typing.Optional[typing.List[\\\"FilterGroup\\\"]]:\\n return self._values.get('webhook_filters')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bf2f73956bf1a5c94f3abc1fd694f8\",\n \"score\": \"0.5737021\",\n \"text\": \"def webhook_filters(self) -> typing.Optional[typing.List[\\\"FilterGroup\\\"]]:\\n return self._values.get('webhook_filters')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421a11c35176b6b631f4fd730f40cad2\",\n \"score\": \"0.5716218\",\n \"text\": \"async def get_filter(self, **kwargs: Any) -> str:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969dec80423f33de6e1b99edf382a34f\",\n \"score\": \"0.5707454\",\n \"text\": \"def build_filters(self, filters=None):\\n built = super(RatingResource, self).build_filters(filters)\\n if 'addon__exact' in built:\\n try:\\n int(built['addon__exact'])\\n except ValueError:\\n app = self.get_app(built['addon__exact'])\\n if app:\\n built['addon__exact'] = str(app.pk)\\n\\n if built.get('user__exact', None) == 'mine':\\n # This is a cheat. Would prefer /mine/ in the URL.\\n user = get_user()\\n if not user:\\n # You must be logged in to use \\\"mine\\\".\\n raise ImmediateHttpResponse(response=http.HttpUnauthorized())\\n\\n built['user__exact'] = user.pk\\n return built\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcab0f9dc749c1b9c05092c27eeb794e\",\n \"score\": \"0.56733215\",\n \"text\": \"def get_filters_params(self, params=None):\\n if not params:\\n params = self.params\\n lookup_params = params.copy() # a dictionary of the query string\\n # Remove all the parameters that are globally and systematically\\n # ignored.\\n for ignored in self.IGNORED_PARAMS:\\n if ignored in lookup_params:\\n del lookup_params[ignored]\\n return lookup_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f7eaafdf3ce8f2e96322d15491634d\",\n \"score\": \"0.5664275\",\n \"text\": \"def apply_filters(self, request, applicable_filters):\\n obj_list = super(SupplierResource, self).apply_filters(request, applicable_filters)\\n \\n if request.GET.has_key('q'):\\n query = request.GET.get('q')\\n obj_list = obj_list.filter(Q(name__icontains=query) |\\n Q(email__icontains=query) |\\n Q(telephone__icontains=query) |\\n Q(notes__icontains=query))\\n \\n return obj_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0756c496335fd71f7ae5c0ec87ca9fb3\",\n \"score\": \"0.5662441\",\n \"text\": \"def filter_items(self):\\n return self._filter_method(self.__toggle_filter, self._data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128f1e5d041eb4309e98f68258c5a468\",\n \"score\": \"0.56569374\",\n \"text\": \"def filter_values(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"filter_values\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a893f89d4eaf8bce36a2e26b86242bf1\",\n \"score\": \"0.5642303\",\n \"text\": \"def scaffold_filters(self, name):\\r\\n if isinstance(name, string_types):\\r\\n attr = self.model._fields.get(name)\\r\\n else:\\r\\n attr = name\\r\\n\\r\\n if attr is None:\\r\\n raise Exception('Failed to find field for filter: %s' % name)\\r\\n\\r\\n # Find name\\r\\n visible_name = None\\r\\n\\r\\n if not isinstance(name, string_types):\\r\\n visible_name = self.get_column_name(attr.name)\\r\\n\\r\\n if not visible_name:\\r\\n visible_name = self.get_column_name(name)\\r\\n\\r\\n # Convert filter\\r\\n type_name = type(attr).__name__\\r\\n flt = self.filter_converter.convert(type_name,\\r\\n attr,\\r\\n visible_name)\\r\\n\\r\\n return flt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0810b01987afd0b38098450d4c9d0bb8\",\n \"score\": \"0.5641344\",\n \"text\": \"def _populate_filters(self, filters=None):\\n\\n if filters is None: filters = self.filter_order\\n if type(filters) == type(''): filters = [filters]\\n\\n return filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7a9ff2d6628e869c38ef82b13aa666\",\n \"score\": \"0.56364703\",\n \"text\": \"def filters(self):\\n self._create_filters()\\n\\n return self._filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a09e4cb9863b5e8be9ca7b3a6acbd2\",\n \"score\": \"0.5635801\",\n \"text\": \"def filters(self, view):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2bdaa69e6e14d4c42cbc19862d268c\",\n \"score\": \"0.56266093\",\n \"text\": \"def filter(self):\\n return self._filter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2bdaa69e6e14d4c42cbc19862d268c\",\n \"score\": \"0.56266093\",\n \"text\": \"def filter(self):\\n return self._filter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2bdaa69e6e14d4c42cbc19862d268c\",\n \"score\": \"0.56266093\",\n \"text\": \"def filter(self):\\n return self._filter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6d3ad9f6f4a1e0dea04957f8219278\",\n \"score\": \"0.56254965\",\n \"text\": \"def filter(self) -> str:\\n return pulumi.get(self, \\\"filter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21f27c4fe72860938068476834ecc26\",\n \"score\": \"0.5612213\",\n \"text\": \"def saveFilter():\\n req = request.get_json()\\n\\n try:\\n fstorage = Filter()\\n fstorage.saveFilter(req['name'], req['value'])\\n return fstorage.getFiltersAsJSON()\\n except FilterError as e:\\n raise SCGUIException(\\\"FilterError: \\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7734b2eba952da95805d327b829c8e4f\",\n \"score\": \"0.55969423\",\n \"text\": \"def get_filters(self):\\n response_ = self._client.service.doGetItemsList( \\\\\\n webapiKey=self._web_key, \\\\\\n countryId=self._country_id, \\\\\\n resultScope=6) # filters struct only\\n return response_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ca4b2714505f5c80a1b1c00448c24e\",\n \"score\": \"0.5593479\",\n \"text\": \"def get_es_filter_fields(self):\\n return getattr(self, 'es_filter_fields', tuple())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ad87098d52087837f1b1cf0bcfb1dc\",\n \"score\": \"0.55905557\",\n \"text\": \"def get_filters(filter_dict=None):\\n\\tloaded = []\\n\\tif filter_dict is None:\\n\\t\\tloaded = custom_filters()\\n\\t\\tused = set(l.field for l in loaded)\\n\\t\\tfor k, v in filter_fields().items():\\n\\t\\t\\tif k in used:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tused.add(k)\\n\\t\\t\\tcl = Filter(field=k, description=v) # New filter for default values.\\n\\t\\t\\tloaded.append(cl)\\n\\telse:\\n\\t\\tfor loaded_field, loaded_value in filter_dict.items():\\n\\t\\t\\tfor f in get_filters(None):\\n\\t\\t\\t\\tif f.from_keyval(loaded_field, loaded_value):\\n\\t\\t\\t\\t\\tloaded.append(f)\\n\\t\\t\\t\\t\\tbreak\\n\\treturn loaded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f94b604397e752ddd6e4fcb7f8cd1837\",\n \"score\": \"0.5577068\",\n \"text\": \"def apply_filters(self, request, applicable_filters):\\n obj_list = super(TableResource, self).apply_filters(request, applicable_filters)\\n \\n if request.GET.has_key('q'):\\n query = request.GET.get('q')\\n obj_list = obj_list.filter(Q(description__icontains=query) |\\n Q(model__model__icontains=query) |\\n Q(model__name__icontains=query) |\\n Q(configuration__configuration__icontains=query))\\n \\n return obj_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a1a59041811f6c3c2c52503ed35e0e\",\n \"score\": \"0.5566312\",\n \"text\": \"def get_filter_value(self):\\n return self._filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b6b7ee1beb7f17ea0619f2db30e68c\",\n \"score\": \"0.5560296\",\n \"text\": \"def filters(self):\\n return self._alarm_filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42dde104485b1c4feff3ff855328201d\",\n \"score\": \"0.55561954\",\n \"text\": \"def filter(self, items):\\n filters = self.schema.get(\\\"filters\\\", [])\\n mapping = {\\n \\\"property\\\": self.filter_property,\\n \\\"dayTime\\\": self.filter_day_time,\\n \\\"randomSample\\\": self.filter_random_sample,\\n \\\"probability\\\": self.filter_probability,\\n }\\n return self.filter_mapping(items, filters, mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5619399e49084a85f8d6014856e724\",\n \"score\": \"0.55478203\",\n \"text\": \"def get_remove_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e924597ae5067277634575c14fd42a5\",\n \"score\": \"0.55449784\",\n \"text\": \"def filter_queryset(self, request, queryset):\\n fieldnames = queryset.model._meta.get_all_field_names()\\n for field, value in request.GET.iteritems():\\n if field in fieldnames:\\n queryset = self.apply_filter(request, queryset, field, value)\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9080ef9d1b5ffa2acc17d66c9076733\",\n \"score\": \"0.553553\",\n \"text\": \"def get_filter():\\n if request.method == \\\"POST\\\":\\n ftr = request.form.get(\\\"filter\\\", \\\"\\\")\\n conn = connector()\\n ftr_data = use_filter(conn, ftr)\\n return render_template(\\\"WebAppWordFilter.html\\\", filter=ftr,\\n data=ftr_data)\\n else:\\n return render_template(\\\"WebAppWordFilter.html\\\", filter=\\\"\\\",\\n data=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a5d8507a057f6f6bbe9124e9a56576\",\n \"score\": \"0.55300486\",\n \"text\": \"def GetFilter(self,name):\\n if name in self.filters.keys():\\n return self.filters[name]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2102a1757fa9d630f3fe3b9fe6ab69ae\",\n \"score\": \"0.55204654\",\n \"text\": \"def getCurrentFilter(self):\\n return self.default_filters[self.current_filter]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b4bb1c9c4038d91c27736bae2bb736\",\n \"score\": \"0.55093217\",\n \"text\": \"def active_filters():\\n return {n: d.copy() for n, d in _active_filters.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c57fe58448338aa169a757e0e7d6456\",\n \"score\": \"0.55068797\",\n \"text\": \"def filter_command(self):\\n l = []\\n query_object = Query(l)\\n self.formatted_filters = self.convert_filters_list(self.selected_filters)\\n query_object.update_filter_list(self.formatted_filters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2319acffc0063c4ff8bc77cc6625dd19\",\n \"score\": \"0.5504566\",\n \"text\": \"def filter_models(self):\\n\\n hide = self.btn_filter.text().startswith(\\\"Hide\\\")\\n\\n if hide:\\n self.proxy_spectral_models.add_filter_function(\\n \\\"is_acceptable\\\", lambda model: model.is_acceptable)\\n else:\\n self.proxy_spectral_models.delete_filter_function(\\\"is_acceptable\\\")\\n\\n text = \\\"{} unacceptable models\\\".format((\\\"Hide\\\", \\\"Show\\\")[hide])\\n self.btn_filter.setText(text)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d603365e5afc97d8b8819570c88ae14\",\n \"score\": \"0.5475208\",\n \"text\": \"def queryset(self, request, queryset):\\n # Compare the requested value (either '80s' or '90s')\\n # to decide how to filter the queryset.\\n if self.value() == 'missing_law':\\n return queryset.filter(law=None)\\n else:\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca3c4494f8e7bf2939495b0c6e76460\",\n \"score\": \"0.5472292\",\n \"text\": \"def formatted_filters(self):\\n if not self._formatted_filters:\\n filters = deepcopy(self.filters)\\n self._formatted_filters = self._format_filter(filters, self._and_props)\\n return self._formatted_filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea88bdf051770250487236ccdf6f2449\",\n \"score\": \"0.5466927\",\n \"text\": \"def get_filter(self, **filter_kwargs):\\n filter_kwargs.update(self.base_filter_kwargs)\\n if filter_kwargs:\\n return [models.Q(**filter_kwargs)]\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e95ba42bb7921697830e07c0c55fc11d\",\n \"score\": \"0.5466506\",\n \"text\": \"def scaffold_filters(self, name):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec6341380118ef0be367515af3d051a\",\n \"score\": \"0.5466304\",\n \"text\": \"def _set_filter_kwargs(self, filters):\\n filter_kwargs = {}\\n for p in filters:\\n pair = p.split('__')\\n dot_string = pair[0]\\n if len(pair) > 1:\\n operation = '__' + pair[1] # 'operation' is something like '__gt', '__isnull', etc.\\n else:\\n operation = ''\\n\\n def do_filter(dot_string, filter_string, current_model):\\n \\\"\\\"\\\"\\n Recursive function that takes 'invoice.client.last_name'\\n and puts out a string like 'invoice__client__last_name' after validating that all the fields are\\n valid and accessible to the user\\n \\\"\\\"\\\"\\n parts = dot_string.split('.', 1)\\n fieldname = parts[0]\\n filter_string = (filter_string + '__' + fieldname).strip('__')\\n if len(parts) > 1:\\n leftover = parts[1]\\n else:\\n leftover = ''\\n\\n if not is_field_in(current_model, fieldname):\\n if self.picky:\\n self.error = ('\\\"%s\\\" is not a valid filter' % fieldname)\\n else:\\n self.warning += '\\\"%s\\\" is not a valid parameter. ' % filter_string.replace('__', '.')\\n return None\\n\\n if leftover:\\n field_type = get_field_type(current_model, fieldname)\\n if filter_string not in self.drilldowns:\\n if self.picky:\\n self.error = 'Error in filters: %s' % filter_string.replace('__', '.')\\n else:\\n self.warning += '\\\"%s\\\" is not a valid parameter. ' % filter_string.replace('__', '.')\\n return None\\n if field_type not in [ForeignKey, OneToOneField, ManyToOneRel, OneToOneRel, ManyToManyField]:\\n if self.picky:\\n self.error = ('Error: %s has no children' % filter_string)\\n else:\\n self.warning += '\\\"%s\\\" is not a valid parameter. ' % filter_string.replace('__', '.')\\n return None\\n\\n # go to the related model\\n current_model = get_model(current_model, fieldname)\\n return do_filter(leftover, filter_string, current_model) # recursion\\n else:\\n return filter_string\\n\\n filter_string = do_filter(dot_string, '', self.model)\\n\\n if filter_string:\\n # __in operator requires a list to work with so making it a special case for now.\\n # And adding it in list to support multiple operators that require similar logic\\n if operation in [\\\"__in\\\"]:\\n comma_separated_multiple_values = self.request.QUERY_PARAMS[p]\\n filter_kwargs[filter_string + operation] = comma_separated_multiple_values.split(\\\",\\\")\\n else:\\n filter_kwargs[filter_string + operation] = self.request.QUERY_PARAMS[p]\\n\\n for k in filter_kwargs:\\n if filter_kwargs[k] in ['true', 'True']:\\n filter_kwargs[k] = True\\n elif filter_kwargs[k] in ['false', 'False']:\\n filter_kwargs[k] = False\\n\\n return filter_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f43805e8e86185db65290bd249fb50e\",\n \"score\": \"0.54537696\",\n \"text\": \"def get_filters(request, mapping):\\n filters = dict([(FILTER_RE.match(k).group(1), v)\\n for (k, v) in request.params.items()\\n if FILTER_RE.match(k)])\\n date_start = filters.get('from', None)\\n date_end = filters.get('to', None)\\n # getting fields list from mapping\\n fields = mapping.get_fields_list()\\n # filtering list of filters from request by object existing fields\\n query_filters = dict([(k, v) for k, v in filters.items()\\n if k in fields and v])\\n return date_start, date_end, query_filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69156bca2033bfca12f4369c7587cc1b\",\n \"score\": \"0.5433845\",\n \"text\": \"def choices_for_request(self):\\n if not issubclass(self.model, Condenser): # pragma: no cover\\n raise TypeError('model must be a type of condenser')\\n\\n choices = ()\\n\\n # text typed in autocomplete input\\n query = self.request.GET.get('q', '').lower().strip()\\n\\n # selected condenser on Fitting admin page\\n condenser_id = self.request.GET.get('condenser', None)\\n\\n if condenser_id:\\n\\n condenser = self.model.objects.get(pk=condenser_id)\\n choices = BottleField.objects.filter(bottle=condenser.bottle)\\n\\n # exclude BottleFields that are already fitted to this condenser\\n if self.fitting:\\n kwargs = {self.fitting + '__condenser': condenser}\\n choices = choices.exclude(**kwargs)\\n\\n if query:\\n choices = choices.filter(field_name__icontains=query)\\n\\n return self.order_choices(choices)[0:self.limit_choices]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526a2e2860dd8d60fa509cbf763d711f\",\n \"score\": \"0.5421987\",\n \"text\": \"def filter(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"filter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4dd8706ef9fd53ce7bf0f45264306d\",\n \"score\": \"0.54015136\",\n \"text\": \"def get_queryset(self):\\n valid = self.is_bound and self.form.is_valid()\\n\\n if self.strict and self.is_bound and not valid:\\n return self.queryset.none()\\n\\n # Start with all the results and filter from there.\\n qs = self.queryset.all()\\n for name, filter_ in self.filters.items():\\n if valid:\\n if name in self.form.data:\\n value = self.form.cleaned_data[name]\\n else:\\n continue\\n else:\\n raw_value = self.form[name].value()\\n try:\\n value = self.form.fields[name].clean(raw_value)\\n except forms.ValidationError:\\n if self.strict:\\n return self.queryset.none()\\n else:\\n continue\\n\\n # At this point we should have valid & clean data.\\n qs = filter_.filter(qs, value)\\n\\n # Optional ordering.\\n if self._meta.order_by:\\n order_field = self.form.fields[self.order_by_field]\\n data = self.form[self.order_by_field].data\\n ordered = None\\n try:\\n ordered = order_field.clean(data)\\n except forms.ValidationError:\\n pass\\n\\n if ordered in EMPTY_VALUES and self.strict:\\n ordered = self.form.fields[self.order_by_field].choices[0][0]\\n\\n if ordered:\\n qs = qs.order_by(*self.get_order_by(ordered))\\n\\n return qs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9627ddd0aded742a6e35c8765bbee32f\",\n \"score\": \"0.5398656\",\n \"text\": \"def all_filters():\\n is_state = storage.all(State).values()\\n is_amenity = storage.all(Amenity).values()\\n return render_template('10-hbnb_filters.html',\\n states=is_state, amenities=is_amenity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df186b44fbb6e02c89a79cc7336a432d\",\n \"score\": \"0.5394143\",\n \"text\": \"def filterables(self):\\n return self._filterables\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9412c3ba0f539cf6282d6fd1c240cd\",\n \"score\": \"0.53934443\",\n \"text\": \"def filters():\\n states = storage.all(State)\\n amenities = storage.all(Amenity)\\n return render_template('10-hbnb_filters.html',\\n states=states, amenities=amenities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b705a3490ec1e9dd37259a7343653a6e\",\n \"score\": \"0.5391118\",\n \"text\": \"def get_basic_filter_values(self, list_values, questionnaires):\\n basic_filter_values = {\\n 'list_values': list_values,\\n 'filter_configuration': self.get_global_filter_configuration(),\\n 'active_filters': get_active_filters(\\n questionnaire_configuration=self.configuration, query_dict=self.request.GET\\n ),\\n 'request': self.request,\\n }\\n\\n # Add the pagination parameters\\n basic_filter_values.update(**get_pagination_parameters(\\n self.request, self.pagination, questionnaires))\\n\\n return basic_filter_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5618c3aef83f2dadfbb340f91889b11\",\n \"score\": \"0.5388985\",\n \"text\": \"def get_filter(self, **filter_kwargs):\\n\\n q_objects = super(ListView, self).get_filter(**filter_kwargs)\\n form = self.get_filter_form()\\n if form:\\n q_objects.extend(form.get_filter())\\n\\n return q_objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82adcfab60e6197b413e890659107a0a\",\n \"score\": \"0.538819\",\n \"text\": \"def filter(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"filter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"296de409cdfdb29333942f0d31aa9968\",\n \"score\": \"0.53880036\",\n \"text\": \"def values(self, filters):\\n return [[val, page] for val, page in self.filter(*filters)\\n if val > -1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48a1c4b8283a6b7be6829b219ce2b970\",\n \"score\": \"0.5365557\",\n \"text\": \"def loadFilters():\\n try:\\n fstorage = Filter()\\n return fstorage.getFiltersAsJSON()\\n except FilterError as e:\\n raise SCGUIException(\\\"FilterError: \\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c4ff9cdc987c04aab757a4537f1426\",\n \"score\": \"0.53458387\",\n \"text\": \"def get_filter_params(db: Session = Depends(get_db)):\\n return crud.get_params(db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92c7d66a7b8ae58c9e657dd5cdf43d4\",\n \"score\": \"0.5342754\",\n \"text\": \"def get_advanced_filter_values(self, active_filters):\\n advanced_filter_select = self.get_advanced_filter_select()\\n\\n # Get a list of all key_paths\\n flat_list = []\\n for filter_keys in dict(advanced_filter_select).values():\\n flat_list.extend(filter_keys)\\n advanced_filter_paths = [f.path for f in flat_list]\\n\\n active_advanced_filters = []\\n for active_filter in active_filters:\\n questiongroup = active_filter.get('questiongroup')\\n key = active_filter.get('key')\\n key_path = f'{questiongroup}__{key}'\\n if key_path not in advanced_filter_paths:\\n continue\\n\\n aggregated_values = get_aggregated_values(\\n questiongroup, key, active_filter['type'],\\n **self.get_filter_params())\\n\\n values_counted = []\\n for c in active_filter.get('choices', []):\\n values_counted.append(\\n (str(c[0]), c[1], aggregated_values.get(c[0], 0)))\\n\\n active_filter.update({\\n 'choices_counted': values_counted,\\n 'key_path': key_path,\\n })\\n active_advanced_filters.append(active_filter)\\n\\n return {\\n 'active_advanced_filters': active_advanced_filters,\\n 'advanced_filter_select': advanced_filter_select,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05570c675afc19ae400dc933f55edc0\",\n \"score\": \"0.53343034\",\n \"text\": \"def build_filter(self, fieldname, operator, value, exclusivity, status=None):\\n\\n filters = list()\\n\\n for i, _ in enumerate(fieldname):\\n filter_dict = dict()\\n\\n filter_dict[\\\"field\\\"] = fieldname[i]\\n filter_dict[\\\"operator\\\"] = operator[i]\\n filter_dict[\\\"exclusive\\\"] = exclusivity[i]\\n filter_dict[\\\"value\\\"] = value[i]\\n\\n filters.append(filter_dict)\\n\\n if status:\\n filter_dict = dict()\\n\\n filter_dict[\\\"field\\\"] = \\\"generic_state\\\"\\n filter_dict[\\\"operator\\\"] = \\\"EXACT\\\"\\n filter_dict[\\\"exclusive\\\"] = False\\n filter_dict[\\\"value\\\"] = status\\n\\n filters.append(filter_dict)\\n\\n return filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e9bee001f4e9ac746171c0c571b1d5\",\n \"score\": \"0.5334269\",\n \"text\": \"def filter(self):\\n return str(self._filter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472196109d3dae210a10b157ba13a52d\",\n \"score\": \"0.533153\",\n \"text\": \"def clear_filters(self):\\r\\n self.__filters = []\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f20c081fbf1323ce7e4ebfc173541b5\",\n \"score\": \"0.53305507\",\n \"text\": \"def queryset(self, request, queryset):\\n if self.value():\\n params = {self.lookup_key: self.value()}\\n return queryset.filter(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cded017a743fb46cdc6afaf70f741b14\",\n \"score\": \"0.53104585\",\n \"text\": \"def get_filter_form(self, **kwargs):\\n\\n form = None\\n if self.filter_form:\\n form = self.filter_form(self.request.GET)\\n elif self.model and hasattr(self.model._meta, '_is_view'):\\n form = VersionFilterForm(self.request.GET)\\n return form\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc83283de9bc5901596809b6bbc97a78\",\n \"score\": \"0.5309066\",\n \"text\": \"def filters(self):\\n\\n args = {}\\n webbrowser.open(url_with_params(\\\"todoist://filters\\\", args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad9f6d595493f7dd09c221568865f977\",\n \"score\": \"0.53052044\",\n \"text\": \"def filter_values_vocab(self, vocab_name, value_filter):\\n selected_values = []\\n for value in self.settings[vocab_name]:\\n if value_filter in value:\\n selected_values.append(value)\\n return selected_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc250c779cdebbe7df34b077ecd3d7b3\",\n \"score\": \"0.5298737\",\n \"text\": \"def filter(self) -> Optional['outputs.BudgetFilterResponse']:\\n return pulumi.get(self, \\\"filter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b8f625c8915483b2606e2de3ad0a08\",\n \"score\": \"0.52969754\",\n \"text\": \"def filters(self) -> Optional[Sequence['outputs.EventSourceMappingFilterCriteriaFilter']]:\\n return pulumi.get(self, \\\"filters\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5869db3e38684e56f3756098e2b1e70f\",\n \"score\": \"0.5288502\",\n \"text\": \"def GetDisplayFilter(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a4a85a63b01e9e77f7c2df887379c1\",\n \"score\": \"0.5288345\",\n \"text\": \"def get_filters(self, dataset: str = \\\"hsapiens_gene_ensembl\\\"):\\n # filters = super(Biomart, self).filters(dataset)\\n # if dataset not in [x for k in self.valid_attributes.keys() for x in self.valid_attributes[k]]:\\n # raise ValueError(\\\"provided dataset (%s) is not found. see valid_attributes\\\" % dataset)\\n url = (\\n \\\"https://{host}/biomart/martservice?type=filters&dataset={dataset}\\\".format(\\n host=self.host, dataset=dataset\\n )\\n )\\n\\n resp = requests.get(url)\\n if resp.ok:\\n if str(resp.text).startswith(\\\"Query ERROR\\\"):\\n return resp.text\\n filters = [text.split(\\\"\\\\t\\\") for text in resp.text.strip().split(\\\"\\\\n\\\")]\\n filters = pd.DataFrame(filters).iloc[:, [0, 1, 3, 5]]\\n filters.columns = [\\\"Filter\\\", \\\"Description\\\", \\\"Additional\\\", \\\"InputType\\\"]\\n return filters\\n return resp.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dbbb35c32267a85b6c2f3b6a780db8e\",\n \"score\": \"0.528081\",\n \"text\": \"def get_queryset(self):\\n EMPTY = ''\\n func = self.request.GET.get(\\\"func\\\")\\n alt_func = self.request.GET.get(\\\"alt_func\\\")\\n exponent = self.request.GET.get(\\\"exponent\\\")\\n flavour = self.request.GET.get(\\\"flavour\\\")\\n queryset = list(filter(lambda img:\\n (func == EMPTY or img.params()['func'] == func) and\\n (alt_func == EMPTY or img.params()['alt_func'] == alt_func) and\\n (exponent == EMPTY or img.params()['power'] == exponent) and\\n (flavour == EMPTY or flavour == \\\"*\\\" or img.params()['type'] == flavour),\\n Image.objects.all()))\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8b1254314442b48ae6d40691319019\",\n \"score\": \"0.5278299\",\n \"text\": \"def _build_filters(cls, filters):\\n # build empty filter; iterate filters dict\\n f = MatchAll()\\n for key, value in filters.items():\\n\\n # handle status meta filtering\\n if key.lower() == \\\"status\\\":\\n status_filter = cls._handle_status_filter(value)\\n f &= status_filter\\n\\n # handle nested filtering -- convert dunders to dots\\n elif \\\"__\\\" in key or \\\".\\\" in key:\\n nested_key = key.lower().replace(\\\"__\\\", \\\".\\\")\\n path = nested_key.split(\\\".\\\")[0]\\n f &= Nested(path=path, filter=Term(**{nested_key: value}))\\n\\n # regular term filter\\n else:\\n key = key.lower()\\n f &= Term(**{key: value})\\n\\n # done\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"835a77c02879ea386a9ec704b9470324\",\n \"score\": \"0.527025\",\n \"text\": \"def filters(self):\\n return {\\n 'gvariant': gvariant\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ef88d0f64068cfdfbef14401172ed4\",\n \"score\": \"0.5267539\",\n \"text\": \"def sanitize_filters(filters):\\n new_filters = {}\\n keys_to_remove = []\\n for k, v in filters.items():\\n if '.' in k:\\n field = k.split('.')[0]\\n operator = v\\n if isinstance(operator, (list, tuple)):\\n operator = operator[0]\\n new_key = str('%s__%s' % (field, operator))\\n value = filters[field]\\n if value:\\n new_filters[new_key] = value\\n\\n keys_to_remove.append(field)\\n if field in new_filters:\\n del new_filters[field]\\n\\n elif k not in keys_to_remove and v:\\n new_filters[k] = v\\n\\n return new_filters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c27d7b4ce5aba130ee20a38718c0471\",\n \"score\": \"0.526235\",\n \"text\": \"def deleteFilter():\\n req = request.args.to_dict()\\n\\n try:\\n fstorage = Filter()\\n fstorage.deleteFilter(req['name'], req['value'])\\n return fstorage.getFiltersAsJSON()\\n except FilterError as e:\\n raise SCGUIException(\\\"FilterError: \\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e158e72249c282baa45be15eeea981f9\",\n \"score\": \"0.52572507\",\n \"text\": \"def __filters_raw(self, repo):\\n p2o_args = self._compose_p2o_params(self.backend_section, repo)\\n filter_raw = p2o_args['filter-raw'] if 'filter-raw' in p2o_args else None\\n filters_raw_prefix = p2o_args['filters-raw-prefix'] \\\\\\n if 'filters-raw-prefix' in p2o_args else None\\n\\n # filter_raw must be converted from the string param to a dict\\n filter_raw_dict = {}\\n if filter_raw:\\n filter_raw_dict['name'] = filter_raw.split(\\\":\\\")[0].replace('\\\"', '')\\n filter_raw_dict['value'] = filter_raw.split(\\\":\\\")[1].replace('\\\"', '')\\n # filters_raw_prefix must be converted from the list param to\\n # DSL query format for a should filter inside a boolean filter\\n filter_raw_should = None\\n if filters_raw_prefix:\\n filter_raw_should = {\\\"should\\\": []}\\n for filter_prefix in filters_raw_prefix:\\n fname = filter_prefix.split(\\\":\\\")[0].replace('\\\"', '')\\n fvalue = filter_prefix.split(\\\":\\\")[1].replace('\\\"', '')\\n filter_raw_should[\\\"should\\\"].append(\\n {\\n \\\"prefix\\\": {fname : fvalue}\\n }\\n )\\n return (filter_raw_dict, filter_raw_should)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1471,"cells":{"query_id":{"kind":"string","value":"9c8cd9ef14c8648c41dbce6c4d7aaf41"},"query":{"kind":"string","value":"Will be deprecated in favor of Simulation.initialize(). Replace in code, will be removed at or before v1.0.0"},"positive_passages":{"kind":"list like","value":[{"docid":"501d6281c92d3fe97c4124f65fc3dce0","score":"0.8208679","text":"def initialize_simulation(self):\n\n warnings.warn(\n \"Simulation.initialize_simulation() is replaced by Simulation.initialize() and will be deprecated at or before defSim v1.0.0\",\n category=FutureWarning)\n return self.initialize()","title":""}],"string":"[\n {\n \"docid\": \"501d6281c92d3fe97c4124f65fc3dce0\",\n \"score\": \"0.8208679\",\n \"text\": \"def initialize_simulation(self):\\n\\n warnings.warn(\\n \\\"Simulation.initialize_simulation() is replaced by Simulation.initialize() and will be deprecated at or before defSim v1.0.0\\\",\\n category=FutureWarning)\\n return self.initialize()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"469c26f0edd3c0dded13ebf120d8c8b2","score":"0.68212414","text":"def _setup_sim(self):\n raise NotImplementedError","title":""},{"docid":"ddcb531260db5f74dbf20d2a6c653f02","score":"0.6753155","text":"def init_sim():\n\n print(\"Creating population\")\n property_sync_check()\n sim.init_sim()","title":""},{"docid":"a4eca7b751d9cac9c8ed00b95a0e60e8","score":"0.6555123","text":"def __init__(self, sim):\n \n self.sim = sim","title":""},{"docid":"f970c84bc604e6bdd43dc49c04ce6dee","score":"0.6452032","text":"def init_sim(self):\n\n logger.debug(\"Creating instance of Core.Engine with Simulation values values \"\n \"target: {target}, mutation: {mutation}, and pop: {pop}\".format(\n target=self.target,\n mutation=self.mutation,\n pop=self.pop\n ))\n self.simulation = Core.Engine(self.target, self.mutation, self.pop)\n self.simulation.gen_population()\n generationsRs.config(text=\"0\")\n self.update_status(\"Population created\")","title":""},{"docid":"188c44f48e0976fe8e88f87852d6a51c","score":"0.63575655","text":"def initialize():\n pass","title":""},{"docid":"417b16cdfac9127cf8aa0918b6069e9b","score":"0.62683","text":"def run_simulation_step(self):\n\n warnings.warn(\n \"Simulation.run_simulation_step() is replaced by Simulation.run_step() and will be deprecated at or before defSim v1.0.0\",\n category=FutureWarning)\n return self.run_step()","title":""},{"docid":"1a75bd7c6e621d3b7cb4d362fa77095c","score":"0.6177357","text":"def __init__(self):\n raise NotImplementedError(\n 'Initialization must be implemented by derived stimulus classes.'\n )","title":""},{"docid":"dd2789dab0db585eeba24cbb69d5dbc7","score":"0.6162097","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.simulated = True","title":""},{"docid":"07f49a67d168c1ca5d86a289c0f2d8c8","score":"0.61303383","text":"def solver_initialize(self):\n self.sim_time = 0. # current simulation time [day]\n self.prepare_linear_system()","title":""},{"docid":"754df85a4d23e458ccbe3db2d0846e47","score":"0.61241084","text":"def setup(self, simgr):\n pass","title":""},{"docid":"ddb9a6d3aad56bf8f8f311830f3421a4","score":"0.6120191","text":"def __init__(self):\n\t\tself.birth = time.time() # time that the simulator object was created.\n\t\tself.n = 0 #counter for total number of samples in simulation. \n\t\tself.sr = 25 #sample rate for simulators\n\t\tself.noiseStd = 1 #standard deviation of the noise\n\t\tself.sampleList = [] #initialize empty sample list that will contain all samples","title":""},{"docid":"ec1698ec4128f8590d851728ca03da3e","score":"0.6106263","text":"def instantiateSimObject(self):\n self.initSimObject()\n self.getParamDefaults()","title":""},{"docid":"4fdfb157ccb860909ab42bfe9fa6b64e","score":"0.60808754","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.simulated = False","title":""},{"docid":"bc5096991d64545e843de7154e6cc0d3","score":"0.6036654","text":"def __init__(self, world):\n\n self.world: World = world\n \"\"\"The world that is being simulated\"\"\"","title":""},{"docid":"7292b2463c452bb2fc7801375981290d","score":"0.5990851","text":"def load_sim():","title":""},{"docid":"5344bd6c29b56809e5510401dbf8deef","score":"0.59883094","text":"def initialize(self):\n raise NotImplementedError","title":""},{"docid":"8ce691c8eaad41bacd9b8ef5fe2efc5e","score":"0.5984462","text":"def run_simulation(self, initialize: bool = True) -> pd.DataFrame:\n\n warnings.warn(\n \"Simulation.run_simulation() is replaced by Simulation.run() and will be deprecated at or before defSim v1.0.0\",\n category=FutureWarning)\n return self.run(initialize=initialize)","title":""},{"docid":"73aba91e13dcecbdb307b4b7c79dc0a0","score":"0.5969146","text":"def _init_sim(config):\n solver = findSolver()\n\n cmds.setAttr(solver + '.selfCollision', 1)\n cmds.setAttr(solver + '.startTime', 1)\n cmds.setAttr(solver + '.solverStatistics', 0) # for easy reading of console output\n cmds.playbackOptions(ps=0, max=config['max_sim_steps']) # 0 playback speed = play every frame\n\n return solver","title":""},{"docid":"efb049f5669700df5b869bd40d3df21a","score":"0.59534264","text":"def __init__(self, no_graphics = True):\n self.env = UnityEnvironment(file_name=\"./Tennis_Linux/Tennis.x86_64\", no_graphics = no_graphics)\n self.brain_name = self.env.brain_names[0]\n self.brain = self.env.brains[self.brain_name]\n\n # reset the environment\n self.env_info = self.env.reset(train_mode=True)[self.brain_name]\n\n # number of agents in the environment\n print('Number of agents:', len(self.env_info.agents))\n\n # number of actions\n self.action_size = self.brain.vector_action_space_size\n print('Number of actions:', self.action_size)\n\n # examine the state space \n state = self.env_info.vector_observations[0]\n print('States look like:', state)\n self.state_size = len(state)\n print('States have length:', self.state_size)","title":""},{"docid":"c21aa0a52a69dab6e82fdf2b81f9e1c9","score":"0.5948905","text":"def sim() -> merlin.Simulation:\n return merlin.Simulation(\n config=[],\n outputs=set(),\n name='test_sim')","title":""},{"docid":"11f2d4db801ec354fdbd3c60b72f33af","score":"0.5940551","text":"def __init__(self, *args):\n# print(_siglist)\n _simulator._time = 0\n arglist = _flatten(*args)\n self._waiters, self._cosim = _makeWaiters(arglist)\n if not self._cosim and _simulator._cosim:\n warn(\"Cosimulation not registered as Simulation argument\")\n self._finished = False\n del _futureEvents[:]\n del _siglist[:]","title":""},{"docid":"5bb9d05cd9b69569865e24aa8a82c7d5","score":"0.5898695","text":"def init(self, data):\n\n self.simulation = Simulation(data['generator'], data['params'], data['fifo'], self._ws)","title":""},{"docid":"c39a915ba421682739f72e91d34b52e2","score":"0.5898405","text":"def _initialize_simulation(self):\n self._static_main_circle = plt.Circle((0, 0), 0.025, color='black', fill=True)\n self._static_reference_mass_circle = plt.Circle((0, self._length), self._radius, color='grey', fill=False,\n linestyle='--')\n self._static_reference_line = plt.Line2D([0.0, 0.0], [0.0, self._length - self._radius], color='grey',\n linestyle='--')\n\n self._dynamic_pendulum_line = plt.Line2D([0.0], [0.0], color='black')\n self._dynamic_mass_circle = plt.Circle((np.inf, np.inf), self._radius, color='black', fill=True)\n self._dynamic_state_text = plt.Text(1.5, 1, \"Pos: \\nVelo:\", horizontalalignment='right')\n\n if self._simulation_ax is None:\n self._simulation_ax = self._create_figure()\n\n self._simulation_ax.axis('equal')\n limit = 1.5 * self._length\n self._simulation_ax.set_xlim(-limit, limit)\n self._simulation_ax.set_ylim(-limit, limit)\n self._simulation_ax.set_axis_off()\n self._simulation_ax.set_title(\"1-DOF Inverted Pendulum Simulation\")\n\n self._simulation_ax.add_artist(self._static_main_circle)\n self._simulation_ax.add_artist(self._static_reference_mass_circle)\n self._simulation_ax.add_artist(self._static_reference_line)\n\n self._simulation_ax.add_artist(self._dynamic_pendulum_line)\n self._simulation_ax.add_artist(self._dynamic_mass_circle)\n self._simulation_ax.add_artist(self._dynamic_state_text)\n\n self._simulation_ready = True","title":""},{"docid":"f7be937a46fb0d0b2d6c88cb1977f897","score":"0.58765113","text":"def initialize(self):\n\n # reset steps\n self.time_steps = 0\n self.influence_steps = 0\n\n # reset tickwise output\n self.initialize_tickwise_output()\n\n if self.seed is None:\n self.seed = random.randint(10000, 99999)\n random.seed(self.seed)\n self.parameter_dict['np_random_generator'] = np.random.default_rng(self.seed)\n\n ## if deprecated ms_rewiring parameter is set in parameter dict, replace with network modifier\n if 'ms_rewiring' in list(self.parameter_dict.keys()):\n warnings.warn(\n \"Setting ms_rewiring in parameter dict is deprecated. Pass an instance of MaslovSneppenModifier in network_modifiers instead.\",\n DeprecationWarning)\n if self.network_modifiers is None:\n self.network_modifiers = [MaslovSneppenModifier(rewiring_prop=self.parameter_dict['ms_rewiring'])]\n else:\n self.network_modifiers.append(MaslovSneppenModifier(rewiring_prop=self.parameter_dict['ms_rewiring']))\n\n # read or generate network if no nx.Graph was provided, apply network modifiers\n if self.network_provided:\n if self.network == 'list':\n self.network = self.parameter_dict.pop('network')\n if not isinstance(self.network, nx.Graph) and self.network is not None:\n self.network = network_init.read_network(self.network)\n\n ## apply network modifiers\n if self.network_modifiers is not None:\n for modifier in self.network_modifiers:\n modifier.rewire_network(network)\n else:\n self.network = network_init.generate_network(self.topology, network_modifiers=self.network_modifiers,\n **self.parameter_dict)\n\n # storing the indices of the agents to access them quicker\n self.agentIDs = list(self.network)\n\n # initialize agent attributes (accepts string realizations and instances of AttributesInitializer classes)\n agents_init.initialize_attributes(self.network, self.attributes_initializer, **self.parameter_dict)\n\n # initialization of distances between neighbors\n self.dissimilarity_calculator.calculate_dissimilarity_networkwide(self.network, **self.parameter_dict)","title":""},{"docid":"a10dae8c1d968eb3ebe05d99854d0f32","score":"0.58589745","text":"def Simulation(parent):\n simulation = parent.createChild(\"Simulation\")\n simulation.createObject(\"EulerImplicitSolver\")\n simulation.createObject(\"CGLinearSolver\", iterations=250, tolerance=1e-20, threshold=1e-20)\n return simulation","title":""},{"docid":"228b70553f8a23ae4a45fb7954e7c928","score":"0.58466333","text":"def initialize_simulator(config):\n\n simulator = config.get('simulation', 'simulator')\n print(\"Initializing {} simulator...\\n\".format(simulator))\n if simulator in config_string_to_set_of_strings(\n config.get('restrictions', 'simulators_pyNN')):\n if simulator == 'spiNNaker':\n try:\n sim = import_module('pyNN.' + simulator)\n except ImportError:\n sim = import_module('spynnaker8')\n else:\n sim = import_module('pyNN.' + simulator)\n\n # From the pyNN documentation:\n # \"Before using any other functions or classes from PyNN, the user\n # must call the setup() function. Calling setup() a second time\n # resets the simulator entirely, destroying any network that may\n # have been created in the meantime.\"\n sim.setup(timestep=config.getfloat('simulation', 'dt'))\n return sim\n if simulator == 'brian2':\n return import_module('brian2')\n if simulator == 'loihi':\n import nxsdk.api.n2a as sim\n return sim\n sim_module_str = None\n if simulator == 'INI':\n spike_code = config.get('conversion', 'spike_code')\n sim_module_str = 'inisim.' + spike_code\n if spike_code == 'temporal_mean_rate':\n sim_module_str += '_' + config.get('simulation', 'keras_backend')\n elif simulator == 'MegaSim':\n sim_module_str = 'megasim.megasim'\n if sim_module_str is None:\n sim_module_str = 'inisim.temporal_mean_rate_theano'\n sim = import_module('snntoolbox.simulation.backends.' + sim_module_str)\n assert sim, \"Simulator {} could not be initialized.\".format(simulator)\n return sim","title":""},{"docid":"89c12a27c41e44be8768e11ea7ef5ef0","score":"0.5825551","text":"def __setup_simulation(self):\n # Nest stuff\n nest.ResetKernel()\n # http://www.nest-simulator.org/sli/setverbosity/\n nest.set_verbosity('M_INFO')\n nest.SetKernelStatus(\n {\n 'resolution': self.dt,\n 'local_num_threads': 1,\n 'overwrite_files': True\n }\n )\n # Since I've patched NEST, this doesn't actually update connectivity\n # But, it's required to ensure that synaptic elements are connected\n # correctly when I form or delete new connections\n nest.EnableStructuralPlasticity()\n nest.SetStructuralPlasticityStatus({\n 'structural_plasticity_update_interval':\n int(self.sp_update_interval),\n })\n\n self.__setup_neurons()\n self.__create_neurons()\n self.__get_regions()\n self.__setup_detectors()\n self.__setup_initial_connection_params()\n self.__create_initial_connections()\n self.__setup_files()\n\n self.dump_data()","title":""},{"docid":"bf575f955b01054f9540a13a9c2744c6","score":"0.58068424","text":"def initialize(self):\n\t\tpass","title":""},{"docid":"4228f0053fc9fdbf5c1b354eaf3a9d1d","score":"0.5793719","text":"def run(self):\n home, work, positions = initialization(self.params)\n simulate(params=self.params,plt=plt,callback=self.callback,home=home,work=work,positions=positions)","title":""},{"docid":"97b5e4bcbc9516d2cd972af379bf2ffb","score":"0.57527983","text":"def task_2_7_1():\n # TODO Task 2.7.1: Your code goes here\n sim_param = SimParam()\n random.seed(sim_param.SEED)\n sim = Simulation(sim_param)\n \n do_simulation_study(sim)\n pass","title":""},{"docid":"e8419f6e099a425ce9a5e2bbb6fbbe83","score":"0.5742213","text":"def startSimulation(self):\n self.master.mainloop()","title":""},{"docid":"dd2caba21d0e67902a4ce992ce087761","score":"0.5727336","text":"def initialize(self, rng):\n # being abstract, i don't know what to do here\n raise NotImplementedError(\n f\"class '{type(self).__name__}' must implement 'initialize'\")","title":""},{"docid":"8601301dd6d8c18d50f3d45e6720ce95","score":"0.57254976","text":"def initialize(self):\r\n\t\tpass","title":""},{"docid":"8d17a6f34186b16ff4476a9362b39b7d","score":"0.57227135","text":"def Initialize():\r\n pass","title":""},{"docid":"c6956e2f3a5cb33be8e1bf3e71b6386c","score":"0.57202876","text":"def simulation(self):\n self.qmg.simulation()","title":""},{"docid":"affc4f297652c37556fd6a5447753513","score":"0.57173765","text":"def _initialize(self):","title":""},{"docid":"cf84de7ae2dfb796abfeb2207130563c","score":"0.5714358","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.1) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","title":""},{"docid":"d8d382d2dde0414dc2ca3712c1e487da","score":"0.5708899","text":"def init(self, sim: \"sims.MeshSimulation\"):\n self.sim = sim\n self.spec = sim.spec\n\n self.attach()","title":""},{"docid":"bf5d8fc6002b7e94475672743cb08870","score":"0.5708061","text":"def initialize(self) -> None:\n ...","title":""},{"docid":"5b3ba65fb93b236d87323d067dedecce","score":"0.57060385","text":"def __init__(self):\n # self.position = vector(self.np_random.rand()*0.01, PLATHEIGHT)\n # self.velocity = vector(self.np_random.rand()*0.0001, 0.0)\n self.position = np.array((0., Constants.PLATFORM_HEIGHT))\n self.velocity = np.array((0., 0.0))\n self.np_random = np.random # overwritten by seed()","title":""},{"docid":"1913205420b06e6fc1c4ca43b29f1563","score":"0.56979334","text":"def __init__(self, lifespan=None, test=False):\n BaseWorld.__init__(self, lifespan)\n self.reward_magnitude = 1.\n self.energy_cost = 0.01 * self.reward_magnitude\n self.jump_fraction = 0.1\n self.name = 'grid_1D_noise'\n self.name_long = 'noisy one dimensional grid world'\n print \"Entering\", self.name_long\n self.num_real_sensors = 3\n # Number of sensors that have no basis in the world. \n # These are noise meant to distract.\n self.num_noise_sensors = 15 \n self.num_sensors = self.num_noise_sensors + self.num_real_sensors\n self.num_actions = 2\n self.action = np.zeros((self.num_actions,1))\n self.world_state = 0 \n self.simple_state = 0 \n self.world_visualize_period = 1e6\n self.brain_visualize_period = 1e3","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.56942075","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.56942075","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.56942075","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.56942075","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.56942075","text":"def initialize(self):\n pass","title":""},{"docid":"0d4ac587230560e08dd42511d6751861","score":"0.5690917","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001) # reduce update_delay to speed up simulation\n sim.run(n_trials=120) # press Esc or close pygame window to quit","title":""},{"docid":"53a9c4bbb9d8db5e083d6783874c551f","score":"0.56885207","text":"def main():\n # load parameters\n P = {}\n with open(PARAMETER_FILE) as pFile:\n P = yaml.load(pFile, Loader=yaml.FullLoader)\n\n init_state = [P[\"startingX\"], P[\"startingY\"], np.radians(P['startingAngle'])]\n robot = Agent(init_state, P['w'], P['l'], P['d'], P['roomWidth'], P['roomHeight'],\n P['maxrpm'], P['lstddev'], P['astddev'], P['mstddev'])\n loop(robot, init_state)\n\n #init_state = [5000, 5000, np.pi/2]\n\n #robot = Agent(init_state=init_state)\n #loop(P, robot, init_state)","title":""},{"docid":"8257b176e2787e5306f6041173a035e1","score":"0.56856984","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","title":""},{"docid":"098d0db20f5ed3ccbd37223dc40c9521","score":"0.5682455","text":"def do_simulation(self):\n self._configure_slurm()\n self._run_simulation()","title":""},{"docid":"4fa1d738e9e12194a1cecdac21df7abe","score":"0.5674467","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n\n self.state_size = self.action_repeat * 6\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])","title":""},{"docid":"a58154aa312ea889c06e16569df223d6","score":"0.56701064","text":"def __init__(self):\n self.armSimulator = Kinematics()","title":""},{"docid":"4f3a30c33c9fa9506503a26aa1ad8682","score":"0.5669107","text":"def __init__(self):\n self.trainable = False\n self.phase = None\n self.model = None\n self.device = None\n self.last_state = None\n self.time_step = None\n # if agent is assumed to know the dynamics of real world\n self.env = None","title":""},{"docid":"baf4667fe4966953c31b3499144a3887","score":"0.56642395","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n\n self.state_size = self.action_repeat * 6\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])","title":""},{"docid":"55402bb166fc0895b7363501e3770ee3","score":"0.56626606","text":"def setupSimulation(self):\n if self.log and self.verbose > 1:\n self.log.info('Simulator: setupSimulation(): instConf:%s ' % (self.instrumentConf))\n\n # Instantiate the Instrument class\n self.telescope = Instrument(lsstDB=self.lsstDB, sessionID=self.sessionID,\n dbTableDict=self.dbTableDict, obsProfile=self.obsProfile,\n instrumentConf=self.instrumentConf, log=self.log, logfile=self.logfile,\n verbose=self.verbose)\n self.telescope.Park()\n\n # Init the Filters class\n self.filters = Filters(lsstDB=self.lsstDB, filtersConf=self.filtersConf, sessionID=self.sessionID,\n dbTableDict=self.dbTableDict, telSeeing=self.telSeeing,\n opticalDesSeeing=self.opticalDesSeeing, cameraSeeing=self.cameraSeeing,\n scaleToNeff=self.scaleToNeff, atmNeffFactor=self.atmNeffFactor,\n log=self.log, logfile=self.logfile, verbose=self.verbose)\n\n # Init the Astronomical Sky\n self.sky = AstronomicalSky(lsstDB=self.lsstDB, configFile=self.astroskyConf,\n obsProfile=self.obsProfile, date=0.,\n sessionID=self.sessionID, dbTableDict=self.dbTableDict, log=self.log,\n logfile=self.logfile, verbose=self.verbose)\n\n self.schedulingData = SchedulingData(self.schedulingDataConf, 0, self.nRun * YEAR, self.sky,\n self.lsstDB, self.sessionID)\n\n # Init the Weather Simulator\n self.weather = Weather(lsstDB=self.lsstDB, date=0., dbTableDict=self.dbTableDict,\n seeingEpoch=self.seeingEpoch, simStartDay=self.simStartDay, log=self.log,\n logfile=self.logfile, verbose=self.verbose)\n\n # Define the telescope as a Resource with a priority queue.\n # self.telResource = Simulation.Resource ()\n\n # We also need a Obs Scheduler\n self.obsScheduler = ObsScheduler(lsstDB=self.lsstDB, schedulingData=self.schedulingData,\n obsProfile=self.obsProfile, dbTableDict=self.dbTableDict,\n telescope=self.telescope, weather=self.weather, sky=self.sky,\n filters=self.filters, sessionID=self.sessionID,\n runSeeingFudge=self.runSeeingFudge, schedulerConf=self.schedulerConf,\n log=self.log, logfile=self.logfile, verbose=self.verbose)\n\n # Create a TAC object and activate it.\n self.tac = TAC(lsstDB=self.lsstDB, nRun=self.nRun, schedulingData=self.schedulingData, sky=self.sky,\n weather=self.weather, obsScheduler=self.obsScheduler, sessionID=self.sessionID,\n fov=self.fov, filters=self.filters, nearEarthConf=self.nearEarthConf,\n weakLensConf=self.weakLensConf, superNovaConf=self.superNovaConf,\n superNovaSubSeqConf=self.superNovaSubSeqConf, kuiperBeltConf=self.kuiperBeltConf,\n WLpropConf=self.WLpropConf, targetList=self.targetList, dbTableDict=self.dbTableDict,\n log=self.log, logfile=self.logfile, verbose=self.verbose)\n return","title":""},{"docid":"4e63bcdef783aad7ef437c0c76edd1cf","score":"0.56589377","text":"def task_2_7_2():\n # TODO Task 2.7.2: Your code goes here or in the function above\n sim_param = SimParam()\n random.seed(sim_param.SEED)\n sim = Simulation(sim_param)\n sim.sim_param.SIM_TIME = 1000000\n \n do_simulation_study(sim)\n pass","title":""},{"docid":"962a920d65c962e6d8d53c5ca2c5dabf","score":"0.56458575","text":"def registerInitialState(self, gameState):\n\n '''\n Make sure you do not delete the following line. If you would like to\n use Manhattan distances instead of maze distances in order to save\n on initialization time, please take a look at\n CaptureAgent.registerInitialState in captureAgents.py.\n '''\n CaptureAgent.registerInitialState(self, gameState)\n\n '''\n Your initialization code goes here, if you need any.\n '''","title":""},{"docid":"df6765943dc6cf62a21b9a081bb9a4e1","score":"0.5644654","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n\n self.state_size = self.action_repeat * 6\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([3., 3., 10.])","title":""},{"docid":"d7ed478eff0282d14075b0228c6fa570","score":"0.56286454","text":"def start():\n env = simpy.Environment() # defining that env is a simpy envirnment class\n\n car = Virus(env) # starts the procces of the simulation the func\n env.run(until=lengh_of_sim+1)","title":""},{"docid":"61d27ac12c48af8b61888e0b5ef754ee","score":"0.562734","text":"def initialize(self): # pragma: no cover\n pass","title":""},{"docid":"945cc72591445f736b58674267346ff2","score":"0.56238854","text":"def _initialize(self):\n pass","title":""},{"docid":"945cc72591445f736b58674267346ff2","score":"0.56238854","text":"def _initialize(self):\n pass","title":""},{"docid":"945cc72591445f736b58674267346ff2","score":"0.56238854","text":"def _initialize(self):\n pass","title":""},{"docid":"f4e8242b6c68e507548ebf412cbfbb05","score":"0.5608538","text":"def initiate(self, session):\n\n # Raise reproman.support.exceptions.CommandError exception if\n # Singularity is not to be found.\n session.execute_command(['singularity', 'selftest'])","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.5608222","text":"def initialize(self):","title":""},{"docid":"2f534f24c501f10f24ed4736234bfda6","score":"0.5604452","text":"async def initialize(self):\n pass","title":""},{"docid":"b56ebaeb62f5c8ae6c0d206cdfe73c26","score":"0.5603592","text":"def __init__(self):\n # initialisation function, it should not wait\n smach.State.__init__(self,outcomes=['someTimes'])\n\n ## Define the value at wich it is performed the state transition\n self.transition_value = random.randint(min_transition_play_normal,max_transition_play_normal)","title":""},{"docid":"b9bdacff4e1a7d7fd4a258f463f719b8","score":"0.56013787","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 1\n\n self.state_size = self.action_repeat * 2\n self.action_low = 390\n self.action_high = 420\n self.action_range = self.action_high - self.action_low \n self.action_size = 1\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])","title":""},{"docid":"66a1bf978d3a47b49e3458091ac6787c","score":"0.55938363","text":"def init(self, state, **runopts):\n pass","title":""},{"docid":"b7bacdfc02e3748042ad0d9ff54be61f","score":"0.55849993","text":"def initialize(self):\n # TODO: might want to change initialization order? order/wait length depends on where the arm position was, and we can't know that\n self.hand.initialize()\n self.wrist.initialize()\n time.sleep(0.5)\n self.elbow.initialize()\n time.sleep(1)\n self.lift_front.initialize()\n self.lift_out.initialize()\n time.sleep(1)\n self.twist.initialize()\n time.sleep(1)","title":""},{"docid":"e704081e9b57ca08622f5bb9b6ba32cd","score":"0.55800724","text":"def __init__(self):\n super(RLGlueWorld, self).__init__()\n\n self.agent = RLGlueAgent()\n self.thread = threading.Thread(target=(lambda: _start_rlglue_agent(self.agent)))\n\n # Start the thread and wait for it to be initialized\n print('Starting RL-Glue thread...')\n\n self.thread.start()\n self.actions = self.agent.numberOfActions()\n self.initial = self.agent.observation()[0] # First observation sent by RL-Glue\n\n print('Started!')","title":""},{"docid":"af98a8d328519af92e46a9c34206894e","score":"0.55772257","text":"def reconfigure_sim(self) -> None:\n # configure our sim_settings but then set the agent to our default\n self.cfg = make_cfg(self.sim_settings)\n self.agent_id: int = self.sim_settings[\"default_agent\"]\n self.cfg.agents[self.agent_id] = self.default_agent_config()\n\n if self.sim is None:\n self.sim = habitat_sim.Simulator(self.cfg)\n\n else: # edge case\n if self.sim.config.sim_cfg.scene_id == self.cfg.sim_cfg.scene_id:\n # we need to force a reset, so change the internal config scene name\n self.sim.config.sim_cfg.scene_id = \"NONE\"\n self.sim.reconfigure(self.cfg)\n\n self.active_scene_graph = self.sim.get_active_scene_graph()\n self.default_agent = self.sim.get_agent(self.agent_id)\n self.agent_body_node = self.default_agent.scene_node\n self.render_camera = self.agent_body_node.node_sensor_suite.get(\"color_sensor\")\n\n Timer.start()\n self.step = -1","title":""},{"docid":"c1f3c6b96536f4f352aa3b3b7ff6e11a","score":"0.5576994","text":"def init() -> None:","title":""},{"docid":"8fc93ecdcdc53214dfd02963936a0a75","score":"0.5575788","text":"def initSimObject(self):\n if self.componentName == \"Root\":\n self.simObjectInstance = getRoot()\n else:\n self.simObjectInstance = self.simObject()","title":""},{"docid":"ff1172e899d2894d2dbdd52258f5bcd3","score":"0.5567018","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=1000) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n for i in a.qt.items(): print(str(i).replace('(','').replace(')',''))","title":""},{"docid":"28d0f3f5dfc86b523e89eaa4ba43f615","score":"0.55600816","text":"def _reset_sim(self):\n self.pr.stop()\n #print (self.init_angles, end = '\\r')\n self.ecm.setJointAngles(self.init_angles)\n self.pr.start()","title":""},{"docid":"a3027d2508342008d4c6b64e7eeb68b3","score":"0.5559209","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n \n self.unique_state_size = np.concatenate(([self.sim.pose] + [self.sim.v] + [self.sim.angular_v])).shape[0]\n \n self.state_size = self.action_repeat * self.unique_state_size\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n \n # Origin\n self.init_pose = init_pose\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])","title":""},{"docid":"be7a2e8dea5623a58e9cd0efcbc76a41","score":"0.55560356","text":"def initialize(*args, **kwargs):\n \n pass","title":""},{"docid":"c8e2b7ce233653b1a760a74c3bee7dea","score":"0.5552046","text":"async def initialize(self) -> None:\n pass","title":""},{"docid":"0f6ab876675987765e8f40a5849f53ea","score":"0.55503315","text":"def __init__(self, session):\n super(JointEstimation, self).__init__(session)\n import models\n self.models = models\n self.experiment_repeats = 1\n self.setup()\n self.num_participants = 10 # desired total participants, not dyads\n self.initial_recruitment_size = 10\n self.completion_bonus_payment = .33\n self.accuracy_bonus_payment = 2\n self.total_test_trials = 15","title":""},{"docid":"f65ed4429bcfaa893e7cf854c5c6159f","score":"0.5545332","text":"def initialize(self, sess):\n sess.run(self._update_target)","title":""},{"docid":"c5320c6ecfaa893abd88dffbc204614d","score":"0.5539388","text":"def initialize(self):\n return","title":""},{"docid":"c5320c6ecfaa893abd88dffbc204614d","score":"0.5539388","text":"def initialize(self):\n return","title":""},{"docid":"c5320c6ecfaa893abd88dffbc204614d","score":"0.5539388","text":"def initialize(self):\n return","title":""},{"docid":"0e50e628522336cabb0321ceec54589f","score":"0.55339056","text":"def main():\n\tif fm_sim == \"snana\":\n \tsampler = abc.ABC_class(nparam,npart,dataLC,tlevels,niter,prior,**prop)\n\telif fm_sim == \"sncosmo\":\n \tsampler = abc.ABC_class(nparam,npart,data,tlevels,niter,prior,**prop)\n model_sim = run_simulation\n\n sampler.sample(model_sim)","title":""},{"docid":"56ce2478f8261714ea92a80faca07932","score":"0.5530867","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=2, display=True) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","title":""},{"docid":"0ef606f36015015159f4eb7ac4fcb028","score":"0.5530597","text":"def run():\n\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit\n\n policy = a.Q_values\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(TrainedAgent, policy) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n print(chr(27) + \"[2J\")\n\n # Now simulate it\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","title":""},{"docid":"2b3ea59fdfec4d68b3b36563d98a6ffd","score":"0.5527462","text":"def simulation_factory(timestep, scheme):\n lsi = LangevinSplittingIntegrator(scheme, timestep=timestep, temperature=temperature,\n collision_rate=collision_rate)\n\n simulation = app.Simulation(topology, system, lsi, platform)\n simulation.context.setPositions(positions)\n simulation.context.setVelocitiesToTemperature(temperature)\n return simulation","title":""},{"docid":"4d2cde19b518d4cc9b853875b6f9ddb0","score":"0.551768","text":"def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime)\n self.sim.reset()\n self.sim.init_pose = self.sim.pose\n self.action_repeat = 4\n\n self.state_size = self.action_repeat * 6\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n \n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.]) \n #self.target = np.append(self.target_pos, np.array([0,0,0]))\n \n if np.linalg.norm(self.sim.init_pose[:3] - self.target_pos) < 1.0:\n self.norm_targ_dist = 1.0\n else:\n self.norm_targ_dist = np.linalg.norm(self.sim.init_pose[:3] - self.target_pos)","title":""},{"docid":"c434486abc7bbd31584a256a30c26977","score":"0.5514013","text":"def __init__(self, root: object, size=50, num_particle=50):\n self.size = size\n self._particles = [] # all particles in the simulation\n self._field = Field(size)\n self.step = 0\n self._view = SimulatorView(root, size)\n self._colours = {State.SUSCEPTIBLE: 'slate blue',\n State.INFECTED: 'red',\n State.RECOVERED: 'spring green',\n State.DEAD: 'black'}\n self.reset(num_particle)","title":""},{"docid":"d54894e133505ea04ae6bd4134d7d383","score":"0.5511776","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.01, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","title":""},{"docid":"9af011961187bfc2b3dc53c83d90a2f1","score":"0.5511652","text":"def setSimulation(self, simulation):\r\n\t\tself.simulationHandle = simulation","title":""},{"docid":"443dae78141c6fd2ffc6b5a31d21dd1d","score":"0.55072814","text":"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n return sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","title":""}],"string":"[\n {\n \"docid\": \"469c26f0edd3c0dded13ebf120d8c8b2\",\n \"score\": \"0.68212414\",\n \"text\": \"def _setup_sim(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcb531260db5f74dbf20d2a6c653f02\",\n \"score\": \"0.6753155\",\n \"text\": \"def init_sim():\\n\\n print(\\\"Creating population\\\")\\n property_sync_check()\\n sim.init_sim()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4eca7b751d9cac9c8ed00b95a0e60e8\",\n \"score\": \"0.6555123\",\n \"text\": \"def __init__(self, sim):\\n \\n self.sim = sim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f970c84bc604e6bdd43dc49c04ce6dee\",\n \"score\": \"0.6452032\",\n \"text\": \"def init_sim(self):\\n\\n logger.debug(\\\"Creating instance of Core.Engine with Simulation values values \\\"\\n \\\"target: {target}, mutation: {mutation}, and pop: {pop}\\\".format(\\n target=self.target,\\n mutation=self.mutation,\\n pop=self.pop\\n ))\\n self.simulation = Core.Engine(self.target, self.mutation, self.pop)\\n self.simulation.gen_population()\\n generationsRs.config(text=\\\"0\\\")\\n self.update_status(\\\"Population created\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188c44f48e0976fe8e88f87852d6a51c\",\n \"score\": \"0.63575655\",\n \"text\": \"def initialize():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417b16cdfac9127cf8aa0918b6069e9b\",\n \"score\": \"0.62683\",\n \"text\": \"def run_simulation_step(self):\\n\\n warnings.warn(\\n \\\"Simulation.run_simulation_step() is replaced by Simulation.run_step() and will be deprecated at or before defSim v1.0.0\\\",\\n category=FutureWarning)\\n return self.run_step()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a75bd7c6e621d3b7cb4d362fa77095c\",\n \"score\": \"0.6177357\",\n \"text\": \"def __init__(self):\\n raise NotImplementedError(\\n 'Initialization must be implemented by derived stimulus classes.'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2789dab0db585eeba24cbb69d5dbc7\",\n \"score\": \"0.6162097\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.simulated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f49a67d168c1ca5d86a289c0f2d8c8\",\n \"score\": \"0.61303383\",\n \"text\": \"def solver_initialize(self):\\n self.sim_time = 0. # current simulation time [day]\\n self.prepare_linear_system()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754df85a4d23e458ccbe3db2d0846e47\",\n \"score\": \"0.61241084\",\n \"text\": \"def setup(self, simgr):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb9a6d3aad56bf8f8f311830f3421a4\",\n \"score\": \"0.6120191\",\n \"text\": \"def __init__(self):\\n\\t\\tself.birth = time.time() # time that the simulator object was created.\\n\\t\\tself.n = 0 #counter for total number of samples in simulation. \\n\\t\\tself.sr = 25 #sample rate for simulators\\n\\t\\tself.noiseStd = 1 #standard deviation of the noise\\n\\t\\tself.sampleList = [] #initialize empty sample list that will contain all samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1698ec4128f8590d851728ca03da3e\",\n \"score\": \"0.6106263\",\n \"text\": \"def instantiateSimObject(self):\\n self.initSimObject()\\n self.getParamDefaults()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fdfb157ccb860909ab42bfe9fa6b64e\",\n \"score\": \"0.60808754\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.simulated = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc5096991d64545e843de7154e6cc0d3\",\n \"score\": \"0.6036654\",\n \"text\": \"def __init__(self, world):\\n\\n self.world: World = world\\n \\\"\\\"\\\"The world that is being simulated\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7292b2463c452bb2fc7801375981290d\",\n \"score\": \"0.5990851\",\n \"text\": \"def load_sim():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5344bd6c29b56809e5510401dbf8deef\",\n \"score\": \"0.59883094\",\n \"text\": \"def initialize(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce691c8eaad41bacd9b8ef5fe2efc5e\",\n \"score\": \"0.5984462\",\n \"text\": \"def run_simulation(self, initialize: bool = True) -> pd.DataFrame:\\n\\n warnings.warn(\\n \\\"Simulation.run_simulation() is replaced by Simulation.run() and will be deprecated at or before defSim v1.0.0\\\",\\n category=FutureWarning)\\n return self.run(initialize=initialize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73aba91e13dcecbdb307b4b7c79dc0a0\",\n \"score\": \"0.5969146\",\n \"text\": \"def _init_sim(config):\\n solver = findSolver()\\n\\n cmds.setAttr(solver + '.selfCollision', 1)\\n cmds.setAttr(solver + '.startTime', 1)\\n cmds.setAttr(solver + '.solverStatistics', 0) # for easy reading of console output\\n cmds.playbackOptions(ps=0, max=config['max_sim_steps']) # 0 playback speed = play every frame\\n\\n return solver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb049f5669700df5b869bd40d3df21a\",\n \"score\": \"0.59534264\",\n \"text\": \"def __init__(self, no_graphics = True):\\n self.env = UnityEnvironment(file_name=\\\"./Tennis_Linux/Tennis.x86_64\\\", no_graphics = no_graphics)\\n self.brain_name = self.env.brain_names[0]\\n self.brain = self.env.brains[self.brain_name]\\n\\n # reset the environment\\n self.env_info = self.env.reset(train_mode=True)[self.brain_name]\\n\\n # number of agents in the environment\\n print('Number of agents:', len(self.env_info.agents))\\n\\n # number of actions\\n self.action_size = self.brain.vector_action_space_size\\n print('Number of actions:', self.action_size)\\n\\n # examine the state space \\n state = self.env_info.vector_observations[0]\\n print('States look like:', state)\\n self.state_size = len(state)\\n print('States have length:', self.state_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21aa0a52a69dab6e82fdf2b81f9e1c9\",\n \"score\": \"0.5948905\",\n \"text\": \"def sim() -> merlin.Simulation:\\n return merlin.Simulation(\\n config=[],\\n outputs=set(),\\n name='test_sim')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f2d4db801ec354fdbd3c60b72f33af\",\n \"score\": \"0.5940551\",\n \"text\": \"def __init__(self, *args):\\n# print(_siglist)\\n _simulator._time = 0\\n arglist = _flatten(*args)\\n self._waiters, self._cosim = _makeWaiters(arglist)\\n if not self._cosim and _simulator._cosim:\\n warn(\\\"Cosimulation not registered as Simulation argument\\\")\\n self._finished = False\\n del _futureEvents[:]\\n del _siglist[:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb9d05cd9b69569865e24aa8a82c7d5\",\n \"score\": \"0.5898695\",\n \"text\": \"def init(self, data):\\n\\n self.simulation = Simulation(data['generator'], data['params'], data['fifo'], self._ws)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39a915ba421682739f72e91d34b52e2\",\n \"score\": \"0.5898405\",\n \"text\": \"def _initialize_simulation(self):\\n self._static_main_circle = plt.Circle((0, 0), 0.025, color='black', fill=True)\\n self._static_reference_mass_circle = plt.Circle((0, self._length), self._radius, color='grey', fill=False,\\n linestyle='--')\\n self._static_reference_line = plt.Line2D([0.0, 0.0], [0.0, self._length - self._radius], color='grey',\\n linestyle='--')\\n\\n self._dynamic_pendulum_line = plt.Line2D([0.0], [0.0], color='black')\\n self._dynamic_mass_circle = plt.Circle((np.inf, np.inf), self._radius, color='black', fill=True)\\n self._dynamic_state_text = plt.Text(1.5, 1, \\\"Pos: \\\\nVelo:\\\", horizontalalignment='right')\\n\\n if self._simulation_ax is None:\\n self._simulation_ax = self._create_figure()\\n\\n self._simulation_ax.axis('equal')\\n limit = 1.5 * self._length\\n self._simulation_ax.set_xlim(-limit, limit)\\n self._simulation_ax.set_ylim(-limit, limit)\\n self._simulation_ax.set_axis_off()\\n self._simulation_ax.set_title(\\\"1-DOF Inverted Pendulum Simulation\\\")\\n\\n self._simulation_ax.add_artist(self._static_main_circle)\\n self._simulation_ax.add_artist(self._static_reference_mass_circle)\\n self._simulation_ax.add_artist(self._static_reference_line)\\n\\n self._simulation_ax.add_artist(self._dynamic_pendulum_line)\\n self._simulation_ax.add_artist(self._dynamic_mass_circle)\\n self._simulation_ax.add_artist(self._dynamic_state_text)\\n\\n self._simulation_ready = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7be937a46fb0d0b2d6c88cb1977f897\",\n \"score\": \"0.58765113\",\n \"text\": \"def initialize(self):\\n\\n # reset steps\\n self.time_steps = 0\\n self.influence_steps = 0\\n\\n # reset tickwise output\\n self.initialize_tickwise_output()\\n\\n if self.seed is None:\\n self.seed = random.randint(10000, 99999)\\n random.seed(self.seed)\\n self.parameter_dict['np_random_generator'] = np.random.default_rng(self.seed)\\n\\n ## if deprecated ms_rewiring parameter is set in parameter dict, replace with network modifier\\n if 'ms_rewiring' in list(self.parameter_dict.keys()):\\n warnings.warn(\\n \\\"Setting ms_rewiring in parameter dict is deprecated. Pass an instance of MaslovSneppenModifier in network_modifiers instead.\\\",\\n DeprecationWarning)\\n if self.network_modifiers is None:\\n self.network_modifiers = [MaslovSneppenModifier(rewiring_prop=self.parameter_dict['ms_rewiring'])]\\n else:\\n self.network_modifiers.append(MaslovSneppenModifier(rewiring_prop=self.parameter_dict['ms_rewiring']))\\n\\n # read or generate network if no nx.Graph was provided, apply network modifiers\\n if self.network_provided:\\n if self.network == 'list':\\n self.network = self.parameter_dict.pop('network')\\n if not isinstance(self.network, nx.Graph) and self.network is not None:\\n self.network = network_init.read_network(self.network)\\n\\n ## apply network modifiers\\n if self.network_modifiers is not None:\\n for modifier in self.network_modifiers:\\n modifier.rewire_network(network)\\n else:\\n self.network = network_init.generate_network(self.topology, network_modifiers=self.network_modifiers,\\n **self.parameter_dict)\\n\\n # storing the indices of the agents to access them quicker\\n self.agentIDs = list(self.network)\\n\\n # initialize agent attributes (accepts string realizations and instances of AttributesInitializer classes)\\n agents_init.initialize_attributes(self.network, self.attributes_initializer, **self.parameter_dict)\\n\\n # initialization of distances between neighbors\\n self.dissimilarity_calculator.calculate_dissimilarity_networkwide(self.network, **self.parameter_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10dae8c1d968eb3ebe05d99854d0f32\",\n \"score\": \"0.58589745\",\n \"text\": \"def Simulation(parent):\\n simulation = parent.createChild(\\\"Simulation\\\")\\n simulation.createObject(\\\"EulerImplicitSolver\\\")\\n simulation.createObject(\\\"CGLinearSolver\\\", iterations=250, tolerance=1e-20, threshold=1e-20)\\n return simulation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228b70553f8a23ae4a45fb7954e7c928\",\n \"score\": \"0.58466333\",\n \"text\": \"def initialize_simulator(config):\\n\\n simulator = config.get('simulation', 'simulator')\\n print(\\\"Initializing {} simulator...\\\\n\\\".format(simulator))\\n if simulator in config_string_to_set_of_strings(\\n config.get('restrictions', 'simulators_pyNN')):\\n if simulator == 'spiNNaker':\\n try:\\n sim = import_module('pyNN.' + simulator)\\n except ImportError:\\n sim = import_module('spynnaker8')\\n else:\\n sim = import_module('pyNN.' + simulator)\\n\\n # From the pyNN documentation:\\n # \\\"Before using any other functions or classes from PyNN, the user\\n # must call the setup() function. Calling setup() a second time\\n # resets the simulator entirely, destroying any network that may\\n # have been created in the meantime.\\\"\\n sim.setup(timestep=config.getfloat('simulation', 'dt'))\\n return sim\\n if simulator == 'brian2':\\n return import_module('brian2')\\n if simulator == 'loihi':\\n import nxsdk.api.n2a as sim\\n return sim\\n sim_module_str = None\\n if simulator == 'INI':\\n spike_code = config.get('conversion', 'spike_code')\\n sim_module_str = 'inisim.' + spike_code\\n if spike_code == 'temporal_mean_rate':\\n sim_module_str += '_' + config.get('simulation', 'keras_backend')\\n elif simulator == 'MegaSim':\\n sim_module_str = 'megasim.megasim'\\n if sim_module_str is None:\\n sim_module_str = 'inisim.temporal_mean_rate_theano'\\n sim = import_module('snntoolbox.simulation.backends.' + sim_module_str)\\n assert sim, \\\"Simulator {} could not be initialized.\\\".format(simulator)\\n return sim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c12a27c41e44be8768e11ea7ef5ef0\",\n \"score\": \"0.5825551\",\n \"text\": \"def __setup_simulation(self):\\n # Nest stuff\\n nest.ResetKernel()\\n # http://www.nest-simulator.org/sli/setverbosity/\\n nest.set_verbosity('M_INFO')\\n nest.SetKernelStatus(\\n {\\n 'resolution': self.dt,\\n 'local_num_threads': 1,\\n 'overwrite_files': True\\n }\\n )\\n # Since I've patched NEST, this doesn't actually update connectivity\\n # But, it's required to ensure that synaptic elements are connected\\n # correctly when I form or delete new connections\\n nest.EnableStructuralPlasticity()\\n nest.SetStructuralPlasticityStatus({\\n 'structural_plasticity_update_interval':\\n int(self.sp_update_interval),\\n })\\n\\n self.__setup_neurons()\\n self.__create_neurons()\\n self.__get_regions()\\n self.__setup_detectors()\\n self.__setup_initial_connection_params()\\n self.__create_initial_connections()\\n self.__setup_files()\\n\\n self.dump_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf575f955b01054f9540a13a9c2744c6\",\n \"score\": \"0.58068424\",\n \"text\": \"def initialize(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4228f0053fc9fdbf5c1b354eaf3a9d1d\",\n \"score\": \"0.5793719\",\n \"text\": \"def run(self):\\n home, work, positions = initialization(self.params)\\n simulate(params=self.params,plt=plt,callback=self.callback,home=home,work=work,positions=positions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b5e4bcbc9516d2cd972af379bf2ffb\",\n \"score\": \"0.57527983\",\n \"text\": \"def task_2_7_1():\\n # TODO Task 2.7.1: Your code goes here\\n sim_param = SimParam()\\n random.seed(sim_param.SEED)\\n sim = Simulation(sim_param)\\n \\n do_simulation_study(sim)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8419f6e099a425ce9a5e2bbb6fbbe83\",\n \"score\": \"0.5742213\",\n \"text\": \"def startSimulation(self):\\n self.master.mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2caba21d0e67902a4ce992ce087761\",\n \"score\": \"0.5727336\",\n \"text\": \"def initialize(self, rng):\\n # being abstract, i don't know what to do here\\n raise NotImplementedError(\\n f\\\"class '{type(self).__name__}' must implement 'initialize'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8601301dd6d8c18d50f3d45e6720ce95\",\n \"score\": \"0.57254976\",\n \"text\": \"def initialize(self):\\r\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d17a6f34186b16ff4476a9362b39b7d\",\n \"score\": \"0.57227135\",\n \"text\": \"def Initialize():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6956e2f3a5cb33be8e1bf3e71b6386c\",\n \"score\": \"0.57202876\",\n \"text\": \"def simulation(self):\\n self.qmg.simulation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"affc4f297652c37556fd6a5447753513\",\n \"score\": \"0.57173765\",\n \"text\": \"def _initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf84de7ae2dfb796abfeb2207130563c\",\n \"score\": \"0.5714358\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.1) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d382d2dde0414dc2ca3712c1e487da\",\n \"score\": \"0.5708899\",\n \"text\": \"def init(self, sim: \\\"sims.MeshSimulation\\\"):\\n self.sim = sim\\n self.spec = sim.spec\\n\\n self.attach()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf5d8fc6002b7e94475672743cb08870\",\n \"score\": \"0.5708061\",\n \"text\": \"def initialize(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3ba65fb93b236d87323d067dedecce\",\n \"score\": \"0.57060385\",\n \"text\": \"def __init__(self):\\n # self.position = vector(self.np_random.rand()*0.01, PLATHEIGHT)\\n # self.velocity = vector(self.np_random.rand()*0.0001, 0.0)\\n self.position = np.array((0., Constants.PLATFORM_HEIGHT))\\n self.velocity = np.array((0., 0.0))\\n self.np_random = np.random # overwritten by seed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1913205420b06e6fc1c4ca43b29f1563\",\n \"score\": \"0.56979334\",\n \"text\": \"def __init__(self, lifespan=None, test=False):\\n BaseWorld.__init__(self, lifespan)\\n self.reward_magnitude = 1.\\n self.energy_cost = 0.01 * self.reward_magnitude\\n self.jump_fraction = 0.1\\n self.name = 'grid_1D_noise'\\n self.name_long = 'noisy one dimensional grid world'\\n print \\\"Entering\\\", self.name_long\\n self.num_real_sensors = 3\\n # Number of sensors that have no basis in the world. \\n # These are noise meant to distract.\\n self.num_noise_sensors = 15 \\n self.num_sensors = self.num_noise_sensors + self.num_real_sensors\\n self.num_actions = 2\\n self.action = np.zeros((self.num_actions,1))\\n self.world_state = 0 \\n self.simple_state = 0 \\n self.world_visualize_period = 1e6\\n self.brain_visualize_period = 1e3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.56942075\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.56942075\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.56942075\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.56942075\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.56942075\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4ac587230560e08dd42511d6751861\",\n \"score\": \"0.5690917\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001) # reduce update_delay to speed up simulation\\n sim.run(n_trials=120) # press Esc or close pygame window to quit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a9c4bbb9d8db5e083d6783874c551f\",\n \"score\": \"0.56885207\",\n \"text\": \"def main():\\n # load parameters\\n P = {}\\n with open(PARAMETER_FILE) as pFile:\\n P = yaml.load(pFile, Loader=yaml.FullLoader)\\n\\n init_state = [P[\\\"startingX\\\"], P[\\\"startingY\\\"], np.radians(P['startingAngle'])]\\n robot = Agent(init_state, P['w'], P['l'], P['d'], P['roomWidth'], P['roomHeight'],\\n P['maxrpm'], P['lstddev'], P['astddev'], P['mstddev'])\\n loop(robot, init_state)\\n\\n #init_state = [5000, 5000, np.pi/2]\\n\\n #robot = Agent(init_state=init_state)\\n #loop(P, robot, init_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8257b176e2787e5306f6041173a035e1\",\n \"score\": \"0.56856984\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098d0db20f5ed3ccbd37223dc40c9521\",\n \"score\": \"0.5682455\",\n \"text\": \"def do_simulation(self):\\n self._configure_slurm()\\n self._run_simulation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa1d738e9e12194a1cecdac21df7abe\",\n \"score\": \"0.5674467\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 3\\n\\n self.state_size = self.action_repeat * 6\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58154aa312ea889c06e16569df223d6\",\n \"score\": \"0.56701064\",\n \"text\": \"def __init__(self):\\n self.armSimulator = Kinematics()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3a30c33c9fa9506503a26aa1ad8682\",\n \"score\": \"0.5669107\",\n \"text\": \"def __init__(self):\\n self.trainable = False\\n self.phase = None\\n self.model = None\\n self.device = None\\n self.last_state = None\\n self.time_step = None\\n # if agent is assumed to know the dynamics of real world\\n self.env = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf4667fe4966953c31b3499144a3887\",\n \"score\": \"0.56642395\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 3\\n\\n self.state_size = self.action_repeat * 6\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55402bb166fc0895b7363501e3770ee3\",\n \"score\": \"0.56626606\",\n \"text\": \"def setupSimulation(self):\\n if self.log and self.verbose > 1:\\n self.log.info('Simulator: setupSimulation(): instConf:%s ' % (self.instrumentConf))\\n\\n # Instantiate the Instrument class\\n self.telescope = Instrument(lsstDB=self.lsstDB, sessionID=self.sessionID,\\n dbTableDict=self.dbTableDict, obsProfile=self.obsProfile,\\n instrumentConf=self.instrumentConf, log=self.log, logfile=self.logfile,\\n verbose=self.verbose)\\n self.telescope.Park()\\n\\n # Init the Filters class\\n self.filters = Filters(lsstDB=self.lsstDB, filtersConf=self.filtersConf, sessionID=self.sessionID,\\n dbTableDict=self.dbTableDict, telSeeing=self.telSeeing,\\n opticalDesSeeing=self.opticalDesSeeing, cameraSeeing=self.cameraSeeing,\\n scaleToNeff=self.scaleToNeff, atmNeffFactor=self.atmNeffFactor,\\n log=self.log, logfile=self.logfile, verbose=self.verbose)\\n\\n # Init the Astronomical Sky\\n self.sky = AstronomicalSky(lsstDB=self.lsstDB, configFile=self.astroskyConf,\\n obsProfile=self.obsProfile, date=0.,\\n sessionID=self.sessionID, dbTableDict=self.dbTableDict, log=self.log,\\n logfile=self.logfile, verbose=self.verbose)\\n\\n self.schedulingData = SchedulingData(self.schedulingDataConf, 0, self.nRun * YEAR, self.sky,\\n self.lsstDB, self.sessionID)\\n\\n # Init the Weather Simulator\\n self.weather = Weather(lsstDB=self.lsstDB, date=0., dbTableDict=self.dbTableDict,\\n seeingEpoch=self.seeingEpoch, simStartDay=self.simStartDay, log=self.log,\\n logfile=self.logfile, verbose=self.verbose)\\n\\n # Define the telescope as a Resource with a priority queue.\\n # self.telResource = Simulation.Resource ()\\n\\n # We also need a Obs Scheduler\\n self.obsScheduler = ObsScheduler(lsstDB=self.lsstDB, schedulingData=self.schedulingData,\\n obsProfile=self.obsProfile, dbTableDict=self.dbTableDict,\\n telescope=self.telescope, weather=self.weather, sky=self.sky,\\n filters=self.filters, sessionID=self.sessionID,\\n runSeeingFudge=self.runSeeingFudge, schedulerConf=self.schedulerConf,\\n log=self.log, logfile=self.logfile, verbose=self.verbose)\\n\\n # Create a TAC object and activate it.\\n self.tac = TAC(lsstDB=self.lsstDB, nRun=self.nRun, schedulingData=self.schedulingData, sky=self.sky,\\n weather=self.weather, obsScheduler=self.obsScheduler, sessionID=self.sessionID,\\n fov=self.fov, filters=self.filters, nearEarthConf=self.nearEarthConf,\\n weakLensConf=self.weakLensConf, superNovaConf=self.superNovaConf,\\n superNovaSubSeqConf=self.superNovaSubSeqConf, kuiperBeltConf=self.kuiperBeltConf,\\n WLpropConf=self.WLpropConf, targetList=self.targetList, dbTableDict=self.dbTableDict,\\n log=self.log, logfile=self.logfile, verbose=self.verbose)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e63bcdef783aad7ef437c0c76edd1cf\",\n \"score\": \"0.56589377\",\n \"text\": \"def task_2_7_2():\\n # TODO Task 2.7.2: Your code goes here or in the function above\\n sim_param = SimParam()\\n random.seed(sim_param.SEED)\\n sim = Simulation(sim_param)\\n sim.sim_param.SIM_TIME = 1000000\\n \\n do_simulation_study(sim)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962a920d65c962e6d8d53c5ca2c5dabf\",\n \"score\": \"0.56458575\",\n \"text\": \"def registerInitialState(self, gameState):\\n\\n '''\\n Make sure you do not delete the following line. If you would like to\\n use Manhattan distances instead of maze distances in order to save\\n on initialization time, please take a look at\\n CaptureAgent.registerInitialState in captureAgents.py.\\n '''\\n CaptureAgent.registerInitialState(self, gameState)\\n\\n '''\\n Your initialization code goes here, if you need any.\\n '''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6765943dc6cf62a21b9a081bb9a4e1\",\n \"score\": \"0.5644654\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 3\\n\\n self.state_size = self.action_repeat * 6\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([3., 3., 10.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ed478eff0282d14075b0228c6fa570\",\n \"score\": \"0.56286454\",\n \"text\": \"def start():\\n env = simpy.Environment() # defining that env is a simpy envirnment class\\n\\n car = Virus(env) # starts the procces of the simulation the func\\n env.run(until=lengh_of_sim+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d27ac12c48af8b61888e0b5ef754ee\",\n \"score\": \"0.562734\",\n \"text\": \"def initialize(self): # pragma: no cover\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945cc72591445f736b58674267346ff2\",\n \"score\": \"0.56238854\",\n \"text\": \"def _initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945cc72591445f736b58674267346ff2\",\n \"score\": \"0.56238854\",\n \"text\": \"def _initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945cc72591445f736b58674267346ff2\",\n \"score\": \"0.56238854\",\n \"text\": \"def _initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e8242b6c68e507548ebf412cbfbb05\",\n \"score\": \"0.5608538\",\n \"text\": \"def initiate(self, session):\\n\\n # Raise reproman.support.exceptions.CommandError exception if\\n # Singularity is not to be found.\\n session.execute_command(['singularity', 'selftest'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.5608222\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f534f24c501f10f24ed4736234bfda6\",\n \"score\": \"0.5604452\",\n \"text\": \"async def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56ebaeb62f5c8ae6c0d206cdfe73c26\",\n \"score\": \"0.5603592\",\n \"text\": \"def __init__(self):\\n # initialisation function, it should not wait\\n smach.State.__init__(self,outcomes=['someTimes'])\\n\\n ## Define the value at wich it is performed the state transition\\n self.transition_value = random.randint(min_transition_play_normal,max_transition_play_normal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bdacff4e1a7d7fd4a258f463f719b8\",\n \"score\": \"0.56013787\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 1\\n\\n self.state_size = self.action_repeat * 2\\n self.action_low = 390\\n self.action_high = 420\\n self.action_range = self.action_high - self.action_low \\n self.action_size = 1\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a1bf978d3a47b49e3458091ac6787c\",\n \"score\": \"0.55938363\",\n \"text\": \"def init(self, state, **runopts):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7bacdfc02e3748042ad0d9ff54be61f\",\n \"score\": \"0.55849993\",\n \"text\": \"def initialize(self):\\n # TODO: might want to change initialization order? order/wait length depends on where the arm position was, and we can't know that\\n self.hand.initialize()\\n self.wrist.initialize()\\n time.sleep(0.5)\\n self.elbow.initialize()\\n time.sleep(1)\\n self.lift_front.initialize()\\n self.lift_out.initialize()\\n time.sleep(1)\\n self.twist.initialize()\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e704081e9b57ca08622f5bb9b6ba32cd\",\n \"score\": \"0.55800724\",\n \"text\": \"def __init__(self):\\n super(RLGlueWorld, self).__init__()\\n\\n self.agent = RLGlueAgent()\\n self.thread = threading.Thread(target=(lambda: _start_rlglue_agent(self.agent)))\\n\\n # Start the thread and wait for it to be initialized\\n print('Starting RL-Glue thread...')\\n\\n self.thread.start()\\n self.actions = self.agent.numberOfActions()\\n self.initial = self.agent.observation()[0] # First observation sent by RL-Glue\\n\\n print('Started!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af98a8d328519af92e46a9c34206894e\",\n \"score\": \"0.55772257\",\n \"text\": \"def reconfigure_sim(self) -> None:\\n # configure our sim_settings but then set the agent to our default\\n self.cfg = make_cfg(self.sim_settings)\\n self.agent_id: int = self.sim_settings[\\\"default_agent\\\"]\\n self.cfg.agents[self.agent_id] = self.default_agent_config()\\n\\n if self.sim is None:\\n self.sim = habitat_sim.Simulator(self.cfg)\\n\\n else: # edge case\\n if self.sim.config.sim_cfg.scene_id == self.cfg.sim_cfg.scene_id:\\n # we need to force a reset, so change the internal config scene name\\n self.sim.config.sim_cfg.scene_id = \\\"NONE\\\"\\n self.sim.reconfigure(self.cfg)\\n\\n self.active_scene_graph = self.sim.get_active_scene_graph()\\n self.default_agent = self.sim.get_agent(self.agent_id)\\n self.agent_body_node = self.default_agent.scene_node\\n self.render_camera = self.agent_body_node.node_sensor_suite.get(\\\"color_sensor\\\")\\n\\n Timer.start()\\n self.step = -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f3c6b96536f4f352aa3b3b7ff6e11a\",\n \"score\": \"0.5576994\",\n \"text\": \"def init() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fc93ecdcdc53214dfd02963936a0a75\",\n \"score\": \"0.5575788\",\n \"text\": \"def initSimObject(self):\\n if self.componentName == \\\"Root\\\":\\n self.simObjectInstance = getRoot()\\n else:\\n self.simObjectInstance = self.simObject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1172e899d2894d2dbdd52258f5bcd3\",\n \"score\": \"0.5567018\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=1000) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n for i in a.qt.items(): print(str(i).replace('(','').replace(')',''))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0f3f5dfc86b523e89eaa4ba43f615\",\n \"score\": \"0.55600816\",\n \"text\": \"def _reset_sim(self):\\n self.pr.stop()\\n #print (self.init_angles, end = '\\\\r')\\n self.ecm.setJointAngles(self.init_angles)\\n self.pr.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3027d2508342008d4c6b64e7eeb68b3\",\n \"score\": \"0.5559209\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 3\\n \\n self.unique_state_size = np.concatenate(([self.sim.pose] + [self.sim.v] + [self.sim.angular_v])).shape[0]\\n \\n self.state_size = self.action_repeat * self.unique_state_size\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n \\n # Origin\\n self.init_pose = init_pose\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be7a2e8dea5623a58e9cd0efcbc76a41\",\n \"score\": \"0.55560356\",\n \"text\": \"def initialize(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e2b7ce233653b1a760a74c3bee7dea\",\n \"score\": \"0.5552046\",\n \"text\": \"async def initialize(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6ab876675987765e8f40a5849f53ea\",\n \"score\": \"0.55503315\",\n \"text\": \"def __init__(self, session):\\n super(JointEstimation, self).__init__(session)\\n import models\\n self.models = models\\n self.experiment_repeats = 1\\n self.setup()\\n self.num_participants = 10 # desired total participants, not dyads\\n self.initial_recruitment_size = 10\\n self.completion_bonus_payment = .33\\n self.accuracy_bonus_payment = 2\\n self.total_test_trials = 15\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65ed4429bcfaa893e7cf854c5c6159f\",\n \"score\": \"0.5545332\",\n \"text\": \"def initialize(self, sess):\\n sess.run(self._update_target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5320c6ecfaa893abd88dffbc204614d\",\n \"score\": \"0.5539388\",\n \"text\": \"def initialize(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5320c6ecfaa893abd88dffbc204614d\",\n \"score\": \"0.5539388\",\n \"text\": \"def initialize(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5320c6ecfaa893abd88dffbc204614d\",\n \"score\": \"0.5539388\",\n \"text\": \"def initialize(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e50e628522336cabb0321ceec54589f\",\n \"score\": \"0.55339056\",\n \"text\": \"def main():\\n\\tif fm_sim == \\\"snana\\\":\\n \\tsampler = abc.ABC_class(nparam,npart,dataLC,tlevels,niter,prior,**prop)\\n\\telif fm_sim == \\\"sncosmo\\\":\\n \\tsampler = abc.ABC_class(nparam,npart,data,tlevels,niter,prior,**prop)\\n model_sim = run_simulation\\n\\n sampler.sample(model_sim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ce2478f8261714ea92a80faca07932\",\n \"score\": \"0.5530867\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=2, display=True) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef606f36015015159f4eb7ac4fcb028\",\n \"score\": \"0.5530597\",\n \"text\": \"def run():\\n\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\\n\\n policy = a.Q_values\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(TrainedAgent, policy) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n print(chr(27) + \\\"[2J\\\")\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3ea59fdfec4d68b3b36563d98a6ffd\",\n \"score\": \"0.5527462\",\n \"text\": \"def simulation_factory(timestep, scheme):\\n lsi = LangevinSplittingIntegrator(scheme, timestep=timestep, temperature=temperature,\\n collision_rate=collision_rate)\\n\\n simulation = app.Simulation(topology, system, lsi, platform)\\n simulation.context.setPositions(positions)\\n simulation.context.setVelocitiesToTemperature(temperature)\\n return simulation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2cde19b518d4cc9b853875b6f9ddb0\",\n \"score\": \"0.551768\",\n \"text\": \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime)\\n self.sim.reset()\\n self.sim.init_pose = self.sim.pose\\n self.action_repeat = 4\\n\\n self.state_size = self.action_repeat * 6\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n \\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.]) \\n #self.target = np.append(self.target_pos, np.array([0,0,0]))\\n \\n if np.linalg.norm(self.sim.init_pose[:3] - self.target_pos) < 1.0:\\n self.norm_targ_dist = 1.0\\n else:\\n self.norm_targ_dist = np.linalg.norm(self.sim.init_pose[:3] - self.target_pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c434486abc7bbd31584a256a30c26977\",\n \"score\": \"0.5514013\",\n \"text\": \"def __init__(self, root: object, size=50, num_particle=50):\\n self.size = size\\n self._particles = [] # all particles in the simulation\\n self._field = Field(size)\\n self.step = 0\\n self._view = SimulatorView(root, size)\\n self._colours = {State.SUSCEPTIBLE: 'slate blue',\\n State.INFECTED: 'red',\\n State.RECOVERED: 'spring green',\\n State.DEAD: 'black'}\\n self.reset(num_particle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54894e133505ea04ae6bd4134d7d383\",\n \"score\": \"0.5511776\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.01, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af011961187bfc2b3dc53c83d90a2f1\",\n \"score\": \"0.5511652\",\n \"text\": \"def setSimulation(self, simulation):\\r\\n\\t\\tself.simulationHandle = simulation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443dae78141c6fd2ffc6b5a31d21dd1d\",\n \"score\": \"0.55072814\",\n \"text\": \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n return sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1472,"cells":{"query_id":{"kind":"string","value":"e832510024261fa3c983fe9e7e7854e3"},"query":{"kind":"string","value":"Delete a new BridgeDomain."},"positive_passages":{"kind":"list like","value":[{"docid":"4b6866a437046c6754ad6e6ff7cc6f3a","score":"0.0","text":"def delete(self, node_type, node_id):\n url = self._url('node', node_type, node_id)\n self._delete(url)","title":""}],"string":"[\n {\n \"docid\": \"4b6866a437046c6754ad6e6ff7cc6f3a\",\n \"score\": \"0.0\",\n \"text\": \"def delete(self, node_type, node_id):\\n url = self._url('node', node_type, node_id)\\n self._delete(url)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"216d0d7b993759eb24ef3fbae02bc061","score":"0.7052366","text":"def delete_domain(self, domain_spec, delete_dependencies=False):\n pass","title":""},{"docid":"f324136af1685db477ea2804a46409e7","score":"0.69567776","text":"def delete_domain(self, domain_id):\n raise exception.NotImplemented() # pragma: no cover","title":""},{"docid":"a8444792de489dc6362e28c5796e1044","score":"0.6765949","text":"def gdomain_del(browser: Browser, domain: str, hostname: str) -> None:\n hostname = fqdn(hostname, domain)\n\n # find the right div for this hostname\n records = get_synthetic_records_div(browser)\n # xpath = \"//div[contains(@class, 'H2OGROB-d-t')]\"\n xpath = f\"//div[contains(text(), '{hostname}')]/../..\"\n divs = records.find_by_xpath(xpath)\n div = divs.first\n\n # click the delete button\n delete_button = get_element_by_substring(\"Delete\", div.find_by_tag(\"button\"))\n delete_button.click()\n\n # wait for the modal dialog\n wait_for_tag(browser, \"h3\", \"Delete synthetic record?\")\n\n # get the form element for the modal dialog\n modal_form = get_element_by_substring(\n \"Delete synthetic record?\", browser.find_by_tag(\"form\")\n )\n modal_button = get_element_by_substring(\"Delete\", modal_form.find_by_tag(\"button\"))\n modal_button.click()\n\n wait_for_success_notification(browser)","title":""},{"docid":"df1ca4f4c2b618e3b1480bba4a14e5ba","score":"0.67142767","text":"def delete(self,\n domain_id,\n ):\n return self._invoke('delete',\n {\n 'domain_id': domain_id,\n })","title":""},{"docid":"2a990e61380fbf5b934cacf33b6c5821","score":"0.63781595","text":"def test_container_project_registry_domain_delete(self):\n pass","title":""},{"docid":"d46a8cd75a70274dfd378bf940c77456","score":"0.6328905","text":"def delete_domain_name(self, domainName: str):\n pass","title":""},{"docid":"33220f192910a49f85bb115ced2b41f6","score":"0.6155003","text":"def test_delete_active_domain(self):\n domain = self.create_domain()\n domain_id = domain['id']\n\n self.addCleanup(self.delete_domain, domain_id)\n\n # domain need to be disabled before deleting\n self.assertRaises(lib_exc.Forbidden, self.domains_client.delete_domain,\n domain_id)","title":""},{"docid":"a30a2cdda9429bfae118bd1cea3552bb","score":"0.611994","text":"def test_delete_relaydomain(self):\n self.ajax_post(\n reverse(\"modoboa_admin_relaydomains:relaydomain_delete\",\n args=[self.rdom.id]),\n {}\n )\n with self.assertRaises(RelayDomain.DoesNotExist):\n RelayDomain.objects.get(name='relaydomain.tld')","title":""},{"docid":"18ad3d27d61f9621ca7f6228b18992fa","score":"0.6032309","text":"def delete(domain_name):\n exists = False\n for key, domain in custom_domains.items():\n exists = domain_name == domain.get('domain')\n id = key\n if exists: break\n if not exists:\n return do_error(404, 'domain not found')\n\n del custom_domains[id]\n return make_response({'domain' : domain_name}, 200)","title":""},{"docid":"209d44d76fc794a3de53b57276725445","score":"0.6026429","text":"def destroy(self, domain):\n return domain.destory()","title":""},{"docid":"f46c04f2e610401739294275d4f4447d","score":"0.5919469","text":"def delete_domain(self, domain, check=True):\n self._client.update(domain, enabled=False)\n self._client.delete(domain.id)\n\n if check:\n self.check_domain_presence(domain, must_present=False)","title":""},{"docid":"313b00ad935ebd3f6f5bdd84aefaf2b0","score":"0.5897445","text":"def on_delete_excluded_domain(self):\n del_crisp: QObject = self.sender()\n self.domain_list.remove(del_crisp.title)\n self.render_domains()","title":""},{"docid":"9251892152d87e1d21f8762f3ed9e4d7","score":"0.58448267","text":"def delete_domain(self, domain_id):\n try:\n url = self.url_path_prefix + \"ldap/%s\" % domain_id\n rc, response = self.request(self.url_path_prefix + \"ldap/%s\" % domain_id, method=\"DELETE\")\n except Exception as error:\n self.module.fail_json(msg=\"Failed to delete LDAP domain. Array Id [%s]. Error [%s].\" % (self.ssid, to_native(error)))","title":""},{"docid":"99f1fc141dd946215602748cb363f366","score":"0.584052","text":"def test_delete_non_existent_domain(self):\n self.assertRaises(lib_exc.NotFound, self.domains_client.delete_domain,\n data_utils.rand_uuid_hex())","title":""},{"docid":"408662cf703d17fc8382e65aac573032","score":"0.58352196","text":"def cmd_host_delete():\n # Parse JSON body w/ API params\n try:\n body = request.data.decode('utf-8')\n in_obj = json.loads(body)\n except Exception as err:\n logger.error(\"Failure: {0}\".format(err))\n return http400(\"JSON Decode failed. Check JSON POST data structure on request.\")\n\n # Validate JSON object basics\n try:\n if 'name' not in in_obj or 'pkh' not in in_obj:\n return http400(\"Missing name/pkh\")\n\n domain = \"21\"\n name = in_obj['name']\n pkh = in_obj['pkh']\n\n if (not valid_name(name)):\n return http400(\"Invalid name param: {}\".format(name))\n\n if not db.valid_domain(domain):\n return http404(\"Domain not found\")\n\n except Exception as err:\n logger.error(\"Failure: {0}\".format(err))\n return http400(\"JSON validation exception\")\n\n # Verify host exists and pkh matches\n try:\n hostinfo = db.get_host(name, domain)\n if hostinfo is None:\n return http404(\"Name parameter not found: {}\".format(name))\n\n # Validate the submitted pkh matches the one in the db\n if (pkh != hostinfo['pkh']):\n return http403(\"pkh does not match existing record.\")\n\n except Exception as err:\n logger.error(\"Failure: {0}\".format(err))\n return http500(\"DB Exception - get host\")\n\n # Validate the signature on the message\n try:\n validate_sig(body, request.headers.get('X-Bitcoin-Sig'), pkh)\n except PermissionError as err:\n logger.error(\"Failure: {0}\".format(err))\n return http403(err)\n\n # Remove from database. Rely on db to filter out dups.\n try:\n if not nsupdate_exec(name, domain, []):\n http500(\"nsupdate failure\")\n db.delete_host(name, domain)\n except Exception as err:\n logger.error(\"Failure: {0}\".format(err))\n return http400(\"DB Exception - delete host\")\n\n return http200(\"Record successfully deleted.\")","title":""},{"docid":"c91625d9b2d56d61ffe978e88f305ce3","score":"0.58131385","text":"def delete(self, dns_zone):\n pass","title":""},{"docid":"4cbe88f63c48bdcc8b04756a04e5dc68","score":"0.57967484","text":"def del_domain(self, domain_name):\n self._domain_set.discard(domain_name)\n self.write_config()","title":""},{"docid":"79b15a5b94fcae1ae2411ada439bba48","score":"0.57900757","text":"def test_delete_relaydomainalias(self):\n self._relay_domain_alias_operation(\n 'del', self.rdom.name, 'relaydomainalias.tld'\n )\n with self.assertRaises(RelayDomain.DoesNotExist):\n RelayDomain.objects.get(name='relaydomainalias.tld')","title":""},{"docid":"21eadbb8e00a378f7e756d011928370b","score":"0.5699527","text":"def handle_delete(self):\n LOG.debug(\"CloudDns handle_delete called.\")\n if self.resource_id:\n try:\n dom = self.cloud_dns().get(self.resource_id)\n dom.delete()\n except NotFound:\n pass","title":""},{"docid":"bf8f5970ce7561aaebfa73398272b7d8","score":"0.5695885","text":"def remove_model(self, d):\n with self.session_scope() as session:\n if isinstance(d, UniprotDomain):\n session.execute(\n 'delete from {0}.uniprot_domain_model where uniprot_domain_id = {1}'\n .format(conf.CONFIGS['db_schema'], d.uniprot_domain_id))\n elif isinstance(d, UniprotDomainPair):\n session.execute(\n 'delete from {0}.uniprot_domain_pair_model where uniprot_domain_pair_id = {1}'\n .format(conf.CONFIGS['db_schema'], d.uniprot_domain_pair_id))\n else:\n raise Exception(\"'d' is of incorrect type!\")","title":""},{"docid":"211b1ff2eeaa9e936631e13c51497300","score":"0.56918234","text":"def deleteZone(self, domain):\n querystr = 'killdns?domain=%s' % domain\n result = self.cQuery(querystr)\n logger.error(\"result: %s\" % result)\n try:\n if result[\"result\"][0][\"status\"] == 1:\n return True\n except Exception as ex:\n logger.error('%s threw exception: %s' % (querystr, ex))\n\n return False","title":""},{"docid":"28417f85a6f50a8043b438427f457e5e","score":"0.56803626","text":"def delete(self, *args, **kwargs):\n if kwargs.pop('validate_glue', True):\n if self.nameserver_set.exists():\n raise ValidationError(\n \"Cannot delete the record {0}. It is a ' 'glue record.\"\n .format(self.record_type()))\n if kwargs.pop('check_cname', True):\n if CNAME.objects.filter(target=self.fqdn):\n raise ValidationError('A CNAME points to this {0} record. '\n 'Change the CNAME before deleting this '\n 'record.'.format(self.record_type()))\n\n from cyder.cydns.utils import prune_tree\n objs_domain = self.domain\n super(BaseAddressRecord, self).delete(*args, **kwargs)\n prune_tree(objs_domain)","title":""},{"docid":"5b7a69ce04b560f95ad527bd2ab60c36","score":"0.5633319","text":"def test_delete(self):\n dom = Domain.objects.get(name=\"test.com\")\n self.ajax_post(\n reverse(\"admin:domain_delete\", args=[dom.id]),\n {}\n )\n with self.assertRaises(Domain.DoesNotExist):\n Domain.objects.get(pk=1)","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.5596321","text":"def delete():","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.5596321","text":"def delete():","title":""},{"docid":"aef1389401297c0d8a1aae3367b254c2","score":"0.55897075","text":"def delete_bd(self, count, start=1):\n for b in range(start, start + count):\n bd_id = self.bd_map[b]\n for j in self.bd_if_range(b):\n pg_if = self.pg_interfaces[j]\n self.vapi.sw_interface_set_l2_bridge(\n rx_sw_if_index=pg_if.sw_if_index, bd_id=bd_id, enable=0\n )\n self.pg_in_bd.remove(pg_if)\n self.logger.info(\n \"pg-interface %s removed from bridge domain ID %d\"\n % (pg_if.name, bd_id)\n )\n self.vapi.bridge_domain_add_del_v2(bd_id=bd_id, is_add=0)\n self.bd_map.pop(b)\n self.bd_list.remove(bd_id)\n self.bd_deleted_list.append(bd_id)\n self.logger.info(\"Bridge domain ID %d deleted\" % bd_id)","title":""},{"docid":"607b40cc2f5d48ae143298933d5e131d","score":"0.55100995","text":"def delete(self) -> None:","title":""},{"docid":"607b40cc2f5d48ae143298933d5e131d","score":"0.55100995","text":"def delete(self) -> None:","title":""},{"docid":"bc167aa2a029b787673b723ef136d0c6","score":"0.54946524","text":"def delete(self):\n self._api.post(self._api.url + \"doDelete\")","title":""},{"docid":"02be08c025147971adb51eb2316e7070","score":"0.546674","text":"def create_domain(self, domain_id, domain):\n raise exception.NotImplemented() # pragma: no cover","title":""},{"docid":"36b1ee98af6c5dfb41278f39ad723cda","score":"0.5461764","text":"def delete(name, **kwargs):\n dom = defineDomain(name)\n if queries.get_vm_state(name) == 1:\n dom.destroy()\n mac = getMAC(name)\n queries.unAssignIP(mac)\n queries.delete_vm(name)\n manageBridges.delConfHost(mac) # send\n res = 'VM {} was destroyed'.format(name)\n dom.undefine()\n return res","title":""},{"docid":"b16919bdc178538fcdaf5a149a90416a","score":"0.54525614","text":"def delete(self, host_name):\n host_name = common.validate_hostname(host_name)\n dn = '%s=%s,%s' % (self.host_key, host_name, self.host_container_dn)\n msg = 'Deleting %s' % dn\n self.log.debug(msg)\n result = self._delete_object(dn)\n self.log.debug('Result: %s' % result)\n return result","title":""},{"docid":"70da5bd337a192b43090bf1d94e04844","score":"0.5437967","text":"def delete(self, backend=...):\n ...","title":""},{"docid":"6afaa0831f3c94079998f97e24aa142d","score":"0.54223454","text":"def delete_by_dn(self, dn_name):\n dn_object = self.moDir.lookupByDn(dn_name)\n if dn_object is not None:\n dn_object.delete()\n self.commit(dn_object)","title":""},{"docid":"4d0e47ab5e189333f7ec90f056ea06f3","score":"0.5421466","text":"def del_resource_record(self):\n self.ssh.run(\"uci delete dhcp.@domain[-1]\")\n self.service_manager.need_restart(SERVICE_DNSMASQ)","title":""},{"docid":"b31a16154e0f0eb56093a1cf1dd1e945","score":"0.542094","text":"def delete_bd(self, bd_dn):\n self.delete_by_dn(bd_dn)","title":""},{"docid":"5c8d477a780b178dee2d4159259d2282","score":"0.5420142","text":"def new_domain(self, domain):\n url = f'{api_base_url}/domains/'\n code, _, data = self.query('POST', url, data={'name': domain})\n if code == 201:\n return data\n elif code == 400:\n raise ParameterError(f'Malformed domain name {domain}')\n elif code == 403:\n raise APIError('Maximum number of domains reached')\n elif code == 409:\n raise ParameterError(f'Could not create domain {domain} ({data})')\n else:\n raise APIError(f'Unexpected error code {code}')","title":""},{"docid":"3f49c71b78871b268bf1c5acf0950e01","score":"0.541126","text":"def deletePBX():\n\n db = DummySession()\n\n try:\n if not session.get('logged_in'):\n return redirect(url_for('index'))\n\n if (settings.DEBUG):\n debugEndpoint()\n\n db = SessionLoader()\n\n form = stripDictVals(request.form.to_dict())\n\n gwid = form['gwid']\n name = form['name']\n\n gateway = db.query(Gateways).filter(Gateways.gwid == gwid)\n gateway.delete(synchronize_session=False)\n address = db.query(Address).filter(Address.tag.contains(\"name:{}\".format(name)))\n address.delete(synchronize_session=False)\n subscriber = db.query(Subscribers).filter(Subscribers.rpid == gwid)\n subscriber.delete(synchronize_session=False)\n address.delete(synchronize_session=False)\n\n domainmultimapping = db.query(dSIPMultiDomainMapping).filter(dSIPMultiDomainMapping.pbx_id == gwid)\n res = domainmultimapping.options(load_only(\"domain_list\", \"attr_list\")).first()\n if res is not None:\n # make sure mapping has domains\n if len(res.domain_list) > 0:\n domains = list(map(int, filter(None, res.domain_list.split(\",\"))))\n domain = db.query(Domain).filter(Domain.id.in_(domains))\n domain.delete(synchronize_session=False)\n # make sure mapping has attributes\n if len(res.attr_list) > 0:\n attrs = list(map(int, filter(None, res.attr_list.split(\",\"))))\n domainattrs = db.query(DomainAttrs).filter(DomainAttrs.id.in_(attrs))\n domainattrs.delete(synchronize_session=False)\n\n # delete the entry in the multi domain mapping table\n domainmultimapping.delete(synchronize_session=False)\n\n db.commit()\n globals.reload_required = True\n return displayEndpointGroups()\n\n except sql_exceptions.SQLAlchemyError as ex:\n debugException(ex)\n error = \"db\"\n db.rollback()\n db.flush()\n return showError(type=error)\n except http_exceptions.HTTPException as ex:\n debugException(ex)\n error = \"http\"\n db.rollback()\n db.flush()\n return showError(type=error)\n except Exception as ex:\n debugException(ex)\n error = \"server\"\n db.rollback()\n db.flush()\n return showError(type=error)\n finally:\n db.close()","title":""},{"docid":"8b8d858e352287b0010408b7912e98f1","score":"0.54095286","text":"def delete(self, identifier):","title":""},{"docid":"136f28557655c9a0d3145b4cc76cd816","score":"0.5407476","text":"async def delete(self, guid: UUID) -> None:\n ...","title":""},{"docid":"be136f17714c31f7b33b224eb6652031","score":"0.5401327","text":"def delete_bridge(self, name: str) -> None:\n self.device_down(name)\n self.run(f\"{IP} link delete {name} type bridge\")","title":""},{"docid":"0a890164abf631c2e8bfa91a29622d92","score":"0.53858835","text":"def delete_domain_ownership_identifier( # pylint: disable=inconsistent-return-statements\n self, resource_group_name: str, name: str, domain_ownership_identifier_name: str, **kwargs: Any\n ) -> None:\n error_map = {\n 401: ClientAuthenticationError,\n 404: ResourceNotFoundError,\n 409: ResourceExistsError,\n 304: ResourceNotModifiedError,\n }\n error_map.update(kwargs.pop(\"error_map\", {}) or {})\n\n _headers = kwargs.pop(\"headers\", {}) or {}\n _params = case_insensitive_dict(kwargs.pop(\"params\", {}) or {})\n\n api_version: Literal[\"2022-09-01\"] = kwargs.pop(\"api_version\", _params.pop(\"api-version\", \"2022-09-01\"))\n cls: ClsType[None] = kwargs.pop(\"cls\", None)\n\n request = build_delete_domain_ownership_identifier_request(\n resource_group_name=resource_group_name,\n name=name,\n domain_ownership_identifier_name=domain_ownership_identifier_name,\n subscription_id=self._config.subscription_id,\n api_version=api_version,\n template_url=self.delete_domain_ownership_identifier.metadata[\"url\"],\n headers=_headers,\n params=_params,\n )\n request = _convert_request(request)\n request.url = self._client.format_url(request.url)\n\n _stream = False\n pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access\n request, stream=_stream, **kwargs\n )\n\n response = pipeline_response.http_response\n\n if response.status_code not in [200, 204]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n if cls:\n return cls(pipeline_response, None, {})","title":""},{"docid":"95e739c7e17eae1d88133546de256f9c","score":"0.5350564","text":"def delete(paths, dandi_instance=\"dandi\", devel_debug=False):\n from ..delete import delete\n\n delete(paths, dandi_instance=dandi_instance, devel_debug=devel_debug)","title":""},{"docid":"75f2307bb3aace01c213bbe918f06eaf","score":"0.5323482","text":"def delete_policy_address(request):\n try:\n response = generic_procbridge_request('delete_client', request.body)\n except:\n return server_error(request)\n return response","title":""},{"docid":"9e21dc0bbdab6fb90a1944564d74156e","score":"0.5321132","text":"def delete():\n pass","title":""},{"docid":"c5fcdebca53f9a6dd9009fe5ada25bea","score":"0.5316228","text":"def delete(cls):\n pass","title":""},{"docid":"df4df7c6d648c33a347d84f0320fee04","score":"0.5312748","text":"def delete_bridge(self, name: str) -> None:\n self.device_down(name)\n self.run(f\"{OVS_VSCTL} del-br {name}\")","title":""},{"docid":"06b6b61aa004c7d9c285d670722cba25","score":"0.53118014","text":"def delete_driver(driver_id):\n result_delete = driver_manager.delete(driver_id)\n return jsonify({\"message\": \"The object was deleted successfully\"})","title":""},{"docid":"ef4cee562ac69106accb6bfd667a010e","score":"0.5311045","text":"def delete(db, hostid):\n _validate_hostid(db, hostid)\n db.execute(\"DELETE FROM host WHERE sid = %d\" % (hostid,))\n device.delete(db, hostid)","title":""},{"docid":"a8d27b3ded5a2479dc160fda3a537981","score":"0.5309628","text":"def delete_epg(self, epg_dn):\n self.delete_by_dn(epg_dn)","title":""},{"docid":"678ecef19e4140f8f03f21a073789413","score":"0.5308238","text":"def remove_domains(self, domains):\n raise NotImplementedError","title":""},{"docid":"32ac17de1fdb94bacf5b5ce9ae287137","score":"0.53077596","text":"def delete(self):\n\n pass","title":""},{"docid":"f63d9458e1974c4df54cfff47129e72b","score":"0.53007925","text":"def delete_record(self, record):\n action = \"/app/dns/dyndns.jsp?\"\n params = {\"action\": \"DELETE\", \"name\": record.name, \"type\": record.type}\n try:\n response = self.connection.request(action=action, params=params)\n except ZonomiException as e:\n if e.message == \"Record not deleted.\":\n raise RecordDoesNotExistError(record_id=record.id, driver=self, value=e.message)\n raise e\n\n return \"DELETED\" in response.objects","title":""},{"docid":"90d540141cf83fb65d40e3a4fc20e047","score":"0.5297295","text":"def delete_host(self, hostname):\n data = NoNoneValueDict({\n 'hostname': hostname\n })\n\n return self.make_request('delete_host', data=data)","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5291471","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5291471","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5291471","text":"def delete(self):\n pass","title":""},{"docid":"933b486f0de1e8bbf8d1b1e70b68dd83","score":"0.5266047","text":"def test_deleting_a_persisted_entity(self, test_domain):\n person = test_domain.repository_for(Person)._dao.create(\n id=3, first_name=\"Jim\", last_name=\"Carrey\"\n )\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\n assert deleted_person is not None\n assert deleted_person.state_.is_destroyed is True\n\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(3)","title":""},{"docid":"70cb1df090b5d6ac85ee2fe777922c31","score":"0.52594876","text":"def delete():\n pass","title":""},{"docid":"68b889b424207a149902148feba9ad45","score":"0.5237945","text":"def delete(self):","title":""},{"docid":"68b889b424207a149902148feba9ad45","score":"0.5237945","text":"def delete(self):","title":""},{"docid":"68b889b424207a149902148feba9ad45","score":"0.5237945","text":"def delete(self):","title":""},{"docid":"4cc36f6253de6570c1dac50ab3193167","score":"0.522314","text":"def delete_business(self):\n self.delete()","title":""},{"docid":"4cc36f6253de6570c1dac50ab3193167","score":"0.522314","text":"def delete_business(self):\n self.delete()","title":""},{"docid":"42f27d943cfc06bb1827efb14a828995","score":"0.5221641","text":"def delete(self):\r\n pass","title":""},{"docid":"42f27d943cfc06bb1827efb14a828995","score":"0.5221641","text":"def delete(self):\r\n pass","title":""},{"docid":"879e42869c4286e0b618df2381034674","score":"0.5216646","text":"def delete(self):\n raise NotImplementedError","title":""},{"docid":"879e42869c4286e0b618df2381034674","score":"0.5216646","text":"def delete(self):\n raise NotImplementedError","title":""},{"docid":"ebe7bfd99cd8eb488c5f251be021d74f","score":"0.5216153","text":"def destroy_vm(args):\n libvirtConn = libvirt.open(None)\n if libvirtConn is None:\n print('Cannot contact hypervisor', file=sys.stderr)\n return 1\n dom = None\n try:\n dom = libvirtConn.lookupByName(args.domain)\n if dom == None:\n print(\"Domain %s does not exist.\" % args.domain, file=sys.stderr)\n return 1\n except Exception as e:\n print(\"Failed looking for Domain %s: %s\" % (args.domain, str(e)))\n return 1\n # from here , domain exists\n if dom.isActive():\n if not args.stop:\n _logger.error(\"Domain %s is running. Only domains that are not running can be destroyed.\" % args.domain)\n libvirtConn.close()\n return 1\n\n shut_res = 0\n _logger.debug('Domain is running, stop it first with force ? %s' % args.force)\n if args.force:\n shut_res = dom.destroyFlags()\n else:\n shut_res = dom.destroyFlags(libvirt.VIR_DOMAIN_DESTROY_GRACEFUL)\n\n libvirtConn.close()\n\n if shut_res != 0:\n _logger.error(\"Failed to stop domain\")\n return 1\n\n return oci_utils.kvm.virt.destroy(args.domain, args.destroy_disks)","title":""},{"docid":"de4dd1e5b53edbd690431f3bead1287f","score":"0.5215939","text":"def test_delete_namespace_using_delete(self):\n pass","title":""},{"docid":"cc2d7cba46b72d1f4d1cc3fb8584bcab","score":"0.51904964","text":"def delete(self):\r\n url = self._url + \"/delete\"\r\n params = {'f' : 'json'}\r\n return self._con.post(url, params)['success']","title":""},{"docid":"d159ca6bce85d2ce06b6896218ee84ed","score":"0.5186467","text":"def deleteLldpManagementaddress(self):\n\t\tquery = ''\n\t\tconn = self.get_connection()\n\t\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\n\t\tconn.request('DELETE', self.get_path() + '/' + query, headers=headers)\n\t\tresponse = conn.getresponse()\n\t\texpected_status = 200\n\t\terrors = {500: 'An unexpected runtime exception', 404: 'Specified management_address does not exist'}\n\t\tpayload = self.get_output(response, expected_status, errors)\n\t\tconn.close()\n\t\tif self.debug:\n\t\t\tprint 'payload:', payload\n\t\tif payload == '':\n\t\t\tpayload = None\n\t\treturn deserialize_string_json(payload)","title":""},{"docid":"fa67d33f16a3aae6811f111aaa385ab1","score":"0.517543","text":"def delete_bridge(self, br_name):\n self.execute('ovs-vsctl del-br {}'.format(br_name))\n br = self.get_bridge(br_name)\n self.bridges.remove(br)","title":""},{"docid":"8cca2e073056ce2125cbccd6022b02b9","score":"0.5168209","text":"def delete(self, hass):\n dr = device_registry.async_get(hass)\n er = entity_registry.async_get(hass)\n\n for e in self.entities.values():\n er.async_remove(e.entity_id)\n\n self.entities = {}\n\n device = dr.async_get_device({(DOMAIN, self.browserID)})\n dr.async_remove_device(device.id)","title":""},{"docid":"584cf3f5cef042c982eb47515585e53d","score":"0.5162038","text":"def delete(self):\n\n\t\tpass","title":""},{"docid":"c3365c3bdc3a93293f26ff02a942b9a0","score":"0.5156468","text":"def delete(isdsAppliance, hostname, ip_addr, check_mode=False, force=False):\n if force is True or _check(isdsAppliance, hostname, ip_addr) is True:\n if check_mode is True:\n return isdsAppliance.create_return_object(changed=True)\n else:\n return isdsAppliance.invoke_delete(\n \"Removing host record\",\n \"/host_records/\" + ip_addr,\n {\n })\n return isdsAppliance.create_return_object()","title":""},{"docid":"c30bce7a253ac4e21d746abb13deddeb","score":"0.51520634","text":"def delete_domain(name: str):\n config_path = flask.current_app.config['CONFIG_PATH']\n removed = False\n\n for _ in os.listdir(config_path):\n\n if os.path.isfile(os.path.join(config_path, _)):\n if _.startswith(name):\n os.remove(os.path.join(config_path, _))\n removed = not os.path.exists(os.path.join(config_path, _))\n break\n\n if removed:\n return flask.jsonify({'success': True}), 200\n else:\n return flask.jsonify({'success': False}), 400","title":""},{"docid":"24ca9863113155417a3ebebadd5dff23","score":"0.51452446","text":"def delete(self):\n self.doapi_manager.request(self.url, method='DELETE')","title":""},{"docid":"24ca9863113155417a3ebebadd5dff23","score":"0.51452446","text":"def delete(self):\n self.doapi_manager.request(self.url, method='DELETE')","title":""},{"docid":"3702b291ec5f4b9c5e9b3aabb155ce45","score":"0.5140637","text":"def test_domain_create_duplicate(self):\n domain_name = data_utils.rand_name('domain-dup')\n domain = self.domains_client.create_domain(name=domain_name)['domain']\n domain_id = domain['id']\n self.addCleanup(self.delete_domain, domain_id)\n # Domain name should be unique\n self.assertRaises(\n lib_exc.Conflict, self.domains_client.create_domain,\n name=domain_name)","title":""},{"docid":"2d43205cfb20a24078fd14a689ed49b2","score":"0.5123957","text":"def delete(self, *args, **kwargs):\n\n print(\"ciao\")\n\n try:\n\n if len(args) != 3:\n raise ValueError(\"Invalid url\")\n\n request = tornado.escape.json_decode(self.request.body)\n\n if \"version\" not in request:\n raise ValueError(\"missing version element\")\n\n if \"match\" not in request:\n raise ValueError(\"missing match element\")\n\n tenant_id = uuid.UUID(args[0])\n tenant = RUNTIME.tenants[tenant_id]\n\n lvap_id = EtherAddress(args[1])\n lvap = tenant.lvaps[lvap_id]\n\n port_id = int(args[2])\n port = lvap.ports[port_id]\n\n match = request['match']\n\n del port.next[match]\n\n except ValueError as ex:\n self.send_error(400, message=ex)\n except KeyError as ex:\n self.send_error(404, message=ex)\n\n self.set_status(204, None)","title":""},{"docid":"68085acd99a14bc2d5dd72a8d6d8620e","score":"0.51209426","text":"def Delete(self):\r\n\t\tself.client.Delete(self.nodeid)","title":""},{"docid":"d38eeb1b622b4bb6f2c3174b9c019257","score":"0.5120391","text":"def test_delete_person(self):\n\n response = DispatchTestHelpers.create_person(\n self.client,\n full_name='Test Person',\n slug='test-person'\n )\n person_id = response.data['id']\n # Generate detail URL\n url = reverse('api-persons-detail', args=[person_id])\n\n # Successful deletion should return 204\n response = self.client.delete(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n self.assertFalse(Person.objects.filter(id=person_id).exists())\n\n # Can't delete an person that has already been deleted\n response = self.client.delete(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)","title":""},{"docid":"32135d7db2e33f38bd87475edfb1460e","score":"0.51072913","text":"def delete(self, network):\n pass","title":""},{"docid":"c92132dc99385cda34fb11637edb6e82","score":"0.51051366","text":"def delete(self, cynetwork):\n id = cynetwork.get_id()\n self.session.delete(self.__url + '/' + str(id))\n del cynetwork","title":""},{"docid":"ee39b243816900b759a61e1e21262b23","score":"0.51043063","text":"def test_delete_an_object_in_repository_by_id(self, test_domain):\n person = test_domain.repository_for(Person)._dao.create(\n id=3, first_name=\"John\", last_name=\"Doe\", age=22\n )\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\n assert deleted_person is not None\n assert deleted_person.state_.is_destroyed is True\n\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(3)","title":""},{"docid":"62cf941da6af7dd20ebf98ec822167b6","score":"0.5085803","text":"def delete(self):\n resp, result = self.http_delete()\n return result","title":""},{"docid":"b0387b42f8c1138b850bc2152130a569","score":"0.50802076","text":"def clear(self, models=None, commit=True, domains=None, indexes=None, everything=False, spinlock=True):\n # the implementation here just deletes the domain, recreates it, then reloads the schema\n domains = domains or []\n if models is not None:\n m = self.get_model_to_index_map()\n for model in models:\n domains.append(self.get_searchdomain_name(m[model.__class__.__name__]))\n\n if indexes is not None:\n for i in indexes:\n domains.append(self.get_searchdomain_name(i))\n\n if models is None and indexes is None and not domains:\n domains = [x for x in self.boto_conn.layer1.describe_domains()['DescribeDomainsResponse']['DescribeDomainsResult']]\n if not everything:\n conn = haystack.connections[self.connection_alias]\n unified_index = conn.get_unified_index()\n index_set = set([self.get_searchdomain_name(i) for i in unified_index.collect_indexes()])\n domains = list(set(domains) & index_set)\n\n self.log.debug('deleting domains: %s' % (', '.join(domains),))\n for d in domains:\n self.boto_conn.layer1.delete_domain(d)\n if spinlock:\n if self.domain_processing_spinlock(domains):\n if commit:\n self.setup()\n else:\n raise CloudsearchDryerExploded('While waiting for a delete domain to finish, we hit our max timeout. Please investigate your SearchDomains.')\n else:\n if commit:\n self.setup()","title":""},{"docid":"d1e9798c0cec5e1ec5d423f8f3a91c46","score":"0.50800407","text":"def delete(self):\n reset_db()\n return OK","title":""},{"docid":"971c27fec929ab8b8acc6f4d34556cec","score":"0.5073962","text":"def cleanup_domains(self, domains):\n for domain_id in domains:\n self.nsxlib.domain.delete(domain_id)","title":""},{"docid":"967c7ddca832a3eb07bf1c398bfad312","score":"0.50705904","text":"def networkDelete(request, networkKey):\n network = get_object_or_404(Network, pk=networkKey)\n network.delete()\n return HttpResponseRedirect(reverse('pdep:index'))","title":""},{"docid":"a1a7db7e329d028556049dd359f7f714","score":"0.5069408","text":"def delete(self, **kw):\n abort(501, 'Delete not implemented')","title":""},{"docid":"034807eeaa6fb4d4ff1b300f825e20a7","score":"0.5068716","text":"def delete_tld(self, context, tld_id):","title":""},{"docid":"898d48ba8bcd53e2dbff0d95f543bd95","score":"0.50573736","text":"def delete_dj_by_id(dj_id=None):\n data_id = storage.get(\"Dj\", dj_id)\n if data_id is None:\n abort(404)\n else:\n storage.delete(data_id)\n storage.save()\n return jsonify({}), 200","title":""},{"docid":"eafbf12191abee5a6a5e5f1fa82f193f","score":"0.50552016","text":"def delete(self):\n handler = service_handler.ServiceHandler(pecan.request)\n handler.delete(\n pecan.request.context['host_name'],\n pecan.request.context['service_description']\n )","title":""},{"docid":"be3bee67fb232d5a77335c8aa5298614","score":"0.50466233","text":"def remove_from_owned_domains(self, domain_name: str):\n self.owned_domain_list.remove(domain_name)\n index = self.owned_domains.findText(domain_name)\n self.owned_domains.removeItem(index)","title":""},{"docid":"23fda611b7c50201f9dc2ed71cff5a49","score":"0.5038844","text":"def delete(self):\n return self.parent.delete_instance(self.name)","title":""},{"docid":"d39b3f42d30c6d4251c90e5c26dd9959","score":"0.50388414","text":"def remove_host(request, storage):\n self = request.node.cls\n\n self.removed_host = getattr(\n self, 'removed_host', self.hsm_hosts[0]\n )\n self.host_object = config.VDS_HOSTS[config.HOSTS.index(self.removed_host)]\n testflow.setup(\n \"Remove host '%s' from %s\", self.removed_host, config.DATA_CENTER_NAME\n )\n assert ll_hosts.remove_host(True, self.removed_host, True), (\n \"Failed to remove host %s\" % self.removed_host\n )","title":""},{"docid":"c4407ce0dd83764277ba6d39bf5a9689","score":"0.5036082","text":"def del_object(id):","title":""},{"docid":"f13b03cdb3efe834e2c758bc7afe4374","score":"0.5034182","text":"def delete(self, address):\n\n if not address:\n logging.error(\"Delete: Missing required parameter - address\")\n return False\n\n return self.query_backend.delete_listener(address)","title":""},{"docid":"8013501ca58b78c33f74271058186b36","score":"0.5033916","text":"def DeleteLoadBalancer(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"DeleteLoadBalancer\", params, headers=headers)\n response = json.loads(body)\n model = models.DeleteLoadBalancerResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""}],"string":"[\n {\n \"docid\": \"216d0d7b993759eb24ef3fbae02bc061\",\n \"score\": \"0.7052366\",\n \"text\": \"def delete_domain(self, domain_spec, delete_dependencies=False):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f324136af1685db477ea2804a46409e7\",\n \"score\": \"0.69567776\",\n \"text\": \"def delete_domain(self, domain_id):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8444792de489dc6362e28c5796e1044\",\n \"score\": \"0.6765949\",\n \"text\": \"def gdomain_del(browser: Browser, domain: str, hostname: str) -> None:\\n hostname = fqdn(hostname, domain)\\n\\n # find the right div for this hostname\\n records = get_synthetic_records_div(browser)\\n # xpath = \\\"//div[contains(@class, 'H2OGROB-d-t')]\\\"\\n xpath = f\\\"//div[contains(text(), '{hostname}')]/../..\\\"\\n divs = records.find_by_xpath(xpath)\\n div = divs.first\\n\\n # click the delete button\\n delete_button = get_element_by_substring(\\\"Delete\\\", div.find_by_tag(\\\"button\\\"))\\n delete_button.click()\\n\\n # wait for the modal dialog\\n wait_for_tag(browser, \\\"h3\\\", \\\"Delete synthetic record?\\\")\\n\\n # get the form element for the modal dialog\\n modal_form = get_element_by_substring(\\n \\\"Delete synthetic record?\\\", browser.find_by_tag(\\\"form\\\")\\n )\\n modal_button = get_element_by_substring(\\\"Delete\\\", modal_form.find_by_tag(\\\"button\\\"))\\n modal_button.click()\\n\\n wait_for_success_notification(browser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1ca4f4c2b618e3b1480bba4a14e5ba\",\n \"score\": \"0.67142767\",\n \"text\": \"def delete(self,\\n domain_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'domain_id': domain_id,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a990e61380fbf5b934cacf33b6c5821\",\n \"score\": \"0.63781595\",\n \"text\": \"def test_container_project_registry_domain_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46a8cd75a70274dfd378bf940c77456\",\n \"score\": \"0.6328905\",\n \"text\": \"def delete_domain_name(self, domainName: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33220f192910a49f85bb115ced2b41f6\",\n \"score\": \"0.6155003\",\n \"text\": \"def test_delete_active_domain(self):\\n domain = self.create_domain()\\n domain_id = domain['id']\\n\\n self.addCleanup(self.delete_domain, domain_id)\\n\\n # domain need to be disabled before deleting\\n self.assertRaises(lib_exc.Forbidden, self.domains_client.delete_domain,\\n domain_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30a2cdda9429bfae118bd1cea3552bb\",\n \"score\": \"0.611994\",\n \"text\": \"def test_delete_relaydomain(self):\\n self.ajax_post(\\n reverse(\\\"modoboa_admin_relaydomains:relaydomain_delete\\\",\\n args=[self.rdom.id]),\\n {}\\n )\\n with self.assertRaises(RelayDomain.DoesNotExist):\\n RelayDomain.objects.get(name='relaydomain.tld')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ad3d27d61f9621ca7f6228b18992fa\",\n \"score\": \"0.6032309\",\n \"text\": \"def delete(domain_name):\\n exists = False\\n for key, domain in custom_domains.items():\\n exists = domain_name == domain.get('domain')\\n id = key\\n if exists: break\\n if not exists:\\n return do_error(404, 'domain not found')\\n\\n del custom_domains[id]\\n return make_response({'domain' : domain_name}, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"209d44d76fc794a3de53b57276725445\",\n \"score\": \"0.6026429\",\n \"text\": \"def destroy(self, domain):\\n return domain.destory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46c04f2e610401739294275d4f4447d\",\n \"score\": \"0.5919469\",\n \"text\": \"def delete_domain(self, domain, check=True):\\n self._client.update(domain, enabled=False)\\n self._client.delete(domain.id)\\n\\n if check:\\n self.check_domain_presence(domain, must_present=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313b00ad935ebd3f6f5bdd84aefaf2b0\",\n \"score\": \"0.5897445\",\n \"text\": \"def on_delete_excluded_domain(self):\\n del_crisp: QObject = self.sender()\\n self.domain_list.remove(del_crisp.title)\\n self.render_domains()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9251892152d87e1d21f8762f3ed9e4d7\",\n \"score\": \"0.58448267\",\n \"text\": \"def delete_domain(self, domain_id):\\n try:\\n url = self.url_path_prefix + \\\"ldap/%s\\\" % domain_id\\n rc, response = self.request(self.url_path_prefix + \\\"ldap/%s\\\" % domain_id, method=\\\"DELETE\\\")\\n except Exception as error:\\n self.module.fail_json(msg=\\\"Failed to delete LDAP domain. Array Id [%s]. Error [%s].\\\" % (self.ssid, to_native(error)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f1fc141dd946215602748cb363f366\",\n \"score\": \"0.584052\",\n \"text\": \"def test_delete_non_existent_domain(self):\\n self.assertRaises(lib_exc.NotFound, self.domains_client.delete_domain,\\n data_utils.rand_uuid_hex())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408662cf703d17fc8382e65aac573032\",\n \"score\": \"0.58352196\",\n \"text\": \"def cmd_host_delete():\\n # Parse JSON body w/ API params\\n try:\\n body = request.data.decode('utf-8')\\n in_obj = json.loads(body)\\n except Exception as err:\\n logger.error(\\\"Failure: {0}\\\".format(err))\\n return http400(\\\"JSON Decode failed. Check JSON POST data structure on request.\\\")\\n\\n # Validate JSON object basics\\n try:\\n if 'name' not in in_obj or 'pkh' not in in_obj:\\n return http400(\\\"Missing name/pkh\\\")\\n\\n domain = \\\"21\\\"\\n name = in_obj['name']\\n pkh = in_obj['pkh']\\n\\n if (not valid_name(name)):\\n return http400(\\\"Invalid name param: {}\\\".format(name))\\n\\n if not db.valid_domain(domain):\\n return http404(\\\"Domain not found\\\")\\n\\n except Exception as err:\\n logger.error(\\\"Failure: {0}\\\".format(err))\\n return http400(\\\"JSON validation exception\\\")\\n\\n # Verify host exists and pkh matches\\n try:\\n hostinfo = db.get_host(name, domain)\\n if hostinfo is None:\\n return http404(\\\"Name parameter not found: {}\\\".format(name))\\n\\n # Validate the submitted pkh matches the one in the db\\n if (pkh != hostinfo['pkh']):\\n return http403(\\\"pkh does not match existing record.\\\")\\n\\n except Exception as err:\\n logger.error(\\\"Failure: {0}\\\".format(err))\\n return http500(\\\"DB Exception - get host\\\")\\n\\n # Validate the signature on the message\\n try:\\n validate_sig(body, request.headers.get('X-Bitcoin-Sig'), pkh)\\n except PermissionError as err:\\n logger.error(\\\"Failure: {0}\\\".format(err))\\n return http403(err)\\n\\n # Remove from database. Rely on db to filter out dups.\\n try:\\n if not nsupdate_exec(name, domain, []):\\n http500(\\\"nsupdate failure\\\")\\n db.delete_host(name, domain)\\n except Exception as err:\\n logger.error(\\\"Failure: {0}\\\".format(err))\\n return http400(\\\"DB Exception - delete host\\\")\\n\\n return http200(\\\"Record successfully deleted.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91625d9b2d56d61ffe978e88f305ce3\",\n \"score\": \"0.58131385\",\n \"text\": \"def delete(self, dns_zone):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cbe88f63c48bdcc8b04756a04e5dc68\",\n \"score\": \"0.57967484\",\n \"text\": \"def del_domain(self, domain_name):\\n self._domain_set.discard(domain_name)\\n self.write_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b15a5b94fcae1ae2411ada439bba48\",\n \"score\": \"0.57900757\",\n \"text\": \"def test_delete_relaydomainalias(self):\\n self._relay_domain_alias_operation(\\n 'del', self.rdom.name, 'relaydomainalias.tld'\\n )\\n with self.assertRaises(RelayDomain.DoesNotExist):\\n RelayDomain.objects.get(name='relaydomainalias.tld')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21eadbb8e00a378f7e756d011928370b\",\n \"score\": \"0.5699527\",\n \"text\": \"def handle_delete(self):\\n LOG.debug(\\\"CloudDns handle_delete called.\\\")\\n if self.resource_id:\\n try:\\n dom = self.cloud_dns().get(self.resource_id)\\n dom.delete()\\n except NotFound:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8f5970ce7561aaebfa73398272b7d8\",\n \"score\": \"0.5695885\",\n \"text\": \"def remove_model(self, d):\\n with self.session_scope() as session:\\n if isinstance(d, UniprotDomain):\\n session.execute(\\n 'delete from {0}.uniprot_domain_model where uniprot_domain_id = {1}'\\n .format(conf.CONFIGS['db_schema'], d.uniprot_domain_id))\\n elif isinstance(d, UniprotDomainPair):\\n session.execute(\\n 'delete from {0}.uniprot_domain_pair_model where uniprot_domain_pair_id = {1}'\\n .format(conf.CONFIGS['db_schema'], d.uniprot_domain_pair_id))\\n else:\\n raise Exception(\\\"'d' is of incorrect type!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211b1ff2eeaa9e936631e13c51497300\",\n \"score\": \"0.56918234\",\n \"text\": \"def deleteZone(self, domain):\\n querystr = 'killdns?domain=%s' % domain\\n result = self.cQuery(querystr)\\n logger.error(\\\"result: %s\\\" % result)\\n try:\\n if result[\\\"result\\\"][0][\\\"status\\\"] == 1:\\n return True\\n except Exception as ex:\\n logger.error('%s threw exception: %s' % (querystr, ex))\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28417f85a6f50a8043b438427f457e5e\",\n \"score\": \"0.56803626\",\n \"text\": \"def delete(self, *args, **kwargs):\\n if kwargs.pop('validate_glue', True):\\n if self.nameserver_set.exists():\\n raise ValidationError(\\n \\\"Cannot delete the record {0}. It is a ' 'glue record.\\\"\\n .format(self.record_type()))\\n if kwargs.pop('check_cname', True):\\n if CNAME.objects.filter(target=self.fqdn):\\n raise ValidationError('A CNAME points to this {0} record. '\\n 'Change the CNAME before deleting this '\\n 'record.'.format(self.record_type()))\\n\\n from cyder.cydns.utils import prune_tree\\n objs_domain = self.domain\\n super(BaseAddressRecord, self).delete(*args, **kwargs)\\n prune_tree(objs_domain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7a69ce04b560f95ad527bd2ab60c36\",\n \"score\": \"0.5633319\",\n \"text\": \"def test_delete(self):\\n dom = Domain.objects.get(name=\\\"test.com\\\")\\n self.ajax_post(\\n reverse(\\\"admin:domain_delete\\\", args=[dom.id]),\\n {}\\n )\\n with self.assertRaises(Domain.DoesNotExist):\\n Domain.objects.get(pk=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.5596321\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.5596321\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef1389401297c0d8a1aae3367b254c2\",\n \"score\": \"0.55897075\",\n \"text\": \"def delete_bd(self, count, start=1):\\n for b in range(start, start + count):\\n bd_id = self.bd_map[b]\\n for j in self.bd_if_range(b):\\n pg_if = self.pg_interfaces[j]\\n self.vapi.sw_interface_set_l2_bridge(\\n rx_sw_if_index=pg_if.sw_if_index, bd_id=bd_id, enable=0\\n )\\n self.pg_in_bd.remove(pg_if)\\n self.logger.info(\\n \\\"pg-interface %s removed from bridge domain ID %d\\\"\\n % (pg_if.name, bd_id)\\n )\\n self.vapi.bridge_domain_add_del_v2(bd_id=bd_id, is_add=0)\\n self.bd_map.pop(b)\\n self.bd_list.remove(bd_id)\\n self.bd_deleted_list.append(bd_id)\\n self.logger.info(\\\"Bridge domain ID %d deleted\\\" % bd_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607b40cc2f5d48ae143298933d5e131d\",\n \"score\": \"0.55100995\",\n \"text\": \"def delete(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607b40cc2f5d48ae143298933d5e131d\",\n \"score\": \"0.55100995\",\n \"text\": \"def delete(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc167aa2a029b787673b723ef136d0c6\",\n \"score\": \"0.54946524\",\n \"text\": \"def delete(self):\\n self._api.post(self._api.url + \\\"doDelete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02be08c025147971adb51eb2316e7070\",\n \"score\": \"0.546674\",\n \"text\": \"def create_domain(self, domain_id, domain):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b1ee98af6c5dfb41278f39ad723cda\",\n \"score\": \"0.5461764\",\n \"text\": \"def delete(name, **kwargs):\\n dom = defineDomain(name)\\n if queries.get_vm_state(name) == 1:\\n dom.destroy()\\n mac = getMAC(name)\\n queries.unAssignIP(mac)\\n queries.delete_vm(name)\\n manageBridges.delConfHost(mac) # send\\n res = 'VM {} was destroyed'.format(name)\\n dom.undefine()\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16919bdc178538fcdaf5a149a90416a\",\n \"score\": \"0.54525614\",\n \"text\": \"def delete(self, host_name):\\n host_name = common.validate_hostname(host_name)\\n dn = '%s=%s,%s' % (self.host_key, host_name, self.host_container_dn)\\n msg = 'Deleting %s' % dn\\n self.log.debug(msg)\\n result = self._delete_object(dn)\\n self.log.debug('Result: %s' % result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70da5bd337a192b43090bf1d94e04844\",\n \"score\": \"0.5437967\",\n \"text\": \"def delete(self, backend=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afaa0831f3c94079998f97e24aa142d\",\n \"score\": \"0.54223454\",\n \"text\": \"def delete_by_dn(self, dn_name):\\n dn_object = self.moDir.lookupByDn(dn_name)\\n if dn_object is not None:\\n dn_object.delete()\\n self.commit(dn_object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0e47ab5e189333f7ec90f056ea06f3\",\n \"score\": \"0.5421466\",\n \"text\": \"def del_resource_record(self):\\n self.ssh.run(\\\"uci delete dhcp.@domain[-1]\\\")\\n self.service_manager.need_restart(SERVICE_DNSMASQ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31a16154e0f0eb56093a1cf1dd1e945\",\n \"score\": \"0.542094\",\n \"text\": \"def delete_bd(self, bd_dn):\\n self.delete_by_dn(bd_dn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8d477a780b178dee2d4159259d2282\",\n \"score\": \"0.5420142\",\n \"text\": \"def new_domain(self, domain):\\n url = f'{api_base_url}/domains/'\\n code, _, data = self.query('POST', url, data={'name': domain})\\n if code == 201:\\n return data\\n elif code == 400:\\n raise ParameterError(f'Malformed domain name {domain}')\\n elif code == 403:\\n raise APIError('Maximum number of domains reached')\\n elif code == 409:\\n raise ParameterError(f'Could not create domain {domain} ({data})')\\n else:\\n raise APIError(f'Unexpected error code {code}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f49c71b78871b268bf1c5acf0950e01\",\n \"score\": \"0.541126\",\n \"text\": \"def deletePBX():\\n\\n db = DummySession()\\n\\n try:\\n if not session.get('logged_in'):\\n return redirect(url_for('index'))\\n\\n if (settings.DEBUG):\\n debugEndpoint()\\n\\n db = SessionLoader()\\n\\n form = stripDictVals(request.form.to_dict())\\n\\n gwid = form['gwid']\\n name = form['name']\\n\\n gateway = db.query(Gateways).filter(Gateways.gwid == gwid)\\n gateway.delete(synchronize_session=False)\\n address = db.query(Address).filter(Address.tag.contains(\\\"name:{}\\\".format(name)))\\n address.delete(synchronize_session=False)\\n subscriber = db.query(Subscribers).filter(Subscribers.rpid == gwid)\\n subscriber.delete(synchronize_session=False)\\n address.delete(synchronize_session=False)\\n\\n domainmultimapping = db.query(dSIPMultiDomainMapping).filter(dSIPMultiDomainMapping.pbx_id == gwid)\\n res = domainmultimapping.options(load_only(\\\"domain_list\\\", \\\"attr_list\\\")).first()\\n if res is not None:\\n # make sure mapping has domains\\n if len(res.domain_list) > 0:\\n domains = list(map(int, filter(None, res.domain_list.split(\\\",\\\"))))\\n domain = db.query(Domain).filter(Domain.id.in_(domains))\\n domain.delete(synchronize_session=False)\\n # make sure mapping has attributes\\n if len(res.attr_list) > 0:\\n attrs = list(map(int, filter(None, res.attr_list.split(\\\",\\\"))))\\n domainattrs = db.query(DomainAttrs).filter(DomainAttrs.id.in_(attrs))\\n domainattrs.delete(synchronize_session=False)\\n\\n # delete the entry in the multi domain mapping table\\n domainmultimapping.delete(synchronize_session=False)\\n\\n db.commit()\\n globals.reload_required = True\\n return displayEndpointGroups()\\n\\n except sql_exceptions.SQLAlchemyError as ex:\\n debugException(ex)\\n error = \\\"db\\\"\\n db.rollback()\\n db.flush()\\n return showError(type=error)\\n except http_exceptions.HTTPException as ex:\\n debugException(ex)\\n error = \\\"http\\\"\\n db.rollback()\\n db.flush()\\n return showError(type=error)\\n except Exception as ex:\\n debugException(ex)\\n error = \\\"server\\\"\\n db.rollback()\\n db.flush()\\n return showError(type=error)\\n finally:\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8d858e352287b0010408b7912e98f1\",\n \"score\": \"0.54095286\",\n \"text\": \"def delete(self, identifier):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136f28557655c9a0d3145b4cc76cd816\",\n \"score\": \"0.5407476\",\n \"text\": \"async def delete(self, guid: UUID) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be136f17714c31f7b33b224eb6652031\",\n \"score\": \"0.5401327\",\n \"text\": \"def delete_bridge(self, name: str) -> None:\\n self.device_down(name)\\n self.run(f\\\"{IP} link delete {name} type bridge\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a890164abf631c2e8bfa91a29622d92\",\n \"score\": \"0.53858835\",\n \"text\": \"def delete_domain_ownership_identifier( # pylint: disable=inconsistent-return-statements\\n self, resource_group_name: str, name: str, domain_ownership_identifier_name: str, **kwargs: Any\\n ) -> None:\\n error_map = {\\n 401: ClientAuthenticationError,\\n 404: ResourceNotFoundError,\\n 409: ResourceExistsError,\\n 304: ResourceNotModifiedError,\\n }\\n error_map.update(kwargs.pop(\\\"error_map\\\", {}) or {})\\n\\n _headers = kwargs.pop(\\\"headers\\\", {}) or {}\\n _params = case_insensitive_dict(kwargs.pop(\\\"params\\\", {}) or {})\\n\\n api_version: Literal[\\\"2022-09-01\\\"] = kwargs.pop(\\\"api_version\\\", _params.pop(\\\"api-version\\\", \\\"2022-09-01\\\"))\\n cls: ClsType[None] = kwargs.pop(\\\"cls\\\", None)\\n\\n request = build_delete_domain_ownership_identifier_request(\\n resource_group_name=resource_group_name,\\n name=name,\\n domain_ownership_identifier_name=domain_ownership_identifier_name,\\n subscription_id=self._config.subscription_id,\\n api_version=api_version,\\n template_url=self.delete_domain_ownership_identifier.metadata[\\\"url\\\"],\\n headers=_headers,\\n params=_params,\\n )\\n request = _convert_request(request)\\n request.url = self._client.format_url(request.url)\\n\\n _stream = False\\n pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access\\n request, stream=_stream, **kwargs\\n )\\n\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200, 204]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response)\\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\\n\\n if cls:\\n return cls(pipeline_response, None, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e739c7e17eae1d88133546de256f9c\",\n \"score\": \"0.5350564\",\n \"text\": \"def delete(paths, dandi_instance=\\\"dandi\\\", devel_debug=False):\\n from ..delete import delete\\n\\n delete(paths, dandi_instance=dandi_instance, devel_debug=devel_debug)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f2307bb3aace01c213bbe918f06eaf\",\n \"score\": \"0.5323482\",\n \"text\": \"def delete_policy_address(request):\\n try:\\n response = generic_procbridge_request('delete_client', request.body)\\n except:\\n return server_error(request)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e21dc0bbdab6fb90a1944564d74156e\",\n \"score\": \"0.5321132\",\n \"text\": \"def delete():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fcdebca53f9a6dd9009fe5ada25bea\",\n \"score\": \"0.5316228\",\n \"text\": \"def delete(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4df7c6d648c33a347d84f0320fee04\",\n \"score\": \"0.5312748\",\n \"text\": \"def delete_bridge(self, name: str) -> None:\\n self.device_down(name)\\n self.run(f\\\"{OVS_VSCTL} del-br {name}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b6b61aa004c7d9c285d670722cba25\",\n \"score\": \"0.53118014\",\n \"text\": \"def delete_driver(driver_id):\\n result_delete = driver_manager.delete(driver_id)\\n return jsonify({\\\"message\\\": \\\"The object was deleted successfully\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4cee562ac69106accb6bfd667a010e\",\n \"score\": \"0.5311045\",\n \"text\": \"def delete(db, hostid):\\n _validate_hostid(db, hostid)\\n db.execute(\\\"DELETE FROM host WHERE sid = %d\\\" % (hostid,))\\n device.delete(db, hostid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d27b3ded5a2479dc160fda3a537981\",\n \"score\": \"0.5309628\",\n \"text\": \"def delete_epg(self, epg_dn):\\n self.delete_by_dn(epg_dn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"678ecef19e4140f8f03f21a073789413\",\n \"score\": \"0.5308238\",\n \"text\": \"def remove_domains(self, domains):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ac17de1fdb94bacf5b5ce9ae287137\",\n \"score\": \"0.53077596\",\n \"text\": \"def delete(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63d9458e1974c4df54cfff47129e72b\",\n \"score\": \"0.53007925\",\n \"text\": \"def delete_record(self, record):\\n action = \\\"/app/dns/dyndns.jsp?\\\"\\n params = {\\\"action\\\": \\\"DELETE\\\", \\\"name\\\": record.name, \\\"type\\\": record.type}\\n try:\\n response = self.connection.request(action=action, params=params)\\n except ZonomiException as e:\\n if e.message == \\\"Record not deleted.\\\":\\n raise RecordDoesNotExistError(record_id=record.id, driver=self, value=e.message)\\n raise e\\n\\n return \\\"DELETED\\\" in response.objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d540141cf83fb65d40e3a4fc20e047\",\n \"score\": \"0.5297295\",\n \"text\": \"def delete_host(self, hostname):\\n data = NoNoneValueDict({\\n 'hostname': hostname\\n })\\n\\n return self.make_request('delete_host', data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5291471\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5291471\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5291471\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933b486f0de1e8bbf8d1b1e70b68dd83\",\n \"score\": \"0.5266047\",\n \"text\": \"def test_deleting_a_persisted_entity(self, test_domain):\\n person = test_domain.repository_for(Person)._dao.create(\\n id=3, first_name=\\\"Jim\\\", last_name=\\\"Carrey\\\"\\n )\\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\\n assert deleted_person is not None\\n assert deleted_person.state_.is_destroyed is True\\n\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70cb1df090b5d6ac85ee2fe777922c31\",\n \"score\": \"0.52594876\",\n \"text\": \"def delete():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b889b424207a149902148feba9ad45\",\n \"score\": \"0.5237945\",\n \"text\": \"def delete(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b889b424207a149902148feba9ad45\",\n \"score\": \"0.5237945\",\n \"text\": \"def delete(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b889b424207a149902148feba9ad45\",\n \"score\": \"0.5237945\",\n \"text\": \"def delete(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc36f6253de6570c1dac50ab3193167\",\n \"score\": \"0.522314\",\n \"text\": \"def delete_business(self):\\n self.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc36f6253de6570c1dac50ab3193167\",\n \"score\": \"0.522314\",\n \"text\": \"def delete_business(self):\\n self.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f27d943cfc06bb1827efb14a828995\",\n \"score\": \"0.5221641\",\n \"text\": \"def delete(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f27d943cfc06bb1827efb14a828995\",\n \"score\": \"0.5221641\",\n \"text\": \"def delete(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879e42869c4286e0b618df2381034674\",\n \"score\": \"0.5216646\",\n \"text\": \"def delete(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879e42869c4286e0b618df2381034674\",\n \"score\": \"0.5216646\",\n \"text\": \"def delete(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebe7bfd99cd8eb488c5f251be021d74f\",\n \"score\": \"0.5216153\",\n \"text\": \"def destroy_vm(args):\\n libvirtConn = libvirt.open(None)\\n if libvirtConn is None:\\n print('Cannot contact hypervisor', file=sys.stderr)\\n return 1\\n dom = None\\n try:\\n dom = libvirtConn.lookupByName(args.domain)\\n if dom == None:\\n print(\\\"Domain %s does not exist.\\\" % args.domain, file=sys.stderr)\\n return 1\\n except Exception as e:\\n print(\\\"Failed looking for Domain %s: %s\\\" % (args.domain, str(e)))\\n return 1\\n # from here , domain exists\\n if dom.isActive():\\n if not args.stop:\\n _logger.error(\\\"Domain %s is running. Only domains that are not running can be destroyed.\\\" % args.domain)\\n libvirtConn.close()\\n return 1\\n\\n shut_res = 0\\n _logger.debug('Domain is running, stop it first with force ? %s' % args.force)\\n if args.force:\\n shut_res = dom.destroyFlags()\\n else:\\n shut_res = dom.destroyFlags(libvirt.VIR_DOMAIN_DESTROY_GRACEFUL)\\n\\n libvirtConn.close()\\n\\n if shut_res != 0:\\n _logger.error(\\\"Failed to stop domain\\\")\\n return 1\\n\\n return oci_utils.kvm.virt.destroy(args.domain, args.destroy_disks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de4dd1e5b53edbd690431f3bead1287f\",\n \"score\": \"0.5215939\",\n \"text\": \"def test_delete_namespace_using_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2d7cba46b72d1f4d1cc3fb8584bcab\",\n \"score\": \"0.51904964\",\n \"text\": \"def delete(self):\\r\\n url = self._url + \\\"/delete\\\"\\r\\n params = {'f' : 'json'}\\r\\n return self._con.post(url, params)['success']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d159ca6bce85d2ce06b6896218ee84ed\",\n \"score\": \"0.5186467\",\n \"text\": \"def deleteLldpManagementaddress(self):\\n\\t\\tquery = ''\\n\\t\\tconn = self.get_connection()\\n\\t\\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\\n\\t\\tconn.request('DELETE', self.get_path() + '/' + query, headers=headers)\\n\\t\\tresponse = conn.getresponse()\\n\\t\\texpected_status = 200\\n\\t\\terrors = {500: 'An unexpected runtime exception', 404: 'Specified management_address does not exist'}\\n\\t\\tpayload = self.get_output(response, expected_status, errors)\\n\\t\\tconn.close()\\n\\t\\tif self.debug:\\n\\t\\t\\tprint 'payload:', payload\\n\\t\\tif payload == '':\\n\\t\\t\\tpayload = None\\n\\t\\treturn deserialize_string_json(payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa67d33f16a3aae6811f111aaa385ab1\",\n \"score\": \"0.517543\",\n \"text\": \"def delete_bridge(self, br_name):\\n self.execute('ovs-vsctl del-br {}'.format(br_name))\\n br = self.get_bridge(br_name)\\n self.bridges.remove(br)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cca2e073056ce2125cbccd6022b02b9\",\n \"score\": \"0.5168209\",\n \"text\": \"def delete(self, hass):\\n dr = device_registry.async_get(hass)\\n er = entity_registry.async_get(hass)\\n\\n for e in self.entities.values():\\n er.async_remove(e.entity_id)\\n\\n self.entities = {}\\n\\n device = dr.async_get_device({(DOMAIN, self.browserID)})\\n dr.async_remove_device(device.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"584cf3f5cef042c982eb47515585e53d\",\n \"score\": \"0.5162038\",\n \"text\": \"def delete(self):\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3365c3bdc3a93293f26ff02a942b9a0\",\n \"score\": \"0.5156468\",\n \"text\": \"def delete(isdsAppliance, hostname, ip_addr, check_mode=False, force=False):\\n if force is True or _check(isdsAppliance, hostname, ip_addr) is True:\\n if check_mode is True:\\n return isdsAppliance.create_return_object(changed=True)\\n else:\\n return isdsAppliance.invoke_delete(\\n \\\"Removing host record\\\",\\n \\\"/host_records/\\\" + ip_addr,\\n {\\n })\\n return isdsAppliance.create_return_object()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30bce7a253ac4e21d746abb13deddeb\",\n \"score\": \"0.51520634\",\n \"text\": \"def delete_domain(name: str):\\n config_path = flask.current_app.config['CONFIG_PATH']\\n removed = False\\n\\n for _ in os.listdir(config_path):\\n\\n if os.path.isfile(os.path.join(config_path, _)):\\n if _.startswith(name):\\n os.remove(os.path.join(config_path, _))\\n removed = not os.path.exists(os.path.join(config_path, _))\\n break\\n\\n if removed:\\n return flask.jsonify({'success': True}), 200\\n else:\\n return flask.jsonify({'success': False}), 400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ca9863113155417a3ebebadd5dff23\",\n \"score\": \"0.51452446\",\n \"text\": \"def delete(self):\\n self.doapi_manager.request(self.url, method='DELETE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ca9863113155417a3ebebadd5dff23\",\n \"score\": \"0.51452446\",\n \"text\": \"def delete(self):\\n self.doapi_manager.request(self.url, method='DELETE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3702b291ec5f4b9c5e9b3aabb155ce45\",\n \"score\": \"0.5140637\",\n \"text\": \"def test_domain_create_duplicate(self):\\n domain_name = data_utils.rand_name('domain-dup')\\n domain = self.domains_client.create_domain(name=domain_name)['domain']\\n domain_id = domain['id']\\n self.addCleanup(self.delete_domain, domain_id)\\n # Domain name should be unique\\n self.assertRaises(\\n lib_exc.Conflict, self.domains_client.create_domain,\\n name=domain_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d43205cfb20a24078fd14a689ed49b2\",\n \"score\": \"0.5123957\",\n \"text\": \"def delete(self, *args, **kwargs):\\n\\n print(\\\"ciao\\\")\\n\\n try:\\n\\n if len(args) != 3:\\n raise ValueError(\\\"Invalid url\\\")\\n\\n request = tornado.escape.json_decode(self.request.body)\\n\\n if \\\"version\\\" not in request:\\n raise ValueError(\\\"missing version element\\\")\\n\\n if \\\"match\\\" not in request:\\n raise ValueError(\\\"missing match element\\\")\\n\\n tenant_id = uuid.UUID(args[0])\\n tenant = RUNTIME.tenants[tenant_id]\\n\\n lvap_id = EtherAddress(args[1])\\n lvap = tenant.lvaps[lvap_id]\\n\\n port_id = int(args[2])\\n port = lvap.ports[port_id]\\n\\n match = request['match']\\n\\n del port.next[match]\\n\\n except ValueError as ex:\\n self.send_error(400, message=ex)\\n except KeyError as ex:\\n self.send_error(404, message=ex)\\n\\n self.set_status(204, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68085acd99a14bc2d5dd72a8d6d8620e\",\n \"score\": \"0.51209426\",\n \"text\": \"def Delete(self):\\r\\n\\t\\tself.client.Delete(self.nodeid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d38eeb1b622b4bb6f2c3174b9c019257\",\n \"score\": \"0.5120391\",\n \"text\": \"def test_delete_person(self):\\n\\n response = DispatchTestHelpers.create_person(\\n self.client,\\n full_name='Test Person',\\n slug='test-person'\\n )\\n person_id = response.data['id']\\n # Generate detail URL\\n url = reverse('api-persons-detail', args=[person_id])\\n\\n # Successful deletion should return 204\\n response = self.client.delete(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\\n self.assertFalse(Person.objects.filter(id=person_id).exists())\\n\\n # Can't delete an person that has already been deleted\\n response = self.client.delete(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32135d7db2e33f38bd87475edfb1460e\",\n \"score\": \"0.51072913\",\n \"text\": \"def delete(self, network):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92132dc99385cda34fb11637edb6e82\",\n \"score\": \"0.51051366\",\n \"text\": \"def delete(self, cynetwork):\\n id = cynetwork.get_id()\\n self.session.delete(self.__url + '/' + str(id))\\n del cynetwork\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee39b243816900b759a61e1e21262b23\",\n \"score\": \"0.51043063\",\n \"text\": \"def test_delete_an_object_in_repository_by_id(self, test_domain):\\n person = test_domain.repository_for(Person)._dao.create(\\n id=3, first_name=\\\"John\\\", last_name=\\\"Doe\\\", age=22\\n )\\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\\n assert deleted_person is not None\\n assert deleted_person.state_.is_destroyed is True\\n\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62cf941da6af7dd20ebf98ec822167b6\",\n \"score\": \"0.5085803\",\n \"text\": \"def delete(self):\\n resp, result = self.http_delete()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0387b42f8c1138b850bc2152130a569\",\n \"score\": \"0.50802076\",\n \"text\": \"def clear(self, models=None, commit=True, domains=None, indexes=None, everything=False, spinlock=True):\\n # the implementation here just deletes the domain, recreates it, then reloads the schema\\n domains = domains or []\\n if models is not None:\\n m = self.get_model_to_index_map()\\n for model in models:\\n domains.append(self.get_searchdomain_name(m[model.__class__.__name__]))\\n\\n if indexes is not None:\\n for i in indexes:\\n domains.append(self.get_searchdomain_name(i))\\n\\n if models is None and indexes is None and not domains:\\n domains = [x for x in self.boto_conn.layer1.describe_domains()['DescribeDomainsResponse']['DescribeDomainsResult']]\\n if not everything:\\n conn = haystack.connections[self.connection_alias]\\n unified_index = conn.get_unified_index()\\n index_set = set([self.get_searchdomain_name(i) for i in unified_index.collect_indexes()])\\n domains = list(set(domains) & index_set)\\n\\n self.log.debug('deleting domains: %s' % (', '.join(domains),))\\n for d in domains:\\n self.boto_conn.layer1.delete_domain(d)\\n if spinlock:\\n if self.domain_processing_spinlock(domains):\\n if commit:\\n self.setup()\\n else:\\n raise CloudsearchDryerExploded('While waiting for a delete domain to finish, we hit our max timeout. Please investigate your SearchDomains.')\\n else:\\n if commit:\\n self.setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e9798c0cec5e1ec5d423f8f3a91c46\",\n \"score\": \"0.50800407\",\n \"text\": \"def delete(self):\\n reset_db()\\n return OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"971c27fec929ab8b8acc6f4d34556cec\",\n \"score\": \"0.5073962\",\n \"text\": \"def cleanup_domains(self, domains):\\n for domain_id in domains:\\n self.nsxlib.domain.delete(domain_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967c7ddca832a3eb07bf1c398bfad312\",\n \"score\": \"0.50705904\",\n \"text\": \"def networkDelete(request, networkKey):\\n network = get_object_or_404(Network, pk=networkKey)\\n network.delete()\\n return HttpResponseRedirect(reverse('pdep:index'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a7db7e329d028556049dd359f7f714\",\n \"score\": \"0.5069408\",\n \"text\": \"def delete(self, **kw):\\n abort(501, 'Delete not implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034807eeaa6fb4d4ff1b300f825e20a7\",\n \"score\": \"0.5068716\",\n \"text\": \"def delete_tld(self, context, tld_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898d48ba8bcd53e2dbff0d95f543bd95\",\n \"score\": \"0.50573736\",\n \"text\": \"def delete_dj_by_id(dj_id=None):\\n data_id = storage.get(\\\"Dj\\\", dj_id)\\n if data_id is None:\\n abort(404)\\n else:\\n storage.delete(data_id)\\n storage.save()\\n return jsonify({}), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafbf12191abee5a6a5e5f1fa82f193f\",\n \"score\": \"0.50552016\",\n \"text\": \"def delete(self):\\n handler = service_handler.ServiceHandler(pecan.request)\\n handler.delete(\\n pecan.request.context['host_name'],\\n pecan.request.context['service_description']\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3bee67fb232d5a77335c8aa5298614\",\n \"score\": \"0.50466233\",\n \"text\": \"def remove_from_owned_domains(self, domain_name: str):\\n self.owned_domain_list.remove(domain_name)\\n index = self.owned_domains.findText(domain_name)\\n self.owned_domains.removeItem(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fda611b7c50201f9dc2ed71cff5a49\",\n \"score\": \"0.5038844\",\n \"text\": \"def delete(self):\\n return self.parent.delete_instance(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39b3f42d30c6d4251c90e5c26dd9959\",\n \"score\": \"0.50388414\",\n \"text\": \"def remove_host(request, storage):\\n self = request.node.cls\\n\\n self.removed_host = getattr(\\n self, 'removed_host', self.hsm_hosts[0]\\n )\\n self.host_object = config.VDS_HOSTS[config.HOSTS.index(self.removed_host)]\\n testflow.setup(\\n \\\"Remove host '%s' from %s\\\", self.removed_host, config.DATA_CENTER_NAME\\n )\\n assert ll_hosts.remove_host(True, self.removed_host, True), (\\n \\\"Failed to remove host %s\\\" % self.removed_host\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4407ce0dd83764277ba6d39bf5a9689\",\n \"score\": \"0.5036082\",\n \"text\": \"def del_object(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f13b03cdb3efe834e2c758bc7afe4374\",\n \"score\": \"0.5034182\",\n \"text\": \"def delete(self, address):\\n\\n if not address:\\n logging.error(\\\"Delete: Missing required parameter - address\\\")\\n return False\\n\\n return self.query_backend.delete_listener(address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8013501ca58b78c33f74271058186b36\",\n \"score\": \"0.5033916\",\n \"text\": \"def DeleteLoadBalancer(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"DeleteLoadBalancer\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.DeleteLoadBalancerResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1473,"cells":{"query_id":{"kind":"string","value":"61d3b6d8df4e0289ea1bd7a1758748b7"},"query":{"kind":"string","value":"Counts the total number of bags contained in the bag"},"positive_passages":{"kind":"list like","value":[{"docid":"f2ba0b2855248ddcd7616a456eabd28b","score":"0.7824124","text":"def inside_bag_count(self):\n if not self.containees:\n return 0\n\n count = 0\n for number, name in self.containees:\n count += number * (1 + Bag.collection[name].inside_bag_count)\n\n return count","title":""}],"string":"[\n {\n \"docid\": \"f2ba0b2855248ddcd7616a456eabd28b\",\n \"score\": \"0.7824124\",\n \"text\": \"def inside_bag_count(self):\\n if not self.containees:\\n return 0\\n\\n count = 0\\n for number, name in self.containees:\\n count += number * (1 + Bag.collection[name].inside_bag_count)\\n\\n return count\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3b7d03054db7468d5c2144763718f1ec","score":"0.8159138","text":"def num_bags(bag_type: str) -> int:\n\n result = 1\n\n for key, value in rules[bag_type].items():\n # We add 1 to num_bags(key) so that we count the actual bag with type key.\n result += value * (num_bags(key) + 1)\n\n return result - 1","title":""},{"docid":"86bdfd46583a66e9ad20cff6a9caf705","score":"0.7997461","text":"def countBags(outerBag):\n count = 0\n if len(rules[outerBag]):\n for innerBag in rules[outerBag]:\n # Add the number of bags of that color\n count += rules[outerBag][innerBag]\n # Add the bags that they contain\n count += rules[outerBag][innerBag] * countBags(innerBag)\n return count\n else:\n return 0","title":""},{"docid":"f7147466fc3d5157aa90521c453d152d","score":"0.7439715","text":"def count_child_bags(bag_colour: str) -> int:\n # Each bag contains it's child bags PLUS their children.\n return sum([(count_child_bags(child_bag_colour) + 1) * quantity for child_bag_colour, quantity in _bags[bag_colour].children.items()])","title":""},{"docid":"496b90f83b4f44fbb5301a026d2ef78f","score":"0.72352433","text":"def count_contents(bag: str) -> int:\n total: int = 0\n contents: dict = parsed[bag]\n \n # Base case: if no contents, total stays 0\n \n # Recursive case:\n if len(contents) > 0: \n for bag_type in contents: \n n: int = contents[bag_type]\n total += n + n * count_contents(bag_type)\n \n return total","title":""},{"docid":"4cef81a045b70a2bf8578d8f18ce7db7","score":"0.7093167","text":"def aggregate_counts(bags_of_words):\n aggregate_counter = Counter() \n for counter in bags_of_words:\n aggregate_counter += counter\n\n return aggregate_counter","title":""},{"docid":"8ce9e804b23f3a79219887b2f045a1b6","score":"0.707282","text":"def number_of_bags(current_bag_type: str, rules: dict) -> int:\n allowed_bags = [rt[1] for rt in rules[current_bag_type]]\n multipliers = [int(rt[0]) for rt in rules[current_bag_type]]\n if not allowed_bags:\n return 1\n else:\n result = sum([multiplier * number_of_bags(ab, rules) for (ab, multiplier) in zip(allowed_bags, multipliers)])\n return result + 1","title":""},{"docid":"ec069b033bf5a93a9b1d1b4acc8c0e35","score":"0.6886578","text":"def count_bags_containing(rules, color):\n root_colors = rules.keys()\n \n return sum([1 for root in root_colors if does_bag_contain(rules, root, color)])","title":""},{"docid":"2ea17fffbdf68497301ffa70a54be6e6","score":"0.6799235","text":"def part_1(data):\n rules = parse_rules(data)\n ways = list(containments(rules, 'shiny gold', []))\n return len(set(way[-1] for way in ways)) # Only count unique outermost bags","title":""},{"docid":"3952d3b08167aac068c14870a715e51a","score":"0.6477009","text":"def tree_count(self):\n total = 0 \n for tree in self:\n if isinstance(self[tree], TAGTreeSet):\n total += self[tree].tree_count()\n else:\n total += 1\n return total","title":""},{"docid":"3d260798967cc262c0473f63c88cb080","score":"0.64728487","text":"def __len__(self):\n return sum(item['qty'] for item in self.basket.values())","title":""},{"docid":"5b7382b3d81c05cc6af4494547e661fe","score":"0.64023954","text":"def Dfs(corpus):\n cnt = Counter()\n for d in corpus:\n s = set(d['bag'])\n for word in s:\n cnt[word] += 1\n return cnt","title":""},{"docid":"0f048cab8ed3c07ad97367a36aa957f8","score":"0.6341221","text":"def size(self):\n\n return len(self.get_basket_items())","title":""},{"docid":"935b2b8205d542f2dc81e09684a43d1c","score":"0.6267463","text":"def valid_bags(bag_dict):\n my_bag = [\"shiny gold\"]\n valid_bag_dict = {}\n while len(my_bag) > 0:\n to_check = my_bag.pop()\n for bag in bag_dict:\n if to_check in bag_dict[bag]:\n my_bag.append(bag)\n valid_bag_dict[bag] = 1\n print (len(valid_bag_dict))","title":""},{"docid":"baa7f8900ea14ee3fd4eac8eacedce22","score":"0.6253775","text":"def count_blign_objects():\n return len([obj for obj in list(bpy.data.objects) if obj.blign == True])","title":""},{"docid":"da0aeb47392414558e7fd89c6ecb3f3d","score":"0.6240883","text":"def branch_count(self):\n sum = 0\n for n in self.tree.values():\n sum += n.get_children_count()\n return sum","title":""},{"docid":"040a687b20baadc4b9119cacc1ed8e56","score":"0.62002516","text":"def count(self):\n return len(self.labels)","title":""},{"docid":"db9dcccb3fd3546fee0a28fc17ac43d4","score":"0.6189848","text":"def contains2(color: str, bags_dict: Dict[str, Dict], counter: int = 0) -> int:\n if not bags_dict[color]:\n return 1\n for bag in bags_dict[color]:\n counter += (bags_dict[color][bag] * contains2(bag, bags_dict, 0))\n return counter + 1","title":""},{"docid":"7f807f8ce0e2c5448b6e80254709dfa8","score":"0.61884373","text":"def bin_counts(self):\n return self.binning_info[\"refl_per_bin\"]","title":""},{"docid":"414730ad6a4af71f9086a3e0b574fbb7","score":"0.61663646","text":"def number_of_animals(self):\n num_herbs = 0\n num_carns = 0\n\n for row in self.island_map:\n for cell in row:\n num_herbs += cell.total_num_animals(species='herbivore')\n num_carns += cell.total_num_animals(species='carnivore')\n return num_herbs, num_carns","title":""},{"docid":"903a9bf9a5c178d0130e4917404abf48","score":"0.6145309","text":"def getCounts(self, agents):\n\n\t\tcounts = {}\n\t\tfor tag in self.tags:\n\t\t\tcounts[tag] = 0\n\n\t\tfor agent in agents:\n\t\t\tcounts[agent.tag] +=1\n\t\t\t\n\t\treturn counts","title":""},{"docid":"880a69215e1872c78946e1bd9b11f0cd","score":"0.61164844","text":"def get_total_dogs(self) -> int:\n return len(self.dogs)","title":""},{"docid":"0cead2beda6288b2929db2bafc4fb196","score":"0.6112249","text":"def Count(self) -> int:","title":""},{"docid":"7a0977f20f6eb2bc9eb41410d6822d1c","score":"0.6094235","text":"def file_to_bags(f) -> int:\n for line in f:\n parent_bag_colour, child_bag_colours = parse_line(line)\n if parent_bag_colour not in _bags:\n _bags[parent_bag_colour] = Bag(parent_bag_colour)\n _bags[parent_bag_colour].add_children(child_bag_colours)\n\n for bag_colour, _ in _bags.items():\n check_contain_shiny_gold(bag_colour)\n\n total_containing_shiny = sum([bag.contain_shiny_gold for bag in _bags.values()])\n\n total_shiny_contains = count_child_bags(\"shiny gold\")\n\n return total_containing_shiny, total_shiny_contains","title":""},{"docid":"c814d677b9b6c81270024271416cf2ae","score":"0.6093544","text":"def count_ships(b) -> int:","title":""},{"docid":"7922bb0186f3f1d04f19f85c5255c82b","score":"0.608899","text":"def count(self) -> int:","title":""},{"docid":"c2a9d8069655a252e5bc3d2a22c4a108","score":"0.6077405","text":"def count_blesses(self):\n return sum(1 for card in self.current_deck if card.type == \"bless\")","title":""},{"docid":"9a9910c7d4fb95c13fb446d3ee2d8d8e","score":"0.60663396","text":"def get_total_breeds(self) -> int:\n # api count:186\n list_breeds = []\n for breed in self.breeds:\n if breed.name not in list_breeds:\n list_breeds.append(breed.name)\n return len(list_breeds)","title":""},{"docid":"74845395fc5b7c5c5c02a76753cb130d","score":"0.6045785","text":"def tagSize(self) -> int:\n size = 1\n for group in self.inflectionalGroups:\n size += group.size()\n return size","title":""},{"docid":"329d639b7d084604e71196677d1a063b","score":"0.6042014","text":"def _get_count(self):\n itemCount = 0\n for item in self.cartitem_set.all():\n itemCount += item.quantity\n\n for writein in self.cartwriteinitem_set.all():\n itemCount += 1\n\n for virtualshop_item in self.cartvirtualshopitem_set.all():\n itemCount += 1\n\n return (itemCount)","title":""},{"docid":"72a183d44d85a7c6daac94db35a48e0f","score":"0.6031002","text":"def bars_count(self):\n return len(self.__bars)","title":""},{"docid":"f3691b744c7a118066eaaad896349690","score":"0.60179776","text":"def count(self):\n cn = self.compoundNames()\n return len(cn)","title":""},{"docid":"0f5ebbac17892edc9e8e7bce3a24ae95","score":"0.60082006","text":"def get_count(self):\n return sum([len(positions) for positions in self.items.values()])","title":""},{"docid":"cdb7c37c312c0e1ea847b3b5a957ca22","score":"0.5968933","text":"def solve(xs):\n counter = collections.Counter(xs)\n for x, cnt in counter.items():\n while x in bag:\n bag.remove(x)\n x += 1\n bag.add(x)\n return len(bag)","title":""},{"docid":"ee7ac33396932d7b0a803607cec5f13b","score":"0.5958713","text":"def count(self):\n return len(self._get_items())","title":""},{"docid":"c8847153b29635620f3db8923cc77606","score":"0.59510857","text":"def neuron_count(self):\n return len(self._biases)","title":""},{"docid":"b957f1b1858b138715dc4fabd4c49c39","score":"0.59504426","text":"def count_nodes(self):\n # return len(self.extract_nodes())\n return sum([1] + [b.count_nodes() for b in self.branches])","title":""},{"docid":"781723e2172d56e0aaebed268b4de93f","score":"0.5932326","text":"def _collect_box_count(self, boxes):\n count = Counter([box.box_id for box in boxes])\n\n # Add the counts in the superboxes.\n for box in boxes:\n if hasattr(box, 'box'):\n count.update(self._collect_box_count(box.box))\n\n return count","title":""},{"docid":"2ec41f69e455087559a775dc56d077d3","score":"0.5927293","text":"def __len__(self):\n return sum([len(bucket) for bucket in self.buckets])","title":""},{"docid":"39000349d4ee3239156883a84460b283","score":"0.5926623","text":"def count(self):\n count = len(self.__children)\n for child in self.__children:\n count += child.count\n return count","title":""},{"docid":"86f691b7339e50dd88382d1dc337edeb","score":"0.5921082","text":"def amount_of_genes(self,category):\n return len(self.genes[category])","title":""},{"docid":"1bba81a6ab2467e983be360b1191328a","score":"0.59209186","text":"def tree_family_count(self):\n total = 0 \n for tree in self:\n if isinstance(self[tree], TAGTreeSet) and tree[-6:] == '.trees':\n total += 1\n elif isinstance(self[tree], TAGTreeSet):\n total += self[tree].tree_family_count()\n return total","title":""},{"docid":"81c3261e3db18fb3e2740f5905e42c36","score":"0.5911013","text":"def num_animals(self):\n pop = self.island.get_population_numbers()\n return sum(pop['Herbivore']) + sum(pop['Carnivore'])","title":""},{"docid":"9ecf101a51091fb30c01499c20cf25b4","score":"0.5908521","text":"def count(self):\n return self.size()","title":""},{"docid":"20c4baef668fe4d147f2fd62cac83b8c","score":"0.58957577","text":"def get_items_count(self):\n return sum(1 for places in self.grid.values() for pos in places)","title":""},{"docid":"4c3155b8f66fe4d616cab4b0eff850ce","score":"0.58875024","text":"def count_taggers(self):\r\n # TODO : must be a better way (group by ?)\r\n return len(set(self.private_tags_class.objects.filter(content_object=self).values_list('owner', flat=True)))","title":""},{"docid":"3fb0dff214d56e88c3063fe7baee364c","score":"0.58859867","text":"def count(self):\n return len(self.items)","title":""},{"docid":"e64c32f3acd43c56727899695cb45b18","score":"0.58728564","text":"def numberOfBonds(self):\n return sum(1 for o in self.structures if isinstance(o, Bond))","title":""},{"docid":"f01f556d828221ab95bb4f0dce3282ef","score":"0.5871036","text":"def total(self):\n return EntryTag.objects.for_category(self).count()","title":""},{"docid":"dbbd74f007e5b631ead4edf29e6f5919","score":"0.58583224","text":"def count(self):\n pass","title":""},{"docid":"dbbd74f007e5b631ead4edf29e6f5919","score":"0.58583224","text":"def count(self):\n pass","title":""},{"docid":"ab57e0ae68c2371dff13d333aeee6d1f","score":"0.5851884","text":"def number_of_counties(self):\n return self._number_of_counties","title":""},{"docid":"ab57e0ae68c2371dff13d333aeee6d1f","score":"0.5851884","text":"def number_of_counties(self):\n return self._number_of_counties","title":""},{"docid":"adc71f53a6a8dec5df049b4f28f0269b","score":"0.5839162","text":"def catcount(self, cat):\n if cat in self.cnt:\n return float(self.cnt[cat])\n return 0","title":""},{"docid":"40117d079063def7b0be342845d4dae5","score":"0.58320206","text":"def vigra_bincount(labels):\n labels = labels.astype(numpy.uint32, copy=False)\n labels = numpy.ravel(labels, order='K').reshape((-1, 1), order='A')\n # We don't care what the 'image' parameter is, but we have to give something\n image = labels.view(numpy.float32)\n counts = vigra.analysis.extractRegionFeatures(image, labels, ['Count'])['Count']\n return counts.astype(numpy.int64)","title":""},{"docid":"9695e03edd727f14ff7ebba186589b91","score":"0.58172184","text":"def bagofwords(sentence_words, vocab):\n bag = [0] * len(vocab)\n for sw in sentence_words:\n for i, word in enumerate(vocab):\n if word == sw:\n bag[i] += 1\n return bag","title":""},{"docid":"7248103ca92fe057df90a49203576ab7","score":"0.5807144","text":"def labelsets_count(self):\n proj = h5py.File(self.project_filename, \"r\")\n label_sets = eval_h5(proj, const.label_sets_list())\n count = len(label_sets.keys())\n proj.close()\n return count","title":""},{"docid":"77dc86ebd2e574a32ade47204b3df038","score":"0.5805245","text":"def count_neighbor_bombs(neighbors, bombs):\n count = 0\n for n in neighbors:\n if n in bombs:\n count +=1\n return count","title":""},{"docid":"95771057696003c349ee4bdf1ddb5fe0","score":"0.58017373","text":"def get_scrobble_count(self):\n return self.scrobbles.count()","title":""},{"docid":"95771057696003c349ee4bdf1ddb5fe0","score":"0.58017373","text":"def get_scrobble_count(self):\n return self.scrobbles.count()","title":""},{"docid":"95771057696003c349ee4bdf1ddb5fe0","score":"0.58017373","text":"def get_scrobble_count(self):\n return self.scrobbles.count()","title":""},{"docid":"3e2e270fd8e33a9c760e1e6745cc42cd","score":"0.5799532","text":"def popcount(a):\n return np.sum(_pop_count_lut[a])","title":""},{"docid":"db98f5c1e086934e4b92f7b5f606d80f","score":"0.5762601","text":"def count(self):\n return self.values.size","title":""},{"docid":"02c07a83ebfce1cba7358da5cb77bafa","score":"0.5756949","text":"def count(self):\n number = self._count_aux(self._root)\n return number","title":""},{"docid":"470451201f3c4c7b3f47b521fc3d6e67","score":"0.57510835","text":"def count(self):\n return self.pipeline.count()","title":""},{"docid":"813e90965a2a4cdf2bf17f3cfd5df3df","score":"0.57340974","text":"def count(self):\n return len(self._items)","title":""},{"docid":"6204cc691b1682234dcf72cef49f5a92","score":"0.57329416","text":"def length(self):\n # Running time: O(b) Where b is the number of buckets\n '''length = 0\n #Loop through all buckets\n for bucket in self.buckets:\n #Count number of key-value entries in each bucket\n length += bucket.length()\n\n return length'''\n\n return self.size","title":""},{"docid":"3e97a871acb2afcdaf4c7b81cd4a3682","score":"0.57326066","text":"def totalcount(self):\n return sum(self.cnt.values())","title":""},{"docid":"f9974bf56d30760c4081ba2b37acaa61","score":"0.5717575","text":"def ncat(self):\n return len(self.categories)","title":""},{"docid":"7710409b65f2324a2e02c3a7bb138896","score":"0.5711229","text":"def count(self, item=True):\n return sum([x.count(item) for x in self.data])","title":""},{"docid":"71225dceb67fb39ca5558e11c935d71d","score":"0.5708771","text":"def __len__(self):\n n = len(self._hyperedges)\n try:\n for m in self._extras.values():\n n += m\n except AttributeError:\n pass\n return n","title":""},{"docid":"c813f0d81141388b55a84e614a88d7eb","score":"0.5704031","text":"def count(dimacs_, constraints_):\n\n _tempdimacs = os.path.dirname(dimacs_) + '/count.dimacs'\n _features, _clauses, _vcount = read_dimacs(dimacs_)\n\n gen_dimacs(_vcount, _clauses, constraints_, _tempdimacs)\n res = int(getoutput(SHARPSAT + ' -q ' + _tempdimacs))\n\n return res","title":""},{"docid":"3c1c0b737ef8bbda99962982f06e76d9","score":"0.5702885","text":"def count_hits(label_dict):\n count = 0\n for label in label_dict:\n if label == DataLabel.NOISE:\n continue\n count += len(label_dict[label]['hits'])\n return count","title":""},{"docid":"7137a3cd993f6409d81bd5ad790bac69","score":"0.56981176","text":"def length(self):\n # TODO: Count number of key-value entries in each of the buckets\n length = 0\n for bucket in self.buckets:\n length += bucket.length()\n return length","title":""},{"docid":"1cd676167d465eb7d7c9ddc025ef3cd8","score":"0.569385","text":"def count_eggs():\n eggs = 12\n return eggs","title":""},{"docid":"861f08ed50676f92ebd2a3e7b8c6c05d","score":"0.5693377","text":"def count_bikes(kdnr):\n\n\treturn models.Bike.objects.filter(kunde=kdnr).count()","title":""},{"docid":"a452cdc73e176cfc8943e088e3443328","score":"0.5688209","text":"def ElementCount(self) -> int:","title":""},{"docid":"066597d39f508951c971d4cfdfc680e1","score":"0.5687972","text":"def get_number_of_labels(label_dict, args):\n\n return len(get_sorted_labels(label_dict, args))","title":""},{"docid":"f6eb06431b5b18062b1f59db4bf8b043","score":"0.5685428","text":"def compute_train_bags(self,n_bags, bag_size,y):\n with torch.no_grad():\n if not torch.is_tensor(y): \n y=torch.IntTensor(y).to(self.device)\n \n #Tensor to return the result. Each bag in a row.\n samples_indexes = torch.zeros((n_bags,bag_size),dtype=torch.int64,device=self.device)\n classes = torch.unique(y)\n n_classes = len(classes)\n \n prevalences = torch.zeros((n_bags,n_classes),device=self.device)\n for i in range(n_bags):\n low = round(bag_size * 0.01)\n high = round(bag_size * 0.99)\n\n ps = torch.randint(low, high, (n_classes - 1,),generator=self.gen,device=self.device)\n ps = torch.cat([ps, torch.tensor([0, bag_size],device=self.device)])\n ps = torch.sort(ps)[0]\n ps = ps[1:] - ps[:-1] #Number of samples per class\n prevalences[i,:] = ps/bag_size\n already_generated=0\n for n, p in zip(classes, ps):\n if p != 0:\n examples_class=torch.where(y==n)[0]\n samples_indexes[i,already_generated:already_generated+p]=examples_class[torch.randint(len(examples_class),(p,),generator=self.gen,device=self.device)]\n already_generated+=p\n suffle = torch.randperm(bag_size)\n samples_indexes[i,:]=samples_indexes[i,suffle]\n return samples_indexes,prevalences","title":""},{"docid":"8346437baf34d7265ec0180b91e03e9e","score":"0.56834364","text":"def count(self):\n rv = 0\n for _ in self:\n rv += 1\n return rv","title":""},{"docid":"8b6945332b05e538b44233b6bdf18f80","score":"0.5678691","text":"def test_num_bags_required_01_example1(self):\n # Arrange\n rules = parse_rules('./Day07/tdata.txt')\n\n # Act\n a = num_bags_required(rules, \"faded blue\")\n b = num_bags_required(rules, \"dotted black\")\n c = num_bags_required(rules, \"vibrant plum\")\n d = num_bags_required(rules, \"dark olive\")\n e = num_bags_required(rules, \"shiny gold\")\n \n # Assert\n self.assertEqual(0, a)\n self.assertEqual(0, b)\n self.assertEqual(11, c)\n self.assertEqual(7, d)\n self.assertEqual(32, e)","title":""},{"docid":"42fb58ce9fdfc7b874d1c42bacc98947","score":"0.5677119","text":"def count(self):\n return len(self)","title":""},{"docid":"a4300a9f737bf56dfc4a7fe51125409b","score":"0.5671487","text":"def getLength(self):\n numItems = 0\n \n for items in self.exploredSet:\n numItems += 1\n \n return numItems","title":""},{"docid":"917c3a55aed9eded2bf8e087a9aebea6","score":"0.5668383","text":"def count(self, categories=None):\n pass","title":""},{"docid":"c931e0eacfb6119d5db086d9b7544f3a","score":"0.5668341","text":"def count(self,op=0):\n print 'count'\n counts=self.read()\n q=dict(zip(self.labels,counts))\n return q","title":""},{"docid":"f919c040cdce3d5a885d594976469655","score":"0.56671906","text":"def nreads(counts):\n return sum([counts[x] for x in counts])","title":""},{"docid":"349cbaa3e5cb17b4b9aa0ca13a31590d","score":"0.5666067","text":"def count_drugs(self) -> int:\n return self._count_model(Drug)","title":""},{"docid":"15730d0e282bb523d20218ad51e47f84","score":"0.56626314","text":"def count_elements_in_dataset(dataset):\r\n return dataset.count()","title":""},{"docid":"7c7501180ec77f0ba6a0aa3eed21084c","score":"0.56625646","text":"def count(self):\n count = 0\n for item in self.table:\n count += bit_count(item)\n\n return -self.filter_size * log(count / self.filter_size)","title":""},{"docid":"cbafb5a07f358c61bb044c784b52f9ee","score":"0.5661566","text":"def bag_of_words(text):\n text_list = text.split()\n cnt = Counter()\n for word in text_list:\n cnt[word] += 1\n\n return cnt","title":""},{"docid":"cd08b67c9c0dbebb2658b37840b00773","score":"0.5658421","text":"def GetNumNodes(self):\n return sum(self.nodeCounts)","title":""},{"docid":"721962f73f783031ec88daf073a8745c","score":"0.5656176","text":"def get_count():\n total = 0\n for counter in SimpleCounterShard.all():\n total += counter.count\n return total","title":""},{"docid":"51775eb7f4a99c5f537dec8444e94dc5","score":"0.5639252","text":"def CountGO(gene2go):\n\n cats = collections.defaultdict(int)\n nmaps = 0\n for k, vv in gene2go.items():\n for v in vv:\n nmaps += 1\n cats[v.mGOId] += 1\n\n return len(gene2go), len(cats), nmaps, cats","title":""},{"docid":"e1934e10c6a0db14a61156ef76d207ec","score":"0.56314844","text":"def count_object(network, class_ids, scores, bounding_boxes, object_label, threshold=0.75):\n detected_objects=0\n while(scores[0][detected_objects]>threshold):\n detected_objects+=1\n count=0\n reqd_idx = network.classes.index(object_label)\n for idx in class_ids[0][:detected_objects]:\n if (idx==reqd_idx):\n count+=1\n return count","title":""},{"docid":"a8a499880bdd60155a0d8c84481692b3","score":"0.5629823","text":"def deep_sum(bag_map: BagMap, key: str) -> int:\n immediate = sum(bag_map[key].values()) # Amount of immediate children\n return immediate + sum(times * deep_sum(bag_map, key) for key, times in bag_map[key].items())","title":""},{"docid":"6c03f1b4e9764ddb41b886e5af798a73","score":"0.56269306","text":"def count_brands() -> int:\n return db.session.scalar(select(db.func.count(DbBrand.id)))","title":""},{"docid":"30959678e030fddef3cbf22ca9172727","score":"0.56226605","text":"def size(self):\n return self.count","title":""},{"docid":"30959678e030fddef3cbf22ca9172727","score":"0.56226605","text":"def size(self):\n return self.count","title":""},{"docid":"30959678e030fddef3cbf22ca9172727","score":"0.56226605","text":"def size(self):\n return self.count","title":""},{"docid":"30959678e030fddef3cbf22ca9172727","score":"0.56226605","text":"def size(self):\n return self.count","title":""},{"docid":"dc85db9e660a0e90d93d40e8c2c16d6e","score":"0.5622088","text":"def totalCount(self):\n return sum(self.values())","title":""}],"string":"[\n {\n \"docid\": \"3b7d03054db7468d5c2144763718f1ec\",\n \"score\": \"0.8159138\",\n \"text\": \"def num_bags(bag_type: str) -> int:\\n\\n result = 1\\n\\n for key, value in rules[bag_type].items():\\n # We add 1 to num_bags(key) so that we count the actual bag with type key.\\n result += value * (num_bags(key) + 1)\\n\\n return result - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86bdfd46583a66e9ad20cff6a9caf705\",\n \"score\": \"0.7997461\",\n \"text\": \"def countBags(outerBag):\\n count = 0\\n if len(rules[outerBag]):\\n for innerBag in rules[outerBag]:\\n # Add the number of bags of that color\\n count += rules[outerBag][innerBag]\\n # Add the bags that they contain\\n count += rules[outerBag][innerBag] * countBags(innerBag)\\n return count\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7147466fc3d5157aa90521c453d152d\",\n \"score\": \"0.7439715\",\n \"text\": \"def count_child_bags(bag_colour: str) -> int:\\n # Each bag contains it's child bags PLUS their children.\\n return sum([(count_child_bags(child_bag_colour) + 1) * quantity for child_bag_colour, quantity in _bags[bag_colour].children.items()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"496b90f83b4f44fbb5301a026d2ef78f\",\n \"score\": \"0.72352433\",\n \"text\": \"def count_contents(bag: str) -> int:\\n total: int = 0\\n contents: dict = parsed[bag]\\n \\n # Base case: if no contents, total stays 0\\n \\n # Recursive case:\\n if len(contents) > 0: \\n for bag_type in contents: \\n n: int = contents[bag_type]\\n total += n + n * count_contents(bag_type)\\n \\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cef81a045b70a2bf8578d8f18ce7db7\",\n \"score\": \"0.7093167\",\n \"text\": \"def aggregate_counts(bags_of_words):\\n aggregate_counter = Counter() \\n for counter in bags_of_words:\\n aggregate_counter += counter\\n\\n return aggregate_counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce9e804b23f3a79219887b2f045a1b6\",\n \"score\": \"0.707282\",\n \"text\": \"def number_of_bags(current_bag_type: str, rules: dict) -> int:\\n allowed_bags = [rt[1] for rt in rules[current_bag_type]]\\n multipliers = [int(rt[0]) for rt in rules[current_bag_type]]\\n if not allowed_bags:\\n return 1\\n else:\\n result = sum([multiplier * number_of_bags(ab, rules) for (ab, multiplier) in zip(allowed_bags, multipliers)])\\n return result + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec069b033bf5a93a9b1d1b4acc8c0e35\",\n \"score\": \"0.6886578\",\n \"text\": \"def count_bags_containing(rules, color):\\n root_colors = rules.keys()\\n \\n return sum([1 for root in root_colors if does_bag_contain(rules, root, color)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ea17fffbdf68497301ffa70a54be6e6\",\n \"score\": \"0.6799235\",\n \"text\": \"def part_1(data):\\n rules = parse_rules(data)\\n ways = list(containments(rules, 'shiny gold', []))\\n return len(set(way[-1] for way in ways)) # Only count unique outermost bags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3952d3b08167aac068c14870a715e51a\",\n \"score\": \"0.6477009\",\n \"text\": \"def tree_count(self):\\n total = 0 \\n for tree in self:\\n if isinstance(self[tree], TAGTreeSet):\\n total += self[tree].tree_count()\\n else:\\n total += 1\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d260798967cc262c0473f63c88cb080\",\n \"score\": \"0.64728487\",\n \"text\": \"def __len__(self):\\n return sum(item['qty'] for item in self.basket.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7382b3d81c05cc6af4494547e661fe\",\n \"score\": \"0.64023954\",\n \"text\": \"def Dfs(corpus):\\n cnt = Counter()\\n for d in corpus:\\n s = set(d['bag'])\\n for word in s:\\n cnt[word] += 1\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f048cab8ed3c07ad97367a36aa957f8\",\n \"score\": \"0.6341221\",\n \"text\": \"def size(self):\\n\\n return len(self.get_basket_items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"935b2b8205d542f2dc81e09684a43d1c\",\n \"score\": \"0.6267463\",\n \"text\": \"def valid_bags(bag_dict):\\n my_bag = [\\\"shiny gold\\\"]\\n valid_bag_dict = {}\\n while len(my_bag) > 0:\\n to_check = my_bag.pop()\\n for bag in bag_dict:\\n if to_check in bag_dict[bag]:\\n my_bag.append(bag)\\n valid_bag_dict[bag] = 1\\n print (len(valid_bag_dict))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa7f8900ea14ee3fd4eac8eacedce22\",\n \"score\": \"0.6253775\",\n \"text\": \"def count_blign_objects():\\n return len([obj for obj in list(bpy.data.objects) if obj.blign == True])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0aeb47392414558e7fd89c6ecb3f3d\",\n \"score\": \"0.6240883\",\n \"text\": \"def branch_count(self):\\n sum = 0\\n for n in self.tree.values():\\n sum += n.get_children_count()\\n return sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"040a687b20baadc4b9119cacc1ed8e56\",\n \"score\": \"0.62002516\",\n \"text\": \"def count(self):\\n return len(self.labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9dcccb3fd3546fee0a28fc17ac43d4\",\n \"score\": \"0.6189848\",\n \"text\": \"def contains2(color: str, bags_dict: Dict[str, Dict], counter: int = 0) -> int:\\n if not bags_dict[color]:\\n return 1\\n for bag in bags_dict[color]:\\n counter += (bags_dict[color][bag] * contains2(bag, bags_dict, 0))\\n return counter + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f807f8ce0e2c5448b6e80254709dfa8\",\n \"score\": \"0.61884373\",\n \"text\": \"def bin_counts(self):\\n return self.binning_info[\\\"refl_per_bin\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"414730ad6a4af71f9086a3e0b574fbb7\",\n \"score\": \"0.61663646\",\n \"text\": \"def number_of_animals(self):\\n num_herbs = 0\\n num_carns = 0\\n\\n for row in self.island_map:\\n for cell in row:\\n num_herbs += cell.total_num_animals(species='herbivore')\\n num_carns += cell.total_num_animals(species='carnivore')\\n return num_herbs, num_carns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903a9bf9a5c178d0130e4917404abf48\",\n \"score\": \"0.6145309\",\n \"text\": \"def getCounts(self, agents):\\n\\n\\t\\tcounts = {}\\n\\t\\tfor tag in self.tags:\\n\\t\\t\\tcounts[tag] = 0\\n\\n\\t\\tfor agent in agents:\\n\\t\\t\\tcounts[agent.tag] +=1\\n\\t\\t\\t\\n\\t\\treturn counts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880a69215e1872c78946e1bd9b11f0cd\",\n \"score\": \"0.61164844\",\n \"text\": \"def get_total_dogs(self) -> int:\\n return len(self.dogs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cead2beda6288b2929db2bafc4fb196\",\n \"score\": \"0.6112249\",\n \"text\": \"def Count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0977f20f6eb2bc9eb41410d6822d1c\",\n \"score\": \"0.6094235\",\n \"text\": \"def file_to_bags(f) -> int:\\n for line in f:\\n parent_bag_colour, child_bag_colours = parse_line(line)\\n if parent_bag_colour not in _bags:\\n _bags[parent_bag_colour] = Bag(parent_bag_colour)\\n _bags[parent_bag_colour].add_children(child_bag_colours)\\n\\n for bag_colour, _ in _bags.items():\\n check_contain_shiny_gold(bag_colour)\\n\\n total_containing_shiny = sum([bag.contain_shiny_gold for bag in _bags.values()])\\n\\n total_shiny_contains = count_child_bags(\\\"shiny gold\\\")\\n\\n return total_containing_shiny, total_shiny_contains\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c814d677b9b6c81270024271416cf2ae\",\n \"score\": \"0.6093544\",\n \"text\": \"def count_ships(b) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7922bb0186f3f1d04f19f85c5255c82b\",\n \"score\": \"0.608899\",\n \"text\": \"def count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a9d8069655a252e5bc3d2a22c4a108\",\n \"score\": \"0.6077405\",\n \"text\": \"def count_blesses(self):\\n return sum(1 for card in self.current_deck if card.type == \\\"bless\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9910c7d4fb95c13fb446d3ee2d8d8e\",\n \"score\": \"0.60663396\",\n \"text\": \"def get_total_breeds(self) -> int:\\n # api count:186\\n list_breeds = []\\n for breed in self.breeds:\\n if breed.name not in list_breeds:\\n list_breeds.append(breed.name)\\n return len(list_breeds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74845395fc5b7c5c5c02a76753cb130d\",\n \"score\": \"0.6045785\",\n \"text\": \"def tagSize(self) -> int:\\n size = 1\\n for group in self.inflectionalGroups:\\n size += group.size()\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329d639b7d084604e71196677d1a063b\",\n \"score\": \"0.6042014\",\n \"text\": \"def _get_count(self):\\n itemCount = 0\\n for item in self.cartitem_set.all():\\n itemCount += item.quantity\\n\\n for writein in self.cartwriteinitem_set.all():\\n itemCount += 1\\n\\n for virtualshop_item in self.cartvirtualshopitem_set.all():\\n itemCount += 1\\n\\n return (itemCount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a183d44d85a7c6daac94db35a48e0f\",\n \"score\": \"0.6031002\",\n \"text\": \"def bars_count(self):\\n return len(self.__bars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3691b744c7a118066eaaad896349690\",\n \"score\": \"0.60179776\",\n \"text\": \"def count(self):\\n cn = self.compoundNames()\\n return len(cn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f5ebbac17892edc9e8e7bce3a24ae95\",\n \"score\": \"0.60082006\",\n \"text\": \"def get_count(self):\\n return sum([len(positions) for positions in self.items.values()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb7c37c312c0e1ea847b3b5a957ca22\",\n \"score\": \"0.5968933\",\n \"text\": \"def solve(xs):\\n counter = collections.Counter(xs)\\n for x, cnt in counter.items():\\n while x in bag:\\n bag.remove(x)\\n x += 1\\n bag.add(x)\\n return len(bag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7ac33396932d7b0a803607cec5f13b\",\n \"score\": \"0.5958713\",\n \"text\": \"def count(self):\\n return len(self._get_items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8847153b29635620f3db8923cc77606\",\n \"score\": \"0.59510857\",\n \"text\": \"def neuron_count(self):\\n return len(self._biases)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b957f1b1858b138715dc4fabd4c49c39\",\n \"score\": \"0.59504426\",\n \"text\": \"def count_nodes(self):\\n # return len(self.extract_nodes())\\n return sum([1] + [b.count_nodes() for b in self.branches])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781723e2172d56e0aaebed268b4de93f\",\n \"score\": \"0.5932326\",\n \"text\": \"def _collect_box_count(self, boxes):\\n count = Counter([box.box_id for box in boxes])\\n\\n # Add the counts in the superboxes.\\n for box in boxes:\\n if hasattr(box, 'box'):\\n count.update(self._collect_box_count(box.box))\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ec41f69e455087559a775dc56d077d3\",\n \"score\": \"0.5927293\",\n \"text\": \"def __len__(self):\\n return sum([len(bucket) for bucket in self.buckets])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39000349d4ee3239156883a84460b283\",\n \"score\": \"0.5926623\",\n \"text\": \"def count(self):\\n count = len(self.__children)\\n for child in self.__children:\\n count += child.count\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f691b7339e50dd88382d1dc337edeb\",\n \"score\": \"0.5921082\",\n \"text\": \"def amount_of_genes(self,category):\\n return len(self.genes[category])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bba81a6ab2467e983be360b1191328a\",\n \"score\": \"0.59209186\",\n \"text\": \"def tree_family_count(self):\\n total = 0 \\n for tree in self:\\n if isinstance(self[tree], TAGTreeSet) and tree[-6:] == '.trees':\\n total += 1\\n elif isinstance(self[tree], TAGTreeSet):\\n total += self[tree].tree_family_count()\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c3261e3db18fb3e2740f5905e42c36\",\n \"score\": \"0.5911013\",\n \"text\": \"def num_animals(self):\\n pop = self.island.get_population_numbers()\\n return sum(pop['Herbivore']) + sum(pop['Carnivore'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ecf101a51091fb30c01499c20cf25b4\",\n \"score\": \"0.5908521\",\n \"text\": \"def count(self):\\n return self.size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c4baef668fe4d147f2fd62cac83b8c\",\n \"score\": \"0.58957577\",\n \"text\": \"def get_items_count(self):\\n return sum(1 for places in self.grid.values() for pos in places)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3155b8f66fe4d616cab4b0eff850ce\",\n \"score\": \"0.58875024\",\n \"text\": \"def count_taggers(self):\\r\\n # TODO : must be a better way (group by ?)\\r\\n return len(set(self.private_tags_class.objects.filter(content_object=self).values_list('owner', flat=True)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fb0dff214d56e88c3063fe7baee364c\",\n \"score\": \"0.58859867\",\n \"text\": \"def count(self):\\n return len(self.items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64c32f3acd43c56727899695cb45b18\",\n \"score\": \"0.58728564\",\n \"text\": \"def numberOfBonds(self):\\n return sum(1 for o in self.structures if isinstance(o, Bond))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01f556d828221ab95bb4f0dce3282ef\",\n \"score\": \"0.5871036\",\n \"text\": \"def total(self):\\n return EntryTag.objects.for_category(self).count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbd74f007e5b631ead4edf29e6f5919\",\n \"score\": \"0.58583224\",\n \"text\": \"def count(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbd74f007e5b631ead4edf29e6f5919\",\n \"score\": \"0.58583224\",\n \"text\": \"def count(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab57e0ae68c2371dff13d333aeee6d1f\",\n \"score\": \"0.5851884\",\n \"text\": \"def number_of_counties(self):\\n return self._number_of_counties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab57e0ae68c2371dff13d333aeee6d1f\",\n \"score\": \"0.5851884\",\n \"text\": \"def number_of_counties(self):\\n return self._number_of_counties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc71f53a6a8dec5df049b4f28f0269b\",\n \"score\": \"0.5839162\",\n \"text\": \"def catcount(self, cat):\\n if cat in self.cnt:\\n return float(self.cnt[cat])\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40117d079063def7b0be342845d4dae5\",\n \"score\": \"0.58320206\",\n \"text\": \"def vigra_bincount(labels):\\n labels = labels.astype(numpy.uint32, copy=False)\\n labels = numpy.ravel(labels, order='K').reshape((-1, 1), order='A')\\n # We don't care what the 'image' parameter is, but we have to give something\\n image = labels.view(numpy.float32)\\n counts = vigra.analysis.extractRegionFeatures(image, labels, ['Count'])['Count']\\n return counts.astype(numpy.int64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9695e03edd727f14ff7ebba186589b91\",\n \"score\": \"0.58172184\",\n \"text\": \"def bagofwords(sentence_words, vocab):\\n bag = [0] * len(vocab)\\n for sw in sentence_words:\\n for i, word in enumerate(vocab):\\n if word == sw:\\n bag[i] += 1\\n return bag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7248103ca92fe057df90a49203576ab7\",\n \"score\": \"0.5807144\",\n \"text\": \"def labelsets_count(self):\\n proj = h5py.File(self.project_filename, \\\"r\\\")\\n label_sets = eval_h5(proj, const.label_sets_list())\\n count = len(label_sets.keys())\\n proj.close()\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77dc86ebd2e574a32ade47204b3df038\",\n \"score\": \"0.5805245\",\n \"text\": \"def count_neighbor_bombs(neighbors, bombs):\\n count = 0\\n for n in neighbors:\\n if n in bombs:\\n count +=1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95771057696003c349ee4bdf1ddb5fe0\",\n \"score\": \"0.58017373\",\n \"text\": \"def get_scrobble_count(self):\\n return self.scrobbles.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95771057696003c349ee4bdf1ddb5fe0\",\n \"score\": \"0.58017373\",\n \"text\": \"def get_scrobble_count(self):\\n return self.scrobbles.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95771057696003c349ee4bdf1ddb5fe0\",\n \"score\": \"0.58017373\",\n \"text\": \"def get_scrobble_count(self):\\n return self.scrobbles.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2e270fd8e33a9c760e1e6745cc42cd\",\n \"score\": \"0.5799532\",\n \"text\": \"def popcount(a):\\n return np.sum(_pop_count_lut[a])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db98f5c1e086934e4b92f7b5f606d80f\",\n \"score\": \"0.5762601\",\n \"text\": \"def count(self):\\n return self.values.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c07a83ebfce1cba7358da5cb77bafa\",\n \"score\": \"0.5756949\",\n \"text\": \"def count(self):\\n number = self._count_aux(self._root)\\n return number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470451201f3c4c7b3f47b521fc3d6e67\",\n \"score\": \"0.57510835\",\n \"text\": \"def count(self):\\n return self.pipeline.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"813e90965a2a4cdf2bf17f3cfd5df3df\",\n \"score\": \"0.57340974\",\n \"text\": \"def count(self):\\n return len(self._items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6204cc691b1682234dcf72cef49f5a92\",\n \"score\": \"0.57329416\",\n \"text\": \"def length(self):\\n # Running time: O(b) Where b is the number of buckets\\n '''length = 0\\n #Loop through all buckets\\n for bucket in self.buckets:\\n #Count number of key-value entries in each bucket\\n length += bucket.length()\\n\\n return length'''\\n\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e97a871acb2afcdaf4c7b81cd4a3682\",\n \"score\": \"0.57326066\",\n \"text\": \"def totalcount(self):\\n return sum(self.cnt.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9974bf56d30760c4081ba2b37acaa61\",\n \"score\": \"0.5717575\",\n \"text\": \"def ncat(self):\\n return len(self.categories)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7710409b65f2324a2e02c3a7bb138896\",\n \"score\": \"0.5711229\",\n \"text\": \"def count(self, item=True):\\n return sum([x.count(item) for x in self.data])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71225dceb67fb39ca5558e11c935d71d\",\n \"score\": \"0.5708771\",\n \"text\": \"def __len__(self):\\n n = len(self._hyperedges)\\n try:\\n for m in self._extras.values():\\n n += m\\n except AttributeError:\\n pass\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c813f0d81141388b55a84e614a88d7eb\",\n \"score\": \"0.5704031\",\n \"text\": \"def count(dimacs_, constraints_):\\n\\n _tempdimacs = os.path.dirname(dimacs_) + '/count.dimacs'\\n _features, _clauses, _vcount = read_dimacs(dimacs_)\\n\\n gen_dimacs(_vcount, _clauses, constraints_, _tempdimacs)\\n res = int(getoutput(SHARPSAT + ' -q ' + _tempdimacs))\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1c0b737ef8bbda99962982f06e76d9\",\n \"score\": \"0.5702885\",\n \"text\": \"def count_hits(label_dict):\\n count = 0\\n for label in label_dict:\\n if label == DataLabel.NOISE:\\n continue\\n count += len(label_dict[label]['hits'])\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7137a3cd993f6409d81bd5ad790bac69\",\n \"score\": \"0.56981176\",\n \"text\": \"def length(self):\\n # TODO: Count number of key-value entries in each of the buckets\\n length = 0\\n for bucket in self.buckets:\\n length += bucket.length()\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd676167d465eb7d7c9ddc025ef3cd8\",\n \"score\": \"0.569385\",\n \"text\": \"def count_eggs():\\n eggs = 12\\n return eggs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861f08ed50676f92ebd2a3e7b8c6c05d\",\n \"score\": \"0.5693377\",\n \"text\": \"def count_bikes(kdnr):\\n\\n\\treturn models.Bike.objects.filter(kunde=kdnr).count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a452cdc73e176cfc8943e088e3443328\",\n \"score\": \"0.5688209\",\n \"text\": \"def ElementCount(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066597d39f508951c971d4cfdfc680e1\",\n \"score\": \"0.5687972\",\n \"text\": \"def get_number_of_labels(label_dict, args):\\n\\n return len(get_sorted_labels(label_dict, args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6eb06431b5b18062b1f59db4bf8b043\",\n \"score\": \"0.5685428\",\n \"text\": \"def compute_train_bags(self,n_bags, bag_size,y):\\n with torch.no_grad():\\n if not torch.is_tensor(y): \\n y=torch.IntTensor(y).to(self.device)\\n \\n #Tensor to return the result. Each bag in a row.\\n samples_indexes = torch.zeros((n_bags,bag_size),dtype=torch.int64,device=self.device)\\n classes = torch.unique(y)\\n n_classes = len(classes)\\n \\n prevalences = torch.zeros((n_bags,n_classes),device=self.device)\\n for i in range(n_bags):\\n low = round(bag_size * 0.01)\\n high = round(bag_size * 0.99)\\n\\n ps = torch.randint(low, high, (n_classes - 1,),generator=self.gen,device=self.device)\\n ps = torch.cat([ps, torch.tensor([0, bag_size],device=self.device)])\\n ps = torch.sort(ps)[0]\\n ps = ps[1:] - ps[:-1] #Number of samples per class\\n prevalences[i,:] = ps/bag_size\\n already_generated=0\\n for n, p in zip(classes, ps):\\n if p != 0:\\n examples_class=torch.where(y==n)[0]\\n samples_indexes[i,already_generated:already_generated+p]=examples_class[torch.randint(len(examples_class),(p,),generator=self.gen,device=self.device)]\\n already_generated+=p\\n suffle = torch.randperm(bag_size)\\n samples_indexes[i,:]=samples_indexes[i,suffle]\\n return samples_indexes,prevalences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8346437baf34d7265ec0180b91e03e9e\",\n \"score\": \"0.56834364\",\n \"text\": \"def count(self):\\n rv = 0\\n for _ in self:\\n rv += 1\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6945332b05e538b44233b6bdf18f80\",\n \"score\": \"0.5678691\",\n \"text\": \"def test_num_bags_required_01_example1(self):\\n # Arrange\\n rules = parse_rules('./Day07/tdata.txt')\\n\\n # Act\\n a = num_bags_required(rules, \\\"faded blue\\\")\\n b = num_bags_required(rules, \\\"dotted black\\\")\\n c = num_bags_required(rules, \\\"vibrant plum\\\")\\n d = num_bags_required(rules, \\\"dark olive\\\")\\n e = num_bags_required(rules, \\\"shiny gold\\\")\\n \\n # Assert\\n self.assertEqual(0, a)\\n self.assertEqual(0, b)\\n self.assertEqual(11, c)\\n self.assertEqual(7, d)\\n self.assertEqual(32, e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42fb58ce9fdfc7b874d1c42bacc98947\",\n \"score\": \"0.5677119\",\n \"text\": \"def count(self):\\n return len(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4300a9f737bf56dfc4a7fe51125409b\",\n \"score\": \"0.5671487\",\n \"text\": \"def getLength(self):\\n numItems = 0\\n \\n for items in self.exploredSet:\\n numItems += 1\\n \\n return numItems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917c3a55aed9eded2bf8e087a9aebea6\",\n \"score\": \"0.5668383\",\n \"text\": \"def count(self, categories=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c931e0eacfb6119d5db086d9b7544f3a\",\n \"score\": \"0.5668341\",\n \"text\": \"def count(self,op=0):\\n print 'count'\\n counts=self.read()\\n q=dict(zip(self.labels,counts))\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f919c040cdce3d5a885d594976469655\",\n \"score\": \"0.56671906\",\n \"text\": \"def nreads(counts):\\n return sum([counts[x] for x in counts])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349cbaa3e5cb17b4b9aa0ca13a31590d\",\n \"score\": \"0.5666067\",\n \"text\": \"def count_drugs(self) -> int:\\n return self._count_model(Drug)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15730d0e282bb523d20218ad51e47f84\",\n \"score\": \"0.56626314\",\n \"text\": \"def count_elements_in_dataset(dataset):\\r\\n return dataset.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7501180ec77f0ba6a0aa3eed21084c\",\n \"score\": \"0.56625646\",\n \"text\": \"def count(self):\\n count = 0\\n for item in self.table:\\n count += bit_count(item)\\n\\n return -self.filter_size * log(count / self.filter_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbafb5a07f358c61bb044c784b52f9ee\",\n \"score\": \"0.5661566\",\n \"text\": \"def bag_of_words(text):\\n text_list = text.split()\\n cnt = Counter()\\n for word in text_list:\\n cnt[word] += 1\\n\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd08b67c9c0dbebb2658b37840b00773\",\n \"score\": \"0.5658421\",\n \"text\": \"def GetNumNodes(self):\\n return sum(self.nodeCounts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"721962f73f783031ec88daf073a8745c\",\n \"score\": \"0.5656176\",\n \"text\": \"def get_count():\\n total = 0\\n for counter in SimpleCounterShard.all():\\n total += counter.count\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51775eb7f4a99c5f537dec8444e94dc5\",\n \"score\": \"0.5639252\",\n \"text\": \"def CountGO(gene2go):\\n\\n cats = collections.defaultdict(int)\\n nmaps = 0\\n for k, vv in gene2go.items():\\n for v in vv:\\n nmaps += 1\\n cats[v.mGOId] += 1\\n\\n return len(gene2go), len(cats), nmaps, cats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1934e10c6a0db14a61156ef76d207ec\",\n \"score\": \"0.56314844\",\n \"text\": \"def count_object(network, class_ids, scores, bounding_boxes, object_label, threshold=0.75):\\n detected_objects=0\\n while(scores[0][detected_objects]>threshold):\\n detected_objects+=1\\n count=0\\n reqd_idx = network.classes.index(object_label)\\n for idx in class_ids[0][:detected_objects]:\\n if (idx==reqd_idx):\\n count+=1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a499880bdd60155a0d8c84481692b3\",\n \"score\": \"0.5629823\",\n \"text\": \"def deep_sum(bag_map: BagMap, key: str) -> int:\\n immediate = sum(bag_map[key].values()) # Amount of immediate children\\n return immediate + sum(times * deep_sum(bag_map, key) for key, times in bag_map[key].items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c03f1b4e9764ddb41b886e5af798a73\",\n \"score\": \"0.56269306\",\n \"text\": \"def count_brands() -> int:\\n return db.session.scalar(select(db.func.count(DbBrand.id)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30959678e030fddef3cbf22ca9172727\",\n \"score\": \"0.56226605\",\n \"text\": \"def size(self):\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30959678e030fddef3cbf22ca9172727\",\n \"score\": \"0.56226605\",\n \"text\": \"def size(self):\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30959678e030fddef3cbf22ca9172727\",\n \"score\": \"0.56226605\",\n \"text\": \"def size(self):\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30959678e030fddef3cbf22ca9172727\",\n \"score\": \"0.56226605\",\n \"text\": \"def size(self):\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc85db9e660a0e90d93d40e8c2c16d6e\",\n \"score\": \"0.5622088\",\n \"text\": \"def totalCount(self):\\n return sum(self.values())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1474,"cells":{"query_id":{"kind":"string","value":"dbfd34aebb1c0ddd81638ddcf1da768c"},"query":{"kind":"string","value":"Get Passive total data"},"positive_passages":{"kind":"list like","value":[{"docid":"4cf6299277031da97255163e759e2785","score":"0.0","text":"def getPT(self):\n if not self.ptapi:\n return False\n command = \"curl -m 25 --insecure -s -u \" + self.ptuser + \":\" + self.ptkey + \" 'https://api.riskiq.net/pt/v2/dns/passive?query=\" + self.ip + \"'\"\n temp = os.popen(command).read()\n try:\n self.processedptdata = json.loads(temp)\n self.processedptdata_results = None\n # Sort and reverse the keys\n # Store the samples in our dictionary so we can sort them\n temp_dict = {}\n try:\n for pt_results in self.processedptdata['results']:\n temp_dict[pt_results['lastSeen']] = [pt_results['firstSeen'], pt_results['resolve'], pt_results['collected']]\n # Sort them by datetime and convert to list\n self.processedptdata_results = sorted(temp_dict.items(), reverse=True)\n except KeyError as e:\n print(f'Error in getPT: {e}')\n return False\n except json.decoder.JSONDecodeError:\n self.processedptdata = None","title":""}],"string":"[\n {\n \"docid\": \"4cf6299277031da97255163e759e2785\",\n \"score\": \"0.0\",\n \"text\": \"def getPT(self):\\n if not self.ptapi:\\n return False\\n command = \\\"curl -m 25 --insecure -s -u \\\" + self.ptuser + \\\":\\\" + self.ptkey + \\\" 'https://api.riskiq.net/pt/v2/dns/passive?query=\\\" + self.ip + \\\"'\\\"\\n temp = os.popen(command).read()\\n try:\\n self.processedptdata = json.loads(temp)\\n self.processedptdata_results = None\\n # Sort and reverse the keys\\n # Store the samples in our dictionary so we can sort them\\n temp_dict = {}\\n try:\\n for pt_results in self.processedptdata['results']:\\n temp_dict[pt_results['lastSeen']] = [pt_results['firstSeen'], pt_results['resolve'], pt_results['collected']]\\n # Sort them by datetime and convert to list\\n self.processedptdata_results = sorted(temp_dict.items(), reverse=True)\\n except KeyError as e:\\n print(f'Error in getPT: {e}')\\n return False\\n except json.decoder.JSONDecodeError:\\n self.processedptdata = None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"231da07473aef7149cd6581d0b1881bc","score":"0.7550525","text":"def patrimony_total(self):\n pass","title":""},{"docid":"f15ad4868023119d2906fb1a4dddec28","score":"0.71757656","text":"def total(self):\n\t\treturn self._total","title":""},{"docid":"8da381950fc5ab676c6552e5da140ed1","score":"0.7028725","text":"def get_total(self, **kwargs):\n LOG.warning('WARNING: /v1/report/total/ endpoint is deprecated, '\n 'please use /v1/report/summary instead.')\n authorized_args = [\n 'begin', 'end', 'tenant_id', 'service', 'all_tenants']\n url = self.get_url('total', kwargs, authorized_args)\n return self.api_client.get(url).json()","title":""},{"docid":"33c4641238e0e1f0a8c50ae68bb8b34e","score":"0.7000786","text":"def total(self):\n return self._evaluate()['hits']['total']","title":""},{"docid":"e3161555d5cbc30ba1f616126811a5a3","score":"0.69918793","text":"def GetTotal(self):\n return(self.total)","title":""},{"docid":"f7456f1fe58109ab5627afd66775b02e","score":"0.69355863","text":"def GOAL_TOTAL() -> int:\n return 21","title":""},{"docid":"dc26d451f775845dadfb7d2163735160","score":"0.6890283","text":"def order_total(user_data):\n total_res = requests.get(url=\"http://127.0.0.1:5000/order_total\", json=user_data)\n return total_res.text","title":""},{"docid":"e4d13f4f52ad86d532ddc056a5ea45be","score":"0.6835333","text":"def total(self):\n if self.dynamic:\n self._update_db_obj()\n return self._db_obj.total","title":""},{"docid":"e245208537e161ad5f9df399d99bff73","score":"0.6768295","text":"def _total_d(self):\n debit = 0.0\n for l in self.data:\n debit += l['debit']\n self.t_credit += l['credit']\n self.t_balance += l['balance']\n return debit","title":""},{"docid":"41d9cb1a5ec2b0e93d12670b96ed6c51","score":"0.6698106","text":"def total(self) -> float:\n return self._total","title":""},{"docid":"71614096becde1bcf13969bc820d36f6","score":"0.6677886","text":"def test_total(self):\n result = self.test_client.total\n\n assert result == 730","title":""},{"docid":"cdaf5ca47981284af861f5cb9c510029","score":"0.6673026","text":"def get_total(self):\n total = 0.00\n\n for _drink in self.drinks:\n total = total + _drink.get_price()\n\n for _food in self.food:\n total = total + _food.get_price()\n\n return total","title":""},{"docid":"5c8e601a3e1982cbaf62d988742043a7","score":"0.6642237","text":"def total(self):\n return self._results.total","title":""},{"docid":"a5a5305b562a78587f4ffa8cd0ea557d","score":"0.65746224","text":"def table_total(self):\n total = 0.00\n\n for customer in self.customers:\n total = total + customer.get_total()\n\n return total","title":""},{"docid":"6a667114d6dd9c20f1984c66cfd353ea","score":"0.6557723","text":"def calculate_agrigate(self):\n self.total = 0.0\n for rec in self.data:\n self.total = self.total + rec[\"value\"]\n\n self.agrigate_data = {\n \"site\": self.site,\n \"utc\": self.timestamp_utc,\n \"local\": self.timestamp_local,\n \"tag\": \"TOTAL\",\n \"value\": round(self.total, 3)}\n self.data.append(self.agrigate_data)","title":""},{"docid":"4906018e89b89b48f45f380c3099258f","score":"0.65393555","text":"def total(self) -> int:\n return self._total","title":""},{"docid":"551a5fdcebdc7904d52c82099a9f5ae1","score":"0.6522489","text":"def get_total_supply() -> int:\n return total_supply","title":""},{"docid":"598eb4cab70fea293482fb4befec95c8","score":"0.6514203","text":"def total(self):\n return self._total_name","title":""},{"docid":"f499e3c7140c536233b59aa5018b00e0","score":"0.6512474","text":"def life_insurance_to_recive_total(self):\n pass","title":""},{"docid":"15bbed91b44091023bf2b045da3e0689","score":"0.6487123","text":"def get_total(self):\r\n \r\n return str(round(self._total, 2))","title":""},{"docid":"fa70a64544e6644936c66f803bcff76d","score":"0.6470242","text":"def total(self):\n return sum(self.d.values())","title":""},{"docid":"fb58f413c2681bd8ca70e9541f5e5ca8","score":"0.6464646","text":"def get_total_stateless(db):\n pass","title":""},{"docid":"865246bbabb6a0f3a989e465995d6b70","score":"0.6405744","text":"def dashboard_alarm_threshold_get_total_records(self):\n return self.request( \"dashboard-alarm-threshold-get-total-records\", {\n }, {\n 'count': [ int, False ],\n } )","title":""},{"docid":"a0530bfec9225d733904f2786927836a","score":"0.64012945","text":"def totals():\n return make_simple_tsv_get_response(TOTALS_FILE, 'totals')","title":""},{"docid":"bf0c4798af47cbb6140205b3b11ff003","score":"0.6397635","text":"def total(self, desired_period: int = 12):\n self._trigger_gather()\n result = Decimal(0)\n for item in self.elements:\n result += item.income.amount(desired_period)\n return(Decimal(result))","title":""},{"docid":"bf0c4798af47cbb6140205b3b11ff003","score":"0.6397635","text":"def total(self, desired_period: int = 12):\n self._trigger_gather()\n result = Decimal(0)\n for item in self.elements:\n result += item.income.amount(desired_period)\n return(Decimal(result))","title":""},{"docid":"af6a32d8af9308060f6c8c592fb87857","score":"0.63970494","text":"def private_pension_total(self):\n pass","title":""},{"docid":"6447c71b9cf7b5b78f0bd37c7bc336a2","score":"0.6390197","text":"def total(self):\n total = sum(self.d.values())\n return total","title":""},{"docid":"258b06e1630c09e10d0cf51e21fe6d50","score":"0.6384356","text":"def get_total_paid(self):\n return sum(self.paid)","title":""},{"docid":"ff50bf910bd09914c172a690bb19f328","score":"0.63843036","text":"async def get_total(self, payload: str = None):\n if payload is None:\n raise RequiredPayload(\"You must provide at least one payload.\")\n data = await self._send_payload(payload)\n return data['total']","title":""},{"docid":"cb563635cc612d3597df1928729db964","score":"0.637894","text":"def get_total_stats(self, **kwargs):\n resp = self.get(_u.build_uri(\"total.stats\", domain=self.domain), kwargs)\n return utils.handle_response(resp)","title":""},{"docid":"7ddd6136f3a0b8a5a4ab76457215d408","score":"0.6372153","text":"def get_total(self):\n\n total = super().get_total()\n if self.qty < 10:\n total += 3.00\n return total","title":""},{"docid":"309fc3fdb0ab9cb54e62551a7ab15cb5","score":"0.6371592","text":"def __puntuacion_total(self):\n disparos = []\n for disparo in self.__disparos:\n total = 0\n for puntaje in disparo['disparos']:\n total += puntaje\n disparo['puntaje_total'] = total\n disparos.append(disparo)\n return disparos","title":""},{"docid":"1833fc38a258ea16ef32140ec4fbea47","score":"0.63343596","text":"def _grand_total(self):\n count = 0\n for product in self.products:\n count += product.price\n return count","title":""},{"docid":"be33d8e0b8d2a4bb9d780cbc1d3f85c1","score":"0.6330881","text":"def get_total(self):\n total = super().get_total()\n\n if self.qty < 10:\n total += 3\n\n return total","title":""},{"docid":"2131cb369f4d94e5b2305b44f1346cc2","score":"0.6325624","text":"def totalValue(self):\n\n\t\tvalue = 0\n\t\tfor bottle in self.bottles:\n\t\t\tvalue += bottle.inflatedCost\n\n\t\treturn value","title":""},{"docid":"a2f27bbef69e61793103a044cb92df1a","score":"0.6323913","text":"def total_additional_charges(self) -> int:\n total = 0\n additional_charges = self.additional_charges\n if additional_charges:\n for charge in additional_charges:\n total += charge['amount']\n return total","title":""},{"docid":"e1d797751488711ad29fd3eef1fb6a18","score":"0.62905407","text":"def get_total(self):\n\n # Total sum\n self.sum = 0.00\n\n # Determine which Check buttons are selected\n # and add the charges to find the total\n if self.check_1.get() == 1:\n self.sum += 30.00\n if self.check_2.get() == 1:\n self.sum += 20.00\n if self.check_3.get() == 1:\n self.sum += 40.00\n if self.check_4.get() == 1:\n self.sum += 100.00\n if self.check_5.get() == 1:\n self.sum += 35.00\n if self.check_6.get() == 1:\n self.sum += 200.00\n if self.check_7.get() == 1:\n self.sum += 20.00\n\n # Convert the sum to string\n # and store in StringVar object\n # to automatically update the total_val label\n self.sum_str.set(self.sum)","title":""},{"docid":"aeeedbbacbc671cc3b7906de58d44fdf","score":"0.6289862","text":"def print_total_value():\n sum = 0.0\n for item in data:\n sum += (item['price'] * item['stock'])\n\n print(f\"total stock value = {sum}\")","title":""},{"docid":"3f4502bb4fdd4543f6a9e23e9ed4a07c","score":"0.6281114","text":"def get_total(self):\n\n base_price = 5\n \n if self.species == \"Christmas melon\":\n base_price = base_price * 1.5 \n\n total = (1 + self.tax) * self.qty * base_price \n\n if self.order_type == \"international\" and self.qty>10:\n total += 3\n\n\n return total","title":""},{"docid":"93c2b68ebd94eee11f0ce8689bd20504","score":"0.62805223","text":"def get_total_redeem(self):\n total = 0\n for redeem in self.get_redeems():\n total += redeem.get_total()\n return total","title":""},{"docid":"3edc2f520a6f690f9cd2924fb9319d67","score":"0.6277657","text":"def subtotal(self):\n return self.cantidad * self.precio","title":""},{"docid":"27389959ed3a952653366943b13a5fd3","score":"0.6247071","text":"def total(evictiondata):\r\n total = 0\r\n for index, row in evictiondata.iterrows():\r\n total += row['filings_2020']","title":""},{"docid":"d8e1214af5e500077ce595e8456a1b2d","score":"0.6244062","text":"def get_total(self):\n\n base_price=5\n if self.species == \"Christmas\":\n base_price=1.5*base_price\n \n total = (1 + self.tax) * self.qty * base_price\n\n if self.order_type==\"international\" and self.qty<10:\n total+=3\n\n return total","title":""},{"docid":"3f38da991f02b844ebbb264c8acaef70","score":"0.62252694","text":"def dashboard_alarm_get_total_records(self):\n return self.request( \"dashboard-alarm-get-total-records\", {\n }, {\n 'count': [ int, False ],\n } )","title":""},{"docid":"c392e6058c52b699214ba527d5d51d0c","score":"0.6217824","text":"def calculate_total(kind):\r\n\ttotal = 0\r\n\tfor item in payments[kind]:\r\n\t\tif item.is_active:\r\n\t\t\ttotal += item.amount\r\n\treturn total","title":""},{"docid":"283559069c5d9fa2e6f41008adb207f1","score":"0.6207658","text":"def get_total(self):\n\n self.base_price = self.get_base_price()\n\n if self.species == \"christmas melon\":\n self.base_price = self.base_price * 1.5\n\n total = (1 + self.tax) * self.qty * self.base_price\n return total","title":""},{"docid":"db7ca1c0eb103be5ad99e17bbab23fd5","score":"0.61917746","text":"def get_totals(self):\n return self._get('app_totals')","title":""},{"docid":"5b1f98c32463028d8232d0411b098d36","score":"0.6188827","text":"def tot(self):\n return self.det + self.out + self.faint + self.late","title":""},{"docid":"527e23d5e37258f1b8bff4d26e6ea348","score":"0.61880326","text":"def __len__(self):\n return self.total","title":""},{"docid":"9f1d9f92e54ca0c16dbab8dcab2ab89b","score":"0.61878496","text":"def __len__(self):\n return int(self.total)","title":""},{"docid":"e3d7e2c980fae7a563be69b13d2fc02f","score":"0.6181774","text":"def get_total(self):\n\n base_price = self.get_base_price()\n if self.species == \"christmas melon\":\n base_price = base_price * 1.5\n\n total = ((1 + self.tax) * self.qty * base_price)\n\n return total","title":""},{"docid":"0ca4ecdb461fbd2bdfe8e26480c7ae68","score":"0.61723113","text":"def get_total(self, _property=None):\n return self._get_total(\"event\", _property)","title":""},{"docid":"0632157c4e866fbac206cbdc650d466f","score":"0.61584985","text":"def allowance_total(self):\n total = 0\n for allowance in self.allowances:\n if allowance.is_active:\n total += allowance.amount\n return total","title":""},{"docid":"f9f4501337ff990a4fde52f0832d69da","score":"0.6151031","text":"def get_total_price(self):\n i = self.get_copy_with_resolved_dependencies()\n total_price = Decimal(0)\n for product in i['products']:\n billed_price = Decimal(str(product.get('price', 0))) * Decimal(str(product.get('quantity')))\n total_price += billed_price\n return total_price","title":""},{"docid":"e3f91885fe85f2c2b2c0b2f9038e8c4f","score":"0.6140047","text":"def total(self, total):\n if self.local_vars_configuration.client_side_validation and total is None: # noqa: E501\n raise ValueError(\"Invalid value for `total`, must not be `None`\") # noqa: E501\n\n self._total = total","title":""},{"docid":"ffcabb70bba25a6188e0ce399ce0cbf0","score":"0.6133254","text":"def calculate_total(self):\n if self.total_price == 0:\n for discount in self.discounts:\n for item in self.items:\n item.add_discount(discount)\n\n for item in self.items:\n self.total_price += item.final_price()\n\n return self.total_price","title":""},{"docid":"4fa791b0f5f884f13488111b732e9592","score":"0.6125913","text":"def total_data_processed(self):\n return self._total_data_processed","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"905c96f4365949af8c0b68f31f81e4f1","score":"0.6120088","text":"def total(self, total):\n\n self._total = total","title":""},{"docid":"2ebd0d8ddf330e82865de52c79229246","score":"0.6115438","text":"def total(self):\n total_price = self.get_total_amount()\n discounts = self.get_total_discount()\n\n return total_price - discounts","title":""},{"docid":"76c8f74b6c1a5f91a69cb157e9211bdf","score":"0.6106633","text":"def get_total(self):\n\n base_price = self.get_base_price()\n\n if self.species == \"Christmas\":\n base_price = base_price * 1.5\n\n total = (1 + self.tax) * self.qty * base_price\n\n return total","title":""},{"docid":"1f9d3714f40889f88a17a263e909f73c","score":"0.6103407","text":"def get_total_appliance(self):\n total = 0\n for appliance in self.get_appliances():\n total += appliance.get_total()\n return total","title":""},{"docid":"91725e6ad8eb92948bd631c3fa1ff664","score":"0.6092907","text":"def total_purchase(self):\n\n total_amount = 0\n #grab all the item\n items = self.item_set.all()\n for item in items:\n total_amount += item.price\n return total_amount","title":""},{"docid":"191a36d143f40f4392c9a73d04174fc4","score":"0.6092074","text":"def get_total(self):\n\n base_price = 5\n total = (1 + int(self.tax)) * int(self.qty) * base_price\n\n return total","title":""},{"docid":"680c52ee3f029716aec78956426a874b","score":"0.60887814","text":"def sub_total():\n return sum(SAVE_PRICE)","title":""},{"docid":"76998057f28956014377988c587dc68f","score":"0.6084187","text":"def total(self):\n return sum(self.meta) + sum(child.total() for child in self.children)","title":""},{"docid":"210950590051129b961614e02f576a7a","score":"0.60731065","text":"def calcularTotal(self):\n subtotales=[]\n for row in range(0,self.tableFactura.rowCount()):\n subtotales.append(float(self.tableFactura.item(row,2).text()))\n importeTotal=sum(subtotales)\n return importeTotal","title":""},{"docid":"49221e00db7096fb5f304ea9d1965c11","score":"0.60566735","text":"def total_management_cost(self):\n total = 0\n total += self.output_dict['insurance_usd']\n total += self.output_dict['construction_permitting_usd']\n total += self.output_dict['bonding_usd']\n total += self.output_dict['project_management_usd']\n total += self.output_dict['markup_contingency_usd']\n total += self.output_dict['engineering_usd']\n total += self.output_dict['site_facility_usd']\n return total","title":""},{"docid":"08dc07b73e948ea931cf94abc1c051c4","score":"0.6053725","text":"def total_points(self):\n total_points = 0.0\n for ingredient in self.ingredients:\n if (ingredient.has_property('ppg')):\n # Use given value if specified\n total_points += ingredient.property('ppg').to('ppg') * ingredient.quantity.to('lb')\n else:\n total_points += EXTRACTS[ingredient.type] * ingredient.quantity.to('lb')\n return(Quantity(total_points, 'points'))","title":""},{"docid":"dcf3daa7aac05cdadcb9df6c21b466e0","score":"0.605202","text":"async def get_garages_total(self):\r\n async with self._db.acquire() as conn:\r\n s = await (await conn.execute(Garage.count())).scalar()\r\n return s","title":""},{"docid":"9cb72aab0a49b469cf4606f6c29d6ac5","score":"0.60519195","text":"def compute_total_paid(self):\n total = 0.0\n for line in self.loan_ids:\n if line.pay:\n total += line.amount\n self.total_paid = total","title":""},{"docid":"b264a723c2f8f405f5c7972481dcb3cc","score":"0.6041364","text":"def test_get_total_users_get(self):\n pass","title":""},{"docid":"55a327f1ea0a52cb7441a365b4969ad8","score":"0.6036034","text":"def calculate_total_capacity(**kwargs):\n members = kwargs.get(\"data\", [])\n total = 0\n if members:\n for member in members:\n capacity = Mapper.get_single_attribute(\n member, \"Capacity\", MappingTable.device.value, output_as_json=True\n )\n total += capacity.get(\"Capacity\", {}).get(\"Value\", 0)\n # returning value in MiB\n return total * 1024","title":""},{"docid":"7f1d654291b3070dfaa0204f3920af9b","score":"0.6035945","text":"def get_total_data():\n return pd.merge(compute_aggregate_load_data(), compute_aggregate_weather_data(),on=\"Date\")","title":""},{"docid":"58a3fb2d05541ad0696462e4ef90fee7","score":"0.60240984","text":"def total_value(self):\n total = 0.0\n for account in self.accounts():\n total += account.available_cash()\n for asset in account.assets():\n total += asset.adjusted_value()\n return total","title":""},{"docid":"631f0049b65b30ece4c652bbee25517e","score":"0.60143274","text":"def total_price(self):\n return self.owner.total_price()","title":""},{"docid":"d6e90e660665e6a49ecb29a91eb82335","score":"0.60093033","text":"def subtotal(self):\n return self.precio_unitario * self.cantidad","title":""},{"docid":"de0949a81db8ead6c8fdf15c50938315","score":"0.6008142","text":"def total_donated():\r\n url = \"http://blockchain.info/rawaddr/1RvRE1XPTboTfujU9dRK9euC6TPnGHzKf?limit=0\" # pylint:disable=C0301\r\n try:\r\n response = json.load(urllib2.urlopen(url))\r\n except urllib2.HTTPError:\r\n return abort(500)\r\n return jsonify(total_received=response['total_received'])","title":""},{"docid":"c68b02aa476df81185eef6b9b0445467","score":"0.59920055","text":"def total_spent(self):\n total_sum = Order.objects.filter(\n email=self.email).aggregate(\n Sum('total_price')\n ).get('total_price__sum')\n return round(total_sum, 4) if total_sum else 0","title":""},{"docid":"3fd6b80f49526e66b42bb27b2029f1b9","score":"0.5991375","text":"def get_total(self):\n # method on the class DomesticMelonOrder\n base_price = 5\n\n if self.species == \"Christmas melons\":\n base_price = base_price * 1.5\n\n total = (1 + self.tax) * self.qty * base_price\n\n return total","title":""},{"docid":"105706d33bb587a32bfa3463424c971e","score":"0.5989816","text":"def total(self) -> int:\n if self._total is None:\n self._total = self.counts.sum()\n return self._total","title":""},{"docid":"eb5dddba07b14bc7da3e45d3571ef799","score":"0.5984897","text":"def extras_total(self):\n total = self.wides + self.no_balls + self.byes + self.leg_byes\n return total","title":""},{"docid":"1b01512344f5a2b164ae90cc2293ff48","score":"0.5980298","text":"def print_sum():\n\n sum = 0\n for item in data:\n sum += item[\"price\"]\n\n print(f\"The sum is: {sum}\")","title":""},{"docid":"61b96dae04b7491958a77584dd2e6b14","score":"0.59795064","text":"def get_total_count(self):\n return self.total_count","title":""},{"docid":"d376b17b1253279e96f2230d64508015","score":"0.59771615","text":"def total_value(self):\n return self.parent.child_total_value[self.action]","title":""},{"docid":"a443031fa84388c4438c31017094cb1f","score":"0.5970782","text":"def total_paid(self) -> Decimal:\n return self.total_principal + self.total_interest","title":""},{"docid":"3eb67cd2f384fdbe2d742c2f2bcc9597","score":"0.59627134","text":"def get_total_haberes(self):\n return float(self.input.get_text(liquidaciones_historicas_catalog.TOTAL_HABERES).replace(\".\", \"\"))","title":""},{"docid":"ac69cc7ff610c2b2e4f6210f8ecdf5cc","score":"0.5960257","text":"def _get_toal_cp_(obj):\n \n fTotal = 0.0\n for item in obj.order_line:\n fTotal += item.purchase_price * item.product_uom_qty\n \n return fTotal","title":""},{"docid":"854c02418eae8c8bc2922d680c09abf2","score":"0.59539336","text":"def total_amortization(self):\n return sum(self.table[\"amortization\"])","title":""},{"docid":"9a8a91f710d4aeb93a77d03a83682485","score":"0.5952401","text":"def get_total(self) -> float:\n if self.__open:\n raise RuntimeError(\"Cash drawer must be closed to count.\")\n total: float = 0.0\n for denom in CashDenomination:\n total += self.__contents[denom] * denom.amount\n return total","title":""},{"docid":"8ddacd56392ef69c73fc641a7e0c00ec","score":"0.5951694","text":"def totalcounts(self):\n return self.datacounts + self.bkgdcounts","title":""},{"docid":"5163d982cdeeb8954023689bbbe1adad","score":"0.5949382","text":"def tot(self, prop=\"基金现值\", date=yesterdayobj()):\n res = 0\n for fund in self.fundtradeobj:\n res += fund.dailyreport(date).iloc[0][prop]\n return res","title":""},{"docid":"dea85063a570efee2af9133a2bab3e91","score":"0.59473103","text":"def show_total(request):\n user_id = request.user\n end_date = datetime.datetime.utcnow()\n start_date = end_date.replace(day=1,\n hour=datetime.time(0, 0, 0).hour,\n minute=datetime.time(0, 0, 0).minute,\n second=datetime.time(0, 0, 0).second)\n total = 0\n incomes_to_date = IncomeHistory.objects.filter(date__range=(start_date, end_date),\n income_id__owner_id=user_id)\n if not incomes_to_date:\n return HttpResponse(0, status=200)\n\n for income in incomes_to_date:\n if income.is_active:\n total = total + income.value\n return HttpResponse(total, status=200)","title":""},{"docid":"6cad441fbabe978434998b85a263d56c","score":"0.5945589","text":"def precipitation_forecast_total(self) -> float:\n return sum(item[1] for item in self.precipitation)","title":""}],"string":"[\n {\n \"docid\": \"231da07473aef7149cd6581d0b1881bc\",\n \"score\": \"0.7550525\",\n \"text\": \"def patrimony_total(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15ad4868023119d2906fb1a4dddec28\",\n \"score\": \"0.71757656\",\n \"text\": \"def total(self):\\n\\t\\treturn self._total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da381950fc5ab676c6552e5da140ed1\",\n \"score\": \"0.7028725\",\n \"text\": \"def get_total(self, **kwargs):\\n LOG.warning('WARNING: /v1/report/total/ endpoint is deprecated, '\\n 'please use /v1/report/summary instead.')\\n authorized_args = [\\n 'begin', 'end', 'tenant_id', 'service', 'all_tenants']\\n url = self.get_url('total', kwargs, authorized_args)\\n return self.api_client.get(url).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c4641238e0e1f0a8c50ae68bb8b34e\",\n \"score\": \"0.7000786\",\n \"text\": \"def total(self):\\n return self._evaluate()['hits']['total']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3161555d5cbc30ba1f616126811a5a3\",\n \"score\": \"0.69918793\",\n \"text\": \"def GetTotal(self):\\n return(self.total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7456f1fe58109ab5627afd66775b02e\",\n \"score\": \"0.69355863\",\n \"text\": \"def GOAL_TOTAL() -> int:\\n return 21\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc26d451f775845dadfb7d2163735160\",\n \"score\": \"0.6890283\",\n \"text\": \"def order_total(user_data):\\n total_res = requests.get(url=\\\"http://127.0.0.1:5000/order_total\\\", json=user_data)\\n return total_res.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d13f4f52ad86d532ddc056a5ea45be\",\n \"score\": \"0.6835333\",\n \"text\": \"def total(self):\\n if self.dynamic:\\n self._update_db_obj()\\n return self._db_obj.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e245208537e161ad5f9df399d99bff73\",\n \"score\": \"0.6768295\",\n \"text\": \"def _total_d(self):\\n debit = 0.0\\n for l in self.data:\\n debit += l['debit']\\n self.t_credit += l['credit']\\n self.t_balance += l['balance']\\n return debit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41d9cb1a5ec2b0e93d12670b96ed6c51\",\n \"score\": \"0.6698106\",\n \"text\": \"def total(self) -> float:\\n return self._total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71614096becde1bcf13969bc820d36f6\",\n \"score\": \"0.6677886\",\n \"text\": \"def test_total(self):\\n result = self.test_client.total\\n\\n assert result == 730\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdaf5ca47981284af861f5cb9c510029\",\n \"score\": \"0.6673026\",\n \"text\": \"def get_total(self):\\n total = 0.00\\n\\n for _drink in self.drinks:\\n total = total + _drink.get_price()\\n\\n for _food in self.food:\\n total = total + _food.get_price()\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8e601a3e1982cbaf62d988742043a7\",\n \"score\": \"0.6642237\",\n \"text\": \"def total(self):\\n return self._results.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a5305b562a78587f4ffa8cd0ea557d\",\n \"score\": \"0.65746224\",\n \"text\": \"def table_total(self):\\n total = 0.00\\n\\n for customer in self.customers:\\n total = total + customer.get_total()\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a667114d6dd9c20f1984c66cfd353ea\",\n \"score\": \"0.6557723\",\n \"text\": \"def calculate_agrigate(self):\\n self.total = 0.0\\n for rec in self.data:\\n self.total = self.total + rec[\\\"value\\\"]\\n\\n self.agrigate_data = {\\n \\\"site\\\": self.site,\\n \\\"utc\\\": self.timestamp_utc,\\n \\\"local\\\": self.timestamp_local,\\n \\\"tag\\\": \\\"TOTAL\\\",\\n \\\"value\\\": round(self.total, 3)}\\n self.data.append(self.agrigate_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4906018e89b89b48f45f380c3099258f\",\n \"score\": \"0.65393555\",\n \"text\": \"def total(self) -> int:\\n return self._total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"551a5fdcebdc7904d52c82099a9f5ae1\",\n \"score\": \"0.6522489\",\n \"text\": \"def get_total_supply() -> int:\\n return total_supply\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598eb4cab70fea293482fb4befec95c8\",\n \"score\": \"0.6514203\",\n \"text\": \"def total(self):\\n return self._total_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f499e3c7140c536233b59aa5018b00e0\",\n \"score\": \"0.6512474\",\n \"text\": \"def life_insurance_to_recive_total(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15bbed91b44091023bf2b045da3e0689\",\n \"score\": \"0.6487123\",\n \"text\": \"def get_total(self):\\r\\n \\r\\n return str(round(self._total, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa70a64544e6644936c66f803bcff76d\",\n \"score\": \"0.6470242\",\n \"text\": \"def total(self):\\n return sum(self.d.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb58f413c2681bd8ca70e9541f5e5ca8\",\n \"score\": \"0.6464646\",\n \"text\": \"def get_total_stateless(db):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865246bbabb6a0f3a989e465995d6b70\",\n \"score\": \"0.6405744\",\n \"text\": \"def dashboard_alarm_threshold_get_total_records(self):\\n return self.request( \\\"dashboard-alarm-threshold-get-total-records\\\", {\\n }, {\\n 'count': [ int, False ],\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0530bfec9225d733904f2786927836a\",\n \"score\": \"0.64012945\",\n \"text\": \"def totals():\\n return make_simple_tsv_get_response(TOTALS_FILE, 'totals')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c4798af47cbb6140205b3b11ff003\",\n \"score\": \"0.6397635\",\n \"text\": \"def total(self, desired_period: int = 12):\\n self._trigger_gather()\\n result = Decimal(0)\\n for item in self.elements:\\n result += item.income.amount(desired_period)\\n return(Decimal(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c4798af47cbb6140205b3b11ff003\",\n \"score\": \"0.6397635\",\n \"text\": \"def total(self, desired_period: int = 12):\\n self._trigger_gather()\\n result = Decimal(0)\\n for item in self.elements:\\n result += item.income.amount(desired_period)\\n return(Decimal(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6a32d8af9308060f6c8c592fb87857\",\n \"score\": \"0.63970494\",\n \"text\": \"def private_pension_total(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6447c71b9cf7b5b78f0bd37c7bc336a2\",\n \"score\": \"0.6390197\",\n \"text\": \"def total(self):\\n total = sum(self.d.values())\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258b06e1630c09e10d0cf51e21fe6d50\",\n \"score\": \"0.6384356\",\n \"text\": \"def get_total_paid(self):\\n return sum(self.paid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff50bf910bd09914c172a690bb19f328\",\n \"score\": \"0.63843036\",\n \"text\": \"async def get_total(self, payload: str = None):\\n if payload is None:\\n raise RequiredPayload(\\\"You must provide at least one payload.\\\")\\n data = await self._send_payload(payload)\\n return data['total']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb563635cc612d3597df1928729db964\",\n \"score\": \"0.637894\",\n \"text\": \"def get_total_stats(self, **kwargs):\\n resp = self.get(_u.build_uri(\\\"total.stats\\\", domain=self.domain), kwargs)\\n return utils.handle_response(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ddd6136f3a0b8a5a4ab76457215d408\",\n \"score\": \"0.6372153\",\n \"text\": \"def get_total(self):\\n\\n total = super().get_total()\\n if self.qty < 10:\\n total += 3.00\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"309fc3fdb0ab9cb54e62551a7ab15cb5\",\n \"score\": \"0.6371592\",\n \"text\": \"def __puntuacion_total(self):\\n disparos = []\\n for disparo in self.__disparos:\\n total = 0\\n for puntaje in disparo['disparos']:\\n total += puntaje\\n disparo['puntaje_total'] = total\\n disparos.append(disparo)\\n return disparos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1833fc38a258ea16ef32140ec4fbea47\",\n \"score\": \"0.63343596\",\n \"text\": \"def _grand_total(self):\\n count = 0\\n for product in self.products:\\n count += product.price\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be33d8e0b8d2a4bb9d780cbc1d3f85c1\",\n \"score\": \"0.6330881\",\n \"text\": \"def get_total(self):\\n total = super().get_total()\\n\\n if self.qty < 10:\\n total += 3\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2131cb369f4d94e5b2305b44f1346cc2\",\n \"score\": \"0.6325624\",\n \"text\": \"def totalValue(self):\\n\\n\\t\\tvalue = 0\\n\\t\\tfor bottle in self.bottles:\\n\\t\\t\\tvalue += bottle.inflatedCost\\n\\n\\t\\treturn value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f27bbef69e61793103a044cb92df1a\",\n \"score\": \"0.6323913\",\n \"text\": \"def total_additional_charges(self) -> int:\\n total = 0\\n additional_charges = self.additional_charges\\n if additional_charges:\\n for charge in additional_charges:\\n total += charge['amount']\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d797751488711ad29fd3eef1fb6a18\",\n \"score\": \"0.62905407\",\n \"text\": \"def get_total(self):\\n\\n # Total sum\\n self.sum = 0.00\\n\\n # Determine which Check buttons are selected\\n # and add the charges to find the total\\n if self.check_1.get() == 1:\\n self.sum += 30.00\\n if self.check_2.get() == 1:\\n self.sum += 20.00\\n if self.check_3.get() == 1:\\n self.sum += 40.00\\n if self.check_4.get() == 1:\\n self.sum += 100.00\\n if self.check_5.get() == 1:\\n self.sum += 35.00\\n if self.check_6.get() == 1:\\n self.sum += 200.00\\n if self.check_7.get() == 1:\\n self.sum += 20.00\\n\\n # Convert the sum to string\\n # and store in StringVar object\\n # to automatically update the total_val label\\n self.sum_str.set(self.sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeeedbbacbc671cc3b7906de58d44fdf\",\n \"score\": \"0.6289862\",\n \"text\": \"def print_total_value():\\n sum = 0.0\\n for item in data:\\n sum += (item['price'] * item['stock'])\\n\\n print(f\\\"total stock value = {sum}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4502bb4fdd4543f6a9e23e9ed4a07c\",\n \"score\": \"0.6281114\",\n \"text\": \"def get_total(self):\\n\\n base_price = 5\\n \\n if self.species == \\\"Christmas melon\\\":\\n base_price = base_price * 1.5 \\n\\n total = (1 + self.tax) * self.qty * base_price \\n\\n if self.order_type == \\\"international\\\" and self.qty>10:\\n total += 3\\n\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c2b68ebd94eee11f0ce8689bd20504\",\n \"score\": \"0.62805223\",\n \"text\": \"def get_total_redeem(self):\\n total = 0\\n for redeem in self.get_redeems():\\n total += redeem.get_total()\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edc2f520a6f690f9cd2924fb9319d67\",\n \"score\": \"0.6277657\",\n \"text\": \"def subtotal(self):\\n return self.cantidad * self.precio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27389959ed3a952653366943b13a5fd3\",\n \"score\": \"0.6247071\",\n \"text\": \"def total(evictiondata):\\r\\n total = 0\\r\\n for index, row in evictiondata.iterrows():\\r\\n total += row['filings_2020']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e1214af5e500077ce595e8456a1b2d\",\n \"score\": \"0.6244062\",\n \"text\": \"def get_total(self):\\n\\n base_price=5\\n if self.species == \\\"Christmas\\\":\\n base_price=1.5*base_price\\n \\n total = (1 + self.tax) * self.qty * base_price\\n\\n if self.order_type==\\\"international\\\" and self.qty<10:\\n total+=3\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f38da991f02b844ebbb264c8acaef70\",\n \"score\": \"0.62252694\",\n \"text\": \"def dashboard_alarm_get_total_records(self):\\n return self.request( \\\"dashboard-alarm-get-total-records\\\", {\\n }, {\\n 'count': [ int, False ],\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c392e6058c52b699214ba527d5d51d0c\",\n \"score\": \"0.6217824\",\n \"text\": \"def calculate_total(kind):\\r\\n\\ttotal = 0\\r\\n\\tfor item in payments[kind]:\\r\\n\\t\\tif item.is_active:\\r\\n\\t\\t\\ttotal += item.amount\\r\\n\\treturn total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283559069c5d9fa2e6f41008adb207f1\",\n \"score\": \"0.6207658\",\n \"text\": \"def get_total(self):\\n\\n self.base_price = self.get_base_price()\\n\\n if self.species == \\\"christmas melon\\\":\\n self.base_price = self.base_price * 1.5\\n\\n total = (1 + self.tax) * self.qty * self.base_price\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db7ca1c0eb103be5ad99e17bbab23fd5\",\n \"score\": \"0.61917746\",\n \"text\": \"def get_totals(self):\\n return self._get('app_totals')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1f98c32463028d8232d0411b098d36\",\n \"score\": \"0.6188827\",\n \"text\": \"def tot(self):\\n return self.det + self.out + self.faint + self.late\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527e23d5e37258f1b8bff4d26e6ea348\",\n \"score\": \"0.61880326\",\n \"text\": \"def __len__(self):\\n return self.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1d9f92e54ca0c16dbab8dcab2ab89b\",\n \"score\": \"0.61878496\",\n \"text\": \"def __len__(self):\\n return int(self.total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d7e2c980fae7a563be69b13d2fc02f\",\n \"score\": \"0.6181774\",\n \"text\": \"def get_total(self):\\n\\n base_price = self.get_base_price()\\n if self.species == \\\"christmas melon\\\":\\n base_price = base_price * 1.5\\n\\n total = ((1 + self.tax) * self.qty * base_price)\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca4ecdb461fbd2bdfe8e26480c7ae68\",\n \"score\": \"0.61723113\",\n \"text\": \"def get_total(self, _property=None):\\n return self._get_total(\\\"event\\\", _property)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0632157c4e866fbac206cbdc650d466f\",\n \"score\": \"0.61584985\",\n \"text\": \"def allowance_total(self):\\n total = 0\\n for allowance in self.allowances:\\n if allowance.is_active:\\n total += allowance.amount\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f4501337ff990a4fde52f0832d69da\",\n \"score\": \"0.6151031\",\n \"text\": \"def get_total_price(self):\\n i = self.get_copy_with_resolved_dependencies()\\n total_price = Decimal(0)\\n for product in i['products']:\\n billed_price = Decimal(str(product.get('price', 0))) * Decimal(str(product.get('quantity')))\\n total_price += billed_price\\n return total_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f91885fe85f2c2b2c0b2f9038e8c4f\",\n \"score\": \"0.6140047\",\n \"text\": \"def total(self, total):\\n if self.local_vars_configuration.client_side_validation and total is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `total`, must not be `None`\\\") # noqa: E501\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcabb70bba25a6188e0ce399ce0cbf0\",\n \"score\": \"0.6133254\",\n \"text\": \"def calculate_total(self):\\n if self.total_price == 0:\\n for discount in self.discounts:\\n for item in self.items:\\n item.add_discount(discount)\\n\\n for item in self.items:\\n self.total_price += item.final_price()\\n\\n return self.total_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa791b0f5f884f13488111b732e9592\",\n \"score\": \"0.6125913\",\n \"text\": \"def total_data_processed(self):\\n return self._total_data_processed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905c96f4365949af8c0b68f31f81e4f1\",\n \"score\": \"0.6120088\",\n \"text\": \"def total(self, total):\\n\\n self._total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ebd0d8ddf330e82865de52c79229246\",\n \"score\": \"0.6115438\",\n \"text\": \"def total(self):\\n total_price = self.get_total_amount()\\n discounts = self.get_total_discount()\\n\\n return total_price - discounts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c8f74b6c1a5f91a69cb157e9211bdf\",\n \"score\": \"0.6106633\",\n \"text\": \"def get_total(self):\\n\\n base_price = self.get_base_price()\\n\\n if self.species == \\\"Christmas\\\":\\n base_price = base_price * 1.5\\n\\n total = (1 + self.tax) * self.qty * base_price\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9d3714f40889f88a17a263e909f73c\",\n \"score\": \"0.6103407\",\n \"text\": \"def get_total_appliance(self):\\n total = 0\\n for appliance in self.get_appliances():\\n total += appliance.get_total()\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91725e6ad8eb92948bd631c3fa1ff664\",\n \"score\": \"0.6092907\",\n \"text\": \"def total_purchase(self):\\n\\n total_amount = 0\\n #grab all the item\\n items = self.item_set.all()\\n for item in items:\\n total_amount += item.price\\n return total_amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191a36d143f40f4392c9a73d04174fc4\",\n \"score\": \"0.6092074\",\n \"text\": \"def get_total(self):\\n\\n base_price = 5\\n total = (1 + int(self.tax)) * int(self.qty) * base_price\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"680c52ee3f029716aec78956426a874b\",\n \"score\": \"0.60887814\",\n \"text\": \"def sub_total():\\n return sum(SAVE_PRICE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76998057f28956014377988c587dc68f\",\n \"score\": \"0.6084187\",\n \"text\": \"def total(self):\\n return sum(self.meta) + sum(child.total() for child in self.children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210950590051129b961614e02f576a7a\",\n \"score\": \"0.60731065\",\n \"text\": \"def calcularTotal(self):\\n subtotales=[]\\n for row in range(0,self.tableFactura.rowCount()):\\n subtotales.append(float(self.tableFactura.item(row,2).text()))\\n importeTotal=sum(subtotales)\\n return importeTotal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49221e00db7096fb5f304ea9d1965c11\",\n \"score\": \"0.60566735\",\n \"text\": \"def total_management_cost(self):\\n total = 0\\n total += self.output_dict['insurance_usd']\\n total += self.output_dict['construction_permitting_usd']\\n total += self.output_dict['bonding_usd']\\n total += self.output_dict['project_management_usd']\\n total += self.output_dict['markup_contingency_usd']\\n total += self.output_dict['engineering_usd']\\n total += self.output_dict['site_facility_usd']\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08dc07b73e948ea931cf94abc1c051c4\",\n \"score\": \"0.6053725\",\n \"text\": \"def total_points(self):\\n total_points = 0.0\\n for ingredient in self.ingredients:\\n if (ingredient.has_property('ppg')):\\n # Use given value if specified\\n total_points += ingredient.property('ppg').to('ppg') * ingredient.quantity.to('lb')\\n else:\\n total_points += EXTRACTS[ingredient.type] * ingredient.quantity.to('lb')\\n return(Quantity(total_points, 'points'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf3daa7aac05cdadcb9df6c21b466e0\",\n \"score\": \"0.605202\",\n \"text\": \"async def get_garages_total(self):\\r\\n async with self._db.acquire() as conn:\\r\\n s = await (await conn.execute(Garage.count())).scalar()\\r\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb72aab0a49b469cf4606f6c29d6ac5\",\n \"score\": \"0.60519195\",\n \"text\": \"def compute_total_paid(self):\\n total = 0.0\\n for line in self.loan_ids:\\n if line.pay:\\n total += line.amount\\n self.total_paid = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b264a723c2f8f405f5c7972481dcb3cc\",\n \"score\": \"0.6041364\",\n \"text\": \"def test_get_total_users_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a327f1ea0a52cb7441a365b4969ad8\",\n \"score\": \"0.6036034\",\n \"text\": \"def calculate_total_capacity(**kwargs):\\n members = kwargs.get(\\\"data\\\", [])\\n total = 0\\n if members:\\n for member in members:\\n capacity = Mapper.get_single_attribute(\\n member, \\\"Capacity\\\", MappingTable.device.value, output_as_json=True\\n )\\n total += capacity.get(\\\"Capacity\\\", {}).get(\\\"Value\\\", 0)\\n # returning value in MiB\\n return total * 1024\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1d654291b3070dfaa0204f3920af9b\",\n \"score\": \"0.6035945\",\n \"text\": \"def get_total_data():\\n return pd.merge(compute_aggregate_load_data(), compute_aggregate_weather_data(),on=\\\"Date\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a3fb2d05541ad0696462e4ef90fee7\",\n \"score\": \"0.60240984\",\n \"text\": \"def total_value(self):\\n total = 0.0\\n for account in self.accounts():\\n total += account.available_cash()\\n for asset in account.assets():\\n total += asset.adjusted_value()\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"631f0049b65b30ece4c652bbee25517e\",\n \"score\": \"0.60143274\",\n \"text\": \"def total_price(self):\\n return self.owner.total_price()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e90e660665e6a49ecb29a91eb82335\",\n \"score\": \"0.60093033\",\n \"text\": \"def subtotal(self):\\n return self.precio_unitario * self.cantidad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0949a81db8ead6c8fdf15c50938315\",\n \"score\": \"0.6008142\",\n \"text\": \"def total_donated():\\r\\n url = \\\"http://blockchain.info/rawaddr/1RvRE1XPTboTfujU9dRK9euC6TPnGHzKf?limit=0\\\" # pylint:disable=C0301\\r\\n try:\\r\\n response = json.load(urllib2.urlopen(url))\\r\\n except urllib2.HTTPError:\\r\\n return abort(500)\\r\\n return jsonify(total_received=response['total_received'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68b02aa476df81185eef6b9b0445467\",\n \"score\": \"0.59920055\",\n \"text\": \"def total_spent(self):\\n total_sum = Order.objects.filter(\\n email=self.email).aggregate(\\n Sum('total_price')\\n ).get('total_price__sum')\\n return round(total_sum, 4) if total_sum else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd6b80f49526e66b42bb27b2029f1b9\",\n \"score\": \"0.5991375\",\n \"text\": \"def get_total(self):\\n # method on the class DomesticMelonOrder\\n base_price = 5\\n\\n if self.species == \\\"Christmas melons\\\":\\n base_price = base_price * 1.5\\n\\n total = (1 + self.tax) * self.qty * base_price\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105706d33bb587a32bfa3463424c971e\",\n \"score\": \"0.5989816\",\n \"text\": \"def total(self) -> int:\\n if self._total is None:\\n self._total = self.counts.sum()\\n return self._total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5dddba07b14bc7da3e45d3571ef799\",\n \"score\": \"0.5984897\",\n \"text\": \"def extras_total(self):\\n total = self.wides + self.no_balls + self.byes + self.leg_byes\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b01512344f5a2b164ae90cc2293ff48\",\n \"score\": \"0.5980298\",\n \"text\": \"def print_sum():\\n\\n sum = 0\\n for item in data:\\n sum += item[\\\"price\\\"]\\n\\n print(f\\\"The sum is: {sum}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61b96dae04b7491958a77584dd2e6b14\",\n \"score\": \"0.59795064\",\n \"text\": \"def get_total_count(self):\\n return self.total_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d376b17b1253279e96f2230d64508015\",\n \"score\": \"0.59771615\",\n \"text\": \"def total_value(self):\\n return self.parent.child_total_value[self.action]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a443031fa84388c4438c31017094cb1f\",\n \"score\": \"0.5970782\",\n \"text\": \"def total_paid(self) -> Decimal:\\n return self.total_principal + self.total_interest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb67cd2f384fdbe2d742c2f2bcc9597\",\n \"score\": \"0.59627134\",\n \"text\": \"def get_total_haberes(self):\\n return float(self.input.get_text(liquidaciones_historicas_catalog.TOTAL_HABERES).replace(\\\".\\\", \\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac69cc7ff610c2b2e4f6210f8ecdf5cc\",\n \"score\": \"0.5960257\",\n \"text\": \"def _get_toal_cp_(obj):\\n \\n fTotal = 0.0\\n for item in obj.order_line:\\n fTotal += item.purchase_price * item.product_uom_qty\\n \\n return fTotal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854c02418eae8c8bc2922d680c09abf2\",\n \"score\": \"0.59539336\",\n \"text\": \"def total_amortization(self):\\n return sum(self.table[\\\"amortization\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a8a91f710d4aeb93a77d03a83682485\",\n \"score\": \"0.5952401\",\n \"text\": \"def get_total(self) -> float:\\n if self.__open:\\n raise RuntimeError(\\\"Cash drawer must be closed to count.\\\")\\n total: float = 0.0\\n for denom in CashDenomination:\\n total += self.__contents[denom] * denom.amount\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddacd56392ef69c73fc641a7e0c00ec\",\n \"score\": \"0.5951694\",\n \"text\": \"def totalcounts(self):\\n return self.datacounts + self.bkgdcounts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5163d982cdeeb8954023689bbbe1adad\",\n \"score\": \"0.5949382\",\n \"text\": \"def tot(self, prop=\\\"基金现值\\\", date=yesterdayobj()):\\n res = 0\\n for fund in self.fundtradeobj:\\n res += fund.dailyreport(date).iloc[0][prop]\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea85063a570efee2af9133a2bab3e91\",\n \"score\": \"0.59473103\",\n \"text\": \"def show_total(request):\\n user_id = request.user\\n end_date = datetime.datetime.utcnow()\\n start_date = end_date.replace(day=1,\\n hour=datetime.time(0, 0, 0).hour,\\n minute=datetime.time(0, 0, 0).minute,\\n second=datetime.time(0, 0, 0).second)\\n total = 0\\n incomes_to_date = IncomeHistory.objects.filter(date__range=(start_date, end_date),\\n income_id__owner_id=user_id)\\n if not incomes_to_date:\\n return HttpResponse(0, status=200)\\n\\n for income in incomes_to_date:\\n if income.is_active:\\n total = total + income.value\\n return HttpResponse(total, status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cad441fbabe978434998b85a263d56c\",\n \"score\": \"0.5945589\",\n \"text\": \"def precipitation_forecast_total(self) -> float:\\n return sum(item[1] for item in self.precipitation)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1475,"cells":{"query_id":{"kind":"string","value":"37e7d438ef02abdf6317338e042de9cd"},"query":{"kind":"string","value":"Test the `config.commands.search.context` setting."},"positive_passages":{"kind":"list like","value":[{"docid":"5df43a952d6d15b82c678129969031a8","score":"0.73624146","text":"def test_context_configuration(self, setup: Any) -> None:\n config.commands.search.context = 2\n\n cmd = SearchCommand(\n \"einstein\",\n \"-i\",\n \"-c\",\n \"2\",\n )\n cmd.execute()\n output = cmd.render_porcelain()\n self._assert(\n output,\n [\n \"einstein::2\",\n \"1::@article{einstein,\",\n \"2::author = {Albert Einstein},\",\n \"2::doi = {http://dx.doi.org/10.1002/andp.19053221004},\",\n \"2::journal = {Annalen der Physik},\",\n ],\n )","title":""}],"string":"[\n {\n \"docid\": \"5df43a952d6d15b82c678129969031a8\",\n \"score\": \"0.73624146\",\n \"text\": \"def test_context_configuration(self, setup: Any) -> None:\\n config.commands.search.context = 2\\n\\n cmd = SearchCommand(\\n \\\"einstein\\\",\\n \\\"-i\\\",\\n \\\"-c\\\",\\n \\\"2\\\",\\n )\\n cmd.execute()\\n output = cmd.render_porcelain()\\n self._assert(\\n output,\\n [\\n \\\"einstein::2\\\",\\n \\\"1::@article{einstein,\\\",\\n \\\"2::author = {Albert Einstein},\\\",\\n \\\"2::doi = {http://dx.doi.org/10.1002/andp.19053221004},\\\",\\n \\\"2::journal = {Annalen der Physik},\\\",\\n ],\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e10689036335fbbb48a0cd39ef5563c5","score":"0.62758994","text":"def test_contexts_search(self):\n from .mockers import create_context, create_contextA, create_contextB\n\n self.create_context(create_context)\n self.create_context(create_contextA)\n self.create_context(create_contextB)\n res = self.testapp.get('/contexts', '', oauth2Header(test_manager), status=200)\n self.assertEqual(len(res.json), 3)","title":""},{"docid":"7b6598b1451cd9fd9ffa83210cf78fa8","score":"0.62084484","text":"def getSearchContext(self): \n return SearchResults.context","title":""},{"docid":"1019db01ca2e2cad5b6467488b3a1fbb","score":"0.58214545","text":"def is_search(self, default=None):\n return self.get(\"search\", default)","title":""},{"docid":"581f4edba9cdfcfb9b8dc992f94e7ff0","score":"0.580631","text":"async def find(self, ctx: CustomContext):\r\n if ctx.invoked_subcommand is None:\r\n await ctx.send_help(str(ctx.command))","title":""},{"docid":"1f779d2c6ce7cb3d62c35a36ff264f89","score":"0.5755496","text":"def test_search(self):\n\n def is_venture2(ci):\n return ci.uid == 'br-2'\n\n self.assertEqual(\n breadth_first_search_ci(self.server_b, is_venture2),\n (self.venture2, True)\n )\n\n self.assertEqual(\n breadth_first_search_ci(self.server_a, is_venture2),\n (None, None)\n )","title":""},{"docid":"79a1d5954d69efc7619bf25740386e52","score":"0.568351","text":"def test_environment_set_in_context(self):\n\n request = RequestFactory().get('/', data={'name_product': \"Ravioli\"})\n view = SearchProductView()\n view.setup(request)\n # we fix the object _list because we do not call\n # SearchProduct as a view\n view.object_list = view.get_queryset()\n context = view.get_context_data()\n self.assertIn('essais', context)\n self.assertIn('name_product', context)","title":""},{"docid":"af655d4f21a16c284cd4f93420f75931","score":"0.5667697","text":"def test_search_get_menu(self):\n menu = self.dialog.get_menu()\n menu = self.dialog.get_menu()\n self.assertIn('w', menu)\n self.assertIn('t', menu)\n self.assertIn('d', menu)\n self.assertIn('e', menu)\n self.assertIn('b', menu)\n self.assertIn('q', menu)","title":""},{"docid":"71bca9ff8513d6bbe55aa666d5354b00","score":"0.5643643","text":"def test_context_args(self, cdp, command):\n handler = self.make_default_handler(self.callback_context_args)\n self._test_context_args_or_regex(cdp, handler, command)","title":""},{"docid":"aa5cee4b7734b587130e9b671e7e7e9c","score":"0.5592355","text":"def ask_for_search_args(self):\n # self._search_args = []\n with SearchDialog(self, title='Search options') as edt:\n send = True\n while send:\n ok = edt.ShowModal()\n if ok == wx.ID_OK:\n if self.editor.search_args:\n break\n else:\n send = False\n print(send)\n return send","title":""},{"docid":"aeb78d427bc1d9c6db34ca5821bce400","score":"0.5587644","text":"def test_product_search_functionality(self):\n response = self.client.get(\n '/products/?', {'q': 'tee'})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.context['search_term'], 'tee')","title":""},{"docid":"aaec366ab8fc4f70ad597bdd327fc2d0","score":"0.55772245","text":"def test_right_context_only_query(self):\n querier = DataMuseQuerier()\n results = querier.get_synonym_query_results(Word(\"the\", \"\"), right_context = \"mall\")\n self.assertTrue(len(results) > 0 )","title":""},{"docid":"daf24877f81b47f6baca43a271bb2d4a","score":"0.55699056","text":"def test_gridsearch_alias(self):\n assert isgridsearch is is_gridsearch","title":""},{"docid":"6c761c3f47b532479c1f927b9c34d73c","score":"0.5542754","text":"def test_contexts_search_with_tags(self):\n from .mockers import create_context, create_contextA, create_contextB\n from .mockers import context_search_by_tags\n\n self.create_context(create_context) # Tagged \"Assignatura\"\n self.create_context(create_contextA) # Tagged \"Assignatura\"\n self.create_context(create_contextB) # Not tagged\n res = self.testapp.get('/contexts?tags', context_search_by_tags, oauth2Header(test_manager), status=200)\n self.assertEqual(len(res.json), 2)","title":""},{"docid":"1b68791bd2e6eb868a1c2c7f4c9c3e2e","score":"0.5491355","text":"def test_command(\n self, setup: Any, args: list[str], expected: list[str], config_overwrite: bool\n ) -> None:\n config.commands.search.ignore_case = config_overwrite\n\n cmd = SearchCommand(*args)\n cmd.execute()\n output = cmd.render_porcelain()\n self._assert(output, expected)","title":""},{"docid":"91a43fa05dd87308a8033e36bc154199","score":"0.5470138","text":"def test_search(self):\n response = self.client.get(\"/?q=Stone\")\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"Stonebag\")","title":""},{"docid":"f606412343223de4329bd91c5ce56204","score":"0.54601735","text":"def setup_parameters_search(context):\n context.parameters = {\n 'exact_list': None,\n 'start_time': None,\n 'end_time': None\n }\n logit.debug('New parameter search set up')","title":""},{"docid":"49d7df596d91107fdc82792bfd8900c6","score":"0.54186153","text":"def test_search_search_by_term(self):\n self.dialog.search_by_term()\n self.assertEqual(self.dialog.activestep.checkinput_func, self.dialog.check_term)","title":""},{"docid":"46b8e3932ebf6d19da8fea5714f5a4ee","score":"0.5392576","text":"def context_mode(self, context):\n self.cli_mode()\n # Set the Mode to Config\n\n self.prompt_mode = \"context\"\n context_prompt = self.cli_prompt.replace('local', context)\n self.set_prompt(context_prompt)\n return self.execute_command(\"context {0}\".format(context))","title":""},{"docid":"2b75bde13fcb9f4a0edac2d827f602e5","score":"0.5389433","text":"def test_contains(self):\n self.assertIn(\"app\", self.config)\n self.assertIn(\"window\", self.config.app)","title":""},{"docid":"e5da044af779a77804b0003b4eeba402","score":"0.53737843","text":"def test_search_available(self):\n name = \"maddison.name\"\n args = [\"search\", name]\n result = self.invoke(args=args)\n assert result.exit_code == 0\n assert name in result.output\n for keyword in [\"premium\", \"type\", \"purchase price\", \"renewal price\"]:\n assert keyword in result.output","title":""},{"docid":"a0abb2f357886014901945e0babd1772","score":"0.5373784","text":"def get_desired_context(self):","title":""},{"docid":"8c4888f1d2fc20be33435abeb94cf931","score":"0.5368296","text":"def test_search(self):\n output = self.run_command(\"pip search pytest\", exitcode=0)\n self.assertIn(\"pytest\", output)\n # due to changing pypi search results, the following results are not guaranteed.\n # TODO: fix this\n # self.assertIn(\"pytest-cov\", output)\n # self.assertIn(\"pytest-env\", output)","title":""},{"docid":"4c10d441a78848a25cd53af4af9fb2de","score":"0.5313205","text":"def test_advanced_search_detail(self):\n utils.login(self)\n response = self.client.get(reverse('spirit:search'))\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"0a1faaaeabbb156ab79c30bb2e843a31","score":"0.5310486","text":"def test_settings_are_in_context(self):\n req = RequestFactory().get('')\n ctx = RequestContext(req)\n\n self.assertTrue('settings' in ctx)\n self.assertEqual(ctx['settings'], settings)","title":""},{"docid":"06a1e6b0c0739e657dd4beb9fe644c99","score":"0.5305073","text":"def test_search(self):\n\n result = self.client.get(\"/search\", query_string={'location': 'San Francisco', 'keyWord': 'music', 'cat_id': 115})\n self.assertIn(\"Results\", result.data)","title":""},{"docid":"cab2f5fa937bfa11a9aee7b53887b690","score":"0.5303475","text":"def init_search(self):\n return False","title":""},{"docid":"cc35c6e6bcfa4556b46722503b47ed7e","score":"0.5301699","text":"def test_template_search(self):\r\n response = self.client.get(\"/search/\")\r\n self.assertTemplateUsed(response, \"search.html\")\r\n self.assertTemplateUsed(response, \"base/main.html\")","title":""},{"docid":"4914c84eb52f3afcb1ee2d826c5863a4","score":"0.5300841","text":"def test_search_case(self):\n rv = search.search(\"test\")\n for k, inst in list(self.instances.items()):\n assert k in rv\n assert len(rv[k]) == 1\n assert rv[k][0][\"name\"] == inst.search_result_name","title":""},{"docid":"a9f8deb586c5e14402ad48482f1da576","score":"0.5298709","text":"def test_index_context(self, client):\n\n response = client.get((reverse(\"contact:index\")))\n assert \"form\" in response.context\n assert \"thanks\" in response.context\n assert response.context[\"thanks\"] == False","title":""},{"docid":"d2c7767b9a6d2ef62a0d1652c4ff1ae3","score":"0.52830696","text":"def test_get_help(self):\n assert self.testcommand.get_help() == self.testcommand.help","title":""},{"docid":"9516235976bea6614cfff2bb688e2078","score":"0.5273061","text":"def test_signin_context(self):\n response = self.client.get(reverse_lazy('login:signin_page'))\n try:\n self.assertTrue('signin_form' in response.context)\n except KeyError:\n self.assertTrue(False)","title":""},{"docid":"519110285279c14ef79315499c1ffaad","score":"0.5246247","text":"def test_event_pre_search_command(self, setup: Any) -> None:\n\n @Event.PreSearchCommand.subscribe\n def hook(command: SearchCommand) -> None:\n command.largs.query = [\"einstein\"]\n\n assert Event.PreSearchCommand.validate()\n\n expected = [\"einstein::1\", \"1::@article{einstein,\", \"1::author = {Albert Einstein},\"]\n\n cmd = SearchCommand(\"knuthwebsite\")\n cmd.execute()\n output = cmd.render_porcelain()\n self._assert(output, expected)","title":""},{"docid":"e002759bc5a952bd61e0112586730e3e","score":"0.5239413","text":"def test_public_contexts_search_with_tags(self):\n from .mockers import create_context, create_contextA, create_contextB\n from .mockers import context_search_by_tags\n\n username = 'messi'\n self.create_user(username)\n\n self.create_context(create_context)\n self.create_context(create_contextA, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='subscribed'))\n self.create_context(create_contextB)\n res = self.testapp.get('/contexts/public?tags', context_search_by_tags, oauth2Header(username), status=200)\n self.assertEqual(len(res.json), 1)","title":""},{"docid":"a6580fb22ad316ebf04910630bc18495","score":"0.522679","text":"def test_search_page(client):\n # check that search only available to logged in users\n result = search(client, \"Note\")\n # landing page should be shown\n assert b\"create a user or login with an existing one\" in result.data\n # with the user the search results page should be shown\n result = login(client, TEST_USER, TEST_PASSWORD)\n result = search(client, \"Note\")\n assert b\"results\" in result.data","title":""},{"docid":"9cb41cba964f453610b87c676d709aa2","score":"0.52198553","text":"def test_search(self):\n result = self.client.get('/search')\n self.assertEqual(result.status_code, 200)\n self.assertIn(b\"Search\", result.data)","title":""},{"docid":"bcbd1394bb7c4081e4abb9ef28c1445b","score":"0.52171105","text":"def test_left_context_only_query(self):\n querier = DataMuseQuerier()\n results = querier.get_synonym_query_results(Word(\"the\", \"\"), left_context = \"to\")\n self.assertTrue(len(results) > 0 )","title":""},{"docid":"f61f3d85aed9df0921ed83a5121d3431","score":"0.51948285","text":"def test_api_authorization_search_get(self):\n pass","title":""},{"docid":"1792dc0ff11cb773b289c81d1b7ef79e","score":"0.5180473","text":"def test_search_wards_enter(self):\n self.wp_sidebar.verify_search_wards_enter()","title":""},{"docid":"6cd9fdb7757483d36758ed180a92d4f4","score":"0.5173186","text":"def is_configured(command):\n return command in COMMANDS","title":""},{"docid":"096c152f58b9a86785d736f5b87bc0f5","score":"0.5156481","text":"def has_context_name():\n return \"name\" in app.context","title":""},{"docid":"3d1514ee20721d8d6cf0517d33464511","score":"0.515161","text":"async def search(self, ctx):\n\n await ctx.message.add_reaction('❌')\n await ctx.send('None or invalid source supplied, try `{}help search`'.format(self.bot.command_prefix))","title":""},{"docid":"d8f8e704911f6e6e3ac80d1645fb3949","score":"0.51476645","text":"def testSeachFunction(self):\n inst = self.createInstance()\n ans = inst.search('key2', 'two')\n self.assertEqual(len(ans), 3, 'this finds only none blankc items')\n ans = inst.search('key2', 'twob')\n self.assertEqual(len(ans), 1, 'insensitive to case finds one entry')\n ans = inst.search('key2', 'twob', False)\n self.assertEqual(len(ans), 0, 'sensitive to case finds no entry')","title":""},{"docid":"93ed33da250bc5ef96877e0ec6323fbf","score":"0.51406145","text":"def search(self) -> global___Search:","title":""},{"docid":"bc83252ba1a8f797794420b0e20e44bc","score":"0.5139536","text":"def test_location_route_takes_search_parameter(self):\n data = self._query_api(search='some search text')\n self.assertFalse(data)","title":""},{"docid":"7eb130c14b5e5569537a30937ff80d5b","score":"0.5136215","text":"def _match_context(context, active_context):\n if context is None:\n # If command has no context, it always matches\n return True\n\n if active_context is None:\n # If the command has a context, and we don't, no match\n return False\n\n # The active_context matches if it starts with context and is followed by\n # the end of the string or the separator\n clen = len(context)\n return (\n active_context.startswith(context) and\n active_context[clen:clen + len(CONTEXT_SEP)] in ('', CONTEXT_SEP)\n )","title":""},{"docid":"fabda2ac70d040550e776a10a7c32806","score":"0.51300794","text":"def can_search_objectives(self):\n pass","title":""},{"docid":"d06ef1fb9e914508ec9e6273dd71485b","score":"0.512584","text":"def test_search_wards_click(self):\n self.wp_sidebar.verify_search_wards_click()","title":""},{"docid":"be1e411d0ef162fdc2178758bfc3fcb1","score":"0.5125268","text":"def test_get_registered_items_positive(\n self, cli_setup_search_ctx_mock, cli_search_items_mock\n ):\n response = self.app.get(\"api/connection\")\n self.assertEqual(response.status_code, 200)\n\n result = json.loads(response.get_data(as_text=True))\n expected_result = [{\"name\": \"some-connection\"}]\n self.assertEqual(result, expected_result)\n\n cli_setup_search_ctx_mock.assert_called_once()\n cli_search_items_mock.assert_called_once()","title":""},{"docid":"32c8263b04d6ab0b6258b05356396c24","score":"0.5124275","text":"def test_search_courses(self):\n\n self.po.search_courses('searchtext')\n # self.assertFalse(po.is_on_page())","title":""},{"docid":"54f60856f92794ac1a2d9482213d7ec9","score":"0.5107705","text":"def has_context_name():\n return 'name' in app.context","title":""},{"docid":"da8c09d11c39b70622cf38ae1b265ce5","score":"0.51073605","text":"async def wordfilter(self, ctx: KurisuContext):\n if ctx.invoked_subcommand is None:\n await ctx.send_help(ctx.command)","title":""},{"docid":"3a796b378dc0c34d8e5d3e0e4936193b","score":"0.5088855","text":"def test_left_and_right_context_query(self):\n querier = DataMuseQuerier()\n results = querier.get_synonym_query_results(Word(\"the\", \"\"), left_context = \"to\", right_context = \"mall\")\n self.assertTrue(len(results) > 0 )","title":""},{"docid":"0a6b399a5ded7b9eb4bce13da07d1ce4","score":"0.5081442","text":"def search_enabled(self):\n return self.SEARCH_PRECOMMIT_HOOK in (self.get_property(\"precommit\") or [])","title":""},{"docid":"739ef4c5def30e123373439dce2ffcca","score":"0.508039","text":"async def octex(self, ctx, *, search_path = None):\r\n\r\n\t\tusage = \"Usage: `{}occ [search_path]`\".format(ctx.prefix)\r\n\t\tif not self.tex: return await ctx.send(\"It looks like I was unable to get the Configuration.tex :(\")\r\n\t\tif search_path is None: return await ctx.send(usage)\r\n\t\t# Let's split up the search path and ensure we have a qualified path to give to the parser\r\n\t\tsearch_path = search_path.replace(\"-\",\" \").replace(\">\",\" \").replace(\"/\",\" \")\r\n\t\tsearch_path = re.sub(\" {2,}\",\" \",search_path)\r\n\t\tsearch_parts = search_path.split()\r\n\t\tif not search_parts: return await ctx.send(usage)\r\n\r\n\t\t# Search for matches in our Sample\r\n\t\tmatches = self.search_sample(search_parts)\r\n\t\tmessage = None\r\n\t\tif matches:\r\n\t\t\tfuzzy,matches = matches # Expand the vars\r\n\t\t\tif len(matches)>1 or fuzzy: # Multiple matches - show a list\r\n\t\t\t\tif fuzzy:\r\n\t\t\t\t\tlimit = 3\r\n\t\t\t\t\ttitle = \"There were no exact matches for that search, perhaps you meant {}the following:\".format(\r\n\t\t\t\t\t\t\"one of \" if len(matches)>1 else \"\"\r\n\t\t\t\t\t)\r\n\t\t\t\telse:\r\n\t\t\t\t\tlimit = 5\r\n\t\t\t\t\tleftover = len(matches)-5 if len(matches)>5 else 0\r\n\t\t\t\t\ttitle = \"There were multiple results for that search, please pick from the following list{}:\".format(\r\n\t\t\t\t\t\t\" ({:,} more omitted)\".format(leftover) if leftover else \"\"\r\n\t\t\t\t\t)\r\n\t\t\t\tindex, message = await PickList.Picker(\r\n\t\t\t\t\ttitle=title,\r\n\t\t\t\t\tlist=[\" -> \".join(x) for x in matches[:3 if fuzzy else 5]],\r\n\t\t\t\t\tctx=ctx\r\n\t\t\t\t).pick()\r\n\t\t\t\tif index < 0:\r\n\t\t\t\t\treturn await message.edit(content=\"Search cancelled.\")\r\n\t\t\t\tmatches = matches[index]\r\n\t\t\telse:\r\n\t\t\t\tmatches = matches[0]\r\n\t\telse:\r\n\t\t\t# Fall back on the original search in case we don't have a Sample.plist,\r\n\t\t\t# or there's a version mismatch - or similar.\r\n\t\t\tmatches = search_parts\r\n\t\tsearch_results = self.tex_search(self.tex, matches)\r\n\t\tif not search_results: return await ctx.send(\"Nothing was found for that search :(\")\r\n\r\n\t\t# We got something to show - let's build a page-picker\r\n\t\treturn await PickList.PagePicker(\r\n\t\t\ttitle=\"Results For: \"+\" -> \".join(matches),\r\n\t\t\tdescription=search_results,\r\n\t\t\ttimeout=300, # Allow 5 minutes before we stop watching the picker\r\n\t\t\tfooter=\"From Configuration.tex for OpenCore v{}\".format(self.tex_version),\r\n\t\t\tctx=ctx,\r\n\t\t\tmessage=message\r\n\t\t).pick()","title":""},{"docid":"713291244e3d4b2c1ca4c8059d4a2500","score":"0.5075484","text":"def KeywordSearch(self, keyWord, mode=HELP_SEARCH_ALL):","title":""},{"docid":"9ae9b4ecfaa6a9a97aa3202a0e3c46d6","score":"0.5067172","text":"def test_pytest_uses_posargs(self, testenv_commands):\n assert any([re.search(r'py\\.test [^\\n]*\\{posargs', command) for command in testenv_commands])","title":""},{"docid":"a3d88a4be8f6374531817ab4ea20d4b2","score":"0.50642264","text":"def test_search_services_with_query_without_model(self):\n search_query_empty_model = Query(\n [Constraint(\"foo\", ConstraintType(\"==\", \"bar\"))], model=None\n )\n oef_search_request, sending_dialogue = self.oef_search_dialogues.create(\n counterparty=str(self.connection.connection_id),\n performative=OefSearchMessage.Performative.SEARCH_SERVICES,\n query=search_query_empty_model,\n )\n self.multiplexer.put(\n Envelope(\n to=oef_search_request.to,\n sender=oef_search_request.sender,\n message=oef_search_request,\n )\n )\n\n envelope = self.multiplexer.get(block=True, timeout=5.0)\n oef_search_response = envelope.message\n oef_search_dialogue = self.oef_search_dialogues.update(oef_search_response)\n assert (\n oef_search_response.performative\n == OefSearchMessage.Performative.SEARCH_RESULT\n )\n assert oef_search_dialogue is not None\n assert oef_search_dialogue == sending_dialogue\n assert oef_search_response.agents == ()","title":""},{"docid":"02badc336ddbe0ec9b0e4f9683b9b347","score":"0.50537103","text":"def test_search(self):\n\n rv = search.search(\"Test\")\n for k, inst in list(self.instances.items()):\n assert k in rv\n assert len(rv[k]) == 1\n assert rv[k][0][\"name\"] == inst.search_result_name\n assert rv[k][0][\"org_id\"] == inst.org_id\n\n # test that term order does not matter\n\n for k, inst in list(self.instances.items()):\n rv = search.search(f\"Test {k}\")\n assert k in rv\n assert len(rv[k]) == 1\n assert rv[k][0][\"name\"] == inst.search_result_name\n assert rv[k][0][\"org_id\"] == inst.org_id\n\n rv = search.search(f\"{k} Test\")\n assert k in rv\n assert len(rv[k]) == 1\n assert rv[k][0][\"name\"] == inst.search_result_name\n assert rv[k][0][\"org_id\"] == inst.org_id\n\n rv = search.search(\"as1\")\n assert len(rv[\"net\"]) == 1\n assert rv[\"net\"][0][\"name\"] == self.instances[\"net\"].search_result_name\n assert rv[\"net\"][0][\"org_id\"] == self.instances[\"net\"].org_id\n\n rv = search.search(\"asn1\")\n assert len(rv[\"net\"]) == 1\n assert rv[\"net\"][0][\"name\"] == self.instances[\"net\"].search_result_name\n assert rv[\"net\"][0][\"org_id\"] == self.instances[\"net\"].org_id","title":""},{"docid":"e1559b02bcbb620bfa877f657523020c","score":"0.50473875","text":"def test_context(self, client):\n def ctx(ins):\n return dict(a=\"1\")\n\n self.reg.register(self.type, \"foo/bar\", \"foo bar\",\n default=False, context=ctx)\n\n context_method = self.reg.context.get((self.type, \"foo/bar\"))\n assert context_method\n assert context_method(None) == dict(a=\"1\")","title":""},{"docid":"359bb82ddf6be0b365004a1d87968153","score":"0.50443554","text":"def test_adv_search_page(self):\n result = self.client.get(\"/adv-search\")\n self.assertEqual(result.status_code, 200)\n self.assertIn(b\"Advanced Search\", result.data)","title":""},{"docid":"9ffec52bf51653b0b208cfca92c0afbb","score":"0.5042195","text":"def test_search_engine():\n test_search = SearchEngine('/home/testFiles')\n assert_equals(None, test_search.search(\"street\"))\n assert_equals(['/home/testFiles/doc4.txt',\n '/home/testFiles/doc2.txt'],\n test_search.search(\"basketball\"))\n assert_equals(['/home/testFiles/doc4.txt', '/home/testFiles/doc5.txt',\n '/home/testFiles/doc3.txt', '/home/testFiles/doc2.txt',\n '/home/testFiles/doc1.txt'],\n test_search.search('food'))","title":""},{"docid":"8add78e9d76fe13ac95ee2b9991e5e75","score":"0.5040621","text":"def search(self):\n pass","title":""},{"docid":"8add78e9d76fe13ac95ee2b9991e5e75","score":"0.5040621","text":"def search(self):\n pass","title":""},{"docid":"a2e8dc6cfd75161bcee0d47a04e90fe0","score":"0.5038879","text":"async def xkcd(self, ctx, *, search: typing.Union[int, str] = None):\n\n async with ctx.typing():\n if search is None:\n await self.random(ctx)\n elif isinstance(search, int):\n await self.num(ctx, search)\n elif search == \"latest\":\n await self.latest(ctx)\n else:\n await self.search(ctx, search=search)","title":""},{"docid":"0284403fd3611743c5bc811d22d27102","score":"0.5035066","text":"def database_parameter_search(context):\n logit.info(f\"using parameters to get data from the database {context.parameters}\")\n context.selection = context.db.query_database(context.parameters)\n logit.debug(f\"data from the database {context.selection}\")","title":""},{"docid":"d6ac260bf4d935f40ac0624adbf80665","score":"0.50315154","text":"def test_context_regex(self, cdp, command):\n handler = self.make_default_handler(self.callback_context_regex1,\n filters=Filters.regex('one two'))\n self._test_context_args_or_regex(cdp, handler, command)","title":""},{"docid":"03f1b0932a6c2eed78276b8380b08548","score":"0.50186235","text":"def search():\n option = ui.get_search_input()\n term = ui.get_input(\"Please type your search term (not case sensitive).\")\n if term != \"-1\":\n ui.message(datastore.search(option, term))","title":""},{"docid":"7587b35853966a5db14548a0b4dcfd2c","score":"0.5017184","text":"def test_contains(self):\n self._token['opt'] = \"This is optional information, and should always return true\"\n self._token['scp'] = \"write:/home/example\"\n\n self.assertTrue('opt' in self._token)\n self.assertTrue('scp' in self._token)\n self.assertFalse('notin' in self._token)","title":""},{"docid":"dca7db08b1d94e750814a335464ebd75","score":"0.4992911","text":"def test_search(self):\n\n\t\tresult = self.app.get('/search?terms=dummy')\n\t\tself.assertEqual(result.status_code, 200)\n\t\tself.assertEqual(result.mimetype, 'text/html')","title":""},{"docid":"458841b019c73d81dbc9577fe5bca048","score":"0.49922562","text":"def test_menu_and_search(self):\n # driver = self.setUp()\n demo = MenuAndSearchTest(self.dr)\n time.sleep(2.0)\n demo.get_sections()['menu'].get_actions('click_menu')\n demo.get_sections()['menu'].get_actions('trend')\n demo.get_sections()['menu'].get_actions('history')\n demo.get_sections()['search'].get_actions('set_query', 'python')\n demo.get_sections()['search'].get_actions('click_search')","title":""},{"docid":"470379e5ec19b7c65680e1c8f5bacf34","score":"0.49898216","text":"def test_event_post_search_command(self, setup: Any) -> None:\n\n @Event.PostSearchCommand.subscribe\n def hook(command: SearchCommand) -> None:\n print(command.hits, [entry.label for entry in command.entries])\n\n assert Event.PostSearchCommand.validate()\n\n with contextlib.redirect_stdout(StringIO()) as file:\n cmd = SearchCommand(\"einstein\")\n cmd.execute()\n assert file.getvalue().strip() == \"1 ['einstein']\"","title":""},{"docid":"73970fe19b4b13b75966143fd1a57dcc","score":"0.4988545","text":"def test_search_entity(self):\n for entity in self.testVector[\"text_params\"]:\n query = self.cat_query.text_search(entity)\n result = self.cat.search_entity(query)\n print(f\"DOCUMENTS: {result.documents}\")\n print(f\"STATUS: {result.status}\")\n print(f\"TOTAL HITS: {result.total_hits}\")\n print(\"*\"*30)","title":""},{"docid":"a18502eb8f9599f2d12a43071a6599e7","score":"0.49825704","text":"def test_index_context(rf):\n sitemaps = {'001': FakeSitemap}\n request = rf.get('/')\n response = resourcesync.index(request, sitemaps, 'mdstore/resourceindex.xml')\n\n # Expected filenames. The full location would look something like\n # http://example.com/resourcelist-001.xml\n resource_list_1 = 'resourcelist-001.xml'\n resource_list_2 = 'resourcelist-002.xml'\n\n locations = response.context_data['sitemaps']\n\n assert resource_list_1 in locations[0]\n assert resource_list_2 in locations[1]","title":""},{"docid":"af737b0228bbfac053b4c2bd3b50ac4a","score":"0.49786898","text":"async def answer_searching(message: types.Message, state: FSMContext):\n await message.answer(phrases.my_commands)","title":""},{"docid":"f16f9ec2cd7b993e1fc7304e3392838c","score":"0.49729362","text":"def test_context_processor(self):\n config = auth0(None)\n self.assertTrue(len(config.keys()) > 0)\n self.assertTrue(type(config) == dict)","title":""},{"docid":"623bdaefc29d0d01c227397512ffb731","score":"0.49686962","text":"def can_search_activities(self):\n pass","title":""},{"docid":"d6c660741b96e0963b709d9dc03776f8","score":"0.49648973","text":"def set_search_properties(\n self, metric: Optional[str], mode: Optional[str], config: Dict, **spec\n ) -> bool:\n return False","title":""},{"docid":"055f6de37164bd5304948defe24c9c2b","score":"0.49557742","text":"def test_contains(self):\n\n self.command_manager.add('dummy', DummyCommand)\n\n self.assertTrue('dummy' in self.command_manager)\n self.assertFalse('bar' in self.command_manager)","title":""},{"docid":"ed3c7874ecf5f9fc291fe3b3f4bb1c2f","score":"0.49549","text":"def test_context_only_load(self):\n ctx = Context(self.context)\n ctx.load()\n self.assertTrue('context' in ctx)\n self.assertTrue('defaults' in ctx)\n self.assertEqual(ctx['context']['value3'], 3)\n self.assertEqual(ctx['context']['value4'], 4)\n self.assertEqual(ctx['defaults']['value2'], 100)","title":""},{"docid":"f24a5944a95a319a717033112800e45a","score":"0.49466544","text":"def test_minerals_search_view(self):\n resp = self.client.get('/search/', {'q':'allophane'})\n self.assertEquals(resp.status_code, 200)\n #context = resp.context['minerals']\n self.assertEqual(resp.resolver_match.func, views.mineral_search)\n self.assertTemplateUsed(resp, 'trace_minerals/index.html')","title":""},{"docid":"1ae2c4e7f0484205e349755156ef18fd","score":"0.49459162","text":"def test_act_on_settings(self):\n pass # TODO(tlarsen)","title":""},{"docid":"1ae2c4e7f0484205e349755156ef18fd","score":"0.49459162","text":"def test_act_on_settings(self):\n pass # TODO(tlarsen)","title":""},{"docid":"483d2bcf11a4ca52133f13bfc9749617","score":"0.4934035","text":"def test_help_is_provided(parser, capsys):","title":""},{"docid":"33bde908bf20f1ced0af04a6b114cc5d","score":"0.4932022","text":"def test_search_exact_match(self, client, project, case):\n # `Sphinx` word is present both in `kuma` and `docs` files\n # But the phrase `Sphinx uses` is available only in kuma docs.\n # So search with this phrase to check\n query_text = r'\"Sphinx uses\"'\n cased_query = getattr(query_text, case)\n query = cased_query()\n\n page_search = PageSearch(query=query)\n results = page_search.execute()\n\n assert len(results) == 2\n assert results[0][\"project\"] == \"kuma\"\n assert results[0][\"path\"] == \"testdocumentation\"\n assert results[0][\"version\"] == \"stable\"\n\n assert results[1][\"project\"] == \"kuma\"\n assert results[1][\"path\"] == \"testdocumentation\"\n assert results[1][\"version\"] == \"latest\"","title":""},{"docid":"be88381763ae036af82924a4799b2fd1","score":"0.4925959","text":"def test_get_context_defaults(mocker):\n mocker.patch.dict(os.environ, {})\n assert get_context() == ALL_NONE_CONTEXT","title":""},{"docid":"3466b288cff4fe1ba9c7473471a0ee81","score":"0.4921266","text":"def test_search_user(self):\n\n # Login\n ts = TSApiWrapper(DISABLE_SSL)\n ts.login(HOSTPORT, USERNAME, PASSWORD)\n\n result = ts.switch_org(int(Constants.ALL_ORG_ID))\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\n # create orgs\n orgs = [\"test_search_user\" + str(i) for i in range(2)]\n for org in orgs:\n result = ts.create_org(org, org + \"_desc\")\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\n\n result = ts.switch_org(int(Constants.DEFAULT_ORG_ID))\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\n\n # search for tsadmin user\n result = ts.search_user(USERNAME)\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\n\n # search for a random user\n result = ts.search_user(\"randomUser\")\n self.assertEqual(result.status, Constants.OPERATION_FAILURE)","title":""},{"docid":"a7e3716dc7f9941a19891c85553e240b","score":"0.49185625","text":"def can_search_catalogs(self):\n # Implemented from template for\n # osid.resource.BinQuerySession.can_search_bins_template\n # NOTE: It is expected that real authentication hints will be\n # handled in a service adapter above the pay grade of this impl.\n return True","title":""},{"docid":"ff398a692451a838ae0f1ac6ce6ff79f","score":"0.49178156","text":"def __contains__(self, othercmd):\n ret = self._contains_cache.get(othercmd)\n if ret is None:\n ret = othercmd in self.commands\n self._contains_cache[othercmd] = ret\n return ret","title":""},{"docid":"908ec20ade51375e34afee11e3d007e5","score":"0.49175754","text":"def test_contexts(self):\n if os.path.isfile(\"appdconfig.yaml\"):\n question = \"File appdconfig.yaml already exists. Do you want to replace it [Y/N]?\"\n answer = \"\"\n while answer not in [\"y\", \"n\"]:\n if sys.version_info[0] < 3:\n answer = raw_input(question).lower()\n else:\n answer = input(question).lower()\n if answer == \"y\": os.remove(\"appdconfig.yaml\")\n else: return\n\n if 'VERBOSE' in locals(): print (\"Try to get appdynamics context file, when file doesn't exist\")\n result = subprocess.call(\"./appdctl.py config get-contexts \", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Create new appdynamics context file and add new context\")\n result = subprocess.call(\"./appdctl.py config set-context EVO.PRO --controller-url https://evobanco.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Append context to appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config set-context EVO.UAT --controller-url https://evobanco-dev.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco-dev\", shell=True)\n self.assertEqual(result, 0)\n result = subprocess.call(\"./appdctl.py config set-context localhost --controller-url http://localhost:8090 --user-API APIClient@customer1\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Add already existing context to appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config set-context EVO.UAT --controller-url https://evobanco-dev.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco-dev\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Get contexts from appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config get-contexts\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Use existing context on appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config use-context EVO.UAT\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Use non existing context on appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config use-context nonexist\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Delete existing context on appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config delete-context localhost\", shell=True)\n self.assertEqual(result, 0)\n\n if 'VERBOSE' in locals(): print (\"Delete non existing context on appdynamics context file\")\n result = subprocess.call(\"./appdctl.py config delete-context nonexist\", shell=True)\n self.assertEqual(result, 0)","title":""},{"docid":"83551bfc579138947aeaa2beeccc8e1d","score":"0.49064502","text":"def test_search(self):\n path = reverse('blog:search')\n assert path, SearchView.as_view().__name__","title":""},{"docid":"65f9450127b40ee6dc80de8dae78fe98","score":"0.49044466","text":"def test_search_init_new_search(self):\n self.dialog.search_parameters = \"something\"\n self.dialog.logentry_queryset = \"some queryset\"\n self.dialog.init_new_search()\n self.assertFalse(hasattr(self.dialog, 'search_parameters'))\n self.assertFalse(hasattr(self.dialog, 'logentry_queryset'))","title":""},{"docid":"6e2820de433dc82cf55305aa58f92fa4","score":"0.48993313","text":"def _check_command_context(self):\n if not isinstance(self.command_context, CommandContext):\n raise TypeError(\"The command context parameter of the Commanding server has to be CommandContext\")","title":""},{"docid":"cccc254cc49c538608326bc83eaeef3b","score":"0.4898871","text":"async def filter(self, ctx):\n\n if ctx.invoked_subcommand is None:\n raise SendHelp()","title":""},{"docid":"616c33ff3fcbbebedf5052c8084e897b","score":"0.48984346","text":"def test_search(self):\n\n qs = Project.objects.order_by('pk')\n request = Mock()\n\n tests = (\n # Label, Term, Expected Results\n ('No Term', '', [self.project.pk, self.other.pk, self.fieldbook.pk, ]),\n ('Project Name', 'Mine', [self.project.pk, ]),\n ('Project ID', str(self.other.pk), [self.other.pk, ]),\n ('Fieldbook ID', 'Project X', [self.fieldbook.pk, ]),\n )\n\n for label, term, expected in tests:\n with self.subTest(label):\n results, _use_distinct = self.admin.get_search_results(request, qs, term)\n self.assertQuerysetEqual(results, expected, lambda x: x.pk)","title":""},{"docid":"4ed346827b3227cfc4a1102db5580e19","score":"0.4895134","text":"def test_search_services_with_query_with_model(self):\n data_model = DataModel(\"foobar\", [Attribute(\"foo\", str, True)])\n search_query = Query(\n [Constraint(\"foo\", ConstraintType(\"==\", \"bar\"))], model=data_model\n )\n oef_search_request, sending_dialogue = self.oef_search_dialogues.create(\n counterparty=str(self.connection.connection_id),\n performative=OefSearchMessage.Performative.SEARCH_SERVICES,\n query=search_query,\n )\n self.multiplexer.put(\n Envelope(\n to=oef_search_request.to,\n sender=oef_search_request.sender,\n message=oef_search_request,\n )\n )\n\n envelope = self.multiplexer.get(block=True, timeout=5.0)\n oef_search_response = envelope.message\n oef_search_dialogue = self.oef_search_dialogues.update(oef_search_response)\n assert (\n oef_search_response.performative\n == OefSearchMessage.Performative.SEARCH_RESULT\n )\n assert oef_search_dialogue is not None\n assert oef_search_dialogue == sending_dialogue\n assert oef_search_response.agents == ()","title":""},{"docid":"d6601ece521252ac28f47388771dc9bb","score":"0.48874626","text":"def suggest(self, query, context):\n pass","title":""},{"docid":"93d610dfe63d0252444c00bce86eb628","score":"0.48862186","text":"def test_describe_saved_search(self):\n runner = CliRunner()\n result = runner.invoke(saved_searches_group, [\"describe\", \"1\"], obj=self.ctx)\n assert result.output == EXPECTED_OUTPUT","title":""},{"docid":"35a6d5d2ba2f62cbd73f5ce976b7cbc9","score":"0.48769417","text":"def test_focus(self):\n self.argv.append(\"--focus\")\n with self.assert_in_stdout(\n \"top context\\n\"\n \" *focused example: PASS\\n\\n\"\n # TODO rest of output\n ):\n self.execute()","title":""},{"docid":"06bd541b9da9f8254accb24100915564","score":"0.48763618","text":"def query_global_configuration(self):","title":""},{"docid":"f64c81d404ffba729586adad7d272193","score":"0.48711967","text":"def test_search():\n request = {\n 'params': {\n 'q': 'music',\n },\n }\n code, _ = search_route(request)\n assert code == 200","title":""}],"string":"[\n {\n \"docid\": \"e10689036335fbbb48a0cd39ef5563c5\",\n \"score\": \"0.62758994\",\n \"text\": \"def test_contexts_search(self):\\n from .mockers import create_context, create_contextA, create_contextB\\n\\n self.create_context(create_context)\\n self.create_context(create_contextA)\\n self.create_context(create_contextB)\\n res = self.testapp.get('/contexts', '', oauth2Header(test_manager), status=200)\\n self.assertEqual(len(res.json), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6598b1451cd9fd9ffa83210cf78fa8\",\n \"score\": \"0.62084484\",\n \"text\": \"def getSearchContext(self): \\n return SearchResults.context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1019db01ca2e2cad5b6467488b3a1fbb\",\n \"score\": \"0.58214545\",\n \"text\": \"def is_search(self, default=None):\\n return self.get(\\\"search\\\", default)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581f4edba9cdfcfb9b8dc992f94e7ff0\",\n \"score\": \"0.580631\",\n \"text\": \"async def find(self, ctx: CustomContext):\\r\\n if ctx.invoked_subcommand is None:\\r\\n await ctx.send_help(str(ctx.command))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f779d2c6ce7cb3d62c35a36ff264f89\",\n \"score\": \"0.5755496\",\n \"text\": \"def test_search(self):\\n\\n def is_venture2(ci):\\n return ci.uid == 'br-2'\\n\\n self.assertEqual(\\n breadth_first_search_ci(self.server_b, is_venture2),\\n (self.venture2, True)\\n )\\n\\n self.assertEqual(\\n breadth_first_search_ci(self.server_a, is_venture2),\\n (None, None)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a1d5954d69efc7619bf25740386e52\",\n \"score\": \"0.568351\",\n \"text\": \"def test_environment_set_in_context(self):\\n\\n request = RequestFactory().get('/', data={'name_product': \\\"Ravioli\\\"})\\n view = SearchProductView()\\n view.setup(request)\\n # we fix the object _list because we do not call\\n # SearchProduct as a view\\n view.object_list = view.get_queryset()\\n context = view.get_context_data()\\n self.assertIn('essais', context)\\n self.assertIn('name_product', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af655d4f21a16c284cd4f93420f75931\",\n \"score\": \"0.5667697\",\n \"text\": \"def test_search_get_menu(self):\\n menu = self.dialog.get_menu()\\n menu = self.dialog.get_menu()\\n self.assertIn('w', menu)\\n self.assertIn('t', menu)\\n self.assertIn('d', menu)\\n self.assertIn('e', menu)\\n self.assertIn('b', menu)\\n self.assertIn('q', menu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71bca9ff8513d6bbe55aa666d5354b00\",\n \"score\": \"0.5643643\",\n \"text\": \"def test_context_args(self, cdp, command):\\n handler = self.make_default_handler(self.callback_context_args)\\n self._test_context_args_or_regex(cdp, handler, command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5cee4b7734b587130e9b671e7e7e9c\",\n \"score\": \"0.5592355\",\n \"text\": \"def ask_for_search_args(self):\\n # self._search_args = []\\n with SearchDialog(self, title='Search options') as edt:\\n send = True\\n while send:\\n ok = edt.ShowModal()\\n if ok == wx.ID_OK:\\n if self.editor.search_args:\\n break\\n else:\\n send = False\\n print(send)\\n return send\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb78d427bc1d9c6db34ca5821bce400\",\n \"score\": \"0.5587644\",\n \"text\": \"def test_product_search_functionality(self):\\n response = self.client.get(\\n '/products/?', {'q': 'tee'})\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response.context['search_term'], 'tee')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaec366ab8fc4f70ad597bdd327fc2d0\",\n \"score\": \"0.55772245\",\n \"text\": \"def test_right_context_only_query(self):\\n querier = DataMuseQuerier()\\n results = querier.get_synonym_query_results(Word(\\\"the\\\", \\\"\\\"), right_context = \\\"mall\\\")\\n self.assertTrue(len(results) > 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf24877f81b47f6baca43a271bb2d4a\",\n \"score\": \"0.55699056\",\n \"text\": \"def test_gridsearch_alias(self):\\n assert isgridsearch is is_gridsearch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c761c3f47b532479c1f927b9c34d73c\",\n \"score\": \"0.5542754\",\n \"text\": \"def test_contexts_search_with_tags(self):\\n from .mockers import create_context, create_contextA, create_contextB\\n from .mockers import context_search_by_tags\\n\\n self.create_context(create_context) # Tagged \\\"Assignatura\\\"\\n self.create_context(create_contextA) # Tagged \\\"Assignatura\\\"\\n self.create_context(create_contextB) # Not tagged\\n res = self.testapp.get('/contexts?tags', context_search_by_tags, oauth2Header(test_manager), status=200)\\n self.assertEqual(len(res.json), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b68791bd2e6eb868a1c2c7f4c9c3e2e\",\n \"score\": \"0.5491355\",\n \"text\": \"def test_command(\\n self, setup: Any, args: list[str], expected: list[str], config_overwrite: bool\\n ) -> None:\\n config.commands.search.ignore_case = config_overwrite\\n\\n cmd = SearchCommand(*args)\\n cmd.execute()\\n output = cmd.render_porcelain()\\n self._assert(output, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a43fa05dd87308a8033e36bc154199\",\n \"score\": \"0.5470138\",\n \"text\": \"def test_search(self):\\n response = self.client.get(\\\"/?q=Stone\\\")\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, \\\"Stonebag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f606412343223de4329bd91c5ce56204\",\n \"score\": \"0.54601735\",\n \"text\": \"def setup_parameters_search(context):\\n context.parameters = {\\n 'exact_list': None,\\n 'start_time': None,\\n 'end_time': None\\n }\\n logit.debug('New parameter search set up')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d7df596d91107fdc82792bfd8900c6\",\n \"score\": \"0.54186153\",\n \"text\": \"def test_search_search_by_term(self):\\n self.dialog.search_by_term()\\n self.assertEqual(self.dialog.activestep.checkinput_func, self.dialog.check_term)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b8e3932ebf6d19da8fea5714f5a4ee\",\n \"score\": \"0.5392576\",\n \"text\": \"def context_mode(self, context):\\n self.cli_mode()\\n # Set the Mode to Config\\n\\n self.prompt_mode = \\\"context\\\"\\n context_prompt = self.cli_prompt.replace('local', context)\\n self.set_prompt(context_prompt)\\n return self.execute_command(\\\"context {0}\\\".format(context))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b75bde13fcb9f4a0edac2d827f602e5\",\n \"score\": \"0.5389433\",\n \"text\": \"def test_contains(self):\\n self.assertIn(\\\"app\\\", self.config)\\n self.assertIn(\\\"window\\\", self.config.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5da044af779a77804b0003b4eeba402\",\n \"score\": \"0.53737843\",\n \"text\": \"def test_search_available(self):\\n name = \\\"maddison.name\\\"\\n args = [\\\"search\\\", name]\\n result = self.invoke(args=args)\\n assert result.exit_code == 0\\n assert name in result.output\\n for keyword in [\\\"premium\\\", \\\"type\\\", \\\"purchase price\\\", \\\"renewal price\\\"]:\\n assert keyword in result.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0abb2f357886014901945e0babd1772\",\n \"score\": \"0.5373784\",\n \"text\": \"def get_desired_context(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c4888f1d2fc20be33435abeb94cf931\",\n \"score\": \"0.5368296\",\n \"text\": \"def test_search(self):\\n output = self.run_command(\\\"pip search pytest\\\", exitcode=0)\\n self.assertIn(\\\"pytest\\\", output)\\n # due to changing pypi search results, the following results are not guaranteed.\\n # TODO: fix this\\n # self.assertIn(\\\"pytest-cov\\\", output)\\n # self.assertIn(\\\"pytest-env\\\", output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c10d441a78848a25cd53af4af9fb2de\",\n \"score\": \"0.5313205\",\n \"text\": \"def test_advanced_search_detail(self):\\n utils.login(self)\\n response = self.client.get(reverse('spirit:search'))\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1faaaeabbb156ab79c30bb2e843a31\",\n \"score\": \"0.5310486\",\n \"text\": \"def test_settings_are_in_context(self):\\n req = RequestFactory().get('')\\n ctx = RequestContext(req)\\n\\n self.assertTrue('settings' in ctx)\\n self.assertEqual(ctx['settings'], settings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a1e6b0c0739e657dd4beb9fe644c99\",\n \"score\": \"0.5305073\",\n \"text\": \"def test_search(self):\\n\\n result = self.client.get(\\\"/search\\\", query_string={'location': 'San Francisco', 'keyWord': 'music', 'cat_id': 115})\\n self.assertIn(\\\"Results\\\", result.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab2f5fa937bfa11a9aee7b53887b690\",\n \"score\": \"0.5303475\",\n \"text\": \"def init_search(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc35c6e6bcfa4556b46722503b47ed7e\",\n \"score\": \"0.5301699\",\n \"text\": \"def test_template_search(self):\\r\\n response = self.client.get(\\\"/search/\\\")\\r\\n self.assertTemplateUsed(response, \\\"search.html\\\")\\r\\n self.assertTemplateUsed(response, \\\"base/main.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4914c84eb52f3afcb1ee2d826c5863a4\",\n \"score\": \"0.5300841\",\n \"text\": \"def test_search_case(self):\\n rv = search.search(\\\"test\\\")\\n for k, inst in list(self.instances.items()):\\n assert k in rv\\n assert len(rv[k]) == 1\\n assert rv[k][0][\\\"name\\\"] == inst.search_result_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f8deb586c5e14402ad48482f1da576\",\n \"score\": \"0.5298709\",\n \"text\": \"def test_index_context(self, client):\\n\\n response = client.get((reverse(\\\"contact:index\\\")))\\n assert \\\"form\\\" in response.context\\n assert \\\"thanks\\\" in response.context\\n assert response.context[\\\"thanks\\\"] == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c7767b9a6d2ef62a0d1652c4ff1ae3\",\n \"score\": \"0.52830696\",\n \"text\": \"def test_get_help(self):\\n assert self.testcommand.get_help() == self.testcommand.help\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9516235976bea6614cfff2bb688e2078\",\n \"score\": \"0.5273061\",\n \"text\": \"def test_signin_context(self):\\n response = self.client.get(reverse_lazy('login:signin_page'))\\n try:\\n self.assertTrue('signin_form' in response.context)\\n except KeyError:\\n self.assertTrue(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"519110285279c14ef79315499c1ffaad\",\n \"score\": \"0.5246247\",\n \"text\": \"def test_event_pre_search_command(self, setup: Any) -> None:\\n\\n @Event.PreSearchCommand.subscribe\\n def hook(command: SearchCommand) -> None:\\n command.largs.query = [\\\"einstein\\\"]\\n\\n assert Event.PreSearchCommand.validate()\\n\\n expected = [\\\"einstein::1\\\", \\\"1::@article{einstein,\\\", \\\"1::author = {Albert Einstein},\\\"]\\n\\n cmd = SearchCommand(\\\"knuthwebsite\\\")\\n cmd.execute()\\n output = cmd.render_porcelain()\\n self._assert(output, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e002759bc5a952bd61e0112586730e3e\",\n \"score\": \"0.5239413\",\n \"text\": \"def test_public_contexts_search_with_tags(self):\\n from .mockers import create_context, create_contextA, create_contextB\\n from .mockers import context_search_by_tags\\n\\n username = 'messi'\\n self.create_user(username)\\n\\n self.create_context(create_context)\\n self.create_context(create_contextA, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='subscribed'))\\n self.create_context(create_contextB)\\n res = self.testapp.get('/contexts/public?tags', context_search_by_tags, oauth2Header(username), status=200)\\n self.assertEqual(len(res.json), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6580fb22ad316ebf04910630bc18495\",\n \"score\": \"0.522679\",\n \"text\": \"def test_search_page(client):\\n # check that search only available to logged in users\\n result = search(client, \\\"Note\\\")\\n # landing page should be shown\\n assert b\\\"create a user or login with an existing one\\\" in result.data\\n # with the user the search results page should be shown\\n result = login(client, TEST_USER, TEST_PASSWORD)\\n result = search(client, \\\"Note\\\")\\n assert b\\\"results\\\" in result.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb41cba964f453610b87c676d709aa2\",\n \"score\": \"0.52198553\",\n \"text\": \"def test_search(self):\\n result = self.client.get('/search')\\n self.assertEqual(result.status_code, 200)\\n self.assertIn(b\\\"Search\\\", result.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcbd1394bb7c4081e4abb9ef28c1445b\",\n \"score\": \"0.52171105\",\n \"text\": \"def test_left_context_only_query(self):\\n querier = DataMuseQuerier()\\n results = querier.get_synonym_query_results(Word(\\\"the\\\", \\\"\\\"), left_context = \\\"to\\\")\\n self.assertTrue(len(results) > 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61f3d85aed9df0921ed83a5121d3431\",\n \"score\": \"0.51948285\",\n \"text\": \"def test_api_authorization_search_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1792dc0ff11cb773b289c81d1b7ef79e\",\n \"score\": \"0.5180473\",\n \"text\": \"def test_search_wards_enter(self):\\n self.wp_sidebar.verify_search_wards_enter()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd9fdb7757483d36758ed180a92d4f4\",\n \"score\": \"0.5173186\",\n \"text\": \"def is_configured(command):\\n return command in COMMANDS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"096c152f58b9a86785d736f5b87bc0f5\",\n \"score\": \"0.5156481\",\n \"text\": \"def has_context_name():\\n return \\\"name\\\" in app.context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d1514ee20721d8d6cf0517d33464511\",\n \"score\": \"0.515161\",\n \"text\": \"async def search(self, ctx):\\n\\n await ctx.message.add_reaction('❌')\\n await ctx.send('None or invalid source supplied, try `{}help search`'.format(self.bot.command_prefix))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8f8e704911f6e6e3ac80d1645fb3949\",\n \"score\": \"0.51476645\",\n \"text\": \"def testSeachFunction(self):\\n inst = self.createInstance()\\n ans = inst.search('key2', 'two')\\n self.assertEqual(len(ans), 3, 'this finds only none blankc items')\\n ans = inst.search('key2', 'twob')\\n self.assertEqual(len(ans), 1, 'insensitive to case finds one entry')\\n ans = inst.search('key2', 'twob', False)\\n self.assertEqual(len(ans), 0, 'sensitive to case finds no entry')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ed33da250bc5ef96877e0ec6323fbf\",\n \"score\": \"0.51406145\",\n \"text\": \"def search(self) -> global___Search:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc83252ba1a8f797794420b0e20e44bc\",\n \"score\": \"0.5139536\",\n \"text\": \"def test_location_route_takes_search_parameter(self):\\n data = self._query_api(search='some search text')\\n self.assertFalse(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb130c14b5e5569537a30937ff80d5b\",\n \"score\": \"0.5136215\",\n \"text\": \"def _match_context(context, active_context):\\n if context is None:\\n # If command has no context, it always matches\\n return True\\n\\n if active_context is None:\\n # If the command has a context, and we don't, no match\\n return False\\n\\n # The active_context matches if it starts with context and is followed by\\n # the end of the string or the separator\\n clen = len(context)\\n return (\\n active_context.startswith(context) and\\n active_context[clen:clen + len(CONTEXT_SEP)] in ('', CONTEXT_SEP)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabda2ac70d040550e776a10a7c32806\",\n \"score\": \"0.51300794\",\n \"text\": \"def can_search_objectives(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06ef1fb9e914508ec9e6273dd71485b\",\n \"score\": \"0.512584\",\n \"text\": \"def test_search_wards_click(self):\\n self.wp_sidebar.verify_search_wards_click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1e411d0ef162fdc2178758bfc3fcb1\",\n \"score\": \"0.5125268\",\n \"text\": \"def test_get_registered_items_positive(\\n self, cli_setup_search_ctx_mock, cli_search_items_mock\\n ):\\n response = self.app.get(\\\"api/connection\\\")\\n self.assertEqual(response.status_code, 200)\\n\\n result = json.loads(response.get_data(as_text=True))\\n expected_result = [{\\\"name\\\": \\\"some-connection\\\"}]\\n self.assertEqual(result, expected_result)\\n\\n cli_setup_search_ctx_mock.assert_called_once()\\n cli_search_items_mock.assert_called_once()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c8263b04d6ab0b6258b05356396c24\",\n \"score\": \"0.5124275\",\n \"text\": \"def test_search_courses(self):\\n\\n self.po.search_courses('searchtext')\\n # self.assertFalse(po.is_on_page())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f60856f92794ac1a2d9482213d7ec9\",\n \"score\": \"0.5107705\",\n \"text\": \"def has_context_name():\\n return 'name' in app.context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8c09d11c39b70622cf38ae1b265ce5\",\n \"score\": \"0.51073605\",\n \"text\": \"async def wordfilter(self, ctx: KurisuContext):\\n if ctx.invoked_subcommand is None:\\n await ctx.send_help(ctx.command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a796b378dc0c34d8e5d3e0e4936193b\",\n \"score\": \"0.5088855\",\n \"text\": \"def test_left_and_right_context_query(self):\\n querier = DataMuseQuerier()\\n results = querier.get_synonym_query_results(Word(\\\"the\\\", \\\"\\\"), left_context = \\\"to\\\", right_context = \\\"mall\\\")\\n self.assertTrue(len(results) > 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6b399a5ded7b9eb4bce13da07d1ce4\",\n \"score\": \"0.5081442\",\n \"text\": \"def search_enabled(self):\\n return self.SEARCH_PRECOMMIT_HOOK in (self.get_property(\\\"precommit\\\") or [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739ef4c5def30e123373439dce2ffcca\",\n \"score\": \"0.508039\",\n \"text\": \"async def octex(self, ctx, *, search_path = None):\\r\\n\\r\\n\\t\\tusage = \\\"Usage: `{}occ [search_path]`\\\".format(ctx.prefix)\\r\\n\\t\\tif not self.tex: return await ctx.send(\\\"It looks like I was unable to get the Configuration.tex :(\\\")\\r\\n\\t\\tif search_path is None: return await ctx.send(usage)\\r\\n\\t\\t# Let's split up the search path and ensure we have a qualified path to give to the parser\\r\\n\\t\\tsearch_path = search_path.replace(\\\"-\\\",\\\" \\\").replace(\\\">\\\",\\\" \\\").replace(\\\"/\\\",\\\" \\\")\\r\\n\\t\\tsearch_path = re.sub(\\\" {2,}\\\",\\\" \\\",search_path)\\r\\n\\t\\tsearch_parts = search_path.split()\\r\\n\\t\\tif not search_parts: return await ctx.send(usage)\\r\\n\\r\\n\\t\\t# Search for matches in our Sample\\r\\n\\t\\tmatches = self.search_sample(search_parts)\\r\\n\\t\\tmessage = None\\r\\n\\t\\tif matches:\\r\\n\\t\\t\\tfuzzy,matches = matches # Expand the vars\\r\\n\\t\\t\\tif len(matches)>1 or fuzzy: # Multiple matches - show a list\\r\\n\\t\\t\\t\\tif fuzzy:\\r\\n\\t\\t\\t\\t\\tlimit = 3\\r\\n\\t\\t\\t\\t\\ttitle = \\\"There were no exact matches for that search, perhaps you meant {}the following:\\\".format(\\r\\n\\t\\t\\t\\t\\t\\t\\\"one of \\\" if len(matches)>1 else \\\"\\\"\\r\\n\\t\\t\\t\\t\\t)\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tlimit = 5\\r\\n\\t\\t\\t\\t\\tleftover = len(matches)-5 if len(matches)>5 else 0\\r\\n\\t\\t\\t\\t\\ttitle = \\\"There were multiple results for that search, please pick from the following list{}:\\\".format(\\r\\n\\t\\t\\t\\t\\t\\t\\\" ({:,} more omitted)\\\".format(leftover) if leftover else \\\"\\\"\\r\\n\\t\\t\\t\\t\\t)\\r\\n\\t\\t\\t\\tindex, message = await PickList.Picker(\\r\\n\\t\\t\\t\\t\\ttitle=title,\\r\\n\\t\\t\\t\\t\\tlist=[\\\" -> \\\".join(x) for x in matches[:3 if fuzzy else 5]],\\r\\n\\t\\t\\t\\t\\tctx=ctx\\r\\n\\t\\t\\t\\t).pick()\\r\\n\\t\\t\\t\\tif index < 0:\\r\\n\\t\\t\\t\\t\\treturn await message.edit(content=\\\"Search cancelled.\\\")\\r\\n\\t\\t\\t\\tmatches = matches[index]\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tmatches = matches[0]\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Fall back on the original search in case we don't have a Sample.plist,\\r\\n\\t\\t\\t# or there's a version mismatch - or similar.\\r\\n\\t\\t\\tmatches = search_parts\\r\\n\\t\\tsearch_results = self.tex_search(self.tex, matches)\\r\\n\\t\\tif not search_results: return await ctx.send(\\\"Nothing was found for that search :(\\\")\\r\\n\\r\\n\\t\\t# We got something to show - let's build a page-picker\\r\\n\\t\\treturn await PickList.PagePicker(\\r\\n\\t\\t\\ttitle=\\\"Results For: \\\"+\\\" -> \\\".join(matches),\\r\\n\\t\\t\\tdescription=search_results,\\r\\n\\t\\t\\ttimeout=300, # Allow 5 minutes before we stop watching the picker\\r\\n\\t\\t\\tfooter=\\\"From Configuration.tex for OpenCore v{}\\\".format(self.tex_version),\\r\\n\\t\\t\\tctx=ctx,\\r\\n\\t\\t\\tmessage=message\\r\\n\\t\\t).pick()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713291244e3d4b2c1ca4c8059d4a2500\",\n \"score\": \"0.5075484\",\n \"text\": \"def KeywordSearch(self, keyWord, mode=HELP_SEARCH_ALL):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae9b4ecfaa6a9a97aa3202a0e3c46d6\",\n \"score\": \"0.5067172\",\n \"text\": \"def test_pytest_uses_posargs(self, testenv_commands):\\n assert any([re.search(r'py\\\\.test [^\\\\n]*\\\\{posargs', command) for command in testenv_commands])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d88a4be8f6374531817ab4ea20d4b2\",\n \"score\": \"0.50642264\",\n \"text\": \"def test_search_services_with_query_without_model(self):\\n search_query_empty_model = Query(\\n [Constraint(\\\"foo\\\", ConstraintType(\\\"==\\\", \\\"bar\\\"))], model=None\\n )\\n oef_search_request, sending_dialogue = self.oef_search_dialogues.create(\\n counterparty=str(self.connection.connection_id),\\n performative=OefSearchMessage.Performative.SEARCH_SERVICES,\\n query=search_query_empty_model,\\n )\\n self.multiplexer.put(\\n Envelope(\\n to=oef_search_request.to,\\n sender=oef_search_request.sender,\\n message=oef_search_request,\\n )\\n )\\n\\n envelope = self.multiplexer.get(block=True, timeout=5.0)\\n oef_search_response = envelope.message\\n oef_search_dialogue = self.oef_search_dialogues.update(oef_search_response)\\n assert (\\n oef_search_response.performative\\n == OefSearchMessage.Performative.SEARCH_RESULT\\n )\\n assert oef_search_dialogue is not None\\n assert oef_search_dialogue == sending_dialogue\\n assert oef_search_response.agents == ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02badc336ddbe0ec9b0e4f9683b9b347\",\n \"score\": \"0.50537103\",\n \"text\": \"def test_search(self):\\n\\n rv = search.search(\\\"Test\\\")\\n for k, inst in list(self.instances.items()):\\n assert k in rv\\n assert len(rv[k]) == 1\\n assert rv[k][0][\\\"name\\\"] == inst.search_result_name\\n assert rv[k][0][\\\"org_id\\\"] == inst.org_id\\n\\n # test that term order does not matter\\n\\n for k, inst in list(self.instances.items()):\\n rv = search.search(f\\\"Test {k}\\\")\\n assert k in rv\\n assert len(rv[k]) == 1\\n assert rv[k][0][\\\"name\\\"] == inst.search_result_name\\n assert rv[k][0][\\\"org_id\\\"] == inst.org_id\\n\\n rv = search.search(f\\\"{k} Test\\\")\\n assert k in rv\\n assert len(rv[k]) == 1\\n assert rv[k][0][\\\"name\\\"] == inst.search_result_name\\n assert rv[k][0][\\\"org_id\\\"] == inst.org_id\\n\\n rv = search.search(\\\"as1\\\")\\n assert len(rv[\\\"net\\\"]) == 1\\n assert rv[\\\"net\\\"][0][\\\"name\\\"] == self.instances[\\\"net\\\"].search_result_name\\n assert rv[\\\"net\\\"][0][\\\"org_id\\\"] == self.instances[\\\"net\\\"].org_id\\n\\n rv = search.search(\\\"asn1\\\")\\n assert len(rv[\\\"net\\\"]) == 1\\n assert rv[\\\"net\\\"][0][\\\"name\\\"] == self.instances[\\\"net\\\"].search_result_name\\n assert rv[\\\"net\\\"][0][\\\"org_id\\\"] == self.instances[\\\"net\\\"].org_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1559b02bcbb620bfa877f657523020c\",\n \"score\": \"0.50473875\",\n \"text\": \"def test_context(self, client):\\n def ctx(ins):\\n return dict(a=\\\"1\\\")\\n\\n self.reg.register(self.type, \\\"foo/bar\\\", \\\"foo bar\\\",\\n default=False, context=ctx)\\n\\n context_method = self.reg.context.get((self.type, \\\"foo/bar\\\"))\\n assert context_method\\n assert context_method(None) == dict(a=\\\"1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359bb82ddf6be0b365004a1d87968153\",\n \"score\": \"0.50443554\",\n \"text\": \"def test_adv_search_page(self):\\n result = self.client.get(\\\"/adv-search\\\")\\n self.assertEqual(result.status_code, 200)\\n self.assertIn(b\\\"Advanced Search\\\", result.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ffec52bf51653b0b208cfca92c0afbb\",\n \"score\": \"0.5042195\",\n \"text\": \"def test_search_engine():\\n test_search = SearchEngine('/home/testFiles')\\n assert_equals(None, test_search.search(\\\"street\\\"))\\n assert_equals(['/home/testFiles/doc4.txt',\\n '/home/testFiles/doc2.txt'],\\n test_search.search(\\\"basketball\\\"))\\n assert_equals(['/home/testFiles/doc4.txt', '/home/testFiles/doc5.txt',\\n '/home/testFiles/doc3.txt', '/home/testFiles/doc2.txt',\\n '/home/testFiles/doc1.txt'],\\n test_search.search('food'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8add78e9d76fe13ac95ee2b9991e5e75\",\n \"score\": \"0.5040621\",\n \"text\": \"def search(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8add78e9d76fe13ac95ee2b9991e5e75\",\n \"score\": \"0.5040621\",\n \"text\": \"def search(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e8dc6cfd75161bcee0d47a04e90fe0\",\n \"score\": \"0.5038879\",\n \"text\": \"async def xkcd(self, ctx, *, search: typing.Union[int, str] = None):\\n\\n async with ctx.typing():\\n if search is None:\\n await self.random(ctx)\\n elif isinstance(search, int):\\n await self.num(ctx, search)\\n elif search == \\\"latest\\\":\\n await self.latest(ctx)\\n else:\\n await self.search(ctx, search=search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0284403fd3611743c5bc811d22d27102\",\n \"score\": \"0.5035066\",\n \"text\": \"def database_parameter_search(context):\\n logit.info(f\\\"using parameters to get data from the database {context.parameters}\\\")\\n context.selection = context.db.query_database(context.parameters)\\n logit.debug(f\\\"data from the database {context.selection}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6ac260bf4d935f40ac0624adbf80665\",\n \"score\": \"0.50315154\",\n \"text\": \"def test_context_regex(self, cdp, command):\\n handler = self.make_default_handler(self.callback_context_regex1,\\n filters=Filters.regex('one two'))\\n self._test_context_args_or_regex(cdp, handler, command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03f1b0932a6c2eed78276b8380b08548\",\n \"score\": \"0.50186235\",\n \"text\": \"def search():\\n option = ui.get_search_input()\\n term = ui.get_input(\\\"Please type your search term (not case sensitive).\\\")\\n if term != \\\"-1\\\":\\n ui.message(datastore.search(option, term))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7587b35853966a5db14548a0b4dcfd2c\",\n \"score\": \"0.5017184\",\n \"text\": \"def test_contains(self):\\n self._token['opt'] = \\\"This is optional information, and should always return true\\\"\\n self._token['scp'] = \\\"write:/home/example\\\"\\n\\n self.assertTrue('opt' in self._token)\\n self.assertTrue('scp' in self._token)\\n self.assertFalse('notin' in self._token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca7db08b1d94e750814a335464ebd75\",\n \"score\": \"0.4992911\",\n \"text\": \"def test_search(self):\\n\\n\\t\\tresult = self.app.get('/search?terms=dummy')\\n\\t\\tself.assertEqual(result.status_code, 200)\\n\\t\\tself.assertEqual(result.mimetype, 'text/html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458841b019c73d81dbc9577fe5bca048\",\n \"score\": \"0.49922562\",\n \"text\": \"def test_menu_and_search(self):\\n # driver = self.setUp()\\n demo = MenuAndSearchTest(self.dr)\\n time.sleep(2.0)\\n demo.get_sections()['menu'].get_actions('click_menu')\\n demo.get_sections()['menu'].get_actions('trend')\\n demo.get_sections()['menu'].get_actions('history')\\n demo.get_sections()['search'].get_actions('set_query', 'python')\\n demo.get_sections()['search'].get_actions('click_search')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470379e5ec19b7c65680e1c8f5bacf34\",\n \"score\": \"0.49898216\",\n \"text\": \"def test_event_post_search_command(self, setup: Any) -> None:\\n\\n @Event.PostSearchCommand.subscribe\\n def hook(command: SearchCommand) -> None:\\n print(command.hits, [entry.label for entry in command.entries])\\n\\n assert Event.PostSearchCommand.validate()\\n\\n with contextlib.redirect_stdout(StringIO()) as file:\\n cmd = SearchCommand(\\\"einstein\\\")\\n cmd.execute()\\n assert file.getvalue().strip() == \\\"1 ['einstein']\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73970fe19b4b13b75966143fd1a57dcc\",\n \"score\": \"0.4988545\",\n \"text\": \"def test_search_entity(self):\\n for entity in self.testVector[\\\"text_params\\\"]:\\n query = self.cat_query.text_search(entity)\\n result = self.cat.search_entity(query)\\n print(f\\\"DOCUMENTS: {result.documents}\\\")\\n print(f\\\"STATUS: {result.status}\\\")\\n print(f\\\"TOTAL HITS: {result.total_hits}\\\")\\n print(\\\"*\\\"*30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18502eb8f9599f2d12a43071a6599e7\",\n \"score\": \"0.49825704\",\n \"text\": \"def test_index_context(rf):\\n sitemaps = {'001': FakeSitemap}\\n request = rf.get('/')\\n response = resourcesync.index(request, sitemaps, 'mdstore/resourceindex.xml')\\n\\n # Expected filenames. The full location would look something like\\n # http://example.com/resourcelist-001.xml\\n resource_list_1 = 'resourcelist-001.xml'\\n resource_list_2 = 'resourcelist-002.xml'\\n\\n locations = response.context_data['sitemaps']\\n\\n assert resource_list_1 in locations[0]\\n assert resource_list_2 in locations[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af737b0228bbfac053b4c2bd3b50ac4a\",\n \"score\": \"0.49786898\",\n \"text\": \"async def answer_searching(message: types.Message, state: FSMContext):\\n await message.answer(phrases.my_commands)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f16f9ec2cd7b993e1fc7304e3392838c\",\n \"score\": \"0.49729362\",\n \"text\": \"def test_context_processor(self):\\n config = auth0(None)\\n self.assertTrue(len(config.keys()) > 0)\\n self.assertTrue(type(config) == dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623bdaefc29d0d01c227397512ffb731\",\n \"score\": \"0.49686962\",\n \"text\": \"def can_search_activities(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c660741b96e0963b709d9dc03776f8\",\n \"score\": \"0.49648973\",\n \"text\": \"def set_search_properties(\\n self, metric: Optional[str], mode: Optional[str], config: Dict, **spec\\n ) -> bool:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"055f6de37164bd5304948defe24c9c2b\",\n \"score\": \"0.49557742\",\n \"text\": \"def test_contains(self):\\n\\n self.command_manager.add('dummy', DummyCommand)\\n\\n self.assertTrue('dummy' in self.command_manager)\\n self.assertFalse('bar' in self.command_manager)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed3c7874ecf5f9fc291fe3b3f4bb1c2f\",\n \"score\": \"0.49549\",\n \"text\": \"def test_context_only_load(self):\\n ctx = Context(self.context)\\n ctx.load()\\n self.assertTrue('context' in ctx)\\n self.assertTrue('defaults' in ctx)\\n self.assertEqual(ctx['context']['value3'], 3)\\n self.assertEqual(ctx['context']['value4'], 4)\\n self.assertEqual(ctx['defaults']['value2'], 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24a5944a95a319a717033112800e45a\",\n \"score\": \"0.49466544\",\n \"text\": \"def test_minerals_search_view(self):\\n resp = self.client.get('/search/', {'q':'allophane'})\\n self.assertEquals(resp.status_code, 200)\\n #context = resp.context['minerals']\\n self.assertEqual(resp.resolver_match.func, views.mineral_search)\\n self.assertTemplateUsed(resp, 'trace_minerals/index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae2c4e7f0484205e349755156ef18fd\",\n \"score\": \"0.49459162\",\n \"text\": \"def test_act_on_settings(self):\\n pass # TODO(tlarsen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae2c4e7f0484205e349755156ef18fd\",\n \"score\": \"0.49459162\",\n \"text\": \"def test_act_on_settings(self):\\n pass # TODO(tlarsen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"483d2bcf11a4ca52133f13bfc9749617\",\n \"score\": \"0.4934035\",\n \"text\": \"def test_help_is_provided(parser, capsys):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bde908bf20f1ced0af04a6b114cc5d\",\n \"score\": \"0.4932022\",\n \"text\": \"def test_search_exact_match(self, client, project, case):\\n # `Sphinx` word is present both in `kuma` and `docs` files\\n # But the phrase `Sphinx uses` is available only in kuma docs.\\n # So search with this phrase to check\\n query_text = r'\\\"Sphinx uses\\\"'\\n cased_query = getattr(query_text, case)\\n query = cased_query()\\n\\n page_search = PageSearch(query=query)\\n results = page_search.execute()\\n\\n assert len(results) == 2\\n assert results[0][\\\"project\\\"] == \\\"kuma\\\"\\n assert results[0][\\\"path\\\"] == \\\"testdocumentation\\\"\\n assert results[0][\\\"version\\\"] == \\\"stable\\\"\\n\\n assert results[1][\\\"project\\\"] == \\\"kuma\\\"\\n assert results[1][\\\"path\\\"] == \\\"testdocumentation\\\"\\n assert results[1][\\\"version\\\"] == \\\"latest\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be88381763ae036af82924a4799b2fd1\",\n \"score\": \"0.4925959\",\n \"text\": \"def test_get_context_defaults(mocker):\\n mocker.patch.dict(os.environ, {})\\n assert get_context() == ALL_NONE_CONTEXT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3466b288cff4fe1ba9c7473471a0ee81\",\n \"score\": \"0.4921266\",\n \"text\": \"def test_search_user(self):\\n\\n # Login\\n ts = TSApiWrapper(DISABLE_SSL)\\n ts.login(HOSTPORT, USERNAME, PASSWORD)\\n\\n result = ts.switch_org(int(Constants.ALL_ORG_ID))\\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\\n # create orgs\\n orgs = [\\\"test_search_user\\\" + str(i) for i in range(2)]\\n for org in orgs:\\n result = ts.create_org(org, org + \\\"_desc\\\")\\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\\n\\n result = ts.switch_org(int(Constants.DEFAULT_ORG_ID))\\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\\n\\n # search for tsadmin user\\n result = ts.search_user(USERNAME)\\n self.assertEqual(result.status, Constants.OPERATION_SUCCESS)\\n\\n # search for a random user\\n result = ts.search_user(\\\"randomUser\\\")\\n self.assertEqual(result.status, Constants.OPERATION_FAILURE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e3716dc7f9941a19891c85553e240b\",\n \"score\": \"0.49185625\",\n \"text\": \"def can_search_catalogs(self):\\n # Implemented from template for\\n # osid.resource.BinQuerySession.can_search_bins_template\\n # NOTE: It is expected that real authentication hints will be\\n # handled in a service adapter above the pay grade of this impl.\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff398a692451a838ae0f1ac6ce6ff79f\",\n \"score\": \"0.49178156\",\n \"text\": \"def __contains__(self, othercmd):\\n ret = self._contains_cache.get(othercmd)\\n if ret is None:\\n ret = othercmd in self.commands\\n self._contains_cache[othercmd] = ret\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"908ec20ade51375e34afee11e3d007e5\",\n \"score\": \"0.49175754\",\n \"text\": \"def test_contexts(self):\\n if os.path.isfile(\\\"appdconfig.yaml\\\"):\\n question = \\\"File appdconfig.yaml already exists. Do you want to replace it [Y/N]?\\\"\\n answer = \\\"\\\"\\n while answer not in [\\\"y\\\", \\\"n\\\"]:\\n if sys.version_info[0] < 3:\\n answer = raw_input(question).lower()\\n else:\\n answer = input(question).lower()\\n if answer == \\\"y\\\": os.remove(\\\"appdconfig.yaml\\\")\\n else: return\\n\\n if 'VERBOSE' in locals(): print (\\\"Try to get appdynamics context file, when file doesn't exist\\\")\\n result = subprocess.call(\\\"./appdctl.py config get-contexts \\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Create new appdynamics context file and add new context\\\")\\n result = subprocess.call(\\\"./appdctl.py config set-context EVO.PRO --controller-url https://evobanco.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Append context to appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config set-context EVO.UAT --controller-url https://evobanco-dev.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco-dev\\\", shell=True)\\n self.assertEqual(result, 0)\\n result = subprocess.call(\\\"./appdctl.py config set-context localhost --controller-url http://localhost:8090 --user-API APIClient@customer1\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Add already existing context to appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config set-context EVO.UAT --controller-url https://evobanco-dev.saas.appdynamics.com:443 --user-API EvoAPIClient@evobanco-dev\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Get contexts from appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config get-contexts\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Use existing context on appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config use-context EVO.UAT\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Use non existing context on appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config use-context nonexist\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Delete existing context on appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config delete-context localhost\\\", shell=True)\\n self.assertEqual(result, 0)\\n\\n if 'VERBOSE' in locals(): print (\\\"Delete non existing context on appdynamics context file\\\")\\n result = subprocess.call(\\\"./appdctl.py config delete-context nonexist\\\", shell=True)\\n self.assertEqual(result, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83551bfc579138947aeaa2beeccc8e1d\",\n \"score\": \"0.49064502\",\n \"text\": \"def test_search(self):\\n path = reverse('blog:search')\\n assert path, SearchView.as_view().__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f9450127b40ee6dc80de8dae78fe98\",\n \"score\": \"0.49044466\",\n \"text\": \"def test_search_init_new_search(self):\\n self.dialog.search_parameters = \\\"something\\\"\\n self.dialog.logentry_queryset = \\\"some queryset\\\"\\n self.dialog.init_new_search()\\n self.assertFalse(hasattr(self.dialog, 'search_parameters'))\\n self.assertFalse(hasattr(self.dialog, 'logentry_queryset'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2820de433dc82cf55305aa58f92fa4\",\n \"score\": \"0.48993313\",\n \"text\": \"def _check_command_context(self):\\n if not isinstance(self.command_context, CommandContext):\\n raise TypeError(\\\"The command context parameter of the Commanding server has to be CommandContext\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cccc254cc49c538608326bc83eaeef3b\",\n \"score\": \"0.4898871\",\n \"text\": \"async def filter(self, ctx):\\n\\n if ctx.invoked_subcommand is None:\\n raise SendHelp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616c33ff3fcbbebedf5052c8084e897b\",\n \"score\": \"0.48984346\",\n \"text\": \"def test_search(self):\\n\\n qs = Project.objects.order_by('pk')\\n request = Mock()\\n\\n tests = (\\n # Label, Term, Expected Results\\n ('No Term', '', [self.project.pk, self.other.pk, self.fieldbook.pk, ]),\\n ('Project Name', 'Mine', [self.project.pk, ]),\\n ('Project ID', str(self.other.pk), [self.other.pk, ]),\\n ('Fieldbook ID', 'Project X', [self.fieldbook.pk, ]),\\n )\\n\\n for label, term, expected in tests:\\n with self.subTest(label):\\n results, _use_distinct = self.admin.get_search_results(request, qs, term)\\n self.assertQuerysetEqual(results, expected, lambda x: x.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed346827b3227cfc4a1102db5580e19\",\n \"score\": \"0.4895134\",\n \"text\": \"def test_search_services_with_query_with_model(self):\\n data_model = DataModel(\\\"foobar\\\", [Attribute(\\\"foo\\\", str, True)])\\n search_query = Query(\\n [Constraint(\\\"foo\\\", ConstraintType(\\\"==\\\", \\\"bar\\\"))], model=data_model\\n )\\n oef_search_request, sending_dialogue = self.oef_search_dialogues.create(\\n counterparty=str(self.connection.connection_id),\\n performative=OefSearchMessage.Performative.SEARCH_SERVICES,\\n query=search_query,\\n )\\n self.multiplexer.put(\\n Envelope(\\n to=oef_search_request.to,\\n sender=oef_search_request.sender,\\n message=oef_search_request,\\n )\\n )\\n\\n envelope = self.multiplexer.get(block=True, timeout=5.0)\\n oef_search_response = envelope.message\\n oef_search_dialogue = self.oef_search_dialogues.update(oef_search_response)\\n assert (\\n oef_search_response.performative\\n == OefSearchMessage.Performative.SEARCH_RESULT\\n )\\n assert oef_search_dialogue is not None\\n assert oef_search_dialogue == sending_dialogue\\n assert oef_search_response.agents == ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6601ece521252ac28f47388771dc9bb\",\n \"score\": \"0.48874626\",\n \"text\": \"def suggest(self, query, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d610dfe63d0252444c00bce86eb628\",\n \"score\": \"0.48862186\",\n \"text\": \"def test_describe_saved_search(self):\\n runner = CliRunner()\\n result = runner.invoke(saved_searches_group, [\\\"describe\\\", \\\"1\\\"], obj=self.ctx)\\n assert result.output == EXPECTED_OUTPUT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a6d5d2ba2f62cbd73f5ce976b7cbc9\",\n \"score\": \"0.48769417\",\n \"text\": \"def test_focus(self):\\n self.argv.append(\\\"--focus\\\")\\n with self.assert_in_stdout(\\n \\\"top context\\\\n\\\"\\n \\\" *focused example: PASS\\\\n\\\\n\\\"\\n # TODO rest of output\\n ):\\n self.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06bd541b9da9f8254accb24100915564\",\n \"score\": \"0.48763618\",\n \"text\": \"def query_global_configuration(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64c81d404ffba729586adad7d272193\",\n \"score\": \"0.48711967\",\n \"text\": \"def test_search():\\n request = {\\n 'params': {\\n 'q': 'music',\\n },\\n }\\n code, _ = search_route(request)\\n assert code == 200\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1476,"cells":{"query_id":{"kind":"string","value":"5156c7a834bb7a3488e6720c34c7a1d2"},"query":{"kind":"string","value":"Pops next token value"},"positive_passages":{"kind":"list like","value":[{"docid":"716e5a5cb8b66a74e098e1bb9a782765","score":"0.7025629","text":"def next(tokens):\n return tokens.next_pair()[1]","title":""}],"string":"[\n {\n \"docid\": \"716e5a5cb8b66a74e098e1bb9a782765\",\n \"score\": \"0.7025629\",\n \"text\": \"def next(tokens):\\n return tokens.next_pair()[1]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1c44fe9e59a741515ad58d732a600e80","score":"0.77126354","text":"def _pop_token(self):\n return self._tokens.pop(0)","title":""},{"docid":"e5f3e681cbe93e988efa84ffc58ddd7a","score":"0.7448798","text":"def pop_next():","title":""},{"docid":"4ca8b0b090b5e7bee0547791c98b5be6","score":"0.7027537","text":"def peek_next_token(self):\n return self.tokens[self.tokens_counter]","title":""},{"docid":"9ca16d8899efcd647744347ccd6a6b41","score":"0.6949655","text":"def next_pair(tokens):\n pair = tokens.pairs.pop(0)\n #print pair\n return pair","title":""},{"docid":"96b0e956eebc99fce3f443683d8f4f9c","score":"0.68594855","text":"def pop(self):\n try:\n return self.tokens.pop()\n except IndexError:\n raise ParsingError","title":""},{"docid":"b0a825fa345da0278f170e951212f0e2","score":"0.6859276","text":"def next_token(self):\n\t\ttry:\n\t\t\ttoken = self.source[self.index]\n\t\t\tself.index += 1\n\t\t\tif self.debug > 2:\n\t\t\t\tprint 'shift',token\n\t\t\treturn token\n\t\texcept IndexError:\n\t\t\traise EndOfTokens()","title":""},{"docid":"aaacbe1d8dce7a1382ff540d221d94f2","score":"0.6748197","text":"def next(self):\n token = self.peek()\n self.position += 1\n return token","title":""},{"docid":"d44b0d200e4072dc78fc4168683e7f2e","score":"0.65652883","text":"def token(self):\r\n return self._xyzzy(self.next, 0)","title":""},{"docid":"4c64e59dc447b607387272a08ce1d1fe","score":"0.6557061","text":"def peek(self) -> item:\n if self.peekCount > 0:\n return self.token[self.peekCount-1]\n self.peekCount = 1\n self.token[0] = self.lex.nextItem()\n return self.token[0]","title":""},{"docid":"907b65c7936d30f060c32c90b2b3b2af","score":"0.651498","text":"def next(self):\n\n token = self.get()\n if token.is_eof():\n raise StopIteration\n return token","title":""},{"docid":"c1827ce1ff8606cf40bf94f736d9a205","score":"0.6447961","text":"def next(self):\n\n if self.eof():\n return None\n\n token = self._data[self._index]\n self._index += 1\n\n return token","title":""},{"docid":"14be3838b4f2080c4bf436fed6ef6681","score":"0.63832986","text":"def peek_token(self):\r\n return self._xyzzy(self.peek, 0)","title":""},{"docid":"5102a1b2d8685e91e98dea9f02ec361c","score":"0.63772905","text":"def pop(self, idx=None) -> Token:\n if idx is None:\n return self.__tokens.pop()\n else:\n return self.__tokens.pop(idx)","title":""},{"docid":"d1ce2989cb0e76ae37315694cf085351","score":"0.636382","text":"def read_next(self) -> TokenTypeStr:\n return TOKEN_TYPE.inverse[self.readNext()]","title":""},{"docid":"8d52f1c0697d22b03322ec3f2cae959d","score":"0.6350362","text":"def _next_token(self):\n if self._token_in_queue:\n return self._pop_token()\n\n while self.state is not None:\n self.state = self.state()\n if self._token_in_queue:\n return self._pop_token()\n\n if self._start != self._pos or self._pos < self._len:\n raise ValueError('not all input consumed')","title":""},{"docid":"dd004d931fe225f0ca7ced316bbd408e","score":"0.6313069","text":"def jumpTokenBack(self, offset):\n raise JumpTokenBack(offset)","title":""},{"docid":"1e9696e0d54f30bf910f3286601f9dee","score":"0.63065654","text":"def peek(self) -> Token:\n if self._next is None:\n raise self.error('Unexpected end of input')\n return self._next","title":""},{"docid":"d8f5c8538916ced6e865aba415346880","score":"0.6271672","text":"def advance(self):\n curr_token = self.tokens[self.tokens_counter]\n self.tokens_counter += 1\n return curr_token","title":""},{"docid":"eb2809d6fe689ca51c54ec4f073f3396","score":"0.6237","text":"def token(self):\n self.last_token = self.lexer.token()\n return self.last_token","title":""},{"docid":"5c926ce6d6b3490ef63774f119dfb11d","score":"0.6233987","text":"def readToken(self):\n return self.tokenStream.next()","title":""},{"docid":"17011ef7ce655dc12100bc23ffb3db11","score":"0.620918","text":"def peek(self):\n if self.position >= len(self.tokens):\n return ''\n else:\n return self.tokens[self.position]","title":""},{"docid":"d5e840101084ef6290dddc8caf77e45e","score":"0.6180218","text":"def __advance_token(self):\r\n self.__cur_token = self.__next_token\r\n self.__next_token = next(self.__root, None)","title":""},{"docid":"06412c8c538670d56b7ad6b35444f5a3","score":"0.6160175","text":"def pop(self):\n if self.top is None:\n return\n else:\n todelete = self.top\n self.top = todelete.next\n return todelete.value","title":""},{"docid":"2f7347311aff164f8b8dba21407adec2","score":"0.6131283","text":"def current(self):\n try:\n return self._token_stream[self._current_index-1]\n except IndexError:\n raise StopIteration","title":""},{"docid":"24c1e01856586ddd9e69fed3898a8aa8","score":"0.6084643","text":"def getAnotherToken(self): \n \n if self.token == '\\0':\n return None\n \n self.value = None\n\n self.skipWhiteSpace() # now leaves self.lastWhiteSpace\n ch = self.char\n ln = self.text[self.line]\n lnLen = len(ln)\n\n self.start = ch\n \n if ln[ch] in ['(', ')', '[', ']', '|', '+', '=', ';', '\\0']:\n self.token = ln[ch]\n ch = ch + 1\n\n elif ln[ch] == '\"':\n self.token = 'dqword'\n ch = ch + 1\n while ch < lnLen and ln[ch] != '\"':\n ch = ch + 1\n if ch >= lnLen:\n raise LexicalError( \"expecting closing quote in word name\", self)\n self.value = ln[self.start+1:ch]\n ch = ch + 1\n \n elif ln[ch] == \"'\":\n self.token = 'sqword'\n ch = ch + 1\n while ch < lnLen and ln[ch] != \"'\":\n ch = ch + 1\n if ch >= lnLen:\n raise LexicalError( \"expecting closing quote in word name\", self)\n self.value = ln[self.start+1:ch]\n ch = ch + 1\n \n elif ln[ch] == '<':\n self.token = 'rule'\n ch = ch + 1\n while ch < lnLen and ln[ch] != '>':\n ch = ch + 1\n if ch >= lnLen:\n raise LexicalError( \"expecting closing angle bracket in rule name\", self)\n self.value = ln[self.start+1:ch]\n ch = ch + 1\n\n elif ln[ch] == '{':\n self.token = 'list'\n ch = ch + 1\n while ch < lnLen and ln[ch] != '}':\n ch = ch + 1\n if ch >= lnLen:\n raise LexicalError( \"expecting closing brace in list name\", self)\n self.value = ln[self.start+1:ch]\n ch = ch + 1\n\n elif isCharOrDigit(ln[ch]):\n self.token = 'word'\n while ch < lnLen and isCharOrDigit(ln[ch]):\n ch = ch + 1\n self.value = ln[self.start:ch]\n\n else:\n raise LexicalError( \"unknown character found\", self)\n\n self.char = ch","title":""},{"docid":"de5cddacf63a27ff3d37a9ec848374dc","score":"0.60647315","text":"def acc():\n self.stack.pop()\n return self.stack[-1]","title":""},{"docid":"430a238f7bc1e3d1b2239e5ba974f267","score":"0.6059106","text":"def next_token(self, context, token):\n raise Exception(\"bad next_token\")","title":""},{"docid":"4bc05ba3f25db01ffff548f2e079e710","score":"0.6051701","text":"def next_token(self):\n\t\ttoken = self.lexer.token()\n\t\tif not token:\n\t\t\traise EndOfTokens\n\t\t# store this token's line number based on its file offset\n\t\ttoken.lineno = self.get_lineno(token.lexpos)\n\t\t# undo any escaped-newline substitution applied to this token's value\n\t\tstart = token.lexpos\n\t\tend = start + len(token.value)\n\t\tfor offset in self.escapes:\n\t\t\tif offset < start or offset >= end:\n\t\t\t\tcontinue\n\t\t\tpos = offset - start\n\t\t\ttoken.value = token.value[:pos] + '\\\\\\n' + token.value[pos+2:]\n\t\t\tif self.debug > 1:\n\t\t\t\tprint 'restored escaped newline at end of line',token.lineno\n\t\t\tbreak\n\t\t# print out each token if we are being verbose\n\t\tif self.debug > 2:\n\t\t\tprint 'shift',token\n\t\treturn token","title":""},{"docid":"827bd5db72f3592308b161180521b2bc","score":"0.6048811","text":"def Token(self) -> _n_1_t_8:","title":""},{"docid":"5fba685d86babf40c57eb6a5a8d8ae14","score":"0.60441077","text":"def next(self):\n temp_node = self.stack.pop()\n self.push_all(temp_node.right)\n return temp_node.val","title":""},{"docid":"327b9b66bfed6ef02eaf66f72e7af566","score":"0.60308325","text":"def pop(self):\n tmp = self.top\n self.top = tmp._next\n tmp._next = None\n\n self._length -= 1\n return tmp.value","title":""},{"docid":"c56217b42547ca0f2c5e0b1cc1f0305a","score":"0.6007044","text":"def pop(self):\n\n if not self.is_empty():\n result = self.top.value\n self.top = self.top.next\n \n return result\n else:\n raise EmptySatckException","title":""},{"docid":"a6f92dc30fb9a91c252bfd01511dbd92","score":"0.6006929","text":"def getNextToken(self):\n while self.currentChar() is not None:\n # print(\"Current:\"+self.currentChar())\n\n if self.currentChar().isspace():\n self.skipWhitespace()\n continue\n\n if self.currentChar().isalpha():\n return self.id()\n\n if self.currentChar().isdigit():\n return self.number()\n\n if self.tryToMatch(Token.get(LexName.LINECOMMENT)):\n self.skipLine()\n continue\n if self.tryToMatch(Token.get(LexName.COMMENTSTART)):\n self.advance(len(Token.get(LexName.COMMENTSTART)))\n self.skipComment()\n continue\n if self.tryToMatch(Token.get(LexName.STRINGL)):\n return self.string()\n for key in Token.getSortedKeys(Token.other):\n tokenValue = Token.get(key)\n if self.tryToMatch(tokenValue):\n self.advance(len(tokenValue))\n return Token(key,tokenValue,self.pos)\n\n return self.error(\"invalid character\")\n\n return Token(LexName.EOF, \"EOF\",self.pos)","title":""},{"docid":"2c7b1f0a17c55bca26d7434351fb1aae","score":"0.5996904","text":"def decrement_token_count(self):\n\n self.token_count -= 1","title":""},{"docid":"9b4d30fa7a9a1f86d80f9e755806df59","score":"0.59844995","text":"def next(self):\n try:\n cur_token = self._token_stream[self._current_index]\n except IndexError:\n raise StopIteration\n self._current_index += 1\n return cur_token","title":""},{"docid":"14eb5761506498f76fdcb215550d05c6","score":"0.5983739","text":"def consume(self):\n token_type, token_string, token_line_number = self.token_data[self.pos]\n self.pos += 1\n return token_type, token_string, token_line_number","title":""},{"docid":"8b22beca5a104bbaff2d4279dc3c7b20","score":"0.59616506","text":"def next(self,end_is_fatal=True):\n\t\ttry:\n\t\t\tself.last_token = self.tokenizer.next_token()\n\t\t\treturn self.last_token\n\t\texcept EndOfTokens:\n\t\t\tif end_is_fatal:\n\t\t\t\traise FatalParseError('unexpected EOF in %s' % self.name)\n\t\t\telse:\n\t\t\t\traise","title":""},{"docid":"a4c80aa4a5f87925675572a30748912f","score":"0.59562445","text":"def __next__(self):\r\n\t\tbuf = None\r\n\t\twhile buf is None or len(buf.strip()) == 0:\r\n\t\t\tbuf = self.line()\r\n\t\t#split once\r\n\t\ttoken, *rest = buf.split(maxsplit=1)\r\n\t\tself.buf = rest[0] if rest else None\r\n\t\treturn token","title":""},{"docid":"03476f56e2e6080c5aac776adf8d4175","score":"0.5939367","text":"def unshift(self, token):\r\n self.unshift_buffer[:0] = [token]","title":""},{"docid":"0fcefca8ece74e7b9673bcc2532b8b27","score":"0.5934602","text":"def next(self) -> Token:\n current = self._next\n try:\n self._next = next(self.iterator)\n except StopIteration:\n if current is None:\n raise\n self._next = None\n self._last = current\n return current","title":""},{"docid":"76c8c91c6757773bb8480c8e1c5d011b","score":"0.59250224","text":"def peek(tokens):\n if len(tokens) == 0:\n raise ParserError, \"While peeking: ran out of tokens.\"\n return tokens[0]","title":""},{"docid":"18b4f10183c8db75e94a8d00ef077608","score":"0.59155077","text":"def next(self) -> int:\n return self.sorted_traversal_output.pop()","title":""},{"docid":"64362ba885d8055e2c66633d41d532b3","score":"0.59042275","text":"def advance(self):\n self.curr_tokens = self.next_tokens","title":""},{"docid":"c587dc8fc6735e962ceaa688f315c125","score":"0.58780307","text":"def peekNonSpace(self) -> item:\n token = self.nextNonSpace()\n self.backup()\n return token","title":""},{"docid":"d7cc990282a97cc421ccba3f7d37abb3","score":"0.5867448","text":"def clearToken():\n token[0] = \"\"\n state[0] = STATE.NORMAL","title":""},{"docid":"23db003a92a1a7a6b39a47cbf50b39f4","score":"0.5862018","text":"def __next__(self):\n return self.cards.pop()","title":""},{"docid":"0db6dc964757b3237f3c9d1805d4f52e","score":"0.5841549","text":"def next_token(self, delimiter=' '):\n\n if self.position >= self.length:\n return None\n\n index = self.input.find(delimiter, self.position)\n if index == -1:\n index = self.length\n\n result = self.input[self.position:index]\n self.position = index + len(delimiter);\n\n return result","title":""},{"docid":"667abcdee96787fd26fc11087a39f57a","score":"0.5823206","text":"def pop(self):\n\t\tif self.top == None:\n\t\t\treturn\n\t\telse:\n\t\t\tpopped_node = self.top\n\t\t\tself.top = self.top.next\n\t\treturn popped_node.value","title":""},{"docid":"8f0d51abdb8c2a670df743129aa9278d","score":"0.5811954","text":"def next():","title":""},{"docid":"559be67848d3bd578d0793ebbf1aa480","score":"0.58066624","text":"def getNextToken(self):\n\n self.tk = LEX_TYPES.LEX_EOF\n self.tkStr = ''\n while self.currCh and isWhitespace(self.currCh):\n self.getNextCh()\n # newline comments\n if self.currCh == '/' and self.nextCh == '/':\n while self.currCh and self.currCh != '\\n':\n self.getNextCh()\n self.getNextCh()\n self.getNextToken()\n return\n # block comments\n if self.currCh == '/' and self.nextCh == '*':\n while self.currCh and (self.currCh != '*' or self.nextCh != '/'):\n self.getNextCh()\n self.getNextCh()\n self.getNextCh()\n self.getNextToken()\n return;\n # record beginning of this token\n self.tokenStart = self.dataPos - 2\n # tokens\n if isAlpha(self.currCh): # IDs\n while isAlpha(self.currCh) or isNumeric(self.currCh):\n self.tkStr += self.currCh\n self.getNextCh()\n self.tk = LEX_TYPES.LEX_ID\n if self.tkStr==\"if\": self.tk = LEX_TYPES.LEX_R_IF\n elif self.tkStr==\"else\": self.tk = LEX_TYPES.LEX_R_ELSE\n elif self.tkStr==\"do\": self.tk = LEX_TYPES.LEX_R_DO\n elif self.tkStr==\"while\": self.tk = LEX_TYPES.LEX_R_WHILE\n elif self.tkStr==\"for\": self.tk = LEX_TYPES.LEX_R_FOR\n elif self.tkStr==\"break\": self.tk = LEX_TYPES.LEX_R_BREAK\n elif self.tkStr==\"continue\": self.tk = LEX_TYPES.LEX_R_CONTINUE\n elif self.tkStr==\"function\": self.tk = LEX_TYPES.LEX_R_FUNCTION\n elif self.tkStr==\"return\": self.tk = LEX_TYPES.LEX_R_RETURN\n elif self.tkStr==\"var\": self.tk = LEX_TYPES.LEX_R_VAR\n elif self.tkStr==\"true\": self.tk = LEX_TYPES.LEX_R_TRUE\n elif self.tkStr==\"false\": self.tk = LEX_TYPES.LEX_R_FALSE\n elif self.tkStr==\"null\": self.tk = LEX_TYPES.LEX_R_NULL\n elif self.tkStr==\"undefined\": self.tk = LEX_TYPES.LEX_R_UNDEFINED\n elif self.tkStr==\"new\": self.tk = LEX_TYPES.LEX_R_NEW\n elif isNumeric(self.currCh):\n isHex = False\n if self.currCh == '0':\n self.tkStr += self.currCh\n self.getNextCh()\n if self.currCh == 'x':\n isHex = True\n self.tkStr += self.currCh\n self.getNextCh()\n self.tk = LEX_TYPES.LEX_INT\n while isNumeric(self.currCh) or (isHex and isHexadecimal(self.currCh)):\n self.tkStr += self.currCh\n self.getNextCh()\n if not isHex and self.currCh == '.':\n self.tk = LEX_TYPES.LEX_FLOAT\n self.tkStr += '.'\n self.getNextCh()\n while isNumeric(self.currCh):\n self.tkStr += self.currCh\n self.getNextCh()\n # do fancy e-style floating point\n if not isHex and (self.currCh == 'e' or self.currCh=='E'):\n self.tk = LEX_TYPES.LEX_FLOAT\n self.tkStr += self.currCh\n self.getNextCh()\n if self.currCh=='-':\n self.tkStr += self.currCh\n self.getNextCh()\n while isNumeric(self.currCh):\n self.tkStr += self.currCh\n self.getNextCh()\n elif self.currCh == '\"':\n # strings...\n self.getNextCh()\n while self.currCh and self.currCh!='\"':\n if self.currCh == '\\\\':\n self.getNextCh()\n if self.currCh == 'n' : self.tkStr += '\\n'\n elif self.currCh == '\"' : self.tkStr += '\"'\n elif self.currCh == '\\\\': self.tkStr += '\\\\'\n else: self.tkStr += self.currCh\n else:\n self.tkStr += self.currCh\n self.getNextCh()\n self.getNextCh()\n self.tk = LEX_TYPES.LEX_STR\n elif self.currCh=='\\'':\n # strings again...\n self.getNextCh()\n while self.currCh and self.currCh!='\\'':\n if self.currCh == '\\\\':\n self.getNextCh()\n if self.currCh == 'n' : self.tkStr += '\\n'\n elif self.currCh == 'a' : self.tkStr += '\\a'\n elif self.currCh == 'r' : self.tkStr += '\\r'\n elif self.currCh == 't' : self.tkStr += '\\t'\n elif self.currCh == '\\'' : self.tkStr += '\\''\n elif self.currCh == '\\\\' : self.tkStr += '\\\\'\n elif self.currCh == 'x' : # hex digits\n self.getNextCh()\n buf = self.currCh\n self.getNextCh()\n buf += self.currCh\n self.tkStr += chr(int(buf, 16))\n else:\n if self.currCh>='0' and self.currCh<='7':\n # octal digits\n buf = self.currCh\n self.getNextCh()\n buf += self.currCh\n self.getNextCh()\n buf += self.currCh\n self.tkStr += chr(int(buf, 8))\n else:\n self.tkStr += self.currCh\n else:\n self.tkStr += self.currCh\n self.getNextCh()\n self.getNextCh()\n self.tk = LEX_TYPES.LEX_STR\n else:\n # single chars\n self.tk = self.currCh\n if self.currCh: self.getNextCh()\n if self.tk=='=' and self.currCh=='=': # ==\n self.tk = LEX_TYPES.LEX_EQUAL\n self.getNextCh()\n if self.currCh=='=': # ===\n self.tk = LEX_TYPES.LEX_TYPEEQUAL\n self.getNextCh()\n elif self.tk=='!' and self.currCh=='=': # // !=\n self.tk = LEX_TYPES.LEX_NEQUAL\n self.getNextCh()\n if self.currCh=='=': # // !==\n self.tk = LEX_TYPES.LEX_NTYPEEQUAL\n self.getNextCh()\n elif self.tk=='<' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_LEQUAL\n self.getNextCh()\n elif self.tk=='<' and self.currCh=='<':\n self.tk = LEX_TYPES.LEX_LSHIFT\n self.getNextCh()\n if self.currCh=='=': # // <<=\n self.tk = LEX_TYPES.LEX_LSHIFTEQUAL\n self.getNextCh()\n elif self.tk=='>' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_GEQUAL\n self.getNextCh()\n elif self.tk=='>' and self.currCh=='>':\n self.tk = LEX_TYPES.LEX_RSHIFT\n self.getNextCh()\n if self.currCh=='=': # // >>=\n self.tk = LEX_TYPES.LEX_RSHIFTEQUAL\n self.getNextCh()\n elif self.currCh=='>': # // >>>\n self.tk = LEX_TYPES.LEX_RSHIFTUNSIGNED\n self.getNextCh()\n elif self.tk=='+' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_PLUSEQUAL\n self.getNextCh()\n elif self.tk=='-' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_MINUSEQUAL\n self.getNextCh()\n elif self.tk=='+' and self.currCh=='+':\n self.tk = LEX_TYPES.LEX_PLUSPLUS\n self.getNextCh()\n elif self.tk=='-' and self.currCh=='-':\n self.tk = LEX_TYPES.LEX_MINUSMINUS\n self.getNextCh()\n elif self.tk=='&' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_ANDEQUAL\n self.getNextCh()\n elif self.tk=='&' and self.currCh=='&':\n self.tk = LEX_TYPES.LEX_ANDAND\n self.getNextCh()\n elif self.tk=='|' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_OREQUAL\n self.getNextCh()\n elif self.tk=='|' and self.currCh=='|':\n self.tk = LEX_TYPES.LEX_OROR\n self.getNextCh()\n elif self.tk=='^' and self.currCh=='=':\n self.tk = LEX_TYPES.LEX_XOREQUAL\n self.getNextCh()\n\n # /* This isn't quite right yet */\n self.tokenLastEnd = self.tokenEnd\n self.tokenEnd = self.dataPos-3","title":""},{"docid":"57ea341d5bab1b85acfb4456d186db9a","score":"0.5798741","text":"def pop(self):\n val = self._lst[-1]\n self._lst = self._lst[:-1]\n return val","title":""},{"docid":"0d14fc7a2c17b25ff11dc8d64a907790","score":"0.5797154","text":"def __next__(self):\n try:\n return self.pop()\n except IndexError:\n raise StopIteration","title":""},{"docid":"2c30ec3b5b7f3c161234f03820372160","score":"0.57851636","text":"def pop(self):\n # TODO: Write function to pop here\n node = self.head\n self.head = self.head.next\n \n return node.value","title":""},{"docid":"667f16ddb4791508fd66e1f937936668","score":"0.5753288","text":"def peek_next(self):\n\t\treturn self.data[self.pos + 1]","title":""},{"docid":"3deb46a9b45c06007f99abc7ecc8be99","score":"0.5749768","text":"def __prevsym(self):\n self.tnum -= 1\n if self.tnum < 0:\n logging.warning('Index out of range, tnum: %s' % self.tnum)\n return False\n self.curr = self.tokens[self.tnum]\n self.prev = self.tokens[self.tnum - 1]","title":""},{"docid":"8ee302ecf678768d5fe288c166f6281d","score":"0.57356846","text":"def getNextValueExpression(self):","title":""},{"docid":"ec133b3bf8bd54682c6dd9feb964833a","score":"0.5729338","text":"def consume_next_move(self):\n tmp = self.next_move\n self.next_move = None\n return tmp","title":""},{"docid":"6aff1965a5b2500cd60cb8c421b32ea1","score":"0.5712636","text":"def curr(self):\n return self.scanner.curr()","title":""},{"docid":"d5717de5a681c317e002f892560dd6d2","score":"0.57085747","text":"def pop_element(self):\n curr = self.top\n\n if curr == None:\n print(\"Stack is already empty.\")\n\n else:\n temp = self.top.data\n self.top = self.top.next_val\n return temp","title":""},{"docid":"c301def0938d7d80ab0023454f6ee831","score":"0.5707097","text":"def next(self) -> int:\n res = self.nodes.pop()\n ans = res.val\n if res.right:\n res = res.right\n while res:\n self.nodes.append(res)\n res = res.left\n return ans","title":""},{"docid":"ab6386e940b1f66d1cf718b84191ac08","score":"0.5706446","text":"def pop(self):\n if self.count > 0:\n # Store value at top of stack\n output = self.stack[self.top]\n\n # Remove top value from stack\n del self.stack[self.top]\n\n # Move top tracker\n self.top -= 1\n\n # Decrement count\n self.count -= 1\n\n return output\n\n return None","title":""},{"docid":"711e037ae0fc790b952a6d53e8420d70","score":"0.57045716","text":"def peek(self):\t\n\t\treturn self.items[-1]","title":""},{"docid":"d777b00720c69662db63193935939191","score":"0.5701458","text":"def advance(self, type_check=None):\n prev_token = copy(self.current_token)\n if type_check:\n assert prev_token.type == type_check\n if self.pos + 1 < len(self.tokens):\n self.pos += 1\n self.current_token = self.tokens[self.pos]\n return prev_token","title":""},{"docid":"a9411ac34acf4538b7a18a9a6a11ccaf","score":"0.5684758","text":"def peek(self):\r\n return self[-1]","title":""},{"docid":"a2e2863ae3eef48b5f3b10cacb27a39d","score":"0.568419","text":"def next(self) -> int:\n if self.hasNext():\n node = self.stack.pop()\n cur = node.right\n while cur:\n self.stack.append(cur)\n cur = cur.left\n return node.val","title":""},{"docid":"dd0e725ddf13717e9fc0ab074c133911","score":"0.5678976","text":"def pop(self):\r\n x = self.top.data\r\n self.top = self.top.next\r\n return x","title":""},{"docid":"ca44dc8b2689bfda63d2df754af6375c","score":"0.56594294","text":"def pop(self):\n if not self.first:\n return None\n v = self.first\n if self.first == self.last:\n self.first = None\n self.last = None\n else:\n self.first = self[self.first]['next']\n del self[v]\n return v","title":""},{"docid":"8c404a919408f49ebbf43ad1dbef3844","score":"0.56551325","text":"def advance(self):\n self.current_command = self.lines.pop(0)","title":""},{"docid":"df96135d18337d5eaefcb344ef50c2f2","score":"0.5653407","text":"def pop(self):\n if self._size > 0:\n temp = self.top.val\n self.top = self.top._next\n self._size -= 1\n return temp\n else:\n raise ValueError('There is nothing in the stack!')","title":""},{"docid":"184753142ae67fb3290c1973cd85a64d","score":"0.5650666","text":"def tokens(self):\n while 1:\n tok = self.token()\n if tok is None: break\n yield tok","title":""},{"docid":"4274e2fbbfac1cd8a0a92a49f5ec3dd4","score":"0.56504685","text":"def get_next_line(self):\n token_to_use = None\n if self.next_token:\n if len(self.next_token) == 2:\n token_to_use = self.next_token[0]\n self.next_token = self.next_token[1].split(\"\\n\", 1)\n else:\n assert self.next_token\n token_to_use = self.next_token[0]\n self.next_token = None\n return token_to_use","title":""},{"docid":"9042f5524ae082b14a6e942ddece4a11","score":"0.564964","text":"def next(self) -> int:\n cur_node = self.stack.pop()\n if cur_node.right:\n self.push_stack(cur_node.right)\n return cur_node.val","title":""},{"docid":"6d47e035d5ad78dbca10ad89fd1ac407","score":"0.56485087","text":"def advance(self):\n\n if self.cursor >= len(self.tokens):\n return '', None\n if self.at(self.cursor) == EOF: return '', None\n token = self.at(self.cursor)\n return token, PLine.from_seed(self, self.cursor+1)","title":""},{"docid":"bb664c6ee42bbafb8da94531765556ea","score":"0.5643372","text":"def pop(self, ):\n\t\tpass","title":""},{"docid":"0e8256f997dfc9965ea115e4fa9cc72a","score":"0.5634845","text":"def __nextsym(self):\n self.tnum += 1\n if self.tnum >= len(self.tokens):\n logging.warning('index out of range, tnum: %s' % self.tnum)\n return False\n self.curr = self.tokens[self.tnum]\n self.prev = self.tokens[self.tnum - 1]\n return True","title":""},{"docid":"46f2f911de7f690e96ba88b6ff07dbd8","score":"0.5632336","text":"def next(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"46f2f911de7f690e96ba88b6ff07dbd8","score":"0.5632336","text":"def next(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"46f2f911de7f690e96ba88b6ff07dbd8","score":"0.5632336","text":"def next(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"64a5dfe86db99d48791db9571089aeb8","score":"0.5631743","text":"def pop(self):\n self.text = self.text[0:len(self.text)-1]","title":""},{"docid":"12fbd352bac3af353569356399621201","score":"0.56218576","text":"def get(self):\n\n # First, check the pushback\n if self.__pushback:\n self.line_position += 1\n return self.__pushback.pop()\n\n # Get raw value\n token_type, value = self.__get_next()\n # End of file\n if not value:\n return None\n\n # Skip all whitespace characters\n while token_type is token_type.Space:\n token_type, value = self.__get_next()\n if not value:\n return None\n\n if token_type is TokenType.Literal:\n curr = \"\"\n # Collect all digits of the number\n while token_type is TokenType.Literal:\n curr += value\n token_type, value = self.__get_next()\n\n # Number literal which begins with 0 and has more than 1 digit is illegal\n if len(curr) > 1 and curr.startswith('0'):\n raise InvalidNumberFormatException(self.filename, (self.line_number, self.line_position - 1), curr)\n\n if token_type and token_type is not token_type.Space:\n # If the last symbol was followed by meaningful, put it in pushback\n self.push_back(Token(token_type, value))\n\n return Token(TokenType.Literal, curr)\n\n return Token(token_type, value)","title":""},{"docid":"3b9a1cd6dbb38cd48ff5252478758d49","score":"0.5620235","text":"def reverse_consume(\n tokens: List[Token], i: int, *, name: str, src: Optional[str] = None\n) -> int:\n while tokens[i - 1].name == name and (src is None or tokens[i - 1].src == src):\n i -= 1\n return i","title":""},{"docid":"1fe27eb2d33e3b579688e2565b22a47b","score":"0.56164205","text":"def next(self) -> int:\n root = self.stack.pop()\n self.pushAll(root.right)\n return root.val","title":""},{"docid":"34d31bb96e43174e10b477ccc05e258a","score":"0.5616356","text":"def pop(self):","title":""},{"docid":"34d31bb96e43174e10b477ccc05e258a","score":"0.5616356","text":"def pop(self):","title":""},{"docid":"cf9d6cf68f8f2e96e3ac125033d7c3ed","score":"0.56060076","text":"def __next__(self) -> str:\n try:\n result = self.expression[self.token_pos]\n self.token_pos += 1\n return result\n except IndexError:\n if self.token_pos == len(self.expression):\n self.token_pos += 1\n return \"\"\n raise StopIteration","title":""},{"docid":"949c65719835eef0138625e9b887dcf9","score":"0.5601451","text":"def pop(self):\n while self.number > self.maxlength:\n self.buffer.popleft()\n self.number -= 1","title":""},{"docid":"7a04d8cc4cc68220420f02314d9bb6f6","score":"0.5600353","text":"def _next():","title":""},{"docid":"a43f21d7b2a6f816d52a4231f232bab7","score":"0.5592252","text":"def pop(self):\n if self.isEmpty():\n raise KeyError(\"the stack is empty\")\n result=self._item.data\n self._size-=1\n self._item=self._item.next\n return result","title":""},{"docid":"532f1e3dd5dc8b0e3b8870b189b1ea0e","score":"0.55828035","text":"def last(parameters: list = None) -> Token:\n return parameters[0].value[-1]","title":""},{"docid":"166f8830cc5f64df89defa06bd85a125","score":"0.5576525","text":"def next(self):","title":""},{"docid":"166f8830cc5f64df89defa06bd85a125","score":"0.5576525","text":"def next(self):","title":""},{"docid":"2a8d664f9729b5e4c12cd7a668c6a652","score":"0.5573545","text":"def token(self):\n raise NotImplementedError","title":""},{"docid":"7dfebccc2ccf385cd7ddeb18ba02b9cd","score":"0.55690104","text":"def pop(self):\n pass","title":""},{"docid":"7dfebccc2ccf385cd7ddeb18ba02b9cd","score":"0.55690104","text":"def pop(self):\n pass","title":""},{"docid":"7dfebccc2ccf385cd7ddeb18ba02b9cd","score":"0.55690104","text":"def pop(self):\n pass","title":""},{"docid":"5786d13f1433fceb95fdfe86aa616eda","score":"0.5564854","text":"def next(self):\n ttype, token, (lineno, indent) = self.gen.next()[:3]\n if lineno == self.curline:\n self.currentscope = self.scope\n return (ttype, token, indent)","title":""},{"docid":"55abf3f5b731001a85e49e969d2e2f1c","score":"0.5551662","text":"def get_next_token(self):\n\n while self.current_char is not None:\n\n if self.current_char.isspace():\n self.skip_whitespace()\n # Continue makes the loop start from the top\n continue\n\n if self.current_char.isdigit():\n return Token(INTEGER, self.integer())\n\n if self.current_char == \"+\":\n self.advance_pos()\n return Token(PLUS, \"+\")\n\n if self.current_char == \"-\":\n self.advance_pos()\n return Token(MINUS, \"-\")\n\n # If it reached here it means it wasn't in one of the if statements hence, an error\n self.error()\n\n # If it skipped the while loop then it means it's the end of the file\n return Token(EOF, None)","title":""},{"docid":"af636d9dd78769d98ee1bfe54ea1e854","score":"0.55402297","text":"def ParseToken(self):\n if self.IsToken():\n return self.ParseWord()\n else:\n return None","title":""},{"docid":"10a6c60a88bf7eb371a4de74fd9c2711","score":"0.5537837","text":"def next(self) -> int:\n if self.hasNext():\n topMost = self.stack.pop(-1)\n if topMost.right:\n self.in_order_left(topMost.right)\n return topMost.val","title":""},{"docid":"c9254265abfe3bdc2f29c7b917cfccfa","score":"0.55335903","text":"def pop(self):\n value = self.head.value\n self.remove(value)\n return value","title":""}],"string":"[\n {\n \"docid\": \"1c44fe9e59a741515ad58d732a600e80\",\n \"score\": \"0.77126354\",\n \"text\": \"def _pop_token(self):\\n return self._tokens.pop(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f3e681cbe93e988efa84ffc58ddd7a\",\n \"score\": \"0.7448798\",\n \"text\": \"def pop_next():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ca8b0b090b5e7bee0547791c98b5be6\",\n \"score\": \"0.7027537\",\n \"text\": \"def peek_next_token(self):\\n return self.tokens[self.tokens_counter]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca16d8899efcd647744347ccd6a6b41\",\n \"score\": \"0.6949655\",\n \"text\": \"def next_pair(tokens):\\n pair = tokens.pairs.pop(0)\\n #print pair\\n return pair\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b0e956eebc99fce3f443683d8f4f9c\",\n \"score\": \"0.68594855\",\n \"text\": \"def pop(self):\\n try:\\n return self.tokens.pop()\\n except IndexError:\\n raise ParsingError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a825fa345da0278f170e951212f0e2\",\n \"score\": \"0.6859276\",\n \"text\": \"def next_token(self):\\n\\t\\ttry:\\n\\t\\t\\ttoken = self.source[self.index]\\n\\t\\t\\tself.index += 1\\n\\t\\t\\tif self.debug > 2:\\n\\t\\t\\t\\tprint 'shift',token\\n\\t\\t\\treturn token\\n\\t\\texcept IndexError:\\n\\t\\t\\traise EndOfTokens()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaacbe1d8dce7a1382ff540d221d94f2\",\n \"score\": \"0.6748197\",\n \"text\": \"def next(self):\\n token = self.peek()\\n self.position += 1\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44b0d200e4072dc78fc4168683e7f2e\",\n \"score\": \"0.65652883\",\n \"text\": \"def token(self):\\r\\n return self._xyzzy(self.next, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c64e59dc447b607387272a08ce1d1fe\",\n \"score\": \"0.6557061\",\n \"text\": \"def peek(self) -> item:\\n if self.peekCount > 0:\\n return self.token[self.peekCount-1]\\n self.peekCount = 1\\n self.token[0] = self.lex.nextItem()\\n return self.token[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"907b65c7936d30f060c32c90b2b3b2af\",\n \"score\": \"0.651498\",\n \"text\": \"def next(self):\\n\\n token = self.get()\\n if token.is_eof():\\n raise StopIteration\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1827ce1ff8606cf40bf94f736d9a205\",\n \"score\": \"0.6447961\",\n \"text\": \"def next(self):\\n\\n if self.eof():\\n return None\\n\\n token = self._data[self._index]\\n self._index += 1\\n\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14be3838b4f2080c4bf436fed6ef6681\",\n \"score\": \"0.63832986\",\n \"text\": \"def peek_token(self):\\r\\n return self._xyzzy(self.peek, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5102a1b2d8685e91e98dea9f02ec361c\",\n \"score\": \"0.63772905\",\n \"text\": \"def pop(self, idx=None) -> Token:\\n if idx is None:\\n return self.__tokens.pop()\\n else:\\n return self.__tokens.pop(idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ce2989cb0e76ae37315694cf085351\",\n \"score\": \"0.636382\",\n \"text\": \"def read_next(self) -> TokenTypeStr:\\n return TOKEN_TYPE.inverse[self.readNext()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d52f1c0697d22b03322ec3f2cae959d\",\n \"score\": \"0.6350362\",\n \"text\": \"def _next_token(self):\\n if self._token_in_queue:\\n return self._pop_token()\\n\\n while self.state is not None:\\n self.state = self.state()\\n if self._token_in_queue:\\n return self._pop_token()\\n\\n if self._start != self._pos or self._pos < self._len:\\n raise ValueError('not all input consumed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd004d931fe225f0ca7ced316bbd408e\",\n \"score\": \"0.6313069\",\n \"text\": \"def jumpTokenBack(self, offset):\\n raise JumpTokenBack(offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9696e0d54f30bf910f3286601f9dee\",\n \"score\": \"0.63065654\",\n \"text\": \"def peek(self) -> Token:\\n if self._next is None:\\n raise self.error('Unexpected end of input')\\n return self._next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8f5c8538916ced6e865aba415346880\",\n \"score\": \"0.6271672\",\n \"text\": \"def advance(self):\\n curr_token = self.tokens[self.tokens_counter]\\n self.tokens_counter += 1\\n return curr_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb2809d6fe689ca51c54ec4f073f3396\",\n \"score\": \"0.6237\",\n \"text\": \"def token(self):\\n self.last_token = self.lexer.token()\\n return self.last_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c926ce6d6b3490ef63774f119dfb11d\",\n \"score\": \"0.6233987\",\n \"text\": \"def readToken(self):\\n return self.tokenStream.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17011ef7ce655dc12100bc23ffb3db11\",\n \"score\": \"0.620918\",\n \"text\": \"def peek(self):\\n if self.position >= len(self.tokens):\\n return ''\\n else:\\n return self.tokens[self.position]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e840101084ef6290dddc8caf77e45e\",\n \"score\": \"0.6180218\",\n \"text\": \"def __advance_token(self):\\r\\n self.__cur_token = self.__next_token\\r\\n self.__next_token = next(self.__root, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06412c8c538670d56b7ad6b35444f5a3\",\n \"score\": \"0.6160175\",\n \"text\": \"def pop(self):\\n if self.top is None:\\n return\\n else:\\n todelete = self.top\\n self.top = todelete.next\\n return todelete.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7347311aff164f8b8dba21407adec2\",\n \"score\": \"0.6131283\",\n \"text\": \"def current(self):\\n try:\\n return self._token_stream[self._current_index-1]\\n except IndexError:\\n raise StopIteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c1e01856586ddd9e69fed3898a8aa8\",\n \"score\": \"0.6084643\",\n \"text\": \"def getAnotherToken(self): \\n \\n if self.token == '\\\\0':\\n return None\\n \\n self.value = None\\n\\n self.skipWhiteSpace() # now leaves self.lastWhiteSpace\\n ch = self.char\\n ln = self.text[self.line]\\n lnLen = len(ln)\\n\\n self.start = ch\\n \\n if ln[ch] in ['(', ')', '[', ']', '|', '+', '=', ';', '\\\\0']:\\n self.token = ln[ch]\\n ch = ch + 1\\n\\n elif ln[ch] == '\\\"':\\n self.token = 'dqword'\\n ch = ch + 1\\n while ch < lnLen and ln[ch] != '\\\"':\\n ch = ch + 1\\n if ch >= lnLen:\\n raise LexicalError( \\\"expecting closing quote in word name\\\", self)\\n self.value = ln[self.start+1:ch]\\n ch = ch + 1\\n \\n elif ln[ch] == \\\"'\\\":\\n self.token = 'sqword'\\n ch = ch + 1\\n while ch < lnLen and ln[ch] != \\\"'\\\":\\n ch = ch + 1\\n if ch >= lnLen:\\n raise LexicalError( \\\"expecting closing quote in word name\\\", self)\\n self.value = ln[self.start+1:ch]\\n ch = ch + 1\\n \\n elif ln[ch] == '<':\\n self.token = 'rule'\\n ch = ch + 1\\n while ch < lnLen and ln[ch] != '>':\\n ch = ch + 1\\n if ch >= lnLen:\\n raise LexicalError( \\\"expecting closing angle bracket in rule name\\\", self)\\n self.value = ln[self.start+1:ch]\\n ch = ch + 1\\n\\n elif ln[ch] == '{':\\n self.token = 'list'\\n ch = ch + 1\\n while ch < lnLen and ln[ch] != '}':\\n ch = ch + 1\\n if ch >= lnLen:\\n raise LexicalError( \\\"expecting closing brace in list name\\\", self)\\n self.value = ln[self.start+1:ch]\\n ch = ch + 1\\n\\n elif isCharOrDigit(ln[ch]):\\n self.token = 'word'\\n while ch < lnLen and isCharOrDigit(ln[ch]):\\n ch = ch + 1\\n self.value = ln[self.start:ch]\\n\\n else:\\n raise LexicalError( \\\"unknown character found\\\", self)\\n\\n self.char = ch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5cddacf63a27ff3d37a9ec848374dc\",\n \"score\": \"0.60647315\",\n \"text\": \"def acc():\\n self.stack.pop()\\n return self.stack[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430a238f7bc1e3d1b2239e5ba974f267\",\n \"score\": \"0.6059106\",\n \"text\": \"def next_token(self, context, token):\\n raise Exception(\\\"bad next_token\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc05ba3f25db01ffff548f2e079e710\",\n \"score\": \"0.6051701\",\n \"text\": \"def next_token(self):\\n\\t\\ttoken = self.lexer.token()\\n\\t\\tif not token:\\n\\t\\t\\traise EndOfTokens\\n\\t\\t# store this token's line number based on its file offset\\n\\t\\ttoken.lineno = self.get_lineno(token.lexpos)\\n\\t\\t# undo any escaped-newline substitution applied to this token's value\\n\\t\\tstart = token.lexpos\\n\\t\\tend = start + len(token.value)\\n\\t\\tfor offset in self.escapes:\\n\\t\\t\\tif offset < start or offset >= end:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tpos = offset - start\\n\\t\\t\\ttoken.value = token.value[:pos] + '\\\\\\\\\\\\n' + token.value[pos+2:]\\n\\t\\t\\tif self.debug > 1:\\n\\t\\t\\t\\tprint 'restored escaped newline at end of line',token.lineno\\n\\t\\t\\tbreak\\n\\t\\t# print out each token if we are being verbose\\n\\t\\tif self.debug > 2:\\n\\t\\t\\tprint 'shift',token\\n\\t\\treturn token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827bd5db72f3592308b161180521b2bc\",\n \"score\": \"0.6048811\",\n \"text\": \"def Token(self) -> _n_1_t_8:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fba685d86babf40c57eb6a5a8d8ae14\",\n \"score\": \"0.60441077\",\n \"text\": \"def next(self):\\n temp_node = self.stack.pop()\\n self.push_all(temp_node.right)\\n return temp_node.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"327b9b66bfed6ef02eaf66f72e7af566\",\n \"score\": \"0.60308325\",\n \"text\": \"def pop(self):\\n tmp = self.top\\n self.top = tmp._next\\n tmp._next = None\\n\\n self._length -= 1\\n return tmp.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56217b42547ca0f2c5e0b1cc1f0305a\",\n \"score\": \"0.6007044\",\n \"text\": \"def pop(self):\\n\\n if not self.is_empty():\\n result = self.top.value\\n self.top = self.top.next\\n \\n return result\\n else:\\n raise EmptySatckException\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f92dc30fb9a91c252bfd01511dbd92\",\n \"score\": \"0.6006929\",\n \"text\": \"def getNextToken(self):\\n while self.currentChar() is not None:\\n # print(\\\"Current:\\\"+self.currentChar())\\n\\n if self.currentChar().isspace():\\n self.skipWhitespace()\\n continue\\n\\n if self.currentChar().isalpha():\\n return self.id()\\n\\n if self.currentChar().isdigit():\\n return self.number()\\n\\n if self.tryToMatch(Token.get(LexName.LINECOMMENT)):\\n self.skipLine()\\n continue\\n if self.tryToMatch(Token.get(LexName.COMMENTSTART)):\\n self.advance(len(Token.get(LexName.COMMENTSTART)))\\n self.skipComment()\\n continue\\n if self.tryToMatch(Token.get(LexName.STRINGL)):\\n return self.string()\\n for key in Token.getSortedKeys(Token.other):\\n tokenValue = Token.get(key)\\n if self.tryToMatch(tokenValue):\\n self.advance(len(tokenValue))\\n return Token(key,tokenValue,self.pos)\\n\\n return self.error(\\\"invalid character\\\")\\n\\n return Token(LexName.EOF, \\\"EOF\\\",self.pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7b1f0a17c55bca26d7434351fb1aae\",\n \"score\": \"0.5996904\",\n \"text\": \"def decrement_token_count(self):\\n\\n self.token_count -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4d30fa7a9a1f86d80f9e755806df59\",\n \"score\": \"0.59844995\",\n \"text\": \"def next(self):\\n try:\\n cur_token = self._token_stream[self._current_index]\\n except IndexError:\\n raise StopIteration\\n self._current_index += 1\\n return cur_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14eb5761506498f76fdcb215550d05c6\",\n \"score\": \"0.5983739\",\n \"text\": \"def consume(self):\\n token_type, token_string, token_line_number = self.token_data[self.pos]\\n self.pos += 1\\n return token_type, token_string, token_line_number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b22beca5a104bbaff2d4279dc3c7b20\",\n \"score\": \"0.59616506\",\n \"text\": \"def next(self,end_is_fatal=True):\\n\\t\\ttry:\\n\\t\\t\\tself.last_token = self.tokenizer.next_token()\\n\\t\\t\\treturn self.last_token\\n\\t\\texcept EndOfTokens:\\n\\t\\t\\tif end_is_fatal:\\n\\t\\t\\t\\traise FatalParseError('unexpected EOF in %s' % self.name)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4c80aa4a5f87925675572a30748912f\",\n \"score\": \"0.59562445\",\n \"text\": \"def __next__(self):\\r\\n\\t\\tbuf = None\\r\\n\\t\\twhile buf is None or len(buf.strip()) == 0:\\r\\n\\t\\t\\tbuf = self.line()\\r\\n\\t\\t#split once\\r\\n\\t\\ttoken, *rest = buf.split(maxsplit=1)\\r\\n\\t\\tself.buf = rest[0] if rest else None\\r\\n\\t\\treturn token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03476f56e2e6080c5aac776adf8d4175\",\n \"score\": \"0.5939367\",\n \"text\": \"def unshift(self, token):\\r\\n self.unshift_buffer[:0] = [token]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fcefca8ece74e7b9673bcc2532b8b27\",\n \"score\": \"0.5934602\",\n \"text\": \"def next(self) -> Token:\\n current = self._next\\n try:\\n self._next = next(self.iterator)\\n except StopIteration:\\n if current is None:\\n raise\\n self._next = None\\n self._last = current\\n return current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c8c91c6757773bb8480c8e1c5d011b\",\n \"score\": \"0.59250224\",\n \"text\": \"def peek(tokens):\\n if len(tokens) == 0:\\n raise ParserError, \\\"While peeking: ran out of tokens.\\\"\\n return tokens[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b4f10183c8db75e94a8d00ef077608\",\n \"score\": \"0.59155077\",\n \"text\": \"def next(self) -> int:\\n return self.sorted_traversal_output.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64362ba885d8055e2c66633d41d532b3\",\n \"score\": \"0.59042275\",\n \"text\": \"def advance(self):\\n self.curr_tokens = self.next_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c587dc8fc6735e962ceaa688f315c125\",\n \"score\": \"0.58780307\",\n \"text\": \"def peekNonSpace(self) -> item:\\n token = self.nextNonSpace()\\n self.backup()\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cc990282a97cc421ccba3f7d37abb3\",\n \"score\": \"0.5867448\",\n \"text\": \"def clearToken():\\n token[0] = \\\"\\\"\\n state[0] = STATE.NORMAL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23db003a92a1a7a6b39a47cbf50b39f4\",\n \"score\": \"0.5862018\",\n \"text\": \"def __next__(self):\\n return self.cards.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db6dc964757b3237f3c9d1805d4f52e\",\n \"score\": \"0.5841549\",\n \"text\": \"def next_token(self, delimiter=' '):\\n\\n if self.position >= self.length:\\n return None\\n\\n index = self.input.find(delimiter, self.position)\\n if index == -1:\\n index = self.length\\n\\n result = self.input[self.position:index]\\n self.position = index + len(delimiter);\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667abcdee96787fd26fc11087a39f57a\",\n \"score\": \"0.5823206\",\n \"text\": \"def pop(self):\\n\\t\\tif self.top == None:\\n\\t\\t\\treturn\\n\\t\\telse:\\n\\t\\t\\tpopped_node = self.top\\n\\t\\t\\tself.top = self.top.next\\n\\t\\treturn popped_node.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0d51abdb8c2a670df743129aa9278d\",\n \"score\": \"0.5811954\",\n \"text\": \"def next():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559be67848d3bd578d0793ebbf1aa480\",\n \"score\": \"0.58066624\",\n \"text\": \"def getNextToken(self):\\n\\n self.tk = LEX_TYPES.LEX_EOF\\n self.tkStr = ''\\n while self.currCh and isWhitespace(self.currCh):\\n self.getNextCh()\\n # newline comments\\n if self.currCh == '/' and self.nextCh == '/':\\n while self.currCh and self.currCh != '\\\\n':\\n self.getNextCh()\\n self.getNextCh()\\n self.getNextToken()\\n return\\n # block comments\\n if self.currCh == '/' and self.nextCh == '*':\\n while self.currCh and (self.currCh != '*' or self.nextCh != '/'):\\n self.getNextCh()\\n self.getNextCh()\\n self.getNextCh()\\n self.getNextToken()\\n return;\\n # record beginning of this token\\n self.tokenStart = self.dataPos - 2\\n # tokens\\n if isAlpha(self.currCh): # IDs\\n while isAlpha(self.currCh) or isNumeric(self.currCh):\\n self.tkStr += self.currCh\\n self.getNextCh()\\n self.tk = LEX_TYPES.LEX_ID\\n if self.tkStr==\\\"if\\\": self.tk = LEX_TYPES.LEX_R_IF\\n elif self.tkStr==\\\"else\\\": self.tk = LEX_TYPES.LEX_R_ELSE\\n elif self.tkStr==\\\"do\\\": self.tk = LEX_TYPES.LEX_R_DO\\n elif self.tkStr==\\\"while\\\": self.tk = LEX_TYPES.LEX_R_WHILE\\n elif self.tkStr==\\\"for\\\": self.tk = LEX_TYPES.LEX_R_FOR\\n elif self.tkStr==\\\"break\\\": self.tk = LEX_TYPES.LEX_R_BREAK\\n elif self.tkStr==\\\"continue\\\": self.tk = LEX_TYPES.LEX_R_CONTINUE\\n elif self.tkStr==\\\"function\\\": self.tk = LEX_TYPES.LEX_R_FUNCTION\\n elif self.tkStr==\\\"return\\\": self.tk = LEX_TYPES.LEX_R_RETURN\\n elif self.tkStr==\\\"var\\\": self.tk = LEX_TYPES.LEX_R_VAR\\n elif self.tkStr==\\\"true\\\": self.tk = LEX_TYPES.LEX_R_TRUE\\n elif self.tkStr==\\\"false\\\": self.tk = LEX_TYPES.LEX_R_FALSE\\n elif self.tkStr==\\\"null\\\": self.tk = LEX_TYPES.LEX_R_NULL\\n elif self.tkStr==\\\"undefined\\\": self.tk = LEX_TYPES.LEX_R_UNDEFINED\\n elif self.tkStr==\\\"new\\\": self.tk = LEX_TYPES.LEX_R_NEW\\n elif isNumeric(self.currCh):\\n isHex = False\\n if self.currCh == '0':\\n self.tkStr += self.currCh\\n self.getNextCh()\\n if self.currCh == 'x':\\n isHex = True\\n self.tkStr += self.currCh\\n self.getNextCh()\\n self.tk = LEX_TYPES.LEX_INT\\n while isNumeric(self.currCh) or (isHex and isHexadecimal(self.currCh)):\\n self.tkStr += self.currCh\\n self.getNextCh()\\n if not isHex and self.currCh == '.':\\n self.tk = LEX_TYPES.LEX_FLOAT\\n self.tkStr += '.'\\n self.getNextCh()\\n while isNumeric(self.currCh):\\n self.tkStr += self.currCh\\n self.getNextCh()\\n # do fancy e-style floating point\\n if not isHex and (self.currCh == 'e' or self.currCh=='E'):\\n self.tk = LEX_TYPES.LEX_FLOAT\\n self.tkStr += self.currCh\\n self.getNextCh()\\n if self.currCh=='-':\\n self.tkStr += self.currCh\\n self.getNextCh()\\n while isNumeric(self.currCh):\\n self.tkStr += self.currCh\\n self.getNextCh()\\n elif self.currCh == '\\\"':\\n # strings...\\n self.getNextCh()\\n while self.currCh and self.currCh!='\\\"':\\n if self.currCh == '\\\\\\\\':\\n self.getNextCh()\\n if self.currCh == 'n' : self.tkStr += '\\\\n'\\n elif self.currCh == '\\\"' : self.tkStr += '\\\"'\\n elif self.currCh == '\\\\\\\\': self.tkStr += '\\\\\\\\'\\n else: self.tkStr += self.currCh\\n else:\\n self.tkStr += self.currCh\\n self.getNextCh()\\n self.getNextCh()\\n self.tk = LEX_TYPES.LEX_STR\\n elif self.currCh=='\\\\'':\\n # strings again...\\n self.getNextCh()\\n while self.currCh and self.currCh!='\\\\'':\\n if self.currCh == '\\\\\\\\':\\n self.getNextCh()\\n if self.currCh == 'n' : self.tkStr += '\\\\n'\\n elif self.currCh == 'a' : self.tkStr += '\\\\a'\\n elif self.currCh == 'r' : self.tkStr += '\\\\r'\\n elif self.currCh == 't' : self.tkStr += '\\\\t'\\n elif self.currCh == '\\\\'' : self.tkStr += '\\\\''\\n elif self.currCh == '\\\\\\\\' : self.tkStr += '\\\\\\\\'\\n elif self.currCh == 'x' : # hex digits\\n self.getNextCh()\\n buf = self.currCh\\n self.getNextCh()\\n buf += self.currCh\\n self.tkStr += chr(int(buf, 16))\\n else:\\n if self.currCh>='0' and self.currCh<='7':\\n # octal digits\\n buf = self.currCh\\n self.getNextCh()\\n buf += self.currCh\\n self.getNextCh()\\n buf += self.currCh\\n self.tkStr += chr(int(buf, 8))\\n else:\\n self.tkStr += self.currCh\\n else:\\n self.tkStr += self.currCh\\n self.getNextCh()\\n self.getNextCh()\\n self.tk = LEX_TYPES.LEX_STR\\n else:\\n # single chars\\n self.tk = self.currCh\\n if self.currCh: self.getNextCh()\\n if self.tk=='=' and self.currCh=='=': # ==\\n self.tk = LEX_TYPES.LEX_EQUAL\\n self.getNextCh()\\n if self.currCh=='=': # ===\\n self.tk = LEX_TYPES.LEX_TYPEEQUAL\\n self.getNextCh()\\n elif self.tk=='!' and self.currCh=='=': # // !=\\n self.tk = LEX_TYPES.LEX_NEQUAL\\n self.getNextCh()\\n if self.currCh=='=': # // !==\\n self.tk = LEX_TYPES.LEX_NTYPEEQUAL\\n self.getNextCh()\\n elif self.tk=='<' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_LEQUAL\\n self.getNextCh()\\n elif self.tk=='<' and self.currCh=='<':\\n self.tk = LEX_TYPES.LEX_LSHIFT\\n self.getNextCh()\\n if self.currCh=='=': # // <<=\\n self.tk = LEX_TYPES.LEX_LSHIFTEQUAL\\n self.getNextCh()\\n elif self.tk=='>' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_GEQUAL\\n self.getNextCh()\\n elif self.tk=='>' and self.currCh=='>':\\n self.tk = LEX_TYPES.LEX_RSHIFT\\n self.getNextCh()\\n if self.currCh=='=': # // >>=\\n self.tk = LEX_TYPES.LEX_RSHIFTEQUAL\\n self.getNextCh()\\n elif self.currCh=='>': # // >>>\\n self.tk = LEX_TYPES.LEX_RSHIFTUNSIGNED\\n self.getNextCh()\\n elif self.tk=='+' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_PLUSEQUAL\\n self.getNextCh()\\n elif self.tk=='-' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_MINUSEQUAL\\n self.getNextCh()\\n elif self.tk=='+' and self.currCh=='+':\\n self.tk = LEX_TYPES.LEX_PLUSPLUS\\n self.getNextCh()\\n elif self.tk=='-' and self.currCh=='-':\\n self.tk = LEX_TYPES.LEX_MINUSMINUS\\n self.getNextCh()\\n elif self.tk=='&' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_ANDEQUAL\\n self.getNextCh()\\n elif self.tk=='&' and self.currCh=='&':\\n self.tk = LEX_TYPES.LEX_ANDAND\\n self.getNextCh()\\n elif self.tk=='|' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_OREQUAL\\n self.getNextCh()\\n elif self.tk=='|' and self.currCh=='|':\\n self.tk = LEX_TYPES.LEX_OROR\\n self.getNextCh()\\n elif self.tk=='^' and self.currCh=='=':\\n self.tk = LEX_TYPES.LEX_XOREQUAL\\n self.getNextCh()\\n\\n # /* This isn't quite right yet */\\n self.tokenLastEnd = self.tokenEnd\\n self.tokenEnd = self.dataPos-3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ea341d5bab1b85acfb4456d186db9a\",\n \"score\": \"0.5798741\",\n \"text\": \"def pop(self):\\n val = self._lst[-1]\\n self._lst = self._lst[:-1]\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d14fc7a2c17b25ff11dc8d64a907790\",\n \"score\": \"0.5797154\",\n \"text\": \"def __next__(self):\\n try:\\n return self.pop()\\n except IndexError:\\n raise StopIteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c30ec3b5b7f3c161234f03820372160\",\n \"score\": \"0.57851636\",\n \"text\": \"def pop(self):\\n # TODO: Write function to pop here\\n node = self.head\\n self.head = self.head.next\\n \\n return node.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667f16ddb4791508fd66e1f937936668\",\n \"score\": \"0.5753288\",\n \"text\": \"def peek_next(self):\\n\\t\\treturn self.data[self.pos + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3deb46a9b45c06007f99abc7ecc8be99\",\n \"score\": \"0.5749768\",\n \"text\": \"def __prevsym(self):\\n self.tnum -= 1\\n if self.tnum < 0:\\n logging.warning('Index out of range, tnum: %s' % self.tnum)\\n return False\\n self.curr = self.tokens[self.tnum]\\n self.prev = self.tokens[self.tnum - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee302ecf678768d5fe288c166f6281d\",\n \"score\": \"0.57356846\",\n \"text\": \"def getNextValueExpression(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec133b3bf8bd54682c6dd9feb964833a\",\n \"score\": \"0.5729338\",\n \"text\": \"def consume_next_move(self):\\n tmp = self.next_move\\n self.next_move = None\\n return tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aff1965a5b2500cd60cb8c421b32ea1\",\n \"score\": \"0.5712636\",\n \"text\": \"def curr(self):\\n return self.scanner.curr()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5717de5a681c317e002f892560dd6d2\",\n \"score\": \"0.57085747\",\n \"text\": \"def pop_element(self):\\n curr = self.top\\n\\n if curr == None:\\n print(\\\"Stack is already empty.\\\")\\n\\n else:\\n temp = self.top.data\\n self.top = self.top.next_val\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c301def0938d7d80ab0023454f6ee831\",\n \"score\": \"0.5707097\",\n \"text\": \"def next(self) -> int:\\n res = self.nodes.pop()\\n ans = res.val\\n if res.right:\\n res = res.right\\n while res:\\n self.nodes.append(res)\\n res = res.left\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6386e940b1f66d1cf718b84191ac08\",\n \"score\": \"0.5706446\",\n \"text\": \"def pop(self):\\n if self.count > 0:\\n # Store value at top of stack\\n output = self.stack[self.top]\\n\\n # Remove top value from stack\\n del self.stack[self.top]\\n\\n # Move top tracker\\n self.top -= 1\\n\\n # Decrement count\\n self.count -= 1\\n\\n return output\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"711e037ae0fc790b952a6d53e8420d70\",\n \"score\": \"0.57045716\",\n \"text\": \"def peek(self):\\t\\n\\t\\treturn self.items[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d777b00720c69662db63193935939191\",\n \"score\": \"0.5701458\",\n \"text\": \"def advance(self, type_check=None):\\n prev_token = copy(self.current_token)\\n if type_check:\\n assert prev_token.type == type_check\\n if self.pos + 1 < len(self.tokens):\\n self.pos += 1\\n self.current_token = self.tokens[self.pos]\\n return prev_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9411ac34acf4538b7a18a9a6a11ccaf\",\n \"score\": \"0.5684758\",\n \"text\": \"def peek(self):\\r\\n return self[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e2863ae3eef48b5f3b10cacb27a39d\",\n \"score\": \"0.568419\",\n \"text\": \"def next(self) -> int:\\n if self.hasNext():\\n node = self.stack.pop()\\n cur = node.right\\n while cur:\\n self.stack.append(cur)\\n cur = cur.left\\n return node.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0e725ddf13717e9fc0ab074c133911\",\n \"score\": \"0.5678976\",\n \"text\": \"def pop(self):\\r\\n x = self.top.data\\r\\n self.top = self.top.next\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca44dc8b2689bfda63d2df754af6375c\",\n \"score\": \"0.56594294\",\n \"text\": \"def pop(self):\\n if not self.first:\\n return None\\n v = self.first\\n if self.first == self.last:\\n self.first = None\\n self.last = None\\n else:\\n self.first = self[self.first]['next']\\n del self[v]\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c404a919408f49ebbf43ad1dbef3844\",\n \"score\": \"0.56551325\",\n \"text\": \"def advance(self):\\n self.current_command = self.lines.pop(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df96135d18337d5eaefcb344ef50c2f2\",\n \"score\": \"0.5653407\",\n \"text\": \"def pop(self):\\n if self._size > 0:\\n temp = self.top.val\\n self.top = self.top._next\\n self._size -= 1\\n return temp\\n else:\\n raise ValueError('There is nothing in the stack!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184753142ae67fb3290c1973cd85a64d\",\n \"score\": \"0.5650666\",\n \"text\": \"def tokens(self):\\n while 1:\\n tok = self.token()\\n if tok is None: break\\n yield tok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4274e2fbbfac1cd8a0a92a49f5ec3dd4\",\n \"score\": \"0.56504685\",\n \"text\": \"def get_next_line(self):\\n token_to_use = None\\n if self.next_token:\\n if len(self.next_token) == 2:\\n token_to_use = self.next_token[0]\\n self.next_token = self.next_token[1].split(\\\"\\\\n\\\", 1)\\n else:\\n assert self.next_token\\n token_to_use = self.next_token[0]\\n self.next_token = None\\n return token_to_use\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9042f5524ae082b14a6e942ddece4a11\",\n \"score\": \"0.564964\",\n \"text\": \"def next(self) -> int:\\n cur_node = self.stack.pop()\\n if cur_node.right:\\n self.push_stack(cur_node.right)\\n return cur_node.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d47e035d5ad78dbca10ad89fd1ac407\",\n \"score\": \"0.56485087\",\n \"text\": \"def advance(self):\\n\\n if self.cursor >= len(self.tokens):\\n return '', None\\n if self.at(self.cursor) == EOF: return '', None\\n token = self.at(self.cursor)\\n return token, PLine.from_seed(self, self.cursor+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb664c6ee42bbafb8da94531765556ea\",\n \"score\": \"0.5643372\",\n \"text\": \"def pop(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e8256f997dfc9965ea115e4fa9cc72a\",\n \"score\": \"0.5634845\",\n \"text\": \"def __nextsym(self):\\n self.tnum += 1\\n if self.tnum >= len(self.tokens):\\n logging.warning('index out of range, tnum: %s' % self.tnum)\\n return False\\n self.curr = self.tokens[self.tnum]\\n self.prev = self.tokens[self.tnum - 1]\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f2f911de7f690e96ba88b6ff07dbd8\",\n \"score\": \"0.5632336\",\n \"text\": \"def next(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f2f911de7f690e96ba88b6ff07dbd8\",\n \"score\": \"0.5632336\",\n \"text\": \"def next(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f2f911de7f690e96ba88b6ff07dbd8\",\n \"score\": \"0.5632336\",\n \"text\": \"def next(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a5dfe86db99d48791db9571089aeb8\",\n \"score\": \"0.5631743\",\n \"text\": \"def pop(self):\\n self.text = self.text[0:len(self.text)-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12fbd352bac3af353569356399621201\",\n \"score\": \"0.56218576\",\n \"text\": \"def get(self):\\n\\n # First, check the pushback\\n if self.__pushback:\\n self.line_position += 1\\n return self.__pushback.pop()\\n\\n # Get raw value\\n token_type, value = self.__get_next()\\n # End of file\\n if not value:\\n return None\\n\\n # Skip all whitespace characters\\n while token_type is token_type.Space:\\n token_type, value = self.__get_next()\\n if not value:\\n return None\\n\\n if token_type is TokenType.Literal:\\n curr = \\\"\\\"\\n # Collect all digits of the number\\n while token_type is TokenType.Literal:\\n curr += value\\n token_type, value = self.__get_next()\\n\\n # Number literal which begins with 0 and has more than 1 digit is illegal\\n if len(curr) > 1 and curr.startswith('0'):\\n raise InvalidNumberFormatException(self.filename, (self.line_number, self.line_position - 1), curr)\\n\\n if token_type and token_type is not token_type.Space:\\n # If the last symbol was followed by meaningful, put it in pushback\\n self.push_back(Token(token_type, value))\\n\\n return Token(TokenType.Literal, curr)\\n\\n return Token(token_type, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9a1cd6dbb38cd48ff5252478758d49\",\n \"score\": \"0.5620235\",\n \"text\": \"def reverse_consume(\\n tokens: List[Token], i: int, *, name: str, src: Optional[str] = None\\n) -> int:\\n while tokens[i - 1].name == name and (src is None or tokens[i - 1].src == src):\\n i -= 1\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe27eb2d33e3b579688e2565b22a47b\",\n \"score\": \"0.56164205\",\n \"text\": \"def next(self) -> int:\\n root = self.stack.pop()\\n self.pushAll(root.right)\\n return root.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d31bb96e43174e10b477ccc05e258a\",\n \"score\": \"0.5616356\",\n \"text\": \"def pop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d31bb96e43174e10b477ccc05e258a\",\n \"score\": \"0.5616356\",\n \"text\": \"def pop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9d6cf68f8f2e96e3ac125033d7c3ed\",\n \"score\": \"0.56060076\",\n \"text\": \"def __next__(self) -> str:\\n try:\\n result = self.expression[self.token_pos]\\n self.token_pos += 1\\n return result\\n except IndexError:\\n if self.token_pos == len(self.expression):\\n self.token_pos += 1\\n return \\\"\\\"\\n raise StopIteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949c65719835eef0138625e9b887dcf9\",\n \"score\": \"0.5601451\",\n \"text\": \"def pop(self):\\n while self.number > self.maxlength:\\n self.buffer.popleft()\\n self.number -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a04d8cc4cc68220420f02314d9bb6f6\",\n \"score\": \"0.5600353\",\n \"text\": \"def _next():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43f21d7b2a6f816d52a4231f232bab7\",\n \"score\": \"0.5592252\",\n \"text\": \"def pop(self):\\n if self.isEmpty():\\n raise KeyError(\\\"the stack is empty\\\")\\n result=self._item.data\\n self._size-=1\\n self._item=self._item.next\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"532f1e3dd5dc8b0e3b8870b189b1ea0e\",\n \"score\": \"0.55828035\",\n \"text\": \"def last(parameters: list = None) -> Token:\\n return parameters[0].value[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166f8830cc5f64df89defa06bd85a125\",\n \"score\": \"0.5576525\",\n \"text\": \"def next(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166f8830cc5f64df89defa06bd85a125\",\n \"score\": \"0.5576525\",\n \"text\": \"def next(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8d664f9729b5e4c12cd7a668c6a652\",\n \"score\": \"0.5573545\",\n \"text\": \"def token(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dfebccc2ccf385cd7ddeb18ba02b9cd\",\n \"score\": \"0.55690104\",\n \"text\": \"def pop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dfebccc2ccf385cd7ddeb18ba02b9cd\",\n \"score\": \"0.55690104\",\n \"text\": \"def pop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dfebccc2ccf385cd7ddeb18ba02b9cd\",\n \"score\": \"0.55690104\",\n \"text\": \"def pop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5786d13f1433fceb95fdfe86aa616eda\",\n \"score\": \"0.5564854\",\n \"text\": \"def next(self):\\n ttype, token, (lineno, indent) = self.gen.next()[:3]\\n if lineno == self.curline:\\n self.currentscope = self.scope\\n return (ttype, token, indent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55abf3f5b731001a85e49e969d2e2f1c\",\n \"score\": \"0.5551662\",\n \"text\": \"def get_next_token(self):\\n\\n while self.current_char is not None:\\n\\n if self.current_char.isspace():\\n self.skip_whitespace()\\n # Continue makes the loop start from the top\\n continue\\n\\n if self.current_char.isdigit():\\n return Token(INTEGER, self.integer())\\n\\n if self.current_char == \\\"+\\\":\\n self.advance_pos()\\n return Token(PLUS, \\\"+\\\")\\n\\n if self.current_char == \\\"-\\\":\\n self.advance_pos()\\n return Token(MINUS, \\\"-\\\")\\n\\n # If it reached here it means it wasn't in one of the if statements hence, an error\\n self.error()\\n\\n # If it skipped the while loop then it means it's the end of the file\\n return Token(EOF, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af636d9dd78769d98ee1bfe54ea1e854\",\n \"score\": \"0.55402297\",\n \"text\": \"def ParseToken(self):\\n if self.IsToken():\\n return self.ParseWord()\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a6c60a88bf7eb371a4de74fd9c2711\",\n \"score\": \"0.5537837\",\n \"text\": \"def next(self) -> int:\\n if self.hasNext():\\n topMost = self.stack.pop(-1)\\n if topMost.right:\\n self.in_order_left(topMost.right)\\n return topMost.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9254265abfe3bdc2f29c7b917cfccfa\",\n \"score\": \"0.55335903\",\n \"text\": \"def pop(self):\\n value = self.head.value\\n self.remove(value)\\n return value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1477,"cells":{"query_id":{"kind":"string","value":"13e3bf1628847f08c6bbeb374b7ecc03"},"query":{"kind":"string","value":"r\"\"\"__init__(GreaterImageFilter self) > GreaterImageFilter"},"positive_passages":{"kind":"list like","value":[{"docid":"3a6e3a1b8ec12a640b671ba41e2e245f","score":"0.812999","text":"def __init__(self):\n _SimpleITK.GreaterImageFilter_swiginit(self, _SimpleITK.new_GreaterImageFilter())","title":""}],"string":"[\n {\n \"docid\": \"3a6e3a1b8ec12a640b671ba41e2e245f\",\n \"score\": \"0.812999\",\n \"text\": \"def __init__(self):\\n _SimpleITK.GreaterImageFilter_swiginit(self, _SimpleITK.new_GreaterImageFilter())\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3516fce8e9fb3bf328b4cc48fdf578fd","score":"0.7975903","text":"def __init__(self):\n _SimpleITK.GreaterEqualImageFilter_swiginit(self, _SimpleITK.new_GreaterEqualImageFilter())","title":""},{"docid":"f164024e19dfe04953f63af16a9ffdb8","score":"0.71127456","text":"def Execute(self, *args):\n return _SimpleITK.GreaterImageFilter_Execute(self, *args)","title":""},{"docid":"082a11075ef7b4f74f1fa8cc5101a683","score":"0.69463205","text":"def __init__(self, *args):\n _Expr.Expr_GreaterThan_swiginit(self,_Expr.new_Expr_GreaterThan(*args))","title":""},{"docid":"2abb0ca7a665e88cbcac20f29c7cbf04","score":"0.68200463","text":"def Execute(self, *args):\n return _SimpleITK.GreaterEqualImageFilter_Execute(self, *args)","title":""},{"docid":"6fc01f86c47a429540681bad06c4ee30","score":"0.65644675","text":"def __gt__(self, other):\n return self._operation(other, \">\")","title":""},{"docid":"f769f82e57d722a0f0c8a89582d36a8e","score":"0.6406268","text":"def _op_greaterthan(self):\n self._do_binary_op(lambda a, b: int(a > b))","title":""},{"docid":"d1128f4687a009210526534da9616de5","score":"0.6396495","text":"def __init__(self):\n _SimpleITK.LessEqualImageFilter_swiginit(self, _SimpleITK.new_LessEqualImageFilter())","title":""},{"docid":"8c2ae8dc1c1a13197b5984fadfa14ff4","score":"0.63914967","text":"def __gt__(self, other):","title":""},{"docid":"ac110f017580a6f006aefcfe7d861254","score":"0.63662004","text":"def __gt__(self, other):\n return self._handle_binary_comparison(\n other,\n primitives.GreaterThan,\n primitives.GreaterThanScalar,\n )","title":""},{"docid":"d20ef47dd8dd7b898501701e13000337","score":"0.6287866","text":"def __init__(self, *args):\n _Expr.Expr_GreaterThanOrEqual_swiginit(self,_Expr.new_Expr_GreaterThanOrEqual(*args))","title":""},{"docid":"e4db8809196b12a403cfc6dfa528fcbc","score":"0.6273326","text":"def Greater(*args):\n return _SimpleITK.Greater(*args)","title":""},{"docid":"5b10c13fd903c499ca4bee2d2d011d1a","score":"0.6266256","text":"def __init__(self):\n _SimpleITK.MinimumMaximumImageFilter_swiginit(self, _SimpleITK.new_MinimumMaximumImageFilter())","title":""},{"docid":"feb5e81a30f1794c4992dc22ce229800","score":"0.6237459","text":"def __gt__(self, other):\n return self.value > other","title":""},{"docid":"7f5c5149ec4ed887ba030fe316f16ad4","score":"0.6199739","text":"def _op_greaterthanequal(self):\n self._do_binary_op(lambda a, b: int(a >= b))","title":""},{"docid":"498f8b11ab93fd75508f19348bf815f6","score":"0.6157496","text":"def __gt__(self, other):\n return self.src > other.src","title":""},{"docid":"f58c6ca3243f1006bb493d273bb2bdcd","score":"0.6147632","text":"def __gt__(self, other):\n return self.value > other.value","title":""},{"docid":"f58c6ca3243f1006bb493d273bb2bdcd","score":"0.6147632","text":"def __gt__(self, other):\n return self.value > other.value","title":""},{"docid":"a10da4f354b0e9de565944a2a4ac5262","score":"0.6139351","text":"def __gt__(self, other: int):\n return self.x > other","title":""},{"docid":"911833183b93b1aadcd253402557aa51","score":"0.61002004","text":"def __ge__(self, other):\n return self._handle_binary_comparison(\n other,\n primitives.GreaterThanEqualTo,\n primitives.GreaterThanEqualToScalar,\n )","title":""},{"docid":"96d7e0607df9ef105ecd9891bfe26739","score":"0.6022101","text":"def __gt__(self, other):\n op = lambda x, y: x > y\n msg = \"Cannot compare types {0} and {1}\"\n return self.__meta_magic(other, op, msg)","title":""},{"docid":"fe03e878d862bbc33825e5de30984e1a","score":"0.60202456","text":"def __init__(self):\n _SimpleITK.AbsoluteValueDifferenceImageFilter_swiginit(self, _SimpleITK.new_AbsoluteValueDifferenceImageFilter())","title":""},{"docid":"3a1910739f9085938bcbfc21f586c938","score":"0.597116","text":"def __init__(self):\n _SimpleITK.MaximumImageFilter_swiginit(self, _SimpleITK.new_MaximumImageFilter())","title":""},{"docid":"6ddac9a0559fef9ba43e49f04a288db3","score":"0.59679735","text":"def __gt__(self, other):\n if isinstance(other, Symbol):\n return _internal._greater(self, other)\n if isinstance(other, numeric_types):\n return _internal._greater_scalar(self, scalar=other)\n else:\n raise TypeError('type %s not supported' % str(type(other)))","title":""},{"docid":"3cd855f507964238688f44c4e8e354f2","score":"0.59448373","text":"def __gt__(self, other):\n return self.area() > other.area()","title":""},{"docid":"061fe9410f0a47c11e43df78c94169a4","score":"0.5931065","text":"def __gt__(self, other):\n return self >= other and self != other","title":""},{"docid":"b9aaa739afa36aad7aa9c541899c63af","score":"0.5920063","text":"def __gt__(self, other):\n return (self.label > other.label)","title":""},{"docid":"58838f9da2627a4990fb7bb1f66822a6","score":"0.5906365","text":"def __init__(self):\n _SimpleITK.LessImageFilter_swiginit(self, _SimpleITK.new_LessImageFilter())","title":""},{"docid":"3e796f19152fe83c56f99b30c2936412","score":"0.59007955","text":"def __gt__(self, other):\n return not (self == other or self < other)","title":""},{"docid":"a6c7c39a68ef1d96989a25fc6929a338","score":"0.589164","text":"def __gt__(self, other):\n self.__command_args.extend(['>', str(other)])\n return self","title":""},{"docid":"bdee305bb12bc9ef8c0cc91cab5cf0cc","score":"0.5879316","text":"def GreaterEqual(*args):\n return _SimpleITK.GreaterEqual(*args)","title":""},{"docid":"77810d9956184acc0a1a0ab6f165f1fe","score":"0.58755404","text":"def __gt__(self, other):\n return self._comp(other) > 0","title":""},{"docid":"31a7ec456772fe14ec666b43d8879329","score":"0.58569014","text":"def __init__(self):\n _SimpleITK.EqualImageFilter_swiginit(self, _SimpleITK.new_EqualImageFilter())","title":""},{"docid":"a99f2df5da20e7982c27689b5c2b173c","score":"0.58473605","text":"def __init__(self):\n _SimpleITK.BinaryThresholdImageFilter_swiginit(self, _SimpleITK.new_BinaryThresholdImageFilter())","title":""},{"docid":"b2786a1aecc1ba3a7f02de6fa1c3782b","score":"0.58335257","text":"def __gt__(self, other):\r\n if isinstance(other, Shape):\r\n if self.name() > other.name():\r\n return True\r\n if self.name() == other.name() and self.area() > other.area():\r\n return True\r\n return False\r\n raise TypeError(\"object passed to greater than is not a Shape\")","title":""},{"docid":"301c46d6c5417c2abcf78ecd98b129b3","score":"0.580836","text":"def __init__(self):\n _SimpleITK.NaryMaximumImageFilter_swiginit(self, _SimpleITK.new_NaryMaximumImageFilter())","title":""},{"docid":"ded20f82861e259691b6e269b79377c8","score":"0.57955796","text":"def __gt__(self, other):\n\n return (not self < other) and (not self == other)","title":""},{"docid":"710bba45ae4862d08b05b4d7332aa754","score":"0.57872903","text":"def __gt__(self, other):\n return self.upper > other.upper > self.lower","title":""},{"docid":"cdda0a364794e05170b2e14bed1bde38","score":"0.5778024","text":"def __init__(self):\n _SimpleITK.ThresholdMaximumConnectedComponentsImageFilter_swiginit(self, _SimpleITK.new_ThresholdMaximumConnectedComponentsImageFilter())","title":""},{"docid":"75cb80b36aef5a0b0742cc097bea6991","score":"0.57778263","text":"def __gt__(self, other):\n try:\n return self.val > other.val\n except AttributeError:\n return self.val > other","title":""},{"docid":"ec797d79b8c5f043543bcc52d0f7ec14","score":"0.5766795","text":"def __gt__(self, other):\n return (self.numerator/self.denominator) > (other.numerator/other.denominator)","title":""},{"docid":"a6853485612de45ae11768ed20348664","score":"0.57625264","text":"def __gt__(self, other):\n self.__class__.__verify_has_same_class(other)\n return other < self","title":""},{"docid":"2f58a55492221fc99f845d2d50b164f0","score":"0.5761019","text":"def __gt__(self, other):\n return not self <= other","title":""},{"docid":"2f58a55492221fc99f845d2d50b164f0","score":"0.5761019","text":"def __gt__(self, other):\n return not self <= other","title":""},{"docid":"2f58a55492221fc99f845d2d50b164f0","score":"0.5761019","text":"def __gt__(self, other):\n return not self <= other","title":""},{"docid":"957540d6d44c8763bd0ce7087aaf4f22","score":"0.57384276","text":"def __gt__(self, value):\r\n return self.get_operation_for(self.__field > value)","title":""},{"docid":"a2ebc3a631c549d805256f1b5a53e17a","score":"0.57241553","text":"def __init__(self):\n _SimpleITK.ShanbhagThresholdImageFilter_swiginit(self, _SimpleITK.new_ShanbhagThresholdImageFilter())","title":""},{"docid":"897d85189c9d8463ba533b1be8baac34","score":"0.572219","text":"def __ge__(self, other):\n return self._operation(other, \">=\")","title":""},{"docid":"5afaa124ef380e0c0ae14f8ac5ec6219","score":"0.5719371","text":"def __gt__(self, other):\n return self._comparison_tuple > other._comparison_tuple","title":""},{"docid":"77e151c29d41ee670720d89b59a94e7d","score":"0.57122487","text":"def __init__(self):\n _SimpleITK.BinomialBlurImageFilter_swiginit(self, _SimpleITK.new_BinomialBlurImageFilter())","title":""},{"docid":"50c5aeb5ea986a62c177c9a49528ff80","score":"0.57099813","text":"def __init__(self):\n _SimpleITK.NotEqualImageFilter_swiginit(self, _SimpleITK.new_NotEqualImageFilter())","title":""},{"docid":"5b15f8c76be9c6c2c9ad147538562625","score":"0.56994975","text":"def __init__(self):\n _SimpleITK.ThresholdImageFilter_swiginit(self, _SimpleITK.new_ThresholdImageFilter())","title":""},{"docid":"d55e05432de1d94f265bf9edeb757331","score":"0.56795394","text":"def __gt__(self, other):\n return not self.__lt__(other)","title":""},{"docid":"d76af530a9792f7de23342d9ca920990","score":"0.5669293","text":"def __init__(self):\n _SimpleITK.MaximumEntropyThresholdImageFilter_swiginit(self, _SimpleITK.new_MaximumEntropyThresholdImageFilter())","title":""},{"docid":"41dcd6d2c2f85ba436b9bc88443ee34a","score":"0.56598055","text":"def __init__(self):\n _SimpleITK.AdaptiveHistogramEqualizationImageFilter_swiginit(self, _SimpleITK.new_AdaptiveHistogramEqualizationImageFilter())","title":""},{"docid":"c59262a8a3350dc5f5e631db57faec3d","score":"0.56560826","text":"def __gt__(self, other):\n cmp = {0: 2, 1: 0, 2: 1}\n return cmp[self.action] != other.action","title":""},{"docid":"03459997b81bb43672244399d0730945","score":"0.56493753","text":"def __gt__(self, a_rhs):\n return a_rhs.__lt__(self)","title":""},{"docid":"e40a1b1234d3629da201ced150199be6","score":"0.56422174","text":"def __init__(self):\n _SimpleITK.DoubleThresholdImageFilter_swiginit(self, _SimpleITK.new_DoubleThresholdImageFilter())","title":""},{"docid":"cb780a0b6e538ca17318d6111ddaa3ae","score":"0.56382346","text":"def __init__(self):\n _SimpleITK.SubtractImageFilter_swiginit(self, _SimpleITK.new_SubtractImageFilter())","title":""},{"docid":"e966b0e28c700afb493921cfcbca5e6a","score":"0.5637381","text":"def __init__(self):\n _SimpleITK.BinaryPruningImageFilter_swiginit(self, _SimpleITK.new_BinaryPruningImageFilter())","title":""},{"docid":"4ff6fd130199745dfe371adf6a126155","score":"0.5632606","text":"def gt(a, b):\n return a > b","title":""},{"docid":"4147df24e2d5dcd8e861caf893f18c3c","score":"0.5631325","text":"def __gt__(self,other):\r\n return random.choice( [ True, False ] )","title":""},{"docid":"8a78b2c0714d4e1650428743d76be691","score":"0.56262636","text":"def __gt__(self, other):\n return int(self.num + other.num) > int(other.num + self.num)","title":""},{"docid":"d05dbb859dc7316e1c19261bc18be1ff","score":"0.56260437","text":"def __init__(self):\n _SimpleITK.TernaryAddImageFilter_swiginit(self, _SimpleITK.new_TernaryAddImageFilter())","title":""},{"docid":"8bee732a0b026d11ed6d7739a6c997f1","score":"0.5600518","text":"def __gt__(self, other):\r\n return self.determine_hand_rank() > other.determine_hand_rank()","title":""},{"docid":"ae09bd6d996cfae164cc785250aa1f19","score":"0.5596626","text":"def __gt__(self, other):\n if float(self) > float(other):\n return True","title":""},{"docid":"ec13252f127f2de8e8e39cfb608a0901","score":"0.5590017","text":"def __ge__(self, other):\n op = lambda x, y: x >= y\n msg = \"Cannot compare types {0} and {1}\"\n return self.__meta_magic(other, op, msg)","title":""},{"docid":"0d54f4720d0b0ba82f444a0dcd991f0f","score":"0.55895245","text":"def __ge__(self, b):\n\n return not self.__lt__(b) # '>=' == 'not(<)'","title":""},{"docid":"58374d138fad9a770cfcd0841cac34b1","score":"0.5574004","text":"def __gt__(self, other: RGB) -> bool:\n return (\n self.red > other.red and self.green > other.green and self.blue > other.blue\n )","title":""},{"docid":"ffb6f11a020fbab6b50472df6410237c","score":"0.55735373","text":"def __gt__(self, other):\n return self._value.__gt__(float(other))","title":""},{"docid":"167f2e5cf50b12dc7e723093e6b5e4bf","score":"0.5561445","text":"def __gt__(self, *args):\n return _pymaxwell.CfVector___gt__(self, *args)","title":""},{"docid":"c5bcd559a7fdd837f2b39e9be550fcab","score":"0.5560354","text":"def __gt__(self, other):\n return self.id > other.id if isinstance(other, Variable) else self.id > other","title":""},{"docid":"3ee097b5f16fb0489a89d9a526571ddd","score":"0.5558126","text":"def __init__(self):\n _SimpleITK.MinimumImageFilter_swiginit(self, _SimpleITK.new_MinimumImageFilter())","title":""},{"docid":"113860496de2cac5682f44e9164e9abf","score":"0.5557949","text":"def __gt__(self, other):\n if self._numer*other._denom > other._numer*self._denom:\n return True\n else:\n return False","title":""},{"docid":"0dc059712a13bd820040a96f2e784d86","score":"0.55441993","text":"def __init__(self):\n _SimpleITK.IsoDataThresholdImageFilter_swiginit(self, _SimpleITK.new_IsoDataThresholdImageFilter())","title":""},{"docid":"93f9577716121ad3c977a1ddbe262892","score":"0.554238","text":"def __gt__(self, other):\n if isinstance(other, INumericalColumn):\n self.scope.check_is_same(other.scope)\n if isinstance(other, NumericalColumnDemo):\n return self._from_ma(self._ma() > other._ma())\n else:\n return self._from_ma(self._ma() > other)","title":""},{"docid":"139519d9b192fb70b5812bab5f0e8601","score":"0.5542278","text":"def __init__(self):\n _SimpleITK.ClampImageFilter_swiginit(self, _SimpleITK.new_ClampImageFilter())","title":""},{"docid":"20ee8a19b533a8f71f473a43acae4421","score":"0.5542161","text":"def __gt__(self, other):\n if isinstance(other,_StatementBuilder):\n # combine the statement, remove from main list\n self._owner.statement_combined(other)\n self._buffer += \" > %s\" % repr(other)\n return self","title":""},{"docid":"080431de322578b491949cdffdf6ebe9","score":"0.55392885","text":"def __init__(self):\n _SimpleITK.TernaryMagnitudeImageFilter_swiginit(self, _SimpleITK.new_TernaryMagnitudeImageFilter())","title":""},{"docid":"efb349786352d4872a68141a258b5f8e","score":"0.5528561","text":"def __gt__(self, other):\n return self.distance > other.distance","title":""},{"docid":"b1b709390e8b023a0385de357d1d5f3e","score":"0.552372","text":"def __init__(self):\n _SimpleITK.BilateralImageFilter_swiginit(self, _SimpleITK.new_BilateralImageFilter())","title":""},{"docid":"dd75674fbf241ffdba32b6d7b0e3c93a","score":"0.552328","text":"def __gt__(self, other):\n blti0, fi0 = self.uvdata_index\n blti1, fi1 = other.uvdata_index\n if self.baseline == other.baseline:\n if fi0 == fi1:\n return blti0 > blti1\n return fi0 > fi1\n return self.baseline > other.baseline","title":""},{"docid":"34478beac4d8b2c6120a4d6e358c4d84","score":"0.55129445","text":"def __ge__(self, other):\n return self > other or self == other","title":""},{"docid":"e20993730bbb59a2e4010138393de3ad","score":"0.5510837","text":"def test_greaterthan(value, other):\n return value > other","title":""},{"docid":"a5ac5ccd4ba6a405e7e5b81c4f82642c","score":"0.5509724","text":"def __init__(self):\n _SimpleITK.YenThresholdImageFilter_swiginit(self, _SimpleITK.new_YenThresholdImageFilter())","title":""},{"docid":"258c3046aef851be06f652b362c4d028","score":"0.5506824","text":"def __gt__(self, other):\n return 'GT_SENTINEL'","title":""},{"docid":"7aafe61fb4bc78a0cf9b68eca8e17a77","score":"0.55024123","text":"def __gt__(self, other):\n call_generator = partial(self.wheel, other)\n return Wheel(call_generator)","title":""},{"docid":"c2dfaa9220b8c01553e899b458b013f1","score":"0.5492557","text":"def __gt__(self, other):\n\n if self == other:\n return False\n elif self._higherEnergyTransition > other._higherEnergyTransition:\n return True\n elif self._higherEnergyTransition == other._higherEnergyTransition:\n if self._lowerEnergyTransition > other._lowerEnergyTransition:\n return True\n else:\n return False\n else:\n return False","title":""},{"docid":"9ca8a727c4489c5c52ffa5ac72f3da28","score":"0.5490644","text":"def __gt__(self, other):\n try:\n return self.heuristic(self) > self.heuristic(other)\n except Exception:\n raise Exception(\"No heuristic provided\")","title":""},{"docid":"167a81c3e56fdcf8e333c086953c15ef","score":"0.5490042","text":"def __ge__(self, other):\n return (self > other) or (self == other)","title":""},{"docid":"167a81c3e56fdcf8e333c086953c15ef","score":"0.5490042","text":"def __ge__(self, other):\n return (self > other) or (self == other)","title":""},{"docid":"a80ce68d60dfd7f920afde619902a873","score":"0.5489619","text":"def __ge__(self, other):\n return self == other or self > other","title":""},{"docid":"1b22b57b661973418fd8f551600afecc","score":"0.54895985","text":"def __gt__(self,other):\n\t\treturn self.character > other.character","title":""},{"docid":"dced470d37d7b652f2c371f972e16b21","score":"0.5489279","text":"def __init__(self):\n _SimpleITK.UnaryMinusImageFilter_swiginit(self, _SimpleITK.new_UnaryMinusImageFilter())","title":""},{"docid":"e9aab879ec6b794dd9a23b4889fc6dc9","score":"0.5487329","text":"def __init__(self):\n _SimpleITK.TriangleThresholdImageFilter_swiginit(self, _SimpleITK.new_TriangleThresholdImageFilter())","title":""},{"docid":"ae7cdc13498ea423c19341cb4e32b85b","score":"0.54790664","text":"def __gt__(self, other):\n assert self.search.query.objective is not None\n obj_type = str(self.search.query.objective.sense)\n if obj_type == \"maximize\":\n return self.get_objective_value() > other.get_objective_value()\n elif obj_type == \"minimize\":\n return self.get_objective_value() < other.get_objective_value()\n else:\n raise Exception(\"Objective type '{}' not recognized!\".format(obj_type))","title":""},{"docid":"8b90fc5aa141a6f138599d5dad5830d9","score":"0.5457435","text":"def __init__(self):\n _SimpleITK.LiThresholdImageFilter_swiginit(self, _SimpleITK.new_LiThresholdImageFilter())","title":""},{"docid":"e8be5e6630d77d2c8d962a312d2b926f","score":"0.5443576","text":"def __init__(self):\n _SimpleITK.HistogramMatchingImageFilter_swiginit(self, _SimpleITK.new_HistogramMatchingImageFilter())","title":""},{"docid":"f25e489ed770e5b34a21b30e166b3bb1","score":"0.5442578","text":"def __gt__(self, other):\n if self.row > other.row:\n return True\n elif self.row == other.row:\n return self.col > other.col\n else:\n return False","title":""},{"docid":"304e75f9210737b00c089456716b0073","score":"0.54406625","text":"def __gt__(self, other: 'LTL'):\n gt = self >= other\n neq = self != other\n return gt and neq","title":""},{"docid":"d69f593c66a4062912288f47c7a9e871","score":"0.54303855","text":"def __init__(self):\n _SimpleITK.HuangThresholdImageFilter_swiginit(self, _SimpleITK.new_HuangThresholdImageFilter())","title":""}],"string":"[\n {\n \"docid\": \"3516fce8e9fb3bf328b4cc48fdf578fd\",\n \"score\": \"0.7975903\",\n \"text\": \"def __init__(self):\\n _SimpleITK.GreaterEqualImageFilter_swiginit(self, _SimpleITK.new_GreaterEqualImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f164024e19dfe04953f63af16a9ffdb8\",\n \"score\": \"0.71127456\",\n \"text\": \"def Execute(self, *args):\\n return _SimpleITK.GreaterImageFilter_Execute(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082a11075ef7b4f74f1fa8cc5101a683\",\n \"score\": \"0.69463205\",\n \"text\": \"def __init__(self, *args):\\n _Expr.Expr_GreaterThan_swiginit(self,_Expr.new_Expr_GreaterThan(*args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abb0ca7a665e88cbcac20f29c7cbf04\",\n \"score\": \"0.68200463\",\n \"text\": \"def Execute(self, *args):\\n return _SimpleITK.GreaterEqualImageFilter_Execute(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc01f86c47a429540681bad06c4ee30\",\n \"score\": \"0.65644675\",\n \"text\": \"def __gt__(self, other):\\n return self._operation(other, \\\">\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f769f82e57d722a0f0c8a89582d36a8e\",\n \"score\": \"0.6406268\",\n \"text\": \"def _op_greaterthan(self):\\n self._do_binary_op(lambda a, b: int(a > b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1128f4687a009210526534da9616de5\",\n \"score\": \"0.6396495\",\n \"text\": \"def __init__(self):\\n _SimpleITK.LessEqualImageFilter_swiginit(self, _SimpleITK.new_LessEqualImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2ae8dc1c1a13197b5984fadfa14ff4\",\n \"score\": \"0.63914967\",\n \"text\": \"def __gt__(self, other):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac110f017580a6f006aefcfe7d861254\",\n \"score\": \"0.63662004\",\n \"text\": \"def __gt__(self, other):\\n return self._handle_binary_comparison(\\n other,\\n primitives.GreaterThan,\\n primitives.GreaterThanScalar,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20ef47dd8dd7b898501701e13000337\",\n \"score\": \"0.6287866\",\n \"text\": \"def __init__(self, *args):\\n _Expr.Expr_GreaterThanOrEqual_swiginit(self,_Expr.new_Expr_GreaterThanOrEqual(*args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4db8809196b12a403cfc6dfa528fcbc\",\n \"score\": \"0.6273326\",\n \"text\": \"def Greater(*args):\\n return _SimpleITK.Greater(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b10c13fd903c499ca4bee2d2d011d1a\",\n \"score\": \"0.6266256\",\n \"text\": \"def __init__(self):\\n _SimpleITK.MinimumMaximumImageFilter_swiginit(self, _SimpleITK.new_MinimumMaximumImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb5e81a30f1794c4992dc22ce229800\",\n \"score\": \"0.6237459\",\n \"text\": \"def __gt__(self, other):\\n return self.value > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5c5149ec4ed887ba030fe316f16ad4\",\n \"score\": \"0.6199739\",\n \"text\": \"def _op_greaterthanequal(self):\\n self._do_binary_op(lambda a, b: int(a >= b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498f8b11ab93fd75508f19348bf815f6\",\n \"score\": \"0.6157496\",\n \"text\": \"def __gt__(self, other):\\n return self.src > other.src\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58c6ca3243f1006bb493d273bb2bdcd\",\n \"score\": \"0.6147632\",\n \"text\": \"def __gt__(self, other):\\n return self.value > other.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58c6ca3243f1006bb493d273bb2bdcd\",\n \"score\": \"0.6147632\",\n \"text\": \"def __gt__(self, other):\\n return self.value > other.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10da4f354b0e9de565944a2a4ac5262\",\n \"score\": \"0.6139351\",\n \"text\": \"def __gt__(self, other: int):\\n return self.x > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911833183b93b1aadcd253402557aa51\",\n \"score\": \"0.61002004\",\n \"text\": \"def __ge__(self, other):\\n return self._handle_binary_comparison(\\n other,\\n primitives.GreaterThanEqualTo,\\n primitives.GreaterThanEqualToScalar,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d7e0607df9ef105ecd9891bfe26739\",\n \"score\": \"0.6022101\",\n \"text\": \"def __gt__(self, other):\\n op = lambda x, y: x > y\\n msg = \\\"Cannot compare types {0} and {1}\\\"\\n return self.__meta_magic(other, op, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe03e878d862bbc33825e5de30984e1a\",\n \"score\": \"0.60202456\",\n \"text\": \"def __init__(self):\\n _SimpleITK.AbsoluteValueDifferenceImageFilter_swiginit(self, _SimpleITK.new_AbsoluteValueDifferenceImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1910739f9085938bcbfc21f586c938\",\n \"score\": \"0.597116\",\n \"text\": \"def __init__(self):\\n _SimpleITK.MaximumImageFilter_swiginit(self, _SimpleITK.new_MaximumImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddac9a0559fef9ba43e49f04a288db3\",\n \"score\": \"0.59679735\",\n \"text\": \"def __gt__(self, other):\\n if isinstance(other, Symbol):\\n return _internal._greater(self, other)\\n if isinstance(other, numeric_types):\\n return _internal._greater_scalar(self, scalar=other)\\n else:\\n raise TypeError('type %s not supported' % str(type(other)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd855f507964238688f44c4e8e354f2\",\n \"score\": \"0.59448373\",\n \"text\": \"def __gt__(self, other):\\n return self.area() > other.area()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061fe9410f0a47c11e43df78c94169a4\",\n \"score\": \"0.5931065\",\n \"text\": \"def __gt__(self, other):\\n return self >= other and self != other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9aaa739afa36aad7aa9c541899c63af\",\n \"score\": \"0.5920063\",\n \"text\": \"def __gt__(self, other):\\n return (self.label > other.label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58838f9da2627a4990fb7bb1f66822a6\",\n \"score\": \"0.5906365\",\n \"text\": \"def __init__(self):\\n _SimpleITK.LessImageFilter_swiginit(self, _SimpleITK.new_LessImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e796f19152fe83c56f99b30c2936412\",\n \"score\": \"0.59007955\",\n \"text\": \"def __gt__(self, other):\\n return not (self == other or self < other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c7c39a68ef1d96989a25fc6929a338\",\n \"score\": \"0.589164\",\n \"text\": \"def __gt__(self, other):\\n self.__command_args.extend(['>', str(other)])\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdee305bb12bc9ef8c0cc91cab5cf0cc\",\n \"score\": \"0.5879316\",\n \"text\": \"def GreaterEqual(*args):\\n return _SimpleITK.GreaterEqual(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77810d9956184acc0a1a0ab6f165f1fe\",\n \"score\": \"0.58755404\",\n \"text\": \"def __gt__(self, other):\\n return self._comp(other) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a7ec456772fe14ec666b43d8879329\",\n \"score\": \"0.58569014\",\n \"text\": \"def __init__(self):\\n _SimpleITK.EqualImageFilter_swiginit(self, _SimpleITK.new_EqualImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a99f2df5da20e7982c27689b5c2b173c\",\n \"score\": \"0.58473605\",\n \"text\": \"def __init__(self):\\n _SimpleITK.BinaryThresholdImageFilter_swiginit(self, _SimpleITK.new_BinaryThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2786a1aecc1ba3a7f02de6fa1c3782b\",\n \"score\": \"0.58335257\",\n \"text\": \"def __gt__(self, other):\\r\\n if isinstance(other, Shape):\\r\\n if self.name() > other.name():\\r\\n return True\\r\\n if self.name() == other.name() and self.area() > other.area():\\r\\n return True\\r\\n return False\\r\\n raise TypeError(\\\"object passed to greater than is not a Shape\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301c46d6c5417c2abcf78ecd98b129b3\",\n \"score\": \"0.580836\",\n \"text\": \"def __init__(self):\\n _SimpleITK.NaryMaximumImageFilter_swiginit(self, _SimpleITK.new_NaryMaximumImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded20f82861e259691b6e269b79377c8\",\n \"score\": \"0.57955796\",\n \"text\": \"def __gt__(self, other):\\n\\n return (not self < other) and (not self == other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"710bba45ae4862d08b05b4d7332aa754\",\n \"score\": \"0.57872903\",\n \"text\": \"def __gt__(self, other):\\n return self.upper > other.upper > self.lower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdda0a364794e05170b2e14bed1bde38\",\n \"score\": \"0.5778024\",\n \"text\": \"def __init__(self):\\n _SimpleITK.ThresholdMaximumConnectedComponentsImageFilter_swiginit(self, _SimpleITK.new_ThresholdMaximumConnectedComponentsImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cb80b36aef5a0b0742cc097bea6991\",\n \"score\": \"0.57778263\",\n \"text\": \"def __gt__(self, other):\\n try:\\n return self.val > other.val\\n except AttributeError:\\n return self.val > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec797d79b8c5f043543bcc52d0f7ec14\",\n \"score\": \"0.5766795\",\n \"text\": \"def __gt__(self, other):\\n return (self.numerator/self.denominator) > (other.numerator/other.denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6853485612de45ae11768ed20348664\",\n \"score\": \"0.57625264\",\n \"text\": \"def __gt__(self, other):\\n self.__class__.__verify_has_same_class(other)\\n return other < self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f58a55492221fc99f845d2d50b164f0\",\n \"score\": \"0.5761019\",\n \"text\": \"def __gt__(self, other):\\n return not self <= other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f58a55492221fc99f845d2d50b164f0\",\n \"score\": \"0.5761019\",\n \"text\": \"def __gt__(self, other):\\n return not self <= other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f58a55492221fc99f845d2d50b164f0\",\n \"score\": \"0.5761019\",\n \"text\": \"def __gt__(self, other):\\n return not self <= other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957540d6d44c8763bd0ce7087aaf4f22\",\n \"score\": \"0.57384276\",\n \"text\": \"def __gt__(self, value):\\r\\n return self.get_operation_for(self.__field > value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ebc3a631c549d805256f1b5a53e17a\",\n \"score\": \"0.57241553\",\n \"text\": \"def __init__(self):\\n _SimpleITK.ShanbhagThresholdImageFilter_swiginit(self, _SimpleITK.new_ShanbhagThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897d85189c9d8463ba533b1be8baac34\",\n \"score\": \"0.572219\",\n \"text\": \"def __ge__(self, other):\\n return self._operation(other, \\\">=\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5afaa124ef380e0c0ae14f8ac5ec6219\",\n \"score\": \"0.5719371\",\n \"text\": \"def __gt__(self, other):\\n return self._comparison_tuple > other._comparison_tuple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e151c29d41ee670720d89b59a94e7d\",\n \"score\": \"0.57122487\",\n \"text\": \"def __init__(self):\\n _SimpleITK.BinomialBlurImageFilter_swiginit(self, _SimpleITK.new_BinomialBlurImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c5aeb5ea986a62c177c9a49528ff80\",\n \"score\": \"0.57099813\",\n \"text\": \"def __init__(self):\\n _SimpleITK.NotEqualImageFilter_swiginit(self, _SimpleITK.new_NotEqualImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b15f8c76be9c6c2c9ad147538562625\",\n \"score\": \"0.56994975\",\n \"text\": \"def __init__(self):\\n _SimpleITK.ThresholdImageFilter_swiginit(self, _SimpleITK.new_ThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d55e05432de1d94f265bf9edeb757331\",\n \"score\": \"0.56795394\",\n \"text\": \"def __gt__(self, other):\\n return not self.__lt__(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76af530a9792f7de23342d9ca920990\",\n \"score\": \"0.5669293\",\n \"text\": \"def __init__(self):\\n _SimpleITK.MaximumEntropyThresholdImageFilter_swiginit(self, _SimpleITK.new_MaximumEntropyThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41dcd6d2c2f85ba436b9bc88443ee34a\",\n \"score\": \"0.56598055\",\n \"text\": \"def __init__(self):\\n _SimpleITK.AdaptiveHistogramEqualizationImageFilter_swiginit(self, _SimpleITK.new_AdaptiveHistogramEqualizationImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59262a8a3350dc5f5e631db57faec3d\",\n \"score\": \"0.56560826\",\n \"text\": \"def __gt__(self, other):\\n cmp = {0: 2, 1: 0, 2: 1}\\n return cmp[self.action] != other.action\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03459997b81bb43672244399d0730945\",\n \"score\": \"0.56493753\",\n \"text\": \"def __gt__(self, a_rhs):\\n return a_rhs.__lt__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40a1b1234d3629da201ced150199be6\",\n \"score\": \"0.56422174\",\n \"text\": \"def __init__(self):\\n _SimpleITK.DoubleThresholdImageFilter_swiginit(self, _SimpleITK.new_DoubleThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb780a0b6e538ca17318d6111ddaa3ae\",\n \"score\": \"0.56382346\",\n \"text\": \"def __init__(self):\\n _SimpleITK.SubtractImageFilter_swiginit(self, _SimpleITK.new_SubtractImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e966b0e28c700afb493921cfcbca5e6a\",\n \"score\": \"0.5637381\",\n \"text\": \"def __init__(self):\\n _SimpleITK.BinaryPruningImageFilter_swiginit(self, _SimpleITK.new_BinaryPruningImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff6fd130199745dfe371adf6a126155\",\n \"score\": \"0.5632606\",\n \"text\": \"def gt(a, b):\\n return a > b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4147df24e2d5dcd8e861caf893f18c3c\",\n \"score\": \"0.5631325\",\n \"text\": \"def __gt__(self,other):\\r\\n return random.choice( [ True, False ] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a78b2c0714d4e1650428743d76be691\",\n \"score\": \"0.56262636\",\n \"text\": \"def __gt__(self, other):\\n return int(self.num + other.num) > int(other.num + self.num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05dbb859dc7316e1c19261bc18be1ff\",\n \"score\": \"0.56260437\",\n \"text\": \"def __init__(self):\\n _SimpleITK.TernaryAddImageFilter_swiginit(self, _SimpleITK.new_TernaryAddImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bee732a0b026d11ed6d7739a6c997f1\",\n \"score\": \"0.5600518\",\n \"text\": \"def __gt__(self, other):\\r\\n return self.determine_hand_rank() > other.determine_hand_rank()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae09bd6d996cfae164cc785250aa1f19\",\n \"score\": \"0.5596626\",\n \"text\": \"def __gt__(self, other):\\n if float(self) > float(other):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec13252f127f2de8e8e39cfb608a0901\",\n \"score\": \"0.5590017\",\n \"text\": \"def __ge__(self, other):\\n op = lambda x, y: x >= y\\n msg = \\\"Cannot compare types {0} and {1}\\\"\\n return self.__meta_magic(other, op, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d54f4720d0b0ba82f444a0dcd991f0f\",\n \"score\": \"0.55895245\",\n \"text\": \"def __ge__(self, b):\\n\\n return not self.__lt__(b) # '>=' == 'not(<)'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58374d138fad9a770cfcd0841cac34b1\",\n \"score\": \"0.5574004\",\n \"text\": \"def __gt__(self, other: RGB) -> bool:\\n return (\\n self.red > other.red and self.green > other.green and self.blue > other.blue\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb6f11a020fbab6b50472df6410237c\",\n \"score\": \"0.55735373\",\n \"text\": \"def __gt__(self, other):\\n return self._value.__gt__(float(other))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167f2e5cf50b12dc7e723093e6b5e4bf\",\n \"score\": \"0.5561445\",\n \"text\": \"def __gt__(self, *args):\\n return _pymaxwell.CfVector___gt__(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5bcd559a7fdd837f2b39e9be550fcab\",\n \"score\": \"0.5560354\",\n \"text\": \"def __gt__(self, other):\\n return self.id > other.id if isinstance(other, Variable) else self.id > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee097b5f16fb0489a89d9a526571ddd\",\n \"score\": \"0.5558126\",\n \"text\": \"def __init__(self):\\n _SimpleITK.MinimumImageFilter_swiginit(self, _SimpleITK.new_MinimumImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"113860496de2cac5682f44e9164e9abf\",\n \"score\": \"0.5557949\",\n \"text\": \"def __gt__(self, other):\\n if self._numer*other._denom > other._numer*self._denom:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc059712a13bd820040a96f2e784d86\",\n \"score\": \"0.55441993\",\n \"text\": \"def __init__(self):\\n _SimpleITK.IsoDataThresholdImageFilter_swiginit(self, _SimpleITK.new_IsoDataThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f9577716121ad3c977a1ddbe262892\",\n \"score\": \"0.554238\",\n \"text\": \"def __gt__(self, other):\\n if isinstance(other, INumericalColumn):\\n self.scope.check_is_same(other.scope)\\n if isinstance(other, NumericalColumnDemo):\\n return self._from_ma(self._ma() > other._ma())\\n else:\\n return self._from_ma(self._ma() > other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139519d9b192fb70b5812bab5f0e8601\",\n \"score\": \"0.5542278\",\n \"text\": \"def __init__(self):\\n _SimpleITK.ClampImageFilter_swiginit(self, _SimpleITK.new_ClampImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ee8a19b533a8f71f473a43acae4421\",\n \"score\": \"0.5542161\",\n \"text\": \"def __gt__(self, other):\\n if isinstance(other,_StatementBuilder):\\n # combine the statement, remove from main list\\n self._owner.statement_combined(other)\\n self._buffer += \\\" > %s\\\" % repr(other)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080431de322578b491949cdffdf6ebe9\",\n \"score\": \"0.55392885\",\n \"text\": \"def __init__(self):\\n _SimpleITK.TernaryMagnitudeImageFilter_swiginit(self, _SimpleITK.new_TernaryMagnitudeImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb349786352d4872a68141a258b5f8e\",\n \"score\": \"0.5528561\",\n \"text\": \"def __gt__(self, other):\\n return self.distance > other.distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1b709390e8b023a0385de357d1d5f3e\",\n \"score\": \"0.552372\",\n \"text\": \"def __init__(self):\\n _SimpleITK.BilateralImageFilter_swiginit(self, _SimpleITK.new_BilateralImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd75674fbf241ffdba32b6d7b0e3c93a\",\n \"score\": \"0.552328\",\n \"text\": \"def __gt__(self, other):\\n blti0, fi0 = self.uvdata_index\\n blti1, fi1 = other.uvdata_index\\n if self.baseline == other.baseline:\\n if fi0 == fi1:\\n return blti0 > blti1\\n return fi0 > fi1\\n return self.baseline > other.baseline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34478beac4d8b2c6120a4d6e358c4d84\",\n \"score\": \"0.55129445\",\n \"text\": \"def __ge__(self, other):\\n return self > other or self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e20993730bbb59a2e4010138393de3ad\",\n \"score\": \"0.5510837\",\n \"text\": \"def test_greaterthan(value, other):\\n return value > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ac5ccd4ba6a405e7e5b81c4f82642c\",\n \"score\": \"0.5509724\",\n \"text\": \"def __init__(self):\\n _SimpleITK.YenThresholdImageFilter_swiginit(self, _SimpleITK.new_YenThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258c3046aef851be06f652b362c4d028\",\n \"score\": \"0.5506824\",\n \"text\": \"def __gt__(self, other):\\n return 'GT_SENTINEL'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aafe61fb4bc78a0cf9b68eca8e17a77\",\n \"score\": \"0.55024123\",\n \"text\": \"def __gt__(self, other):\\n call_generator = partial(self.wheel, other)\\n return Wheel(call_generator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2dfaa9220b8c01553e899b458b013f1\",\n \"score\": \"0.5492557\",\n \"text\": \"def __gt__(self, other):\\n\\n if self == other:\\n return False\\n elif self._higherEnergyTransition > other._higherEnergyTransition:\\n return True\\n elif self._higherEnergyTransition == other._higherEnergyTransition:\\n if self._lowerEnergyTransition > other._lowerEnergyTransition:\\n return True\\n else:\\n return False\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca8a727c4489c5c52ffa5ac72f3da28\",\n \"score\": \"0.5490644\",\n \"text\": \"def __gt__(self, other):\\n try:\\n return self.heuristic(self) > self.heuristic(other)\\n except Exception:\\n raise Exception(\\\"No heuristic provided\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167a81c3e56fdcf8e333c086953c15ef\",\n \"score\": \"0.5490042\",\n \"text\": \"def __ge__(self, other):\\n return (self > other) or (self == other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167a81c3e56fdcf8e333c086953c15ef\",\n \"score\": \"0.5490042\",\n \"text\": \"def __ge__(self, other):\\n return (self > other) or (self == other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80ce68d60dfd7f920afde619902a873\",\n \"score\": \"0.5489619\",\n \"text\": \"def __ge__(self, other):\\n return self == other or self > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b22b57b661973418fd8f551600afecc\",\n \"score\": \"0.54895985\",\n \"text\": \"def __gt__(self,other):\\n\\t\\treturn self.character > other.character\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dced470d37d7b652f2c371f972e16b21\",\n \"score\": \"0.5489279\",\n \"text\": \"def __init__(self):\\n _SimpleITK.UnaryMinusImageFilter_swiginit(self, _SimpleITK.new_UnaryMinusImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9aab879ec6b794dd9a23b4889fc6dc9\",\n \"score\": \"0.5487329\",\n \"text\": \"def __init__(self):\\n _SimpleITK.TriangleThresholdImageFilter_swiginit(self, _SimpleITK.new_TriangleThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7cdc13498ea423c19341cb4e32b85b\",\n \"score\": \"0.54790664\",\n \"text\": \"def __gt__(self, other):\\n assert self.search.query.objective is not None\\n obj_type = str(self.search.query.objective.sense)\\n if obj_type == \\\"maximize\\\":\\n return self.get_objective_value() > other.get_objective_value()\\n elif obj_type == \\\"minimize\\\":\\n return self.get_objective_value() < other.get_objective_value()\\n else:\\n raise Exception(\\\"Objective type '{}' not recognized!\\\".format(obj_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b90fc5aa141a6f138599d5dad5830d9\",\n \"score\": \"0.5457435\",\n \"text\": \"def __init__(self):\\n _SimpleITK.LiThresholdImageFilter_swiginit(self, _SimpleITK.new_LiThresholdImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8be5e6630d77d2c8d962a312d2b926f\",\n \"score\": \"0.5443576\",\n \"text\": \"def __init__(self):\\n _SimpleITK.HistogramMatchingImageFilter_swiginit(self, _SimpleITK.new_HistogramMatchingImageFilter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25e489ed770e5b34a21b30e166b3bb1\",\n \"score\": \"0.5442578\",\n \"text\": \"def __gt__(self, other):\\n if self.row > other.row:\\n return True\\n elif self.row == other.row:\\n return self.col > other.col\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304e75f9210737b00c089456716b0073\",\n \"score\": \"0.54406625\",\n \"text\": \"def __gt__(self, other: 'LTL'):\\n gt = self >= other\\n neq = self != other\\n return gt and neq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69f593c66a4062912288f47c7a9e871\",\n \"score\": \"0.54303855\",\n \"text\": \"def __init__(self):\\n _SimpleITK.HuangThresholdImageFilter_swiginit(self, _SimpleITK.new_HuangThresholdImageFilter())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1478,"cells":{"query_id":{"kind":"string","value":"847ca392c243018c190d6237ec0e2352"},"query":{"kind":"string","value":"list all the G(r) plotted on the figure now"},"positive_passages":{"kind":"list like","value":[{"docid":"242e919f2ea08f56bc288b869e8ee391","score":"0.6194304","text":"def get_current_grs(self):\n return list(self._grDict.keys())","title":""}],"string":"[\n {\n \"docid\": \"242e919f2ea08f56bc288b869e8ee391\",\n \"score\": \"0.6194304\",\n \"text\": \"def get_current_grs(self):\\n return list(self._grDict.keys())\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"065bd8f9420f9d7fe78a71dd623e2dcf","score":"0.69316924","text":"def plot(self):\n return []","title":""},{"docid":"62de181701f8f7af09e91c98fc09444f","score":"0.63560665","text":"def show_plots():","title":""},{"docid":"62de181701f8f7af09e91c98fc09444f","score":"0.63560665","text":"def show_plots():","title":""},{"docid":"14b17e42080391c98fd0b8a7cd66de29","score":"0.62527966","text":"def showFigures():\n pyplot.show()","title":""},{"docid":"b15382ff7e5413c58f939ddcb600f9cc","score":"0.6202436","text":"def plot(self):","title":""},{"docid":"a4240a5aadf84ac63cc47713c81c8741","score":"0.61292875","text":"def plot(self):\n pass","title":""},{"docid":"63bc38324904f72fb33d1d18e0e8be16","score":"0.60767907","text":"def MakeFigures(self):\r\n self.PlotDistN()\r\n self.PlotPrevalences()","title":""},{"docid":"3ef468de5fefb40a5f06870dfa55fc00","score":"0.603339","text":"def cmd_plot():","title":""},{"docid":"c7e62b0d719934abc9f4883443d594e2","score":"0.5980389","text":"def get_plot(self):\n pass","title":""},{"docid":"4645ffc99e2fd6302b4a462a9e4ba8cd","score":"0.589725","text":"def generate_plots():\n logger(\"plots\").info(\"Generating plots\")","title":""},{"docid":"57cab97920f207c6091141a1e4f549e2","score":"0.5846857","text":"def scree_plot(self):\n pass","title":""},{"docid":"4aa73fa5a14c851f206804327d30bd22","score":"0.5829319","text":"def all_figure_numbers(cls):\n return list(cls._figures.keys())","title":""},{"docid":"397af6e240b5f0a90c2e67fa6a935630","score":"0.58169323","text":"def print_figure(self):\n return [\n [\n pos[0] * self.pixel_ratio - 1,\n pos[1] * self.pixel_ratio + 1,\n self.pixel_ratio - 1,\n self.pixel_ratio - 1\n ]\n for pos in self.repr_figure_on_board()\n ]","title":""},{"docid":"31336f0b9696eaa72c7fbe20069312df","score":"0.5773237","text":"def plots(self):\n return list(self.metadata.get(\"plots\", {}))","title":""},{"docid":"c1b0937d10a7d7b9d27a08eea0bd22c4","score":"0.5761019","text":"def show_fig416(self):\n\t\tradiusplot = []\n\t\tdeltamplot = []\n\t\tmplot = []\n\t\tsigmaplot = []\n\t\tvelocityplot = []\n\t\tomegaplot = []\n\n\t\ti=0\n\t\tfor star in self.galaxy:\n\t\t\ti+=1\n\t\t\tif i % 100000 == 0:\n\t\t\t\tprint(i)\n\t\t\tradiusplot.append(star.radius)\n\t\t\tdeltamplot.append(self.deltaM[star.inring]*100)\n\t\t\tmplot.append(self.Mr[star.inring])\n\t\t\tsigmaplot.append(star.area_density)\n\t\t\tvelocityplot.append(star.va*3.086e+16/7.5e15)\n\t\t\tomegaplot.append(self.omega[star.inring])\n\n\t\tfig, axarr = plt.subplots(2,sharex=True,figsize=(9,10))\n\t\taxarr[0].plot(radiusplot,deltamplot,'ko', ms=0.5)\n\t\taxarr[0].plot(radiusplot,mplot,'ko',ms=0.5)\n\t\t# Make the y-axis label, ticks and tick labels match the line color.\n\t\taxarr[0].set_ylabel(r'$100 \\times M(r)/M_{\\odot}$, $M(<r)/M_{\\odot}$',\\\n\t\t\t\t\t\t\t\tcolor='k')\n\t\taxarr[0].set_xlim([0,30])\n\t\taxarr[0].set_ylim([0,1.1e11])\n\t\taxarr[0].text(22,9e10,r'$M(<r)$')\n\t\taxarr[0].text(11,5e10,r'$100\\times \\Delta M(r)$')\n\t\taxarr[0].text(22,1e10,r'$N=10^6$')\n\n\t\tax1 = axarr[0].twinx()\n\t\tax1.plot(radiusplot, sigmaplot, 'ro',ms=0.5)\n\t\tax1.set_ylabel(r'$\\sigma (r) (M_{\\odot}$ kpc$^{-2}$)', color='r')\n\t\tax1.tick_params('y', which='both',colors='r')\n\t\tax1.set_ylim([0,1e9])\n\t\tax1.set_xlim([0,30])\n\t\tax1.spines['right'].set_color('red')\n\n\t\taxarr[1].plot(radiusplot,velocityplot,'ko',ms=0.5)\n\t\taxarr[1].set_ylabel(r'$v_{rot}$ (km s$^{-1}$)', color='k')\n\t\taxarr[1].set_xlabel(r'$r$ (kpc)')\n\t\taxarr[1].set_xlim([0,30])\n\t\taxarr[1].set_ylim([0,250])\n\n\t\tax2 = axarr[1].twinx()\n\t\tax2.plot(radiusplot,omegaplot,'ro',ms=0.5)\n\t\tax2.set_ylabel(r'$\\Omega (r)$', color='r')\n\t\tax2.tick_params('y', which='both',colors='r')\n\t\tax2.set_ylim([0,40])\n\t\tax2.set_xlim([0,30])\n\t\tax2.spines['right'].set_color('red')\n\n\t\tfig.tight_layout()\n\t\tfig.savefig(\"/Users/user/416.png\",format='png', dpi=1000)\n\t\tplt.show()","title":""},{"docid":"aa5443c3f98dcf773486e9e6fa8175b2","score":"0.57598656","text":"def get_chrom_axes(grid=None, parent_gridelement=None, fig=None,\n ylabel=\"\", plot_xlabels=True, color=None, plotfun=None):\n if plotfun is None:\n plotfun = plt.plot\n if grid is None:\n grid, parent_gridelement = get_chrom_grid(parent_gridelement=parent_gridelement)\n if fig is None:\n fig = plt.figure(1, figsize=(20, 2))\n fig.subplots_adjust(wspace=0)\n # print color\n if color is None:\n print(\"changed to current color\")\n ax = plt.gca()\n color = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"][0]\n color = converter.to_rgb(color)\n chroms = series.index.get_level_values(level=0).unique()\n for i, (chrom, gr) in enumerate(zip(chroms, grid)):\n if i == 0:\n ax = fig.add_subplot(gr)\n else:\n ax = fig.add_subplot(gr, sharey=ax)\n ax.set_yticks([])\n\n if i % 2 == 0:\n color1 = tuple(np.array(color) * 0.6)\n if plot_xlabels:\n ax.set_xlabel('Chr' + chrom.rsplit(\"_\")[1])\n else:\n color1 = color\n # print color1\n ax.set_frame_on(False)\n ax.set_xticks([])\n ax.set_prop_cycler([color1])\n ylim = ax.get_ylim()\n # we could also get the parent element with gr.get_topmost_subplotspec()\n total_ax = fig.add_subplot(parent_gridelement)\n total_ax.set_frame_on(False)\n total_ax.spines['top'].set_visible(True)\n total_ax.set_ylim(ylim)\n total_ax.set_xticks([])\n if not ylabel and series.name is not None:\n ylabel = series.name\n total_ax.set_ylabel(ylabel, fontsize=14)\n if plot_xlabels:\n total_ax.set_xlabel(\"Chromosomes\", labelpad=25, fontsize=14)\n return fig.get_axes()","title":""},{"docid":"481f86e1d57272a8d68181be7ead75ad","score":"0.57525766","text":"def grads(self):\r\n return []","title":""},{"docid":"af19b9647b5f155b753fe51707b4c81d","score":"0.5750883","text":"def plot_results(self, save_filename = None, plot = True):\n \n # Check if the length of the gc is not good, and append the last\n # gradient\n if len(self.__gc__) != len(self.__fe__):\n #grad, grad_err = self.ensemble.get_fc_from_self_consistency(True, True)\n# grad, grad_err = self.ensemble.get_preconditioned_gradient(True, True)\n# grad = self.__gc__[-1]\n# grad_err = self.__gc_err__[-1]\n# \n# # Initialize the symmetry\n# qe_sym = CC.symmetries.QE_Symmetry(self.dyn.structure)\n# qe_sym.SetupQPoint(verbose = True)\n# \n# struct_grad, struct_grad_err = self.ensemble.get_average_forces(True)\n# \n# qe_sym.SymmetrizeVector(struct_grad)\n# qe_sym.SymmetrizeVector(struct_grad_err)\n# \n# # Get the gradient modulus\n# gc = np.einsum(\"abc, acb\", grad, grad)\n# gc_err = np.einsum(\"abc, acb\", grad_err, grad_err)\n \n self.__gw__.append(self.__gw__[-1])#np.sqrt( np.einsum(\"ij, ij\", struct_grad, struct_grad)))\n self.__gw_err__.append(self.__gw_err__[-1])#np.sqrt( np.einsum(\"ij, ij\", struct_grad_err, struct_grad_err) / qe_sym.QE_nsymq))\n \n self.__gc__.append(self.__gc__[-1])\n self.__gc_err__.append(self.__gc_err__[-1])\n \n # Convert the data in numpy arrays\n fe = np.array(self.__fe__) * __RyTomev__\n fe_err = np.array(self.__fe_err__) * __RyTomev__\n \n gc = np.array(self.__gc__) * __RyTomev__\n gc_err = np.array(self.__gc_err__) * __RyTomev__\n \n gw = np.array(self.__gw__) * __RyTomev__\n gw_err = np.array(self.__gw_err__) * __RyTomev__\n \n kl = np.array(self.__KL__, dtype = np.float64)\n \n steps = np.arange(len(fe))\n \n \n # Check if the results need to be saved on a file\n if save_filename is not None: \n #print \"Lengths:\", len(fe), len(gc), len(gw), len(kl), len(steps) \n print (\"Saving the minimization results...\")\n save_data = [steps, fe, fe_err, gc, gc_err, gw, gw_err, kl]\n np.savetxt(save_filename, np.real(np.transpose(save_data)),\n header = \"Steps; Free energy +- error [meV]; FC gradient +- error [bohr^2]; Structure gradient +- error [meV / A]; Kong-Liu N_eff\")\n print (\"Minimization data saved in %s.\" % save_filename)\n \n \n # Plot\n if plot:\n if not __MATPLOTLIB__:\n print (\"MATPLOTLIB NOT FOUND.\")\n raise ImportError(\"Error, matplotlib required to plot\")\n plt.figure()\n plt.title(\"Free energy\")\n plt.errorbar(steps, fe, yerr = fe_err, label = \"Free energy\")\n plt.ylabel(r\"$F$ [meV]\")\n plt.xlabel(\"steps\")\n plt.tight_layout()\n \n plt.figure()\n plt.title(\"FC matrix gradient\")\n plt.errorbar(steps, gc, yerr = gc_err, label = \"gradient\")\n plt.ylabel(r\"$|\\vec g|$ [bohr^2]\")\n plt.xlabel(\"steps\")\n plt.tight_layout()\n \n plt.figure()\n plt.title(\"Structure gradient\")\n plt.errorbar(steps, gw, yerr = gw_err, label = \"gradient\")\n plt.ylabel(r\"$|\\vec g|$ [meV / A]\")\n plt.xlabel(\"steps\")\n plt.tight_layout()\n \n plt.figure()\n plt.title(\"Kong-Liu effective sample size\")\n plt.plot(steps, kl)\n plt.ylabel(r\"$\\frac{N_{eff}}{N_0}$\")\n plt.xlabel(\"steps\")\n plt.tight_layout()\n \n plt.show()","title":""},{"docid":"9f3c9a1bb0ada998931a6b2323b0b754","score":"0.5749016","text":"def plotGeneration(self):\n to_plot = self.__fitnesses\n # plt.clf()\n plt.plot(to_plot, 'g^')\n plt.ylabel('fitness')\n plt.xlabel('individual')\n plt.show()\n self.__fitnesses = []","title":""},{"docid":"7733d9c56c3559639426f5bd708eb6a7","score":"0.5736658","text":"def show_plots(self):\n pass","title":""},{"docid":"d8c269297eb365ad945dffe642d4a1b1","score":"0.573472","text":"def draw_plots(self):\n for f in self.figurePlots:\n f.draw_plot()","title":""},{"docid":"f07f725da8a49b52841b5ee4d9ed54f1","score":"0.57297593","text":"def plot(self):\n plt.figure()\n if self.exog == None:\n self.plotting_proc(0)\n else:\n for g in range(len(self.groups)):\n self.plotting_proc(g)\n plt.ylim(ymax=1.05)\n plt.ylabel('Survival')\n plt.xlabel('Time')","title":""},{"docid":"4b3c3f04e9e167fff37c9c266fcb501b","score":"0.57122016","text":"def display_generation(self):\n if self.t == 0:\n plt.subplot(121)\n plt.subplot(122)\n\n plt.sca(plt.gcf().axes[0])\n plt.cla()\n plt.plot(self.archive[:self.t + 1, :, I_GENOME[0]].reshape(-1), self.archive[:self.t + 1, :, I_GENOME[1]].reshape(-1), \"k.\", label=\"archive\")\n plt.plot(self.population[self.t, :, I_GENOME[0]], self.population[self.t, :, I_GENOME[1]], \"b.\", label=\"population\")\n plt.plot(self.offsprings[self.t, :, I_GENOME[0]], self.offsprings[self.t, :, I_GENOME[1]], \"r.\", label=\"offsprings\")\n plt.axis([-5, 5, -5, 5])\n if self.t == 0:\n plt.legend()\n\n plt.sca(plt.gcf().axes[1])\n plt.plot(self.archive[:self.t + 1, :, I_BEHAVIOR].reshape(-1), np.full(((self.t + 1) * self.n_selected), self.t),\n \"k.\", markersize=10, label=\"archive\")\n plt.plot(self.population[self.t, :, I_BEHAVIOR], np.full(self.n_pop, self.t + 0.1),\n \"b.\", markersize=10, label=\"population\")\n plt.plot(self.offsprings[self.t, :, I_BEHAVIOR], np.full(self.n_offspring, self.t + 0.2),\n \"r.\", markersize=10, label=\"offsprings\")\n\n novelty_landscape = self.compute_novelty(np.linspace(-5, 5, 100))\n plt.plot(np.linspace(-5, 5, 100), novelty_landscape + self.t, color=\"g\", label=\"novelty\")\n\n selected = np.argwhere(self.population[self.t, :, I_SELECTED_POP] == 1.)\n plt.plot(self.population[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.1),\n \"bo\", markersize=14, fillstyle=\"none\", label=\"selected for population\")\n selected = np.argwhere(self.offsprings[self.t, :, I_SELECTED_POP] == 1.)\n plt.plot(self.offsprings[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.2),\n \"bo\", markersize=14, fillstyle=\"none\")\n selected = np.argwhere(self.offsprings[self.t, :, I_SELECTED_ARC] == 1.)\n plt.plot(self.offsprings[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.2),\n \"kd\", markersize=14, fillstyle=\"none\", label=\"selected for population\")\n plt.xlim([-5, 5])\n if self.t == 0:\n plt.legend()\n\n plt.title(\"generation \" + str(self.t))\n plt.show(block=False)\n plt.waitforbuttonpress()","title":""},{"docid":"a5daba460d1ea8430681ac4a6a3ed867","score":"0.56904256","text":"def main():\n plot_grid()\n import matplotlib.pyplot as plt\n plt.show()","title":""},{"docid":"3890ba7b749cc8509f95066ccc461c5a","score":"0.5680215","text":"def plot(self):\n return hdmr_plot(self)","title":""},{"docid":"cb26ba2a048335db2dc0f167f7eb11bf","score":"0.5666373","text":"def plot_gal_region(self, gal_region, output_stamps=None, ncols=6, savefig=True, name_plot=None):\n\n\t\timport matplotlib.pyplot as plt\n\t\tfrom matplotlib.colors import ListedColormap\n\n\t\tfilters = self.filters\n\t\tnbands = len(filters)\n\n\t\tif output_stamps is None:\n\t\t\tif self.run_image_processing == False:\n\t\t\t\tprint ('output_stamps is required input if run_image_processing=False!')\n\t\t\t\tsys.exit()\n\t\t\telse:\n\t\t\t\toutput_stamps = output_stamps1\n\n\t\trows, cols = np.where(gal_region==0)\n\t\tgal_region[rows,cols] = float('nan')\n\n\t\tmap_plots, nrows = mapping_multiplots(nbands,ncols)\n\n\t\tfig1 = plt.figure(figsize=(ncols*4,nrows*4))\n\t\tfor bb in range(0,nbands):\n\t\t\tyy, xx = np.where(map_plots==bb+1)\n\t\t\tf1 = fig1.add_subplot(nrows, ncols, bb+1)\n\t\t\tif map_plots[yy[0]][xx[0]-1] == 0:\n\t\t\t\tplt.ylabel('[pixel]', fontsize=15)\n\t\t\tif map_plots[yy[0]+1][xx[0]] == 0:\n\t\t\t\tplt.xlabel('[pixel]', fontsize=15)\n\n\t\t\thdu = fits.open(output_stamps[\"name_img_%s\" % filters[bb]])\n\t\t\tplt.imshow(np.log10(hdu[0].data), origin='lower')\n\t\t\thdu.close()\n\t\t\tplt.imshow(gal_region, origin='lower', cmap=ListedColormap(['brown']), alpha=0.5)\n\t\t\tf1.text(0.5, 0.93, filters[bb], horizontalalignment='center', verticalalignment='center', \n\t\t\t\t\ttransform = f1.transAxes, fontsize=18, color='black')\n\n\t\tplt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.15)\n\n\t\tif savefig is True:\n\t\t\tif name_plot is None:\n\t\t\t\tname_plot = 'gal_region.png'\n\t\t\tplt.savefig(name_plot)\n\t\telse:\n\t\t\tplt.show()","title":""},{"docid":"6fc8e2b916489a2fb12dbb0bd050a44c","score":"0.5660283","text":"def plot_all_nav(directory = '.'):\n fig = plt.figure()\n ax1 = fig.add_subplot(221)\n ax2 = fig.add_subplot(222)\n ax3 = fig.add_subplot(614, sharex = ax2)\n ax4 = fig.add_subplot(615, sharex = ax2)\n ax5 = fig.add_subplot(616, sharex = ax2)\n flist = glob(directory + '/*.all')\n flist.sort()\n clist = ['b','r','g','k','y','c']\n n = 0\n for f in flist:\n a = allRead(f)\n if os.path.exists(f + '.nav'):\n a.load_navarray()\n else:\n a._build_navarray()\n a.save_navarray()\n ax1.plot(a.navarray['80'][:,1],a.navarray['80'][:,2], clist[n])\n ax1.set_xlabel('Longitude (Degrees)')\n ax1.set_ylabel('Latitude (Degrees)')\n ax1.grid(True)\n ax2.plot(a.navarray['65'][:,0],a.navarray['65'][:,4], clist[n])\n ax2.set_ylabel('Heading (Degrees)')\n ax2.set_xlabel('Time (Seconds)')\n ax2.grid(True)\n if a.navarray.has_key('104'):\n ax3.plot(a.navarray['104'][:,0],a.navarray['104'][:,1], clist[n])\n ax3.set_ylabel('Height (Meters)')\n ax3.grid(True)\n ax4.plot(a.navarray['65'][:,0],a.navarray['65'][:,1], clist[n])\n ax4.plot(a.navarray['65'][:,0],a.navarray['65'][:,2], clist[n] + '--')\n ax4.set_ylabel('Degress')\n ax4.legend(('Roll','Pitch'))\n ax4.grid(True)\n ax5.plot(a.navarray['65'][:,0],a.navarray['65'][:,3], clist[n])\n ax5.set_ylabel('Heave (Meters)')\n ax5.set_xlabel('Time (Seconds)')\n ax5.grid(True)\n n += 1\n if n >= len(clist):\n n = 0\n plt.draw()","title":""},{"docid":"aa2c2800df6982631017383eec11be5b","score":"0.5659238","text":"def gcfn():\n currentFig = mpl.pyplot.gcf()\n for k in mpl.pyplot.get_fignums():\n mpl.pyplot.figure(k)\n if mpl.pyplot.gcf() == currentFig:\n return k","title":""},{"docid":"637658b46fae402dc3bf5c890a9acef9","score":"0.5655926","text":"def _calculate_glare(self) -> List[DrawObject]:\n return [\n GlareObject.generate_random_figure(*self._src_image.size)\n for _ in range(self._aug_numberofoptions)\n ]","title":""},{"docid":"63c5ad9601c8c48becd2568a0ed40cbd","score":"0.5645673","text":"def qf_plot(self):\n lgm = self.lg_masses()\n sfr = np.array([galaxy.sfr for galaxy in self.galaxies])\n hq, edges = np.histogram(lgm[sfr == 0], self.bins)\n h, edges = np.histogram(lgm, self.bins)\n qf = hq/h\n plt.clf()\n plt.plot(self.lgM, qf, label='Current')\n plt.plot(self.lgM, self.qf_end, label='Target (GAMA groups)')\n plt.legend()\n plt.xlabel(r'log $M^*/M_\\odot$')\n plt.ylabel('Red fraction')\n plt.show()","title":""},{"docid":"a04952ae541c725ca7d1d4dabb11fae8","score":"0.5628412","text":"def plot_locations(self, fig: plt.figure = None):\n\n CoB = data['Structures']['CoB']\n\n\n if fig is None:\n fig, ax = plt.subplots()\n ax.set_aspect(1.0)\n plt.xlim(0, 15)\n plt.ylim(0, 15)\n\n coordinate_history = []\n xs = []\n zs = []\n text = []\n for name, location in self.__components.items():\n weight = location[0]\n if len(location[1]) == 2:\n x = location[1][0]\n z = location[1][1]\n if len(location[1]) == 3:\n x = location[1][0]\n z = location[1][1]\n y = location[1][2]\n i = 1\n print(x,z)\n if (x, z) in coordinate_history:\n i = 3\n matplotlib.rcParams.update({'font.size': 8})\n plt.scatter(x, z, s=np.sqrt(weight), c='k')\n #plt.annotate(name, xy=(x+i*0.1, z+i*0.1))\n coordinate_history.append((x,z))\n xs.append(x)\n zs.append(z)\n text.append(name)\n texts = []\n for (a,b,c) in zip(xs,zs,text):\n texts.append(plt.text(a,b,c))\n\n plt.scatter(self.__cg[0], self.__cg[1], c='r')\n plt.annotate('CG', xy=self.__cg)\n plt.scatter(CoB[0], CoB[1], c='b')\n plt.annotate('CoB', xy=CoB)\n\n # fuselage = self.__plot_cilinder(9, 2.5)\n # wing = self.__plot_cilinder(2.25, 0.5, offset=(3, 1.5))\n #\n # plt.plot(fuselage[0], fuselage[1], 'k-')\n # plt.plot(wing[0], wing[1], 'k-')\n\n plt.grid()\n plt.xlabel('X Position [m]')\n plt.ylabel('Z Position [m]')\n plt.title('CG Location')\n adjust_text(texts)\n plt.show()","title":""},{"docid":"15a56f98af91d09fc31a4bae3efc9045","score":"0.56251925","text":"def get_all_fig_managers(cls):\n return list(cls._figures.values())","title":""},{"docid":"1c06457a4b0cb4d1eb0037b85af70ca1","score":"0.56244075","text":"def plotGrid(self,g):\n # Get system size\n N = g.shape[0]\n \n # Rotate grid, such that the view is correct (Maybe not necessary)\n rotg = np.rot90(g,3)\n\n\n # Extend the matrix, so we plot 9 unit cells\n tmp = np.concatenate((rotg,rotg),axis=0)\n tmp = np.concatenate((tmp,tmp),axis=0)\n tmp2 = np.concatenate((tmp,tmp),axis=1)\n rotg = np.concatenate((tmp2,tmp2),axis=1)\n\n # Find Ag index and pair them in a list\n indexAg = np.where(rotg==1)\n PairsAg = list( [indexAg[0][i], indexAg[1][i] ] for i in range(len(indexAg[0])))\n\n # Find O index and pair them in a list\n indexO = np.where(rotg==2)\n PairsO = list( [indexO[0][i], indexO[1][i] ] for i in range(len(indexO[0])))\n\n # Define the shearing matrix\n shearMat = np.array([[1, 0.5],[0,1]])\n\n PairsAg = np.subtract(PairsAg,(N*2))\n PairsO = np.subtract(PairsO,(N*2))\n\n # Multiply all coordinate pairs with the shearing matrix.\n # The \".T\" transposes such that CoordsAg[0] are x coordinates, CoordsAg[1] are y coordinates etc. \n CoordsAg = np.array(list( np.dot(shearMat,i) for i in PairsAg)).T\n CoordsO = np.array(list( np.dot(shearMat,i) for i in PairsO)).T\n\n # CoordsAg = np.subtract(CoordsAg,(N-1))\n # CoordsO = np.subtract(CoordsO,(N-1))\n\n # Update the x and y values of the plots\n self.AgPlot.set_xdata(CoordsAg[0])\n self.AgPlot.set_ydata(CoordsAg[1])\n self.OPlot.set_xdata(CoordsO[0])\n self.OPlot.set_ydata(CoordsO[1])\n\n # Pause a millisecond, so pyplot updates the graphics\n plt.pause(.001)","title":""},{"docid":"d65eb680a65479e68e06b6586b1d97f9","score":"0.5612948","text":"def _gripper_visualization(self):\n pass","title":""},{"docid":"749f8c41ec0b1211cad399b0678ded70","score":"0.5606378","text":"def all_figures(cls):\n return list(cls._figures.values())","title":""},{"docid":"7a2b239ef13e854a788f4860869c7aa3","score":"0.5606249","text":"def gp_only():\n plotter = Plotter('GP only tree size vs test error', 'Tree size', 'Test error')\n directory = '../result_data/gp_only/'\n gp_only_files = os.listdir(directory)\n for filename in gp_only_files:\n plotter.add_trace_from_csv(os.path.join(directory, filename), 'tree size', 'test error')\n plotter.plot()","title":""},{"docid":"39ff76e55e2df27279256ede7c46a823","score":"0.5597997","text":"def plot(self):\n info('*** Plot\\n')\n self.itfs = [t for t in self.itfs if t in self.data]\n gui(self.data, (self.ast, self.aet), self.itfs)","title":""},{"docid":"f656c7e2bdd86aeda0af3098cbe01b82","score":"0.55958873","text":"def _plot(self, Xup, Xdown, ifig, num, numfiles):","title":""},{"docid":"977de0e4ec2e7603f5a5a34f07e12e4d","score":"0.5592947","text":"def return_figures():\n\n df = query_generation('DE', 14)\n\n graph_one = []\n x_val = df.index\n\n for energy_source in df.columns:\n\n y_val = df[energy_source].tolist()\n\n graph_one.append(\n go.Scatter(\n x=x_val,\n y=y_val,\n mode='lines',\n name=energy_source,\n stackgroup = 'one'\n )\n )\n\n layout_one = dict(title='Generation in Germany during the last 14 days',\n xaxis=dict(title='Date'),\n yaxis=dict(title='Net Generation (MW)'),\n colorway = ['#008000', '#ffa500', '#ff0000', '#000080', '#008080', '#808080', '#a52a2a', '#1e90ff', '#ffc40c'],\n plot_bgcolor = '#E8E8E8',\n hovermode = 'closest', \n hoverdistance = -1,\n height = 500\n )\n\n # append all charts to the figures list\n figures = []\n figures.append(dict(data=graph_one, layout=layout_one))\n\n return figures","title":""},{"docid":"438a7f813c2d87f2fa6f0e2b75473a37","score":"0.55875325","text":"def init_plots(self):\n return None","title":""},{"docid":"0296bc833cf8baa3c2dc38e225f2950b","score":"0.55847204","text":"def get_graphics_list( self ):\r\n return self.OGLDrawables[:] # This is a shallow copy and intentionally so\r","title":""},{"docid":"fd17cc2dce0d1aeea525f7003402fcb3","score":"0.5576919","text":"def create_plots(self):\n from matplotlib.pylab import figure, subplot, scatter, ylabel, xlabel, \\\n xlim, ylim, rcParams, colorbar, axis, title\n \n # Alias data from the search results\n evo = self.results['evolution']\n params = self.results['parameters']\n p_names = params.keys()\n p_names.sort()\n Np = len(p_names) # number of parameters\n\n # Set color axis variables and key flag strings\n vargs = {}\n normstr = ''\n boundsstr = ''\n if self.norm_caxis:\n vargs['vmin'] = min([g.fitness.min() for g in evo])\n vargs['vmax'] = max([g.fitness.max() for g in evo])\n normstr = '_norm'\n if self.use_bounds:\n boundsstr = '_full'\n\n # Create per-generation param x param plots\n rcParams['figure.figsize'] = self.figsize_inches\n self.figure = {}\n for gen in xrange(len(evo)):\n f = figure()\n f.set_size_inches(self.figsize_inches)\n for j in xrange(0,Np-1):\n for i in xrange(j+1,Np):\n subplot(Np-1, Np-1, (Np-1)*(i-1) + (j+1))\n scatter(evo[gen][p_names[j]], evo[gen][p_names[i]], \n c=evo[gen].fitness, s=self.marker_size, \n edgecolors='none', **vargs)\n if j==0:\n ylabel(p_names[i])\n if i==Np-1:\n xlabel(p_names[j])\n if j==0 and i==1:\n title('generation_%02d'%(gen+1))\n if self.use_bounds:\n xlim(params[p_names[j]])\n ylim(params[p_names[i]])\n else:\n axis('tight')\n \n # Add colorbar in upper right\n subplot(Np-1, Np-1, Np-1)\n colorbar(fraction=0.5, aspect=10)\n axis('off') \n \n # Store this figure in the plots dictionary\n figkey = 'gen%02d%s%s'%(gen+1, normstr, boundsstr)\n self.figure[figkey] = f","title":""},{"docid":"d140a6197a2df2cd78c5a62b3d2e28e2","score":"0.5555535","text":"def showCalData(data):\n gs=gridspec.GridSpec(4,4)\n for gradData in dat.magDataGrad:\n for k,key in enumerate(gradData.keys()):\n ax=subplot(gs[k])\n ax.plot(gradData[key])\n ax.set_ylim([-0.2,0.2])\n\n for k,key in enumerate(dat.magDataRaw.keys()):\n ax=subplot(gs[k+8])\n ax.plot(dat.magDataRaw[key])\n ax.set_ylim([-0.2,0.2])","title":""},{"docid":"5546e08a25a4f089327accfe766dbecb","score":"0.5539796","text":"def ax(self):\n return plt.gca()","title":""},{"docid":"bf3027284a510e8e24ce73c9a6471c53","score":"0.5536739","text":"def plotting(self):\n raise NotImplementedError('Must implement plotting method!')","title":""},{"docid":"b81061b5518d63aee3cec88e079fd127","score":"0.5533772","text":"def showFigures():\n mlab.show()","title":""},{"docid":"509edc5bc45b5c20d8101a701e1cdbe4","score":"0.55281097","text":"def init_graphics(self):\n self.rows, self.cols = self.stdscr.getmaxyx()\n self.graphics = []","title":""},{"docid":"10014b3b70ee8549d2d2daf38efecf8b","score":"0.55235696","text":"def plotting(self):\n self.plot.figure()\n exps = self.repo.get(['name', self.config.get('experiment')])\n plot_data = self.data(exps)\n\n if isinstance(plot_data, list):\n for plot, data in enumerate(plot_data):\n self.draw(data, plot)\n else:\n self.draw(plot_data)\n\n if self.config.get('styles.grid', False):\n self.plot.grid(True)\n\n if self.config.get('styles.axis', False):\n self.plot.axis(self.config.get('styles.axis'))\n\n if self.config.get('styles.ticks', False):\n self.plot.xticks(**self.config.get('styles.ticks.xticks', {}))\n self.plot.yticks(**self.config.get('styles.ticks.yticks', {}))\n\n self.labeling()\n\n return self.plot","title":""},{"docid":"0a11a909dc25bcb936bdc9f08c6ad02a","score":"0.5498557","text":"def plot(self):\n return plt","title":""},{"docid":"2ab3b49502d7b205837b7868651ec088","score":"0.54825664","text":"def do_all():\n plot_folder = 'plots/paperplots/'\n results_folder = 'results/'\n ex1_plot(plot_folder,results_folder)\n ex2_plot(plot_folder,results_folder)\n ex3_plot(plot_folder,results_folder)","title":""},{"docid":"dc67e2c9c5f778e7ef2c4bafa0ae6e74","score":"0.5478483","text":"def make_plot(self):\n plt.figure(figsize=self.figsize)\n self.get_lines()\n self.get_axes()\n self.get_xticks()\n self.get_yticks()\n self.get_labels()\n self.get_grid()\n self.get_legend()\n self.output_plot()","title":""},{"docid":"5240c753d3910da3081b1456509162b5","score":"0.54632396","text":"def visualize_grid(self):\n self.visualize_demos([])","title":""},{"docid":"5827d8565c4852f7fb2132fdad2bcfd8","score":"0.54569346","text":"def graphics(self):\n return self._graphics","title":""},{"docid":"dcebd1d3940e28dd8be60f8cc0b32951","score":"0.5452585","text":"def displayFigures() -> None:\r\n for figure in MAIN_FIGURE_LIST:\r\n glEnable(GL_BLEND)\r\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)\r\n glColor4f(*(figure.rgba))\r\n\r\n glPushMatrix()\r\n glTranslatef(*figure.figPos)\r\n glScalef(*figure.figScale)\r\n glRotatef(figure.figRot, 0, 0, 1.0)\r\n glMultMatrixf(figure.figShear)\r\n glMultMatrixf(figure.figReflex)\r\n\r\n glBegin(GL_POLYGON)\r\n for point in figure.pointsList:\r\n glVertex2f(*point())\r\n\r\n glEnd()\r\n glPopMatrix()","title":""},{"docid":"30d2cfb6110ad1695aaf84f2db701769","score":"0.5428176","text":"def visualize(self):\r\n pass","title":""},{"docid":"670d9a24824a3565fa14e41d69ce71ab","score":"0.54272133","text":"def displayG(self):\n G=nx.Graph();\n G=nx.read_weighted_edgelist(self.dataset+\"/teamsG.txt\")\n nodes=list(G.nodes())\n #print(nodes)\n for i in range(self.N):\n if str(i) not in nodes:\n G.add_node(i)\n edges = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0]\n pos = nx.spring_layout(G) # positions for all nodes\n # nodes\n nx.draw_networkx_nodes(G, pos, node_size=300)\n # edges\n nx.draw_networkx_edges(G, pos, edgelist=edges, width=2)\n # labels\n nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif')\n plt.axis('off')\n plt.show()","title":""},{"docid":"a37073089f01e35623926adbba93ce52","score":"0.5427033","text":"def plot(self):\n # Set up the different parametrization for the four plots.\n x_names = ['REL_TSINCEOSM1', 'REL_TSINCEOSM1', 'REL_TSINCEOSM2', 'REL_TSINCEOSM2']\n y_names = ['SHIFT1_DRIFT', 'SHIFT2_DRIFT', 'SHIFT1_DRIFT', 'SHIFT2_DRIFT']\n titles = ['OSM1 SHIFT1', 'OSM1 SHIFT2', 'OSM2 SHIFT1', 'OSM2 SHIFT2']\n locations = [(1, 1), (2, 1), (1, 2), (2, 2)]\n\n # Set the min and max for the scale so that each plot is plotted on the same scale\n c_min = self.data.EXPSTART.min()\n c_max = self.data.EXPSTART.max()\n\n segment_groups, _ = self.results\n\n # Plot drift v time for each segment/stripe\n trace_counts = {}\n for segment, group in segment_groups:\n segment_count = 0\n\n # Plot per grating\n for grating, grating_group in group.groupby('OPT_ELEM'):\n for i, (x, y, axes, name) in enumerate(zip(x_names, y_names, locations, titles)):\n self.figure.add_trace(\n go.Scattergl(\n x=grating_group[x],\n y=grating_group[y],\n mode='markers',\n text=grating_group.hover_text,\n name=f'{segment} {grating}',\n legendgroup=grating,\n showlegend=True if i == 0 else False,\n marker=dict(\n color=grating_group.EXPSTART,\n cmin=c_min,\n cmax=c_max,\n colorscale='Viridis',\n showscale=True,\n colorbar=dict(\n title='Observation Date',\n tickmode='array',\n ticks='outside',\n tickvals=[self.data.EXPSTART.min(), self.data.EXPSTART.mean(),\n self.data.EXPSTART.max()],\n ticktext=[\n f'{Time(self.data.EXPSTART.min(), format=\"mjd\").to_datetime().date()}',\n f'{Time(self.data.EXPSTART.mean(), format=\"mjd\").to_datetime().date()}',\n f'{Time(self.data.EXPSTART.max(), format=\"mjd\").to_datetime().date()}'\n ],\n len=0.65,\n y=0,\n yanchor='bottom'\n )\n ),\n ),\n *axes\n )\n\n segment_count += 1\n\n trace_counts[segment] = segment_count\n\n # Create figure buttons for LP\n titles = [f'<a href=\"{self.docs}\">{self.name} All Stripes'] + [\n f'<a href=\"{self.docs}\">{self.name} {stripe}</a>'\n for stripe in trace_counts.keys()\n ]\n\n labels = ['All Stripes'] + [f'{stripe}' for stripe in trace_counts.keys()]\n\n # Create trace visibility options\n traces = list(trace_counts.values())\n all_visible = create_visibility(traces, [True, True, True]) # all stripes, NUVA, NUVB, NUVC\n nuva = create_visibility(traces, [True, False, False])\n nuvb = create_visibility(traces, [False, True, False])\n nuvc = create_visibility(traces, [False, False, True])\n\n updatemenus = [\n go.layout.Updatemenu(\n buttons=[\n dict(\n label=label,\n method='update',\n args=[{'visible': visible}, {'title': title}]\n ) for label, title, visible in zip(labels, titles, [all_visible, nuva, nuvb, nuvc])\n ]\n )\n ]\n\n # Set the layout. Refer to the commented plot grid to make sense of this.\n layout = go.Layout(\n title=f'<a href=\"{self.docs}\">{self.name}</a>',\n xaxis=dict(title='Time since last OSM1 move [s]', matches='x3'),\n xaxis3=dict(title='Time since last OSM1 move [s]'),\n xaxis2=dict(title='Time since last OSM2 move [s]', matches='x4'),\n xaxis4=dict(title='Time since last OSM2 move [s]'),\n yaxis=dict(title='SHIFT1 drift rate [pixels/sec]'),\n yaxis3=dict(title='SHIFT2 drift rate [pixels/sec]'),\n yaxis2=dict(title='SHIFT1 drift rate [pixels/sec]'),\n yaxis4=dict(title='SHIFT2 drift rate [pixels/sec]'),\n updatemenus=updatemenus\n )\n\n self.figure.update_layout(layout)","title":""},{"docid":"0443e116ecc4d0059b688417b296b10d","score":"0.54242045","text":"def plot_glucose_sliceXY(self):\n if self.tick % 75 == 0: # every 75 hours, save figure\n plt.figure()\n plt.imshow(self.grid.glucose[:,:,int(self.zSize//2)], cmap='inferno')\n plt.title('@time '+str(self.tick)+ ' [h]')\n plt.xlabel('Width [mm]')\n plt.ylabel('Height [mm]')\n plt.title('Time step = '+str(self.tick)+' [h]')\n plt.colorbar()\n plt.savefig('DeepRL-PT/out/cell_proliferations/glucose@_'+str(self.tick)+'_'+str(self.xSize)+'x'+str(self.ySize)+'x'+str(self.zSize)+'.png')\n plt.close()","title":""},{"docid":"8b77be9fa7ddf4ac488dea57c8d7111a","score":"0.5415339","text":"def _initialize_graphics(self, modules=None):\n import matplotlib.pyplot as PLT\n for i in range(len(self.trainer.monitored_modules if modules is None else modules)):\n self.hinton_figures.append(PLT.figure())","title":""},{"docid":"cde45ce234cbec6b2a2d223b6bbdd5b0","score":"0.5407187","text":"def plot_GP(X, f):\n plt.figure()\n for i in range(f.shape[0]):\n plt.plot(X[:], Z[i, :])","title":""},{"docid":"dea3493c555ac2351ae7ea6bec10b0c0","score":"0.53991556","text":"def plot_gr50(dfgr, ax, labels, colors):\n drug = ax.get_title()\n # print(drug)\n ylim = ax.get_ylim()\n xlim = ax.get_xlim()\n dfgr_drug = dfgr[dfgr.agent == drug].copy()\n for line, color in zip(labels, colors):\n gr50 = dfgr_drug[dfgr_drug.cell_line == line]['GR50'].values[0]\n ax.plot([gr50, gr50], [ylim[0], (ylim[0] + 0.2)], color)\n ax.set_ylim(ylim)\n ax.set_xlim(xlim)","title":""},{"docid":"490feefca9e2b3df8b7ba15dfe2b1a36","score":"0.53975236","text":"def visualize(self):\n pass","title":""},{"docid":"616057a28000824ed3d8db6097a5afef","score":"0.5393877","text":"def __init__(self, skeletons):\n self.fig = plt.figure()\n\n \"\"\" Set plot boundaries accordingly to the data ranges \"\"\"\n x = skeletons[:,:,0]\n xmin = x.min()\n xmax = x.max()\n y = skeletons[:, :, 2]\n ymin = y.min()\n ymax = y.max()\n self.ax = plt.axes(xlim=(xmin-10, xmax+10), ylim=(ymin-10, ymax+10)) # plt.axes(xlim=(-1.5, 1.5), ylim=(-1.2, 1.2))\n #\n self.ax.axis('off')\n self.line_one = self.ax.plot([], [], lw=5, c='b', marker=\"s\")[0]\n self.line_two = self.ax.plot([], [], lw=5, c='b', marker=\"s\")[0]\n self.line_three = self.ax.plot([], [], lw=5, c='b', marker=\"s\")[0]\n\n self.r_fing_1 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.r_fing_2 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.r_fing_3 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.r_fing_4 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.r_fing_5 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n\n self.l_fing_1 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.l_fing_2 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.l_fing_3 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.l_fing_4 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]\n self.l_fing_5 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\"s\")[0]","title":""},{"docid":"978b0e070115aea5c29009e06449acc6","score":"0.53871536","text":"def plot(self, *args):\n ...","title":""},{"docid":"b9d298d32aa02aec8b6a1c793a8ec68d","score":"0.53838754","text":"def plot_data(Figures,Data,fr):\r\n\ttxt=[]\r\n\r\n\tif FIGS == -1:\r\n\t\tplt.close(\"all\")\r\n\t\tfr = True\r\n\r\n\tdatacount=1 \r\n\tfor fig in range(1,len(Figures)+1):\r\n\t\tplt.figure(fig)\r\n\r\n\r\n\t\tfor plot in range(2,len(Figures[fig-1]),2):\r\n\t\t\tif plot + 1 < len(Figures[fig-1]):\r\n\t\t\t\tplt.plot(Data[0], Data[datacount], label=Figures[fig-1][plot+1])\r\n\t\t\t\tplt.legend(fancybox=True,fontsize=11).draggable()\r\n\t\t\telse: \r\n\t\t\t\tplt.plot(Data[0], Data[datacount])\r\n\t\t\tdatacount+=1\r\n\t\t\tplt.hold(True)\t\r\n\t\tplt.hold(False)\r\n\t\tplt.grid()\r\n\t\tplt.xlabel('Time [s]')\r\n\t\tplt.ylabel(Figures[fig-1][1])\r\n\t\tplt.ticklabel_format(style='sci', axis='y', scilimits=(-4,4))\r\n\t\tplt.autoscale(tight=False)\r\n\t\tplt.tight_layout()\r\n\t\tplt.margins(0.001, 0.1)\r\n\t\tplace_fig_win(fig, fr)\r\n\r\n\t\ttry:\r\n\t\t\ttxt.append(plt.figtext(0.3, 0.96, 'Latest value: ' + str(Data[datacount-1][-1])))\r\n\t\texcept:\r\n\t\t\ttxt.append(plt.figtext(0.3, 0.96, 'Latest value: ' + str(Data[datacount-1])))\r\n\r\n\tplt.pause(3-len(Figures)/30)\r\n\tfor tx in txt: tx.remove()","title":""},{"docid":"d9039e762203d2f5d4bfd004206a6f6c","score":"0.53796315","text":"def Clf():\r\n Brewer.ClearIter()\r\n pyplot.clf()","title":""},{"docid":"d9039e762203d2f5d4bfd004206a6f6c","score":"0.53796315","text":"def Clf():\r\n Brewer.ClearIter()\r\n pyplot.clf()","title":""},{"docid":"fb11a54ff25fbf1caa6db2d5f469c30e","score":"0.5378374","text":"def print_grid(nodes, color='ro'):\n x_axis = []\n y_axis = []\n # convert points to coords\n for n in nodes:\n x, y = n\n x_axis.append(x)\n y_axis.append(y)\n plt.plot(x_axis, y_axis, color)\n #plt.show(block=False)\n return (x_axis, y_axis)","title":""},{"docid":"d46145faf02c2616ffa2d98d30db595b","score":"0.53757423","text":"def plot_figure(self) -> None:\n plt.close()\n plt.ion()\n if not self.back:\n self.figure, self.axes = plt.subplots(self.back + 1)\n self.axes.plot(\n self.data.bins, self.data.z2n, label='Z2n Power',\n color='tab:blue', linewidth=2)\n try:\n self.axes.plot(\n self.data.gauss.bins, self.data.gauss.z2n,\n color='tab:red', label='Gaussian Fit', linewidth=1)\n except AttributeError:\n pass\n self.axes.set_xlabel('Frequency (Hz)')\n self.axes.set_ylabel('Power')\n self.axes.legend(loc='best')\n else:\n self.figure, self.axes = plt.subplots(\n self.back + 1, sharex=True, sharey=True)\n self.axes[0].plot(\n self.data.bins, self.data.z2n, label='Z2n Power',\n color='tab:blue', linewidth=2)\n try:\n self.axes[0].plot(\n self.data.gauss.bins, self.data.gauss.z2n,\n color='tab:red', label='Gaussian Fit', linewidth=1)\n except AttributeError:\n pass\n self.axes[1].plot(\n self.noise.bins, self.noise.z2n, color='tab:cyan',\n label='Background')\n self.axes[0].set_xlabel('Frequency (Hz)')\n self.axes[0].set_ylabel('Power')\n self.axes[1].set_xlabel('Frequency (Hz)')\n self.axes[1].set_ylabel('Power')\n self.axes[0].legend(loc='best')\n self.axes[1].legend(loc='best')\n plt.tight_layout()","title":""},{"docid":"ff6953f19c1fc9947b3563c76967bc6d","score":"0.5359109","text":"def graphs(self):\n return list(self.graph.keys())","title":""},{"docid":"f9db9261766db405735718a56421195c","score":"0.53575796","text":"def plot_gbrv_convergence(self, ax_list=None, **kwargs):\n import matplotlib.pyplot as plt\n stypes = (\"fcc\", \"bcc\")\n if ax_list is None:\n fig, ax_list = plt.subplots(nrows=len(stypes), ncols=1, sharex=True, squeeze=False)\n ax_list = ax_list.ravel()\n else:\n fig = plt.gcf()\n\n #ax_list, fig, plt = get_axarray_fig_plt(ax_list, nrows=len(stypes), ncols=1, sharex=True, squeeze=False)\n\n if len(stypes) != len(ax_list):\n raise ValueError(\"len(stypes)=%s != len(ax_list)=%s\" % (len(stypes), len(ax_list)))\n\n for i, (ax, stype) in enumerate(zip(ax_list, stypes)):\n trial = \"gbrv_\" + stype\n d = self[trial]\n ecuts = list(d.keys())\n values = np.array([float(d[ecut][\"a0_rel_err\"]) for ecut in ecuts])\n\n ax.grid(True)\n ax.set_ylabel(\"$\\Delta$\" + trial + \"a0_rel_err\")\n\n # Plot difference pseudo - ref.\n ax.plot(ecuts, values, \"bo-\")\n #ax.hlines(y=0.0, xmin=min(ecuts), xmax=max(ecuts), color=\"red\")\n if i == len(ax_list) - 1: ax.set_xlabel(\"Ecut [Ha]\")\n\n return fig","title":""},{"docid":"0ac021ff7cfdc7460a2f6b23a88b1881","score":"0.53514504","text":"def graphglob (dico):\n\t\n\tx= list()\n\ty=list()\n\tfor prot1 in sorted(dico.keys()): #parcourt les conformations dans le dictionnaire de sortie de la fonction RMSD\n\t\tfor prot2 in sorted(dico[prot1].keys()):#Parcourt les conformations (2ieme clé du dictionnaire)\n\t\t\tx.append(prot1)\t\t\t#concatenation des identifiants des conformations rencontrées\n\t\t\ty.append(dico[prot1][prot2]) # Concaténation des valeurs de RMSD globaux dans Y \n\n\n\tplt.plot(x, y) #trace le graphique des valeurs de RMSD en fonction des conformations\n\tplt.title(\"Graphique des RMSD globaux\")\n\tplt.xlabel('Conformation')\n\tplt.ylabel('RMSD globales')\n\tplt.show()\n\t\n\treturn ()","title":""},{"docid":"31350b1cd60a02ca06a26b6540751ccb","score":"0.5342083","text":"def setup_axes(): \n\tfig = plt.figure(figsize = (14, 7)) \n\taxes = 2 * [None] \n\tfor i in range(2): \n\t\taxes[i] = fig.add_subplot(121 + i, facecolor = \"white\") \n\t\taxes[i].set_xlabel(r\"$R_\\text{gal}$ [kpc]\") \n\t\taxes[i].set_xlim([-1, 21]) \n\taxes[0].set_ylim([-0.55, 0.75]) \n\taxes[1].set_ylim([-0.1, 0.5]) \n\taxes[0].set_ylabel(\"[X/H]\") \n\taxes[1].set_ylabel(\"[O/Fe]\") \n\treturn axes","title":""},{"docid":"a8bb6ed69507fd203044fde08d4e9fdb","score":"0.5341665","text":"def showPlot4():\n # TODO: Your code goes here\n roomDim = 25\n numOfRobots = 5\n posMinCoverage = [0, 0.25, 0.5, 0.75, 1.0]\n simResult = []\n meanTime = []\n for minCoverage in posMinCoverage:\n for robotNum in range(1, numOfRobots + 1):\n simResult = runSimulation(robotNum, 1.0, roomDim, roomDim, minCoverage, 1, Robot, False)\n meanTime.append(avgLenListOfLists(simResult))\n pylab.plot(robotCoverage, meanTime)\n pylab.xlabel('Minimum area to be cleaned')\n pylab.ylabel('Timesteps')\n pylab.title('Time to clean a square room of size 25 X 25, for various number of robots and various minimum coverage areas')\n pylab.show()","title":""},{"docid":"65661f7551a09d053ec3075514af218c","score":"0.53413105","text":"def plot_graph(fig, g, iteration: int = -1) -> None:\n # retrieve indices into g's \"x\" vector -> start of each individual variable\n # plot (x,y) positions from each pose/landmark\n p, l = g.get_poses_landmarks()\n\n plt.clf()\n\n if len(l) > 0:\n landmarkIdxX = l\n landmarkIdxY = l + 1\n plt.scatter(g.x[landmarkIdxX], g.x[landmarkIdxY], 4, color=\"r\", marker=\"o\")\n\n if len(p) > 0:\n pIdxX = p\n pIdxY = p + 1\n plt.scatter(g.x[pIdxX], g.x[pIdxY], 4, color=\"b\", marker=\"x\")\n\n fig.canvas.draw()\n plt.pause(1)\n\n os.makedirs(\"plots\", exist_ok=True)\n filename = os.path.join(\"plots\", f\"lsslam_{iteration:03d}.png\")\n plt.savefig(filename)","title":""},{"docid":"f020fe2314cbbe9610b951b5ab9cbe08","score":"0.5341108","text":"def get_figure(self):\n pass","title":""},{"docid":"d7760097f380502dacd62ba84352ba54","score":"0.5335527","text":"def plot_snapshot():\n plt.figure(figsize=(20, 15))\n\n plt.subplot(311)\n plt.ylabel('Rewards', fontsize=16)\n plt.plot(reward_record, alpha=0.15, color='red')\n plt.plot(reward_average_plot, alpha=0.9, color='red', linewidth=3.0)\n plt.xlim(-0.5, i_episode)\n plt.ylim(-10, 3)\n\n plt.subplot(312)\n plt.ylabel('Number of Flickers', fontsize=16)\n plt.plot(flicker_record, alpha=0.15, color='green')\n plt.plot(flicker_average_plot, alpha=0.9, color='green', linewidth=3.0)\n plt.xlim(-0.5, i_episode)\n plt.ylim(-0.2, 25)\n\n plt.subplot(313)\n plt.ylabel('Wait Time', fontsize=16)\n plt.plot(queue_record, alpha=0.15, color='blue')\n plt.plot(wait_average_plot, alpha=0.9, color='blue', linewidth=3.0)\n plt.xlim(-0.5, i_episode)\n plt.ylim(-0.5, 9000)\n\n if folder == \"RL\":\n if all(options):\n PATH = \"Plots/\" + folder + \"/all\" + \"/{}.png\".format('plot_all')\n elif not any(options):\n PATH = \"Plots/\" + folder + \"/vanilla_dqn\" + \"/{}.png\".format('plot_vanilla_dqn')\n else:\n zero_idx = list(options).index(0)\n PATH = \"Plots/\" + folder + \"/no_\" + options_list[zero_idx] + \"_dqn/plot_no_\" + options_list[zero_idx] + \"_dqn.png\"\n else:\n PATH = \"Plots/\" + folder + \"/plot_{}\".format(folder) + \".png\"\n\n plt.savefig(PATH)\n plt.close()","title":""},{"docid":"9445dda2a40b73f554d9d69ef922aaa0","score":"0.5330961","text":"def list_figures(experiment_id, operator_id, run_id):\n return platiagro.list_figures(experiment_id=experiment_id,\n operator_id=operator_id,\n run_id=run_id)","title":""},{"docid":"1cbe22fc84c49242caf577f599c0386b","score":"0.5329918","text":"def build_stock_gdp():\n axes = []\n for comp in found_years:\n x = np.arange(found_years[comp], 2019)\n if comp == 'LGF':\n x = np.arange(found_years[comp], 2017)\n lst = []\n for el in database:\n if database[el][1][comp] is not None:\n lst.append(database[el][1][comp])\n y = np.array(lst)\n axes.extend([x, y])\n\n lst = []\n x = np.arange(1962, 2019)\n for el in gdp_info:\n if int(el) >= 1962:\n lst.append(gdp_info[el]/100)\n y = np.array(lst)\n axes.extend([x, y])\n\n fig, ax = plt.subplots()\n l1, = plt.plot(axes[0], axes[1], linestyle='solid')\n l2, = plt.plot(axes[2], axes[3], linestyle='solid')\n l3, = plt.plot(axes[4], axes[5], linestyle='solid')\n l4, = plt.plot(axes[6], axes[7], linestyle='solid')\n l5, = plt.plot(axes[8], axes[9], linestyle='solid')\n l6, = plt.plot(axes[10], axes[11], linestyle='solid')\n l7, = plt.plot(axes[12], axes[13], linestyle='solid')\n l8, = plt.plot(axes[14], axes[15], linestyle='solid')\n l9, = plt.plot(axes[16], axes[17], linestyle='solid')\n\n plt.subplots_adjust(left=0.3)\n lgnd = ax.legend(list(found_years.keys()),\n loc='upper center', ncol=4, fontsize='xx-small', shadow=True)\n\n lgnd.get_frame().set_facecolor('#ffb19a')\n\n rax = plt.axes([0.04, 0.5, 0.17, 0.37],\n facecolor='lightgoldenrodyellow')\n check = CheckButtons(rax, tuple(found_years.keys()), (True,\n True, True, True, True, True, True, True))\n\n def func(label):\n for line, comp in zip([l1, l2, l3, l4, l5, l6, l7, l8], list(found_years.keys())):\n if label == comp:\n line.set_visible(not line.get_visible())\n plt.draw()\n\n check.on_clicked(func)\n\n plt.show()","title":""},{"docid":"00991f0301d36367c760f669f52d97c1","score":"0.5329416","text":"def show_fig417(self,j):\n\t\txpos = []\n\t\typos = []\n\t\tfor star in self.galaxy:\n\t\t\txpos.append(star.x)\n\t\t\typos.append(star.y)\n\t\tplt.figure(figsize=(9,9))\n\t\tplt.plot(xpos,ypos,'go',ms=0.25)\n\t\ttemp = -15\n\t\t\"\"\"\n\t\tfor i in range(self.M+1):\n\t\t\tplt.axhline(temp,-15,15,c='r',lw=0.5)\n\t\t\tplt.axvline(temp,-15,15,c='r',lw=0.5)\n\t\t\ttemp += self.DELTA\n\t\t\"\"\"\n\t\tplt.axis([-30,30,-30,30])\n\t\t#plt.savefig(\"/Users/user/417.png\",format='png', dpi=1000)\n\t\tplt.savefig(\"/Users/user/417_%d.png\"%j,format='png')","title":""},{"docid":"58339152b20921e2027a48ba3763274d","score":"0.5318704","text":"def showPlot2():\n # TODO: Your code goes here\n roomDim = 25\n numOfRobots = 10\n simResult = []\n meanTime = []\n for robotNum in range(1, numOfRobots + 1):\n simResult = runSimulation(robotNum, 1.0, roomDim, roomDim, 0.75, 100, Robot, False)\n meanTime.append(avgLenListOfLists(simResult))\n pylab.plot([num for num in range(1, numOfRobots + 1)], meanTime)\n pylab.xlabel('Number of robots')\n pylab.ylabel('Timesteps')\n pylab.title('Time to clean 75percent of square room of sie 25 X 25, for various number of robots')\n pylab.show()","title":""},{"docid":"c18d6dd6d08902accddbcfb8f3194e04","score":"0.53155","text":"def plot_path(self):\n\n if self._dataset_is_binned: # this means reall data\n self.plot_path_real_data()\n return\n \n # self._numb_shared_lat = numb_shared_lat\n # self._numb_grouped_lat = numb_grouped_lat\n # self._numb_trial_lat = numb_trial_lat\n\n # first shared latents:\n if self._numb_shared_lat > 0 or self._meta_data[\"K_alpha\"]:\n\n numb_plots_hor = max(self._numb_shared_lat,self._meta_data[\"K_alpha\"])\n if numb_plots_hor == 1:\n numb_plots_hor += 1\n\n fig, axes = plt.subplots(2, numb_plots_hor)\n\n for lat_i in range(0,self._meta_data[\"K_alpha\"]):\n axes[0, lat_i].plot(self._true_latent_alpha[lat_i,:])\n for lat_i in range(0,self._numb_shared_lat):\n axes[1, lat_i].plot(self._bin_times,self._found_shared[lat_i,:])\n\n fig.suptitle(\"Shared latents : \")\n\n # then group latents:\n if self._numb_grouped_lat > 0 or self._meta_data[\"K_beta\"]: \n \n numb_plots_hor = (max(self._numb_grouped_lat,self._meta_data[\"K_beta\"]) + 1) * self._numb_conditions \n if numb_plots_hor == 1:\n numb_plots_hor += 1\n fig, axes = plt.subplots(2, numb_plots_hor)\n counter = 0\n for cond_i in range(0, self._numb_conditions):\n for lat_i in range(0,self._meta_data[\"K_beta\"]):\n axes[0, counter+lat_i].plot(self._true_latent_beta[cond_i,lat_i,:])\n #axes[1].set_ylim([-2.5, 2.5])\n for lat_i in range(0,self._numb_grouped_lat):\n axes[1, counter+lat_i].plot(self._bin_times,self._found_cond[cond_i,lat_i,:])\n\n counter += max(self._numb_grouped_lat,self._meta_data[\"K_beta\"]) + 1\n\n fig.suptitle(\"Group latents : \")\n\n # finally the trial latents:\n if self._numb_trial_lat > 0 or self._meta_data[\"K_gamma\"]: \n \n numb_plots_hor = (max(self._numb_trial_lat,self._meta_data[\"K_gamma\"]))\n if numb_plots_hor == 1:\n numb_plots_hor += 1\n fig, axes = plt.subplots(min(self._numb_trials - self._numb_test_trials, 20), numb_plots_hor)\n \n for trial_i in range(0, min(self._numb_trials - self._numb_test_trials, 20)):\n for lat_i in range(0,self._meta_data[\"K_gamma\"]):\n axes[trial_i, lat_i].plot(self._bin_times, self._true_latent_gamma[trial_i,lat_i,:])\n #plt.hold(True)\n #axes[1].set_ylim([-2.5, 2.5])\n for lat_i in range(0,self._numb_trial_lat):\n axes[trial_i, lat_i].plot(self._bin_times,self._found_latents[trial_i,lat_i,:])\n\n fig.suptitle(\"Trial latents : \")\n\n\n plt.show()","title":""},{"docid":"63f02a645d33e9e9cc3b2a97d51970cc","score":"0.53062576","text":"def granule_figure(monit_gr, pscommon):\n granule_pop_figure(monit_gr['s'].values, monit_gr['s_syn_self'].values, monit_gr['s'].times, pscommon['resample_dt'], pscommon['burnin'])","title":""},{"docid":"b9f49551a4a0db6e78eee17ab37002b3","score":"0.5300304","text":"def showSOCEstimates():\n \"\"\"this is primarilty to produce a figure in my thesis. THis is NOT NEEDED ON BLIZZARD.\"\"\"\n plt.figure(1, figsize=(1080 / my_dpi, 900 / my_dpi), dpi=my_dpi)\n\n # plot1; the original battery graphs recieved from OEM\n ax = plt.subplot(211)\n ax.plot(SOC.d[\"-20\"][\"Ys\"], SOC.d[\"-20\"][\"Xs\"], color='blue', label=\"-20C\")\n ax.plot(SOC.d[\"-10\"][\"Ys\"], SOC.d[\"-10\"][\"Xs\"], color='cyan', label=\"-10C\")\n ax.plot(SOC.d[\"0\"][\"Ys\"], SOC.d[\"0\"][\"Xs\"], color='green', label=\"0C\")\n ax.plot(SOC.d[\"23\"][\"Ys\"], SOC.d[\"23\"][\"Xs\"], color='yellow', label=\"23C\")\n ax.plot(SOC.d[\"45\"][\"Ys\"], SOC.d[\"45\"][\"Xs\"], color='pink', label=\"45C\")\n ax.set_axisbelow(True)\n # ax.legend(numpoints=1, ncol = 1, loc='best',columnspacing=0,labelspacing=1,handletextpad=0,borderpad=.15,markerscale=0.2)\n plt.ylim(20000, 0)\n ax.yaxis.grid(color='gray', linestyle='solid')\n ax.xaxis.grid(color='gray', linestyle='solid')\n plt.ylabel(\"capacity in mAh\")\n plt.title(\"voltage vs capacity\")\n\n ax3 = plt.subplot(212)\n SampVs = linspace(32, 16, 100)\n ax3.plot(SampVs, SOC.SOCVals(-10, SampVs), color='cyan', label=\"-10C\", marker=\"o\", markersize=1, linestyle=\"-\")\n ax3.plot(SampVs, SOC.SOCVals(-20, SampVs), color='blue', label=\"-20C\", marker=\"o\", markersize=1, linestyle=\"-\")\n ax3.plot(SampVs, SOC.SOCVals(0, SampVs), color='green', label=\"0C\", marker=\"o\", markersize=1, linestyle=\"-\")\n ax3.plot(SampVs, SOC.SOCVals(23, SampVs), color='yellow', label=\"23C\", marker=\"o\", markersize=1, linestyle=\"-\")\n ax3.plot(SampVs, SOC.SOCVals(45, SampVs), color='pink', label=\"45C\", marker=\"o\", markersize=1, linestyle=\"-\")\n ax3.legend(numpoints=1, ncol=3, loc='best', columnspacing=0, labelspacing=1, handletextpad=0, borderpad=.15,\n markerscale=2)\n # plt.ylim(0,1)\n ax3.yaxis.grid(color='gray', linestyle='solid')\n ax3.xaxis.grid(color='gray', linestyle='solid')\n plt.xlabel(\"voltage\")\n plt.ylabel(\"SOC (% of wh)\")\n\n plt.show()\n plt.close() # THIS IS CRUCIAL SEE: http://stackoverflow.com/questions/26132693/matplotlib-saving-state-between-different-uses-of-io-bytesio","title":""},{"docid":"c7ff6677e6a357089886b9d2173cf095","score":"0.52974087","text":"def drawAll():\n for item in drawList:\n item.draw()\n sliderLine.draw()\n slider.draw()\n for label in labelStims:\n label.draw()\n if nextVisible: next.draw()","title":""},{"docid":"3874abd6f641ef7c9a41133891963c8e","score":"0.5291301","text":"def GGG(self):\n\t\treturn self._ggg.value","title":""},{"docid":"1e9d8511504258c41eb1ad13dea6d4db","score":"0.52893806","text":"def main():\n FIG = {} # plot dictionary\n FIG['eqarea'] = 1 # eqarea is figure 1\n in_file, plot_key, coord, crd = 'pmag_results.txt', 'all', \"0\", 'g'\n plotE, contour = 0, 0\n dir_path = '.'\n fmt = 'svg'\n verbose = pmagplotlib.verbose\n if '-h' in sys.argv:\n print(main.__doc__)\n sys.exit()\n if '-WD' in sys.argv:\n ind = sys.argv.index('-WD')\n dir_path = sys.argv[ind+1]\n pmagplotlib.plot_init(FIG['eqarea'], 5, 5)\n if '-f' in sys.argv:\n ind = sys.argv.index(\"-f\")\n in_file = dir_path+\"/\"+sys.argv[ind+1]\n if '-obj' in sys.argv:\n ind = sys.argv.index('-obj')\n plot_by = sys.argv[ind+1]\n if plot_by == 'all':\n plot_key = 'all'\n if plot_by == 'sit':\n plot_key = 'er_site_name'\n if plot_by == 'sam':\n plot_key = 'er_sample_name'\n if plot_by == 'spc':\n plot_key = 'er_specimen_name'\n if '-c' in sys.argv:\n contour = 1\n plt = 0\n if '-sav' in sys.argv:\n plt = 1\n verbose = 0\n if '-ell' in sys.argv:\n plotE = 1\n ind = sys.argv.index('-ell')\n ell_type = sys.argv[ind+1]\n if ell_type == 'F':\n dist = 'F'\n if ell_type == 'K':\n dist = 'K'\n if ell_type == 'B':\n dist = 'B'\n if ell_type == 'Be':\n dist = 'BE'\n if ell_type == 'Bv':\n dist = 'BV'\n FIG['bdirs'] = 2\n pmagplotlib.plot_init(FIG['bdirs'], 5, 5)\n if '-crd' in sys.argv:\n ind = sys.argv.index(\"-crd\")\n crd = sys.argv[ind+1]\n if crd == 's':\n coord = \"-1\"\n if crd == 'g':\n coord = \"0\"\n if crd == 't':\n coord = \"100\"\n if '-fmt' in sys.argv:\n ind = sys.argv.index(\"-fmt\")\n fmt = sys.argv[ind+1]\n Dec_keys = ['site_dec', 'sample_dec', 'specimen_dec',\n 'measurement_dec', 'average_dec', 'none']\n Inc_keys = ['site_inc', 'sample_inc', 'specimen_inc',\n 'measurement_inc', 'average_inc', 'none']\n Tilt_keys = ['tilt_correction', 'site_tilt_correction',\n 'sample_tilt_correction', 'specimen_tilt_correction', 'none']\n Dir_type_keys = ['', 'site_direction_type',\n 'sample_direction_type', 'specimen_direction_type']\n Name_keys = ['er_specimen_name', 'er_sample_name',\n 'er_site_name', 'pmag_result_name']\n data, file_type = pmag.magic_read(in_file)\n if file_type == 'pmag_results' and plot_key != \"all\":\n plot_key = plot_key+'s' # need plural for results table\n if verbose:\n print(len(data), ' records read from ', in_file)\n #\n #\n # find desired dec,inc data:\n #\n dir_type_key = ''\n #\n # get plotlist if not plotting all records\n #\n plotlist = []\n if plot_key != \"all\":\n plots = pmag.get_dictitem(data, plot_key, '', 'F')\n for rec in plots:\n if rec[plot_key] not in plotlist:\n plotlist.append(rec[plot_key])\n plotlist.sort()\n else:\n plotlist.append('All')\n for plot in plotlist:\n # if verbose: print plot\n DIblock = []\n GCblock = []\n SLblock, SPblock = [], []\n title = plot\n mode = 1\n dec_key, inc_key, tilt_key, name_key, k = \"\", \"\", \"\", \"\", 0\n if plot != \"All\":\n odata = pmag.get_dictitem(data, plot_key, plot, 'T')\n else:\n odata = data # data for this obj\n for dec_key in Dec_keys:\n # get all records with this dec_key not blank\n Decs = pmag.get_dictitem(odata, dec_key, '', 'F')\n if len(Decs) > 0:\n break\n for inc_key in Inc_keys:\n # get all records with this inc_key not blank\n Incs = pmag.get_dictitem(Decs, inc_key, '', 'F')\n if len(Incs) > 0:\n break\n for tilt_key in Tilt_keys:\n if tilt_key in Incs[0].keys():\n break # find the tilt_key for these records\n if tilt_key == 'none': # no tilt key in data, need to fix this with fake data which will be unknown tilt\n tilt_key = 'tilt_correction'\n for rec in Incs:\n rec[tilt_key] = ''\n # get all records matching specified coordinate system\n cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T')\n if coord == '0': # geographic\n # get all the blank records - assume geographic\n udata = pmag.get_dictitem(Incs, tilt_key, '', 'T')\n if len(cdata) == 0:\n crd = ''\n if len(udata) > 0:\n for d in udata:\n cdata.append(d)\n crd = crd+'u'\n for name_key in Name_keys:\n # get all records with this name_key not blank\n Names = pmag.get_dictitem(cdata, name_key, '', 'F')\n if len(Names) > 0:\n break\n for dir_type_key in Dir_type_keys:\n # get all records with this direction type\n Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F')\n if len(Dirs) > 0:\n break\n if dir_type_key == \"\":\n dir_type_key = 'direction_type'\n locations, site, sample, specimen = \"\", \"\", \"\", \"\"\n for rec in cdata: # pick out the data\n if 'er_location_name' in rec.keys() and rec['er_location_name'] != \"\" and rec['er_location_name'] not in locations:\n locations = locations + \\\n rec['er_location_name'].replace(\"/\", \"\")+\"_\"\n if 'er_location_names' in rec.keys() and rec['er_location_names'] != \"\":\n locs = rec['er_location_names'].split(':')\n for loc in locs:\n if loc not in locations:\n locations = locations+loc.replace(\"/\", \"\")+'_'\n if plot_key == 'er_site_name' or plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':\n site = rec['er_site_name']\n if plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':\n sample = rec['er_sample_name']\n if plot_key == 'er_specimen_name':\n specimen = rec['er_specimen_name']\n if plot_key == 'er_site_names' or plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':\n site = rec['er_site_names']\n if plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':\n sample = rec['er_sample_names']\n if plot_key == 'er_specimen_names':\n specimen = rec['er_specimen_names']\n if dir_type_key not in rec.keys() or rec[dir_type_key] == \"\":\n rec[dir_type_key] = 'l'\n if 'magic_method_codes' not in rec.keys():\n rec['magic_method_codes'] = \"\"\n DIblock.append([float(rec[dec_key]), float(rec[inc_key])])\n SLblock.append([rec[name_key], rec['magic_method_codes']])\n if rec[tilt_key] == coord and rec[dir_type_key] != 'l' and rec[dec_key] != \"\" and rec[inc_key] != \"\":\n GCblock.append([float(rec[dec_key]), float(rec[inc_key])])\n SPblock.append([rec[name_key], rec['magic_method_codes']])\n if len(DIblock) == 0 and len(GCblock) == 0:\n if verbose:\n print(\"no records for plotting\")\n sys.exit()\n if verbose:\n for k in range(len(SLblock)):\n print('%s %s %7.1f %7.1f' % (\n SLblock[k][0], SLblock[k][1], DIblock[k][0], DIblock[k][1]))\n for k in range(len(SPblock)):\n print('%s %s %7.1f %7.1f' % (\n SPblock[k][0], SPblock[k][1], GCblock[k][0], GCblock[k][1]))\n if len(DIblock) > 0:\n if contour == 0:\n pmagplotlib.plot_eq(FIG['eqarea'], DIblock, title)\n else:\n pmagplotlib.plot_eq_cont(FIG['eqarea'], DIblock)\n else:\n pmagplotlib.plot_net(FIG['eqarea'])\n if len(GCblock) > 0:\n for rec in GCblock:\n pmagplotlib.plot_circ(FIG['eqarea'], rec, 90., 'g')\n if plotE == 1:\n ppars = pmag.doprinc(DIblock) # get principal directions\n nDIs, rDIs, npars, rpars = [], [], [], []\n for rec in DIblock:\n angle = pmag.angle([rec[0], rec[1]], [\n ppars['dec'], ppars['inc']])\n if angle > 90.:\n rDIs.append(rec)\n else:\n nDIs.append(rec)\n if dist == 'B': # do on whole dataset\n etitle = \"Bingham confidence ellipse\"\n bpars = pmag.dobingham(DIblock)\n for key in bpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (bpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (bpars[key]))\n npars.append(bpars['dec'])\n npars.append(bpars['inc'])\n npars.append(bpars['Zeta'])\n npars.append(bpars['Zdec'])\n npars.append(bpars['Zinc'])\n npars.append(bpars['Eta'])\n npars.append(bpars['Edec'])\n npars.append(bpars['Einc'])\n if dist == 'F':\n etitle = \"Fisher confidence cone\"\n if len(nDIs) > 2:\n fpars = pmag.fisher_mean(nDIs)\n for key in fpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (fpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (fpars[key]))\n mode += 1\n npars.append(fpars['dec'])\n npars.append(fpars['inc'])\n npars.append(fpars['alpha95']) # Beta\n npars.append(fpars['dec'])\n isign = abs(fpars['inc'])/fpars['inc']\n npars.append(fpars['inc']-isign*90.) # Beta inc\n npars.append(fpars['alpha95']) # gamma\n npars.append(fpars['dec']+90.) # Beta dec\n npars.append(0.) # Beta inc\n if len(rDIs) > 2:\n fpars = pmag.fisher_mean(rDIs)\n if verbose:\n print(\"mode \", mode)\n for key in fpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (fpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (fpars[key]))\n mode += 1\n rpars.append(fpars['dec'])\n rpars.append(fpars['inc'])\n rpars.append(fpars['alpha95']) # Beta\n rpars.append(fpars['dec'])\n isign = abs(fpars['inc'])/fpars['inc']\n rpars.append(fpars['inc']-isign*90.) # Beta inc\n rpars.append(fpars['alpha95']) # gamma\n rpars.append(fpars['dec']+90.) # Beta dec\n rpars.append(0.) # Beta inc\n if dist == 'K':\n etitle = \"Kent confidence ellipse\"\n if len(nDIs) > 3:\n kpars = pmag.dokent(nDIs, len(nDIs))\n if verbose:\n print(\"mode \", mode)\n for key in kpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (kpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (kpars[key]))\n mode += 1\n npars.append(kpars['dec'])\n npars.append(kpars['inc'])\n npars.append(kpars['Zeta'])\n npars.append(kpars['Zdec'])\n npars.append(kpars['Zinc'])\n npars.append(kpars['Eta'])\n npars.append(kpars['Edec'])\n npars.append(kpars['Einc'])\n if len(rDIs) > 3:\n kpars = pmag.dokent(rDIs, len(rDIs))\n if verbose:\n print(\"mode \", mode)\n for key in kpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (kpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (kpars[key]))\n mode += 1\n rpars.append(kpars['dec'])\n rpars.append(kpars['inc'])\n rpars.append(kpars['Zeta'])\n rpars.append(kpars['Zdec'])\n rpars.append(kpars['Zinc'])\n rpars.append(kpars['Eta'])\n rpars.append(kpars['Edec'])\n rpars.append(kpars['Einc'])\n else: # assume bootstrap\n if dist == 'BE':\n if len(nDIs) > 5:\n BnDIs = pmag.di_boot(nDIs)\n Bkpars = pmag.dokent(BnDIs, 1.)\n if verbose:\n print(\"mode \", mode)\n for key in Bkpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (Bkpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (Bkpars[key]))\n mode += 1\n npars.append(Bkpars['dec'])\n npars.append(Bkpars['inc'])\n npars.append(Bkpars['Zeta'])\n npars.append(Bkpars['Zdec'])\n npars.append(Bkpars['Zinc'])\n npars.append(Bkpars['Eta'])\n npars.append(Bkpars['Edec'])\n npars.append(Bkpars['Einc'])\n if len(rDIs) > 5:\n BrDIs = pmag.di_boot(rDIs)\n Bkpars = pmag.dokent(BrDIs, 1.)\n if verbose:\n print(\"mode \", mode)\n for key in Bkpars.keys():\n if key != 'n' and verbose:\n print(\" \", key, '%7.1f' % (Bkpars[key]))\n if key == 'n' and verbose:\n print(\" \", key, ' %i' % (Bkpars[key]))\n mode += 1\n rpars.append(Bkpars['dec'])\n rpars.append(Bkpars['inc'])\n rpars.append(Bkpars['Zeta'])\n rpars.append(Bkpars['Zdec'])\n rpars.append(Bkpars['Zinc'])\n rpars.append(Bkpars['Eta'])\n rpars.append(Bkpars['Edec'])\n rpars.append(Bkpars['Einc'])\n etitle = \"Bootstrapped confidence ellipse\"\n elif dist == 'BV':\n sym = {'lower': ['o', 'c'], 'upper': [\n 'o', 'g'], 'size': 3, 'edgecolor': 'face'}\n if len(nDIs) > 5:\n BnDIs = pmag.di_boot(nDIs)\n pmagplotlib.plot_eq_sym(\n FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym)\n if len(rDIs) > 5:\n BrDIs = pmag.di_boot(rDIs)\n if len(nDIs) > 5: # plot on existing plots\n pmagplotlib.plot_di_sym(FIG['bdirs'], BrDIs, sym)\n else:\n pmagplotlib.plot_eq(\n FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors')\n if dist == 'B':\n if len(nDIs) > 3 or len(rDIs) > 3:\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)\n elif len(nDIs) > 3 and dist != 'BV':\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)\n if len(rDIs) > 3:\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)\n elif len(rDIs) > 3 and dist != 'BV':\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)\n if verbose:\n pmagplotlib.draw_figs(FIG)\n #\n files = {}\n locations = locations[:-1]\n for key in FIG.keys():\n if pmagplotlib.isServer: # use server plot naming convention\n filename = 'LO:_'+locations+'_SI:_'+site+'_SA:_'+sample + \\\n '_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt\n else: # use more readable plot naming convention\n filename = ''\n for item in [locations, site, sample, specimen, crd, key]:\n if item:\n item = item.replace(' ', '_')\n filename += item + '_'\n if filename.endswith('_'):\n filename = filename[:-1]\n filename += \".{}\".format(fmt)\n files[key] = filename\n if pmagplotlib.isServer:\n black = '#000000'\n purple = '#800080'\n titles = {}\n titles['eq'] = 'Equal Area Plot'\n FIG = pmagplotlib.add_borders(FIG, titles, black, purple)\n pmagplotlib.save_plots(FIG, files)\n elif verbose:\n ans = raw_input(\n \" S[a]ve to save plot, [q]uit, Return to continue: \")\n if ans == \"q\":\n sys.exit()\n if ans == \"a\":\n pmagplotlib.save_plots(FIG, files)\n if plt:\n pmagplotlib.save_plots(FIG, files)","title":""},{"docid":"7d7b545b23011cb461451d62918075d6","score":"0.5289112","text":"def plot_output(depth_img, grasp_q_img, grasp_angle_img, no_grasps=1, grasp_width_img=None):\n gs = detect_grasps(grasp_q_img, grasp_angle_img,\n width_img=grasp_width_img, no_grasps=1)\n\n fig = plt.figure(figsize=(10, 10))\n\n ax = fig.add_subplot(1, 1, 1)\n ax.imshow(depth_img, cmap='gray')\n for g in gs:\n g.plot(ax)\n ax.set_title('Depth')\n ax.axis('off')\n\n return gs","title":""},{"docid":"ea91ae969c85240a5c5feb3f3cf4af73","score":"0.52865165","text":"def set_globalplot_properties():\n\n global colors\n\n plt.rcdefaults()\n plt.rc('font', weight='bold')\n plt.rc('legend', numpoints=1, fontsize='x-large')\n plt.rc('text', usetex=True)\n #colors = ['r', 'b', 'g', 'c']\n colors = ['#7b3294','#a4225f', '#a6dba0', '#d7191c']\n #,'#d01c8b','#a6611a']","title":""},{"docid":"241d87f62e870c563d253990c1ec470c","score":"0.5285527","text":"def printGraph(self):\n for s, p, o in self.g:\n print s, p, o","title":""},{"docid":"a2f1288c89046e498703f8391eace170","score":"0.52843493","text":"def afficher_fig(figure):\n\tR = [[[figure[i][j][k].num for k in range (len(figure[0][0]))] for j in range (len(figure[0]))] for i in range (len(figure))]\n\tprint(R)","title":""},{"docid":"1f13b15ae5d14b15295325d819f845f1","score":"0.5283384","text":"def graph_per_region():\r\n table = callflie()\r\n years_2558 = [each for each in table if \"2558\" in each]\r\n years_2557 = [each for each in table if \"2557\" in each]\r\n years_2556 = [each for each in table if \"2556\" in each]\r\n years_2555 = [each for each in table if \"2555\" in each]\r\n \r\n values58 = locations(years_2558)\r\n values57 = locations(years_2557)\r\n values56 = locations(years_2556)\r\n values55 = locations(years_2555)\r\n\r\n\r\n x = [\"South\", \"West\", \"East north\", \"Center\", \"East\", \"North\"]\r\n ghp = plt.gca()\r\n ghp.set_title(u'จำนวนคนต่อครัวที่รายได้ต่ำกว่า 30,000 บาท แบ่งตามภาค',fontname='JasmineUPC',fontsize=20)\r\n ghp.set_xlabel(u'Region',fontname='JasmineUPC',fontsize=20)\r\n ghp.set_ylabel(u'Amount people',fontname='JasmineUPC',fontsize=20)\r\n ghp.plot(x,values58,'-om')\r\n ghp.plot(x,values57,'-oc')\r\n ghp.plot(x,values56,'-oy')\r\n ghp.plot(x,values55,'-or')\r\n ghp.legend([u'2558',u'2557',u'2556',u'2555'],loc=1,fancybox=1,shadow=1)\r\n plt.show()","title":""},{"docid":"bd43d8bc8e370246a9eec1f65f0eb9f5","score":"0.52823603","text":"def prepare_plots(self):\n pass","title":""},{"docid":"13f7a0ea36841b9bb1e53c6e064ed794","score":"0.52801067","text":"def figure_reproduction():\n\n files = np.array(sorted(glob.glob(\"dataset2/*.ema\")))\n total_samples = len(files)\n electrodes = 14\n init_pos = np.zeros((total_samples,electrodes))\n labels = [\"T3\", \"T2\", \"T1\", \"Lower incisor\", \"Upper lip\", \"Lower lip\"]\n for i,fname in enumerate(files):\n data = ema_read(fname)\n init_pos[i,:] = data[0,:]\n\n sns.set_style(\"white\")\n idx = [0,1,2,3,5,6]\n\n sampled = np.random.choice(range(total_samples),200)\n markers = [\".\", \"v\", \"^\", \"<\",\"<\", \"s\",\"+\"]\n\n img = plt.imread(\"arthead.png\")\n plt.imshow(img, extent=[-6.75, 21.25, -10, 8])\n\n for label in idx:\n plt.scatter(init_pos[sampled,2*label],\n init_pos[sampled,2*label+1],\n marker=markers[label])\n plt.xlabel(\"x position\")\n plt.ylabel(\"y position\")\n plt.xlim([-5, 10])\n leg = plt.legend(labels,loc=3,frameon=False)\n leg.get_frame().set_linewidth(0.0)\n plt.title(\"Initial position of electrodes in MNGU0 dataset\")\n fig = plt.figure(num=1, figsize=(4.2,4.2), dpi=80, facecolor=\"w\",\n edgecolor=\"k\")\n \n plt.savefig(\"paper/init_pos.pgf\")\n\n return 0","title":""},{"docid":"1cc0d66ccb2f4cc4a0fb4a302ab49570","score":"0.52733195","text":"def figure(self):\n self._idx += 1\n\n # Check if there are too many plots in the contextmanager\n if self._idx >= self.nrows * self.ncols:\n raise RuntimeError(\n \"Invalid number of plots in the canvas! Increase \"\n \"the number of rows and cols to add more plots.\"\n )\n\n return plt.gcf()","title":""},{"docid":"7b7aa24e5c507e97c6a35b63d683a106","score":"0.527105","text":"def _plot(self, p):\n p.next(None, {'discrete': True})\n p.plot_3d('info.flipped', 'score_stats.purity', 'golds')\n p.plot_3d('info.flipped', 'score_stats.completeness', 'golds')\n p.plot_3d('info.flipped', 'score_stats.retired', 'golds')\n p.plot_3d('info.flipped', 'score_stats.mse_t', 'golds')\n p.plot_3d('info.flipped', 'score_stats.mse', 'golds')\n p.plot_3d('thresholds.0', 'thresholds.1', 'golds',\n axes={'x': 'Bogus Threshold',\n 'y': 'Real Threshold'})\n\n # Detect whether there are multiple trials per gold label amount\n # and fraction of labels flipped. Used to determine whether to\n # plot points or plot error bars.\n p.kwargs['keys'] = ['info.flipped', 'golds']\n d = plots.FD.bin_data(p.get_data(), 0)\n d = plots.FD.bin_data(d[next(iter(d))], 1)\n print(d)\n ebar = bool(len(next(iter(d.values()))) > 1)\n p.kwargs._reset()\n\n if ebar:\n p.next(None, {'discrete': True})\n p.plot_ebar_d('info.flipped', 'score_stats.purity', 'golds')\n p.plot_ebar_d('info.flipped', 'score_stats.completeness', 'golds')\n p.plot_ebar_d('info.flipped', 'score_stats.retired', 'golds')\n p.plot_ebar_d('info.flipped', 'score_stats.mse_t', 'golds')\n p.plot_ebar_d('info.flipped', 'score_stats.mse', 'golds')\n p.plot_3d('thresholds.0', 'thresholds.1', 'golds',\n axes={'x': 'Bogus Threshold',\n 'y': 'Real Threshold'})\n\n p.next(None, {'discrete': False})\n p.plot_3d('thresholds.0', 'thresholds.1', 'info.flipped',\n axes={'x': 'Bogus Threshold',\n 'y': 'Real Threshold'})\n\n p.next(None, {'discrete': True})\n if ebar:\n p.plot_ebar_d('info.flipped', 'score_stats.mse_t', 'golds')\n p.plot_ebar_d('info.flipped', 'score_stats.mse', 'golds')\n else:\n p.plot_3d('info.flipped', 'score_stats.mse_t', 'golds')\n p.plot_3d('info.flipped', 'score_stats.mse', 'golds')\n\n p.run()","title":""},{"docid":"3a969cced39e3eeeac633b6256e103c8","score":"0.5262232","text":"def plt_patch(self):","title":""},{"docid":"92e38a76290ddc7f285c413438af4160","score":"0.52616775","text":"def plotAll(n):\n\tplt.hold('on')\n\tplt.plot(getN(n),seq1(n,f1),getN(n),seq1(n,f2),getN(n),seq1(n,f3),getN(n),seq1(n,f4),getN(n),seq1(n,f5),)\n\tplt.legend(['Seq1','Seq2','Seq3','Seq4','Seq5'])\n\tplt.show()","title":""},{"docid":"82919fb7be05721c2ad43c09d48e018c","score":"0.52594584","text":"def rrt_graph_list(num_trials, startpos, endpos, n_iter, radius, step_size, threshold):\n graphs = []\n for i in range(0, num_trials):\n print(\"Trial: \", i)\n G = rrt(startpos, endpos, [], n_iter, radius, step_size, threshold)\n graphs.append(G)\n\n return graphs","title":""},{"docid":"d18682ae066026b069267a2489f6ef71","score":"0.5257661","text":"def getAvailableGeoms(self, nodes, showUndisplay = 0):\n # if no nodes specified no geometries.\n if not nodes:\n return\n molecules, atms = self.getNodes(nodes)\n geomsAvailable = []\n for mol in molecules:\n #if hasattr(mol,\"geomContainer\"):\n geomC = mol.geomContainer\n childGeomsName = self.getChildrenGeomsName(mol)\n \n # We only put the one we are ineterested in in the list\n # of geomsAvailable \n for geomName in list(geomC.geoms.keys()):\n if geomName in ['master','selectionSpheres'] :\n continue\n if geomName in geomC.atoms:\n if geomName in childGeomsName and geomC.geoms[geomName].children==[]:\n continue\n childgnames=[]\n if geomC.geoms[geomName].children != []:\n childnames = [x.name for x in geomC.geoms[geomName].children]\n childgnames=childgnames+childnames\n \n for child in childgnames:\n if geomC.atoms[geomName]==[] and geomC.atoms[child]!=[]:\n if not geomName in geomsAvailable:\n geomsAvailable.append(geomName)\n else: \n if geomC.atoms[geomName]!=[]:\n if not geomName in geomsAvailable:\n geomsAvailable.append(geomName) \n \n \n return geomsAvailable","title":""}],"string":"[\n {\n \"docid\": \"065bd8f9420f9d7fe78a71dd623e2dcf\",\n \"score\": \"0.69316924\",\n \"text\": \"def plot(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62de181701f8f7af09e91c98fc09444f\",\n \"score\": \"0.63560665\",\n \"text\": \"def show_plots():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62de181701f8f7af09e91c98fc09444f\",\n \"score\": \"0.63560665\",\n \"text\": \"def show_plots():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b17e42080391c98fd0b8a7cd66de29\",\n \"score\": \"0.62527966\",\n \"text\": \"def showFigures():\\n pyplot.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15382ff7e5413c58f939ddcb600f9cc\",\n \"score\": \"0.6202436\",\n \"text\": \"def plot(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4240a5aadf84ac63cc47713c81c8741\",\n \"score\": \"0.61292875\",\n \"text\": \"def plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63bc38324904f72fb33d1d18e0e8be16\",\n \"score\": \"0.60767907\",\n \"text\": \"def MakeFigures(self):\\r\\n self.PlotDistN()\\r\\n self.PlotPrevalences()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef468de5fefb40a5f06870dfa55fc00\",\n \"score\": \"0.603339\",\n \"text\": \"def cmd_plot():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e62b0d719934abc9f4883443d594e2\",\n \"score\": \"0.5980389\",\n \"text\": \"def get_plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4645ffc99e2fd6302b4a462a9e4ba8cd\",\n \"score\": \"0.589725\",\n \"text\": \"def generate_plots():\\n logger(\\\"plots\\\").info(\\\"Generating plots\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cab97920f207c6091141a1e4f549e2\",\n \"score\": \"0.5846857\",\n \"text\": \"def scree_plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aa73fa5a14c851f206804327d30bd22\",\n \"score\": \"0.5829319\",\n \"text\": \"def all_figure_numbers(cls):\\n return list(cls._figures.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397af6e240b5f0a90c2e67fa6a935630\",\n \"score\": \"0.58169323\",\n \"text\": \"def print_figure(self):\\n return [\\n [\\n pos[0] * self.pixel_ratio - 1,\\n pos[1] * self.pixel_ratio + 1,\\n self.pixel_ratio - 1,\\n self.pixel_ratio - 1\\n ]\\n for pos in self.repr_figure_on_board()\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31336f0b9696eaa72c7fbe20069312df\",\n \"score\": \"0.5773237\",\n \"text\": \"def plots(self):\\n return list(self.metadata.get(\\\"plots\\\", {}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1b0937d10a7d7b9d27a08eea0bd22c4\",\n \"score\": \"0.5761019\",\n \"text\": \"def show_fig416(self):\\n\\t\\tradiusplot = []\\n\\t\\tdeltamplot = []\\n\\t\\tmplot = []\\n\\t\\tsigmaplot = []\\n\\t\\tvelocityplot = []\\n\\t\\tomegaplot = []\\n\\n\\t\\ti=0\\n\\t\\tfor star in self.galaxy:\\n\\t\\t\\ti+=1\\n\\t\\t\\tif i % 100000 == 0:\\n\\t\\t\\t\\tprint(i)\\n\\t\\t\\tradiusplot.append(star.radius)\\n\\t\\t\\tdeltamplot.append(self.deltaM[star.inring]*100)\\n\\t\\t\\tmplot.append(self.Mr[star.inring])\\n\\t\\t\\tsigmaplot.append(star.area_density)\\n\\t\\t\\tvelocityplot.append(star.va*3.086e+16/7.5e15)\\n\\t\\t\\tomegaplot.append(self.omega[star.inring])\\n\\n\\t\\tfig, axarr = plt.subplots(2,sharex=True,figsize=(9,10))\\n\\t\\taxarr[0].plot(radiusplot,deltamplot,'ko', ms=0.5)\\n\\t\\taxarr[0].plot(radiusplot,mplot,'ko',ms=0.5)\\n\\t\\t# Make the y-axis label, ticks and tick labels match the line color.\\n\\t\\taxarr[0].set_ylabel(r'$100 \\\\times M(r)/M_{\\\\odot}$, $M(<r)/M_{\\\\odot}$',\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\tcolor='k')\\n\\t\\taxarr[0].set_xlim([0,30])\\n\\t\\taxarr[0].set_ylim([0,1.1e11])\\n\\t\\taxarr[0].text(22,9e10,r'$M(<r)$')\\n\\t\\taxarr[0].text(11,5e10,r'$100\\\\times \\\\Delta M(r)$')\\n\\t\\taxarr[0].text(22,1e10,r'$N=10^6$')\\n\\n\\t\\tax1 = axarr[0].twinx()\\n\\t\\tax1.plot(radiusplot, sigmaplot, 'ro',ms=0.5)\\n\\t\\tax1.set_ylabel(r'$\\\\sigma (r) (M_{\\\\odot}$ kpc$^{-2}$)', color='r')\\n\\t\\tax1.tick_params('y', which='both',colors='r')\\n\\t\\tax1.set_ylim([0,1e9])\\n\\t\\tax1.set_xlim([0,30])\\n\\t\\tax1.spines['right'].set_color('red')\\n\\n\\t\\taxarr[1].plot(radiusplot,velocityplot,'ko',ms=0.5)\\n\\t\\taxarr[1].set_ylabel(r'$v_{rot}$ (km s$^{-1}$)', color='k')\\n\\t\\taxarr[1].set_xlabel(r'$r$ (kpc)')\\n\\t\\taxarr[1].set_xlim([0,30])\\n\\t\\taxarr[1].set_ylim([0,250])\\n\\n\\t\\tax2 = axarr[1].twinx()\\n\\t\\tax2.plot(radiusplot,omegaplot,'ro',ms=0.5)\\n\\t\\tax2.set_ylabel(r'$\\\\Omega (r)$', color='r')\\n\\t\\tax2.tick_params('y', which='both',colors='r')\\n\\t\\tax2.set_ylim([0,40])\\n\\t\\tax2.set_xlim([0,30])\\n\\t\\tax2.spines['right'].set_color('red')\\n\\n\\t\\tfig.tight_layout()\\n\\t\\tfig.savefig(\\\"/Users/user/416.png\\\",format='png', dpi=1000)\\n\\t\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5443c3f98dcf773486e9e6fa8175b2\",\n \"score\": \"0.57598656\",\n \"text\": \"def get_chrom_axes(grid=None, parent_gridelement=None, fig=None,\\n ylabel=\\\"\\\", plot_xlabels=True, color=None, plotfun=None):\\n if plotfun is None:\\n plotfun = plt.plot\\n if grid is None:\\n grid, parent_gridelement = get_chrom_grid(parent_gridelement=parent_gridelement)\\n if fig is None:\\n fig = plt.figure(1, figsize=(20, 2))\\n fig.subplots_adjust(wspace=0)\\n # print color\\n if color is None:\\n print(\\\"changed to current color\\\")\\n ax = plt.gca()\\n color = plt.rcParams[\\\"axes.prop_cycle\\\"].by_key()[\\\"color\\\"][0]\\n color = converter.to_rgb(color)\\n chroms = series.index.get_level_values(level=0).unique()\\n for i, (chrom, gr) in enumerate(zip(chroms, grid)):\\n if i == 0:\\n ax = fig.add_subplot(gr)\\n else:\\n ax = fig.add_subplot(gr, sharey=ax)\\n ax.set_yticks([])\\n\\n if i % 2 == 0:\\n color1 = tuple(np.array(color) * 0.6)\\n if plot_xlabels:\\n ax.set_xlabel('Chr' + chrom.rsplit(\\\"_\\\")[1])\\n else:\\n color1 = color\\n # print color1\\n ax.set_frame_on(False)\\n ax.set_xticks([])\\n ax.set_prop_cycler([color1])\\n ylim = ax.get_ylim()\\n # we could also get the parent element with gr.get_topmost_subplotspec()\\n total_ax = fig.add_subplot(parent_gridelement)\\n total_ax.set_frame_on(False)\\n total_ax.spines['top'].set_visible(True)\\n total_ax.set_ylim(ylim)\\n total_ax.set_xticks([])\\n if not ylabel and series.name is not None:\\n ylabel = series.name\\n total_ax.set_ylabel(ylabel, fontsize=14)\\n if plot_xlabels:\\n total_ax.set_xlabel(\\\"Chromosomes\\\", labelpad=25, fontsize=14)\\n return fig.get_axes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"481f86e1d57272a8d68181be7ead75ad\",\n \"score\": \"0.57525766\",\n \"text\": \"def grads(self):\\r\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af19b9647b5f155b753fe51707b4c81d\",\n \"score\": \"0.5750883\",\n \"text\": \"def plot_results(self, save_filename = None, plot = True):\\n \\n # Check if the length of the gc is not good, and append the last\\n # gradient\\n if len(self.__gc__) != len(self.__fe__):\\n #grad, grad_err = self.ensemble.get_fc_from_self_consistency(True, True)\\n# grad, grad_err = self.ensemble.get_preconditioned_gradient(True, True)\\n# grad = self.__gc__[-1]\\n# grad_err = self.__gc_err__[-1]\\n# \\n# # Initialize the symmetry\\n# qe_sym = CC.symmetries.QE_Symmetry(self.dyn.structure)\\n# qe_sym.SetupQPoint(verbose = True)\\n# \\n# struct_grad, struct_grad_err = self.ensemble.get_average_forces(True)\\n# \\n# qe_sym.SymmetrizeVector(struct_grad)\\n# qe_sym.SymmetrizeVector(struct_grad_err)\\n# \\n# # Get the gradient modulus\\n# gc = np.einsum(\\\"abc, acb\\\", grad, grad)\\n# gc_err = np.einsum(\\\"abc, acb\\\", grad_err, grad_err)\\n \\n self.__gw__.append(self.__gw__[-1])#np.sqrt( np.einsum(\\\"ij, ij\\\", struct_grad, struct_grad)))\\n self.__gw_err__.append(self.__gw_err__[-1])#np.sqrt( np.einsum(\\\"ij, ij\\\", struct_grad_err, struct_grad_err) / qe_sym.QE_nsymq))\\n \\n self.__gc__.append(self.__gc__[-1])\\n self.__gc_err__.append(self.__gc_err__[-1])\\n \\n # Convert the data in numpy arrays\\n fe = np.array(self.__fe__) * __RyTomev__\\n fe_err = np.array(self.__fe_err__) * __RyTomev__\\n \\n gc = np.array(self.__gc__) * __RyTomev__\\n gc_err = np.array(self.__gc_err__) * __RyTomev__\\n \\n gw = np.array(self.__gw__) * __RyTomev__\\n gw_err = np.array(self.__gw_err__) * __RyTomev__\\n \\n kl = np.array(self.__KL__, dtype = np.float64)\\n \\n steps = np.arange(len(fe))\\n \\n \\n # Check if the results need to be saved on a file\\n if save_filename is not None: \\n #print \\\"Lengths:\\\", len(fe), len(gc), len(gw), len(kl), len(steps) \\n print (\\\"Saving the minimization results...\\\")\\n save_data = [steps, fe, fe_err, gc, gc_err, gw, gw_err, kl]\\n np.savetxt(save_filename, np.real(np.transpose(save_data)),\\n header = \\\"Steps; Free energy +- error [meV]; FC gradient +- error [bohr^2]; Structure gradient +- error [meV / A]; Kong-Liu N_eff\\\")\\n print (\\\"Minimization data saved in %s.\\\" % save_filename)\\n \\n \\n # Plot\\n if plot:\\n if not __MATPLOTLIB__:\\n print (\\\"MATPLOTLIB NOT FOUND.\\\")\\n raise ImportError(\\\"Error, matplotlib required to plot\\\")\\n plt.figure()\\n plt.title(\\\"Free energy\\\")\\n plt.errorbar(steps, fe, yerr = fe_err, label = \\\"Free energy\\\")\\n plt.ylabel(r\\\"$F$ [meV]\\\")\\n plt.xlabel(\\\"steps\\\")\\n plt.tight_layout()\\n \\n plt.figure()\\n plt.title(\\\"FC matrix gradient\\\")\\n plt.errorbar(steps, gc, yerr = gc_err, label = \\\"gradient\\\")\\n plt.ylabel(r\\\"$|\\\\vec g|$ [bohr^2]\\\")\\n plt.xlabel(\\\"steps\\\")\\n plt.tight_layout()\\n \\n plt.figure()\\n plt.title(\\\"Structure gradient\\\")\\n plt.errorbar(steps, gw, yerr = gw_err, label = \\\"gradient\\\")\\n plt.ylabel(r\\\"$|\\\\vec g|$ [meV / A]\\\")\\n plt.xlabel(\\\"steps\\\")\\n plt.tight_layout()\\n \\n plt.figure()\\n plt.title(\\\"Kong-Liu effective sample size\\\")\\n plt.plot(steps, kl)\\n plt.ylabel(r\\\"$\\\\frac{N_{eff}}{N_0}$\\\")\\n plt.xlabel(\\\"steps\\\")\\n plt.tight_layout()\\n \\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f3c9a1bb0ada998931a6b2323b0b754\",\n \"score\": \"0.5749016\",\n \"text\": \"def plotGeneration(self):\\n to_plot = self.__fitnesses\\n # plt.clf()\\n plt.plot(to_plot, 'g^')\\n plt.ylabel('fitness')\\n plt.xlabel('individual')\\n plt.show()\\n self.__fitnesses = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7733d9c56c3559639426f5bd708eb6a7\",\n \"score\": \"0.5736658\",\n \"text\": \"def show_plots(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c269297eb365ad945dffe642d4a1b1\",\n \"score\": \"0.573472\",\n \"text\": \"def draw_plots(self):\\n for f in self.figurePlots:\\n f.draw_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07f725da8a49b52841b5ee4d9ed54f1\",\n \"score\": \"0.57297593\",\n \"text\": \"def plot(self):\\n plt.figure()\\n if self.exog == None:\\n self.plotting_proc(0)\\n else:\\n for g in range(len(self.groups)):\\n self.plotting_proc(g)\\n plt.ylim(ymax=1.05)\\n plt.ylabel('Survival')\\n plt.xlabel('Time')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3c3f04e9e167fff37c9c266fcb501b\",\n \"score\": \"0.57122016\",\n \"text\": \"def display_generation(self):\\n if self.t == 0:\\n plt.subplot(121)\\n plt.subplot(122)\\n\\n plt.sca(plt.gcf().axes[0])\\n plt.cla()\\n plt.plot(self.archive[:self.t + 1, :, I_GENOME[0]].reshape(-1), self.archive[:self.t + 1, :, I_GENOME[1]].reshape(-1), \\\"k.\\\", label=\\\"archive\\\")\\n plt.plot(self.population[self.t, :, I_GENOME[0]], self.population[self.t, :, I_GENOME[1]], \\\"b.\\\", label=\\\"population\\\")\\n plt.plot(self.offsprings[self.t, :, I_GENOME[0]], self.offsprings[self.t, :, I_GENOME[1]], \\\"r.\\\", label=\\\"offsprings\\\")\\n plt.axis([-5, 5, -5, 5])\\n if self.t == 0:\\n plt.legend()\\n\\n plt.sca(plt.gcf().axes[1])\\n plt.plot(self.archive[:self.t + 1, :, I_BEHAVIOR].reshape(-1), np.full(((self.t + 1) * self.n_selected), self.t),\\n \\\"k.\\\", markersize=10, label=\\\"archive\\\")\\n plt.plot(self.population[self.t, :, I_BEHAVIOR], np.full(self.n_pop, self.t + 0.1),\\n \\\"b.\\\", markersize=10, label=\\\"population\\\")\\n plt.plot(self.offsprings[self.t, :, I_BEHAVIOR], np.full(self.n_offspring, self.t + 0.2),\\n \\\"r.\\\", markersize=10, label=\\\"offsprings\\\")\\n\\n novelty_landscape = self.compute_novelty(np.linspace(-5, 5, 100))\\n plt.plot(np.linspace(-5, 5, 100), novelty_landscape + self.t, color=\\\"g\\\", label=\\\"novelty\\\")\\n\\n selected = np.argwhere(self.population[self.t, :, I_SELECTED_POP] == 1.)\\n plt.plot(self.population[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.1),\\n \\\"bo\\\", markersize=14, fillstyle=\\\"none\\\", label=\\\"selected for population\\\")\\n selected = np.argwhere(self.offsprings[self.t, :, I_SELECTED_POP] == 1.)\\n plt.plot(self.offsprings[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.2),\\n \\\"bo\\\", markersize=14, fillstyle=\\\"none\\\")\\n selected = np.argwhere(self.offsprings[self.t, :, I_SELECTED_ARC] == 1.)\\n plt.plot(self.offsprings[self.t, selected, I_BEHAVIOR], np.full(len(selected), self.t + 0.2),\\n \\\"kd\\\", markersize=14, fillstyle=\\\"none\\\", label=\\\"selected for population\\\")\\n plt.xlim([-5, 5])\\n if self.t == 0:\\n plt.legend()\\n\\n plt.title(\\\"generation \\\" + str(self.t))\\n plt.show(block=False)\\n plt.waitforbuttonpress()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5daba460d1ea8430681ac4a6a3ed867\",\n \"score\": \"0.56904256\",\n \"text\": \"def main():\\n plot_grid()\\n import matplotlib.pyplot as plt\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3890ba7b749cc8509f95066ccc461c5a\",\n \"score\": \"0.5680215\",\n \"text\": \"def plot(self):\\n return hdmr_plot(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb26ba2a048335db2dc0f167f7eb11bf\",\n \"score\": \"0.5666373\",\n \"text\": \"def plot_gal_region(self, gal_region, output_stamps=None, ncols=6, savefig=True, name_plot=None):\\n\\n\\t\\timport matplotlib.pyplot as plt\\n\\t\\tfrom matplotlib.colors import ListedColormap\\n\\n\\t\\tfilters = self.filters\\n\\t\\tnbands = len(filters)\\n\\n\\t\\tif output_stamps is None:\\n\\t\\t\\tif self.run_image_processing == False:\\n\\t\\t\\t\\tprint ('output_stamps is required input if run_image_processing=False!')\\n\\t\\t\\t\\tsys.exit()\\n\\t\\t\\telse:\\n\\t\\t\\t\\toutput_stamps = output_stamps1\\n\\n\\t\\trows, cols = np.where(gal_region==0)\\n\\t\\tgal_region[rows,cols] = float('nan')\\n\\n\\t\\tmap_plots, nrows = mapping_multiplots(nbands,ncols)\\n\\n\\t\\tfig1 = plt.figure(figsize=(ncols*4,nrows*4))\\n\\t\\tfor bb in range(0,nbands):\\n\\t\\t\\tyy, xx = np.where(map_plots==bb+1)\\n\\t\\t\\tf1 = fig1.add_subplot(nrows, ncols, bb+1)\\n\\t\\t\\tif map_plots[yy[0]][xx[0]-1] == 0:\\n\\t\\t\\t\\tplt.ylabel('[pixel]', fontsize=15)\\n\\t\\t\\tif map_plots[yy[0]+1][xx[0]] == 0:\\n\\t\\t\\t\\tplt.xlabel('[pixel]', fontsize=15)\\n\\n\\t\\t\\thdu = fits.open(output_stamps[\\\"name_img_%s\\\" % filters[bb]])\\n\\t\\t\\tplt.imshow(np.log10(hdu[0].data), origin='lower')\\n\\t\\t\\thdu.close()\\n\\t\\t\\tplt.imshow(gal_region, origin='lower', cmap=ListedColormap(['brown']), alpha=0.5)\\n\\t\\t\\tf1.text(0.5, 0.93, filters[bb], horizontalalignment='center', verticalalignment='center', \\n\\t\\t\\t\\t\\ttransform = f1.transAxes, fontsize=18, color='black')\\n\\n\\t\\tplt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.15)\\n\\n\\t\\tif savefig is True:\\n\\t\\t\\tif name_plot is None:\\n\\t\\t\\t\\tname_plot = 'gal_region.png'\\n\\t\\t\\tplt.savefig(name_plot)\\n\\t\\telse:\\n\\t\\t\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc8e2b916489a2fb12dbb0bd050a44c\",\n \"score\": \"0.5660283\",\n \"text\": \"def plot_all_nav(directory = '.'):\\n fig = plt.figure()\\n ax1 = fig.add_subplot(221)\\n ax2 = fig.add_subplot(222)\\n ax3 = fig.add_subplot(614, sharex = ax2)\\n ax4 = fig.add_subplot(615, sharex = ax2)\\n ax5 = fig.add_subplot(616, sharex = ax2)\\n flist = glob(directory + '/*.all')\\n flist.sort()\\n clist = ['b','r','g','k','y','c']\\n n = 0\\n for f in flist:\\n a = allRead(f)\\n if os.path.exists(f + '.nav'):\\n a.load_navarray()\\n else:\\n a._build_navarray()\\n a.save_navarray()\\n ax1.plot(a.navarray['80'][:,1],a.navarray['80'][:,2], clist[n])\\n ax1.set_xlabel('Longitude (Degrees)')\\n ax1.set_ylabel('Latitude (Degrees)')\\n ax1.grid(True)\\n ax2.plot(a.navarray['65'][:,0],a.navarray['65'][:,4], clist[n])\\n ax2.set_ylabel('Heading (Degrees)')\\n ax2.set_xlabel('Time (Seconds)')\\n ax2.grid(True)\\n if a.navarray.has_key('104'):\\n ax3.plot(a.navarray['104'][:,0],a.navarray['104'][:,1], clist[n])\\n ax3.set_ylabel('Height (Meters)')\\n ax3.grid(True)\\n ax4.plot(a.navarray['65'][:,0],a.navarray['65'][:,1], clist[n])\\n ax4.plot(a.navarray['65'][:,0],a.navarray['65'][:,2], clist[n] + '--')\\n ax4.set_ylabel('Degress')\\n ax4.legend(('Roll','Pitch'))\\n ax4.grid(True)\\n ax5.plot(a.navarray['65'][:,0],a.navarray['65'][:,3], clist[n])\\n ax5.set_ylabel('Heave (Meters)')\\n ax5.set_xlabel('Time (Seconds)')\\n ax5.grid(True)\\n n += 1\\n if n >= len(clist):\\n n = 0\\n plt.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2c2800df6982631017383eec11be5b\",\n \"score\": \"0.5659238\",\n \"text\": \"def gcfn():\\n currentFig = mpl.pyplot.gcf()\\n for k in mpl.pyplot.get_fignums():\\n mpl.pyplot.figure(k)\\n if mpl.pyplot.gcf() == currentFig:\\n return k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637658b46fae402dc3bf5c890a9acef9\",\n \"score\": \"0.5655926\",\n \"text\": \"def _calculate_glare(self) -> List[DrawObject]:\\n return [\\n GlareObject.generate_random_figure(*self._src_image.size)\\n for _ in range(self._aug_numberofoptions)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c5ad9601c8c48becd2568a0ed40cbd\",\n \"score\": \"0.5645673\",\n \"text\": \"def qf_plot(self):\\n lgm = self.lg_masses()\\n sfr = np.array([galaxy.sfr for galaxy in self.galaxies])\\n hq, edges = np.histogram(lgm[sfr == 0], self.bins)\\n h, edges = np.histogram(lgm, self.bins)\\n qf = hq/h\\n plt.clf()\\n plt.plot(self.lgM, qf, label='Current')\\n plt.plot(self.lgM, self.qf_end, label='Target (GAMA groups)')\\n plt.legend()\\n plt.xlabel(r'log $M^*/M_\\\\odot$')\\n plt.ylabel('Red fraction')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a04952ae541c725ca7d1d4dabb11fae8\",\n \"score\": \"0.5628412\",\n \"text\": \"def plot_locations(self, fig: plt.figure = None):\\n\\n CoB = data['Structures']['CoB']\\n\\n\\n if fig is None:\\n fig, ax = plt.subplots()\\n ax.set_aspect(1.0)\\n plt.xlim(0, 15)\\n plt.ylim(0, 15)\\n\\n coordinate_history = []\\n xs = []\\n zs = []\\n text = []\\n for name, location in self.__components.items():\\n weight = location[0]\\n if len(location[1]) == 2:\\n x = location[1][0]\\n z = location[1][1]\\n if len(location[1]) == 3:\\n x = location[1][0]\\n z = location[1][1]\\n y = location[1][2]\\n i = 1\\n print(x,z)\\n if (x, z) in coordinate_history:\\n i = 3\\n matplotlib.rcParams.update({'font.size': 8})\\n plt.scatter(x, z, s=np.sqrt(weight), c='k')\\n #plt.annotate(name, xy=(x+i*0.1, z+i*0.1))\\n coordinate_history.append((x,z))\\n xs.append(x)\\n zs.append(z)\\n text.append(name)\\n texts = []\\n for (a,b,c) in zip(xs,zs,text):\\n texts.append(plt.text(a,b,c))\\n\\n plt.scatter(self.__cg[0], self.__cg[1], c='r')\\n plt.annotate('CG', xy=self.__cg)\\n plt.scatter(CoB[0], CoB[1], c='b')\\n plt.annotate('CoB', xy=CoB)\\n\\n # fuselage = self.__plot_cilinder(9, 2.5)\\n # wing = self.__plot_cilinder(2.25, 0.5, offset=(3, 1.5))\\n #\\n # plt.plot(fuselage[0], fuselage[1], 'k-')\\n # plt.plot(wing[0], wing[1], 'k-')\\n\\n plt.grid()\\n plt.xlabel('X Position [m]')\\n plt.ylabel('Z Position [m]')\\n plt.title('CG Location')\\n adjust_text(texts)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a56f98af91d09fc31a4bae3efc9045\",\n \"score\": \"0.56251925\",\n \"text\": \"def get_all_fig_managers(cls):\\n return list(cls._figures.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c06457a4b0cb4d1eb0037b85af70ca1\",\n \"score\": \"0.56244075\",\n \"text\": \"def plotGrid(self,g):\\n # Get system size\\n N = g.shape[0]\\n \\n # Rotate grid, such that the view is correct (Maybe not necessary)\\n rotg = np.rot90(g,3)\\n\\n\\n # Extend the matrix, so we plot 9 unit cells\\n tmp = np.concatenate((rotg,rotg),axis=0)\\n tmp = np.concatenate((tmp,tmp),axis=0)\\n tmp2 = np.concatenate((tmp,tmp),axis=1)\\n rotg = np.concatenate((tmp2,tmp2),axis=1)\\n\\n # Find Ag index and pair them in a list\\n indexAg = np.where(rotg==1)\\n PairsAg = list( [indexAg[0][i], indexAg[1][i] ] for i in range(len(indexAg[0])))\\n\\n # Find O index and pair them in a list\\n indexO = np.where(rotg==2)\\n PairsO = list( [indexO[0][i], indexO[1][i] ] for i in range(len(indexO[0])))\\n\\n # Define the shearing matrix\\n shearMat = np.array([[1, 0.5],[0,1]])\\n\\n PairsAg = np.subtract(PairsAg,(N*2))\\n PairsO = np.subtract(PairsO,(N*2))\\n\\n # Multiply all coordinate pairs with the shearing matrix.\\n # The \\\".T\\\" transposes such that CoordsAg[0] are x coordinates, CoordsAg[1] are y coordinates etc. \\n CoordsAg = np.array(list( np.dot(shearMat,i) for i in PairsAg)).T\\n CoordsO = np.array(list( np.dot(shearMat,i) for i in PairsO)).T\\n\\n # CoordsAg = np.subtract(CoordsAg,(N-1))\\n # CoordsO = np.subtract(CoordsO,(N-1))\\n\\n # Update the x and y values of the plots\\n self.AgPlot.set_xdata(CoordsAg[0])\\n self.AgPlot.set_ydata(CoordsAg[1])\\n self.OPlot.set_xdata(CoordsO[0])\\n self.OPlot.set_ydata(CoordsO[1])\\n\\n # Pause a millisecond, so pyplot updates the graphics\\n plt.pause(.001)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65eb680a65479e68e06b6586b1d97f9\",\n \"score\": \"0.5612948\",\n \"text\": \"def _gripper_visualization(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749f8c41ec0b1211cad399b0678ded70\",\n \"score\": \"0.5606378\",\n \"text\": \"def all_figures(cls):\\n return list(cls._figures.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2b239ef13e854a788f4860869c7aa3\",\n \"score\": \"0.5606249\",\n \"text\": \"def gp_only():\\n plotter = Plotter('GP only tree size vs test error', 'Tree size', 'Test error')\\n directory = '../result_data/gp_only/'\\n gp_only_files = os.listdir(directory)\\n for filename in gp_only_files:\\n plotter.add_trace_from_csv(os.path.join(directory, filename), 'tree size', 'test error')\\n plotter.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ff76e55e2df27279256ede7c46a823\",\n \"score\": \"0.5597997\",\n \"text\": \"def plot(self):\\n info('*** Plot\\\\n')\\n self.itfs = [t for t in self.itfs if t in self.data]\\n gui(self.data, (self.ast, self.aet), self.itfs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f656c7e2bdd86aeda0af3098cbe01b82\",\n \"score\": \"0.55958873\",\n \"text\": \"def _plot(self, Xup, Xdown, ifig, num, numfiles):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977de0e4ec2e7603f5a5a34f07e12e4d\",\n \"score\": \"0.5592947\",\n \"text\": \"def return_figures():\\n\\n df = query_generation('DE', 14)\\n\\n graph_one = []\\n x_val = df.index\\n\\n for energy_source in df.columns:\\n\\n y_val = df[energy_source].tolist()\\n\\n graph_one.append(\\n go.Scatter(\\n x=x_val,\\n y=y_val,\\n mode='lines',\\n name=energy_source,\\n stackgroup = 'one'\\n )\\n )\\n\\n layout_one = dict(title='Generation in Germany during the last 14 days',\\n xaxis=dict(title='Date'),\\n yaxis=dict(title='Net Generation (MW)'),\\n colorway = ['#008000', '#ffa500', '#ff0000', '#000080', '#008080', '#808080', '#a52a2a', '#1e90ff', '#ffc40c'],\\n plot_bgcolor = '#E8E8E8',\\n hovermode = 'closest', \\n hoverdistance = -1,\\n height = 500\\n )\\n\\n # append all charts to the figures list\\n figures = []\\n figures.append(dict(data=graph_one, layout=layout_one))\\n\\n return figures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438a7f813c2d87f2fa6f0e2b75473a37\",\n \"score\": \"0.55875325\",\n \"text\": \"def init_plots(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0296bc833cf8baa3c2dc38e225f2950b\",\n \"score\": \"0.55847204\",\n \"text\": \"def get_graphics_list( self ):\\r\\n return self.OGLDrawables[:] # This is a shallow copy and intentionally so\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd17cc2dce0d1aeea525f7003402fcb3\",\n \"score\": \"0.5576919\",\n \"text\": \"def create_plots(self):\\n from matplotlib.pylab import figure, subplot, scatter, ylabel, xlabel, \\\\\\n xlim, ylim, rcParams, colorbar, axis, title\\n \\n # Alias data from the search results\\n evo = self.results['evolution']\\n params = self.results['parameters']\\n p_names = params.keys()\\n p_names.sort()\\n Np = len(p_names) # number of parameters\\n\\n # Set color axis variables and key flag strings\\n vargs = {}\\n normstr = ''\\n boundsstr = ''\\n if self.norm_caxis:\\n vargs['vmin'] = min([g.fitness.min() for g in evo])\\n vargs['vmax'] = max([g.fitness.max() for g in evo])\\n normstr = '_norm'\\n if self.use_bounds:\\n boundsstr = '_full'\\n\\n # Create per-generation param x param plots\\n rcParams['figure.figsize'] = self.figsize_inches\\n self.figure = {}\\n for gen in xrange(len(evo)):\\n f = figure()\\n f.set_size_inches(self.figsize_inches)\\n for j in xrange(0,Np-1):\\n for i in xrange(j+1,Np):\\n subplot(Np-1, Np-1, (Np-1)*(i-1) + (j+1))\\n scatter(evo[gen][p_names[j]], evo[gen][p_names[i]], \\n c=evo[gen].fitness, s=self.marker_size, \\n edgecolors='none', **vargs)\\n if j==0:\\n ylabel(p_names[i])\\n if i==Np-1:\\n xlabel(p_names[j])\\n if j==0 and i==1:\\n title('generation_%02d'%(gen+1))\\n if self.use_bounds:\\n xlim(params[p_names[j]])\\n ylim(params[p_names[i]])\\n else:\\n axis('tight')\\n \\n # Add colorbar in upper right\\n subplot(Np-1, Np-1, Np-1)\\n colorbar(fraction=0.5, aspect=10)\\n axis('off') \\n \\n # Store this figure in the plots dictionary\\n figkey = 'gen%02d%s%s'%(gen+1, normstr, boundsstr)\\n self.figure[figkey] = f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d140a6197a2df2cd78c5a62b3d2e28e2\",\n \"score\": \"0.5555535\",\n \"text\": \"def showCalData(data):\\n gs=gridspec.GridSpec(4,4)\\n for gradData in dat.magDataGrad:\\n for k,key in enumerate(gradData.keys()):\\n ax=subplot(gs[k])\\n ax.plot(gradData[key])\\n ax.set_ylim([-0.2,0.2])\\n\\n for k,key in enumerate(dat.magDataRaw.keys()):\\n ax=subplot(gs[k+8])\\n ax.plot(dat.magDataRaw[key])\\n ax.set_ylim([-0.2,0.2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5546e08a25a4f089327accfe766dbecb\",\n \"score\": \"0.5539796\",\n \"text\": \"def ax(self):\\n return plt.gca()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3027284a510e8e24ce73c9a6471c53\",\n \"score\": \"0.5536739\",\n \"text\": \"def plotting(self):\\n raise NotImplementedError('Must implement plotting method!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81061b5518d63aee3cec88e079fd127\",\n \"score\": \"0.5533772\",\n \"text\": \"def showFigures():\\n mlab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509edc5bc45b5c20d8101a701e1cdbe4\",\n \"score\": \"0.55281097\",\n \"text\": \"def init_graphics(self):\\n self.rows, self.cols = self.stdscr.getmaxyx()\\n self.graphics = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10014b3b70ee8549d2d2daf38efecf8b\",\n \"score\": \"0.55235696\",\n \"text\": \"def plotting(self):\\n self.plot.figure()\\n exps = self.repo.get(['name', self.config.get('experiment')])\\n plot_data = self.data(exps)\\n\\n if isinstance(plot_data, list):\\n for plot, data in enumerate(plot_data):\\n self.draw(data, plot)\\n else:\\n self.draw(plot_data)\\n\\n if self.config.get('styles.grid', False):\\n self.plot.grid(True)\\n\\n if self.config.get('styles.axis', False):\\n self.plot.axis(self.config.get('styles.axis'))\\n\\n if self.config.get('styles.ticks', False):\\n self.plot.xticks(**self.config.get('styles.ticks.xticks', {}))\\n self.plot.yticks(**self.config.get('styles.ticks.yticks', {}))\\n\\n self.labeling()\\n\\n return self.plot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a11a909dc25bcb936bdc9f08c6ad02a\",\n \"score\": \"0.5498557\",\n \"text\": \"def plot(self):\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab3b49502d7b205837b7868651ec088\",\n \"score\": \"0.54825664\",\n \"text\": \"def do_all():\\n plot_folder = 'plots/paperplots/'\\n results_folder = 'results/'\\n ex1_plot(plot_folder,results_folder)\\n ex2_plot(plot_folder,results_folder)\\n ex3_plot(plot_folder,results_folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc67e2c9c5f778e7ef2c4bafa0ae6e74\",\n \"score\": \"0.5478483\",\n \"text\": \"def make_plot(self):\\n plt.figure(figsize=self.figsize)\\n self.get_lines()\\n self.get_axes()\\n self.get_xticks()\\n self.get_yticks()\\n self.get_labels()\\n self.get_grid()\\n self.get_legend()\\n self.output_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5240c753d3910da3081b1456509162b5\",\n \"score\": \"0.54632396\",\n \"text\": \"def visualize_grid(self):\\n self.visualize_demos([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5827d8565c4852f7fb2132fdad2bcfd8\",\n \"score\": \"0.54569346\",\n \"text\": \"def graphics(self):\\n return self._graphics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcebd1d3940e28dd8be60f8cc0b32951\",\n \"score\": \"0.5452585\",\n \"text\": \"def displayFigures() -> None:\\r\\n for figure in MAIN_FIGURE_LIST:\\r\\n glEnable(GL_BLEND)\\r\\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)\\r\\n glColor4f(*(figure.rgba))\\r\\n\\r\\n glPushMatrix()\\r\\n glTranslatef(*figure.figPos)\\r\\n glScalef(*figure.figScale)\\r\\n glRotatef(figure.figRot, 0, 0, 1.0)\\r\\n glMultMatrixf(figure.figShear)\\r\\n glMultMatrixf(figure.figReflex)\\r\\n\\r\\n glBegin(GL_POLYGON)\\r\\n for point in figure.pointsList:\\r\\n glVertex2f(*point())\\r\\n\\r\\n glEnd()\\r\\n glPopMatrix()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d2cfb6110ad1695aaf84f2db701769\",\n \"score\": \"0.5428176\",\n \"text\": \"def visualize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670d9a24824a3565fa14e41d69ce71ab\",\n \"score\": \"0.54272133\",\n \"text\": \"def displayG(self):\\n G=nx.Graph();\\n G=nx.read_weighted_edgelist(self.dataset+\\\"/teamsG.txt\\\")\\n nodes=list(G.nodes())\\n #print(nodes)\\n for i in range(self.N):\\n if str(i) not in nodes:\\n G.add_node(i)\\n edges = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0]\\n pos = nx.spring_layout(G) # positions for all nodes\\n # nodes\\n nx.draw_networkx_nodes(G, pos, node_size=300)\\n # edges\\n nx.draw_networkx_edges(G, pos, edgelist=edges, width=2)\\n # labels\\n nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif')\\n plt.axis('off')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37073089f01e35623926adbba93ce52\",\n \"score\": \"0.5427033\",\n \"text\": \"def plot(self):\\n # Set up the different parametrization for the four plots.\\n x_names = ['REL_TSINCEOSM1', 'REL_TSINCEOSM1', 'REL_TSINCEOSM2', 'REL_TSINCEOSM2']\\n y_names = ['SHIFT1_DRIFT', 'SHIFT2_DRIFT', 'SHIFT1_DRIFT', 'SHIFT2_DRIFT']\\n titles = ['OSM1 SHIFT1', 'OSM1 SHIFT2', 'OSM2 SHIFT1', 'OSM2 SHIFT2']\\n locations = [(1, 1), (2, 1), (1, 2), (2, 2)]\\n\\n # Set the min and max for the scale so that each plot is plotted on the same scale\\n c_min = self.data.EXPSTART.min()\\n c_max = self.data.EXPSTART.max()\\n\\n segment_groups, _ = self.results\\n\\n # Plot drift v time for each segment/stripe\\n trace_counts = {}\\n for segment, group in segment_groups:\\n segment_count = 0\\n\\n # Plot per grating\\n for grating, grating_group in group.groupby('OPT_ELEM'):\\n for i, (x, y, axes, name) in enumerate(zip(x_names, y_names, locations, titles)):\\n self.figure.add_trace(\\n go.Scattergl(\\n x=grating_group[x],\\n y=grating_group[y],\\n mode='markers',\\n text=grating_group.hover_text,\\n name=f'{segment} {grating}',\\n legendgroup=grating,\\n showlegend=True if i == 0 else False,\\n marker=dict(\\n color=grating_group.EXPSTART,\\n cmin=c_min,\\n cmax=c_max,\\n colorscale='Viridis',\\n showscale=True,\\n colorbar=dict(\\n title='Observation Date',\\n tickmode='array',\\n ticks='outside',\\n tickvals=[self.data.EXPSTART.min(), self.data.EXPSTART.mean(),\\n self.data.EXPSTART.max()],\\n ticktext=[\\n f'{Time(self.data.EXPSTART.min(), format=\\\"mjd\\\").to_datetime().date()}',\\n f'{Time(self.data.EXPSTART.mean(), format=\\\"mjd\\\").to_datetime().date()}',\\n f'{Time(self.data.EXPSTART.max(), format=\\\"mjd\\\").to_datetime().date()}'\\n ],\\n len=0.65,\\n y=0,\\n yanchor='bottom'\\n )\\n ),\\n ),\\n *axes\\n )\\n\\n segment_count += 1\\n\\n trace_counts[segment] = segment_count\\n\\n # Create figure buttons for LP\\n titles = [f'<a href=\\\"{self.docs}\\\">{self.name} All Stripes'] + [\\n f'<a href=\\\"{self.docs}\\\">{self.name} {stripe}</a>'\\n for stripe in trace_counts.keys()\\n ]\\n\\n labels = ['All Stripes'] + [f'{stripe}' for stripe in trace_counts.keys()]\\n\\n # Create trace visibility options\\n traces = list(trace_counts.values())\\n all_visible = create_visibility(traces, [True, True, True]) # all stripes, NUVA, NUVB, NUVC\\n nuva = create_visibility(traces, [True, False, False])\\n nuvb = create_visibility(traces, [False, True, False])\\n nuvc = create_visibility(traces, [False, False, True])\\n\\n updatemenus = [\\n go.layout.Updatemenu(\\n buttons=[\\n dict(\\n label=label,\\n method='update',\\n args=[{'visible': visible}, {'title': title}]\\n ) for label, title, visible in zip(labels, titles, [all_visible, nuva, nuvb, nuvc])\\n ]\\n )\\n ]\\n\\n # Set the layout. Refer to the commented plot grid to make sense of this.\\n layout = go.Layout(\\n title=f'<a href=\\\"{self.docs}\\\">{self.name}</a>',\\n xaxis=dict(title='Time since last OSM1 move [s]', matches='x3'),\\n xaxis3=dict(title='Time since last OSM1 move [s]'),\\n xaxis2=dict(title='Time since last OSM2 move [s]', matches='x4'),\\n xaxis4=dict(title='Time since last OSM2 move [s]'),\\n yaxis=dict(title='SHIFT1 drift rate [pixels/sec]'),\\n yaxis3=dict(title='SHIFT2 drift rate [pixels/sec]'),\\n yaxis2=dict(title='SHIFT1 drift rate [pixels/sec]'),\\n yaxis4=dict(title='SHIFT2 drift rate [pixels/sec]'),\\n updatemenus=updatemenus\\n )\\n\\n self.figure.update_layout(layout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0443e116ecc4d0059b688417b296b10d\",\n \"score\": \"0.54242045\",\n \"text\": \"def plot_glucose_sliceXY(self):\\n if self.tick % 75 == 0: # every 75 hours, save figure\\n plt.figure()\\n plt.imshow(self.grid.glucose[:,:,int(self.zSize//2)], cmap='inferno')\\n plt.title('@time '+str(self.tick)+ ' [h]')\\n plt.xlabel('Width [mm]')\\n plt.ylabel('Height [mm]')\\n plt.title('Time step = '+str(self.tick)+' [h]')\\n plt.colorbar()\\n plt.savefig('DeepRL-PT/out/cell_proliferations/glucose@_'+str(self.tick)+'_'+str(self.xSize)+'x'+str(self.ySize)+'x'+str(self.zSize)+'.png')\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b77be9fa7ddf4ac488dea57c8d7111a\",\n \"score\": \"0.5415339\",\n \"text\": \"def _initialize_graphics(self, modules=None):\\n import matplotlib.pyplot as PLT\\n for i in range(len(self.trainer.monitored_modules if modules is None else modules)):\\n self.hinton_figures.append(PLT.figure())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde45ce234cbec6b2a2d223b6bbdd5b0\",\n \"score\": \"0.5407187\",\n \"text\": \"def plot_GP(X, f):\\n plt.figure()\\n for i in range(f.shape[0]):\\n plt.plot(X[:], Z[i, :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea3493c555ac2351ae7ea6bec10b0c0\",\n \"score\": \"0.53991556\",\n \"text\": \"def plot_gr50(dfgr, ax, labels, colors):\\n drug = ax.get_title()\\n # print(drug)\\n ylim = ax.get_ylim()\\n xlim = ax.get_xlim()\\n dfgr_drug = dfgr[dfgr.agent == drug].copy()\\n for line, color in zip(labels, colors):\\n gr50 = dfgr_drug[dfgr_drug.cell_line == line]['GR50'].values[0]\\n ax.plot([gr50, gr50], [ylim[0], (ylim[0] + 0.2)], color)\\n ax.set_ylim(ylim)\\n ax.set_xlim(xlim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490feefca9e2b3df8b7ba15dfe2b1a36\",\n \"score\": \"0.53975236\",\n \"text\": \"def visualize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616057a28000824ed3d8db6097a5afef\",\n \"score\": \"0.5393877\",\n \"text\": \"def __init__(self, skeletons):\\n self.fig = plt.figure()\\n\\n \\\"\\\"\\\" Set plot boundaries accordingly to the data ranges \\\"\\\"\\\"\\n x = skeletons[:,:,0]\\n xmin = x.min()\\n xmax = x.max()\\n y = skeletons[:, :, 2]\\n ymin = y.min()\\n ymax = y.max()\\n self.ax = plt.axes(xlim=(xmin-10, xmax+10), ylim=(ymin-10, ymax+10)) # plt.axes(xlim=(-1.5, 1.5), ylim=(-1.2, 1.2))\\n #\\n self.ax.axis('off')\\n self.line_one = self.ax.plot([], [], lw=5, c='b', marker=\\\"s\\\")[0]\\n self.line_two = self.ax.plot([], [], lw=5, c='b', marker=\\\"s\\\")[0]\\n self.line_three = self.ax.plot([], [], lw=5, c='b', marker=\\\"s\\\")[0]\\n\\n self.r_fing_1 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.r_fing_2 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.r_fing_3 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.r_fing_4 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.r_fing_5 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n\\n self.l_fing_1 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.l_fing_2 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.l_fing_3 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.l_fing_4 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\\n self.l_fing_5 = self.ax.plot([], [], lw=2, markersize=2, c='b', marker=\\\"s\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978b0e070115aea5c29009e06449acc6\",\n \"score\": \"0.53871536\",\n \"text\": \"def plot(self, *args):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9d298d32aa02aec8b6a1c793a8ec68d\",\n \"score\": \"0.53838754\",\n \"text\": \"def plot_data(Figures,Data,fr):\\r\\n\\ttxt=[]\\r\\n\\r\\n\\tif FIGS == -1:\\r\\n\\t\\tplt.close(\\\"all\\\")\\r\\n\\t\\tfr = True\\r\\n\\r\\n\\tdatacount=1 \\r\\n\\tfor fig in range(1,len(Figures)+1):\\r\\n\\t\\tplt.figure(fig)\\r\\n\\r\\n\\r\\n\\t\\tfor plot in range(2,len(Figures[fig-1]),2):\\r\\n\\t\\t\\tif plot + 1 < len(Figures[fig-1]):\\r\\n\\t\\t\\t\\tplt.plot(Data[0], Data[datacount], label=Figures[fig-1][plot+1])\\r\\n\\t\\t\\t\\tplt.legend(fancybox=True,fontsize=11).draggable()\\r\\n\\t\\t\\telse: \\r\\n\\t\\t\\t\\tplt.plot(Data[0], Data[datacount])\\r\\n\\t\\t\\tdatacount+=1\\r\\n\\t\\t\\tplt.hold(True)\\t\\r\\n\\t\\tplt.hold(False)\\r\\n\\t\\tplt.grid()\\r\\n\\t\\tplt.xlabel('Time [s]')\\r\\n\\t\\tplt.ylabel(Figures[fig-1][1])\\r\\n\\t\\tplt.ticklabel_format(style='sci', axis='y', scilimits=(-4,4))\\r\\n\\t\\tplt.autoscale(tight=False)\\r\\n\\t\\tplt.tight_layout()\\r\\n\\t\\tplt.margins(0.001, 0.1)\\r\\n\\t\\tplace_fig_win(fig, fr)\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\ttxt.append(plt.figtext(0.3, 0.96, 'Latest value: ' + str(Data[datacount-1][-1])))\\r\\n\\t\\texcept:\\r\\n\\t\\t\\ttxt.append(plt.figtext(0.3, 0.96, 'Latest value: ' + str(Data[datacount-1])))\\r\\n\\r\\n\\tplt.pause(3-len(Figures)/30)\\r\\n\\tfor tx in txt: tx.remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9039e762203d2f5d4bfd004206a6f6c\",\n \"score\": \"0.53796315\",\n \"text\": \"def Clf():\\r\\n Brewer.ClearIter()\\r\\n pyplot.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9039e762203d2f5d4bfd004206a6f6c\",\n \"score\": \"0.53796315\",\n \"text\": \"def Clf():\\r\\n Brewer.ClearIter()\\r\\n pyplot.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb11a54ff25fbf1caa6db2d5f469c30e\",\n \"score\": \"0.5378374\",\n \"text\": \"def print_grid(nodes, color='ro'):\\n x_axis = []\\n y_axis = []\\n # convert points to coords\\n for n in nodes:\\n x, y = n\\n x_axis.append(x)\\n y_axis.append(y)\\n plt.plot(x_axis, y_axis, color)\\n #plt.show(block=False)\\n return (x_axis, y_axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46145faf02c2616ffa2d98d30db595b\",\n \"score\": \"0.53757423\",\n \"text\": \"def plot_figure(self) -> None:\\n plt.close()\\n plt.ion()\\n if not self.back:\\n self.figure, self.axes = plt.subplots(self.back + 1)\\n self.axes.plot(\\n self.data.bins, self.data.z2n, label='Z2n Power',\\n color='tab:blue', linewidth=2)\\n try:\\n self.axes.plot(\\n self.data.gauss.bins, self.data.gauss.z2n,\\n color='tab:red', label='Gaussian Fit', linewidth=1)\\n except AttributeError:\\n pass\\n self.axes.set_xlabel('Frequency (Hz)')\\n self.axes.set_ylabel('Power')\\n self.axes.legend(loc='best')\\n else:\\n self.figure, self.axes = plt.subplots(\\n self.back + 1, sharex=True, sharey=True)\\n self.axes[0].plot(\\n self.data.bins, self.data.z2n, label='Z2n Power',\\n color='tab:blue', linewidth=2)\\n try:\\n self.axes[0].plot(\\n self.data.gauss.bins, self.data.gauss.z2n,\\n color='tab:red', label='Gaussian Fit', linewidth=1)\\n except AttributeError:\\n pass\\n self.axes[1].plot(\\n self.noise.bins, self.noise.z2n, color='tab:cyan',\\n label='Background')\\n self.axes[0].set_xlabel('Frequency (Hz)')\\n self.axes[0].set_ylabel('Power')\\n self.axes[1].set_xlabel('Frequency (Hz)')\\n self.axes[1].set_ylabel('Power')\\n self.axes[0].legend(loc='best')\\n self.axes[1].legend(loc='best')\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6953f19c1fc9947b3563c76967bc6d\",\n \"score\": \"0.5359109\",\n \"text\": \"def graphs(self):\\n return list(self.graph.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9db9261766db405735718a56421195c\",\n \"score\": \"0.53575796\",\n \"text\": \"def plot_gbrv_convergence(self, ax_list=None, **kwargs):\\n import matplotlib.pyplot as plt\\n stypes = (\\\"fcc\\\", \\\"bcc\\\")\\n if ax_list is None:\\n fig, ax_list = plt.subplots(nrows=len(stypes), ncols=1, sharex=True, squeeze=False)\\n ax_list = ax_list.ravel()\\n else:\\n fig = plt.gcf()\\n\\n #ax_list, fig, plt = get_axarray_fig_plt(ax_list, nrows=len(stypes), ncols=1, sharex=True, squeeze=False)\\n\\n if len(stypes) != len(ax_list):\\n raise ValueError(\\\"len(stypes)=%s != len(ax_list)=%s\\\" % (len(stypes), len(ax_list)))\\n\\n for i, (ax, stype) in enumerate(zip(ax_list, stypes)):\\n trial = \\\"gbrv_\\\" + stype\\n d = self[trial]\\n ecuts = list(d.keys())\\n values = np.array([float(d[ecut][\\\"a0_rel_err\\\"]) for ecut in ecuts])\\n\\n ax.grid(True)\\n ax.set_ylabel(\\\"$\\\\Delta$\\\" + trial + \\\"a0_rel_err\\\")\\n\\n # Plot difference pseudo - ref.\\n ax.plot(ecuts, values, \\\"bo-\\\")\\n #ax.hlines(y=0.0, xmin=min(ecuts), xmax=max(ecuts), color=\\\"red\\\")\\n if i == len(ax_list) - 1: ax.set_xlabel(\\\"Ecut [Ha]\\\")\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac021ff7cfdc7460a2f6b23a88b1881\",\n \"score\": \"0.53514504\",\n \"text\": \"def graphglob (dico):\\n\\t\\n\\tx= list()\\n\\ty=list()\\n\\tfor prot1 in sorted(dico.keys()): #parcourt les conformations dans le dictionnaire de sortie de la fonction RMSD\\n\\t\\tfor prot2 in sorted(dico[prot1].keys()):#Parcourt les conformations (2ieme clé du dictionnaire)\\n\\t\\t\\tx.append(prot1)\\t\\t\\t#concatenation des identifiants des conformations rencontrées\\n\\t\\t\\ty.append(dico[prot1][prot2]) # Concaténation des valeurs de RMSD globaux dans Y \\n\\n\\n\\tplt.plot(x, y) #trace le graphique des valeurs de RMSD en fonction des conformations\\n\\tplt.title(\\\"Graphique des RMSD globaux\\\")\\n\\tplt.xlabel('Conformation')\\n\\tplt.ylabel('RMSD globales')\\n\\tplt.show()\\n\\t\\n\\treturn ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31350b1cd60a02ca06a26b6540751ccb\",\n \"score\": \"0.5342083\",\n \"text\": \"def setup_axes(): \\n\\tfig = plt.figure(figsize = (14, 7)) \\n\\taxes = 2 * [None] \\n\\tfor i in range(2): \\n\\t\\taxes[i] = fig.add_subplot(121 + i, facecolor = \\\"white\\\") \\n\\t\\taxes[i].set_xlabel(r\\\"$R_\\\\text{gal}$ [kpc]\\\") \\n\\t\\taxes[i].set_xlim([-1, 21]) \\n\\taxes[0].set_ylim([-0.55, 0.75]) \\n\\taxes[1].set_ylim([-0.1, 0.5]) \\n\\taxes[0].set_ylabel(\\\"[X/H]\\\") \\n\\taxes[1].set_ylabel(\\\"[O/Fe]\\\") \\n\\treturn axes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bb6ed69507fd203044fde08d4e9fdb\",\n \"score\": \"0.5341665\",\n \"text\": \"def showPlot4():\\n # TODO: Your code goes here\\n roomDim = 25\\n numOfRobots = 5\\n posMinCoverage = [0, 0.25, 0.5, 0.75, 1.0]\\n simResult = []\\n meanTime = []\\n for minCoverage in posMinCoverage:\\n for robotNum in range(1, numOfRobots + 1):\\n simResult = runSimulation(robotNum, 1.0, roomDim, roomDim, minCoverage, 1, Robot, False)\\n meanTime.append(avgLenListOfLists(simResult))\\n pylab.plot(robotCoverage, meanTime)\\n pylab.xlabel('Minimum area to be cleaned')\\n pylab.ylabel('Timesteps')\\n pylab.title('Time to clean a square room of size 25 X 25, for various number of robots and various minimum coverage areas')\\n pylab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65661f7551a09d053ec3075514af218c\",\n \"score\": \"0.53413105\",\n \"text\": \"def plot_graph(fig, g, iteration: int = -1) -> None:\\n # retrieve indices into g's \\\"x\\\" vector -> start of each individual variable\\n # plot (x,y) positions from each pose/landmark\\n p, l = g.get_poses_landmarks()\\n\\n plt.clf()\\n\\n if len(l) > 0:\\n landmarkIdxX = l\\n landmarkIdxY = l + 1\\n plt.scatter(g.x[landmarkIdxX], g.x[landmarkIdxY], 4, color=\\\"r\\\", marker=\\\"o\\\")\\n\\n if len(p) > 0:\\n pIdxX = p\\n pIdxY = p + 1\\n plt.scatter(g.x[pIdxX], g.x[pIdxY], 4, color=\\\"b\\\", marker=\\\"x\\\")\\n\\n fig.canvas.draw()\\n plt.pause(1)\\n\\n os.makedirs(\\\"plots\\\", exist_ok=True)\\n filename = os.path.join(\\\"plots\\\", f\\\"lsslam_{iteration:03d}.png\\\")\\n plt.savefig(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f020fe2314cbbe9610b951b5ab9cbe08\",\n \"score\": \"0.5341108\",\n \"text\": \"def get_figure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7760097f380502dacd62ba84352ba54\",\n \"score\": \"0.5335527\",\n \"text\": \"def plot_snapshot():\\n plt.figure(figsize=(20, 15))\\n\\n plt.subplot(311)\\n plt.ylabel('Rewards', fontsize=16)\\n plt.plot(reward_record, alpha=0.15, color='red')\\n plt.plot(reward_average_plot, alpha=0.9, color='red', linewidth=3.0)\\n plt.xlim(-0.5, i_episode)\\n plt.ylim(-10, 3)\\n\\n plt.subplot(312)\\n plt.ylabel('Number of Flickers', fontsize=16)\\n plt.plot(flicker_record, alpha=0.15, color='green')\\n plt.plot(flicker_average_plot, alpha=0.9, color='green', linewidth=3.0)\\n plt.xlim(-0.5, i_episode)\\n plt.ylim(-0.2, 25)\\n\\n plt.subplot(313)\\n plt.ylabel('Wait Time', fontsize=16)\\n plt.plot(queue_record, alpha=0.15, color='blue')\\n plt.plot(wait_average_plot, alpha=0.9, color='blue', linewidth=3.0)\\n plt.xlim(-0.5, i_episode)\\n plt.ylim(-0.5, 9000)\\n\\n if folder == \\\"RL\\\":\\n if all(options):\\n PATH = \\\"Plots/\\\" + folder + \\\"/all\\\" + \\\"/{}.png\\\".format('plot_all')\\n elif not any(options):\\n PATH = \\\"Plots/\\\" + folder + \\\"/vanilla_dqn\\\" + \\\"/{}.png\\\".format('plot_vanilla_dqn')\\n else:\\n zero_idx = list(options).index(0)\\n PATH = \\\"Plots/\\\" + folder + \\\"/no_\\\" + options_list[zero_idx] + \\\"_dqn/plot_no_\\\" + options_list[zero_idx] + \\\"_dqn.png\\\"\\n else:\\n PATH = \\\"Plots/\\\" + folder + \\\"/plot_{}\\\".format(folder) + \\\".png\\\"\\n\\n plt.savefig(PATH)\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9445dda2a40b73f554d9d69ef922aaa0\",\n \"score\": \"0.5330961\",\n \"text\": \"def list_figures(experiment_id, operator_id, run_id):\\n return platiagro.list_figures(experiment_id=experiment_id,\\n operator_id=operator_id,\\n run_id=run_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cbe22fc84c49242caf577f599c0386b\",\n \"score\": \"0.5329918\",\n \"text\": \"def build_stock_gdp():\\n axes = []\\n for comp in found_years:\\n x = np.arange(found_years[comp], 2019)\\n if comp == 'LGF':\\n x = np.arange(found_years[comp], 2017)\\n lst = []\\n for el in database:\\n if database[el][1][comp] is not None:\\n lst.append(database[el][1][comp])\\n y = np.array(lst)\\n axes.extend([x, y])\\n\\n lst = []\\n x = np.arange(1962, 2019)\\n for el in gdp_info:\\n if int(el) >= 1962:\\n lst.append(gdp_info[el]/100)\\n y = np.array(lst)\\n axes.extend([x, y])\\n\\n fig, ax = plt.subplots()\\n l1, = plt.plot(axes[0], axes[1], linestyle='solid')\\n l2, = plt.plot(axes[2], axes[3], linestyle='solid')\\n l3, = plt.plot(axes[4], axes[5], linestyle='solid')\\n l4, = plt.plot(axes[6], axes[7], linestyle='solid')\\n l5, = plt.plot(axes[8], axes[9], linestyle='solid')\\n l6, = plt.plot(axes[10], axes[11], linestyle='solid')\\n l7, = plt.plot(axes[12], axes[13], linestyle='solid')\\n l8, = plt.plot(axes[14], axes[15], linestyle='solid')\\n l9, = plt.plot(axes[16], axes[17], linestyle='solid')\\n\\n plt.subplots_adjust(left=0.3)\\n lgnd = ax.legend(list(found_years.keys()),\\n loc='upper center', ncol=4, fontsize='xx-small', shadow=True)\\n\\n lgnd.get_frame().set_facecolor('#ffb19a')\\n\\n rax = plt.axes([0.04, 0.5, 0.17, 0.37],\\n facecolor='lightgoldenrodyellow')\\n check = CheckButtons(rax, tuple(found_years.keys()), (True,\\n True, True, True, True, True, True, True))\\n\\n def func(label):\\n for line, comp in zip([l1, l2, l3, l4, l5, l6, l7, l8], list(found_years.keys())):\\n if label == comp:\\n line.set_visible(not line.get_visible())\\n plt.draw()\\n\\n check.on_clicked(func)\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00991f0301d36367c760f669f52d97c1\",\n \"score\": \"0.5329416\",\n \"text\": \"def show_fig417(self,j):\\n\\t\\txpos = []\\n\\t\\typos = []\\n\\t\\tfor star in self.galaxy:\\n\\t\\t\\txpos.append(star.x)\\n\\t\\t\\typos.append(star.y)\\n\\t\\tplt.figure(figsize=(9,9))\\n\\t\\tplt.plot(xpos,ypos,'go',ms=0.25)\\n\\t\\ttemp = -15\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tfor i in range(self.M+1):\\n\\t\\t\\tplt.axhline(temp,-15,15,c='r',lw=0.5)\\n\\t\\t\\tplt.axvline(temp,-15,15,c='r',lw=0.5)\\n\\t\\t\\ttemp += self.DELTA\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tplt.axis([-30,30,-30,30])\\n\\t\\t#plt.savefig(\\\"/Users/user/417.png\\\",format='png', dpi=1000)\\n\\t\\tplt.savefig(\\\"/Users/user/417_%d.png\\\"%j,format='png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58339152b20921e2027a48ba3763274d\",\n \"score\": \"0.5318704\",\n \"text\": \"def showPlot2():\\n # TODO: Your code goes here\\n roomDim = 25\\n numOfRobots = 10\\n simResult = []\\n meanTime = []\\n for robotNum in range(1, numOfRobots + 1):\\n simResult = runSimulation(robotNum, 1.0, roomDim, roomDim, 0.75, 100, Robot, False)\\n meanTime.append(avgLenListOfLists(simResult))\\n pylab.plot([num for num in range(1, numOfRobots + 1)], meanTime)\\n pylab.xlabel('Number of robots')\\n pylab.ylabel('Timesteps')\\n pylab.title('Time to clean 75percent of square room of sie 25 X 25, for various number of robots')\\n pylab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c18d6dd6d08902accddbcfb8f3194e04\",\n \"score\": \"0.53155\",\n \"text\": \"def plot_path(self):\\n\\n if self._dataset_is_binned: # this means reall data\\n self.plot_path_real_data()\\n return\\n \\n # self._numb_shared_lat = numb_shared_lat\\n # self._numb_grouped_lat = numb_grouped_lat\\n # self._numb_trial_lat = numb_trial_lat\\n\\n # first shared latents:\\n if self._numb_shared_lat > 0 or self._meta_data[\\\"K_alpha\\\"]:\\n\\n numb_plots_hor = max(self._numb_shared_lat,self._meta_data[\\\"K_alpha\\\"])\\n if numb_plots_hor == 1:\\n numb_plots_hor += 1\\n\\n fig, axes = plt.subplots(2, numb_plots_hor)\\n\\n for lat_i in range(0,self._meta_data[\\\"K_alpha\\\"]):\\n axes[0, lat_i].plot(self._true_latent_alpha[lat_i,:])\\n for lat_i in range(0,self._numb_shared_lat):\\n axes[1, lat_i].plot(self._bin_times,self._found_shared[lat_i,:])\\n\\n fig.suptitle(\\\"Shared latents : \\\")\\n\\n # then group latents:\\n if self._numb_grouped_lat > 0 or self._meta_data[\\\"K_beta\\\"]: \\n \\n numb_plots_hor = (max(self._numb_grouped_lat,self._meta_data[\\\"K_beta\\\"]) + 1) * self._numb_conditions \\n if numb_plots_hor == 1:\\n numb_plots_hor += 1\\n fig, axes = plt.subplots(2, numb_plots_hor)\\n counter = 0\\n for cond_i in range(0, self._numb_conditions):\\n for lat_i in range(0,self._meta_data[\\\"K_beta\\\"]):\\n axes[0, counter+lat_i].plot(self._true_latent_beta[cond_i,lat_i,:])\\n #axes[1].set_ylim([-2.5, 2.5])\\n for lat_i in range(0,self._numb_grouped_lat):\\n axes[1, counter+lat_i].plot(self._bin_times,self._found_cond[cond_i,lat_i,:])\\n\\n counter += max(self._numb_grouped_lat,self._meta_data[\\\"K_beta\\\"]) + 1\\n\\n fig.suptitle(\\\"Group latents : \\\")\\n\\n # finally the trial latents:\\n if self._numb_trial_lat > 0 or self._meta_data[\\\"K_gamma\\\"]: \\n \\n numb_plots_hor = (max(self._numb_trial_lat,self._meta_data[\\\"K_gamma\\\"]))\\n if numb_plots_hor == 1:\\n numb_plots_hor += 1\\n fig, axes = plt.subplots(min(self._numb_trials - self._numb_test_trials, 20), numb_plots_hor)\\n \\n for trial_i in range(0, min(self._numb_trials - self._numb_test_trials, 20)):\\n for lat_i in range(0,self._meta_data[\\\"K_gamma\\\"]):\\n axes[trial_i, lat_i].plot(self._bin_times, self._true_latent_gamma[trial_i,lat_i,:])\\n #plt.hold(True)\\n #axes[1].set_ylim([-2.5, 2.5])\\n for lat_i in range(0,self._numb_trial_lat):\\n axes[trial_i, lat_i].plot(self._bin_times,self._found_latents[trial_i,lat_i,:])\\n\\n fig.suptitle(\\\"Trial latents : \\\")\\n\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f02a645d33e9e9cc3b2a97d51970cc\",\n \"score\": \"0.53062576\",\n \"text\": \"def granule_figure(monit_gr, pscommon):\\n granule_pop_figure(monit_gr['s'].values, monit_gr['s_syn_self'].values, monit_gr['s'].times, pscommon['resample_dt'], pscommon['burnin'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f49551a4a0db6e78eee17ab37002b3\",\n \"score\": \"0.5300304\",\n \"text\": \"def showSOCEstimates():\\n \\\"\\\"\\\"this is primarilty to produce a figure in my thesis. THis is NOT NEEDED ON BLIZZARD.\\\"\\\"\\\"\\n plt.figure(1, figsize=(1080 / my_dpi, 900 / my_dpi), dpi=my_dpi)\\n\\n # plot1; the original battery graphs recieved from OEM\\n ax = plt.subplot(211)\\n ax.plot(SOC.d[\\\"-20\\\"][\\\"Ys\\\"], SOC.d[\\\"-20\\\"][\\\"Xs\\\"], color='blue', label=\\\"-20C\\\")\\n ax.plot(SOC.d[\\\"-10\\\"][\\\"Ys\\\"], SOC.d[\\\"-10\\\"][\\\"Xs\\\"], color='cyan', label=\\\"-10C\\\")\\n ax.plot(SOC.d[\\\"0\\\"][\\\"Ys\\\"], SOC.d[\\\"0\\\"][\\\"Xs\\\"], color='green', label=\\\"0C\\\")\\n ax.plot(SOC.d[\\\"23\\\"][\\\"Ys\\\"], SOC.d[\\\"23\\\"][\\\"Xs\\\"], color='yellow', label=\\\"23C\\\")\\n ax.plot(SOC.d[\\\"45\\\"][\\\"Ys\\\"], SOC.d[\\\"45\\\"][\\\"Xs\\\"], color='pink', label=\\\"45C\\\")\\n ax.set_axisbelow(True)\\n # ax.legend(numpoints=1, ncol = 1, loc='best',columnspacing=0,labelspacing=1,handletextpad=0,borderpad=.15,markerscale=0.2)\\n plt.ylim(20000, 0)\\n ax.yaxis.grid(color='gray', linestyle='solid')\\n ax.xaxis.grid(color='gray', linestyle='solid')\\n plt.ylabel(\\\"capacity in mAh\\\")\\n plt.title(\\\"voltage vs capacity\\\")\\n\\n ax3 = plt.subplot(212)\\n SampVs = linspace(32, 16, 100)\\n ax3.plot(SampVs, SOC.SOCVals(-10, SampVs), color='cyan', label=\\\"-10C\\\", marker=\\\"o\\\", markersize=1, linestyle=\\\"-\\\")\\n ax3.plot(SampVs, SOC.SOCVals(-20, SampVs), color='blue', label=\\\"-20C\\\", marker=\\\"o\\\", markersize=1, linestyle=\\\"-\\\")\\n ax3.plot(SampVs, SOC.SOCVals(0, SampVs), color='green', label=\\\"0C\\\", marker=\\\"o\\\", markersize=1, linestyle=\\\"-\\\")\\n ax3.plot(SampVs, SOC.SOCVals(23, SampVs), color='yellow', label=\\\"23C\\\", marker=\\\"o\\\", markersize=1, linestyle=\\\"-\\\")\\n ax3.plot(SampVs, SOC.SOCVals(45, SampVs), color='pink', label=\\\"45C\\\", marker=\\\"o\\\", markersize=1, linestyle=\\\"-\\\")\\n ax3.legend(numpoints=1, ncol=3, loc='best', columnspacing=0, labelspacing=1, handletextpad=0, borderpad=.15,\\n markerscale=2)\\n # plt.ylim(0,1)\\n ax3.yaxis.grid(color='gray', linestyle='solid')\\n ax3.xaxis.grid(color='gray', linestyle='solid')\\n plt.xlabel(\\\"voltage\\\")\\n plt.ylabel(\\\"SOC (% of wh)\\\")\\n\\n plt.show()\\n plt.close() # THIS IS CRUCIAL SEE: http://stackoverflow.com/questions/26132693/matplotlib-saving-state-between-different-uses-of-io-bytesio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ff6677e6a357089886b9d2173cf095\",\n \"score\": \"0.52974087\",\n \"text\": \"def drawAll():\\n for item in drawList:\\n item.draw()\\n sliderLine.draw()\\n slider.draw()\\n for label in labelStims:\\n label.draw()\\n if nextVisible: next.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3874abd6f641ef7c9a41133891963c8e\",\n \"score\": \"0.5291301\",\n \"text\": \"def GGG(self):\\n\\t\\treturn self._ggg.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9d8511504258c41eb1ad13dea6d4db\",\n \"score\": \"0.52893806\",\n \"text\": \"def main():\\n FIG = {} # plot dictionary\\n FIG['eqarea'] = 1 # eqarea is figure 1\\n in_file, plot_key, coord, crd = 'pmag_results.txt', 'all', \\\"0\\\", 'g'\\n plotE, contour = 0, 0\\n dir_path = '.'\\n fmt = 'svg'\\n verbose = pmagplotlib.verbose\\n if '-h' in sys.argv:\\n print(main.__doc__)\\n sys.exit()\\n if '-WD' in sys.argv:\\n ind = sys.argv.index('-WD')\\n dir_path = sys.argv[ind+1]\\n pmagplotlib.plot_init(FIG['eqarea'], 5, 5)\\n if '-f' in sys.argv:\\n ind = sys.argv.index(\\\"-f\\\")\\n in_file = dir_path+\\\"/\\\"+sys.argv[ind+1]\\n if '-obj' in sys.argv:\\n ind = sys.argv.index('-obj')\\n plot_by = sys.argv[ind+1]\\n if plot_by == 'all':\\n plot_key = 'all'\\n if plot_by == 'sit':\\n plot_key = 'er_site_name'\\n if plot_by == 'sam':\\n plot_key = 'er_sample_name'\\n if plot_by == 'spc':\\n plot_key = 'er_specimen_name'\\n if '-c' in sys.argv:\\n contour = 1\\n plt = 0\\n if '-sav' in sys.argv:\\n plt = 1\\n verbose = 0\\n if '-ell' in sys.argv:\\n plotE = 1\\n ind = sys.argv.index('-ell')\\n ell_type = sys.argv[ind+1]\\n if ell_type == 'F':\\n dist = 'F'\\n if ell_type == 'K':\\n dist = 'K'\\n if ell_type == 'B':\\n dist = 'B'\\n if ell_type == 'Be':\\n dist = 'BE'\\n if ell_type == 'Bv':\\n dist = 'BV'\\n FIG['bdirs'] = 2\\n pmagplotlib.plot_init(FIG['bdirs'], 5, 5)\\n if '-crd' in sys.argv:\\n ind = sys.argv.index(\\\"-crd\\\")\\n crd = sys.argv[ind+1]\\n if crd == 's':\\n coord = \\\"-1\\\"\\n if crd == 'g':\\n coord = \\\"0\\\"\\n if crd == 't':\\n coord = \\\"100\\\"\\n if '-fmt' in sys.argv:\\n ind = sys.argv.index(\\\"-fmt\\\")\\n fmt = sys.argv[ind+1]\\n Dec_keys = ['site_dec', 'sample_dec', 'specimen_dec',\\n 'measurement_dec', 'average_dec', 'none']\\n Inc_keys = ['site_inc', 'sample_inc', 'specimen_inc',\\n 'measurement_inc', 'average_inc', 'none']\\n Tilt_keys = ['tilt_correction', 'site_tilt_correction',\\n 'sample_tilt_correction', 'specimen_tilt_correction', 'none']\\n Dir_type_keys = ['', 'site_direction_type',\\n 'sample_direction_type', 'specimen_direction_type']\\n Name_keys = ['er_specimen_name', 'er_sample_name',\\n 'er_site_name', 'pmag_result_name']\\n data, file_type = pmag.magic_read(in_file)\\n if file_type == 'pmag_results' and plot_key != \\\"all\\\":\\n plot_key = plot_key+'s' # need plural for results table\\n if verbose:\\n print(len(data), ' records read from ', in_file)\\n #\\n #\\n # find desired dec,inc data:\\n #\\n dir_type_key = ''\\n #\\n # get plotlist if not plotting all records\\n #\\n plotlist = []\\n if plot_key != \\\"all\\\":\\n plots = pmag.get_dictitem(data, plot_key, '', 'F')\\n for rec in plots:\\n if rec[plot_key] not in plotlist:\\n plotlist.append(rec[plot_key])\\n plotlist.sort()\\n else:\\n plotlist.append('All')\\n for plot in plotlist:\\n # if verbose: print plot\\n DIblock = []\\n GCblock = []\\n SLblock, SPblock = [], []\\n title = plot\\n mode = 1\\n dec_key, inc_key, tilt_key, name_key, k = \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", 0\\n if plot != \\\"All\\\":\\n odata = pmag.get_dictitem(data, plot_key, plot, 'T')\\n else:\\n odata = data # data for this obj\\n for dec_key in Dec_keys:\\n # get all records with this dec_key not blank\\n Decs = pmag.get_dictitem(odata, dec_key, '', 'F')\\n if len(Decs) > 0:\\n break\\n for inc_key in Inc_keys:\\n # get all records with this inc_key not blank\\n Incs = pmag.get_dictitem(Decs, inc_key, '', 'F')\\n if len(Incs) > 0:\\n break\\n for tilt_key in Tilt_keys:\\n if tilt_key in Incs[0].keys():\\n break # find the tilt_key for these records\\n if tilt_key == 'none': # no tilt key in data, need to fix this with fake data which will be unknown tilt\\n tilt_key = 'tilt_correction'\\n for rec in Incs:\\n rec[tilt_key] = ''\\n # get all records matching specified coordinate system\\n cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T')\\n if coord == '0': # geographic\\n # get all the blank records - assume geographic\\n udata = pmag.get_dictitem(Incs, tilt_key, '', 'T')\\n if len(cdata) == 0:\\n crd = ''\\n if len(udata) > 0:\\n for d in udata:\\n cdata.append(d)\\n crd = crd+'u'\\n for name_key in Name_keys:\\n # get all records with this name_key not blank\\n Names = pmag.get_dictitem(cdata, name_key, '', 'F')\\n if len(Names) > 0:\\n break\\n for dir_type_key in Dir_type_keys:\\n # get all records with this direction type\\n Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F')\\n if len(Dirs) > 0:\\n break\\n if dir_type_key == \\\"\\\":\\n dir_type_key = 'direction_type'\\n locations, site, sample, specimen = \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"\\n for rec in cdata: # pick out the data\\n if 'er_location_name' in rec.keys() and rec['er_location_name'] != \\\"\\\" and rec['er_location_name'] not in locations:\\n locations = locations + \\\\\\n rec['er_location_name'].replace(\\\"/\\\", \\\"\\\")+\\\"_\\\"\\n if 'er_location_names' in rec.keys() and rec['er_location_names'] != \\\"\\\":\\n locs = rec['er_location_names'].split(':')\\n for loc in locs:\\n if loc not in locations:\\n locations = locations+loc.replace(\\\"/\\\", \\\"\\\")+'_'\\n if plot_key == 'er_site_name' or plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':\\n site = rec['er_site_name']\\n if plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':\\n sample = rec['er_sample_name']\\n if plot_key == 'er_specimen_name':\\n specimen = rec['er_specimen_name']\\n if plot_key == 'er_site_names' or plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':\\n site = rec['er_site_names']\\n if plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':\\n sample = rec['er_sample_names']\\n if plot_key == 'er_specimen_names':\\n specimen = rec['er_specimen_names']\\n if dir_type_key not in rec.keys() or rec[dir_type_key] == \\\"\\\":\\n rec[dir_type_key] = 'l'\\n if 'magic_method_codes' not in rec.keys():\\n rec['magic_method_codes'] = \\\"\\\"\\n DIblock.append([float(rec[dec_key]), float(rec[inc_key])])\\n SLblock.append([rec[name_key], rec['magic_method_codes']])\\n if rec[tilt_key] == coord and rec[dir_type_key] != 'l' and rec[dec_key] != \\\"\\\" and rec[inc_key] != \\\"\\\":\\n GCblock.append([float(rec[dec_key]), float(rec[inc_key])])\\n SPblock.append([rec[name_key], rec['magic_method_codes']])\\n if len(DIblock) == 0 and len(GCblock) == 0:\\n if verbose:\\n print(\\\"no records for plotting\\\")\\n sys.exit()\\n if verbose:\\n for k in range(len(SLblock)):\\n print('%s %s %7.1f %7.1f' % (\\n SLblock[k][0], SLblock[k][1], DIblock[k][0], DIblock[k][1]))\\n for k in range(len(SPblock)):\\n print('%s %s %7.1f %7.1f' % (\\n SPblock[k][0], SPblock[k][1], GCblock[k][0], GCblock[k][1]))\\n if len(DIblock) > 0:\\n if contour == 0:\\n pmagplotlib.plot_eq(FIG['eqarea'], DIblock, title)\\n else:\\n pmagplotlib.plot_eq_cont(FIG['eqarea'], DIblock)\\n else:\\n pmagplotlib.plot_net(FIG['eqarea'])\\n if len(GCblock) > 0:\\n for rec in GCblock:\\n pmagplotlib.plot_circ(FIG['eqarea'], rec, 90., 'g')\\n if plotE == 1:\\n ppars = pmag.doprinc(DIblock) # get principal directions\\n nDIs, rDIs, npars, rpars = [], [], [], []\\n for rec in DIblock:\\n angle = pmag.angle([rec[0], rec[1]], [\\n ppars['dec'], ppars['inc']])\\n if angle > 90.:\\n rDIs.append(rec)\\n else:\\n nDIs.append(rec)\\n if dist == 'B': # do on whole dataset\\n etitle = \\\"Bingham confidence ellipse\\\"\\n bpars = pmag.dobingham(DIblock)\\n for key in bpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (bpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (bpars[key]))\\n npars.append(bpars['dec'])\\n npars.append(bpars['inc'])\\n npars.append(bpars['Zeta'])\\n npars.append(bpars['Zdec'])\\n npars.append(bpars['Zinc'])\\n npars.append(bpars['Eta'])\\n npars.append(bpars['Edec'])\\n npars.append(bpars['Einc'])\\n if dist == 'F':\\n etitle = \\\"Fisher confidence cone\\\"\\n if len(nDIs) > 2:\\n fpars = pmag.fisher_mean(nDIs)\\n for key in fpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (fpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (fpars[key]))\\n mode += 1\\n npars.append(fpars['dec'])\\n npars.append(fpars['inc'])\\n npars.append(fpars['alpha95']) # Beta\\n npars.append(fpars['dec'])\\n isign = abs(fpars['inc'])/fpars['inc']\\n npars.append(fpars['inc']-isign*90.) # Beta inc\\n npars.append(fpars['alpha95']) # gamma\\n npars.append(fpars['dec']+90.) # Beta dec\\n npars.append(0.) # Beta inc\\n if len(rDIs) > 2:\\n fpars = pmag.fisher_mean(rDIs)\\n if verbose:\\n print(\\\"mode \\\", mode)\\n for key in fpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (fpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (fpars[key]))\\n mode += 1\\n rpars.append(fpars['dec'])\\n rpars.append(fpars['inc'])\\n rpars.append(fpars['alpha95']) # Beta\\n rpars.append(fpars['dec'])\\n isign = abs(fpars['inc'])/fpars['inc']\\n rpars.append(fpars['inc']-isign*90.) # Beta inc\\n rpars.append(fpars['alpha95']) # gamma\\n rpars.append(fpars['dec']+90.) # Beta dec\\n rpars.append(0.) # Beta inc\\n if dist == 'K':\\n etitle = \\\"Kent confidence ellipse\\\"\\n if len(nDIs) > 3:\\n kpars = pmag.dokent(nDIs, len(nDIs))\\n if verbose:\\n print(\\\"mode \\\", mode)\\n for key in kpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (kpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (kpars[key]))\\n mode += 1\\n npars.append(kpars['dec'])\\n npars.append(kpars['inc'])\\n npars.append(kpars['Zeta'])\\n npars.append(kpars['Zdec'])\\n npars.append(kpars['Zinc'])\\n npars.append(kpars['Eta'])\\n npars.append(kpars['Edec'])\\n npars.append(kpars['Einc'])\\n if len(rDIs) > 3:\\n kpars = pmag.dokent(rDIs, len(rDIs))\\n if verbose:\\n print(\\\"mode \\\", mode)\\n for key in kpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (kpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (kpars[key]))\\n mode += 1\\n rpars.append(kpars['dec'])\\n rpars.append(kpars['inc'])\\n rpars.append(kpars['Zeta'])\\n rpars.append(kpars['Zdec'])\\n rpars.append(kpars['Zinc'])\\n rpars.append(kpars['Eta'])\\n rpars.append(kpars['Edec'])\\n rpars.append(kpars['Einc'])\\n else: # assume bootstrap\\n if dist == 'BE':\\n if len(nDIs) > 5:\\n BnDIs = pmag.di_boot(nDIs)\\n Bkpars = pmag.dokent(BnDIs, 1.)\\n if verbose:\\n print(\\\"mode \\\", mode)\\n for key in Bkpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (Bkpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (Bkpars[key]))\\n mode += 1\\n npars.append(Bkpars['dec'])\\n npars.append(Bkpars['inc'])\\n npars.append(Bkpars['Zeta'])\\n npars.append(Bkpars['Zdec'])\\n npars.append(Bkpars['Zinc'])\\n npars.append(Bkpars['Eta'])\\n npars.append(Bkpars['Edec'])\\n npars.append(Bkpars['Einc'])\\n if len(rDIs) > 5:\\n BrDIs = pmag.di_boot(rDIs)\\n Bkpars = pmag.dokent(BrDIs, 1.)\\n if verbose:\\n print(\\\"mode \\\", mode)\\n for key in Bkpars.keys():\\n if key != 'n' and verbose:\\n print(\\\" \\\", key, '%7.1f' % (Bkpars[key]))\\n if key == 'n' and verbose:\\n print(\\\" \\\", key, ' %i' % (Bkpars[key]))\\n mode += 1\\n rpars.append(Bkpars['dec'])\\n rpars.append(Bkpars['inc'])\\n rpars.append(Bkpars['Zeta'])\\n rpars.append(Bkpars['Zdec'])\\n rpars.append(Bkpars['Zinc'])\\n rpars.append(Bkpars['Eta'])\\n rpars.append(Bkpars['Edec'])\\n rpars.append(Bkpars['Einc'])\\n etitle = \\\"Bootstrapped confidence ellipse\\\"\\n elif dist == 'BV':\\n sym = {'lower': ['o', 'c'], 'upper': [\\n 'o', 'g'], 'size': 3, 'edgecolor': 'face'}\\n if len(nDIs) > 5:\\n BnDIs = pmag.di_boot(nDIs)\\n pmagplotlib.plot_eq_sym(\\n FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym)\\n if len(rDIs) > 5:\\n BrDIs = pmag.di_boot(rDIs)\\n if len(nDIs) > 5: # plot on existing plots\\n pmagplotlib.plot_di_sym(FIG['bdirs'], BrDIs, sym)\\n else:\\n pmagplotlib.plot_eq(\\n FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors')\\n if dist == 'B':\\n if len(nDIs) > 3 or len(rDIs) > 3:\\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)\\n elif len(nDIs) > 3 and dist != 'BV':\\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)\\n if len(rDIs) > 3:\\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)\\n elif len(rDIs) > 3 and dist != 'BV':\\n pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)\\n if verbose:\\n pmagplotlib.draw_figs(FIG)\\n #\\n files = {}\\n locations = locations[:-1]\\n for key in FIG.keys():\\n if pmagplotlib.isServer: # use server plot naming convention\\n filename = 'LO:_'+locations+'_SI:_'+site+'_SA:_'+sample + \\\\\\n '_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt\\n else: # use more readable plot naming convention\\n filename = ''\\n for item in [locations, site, sample, specimen, crd, key]:\\n if item:\\n item = item.replace(' ', '_')\\n filename += item + '_'\\n if filename.endswith('_'):\\n filename = filename[:-1]\\n filename += \\\".{}\\\".format(fmt)\\n files[key] = filename\\n if pmagplotlib.isServer:\\n black = '#000000'\\n purple = '#800080'\\n titles = {}\\n titles['eq'] = 'Equal Area Plot'\\n FIG = pmagplotlib.add_borders(FIG, titles, black, purple)\\n pmagplotlib.save_plots(FIG, files)\\n elif verbose:\\n ans = raw_input(\\n \\\" S[a]ve to save plot, [q]uit, Return to continue: \\\")\\n if ans == \\\"q\\\":\\n sys.exit()\\n if ans == \\\"a\\\":\\n pmagplotlib.save_plots(FIG, files)\\n if plt:\\n pmagplotlib.save_plots(FIG, files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7b545b23011cb461451d62918075d6\",\n \"score\": \"0.5289112\",\n \"text\": \"def plot_output(depth_img, grasp_q_img, grasp_angle_img, no_grasps=1, grasp_width_img=None):\\n gs = detect_grasps(grasp_q_img, grasp_angle_img,\\n width_img=grasp_width_img, no_grasps=1)\\n\\n fig = plt.figure(figsize=(10, 10))\\n\\n ax = fig.add_subplot(1, 1, 1)\\n ax.imshow(depth_img, cmap='gray')\\n for g in gs:\\n g.plot(ax)\\n ax.set_title('Depth')\\n ax.axis('off')\\n\\n return gs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea91ae969c85240a5c5feb3f3cf4af73\",\n \"score\": \"0.52865165\",\n \"text\": \"def set_globalplot_properties():\\n\\n global colors\\n\\n plt.rcdefaults()\\n plt.rc('font', weight='bold')\\n plt.rc('legend', numpoints=1, fontsize='x-large')\\n plt.rc('text', usetex=True)\\n #colors = ['r', 'b', 'g', 'c']\\n colors = ['#7b3294','#a4225f', '#a6dba0', '#d7191c']\\n #,'#d01c8b','#a6611a']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241d87f62e870c563d253990c1ec470c\",\n \"score\": \"0.5285527\",\n \"text\": \"def printGraph(self):\\n for s, p, o in self.g:\\n print s, p, o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f1288c89046e498703f8391eace170\",\n \"score\": \"0.52843493\",\n \"text\": \"def afficher_fig(figure):\\n\\tR = [[[figure[i][j][k].num for k in range (len(figure[0][0]))] for j in range (len(figure[0]))] for i in range (len(figure))]\\n\\tprint(R)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f13b15ae5d14b15295325d819f845f1\",\n \"score\": \"0.5283384\",\n \"text\": \"def graph_per_region():\\r\\n table = callflie()\\r\\n years_2558 = [each for each in table if \\\"2558\\\" in each]\\r\\n years_2557 = [each for each in table if \\\"2557\\\" in each]\\r\\n years_2556 = [each for each in table if \\\"2556\\\" in each]\\r\\n years_2555 = [each for each in table if \\\"2555\\\" in each]\\r\\n \\r\\n values58 = locations(years_2558)\\r\\n values57 = locations(years_2557)\\r\\n values56 = locations(years_2556)\\r\\n values55 = locations(years_2555)\\r\\n\\r\\n\\r\\n x = [\\\"South\\\", \\\"West\\\", \\\"East north\\\", \\\"Center\\\", \\\"East\\\", \\\"North\\\"]\\r\\n ghp = plt.gca()\\r\\n ghp.set_title(u'จำนวนคนต่อครัวที่รายได้ต่ำกว่า 30,000 บาท แบ่งตามภาค',fontname='JasmineUPC',fontsize=20)\\r\\n ghp.set_xlabel(u'Region',fontname='JasmineUPC',fontsize=20)\\r\\n ghp.set_ylabel(u'Amount people',fontname='JasmineUPC',fontsize=20)\\r\\n ghp.plot(x,values58,'-om')\\r\\n ghp.plot(x,values57,'-oc')\\r\\n ghp.plot(x,values56,'-oy')\\r\\n ghp.plot(x,values55,'-or')\\r\\n ghp.legend([u'2558',u'2557',u'2556',u'2555'],loc=1,fancybox=1,shadow=1)\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd43d8bc8e370246a9eec1f65f0eb9f5\",\n \"score\": \"0.52823603\",\n \"text\": \"def prepare_plots(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f7a0ea36841b9bb1e53c6e064ed794\",\n \"score\": \"0.52801067\",\n \"text\": \"def figure_reproduction():\\n\\n files = np.array(sorted(glob.glob(\\\"dataset2/*.ema\\\")))\\n total_samples = len(files)\\n electrodes = 14\\n init_pos = np.zeros((total_samples,electrodes))\\n labels = [\\\"T3\\\", \\\"T2\\\", \\\"T1\\\", \\\"Lower incisor\\\", \\\"Upper lip\\\", \\\"Lower lip\\\"]\\n for i,fname in enumerate(files):\\n data = ema_read(fname)\\n init_pos[i,:] = data[0,:]\\n\\n sns.set_style(\\\"white\\\")\\n idx = [0,1,2,3,5,6]\\n\\n sampled = np.random.choice(range(total_samples),200)\\n markers = [\\\".\\\", \\\"v\\\", \\\"^\\\", \\\"<\\\",\\\"<\\\", \\\"s\\\",\\\"+\\\"]\\n\\n img = plt.imread(\\\"arthead.png\\\")\\n plt.imshow(img, extent=[-6.75, 21.25, -10, 8])\\n\\n for label in idx:\\n plt.scatter(init_pos[sampled,2*label],\\n init_pos[sampled,2*label+1],\\n marker=markers[label])\\n plt.xlabel(\\\"x position\\\")\\n plt.ylabel(\\\"y position\\\")\\n plt.xlim([-5, 10])\\n leg = plt.legend(labels,loc=3,frameon=False)\\n leg.get_frame().set_linewidth(0.0)\\n plt.title(\\\"Initial position of electrodes in MNGU0 dataset\\\")\\n fig = plt.figure(num=1, figsize=(4.2,4.2), dpi=80, facecolor=\\\"w\\\",\\n edgecolor=\\\"k\\\")\\n \\n plt.savefig(\\\"paper/init_pos.pgf\\\")\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc0d66ccb2f4cc4a0fb4a302ab49570\",\n \"score\": \"0.52733195\",\n \"text\": \"def figure(self):\\n self._idx += 1\\n\\n # Check if there are too many plots in the contextmanager\\n if self._idx >= self.nrows * self.ncols:\\n raise RuntimeError(\\n \\\"Invalid number of plots in the canvas! Increase \\\"\\n \\\"the number of rows and cols to add more plots.\\\"\\n )\\n\\n return plt.gcf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b7aa24e5c507e97c6a35b63d683a106\",\n \"score\": \"0.527105\",\n \"text\": \"def _plot(self, p):\\n p.next(None, {'discrete': True})\\n p.plot_3d('info.flipped', 'score_stats.purity', 'golds')\\n p.plot_3d('info.flipped', 'score_stats.completeness', 'golds')\\n p.plot_3d('info.flipped', 'score_stats.retired', 'golds')\\n p.plot_3d('info.flipped', 'score_stats.mse_t', 'golds')\\n p.plot_3d('info.flipped', 'score_stats.mse', 'golds')\\n p.plot_3d('thresholds.0', 'thresholds.1', 'golds',\\n axes={'x': 'Bogus Threshold',\\n 'y': 'Real Threshold'})\\n\\n # Detect whether there are multiple trials per gold label amount\\n # and fraction of labels flipped. Used to determine whether to\\n # plot points or plot error bars.\\n p.kwargs['keys'] = ['info.flipped', 'golds']\\n d = plots.FD.bin_data(p.get_data(), 0)\\n d = plots.FD.bin_data(d[next(iter(d))], 1)\\n print(d)\\n ebar = bool(len(next(iter(d.values()))) > 1)\\n p.kwargs._reset()\\n\\n if ebar:\\n p.next(None, {'discrete': True})\\n p.plot_ebar_d('info.flipped', 'score_stats.purity', 'golds')\\n p.plot_ebar_d('info.flipped', 'score_stats.completeness', 'golds')\\n p.plot_ebar_d('info.flipped', 'score_stats.retired', 'golds')\\n p.plot_ebar_d('info.flipped', 'score_stats.mse_t', 'golds')\\n p.plot_ebar_d('info.flipped', 'score_stats.mse', 'golds')\\n p.plot_3d('thresholds.0', 'thresholds.1', 'golds',\\n axes={'x': 'Bogus Threshold',\\n 'y': 'Real Threshold'})\\n\\n p.next(None, {'discrete': False})\\n p.plot_3d('thresholds.0', 'thresholds.1', 'info.flipped',\\n axes={'x': 'Bogus Threshold',\\n 'y': 'Real Threshold'})\\n\\n p.next(None, {'discrete': True})\\n if ebar:\\n p.plot_ebar_d('info.flipped', 'score_stats.mse_t', 'golds')\\n p.plot_ebar_d('info.flipped', 'score_stats.mse', 'golds')\\n else:\\n p.plot_3d('info.flipped', 'score_stats.mse_t', 'golds')\\n p.plot_3d('info.flipped', 'score_stats.mse', 'golds')\\n\\n p.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a969cced39e3eeeac633b6256e103c8\",\n \"score\": \"0.5262232\",\n \"text\": \"def plt_patch(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e38a76290ddc7f285c413438af4160\",\n \"score\": \"0.52616775\",\n \"text\": \"def plotAll(n):\\n\\tplt.hold('on')\\n\\tplt.plot(getN(n),seq1(n,f1),getN(n),seq1(n,f2),getN(n),seq1(n,f3),getN(n),seq1(n,f4),getN(n),seq1(n,f5),)\\n\\tplt.legend(['Seq1','Seq2','Seq3','Seq4','Seq5'])\\n\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82919fb7be05721c2ad43c09d48e018c\",\n \"score\": \"0.52594584\",\n \"text\": \"def rrt_graph_list(num_trials, startpos, endpos, n_iter, radius, step_size, threshold):\\n graphs = []\\n for i in range(0, num_trials):\\n print(\\\"Trial: \\\", i)\\n G = rrt(startpos, endpos, [], n_iter, radius, step_size, threshold)\\n graphs.append(G)\\n\\n return graphs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18682ae066026b069267a2489f6ef71\",\n \"score\": \"0.5257661\",\n \"text\": \"def getAvailableGeoms(self, nodes, showUndisplay = 0):\\n # if no nodes specified no geometries.\\n if not nodes:\\n return\\n molecules, atms = self.getNodes(nodes)\\n geomsAvailable = []\\n for mol in molecules:\\n #if hasattr(mol,\\\"geomContainer\\\"):\\n geomC = mol.geomContainer\\n childGeomsName = self.getChildrenGeomsName(mol)\\n \\n # We only put the one we are ineterested in in the list\\n # of geomsAvailable \\n for geomName in list(geomC.geoms.keys()):\\n if geomName in ['master','selectionSpheres'] :\\n continue\\n if geomName in geomC.atoms:\\n if geomName in childGeomsName and geomC.geoms[geomName].children==[]:\\n continue\\n childgnames=[]\\n if geomC.geoms[geomName].children != []:\\n childnames = [x.name for x in geomC.geoms[geomName].children]\\n childgnames=childgnames+childnames\\n \\n for child in childgnames:\\n if geomC.atoms[geomName]==[] and geomC.atoms[child]!=[]:\\n if not geomName in geomsAvailable:\\n geomsAvailable.append(geomName)\\n else: \\n if geomC.atoms[geomName]!=[]:\\n if not geomName in geomsAvailable:\\n geomsAvailable.append(geomName) \\n \\n \\n return geomsAvailable\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1479,"cells":{"query_id":{"kind":"string","value":"31f6c1ca506d156c73e219cee470ccee"},"query":{"kind":"string","value":"Generate a point in a circle, or can think of it as a vector pointing a random direction with a random magnitude <= radius"},"positive_passages":{"kind":"list like","value":[{"docid":"d4835c19c0b6821c9b1897a49c1ba71c","score":"0.81278366","text":"def rand_in_circle(center: Vec2d, radius: float) -> Vec2d:\n # random angle\n angle = 2 * math.pi * random.random()\n # random radius\n r = radius * random.random()\n # calculating coordinates\n return Vec2d(\n r * math.cos(angle) + center.x,\n r * math.sin(angle) + center.y\n )","title":""}],"string":"[\n {\n \"docid\": \"d4835c19c0b6821c9b1897a49c1ba71c\",\n \"score\": \"0.81278366\",\n \"text\": \"def rand_in_circle(center: Vec2d, radius: float) -> Vec2d:\\n # random angle\\n angle = 2 * math.pi * random.random()\\n # random radius\\n r = radius * random.random()\\n # calculating coordinates\\n return Vec2d(\\n r * math.cos(angle) + center.x,\\n r * math.sin(angle) + center.y\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5d91a770b378eed66a786ea00913c8e5","score":"0.7678962","text":"def generate_random_circle():\n\n exp_range = numpy.random.exponential(scale=0.1, size=100)\n d = cumsum(exp_range[exp_range > 0.05])\n t = d[d < 2*pi]\n\n x = sin(t)\n y = cos(t)\n\n return t, x, y","title":""},{"docid":"429655725f5b2782a097d8d6cc0074fa","score":"0.767058","text":"def random_circle(bound_x, bound_y):\n radius = random.randint(100, 1000)\n cx = random.randint(MARGIN, bound_x - radius)\n cy = random.randint(MARGIN, bound_y - radius)\n steps = 360.0 / random.randint(3, 12)\n return circle(cx, cy, radius, arc=steps)","title":""},{"docid":"748ca80da8e984cc952b2b4ae86688c5","score":"0.7482401","text":"def random_point(object_radius=32):\n frame = object_radius // 2 + 1\n x = random.randint(frame, constants.field_width - frame)\n y = random.randint(frame, constants.field_height - frame)\n return geometry.Point(x, y)","title":""},{"docid":"7eaf4010fc33735206649d367114dca5","score":"0.7469423","text":"def random_point_inside(self):\n z = self.maxdist*np.random.random()\n delta_x = self.cam_model.getDeltaX(self.cam_model.width/2, z)\n delta_y = self.cam_model.getDeltaY(self.cam_model.height/2, z)\n point = np.array([0, 0, z, 1])\n point[:2] = np.array([delta_x,delta_y]) * (2*np.random.random_sample(2)-1.)\n random_point = np.dot(self.transform, point)[:3]\n return random_point","title":""},{"docid":"112654677e370c96a6d869ac66c992a9","score":"0.7429767","text":"def random_direction_vector (mag=1.0):\n\n #The objective here is to generate a point in a random direction on a sphere,\n #so that any region of the sphere of a given area is expected to contain the\n #same number of randomly generated points. The algorithm is to generate two\n #random numbers on [0,1], u and v. Then\n #\n # RA = 2 * pi * u [range 0-2pi]\n # DEC = asin(2v - 1) [range -pi - pi]\n #\n #This algorithm is taken from http://mathworld.wolfram.com/SpherePointPicking.html.\n #That article uses spherical coordinates THETA and PHI, where THETA is the\n #azimuthal angle (equivalent to RA) and PHI is the zenith angle. The conversion\n #between zenith angle and declination is PHI = PI/2 - DEC, so cos PHI = sin DEC.\n #Therefore, the acos in the Wolfram formula is replaced by asin here.\n #\n #Also note that the solid angle of an annulus on the celestial sphere scales as\n #sin DEC_MAX - sin DEC_MIN (a result which can easily be proved by integrating over the sphere),\n #which is consistent with this formula.\n \n u = random.random()\n v = random.random()\n ra = 2 * pi * u\n dec = math2.asin2(2*v - 1)\n vec = Vector()\n vec.set_xyz(ra, dec) #convert to Cartesian coordinates\n vec *= mag #scale by magnitude\n return(vec)","title":""},{"docid":"538f2ae966be8ec8e9059536cc7ee434","score":"0.7360734","text":"def random_circle ():\n cx = random.randint(8, constants.MAX_X-8) \n cy = random.randint(8, constants.MAX_Y-8) \n\n max_r = min(get_max_x(cx), get_max_y(cy))\n r = random.randint(8, max_r)\n\n border = random.choice(constants.BORDER_COLORS)\n body = random.choice(constants.COLORS)\n\n return make_circle(cx, cy, r, border, body)","title":""},{"docid":"269d13d0c80780e1dac9d53a18c943ce","score":"0.7290372","text":"def create_random_point(x0,y0,distance): \n r = distance/ 111300\n u = np.random.uniform(0,1)\n v = np.random.uniform(0,1)\n w = r * np.sqrt(u)\n t = 2 * np.pi * v\n x = w * np.cos(t)\n x1 = x / np.cos(y0)\n y = w * np.sin(t)\n return (x0+x1, y0 +y)","title":""},{"docid":"2053d4abe7d7eebfd0afb5d490543f04","score":"0.71617085","text":"def create_random_point(x0,y0,distance=500): \n r = distance/ 111300\n u = np.random.uniform(0,1)\n v = np.random.uniform(0,1)\n w = r * np.sqrt(u)\n t = 2 * np.pi * v\n x = w * np.cos(t)\n x1 = x / np.cos(y0)\n y = w * np.sin(t)\n return (x0+x1, y0 +y)","title":""},{"docid":"1e7de7abda49a611b34745d9476e7e6e","score":"0.7159202","text":"def generate_position(self): \n return sqrt(np.random.random()), np.random.random() * 2 * pi","title":""},{"docid":"d065e16b3de71cca2b17a63e2a7b1edf","score":"0.707648","text":"def create_random_point(x0,y0,z0,distance): \n \n r = distance\n u = np.random.uniform(0,1)\n v = np.random.uniform(0,1)\n w = r * np.sqrt(u)\n t = 2 * np.pi * v\n x = w * np.cos(t)\n x1 = x / np.cos(y0)\n y1 = w * np.sin(t)\n \n return [x0+x1, y0 +y1, z0]","title":""},{"docid":"2dad05dad252810a163b1e31be60eb35","score":"0.70186436","text":"def random_points_on_sphere(radius,\n num,\n rng):\n coords = jax.random.normal(rng, shape=(num, 3))\n magnitude = jnp.sqrt(jnp.sum(jnp.square(coords), axis=-1, keepdims=True))\n coords /= magnitude\n coords *= radius\n return coords","title":""},{"docid":"8685d05ed574385ef6f70339827611b8","score":"0.69379926","text":"def random_in_unit_sphere():\n\twhile True:\n\t\tp = Vec3(random.random(), random.random(), random.random()) * 2 - Vec3(1, 1, 1)\n\t\tif p.length_squared() < 1:\n\t\t\treturn p","title":""},{"docid":"016148ceef0491758170719aa7f195ca","score":"0.6931069","text":"def uniform_sample_circle(radius, num_samples, normal=np.array([0., 0., 1.])):\n theta = np.random.rand(num_samples) * 2 * np.pi\n return radius * np.stack((np.cos(theta), np.sin(theta),\n np.zeros_like(theta)), axis=1)","title":""},{"docid":"d84a681a41b29be030f6badaf3c2f826","score":"0.690921","text":"def generate_circle(r=1.0, s=0.01):\n\n t = arange(0, 2*pi, s)\n x = sin(t)*r\n y = cos(t)*r\n\n return t, x, y","title":""},{"docid":"d7f7c08c066ec1bf576257fd9fe52883","score":"0.6886885","text":"def generate_circle(position=[50,50], radius=10):\n \n position = np.array([50,50])\n circle = position+ np.array([[radius*np.cos(x), radius*np.sin(x)] for x in np.arange(0,2*np.pi, 0.01)])\n\n return circle","title":""},{"docid":"be26570aab2692d7e89d8746a30f050e","score":"0.66828287","text":"def random_point():\n lat = random.uniform(-90, 90)\n lgt = random.uniform(-180, 180)\n return (lat, lgt)","title":""},{"docid":"f561a3b73bd89876550ee112c38266ac","score":"0.66455334","text":"def circle(radius):\n return PI * radius * radius","title":""},{"docid":"c81958b6431ec98e11eeb47caae3d35f","score":"0.6628533","text":"def generate_position(self): \n return np.random.power(4), np.random.random() * 2 * pi","title":""},{"docid":"315fd7fd5a10846417d891b6f2240bc8","score":"0.65938413","text":"def getRandomPoint(self):\n a = random.uniform(0, 1)\n b = random.uniform(0, 1)\n px = (1.0 - sqrt(a)) * self.p1.x + \\\n (sqrt(a) * (1.0 - b)) * self.p2.x + \\\n (sqrt(a) * b) * self.p3.x\n py = (1.0 - sqrt(a)) * self.p1.y + \\\n (sqrt(a) * (1.0 - b)) * self.p2.y + \\\n (sqrt(a) * b) * self.p3.y\n pz = (1.0 - sqrt(a)) * self.p1.z + \\\n (sqrt(a) * (1.0 - b)) * self.p2.z + \\\n (sqrt(a) * b) * self.p3.z\n return Point(px, py, pz)","title":""},{"docid":"b8f3a0a0fbdeceb770a72a11d14cf34d","score":"0.6590845","text":"def get_random_direction():\n phi = np.random.uniform(0, np.pi * 2)\n costheta = np.random.uniform(-1, 1)\n\n theta = np.arccos(costheta)\n x = np.sin(theta) * np.cos(phi)\n y = np.sin(theta) * np.sin(phi)\n z = np.cos(theta)\n return np.array([x, y, z])","title":""},{"docid":"e5509d5f5a540ec75fe57a89e8801c07","score":"0.65896124","text":"def CreateCircle(Point,Normal,Radius):\n Plane=rs.PlaneFromNormal(Point,Normal)\n Circle=rs.AddCircle(Plane,Radius)\n return Circle","title":""},{"docid":"5cf9a9b4e622682c1d6bf8d995563ffe","score":"0.65574086","text":"def get_arc_point(self, ang=0, r=1): \r\n return Point(x=self.x + cos(ang) * r, \\\r\n y=self.y + sin(ang) * r)","title":""},{"docid":"e571ad9dc8b0fa0afa28e94055a3af4f","score":"0.6514502","text":"def random_point(self):\n from random import randint\n from sys import maxint\n\n # The lower and upper\n lower, upper = -maxint-1, maxint\n\n if self.slope is S.Infinity:\n if isinstance(self, Ray):\n if self.ydirection is S.Infinity:\n lower = self.p1[1]\n else:\n upper = self.p1[1]\n elif isinstance(self, Segment):\n lower = self.p1[1]\n upper = self.p2[1]\n\n x = self.p1[0]\n y = randint(lower, upper)\n else:\n if isinstance(self, Ray):\n if self.xdirection is S.Infinity:\n lower = self.p1[0]\n else:\n upper = self.p1[0]\n elif isinstance(self, Segment):\n lower = self.p1[0]\n upper = self.p2[0]\n\n a,b,c = self.coefficients\n x = randint(lower, upper)\n y = simplify( (-c - a*x) / b )\n return Point(x, y)","title":""},{"docid":"7918855abdf6ed8797b55551b3b11761","score":"0.651013","text":"def random_points_in_sphere(radius,\n num,\n rng):\n coords_rng, scale_rng = jax.random.split(rng)\n\n coords = random_points_on_sphere(radius, num, coords_rng)\n scale = jax.random.uniform(scale_rng, shape=(num,))\n scale = jnp.cbrt(scale)\n coords *= scale[:, jnp.newaxis]\n return coords","title":""},{"docid":"ad9f49b691a632e51ec242794307ecb4","score":"0.64982957","text":"def random_3d_point(r=1):\n r = rnd.random() * r\n phi = rnd.random() * 2 * np.pi\n theta = rnd.random() * 2 * np.pi\n\n x = r * sin(phi) * cos(theta)\n y = r * sin(phi) * sin(theta)\n z = r * cos(phi)\n\n return np.array([x, y, z])","title":""},{"docid":"99375f8571f3c14d9fffeb72b649c070","score":"0.646699","text":"def random_point():\n return Decimal(uniform(-1, 1))","title":""},{"docid":"7a6243ce084b7361004b892ce8b23ff2","score":"0.6443925","text":"def makepoint (xmin, xmax, ymin, ymax):\n x = random.uniform(xmin, xmax)\n y = random.uniform(ymin, ymax)\n p = Point(x,y)\n return p","title":""},{"docid":"ae4b940621bbb9d37003c6cb35800862","score":"0.64433503","text":"def create_circle(x_center, y_center, radius):\r\n cv.create_oval(x_center-radius, y_center-radius, x_center+radius, y_center+radius, outline = 'blue', width = 1)","title":""},{"docid":"42b2066220d166f38cfb21de7dfbc8f3","score":"0.64359844","text":"def random_unit_vector():\n\n\n phi = np.random.uniform(0,np.pi*2)\n costheta = np.random.uniform(-1,1)\n\n theta = np.arccos( costheta )\n x = np.sin( theta) * np.cos( phi )\n y = np.sin( theta) * np.sin( phi )\n z = np.cos( theta )\n vect = (x,y,z)\n vect = np.asarray(vect, dtype=np.float32)\n return vect","title":""},{"docid":"5871515a72487f2b552bdd3444244abe","score":"0.64317334","text":"def draw_circle(point, radius):\n for i in xrange(0, 360):\n x = point[0] + int(radius * cos(pi / 180 * i))\n y = point[1] + int(radius * sin(pi / 180 * i))\n yield (x, y)","title":""},{"docid":"f206a7c7effef07593ebd4790e152800","score":"0.6388496","text":"def randPosition():\n x = random.randint(20, Engine.window_width - 20)\n y = random.randint(20, Engine.window_height - 20)\n \n return Vec2d(x, y)","title":""},{"docid":"4dde00ebb9a39d24f813f93906626ebf","score":"0.6305905","text":"def make_random_coordinate():\n return make_coordinate(randint(0, 100), randint(0, 100))","title":""},{"docid":"6e5ad31883991f56ed1cd93d86490820","score":"0.6301807","text":"def get_random_point(self) -> Point:\n an_x = random_between(self.right, self.left)\n a_y = random_between(self.top, self.bottom)\n return Point(x = an_x, y = a_y)","title":""},{"docid":"c23efd0056ff4c15a5c5c67c33a51e1b","score":"0.6301125","text":"def make_circle(N=64, center=np.array([32, 32]), radius=20):\n XX, YY = np.meshgrid(np.arange(N), np.arange(N))\n XY = np.stack([XX, YY], axis=-1)\n val = np.linalg.norm(XY - center[np.newaxis, np.newaxis, :], axis=2)\n result = np.zeros((N, N))\n result += 1. * (val < radius)\n return result","title":""},{"docid":"40f2ea7b1c18aee6a0e546907c3d9a1a","score":"0.6283758","text":"def gen_polar_direc(N=1):\n import numpy.random.random_sample\n import numpy.pi\n out = np.zeros((N,2),dtype=float)\n for i in range(N):\n out[i,0] = (2*pi)*random_sample()\n out[i,1] = (pi)*random_sample()\n return out","title":""},{"docid":"94dc90ba8c7959b1ceb6bd9384fb2db2","score":"0.62657225","text":"def truecircle(radius, r):\n if radius == 0:\n return np.zeros_like(r)\n else:\n samples = r.shape[0]\n one_pixel = 2 / samples\n radius_plus = radius + (one_pixel / 2)\n intermediate = (radius_plus - r) * (samples / 2)\n return np.minimum(np.maximum(intermediate, 0), 1)","title":""},{"docid":"3a3ceb9cf535fa11aa0fd1f6158fb3b3","score":"0.62604505","text":"def random_unit_vector():\n r = np.random.randn(2)\n return r/np.sqrt(np.sum(r**2))","title":""},{"docid":"4d82fc90c13d0e7a77534f94e40c888b","score":"0.62464833","text":"def random_upto(self):\r\n return Point(random.randint(0,self.x),random.randint(0,self.y))","title":""},{"docid":"bf545afa4988aa6fedd64368247dda37","score":"0.6205509","text":"def circle_points(self, num_pts, radius=1):\n t = np.linspace(0, 2*np.pi, num_pts, endpoint=False)\n x = radius * np.cos(t)\n y = radius * np.sin(t)\n \n self.template_points = np.c_[x, y]","title":""},{"docid":"668416a4db69e21909472287863b32c4","score":"0.62023145","text":"def Circle(radius=1.0, center=(0.0,0.0), points=32):\n p = []\n for i in range(points):\n a = 2.0*pi*float(i)/points\n p.append((center[0]+radius*sin(a), center[1]+radius*cos(a)))\n return Polygon(p)","title":""},{"docid":"6b3d77e53da012a5f8fbdab4c55cba3f","score":"0.6191863","text":"def randomize_direction(self, polar_options):\n scalar = float(random.choice(polar_options))/100.\n angle = scalar*2*pi\n vx = self.speed*cos(angle)\n vy = self.speed*sin(angle)\n self.velocity = (vx, vy)","title":""},{"docid":"5765dd76b3b8fc6a55fa8997f98ab4d6","score":"0.6157601","text":"def point_on_circle(angle, radius):\n x = sin(radians(angle)) * radius\n y = cos(radians(angle)) * radius\n return x,y","title":""},{"docid":"716e6479c2fa4a1fea7b35ae35d1e69c","score":"0.6125484","text":"def _point_from_polar(r, phi, tx, ty):\n x, y = pol2cart(r, phi)\n return Point(x=x + tx, y=y + ty)","title":""},{"docid":"a3f39c108d446cf60a04fe5030a91af6","score":"0.611002","text":"def find_pi(n):\n inside=0\n\n for i in range(0,n):\n x=random()\n y=random()\n if (x*x+y*y)**(0.5)<=1: # if i falls inside the circle\n inside+=1\n\n pi=4*inside/n\n return pi","title":""},{"docid":"23d2996ebf51f3ac187881cef39743fe","score":"0.60941553","text":"def _random_sphere(m, n, r):\n a = np.random.randn(m, n)\n s2 = np.sum(a**2, axis=1)\n base = gammainc(n/2, s2/2)**(1/n) * r / np.sqrt(s2)\n a = a * (np.tile(base, (n, 1))).T\n\n return a","title":""},{"docid":"60cb59f4da85ac3d87ab66088af60c25","score":"0.60882926","text":"def _build_filled_circle_xy_vbo(self, radius):\n v = [0., 0., 0.]\n for angle in range(0, 361, 5):\n v.extend([radius * sin(radians(angle)), radius * cos(radians(angle)), 0.])\n return vbo.VBO(v)","title":""},{"docid":"1485ce2106a522d8a1a9dc472bb3da6b","score":"0.60827416","text":"def random_unit():\r\n vec = Vector2D(randint(-10000, 10000), randint(-10000, 10000))\r\n vec.normalise()\r\n return vec","title":""},{"docid":"5449c8653f73fe59d88ef01f5d3427f8","score":"0.60821027","text":"def point_to_circle(point, center, radius, normal, epsilon=1e-6):\n # signed distance from point to plane of circle\n diff = point - center\n dist_to_plane = diff.dot(normal)\n\n # projection of P - C onto plane is Q - C = P - C - dist_to_plane * normal\n diff_in_plane = diff - dist_to_plane * normal\n sqr_len = diff_in_plane.dot(diff_in_plane)\n\n if sqr_len >= epsilon:\n closest_point_circle = (\n center + (radius / math.sqrt(sqr_len)) * diff_in_plane)\n dist = np.linalg.norm(point - closest_point_circle)\n else: # on the line defined by center and normal of the circle\n plane_direction = norm_vector(pr.perpendicular_to_vector(normal))\n closest_point_circle = center + radius * plane_direction\n dist = math.sqrt(radius * radius + dist_to_plane * dist_to_plane)\n\n return dist, closest_point_circle","title":""},{"docid":"44c8af9c54956b02b8e53de5864faa9b","score":"0.60750455","text":"def generateRandomPoint(self, fromPoint=(), maxdistance=0):\n if fromPoint and maxdistance:\n offset_x = cbrandom.randint(-maxdistance,maxdistance)\n offset_y = cbrandom.randint(-maxdistance,maxdistance)\n startX = fromPoint[0] - maxdistance\n endX = fromPoint[0] + maxdistance\n startY = fromPoint[1] - maxdistance\n endY = fromPoint[1] + maxdistance\n else:\n # If one of the not required param is missing, always use the normal feature\n startX = 1\n endX = self.levelSize[0]-1\n startY = 1\n endY = self.levelSize[1]-1\n return self.normalizePointOnLevel(cbrandom.randint(startX,endX), cbrandom.randint(startY,endY))","title":""},{"docid":"1a35a366a05251201a7bba76f8ef4e4e","score":"0.6074381","text":"def generate_sphere_random_sampling(vertex_number=100, radius=1.0):\n coords = np.zeros((vertex_number, 3))\n for i in range(vertex_number):\n M = np.random.normal(size=(3, 3))\n Q, R = np.linalg.qr(M)\n coords[i, :] = Q[:, 0].transpose() * np.sign(R[0, 0])\n if radius != 1:\n coords = radius * coords\n return tri_from_hull(coords)","title":""},{"docid":"2470a7f55d09f6df047659d4632208a0","score":"0.60735345","text":"def circle(a):\n return math.pi*a*a","title":""},{"docid":"2a76954854cfb24cd3c354c84e708a98","score":"0.60680497","text":"def offset_circle(radius, x, y, center):\n x, y = optimize_xy_separable(x, y)\n # no in-place ops, x, y are shared memory\n x = x - center[0]\n y = y - center[1]\n # not cart to polar; computing theta is waste work\n r = np.hypot(x, y)\n return circle(radius, r)","title":""},{"docid":"44798cc94cda367551234e5eef61d1c0","score":"0.60585016","text":"def estimate_pi():\n circle_points = 0\n square_points = 0\n result = 0\n\n while square_points < 1000:\n x = random.uniform(-1, 1)\n y = random.uniform(-1, 1)\n\n if (x**2 + y**2) <= 1:\n circle_points += 1\n square_points += 1\n\n result = 4 * circle_points / square_points\n return result","title":""},{"docid":"07e388b0b222e0c82ccc15a72b0a0a0a","score":"0.605186","text":"def render_target(radius=4):\n t = np.zeros([radius*2,radius*2])\n center = np.array([radius-.5,radius-.5])\n for i in range(radius*2):\n for j in range(radius*2):\n distance = np.abs(np.linalg.norm(center-np.array([i,j])))\n t[i,j] = np.clip((radius-distance)/radius,0,1)\n t[radius,radius] = 0\n t[radius-1,radius] = 0\n t[radius,radius-1] = 0\n t[radius-1,radius-1] = 0\n return t","title":""},{"docid":"4f3c8d106a8c84f9600c8657ec9421e6","score":"0.6050687","text":"def monte_carlo(n_samples, radius = 1):\n\tin_circle_count = 0\n\tfor i in range(n_samples):\n\t\t\n\t\t# Sample x, y from the uniform distribution\n\t\tx = random.uniform(0, radius)\n\t\ty = random.uniform(0, radius)\n\t\t\n\t\t# Count the number of points inside the circle\n\t\tif(in_circle(x, y, radius)):\n\t\t\tin_circle_count += 1\n\n\t# Since we've generated points in upper right quadrant ([0,radius], [0, radius])\n\t# We need to multiply the number of points by 4\t\n\tpi_estimate = 4 * in_circle_count / (n_samples)\n\n\treturn pi_estimate","title":""},{"docid":"5a8d0c6067d3184cdc69ec1d1eda918f","score":"0.604494","text":"def random_angle():\n angle = random.random() * 2 * math.pi\n return angle","title":""},{"docid":"3ec7566b2f64e75652e908b1e19ee252","score":"0.6036802","text":"def uniform():\n def gen(latent_start=None):\n while True:\n x, y, t = _rand(), _rand(), _rand()*np.pi\n yield makeTransform(t,x,y), [x,y, math.cos(t), math.sin(t)]\n\n return gen","title":""},{"docid":"5e7780a0a5eb924b83ca749ae023ba51","score":"0.60340804","text":"def Circle(self,center, rad):\n return mshr.Circle(center, rad)","title":""},{"docid":"d3caf06d58563340adc48221df0a14c4","score":"0.6031237","text":"def create_circle(self, x, y, radius, canvas, **kwargs):\n x_1 = x - radius\n y_1 = y - radius\n x_2 = x + radius\n y_2 = y + radius\n return canvas.create_oval(x_1, y_1, x_2, y_2, **kwargs)","title":""},{"docid":"72861b8ad488d018df319fa851f81481","score":"0.6028996","text":"def pnt_circle(pnt, norm_dir):\n\n if norm_dir == 0:\n return pnt_circle_x(pnt)\n elif norm_dir == 1:\n return pnt_circle_y(pnt)\n elif norm_dir == 2:\n return pnt_circle_z(pnt)\n else:\n raise ValueError('Invalid norm direction.')","title":""},{"docid":"67ef19bfa30587160d7c88bc7e7703ac","score":"0.6021416","text":"def generate_coord(N):\r\n v = np.ones(N)\r\n list_spin = [-1,1]\r\n for i in range(N):\r\n v[i] = np.random.choice(list_spin)\r\n return v","title":""},{"docid":"b18aac808c201743815cba8516dcddeb","score":"0.6016708","text":"def gen_circle_path(self, radius, points, center):\n self.x_radius = radius[0]\n self.y_radius = radius[1]\n self.x_center = center[0]\n self.y_center = center[1]\n\n self.x_radius_variable.set(self.x_radius)\n self.y_radius_variable.set(self.y_radius)\n self.x_center_variable.set(self.x_center)\n self.y_center_variable.set(self.y_center)\n\n self.pt.gen_circle_path(radius, points, center)\n self.canvas.delete(self.PATH_TAG)\n self._draw_path()","title":""},{"docid":"815e3c28c5ae4893de6dd04b12716b8b","score":"0.6011167","text":"def circle(x, y, r):\n global _surface\n\n ws = stddraw._factorX(2*r)\n hs = stddraw._factorY(2*r)\n if (ws <= 1) and (hs <= 1):\n stddraw._pixel(x, y)\n else:\n xs = stddraw._scaleX(x)\n ys = stddraw._scaleY(y)\n pygame.draw.ellipse(_surface,\n stddraw.pygameColor(stddraw.BLACK),\n pygame.Rect(xs-ws/2, ys-hs/2, ws, hs), 1\n )","title":""},{"docid":"c78d57f42036035312b0d662c835e50b","score":"0.5998429","text":"def circle(t, r):\n\tarc(t, r, 360)","title":""},{"docid":"8f62032318964c914396b7c333650115","score":"0.59951895","text":"def pick_point(self):\n x, y = 0.0, 0.0\n\n MAX_ITTERATIONS = 1000 # Protect against unpopulated areas causing infinite spin.\n for _ in range(MAX_ITTERATIONS):\n if self.restrict_to_area:\n radius = randint(0, int(self.area_radius_pixels + 0.5))\n angle = random() * 2 * math.pi\n ox = math.sin(angle) * radius\n oy = math.cos(angle) * radius\n x = int(self.area_centre_xy[0] + ox)\n y = int(self.area_centre_xy[1] + oy)\n else:\n x, y = randint(0, self.map_x_limit - 1), randint(0, self.map_y_limit - 1)\n p = (self.population_density[y][x] / 255.0)\n if p > random():\n break\n\n # Dither our pixels, otherwise all points will be on pixel grid\n x += random()\n y += random()\n\n return self.xy_to_lon_lat((x, y))","title":""},{"docid":"a2b802244b33d32cf2e6493825259977","score":"0.5970713","text":"def __init__(self):\n self.x = randint(0, 15)\n self.y = randint(0, 15)\n self.pos = Vector2(self.x, self.y)","title":""},{"docid":"60cc57a090df063506787932f29712ad","score":"0.59686905","text":"def _sample_pose_circ(rng, params, constraints):\n for _ in range(1000):\n r = (params.inner_w + params.inner_h) / 4. + params.mid_margin\n r = min(r, params.lim_euc_dist)\n\n phi = rng.uniform(0, 2*np.pi)\n\n x, y = pol2cart(r, phi)\n theta = rng.uniform(0, 2*np.pi)\n # theta_e = rng.uniform(0, 2*np.pi)\n\n # while np.abs(theta - theta_e) >= params.lim_ang_dist:\n # theta_e = rng.uniform(0, 2*np.pi)\n\n start_pt = OrientedPoint(x, y, theta)\n end_pt = OrientedPoint(-x, -y, theta)\n\n y_off = end_pt.y - start_pt.y\n x_off = end_pt.x - start_pt.x\n new_theta = np.arctan2(y_off, x_off)\n\n start_pt = OrientedPoint(x, y, new_theta)\n end_pt = OrientedPoint(-x, -y, new_theta)\n\n if _satisfies_constraints(start_pt, constraints) and \\\n _satisfies_constraints(end_pt, constraints):\n return start_pt, end_pt\n\n raise SpaceSeemsEmptyError(\"Something went wrong, the sampling space looks empty.\")","title":""},{"docid":"14c056587bd607b43127dda183036e7e","score":"0.59610903","text":"def rand_point (area : pygame.Rect) :\n return [random.randint(area.left, area.right), random.randint(area.top, area.bottom)]","title":""},{"docid":"3f4ace9b2376d8fc22c41263eadd0fd4","score":"0.59573126","text":"def circle(t, r):\r\n arc(t, r, 360)","title":""},{"docid":"dadd778a036de9fcf7e5e999d39404b7","score":"0.59525836","text":"def Random_xy(Rmax,model,Npoints,Nknots,ellipticity,PA):\n R_random = scale_radius * Random_radius(Rmax/scale_radius,model,Npoints,Nknots)\n PA_random = 2 * np.pi * np.random.random_sample(Npoints) # in rd\n theta_random = 2 * np.pi * np.random.random_sample(Npoints) # in rd\n u_random = R_random * np.cos(theta_random)\n v_random = R_random * np.sin(theta_random) * (1-ellipticity)\n x0_random = RA_cen_init + (RA_cen - RA_cen_init) * np.cos(Dec_cen_init * degree) \n y0_random = Dec_cen\n x_random = x0_random - u_random*np.sin(PA*degree) - v_random*np.cos(PA*degree)\n y_random = y0_random + u_random*np.cos(PA*degree) - v_random*np.sin(PA*degree)\n\n if verbosity >= 3:\n print(\"IS TRUE : \", x0_random == RA_cen_init, y0_random == Dec_cen_init)\n return(x_random, y_random)","title":""},{"docid":"e77c74f92e7b680bb7b7fc22cb71b64e","score":"0.59516895","text":"def random_ellipse ():\n cx = random.randint(8, constants.MAX_X-8) \n cy = random.randint(8, constants.MAX_Y-8) \n\n rx = random.randint(8, get_max_x(cx))\n ry = random.randint(8, get_max_y(cy))\n\n border = random.choice(constants.BORDER_COLORS)\n body = random.choice(constants.COLORS)\n\n return make_ellipse(cx, cy, rx, ry, border, body)","title":""},{"docid":"5491f746920673bd013d2b86a0f36c02","score":"0.5945056","text":"def randForce():\n x = random.uniform(-GuiBoid.normal_speed, GuiBoid.normal_speed)\n y = random.uniform(-GuiBoid.normal_speed, GuiBoid.normal_speed)\n \n # For divbyzero in vector calcs\n if x == 0 and y == 0:\n return randForce()\n \n return Vec2d(x, y)","title":""},{"docid":"f5d4951c1dbbc420b1c87bc79818b77a","score":"0.5944536","text":"def get_random_x():\n return round(np.random.uniform(-1, 1), 2)","title":""},{"docid":"8fb94d6d531010f59e5bc9e756b2e168","score":"0.59416074","text":"def monte_carlo_pi():\n inside: int = 0\n\n for i in range(1_000_000):\n x = random()\n y = random()\n distance = math.sqrt(pow(abs(0.5 - x), 2) + pow(abs(0.5 - y), 2))\n if distance <= 0.5:\n inside += 1\n\n print(4 * inside / 1_000_000) # multiply by 4 to account for x < 0 and/or y < 0","title":""},{"docid":"9742ad15dcf2ef685e1cd5a5af9545c6","score":"0.59367836","text":"def sample_circle(c, N=32):\n \n assert len(c) == 3\n \n # Get x, y and radius\n x, y, r = c\n \n # Sample N points, but add one to close the loop\n a = np.linspace(0,2*np.pi, N+1)\n \n # Prepare array\n pp = np.empty((len(a), 2), dtype=np.float32)\n \n # Apply polar coordinates\n pp[:,0] = np.cos(a) * r + x\n pp[:,1] = np.sin(a) * r + y\n \n # Return as a pointset\n return PointSet(pp)","title":""},{"docid":"186b668a84b3e037c1793af48ea51431","score":"0.5932039","text":"def rand_2D (multi : float = 1, direction = 0) :\n x = random.choice([-1,1])*random.random()\n y = random.choice([-1,1])*random.random()\n vec = [(x*multi)/math.sqrt(x**2 + y**2),(y*multi)/math.sqrt(x**2 + y**2)]\n if direction == 0 :\n return vec\n elif direction == 1 :\n vec[1] = -abs(vec[1])\n return vec\n elif direction == 2 :\n vec[1] = abs(vec[1])\n return vec\n elif direction == 3 :\n vec[0] = -abs(vec[0])\n return vec\n elif direction == 4 :\n vec[0] = abs(vec[0])\n return vec","title":""},{"docid":"bede6819f794fe647fa142907d659b44","score":"0.5920978","text":"def getRandomRoomCoordinate(self) -> Vec3:\n newX = random.uniform(-self.roomSize.x / 2, self.roomSize.x / 2)\n newY = random.uniform(-self.roomSize.y / 2, self.roomSize.y / 2)\n newZ = random.uniform(0 + 0.3, self.roomSize.z)\n return Vec3(newX, newY, newZ)","title":""},{"docid":"66c9fc2cc2ccf8bc908863b13dc6873f","score":"0.59202343","text":"def create_sphere():\n return rt.sphere(radius=5)","title":""},{"docid":"96a5b424996b2e6db4d9c1124b837fbc","score":"0.59189826","text":"def circle(r,n):\n arc = np.linspace(0,360,n+1)\n x = np.cos(arc*np.pi/180.)*r\n y = np.sin(arc*np.pi/180.)*r\n\n return x, y","title":""},{"docid":"7df79dd8df124be6ae2686c7919c2366","score":"0.5914445","text":"def circle_points(resolution, center, radius): \n radians = np.linspace(0, 2*np.pi, resolution)\n c = center[1] + radius*np.cos(radians)#polar co-ordinates\n r = center[0] + radius*np.sin(radians)\n \n return np.array([c, r]).T","title":""},{"docid":"524fefbc8c5cf0aa212f3db43b5b6371","score":"0.59107274","text":"def RandomPoint():\n global APP\n return Point(rand(0, APP['dims']['w']), rand(0, APP['dims']['h']))","title":""},{"docid":"0282de30eb63663e5baccf6f07956b15","score":"0.5897744","text":"def circle(t, r):\n arc(t, r, 360)","title":""},{"docid":"6861fbe22b9ad9cf56556a5cd947a3cd","score":"0.58918506","text":"def raffleDestination():\r\n raffle = np.random.uniform(0, 1) # choose between city center or city periphery\r\n if raffle > 0.5:\r\n point = (np.random.uniform(2000, 4000), np.random.uniform(2000, 4000)) # city center\r\n else:\r\n point = (np.random.uniform(0, 6000), np.random.uniform(0, 6000)) # city periphery\r\n while (point[0] <= 4000 and point[0] >= 2000) and (point[1] <= 4000 and point[1] >= 2000): # must be outside of the city center\r\n # center\r\n point = (np.random.uniform(0, 6000), np.random.uniform(0, 6000))\r\n return point","title":""},{"docid":"127954187564cf2ad47179b4dcc9dff1","score":"0.58912385","text":"def circle(t,r): \n arc(t,360,r)","title":""},{"docid":"f73aa7408cb6666a7b776f19d91d22ec","score":"0.58878446","text":"def from_polar(r, theta):\n return Vector(r * math.cos(theta), r * math.sin(theta));","title":""},{"docid":"55ad82d7bb7f69202af17399beea71a4","score":"0.588418","text":"def __call__(self, particle, rand):\n\n # TODO: once numpypy supports the numpy.random module, use it instead.\n r1 = rand_uniform(0, self.phi1, self.dims, rand)\n r2 = rand_uniform(0, self.phi2, self.dims, rand)\n\n grel = particle.nbestpos - particle.pos\n prel = particle.pbestpos - particle.pos\n newvel = self.const * (particle.vel + grel*r1 + prel*r2)\n\n if self.restrictvel:\n self.vcube.constrain_vec(newvel)\n\n return particle.pos + newvel, newvel","title":""},{"docid":"ef28495f0dc2e6ac2b26cc8306846a09","score":"0.58346266","text":"def circle(center,radius,res=0.01,filled=True):\n numdivs = int(math.ceil(radius*math.pi*2/res))\n glNormal3f(0,0,1)\n if filled:\n glBegin(GL_TRIANGLE_FAN)\n glVertex2f(*center)\n else:\n glBegin(GL_LINE_STRIP)\n for i in xrange(numdivs+1):\n u = float(i)/float(numdivs)*math.pi*2\n glVertex2f(center[0]+radius*math.cos(u),center[1]+radius*math.sin(u))\n glEnd()","title":""},{"docid":"6136b550b7e7acee0aec8e72aa40adda","score":"0.58265775","text":"def make_random_points(min_corner, max_corner, num_points):\n xmin, ymin = min_corner\n xmax, ymax = max_corner\n return ((uniform(xmin, xmax), uniform(ymin, ymax)) \\\n for i in xrange(num_points))","title":""},{"docid":"15e79ef91c26f2955274f1c3f7bf6374","score":"0.5822397","text":"def makeCircle(cx,cy,r):\n # just the clockwise circumference starting at (-r,0)\n # using r^2=x^2+y^2\n # create the presized list and fill it in from both ends\n\n xyList=[]\n\n r=int(round(r,0))\n\n # do -90 to +90\n for x in range(-r,+r):\n y=int(round(math.sqrt(r*r-x*x),0))\n xyList.append((cx+x,cy-y))\n # now do +90 to -90\n for x in range(+r,-r,-1):\n y=int(round(math.sqrt(r*r-x*x),0))\n xyList.append((cx+x,cy+y))\n return xyList","title":""},{"docid":"a8ecf6307f1e02ff1b3abfa35b12778a","score":"0.58202535","text":"def random_element(self):\n while True:\n v = self.vector_space().random_element()\n try:\n g1 = self.reflection(v)\n break\n except ValueError: # v has norm zero\n pass\n g2 = self.translation(self.vector_space().random_element())\n return g1 * g2","title":""},{"docid":"91e0e87e51b28ffff43b4534959c8c34","score":"0.58189756","text":"def get_coord_circle (arg,x_center,y_center,radius):\n x = x_center+radius*np.cos(arg)\n y = y_center+radius*np.sin(arg)\n return np.concatenate((x.reshape((1,-1)),y.reshape((1,-1))))","title":""},{"docid":"28cba6849ce0b73c1b0a708ea4955916","score":"0.5816769","text":"def circle_points(center: np.ndarray, radius: float, steps: int = 60, endpoint: bool = False) -> np.ndarray:\n points = [\n [center[0] + np.cos(angle) * radius, center[1] + np.sin(angle) * radius]\n for angle in np.linspace(0, 2 * np.pi, steps, endpoint=endpoint)]\n return np.array(points).reshape(-1, 2)","title":""},{"docid":"45972f6497e062b097865b16b28b69f2","score":"0.58116984","text":"def random_point(n, low=-10, high=10):\n return np.random.uniform(low=low, high=high, size=(n))","title":""},{"docid":"d9e7c454b6ebb664553ec234a524f648","score":"0.581134","text":"def get_uniform_sphere(size,\n lon_lim=[0., 360]*u.deg,\n lat_lim=[-90., 90]*u.deg,\n dist_lim=[0, 1.]*u.one,\n random_state=None):\n if random_state is None:\n random_state = np.random\n\n rep = get_uniform_spherical_angles(size=size,\n lon_lim=lon_lim,\n lat_lim=lat_lim,\n random_state=random_state)\n\n # R distributed as R^2\n r = np.cbrt(random_state.uniform(dist_lim[0].value**3,\n dist_lim[1].value**3,\n size=size)) * dist_lim.unit\n\n return coord.SphericalRepresentation(lon=rep.lon,\n lat=rep.lat,\n distance=r)","title":""},{"docid":"f40fc0bba0a732045a17bfaec900eede","score":"0.5787312","text":"def getRandomPosition(self):\n # TODO: Your code goes here\n return Position(random.uniform(0,self.width), random.uniform(0,self.height))","title":""},{"docid":"72b72093c33af9b1e7130732b8a74ea6","score":"0.57861334","text":"def circle(t, r):\n print \"circle radius:\", r\n arc(t, r, 360)","title":""},{"docid":"cf9054fef151d37dba858d2578df772c","score":"0.5782994","text":"def getRandomPosition(self):\n x_coord = round(random.uniform(0, self.getWidth()-0.01), 2)\n y_coord = round(random.uniform(0, self.getHeight()-0.01), 2)\n return Position(x_coord, y_coord)","title":""},{"docid":"86e821588b4bd0262b98105443c42bdf","score":"0.578286","text":"def AddCircle(self, psi,center,normal,r,d) :\n rx = self.px - center[0]\n ry = self.py - center[1]\n rz = self.pz - center[2]\n #normal = normal/norm(normal,2) #matlab calls for the 2 norm\n normal = normal/np.linalg.norm(normal) #linalg gives the 2-norm by default\n alpha = np.zeros_like(rx)\n z = rx*normal[0] + ry*normal[1] + rz*normal[2]\n inCylinder = (rx**2+ry**2+rz**2 - z**2) < r**2\n inLayerP = (z> 0) & (z<= d/2) & inCylinder\n inLayerM = (z<=0) & (z>=-d/2) & inCylinder\n alpha[inLayerP] = -pi*(2*z[inLayerP]/d - 1.)\n alpha[inLayerM] = -pi*(2*z[inLayerM]/d + 1.)\n #psi = np.multiply( psi , exp(1j*alpha) )\n psi = psi * exp(1j*alpha)\n return psi","title":""},{"docid":"72cb90c90681b3143ba48427f11424c5","score":"0.5758467","text":"def random_three_vector():\n phi = np.random.uniform(0,np.pi*2)\n costheta = np.random.uniform(-1,1)\n\n theta = np.arccos( costheta )\n x = np.sin( theta) * np.cos( phi )\n y = np.sin( theta) * np.sin( phi )\n z = np.cos( theta )\n return (x,y,z)","title":""},{"docid":"d724fa899cbe46dcefe7ff36bcbdeced","score":"0.5757415","text":"def define_circle(p1, p2, p3):\n temp = p2[0] * p2[0] + p2[1] * p2[1]\n bc = (p1[0] * p1[0] + p1[1] * p1[1] - temp) / 2\n cd = (temp - p3[0] * p3[0] - p3[1] * p3[1]) / 2\n det = (p1[0] - p2[0]) * (p2[1] - p3[1]) - (p2[0] - p3[0]) * (p1[1] - p2[1])\n\n if abs(det) < 1.0e-6:\n return (None, np.inf)\n\n # Center of circle\n cx = (bc * (p2[1] - p3[1]) - cd * (p1[1] - p2[1])) / det\n cy = ((p1[0] - p2[0]) * cd - (p2[0] - p3[0]) * bc) / det\n\n radius = np.sqrt((cx - p1[0])**2 + (cy - p1[1])**2)\n return ((cx, cy), radius)","title":""}],"string":"[\n {\n \"docid\": \"5d91a770b378eed66a786ea00913c8e5\",\n \"score\": \"0.7678962\",\n \"text\": \"def generate_random_circle():\\n\\n exp_range = numpy.random.exponential(scale=0.1, size=100)\\n d = cumsum(exp_range[exp_range > 0.05])\\n t = d[d < 2*pi]\\n\\n x = sin(t)\\n y = cos(t)\\n\\n return t, x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429655725f5b2782a097d8d6cc0074fa\",\n \"score\": \"0.767058\",\n \"text\": \"def random_circle(bound_x, bound_y):\\n radius = random.randint(100, 1000)\\n cx = random.randint(MARGIN, bound_x - radius)\\n cy = random.randint(MARGIN, bound_y - radius)\\n steps = 360.0 / random.randint(3, 12)\\n return circle(cx, cy, radius, arc=steps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748ca80da8e984cc952b2b4ae86688c5\",\n \"score\": \"0.7482401\",\n \"text\": \"def random_point(object_radius=32):\\n frame = object_radius // 2 + 1\\n x = random.randint(frame, constants.field_width - frame)\\n y = random.randint(frame, constants.field_height - frame)\\n return geometry.Point(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eaf4010fc33735206649d367114dca5\",\n \"score\": \"0.7469423\",\n \"text\": \"def random_point_inside(self):\\n z = self.maxdist*np.random.random()\\n delta_x = self.cam_model.getDeltaX(self.cam_model.width/2, z)\\n delta_y = self.cam_model.getDeltaY(self.cam_model.height/2, z)\\n point = np.array([0, 0, z, 1])\\n point[:2] = np.array([delta_x,delta_y]) * (2*np.random.random_sample(2)-1.)\\n random_point = np.dot(self.transform, point)[:3]\\n return random_point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112654677e370c96a6d869ac66c992a9\",\n \"score\": \"0.7429767\",\n \"text\": \"def random_direction_vector (mag=1.0):\\n\\n #The objective here is to generate a point in a random direction on a sphere,\\n #so that any region of the sphere of a given area is expected to contain the\\n #same number of randomly generated points. The algorithm is to generate two\\n #random numbers on [0,1], u and v. Then\\n #\\n # RA = 2 * pi * u [range 0-2pi]\\n # DEC = asin(2v - 1) [range -pi - pi]\\n #\\n #This algorithm is taken from http://mathworld.wolfram.com/SpherePointPicking.html.\\n #That article uses spherical coordinates THETA and PHI, where THETA is the\\n #azimuthal angle (equivalent to RA) and PHI is the zenith angle. The conversion\\n #between zenith angle and declination is PHI = PI/2 - DEC, so cos PHI = sin DEC.\\n #Therefore, the acos in the Wolfram formula is replaced by asin here.\\n #\\n #Also note that the solid angle of an annulus on the celestial sphere scales as\\n #sin DEC_MAX - sin DEC_MIN (a result which can easily be proved by integrating over the sphere),\\n #which is consistent with this formula.\\n \\n u = random.random()\\n v = random.random()\\n ra = 2 * pi * u\\n dec = math2.asin2(2*v - 1)\\n vec = Vector()\\n vec.set_xyz(ra, dec) #convert to Cartesian coordinates\\n vec *= mag #scale by magnitude\\n return(vec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538f2ae966be8ec8e9059536cc7ee434\",\n \"score\": \"0.7360734\",\n \"text\": \"def random_circle ():\\n cx = random.randint(8, constants.MAX_X-8) \\n cy = random.randint(8, constants.MAX_Y-8) \\n\\n max_r = min(get_max_x(cx), get_max_y(cy))\\n r = random.randint(8, max_r)\\n\\n border = random.choice(constants.BORDER_COLORS)\\n body = random.choice(constants.COLORS)\\n\\n return make_circle(cx, cy, r, border, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269d13d0c80780e1dac9d53a18c943ce\",\n \"score\": \"0.7290372\",\n \"text\": \"def create_random_point(x0,y0,distance): \\n r = distance/ 111300\\n u = np.random.uniform(0,1)\\n v = np.random.uniform(0,1)\\n w = r * np.sqrt(u)\\n t = 2 * np.pi * v\\n x = w * np.cos(t)\\n x1 = x / np.cos(y0)\\n y = w * np.sin(t)\\n return (x0+x1, y0 +y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2053d4abe7d7eebfd0afb5d490543f04\",\n \"score\": \"0.71617085\",\n \"text\": \"def create_random_point(x0,y0,distance=500): \\n r = distance/ 111300\\n u = np.random.uniform(0,1)\\n v = np.random.uniform(0,1)\\n w = r * np.sqrt(u)\\n t = 2 * np.pi * v\\n x = w * np.cos(t)\\n x1 = x / np.cos(y0)\\n y = w * np.sin(t)\\n return (x0+x1, y0 +y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7de7abda49a611b34745d9476e7e6e\",\n \"score\": \"0.7159202\",\n \"text\": \"def generate_position(self): \\n return sqrt(np.random.random()), np.random.random() * 2 * pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d065e16b3de71cca2b17a63e2a7b1edf\",\n \"score\": \"0.707648\",\n \"text\": \"def create_random_point(x0,y0,z0,distance): \\n \\n r = distance\\n u = np.random.uniform(0,1)\\n v = np.random.uniform(0,1)\\n w = r * np.sqrt(u)\\n t = 2 * np.pi * v\\n x = w * np.cos(t)\\n x1 = x / np.cos(y0)\\n y1 = w * np.sin(t)\\n \\n return [x0+x1, y0 +y1, z0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dad05dad252810a163b1e31be60eb35\",\n \"score\": \"0.70186436\",\n \"text\": \"def random_points_on_sphere(radius,\\n num,\\n rng):\\n coords = jax.random.normal(rng, shape=(num, 3))\\n magnitude = jnp.sqrt(jnp.sum(jnp.square(coords), axis=-1, keepdims=True))\\n coords /= magnitude\\n coords *= radius\\n return coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8685d05ed574385ef6f70339827611b8\",\n \"score\": \"0.69379926\",\n \"text\": \"def random_in_unit_sphere():\\n\\twhile True:\\n\\t\\tp = Vec3(random.random(), random.random(), random.random()) * 2 - Vec3(1, 1, 1)\\n\\t\\tif p.length_squared() < 1:\\n\\t\\t\\treturn p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016148ceef0491758170719aa7f195ca\",\n \"score\": \"0.6931069\",\n \"text\": \"def uniform_sample_circle(radius, num_samples, normal=np.array([0., 0., 1.])):\\n theta = np.random.rand(num_samples) * 2 * np.pi\\n return radius * np.stack((np.cos(theta), np.sin(theta),\\n np.zeros_like(theta)), axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84a681a41b29be030f6badaf3c2f826\",\n \"score\": \"0.690921\",\n \"text\": \"def generate_circle(r=1.0, s=0.01):\\n\\n t = arange(0, 2*pi, s)\\n x = sin(t)*r\\n y = cos(t)*r\\n\\n return t, x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f7c08c066ec1bf576257fd9fe52883\",\n \"score\": \"0.6886885\",\n \"text\": \"def generate_circle(position=[50,50], radius=10):\\n \\n position = np.array([50,50])\\n circle = position+ np.array([[radius*np.cos(x), radius*np.sin(x)] for x in np.arange(0,2*np.pi, 0.01)])\\n\\n return circle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be26570aab2692d7e89d8746a30f050e\",\n \"score\": \"0.66828287\",\n \"text\": \"def random_point():\\n lat = random.uniform(-90, 90)\\n lgt = random.uniform(-180, 180)\\n return (lat, lgt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f561a3b73bd89876550ee112c38266ac\",\n \"score\": \"0.66455334\",\n \"text\": \"def circle(radius):\\n return PI * radius * radius\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81958b6431ec98e11eeb47caae3d35f\",\n \"score\": \"0.6628533\",\n \"text\": \"def generate_position(self): \\n return np.random.power(4), np.random.random() * 2 * pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"315fd7fd5a10846417d891b6f2240bc8\",\n \"score\": \"0.65938413\",\n \"text\": \"def getRandomPoint(self):\\n a = random.uniform(0, 1)\\n b = random.uniform(0, 1)\\n px = (1.0 - sqrt(a)) * self.p1.x + \\\\\\n (sqrt(a) * (1.0 - b)) * self.p2.x + \\\\\\n (sqrt(a) * b) * self.p3.x\\n py = (1.0 - sqrt(a)) * self.p1.y + \\\\\\n (sqrt(a) * (1.0 - b)) * self.p2.y + \\\\\\n (sqrt(a) * b) * self.p3.y\\n pz = (1.0 - sqrt(a)) * self.p1.z + \\\\\\n (sqrt(a) * (1.0 - b)) * self.p2.z + \\\\\\n (sqrt(a) * b) * self.p3.z\\n return Point(px, py, pz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f3a0a0fbdeceb770a72a11d14cf34d\",\n \"score\": \"0.6590845\",\n \"text\": \"def get_random_direction():\\n phi = np.random.uniform(0, np.pi * 2)\\n costheta = np.random.uniform(-1, 1)\\n\\n theta = np.arccos(costheta)\\n x = np.sin(theta) * np.cos(phi)\\n y = np.sin(theta) * np.sin(phi)\\n z = np.cos(theta)\\n return np.array([x, y, z])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5509d5f5a540ec75fe57a89e8801c07\",\n \"score\": \"0.65896124\",\n \"text\": \"def CreateCircle(Point,Normal,Radius):\\n Plane=rs.PlaneFromNormal(Point,Normal)\\n Circle=rs.AddCircle(Plane,Radius)\\n return Circle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf9a9b4e622682c1d6bf8d995563ffe\",\n \"score\": \"0.65574086\",\n \"text\": \"def get_arc_point(self, ang=0, r=1): \\r\\n return Point(x=self.x + cos(ang) * r, \\\\\\r\\n y=self.y + sin(ang) * r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e571ad9dc8b0fa0afa28e94055a3af4f\",\n \"score\": \"0.6514502\",\n \"text\": \"def random_point(self):\\n from random import randint\\n from sys import maxint\\n\\n # The lower and upper\\n lower, upper = -maxint-1, maxint\\n\\n if self.slope is S.Infinity:\\n if isinstance(self, Ray):\\n if self.ydirection is S.Infinity:\\n lower = self.p1[1]\\n else:\\n upper = self.p1[1]\\n elif isinstance(self, Segment):\\n lower = self.p1[1]\\n upper = self.p2[1]\\n\\n x = self.p1[0]\\n y = randint(lower, upper)\\n else:\\n if isinstance(self, Ray):\\n if self.xdirection is S.Infinity:\\n lower = self.p1[0]\\n else:\\n upper = self.p1[0]\\n elif isinstance(self, Segment):\\n lower = self.p1[0]\\n upper = self.p2[0]\\n\\n a,b,c = self.coefficients\\n x = randint(lower, upper)\\n y = simplify( (-c - a*x) / b )\\n return Point(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7918855abdf6ed8797b55551b3b11761\",\n \"score\": \"0.651013\",\n \"text\": \"def random_points_in_sphere(radius,\\n num,\\n rng):\\n coords_rng, scale_rng = jax.random.split(rng)\\n\\n coords = random_points_on_sphere(radius, num, coords_rng)\\n scale = jax.random.uniform(scale_rng, shape=(num,))\\n scale = jnp.cbrt(scale)\\n coords *= scale[:, jnp.newaxis]\\n return coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad9f49b691a632e51ec242794307ecb4\",\n \"score\": \"0.64982957\",\n \"text\": \"def random_3d_point(r=1):\\n r = rnd.random() * r\\n phi = rnd.random() * 2 * np.pi\\n theta = rnd.random() * 2 * np.pi\\n\\n x = r * sin(phi) * cos(theta)\\n y = r * sin(phi) * sin(theta)\\n z = r * cos(phi)\\n\\n return np.array([x, y, z])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99375f8571f3c14d9fffeb72b649c070\",\n \"score\": \"0.646699\",\n \"text\": \"def random_point():\\n return Decimal(uniform(-1, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6243ce084b7361004b892ce8b23ff2\",\n \"score\": \"0.6443925\",\n \"text\": \"def makepoint (xmin, xmax, ymin, ymax):\\n x = random.uniform(xmin, xmax)\\n y = random.uniform(ymin, ymax)\\n p = Point(x,y)\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4b940621bbb9d37003c6cb35800862\",\n \"score\": \"0.64433503\",\n \"text\": \"def create_circle(x_center, y_center, radius):\\r\\n cv.create_oval(x_center-radius, y_center-radius, x_center+radius, y_center+radius, outline = 'blue', width = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b2066220d166f38cfb21de7dfbc8f3\",\n \"score\": \"0.64359844\",\n \"text\": \"def random_unit_vector():\\n\\n\\n phi = np.random.uniform(0,np.pi*2)\\n costheta = np.random.uniform(-1,1)\\n\\n theta = np.arccos( costheta )\\n x = np.sin( theta) * np.cos( phi )\\n y = np.sin( theta) * np.sin( phi )\\n z = np.cos( theta )\\n vect = (x,y,z)\\n vect = np.asarray(vect, dtype=np.float32)\\n return vect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5871515a72487f2b552bdd3444244abe\",\n \"score\": \"0.64317334\",\n \"text\": \"def draw_circle(point, radius):\\n for i in xrange(0, 360):\\n x = point[0] + int(radius * cos(pi / 180 * i))\\n y = point[1] + int(radius * sin(pi / 180 * i))\\n yield (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f206a7c7effef07593ebd4790e152800\",\n \"score\": \"0.6388496\",\n \"text\": \"def randPosition():\\n x = random.randint(20, Engine.window_width - 20)\\n y = random.randint(20, Engine.window_height - 20)\\n \\n return Vec2d(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dde00ebb9a39d24f813f93906626ebf\",\n \"score\": \"0.6305905\",\n \"text\": \"def make_random_coordinate():\\n return make_coordinate(randint(0, 100), randint(0, 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5ad31883991f56ed1cd93d86490820\",\n \"score\": \"0.6301807\",\n \"text\": \"def get_random_point(self) -> Point:\\n an_x = random_between(self.right, self.left)\\n a_y = random_between(self.top, self.bottom)\\n return Point(x = an_x, y = a_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c23efd0056ff4c15a5c5c67c33a51e1b\",\n \"score\": \"0.6301125\",\n \"text\": \"def make_circle(N=64, center=np.array([32, 32]), radius=20):\\n XX, YY = np.meshgrid(np.arange(N), np.arange(N))\\n XY = np.stack([XX, YY], axis=-1)\\n val = np.linalg.norm(XY - center[np.newaxis, np.newaxis, :], axis=2)\\n result = np.zeros((N, N))\\n result += 1. * (val < radius)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f2ea7b1c18aee6a0e546907c3d9a1a\",\n \"score\": \"0.6283758\",\n \"text\": \"def gen_polar_direc(N=1):\\n import numpy.random.random_sample\\n import numpy.pi\\n out = np.zeros((N,2),dtype=float)\\n for i in range(N):\\n out[i,0] = (2*pi)*random_sample()\\n out[i,1] = (pi)*random_sample()\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94dc90ba8c7959b1ceb6bd9384fb2db2\",\n \"score\": \"0.62657225\",\n \"text\": \"def truecircle(radius, r):\\n if radius == 0:\\n return np.zeros_like(r)\\n else:\\n samples = r.shape[0]\\n one_pixel = 2 / samples\\n radius_plus = radius + (one_pixel / 2)\\n intermediate = (radius_plus - r) * (samples / 2)\\n return np.minimum(np.maximum(intermediate, 0), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3ceb9cf535fa11aa0fd1f6158fb3b3\",\n \"score\": \"0.62604505\",\n \"text\": \"def random_unit_vector():\\n r = np.random.randn(2)\\n return r/np.sqrt(np.sum(r**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d82fc90c13d0e7a77534f94e40c888b\",\n \"score\": \"0.62464833\",\n \"text\": \"def random_upto(self):\\r\\n return Point(random.randint(0,self.x),random.randint(0,self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf545afa4988aa6fedd64368247dda37\",\n \"score\": \"0.6205509\",\n \"text\": \"def circle_points(self, num_pts, radius=1):\\n t = np.linspace(0, 2*np.pi, num_pts, endpoint=False)\\n x = radius * np.cos(t)\\n y = radius * np.sin(t)\\n \\n self.template_points = np.c_[x, y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668416a4db69e21909472287863b32c4\",\n \"score\": \"0.62023145\",\n \"text\": \"def Circle(radius=1.0, center=(0.0,0.0), points=32):\\n p = []\\n for i in range(points):\\n a = 2.0*pi*float(i)/points\\n p.append((center[0]+radius*sin(a), center[1]+radius*cos(a)))\\n return Polygon(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b3d77e53da012a5f8fbdab4c55cba3f\",\n \"score\": \"0.6191863\",\n \"text\": \"def randomize_direction(self, polar_options):\\n scalar = float(random.choice(polar_options))/100.\\n angle = scalar*2*pi\\n vx = self.speed*cos(angle)\\n vy = self.speed*sin(angle)\\n self.velocity = (vx, vy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5765dd76b3b8fc6a55fa8997f98ab4d6\",\n \"score\": \"0.6157601\",\n \"text\": \"def point_on_circle(angle, radius):\\n x = sin(radians(angle)) * radius\\n y = cos(radians(angle)) * radius\\n return x,y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716e6479c2fa4a1fea7b35ae35d1e69c\",\n \"score\": \"0.6125484\",\n \"text\": \"def _point_from_polar(r, phi, tx, ty):\\n x, y = pol2cart(r, phi)\\n return Point(x=x + tx, y=y + ty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f39c108d446cf60a04fe5030a91af6\",\n \"score\": \"0.611002\",\n \"text\": \"def find_pi(n):\\n inside=0\\n\\n for i in range(0,n):\\n x=random()\\n y=random()\\n if (x*x+y*y)**(0.5)<=1: # if i falls inside the circle\\n inside+=1\\n\\n pi=4*inside/n\\n return pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d2996ebf51f3ac187881cef39743fe\",\n \"score\": \"0.60941553\",\n \"text\": \"def _random_sphere(m, n, r):\\n a = np.random.randn(m, n)\\n s2 = np.sum(a**2, axis=1)\\n base = gammainc(n/2, s2/2)**(1/n) * r / np.sqrt(s2)\\n a = a * (np.tile(base, (n, 1))).T\\n\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cb59f4da85ac3d87ab66088af60c25\",\n \"score\": \"0.60882926\",\n \"text\": \"def _build_filled_circle_xy_vbo(self, radius):\\n v = [0., 0., 0.]\\n for angle in range(0, 361, 5):\\n v.extend([radius * sin(radians(angle)), radius * cos(radians(angle)), 0.])\\n return vbo.VBO(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1485ce2106a522d8a1a9dc472bb3da6b\",\n \"score\": \"0.60827416\",\n \"text\": \"def random_unit():\\r\\n vec = Vector2D(randint(-10000, 10000), randint(-10000, 10000))\\r\\n vec.normalise()\\r\\n return vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5449c8653f73fe59d88ef01f5d3427f8\",\n \"score\": \"0.60821027\",\n \"text\": \"def point_to_circle(point, center, radius, normal, epsilon=1e-6):\\n # signed distance from point to plane of circle\\n diff = point - center\\n dist_to_plane = diff.dot(normal)\\n\\n # projection of P - C onto plane is Q - C = P - C - dist_to_plane * normal\\n diff_in_plane = diff - dist_to_plane * normal\\n sqr_len = diff_in_plane.dot(diff_in_plane)\\n\\n if sqr_len >= epsilon:\\n closest_point_circle = (\\n center + (radius / math.sqrt(sqr_len)) * diff_in_plane)\\n dist = np.linalg.norm(point - closest_point_circle)\\n else: # on the line defined by center and normal of the circle\\n plane_direction = norm_vector(pr.perpendicular_to_vector(normal))\\n closest_point_circle = center + radius * plane_direction\\n dist = math.sqrt(radius * radius + dist_to_plane * dist_to_plane)\\n\\n return dist, closest_point_circle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c8af9c54956b02b8e53de5864faa9b\",\n \"score\": \"0.60750455\",\n \"text\": \"def generateRandomPoint(self, fromPoint=(), maxdistance=0):\\n if fromPoint and maxdistance:\\n offset_x = cbrandom.randint(-maxdistance,maxdistance)\\n offset_y = cbrandom.randint(-maxdistance,maxdistance)\\n startX = fromPoint[0] - maxdistance\\n endX = fromPoint[0] + maxdistance\\n startY = fromPoint[1] - maxdistance\\n endY = fromPoint[1] + maxdistance\\n else:\\n # If one of the not required param is missing, always use the normal feature\\n startX = 1\\n endX = self.levelSize[0]-1\\n startY = 1\\n endY = self.levelSize[1]-1\\n return self.normalizePointOnLevel(cbrandom.randint(startX,endX), cbrandom.randint(startY,endY))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a35a366a05251201a7bba76f8ef4e4e\",\n \"score\": \"0.6074381\",\n \"text\": \"def generate_sphere_random_sampling(vertex_number=100, radius=1.0):\\n coords = np.zeros((vertex_number, 3))\\n for i in range(vertex_number):\\n M = np.random.normal(size=(3, 3))\\n Q, R = np.linalg.qr(M)\\n coords[i, :] = Q[:, 0].transpose() * np.sign(R[0, 0])\\n if radius != 1:\\n coords = radius * coords\\n return tri_from_hull(coords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2470a7f55d09f6df047659d4632208a0\",\n \"score\": \"0.60735345\",\n \"text\": \"def circle(a):\\n return math.pi*a*a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a76954854cfb24cd3c354c84e708a98\",\n \"score\": \"0.60680497\",\n \"text\": \"def offset_circle(radius, x, y, center):\\n x, y = optimize_xy_separable(x, y)\\n # no in-place ops, x, y are shared memory\\n x = x - center[0]\\n y = y - center[1]\\n # not cart to polar; computing theta is waste work\\n r = np.hypot(x, y)\\n return circle(radius, r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44798cc94cda367551234e5eef61d1c0\",\n \"score\": \"0.60585016\",\n \"text\": \"def estimate_pi():\\n circle_points = 0\\n square_points = 0\\n result = 0\\n\\n while square_points < 1000:\\n x = random.uniform(-1, 1)\\n y = random.uniform(-1, 1)\\n\\n if (x**2 + y**2) <= 1:\\n circle_points += 1\\n square_points += 1\\n\\n result = 4 * circle_points / square_points\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e388b0b222e0c82ccc15a72b0a0a0a\",\n \"score\": \"0.605186\",\n \"text\": \"def render_target(radius=4):\\n t = np.zeros([radius*2,radius*2])\\n center = np.array([radius-.5,radius-.5])\\n for i in range(radius*2):\\n for j in range(radius*2):\\n distance = np.abs(np.linalg.norm(center-np.array([i,j])))\\n t[i,j] = np.clip((radius-distance)/radius,0,1)\\n t[radius,radius] = 0\\n t[radius-1,radius] = 0\\n t[radius,radius-1] = 0\\n t[radius-1,radius-1] = 0\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3c8d106a8c84f9600c8657ec9421e6\",\n \"score\": \"0.6050687\",\n \"text\": \"def monte_carlo(n_samples, radius = 1):\\n\\tin_circle_count = 0\\n\\tfor i in range(n_samples):\\n\\t\\t\\n\\t\\t# Sample x, y from the uniform distribution\\n\\t\\tx = random.uniform(0, radius)\\n\\t\\ty = random.uniform(0, radius)\\n\\t\\t\\n\\t\\t# Count the number of points inside the circle\\n\\t\\tif(in_circle(x, y, radius)):\\n\\t\\t\\tin_circle_count += 1\\n\\n\\t# Since we've generated points in upper right quadrant ([0,radius], [0, radius])\\n\\t# We need to multiply the number of points by 4\\t\\n\\tpi_estimate = 4 * in_circle_count / (n_samples)\\n\\n\\treturn pi_estimate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8d0c6067d3184cdc69ec1d1eda918f\",\n \"score\": \"0.604494\",\n \"text\": \"def random_angle():\\n angle = random.random() * 2 * math.pi\\n return angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec7566b2f64e75652e908b1e19ee252\",\n \"score\": \"0.6036802\",\n \"text\": \"def uniform():\\n def gen(latent_start=None):\\n while True:\\n x, y, t = _rand(), _rand(), _rand()*np.pi\\n yield makeTransform(t,x,y), [x,y, math.cos(t), math.sin(t)]\\n\\n return gen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7780a0a5eb924b83ca749ae023ba51\",\n \"score\": \"0.60340804\",\n \"text\": \"def Circle(self,center, rad):\\n return mshr.Circle(center, rad)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3caf06d58563340adc48221df0a14c4\",\n \"score\": \"0.6031237\",\n \"text\": \"def create_circle(self, x, y, radius, canvas, **kwargs):\\n x_1 = x - radius\\n y_1 = y - radius\\n x_2 = x + radius\\n y_2 = y + radius\\n return canvas.create_oval(x_1, y_1, x_2, y_2, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72861b8ad488d018df319fa851f81481\",\n \"score\": \"0.6028996\",\n \"text\": \"def pnt_circle(pnt, norm_dir):\\n\\n if norm_dir == 0:\\n return pnt_circle_x(pnt)\\n elif norm_dir == 1:\\n return pnt_circle_y(pnt)\\n elif norm_dir == 2:\\n return pnt_circle_z(pnt)\\n else:\\n raise ValueError('Invalid norm direction.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ef19bfa30587160d7c88bc7e7703ac\",\n \"score\": \"0.6021416\",\n \"text\": \"def generate_coord(N):\\r\\n v = np.ones(N)\\r\\n list_spin = [-1,1]\\r\\n for i in range(N):\\r\\n v[i] = np.random.choice(list_spin)\\r\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18aac808c201743815cba8516dcddeb\",\n \"score\": \"0.6016708\",\n \"text\": \"def gen_circle_path(self, radius, points, center):\\n self.x_radius = radius[0]\\n self.y_radius = radius[1]\\n self.x_center = center[0]\\n self.y_center = center[1]\\n\\n self.x_radius_variable.set(self.x_radius)\\n self.y_radius_variable.set(self.y_radius)\\n self.x_center_variable.set(self.x_center)\\n self.y_center_variable.set(self.y_center)\\n\\n self.pt.gen_circle_path(radius, points, center)\\n self.canvas.delete(self.PATH_TAG)\\n self._draw_path()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815e3c28c5ae4893de6dd04b12716b8b\",\n \"score\": \"0.6011167\",\n \"text\": \"def circle(x, y, r):\\n global _surface\\n\\n ws = stddraw._factorX(2*r)\\n hs = stddraw._factorY(2*r)\\n if (ws <= 1) and (hs <= 1):\\n stddraw._pixel(x, y)\\n else:\\n xs = stddraw._scaleX(x)\\n ys = stddraw._scaleY(y)\\n pygame.draw.ellipse(_surface,\\n stddraw.pygameColor(stddraw.BLACK),\\n pygame.Rect(xs-ws/2, ys-hs/2, ws, hs), 1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c78d57f42036035312b0d662c835e50b\",\n \"score\": \"0.5998429\",\n \"text\": \"def circle(t, r):\\n\\tarc(t, r, 360)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f62032318964c914396b7c333650115\",\n \"score\": \"0.59951895\",\n \"text\": \"def pick_point(self):\\n x, y = 0.0, 0.0\\n\\n MAX_ITTERATIONS = 1000 # Protect against unpopulated areas causing infinite spin.\\n for _ in range(MAX_ITTERATIONS):\\n if self.restrict_to_area:\\n radius = randint(0, int(self.area_radius_pixels + 0.5))\\n angle = random() * 2 * math.pi\\n ox = math.sin(angle) * radius\\n oy = math.cos(angle) * radius\\n x = int(self.area_centre_xy[0] + ox)\\n y = int(self.area_centre_xy[1] + oy)\\n else:\\n x, y = randint(0, self.map_x_limit - 1), randint(0, self.map_y_limit - 1)\\n p = (self.population_density[y][x] / 255.0)\\n if p > random():\\n break\\n\\n # Dither our pixels, otherwise all points will be on pixel grid\\n x += random()\\n y += random()\\n\\n return self.xy_to_lon_lat((x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b802244b33d32cf2e6493825259977\",\n \"score\": \"0.5970713\",\n \"text\": \"def __init__(self):\\n self.x = randint(0, 15)\\n self.y = randint(0, 15)\\n self.pos = Vector2(self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cc57a090df063506787932f29712ad\",\n \"score\": \"0.59686905\",\n \"text\": \"def _sample_pose_circ(rng, params, constraints):\\n for _ in range(1000):\\n r = (params.inner_w + params.inner_h) / 4. + params.mid_margin\\n r = min(r, params.lim_euc_dist)\\n\\n phi = rng.uniform(0, 2*np.pi)\\n\\n x, y = pol2cart(r, phi)\\n theta = rng.uniform(0, 2*np.pi)\\n # theta_e = rng.uniform(0, 2*np.pi)\\n\\n # while np.abs(theta - theta_e) >= params.lim_ang_dist:\\n # theta_e = rng.uniform(0, 2*np.pi)\\n\\n start_pt = OrientedPoint(x, y, theta)\\n end_pt = OrientedPoint(-x, -y, theta)\\n\\n y_off = end_pt.y - start_pt.y\\n x_off = end_pt.x - start_pt.x\\n new_theta = np.arctan2(y_off, x_off)\\n\\n start_pt = OrientedPoint(x, y, new_theta)\\n end_pt = OrientedPoint(-x, -y, new_theta)\\n\\n if _satisfies_constraints(start_pt, constraints) and \\\\\\n _satisfies_constraints(end_pt, constraints):\\n return start_pt, end_pt\\n\\n raise SpaceSeemsEmptyError(\\\"Something went wrong, the sampling space looks empty.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c056587bd607b43127dda183036e7e\",\n \"score\": \"0.59610903\",\n \"text\": \"def rand_point (area : pygame.Rect) :\\n return [random.randint(area.left, area.right), random.randint(area.top, area.bottom)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4ace9b2376d8fc22c41263eadd0fd4\",\n \"score\": \"0.59573126\",\n \"text\": \"def circle(t, r):\\r\\n arc(t, r, 360)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadd778a036de9fcf7e5e999d39404b7\",\n \"score\": \"0.59525836\",\n \"text\": \"def Random_xy(Rmax,model,Npoints,Nknots,ellipticity,PA):\\n R_random = scale_radius * Random_radius(Rmax/scale_radius,model,Npoints,Nknots)\\n PA_random = 2 * np.pi * np.random.random_sample(Npoints) # in rd\\n theta_random = 2 * np.pi * np.random.random_sample(Npoints) # in rd\\n u_random = R_random * np.cos(theta_random)\\n v_random = R_random * np.sin(theta_random) * (1-ellipticity)\\n x0_random = RA_cen_init + (RA_cen - RA_cen_init) * np.cos(Dec_cen_init * degree) \\n y0_random = Dec_cen\\n x_random = x0_random - u_random*np.sin(PA*degree) - v_random*np.cos(PA*degree)\\n y_random = y0_random + u_random*np.cos(PA*degree) - v_random*np.sin(PA*degree)\\n\\n if verbosity >= 3:\\n print(\\\"IS TRUE : \\\", x0_random == RA_cen_init, y0_random == Dec_cen_init)\\n return(x_random, y_random)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77c74f92e7b680bb7b7fc22cb71b64e\",\n \"score\": \"0.59516895\",\n \"text\": \"def random_ellipse ():\\n cx = random.randint(8, constants.MAX_X-8) \\n cy = random.randint(8, constants.MAX_Y-8) \\n\\n rx = random.randint(8, get_max_x(cx))\\n ry = random.randint(8, get_max_y(cy))\\n\\n border = random.choice(constants.BORDER_COLORS)\\n body = random.choice(constants.COLORS)\\n\\n return make_ellipse(cx, cy, rx, ry, border, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5491f746920673bd013d2b86a0f36c02\",\n \"score\": \"0.5945056\",\n \"text\": \"def randForce():\\n x = random.uniform(-GuiBoid.normal_speed, GuiBoid.normal_speed)\\n y = random.uniform(-GuiBoid.normal_speed, GuiBoid.normal_speed)\\n \\n # For divbyzero in vector calcs\\n if x == 0 and y == 0:\\n return randForce()\\n \\n return Vec2d(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d4951c1dbbc420b1c87bc79818b77a\",\n \"score\": \"0.5944536\",\n \"text\": \"def get_random_x():\\n return round(np.random.uniform(-1, 1), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb94d6d531010f59e5bc9e756b2e168\",\n \"score\": \"0.59416074\",\n \"text\": \"def monte_carlo_pi():\\n inside: int = 0\\n\\n for i in range(1_000_000):\\n x = random()\\n y = random()\\n distance = math.sqrt(pow(abs(0.5 - x), 2) + pow(abs(0.5 - y), 2))\\n if distance <= 0.5:\\n inside += 1\\n\\n print(4 * inside / 1_000_000) # multiply by 4 to account for x < 0 and/or y < 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9742ad15dcf2ef685e1cd5a5af9545c6\",\n \"score\": \"0.59367836\",\n \"text\": \"def sample_circle(c, N=32):\\n \\n assert len(c) == 3\\n \\n # Get x, y and radius\\n x, y, r = c\\n \\n # Sample N points, but add one to close the loop\\n a = np.linspace(0,2*np.pi, N+1)\\n \\n # Prepare array\\n pp = np.empty((len(a), 2), dtype=np.float32)\\n \\n # Apply polar coordinates\\n pp[:,0] = np.cos(a) * r + x\\n pp[:,1] = np.sin(a) * r + y\\n \\n # Return as a pointset\\n return PointSet(pp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"186b668a84b3e037c1793af48ea51431\",\n \"score\": \"0.5932039\",\n \"text\": \"def rand_2D (multi : float = 1, direction = 0) :\\n x = random.choice([-1,1])*random.random()\\n y = random.choice([-1,1])*random.random()\\n vec = [(x*multi)/math.sqrt(x**2 + y**2),(y*multi)/math.sqrt(x**2 + y**2)]\\n if direction == 0 :\\n return vec\\n elif direction == 1 :\\n vec[1] = -abs(vec[1])\\n return vec\\n elif direction == 2 :\\n vec[1] = abs(vec[1])\\n return vec\\n elif direction == 3 :\\n vec[0] = -abs(vec[0])\\n return vec\\n elif direction == 4 :\\n vec[0] = abs(vec[0])\\n return vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bede6819f794fe647fa142907d659b44\",\n \"score\": \"0.5920978\",\n \"text\": \"def getRandomRoomCoordinate(self) -> Vec3:\\n newX = random.uniform(-self.roomSize.x / 2, self.roomSize.x / 2)\\n newY = random.uniform(-self.roomSize.y / 2, self.roomSize.y / 2)\\n newZ = random.uniform(0 + 0.3, self.roomSize.z)\\n return Vec3(newX, newY, newZ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c9fc2cc2ccf8bc908863b13dc6873f\",\n \"score\": \"0.59202343\",\n \"text\": \"def create_sphere():\\n return rt.sphere(radius=5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96a5b424996b2e6db4d9c1124b837fbc\",\n \"score\": \"0.59189826\",\n \"text\": \"def circle(r,n):\\n arc = np.linspace(0,360,n+1)\\n x = np.cos(arc*np.pi/180.)*r\\n y = np.sin(arc*np.pi/180.)*r\\n\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df79dd8df124be6ae2686c7919c2366\",\n \"score\": \"0.5914445\",\n \"text\": \"def circle_points(resolution, center, radius): \\n radians = np.linspace(0, 2*np.pi, resolution)\\n c = center[1] + radius*np.cos(radians)#polar co-ordinates\\n r = center[0] + radius*np.sin(radians)\\n \\n return np.array([c, r]).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524fefbc8c5cf0aa212f3db43b5b6371\",\n \"score\": \"0.59107274\",\n \"text\": \"def RandomPoint():\\n global APP\\n return Point(rand(0, APP['dims']['w']), rand(0, APP['dims']['h']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0282de30eb63663e5baccf6f07956b15\",\n \"score\": \"0.5897744\",\n \"text\": \"def circle(t, r):\\n arc(t, r, 360)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6861fbe22b9ad9cf56556a5cd947a3cd\",\n \"score\": \"0.58918506\",\n \"text\": \"def raffleDestination():\\r\\n raffle = np.random.uniform(0, 1) # choose between city center or city periphery\\r\\n if raffle > 0.5:\\r\\n point = (np.random.uniform(2000, 4000), np.random.uniform(2000, 4000)) # city center\\r\\n else:\\r\\n point = (np.random.uniform(0, 6000), np.random.uniform(0, 6000)) # city periphery\\r\\n while (point[0] <= 4000 and point[0] >= 2000) and (point[1] <= 4000 and point[1] >= 2000): # must be outside of the city center\\r\\n # center\\r\\n point = (np.random.uniform(0, 6000), np.random.uniform(0, 6000))\\r\\n return point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127954187564cf2ad47179b4dcc9dff1\",\n \"score\": \"0.58912385\",\n \"text\": \"def circle(t,r): \\n arc(t,360,r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73aa7408cb6666a7b776f19d91d22ec\",\n \"score\": \"0.58878446\",\n \"text\": \"def from_polar(r, theta):\\n return Vector(r * math.cos(theta), r * math.sin(theta));\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ad82d7bb7f69202af17399beea71a4\",\n \"score\": \"0.588418\",\n \"text\": \"def __call__(self, particle, rand):\\n\\n # TODO: once numpypy supports the numpy.random module, use it instead.\\n r1 = rand_uniform(0, self.phi1, self.dims, rand)\\n r2 = rand_uniform(0, self.phi2, self.dims, rand)\\n\\n grel = particle.nbestpos - particle.pos\\n prel = particle.pbestpos - particle.pos\\n newvel = self.const * (particle.vel + grel*r1 + prel*r2)\\n\\n if self.restrictvel:\\n self.vcube.constrain_vec(newvel)\\n\\n return particle.pos + newvel, newvel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef28495f0dc2e6ac2b26cc8306846a09\",\n \"score\": \"0.58346266\",\n \"text\": \"def circle(center,radius,res=0.01,filled=True):\\n numdivs = int(math.ceil(radius*math.pi*2/res))\\n glNormal3f(0,0,1)\\n if filled:\\n glBegin(GL_TRIANGLE_FAN)\\n glVertex2f(*center)\\n else:\\n glBegin(GL_LINE_STRIP)\\n for i in xrange(numdivs+1):\\n u = float(i)/float(numdivs)*math.pi*2\\n glVertex2f(center[0]+radius*math.cos(u),center[1]+radius*math.sin(u))\\n glEnd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6136b550b7e7acee0aec8e72aa40adda\",\n \"score\": \"0.58265775\",\n \"text\": \"def make_random_points(min_corner, max_corner, num_points):\\n xmin, ymin = min_corner\\n xmax, ymax = max_corner\\n return ((uniform(xmin, xmax), uniform(ymin, ymax)) \\\\\\n for i in xrange(num_points))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e79ef91c26f2955274f1c3f7bf6374\",\n \"score\": \"0.5822397\",\n \"text\": \"def makeCircle(cx,cy,r):\\n # just the clockwise circumference starting at (-r,0)\\n # using r^2=x^2+y^2\\n # create the presized list and fill it in from both ends\\n\\n xyList=[]\\n\\n r=int(round(r,0))\\n\\n # do -90 to +90\\n for x in range(-r,+r):\\n y=int(round(math.sqrt(r*r-x*x),0))\\n xyList.append((cx+x,cy-y))\\n # now do +90 to -90\\n for x in range(+r,-r,-1):\\n y=int(round(math.sqrt(r*r-x*x),0))\\n xyList.append((cx+x,cy+y))\\n return xyList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ecf6307f1e02ff1b3abfa35b12778a\",\n \"score\": \"0.58202535\",\n \"text\": \"def random_element(self):\\n while True:\\n v = self.vector_space().random_element()\\n try:\\n g1 = self.reflection(v)\\n break\\n except ValueError: # v has norm zero\\n pass\\n g2 = self.translation(self.vector_space().random_element())\\n return g1 * g2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e0e87e51b28ffff43b4534959c8c34\",\n \"score\": \"0.58189756\",\n \"text\": \"def get_coord_circle (arg,x_center,y_center,radius):\\n x = x_center+radius*np.cos(arg)\\n y = y_center+radius*np.sin(arg)\\n return np.concatenate((x.reshape((1,-1)),y.reshape((1,-1))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28cba6849ce0b73c1b0a708ea4955916\",\n \"score\": \"0.5816769\",\n \"text\": \"def circle_points(center: np.ndarray, radius: float, steps: int = 60, endpoint: bool = False) -> np.ndarray:\\n points = [\\n [center[0] + np.cos(angle) * radius, center[1] + np.sin(angle) * radius]\\n for angle in np.linspace(0, 2 * np.pi, steps, endpoint=endpoint)]\\n return np.array(points).reshape(-1, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45972f6497e062b097865b16b28b69f2\",\n \"score\": \"0.58116984\",\n \"text\": \"def random_point(n, low=-10, high=10):\\n return np.random.uniform(low=low, high=high, size=(n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9e7c454b6ebb664553ec234a524f648\",\n \"score\": \"0.581134\",\n \"text\": \"def get_uniform_sphere(size,\\n lon_lim=[0., 360]*u.deg,\\n lat_lim=[-90., 90]*u.deg,\\n dist_lim=[0, 1.]*u.one,\\n random_state=None):\\n if random_state is None:\\n random_state = np.random\\n\\n rep = get_uniform_spherical_angles(size=size,\\n lon_lim=lon_lim,\\n lat_lim=lat_lim,\\n random_state=random_state)\\n\\n # R distributed as R^2\\n r = np.cbrt(random_state.uniform(dist_lim[0].value**3,\\n dist_lim[1].value**3,\\n size=size)) * dist_lim.unit\\n\\n return coord.SphericalRepresentation(lon=rep.lon,\\n lat=rep.lat,\\n distance=r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40fc0bba0a732045a17bfaec900eede\",\n \"score\": \"0.5787312\",\n \"text\": \"def getRandomPosition(self):\\n # TODO: Your code goes here\\n return Position(random.uniform(0,self.width), random.uniform(0,self.height))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b72093c33af9b1e7130732b8a74ea6\",\n \"score\": \"0.57861334\",\n \"text\": \"def circle(t, r):\\n print \\\"circle radius:\\\", r\\n arc(t, r, 360)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9054fef151d37dba858d2578df772c\",\n \"score\": \"0.5782994\",\n \"text\": \"def getRandomPosition(self):\\n x_coord = round(random.uniform(0, self.getWidth()-0.01), 2)\\n y_coord = round(random.uniform(0, self.getHeight()-0.01), 2)\\n return Position(x_coord, y_coord)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e821588b4bd0262b98105443c42bdf\",\n \"score\": \"0.578286\",\n \"text\": \"def AddCircle(self, psi,center,normal,r,d) :\\n rx = self.px - center[0]\\n ry = self.py - center[1]\\n rz = self.pz - center[2]\\n #normal = normal/norm(normal,2) #matlab calls for the 2 norm\\n normal = normal/np.linalg.norm(normal) #linalg gives the 2-norm by default\\n alpha = np.zeros_like(rx)\\n z = rx*normal[0] + ry*normal[1] + rz*normal[2]\\n inCylinder = (rx**2+ry**2+rz**2 - z**2) < r**2\\n inLayerP = (z> 0) & (z<= d/2) & inCylinder\\n inLayerM = (z<=0) & (z>=-d/2) & inCylinder\\n alpha[inLayerP] = -pi*(2*z[inLayerP]/d - 1.)\\n alpha[inLayerM] = -pi*(2*z[inLayerM]/d + 1.)\\n #psi = np.multiply( psi , exp(1j*alpha) )\\n psi = psi * exp(1j*alpha)\\n return psi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72cb90c90681b3143ba48427f11424c5\",\n \"score\": \"0.5758467\",\n \"text\": \"def random_three_vector():\\n phi = np.random.uniform(0,np.pi*2)\\n costheta = np.random.uniform(-1,1)\\n\\n theta = np.arccos( costheta )\\n x = np.sin( theta) * np.cos( phi )\\n y = np.sin( theta) * np.sin( phi )\\n z = np.cos( theta )\\n return (x,y,z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d724fa899cbe46dcefe7ff36bcbdeced\",\n \"score\": \"0.5757415\",\n \"text\": \"def define_circle(p1, p2, p3):\\n temp = p2[0] * p2[0] + p2[1] * p2[1]\\n bc = (p1[0] * p1[0] + p1[1] * p1[1] - temp) / 2\\n cd = (temp - p3[0] * p3[0] - p3[1] * p3[1]) / 2\\n det = (p1[0] - p2[0]) * (p2[1] - p3[1]) - (p2[0] - p3[0]) * (p1[1] - p2[1])\\n\\n if abs(det) < 1.0e-6:\\n return (None, np.inf)\\n\\n # Center of circle\\n cx = (bc * (p2[1] - p3[1]) - cd * (p1[1] - p2[1])) / det\\n cy = ((p1[0] - p2[0]) * cd - (p2[0] - p3[0]) * bc) / det\\n\\n radius = np.sqrt((cx - p1[0])**2 + (cy - p1[1])**2)\\n return ((cx, cy), radius)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1480,"cells":{"query_id":{"kind":"string","value":"06680ac87d9b16ae182ac51377a8d556"},"query":{"kind":"string","value":"Test the accuracy of a model on some labelled test dataset."},"positive_passages":{"kind":"list like","value":[{"docid":"074b10b233659b2c8690b8d1868318ee","score":"0.0","text":"def test(model, dataloader):\n # Switch the model to eval mode.\n model.eval()\n device = model.device\n\n time_start = time.time()\n batch_time = 0.0\n accuracy = 0.0\n\n # Deactivate autograd for evaluation.\n with torch.no_grad():\n count = 0\n for batch in dataloader:\n batch_start = time.time()\n\n\n premises = batch[\"premise\"].to(device)\n prem_pad = batch[\"prem_pad\"].to(device)\n prem_sem = batch[\"prem_sem\"].to(device)\n premises_lengths = batch[\"premise_length\"].to(device)\n hypotheses = batch[\"hypothesis\"].to(device)\n hyp_pad = batch[\"hyp_pad\"].to(device)\n hyp_sem = batch[\"hyp_sem\"].to(device)\n hypotheses_lengths = batch[\"hypothesis_length\"].to(device)\n abb_1_p = batch[\"abb_1_p\"].to(device)\n abb_2_p = batch[\"abb_2_p\"].to(device)\n abb_3_p = batch[\"abb_3_p\"].to(device)\n abb_4_p = batch[\"abb_4_p\"].to(device)\n abb_5_p = batch[\"abb_5_p\"].to(device)\n con_1_p = batch[\"con_1_p\"].to(device)\n con_2_p = batch[\"con_2_p\"].to(device)\n con_3_p = batch[\"con_3_p\"].to(device)\n con_4_p = batch[\"con_4_p\"].to(device)\n con_5_p = batch[\"con_5_p\"].to(device)\n rel_pad_0_p = batch[\"rel_pad_0_p\"].to(device)\n rel_ent_0_p = batch[\"rel_ent_0_p\"].to(device)\n rel_mask_0_p = batch[\"rel_mask_0_p\"].to(device)\n rel_pad_1_p = batch[\"rel_pad_1_p\"].to(device)\n rel_ent_1_p = batch[\"rel_ent_1_p\"].to(device)\n rel_mask_1_p = batch[\"rel_mask_1_p\"].to(device)\n rel_pad_2_p = batch[\"rel_pad_2_p\"].to(device)\n rel_ent_2_p = batch[\"rel_ent_2_p\"].to(device)\n rel_mask_2_p = batch[\"rel_mask_2_p\"].to(device)\n rel_pad_3_p = batch[\"rel_pad_3_p\"].to(device)\n rel_ent_3_p = batch[\"rel_ent_3_p\"].to(device)\n rel_mask_3_p = batch[\"rel_mask_3_p\"].to(device)\n rel_pad_4_p = batch[\"rel_pad_4_p\"].to(device)\n rel_ent_4_p = batch[\"rel_ent_4_p\"].to(device)\n rel_mask_4_p = batch[\"rel_mask_4_p\"].to(device)\n rel_pad_5_p = batch[\"rel_pad_5_p\"].to(device)\n rel_ent_5_p = batch[\"rel_ent_5_p\"].to(device)\n rel_mask_5_p = batch[\"rel_mask_5_p\"].to(device)\n abb_1_h = batch[\"abb_1_h\"].to(device)\n abb_2_h = batch[\"abb_2_h\"].to(device)\n abb_3_h = batch[\"abb_3_h\"].to(device)\n abb_4_h = batch[\"abb_4_h\"].to(device)\n abb_5_h = batch[\"abb_5_h\"].to(device)\n con_1_h = batch[\"con_1_h\"].to(device)\n con_2_h = batch[\"con_2_h\"].to(device)\n con_3_h = batch[\"con_3_h\"].to(device)\n con_4_h = batch[\"con_4_h\"].to(device)\n con_5_h = batch[\"con_5_h\"].to(device)\n rel_pad_0_h = batch[\"rel_pad_0_h\"].to(device)\n rel_ent_0_h = batch[\"rel_ent_0_h\"].to(device)\n rel_mask_0_h = batch[\"rel_mask_0_h\"].to(device)\n rel_pad_1_h = batch[\"rel_pad_1_h\"].to(device)\n rel_ent_1_h = batch[\"rel_ent_1_h\"].to(device)\n rel_mask_1_h = batch[\"rel_mask_1_h\"].to(device)\n rel_pad_2_h = batch[\"rel_pad_2_h\"].to(device)\n rel_ent_2_h = batch[\"rel_ent_2_h\"].to(device)\n rel_mask_2_h = batch[\"rel_mask_2_h\"].to(device)\n rel_pad_3_h = batch[\"rel_pad_3_h\"].to(device)\n rel_ent_3_h = batch[\"rel_ent_3_h\"].to(device)\n rel_mask_3_h = batch[\"rel_mask_3_h\"].to(device)\n rel_pad_4_h = batch[\"rel_pad_4_h\"].to(device)\n rel_ent_4_h = batch[\"rel_ent_4_h\"].to(device)\n rel_mask_4_h = batch[\"rel_mask_4_h\"].to(device)\n rel_pad_5_h = batch[\"rel_pad_5_h\"].to(device)\n rel_ent_5_h = batch[\"rel_ent_5_h\"].to(device)\n rel_mask_5_h = batch[\"rel_mask_5_h\"].to(device) \n weight_rel = batch[\"weight_rel\"].to(device)\n labels = batch[\"label\"].to(device)\n\n logits, probs = model(premises,\n prem_pad,\n prem_sem,\n premises_lengths,\n hypotheses,\n hyp_pad,\n hyp_sem,\n hypotheses_lengths,\n abb_1_p,\n abb_2_p,\n abb_3_p,\n abb_4_p,\n abb_5_p,\n con_1_p,\n con_2_p,\n con_3_p,\n con_4_p,\n con_5_p,\n rel_pad_0_p,\n rel_ent_0_p,\n rel_mask_0_p,\n rel_pad_1_p,\n rel_ent_1_p,\n rel_mask_1_p,\n rel_pad_2_p,\n rel_ent_2_p,\n rel_mask_2_p,\n rel_pad_3_p,\n rel_ent_3_p,\n rel_mask_3_p,\n rel_pad_4_p,\n rel_ent_4_p,\n rel_mask_4_p,\n rel_pad_5_p,\n rel_ent_5_p,\n rel_mask_5_p,\n abb_1_h,\n abb_2_h,\n abb_3_h,\n abb_4_h,\n abb_5_h,\n con_1_h,\n con_2_h,\n con_3_h,\n con_4_h,\n con_5_h,\n rel_pad_0_h,\n rel_ent_0_h,\n rel_mask_0_h,\n rel_pad_1_h,\n rel_ent_1_h,\n rel_mask_1_h,\n rel_pad_2_h,\n rel_ent_2_h,\n rel_mask_2_h,\n rel_pad_3_h,\n rel_ent_3_h,\n rel_mask_3_h,\n rel_pad_4_h,\n rel_ent_4_h,\n rel_mask_4_h,\n rel_pad_5_h,\n rel_ent_5_h,\n rel_mask_5_h,\n weight_rel)\n \n accuracy += correct_predictions(probs, labels)\n batch_time += time.time() - batch_start\n\n batch_time /= len(dataloader)\n total_time = time.time() - time_start\n accuracy /= (len(dataloader.dataset))\n\n return batch_time, total_time, accuracy","title":""}],"string":"[\n {\n \"docid\": \"074b10b233659b2c8690b8d1868318ee\",\n \"score\": \"0.0\",\n \"text\": \"def test(model, dataloader):\\n # Switch the model to eval mode.\\n model.eval()\\n device = model.device\\n\\n time_start = time.time()\\n batch_time = 0.0\\n accuracy = 0.0\\n\\n # Deactivate autograd for evaluation.\\n with torch.no_grad():\\n count = 0\\n for batch in dataloader:\\n batch_start = time.time()\\n\\n\\n premises = batch[\\\"premise\\\"].to(device)\\n prem_pad = batch[\\\"prem_pad\\\"].to(device)\\n prem_sem = batch[\\\"prem_sem\\\"].to(device)\\n premises_lengths = batch[\\\"premise_length\\\"].to(device)\\n hypotheses = batch[\\\"hypothesis\\\"].to(device)\\n hyp_pad = batch[\\\"hyp_pad\\\"].to(device)\\n hyp_sem = batch[\\\"hyp_sem\\\"].to(device)\\n hypotheses_lengths = batch[\\\"hypothesis_length\\\"].to(device)\\n abb_1_p = batch[\\\"abb_1_p\\\"].to(device)\\n abb_2_p = batch[\\\"abb_2_p\\\"].to(device)\\n abb_3_p = batch[\\\"abb_3_p\\\"].to(device)\\n abb_4_p = batch[\\\"abb_4_p\\\"].to(device)\\n abb_5_p = batch[\\\"abb_5_p\\\"].to(device)\\n con_1_p = batch[\\\"con_1_p\\\"].to(device)\\n con_2_p = batch[\\\"con_2_p\\\"].to(device)\\n con_3_p = batch[\\\"con_3_p\\\"].to(device)\\n con_4_p = batch[\\\"con_4_p\\\"].to(device)\\n con_5_p = batch[\\\"con_5_p\\\"].to(device)\\n rel_pad_0_p = batch[\\\"rel_pad_0_p\\\"].to(device)\\n rel_ent_0_p = batch[\\\"rel_ent_0_p\\\"].to(device)\\n rel_mask_0_p = batch[\\\"rel_mask_0_p\\\"].to(device)\\n rel_pad_1_p = batch[\\\"rel_pad_1_p\\\"].to(device)\\n rel_ent_1_p = batch[\\\"rel_ent_1_p\\\"].to(device)\\n rel_mask_1_p = batch[\\\"rel_mask_1_p\\\"].to(device)\\n rel_pad_2_p = batch[\\\"rel_pad_2_p\\\"].to(device)\\n rel_ent_2_p = batch[\\\"rel_ent_2_p\\\"].to(device)\\n rel_mask_2_p = batch[\\\"rel_mask_2_p\\\"].to(device)\\n rel_pad_3_p = batch[\\\"rel_pad_3_p\\\"].to(device)\\n rel_ent_3_p = batch[\\\"rel_ent_3_p\\\"].to(device)\\n rel_mask_3_p = batch[\\\"rel_mask_3_p\\\"].to(device)\\n rel_pad_4_p = batch[\\\"rel_pad_4_p\\\"].to(device)\\n rel_ent_4_p = batch[\\\"rel_ent_4_p\\\"].to(device)\\n rel_mask_4_p = batch[\\\"rel_mask_4_p\\\"].to(device)\\n rel_pad_5_p = batch[\\\"rel_pad_5_p\\\"].to(device)\\n rel_ent_5_p = batch[\\\"rel_ent_5_p\\\"].to(device)\\n rel_mask_5_p = batch[\\\"rel_mask_5_p\\\"].to(device)\\n abb_1_h = batch[\\\"abb_1_h\\\"].to(device)\\n abb_2_h = batch[\\\"abb_2_h\\\"].to(device)\\n abb_3_h = batch[\\\"abb_3_h\\\"].to(device)\\n abb_4_h = batch[\\\"abb_4_h\\\"].to(device)\\n abb_5_h = batch[\\\"abb_5_h\\\"].to(device)\\n con_1_h = batch[\\\"con_1_h\\\"].to(device)\\n con_2_h = batch[\\\"con_2_h\\\"].to(device)\\n con_3_h = batch[\\\"con_3_h\\\"].to(device)\\n con_4_h = batch[\\\"con_4_h\\\"].to(device)\\n con_5_h = batch[\\\"con_5_h\\\"].to(device)\\n rel_pad_0_h = batch[\\\"rel_pad_0_h\\\"].to(device)\\n rel_ent_0_h = batch[\\\"rel_ent_0_h\\\"].to(device)\\n rel_mask_0_h = batch[\\\"rel_mask_0_h\\\"].to(device)\\n rel_pad_1_h = batch[\\\"rel_pad_1_h\\\"].to(device)\\n rel_ent_1_h = batch[\\\"rel_ent_1_h\\\"].to(device)\\n rel_mask_1_h = batch[\\\"rel_mask_1_h\\\"].to(device)\\n rel_pad_2_h = batch[\\\"rel_pad_2_h\\\"].to(device)\\n rel_ent_2_h = batch[\\\"rel_ent_2_h\\\"].to(device)\\n rel_mask_2_h = batch[\\\"rel_mask_2_h\\\"].to(device)\\n rel_pad_3_h = batch[\\\"rel_pad_3_h\\\"].to(device)\\n rel_ent_3_h = batch[\\\"rel_ent_3_h\\\"].to(device)\\n rel_mask_3_h = batch[\\\"rel_mask_3_h\\\"].to(device)\\n rel_pad_4_h = batch[\\\"rel_pad_4_h\\\"].to(device)\\n rel_ent_4_h = batch[\\\"rel_ent_4_h\\\"].to(device)\\n rel_mask_4_h = batch[\\\"rel_mask_4_h\\\"].to(device)\\n rel_pad_5_h = batch[\\\"rel_pad_5_h\\\"].to(device)\\n rel_ent_5_h = batch[\\\"rel_ent_5_h\\\"].to(device)\\n rel_mask_5_h = batch[\\\"rel_mask_5_h\\\"].to(device) \\n weight_rel = batch[\\\"weight_rel\\\"].to(device)\\n labels = batch[\\\"label\\\"].to(device)\\n\\n logits, probs = model(premises,\\n prem_pad,\\n prem_sem,\\n premises_lengths,\\n hypotheses,\\n hyp_pad,\\n hyp_sem,\\n hypotheses_lengths,\\n abb_1_p,\\n abb_2_p,\\n abb_3_p,\\n abb_4_p,\\n abb_5_p,\\n con_1_p,\\n con_2_p,\\n con_3_p,\\n con_4_p,\\n con_5_p,\\n rel_pad_0_p,\\n rel_ent_0_p,\\n rel_mask_0_p,\\n rel_pad_1_p,\\n rel_ent_1_p,\\n rel_mask_1_p,\\n rel_pad_2_p,\\n rel_ent_2_p,\\n rel_mask_2_p,\\n rel_pad_3_p,\\n rel_ent_3_p,\\n rel_mask_3_p,\\n rel_pad_4_p,\\n rel_ent_4_p,\\n rel_mask_4_p,\\n rel_pad_5_p,\\n rel_ent_5_p,\\n rel_mask_5_p,\\n abb_1_h,\\n abb_2_h,\\n abb_3_h,\\n abb_4_h,\\n abb_5_h,\\n con_1_h,\\n con_2_h,\\n con_3_h,\\n con_4_h,\\n con_5_h,\\n rel_pad_0_h,\\n rel_ent_0_h,\\n rel_mask_0_h,\\n rel_pad_1_h,\\n rel_ent_1_h,\\n rel_mask_1_h,\\n rel_pad_2_h,\\n rel_ent_2_h,\\n rel_mask_2_h,\\n rel_pad_3_h,\\n rel_ent_3_h,\\n rel_mask_3_h,\\n rel_pad_4_h,\\n rel_ent_4_h,\\n rel_mask_4_h,\\n rel_pad_5_h,\\n rel_ent_5_h,\\n rel_mask_5_h,\\n weight_rel)\\n \\n accuracy += correct_predictions(probs, labels)\\n batch_time += time.time() - batch_start\\n\\n batch_time /= len(dataloader)\\n total_time = time.time() - time_start\\n accuracy /= (len(dataloader.dataset))\\n\\n return batch_time, total_time, accuracy\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"948ec61063e23c45cf71c53a41bbcd25","score":"0.7570441","text":"def test(self, dataset):\n self.model.eval() \n with torch.no_grad():\n total_samples, corrects = 0, 0\n pred_class, label_class = [], []\n for images, labels in dataset:\n _, pred_labels = self.predict(images)\n\n corrects += torch.sum(labels == pred_labels).double()\n total_samples += labels.shape[0]\n \n for p, l in zip(pred_labels, labels):\n pred_class.append(p)\n label_class.append(l)\n \n accuracy = corrects / total_samples\n self.logger.info('\\n')\n self.logger.info('Accuracy: {:.3}\\n\\n'.format(accuracy))\n self.logger.info(classification_report(label_class, pred_class))\n \n return label_class, pred_class","title":""},{"docid":"f78d3d90870a28a7b1ae35ceed93abba","score":"0.74879396","text":"def test(model, X_test, y_test, config):\n #ytest = onehotencoding(y_test)\n cost_test,logits = model.forward_pass(X_test,y_test)\n predicted = np.argmax(logits,axis = 1)\n test = np.argmax(y_test,axis = 1)\n accuracy = np.sum(predicted == test)/len(test)\n return accuracy","title":""},{"docid":"8bed5881898afd2a040bef4706e54c1a","score":"0.7437546","text":"def test_model(self,X,Y):\n\t\tif hasattr(self.model, \"evals\"):\n\t\t\tlabels = np.array(Y)\n\t\t\tp_labels = self.model.evals(X)\n\n\t\telse:\n\t\t\tlabels = []\t\t\t\t\t\t# List of actual labels\n\t\t\tp_labels = []\t\t\t\t\t# List of model's predictions\n\t\t\tsuccess = 0\t\t\t\t\t\t# Number of correct predictions\n\t\t\ttotal_count = 0\t\t\t\t\t# Number of images\n\n\t\t\tfor i in range(len(X)):\n\t\t\t\t\n\t\t\t\tx = X[i]\t\t\t\t\t# Test input\n\t\t\t\ty = Y[i]\t\t\t\t\t# Actual label\n\t\t\t\ty_ = self.model.eval(x)\t\t# Model's prediction\n\t\t\t\tlabels.append(y)\n\t\t\t\tp_labels.append(y_)\n\n\t\t\t\tif y == y_:\n\t\t\t\t\tsuccess += 1\n\t\t\t\ttotal_count +=1 \t\t\n\t\t\n\t\tprint(\"Computing Confusion Matrix\")\n\t\t# Compute Confusion Matrix\n\t\tgetConfusionMatrixPlot(labels,p_labels,CLASS_LABELS)","title":""},{"docid":"3afb6ec3a69d89b5c9e10de7e1e7850d","score":"0.7384026","text":"def evaluate_model(model, X_test, Y_test, category_names):\n y_pred = model.predict(X_test)\n print(classification_report(y_pred, Y_test.values, target_names=category_names))\n # print raw accuracy score \n print('Accuracy Score: {}'.format(np.mean(Y_test.values == y_pred)))","title":""},{"docid":"ded7cd65863ec3d6aee9da8d43bba6bd","score":"0.7348681","text":"def evaluate_accuracy(model, testloader, device):\n correct = 0\n total = 0\n with torch.no_grad():\n for data in testloader:\n images, labels = data\n images, labels = images.to(device), labels.to(device)\n outputs = model(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n return correct / total","title":""},{"docid":"ad70a05715df1630b9da59eca37904fe","score":"0.7281572","text":"def test(self):\n test_loss, test_acc = self.model.evaluate(self.test_data_gen, \n verbose=1)\n print(f'Test loss: {test_loss}, test accuracy: {test_acc}')","title":""},{"docid":"7926775089db74b6d0fbe063e981f616","score":"0.72520256","text":"def test(self, model, test_data, test_label):\n # One hot encode\n onehot_test_label = pd.get_dummies(test_label)\n # Make predictions and output result\n return model.evaluate(test_data, onehot_test_label)[1]","title":""},{"docid":"c3bd6d9e0f168dc9b35baf09dd7d08f6","score":"0.7226808","text":"def get_testing_accuracy(self):\n\n self.active_model = DataModel(self.training_model.data)\n testing_data = self.testing_model.data\n total_count_of_data_points = len(testing_data)\n\n total_correct_predictions = 0\n for i in range(total_count_of_data_points):\n correct_classification = testing_data[i][-1]\n point = np.delete(testing_data, -1, axis=1)[i]\n if self.get_classification(point) == correct_classification:\n total_correct_predictions = total_correct_predictions + 1\n accuracy = 100 * (total_correct_predictions / total_count_of_data_points)\n return accuracy","title":""},{"docid":"ba5ac00c21946fc690067f845f147b77","score":"0.72142124","text":"def test(self, dataset, labels, tf_idf=False):\n correct = 0\n predictions = []\n\n # infer for every article\n for i, text in enumerate(dataset):\n cat = self.infer(text, tf_idf)\n predictions.append(cat)\n\n if cat == labels[i]:\n correct += 1\n\n # calculate accuracy\n accuracy = correct/len(dataset)\n\n return predictions, accuracy","title":""},{"docid":"e9e575bfb67e9cd867edb796fa975d8a","score":"0.71954083","text":"def ComputeAccuracy(test_set, test_set_predicted):","title":""},{"docid":"54d05c19c6fd77b771147f67635d6d88","score":"0.7150339","text":"def test(model, test_data, test_label):\n\n #TODO: Fix\n\n acc_bb_sum = 0\n acc_pixel_sum = 0\n acc_type_sum = 0\n number_of_batches = 0\n for i in range(0, len(test_data), model.batch_size):\n if(len(test_data)-i >= model.batch_size):\n\n # Batching:\n inputs = test_data[i:i + model.batch_size, :, :, np.newaxis]\n labels = test_label[i:i+model.batch_size]\n batch_molecules = test_data[i:i + model.batch_size]\n\n output_bb, output_pixel, output_type = model.call(inputs)\n acc_bb, acc_pixel, acc_type = model.accuracy_function(output_bb, output_pixel, output_type, label_bb, label_pixel, label_type)\n acc_bb_sum += acc_bb\n acc_pixel_sum += acc_pixel\n acc_type_sum += acc_type\n number_of_batches += 1\n \n acc_bb = acc_bb_sum / number_of_batches\n acc_pixel = acc_pixel_sum / number_of_batches\n acc_type = acc_type_sum / number_of_batches\n\n return acc_bb, acc_pixel, acc_type","title":""},{"docid":"48321573d46bf6db1648485540b80e82","score":"0.7114068","text":"def test(model, dataset):\n avg_loss = tfe.metrics.Mean('loss', dtype=tf.float32)\n accuracy = tfe.metrics.Accuracy('accuracy', dtype=tf.float32)\n\n for (images, labels) in dataset:\n logits = model(images, training=False)\n avg_loss(loss(logits, labels))\n accuracy(\n tf.argmax(logits, axis=1, output_type=tf.int64),\n tf.cast(labels, tf.int64))\n if ddl.rank() == 0:\n print('Test set: Average loss: %.4f, Accuracy: %4f%%\\n' %\n (avg_loss.result(), 100 * accuracy.result()))\n with tf.contrib.summary.always_record_summaries():\n tf.contrib.summary.scalar('loss', avg_loss.result())\n tf.contrib.summary.scalar('accuracy', accuracy.result())","title":""},{"docid":"92ed292f50cde6689037992f6794decd","score":"0.7109705","text":"def test_binary_classification(self):\n model_predictions = self.predict(self.x_test)\n right_predicts = 0\n for prediction, observation in zip(model_predictions, self.y_test):\n if observation == 1 and 0.5 <= prediction <= 1:\n right_predicts += 1\n elif observation == 0 and prediction < 0.5:\n right_predicts += 1\n else:\n pass\n model_accuracy = right_predicts / model_predictions.shape[0] * 100\n print(f'The Binary Classification Model has an accuracy of {round(model_accuracy, 4)}%')","title":""},{"docid":"3a37ee7f038ec25434a1cb5f58c3dcb8","score":"0.7089755","text":"def accuracy(self, X_test, y_test):\n # Predicted values with the given X's.\n y_pred = self.predict(X_test)\n\n # Sum of which the prediction is the same as the target output.\n return sum(y_pred == y_test)","title":""},{"docid":"b4b109a3a113919532200024d16cb928","score":"0.70825845","text":"def accuracy(data_loader, model):\n training = model.training\n model.eval()\n correct, total = 0, 0\n for data, target in data_loader:\n if use_cuda:\n data, target = Variable(\n data.cuda(), volatile=True), Variable(target.cuda())\n else:\n data, target = Variable(data, volatile=True), Variable(target)\n output = model(data)\n pred = model.predict(output)\n correct += (pred == target.data).sum()\n total += target.size(0)\n model.train(training)\n return correct, total","title":""},{"docid":"f80f205ca84a9fa9b02c67eb687bd62a","score":"0.70442224","text":"def test(self):\n self.eval()\n test_mask = self.data.test_mask\n labels = self.data.y\n output = self.forward(self.data)\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\n None\n return acc_test.item()","title":""},{"docid":"f80f205ca84a9fa9b02c67eb687bd62a","score":"0.70442224","text":"def test(self):\n self.eval()\n test_mask = self.data.test_mask\n labels = self.data.y\n output = self.forward(self.data)\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\n None\n return acc_test.item()","title":""},{"docid":"f80f205ca84a9fa9b02c67eb687bd62a","score":"0.70442224","text":"def test(self):\n self.eval()\n test_mask = self.data.test_mask\n labels = self.data.y\n output = self.forward(self.data)\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\n None\n return acc_test.item()","title":""},{"docid":"960da008c5a27577509f9492754e87f7","score":"0.7029079","text":"def evaluate_on_test_set(self):\n accuracies = 0\n\n test_loader = DataLoader(self.data_test, self.batch_size)\n\n #hidden = self.model.init_hidden(self.batch_size).to(self.device)\n\n with torch.no_grad():\n for j, data in enumerate(test_loader, 0):\n test_inputs, test_labels = data[0].to(self.device), data[1].to(self.device)\n\n #outputs = self.model(test_inputs, hidden)\n outputs = self.model(test_inputs)\n\n loss = self.loss_fn(outputs, test_labels)\n\n accuracies += self.accuracy(outputs, test_labels)\n\n print(\"Accuracy on test set : {:05.3f} %\".format(100 * accuracies / len(test_loader)))\n\n return accuracies / len(test_loader)","title":""},{"docid":"2824e825d88c007c43fbf48fabf50aaa","score":"0.701956","text":"def evaluate_model(self):\n X_test, y_test = self.data\n score = self.model.evaluate(X_test, y_test, verbose=1)\n print(\"Test loss:\", score[0])\n print(\"Test accuracy:\", score[1])","title":""},{"docid":"27f264207e241d5f44e8e0cddb14b247","score":"0.7011244","text":"def predict_and_test(model, X_test, y_test):\n y_hat = np.array(model.predict(X_test))\n correct = np.sum(y_hat == y_test)\n samples = y_test.size\n return correct / samples","title":""},{"docid":"1b587fcad4d3dc44c9fd7a1d4d1e143d","score":"0.6993456","text":"def test_training_images(pos_testpath, neg_testpath, trained_model):\n images = get_gray_imgs(pos_testpath, neg_testpath)\n\n positive_counter = 0\n negative_counter = 0\n pos_acc = 0\n neg_acc = 0\n\n for gray_img, label in images:\n if label == 1:\n positive_counter += 1.0\n elif label == -1:\n negative_counter += 1.0\n\n prediction = calculate_final_hypothesis(gray_img, trained_model[0], trained_model[1], trained_model[2],\n trained_model[3])\n\n if prediction == label and label == 1:\n pos_acc += 1.0\n if prediction == label and label == -1:\n neg_acc += 1.0\n\n print \"positive accuracy\", pos_acc / positive_counter\n print \"negative accuracy\", neg_acc / negative_counter\n print \"overall accuracy\", (pos_acc + neg_acc) / (positive_counter + negative_counter)\n return","title":""},{"docid":"576bde21b6a215c68f3fcde962d31461","score":"0.69915503","text":"def evaluate_model(model,X_test,y_test):\n # print classification_report\n print(\"Evaluation the model .....\")\n y_pred = model.predict(X_test)\n print('Accuracy: {}'.format(np.mean(y_test.values == y_pred)))\n print(\"Classification report:\")\n print(classification_report(y_test.values, y_pred, target_names=y_test.columns))","title":""},{"docid":"72e7b61b1718bf034bf4babcf23f3828","score":"0.697071","text":"def accuracy(test_loader, model):\n model.eval()\n correct = 0.\n total = 0.\n with torch.no_grad():\n for i, (x, target) in enumerate(test_loader):\n target = target.cuda()\n input_var = x.cuda()\n target_var = target.cuda()\n\n output = model(input_var)\n output = output.float()\n\n for j, pred in enumerate(output):\n l = pred.argmax()\n total += 1\n if l == target[j]:\n correct += 1\n return correct/total","title":""},{"docid":"019968139235fb2c96ee7b2ea6f84ee6","score":"0.6950458","text":"def run_test(self, dataset, clf, model_name, dataset_name):\n\n (x_train, y_train), (x_test, y_test), num_classes, input_shape = self.prepare_data(dataset)\n\n original_x_test = x_test\n original_y_test = y_test\n\n y_predict = clf.predict(x_test)\n\n temp_train = []\n temp_predict = []\n temp_test = []\n for i, vect in enumerate(y_train):\n temp_train.append(np.argmax(y_train[i]))\n\n for i, vect in enumerate(y_predict):\n temp_predict.append(np.argmax(y_predict[i]))\n\n for i, vect in enumerate(y_test):\n temp_test.append(np.argmax(y_test[i]))\n\n y_train = temp_train\n y_predict = temp_predict\n y_test = temp_test\n\n labels = [y for y in y_train]\n\n labels = sorted(list(set(labels)))\n print(\"\\nConfusion matrix:\")\n print(\"Labels: {0}\\n\".format(\",\".join([str(y) for y in labels])))\n print(confusion_matrix(y_test, y_predict, labels=labels))\n\n print(\"\\nClassification report:\")\n print(classification_report(y_test, y_predict))\n\n score = clf.evaluate(original_x_test, original_y_test, verbose=1)\n\n print('Test loss:', score[0])\n print('Test accuracy:', score[1])","title":""},{"docid":"0dbd102f4f5d5c8fc9a766ad73822311","score":"0.6939115","text":"def test(self, dataloader):\n # enable eval mode\n self._net.train(False)\n num_correct = 0\n num_total = 0\n with torch.no_grad():\n for img, label in dataloader:\n # Data\n img = img.cuda()\n label = label.cuda()\n # forward pass\n score = self._net(img)\n # prediction\n max_score, prediction = torch.max(score, 1)\n # statistic\n num_total += label.size(0)\n num_correct += torch.sum(prediction == label.data).item()\n # switch back to train mode\n self._net.train(False)\n return 100 * num_correct / num_total","title":""},{"docid":"e0a936cf6ab31d5d1a2feb073242fe2a","score":"0.6918965","text":"def accuracy(self, X_test, y_test):\n _, self.b, self.w = self.fit()\n preds = self.predict(X_test)\n accuracy = accuracy_score(y_test, preds)\n return accuracy","title":""},{"docid":"5343eedc5500ba04b9c91f7def3765d0","score":"0.69107103","text":"def test_model(net, dataset, loss_module, batch_size):\n iters_needed = dataset._num_examples // batch_size # iterations needed to check the whole dataset\n loss = []\n count = 0\n acc = []\n\n for i in range(iters_needed):\n imgs, labels = dataset.next_batch(batch_size)\n labels = torch.tensor(labels)\n imgs = imgs.reshape(imgs.shape[0], -1)\n with torch.no_grad():\n preds = net(imgs)\n count += labels.shape[0]\n acc.append(accuracy(preds, labels))\n loss.append(loss_module(preds, torch.max(labels, 1)[1]).item())\n\n acc = np.mean(acc)\n loss = np.mean(loss)\n\n return acc, loss","title":""},{"docid":"337a50f0a03e73dd29b5794dce9bbf40","score":"0.6903235","text":"def predict(self, test_loader):\n self.eval()\n correct = 0.\n\n for batch_idx, (X_test, y_test) in enumerate(test_loader):\n X_test, y_test = X_test.to(self.device), y_test.to(self.device)\n output = F.softmax(self.forward(X_test), dim=1)\n y_pred = output.data.max(1)[1]\n\n correct += y_pred.eq(y_test.view(-1).data).sum()\n\n accuracy = 100. * float(correct) / len(test_loader.dataset)\n\n return accuracy","title":""},{"docid":"a6fc07b4afda4be150088dcf18ae0125","score":"0.68791044","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_pred = model.predict(X_test)\n for i in range(Y_test.shape[1]):\n accuracy = '%25s accuracy : %.2f' \\\n %(category_names[i],\n accuracy_score(Y_test[:,i], Y_pred[:,i]))\n print(accuracy)\n with open('result.log','w') as file:\n print(classification_report(Y_test, Y_pred,\n target_names=category_names))\n file.write(classification_report(Y_test, Y_pred,\n target_names=category_names))\n file.close()","title":""},{"docid":"5c5f9e8a4d802d88c9ddaa8672d3c8d0","score":"0.6875846","text":"def test_model(loader, model):\n correct = 0\n total = 0\n result = []\n model.eval()\n for data1, data2, lengths1, lengths2, labels in loader:\n data_batch1, data_batch2, lengths_batch1, lengths_batch2, label_batch = data1, data2, lengths1, lengths2, labels\n outputs = F.softmax(model(data_batch1, lengths_batch1, data_batch2, lengths_batch2), dim=1)\n predicted = outputs.max(1, keepdim=True)[1]\n total += labels.size(0)\n correct += predicted.eq(labels.view_as(predicted)).sum().item()\n result.append([labels, predicted])\n return (100 * correct / total) , result","title":""},{"docid":"e2789e9ea18d419b40f12cb7d60ca508","score":"0.681634","text":"def evaluate_model(model, X_test, Y_test, category_names): \n pred = model.predict(X_test)\n for i, col in enumerate(Y_test):\n print(\"Column: {}\".format(col))\n print(classification_report(Y_test[col], pred[:, i]))","title":""},{"docid":"bc0c3cb0237f08c5d929d65a09a0902e","score":"0.6810412","text":"def test(self, test_data, test_labels):\n correct = 0.0;\n prediction_histogram = {k: 0 for k in range(10)};\n \n # Predict\n prediction = self.predict(test_data);\n \n # Compute statistics\n for j in range(np.size(prediction)):\n prediction_histogram[prediction[j]] += 1;\n if (prediction[j] == test_labels[j]):\n correct += 1.0;\n \n return (correct / np.size(test_data,0), prediction_histogram, (self.W, self.V));","title":""},{"docid":"7871c16f0d2c3b2c1791f7794d5835e5","score":"0.68010974","text":"def score(model, testset, trainset):\n correct = 0\n total = 0\n\n for test_cls, test_images in testset.items():\n for image in test_images:\n total += 1\n if isinstance(model, KnnModel):\n cls_pred = model.predict_class(image, trainset, threshold=0.90)\n else:\n cls_pred = model.predict_class(image, trainset, threshold=0.5)\n\n correct += (test_cls == cls_pred)\n\n return correct / total","title":""},{"docid":"5e4ebaa038a27f35e195b04820a07eb5","score":"0.6792031","text":"def accuracy(labels_test, Y_vote):\r\n # predicted classes\r\n y_hat = np.argmax(Y_vote, axis=1)\r\n\r\n # all cases where predicted class was correct\r\n mask = y_hat == labels_test\r\n\r\n antimask = y_hat != labels_test\r\n\r\n for i in range(0, len(antimask)):\r\n if not (y_hat[i] == labels_test[i]):\r\n print(\"index:\" + str(i) + \" ,predicted: \" + str(y_hat[i]) + \" ,actual: \" + str(labels_test[i]))\r\n\r\n return np.float32(np.count_nonzero(mask)) / len(labels_test)","title":""},{"docid":"493b322dc82a4e3df2d5f9b3d0bd0797","score":"0.6787765","text":"def check_model(X_train, X_test, y_train, y_test, model):\n\n # fit the model on the train data\n model.fit(X_train, y_train)\n # predict the test data\n test_score = model.score(X_test, y_test)\n train_score = model.score(X_train, y_train)\n\n return test_score, train_score, model","title":""},{"docid":"f183f06fbc1f236b5f8695c691e96b6a","score":"0.67734265","text":"def evaluate_model(model, X_test, Y_test, category_names):\n # make prediction on test set\n Y_pred = model.predict(X_test)\n \n # print evaluation metrics \n print(classification_report(Y_test,Y_pred,target_names=category_names))","title":""},{"docid":"79790a52e6a25ab50f829fbdeae9e101","score":"0.67720956","text":"def test(self, idx_test):\n self.eval()\n output = self.predict()\n loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])\n acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])\n None\n return acc_test.item()","title":""},{"docid":"79790a52e6a25ab50f829fbdeae9e101","score":"0.67720956","text":"def test(self, idx_test):\n self.eval()\n output = self.predict()\n loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])\n acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])\n None\n return acc_test.item()","title":""},{"docid":"ce857cc1eb62db64c4d03eeecb151e4d","score":"0.67680514","text":"def test(model, data_loader):\n print('===> Evaluate mode')\n\n # Switch to evaluate mode\n model.eval()\n\n test_loss = 0\n correct = 0\n for data, target in data_loader:\n target_indices = target\n target_one_hot = utils.one_hot_encode(\n target_indices, length=model.digits.num_unit)\n\n data, target = Variable(data, volatile=True), Variable(target_one_hot)\n\n if args.cuda:\n data = data.cuda()\n target = target.cuda()\n\n output = model(data)\n\n # sum up batch loss\n test_loss += model.loss(output, target, size_average=False).data[0]\n\n # evaluate\n v_magnitud = torch.sqrt((output**2).sum(dim=2, keepdim=True))\n pred = v_magnitud.data.max(1, keepdim=True)[1].cpu()\n correct += pred.eq(target_indices.view_as(pred)).sum()\n\n test_loss /= len(data_loader.dataset)\n\n mesg = 'Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(\n test_loss,\n correct,\n len(data_loader.dataset),\n 100. * correct / len(data_loader.dataset))\n print(mesg)","title":""},{"docid":"bbb680a285ea58c96dd92154b19c583c","score":"0.67655736","text":"def get_accuracy(self):\n accuracy = 0\n m = self.test_ex.shape[0]\n w_size = [(self.hidden_layer_size, self.input_layer_size), (self.num_labels, self.hidden_layer_size)]\n res, no, na, ni = NeuralNetwork.forward_prop(self.weights_bias, self.test_ex, w_size)\n for i in range(m):\n if np.argmax(res[i]) == self.test_labels[i]:\n accuracy += 1\n print('accuracy :', 100 * accuracy / m, '%')","title":""},{"docid":"d30b5ae1051eb22068bced71fc5f96ac","score":"0.6760537","text":"def test(self, inputs, targets, test_log):\n print(\"Let's test!\\n\")\n\n n_correct = 0\n n_wrong = 0\n \n for i in range(0, len(targets)):\n #print(\"\\tStart argument...\\n\")\n\n data_point = (inputs[i], targets[i])\n test_args = Test_Arguments(data_point)\n \n # Run the ensemble \n (out_0, out_1) = self.test_recursive(test_args, test_log)\n \n #log accuracy of m\n m_accuracy = test_args.total_correct_m / test_args.argument_length\n test_log.log_accuracy(\"m\", m_accuracy)\n\n if out_0 > out_1 and targets[i] == label_0:\n n_correct += 1\n elif out_1 > out_0 and targets[i] == label_1:\n n_correct += 1\n else:\n n_wrong += 1\n \n print(\"Accuracy Ensemble: \", n_correct/(n_correct + n_wrong))\n print(\"Accuracy Main: \", test_log.data_m.get_accuracy())\n print(\"Accuracy C0: \", test_log.data_c0.get_accuracy())\n print(\"Accuracy C1: \", test_log.data_c1.get_accuracy())\n \n test_log.reset_accuracies()\n\n return test_log","title":""},{"docid":"c9124ed98f4a3db0a213437044487f03","score":"0.6753827","text":"def accuracy(y_test, y_pred):\n return (y_pred == y_test).astype(int).mean()","title":""},{"docid":"11ec3fd9385a309995688210f2758b0b","score":"0.6753732","text":"def test(work_dir, pkl_path, load_epoch, silent=False):\r\n load_path = os.path.join(work_dir, 'epoch_{}.pth'.format(load_epoch))\r\n sys.path.append(work_dir)\r\n from config import model_cfg\r\n\r\n for attr in ['type', 'num_steps', 'num_tasks']:\r\n assert attr in model_cfg\r\n if not silent: print (\"Generating model: {}\".format(model_cfg['type']))\r\n trainer = Trainer(model_cfg)\r\n\r\n if not silent: \r\n print (model_cfg)\r\n print (\"Loading trained model from {}\".format(load_path))\r\n print ('----------------------------------------------')\r\n trainer.load_model(load_path)\r\n\r\n scores, task_ids, props = _parse_pkl(pkl_path)\r\n task_preds = trainer.predict(scores, props)\r\n\r\n task_ids = np.array(task_ids)\r\n accuracy = (np.sum(task_ids == task_preds) / len(task_ids)) * 100\r\n if not silent: \r\n print (task_ids)\r\n print (task_preds)\r\n print (np.sum(task_ids == task_preds))\r\n print (len(task_ids))\r\n print (\"Accuracy: %.3f\" % ((np.sum(task_ids == task_preds) / len(task_ids)) * 100))\r\n \r\n return accuracy","title":""},{"docid":"0afa0e95e86ad7cea5e18ac1d23a75d2","score":"0.67516047","text":"def test(self,dataset):\n outputs = self.use(dataset)\n costs = np.ones((len(outputs),1))\n # Compute classification error\n for xy,pred,cost in zip(dataset,outputs,costs):\n x,y = xy\n if y == pred[0]:\n cost[0] = 0\n\n return outputs,costs","title":""},{"docid":"2ff85a6be2dbc836a3013ebcf11709e9","score":"0.6750377","text":"def test_step(images, labels):\n predictions = model(images)\n\n loss = entropy(labels, predictions)\n accuracy = metrics(labels, predictions)\n\n return loss, accuracy","title":""},{"docid":"a5e57d045f02811f087abe456b6c4598","score":"0.67496157","text":"def evaluate_model(model, X_test, Y_test, category_names):\n\n # Score the model on test cases\n Y_predicted = model.predict(X_test)\n\n # Generate the classification report\n for colId, colName in enumerate(Y_test):\n print(colName)\n print(classification_report(Y_test[colName], Y_predicted[:, colId]))","title":""},{"docid":"85d01d740459f09fdaf6fa2e27a79f05","score":"0.67440253","text":"def evaluate_accuracy(\n y_true: np.ndarray, y_pred: np.ndarray, dataset_name: str\n) -> None:\n acc = accuracy_score(y_true, y_pred)\n\n logger.info(f\"{dataset_name} set accuracy: {acc}\")","title":""},{"docid":"057c0ad525f0fc92b7f4e524b337f01e","score":"0.6734119","text":"def evaluate(model, X_test, y_test, checkfile = 'weights.hdf5'):\n model.load_weights(checkfile)\n predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0))) \n for feature in X_test]\n test_list = y_test.argmax(axis=1)\n f1 = f1_score(test_list, predictions, average = 'macro')\n accuracy = accuracy_score(test_list,predictions) \n return test_list, predictions, f1, accuracy","title":""},{"docid":"21d7ea8a88ab5220bba4844919208b38","score":"0.6728676","text":"def model_accuracy(trained_model, features, targets):\n accuracy_score = trained_model.score(features, targets)\n return accuracy_score","title":""},{"docid":"c74746ccdc0ea532b5e296045966dad0","score":"0.67236334","text":"def testModel(self, testData):\n\n # make testData into a string list list\n testLL = []\n for line in (testData.splitlines()):\n testLL.append(line)\n\n # predict and test each line\n TP_R, TN_R, FP_R, FN_R = 0, 0, 0, 0\n TP_B, TN_B, FP_B, FN_B = 0, 0, 0, 0\n for line in testLL:\n actualClass = line.split()[0]\n # print(self.estimateLogProbability(line)['red'], self.estimateLogProbability(line)['blue'])\n if self.estimateLogProbability(line)['red'] > self.estimateLogProbability(line)['blue']:\n predictedClass = \"RED\"\n else:\n predictedClass = \"BLUE\"\n # print(actualClass, predictedClass)\n\n # calculate\n if (actualClass == \"RED\" and predictedClass == \"RED\"): \n TP_R += 1\n TN_B += 1\n elif (actualClass == \"RED\" and predictedClass == \"BLUE\"):\n FN_R += 1\n FP_B += 1\n elif (actualClass == \"BLUE\" and predictedClass == \"RED\"):\n FP_R += 1\n FN_B += 1\n elif (actualClass == \"BLUE\" and predictedClass == \"BLUE\"):\n TN_R += 1\n TP_B += 1\n\n # overall accuracy, calculate use either red or blue is fine\n accu_overall = (TP_R+TN_R)/(TP_R+TN_R+FN_R+FP_R)\n # precision for red\n preci_red = (TP_R)/(TP_R+FP_R)\n # precison for blue\n preci_blue = TP_B/(TP_B+FP_B)\n # recall for red\n recall_red = (TP_R)/(TP_R+FN_R)\n # recall for blue\n recall_blue = TP_B/(TP_B+FN_B)\n\n\n\n return {'overall accuracy': accu_overall,\n 'precision for red': preci_red,\n 'precision for blue': preci_blue,\n 'recall for red': recall_red,\n 'recall for blue': recall_blue}","title":""},{"docid":"97651a9af26d44a00b2534dc9ef9e3ab","score":"0.6722877","text":"def test_model(model, test_input, test_target, test_class):\r\n model.train(False)\r\n \r\n pred_x, pred_y, pred_z = model(test_input)\r\n class_err = (pred_x.data.max(1)[1].numpy() != test_class[:,0].numpy()).sum()\r\n class_err += (pred_y.data.max(1)[1].numpy() != test_class[:,1].numpy()).sum()\r\n comp_err = (pred_z.data.max(1)[1].numpy() != test_target.numpy()).sum() \r\n\r\n pred_class = torch.stack([pred_x.data.max(1)[1], pred_y.data.max(1)[1]], dim=1)\r\n print(\"Naive comparison error: {}\".format((compare_digits(pred_class) - test_target.int()).abs().sum().item()))\r\n \r\n return class_err, class_err /test_target.size(0) /2*100, comp_err, comp_err /test_target.size(0) *100","title":""},{"docid":"1c1fe2bffecad427c690691028dd4211","score":"0.6721459","text":"def evaluate(self, dataset):\r\n score = self.model.evaluate(dataset.test_images, [dataset.test_labels, dataset.test_labels], verbose=1)\r\n print(self.model.metrics_names)\r\n print('Test score:', score)","title":""},{"docid":"9b42e0693013f7d2fcb8abb07a9a9f62","score":"0.67200595","text":"def test_model_building(self):\n preds = self.lda.predict()\n confusion = confusion_matrix(self.y, preds)\n self.assertTrue(confusion[0, 0] > 90)\n self.assertTrue(confusion[1, 1] > 90)","title":""},{"docid":"b742a6ceeb678e7e1e6c3fcd87fe26d2","score":"0.6720059","text":"def evaluate_model(model, X_test, Y_test, category_names):\n y_pred = model.predict(X_test)\n\n for i in range(len(category_names)):\n print('---------------------------------------------------------------------------')\n print(\"Category:\", category_names[i])\n print(classification_report(Y_test.iloc[:, i].values, y_pred[:, i]))\n print(\"Accuracy: \", accuracy_score(Y_test.iloc[:, i].values, y_pred[:, i]))\n print(\"Best Parameters:\", model.best_params_)","title":""},{"docid":"8ca96b29a8b8421903f42e1a44590676","score":"0.67182577","text":"def test_step(X, labels, batch_size=50):\n batch_num = int(len(X) / batch_size)\n testing_accuracy = 0\n prev = 0\n predicted_vector = []\n for i in range(batch_num+1):\n x_dev_batch = X[prev:prev+batch_size]\n y_dev_batch = labels[prev:prev+batch_size]\n prev += batch_size\n pred_list, test_acc = sess.run(\n [cnn.predictions, cnn.accuracy],\n feed_dict={\n cnn.X: x_dev_batch,\n cnn.y: y_dev_batch,\n cnn.dropout_keep_prob: 1.0})\n testing_accuracy += test_acc\n predicted_vector.extend(list(pred_list))\n\n if i == (batch_num-1):\n batch_size = int(len(X) % batch_size)\n\n testing_accuracy /= batch_num\n pickle.dump(predicted_vector, open(\"{}\".format(sys.argv[2]), \"wb\"))\n print(\"Testing accuracy %g\" % testing_accuracy)","title":""},{"docid":"d1671dea4360bdb2a6129777452bcf52","score":"0.67172253","text":"def test(self):\n self._reset_metrics()\n self.model.eval()\n total = 0\n test_loss = 0\n with torch.no_grad():\n old_time = time()\n for batch, data in enumerate(self.data_bundle.test_dataset):\n inputs, labels = data\n inputs, labels = inputs.to(self.device), labels.to(self.device)\n\n outputs = self.model(inputs)\n loss = self.criterion(outputs, labels)\n _, predicted = torch.max(outputs.data, 1)\n\n total += labels.size(0)\n test_loss += loss.item()\n\n self._eval_metrics(labels, outputs)\n\n if batch % 10 == 0 or batch == (len(self.data_bundle.test_dataset)-1):\n self._print_status(batch, old_time, self.data_bundle.test_dataset)\n old_time = time()\n\n test_metrics = self._track_metrics('test', test_loss, total)\n return test_metrics","title":""},{"docid":"5e8e7734dda9f7655f3dec7cf24650b2","score":"0.6711027","text":"def accuracy(preds, targets):\n return (preds == targets).mean()","title":""},{"docid":"b34493360d77d6bdedb38aaba2c0dcb6","score":"0.67067784","text":"def test_network(model, optimizer):\n\n train_loss, train_accuracy = feed_data(model, optimizer, train_iter, False)\n test_loss, test_accuracy = feed_data(model, optimizer, test_iter, False)\n\n print('Training Data: accuracy: {} \\t loss: {} \\n'\n 'Testing Data: accuracy: {} \\t loss: {}'.format(train_accuracy, train_loss,\n test_accuracy, test_loss))","title":""},{"docid":"c8ec0592d6dcd927345109dc347d4228","score":"0.670525","text":"def test_epoch(self):\n self.model.eval()\n\n corrects = 0\n print('Performing Test Epoch')\n for test_inputs, test_labels in self.test_dl:\n test_inputs = test_inputs.to(self.device)\n test_labels = test_labels.to(self.device)\n\n test_output = self.model(test_inputs)\n test_output_labels = torch.max(test_output, dim=1)[1]\n corrects += int(torch.sum(test_output_labels == test_labels))\n\n del(test_inputs)\n del(test_labels)\n del(test_output)\n\n return corrects/len(self.test_dl.dataset)","title":""},{"docid":"9b22fa8e1eaeb668fa77c0e383d88675","score":"0.67051363","text":"def accuracy(self, X_test, t_test):\n #Fill in the code\n c = self.classify(X_test)\n # IPython.embed(header=\"acc\")\n return np.sum(c == t_test) / t_test.shape[0]","title":""},{"docid":"01333b30f040b59c7f87108a8e6a3ed0","score":"0.66959625","text":"def simple_accuracy(preds, labels):\n return (preds == labels).mean()","title":""},{"docid":"0eba31cdf22aa46b994c0fa1db3ea1ed","score":"0.6691743","text":"def accuracy(self, input, labels):\n predictions = self.predict(input)\n count = 0\n for prediction, target in zip(predictions, labels):\n if prediction == target:\n count += 1\n return count/len(labels)","title":""},{"docid":"518972fa1fb139bf015b89a1dcbb3470","score":"0.668921","text":"def evaluate_model(model, X_test, Y_test, category_names):\n\t\n Y_pred = model.predict(X_test)\n\t\n i = 0\n for col in Y_test:\n print(col)\n print(classification_report(Y_test[col], Y_pred[:, i]))\n i = i + 1","title":""},{"docid":"d5b7a045c07e7aa2801dfb2a109a241d","score":"0.66876626","text":"def test_evaluate(model, test_dataloader):\r\n # Put the model into the evaluation mode. The dropout layers are disabled during\r\n # the test time.\r\n model.eval()\r\n\r\n # Tracking variables\r\n# val_accuracy = []\r\n test_loss = []\r\n\r\n # For each batch in our validation set...\r\n for batch in test_dataloader:\r\n # Load batch to GPU\r\n b_input_ids, b_attn_mask, b_labels = tuple(t.to(dev) for t in batch)\r\n\r\n # Compute logits\r\n with torch.no_grad():\r\n logits = model(b_input_ids, b_attn_mask)\r\n\r\n # Compute loss\r\n loss = loss_fn(logits, b_labels)\r\n test_loss.append(loss.item())\r\n\r\n # Get the predictions\r\n preds = torch.argmax(logits, dim=1).flatten()\r\n\r\n # Calculate the accuracy rate\r\n# accuracy = (preds == b_labels).cpu().numpy().mean() * 100\r\n# val_accuracy.append(accuracy)\r\n\r\n # Compute the average accuracy and loss over the validation set.\r\n test_loss = np.mean(test_loss)\r\n# val_accuracy = np.mean(val_accuracy)\r\n\r\n return test_loss","title":""},{"docid":"4291aa6db212a53b519468ba317daa78","score":"0.66854525","text":"def test_network(sess, test_data, test_labels):\n\n start_index = 0\n total_elements = len(test_labels)\n denominator = total_elements\n accuracy_values = []\n image_accuracy_values = []\n while start_index < total_elements:\n\n \"\"\"Data gathering\"\"\"\n test_data_real = data_set.load_image_data(test_data, start_index, params.testing_batch_size, params)\n test_labels_real = data_set.load_image_data(test_labels, start_index, params.testing_batch_size, params, binary=True)\n\n \"\"\"Model execution\"\"\"\n accuracy_pred, all_accuracy, predictions = sess.run([accuracy, accuracy_all_true,prediction_round_values],\n feed_dict={x: test_data_real, y: test_labels_real, keep_prob: 1.0})\n\n \"\"\"Accuracy Calculation\"\"\"\n for i in xrange(len(test_labels_real)):\n label_array = np.array(test_labels_real[i]).reshape((params.image_size,params.image_size))\n prediction_array = np.array(predictions[i]).reshape((params.image_size,params.image_size))\n accuracy_values.append(vs.calculate_test_accuracy(prediction_array,label_array))\n image_accuracy_values.append(all_accuracy)\n\n if start_index % params.testing_display == 0:\n u.printf ('Tested {} data items'.format(start_index))\n\n start_index += params.testing_batch_size\n\n \"\"\"Testing output\"\"\"\n if total_elements >= params.testing_batch_size:\n denominator = float(total_elements/params.testing_batch_size)\n\n accuracy_array = np.array(accuracy_values).sum(0)\n u.printf(\"Test Accuracy: {} Image Accuracy: {} F1: {} Recall: {} Precision: {}\".format((accuracy_array[0] / total_elements),\n ((sum(image_accuracy_values) / denominator)/100),\n (accuracy_array[1] / total_elements),\n (accuracy_array[2] / total_elements),\n (accuracy_array[3] / total_elements)))\n\n u.printf(\"Debug: Accuracy{} TP: {} TN: {} FP: {} FN: {}\".format(accuracy_pred,accuracy_array[4],accuracy_array[5],accuracy_array[6],accuracy_array[7]))","title":""},{"docid":"67f55079474540a8e3fb90ea8e8ddab6","score":"0.66835564","text":"def test_on_data(X,y,classifiers):\n\n\t#data = np.array(data)\n\t#X = data[:,0:data.shape[1]-2]\n\t#y = data[:,-1]\n\t#y = y.reshape((y.shape[0]))\n\t#y = LabelBinarizer(neg_label=-1,pos_label=1).fit_transform(y)\n\tX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state = 42)\n\n\taccuracies = []\n\tfor clf_name,clf in classifiers.items():\n\t\tstart = time.time()\n\t\tclf = clf\n\t\tclf.fit(X_train,y_train)\n\t\tclf_time = time.time()-start\n\t\tif clf_name is 'SVM':\n\t\t\tclf_accuracy = round(np.mean(y_test == clf.predict(X_test)) * 100,2)\n\t\t\taccuracies.append(clf_accuracy)\n\t\telse:\n\t\t\tclf_accuracy = round(np.mean(y_test == clf.predict_all(X_test)) * 100,2) \n\t\t\taccuracies.append(clf_accuracy) \n\t\tprint(\"'%s'( time taken: \"%clf_name, clf_time, \", accuracy: \", clf_accuracy, \"%)\")","title":""},{"docid":"0e18b73318263c64142676928bd6ef1e","score":"0.6681391","text":"def evaluate_model(model, data_loader, device):\n correct_predictions = 0\n number_examples = 0\n for images, targets in data_loader:\n # Send data to device\n images, targets = images.to(device), targets.to(device)\n predictions = model(images)\n\n # Calculate number of correct predictions\n predicted_labels = torch.argmax(predictions, dim=1)\n correct_predictions += sum(predicted_labels == targets)\n number_examples += len(targets)\n\n accuracy = correct_predictions / number_examples\n\n return accuracy","title":""},{"docid":"a1b970aaa9afd4186b89c42607e87840","score":"0.6680198","text":"def evaluate_model(model, X_test, Y_test, category_names):\n y_pred = model.predict(X_test)\n print(classification_report(Y_test, y_pred, target_names=category_names))","title":""},{"docid":"740c54b7b89beac3c1abb2a1e7db9a36","score":"0.66731864","text":"def test_iris_dataset(self):\n model = AutoML(\n explain_level=0, verbose=0, random_state=1, results_path=self.automl_dir\n )\n score = model.fit(iris.data, iris.target).score(iris.data, iris.target)\n self.assertGreater(score, 0.5)","title":""},{"docid":"4e3e02d72856759ef618976ce4152199","score":"0.6667584","text":"def testStep(inputATensor,inputBTensor,label):\n modelPrediction=model(inputATensor,inputBTensor,False)\n loss=lossFunction(y_true=label,y_pred=modelPrediction)\n testLoss(loss)\n testAcuracy(y_true=label,y_pred=tf.cast(modelPrediction>=0.5,tf.int32))\n testAUC(y_true=label,y_pred=modelPrediction)\n testRecall(y_true=label,y_pred=modelPrediction)\n testPrecision(y_true=label,y_pred=modelPrediction)","title":""},{"docid":"c095536fd63168b601e209cd9ec08515","score":"0.6664447","text":"def test_all(self):\n self.predictions = []\n self.accuracies = []\n\n for key, model in self.models.items():\n predict_y = model.predict(self.processed['test_X'])\n self.predictions.append(predict_y)\n accuracy = accuracy_score(self.processed['test_y'], predict_y)\n self.accuracies.append(accuracy)","title":""},{"docid":"bc9919b2ac5c9dcd02b71e875ee9b774","score":"0.6663886","text":"def evaluate_model(model, X_test, Y_test, category_names):\n\n Y_pred = model.predict(X_test)\n\n # Print out the full classification report\n print(classification_report(Y_test, Y_pred, target_names=category_names))","title":""},{"docid":"0eba4e4e7c5e903734327fd458f3af34","score":"0.6661462","text":"def test(test_loader, model, names, classes):\r\n # switch to evaluate mode\r\n #model = torch.load(os.path.join(args.outf, 'model_best.pth.tar'))\r\n model.eval()\r\n top1 = AverageMeter()\r\n # Evaluate all the validation set\r\n correct, total = 0,0\r\n for i, (input, target) in enumerate(test_loader):\r\n if cuda:\r\n input = input.cuda(async=True)\r\n target = target.cuda(async=True)\r\n with torch.no_grad():\r\n input_var = torch.autograd.Variable(input)\r\n target_var = torch.autograd.Variable(target)\r\n\r\n # compute output\r\n output = model(input_var)\r\n prec1 = accuracy(output.data, target, topk=(1,))\r\n top1.update(prec1[0], input.size(0))\r\n _, predicted = output.max(1)\r\n total += target.size(0)\r\n correct += predicted.eq(target_var).sum().item() \r\n '''\r\n # Take last layer output\r\n if isinstance(output, tuple):\r\n output = output[len(output)-1]\r\n\r\n # print (output.data.max(1, keepdim=True)[1])\r\n lab = classes[numpy.asscalar(output.data.max(1, keepdim=True)[1].cpu().numpy())]\r\n print (\"Images: \" + next(names) + \", Classified as: \" + lab)\r\n '''\r\n print(' * test acc {acc}'.format(acc=numpy.asscalar(top1.avg.cpu().numpy())))\r\n # print(' ** acc {acc}'.format(acc=100.*correct/total))\r\n return top1.avg","title":""},{"docid":"4de8aab748c9efe37e158ab3d07a9aaf","score":"0.66569453","text":"def evaluate_model(model, X_test, y_test, category_names):\n y_pred = model.predict(X_test)\n print(classification_report(y_test, y_pred, target_names=category_names))","title":""},{"docid":"69a1363c84c48ac713e951d00d4097f7","score":"0.665502","text":"def evaluate_model(model, X_test, Y_test, category_names): \n # predict target using the machine learning model\n Y_pred = model.predict(X_test)\n\t\n # print metrics\n metrics = metrics(Y_test, Y_pred)\n print(metrics)","title":""},{"docid":"45b41a65acc954ef5e4a93037ce32d11","score":"0.66415954","text":"def test(model, test_loader, device):\n model.eval() # put model in evaluation mode\n test_loss = 0 # validation loss\n correct = 0\n with torch.no_grad():\n for data, target in test_loader:\n data, target = data.to(device), target.to(device)\n output = model.forward(data)\n test_loss += f.nll_loss(output, target, reduction=\"sum\").item()\n\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n test_loss /= len(test_loader.dataset) # compute validation loss of neural network\n accuracy = 100. * correct / len(test_loader.dataset)\n logger.debug(f\"Validation has finished:\\n\"\n f\"\\n Validation loss: {test_loss}\\n\"\n f\"\\n Accuracy: {accuracy} %\\n\")\n return test_loss, accuracy","title":""},{"docid":"8759d63fce198de4ff44b8a993cd4f62","score":"0.66390675","text":"def test_testing(self):\n references, corpus = pre.get_input(self.testfilename, labeled=True)\n references = [references[0], references[1], references[3]] + [references[2] \n + references[4] + references[5]]\n pred = lear.train(references, corpus)\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\n self.assertEqual([round(prob) for prob in \n sum(lear.test(references, corpus, pred), [])], classes)","title":""},{"docid":"91db36d923b251a83f90d88eaf4d2963","score":"0.6637648","text":"def test(device):\n correct = 0\n total = 0\n with torch.no_grad():\n net.eval()\n for data in testloader:\n images, labels = data\n images, labels = images.to(device), labels.to(device)\n outputs = net(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\n return correct/total","title":""},{"docid":"5d49efed2d47d2d930b81f76e73ad67e","score":"0.6637189","text":"def findOveralAccuracy(trainData,testData):\r\n pass","title":""},{"docid":"4510b3fe5d18b0d7c0bad2d985e691b5","score":"0.66358894","text":"def test_model_AUC(self):\n\n x_data = np.load('test_data/train_X.npy')\n y_data = np.load('test_data/train_Y.npy')\n train_set = [y_data, x_data]\n\n test_model = models.Model()\n test_model.train_set = train_set\n test_model.train_model(train_set)\n\n x_test = np.load(\"test_data/test_X.npy\")\n y_test = np.load(\"test_data/test_Y.npy\")\n test_set = [y_test, x_test]\n test_model.evaluate_model(test_set, train_set)\n print(test_model.evaluation_metrics)\n\n train_auc = round(test_model.evaluation_metrics['train_AUC'], 2)\n test_auc = round(test_model.evaluation_metrics['test_AUC'], 2)\n\n self.assertEqual(0.85, train_auc,\n msg=\"Train AUC not expected. Got {}, expected 0.85\".format(train_auc))\n\n self.assertEqual(0.77, test_auc,\n msg=\"Test AUC not expected. Got {}, expected 0.77\".format(test_auc))","title":""},{"docid":"ea8e511bd98df27b11f88aaa082b6adf","score":"0.66349304","text":"def test_training(self):\n references, corpus = pre.get_input(self.testfilename, labeled=True)\n references = [references[0], references[1], references[3]] + [references[2] \n + references[4] + references[5]]\n pred = lear.train(references, corpus)\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\n self.assertEqual(pred.predict(sum(sim_vectors, [])).tolist(), classes)","title":""},{"docid":"09fcf90024b9ff63cc84466ebb813db5","score":"0.6626775","text":"def test_model(model, test_generator):\n\n prediction = model.predict(test_generator)\n _, accuracy = model.evaluate(test_generator)\n\n print(\"Test accuracy: {}\".format(accuracy))\n\n return prediction, accuracy","title":""},{"docid":"29723bb4b5371a4f482becb1c2316363","score":"0.66246706","text":"def test(self, test_data, artist_embeddings):\n\n num_correct = 0\n for artist, song, lyrics in test_data:\n W = lyrics_to_word_matrix(lyrics, self.vocab)\n sv = compute_song_vector(W)\n S = [sv]\n av = compute_artist_vector(np.array(S))\n\n if artist in [neigbour[0] for neigbour in nearest_neighbors(artist_embeddings, av, 50)]:\n num_correct += 1\n\n print(\"Number correct: {}\".format(num_correct))\n print(\"Number total: {}\".format(len(test_data)))\n print(\"Learner accuracy {}%\".format((num_correct/len(test_data))*100))","title":""},{"docid":"c7ed9c54a594028100c69c97d429804f","score":"0.6621816","text":"def evaluate_model(model, X_test, Y_test, category_names):\n \n y_pred = model.predict(X_test)\n \n print('Accuracy: ', accuracy_score(Y_test, y_pred))\n print('Precision: ', precision_score(Y_test, y_pred, average='micro'))\n print('Recall: ', recall_score(Y_test, y_pred, average='micro'))\n print('F1 Score: ',f1_score(Y_test, y_pred, average='micro'))\n\n # Print out the full classification report\n for i, col in enumerate(category_names):\n print('{} category metrics: '.format(col))\n print(classification_report(Y_test.iloc[:,i], y_pred[:,i]))","title":""},{"docid":"a349a47a21c07ab33fa22e5d5a10856a","score":"0.66214067","text":"def accuracy(labels,predictions):\n correct = 1.*np.sum((labels+predictions)==2)\n total = 1.*np.sum(labels==1)\n print('correct',correct)\n print('total',total)\n try:\n return correct/total\n except:\n return 1.","title":""},{"docid":"2e372b35acc4494b19d8604553951b7e","score":"0.6618151","text":"def test(self,x,y,lr):\n pred=lr.predict(x)\n true=y\n test_acc=accuracy_score(true,pred)\n test_recall=recall_score(true,pred)\n test_precision=precision_score(true,pred)\n test_f1score=f1_score(true,pred)\n return test_acc","title":""},{"docid":"f89032d2f9eca644d6fd233edfa9202c","score":"0.6615105","text":"def test_model(model, batch_size, device, seed, test_dataset):\n set_seed(seed)\n\n # Set model to evaluation mode\n model.eval()\n\n test_results = {}\n\n with torch.no_grad():\n test_loader = DataLoader(test_dataset, batch_size=batch_size,\n generator=torch.Generator().manual_seed(42), pin_memory=True,\n num_workers=6)\n accuracy = evaluate_model(model, test_loader, device)\n test_results['accuracy'] = accuracy.item()\n\n # Test accuracy\n print(f\"Test accuracy: {accuracy.item():.4f}\")\n\n # Set model back to train mode\n model.train()\n\n return test_results","title":""},{"docid":"8198e00d12de958ac52fb0da56a7410c","score":"0.66064936","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_preds = model.predict(X_test)\n\n #transpose the values for each categories\n Y_preds_transpose = np.transpose(Y_preds)\n Y_test_transpose = np.transpose(Y_test)\n scores = []\n\n #evaluate the model by categories\n for e in range(Y_preds_transpose.shape[0]):\n validation = classification_report(Y_test_transpose.iloc[e,:].values, Y_preds_transpose[e], output_dict=True)\n scores.append((category_names[e], validation))\n\n #print out scores\n for score in scores:\n pprint.pprint(score)","title":""},{"docid":"c83277430e93da63e5c6d72e93dd80d5","score":"0.6598353","text":"def test(model, test_loader):\n print('\\nTesting')\n model.eval()\n correct = 0\n total = 0\n with torch.no_grad():\n for batch_idx, (inputs, targets) in enumerate(test_loader):\n inputs, targets = inputs.cuda(), targets.cuda()\n outputs = model(inputs)\n _, predicted = outputs.max(1)\n total += targets.size(0)\n correct += predicted.eq(targets).sum().item()\n\n progress_bar(batch_idx, len(test_loader), 'Acc: %.3f%% (%d/%d)'\n % (100. * correct/total, correct, total))\n\n return correct * 100 / total","title":""},{"docid":"eba28bfd3730f27f748fec8939eee4c1","score":"0.65945745","text":"def assess_test_data(self, mat, true_labels):\n m = mat.columns.size\n res = pd.Series([self.classify(mat.iloc[:, i]) for i in range(m)], index=mat.columns)\n n_correct = (res == true_labels).sum()\n n_incorrect = m - n_correct\n return res, n_correct, n_incorrect","title":""},{"docid":"8ada649b1f97104de55805b21695f094","score":"0.6589399","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_pred = model.predict(X_test)\n for i, col in enumerate(category_names):\n print(classification_report(Y_test[col], Y_pred[:, i]))","title":""},{"docid":"05cadcb17e87c77ff54513708cebec4f","score":"0.65890366","text":"def accuracy_mean_iou(test_set, labels, model1, model2, model3):\n model1_score = model1.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\n model2_score = model2.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\n model3_score = model3.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\n return model1_score, model2_score, model3_score","title":""},{"docid":"1e84c8afb17776c444a02bcaa110f913","score":"0.658901","text":"def evaluate_model(model, X_test, Y_test, category_names):\n y_pred = model.predict(X_test)\n for i in range(0, len(category_names)):\n print(classification_report(Y_test.values[:, i], y_pred[:, i]))","title":""},{"docid":"d3039f912208cf49a880478dc58199fc","score":"0.65875393","text":"def test_classification_net(model, data_loader, device):\n logits, labels = get_logits_labels(model, data_loader, device)\n return test_classification_net_logits(logits, labels)","title":""},{"docid":"c47962bc5cd2a1eea72d2d5f96afbe66","score":"0.6586379","text":"def KNN_Test(self, X_test, y_test):\n KNN_predict = self.KNN.predict(X_test)\n KNN_score = np.mean(KNN_predict == np.ravel(y_test,order='C'))\n # return (\"KNN Score: {:.9f}\".format(KNN_score))\n return round(KNN_score, 5)\n # Update score in case of retraining","title":""},{"docid":"a1d7cdd61c9e2a60417ff340e5663a38","score":"0.6581354","text":"def test(self, test_loader, get_auc = True): \n label_score = []\n batch_idx = -1\n for data, target, _ in test_loader:\n batch_idx += 1\n data, target = data.to(self.device), target.to(self.device)\n data = data.to(torch.float)\n target = target.to(torch.float)\n target = torch.squeeze(target)\n\n logits = self.model(data)\n logits = torch.squeeze(logits, dim = 1)\n sigmoid_logits = torch.sigmoid(logits)\n scores = sigmoid_logits\n label_score += list(zip(target.cpu().data.numpy().tolist(),\n scores.cpu().data.numpy().tolist()))\n # Compute test score\n labels, scores = zip(*label_score)\n labels = np.array(labels)\n scores = np.array(scores)\n pos_scores = scores[labels==1]\n neg_scores = scores[labels==0]\n auc = -1\n if get_auc:\n auc = roc_auc_score(labels, scores)\n return auc, pos_scores, neg_scores","title":""},{"docid":"0a747b8ff460aff894dccf380bf59dfa","score":"0.657953","text":"def evaluate(self, X_test, y_test):\n\n accuracy_model=self.model.evaluate(X_test, y_test, verbose=0)[1]\n return accuracy_model","title":""},{"docid":"0c333601f4c9d59fa9c64632b45556ff","score":"0.6579035","text":"def testData(model_path, xtest, ytest):\n\n model = load_model(model_path)\n ycapa = model.predict(xtest, batch_size=480)\n\n ycapa = np.argmax(ycapa, axis=-1)\n # ytest = np.argmax(ytest, axis=-1)\n\n util.log(f\"\\nTest metrics: {ytest.shape} {ycapa.shape}\")\n util.print_metrics_per_class(ytest, ycapa)\n\n return 1","title":""},{"docid":"28899e709e1be9e9dd72045d04897555","score":"0.6576536","text":"def validate_model(model, test_data_provider):\n model.start_prediction()\n test_loader = test_data_provider.loader\n test_loss = 0\n correct = 0\n for data, target in test_loader:\n data, target = Variable(data), Variable(target)\n _, pred, loss_val = model.validate_batch(data, target)\n test_loss += loss_val\n correct += pred.eq(target.data).cpu().sum()\n test_loss /= len(test_loader) # loss function already averages over batch size\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n test_loss, # 0.1023\n correct, # 17\n test_loader.example_count(), # out of 100\n 100. * correct / test_loader.example_count())) # (10%)\n return float(correct) / test_loader.example_count()","title":""},{"docid":"df1ba7ecff193f7ab185d268c972ff7e","score":"0.65748125","text":"def evaluate_model(model, X_test, Y_test, category_names):\n \n Y_pred = model.predict(X_test)\n \n for col in range(Y_pred.shape[1]):\n print(\"Category: {}\".format(category_names[col]))\n print(classification_report(Y_test[:, col], Y_pred[:, col]))","title":""}],"string":"[\n {\n \"docid\": \"948ec61063e23c45cf71c53a41bbcd25\",\n \"score\": \"0.7570441\",\n \"text\": \"def test(self, dataset):\\n self.model.eval() \\n with torch.no_grad():\\n total_samples, corrects = 0, 0\\n pred_class, label_class = [], []\\n for images, labels in dataset:\\n _, pred_labels = self.predict(images)\\n\\n corrects += torch.sum(labels == pred_labels).double()\\n total_samples += labels.shape[0]\\n \\n for p, l in zip(pred_labels, labels):\\n pred_class.append(p)\\n label_class.append(l)\\n \\n accuracy = corrects / total_samples\\n self.logger.info('\\\\n')\\n self.logger.info('Accuracy: {:.3}\\\\n\\\\n'.format(accuracy))\\n self.logger.info(classification_report(label_class, pred_class))\\n \\n return label_class, pred_class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78d3d90870a28a7b1ae35ceed93abba\",\n \"score\": \"0.74879396\",\n \"text\": \"def test(model, X_test, y_test, config):\\n #ytest = onehotencoding(y_test)\\n cost_test,logits = model.forward_pass(X_test,y_test)\\n predicted = np.argmax(logits,axis = 1)\\n test = np.argmax(y_test,axis = 1)\\n accuracy = np.sum(predicted == test)/len(test)\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bed5881898afd2a040bef4706e54c1a\",\n \"score\": \"0.7437546\",\n \"text\": \"def test_model(self,X,Y):\\n\\t\\tif hasattr(self.model, \\\"evals\\\"):\\n\\t\\t\\tlabels = np.array(Y)\\n\\t\\t\\tp_labels = self.model.evals(X)\\n\\n\\t\\telse:\\n\\t\\t\\tlabels = []\\t\\t\\t\\t\\t\\t# List of actual labels\\n\\t\\t\\tp_labels = []\\t\\t\\t\\t\\t# List of model's predictions\\n\\t\\t\\tsuccess = 0\\t\\t\\t\\t\\t\\t# Number of correct predictions\\n\\t\\t\\ttotal_count = 0\\t\\t\\t\\t\\t# Number of images\\n\\n\\t\\t\\tfor i in range(len(X)):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tx = X[i]\\t\\t\\t\\t\\t# Test input\\n\\t\\t\\t\\ty = Y[i]\\t\\t\\t\\t\\t# Actual label\\n\\t\\t\\t\\ty_ = self.model.eval(x)\\t\\t# Model's prediction\\n\\t\\t\\t\\tlabels.append(y)\\n\\t\\t\\t\\tp_labels.append(y_)\\n\\n\\t\\t\\t\\tif y == y_:\\n\\t\\t\\t\\t\\tsuccess += 1\\n\\t\\t\\t\\ttotal_count +=1 \\t\\t\\n\\t\\t\\n\\t\\tprint(\\\"Computing Confusion Matrix\\\")\\n\\t\\t# Compute Confusion Matrix\\n\\t\\tgetConfusionMatrixPlot(labels,p_labels,CLASS_LABELS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afb6ec3a69d89b5c9e10de7e1e7850d\",\n \"score\": \"0.7384026\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n y_pred = model.predict(X_test)\\n print(classification_report(y_pred, Y_test.values, target_names=category_names))\\n # print raw accuracy score \\n print('Accuracy Score: {}'.format(np.mean(Y_test.values == y_pred)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded7cd65863ec3d6aee9da8d43bba6bd\",\n \"score\": \"0.7348681\",\n \"text\": \"def evaluate_accuracy(model, testloader, device):\\n correct = 0\\n total = 0\\n with torch.no_grad():\\n for data in testloader:\\n images, labels = data\\n images, labels = images.to(device), labels.to(device)\\n outputs = model(images)\\n _, predicted = torch.max(outputs.data, 1)\\n total += labels.size(0)\\n correct += (predicted == labels).sum().item()\\n return correct / total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad70a05715df1630b9da59eca37904fe\",\n \"score\": \"0.7281572\",\n \"text\": \"def test(self):\\n test_loss, test_acc = self.model.evaluate(self.test_data_gen, \\n verbose=1)\\n print(f'Test loss: {test_loss}, test accuracy: {test_acc}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7926775089db74b6d0fbe063e981f616\",\n \"score\": \"0.72520256\",\n \"text\": \"def test(self, model, test_data, test_label):\\n # One hot encode\\n onehot_test_label = pd.get_dummies(test_label)\\n # Make predictions and output result\\n return model.evaluate(test_data, onehot_test_label)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3bd6d9e0f168dc9b35baf09dd7d08f6\",\n \"score\": \"0.7226808\",\n \"text\": \"def get_testing_accuracy(self):\\n\\n self.active_model = DataModel(self.training_model.data)\\n testing_data = self.testing_model.data\\n total_count_of_data_points = len(testing_data)\\n\\n total_correct_predictions = 0\\n for i in range(total_count_of_data_points):\\n correct_classification = testing_data[i][-1]\\n point = np.delete(testing_data, -1, axis=1)[i]\\n if self.get_classification(point) == correct_classification:\\n total_correct_predictions = total_correct_predictions + 1\\n accuracy = 100 * (total_correct_predictions / total_count_of_data_points)\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5ac00c21946fc690067f845f147b77\",\n \"score\": \"0.72142124\",\n \"text\": \"def test(self, dataset, labels, tf_idf=False):\\n correct = 0\\n predictions = []\\n\\n # infer for every article\\n for i, text in enumerate(dataset):\\n cat = self.infer(text, tf_idf)\\n predictions.append(cat)\\n\\n if cat == labels[i]:\\n correct += 1\\n\\n # calculate accuracy\\n accuracy = correct/len(dataset)\\n\\n return predictions, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e575bfb67e9cd867edb796fa975d8a\",\n \"score\": \"0.71954083\",\n \"text\": \"def ComputeAccuracy(test_set, test_set_predicted):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d05c19c6fd77b771147f67635d6d88\",\n \"score\": \"0.7150339\",\n \"text\": \"def test(model, test_data, test_label):\\n\\n #TODO: Fix\\n\\n acc_bb_sum = 0\\n acc_pixel_sum = 0\\n acc_type_sum = 0\\n number_of_batches = 0\\n for i in range(0, len(test_data), model.batch_size):\\n if(len(test_data)-i >= model.batch_size):\\n\\n # Batching:\\n inputs = test_data[i:i + model.batch_size, :, :, np.newaxis]\\n labels = test_label[i:i+model.batch_size]\\n batch_molecules = test_data[i:i + model.batch_size]\\n\\n output_bb, output_pixel, output_type = model.call(inputs)\\n acc_bb, acc_pixel, acc_type = model.accuracy_function(output_bb, output_pixel, output_type, label_bb, label_pixel, label_type)\\n acc_bb_sum += acc_bb\\n acc_pixel_sum += acc_pixel\\n acc_type_sum += acc_type\\n number_of_batches += 1\\n \\n acc_bb = acc_bb_sum / number_of_batches\\n acc_pixel = acc_pixel_sum / number_of_batches\\n acc_type = acc_type_sum / number_of_batches\\n\\n return acc_bb, acc_pixel, acc_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48321573d46bf6db1648485540b80e82\",\n \"score\": \"0.7114068\",\n \"text\": \"def test(model, dataset):\\n avg_loss = tfe.metrics.Mean('loss', dtype=tf.float32)\\n accuracy = tfe.metrics.Accuracy('accuracy', dtype=tf.float32)\\n\\n for (images, labels) in dataset:\\n logits = model(images, training=False)\\n avg_loss(loss(logits, labels))\\n accuracy(\\n tf.argmax(logits, axis=1, output_type=tf.int64),\\n tf.cast(labels, tf.int64))\\n if ddl.rank() == 0:\\n print('Test set: Average loss: %.4f, Accuracy: %4f%%\\\\n' %\\n (avg_loss.result(), 100 * accuracy.result()))\\n with tf.contrib.summary.always_record_summaries():\\n tf.contrib.summary.scalar('loss', avg_loss.result())\\n tf.contrib.summary.scalar('accuracy', accuracy.result())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ed292f50cde6689037992f6794decd\",\n \"score\": \"0.7109705\",\n \"text\": \"def test_binary_classification(self):\\n model_predictions = self.predict(self.x_test)\\n right_predicts = 0\\n for prediction, observation in zip(model_predictions, self.y_test):\\n if observation == 1 and 0.5 <= prediction <= 1:\\n right_predicts += 1\\n elif observation == 0 and prediction < 0.5:\\n right_predicts += 1\\n else:\\n pass\\n model_accuracy = right_predicts / model_predictions.shape[0] * 100\\n print(f'The Binary Classification Model has an accuracy of {round(model_accuracy, 4)}%')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a37ee7f038ec25434a1cb5f58c3dcb8\",\n \"score\": \"0.7089755\",\n \"text\": \"def accuracy(self, X_test, y_test):\\n # Predicted values with the given X's.\\n y_pred = self.predict(X_test)\\n\\n # Sum of which the prediction is the same as the target output.\\n return sum(y_pred == y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4b109a3a113919532200024d16cb928\",\n \"score\": \"0.70825845\",\n \"text\": \"def accuracy(data_loader, model):\\n training = model.training\\n model.eval()\\n correct, total = 0, 0\\n for data, target in data_loader:\\n if use_cuda:\\n data, target = Variable(\\n data.cuda(), volatile=True), Variable(target.cuda())\\n else:\\n data, target = Variable(data, volatile=True), Variable(target)\\n output = model(data)\\n pred = model.predict(output)\\n correct += (pred == target.data).sum()\\n total += target.size(0)\\n model.train(training)\\n return correct, total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80f205ca84a9fa9b02c67eb687bd62a\",\n \"score\": \"0.70442224\",\n \"text\": \"def test(self):\\n self.eval()\\n test_mask = self.data.test_mask\\n labels = self.data.y\\n output = self.forward(self.data)\\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\\n None\\n return acc_test.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80f205ca84a9fa9b02c67eb687bd62a\",\n \"score\": \"0.70442224\",\n \"text\": \"def test(self):\\n self.eval()\\n test_mask = self.data.test_mask\\n labels = self.data.y\\n output = self.forward(self.data)\\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\\n None\\n return acc_test.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80f205ca84a9fa9b02c67eb687bd62a\",\n \"score\": \"0.70442224\",\n \"text\": \"def test(self):\\n self.eval()\\n test_mask = self.data.test_mask\\n labels = self.data.y\\n output = self.forward(self.data)\\n loss_test = F.nll_loss(output[test_mask], labels[test_mask])\\n acc_test = utils.accuracy(output[test_mask], labels[test_mask])\\n None\\n return acc_test.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960da008c5a27577509f9492754e87f7\",\n \"score\": \"0.7029079\",\n \"text\": \"def evaluate_on_test_set(self):\\n accuracies = 0\\n\\n test_loader = DataLoader(self.data_test, self.batch_size)\\n\\n #hidden = self.model.init_hidden(self.batch_size).to(self.device)\\n\\n with torch.no_grad():\\n for j, data in enumerate(test_loader, 0):\\n test_inputs, test_labels = data[0].to(self.device), data[1].to(self.device)\\n\\n #outputs = self.model(test_inputs, hidden)\\n outputs = self.model(test_inputs)\\n\\n loss = self.loss_fn(outputs, test_labels)\\n\\n accuracies += self.accuracy(outputs, test_labels)\\n\\n print(\\\"Accuracy on test set : {:05.3f} %\\\".format(100 * accuracies / len(test_loader)))\\n\\n return accuracies / len(test_loader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2824e825d88c007c43fbf48fabf50aaa\",\n \"score\": \"0.701956\",\n \"text\": \"def evaluate_model(self):\\n X_test, y_test = self.data\\n score = self.model.evaluate(X_test, y_test, verbose=1)\\n print(\\\"Test loss:\\\", score[0])\\n print(\\\"Test accuracy:\\\", score[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27f264207e241d5f44e8e0cddb14b247\",\n \"score\": \"0.7011244\",\n \"text\": \"def predict_and_test(model, X_test, y_test):\\n y_hat = np.array(model.predict(X_test))\\n correct = np.sum(y_hat == y_test)\\n samples = y_test.size\\n return correct / samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b587fcad4d3dc44c9fd7a1d4d1e143d\",\n \"score\": \"0.6993456\",\n \"text\": \"def test_training_images(pos_testpath, neg_testpath, trained_model):\\n images = get_gray_imgs(pos_testpath, neg_testpath)\\n\\n positive_counter = 0\\n negative_counter = 0\\n pos_acc = 0\\n neg_acc = 0\\n\\n for gray_img, label in images:\\n if label == 1:\\n positive_counter += 1.0\\n elif label == -1:\\n negative_counter += 1.0\\n\\n prediction = calculate_final_hypothesis(gray_img, trained_model[0], trained_model[1], trained_model[2],\\n trained_model[3])\\n\\n if prediction == label and label == 1:\\n pos_acc += 1.0\\n if prediction == label and label == -1:\\n neg_acc += 1.0\\n\\n print \\\"positive accuracy\\\", pos_acc / positive_counter\\n print \\\"negative accuracy\\\", neg_acc / negative_counter\\n print \\\"overall accuracy\\\", (pos_acc + neg_acc) / (positive_counter + negative_counter)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576bde21b6a215c68f3fcde962d31461\",\n \"score\": \"0.69915503\",\n \"text\": \"def evaluate_model(model,X_test,y_test):\\n # print classification_report\\n print(\\\"Evaluation the model .....\\\")\\n y_pred = model.predict(X_test)\\n print('Accuracy: {}'.format(np.mean(y_test.values == y_pred)))\\n print(\\\"Classification report:\\\")\\n print(classification_report(y_test.values, y_pred, target_names=y_test.columns))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e7b61b1718bf034bf4babcf23f3828\",\n \"score\": \"0.697071\",\n \"text\": \"def accuracy(test_loader, model):\\n model.eval()\\n correct = 0.\\n total = 0.\\n with torch.no_grad():\\n for i, (x, target) in enumerate(test_loader):\\n target = target.cuda()\\n input_var = x.cuda()\\n target_var = target.cuda()\\n\\n output = model(input_var)\\n output = output.float()\\n\\n for j, pred in enumerate(output):\\n l = pred.argmax()\\n total += 1\\n if l == target[j]:\\n correct += 1\\n return correct/total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019968139235fb2c96ee7b2ea6f84ee6\",\n \"score\": \"0.6950458\",\n \"text\": \"def run_test(self, dataset, clf, model_name, dataset_name):\\n\\n (x_train, y_train), (x_test, y_test), num_classes, input_shape = self.prepare_data(dataset)\\n\\n original_x_test = x_test\\n original_y_test = y_test\\n\\n y_predict = clf.predict(x_test)\\n\\n temp_train = []\\n temp_predict = []\\n temp_test = []\\n for i, vect in enumerate(y_train):\\n temp_train.append(np.argmax(y_train[i]))\\n\\n for i, vect in enumerate(y_predict):\\n temp_predict.append(np.argmax(y_predict[i]))\\n\\n for i, vect in enumerate(y_test):\\n temp_test.append(np.argmax(y_test[i]))\\n\\n y_train = temp_train\\n y_predict = temp_predict\\n y_test = temp_test\\n\\n labels = [y for y in y_train]\\n\\n labels = sorted(list(set(labels)))\\n print(\\\"\\\\nConfusion matrix:\\\")\\n print(\\\"Labels: {0}\\\\n\\\".format(\\\",\\\".join([str(y) for y in labels])))\\n print(confusion_matrix(y_test, y_predict, labels=labels))\\n\\n print(\\\"\\\\nClassification report:\\\")\\n print(classification_report(y_test, y_predict))\\n\\n score = clf.evaluate(original_x_test, original_y_test, verbose=1)\\n\\n print('Test loss:', score[0])\\n print('Test accuracy:', score[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbd102f4f5d5c8fc9a766ad73822311\",\n \"score\": \"0.6939115\",\n \"text\": \"def test(self, dataloader):\\n # enable eval mode\\n self._net.train(False)\\n num_correct = 0\\n num_total = 0\\n with torch.no_grad():\\n for img, label in dataloader:\\n # Data\\n img = img.cuda()\\n label = label.cuda()\\n # forward pass\\n score = self._net(img)\\n # prediction\\n max_score, prediction = torch.max(score, 1)\\n # statistic\\n num_total += label.size(0)\\n num_correct += torch.sum(prediction == label.data).item()\\n # switch back to train mode\\n self._net.train(False)\\n return 100 * num_correct / num_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a936cf6ab31d5d1a2feb073242fe2a\",\n \"score\": \"0.6918965\",\n \"text\": \"def accuracy(self, X_test, y_test):\\n _, self.b, self.w = self.fit()\\n preds = self.predict(X_test)\\n accuracy = accuracy_score(y_test, preds)\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5343eedc5500ba04b9c91f7def3765d0\",\n \"score\": \"0.69107103\",\n \"text\": \"def test_model(net, dataset, loss_module, batch_size):\\n iters_needed = dataset._num_examples // batch_size # iterations needed to check the whole dataset\\n loss = []\\n count = 0\\n acc = []\\n\\n for i in range(iters_needed):\\n imgs, labels = dataset.next_batch(batch_size)\\n labels = torch.tensor(labels)\\n imgs = imgs.reshape(imgs.shape[0], -1)\\n with torch.no_grad():\\n preds = net(imgs)\\n count += labels.shape[0]\\n acc.append(accuracy(preds, labels))\\n loss.append(loss_module(preds, torch.max(labels, 1)[1]).item())\\n\\n acc = np.mean(acc)\\n loss = np.mean(loss)\\n\\n return acc, loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"337a50f0a03e73dd29b5794dce9bbf40\",\n \"score\": \"0.6903235\",\n \"text\": \"def predict(self, test_loader):\\n self.eval()\\n correct = 0.\\n\\n for batch_idx, (X_test, y_test) in enumerate(test_loader):\\n X_test, y_test = X_test.to(self.device), y_test.to(self.device)\\n output = F.softmax(self.forward(X_test), dim=1)\\n y_pred = output.data.max(1)[1]\\n\\n correct += y_pred.eq(y_test.view(-1).data).sum()\\n\\n accuracy = 100. * float(correct) / len(test_loader.dataset)\\n\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6fc07b4afda4be150088dcf18ae0125\",\n \"score\": \"0.68791044\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_pred = model.predict(X_test)\\n for i in range(Y_test.shape[1]):\\n accuracy = '%25s accuracy : %.2f' \\\\\\n %(category_names[i],\\n accuracy_score(Y_test[:,i], Y_pred[:,i]))\\n print(accuracy)\\n with open('result.log','w') as file:\\n print(classification_report(Y_test, Y_pred,\\n target_names=category_names))\\n file.write(classification_report(Y_test, Y_pred,\\n target_names=category_names))\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5f9e8a4d802d88c9ddaa8672d3c8d0\",\n \"score\": \"0.6875846\",\n \"text\": \"def test_model(loader, model):\\n correct = 0\\n total = 0\\n result = []\\n model.eval()\\n for data1, data2, lengths1, lengths2, labels in loader:\\n data_batch1, data_batch2, lengths_batch1, lengths_batch2, label_batch = data1, data2, lengths1, lengths2, labels\\n outputs = F.softmax(model(data_batch1, lengths_batch1, data_batch2, lengths_batch2), dim=1)\\n predicted = outputs.max(1, keepdim=True)[1]\\n total += labels.size(0)\\n correct += predicted.eq(labels.view_as(predicted)).sum().item()\\n result.append([labels, predicted])\\n return (100 * correct / total) , result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2789e9ea18d419b40f12cb7d60ca508\",\n \"score\": \"0.681634\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names): \\n pred = model.predict(X_test)\\n for i, col in enumerate(Y_test):\\n print(\\\"Column: {}\\\".format(col))\\n print(classification_report(Y_test[col], pred[:, i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0c3cb0237f08c5d929d65a09a0902e\",\n \"score\": \"0.6810412\",\n \"text\": \"def test(self, test_data, test_labels):\\n correct = 0.0;\\n prediction_histogram = {k: 0 for k in range(10)};\\n \\n # Predict\\n prediction = self.predict(test_data);\\n \\n # Compute statistics\\n for j in range(np.size(prediction)):\\n prediction_histogram[prediction[j]] += 1;\\n if (prediction[j] == test_labels[j]):\\n correct += 1.0;\\n \\n return (correct / np.size(test_data,0), prediction_histogram, (self.W, self.V));\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7871c16f0d2c3b2c1791f7794d5835e5\",\n \"score\": \"0.68010974\",\n \"text\": \"def score(model, testset, trainset):\\n correct = 0\\n total = 0\\n\\n for test_cls, test_images in testset.items():\\n for image in test_images:\\n total += 1\\n if isinstance(model, KnnModel):\\n cls_pred = model.predict_class(image, trainset, threshold=0.90)\\n else:\\n cls_pred = model.predict_class(image, trainset, threshold=0.5)\\n\\n correct += (test_cls == cls_pred)\\n\\n return correct / total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4ebaa038a27f35e195b04820a07eb5\",\n \"score\": \"0.6792031\",\n \"text\": \"def accuracy(labels_test, Y_vote):\\r\\n # predicted classes\\r\\n y_hat = np.argmax(Y_vote, axis=1)\\r\\n\\r\\n # all cases where predicted class was correct\\r\\n mask = y_hat == labels_test\\r\\n\\r\\n antimask = y_hat != labels_test\\r\\n\\r\\n for i in range(0, len(antimask)):\\r\\n if not (y_hat[i] == labels_test[i]):\\r\\n print(\\\"index:\\\" + str(i) + \\\" ,predicted: \\\" + str(y_hat[i]) + \\\" ,actual: \\\" + str(labels_test[i]))\\r\\n\\r\\n return np.float32(np.count_nonzero(mask)) / len(labels_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493b322dc82a4e3df2d5f9b3d0bd0797\",\n \"score\": \"0.6787765\",\n \"text\": \"def check_model(X_train, X_test, y_train, y_test, model):\\n\\n # fit the model on the train data\\n model.fit(X_train, y_train)\\n # predict the test data\\n test_score = model.score(X_test, y_test)\\n train_score = model.score(X_train, y_train)\\n\\n return test_score, train_score, model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f183f06fbc1f236b5f8695c691e96b6a\",\n \"score\": \"0.67734265\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n # make prediction on test set\\n Y_pred = model.predict(X_test)\\n \\n # print evaluation metrics \\n print(classification_report(Y_test,Y_pred,target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79790a52e6a25ab50f829fbdeae9e101\",\n \"score\": \"0.67720956\",\n \"text\": \"def test(self, idx_test):\\n self.eval()\\n output = self.predict()\\n loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])\\n acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])\\n None\\n return acc_test.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79790a52e6a25ab50f829fbdeae9e101\",\n \"score\": \"0.67720956\",\n \"text\": \"def test(self, idx_test):\\n self.eval()\\n output = self.predict()\\n loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])\\n acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])\\n None\\n return acc_test.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce857cc1eb62db64c4d03eeecb151e4d\",\n \"score\": \"0.67680514\",\n \"text\": \"def test(model, data_loader):\\n print('===> Evaluate mode')\\n\\n # Switch to evaluate mode\\n model.eval()\\n\\n test_loss = 0\\n correct = 0\\n for data, target in data_loader:\\n target_indices = target\\n target_one_hot = utils.one_hot_encode(\\n target_indices, length=model.digits.num_unit)\\n\\n data, target = Variable(data, volatile=True), Variable(target_one_hot)\\n\\n if args.cuda:\\n data = data.cuda()\\n target = target.cuda()\\n\\n output = model(data)\\n\\n # sum up batch loss\\n test_loss += model.loss(output, target, size_average=False).data[0]\\n\\n # evaluate\\n v_magnitud = torch.sqrt((output**2).sum(dim=2, keepdim=True))\\n pred = v_magnitud.data.max(1, keepdim=True)[1].cpu()\\n correct += pred.eq(target_indices.view_as(pred)).sum()\\n\\n test_loss /= len(data_loader.dataset)\\n\\n mesg = 'Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(\\n test_loss,\\n correct,\\n len(data_loader.dataset),\\n 100. * correct / len(data_loader.dataset))\\n print(mesg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb680a285ea58c96dd92154b19c583c\",\n \"score\": \"0.67655736\",\n \"text\": \"def get_accuracy(self):\\n accuracy = 0\\n m = self.test_ex.shape[0]\\n w_size = [(self.hidden_layer_size, self.input_layer_size), (self.num_labels, self.hidden_layer_size)]\\n res, no, na, ni = NeuralNetwork.forward_prop(self.weights_bias, self.test_ex, w_size)\\n for i in range(m):\\n if np.argmax(res[i]) == self.test_labels[i]:\\n accuracy += 1\\n print('accuracy :', 100 * accuracy / m, '%')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30b5ae1051eb22068bced71fc5f96ac\",\n \"score\": \"0.6760537\",\n \"text\": \"def test(self, inputs, targets, test_log):\\n print(\\\"Let's test!\\\\n\\\")\\n\\n n_correct = 0\\n n_wrong = 0\\n \\n for i in range(0, len(targets)):\\n #print(\\\"\\\\tStart argument...\\\\n\\\")\\n\\n data_point = (inputs[i], targets[i])\\n test_args = Test_Arguments(data_point)\\n \\n # Run the ensemble \\n (out_0, out_1) = self.test_recursive(test_args, test_log)\\n \\n #log accuracy of m\\n m_accuracy = test_args.total_correct_m / test_args.argument_length\\n test_log.log_accuracy(\\\"m\\\", m_accuracy)\\n\\n if out_0 > out_1 and targets[i] == label_0:\\n n_correct += 1\\n elif out_1 > out_0 and targets[i] == label_1:\\n n_correct += 1\\n else:\\n n_wrong += 1\\n \\n print(\\\"Accuracy Ensemble: \\\", n_correct/(n_correct + n_wrong))\\n print(\\\"Accuracy Main: \\\", test_log.data_m.get_accuracy())\\n print(\\\"Accuracy C0: \\\", test_log.data_c0.get_accuracy())\\n print(\\\"Accuracy C1: \\\", test_log.data_c1.get_accuracy())\\n \\n test_log.reset_accuracies()\\n\\n return test_log\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9124ed98f4a3db0a213437044487f03\",\n \"score\": \"0.6753827\",\n \"text\": \"def accuracy(y_test, y_pred):\\n return (y_pred == y_test).astype(int).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ec3fd9385a309995688210f2758b0b\",\n \"score\": \"0.6753732\",\n \"text\": \"def test(work_dir, pkl_path, load_epoch, silent=False):\\r\\n load_path = os.path.join(work_dir, 'epoch_{}.pth'.format(load_epoch))\\r\\n sys.path.append(work_dir)\\r\\n from config import model_cfg\\r\\n\\r\\n for attr in ['type', 'num_steps', 'num_tasks']:\\r\\n assert attr in model_cfg\\r\\n if not silent: print (\\\"Generating model: {}\\\".format(model_cfg['type']))\\r\\n trainer = Trainer(model_cfg)\\r\\n\\r\\n if not silent: \\r\\n print (model_cfg)\\r\\n print (\\\"Loading trained model from {}\\\".format(load_path))\\r\\n print ('----------------------------------------------')\\r\\n trainer.load_model(load_path)\\r\\n\\r\\n scores, task_ids, props = _parse_pkl(pkl_path)\\r\\n task_preds = trainer.predict(scores, props)\\r\\n\\r\\n task_ids = np.array(task_ids)\\r\\n accuracy = (np.sum(task_ids == task_preds) / len(task_ids)) * 100\\r\\n if not silent: \\r\\n print (task_ids)\\r\\n print (task_preds)\\r\\n print (np.sum(task_ids == task_preds))\\r\\n print (len(task_ids))\\r\\n print (\\\"Accuracy: %.3f\\\" % ((np.sum(task_ids == task_preds) / len(task_ids)) * 100))\\r\\n \\r\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0afa0e95e86ad7cea5e18ac1d23a75d2\",\n \"score\": \"0.67516047\",\n \"text\": \"def test(self,dataset):\\n outputs = self.use(dataset)\\n costs = np.ones((len(outputs),1))\\n # Compute classification error\\n for xy,pred,cost in zip(dataset,outputs,costs):\\n x,y = xy\\n if y == pred[0]:\\n cost[0] = 0\\n\\n return outputs,costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ff85a6be2dbc836a3013ebcf11709e9\",\n \"score\": \"0.6750377\",\n \"text\": \"def test_step(images, labels):\\n predictions = model(images)\\n\\n loss = entropy(labels, predictions)\\n accuracy = metrics(labels, predictions)\\n\\n return loss, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e57d045f02811f087abe456b6c4598\",\n \"score\": \"0.67496157\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n\\n # Score the model on test cases\\n Y_predicted = model.predict(X_test)\\n\\n # Generate the classification report\\n for colId, colName in enumerate(Y_test):\\n print(colName)\\n print(classification_report(Y_test[colName], Y_predicted[:, colId]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d01d740459f09fdaf6fa2e27a79f05\",\n \"score\": \"0.67440253\",\n \"text\": \"def evaluate_accuracy(\\n y_true: np.ndarray, y_pred: np.ndarray, dataset_name: str\\n) -> None:\\n acc = accuracy_score(y_true, y_pred)\\n\\n logger.info(f\\\"{dataset_name} set accuracy: {acc}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057c0ad525f0fc92b7f4e524b337f01e\",\n \"score\": \"0.6734119\",\n \"text\": \"def evaluate(model, X_test, y_test, checkfile = 'weights.hdf5'):\\n model.load_weights(checkfile)\\n predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0))) \\n for feature in X_test]\\n test_list = y_test.argmax(axis=1)\\n f1 = f1_score(test_list, predictions, average = 'macro')\\n accuracy = accuracy_score(test_list,predictions) \\n return test_list, predictions, f1, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21d7ea8a88ab5220bba4844919208b38\",\n \"score\": \"0.6728676\",\n \"text\": \"def model_accuracy(trained_model, features, targets):\\n accuracy_score = trained_model.score(features, targets)\\n return accuracy_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c74746ccdc0ea532b5e296045966dad0\",\n \"score\": \"0.67236334\",\n \"text\": \"def testModel(self, testData):\\n\\n # make testData into a string list list\\n testLL = []\\n for line in (testData.splitlines()):\\n testLL.append(line)\\n\\n # predict and test each line\\n TP_R, TN_R, FP_R, FN_R = 0, 0, 0, 0\\n TP_B, TN_B, FP_B, FN_B = 0, 0, 0, 0\\n for line in testLL:\\n actualClass = line.split()[0]\\n # print(self.estimateLogProbability(line)['red'], self.estimateLogProbability(line)['blue'])\\n if self.estimateLogProbability(line)['red'] > self.estimateLogProbability(line)['blue']:\\n predictedClass = \\\"RED\\\"\\n else:\\n predictedClass = \\\"BLUE\\\"\\n # print(actualClass, predictedClass)\\n\\n # calculate\\n if (actualClass == \\\"RED\\\" and predictedClass == \\\"RED\\\"): \\n TP_R += 1\\n TN_B += 1\\n elif (actualClass == \\\"RED\\\" and predictedClass == \\\"BLUE\\\"):\\n FN_R += 1\\n FP_B += 1\\n elif (actualClass == \\\"BLUE\\\" and predictedClass == \\\"RED\\\"):\\n FP_R += 1\\n FN_B += 1\\n elif (actualClass == \\\"BLUE\\\" and predictedClass == \\\"BLUE\\\"):\\n TN_R += 1\\n TP_B += 1\\n\\n # overall accuracy, calculate use either red or blue is fine\\n accu_overall = (TP_R+TN_R)/(TP_R+TN_R+FN_R+FP_R)\\n # precision for red\\n preci_red = (TP_R)/(TP_R+FP_R)\\n # precison for blue\\n preci_blue = TP_B/(TP_B+FP_B)\\n # recall for red\\n recall_red = (TP_R)/(TP_R+FN_R)\\n # recall for blue\\n recall_blue = TP_B/(TP_B+FN_B)\\n\\n\\n\\n return {'overall accuracy': accu_overall,\\n 'precision for red': preci_red,\\n 'precision for blue': preci_blue,\\n 'recall for red': recall_red,\\n 'recall for blue': recall_blue}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97651a9af26d44a00b2534dc9ef9e3ab\",\n \"score\": \"0.6722877\",\n \"text\": \"def test_model(model, test_input, test_target, test_class):\\r\\n model.train(False)\\r\\n \\r\\n pred_x, pred_y, pred_z = model(test_input)\\r\\n class_err = (pred_x.data.max(1)[1].numpy() != test_class[:,0].numpy()).sum()\\r\\n class_err += (pred_y.data.max(1)[1].numpy() != test_class[:,1].numpy()).sum()\\r\\n comp_err = (pred_z.data.max(1)[1].numpy() != test_target.numpy()).sum() \\r\\n\\r\\n pred_class = torch.stack([pred_x.data.max(1)[1], pred_y.data.max(1)[1]], dim=1)\\r\\n print(\\\"Naive comparison error: {}\\\".format((compare_digits(pred_class) - test_target.int()).abs().sum().item()))\\r\\n \\r\\n return class_err, class_err /test_target.size(0) /2*100, comp_err, comp_err /test_target.size(0) *100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1fe2bffecad427c690691028dd4211\",\n \"score\": \"0.6721459\",\n \"text\": \"def evaluate(self, dataset):\\r\\n score = self.model.evaluate(dataset.test_images, [dataset.test_labels, dataset.test_labels], verbose=1)\\r\\n print(self.model.metrics_names)\\r\\n print('Test score:', score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b42e0693013f7d2fcb8abb07a9a9f62\",\n \"score\": \"0.67200595\",\n \"text\": \"def test_model_building(self):\\n preds = self.lda.predict()\\n confusion = confusion_matrix(self.y, preds)\\n self.assertTrue(confusion[0, 0] > 90)\\n self.assertTrue(confusion[1, 1] > 90)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b742a6ceeb678e7e1e6c3fcd87fe26d2\",\n \"score\": \"0.6720059\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n y_pred = model.predict(X_test)\\n\\n for i in range(len(category_names)):\\n print('---------------------------------------------------------------------------')\\n print(\\\"Category:\\\", category_names[i])\\n print(classification_report(Y_test.iloc[:, i].values, y_pred[:, i]))\\n print(\\\"Accuracy: \\\", accuracy_score(Y_test.iloc[:, i].values, y_pred[:, i]))\\n print(\\\"Best Parameters:\\\", model.best_params_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca96b29a8b8421903f42e1a44590676\",\n \"score\": \"0.67182577\",\n \"text\": \"def test_step(X, labels, batch_size=50):\\n batch_num = int(len(X) / batch_size)\\n testing_accuracy = 0\\n prev = 0\\n predicted_vector = []\\n for i in range(batch_num+1):\\n x_dev_batch = X[prev:prev+batch_size]\\n y_dev_batch = labels[prev:prev+batch_size]\\n prev += batch_size\\n pred_list, test_acc = sess.run(\\n [cnn.predictions, cnn.accuracy],\\n feed_dict={\\n cnn.X: x_dev_batch,\\n cnn.y: y_dev_batch,\\n cnn.dropout_keep_prob: 1.0})\\n testing_accuracy += test_acc\\n predicted_vector.extend(list(pred_list))\\n\\n if i == (batch_num-1):\\n batch_size = int(len(X) % batch_size)\\n\\n testing_accuracy /= batch_num\\n pickle.dump(predicted_vector, open(\\\"{}\\\".format(sys.argv[2]), \\\"wb\\\"))\\n print(\\\"Testing accuracy %g\\\" % testing_accuracy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1671dea4360bdb2a6129777452bcf52\",\n \"score\": \"0.67172253\",\n \"text\": \"def test(self):\\n self._reset_metrics()\\n self.model.eval()\\n total = 0\\n test_loss = 0\\n with torch.no_grad():\\n old_time = time()\\n for batch, data in enumerate(self.data_bundle.test_dataset):\\n inputs, labels = data\\n inputs, labels = inputs.to(self.device), labels.to(self.device)\\n\\n outputs = self.model(inputs)\\n loss = self.criterion(outputs, labels)\\n _, predicted = torch.max(outputs.data, 1)\\n\\n total += labels.size(0)\\n test_loss += loss.item()\\n\\n self._eval_metrics(labels, outputs)\\n\\n if batch % 10 == 0 or batch == (len(self.data_bundle.test_dataset)-1):\\n self._print_status(batch, old_time, self.data_bundle.test_dataset)\\n old_time = time()\\n\\n test_metrics = self._track_metrics('test', test_loss, total)\\n return test_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8e7734dda9f7655f3dec7cf24650b2\",\n \"score\": \"0.6711027\",\n \"text\": \"def accuracy(preds, targets):\\n return (preds == targets).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34493360d77d6bdedb38aaba2c0dcb6\",\n \"score\": \"0.67067784\",\n \"text\": \"def test_network(model, optimizer):\\n\\n train_loss, train_accuracy = feed_data(model, optimizer, train_iter, False)\\n test_loss, test_accuracy = feed_data(model, optimizer, test_iter, False)\\n\\n print('Training Data: accuracy: {} \\\\t loss: {} \\\\n'\\n 'Testing Data: accuracy: {} \\\\t loss: {}'.format(train_accuracy, train_loss,\\n test_accuracy, test_loss))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ec0592d6dcd927345109dc347d4228\",\n \"score\": \"0.670525\",\n \"text\": \"def test_epoch(self):\\n self.model.eval()\\n\\n corrects = 0\\n print('Performing Test Epoch')\\n for test_inputs, test_labels in self.test_dl:\\n test_inputs = test_inputs.to(self.device)\\n test_labels = test_labels.to(self.device)\\n\\n test_output = self.model(test_inputs)\\n test_output_labels = torch.max(test_output, dim=1)[1]\\n corrects += int(torch.sum(test_output_labels == test_labels))\\n\\n del(test_inputs)\\n del(test_labels)\\n del(test_output)\\n\\n return corrects/len(self.test_dl.dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b22fa8e1eaeb668fa77c0e383d88675\",\n \"score\": \"0.67051363\",\n \"text\": \"def accuracy(self, X_test, t_test):\\n #Fill in the code\\n c = self.classify(X_test)\\n # IPython.embed(header=\\\"acc\\\")\\n return np.sum(c == t_test) / t_test.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01333b30f040b59c7f87108a8e6a3ed0\",\n \"score\": \"0.66959625\",\n \"text\": \"def simple_accuracy(preds, labels):\\n return (preds == labels).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eba31cdf22aa46b994c0fa1db3ea1ed\",\n \"score\": \"0.6691743\",\n \"text\": \"def accuracy(self, input, labels):\\n predictions = self.predict(input)\\n count = 0\\n for prediction, target in zip(predictions, labels):\\n if prediction == target:\\n count += 1\\n return count/len(labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518972fa1fb139bf015b89a1dcbb3470\",\n \"score\": \"0.668921\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n\\t\\n Y_pred = model.predict(X_test)\\n\\t\\n i = 0\\n for col in Y_test:\\n print(col)\\n print(classification_report(Y_test[col], Y_pred[:, i]))\\n i = i + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5b7a045c07e7aa2801dfb2a109a241d\",\n \"score\": \"0.66876626\",\n \"text\": \"def test_evaluate(model, test_dataloader):\\r\\n # Put the model into the evaluation mode. The dropout layers are disabled during\\r\\n # the test time.\\r\\n model.eval()\\r\\n\\r\\n # Tracking variables\\r\\n# val_accuracy = []\\r\\n test_loss = []\\r\\n\\r\\n # For each batch in our validation set...\\r\\n for batch in test_dataloader:\\r\\n # Load batch to GPU\\r\\n b_input_ids, b_attn_mask, b_labels = tuple(t.to(dev) for t in batch)\\r\\n\\r\\n # Compute logits\\r\\n with torch.no_grad():\\r\\n logits = model(b_input_ids, b_attn_mask)\\r\\n\\r\\n # Compute loss\\r\\n loss = loss_fn(logits, b_labels)\\r\\n test_loss.append(loss.item())\\r\\n\\r\\n # Get the predictions\\r\\n preds = torch.argmax(logits, dim=1).flatten()\\r\\n\\r\\n # Calculate the accuracy rate\\r\\n# accuracy = (preds == b_labels).cpu().numpy().mean() * 100\\r\\n# val_accuracy.append(accuracy)\\r\\n\\r\\n # Compute the average accuracy and loss over the validation set.\\r\\n test_loss = np.mean(test_loss)\\r\\n# val_accuracy = np.mean(val_accuracy)\\r\\n\\r\\n return test_loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4291aa6db212a53b519468ba317daa78\",\n \"score\": \"0.66854525\",\n \"text\": \"def test_network(sess, test_data, test_labels):\\n\\n start_index = 0\\n total_elements = len(test_labels)\\n denominator = total_elements\\n accuracy_values = []\\n image_accuracy_values = []\\n while start_index < total_elements:\\n\\n \\\"\\\"\\\"Data gathering\\\"\\\"\\\"\\n test_data_real = data_set.load_image_data(test_data, start_index, params.testing_batch_size, params)\\n test_labels_real = data_set.load_image_data(test_labels, start_index, params.testing_batch_size, params, binary=True)\\n\\n \\\"\\\"\\\"Model execution\\\"\\\"\\\"\\n accuracy_pred, all_accuracy, predictions = sess.run([accuracy, accuracy_all_true,prediction_round_values],\\n feed_dict={x: test_data_real, y: test_labels_real, keep_prob: 1.0})\\n\\n \\\"\\\"\\\"Accuracy Calculation\\\"\\\"\\\"\\n for i in xrange(len(test_labels_real)):\\n label_array = np.array(test_labels_real[i]).reshape((params.image_size,params.image_size))\\n prediction_array = np.array(predictions[i]).reshape((params.image_size,params.image_size))\\n accuracy_values.append(vs.calculate_test_accuracy(prediction_array,label_array))\\n image_accuracy_values.append(all_accuracy)\\n\\n if start_index % params.testing_display == 0:\\n u.printf ('Tested {} data items'.format(start_index))\\n\\n start_index += params.testing_batch_size\\n\\n \\\"\\\"\\\"Testing output\\\"\\\"\\\"\\n if total_elements >= params.testing_batch_size:\\n denominator = float(total_elements/params.testing_batch_size)\\n\\n accuracy_array = np.array(accuracy_values).sum(0)\\n u.printf(\\\"Test Accuracy: {} Image Accuracy: {} F1: {} Recall: {} Precision: {}\\\".format((accuracy_array[0] / total_elements),\\n ((sum(image_accuracy_values) / denominator)/100),\\n (accuracy_array[1] / total_elements),\\n (accuracy_array[2] / total_elements),\\n (accuracy_array[3] / total_elements)))\\n\\n u.printf(\\\"Debug: Accuracy{} TP: {} TN: {} FP: {} FN: {}\\\".format(accuracy_pred,accuracy_array[4],accuracy_array[5],accuracy_array[6],accuracy_array[7]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f55079474540a8e3fb90ea8e8ddab6\",\n \"score\": \"0.66835564\",\n \"text\": \"def test_on_data(X,y,classifiers):\\n\\n\\t#data = np.array(data)\\n\\t#X = data[:,0:data.shape[1]-2]\\n\\t#y = data[:,-1]\\n\\t#y = y.reshape((y.shape[0]))\\n\\t#y = LabelBinarizer(neg_label=-1,pos_label=1).fit_transform(y)\\n\\tX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state = 42)\\n\\n\\taccuracies = []\\n\\tfor clf_name,clf in classifiers.items():\\n\\t\\tstart = time.time()\\n\\t\\tclf = clf\\n\\t\\tclf.fit(X_train,y_train)\\n\\t\\tclf_time = time.time()-start\\n\\t\\tif clf_name is 'SVM':\\n\\t\\t\\tclf_accuracy = round(np.mean(y_test == clf.predict(X_test)) * 100,2)\\n\\t\\t\\taccuracies.append(clf_accuracy)\\n\\t\\telse:\\n\\t\\t\\tclf_accuracy = round(np.mean(y_test == clf.predict_all(X_test)) * 100,2) \\n\\t\\t\\taccuracies.append(clf_accuracy) \\n\\t\\tprint(\\\"'%s'( time taken: \\\"%clf_name, clf_time, \\\", accuracy: \\\", clf_accuracy, \\\"%)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e18b73318263c64142676928bd6ef1e\",\n \"score\": \"0.6681391\",\n \"text\": \"def evaluate_model(model, data_loader, device):\\n correct_predictions = 0\\n number_examples = 0\\n for images, targets in data_loader:\\n # Send data to device\\n images, targets = images.to(device), targets.to(device)\\n predictions = model(images)\\n\\n # Calculate number of correct predictions\\n predicted_labels = torch.argmax(predictions, dim=1)\\n correct_predictions += sum(predicted_labels == targets)\\n number_examples += len(targets)\\n\\n accuracy = correct_predictions / number_examples\\n\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b970aaa9afd4186b89c42607e87840\",\n \"score\": \"0.6680198\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n y_pred = model.predict(X_test)\\n print(classification_report(Y_test, y_pred, target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740c54b7b89beac3c1abb2a1e7db9a36\",\n \"score\": \"0.66731864\",\n \"text\": \"def test_iris_dataset(self):\\n model = AutoML(\\n explain_level=0, verbose=0, random_state=1, results_path=self.automl_dir\\n )\\n score = model.fit(iris.data, iris.target).score(iris.data, iris.target)\\n self.assertGreater(score, 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3e02d72856759ef618976ce4152199\",\n \"score\": \"0.6667584\",\n \"text\": \"def testStep(inputATensor,inputBTensor,label):\\n modelPrediction=model(inputATensor,inputBTensor,False)\\n loss=lossFunction(y_true=label,y_pred=modelPrediction)\\n testLoss(loss)\\n testAcuracy(y_true=label,y_pred=tf.cast(modelPrediction>=0.5,tf.int32))\\n testAUC(y_true=label,y_pred=modelPrediction)\\n testRecall(y_true=label,y_pred=modelPrediction)\\n testPrecision(y_true=label,y_pred=modelPrediction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c095536fd63168b601e209cd9ec08515\",\n \"score\": \"0.6664447\",\n \"text\": \"def test_all(self):\\n self.predictions = []\\n self.accuracies = []\\n\\n for key, model in self.models.items():\\n predict_y = model.predict(self.processed['test_X'])\\n self.predictions.append(predict_y)\\n accuracy = accuracy_score(self.processed['test_y'], predict_y)\\n self.accuracies.append(accuracy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9919b2ac5c9dcd02b71e875ee9b774\",\n \"score\": \"0.6663886\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n\\n Y_pred = model.predict(X_test)\\n\\n # Print out the full classification report\\n print(classification_report(Y_test, Y_pred, target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eba4e4e7c5e903734327fd458f3af34\",\n \"score\": \"0.6661462\",\n \"text\": \"def test(test_loader, model, names, classes):\\r\\n # switch to evaluate mode\\r\\n #model = torch.load(os.path.join(args.outf, 'model_best.pth.tar'))\\r\\n model.eval()\\r\\n top1 = AverageMeter()\\r\\n # Evaluate all the validation set\\r\\n correct, total = 0,0\\r\\n for i, (input, target) in enumerate(test_loader):\\r\\n if cuda:\\r\\n input = input.cuda(async=True)\\r\\n target = target.cuda(async=True)\\r\\n with torch.no_grad():\\r\\n input_var = torch.autograd.Variable(input)\\r\\n target_var = torch.autograd.Variable(target)\\r\\n\\r\\n # compute output\\r\\n output = model(input_var)\\r\\n prec1 = accuracy(output.data, target, topk=(1,))\\r\\n top1.update(prec1[0], input.size(0))\\r\\n _, predicted = output.max(1)\\r\\n total += target.size(0)\\r\\n correct += predicted.eq(target_var).sum().item() \\r\\n '''\\r\\n # Take last layer output\\r\\n if isinstance(output, tuple):\\r\\n output = output[len(output)-1]\\r\\n\\r\\n # print (output.data.max(1, keepdim=True)[1])\\r\\n lab = classes[numpy.asscalar(output.data.max(1, keepdim=True)[1].cpu().numpy())]\\r\\n print (\\\"Images: \\\" + next(names) + \\\", Classified as: \\\" + lab)\\r\\n '''\\r\\n print(' * test acc {acc}'.format(acc=numpy.asscalar(top1.avg.cpu().numpy())))\\r\\n # print(' ** acc {acc}'.format(acc=100.*correct/total))\\r\\n return top1.avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de8aab748c9efe37e158ab3d07a9aaf\",\n \"score\": \"0.66569453\",\n \"text\": \"def evaluate_model(model, X_test, y_test, category_names):\\n y_pred = model.predict(X_test)\\n print(classification_report(y_test, y_pred, target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a1363c84c48ac713e951d00d4097f7\",\n \"score\": \"0.665502\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names): \\n # predict target using the machine learning model\\n Y_pred = model.predict(X_test)\\n\\t\\n # print metrics\\n metrics = metrics(Y_test, Y_pred)\\n print(metrics)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b41a65acc954ef5e4a93037ce32d11\",\n \"score\": \"0.66415954\",\n \"text\": \"def test(model, test_loader, device):\\n model.eval() # put model in evaluation mode\\n test_loss = 0 # validation loss\\n correct = 0\\n with torch.no_grad():\\n for data, target in test_loader:\\n data, target = data.to(device), target.to(device)\\n output = model.forward(data)\\n test_loss += f.nll_loss(output, target, reduction=\\\"sum\\\").item()\\n\\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\\n correct += pred.eq(target.view_as(pred)).sum().item()\\n\\n test_loss /= len(test_loader.dataset) # compute validation loss of neural network\\n accuracy = 100. * correct / len(test_loader.dataset)\\n logger.debug(f\\\"Validation has finished:\\\\n\\\"\\n f\\\"\\\\n Validation loss: {test_loss}\\\\n\\\"\\n f\\\"\\\\n Accuracy: {accuracy} %\\\\n\\\")\\n return test_loss, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8759d63fce198de4ff44b8a993cd4f62\",\n \"score\": \"0.66390675\",\n \"text\": \"def test_testing(self):\\n references, corpus = pre.get_input(self.testfilename, labeled=True)\\n references = [references[0], references[1], references[3]] + [references[2] \\n + references[4] + references[5]]\\n pred = lear.train(references, corpus)\\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\\n self.assertEqual([round(prob) for prob in \\n sum(lear.test(references, corpus, pred), [])], classes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91db36d923b251a83f90d88eaf4d2963\",\n \"score\": \"0.6637648\",\n \"text\": \"def test(device):\\n correct = 0\\n total = 0\\n with torch.no_grad():\\n net.eval()\\n for data in testloader:\\n images, labels = data\\n images, labels = images.to(device), labels.to(device)\\n outputs = net(images)\\n _, predicted = torch.max(outputs.data, 1)\\n total += labels.size(0)\\n correct += (predicted == labels).sum().item()\\n\\n return correct/total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d49efed2d47d2d930b81f76e73ad67e\",\n \"score\": \"0.6637189\",\n \"text\": \"def findOveralAccuracy(trainData,testData):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4510b3fe5d18b0d7c0bad2d985e691b5\",\n \"score\": \"0.66358894\",\n \"text\": \"def test_model_AUC(self):\\n\\n x_data = np.load('test_data/train_X.npy')\\n y_data = np.load('test_data/train_Y.npy')\\n train_set = [y_data, x_data]\\n\\n test_model = models.Model()\\n test_model.train_set = train_set\\n test_model.train_model(train_set)\\n\\n x_test = np.load(\\\"test_data/test_X.npy\\\")\\n y_test = np.load(\\\"test_data/test_Y.npy\\\")\\n test_set = [y_test, x_test]\\n test_model.evaluate_model(test_set, train_set)\\n print(test_model.evaluation_metrics)\\n\\n train_auc = round(test_model.evaluation_metrics['train_AUC'], 2)\\n test_auc = round(test_model.evaluation_metrics['test_AUC'], 2)\\n\\n self.assertEqual(0.85, train_auc,\\n msg=\\\"Train AUC not expected. Got {}, expected 0.85\\\".format(train_auc))\\n\\n self.assertEqual(0.77, test_auc,\\n msg=\\\"Test AUC not expected. Got {}, expected 0.77\\\".format(test_auc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea8e511bd98df27b11f88aaa082b6adf\",\n \"score\": \"0.66349304\",\n \"text\": \"def test_training(self):\\n references, corpus = pre.get_input(self.testfilename, labeled=True)\\n references = [references[0], references[1], references[3]] + [references[2] \\n + references[4] + references[5]]\\n pred = lear.train(references, corpus)\\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\\n self.assertEqual(pred.predict(sum(sim_vectors, [])).tolist(), classes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09fcf90024b9ff63cc84466ebb813db5\",\n \"score\": \"0.6626775\",\n \"text\": \"def test_model(model, test_generator):\\n\\n prediction = model.predict(test_generator)\\n _, accuracy = model.evaluate(test_generator)\\n\\n print(\\\"Test accuracy: {}\\\".format(accuracy))\\n\\n return prediction, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29723bb4b5371a4f482becb1c2316363\",\n \"score\": \"0.66246706\",\n \"text\": \"def test(self, test_data, artist_embeddings):\\n\\n num_correct = 0\\n for artist, song, lyrics in test_data:\\n W = lyrics_to_word_matrix(lyrics, self.vocab)\\n sv = compute_song_vector(W)\\n S = [sv]\\n av = compute_artist_vector(np.array(S))\\n\\n if artist in [neigbour[0] for neigbour in nearest_neighbors(artist_embeddings, av, 50)]:\\n num_correct += 1\\n\\n print(\\\"Number correct: {}\\\".format(num_correct))\\n print(\\\"Number total: {}\\\".format(len(test_data)))\\n print(\\\"Learner accuracy {}%\\\".format((num_correct/len(test_data))*100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ed9c54a594028100c69c97d429804f\",\n \"score\": \"0.6621816\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n \\n y_pred = model.predict(X_test)\\n \\n print('Accuracy: ', accuracy_score(Y_test, y_pred))\\n print('Precision: ', precision_score(Y_test, y_pred, average='micro'))\\n print('Recall: ', recall_score(Y_test, y_pred, average='micro'))\\n print('F1 Score: ',f1_score(Y_test, y_pred, average='micro'))\\n\\n # Print out the full classification report\\n for i, col in enumerate(category_names):\\n print('{} category metrics: '.format(col))\\n print(classification_report(Y_test.iloc[:,i], y_pred[:,i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a349a47a21c07ab33fa22e5d5a10856a\",\n \"score\": \"0.66214067\",\n \"text\": \"def accuracy(labels,predictions):\\n correct = 1.*np.sum((labels+predictions)==2)\\n total = 1.*np.sum(labels==1)\\n print('correct',correct)\\n print('total',total)\\n try:\\n return correct/total\\n except:\\n return 1.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e372b35acc4494b19d8604553951b7e\",\n \"score\": \"0.6618151\",\n \"text\": \"def test(self,x,y,lr):\\n pred=lr.predict(x)\\n true=y\\n test_acc=accuracy_score(true,pred)\\n test_recall=recall_score(true,pred)\\n test_precision=precision_score(true,pred)\\n test_f1score=f1_score(true,pred)\\n return test_acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89032d2f9eca644d6fd233edfa9202c\",\n \"score\": \"0.6615105\",\n \"text\": \"def test_model(model, batch_size, device, seed, test_dataset):\\n set_seed(seed)\\n\\n # Set model to evaluation mode\\n model.eval()\\n\\n test_results = {}\\n\\n with torch.no_grad():\\n test_loader = DataLoader(test_dataset, batch_size=batch_size,\\n generator=torch.Generator().manual_seed(42), pin_memory=True,\\n num_workers=6)\\n accuracy = evaluate_model(model, test_loader, device)\\n test_results['accuracy'] = accuracy.item()\\n\\n # Test accuracy\\n print(f\\\"Test accuracy: {accuracy.item():.4f}\\\")\\n\\n # Set model back to train mode\\n model.train()\\n\\n return test_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8198e00d12de958ac52fb0da56a7410c\",\n \"score\": \"0.66064936\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_preds = model.predict(X_test)\\n\\n #transpose the values for each categories\\n Y_preds_transpose = np.transpose(Y_preds)\\n Y_test_transpose = np.transpose(Y_test)\\n scores = []\\n\\n #evaluate the model by categories\\n for e in range(Y_preds_transpose.shape[0]):\\n validation = classification_report(Y_test_transpose.iloc[e,:].values, Y_preds_transpose[e], output_dict=True)\\n scores.append((category_names[e], validation))\\n\\n #print out scores\\n for score in scores:\\n pprint.pprint(score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83277430e93da63e5c6d72e93dd80d5\",\n \"score\": \"0.6598353\",\n \"text\": \"def test(model, test_loader):\\n print('\\\\nTesting')\\n model.eval()\\n correct = 0\\n total = 0\\n with torch.no_grad():\\n for batch_idx, (inputs, targets) in enumerate(test_loader):\\n inputs, targets = inputs.cuda(), targets.cuda()\\n outputs = model(inputs)\\n _, predicted = outputs.max(1)\\n total += targets.size(0)\\n correct += predicted.eq(targets).sum().item()\\n\\n progress_bar(batch_idx, len(test_loader), 'Acc: %.3f%% (%d/%d)'\\n % (100. * correct/total, correct, total))\\n\\n return correct * 100 / total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba28bfd3730f27f748fec8939eee4c1\",\n \"score\": \"0.65945745\",\n \"text\": \"def assess_test_data(self, mat, true_labels):\\n m = mat.columns.size\\n res = pd.Series([self.classify(mat.iloc[:, i]) for i in range(m)], index=mat.columns)\\n n_correct = (res == true_labels).sum()\\n n_incorrect = m - n_correct\\n return res, n_correct, n_incorrect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ada649b1f97104de55805b21695f094\",\n \"score\": \"0.6589399\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_pred = model.predict(X_test)\\n for i, col in enumerate(category_names):\\n print(classification_report(Y_test[col], Y_pred[:, i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05cadcb17e87c77ff54513708cebec4f\",\n \"score\": \"0.65890366\",\n \"text\": \"def accuracy_mean_iou(test_set, labels, model1, model2, model3):\\n model1_score = model1.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\\n model2_score = model2.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\\n model3_score = model3.evaluate(test_set, labels, batch_size=4, verbose=0)[1]\\n return model1_score, model2_score, model3_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e84c8afb17776c444a02bcaa110f913\",\n \"score\": \"0.658901\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n y_pred = model.predict(X_test)\\n for i in range(0, len(category_names)):\\n print(classification_report(Y_test.values[:, i], y_pred[:, i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3039f912208cf49a880478dc58199fc\",\n \"score\": \"0.65875393\",\n \"text\": \"def test_classification_net(model, data_loader, device):\\n logits, labels = get_logits_labels(model, data_loader, device)\\n return test_classification_net_logits(logits, labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47962bc5cd2a1eea72d2d5f96afbe66\",\n \"score\": \"0.6586379\",\n \"text\": \"def KNN_Test(self, X_test, y_test):\\n KNN_predict = self.KNN.predict(X_test)\\n KNN_score = np.mean(KNN_predict == np.ravel(y_test,order='C'))\\n # return (\\\"KNN Score: {:.9f}\\\".format(KNN_score))\\n return round(KNN_score, 5)\\n # Update score in case of retraining\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1d7cdd61c9e2a60417ff340e5663a38\",\n \"score\": \"0.6581354\",\n \"text\": \"def test(self, test_loader, get_auc = True): \\n label_score = []\\n batch_idx = -1\\n for data, target, _ in test_loader:\\n batch_idx += 1\\n data, target = data.to(self.device), target.to(self.device)\\n data = data.to(torch.float)\\n target = target.to(torch.float)\\n target = torch.squeeze(target)\\n\\n logits = self.model(data)\\n logits = torch.squeeze(logits, dim = 1)\\n sigmoid_logits = torch.sigmoid(logits)\\n scores = sigmoid_logits\\n label_score += list(zip(target.cpu().data.numpy().tolist(),\\n scores.cpu().data.numpy().tolist()))\\n # Compute test score\\n labels, scores = zip(*label_score)\\n labels = np.array(labels)\\n scores = np.array(scores)\\n pos_scores = scores[labels==1]\\n neg_scores = scores[labels==0]\\n auc = -1\\n if get_auc:\\n auc = roc_auc_score(labels, scores)\\n return auc, pos_scores, neg_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a747b8ff460aff894dccf380bf59dfa\",\n \"score\": \"0.657953\",\n \"text\": \"def evaluate(self, X_test, y_test):\\n\\n accuracy_model=self.model.evaluate(X_test, y_test, verbose=0)[1]\\n return accuracy_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c333601f4c9d59fa9c64632b45556ff\",\n \"score\": \"0.6579035\",\n \"text\": \"def testData(model_path, xtest, ytest):\\n\\n model = load_model(model_path)\\n ycapa = model.predict(xtest, batch_size=480)\\n\\n ycapa = np.argmax(ycapa, axis=-1)\\n # ytest = np.argmax(ytest, axis=-1)\\n\\n util.log(f\\\"\\\\nTest metrics: {ytest.shape} {ycapa.shape}\\\")\\n util.print_metrics_per_class(ytest, ycapa)\\n\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28899e709e1be9e9dd72045d04897555\",\n \"score\": \"0.6576536\",\n \"text\": \"def validate_model(model, test_data_provider):\\n model.start_prediction()\\n test_loader = test_data_provider.loader\\n test_loss = 0\\n correct = 0\\n for data, target in test_loader:\\n data, target = Variable(data), Variable(target)\\n _, pred, loss_val = model.validate_batch(data, target)\\n test_loss += loss_val\\n correct += pred.eq(target.data).cpu().sum()\\n test_loss /= len(test_loader) # loss function already averages over batch size\\n print('\\\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n test_loss, # 0.1023\\n correct, # 17\\n test_loader.example_count(), # out of 100\\n 100. * correct / test_loader.example_count())) # (10%)\\n return float(correct) / test_loader.example_count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1ba7ecff193f7ab185d268c972ff7e\",\n \"score\": \"0.65748125\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n \\n Y_pred = model.predict(X_test)\\n \\n for col in range(Y_pred.shape[1]):\\n print(\\\"Category: {}\\\".format(category_names[col]))\\n print(classification_report(Y_test[:, col], Y_pred[:, col]))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1481,"cells":{"query_id":{"kind":"string","value":"7486922858b721cc978be76d5a1a6712"},"query":{"kind":"string","value":"Verify that the filepath argument is a valid file."},"positive_passages":{"kind":"list like","value":[{"docid":"6f18d6f3c6e04367161933d692e6fbb5","score":"0.7751014","text":"def verify_filepath(filepath: str) -> str:\n filepath = os.path.abspath(os.path.expandvars(os.path.expanduser(filepath)))\n if not os.path.isfile(filepath):\n raise ValueError(f\"'{filepath}' is not a valid filepath\")\n return filepath","title":""}],"string":"[\n {\n \"docid\": \"6f18d6f3c6e04367161933d692e6fbb5\",\n \"score\": \"0.7751014\",\n \"text\": \"def verify_filepath(filepath: str) -> str:\\n filepath = os.path.abspath(os.path.expandvars(os.path.expanduser(filepath)))\\n if not os.path.isfile(filepath):\\n raise ValueError(f\\\"'{filepath}' is not a valid filepath\\\")\\n return filepath\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7b3f148a410921b24e902a0a56697f98","score":"0.81745076","text":"def valid_file(filepath):\n return os.path.isfile(filepath)","title":""},{"docid":"8c15f7c1c876f4b72d4f5a952f1150be","score":"0.7708852","text":"def chkfile(path):\n if not os.path.isfile(path):\n msg = \"{0} is not a file.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"3f825f0709fcaf92147f6228d5a383db","score":"0.7609666","text":"def checkFile(filename):\n\n valid = True\n\n if filename is None or not os.path.isfile(filename):\n valid = False\n\n if valid:\n try:\n with open(filename, \"rb\") as f:\n pass\n except:\n valid = False\n\n if not valid:\n raise Exception(\"unable to read file '%s'\" % filename)","title":""},{"docid":"d74506ccf54d5165c4bbc77a94609733","score":"0.75992125","text":"def check_file(file_path: Union[str, Path]):\n\n file_path = Path(file_path)\n return file_path.is_file()","title":""},{"docid":"cfac8d9d14ca33403a15749027fe0837","score":"0.75611776","text":"def file_is_valid(parser, arg): \n if not os.path.isfile(arg): \n parser.error(\"The file %s doesn't exist.\" % arg) \n else: \n return open(arg, 'r')","title":""},{"docid":"fe8ce0ffb79cff3a33352034a47b4623","score":"0.7451672","text":"def _check_valid_path(path):\n if not os.path.exists(path):\n raise ValueError(f'Path \"{path}\" does not exist.')\n if not os.path.isfile(path):\n raise TypeError(f'Path \"{path}\" is not a file.')","title":""},{"docid":"aaeb48f6750d7d7fcc8f14caf21a57c2","score":"0.74476874","text":"def file_or_fail(file_path: str) -> None:\n if not os.path.exists(file_path):\n raise FileNotFoundError(file_path)\n\n if not os.path.isfile(file_path):\n raise ValueError(f'Argument not a file at: {file_path}')","title":""},{"docid":"9df959a49b8f645dd9a037e4d866fdfe","score":"0.7446648","text":"def is_valid_file(parser, x):\n\n if not os.path.exists(x) or os.path.isdir(x):\n parser.error(\"The file specified is not a file or doesn't exist!\")\n return False\n\n return x","title":""},{"docid":"7d7389d9a92a81d2833b21c6c8c5d1d9","score":"0.7443289","text":"def is_valid_file(parser, arg):\n arg = os.path.abspath(arg)\n if not os.path.exists(arg):\n parser.error(\"The file %s doesn't exist \" % arg)\n else:\n return arg","title":""},{"docid":"7d7389d9a92a81d2833b21c6c8c5d1d9","score":"0.7443289","text":"def is_valid_file(parser, arg):\n arg = os.path.abspath(arg)\n if not os.path.exists(arg):\n parser.error(\"The file %s doesn't exist \" % arg)\n else:\n return arg","title":""},{"docid":"a5a20e5ad9392af2ed62f354c94d2645","score":"0.7441992","text":"def isValidFile( fileName ):\n\timport argparse\n\timport os\n\tif not os.path.isfile( fileName ):\n\t\traise argparse.ArgumentTypeError( \"File %s does not exist\"%fileName )\n\telse:\n\t\treturn fileName","title":""},{"docid":"981c28f9f2b6a896cf6b6df6f2383ef8","score":"0.7405622","text":"def is_valid_file(parser, arg):\n if not os.path.exists(arg):\n parser.error(\"The file %s does not exist!\"%arg)\n else:\n return open(arg,'r') #return an open file handle","title":""},{"docid":"9cbe2010a4eecd00aade0023c7f5f91b","score":"0.73774254","text":"def is_valid_file(parser, arg):\n arg = os.path.abspath(arg)\n if not os.path.exists(arg):\n parser.error(\"The file %s doesn't exist \" % arg)\n else:\n return arg","title":""},{"docid":"83c2f1870fabc9ac2d91b6c02defc272","score":"0.7345188","text":"def is_valid_file(parser, arg):\n if not os.path.exists(arg):\n parser.error(\"The file %s does not exist!\" % arg)\n else:\n return os.path.abspath(arg)","title":""},{"docid":"428a633aad855a2d88b00c014a27b81f","score":"0.72743034","text":"def check_file(f):\n if not os.path.isfile(f):\n raise ValueError(f'File {f} does not exist')","title":""},{"docid":"382d1d628989982161541a9faf4df6e6","score":"0.7272897","text":"def is_valid_file(self, filename):\n if subprocess.os.path.exists(filename):\n return True\n return False","title":""},{"docid":"1d84cedcbde452cfc92c6a1bc754f597","score":"0.72688854","text":"def check_file(abspath, ext=None):\n check_type(abspath, str)\n check_type(ext, [str, type(None)])\n\n if not os.path.isfile(abspath):\n raise ValueError(f'File {abspath} does not exist. '\n f'Specify a valid path to file.')\n\n if ext:\n # print(os.path)\n file_name, extension = os.path.splitext(str(abspath))\n if not extension == ext:\n raise ValueError(f'File {abspath} should be a {ext} file. '\n f'Specify a valid path to a {ext} file.')","title":""},{"docid":"4822f6b252a8706950c346882dbff658","score":"0.72588295","text":"def validate_filepath(self):\n self.status_widget.clear(1)\n\n path = self.file_widget.get_path()\n\n self._file_valid = utils.is_file_path_valid(path)\n\n if not self._file_valid:\n self.status_widget.add_error(1, \"Invalid file path\")\n\n self.validate_all_values()","title":""},{"docid":"73a67c493cc25d977778d0f01ad805ea","score":"0.72427493","text":"def is_valid_file(arg): #Currently not doing this at this point. ? Should it be done here\n if not os.path.isfile(arg):\n self.parser.error('The file {} does not exist!'.format(arg))\n else:\n # File exists so return the filename\n return arg","title":""},{"docid":"bb9c2b0c111e590aab56529372b4c92e","score":"0.7228502","text":"def validate_file_exists(path: Union[Path, str]):\n if not path:\n return False\n else:\n path = Path(path)\n if path.is_file():\n return True\n else:\n valid_path_length(path)\n return False","title":""},{"docid":"8d91d901e308e91215cee9d81dd63f15","score":"0.7210747","text":"def validate_filepath(self):\n self.status_widget.clear(1)\n\n path = self.file_widget.get_path()\n self._file_valid = utils.is_file_path_valid(path)\n\n if not self._file_valid:\n self.status_widget.add_error(1, \"Invalid file path\")\n\n self.validate_all_values()","title":""},{"docid":"b9fb66ffaf59d8850573d8e9a3430050","score":"0.7206458","text":"def check_file(self, filepath):\n if self.regex is not None:\n fname = os.path.basename(filepath)\n match = self.regex_c.match(fname)\n if not match:\n self.logger.info(f\"filename {fname} did not match regex\")\n return False\n\n # file did not fail any checks we set for it\n return True","title":""},{"docid":"8410508ee03fbac6a0b88a5655200bed","score":"0.7182967","text":"def is_valid_file(file, parser):\n\n if os.path.exists(file):\n return file\n else:\n raise parser.error(\"Unable to locate %s: No such file or directory\" % file)","title":""},{"docid":"9d8d12b13d5dc8f44e030c1ccf10e1df","score":"0.7174319","text":"def file_checker(filename):\n if not os.path.isfile(filename):\n raise ValueError(\"File %s is not existed!\" % (filename))\n\n return True","title":""},{"docid":"13da947afcc76c9804927a821f6f26b8","score":"0.71536183","text":"def is_file(filename):\n if not os.path.isfile(filename):\n msg = \"{0} is not a file\".format(filename)\n raise argparse.ArgumentTypeError(msg)\n else:\n return filename","title":""},{"docid":"13da947afcc76c9804927a821f6f26b8","score":"0.71536183","text":"def is_file(filename):\n if not os.path.isfile(filename):\n msg = \"{0} is not a file\".format(filename)\n raise argparse.ArgumentTypeError(msg)\n else:\n return filename","title":""},{"docid":"13da947afcc76c9804927a821f6f26b8","score":"0.71536183","text":"def is_file(filename):\n if not os.path.isfile(filename):\n msg = \"{0} is not a file\".format(filename)\n raise argparse.ArgumentTypeError(msg)\n else:\n return filename","title":""},{"docid":"13da947afcc76c9804927a821f6f26b8","score":"0.71536183","text":"def is_file(filename):\n if not os.path.isfile(filename):\n msg = \"{0} is not a file\".format(filename)\n raise argparse.ArgumentTypeError(msg)\n else:\n return filename","title":""},{"docid":"13da947afcc76c9804927a821f6f26b8","score":"0.71536183","text":"def is_file(filename):\n if not os.path.isfile(filename):\n msg = \"{0} is not a file\".format(filename)\n raise argparse.ArgumentTypeError(msg)\n else:\n return filename","title":""},{"docid":"4608919503ca652c6627ec4c72e8186d","score":"0.71234065","text":"def is_valid_file(parser, arg):\n if not os.path.isfile(arg):\n parser.error(\"The file %s does not seem to be a file at all! Exiting for safety reasons.\" %arg)\n sys.exit(1)\n else:\n if validate_input(arg):\n return True\n else:\n print \"Invalid JSON. Exiting\"\n sys.exit(1)","title":""},{"docid":"dae59589e56ee05db55bf04ec8ea20b5","score":"0.7105801","text":"def isfile(self, filepath: Union[str, Path]) -> bool:\n return osp.isfile(filepath)","title":""},{"docid":"0f6d80cfdebc90cee5eb8aa75b8577d8","score":"0.7062038","text":"def is_valid_file(arg_parse, arg):\n if not os.path.exists(arg):\n arg_parse.error(\"The file %s does not exist!\" % arg)\n elif os.path.splitext(arg)[-1] not in ['.pdf', '.dxf']:\n arg_parse.error(\"supported filetypes are .pdf and .dxf\")\n else:\n return arg","title":""},{"docid":"aed1180a6ffc1a66a4170bec87f72c49","score":"0.70509124","text":"def valid_filename(filename: str) -> str:\n if not os.path.isfile(filename):\n raise argparse.ArgumentTypeError(\n f\"'File '{filename}' does not exist!\")\n return filename","title":""},{"docid":"455064028868ed776a7cd675f7f79722","score":"0.7046568","text":"def fileValidation(self, filename):\n if not os.path.exists(filename):\n print(\"error: file '{}' does not exist\".format (filename), file=sys.stderr)\n sys.exit(1)","title":""},{"docid":"5d8942911674b5b32ea3b21af7f58520","score":"0.7036292","text":"def is_valid_file(x, expected_size):\n if not os.path.exists(x):\n # Argparse uses the ArgumentTypeError to give a rejection message like:\n # error: argument input: x does not exist\n raise argparse.ArgumentTypeError(\"{0} does not exist\".format(x))\n elif os.path.getsize(x) != expected_size:\n raise argparse.ArgumentTypeError(\n \"{0} is not valid, expected filesize: {1} bytes\".format(x, expected_size)) \n return x","title":""},{"docid":"c5408ab784dab27ede4f9872dc35c87d","score":"0.7023431","text":"def check_filename(path, arg_name=\"\"):\n if arg_name == \"\":\n arg_name = \"File path\"\n else:\n arg_name = \"'{}'\".format(arg_name)\n if not path:\n raise ParamValueError('{} is not allowed None or empty!'.format(arg_name))\n if not isinstance(path, str):\n raise ParamValueError(\"File path: {} is not string.\".format(path))\n if path.endswith(\"/\"):\n raise ParamValueError(\"File path can not end with '/'\")\n file_name = os.path.basename(path)\n\n # '#', ':', '|', ' ', '}', '\"', '+', '!', ']', '[', '\\\\', '`',\n # '&', '.', '/', '@', \"'\", '^', ',', '_', '<', ';', '~', '>',\n # '*', '(', '%', ')', '-', '=', '{', '?', '$'\n forbidden_symbols = set(r'\\/:*?\"<>|`&\\';')\n\n if set(file_name) & forbidden_symbols:\n raise ParamValueError(r\"File name should not contains \\/:*?\\\"<>|`&;\\'\")\n\n if file_name.startswith(' ') or file_name.endswith(' '):\n raise ParamValueError(\"File name should not start/end with space.\")\n\n return True","title":""},{"docid":"34f03d14cd4c59b9bf42ed8ac274d9eb","score":"0.7018437","text":"def check_input_file(filename):\n if not (os.path.isfile(filename) and os.access(filename, os.R_OK)):\n raise IOError(f'ERROR: COULD NOT OPEN INPUT FILE {filename}!\\n')","title":""},{"docid":"382aaa954b7ac357cbe8854d3fb1bf72","score":"0.701571","text":"def is_file(path: Path) -> Path:\n\n if not path.is_file():\n msg = f\"{path} is not a file\"\n raise ArgumentTypeError(msg)\n\n return path","title":""},{"docid":"533d4d025d1cd1fc7729973d09e19668","score":"0.6996733","text":"def check_input_file(input_file_path):\n if not os.path.exists(input_file_path):\n raise click.ClickException(\"input_file path doesn't exist\")\n if not os.path.isfile(input_file_path):\n raise click.ClickException(\"input_file isn't a file\")","title":""},{"docid":"0914174929199c33c5b5242a0dc52cf7","score":"0.6978147","text":"def check_file_exists(filepath):\n if not op.exists(filepath):\n raise IOError('File {} not found.'.format(filepath))","title":""},{"docid":"e4ac128e889479c32d299dfa50521788","score":"0.6942239","text":"def isFilePathValid(fileName, checkExist=True):\n try:\n with open(fileName, 'x') as tempFile:\n return True\n except OSError:\n return checkExist and os.path.exists(fileName)","title":""},{"docid":"4e312645ac6b398a94048a059562b063","score":"0.6924955","text":"def _args_check_file(value):\n strval = value\n if not os.path.isfile(strval):\n raise argparse.ArgumentTypeError('File \"%s\" not found.' % value)\n return strval","title":""},{"docid":"314f0ccd4373a276e3985a29ca8375c2","score":"0.6903918","text":"def check_file_validity(file_):\n if not os.access(file_, os.F_OK):\n raise MonitorError(\"File '%s' does not exist\" % file_)\n if not os.access(file_, os.R_OK):\n raise MonitorError(\"File '%s' not readable\" % file_)\n if os.path.isdir(file_):\n raise MonitorError(\"File '%s' is a directory\" % file_)","title":""},{"docid":"22df7a1b753b3d3f30db56799dcdd650","score":"0.68994594","text":"def is_file(path):\n\n\tif not os.path.isfile(path):\n\t\tif os.path.isdir(path):\n\t\t\tmsg = \"{0} is a directory\".format(path)\n\t\telse:\n\t\t\tmsg = \"{0} does not exist.\".format(path)\n\t\traise argparse.ArgumentTypeError(msg)\n\treturn path","title":""},{"docid":"d51e5e6a6884a79af3c6bd4bc3d1995e","score":"0.6883056","text":"def is_file(inputfile):\n inputfile = realpath(inputfile)\n if not isfile(inputfile):\n logger.error('Input is not a file!')\n sys.exit(0)\n return inputfile","title":""},{"docid":"43431fe9c6fe9d8210fe590a4e40974d","score":"0.68723494","text":"def is_file(path: str) -> bool:\n return os.path.isfile(path)","title":""},{"docid":"662c44b4da2858998c1ce93f8cbb17e1","score":"0.6850036","text":"def _check_filename(self, filename):\n if len(os.path.dirname(filename)) != 0:\n raise ValueError(f\"Expecting file name but got path {filename}\")","title":""},{"docid":"fd108eb8bc9df0426280c11e5c689ce0","score":"0.6847535","text":"def __is_file_valid(self, path):\n if not os.path.exists(path):\n raise Exception(\"The path to the given file does not exist:\\n\" + path)\n if not path.endswith(\".pdf\"):\n raise Exception(\"The given file is not a .pdf file:\\n\" + path)\n return True","title":""},{"docid":"a11673839a469f93b0017edfe36f19f0","score":"0.6846054","text":"def check_file(path2check,filename_regex):\n\n # check string is valid path\n if not os.path.isfile(path2check):\n print \"Error: %s is not a file\\n\" % path2check\n sys.exit(1)\n\n filename = os.path.basename(path2check)\n if not filename_regex.match(filename):\n print \"Error: Expected SIT_YYMMDD.xls, but got %s\" % filename\n sys.exit(1)","title":""},{"docid":"358b6799cd6ff9bf19428f1840481532","score":"0.6839836","text":"def CheckFile(filename):\n if not os.path.exists(filename):\n raise FileNotFoundError('%s: file not found' % filename)\n elif not os.access(filename, os.R_OK):\n raise FileNotReadableError('%s: file not readable' % filename)","title":""},{"docid":"fed881578f5f7be36ea78807465e1e75","score":"0.68378216","text":"def checkFile(filename):\r\n\r\n if not os.path.isfile(filename):\r\n raise SqlmapFilePathException(\"unable to read file '%s'\" % filename)","title":""},{"docid":"db605eb566df9762d512e313a7e8a650","score":"0.6832033","text":"def is_file(filename):\n try:\n open(filename, \"r\")\n return True\n except IOError:\n return False","title":""},{"docid":"f25216fbc2f85faee1f2a43dd9b8c38b","score":"0.68318945","text":"def is_file(self, path: PurePath) -> bool:","title":""},{"docid":"b5f1bf2f1bc38c636c44471f49149acb","score":"0.6824413","text":"def is_file_valid(file_path: str) -> bool:\n if not file_path.endswith(\".tml\"):\n return False\n\n try:\n __read_tml(file_path)\n except:\n return False\n return True","title":""},{"docid":"fb41b87acbcf986e9d6f32a1a9c1ce46","score":"0.67919314","text":"def isfile(path):\n if not os.path.isfile(path):\n if os.path.isdir(path):\n msg = \"{0} is a directory\".format(path)\n else:\n msg = \"{0} does not exist.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"fb41b87acbcf986e9d6f32a1a9c1ce46","score":"0.67919314","text":"def isfile(path):\n if not os.path.isfile(path):\n if os.path.isdir(path):\n msg = \"{0} is a directory\".format(path)\n else:\n msg = \"{0} does not exist.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"fb41b87acbcf986e9d6f32a1a9c1ce46","score":"0.67919314","text":"def isfile(path):\n if not os.path.isfile(path):\n if os.path.isdir(path):\n msg = \"{0} is a directory\".format(path)\n else:\n msg = \"{0} does not exist.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"fb41b87acbcf986e9d6f32a1a9c1ce46","score":"0.67919314","text":"def isfile(path):\n if not os.path.isfile(path):\n if os.path.isdir(path):\n msg = \"{0} is a directory\".format(path)\n else:\n msg = \"{0} does not exist.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"fb41b87acbcf986e9d6f32a1a9c1ce46","score":"0.67919314","text":"def isfile(path):\n if not os.path.isfile(path):\n if os.path.isdir(path):\n msg = \"{0} is a directory\".format(path)\n else:\n msg = \"{0} does not exist.\".format(path)\n raise argparse.ArgumentTypeError(msg)\n return path","title":""},{"docid":"c41c4d4050d0240cbb4fb5d2029b4f80","score":"0.6783844","text":"def check_file(path2check,filename_regex):\n\n\n # check string is valid path\n if not os.path.isfile(path2check):\n print \"Error: %s is not a file\\n\" % path2check\n sys.exit(1)\n\n filename = os.path.basename(path2check)\n if not filename_regex.match(filename):\n print \"Error: Expected SIT_YYMMDD.xls, but got %s\" % filename\n sys.exit(1)","title":""},{"docid":"3362cb0a1fe8170180abf1d0721ff6af","score":"0.6776304","text":"def validate(self, file_path):\n existing_path = os.path.exists(file_path)\n same_path = (file_path == self.external_var.get())\n \n name, format = os.path.splitext(file_path)\n correct_format = (format.lower() in self.file_formats)\n\n return not same_path and existing_path and correct_format","title":""},{"docid":"cdf69fba39ee9499eb0934604147d214","score":"0.6771543","text":"def isfile(path):\r\n if not os.path.isfile(path):\r\n if os.path.isdir(path):\r\n msg = \"{0} is a directory\".format(path)\r\n else:\r\n msg = \"{0} does not exist.\".format(path)\r\n raise argparse.ArgumentTypeError(msg)\r\n return path","title":""},{"docid":"6002107f997cba432c0a5a90d1c868c2","score":"0.6756233","text":"def __is_file_valid(self, path):\n if not os.path.exists(path):\n raise Exception('Path does not exist:\\n' + str(path))\n elif not path.endswith('.pdf'):\n raise Exception('File is not a .pdf file:\\n' + str(path))\n return True","title":""},{"docid":"38720ca435d5edc13d018d848b3f6a9b","score":"0.674048","text":"def _filecheck(self, filename):\n if (self._filecheck_pattern is not None and\n not self._filecheck_pattern.search(filename)):\n return False\n else:\n return True","title":""},{"docid":"2d6e502c2324f335904b70b459b65c81","score":"0.6728257","text":"def isfile(self, path):\n pass","title":""},{"docid":"347f20ee4cc3a5fe05abb4bcf8d68541","score":"0.67261446","text":"def check_filename(path):\n if not isinstance(path, str):\n raise ValueError(\"path: {} is not string\".format(path))\n filename = os.path.basename(path)\n\n # '#', ':', '|', ' ', '}', '\"', '+', '!', ']', '[', '\\\\', '`',\n # '&', '.', '/', '@', \"'\", '^', ',', '_', '<', ';', '~', '>',\n # '*', '(', '%', ')', '-', '=', '{', '?', '$'\n forbidden_symbols = set(r'\\/:*?\"<>|`&\\';')\n\n if set(filename) & forbidden_symbols:\n raise ValueError(r\"filename should not contains \\/:*?\\\"<>|`&;\\'\")\n\n if filename.startswith(' ') or filename.endswith(' '):\n raise ValueError(\"filename should not start/end with space\")\n\n return True","title":""},{"docid":"1cf76d2a8da8c938843c466ffa53b2e3","score":"0.6711318","text":"def verify_file(filename):\n\ttry:\n\t\textension = filename.split('.')[-1]\n\t\tif extension == \"gz\":\n\t\t\tfp = gzip.open(filename, 'rt')\n\t\telse:\n\t\t\tfp = open(filename)\n\t\tone = fp.readline()\n\t\tfp.close()\n\texcept IOError as err:\n\t\tsys.exit(gettime() + \"Error reading the file {0}: {1}\".format(filename, err))\n\treturn(filename)","title":""},{"docid":"334d626308dc3e1218c26ce3760b8da3","score":"0.6652371","text":"def assertFile(self, path):\n print(\"Check file %s\" % path)\n self.assertTrue(os.path.isfile(path))","title":""},{"docid":"f3a073f1e720d61ea0ca424c17160cdd","score":"0.664469","text":"def is_file(path):\n return os.path.isfile(str(path))","title":""},{"docid":"3ba7e8088db1c4f2df64243b0a58a79b","score":"0.66247493","text":"def check_input_file(self, path):\n if not gf.file_can_be_read(path):\n self.print_error(u\"Unable to read file '%s'\" % (path))\n self.print_error(u\"Make sure the file path is written/escaped correctly and that you have read permission on it\")\n return False\n return True","title":""},{"docid":"d77aad098b39c03b23bf7e4cc849e385","score":"0.6615429","text":"def check_valid_file(path, uuid):\n try:\n get_header(path)\n return True\n except (IOError, ValueError):\n handle_deleted_file(uuid)\n return False","title":""},{"docid":"b004bfcded2fda9a976894ad8ee28535","score":"0.6583151","text":"def _check_file(self, file_path):\n\n # set logger\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n logger.debug(\"Running check_file.\")\n\n if not os.path.isfile(file_path):\n logger.error(file_path + \"was not found. Exiting.\")\n sys.exit(1)","title":""},{"docid":"3dc213218b8c4c6ec6f6beba539e5cc1","score":"0.65462637","text":"def validate_file_exists(path):\n\n if not os.path.exists(path):\n raise exceptions.FileDoesntExistException(path=path)\n if os.path.isdir(path):\n raise exceptions.FileIsADirectoryException(path=path)","title":""},{"docid":"df5470a40d95c5a3eb081219bd933926","score":"0.6545388","text":"def isFile(path):\n return os.path.isfile(path)","title":""},{"docid":"de234b323533aaebb256e372c0d72bc2","score":"0.6542922","text":"def _validateFilename(self, filePath):\n assert os.path.basename(filePath[0]) == \"data_mutations_extended_{}.txt\".format(\n self.center\n )","title":""},{"docid":"b10274106f1ee75234421a3e7577d942","score":"0.65408105","text":"def validateFile(self, fileName):\n try: # try to open a file\n f = open(fileName, \"r\")\n except IOError: # catch error\n print(\"Wrong name of the file!\")\n return False # exit\n\n f.close()\n return True # exit when no problems found","title":""},{"docid":"00a0f55cb7e1ffcc8b2c61e230d3dfe5","score":"0.6512724","text":"def check_file(filepath, permissions='r', **kwargs):\n if not os.path.exists(filepath):\n return MESSAGES['FILE_NOT_FOUND']\n\n permissions_warning = check_permissions(filepath, permissions)\n if permissions_warning:\n return permissions_warning","title":""},{"docid":"cbe7dc8716c1cdf3c6bbecc9ff063d43","score":"0.650076","text":"def _check_existence(filename):\n if not os.path.isfile(filename):\n raise RuntimeError(\"%s doesn't exist.\" % filename)","title":""},{"docid":"7424295a483d2746b8bedde290efb9db","score":"0.64806426","text":"def validate_file(self):\n # no file set\n if self._file is None:\n self._new_file = True\n self._valid = False\n return False\n\n # check if file exists already\n if os.path.exists(self._file):\n self._new_file = False\n else:\n self._new_file = True\n\n # if it's a dir it's not valid\n if os.path.isdir(self._file):\n self._valid = False\n else:\n self._valid = True","title":""},{"docid":"b97f3acf4f04d1f2e9985083b8160135","score":"0.64797765","text":"def filecheck(filename):\n try:\n open(filename, 'r')\n return 1\n except IOError:\n print \"Error: A required file does not exist.\\n\" \\\n \"- field_find.txt (required)\\n\" \\\n \"- field_update.txt (required)\\n\" \\\n \"- your entered JSON file (required)\\n\\n\" \\\n \"Please check your files and try again.\"\n exit()","title":""},{"docid":"e807c65c52d22a14ac3bd23b9adab7f1","score":"0.6479745","text":"def is_good_file(self, filename):\n return self.goodRE.match(filename)","title":""},{"docid":"8130f5a83b7820c5743e0d5bdf1a9a58","score":"0.6470759","text":"def test_check_file_type(file_path, file_types):\n output = check_file_type(file_path, file_types)\n if not output:\n raise AssertionError\n\n # check for false\n output = check_file_type(file_path + \".nonesense\", file_types)\n if output:\n raise AssertionError","title":""},{"docid":"2ef5c8c413559c3394a1b10b121fda46","score":"0.6448945","text":"def check_root_filepath(filepath):\r\n if \".root\" in filepath:\r\n return True \r\n else:\r\n print(\"ERROR: \" + filepath + \" is not a path to a .root file\")\r\n return False","title":""},{"docid":"acba7b9437af8131c02626f04d62a859","score":"0.6443618","text":"def is_allowed_file(filename):\n # Make sure file has a file type, and check what file type it is\n return '.' in filename and filename.split('.')[-1].lower() in ALLOWED_FILE_EXTENSIONS","title":""},{"docid":"0b78ba1a4abf81d78c7534cf9f99f957","score":"0.64423394","text":"def _check_strategy_file(strategy_filename):\n if not isinstance(strategy_filename, str):\n raise TypeError(f\"For 'build_searched_strategy', the argument 'strategy_filename' should be string, \"\n f\"but got {type(strategy_filename)}.\")\n\n if not os.path.isfile(strategy_filename):\n raise ValueError(f\"For 'build_searched_strategy', no such strategy file: {strategy_filename}. \"\n f\"Please check whether the 'strategy_filename' exists.\")\n\n if os.path.getsize(strategy_filename) == 0:\n raise ValueError(f\"For 'build_searched_strategy', the strategy file {strategy_filename} should not \"\n f\"be empty. Please check whether the 'strategy_filename' is correct.\")","title":""},{"docid":"c253c59352d5ede38d18d7e94747c816","score":"0.64392763","text":"def isfile(f):\n\n return isinstance(f, file)","title":""},{"docid":"6dd33444bacc511b7fb2ccd293c24f73","score":"0.64330333","text":"def type_file_exists(value):\n if not os.path.exists(value):\n raise argparse.ArgumentTypeError(\"file not found: %s\" % value)\n elif not os.path.isfile(value):\n raise argparse.ArgumentTypeError(\"not a file: %s\" % value)\n return value","title":""},{"docid":"93af40058cb114d2632538968b7cba1c","score":"0.6426889","text":"def check_file_arg(a):\n if path.exists(a):\n try:\n validyaml = yaml.safe_load(a)\n except ValueError as e:\n return False\n return True","title":""},{"docid":"dec484ba90f1ecfd78cd422d8b0089dc","score":"0.64253664","text":"def isFile(FileName):\r\n if type(FileName) == str:\r\n return os.path.isfile(FileName)","title":""},{"docid":"bc39618595175309b76fdc248e2f16cd","score":"0.64159495","text":"def is_valid_file(parser: ArgumentParser, arg: str) -> Optional[str]:\n if not os.path.exists(arg) and arg.find(\"/\") > -1:\n parser.error(\"The menu file %s does not exist!\" % arg)\n return None\n else:\n if arg.find(\"/\") > -1:\n data_file: TextIO\n with open(arg, 'r') as data_file:\n if not data_file.readable:\n parser.error(\"The menu file %s is not readable!\" % arg)\n return None\n return arg","title":""},{"docid":"a3d0a35c52a4f8816f6be4421ce6e3ca","score":"0.6398936","text":"def validate_filename(self, fileName, errorMsg):\r\n if len(fileName) < 1:\r\n raise (ValueError(errorMsg))","title":""},{"docid":"e63aeffe73b87ae3f9b6874d1d7adf11","score":"0.6386394","text":"def is_file(param):\n\tif not os.path.isfile(param):\n\t\traise argparse.ArgumentTypeError('Parameter much be a tab delimited .txt file')\n\n\treturn param","title":""},{"docid":"57949d3d70a1fa5831b4023b35868037","score":"0.63861346","text":"def check_file(filename):\n if os.path.exists(filename) and os.access(filename, os.R_OK):\n return True\n else:\n print(f'File: {filename} does not exist or is not readable.')\n return False","title":""},{"docid":"5bab6cf5783f8fb4b55526525bbe85b7","score":"0.63723373","text":"def check_file_exists(filename):\n if not FileClass.is_file_exists(filename):\n UtilClass.error(\"Input files path %s is None or not existed!\\n\" % filename)","title":""},{"docid":"b6c41146c3582c8d599dd822637ef525","score":"0.63705343","text":"def check_file(file):\n\n\tif not os.path.isfile(file):\n\t\tprint(\"Error! File does not exist:\", file)\n\t\tprint(\"Exiting now...\")\n\t\treturn False\n\n\treturn True","title":""},{"docid":"d5a0206de63a82c210773c95c075b11c","score":"0.6369978","text":"def isfile(self, relpath):","title":""},{"docid":"ac235febbe6f420956a7f91a47c2b67b","score":"0.6363512","text":"def check_file(f):\n global HELP_MSG\n allowed_inputs_file = [\"png\"]\n try:\n if f.split('.')[-1] in allowed_inputs_file:\n return True\n else:\n return False\n except Exception:\n print(HELP_MSG)\n return False","title":""},{"docid":"1e9ab2c52d39161489284336184203e9","score":"0.6351157","text":"def check(src):\n if not exists(src):\n raise FileError(src)","title":""},{"docid":"c7ea862517b3d1f25adabe5b6f2fbbb7","score":"0.63506377","text":"def is_file_valid(event_msg):\n allowed_file_types = ['application/pdf', 'image/png', 'image/jpeg', 'image/gif', 'image/bmp', 'image/tiff']\n\n file_type = False\n file_size = False\n\n user_file = event_msg.file\n\n logger.info('file.name: ' + user_file.name)\n logger.info('file.size: ' + str(user_file.size)) #size in bytes of this file.\n\n # logger.info('allowed_file_types index: ' + str(allowed_file_types.index(user_file.mime_type)))\n\n if user_file.mime_type in allowed_file_types:\n logger.info('user_file.mime_type in allowed_file_types: ' + user_file.mime_type)\n file_type = True\n\n if user_file.size <= 1048576:\n file_size = True\n\n logger.info('file_type: {!s} and file_size: {!s}'.format(file_type, file_size))\n\n if file_type and file_size:\n return True\n # else:\n # return False","title":""},{"docid":"466961f257c4fd3a94bd8c4ae613af56","score":"0.634299","text":"def _validate_data_file(path):\n match = re.search(r'\\.(\\w+)$', str(path))\n extension = None\n if match:\n extension = match.group(1).lower()\n else:\n return 'invalid file extension'\n\n if extension == 'dat':\n # mirror file\n try:\n count = 0\n with open(str(path)) as f:\n for line in f.readlines():\n parts = line.split(\"\\t\")\n if len(parts) > 0:\n float(parts[0])\n if len(parts) > 1:\n float(parts[1])\n count += 1\n if count == 0:\n return 'no data rows found in file'\n except ValueError as e:\n return 'invalid file format: {}'.format(e)\n elif extension == 'zip':\n # undulator magnetic data file\n #TODO(pjm): add additional zip file validation\n zip_file = zipfile.ZipFile(str(path))\n is_valid = False\n for f in zip_file.namelist():\n if re.search('\\.txt', f.lower()):\n is_valid = True\n break\n if not is_valid:\n return 'zip file missing txt index file'\n else:\n return 'invalid file type: {}'.format(extension)\n return None","title":""}],"string":"[\n {\n \"docid\": \"7b3f148a410921b24e902a0a56697f98\",\n \"score\": \"0.81745076\",\n \"text\": \"def valid_file(filepath):\\n return os.path.isfile(filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c15f7c1c876f4b72d4f5a952f1150be\",\n \"score\": \"0.7708852\",\n \"text\": \"def chkfile(path):\\n if not os.path.isfile(path):\\n msg = \\\"{0} is not a file.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f825f0709fcaf92147f6228d5a383db\",\n \"score\": \"0.7609666\",\n \"text\": \"def checkFile(filename):\\n\\n valid = True\\n\\n if filename is None or not os.path.isfile(filename):\\n valid = False\\n\\n if valid:\\n try:\\n with open(filename, \\\"rb\\\") as f:\\n pass\\n except:\\n valid = False\\n\\n if not valid:\\n raise Exception(\\\"unable to read file '%s'\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74506ccf54d5165c4bbc77a94609733\",\n \"score\": \"0.75992125\",\n \"text\": \"def check_file(file_path: Union[str, Path]):\\n\\n file_path = Path(file_path)\\n return file_path.is_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfac8d9d14ca33403a15749027fe0837\",\n \"score\": \"0.75611776\",\n \"text\": \"def file_is_valid(parser, arg): \\n if not os.path.isfile(arg): \\n parser.error(\\\"The file %s doesn't exist.\\\" % arg) \\n else: \\n return open(arg, 'r')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8ce0ffb79cff3a33352034a47b4623\",\n \"score\": \"0.7451672\",\n \"text\": \"def _check_valid_path(path):\\n if not os.path.exists(path):\\n raise ValueError(f'Path \\\"{path}\\\" does not exist.')\\n if not os.path.isfile(path):\\n raise TypeError(f'Path \\\"{path}\\\" is not a file.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaeb48f6750d7d7fcc8f14caf21a57c2\",\n \"score\": \"0.74476874\",\n \"text\": \"def file_or_fail(file_path: str) -> None:\\n if not os.path.exists(file_path):\\n raise FileNotFoundError(file_path)\\n\\n if not os.path.isfile(file_path):\\n raise ValueError(f'Argument not a file at: {file_path}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df959a49b8f645dd9a037e4d866fdfe\",\n \"score\": \"0.7446648\",\n \"text\": \"def is_valid_file(parser, x):\\n\\n if not os.path.exists(x) or os.path.isdir(x):\\n parser.error(\\\"The file specified is not a file or doesn't exist!\\\")\\n return False\\n\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7389d9a92a81d2833b21c6c8c5d1d9\",\n \"score\": \"0.7443289\",\n \"text\": \"def is_valid_file(parser, arg):\\n arg = os.path.abspath(arg)\\n if not os.path.exists(arg):\\n parser.error(\\\"The file %s doesn't exist \\\" % arg)\\n else:\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7389d9a92a81d2833b21c6c8c5d1d9\",\n \"score\": \"0.7443289\",\n \"text\": \"def is_valid_file(parser, arg):\\n arg = os.path.abspath(arg)\\n if not os.path.exists(arg):\\n parser.error(\\\"The file %s doesn't exist \\\" % arg)\\n else:\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a20e5ad9392af2ed62f354c94d2645\",\n \"score\": \"0.7441992\",\n \"text\": \"def isValidFile( fileName ):\\n\\timport argparse\\n\\timport os\\n\\tif not os.path.isfile( fileName ):\\n\\t\\traise argparse.ArgumentTypeError( \\\"File %s does not exist\\\"%fileName )\\n\\telse:\\n\\t\\treturn fileName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"981c28f9f2b6a896cf6b6df6f2383ef8\",\n \"score\": \"0.7405622\",\n \"text\": \"def is_valid_file(parser, arg):\\n if not os.path.exists(arg):\\n parser.error(\\\"The file %s does not exist!\\\"%arg)\\n else:\\n return open(arg,'r') #return an open file handle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cbe2010a4eecd00aade0023c7f5f91b\",\n \"score\": \"0.73774254\",\n \"text\": \"def is_valid_file(parser, arg):\\n arg = os.path.abspath(arg)\\n if not os.path.exists(arg):\\n parser.error(\\\"The file %s doesn't exist \\\" % arg)\\n else:\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c2f1870fabc9ac2d91b6c02defc272\",\n \"score\": \"0.7345188\",\n \"text\": \"def is_valid_file(parser, arg):\\n if not os.path.exists(arg):\\n parser.error(\\\"The file %s does not exist!\\\" % arg)\\n else:\\n return os.path.abspath(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428a633aad855a2d88b00c014a27b81f\",\n \"score\": \"0.72743034\",\n \"text\": \"def check_file(f):\\n if not os.path.isfile(f):\\n raise ValueError(f'File {f} does not exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382d1d628989982161541a9faf4df6e6\",\n \"score\": \"0.7272897\",\n \"text\": \"def is_valid_file(self, filename):\\n if subprocess.os.path.exists(filename):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d84cedcbde452cfc92c6a1bc754f597\",\n \"score\": \"0.72688854\",\n \"text\": \"def check_file(abspath, ext=None):\\n check_type(abspath, str)\\n check_type(ext, [str, type(None)])\\n\\n if not os.path.isfile(abspath):\\n raise ValueError(f'File {abspath} does not exist. '\\n f'Specify a valid path to file.')\\n\\n if ext:\\n # print(os.path)\\n file_name, extension = os.path.splitext(str(abspath))\\n if not extension == ext:\\n raise ValueError(f'File {abspath} should be a {ext} file. '\\n f'Specify a valid path to a {ext} file.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4822f6b252a8706950c346882dbff658\",\n \"score\": \"0.72588295\",\n \"text\": \"def validate_filepath(self):\\n self.status_widget.clear(1)\\n\\n path = self.file_widget.get_path()\\n\\n self._file_valid = utils.is_file_path_valid(path)\\n\\n if not self._file_valid:\\n self.status_widget.add_error(1, \\\"Invalid file path\\\")\\n\\n self.validate_all_values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a67c493cc25d977778d0f01ad805ea\",\n \"score\": \"0.72427493\",\n \"text\": \"def is_valid_file(arg): #Currently not doing this at this point. ? Should it be done here\\n if not os.path.isfile(arg):\\n self.parser.error('The file {} does not exist!'.format(arg))\\n else:\\n # File exists so return the filename\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9c2b0c111e590aab56529372b4c92e\",\n \"score\": \"0.7228502\",\n \"text\": \"def validate_file_exists(path: Union[Path, str]):\\n if not path:\\n return False\\n else:\\n path = Path(path)\\n if path.is_file():\\n return True\\n else:\\n valid_path_length(path)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d91d901e308e91215cee9d81dd63f15\",\n \"score\": \"0.7210747\",\n \"text\": \"def validate_filepath(self):\\n self.status_widget.clear(1)\\n\\n path = self.file_widget.get_path()\\n self._file_valid = utils.is_file_path_valid(path)\\n\\n if not self._file_valid:\\n self.status_widget.add_error(1, \\\"Invalid file path\\\")\\n\\n self.validate_all_values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9fb66ffaf59d8850573d8e9a3430050\",\n \"score\": \"0.7206458\",\n \"text\": \"def check_file(self, filepath):\\n if self.regex is not None:\\n fname = os.path.basename(filepath)\\n match = self.regex_c.match(fname)\\n if not match:\\n self.logger.info(f\\\"filename {fname} did not match regex\\\")\\n return False\\n\\n # file did not fail any checks we set for it\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8410508ee03fbac6a0b88a5655200bed\",\n \"score\": \"0.7182967\",\n \"text\": \"def is_valid_file(file, parser):\\n\\n if os.path.exists(file):\\n return file\\n else:\\n raise parser.error(\\\"Unable to locate %s: No such file or directory\\\" % file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d8d12b13d5dc8f44e030c1ccf10e1df\",\n \"score\": \"0.7174319\",\n \"text\": \"def file_checker(filename):\\n if not os.path.isfile(filename):\\n raise ValueError(\\\"File %s is not existed!\\\" % (filename))\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13da947afcc76c9804927a821f6f26b8\",\n \"score\": \"0.71536183\",\n \"text\": \"def is_file(filename):\\n if not os.path.isfile(filename):\\n msg = \\\"{0} is not a file\\\".format(filename)\\n raise argparse.ArgumentTypeError(msg)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13da947afcc76c9804927a821f6f26b8\",\n \"score\": \"0.71536183\",\n \"text\": \"def is_file(filename):\\n if not os.path.isfile(filename):\\n msg = \\\"{0} is not a file\\\".format(filename)\\n raise argparse.ArgumentTypeError(msg)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13da947afcc76c9804927a821f6f26b8\",\n \"score\": \"0.71536183\",\n \"text\": \"def is_file(filename):\\n if not os.path.isfile(filename):\\n msg = \\\"{0} is not a file\\\".format(filename)\\n raise argparse.ArgumentTypeError(msg)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13da947afcc76c9804927a821f6f26b8\",\n \"score\": \"0.71536183\",\n \"text\": \"def is_file(filename):\\n if not os.path.isfile(filename):\\n msg = \\\"{0} is not a file\\\".format(filename)\\n raise argparse.ArgumentTypeError(msg)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13da947afcc76c9804927a821f6f26b8\",\n \"score\": \"0.71536183\",\n \"text\": \"def is_file(filename):\\n if not os.path.isfile(filename):\\n msg = \\\"{0} is not a file\\\".format(filename)\\n raise argparse.ArgumentTypeError(msg)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4608919503ca652c6627ec4c72e8186d\",\n \"score\": \"0.71234065\",\n \"text\": \"def is_valid_file(parser, arg):\\n if not os.path.isfile(arg):\\n parser.error(\\\"The file %s does not seem to be a file at all! Exiting for safety reasons.\\\" %arg)\\n sys.exit(1)\\n else:\\n if validate_input(arg):\\n return True\\n else:\\n print \\\"Invalid JSON. Exiting\\\"\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae59589e56ee05db55bf04ec8ea20b5\",\n \"score\": \"0.7105801\",\n \"text\": \"def isfile(self, filepath: Union[str, Path]) -> bool:\\n return osp.isfile(filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6d80cfdebc90cee5eb8aa75b8577d8\",\n \"score\": \"0.7062038\",\n \"text\": \"def is_valid_file(arg_parse, arg):\\n if not os.path.exists(arg):\\n arg_parse.error(\\\"The file %s does not exist!\\\" % arg)\\n elif os.path.splitext(arg)[-1] not in ['.pdf', '.dxf']:\\n arg_parse.error(\\\"supported filetypes are .pdf and .dxf\\\")\\n else:\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed1180a6ffc1a66a4170bec87f72c49\",\n \"score\": \"0.70509124\",\n \"text\": \"def valid_filename(filename: str) -> str:\\n if not os.path.isfile(filename):\\n raise argparse.ArgumentTypeError(\\n f\\\"'File '{filename}' does not exist!\\\")\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455064028868ed776a7cd675f7f79722\",\n \"score\": \"0.7046568\",\n \"text\": \"def fileValidation(self, filename):\\n if not os.path.exists(filename):\\n print(\\\"error: file '{}' does not exist\\\".format (filename), file=sys.stderr)\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d8942911674b5b32ea3b21af7f58520\",\n \"score\": \"0.7036292\",\n \"text\": \"def is_valid_file(x, expected_size):\\n if not os.path.exists(x):\\n # Argparse uses the ArgumentTypeError to give a rejection message like:\\n # error: argument input: x does not exist\\n raise argparse.ArgumentTypeError(\\\"{0} does not exist\\\".format(x))\\n elif os.path.getsize(x) != expected_size:\\n raise argparse.ArgumentTypeError(\\n \\\"{0} is not valid, expected filesize: {1} bytes\\\".format(x, expected_size)) \\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5408ab784dab27ede4f9872dc35c87d\",\n \"score\": \"0.7023431\",\n \"text\": \"def check_filename(path, arg_name=\\\"\\\"):\\n if arg_name == \\\"\\\":\\n arg_name = \\\"File path\\\"\\n else:\\n arg_name = \\\"'{}'\\\".format(arg_name)\\n if not path:\\n raise ParamValueError('{} is not allowed None or empty!'.format(arg_name))\\n if not isinstance(path, str):\\n raise ParamValueError(\\\"File path: {} is not string.\\\".format(path))\\n if path.endswith(\\\"/\\\"):\\n raise ParamValueError(\\\"File path can not end with '/'\\\")\\n file_name = os.path.basename(path)\\n\\n # '#', ':', '|', ' ', '}', '\\\"', '+', '!', ']', '[', '\\\\\\\\', '`',\\n # '&', '.', '/', '@', \\\"'\\\", '^', ',', '_', '<', ';', '~', '>',\\n # '*', '(', '%', ')', '-', '=', '{', '?', '$'\\n forbidden_symbols = set(r'\\\\/:*?\\\"<>|`&\\\\';')\\n\\n if set(file_name) & forbidden_symbols:\\n raise ParamValueError(r\\\"File name should not contains \\\\/:*?\\\\\\\"<>|`&;\\\\'\\\")\\n\\n if file_name.startswith(' ') or file_name.endswith(' '):\\n raise ParamValueError(\\\"File name should not start/end with space.\\\")\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34f03d14cd4c59b9bf42ed8ac274d9eb\",\n \"score\": \"0.7018437\",\n \"text\": \"def check_input_file(filename):\\n if not (os.path.isfile(filename) and os.access(filename, os.R_OK)):\\n raise IOError(f'ERROR: COULD NOT OPEN INPUT FILE {filename}!\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382aaa954b7ac357cbe8854d3fb1bf72\",\n \"score\": \"0.701571\",\n \"text\": \"def is_file(path: Path) -> Path:\\n\\n if not path.is_file():\\n msg = f\\\"{path} is not a file\\\"\\n raise ArgumentTypeError(msg)\\n\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d4d025d1cd1fc7729973d09e19668\",\n \"score\": \"0.6996733\",\n \"text\": \"def check_input_file(input_file_path):\\n if not os.path.exists(input_file_path):\\n raise click.ClickException(\\\"input_file path doesn't exist\\\")\\n if not os.path.isfile(input_file_path):\\n raise click.ClickException(\\\"input_file isn't a file\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0914174929199c33c5b5242a0dc52cf7\",\n \"score\": \"0.6978147\",\n \"text\": \"def check_file_exists(filepath):\\n if not op.exists(filepath):\\n raise IOError('File {} not found.'.format(filepath))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ac128e889479c32d299dfa50521788\",\n \"score\": \"0.6942239\",\n \"text\": \"def isFilePathValid(fileName, checkExist=True):\\n try:\\n with open(fileName, 'x') as tempFile:\\n return True\\n except OSError:\\n return checkExist and os.path.exists(fileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e312645ac6b398a94048a059562b063\",\n \"score\": \"0.6924955\",\n \"text\": \"def _args_check_file(value):\\n strval = value\\n if not os.path.isfile(strval):\\n raise argparse.ArgumentTypeError('File \\\"%s\\\" not found.' % value)\\n return strval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314f0ccd4373a276e3985a29ca8375c2\",\n \"score\": \"0.6903918\",\n \"text\": \"def check_file_validity(file_):\\n if not os.access(file_, os.F_OK):\\n raise MonitorError(\\\"File '%s' does not exist\\\" % file_)\\n if not os.access(file_, os.R_OK):\\n raise MonitorError(\\\"File '%s' not readable\\\" % file_)\\n if os.path.isdir(file_):\\n raise MonitorError(\\\"File '%s' is a directory\\\" % file_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22df7a1b753b3d3f30db56799dcdd650\",\n \"score\": \"0.68994594\",\n \"text\": \"def is_file(path):\\n\\n\\tif not os.path.isfile(path):\\n\\t\\tif os.path.isdir(path):\\n\\t\\t\\tmsg = \\\"{0} is a directory\\\".format(path)\\n\\t\\telse:\\n\\t\\t\\tmsg = \\\"{0} does not exist.\\\".format(path)\\n\\t\\traise argparse.ArgumentTypeError(msg)\\n\\treturn path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d51e5e6a6884a79af3c6bd4bc3d1995e\",\n \"score\": \"0.6883056\",\n \"text\": \"def is_file(inputfile):\\n inputfile = realpath(inputfile)\\n if not isfile(inputfile):\\n logger.error('Input is not a file!')\\n sys.exit(0)\\n return inputfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43431fe9c6fe9d8210fe590a4e40974d\",\n \"score\": \"0.68723494\",\n \"text\": \"def is_file(path: str) -> bool:\\n return os.path.isfile(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662c44b4da2858998c1ce93f8cbb17e1\",\n \"score\": \"0.6850036\",\n \"text\": \"def _check_filename(self, filename):\\n if len(os.path.dirname(filename)) != 0:\\n raise ValueError(f\\\"Expecting file name but got path {filename}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd108eb8bc9df0426280c11e5c689ce0\",\n \"score\": \"0.6847535\",\n \"text\": \"def __is_file_valid(self, path):\\n if not os.path.exists(path):\\n raise Exception(\\\"The path to the given file does not exist:\\\\n\\\" + path)\\n if not path.endswith(\\\".pdf\\\"):\\n raise Exception(\\\"The given file is not a .pdf file:\\\\n\\\" + path)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11673839a469f93b0017edfe36f19f0\",\n \"score\": \"0.6846054\",\n \"text\": \"def check_file(path2check,filename_regex):\\n\\n # check string is valid path\\n if not os.path.isfile(path2check):\\n print \\\"Error: %s is not a file\\\\n\\\" % path2check\\n sys.exit(1)\\n\\n filename = os.path.basename(path2check)\\n if not filename_regex.match(filename):\\n print \\\"Error: Expected SIT_YYMMDD.xls, but got %s\\\" % filename\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358b6799cd6ff9bf19428f1840481532\",\n \"score\": \"0.6839836\",\n \"text\": \"def CheckFile(filename):\\n if not os.path.exists(filename):\\n raise FileNotFoundError('%s: file not found' % filename)\\n elif not os.access(filename, os.R_OK):\\n raise FileNotReadableError('%s: file not readable' % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed881578f5f7be36ea78807465e1e75\",\n \"score\": \"0.68378216\",\n \"text\": \"def checkFile(filename):\\r\\n\\r\\n if not os.path.isfile(filename):\\r\\n raise SqlmapFilePathException(\\\"unable to read file '%s'\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db605eb566df9762d512e313a7e8a650\",\n \"score\": \"0.6832033\",\n \"text\": \"def is_file(filename):\\n try:\\n open(filename, \\\"r\\\")\\n return True\\n except IOError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25216fbc2f85faee1f2a43dd9b8c38b\",\n \"score\": \"0.68318945\",\n \"text\": \"def is_file(self, path: PurePath) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f1bf2f1bc38c636c44471f49149acb\",\n \"score\": \"0.6824413\",\n \"text\": \"def is_file_valid(file_path: str) -> bool:\\n if not file_path.endswith(\\\".tml\\\"):\\n return False\\n\\n try:\\n __read_tml(file_path)\\n except:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb41b87acbcf986e9d6f32a1a9c1ce46\",\n \"score\": \"0.67919314\",\n \"text\": \"def isfile(path):\\n if not os.path.isfile(path):\\n if os.path.isdir(path):\\n msg = \\\"{0} is a directory\\\".format(path)\\n else:\\n msg = \\\"{0} does not exist.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb41b87acbcf986e9d6f32a1a9c1ce46\",\n \"score\": \"0.67919314\",\n \"text\": \"def isfile(path):\\n if not os.path.isfile(path):\\n if os.path.isdir(path):\\n msg = \\\"{0} is a directory\\\".format(path)\\n else:\\n msg = \\\"{0} does not exist.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb41b87acbcf986e9d6f32a1a9c1ce46\",\n \"score\": \"0.67919314\",\n \"text\": \"def isfile(path):\\n if not os.path.isfile(path):\\n if os.path.isdir(path):\\n msg = \\\"{0} is a directory\\\".format(path)\\n else:\\n msg = \\\"{0} does not exist.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb41b87acbcf986e9d6f32a1a9c1ce46\",\n \"score\": \"0.67919314\",\n \"text\": \"def isfile(path):\\n if not os.path.isfile(path):\\n if os.path.isdir(path):\\n msg = \\\"{0} is a directory\\\".format(path)\\n else:\\n msg = \\\"{0} does not exist.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb41b87acbcf986e9d6f32a1a9c1ce46\",\n \"score\": \"0.67919314\",\n \"text\": \"def isfile(path):\\n if not os.path.isfile(path):\\n if os.path.isdir(path):\\n msg = \\\"{0} is a directory\\\".format(path)\\n else:\\n msg = \\\"{0} does not exist.\\\".format(path)\\n raise argparse.ArgumentTypeError(msg)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41c4d4050d0240cbb4fb5d2029b4f80\",\n \"score\": \"0.6783844\",\n \"text\": \"def check_file(path2check,filename_regex):\\n\\n\\n # check string is valid path\\n if not os.path.isfile(path2check):\\n print \\\"Error: %s is not a file\\\\n\\\" % path2check\\n sys.exit(1)\\n\\n filename = os.path.basename(path2check)\\n if not filename_regex.match(filename):\\n print \\\"Error: Expected SIT_YYMMDD.xls, but got %s\\\" % filename\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3362cb0a1fe8170180abf1d0721ff6af\",\n \"score\": \"0.6776304\",\n \"text\": \"def validate(self, file_path):\\n existing_path = os.path.exists(file_path)\\n same_path = (file_path == self.external_var.get())\\n \\n name, format = os.path.splitext(file_path)\\n correct_format = (format.lower() in self.file_formats)\\n\\n return not same_path and existing_path and correct_format\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf69fba39ee9499eb0934604147d214\",\n \"score\": \"0.6771543\",\n \"text\": \"def isfile(path):\\r\\n if not os.path.isfile(path):\\r\\n if os.path.isdir(path):\\r\\n msg = \\\"{0} is a directory\\\".format(path)\\r\\n else:\\r\\n msg = \\\"{0} does not exist.\\\".format(path)\\r\\n raise argparse.ArgumentTypeError(msg)\\r\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6002107f997cba432c0a5a90d1c868c2\",\n \"score\": \"0.6756233\",\n \"text\": \"def __is_file_valid(self, path):\\n if not os.path.exists(path):\\n raise Exception('Path does not exist:\\\\n' + str(path))\\n elif not path.endswith('.pdf'):\\n raise Exception('File is not a .pdf file:\\\\n' + str(path))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38720ca435d5edc13d018d848b3f6a9b\",\n \"score\": \"0.674048\",\n \"text\": \"def _filecheck(self, filename):\\n if (self._filecheck_pattern is not None and\\n not self._filecheck_pattern.search(filename)):\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d6e502c2324f335904b70b459b65c81\",\n \"score\": \"0.6728257\",\n \"text\": \"def isfile(self, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"347f20ee4cc3a5fe05abb4bcf8d68541\",\n \"score\": \"0.67261446\",\n \"text\": \"def check_filename(path):\\n if not isinstance(path, str):\\n raise ValueError(\\\"path: {} is not string\\\".format(path))\\n filename = os.path.basename(path)\\n\\n # '#', ':', '|', ' ', '}', '\\\"', '+', '!', ']', '[', '\\\\\\\\', '`',\\n # '&', '.', '/', '@', \\\"'\\\", '^', ',', '_', '<', ';', '~', '>',\\n # '*', '(', '%', ')', '-', '=', '{', '?', '$'\\n forbidden_symbols = set(r'\\\\/:*?\\\"<>|`&\\\\';')\\n\\n if set(filename) & forbidden_symbols:\\n raise ValueError(r\\\"filename should not contains \\\\/:*?\\\\\\\"<>|`&;\\\\'\\\")\\n\\n if filename.startswith(' ') or filename.endswith(' '):\\n raise ValueError(\\\"filename should not start/end with space\\\")\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf76d2a8da8c938843c466ffa53b2e3\",\n \"score\": \"0.6711318\",\n \"text\": \"def verify_file(filename):\\n\\ttry:\\n\\t\\textension = filename.split('.')[-1]\\n\\t\\tif extension == \\\"gz\\\":\\n\\t\\t\\tfp = gzip.open(filename, 'rt')\\n\\t\\telse:\\n\\t\\t\\tfp = open(filename)\\n\\t\\tone = fp.readline()\\n\\t\\tfp.close()\\n\\texcept IOError as err:\\n\\t\\tsys.exit(gettime() + \\\"Error reading the file {0}: {1}\\\".format(filename, err))\\n\\treturn(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334d626308dc3e1218c26ce3760b8da3\",\n \"score\": \"0.6652371\",\n \"text\": \"def assertFile(self, path):\\n print(\\\"Check file %s\\\" % path)\\n self.assertTrue(os.path.isfile(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a073f1e720d61ea0ca424c17160cdd\",\n \"score\": \"0.664469\",\n \"text\": \"def is_file(path):\\n return os.path.isfile(str(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba7e8088db1c4f2df64243b0a58a79b\",\n \"score\": \"0.66247493\",\n \"text\": \"def check_input_file(self, path):\\n if not gf.file_can_be_read(path):\\n self.print_error(u\\\"Unable to read file '%s'\\\" % (path))\\n self.print_error(u\\\"Make sure the file path is written/escaped correctly and that you have read permission on it\\\")\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77aad098b39c03b23bf7e4cc849e385\",\n \"score\": \"0.6615429\",\n \"text\": \"def check_valid_file(path, uuid):\\n try:\\n get_header(path)\\n return True\\n except (IOError, ValueError):\\n handle_deleted_file(uuid)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b004bfcded2fda9a976894ad8ee28535\",\n \"score\": \"0.6583151\",\n \"text\": \"def _check_file(self, file_path):\\n\\n # set logger\\n logger = logging.getLogger(sys._getframe().f_code.co_name)\\n logger.debug(\\\"Running check_file.\\\")\\n\\n if not os.path.isfile(file_path):\\n logger.error(file_path + \\\"was not found. Exiting.\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc213218b8c4c6ec6f6beba539e5cc1\",\n \"score\": \"0.65462637\",\n \"text\": \"def validate_file_exists(path):\\n\\n if not os.path.exists(path):\\n raise exceptions.FileDoesntExistException(path=path)\\n if os.path.isdir(path):\\n raise exceptions.FileIsADirectoryException(path=path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df5470a40d95c5a3eb081219bd933926\",\n \"score\": \"0.6545388\",\n \"text\": \"def isFile(path):\\n return os.path.isfile(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de234b323533aaebb256e372c0d72bc2\",\n \"score\": \"0.6542922\",\n \"text\": \"def _validateFilename(self, filePath):\\n assert os.path.basename(filePath[0]) == \\\"data_mutations_extended_{}.txt\\\".format(\\n self.center\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10274106f1ee75234421a3e7577d942\",\n \"score\": \"0.65408105\",\n \"text\": \"def validateFile(self, fileName):\\n try: # try to open a file\\n f = open(fileName, \\\"r\\\")\\n except IOError: # catch error\\n print(\\\"Wrong name of the file!\\\")\\n return False # exit\\n\\n f.close()\\n return True # exit when no problems found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a0f55cb7e1ffcc8b2c61e230d3dfe5\",\n \"score\": \"0.6512724\",\n \"text\": \"def check_file(filepath, permissions='r', **kwargs):\\n if not os.path.exists(filepath):\\n return MESSAGES['FILE_NOT_FOUND']\\n\\n permissions_warning = check_permissions(filepath, permissions)\\n if permissions_warning:\\n return permissions_warning\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe7dc8716c1cdf3c6bbecc9ff063d43\",\n \"score\": \"0.650076\",\n \"text\": \"def _check_existence(filename):\\n if not os.path.isfile(filename):\\n raise RuntimeError(\\\"%s doesn't exist.\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7424295a483d2746b8bedde290efb9db\",\n \"score\": \"0.64806426\",\n \"text\": \"def validate_file(self):\\n # no file set\\n if self._file is None:\\n self._new_file = True\\n self._valid = False\\n return False\\n\\n # check if file exists already\\n if os.path.exists(self._file):\\n self._new_file = False\\n else:\\n self._new_file = True\\n\\n # if it's a dir it's not valid\\n if os.path.isdir(self._file):\\n self._valid = False\\n else:\\n self._valid = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97f3acf4f04d1f2e9985083b8160135\",\n \"score\": \"0.64797765\",\n \"text\": \"def filecheck(filename):\\n try:\\n open(filename, 'r')\\n return 1\\n except IOError:\\n print \\\"Error: A required file does not exist.\\\\n\\\" \\\\\\n \\\"- field_find.txt (required)\\\\n\\\" \\\\\\n \\\"- field_update.txt (required)\\\\n\\\" \\\\\\n \\\"- your entered JSON file (required)\\\\n\\\\n\\\" \\\\\\n \\\"Please check your files and try again.\\\"\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e807c65c52d22a14ac3bd23b9adab7f1\",\n \"score\": \"0.6479745\",\n \"text\": \"def is_good_file(self, filename):\\n return self.goodRE.match(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8130f5a83b7820c5743e0d5bdf1a9a58\",\n \"score\": \"0.6470759\",\n \"text\": \"def test_check_file_type(file_path, file_types):\\n output = check_file_type(file_path, file_types)\\n if not output:\\n raise AssertionError\\n\\n # check for false\\n output = check_file_type(file_path + \\\".nonesense\\\", file_types)\\n if output:\\n raise AssertionError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef5c8c413559c3394a1b10b121fda46\",\n \"score\": \"0.6448945\",\n \"text\": \"def check_root_filepath(filepath):\\r\\n if \\\".root\\\" in filepath:\\r\\n return True \\r\\n else:\\r\\n print(\\\"ERROR: \\\" + filepath + \\\" is not a path to a .root file\\\")\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acba7b9437af8131c02626f04d62a859\",\n \"score\": \"0.6443618\",\n \"text\": \"def is_allowed_file(filename):\\n # Make sure file has a file type, and check what file type it is\\n return '.' in filename and filename.split('.')[-1].lower() in ALLOWED_FILE_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b78ba1a4abf81d78c7534cf9f99f957\",\n \"score\": \"0.64423394\",\n \"text\": \"def _check_strategy_file(strategy_filename):\\n if not isinstance(strategy_filename, str):\\n raise TypeError(f\\\"For 'build_searched_strategy', the argument 'strategy_filename' should be string, \\\"\\n f\\\"but got {type(strategy_filename)}.\\\")\\n\\n if not os.path.isfile(strategy_filename):\\n raise ValueError(f\\\"For 'build_searched_strategy', no such strategy file: {strategy_filename}. \\\"\\n f\\\"Please check whether the 'strategy_filename' exists.\\\")\\n\\n if os.path.getsize(strategy_filename) == 0:\\n raise ValueError(f\\\"For 'build_searched_strategy', the strategy file {strategy_filename} should not \\\"\\n f\\\"be empty. Please check whether the 'strategy_filename' is correct.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c253c59352d5ede38d18d7e94747c816\",\n \"score\": \"0.64392763\",\n \"text\": \"def isfile(f):\\n\\n return isinstance(f, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dd33444bacc511b7fb2ccd293c24f73\",\n \"score\": \"0.64330333\",\n \"text\": \"def type_file_exists(value):\\n if not os.path.exists(value):\\n raise argparse.ArgumentTypeError(\\\"file not found: %s\\\" % value)\\n elif not os.path.isfile(value):\\n raise argparse.ArgumentTypeError(\\\"not a file: %s\\\" % value)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93af40058cb114d2632538968b7cba1c\",\n \"score\": \"0.6426889\",\n \"text\": \"def check_file_arg(a):\\n if path.exists(a):\\n try:\\n validyaml = yaml.safe_load(a)\\n except ValueError as e:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec484ba90f1ecfd78cd422d8b0089dc\",\n \"score\": \"0.64253664\",\n \"text\": \"def isFile(FileName):\\r\\n if type(FileName) == str:\\r\\n return os.path.isfile(FileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc39618595175309b76fdc248e2f16cd\",\n \"score\": \"0.64159495\",\n \"text\": \"def is_valid_file(parser: ArgumentParser, arg: str) -> Optional[str]:\\n if not os.path.exists(arg) and arg.find(\\\"/\\\") > -1:\\n parser.error(\\\"The menu file %s does not exist!\\\" % arg)\\n return None\\n else:\\n if arg.find(\\\"/\\\") > -1:\\n data_file: TextIO\\n with open(arg, 'r') as data_file:\\n if not data_file.readable:\\n parser.error(\\\"The menu file %s is not readable!\\\" % arg)\\n return None\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d0a35c52a4f8816f6be4421ce6e3ca\",\n \"score\": \"0.6398936\",\n \"text\": \"def validate_filename(self, fileName, errorMsg):\\r\\n if len(fileName) < 1:\\r\\n raise (ValueError(errorMsg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63aeffe73b87ae3f9b6874d1d7adf11\",\n \"score\": \"0.6386394\",\n \"text\": \"def is_file(param):\\n\\tif not os.path.isfile(param):\\n\\t\\traise argparse.ArgumentTypeError('Parameter much be a tab delimited .txt file')\\n\\n\\treturn param\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57949d3d70a1fa5831b4023b35868037\",\n \"score\": \"0.63861346\",\n \"text\": \"def check_file(filename):\\n if os.path.exists(filename) and os.access(filename, os.R_OK):\\n return True\\n else:\\n print(f'File: {filename} does not exist or is not readable.')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bab6cf5783f8fb4b55526525bbe85b7\",\n \"score\": \"0.63723373\",\n \"text\": \"def check_file_exists(filename):\\n if not FileClass.is_file_exists(filename):\\n UtilClass.error(\\\"Input files path %s is None or not existed!\\\\n\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c41146c3582c8d599dd822637ef525\",\n \"score\": \"0.63705343\",\n \"text\": \"def check_file(file):\\n\\n\\tif not os.path.isfile(file):\\n\\t\\tprint(\\\"Error! File does not exist:\\\", file)\\n\\t\\tprint(\\\"Exiting now...\\\")\\n\\t\\treturn False\\n\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a0206de63a82c210773c95c075b11c\",\n \"score\": \"0.6369978\",\n \"text\": \"def isfile(self, relpath):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac235febbe6f420956a7f91a47c2b67b\",\n \"score\": \"0.6363512\",\n \"text\": \"def check_file(f):\\n global HELP_MSG\\n allowed_inputs_file = [\\\"png\\\"]\\n try:\\n if f.split('.')[-1] in allowed_inputs_file:\\n return True\\n else:\\n return False\\n except Exception:\\n print(HELP_MSG)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9ab2c52d39161489284336184203e9\",\n \"score\": \"0.6351157\",\n \"text\": \"def check(src):\\n if not exists(src):\\n raise FileError(src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ea862517b3d1f25adabe5b6f2fbbb7\",\n \"score\": \"0.63506377\",\n \"text\": \"def is_file_valid(event_msg):\\n allowed_file_types = ['application/pdf', 'image/png', 'image/jpeg', 'image/gif', 'image/bmp', 'image/tiff']\\n\\n file_type = False\\n file_size = False\\n\\n user_file = event_msg.file\\n\\n logger.info('file.name: ' + user_file.name)\\n logger.info('file.size: ' + str(user_file.size)) #size in bytes of this file.\\n\\n # logger.info('allowed_file_types index: ' + str(allowed_file_types.index(user_file.mime_type)))\\n\\n if user_file.mime_type in allowed_file_types:\\n logger.info('user_file.mime_type in allowed_file_types: ' + user_file.mime_type)\\n file_type = True\\n\\n if user_file.size <= 1048576:\\n file_size = True\\n\\n logger.info('file_type: {!s} and file_size: {!s}'.format(file_type, file_size))\\n\\n if file_type and file_size:\\n return True\\n # else:\\n # return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466961f257c4fd3a94bd8c4ae613af56\",\n \"score\": \"0.634299\",\n \"text\": \"def _validate_data_file(path):\\n match = re.search(r'\\\\.(\\\\w+)$', str(path))\\n extension = None\\n if match:\\n extension = match.group(1).lower()\\n else:\\n return 'invalid file extension'\\n\\n if extension == 'dat':\\n # mirror file\\n try:\\n count = 0\\n with open(str(path)) as f:\\n for line in f.readlines():\\n parts = line.split(\\\"\\\\t\\\")\\n if len(parts) > 0:\\n float(parts[0])\\n if len(parts) > 1:\\n float(parts[1])\\n count += 1\\n if count == 0:\\n return 'no data rows found in file'\\n except ValueError as e:\\n return 'invalid file format: {}'.format(e)\\n elif extension == 'zip':\\n # undulator magnetic data file\\n #TODO(pjm): add additional zip file validation\\n zip_file = zipfile.ZipFile(str(path))\\n is_valid = False\\n for f in zip_file.namelist():\\n if re.search('\\\\.txt', f.lower()):\\n is_valid = True\\n break\\n if not is_valid:\\n return 'zip file missing txt index file'\\n else:\\n return 'invalid file type: {}'.format(extension)\\n return None\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1482,"cells":{"query_id":{"kind":"string","value":"e4689a2a4bb3d8dde2e8fd0626a3ea9f"},"query":{"kind":"string","value":"Execute a serverside transaction"},"positive_passages":{"kind":"list like","value":[{"docid":"4a1b857f9e5292ec65e493069ace3cef","score":"0.0","text":"def transaction(self, collections, action, wait_for_sync = False, lock_timeout = None, params = None):\n payload = {\n \"collections\": collections,\n \"action\": action,\n \"wait_for_sync\": wait_for_sync\n }\n if lock_timeout is not None:\n payload[\"lock_timeout\"] = lock_timeout\n if params is not None:\n payload[\"params\"] = params\n\n self.connection.report_start(action)\n\n response = self.connection.session.post(self.transaction_URL, data = json.dumps(payload, default=str))\n\n self.connection.report_item()\n\n data = response.json()\n\n if (response.status_code == 200 or response.status_code == 201 or response.status_code == 202) and not data.get(\"error\"):\n return data\n else:\n raise TransactionError(data[\"errorMessage\"], action, data)","title":""}],"string":"[\n {\n \"docid\": \"4a1b857f9e5292ec65e493069ace3cef\",\n \"score\": \"0.0\",\n \"text\": \"def transaction(self, collections, action, wait_for_sync = False, lock_timeout = None, params = None):\\n payload = {\\n \\\"collections\\\": collections,\\n \\\"action\\\": action,\\n \\\"wait_for_sync\\\": wait_for_sync\\n }\\n if lock_timeout is not None:\\n payload[\\\"lock_timeout\\\"] = lock_timeout\\n if params is not None:\\n payload[\\\"params\\\"] = params\\n\\n self.connection.report_start(action)\\n\\n response = self.connection.session.post(self.transaction_URL, data = json.dumps(payload, default=str))\\n\\n self.connection.report_item()\\n\\n data = response.json()\\n\\n if (response.status_code == 200 or response.status_code == 201 or response.status_code == 202) and not data.get(\\\"error\\\"):\\n return data\\n else:\\n raise TransactionError(data[\\\"errorMessage\\\"], action, data)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b2f4adf30873647179657713cd0f4368","score":"0.7078037","text":"def transaction(self, *args, **kwargs):\n return self._operate(Connection.transaction, args, kwargs)","title":""},{"docid":"ba488214cac0d5aab92031b2d45241dc","score":"0.65641505","text":"def test_execute_as_transaction(self):\n @execute_as_transaction\n def function():\n # retrieve transaction id\n with qdb.sql_connection.TRN:\n sql = \"SELECT txid_current();\"\n qdb.sql_connection.TRN.add(sql)\n return qdb.sql_connection.TRN.execute_fetchlast()\n\n f1 = function()\n f2 = function()\n self.assertNotEqual(f1, f2)","title":""},{"docid":"dbf85b4379485c20eba40aa610193ab7","score":"0.649355","text":"def execute_txn(self, card, transaction, message=''):\n fields = {}\n if message:\n fields['message'] = message\n return self._post('/me/cards/' + card + '/transactions/' + transaction + '/commit', fields)","title":""},{"docid":"c40f3aa7ba75338f4f74e4a2da2c5056","score":"0.6371799","text":"def onTransaction(self, command, args, response):\n raise NotImplementedError","title":""},{"docid":"953201d2b36e803a78487ced93f633b2","score":"0.6332858","text":"def commit(self, tx):","title":""},{"docid":"eeb79144b48cc547bd8eda52b1d4704d","score":"0.63061035","text":"def _exec(self):\r\n if not self._in_transaction:\r\n raise Error('Not in transaction')\r\n\r\n futures_and_postprocessors = self._transaction_response_queue\r\n self._transaction_response_queue = None\r\n\r\n # Get transaction answers.\r\n multi_bulk_reply = yield from self._query(b'exec', _bypass=True)\r\n\r\n if multi_bulk_reply is None:\r\n # We get None when a transaction failed.\r\n raise TransactionError('Transaction failed.')\r\n else:\r\n assert isinstance(multi_bulk_reply, MultiBulkReply)\r\n\r\n for f in multi_bulk_reply.iter_raw():\r\n answer = yield from f\r\n f2, call = futures_and_postprocessors.popleft()\r\n\r\n if isinstance(answer, Exception):\r\n f2.set_exception(answer)\r\n else:\r\n if call:\r\n self._pipelined_calls.remove(call)\r\n\r\n f2.set_result(answer)\r\n\r\n self._transaction_response_queue = deque()\r\n self._in_transaction = False\r\n self._transaction = None","title":""},{"docid":"3a7e968461d33863af75ec307cc9f8b7","score":"0.61880314","text":"def commit_transaction(*args):\n return _xapian.WritableDatabase_commit_transaction(*args)","title":""},{"docid":"c4570dd02fbb7291ccc4c8078f9fdce2","score":"0.61740094","text":"def test_commit_transaction_commit_saved_trans(auth_client, single_transaction):\n r = auth_client.commit_transaction('DEFAULT', single_transaction, {'commit': True})\n assert r.json()['status'] == 'Committed'","title":""},{"docid":"8043a91958a9239cdadbb2f8660b12bf","score":"0.6117224","text":"def run_in_transaction(function=None):\n def wrapper_run_in_transaction(*args, **kwargs):\n return db.run_in_transaction(function, *args, **kwargs)\n\n return wrapper_run_in_transaction","title":""},{"docid":"5af2d91dbf75a021aee499836eba7ea9","score":"0.61158013","text":"def onTransaction(self, command, args):\n raise NotImplementedError","title":""},{"docid":"5af2d91dbf75a021aee499836eba7ea9","score":"0.61158013","text":"def onTransaction(self, command, args):\n raise NotImplementedError","title":""},{"docid":"f0f50ea54562ad3b6098d0cb4fd106d1","score":"0.6114163","text":"def executeTransaction(mydb, queryID):\n\n mydb.commit()\n\n if (queryID == 0):\n print (\"########## Transacção feita. (UPDATE) ##########\")\n elif (queryID == 1):\n print (\"########## Transacção feita. (DELETE) ##########\")","title":""},{"docid":"c589dbd795a862204683400db247a9d4","score":"0.61043","text":"def commit(self):\n if not self.supportsTransactions:\n return\n result = self.proxy.commit()\n if result:\n self._raiseConnectionError(\n api.OperationalError, \"Error during commit: %s\" % result\n )","title":""},{"docid":"70baa573a590bf3a0e9c563e848f6702","score":"0.6103561","text":"def transaction(request, connection):\n transaction = connection.begin() # begin a non-ORM transaction\n\n def finalize():\n print (\"Rollback\")\n transaction.rollback()\n\n request.addfinalizer(finalize)\n return connection","title":""},{"docid":"0bd44d346bd19b4e270ebe77499f807f","score":"0.6092111","text":"def transaction():\n\n return connection","title":""},{"docid":"c09bf86515404fc5c0bbdfb4c0898dee","score":"0.6063428","text":"def transaction(request, connection):\n transaction = connection.begin()\n\n def fin():\n print (\"Rollback\")\n transaction.rollback()\n\n request.addfinalizer(fin)\n return connection","title":""},{"docid":"487ce722562570809264d418f59c7026","score":"0.603903","text":"def start_transaction_sql(self):\r\n return \"BEGIN TRANSACTION\"","title":""},{"docid":"9e631d60a71956526d7f504a9d57d869","score":"0.59798753","text":"def _exec(self, query, args):\n from django.db import connections, transaction\n\n cursor = connections['amavis'].cursor()\n cursor.execute(query, args)\n transaction.commit_unless_managed(using='amavis')","title":""},{"docid":"4ee25d02f88966a71e6da37621b22932","score":"0.5925089","text":"def test_transactions(self):\r\n @asyncio.coroutine\r\n def test():\r\n # Create connection\r\n connection = yield from Pool.create(host=HOST, port=PORT, poolsize=3)\r\n\r\n t1 = yield from connection.multi()\r\n t2 = yield from connection.multi()\r\n yield from connection.multi()\r\n\r\n # Fourth transaction should fail. (Pool is full)\r\n with self.assertRaises(NoAvailableConnectionsInPoolError) as e:\r\n yield from connection.multi()\r\n self.assertIn('No available connections in the pool', e.exception.args[0])\r\n\r\n # Run commands in transaction\r\n yield from t1.set(u'key', u'value')\r\n yield from t2.set(u'key2', u'value2')\r\n\r\n # Commit.\r\n yield from t1.exec()\r\n yield from t2.exec()\r\n\r\n # Check\r\n result1 = yield from connection.get(u'key')\r\n result2 = yield from connection.get(u'key2')\r\n\r\n self.assertEqual(result1, u'value')\r\n self.assertEqual(result2, u'value2')\r\n\r\n self.loop.run_until_complete(test())","title":""},{"docid":"ff79fa501c0c6ca863e03a146c691ba4","score":"0.5892442","text":"def run_transaction(self, script, write_collections,\n max_transaction_size=None, intermediate_commit_size=None, intermediate_commit_count=None):\n try:\n return self.db.execute_transaction(\n script,\n write=write_collections,\n sync=True,\n allow_implicit=True,\n max_size=max_transaction_size,\n intermediate_commit_size=intermediate_commit_size,\n intermediate_commit_count=intermediate_commit_count\n )\n except TransactionExecuteError as e:\n return e","title":""},{"docid":"3982b4960c2b64cd4e8f15fcd860e2ef","score":"0.58901334","text":"def run_in_transaction(self, func, *args, **kw):\n deadline = time.time() + kw.pop(\"timeout_secs\", DEFAULT_RETRY_TIMEOUT_SECS)\n commit_request_options = kw.pop(\"commit_request_options\", None)\n transaction_tag = kw.pop(\"transaction_tag\", None)\n attempts = 0\n\n while True:\n if self._transaction is None:\n txn = self.transaction()\n txn.transaction_tag = transaction_tag\n else:\n txn = self._transaction\n\n try:\n attempts += 1\n return_value = func(txn, *args, **kw)\n except Aborted as exc:\n del self._transaction\n _delay_until_retry(exc, deadline, attempts)\n continue\n except GoogleAPICallError:\n del self._transaction\n raise\n except Exception:\n txn.rollback()\n raise\n\n try:\n txn.commit(\n return_commit_stats=self._database.log_commit_stats,\n request_options=commit_request_options,\n )\n except Aborted as exc:\n del self._transaction\n _delay_until_retry(exc, deadline, attempts)\n except GoogleAPICallError:\n del self._transaction\n raise\n else:\n if self._database.log_commit_stats and txn.commit_stats:\n self._database.logger.info(\n \"CommitStats: {}\".format(txn.commit_stats),\n extra={\"commit_stats\": txn.commit_stats},\n )\n return return_value","title":""},{"docid":"c77380ffe2059d1c211b3858cfa8689d","score":"0.5887045","text":"def commitTransaction(self):\r\n\t\tself.connection.commit()\r\n\t\tself.connection.autocommit(True)","title":""},{"docid":"3709d88108e23d47f65c853130b6a98f","score":"0.58859867","text":"def startTransaction(self):\r\n\t\tself.connection.autocommit(False)","title":""},{"docid":"df2320de07b99f0079f71daf5975c800","score":"0.58804554","text":"def begin_transaction(self):\n self.__check_closed()\n if self.__current_transaction_level == 0:\n # concurrent DEFERRED transactions have problems with locks on Windows (This is strange, as the problem does not happen in Java nor .NET)\n self.__cursor.execute(\"BEGIN IMMEDIATE TRANSACTION\")\n else:\n self.__cursor.execute(\"SAVEPOINT sp_%d\" % self.__current_transaction_level)\n self.__current_transaction_level += 1 # in case SAVEPOINT failed","title":""},{"docid":"4d55d4032e043a8ceeb6d17c0f291bf6","score":"0.58761424","text":"def start_transaction(self):\n if self.dry_run:\n self.pending_transactions += 1\n transaction.commit_unless_managed(using=self.db_alias)\n transaction.enter_transaction_management(using=self.db_alias)\n transaction.managed(True, using=self.db_alias)","title":""},{"docid":"05226bb4fb29e65b076fc8cca5df8b1f","score":"0.5848467","text":"def transaction(request):\n data = {key: val[0] for key, val in request.POST.lists()}\n data['user_id'] = request.user.id\n Transaction.make_transaction(data) # pylint:disable = E1120\n LOGGER.info('Transaction %s', str(data))\n return HttpResponse(200)","title":""},{"docid":"c0dc37f64cb2f1dd2dbd68ff24c732b3","score":"0.5837106","text":"def transaction():\n if not _is_sqlite():\n yield # For postgresql, alembic ensures transactional context.\n else:\n try:\n op.execute(\"BEGIN\")\n yield\n op.execute(\"END\")\n except OperationalError:\n op.execute(\"ROLLBACK\")\n raise","title":""},{"docid":"e6592a5aa9b09e18070c6122045a060d","score":"0.5834964","text":"def execute():","title":""},{"docid":"4a4eac8d8d16d1b9f3d24facf280d8ac","score":"0.58244205","text":"def test_send_tx(self):\n pass","title":""},{"docid":"3bc01c858ab92eeb9eb768e4c855e27c","score":"0.5819418","text":"def commit_transaction(self):\n if self.dry_run:\n return\n transaction.commit(using=self.db_alias)\n transaction.leave_transaction_management(using=self.db_alias)","title":""},{"docid":"08375e1f5e49ecbd036c1cdba8d511cf","score":"0.58076966","text":"def after_begin(self, session, transaction, connection):","title":""},{"docid":"f120036e68f371711d837362f9bb4ad4","score":"0.5795015","text":"def begin_transaction(self):\n self._cursor.execute(\"begin transaction;\")","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.5778899","text":"def commit(self):","title":""},{"docid":"443588a00f8110859f67db1b56d47dee","score":"0.57631683","text":"async def recv_execute(self, data):\n trans_id, query, args = data\n\n if trans_id != self.current_trans_id:\n begun = await self.begin_transaction(trans_id)\n if not begun:\n # This EXECUTE refers to an old transaction which we already completed.\n # With a sane coordinator, this should not happen; it will only move to\n # the next transaction once the previous one is complete.\n return False\n\n self.logger.info(f\"EXECUTE ({query}) for transaction {trans_id} in database.\")\n try:\n self.data_db_cur.execute(query, args)\n self.logger.info(\"Done.\")\n except psycopg2.Error as e:\n self.do_abort(trans_id)\n self.logger.error(f\"EXECUTE failed: {str(e)}\")\n return False\n return True","title":""},{"docid":"ac6ed66dc82be25f578ed8aa9afb1a54","score":"0.57566977","text":"def transaction(session): # noqa\n try:\n yield session\n session.commit()\n except BaseException:\n session.rollback()\n raise","title":""},{"docid":"8162ed170774bbdd244cc25ae3e3acc6","score":"0.57549876","text":"def test_transaction_isolation(self):\n conn1 = self.get_connection()\n conn2 = self.get_connection()\n\n if not conn1.graph_features['supportsTransactions']:\n return\n\n with conn1.transaction():\n v1, v2, v3 = conn1.execute(\n \"\"\"\n def v1 = g.addVertex([val:1, str:\"vertex 1\"])\n def v2 = g.addVertex([val:2, str:\"vertex 2\"])\n def v3 = g.addVertex([val:3, str:\"vertex 3\"])\n [v1, v2, v3]\n \"\"\"\n )\n\n conn1.open_transaction()\n conn2.open_transaction()\n\n v1_1 = conn1.execute(\n \"\"\"\n def v1 = g.v(eid)\n v1.setProperty(\"str\", \"v1\")\n v1\n \"\"\",\n params={'eid':v1['_id']}\n )\n\n v1_2 = conn2.execute(\n \"\"\"\n g.v(eid)\n \"\"\",\n params={'eid':v1['_id']}\n )\n\n assert v1_2['_properties']['str'] == 'vertex 1'","title":""},{"docid":"018e2d9cf7c26b8f814c1ed497b36950","score":"0.57548404","text":"def post_begin_transaction(\n self, response: datastore.BeginTransactionResponse\n ) -> datastore.BeginTransactionResponse:\n return response","title":""},{"docid":"834015527747f10991349f9d18ff5e61","score":"0.575081","text":"def _ConfirmTransaction(self):\n self.assertEqual(b'1', self.sock.recv(1))\n self.sock.sendall(b'1')\n self.assertEqual(b'1', self.sock.recv(1))","title":""},{"docid":"1eb6c0d8d8a643b786bbb9f167226555","score":"0.5747632","text":"def onTransaction(self, command, args, response):\n handler = self.handlers.get(command, self.onUnknown)\n return handler(command, args, response)","title":""},{"docid":"a718febbbd34fc6454e35f123ae88875","score":"0.5721962","text":"def createTransaction(self):\n return None","title":""},{"docid":"5e04630e320e73206f60697324e53dd0","score":"0.57190585","text":"def test_transaction_calls(mockstomp):\n stomp = StompTransport()\n stomp.connect()\n mockconn = mockstomp.Connection.return_value\n\n stomp._transaction_begin(mock.sentinel.txid)\n mockconn.begin.assert_called_once_with(transaction=mock.sentinel.txid)\n\n stomp._send(\"destination\", mock.sentinel.message, transaction=mock.sentinel.txid)\n mockconn.send.assert_called_once_with(\n \"/queue/destination\",\n mock.sentinel.message,\n headers={\"persistent\": \"true\"},\n transaction=mock.sentinel.txid,\n )\n\n stomp._transaction_abort(mock.sentinel.txid)\n mockconn.abort.assert_called_once_with(mock.sentinel.txid)\n\n stomp._transaction_commit(mock.sentinel.txid)\n mockconn.commit.assert_called_once_with(mock.sentinel.txid)","title":""},{"docid":"4c7fa33fc1b78848f1f54ffa8f540145","score":"0.57154214","text":"def Transaction():\n acm.BeginTransaction()\n try:\n yield None\n acm.CommitTransaction()\n except:\n acm.AbortTransaction()\n raise","title":""},{"docid":"b3948e8cff0784121ba79fdce6d08d80","score":"0.5712536","text":"def on_post(self, req, resp):\n tx_data = str(req.body['tx_data'])\n net = str(req.body['net']).lower()\n error, tx_hash = eth_helper.raw_transaction(tx_data, net)\n\n if error is None:\n message = {\n 'success': True,\n 'tx_hash': tx_hash,\n 'message': 'Transaction initiated successfully.'\n }\n else:\n message = {\n 'success': False,\n 'error': error,\n 'message': 'Error occurred while initiating the transaction.'\n }\n\n resp.status = falcon.HTTP_200\n resp.body = json.dumps(message)","title":""},{"docid":"c1e076c0bc0bb1eb3eaaf0aca562bf21","score":"0.57091737","text":"def test_manualTransactions(self):\n c = self.conn.cursor()\n d = defer.Deferred()\n d.addCallback(\n lambda c: c.execute(\"begin\"))\n d.addCallback(\n lambda c: c.execute(\"insert into simple values (%s)\", (1, )))\n d.addCallback(\n lambda c: c.execute(\"insert into simple values (%s)\", (2, )))\n d.addCallback(\n lambda c: c.execute(\"select * from simple order by x\"))\n d.addCallback(\n lambda c: self.assertEquals(c.fetchall(), [(1, ), (2, )]))\n d.addCallback(\n lambda _: c.execute(\"rollback\"))\n d.addCallback(\n lambda c: c.execute(\"select * from simple\"))\n d.addCallback(\n lambda c: self.assertEquals(c.fetchall(), []))\n d.callback(c)\n return d","title":""},{"docid":"61b89a6bd91cff8c0296a1014fa7ea75","score":"0.5706442","text":"def commit_tx(self, txn):\n # container should be in the open state\n if self.coh == 0:\n raise DaosApiError(\"Container needs to be open.\")\n\n func = self.context.get_function('commit-tx')\n ret = func(txn, None)\n if ret != 0:\n raise DaosApiError(\"TX commit returned non-zero. RC: {0}\"\n .format(ret))","title":""},{"docid":"2fef2b9695d8b35727357b03a5345dbd","score":"0.57001287","text":"def commit(self):\n self.__check_closed()\n assert self.__current_transaction_level > 0\n\n if self.__current_transaction_level == 1:\n self.__cursor.execute(\"COMMIT\")\n else:\n self.__cursor.execute(\"RELEASE sp_%d\" % (self.__current_transaction_level - 1))\n self.__current_transaction_level -= 1 # in case commit failed","title":""},{"docid":"597d14849e6a421fef82f0a1a45edb6c","score":"0.5696073","text":"def void(transaction_id):\r\n\r\n return Configuration.gateway().transaction.void(transaction_id)","title":""},{"docid":"b5fd2f0d6412944c305e8fa4e7ff94f5","score":"0.569463","text":"def _commit(self):\n self._engine.commit()","title":""},{"docid":"df619386e32e855503d123311606645f","score":"0.56921405","text":"def exec(conn, args, stmt):\n cur = conn.cursor()\n try:\n cur.execute(stmt)\n except:\n raise\n finally:\n conn.commit()\n cur.close()","title":""},{"docid":"1cd4094856b8c062bd890ccd7abdc14e","score":"0.5687622","text":"def sql_execute_query(query):\n with transaction.atomic(), connection.cursor() as cursor:\n cursor.execute(query)\n return","title":""},{"docid":"82d768ca9f70a5a1b4205c8d73a507db","score":"0.56838685","text":"def commit_transaction(self, src_index, dst_index):\n src = self.accounts[src_index]\n dst = self.accounts[dst_index]\n\n if src.id == dst.id:\n return None\n\n src_account = src\n dst_account = dst\n\n # In some random cases, flushes the account to have empty balance\n if random.random() < 0.05:\n amount = src_account.balance\n else:\n amount = round(random.random() * src_account.balance, 8)\n\n if amount > 0:\n src_account.balance = round(src_account.balance - amount, 8)\n dst_account.balance = round(dst_account.balance + amount, 8)\n\n trx = db.Transaction(amount=amount, source=src_account,\n destination=dst_account, time=self.turn_number)\n return trx\n\n return None","title":""},{"docid":"505a9a964de536f8562635a8f8d6fb42","score":"0.56745607","text":"def execute(self, *args):\n return self.conn.execute(*args)","title":""},{"docid":"dfb85bc9ef644611569590b4be1a7bca","score":"0.56647146","text":"def run_transaction(conn, op, max_retries=3):\n # leaving this block the transaction will commit or rollback\n # (if leaving with an exception)\n with conn:\n for retry in range(1, max_retries + 1):\n try:\n op(conn)\n\n # If we reach this point, we were able to commit, so we break\n # from the retry loop.\n return\n\n except SerializationFailure as e:\n # This is a retry error, so we roll back the current\n # transaction and sleep for a bit before retrying. The\n # sleep time increases for each failed transaction.\n logging.debug(\"got error: %s\", e)\n conn.rollback()\n logging.debug(\"EXECUTE SERIALIZATION_FAILURE BRANCH\")\n sleep_ms = (2 ** retry) * 0.1 * (random.random() + 0.5)\n logging.debug(\"Sleeping %s seconds\", sleep_ms)\n time.sleep(sleep_ms)\n\n except psycopg2.Error as e:\n logging.debug(\"got error: %s\", e)\n logging.debug(\"EXECUTE NON-SERIALIZATION_FAILURE BRANCH\")\n raise e\n\n raise ValueError(f\"Transaction did not succeed after {max_retries} retries\")","title":""},{"docid":"40af7a98a1e86ce4de89052ad481317a","score":"0.56619096","text":"def commit(self):\n if not self.conn.async:\n self.conn.commit()","title":""},{"docid":"a6b1f79f08ad3b8d31bd063db8fe4dfa","score":"0.56513673","text":"def commit(using=None):\n get_connection(using).commit()","title":""},{"docid":"a864ac9f3d41689e7211230f721b6255","score":"0.56513363","text":"def commit_transaction(self):\n if not self._transaction:\n raise Exception('No transaction running!')\n self._txn.Commit(self._tid)\n self._tid = None\n self._transaction = False","title":""},{"docid":"c0e02b1deb726a172ba67646bbc7a718","score":"0.5647678","text":"def _transaction(stub, metrics):\n session = None\n try:\n session = stub.CreateSession(\n spanner_pb2.CreateSessionRequest(database=_DATABASE))\n\n txn_options = transaction_pb2.TransactionOptions(\n read_write=transaction_pb2.TransactionOptions.ReadWrite())\n txn_request = spanner_pb2.BeginTransactionRequest(\n session=session.name,\n options=txn_options,\n )\n\n # Probing BeginTransaction call\n start = time.time()\n txn = stub.BeginTransaction(txn_request)\n latency = (time.time() - start) * 1000\n metrics['begin_transaction_latency_ms'] = latency\n\n # Probing Commit call\n commit_request = spanner_pb2.CommitRequest(\n session=session.name, transaction_id=txn.id)\n start = time.time()\n stub.Commit(commit_request)\n latency = (time.time() - start) * 1000\n metrics['commit_latency_ms'] = latency\n\n # Probing Rollback call\n txn = stub.BeginTransaction(txn_request)\n rollback_request = spanner_pb2.RollbackRequest(\n session=session.name, transaction_id=txn.id)\n start = time.time()\n stub.Rollback(rollback_request)\n latency = (time.time() - start) * 1000\n metrics['rollback_latency_ms'] = latency\n\n finally:\n if session is not None:\n stub.DeleteSession(spanner_pb2.DeleteSessionRequest(name=session.name))","title":""},{"docid":"c0b08d697a85d994b29b11ec1da2af76","score":"0.5638304","text":"async def log_transaction(self, **params):\n\t\tif params.get(\"message\"):\n\t\t\tparams = json.loads(params.get(\"message\", \"{}\"))\n\t\t\n\t\tif not params:\n\t\t\treturn {\"error\":400, \"reason\":\"Missed required fields\"}\n\n\t\tcoinid = params.get(\"coinid\")\n\n\t\tif not coinid in [\"QTUM\", \"PUT\"]:\n\t\t\treturn {\"error\":400, \"reason\": \"Missed or invalid coinid\"}\n\n\t\tdatabase = client[settings.TXS]\n\t\tsource_collection = database[coinid]\n\n\t\tawait source_collection.find_one_and_update({\"txid\":params.get(\"txid\")},{\"$set\":{\n\t\t\t\t\"blocknumber\":params.get(\"blocknumber\"),\n\t\t\t\t\"blockhash\":params.get(\"blockhash\"),\n\t\t\t\t\"gasLimit\":params.get(\"gasLimit\"),\n\t\t\t\t\"gasPrice\":params.get(\"gasPrice\"),\n\t\t\t}})\n\t\treturn {\"success\":True}","title":""},{"docid":"58dab38bf11228796415acd0d27a8a7a","score":"0.5636962","text":"def _perform_database_action(action, *args):\r\n session = _get_session()\r\n getattr(session, action)(*args)\r\n session.commit()","title":""},{"docid":"30a485601fc154e53fd0dd2b03b6b5da","score":"0.5634563","text":"def commit(self):\n if self.in_transaction:\n self.__private_cursor__.execute(\"COMMIT\")\n self.in_transaction = False","title":""},{"docid":"2bfacea4fd541c9e71f019d5757cffd5","score":"0.56310683","text":"async def send(self, transaction: Any, sync: bool = True, **kwargs: Any) -> None:\n await self._send(transaction, None, None, sync=sync, **kwargs)","title":""},{"docid":"3def92f430044d5807e461dfa65287dd","score":"0.5627344","text":"def commit(self) -> None:","title":""},{"docid":"b48945318c3604e454718ae9ff61c71d","score":"0.5625734","text":"def execute(self, *args, **kwargs):\n return self._operate(Connection.execute, args, kwargs)","title":""},{"docid":"8ed191e54103d8e7867ba47725a57bf0","score":"0.56246305","text":"def execute(self, context):\n logging.info(f\"Running SQL :{self.sql}\")\n self.hook = TrinoHook()\n query = self.hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters)\n if self.xcom_push:\n return query","title":""},{"docid":"50046687c4334b965a8536f72755317f","score":"0.5621117","text":"def transaction(session):\n def wrapper(fn):\n @wraps(fn)\n def decorated_view(*args, **kwargs):\n start = time.time()\n stmp_id = id_gen()\n try:\n logger.debug('Transaction %s - START' % stmp_id)\n res = fn(session, *args, **kwargs)\n session.commit()\n elapsed = round(time.time() - start, 4)\n logger.debug('Transaction %s - STOP - %s' % (stmp_id, elapsed))\n return res \n except IntegrityError as ex:\n elapsed = round(time.time() - start, 4)\n logger.error('Transaction %s - ERROR: %s - %s' % (stmp_id, ex, elapsed))\n session.rollback()\n raise TransactionError(ex)\n except DBAPIError as ex:\n elapsed = round(time.time() - start, 4)\n logger.error('Transaction %s - ERROR: %s - %s' % (stmp_id, ex, elapsed))\n session.rollback()\n raise TransactionError(ex)\n \n return decorated_view\n return wrapper","title":""},{"docid":"69941950f0aa029249f09e28fbc7ee90","score":"0.56188357","text":"def transaction(self, command, data=''):\n self.write(command, data)\n time.sleep(0.1)\n response_command, status, data = self.read()\n if response_command != command:\n raise RuntimeError(\"Response to invalid command in transaction\")\n return status, data","title":""},{"docid":"af716a54491b6f0cf93000acc79fc920","score":"0.5608859","text":"def tx_subtask_invoke(tst, wrapper):\n return tst.execute(wrapper, *tst.save_args, **tst.save_kwargs)","title":""},{"docid":"1c9430ae6c2431915a34abf320cd7fe1","score":"0.5602103","text":"def executePendingTransactions(self):\n self.__fragmentActivity.frame.update_idletasks()\n pass","title":""},{"docid":"0c8a88d3c2647ed4158a49dce382a7af","score":"0.5591583","text":"def transactionally(transactionCreator):\n def thunk(operation):\n return inTransaction(transactionCreator, operation)\n return thunk","title":""},{"docid":"a086270d1db51d2d5311f1c0f43763d7","score":"0.55832964","text":"def execute_query(self):","title":""},{"docid":"422aa225bf14b843defc41bdd0c7087b","score":"0.55783856","text":"def zsa_savepoints(conn):\n from transaction.interfaces import ISynchronizer\n from zope.interface import implementer\n\n @implementer(ISynchronizer)\n class Savepoints(object):\n def __init__(self, conn):\n self.conn = conn\n self.sp = None\n self.state = None\n\n def beforeCompletion(self, transaction):\n pass\n\n def afterCompletion(self, transaction):\n # txn be aborted a second time in manager.begin()\n if self.sp is None:\n return\n if self.state == 'commit':\n self.state = 'completion'\n self.sp.commit()\n else:\n self.state = 'abort'\n self.sp.rollback()\n self.sp = None\n self.state = 'done'\n\n def newTransaction(self, transaction):\n self.state = 'new'\n self.sp = self.conn.begin_nested()\n self.state = 'begun'\n transaction.addBeforeCommitHook(self._registerCommit)\n\n def _registerCommit(self):\n self.state = 'commit'\n\n zsa_savepoints = Savepoints(conn)\n\n import transaction\n transaction.manager.registerSynch(zsa_savepoints)\n\n yield zsa_savepoints\n transaction.manager.unregisterSynch(zsa_savepoints)","title":""},{"docid":"bdde10c744902c313ece4b4b4cbf9555","score":"0.557831","text":"def autonested_transaction(sess):\n if sess.is_active:\n session_context = sess.begin(nested=True)\n else:\n session_context = sess.begin(subtransactions=True)\n with session_context as tx:\n yield tx","title":""},{"docid":"d43b291d94445ed319ac67089e2db2aa","score":"0.5577309","text":"def commit(self):\n self._transaction_strategy(self)","title":""},{"docid":"8fbb3128002356aa2dad89eaaf18012e","score":"0.55714905","text":"def commit(self):\n\n if self.transaction is None:\n if self.transactional:\n self.begin()\n else:\n raise exceptions.InvalidRequestError(\"No transaction is begun.\")\n\n self.transaction.commit()\n if self.transaction is None and self.transactional:\n self.begin()","title":""},{"docid":"9c89a304f9c1e9f5302aff25823e5aa4","score":"0.5563152","text":"def start_transaction(self):\n if self._transaction:\n raise Exception('Transaction already running!')\n self._transaction = True\n self._tid = self._txn.Begin(Empty())","title":""},{"docid":"0cb143854b9a80c942432d2fdb6da38a","score":"0.55472463","text":"def test_commit_transaction_wont_commit_if_commit_param_false(auth_client, single_transaction):\n r = auth_client.commit_transaction('DEFAULT', single_transaction, {'commit': False})\n assert r.json()['status'] == 'Saved'","title":""},{"docid":"be95cd074bd261d44a7b3ff64c117e2f","score":"0.554703","text":"def send_prepared(self, txn_hash):\n\n params = {\"transactionHash\": txn_hash}\n\n return self._post_request('transactions/send/prepared', params)","title":""},{"docid":"1b1015b37abe50e51166b966f291b126","score":"0.55434597","text":"def runInTransaction(cls, fn):\r\n try:\r\n transaction = cls.ensureGet()\r\n except IllegalStateException:\r\n transaction = cls._transactions.local = LockingTransaction()\r\n\r\n if transaction._info:\r\n # Already running transaction... execute current transaction in it. No nested transactions in the same thread\r\n return apply(fn)\r\n\r\n return transaction.run(fn)","title":""},{"docid":"80fa70ac98a4bdc080a07dbebb235f62","score":"0.5541767","text":"def commit_db_transaction(state: State) -> None:\n if state._tx_stack[-1] is None:\n raise Exception(\"Current transaction is not a db transaction\")\n state_root(state)\n state._tx_stack.pop().commit()\n if state._tx_stack != []:\n state._current_tx = state._tx_stack[-1]\n else:\n state._current_tx = None","title":""},{"docid":"cec1fcbbc857262b0c9a74bf84a4a398","score":"0.5533833","text":"def session_commit():\r\n db.session.commit()","title":""},{"docid":"3cc9564cdb16290f8df84367a42cecdf","score":"0.55310845","text":"def transaction(self,\n statements,\n cursor_factory=None,\n auto_rollback=True):\n cursors = []\n transaction_future = Future()\n\n queue = self._statement_generator(statements)\n\n def exec_statements(future):\n try:\n cursor = future.result()\n cursors.append(cursor)\n except Exception as error:\n if auto_rollback and not self.closed:\n self._rollback(transaction_future, error)\n else:\n future_set_exc_info(transaction_future, sys.exc_info())\n return\n\n try:\n operation, parameters = next(queue)\n except StopIteration:\n transaction_future.set_result(cursors[1:-1])\n return\n\n f = self.execute(operation, parameters, cursor_factory)\n self.ioloop.add_future(f, exec_statements)\n\n self.ioloop.add_future(self.execute(\"BEGIN;\"), exec_statements)\n return transaction_future","title":""},{"docid":"4eb8c9278a7b06e632930591c57de7a5","score":"0.5512913","text":"def transaction_commit(user=None, note=''):\n transaction_note(user, note)\n import transaction\n \n # new semantics as of 3.3.0c1: if an exception occurs while committing, the current\n # transaction must be aborted explicitly. \n try:\n transaction.commit()\n except:\n transaction.abort()\n raise","title":""},{"docid":"5092a88a94f7d9573d1bebc7fff9ae03","score":"0.5511806","text":"def savepoint_commit_sql(self, sid):\r\n return \"COMMIT TRANSACTION %s\" % sid","title":""},{"docid":"0e45425ef3c75453a17c7bdd45f24315","score":"0.5492369","text":"def transactional(operation):\n def transactional_operation(*args, **kwargs):\n return datastore.RunInTransaction(operation, *args, **kwargs)\n return transactional_operation","title":""},{"docid":"d97737bc7d39d58d85a215dd1581c1ae","score":"0.54916525","text":"def do_begin(conn):\n conn.execute(\"BEGIN\")","title":""},{"docid":"260acba5c64edc7127cf302981c919f6","score":"0.5485624","text":"def commit(self):\n #print \"--> commit\"\n pass","title":""},{"docid":"a7d27c4e16f498b1fd427bf7ab3651c3","score":"0.54802924","text":"def commit(self):\n self._local.conn.commit()","title":""},{"docid":"f4091f8a6b82c0577d743bd555d28586","score":"0.5479733","text":"def sync_transaction(self):\n sock = self.connect_and_send('sync:')\n try:\n yield sock\n finally:\n try: sync_send_req(sock, 'QUIT', '')\n except socket.error: pass\n try: sock.close()\n except socket.error: pass","title":""},{"docid":"b7e6b69fc93d33022dc9e8ffa8a63317","score":"0.54613274","text":"def commit(self):\n self.CheckOpen()\n request = sql_pb2.ExecOpRequest()\n request.op.type = client_pb2.OpProto.COMMIT\n self.MakeRequest('ExecOp', request)","title":""},{"docid":"77cb8ca8d3ddec5e0b697d600b46e5f2","score":"0.5456491","text":"def create_transaction(self, *args, **kwargs):\n return self.get_vm().create_transaction(*args, **kwargs)","title":""},{"docid":"35bebf7c6a3ee02f124b4c55647720ce","score":"0.5450554","text":"def commit(self):\n return CSQLRelay.commit(self.connection)","title":""},{"docid":"26dcec96362c20cd939930623554548f","score":"0.5444752","text":"def execute(self):\n self.bitshares.blocking = \"head\"\n r = self.bitshares.txbuffer.broadcast()\n self.bitshares.blocking = False\n return r","title":""},{"docid":"a92098a7754c503e3471d239e9a6f632","score":"0.5443431","text":"def commit(self):\n\n\t\tpass","title":""},{"docid":"1b5cf500a1f23487a08eb8355af2a431","score":"0.54395956","text":"def commit(self):\n fut = self._execute(self._conn.commit)\n return fut","title":""},{"docid":"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0","score":"0.5437911","text":"def commit(self):\n pass","title":""},{"docid":"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0","score":"0.5437911","text":"def commit(self):\n pass","title":""},{"docid":"ca7f689bfa2c86c4a644cbbffb007d58","score":"0.54288435","text":"def transaction_after_request(response, base_status_code_error=500):\n if view_has_annotation(NO_AUTO_TRANSACTION_ATTR):\n return response\n if response.status_code >= base_status_code_error:\n try:\n commands.rollback()\n except OperationFailure:\n logger.exception('Transaction rollback failed after request')\n else:\n try:\n commands.commit()\n except OperationFailure as error:\n # transaction commit failed, log and reraise\n message = utils.get_error_message(error)\n if messages.LOCK_ERROR in message:\n commands.rollback()\n return utils.handle_error(LOCK_ERROR_CODE)\n raise\n return response","title":""},{"docid":"ded2ffc3598110189aabea98e1ca8b73","score":"0.5421346","text":"def create(params):\r\n return Configuration.gateway().transaction.create(params)","title":""},{"docid":"a55663d7304146779dade101de20ec01","score":"0.5419232","text":"def commit(self):\n self.__parent._trans_commit(self)\n self.__isactive = False","title":""},{"docid":"b23b1a5e21662dfaf38d96b9a2aca50f","score":"0.54062235","text":"def execute(self, parameters=None, messages=None):\n self._is_executing = True\n try:\n self._commit_temporaries()\n except:\n pass \n self.tool.main(self.par, parameters, messages)\n try:\n self.clear_temporary_dbs()\n except:\n pass\n self._is_executing = False\n self.show_outputs()","title":""},{"docid":"fe2cd039407c982ffdd398e6c5ec0b3d","score":"0.5404173","text":"async def execute_transaction(\n self, tx: TypedTransaction, from_address, block_height: Optional[int]\n ) -> Optional[bytes]:\n evm_tx = tx.tx.to_evm_tx()\n evm_tx.set_quark_chain_config(self.env.quark_chain_config)\n branch = Branch(evm_tx.from_full_shard_id)\n if branch.value not in self.branch_to_slaves:\n return None\n\n futures = []\n for slave in self.branch_to_slaves[branch.value]:\n futures.append(slave.execute_transaction(tx, from_address, block_height))\n responses = await asyncio.gather(*futures)\n # failed response will return as None\n success = all(r is not None for r in responses) and len(set(responses)) == 1\n if not success:\n return None\n\n check(len(responses) >= 1)\n return responses[0]","title":""}],"string":"[\n {\n \"docid\": \"b2f4adf30873647179657713cd0f4368\",\n \"score\": \"0.7078037\",\n \"text\": \"def transaction(self, *args, **kwargs):\\n return self._operate(Connection.transaction, args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba488214cac0d5aab92031b2d45241dc\",\n \"score\": \"0.65641505\",\n \"text\": \"def test_execute_as_transaction(self):\\n @execute_as_transaction\\n def function():\\n # retrieve transaction id\\n with qdb.sql_connection.TRN:\\n sql = \\\"SELECT txid_current();\\\"\\n qdb.sql_connection.TRN.add(sql)\\n return qdb.sql_connection.TRN.execute_fetchlast()\\n\\n f1 = function()\\n f2 = function()\\n self.assertNotEqual(f1, f2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf85b4379485c20eba40aa610193ab7\",\n \"score\": \"0.649355\",\n \"text\": \"def execute_txn(self, card, transaction, message=''):\\n fields = {}\\n if message:\\n fields['message'] = message\\n return self._post('/me/cards/' + card + '/transactions/' + transaction + '/commit', fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c40f3aa7ba75338f4f74e4a2da2c5056\",\n \"score\": \"0.6371799\",\n \"text\": \"def onTransaction(self, command, args, response):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953201d2b36e803a78487ced93f633b2\",\n \"score\": \"0.6332858\",\n \"text\": \"def commit(self, tx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb79144b48cc547bd8eda52b1d4704d\",\n \"score\": \"0.63061035\",\n \"text\": \"def _exec(self):\\r\\n if not self._in_transaction:\\r\\n raise Error('Not in transaction')\\r\\n\\r\\n futures_and_postprocessors = self._transaction_response_queue\\r\\n self._transaction_response_queue = None\\r\\n\\r\\n # Get transaction answers.\\r\\n multi_bulk_reply = yield from self._query(b'exec', _bypass=True)\\r\\n\\r\\n if multi_bulk_reply is None:\\r\\n # We get None when a transaction failed.\\r\\n raise TransactionError('Transaction failed.')\\r\\n else:\\r\\n assert isinstance(multi_bulk_reply, MultiBulkReply)\\r\\n\\r\\n for f in multi_bulk_reply.iter_raw():\\r\\n answer = yield from f\\r\\n f2, call = futures_and_postprocessors.popleft()\\r\\n\\r\\n if isinstance(answer, Exception):\\r\\n f2.set_exception(answer)\\r\\n else:\\r\\n if call:\\r\\n self._pipelined_calls.remove(call)\\r\\n\\r\\n f2.set_result(answer)\\r\\n\\r\\n self._transaction_response_queue = deque()\\r\\n self._in_transaction = False\\r\\n self._transaction = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a7e968461d33863af75ec307cc9f8b7\",\n \"score\": \"0.61880314\",\n \"text\": \"def commit_transaction(*args):\\n return _xapian.WritableDatabase_commit_transaction(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4570dd02fbb7291ccc4c8078f9fdce2\",\n \"score\": \"0.61740094\",\n \"text\": \"def test_commit_transaction_commit_saved_trans(auth_client, single_transaction):\\n r = auth_client.commit_transaction('DEFAULT', single_transaction, {'commit': True})\\n assert r.json()['status'] == 'Committed'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8043a91958a9239cdadbb2f8660b12bf\",\n \"score\": \"0.6117224\",\n \"text\": \"def run_in_transaction(function=None):\\n def wrapper_run_in_transaction(*args, **kwargs):\\n return db.run_in_transaction(function, *args, **kwargs)\\n\\n return wrapper_run_in_transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af2d91dbf75a021aee499836eba7ea9\",\n \"score\": \"0.61158013\",\n \"text\": \"def onTransaction(self, command, args):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af2d91dbf75a021aee499836eba7ea9\",\n \"score\": \"0.61158013\",\n \"text\": \"def onTransaction(self, command, args):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f50ea54562ad3b6098d0cb4fd106d1\",\n \"score\": \"0.6114163\",\n \"text\": \"def executeTransaction(mydb, queryID):\\n\\n mydb.commit()\\n\\n if (queryID == 0):\\n print (\\\"########## Transacção feita. (UPDATE) ##########\\\")\\n elif (queryID == 1):\\n print (\\\"########## Transacção feita. (DELETE) ##########\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c589dbd795a862204683400db247a9d4\",\n \"score\": \"0.61043\",\n \"text\": \"def commit(self):\\n if not self.supportsTransactions:\\n return\\n result = self.proxy.commit()\\n if result:\\n self._raiseConnectionError(\\n api.OperationalError, \\\"Error during commit: %s\\\" % result\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70baa573a590bf3a0e9c563e848f6702\",\n \"score\": \"0.6103561\",\n \"text\": \"def transaction(request, connection):\\n transaction = connection.begin() # begin a non-ORM transaction\\n\\n def finalize():\\n print (\\\"Rollback\\\")\\n transaction.rollback()\\n\\n request.addfinalizer(finalize)\\n return connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd44d346bd19b4e270ebe77499f807f\",\n \"score\": \"0.6092111\",\n \"text\": \"def transaction():\\n\\n return connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09bf86515404fc5c0bbdfb4c0898dee\",\n \"score\": \"0.6063428\",\n \"text\": \"def transaction(request, connection):\\n transaction = connection.begin()\\n\\n def fin():\\n print (\\\"Rollback\\\")\\n transaction.rollback()\\n\\n request.addfinalizer(fin)\\n return connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487ce722562570809264d418f59c7026\",\n \"score\": \"0.603903\",\n \"text\": \"def start_transaction_sql(self):\\r\\n return \\\"BEGIN TRANSACTION\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e631d60a71956526d7f504a9d57d869\",\n \"score\": \"0.59798753\",\n \"text\": \"def _exec(self, query, args):\\n from django.db import connections, transaction\\n\\n cursor = connections['amavis'].cursor()\\n cursor.execute(query, args)\\n transaction.commit_unless_managed(using='amavis')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee25d02f88966a71e6da37621b22932\",\n \"score\": \"0.5925089\",\n \"text\": \"def test_transactions(self):\\r\\n @asyncio.coroutine\\r\\n def test():\\r\\n # Create connection\\r\\n connection = yield from Pool.create(host=HOST, port=PORT, poolsize=3)\\r\\n\\r\\n t1 = yield from connection.multi()\\r\\n t2 = yield from connection.multi()\\r\\n yield from connection.multi()\\r\\n\\r\\n # Fourth transaction should fail. (Pool is full)\\r\\n with self.assertRaises(NoAvailableConnectionsInPoolError) as e:\\r\\n yield from connection.multi()\\r\\n self.assertIn('No available connections in the pool', e.exception.args[0])\\r\\n\\r\\n # Run commands in transaction\\r\\n yield from t1.set(u'key', u'value')\\r\\n yield from t2.set(u'key2', u'value2')\\r\\n\\r\\n # Commit.\\r\\n yield from t1.exec()\\r\\n yield from t2.exec()\\r\\n\\r\\n # Check\\r\\n result1 = yield from connection.get(u'key')\\r\\n result2 = yield from connection.get(u'key2')\\r\\n\\r\\n self.assertEqual(result1, u'value')\\r\\n self.assertEqual(result2, u'value2')\\r\\n\\r\\n self.loop.run_until_complete(test())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff79fa501c0c6ca863e03a146c691ba4\",\n \"score\": \"0.5892442\",\n \"text\": \"def run_transaction(self, script, write_collections,\\n max_transaction_size=None, intermediate_commit_size=None, intermediate_commit_count=None):\\n try:\\n return self.db.execute_transaction(\\n script,\\n write=write_collections,\\n sync=True,\\n allow_implicit=True,\\n max_size=max_transaction_size,\\n intermediate_commit_size=intermediate_commit_size,\\n intermediate_commit_count=intermediate_commit_count\\n )\\n except TransactionExecuteError as e:\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3982b4960c2b64cd4e8f15fcd860e2ef\",\n \"score\": \"0.58901334\",\n \"text\": \"def run_in_transaction(self, func, *args, **kw):\\n deadline = time.time() + kw.pop(\\\"timeout_secs\\\", DEFAULT_RETRY_TIMEOUT_SECS)\\n commit_request_options = kw.pop(\\\"commit_request_options\\\", None)\\n transaction_tag = kw.pop(\\\"transaction_tag\\\", None)\\n attempts = 0\\n\\n while True:\\n if self._transaction is None:\\n txn = self.transaction()\\n txn.transaction_tag = transaction_tag\\n else:\\n txn = self._transaction\\n\\n try:\\n attempts += 1\\n return_value = func(txn, *args, **kw)\\n except Aborted as exc:\\n del self._transaction\\n _delay_until_retry(exc, deadline, attempts)\\n continue\\n except GoogleAPICallError:\\n del self._transaction\\n raise\\n except Exception:\\n txn.rollback()\\n raise\\n\\n try:\\n txn.commit(\\n return_commit_stats=self._database.log_commit_stats,\\n request_options=commit_request_options,\\n )\\n except Aborted as exc:\\n del self._transaction\\n _delay_until_retry(exc, deadline, attempts)\\n except GoogleAPICallError:\\n del self._transaction\\n raise\\n else:\\n if self._database.log_commit_stats and txn.commit_stats:\\n self._database.logger.info(\\n \\\"CommitStats: {}\\\".format(txn.commit_stats),\\n extra={\\\"commit_stats\\\": txn.commit_stats},\\n )\\n return return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77380ffe2059d1c211b3858cfa8689d\",\n \"score\": \"0.5887045\",\n \"text\": \"def commitTransaction(self):\\r\\n\\t\\tself.connection.commit()\\r\\n\\t\\tself.connection.autocommit(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3709d88108e23d47f65c853130b6a98f\",\n \"score\": \"0.58859867\",\n \"text\": \"def startTransaction(self):\\r\\n\\t\\tself.connection.autocommit(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2320de07b99f0079f71daf5975c800\",\n \"score\": \"0.58804554\",\n \"text\": \"def begin_transaction(self):\\n self.__check_closed()\\n if self.__current_transaction_level == 0:\\n # concurrent DEFERRED transactions have problems with locks on Windows (This is strange, as the problem does not happen in Java nor .NET)\\n self.__cursor.execute(\\\"BEGIN IMMEDIATE TRANSACTION\\\")\\n else:\\n self.__cursor.execute(\\\"SAVEPOINT sp_%d\\\" % self.__current_transaction_level)\\n self.__current_transaction_level += 1 # in case SAVEPOINT failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d55d4032e043a8ceeb6d17c0f291bf6\",\n \"score\": \"0.58761424\",\n \"text\": \"def start_transaction(self):\\n if self.dry_run:\\n self.pending_transactions += 1\\n transaction.commit_unless_managed(using=self.db_alias)\\n transaction.enter_transaction_management(using=self.db_alias)\\n transaction.managed(True, using=self.db_alias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05226bb4fb29e65b076fc8cca5df8b1f\",\n \"score\": \"0.5848467\",\n \"text\": \"def transaction(request):\\n data = {key: val[0] for key, val in request.POST.lists()}\\n data['user_id'] = request.user.id\\n Transaction.make_transaction(data) # pylint:disable = E1120\\n LOGGER.info('Transaction %s', str(data))\\n return HttpResponse(200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0dc37f64cb2f1dd2dbd68ff24c732b3\",\n \"score\": \"0.5837106\",\n \"text\": \"def transaction():\\n if not _is_sqlite():\\n yield # For postgresql, alembic ensures transactional context.\\n else:\\n try:\\n op.execute(\\\"BEGIN\\\")\\n yield\\n op.execute(\\\"END\\\")\\n except OperationalError:\\n op.execute(\\\"ROLLBACK\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6592a5aa9b09e18070c6122045a060d\",\n \"score\": \"0.5834964\",\n \"text\": \"def execute():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4eac8d8d16d1b9f3d24facf280d8ac\",\n \"score\": \"0.58244205\",\n \"text\": \"def test_send_tx(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bc01c858ab92eeb9eb768e4c855e27c\",\n \"score\": \"0.5819418\",\n \"text\": \"def commit_transaction(self):\\n if self.dry_run:\\n return\\n transaction.commit(using=self.db_alias)\\n transaction.leave_transaction_management(using=self.db_alias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08375e1f5e49ecbd036c1cdba8d511cf\",\n \"score\": \"0.58076966\",\n \"text\": \"def after_begin(self, session, transaction, connection):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f120036e68f371711d837362f9bb4ad4\",\n \"score\": \"0.5795015\",\n \"text\": \"def begin_transaction(self):\\n self._cursor.execute(\\\"begin transaction;\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.5778899\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443588a00f8110859f67db1b56d47dee\",\n \"score\": \"0.57631683\",\n \"text\": \"async def recv_execute(self, data):\\n trans_id, query, args = data\\n\\n if trans_id != self.current_trans_id:\\n begun = await self.begin_transaction(trans_id)\\n if not begun:\\n # This EXECUTE refers to an old transaction which we already completed.\\n # With a sane coordinator, this should not happen; it will only move to\\n # the next transaction once the previous one is complete.\\n return False\\n\\n self.logger.info(f\\\"EXECUTE ({query}) for transaction {trans_id} in database.\\\")\\n try:\\n self.data_db_cur.execute(query, args)\\n self.logger.info(\\\"Done.\\\")\\n except psycopg2.Error as e:\\n self.do_abort(trans_id)\\n self.logger.error(f\\\"EXECUTE failed: {str(e)}\\\")\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac6ed66dc82be25f578ed8aa9afb1a54\",\n \"score\": \"0.57566977\",\n \"text\": \"def transaction(session): # noqa\\n try:\\n yield session\\n session.commit()\\n except BaseException:\\n session.rollback()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8162ed170774bbdd244cc25ae3e3acc6\",\n \"score\": \"0.57549876\",\n \"text\": \"def test_transaction_isolation(self):\\n conn1 = self.get_connection()\\n conn2 = self.get_connection()\\n\\n if not conn1.graph_features['supportsTransactions']:\\n return\\n\\n with conn1.transaction():\\n v1, v2, v3 = conn1.execute(\\n \\\"\\\"\\\"\\n def v1 = g.addVertex([val:1, str:\\\"vertex 1\\\"])\\n def v2 = g.addVertex([val:2, str:\\\"vertex 2\\\"])\\n def v3 = g.addVertex([val:3, str:\\\"vertex 3\\\"])\\n [v1, v2, v3]\\n \\\"\\\"\\\"\\n )\\n\\n conn1.open_transaction()\\n conn2.open_transaction()\\n\\n v1_1 = conn1.execute(\\n \\\"\\\"\\\"\\n def v1 = g.v(eid)\\n v1.setProperty(\\\"str\\\", \\\"v1\\\")\\n v1\\n \\\"\\\"\\\",\\n params={'eid':v1['_id']}\\n )\\n\\n v1_2 = conn2.execute(\\n \\\"\\\"\\\"\\n g.v(eid)\\n \\\"\\\"\\\",\\n params={'eid':v1['_id']}\\n )\\n\\n assert v1_2['_properties']['str'] == 'vertex 1'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018e2d9cf7c26b8f814c1ed497b36950\",\n \"score\": \"0.57548404\",\n \"text\": \"def post_begin_transaction(\\n self, response: datastore.BeginTransactionResponse\\n ) -> datastore.BeginTransactionResponse:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"834015527747f10991349f9d18ff5e61\",\n \"score\": \"0.575081\",\n \"text\": \"def _ConfirmTransaction(self):\\n self.assertEqual(b'1', self.sock.recv(1))\\n self.sock.sendall(b'1')\\n self.assertEqual(b'1', self.sock.recv(1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb6c0d8d8a643b786bbb9f167226555\",\n \"score\": \"0.5747632\",\n \"text\": \"def onTransaction(self, command, args, response):\\n handler = self.handlers.get(command, self.onUnknown)\\n return handler(command, args, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a718febbbd34fc6454e35f123ae88875\",\n \"score\": \"0.5721962\",\n \"text\": \"def createTransaction(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e04630e320e73206f60697324e53dd0\",\n \"score\": \"0.57190585\",\n \"text\": \"def test_transaction_calls(mockstomp):\\n stomp = StompTransport()\\n stomp.connect()\\n mockconn = mockstomp.Connection.return_value\\n\\n stomp._transaction_begin(mock.sentinel.txid)\\n mockconn.begin.assert_called_once_with(transaction=mock.sentinel.txid)\\n\\n stomp._send(\\\"destination\\\", mock.sentinel.message, transaction=mock.sentinel.txid)\\n mockconn.send.assert_called_once_with(\\n \\\"/queue/destination\\\",\\n mock.sentinel.message,\\n headers={\\\"persistent\\\": \\\"true\\\"},\\n transaction=mock.sentinel.txid,\\n )\\n\\n stomp._transaction_abort(mock.sentinel.txid)\\n mockconn.abort.assert_called_once_with(mock.sentinel.txid)\\n\\n stomp._transaction_commit(mock.sentinel.txid)\\n mockconn.commit.assert_called_once_with(mock.sentinel.txid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c7fa33fc1b78848f1f54ffa8f540145\",\n \"score\": \"0.57154214\",\n \"text\": \"def Transaction():\\n acm.BeginTransaction()\\n try:\\n yield None\\n acm.CommitTransaction()\\n except:\\n acm.AbortTransaction()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3948e8cff0784121ba79fdce6d08d80\",\n \"score\": \"0.5712536\",\n \"text\": \"def on_post(self, req, resp):\\n tx_data = str(req.body['tx_data'])\\n net = str(req.body['net']).lower()\\n error, tx_hash = eth_helper.raw_transaction(tx_data, net)\\n\\n if error is None:\\n message = {\\n 'success': True,\\n 'tx_hash': tx_hash,\\n 'message': 'Transaction initiated successfully.'\\n }\\n else:\\n message = {\\n 'success': False,\\n 'error': error,\\n 'message': 'Error occurred while initiating the transaction.'\\n }\\n\\n resp.status = falcon.HTTP_200\\n resp.body = json.dumps(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e076c0bc0bb1eb3eaaf0aca562bf21\",\n \"score\": \"0.57091737\",\n \"text\": \"def test_manualTransactions(self):\\n c = self.conn.cursor()\\n d = defer.Deferred()\\n d.addCallback(\\n lambda c: c.execute(\\\"begin\\\"))\\n d.addCallback(\\n lambda c: c.execute(\\\"insert into simple values (%s)\\\", (1, )))\\n d.addCallback(\\n lambda c: c.execute(\\\"insert into simple values (%s)\\\", (2, )))\\n d.addCallback(\\n lambda c: c.execute(\\\"select * from simple order by x\\\"))\\n d.addCallback(\\n lambda c: self.assertEquals(c.fetchall(), [(1, ), (2, )]))\\n d.addCallback(\\n lambda _: c.execute(\\\"rollback\\\"))\\n d.addCallback(\\n lambda c: c.execute(\\\"select * from simple\\\"))\\n d.addCallback(\\n lambda c: self.assertEquals(c.fetchall(), []))\\n d.callback(c)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61b89a6bd91cff8c0296a1014fa7ea75\",\n \"score\": \"0.5706442\",\n \"text\": \"def commit_tx(self, txn):\\n # container should be in the open state\\n if self.coh == 0:\\n raise DaosApiError(\\\"Container needs to be open.\\\")\\n\\n func = self.context.get_function('commit-tx')\\n ret = func(txn, None)\\n if ret != 0:\\n raise DaosApiError(\\\"TX commit returned non-zero. RC: {0}\\\"\\n .format(ret))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fef2b9695d8b35727357b03a5345dbd\",\n \"score\": \"0.57001287\",\n \"text\": \"def commit(self):\\n self.__check_closed()\\n assert self.__current_transaction_level > 0\\n\\n if self.__current_transaction_level == 1:\\n self.__cursor.execute(\\\"COMMIT\\\")\\n else:\\n self.__cursor.execute(\\\"RELEASE sp_%d\\\" % (self.__current_transaction_level - 1))\\n self.__current_transaction_level -= 1 # in case commit failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597d14849e6a421fef82f0a1a45edb6c\",\n \"score\": \"0.5696073\",\n \"text\": \"def void(transaction_id):\\r\\n\\r\\n return Configuration.gateway().transaction.void(transaction_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fd2f0d6412944c305e8fa4e7ff94f5\",\n \"score\": \"0.569463\",\n \"text\": \"def _commit(self):\\n self._engine.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df619386e32e855503d123311606645f\",\n \"score\": \"0.56921405\",\n \"text\": \"def exec(conn, args, stmt):\\n cur = conn.cursor()\\n try:\\n cur.execute(stmt)\\n except:\\n raise\\n finally:\\n conn.commit()\\n cur.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd4094856b8c062bd890ccd7abdc14e\",\n \"score\": \"0.5687622\",\n \"text\": \"def sql_execute_query(query):\\n with transaction.atomic(), connection.cursor() as cursor:\\n cursor.execute(query)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d768ca9f70a5a1b4205c8d73a507db\",\n \"score\": \"0.56838685\",\n \"text\": \"def commit_transaction(self, src_index, dst_index):\\n src = self.accounts[src_index]\\n dst = self.accounts[dst_index]\\n\\n if src.id == dst.id:\\n return None\\n\\n src_account = src\\n dst_account = dst\\n\\n # In some random cases, flushes the account to have empty balance\\n if random.random() < 0.05:\\n amount = src_account.balance\\n else:\\n amount = round(random.random() * src_account.balance, 8)\\n\\n if amount > 0:\\n src_account.balance = round(src_account.balance - amount, 8)\\n dst_account.balance = round(dst_account.balance + amount, 8)\\n\\n trx = db.Transaction(amount=amount, source=src_account,\\n destination=dst_account, time=self.turn_number)\\n return trx\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505a9a964de536f8562635a8f8d6fb42\",\n \"score\": \"0.56745607\",\n \"text\": \"def execute(self, *args):\\n return self.conn.execute(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb85bc9ef644611569590b4be1a7bca\",\n \"score\": \"0.56647146\",\n \"text\": \"def run_transaction(conn, op, max_retries=3):\\n # leaving this block the transaction will commit or rollback\\n # (if leaving with an exception)\\n with conn:\\n for retry in range(1, max_retries + 1):\\n try:\\n op(conn)\\n\\n # If we reach this point, we were able to commit, so we break\\n # from the retry loop.\\n return\\n\\n except SerializationFailure as e:\\n # This is a retry error, so we roll back the current\\n # transaction and sleep for a bit before retrying. The\\n # sleep time increases for each failed transaction.\\n logging.debug(\\\"got error: %s\\\", e)\\n conn.rollback()\\n logging.debug(\\\"EXECUTE SERIALIZATION_FAILURE BRANCH\\\")\\n sleep_ms = (2 ** retry) * 0.1 * (random.random() + 0.5)\\n logging.debug(\\\"Sleeping %s seconds\\\", sleep_ms)\\n time.sleep(sleep_ms)\\n\\n except psycopg2.Error as e:\\n logging.debug(\\\"got error: %s\\\", e)\\n logging.debug(\\\"EXECUTE NON-SERIALIZATION_FAILURE BRANCH\\\")\\n raise e\\n\\n raise ValueError(f\\\"Transaction did not succeed after {max_retries} retries\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40af7a98a1e86ce4de89052ad481317a\",\n \"score\": \"0.56619096\",\n \"text\": \"def commit(self):\\n if not self.conn.async:\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b1f79f08ad3b8d31bd063db8fe4dfa\",\n \"score\": \"0.56513673\",\n \"text\": \"def commit(using=None):\\n get_connection(using).commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a864ac9f3d41689e7211230f721b6255\",\n \"score\": \"0.56513363\",\n \"text\": \"def commit_transaction(self):\\n if not self._transaction:\\n raise Exception('No transaction running!')\\n self._txn.Commit(self._tid)\\n self._tid = None\\n self._transaction = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0e02b1deb726a172ba67646bbc7a718\",\n \"score\": \"0.5647678\",\n \"text\": \"def _transaction(stub, metrics):\\n session = None\\n try:\\n session = stub.CreateSession(\\n spanner_pb2.CreateSessionRequest(database=_DATABASE))\\n\\n txn_options = transaction_pb2.TransactionOptions(\\n read_write=transaction_pb2.TransactionOptions.ReadWrite())\\n txn_request = spanner_pb2.BeginTransactionRequest(\\n session=session.name,\\n options=txn_options,\\n )\\n\\n # Probing BeginTransaction call\\n start = time.time()\\n txn = stub.BeginTransaction(txn_request)\\n latency = (time.time() - start) * 1000\\n metrics['begin_transaction_latency_ms'] = latency\\n\\n # Probing Commit call\\n commit_request = spanner_pb2.CommitRequest(\\n session=session.name, transaction_id=txn.id)\\n start = time.time()\\n stub.Commit(commit_request)\\n latency = (time.time() - start) * 1000\\n metrics['commit_latency_ms'] = latency\\n\\n # Probing Rollback call\\n txn = stub.BeginTransaction(txn_request)\\n rollback_request = spanner_pb2.RollbackRequest(\\n session=session.name, transaction_id=txn.id)\\n start = time.time()\\n stub.Rollback(rollback_request)\\n latency = (time.time() - start) * 1000\\n metrics['rollback_latency_ms'] = latency\\n\\n finally:\\n if session is not None:\\n stub.DeleteSession(spanner_pb2.DeleteSessionRequest(name=session.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b08d697a85d994b29b11ec1da2af76\",\n \"score\": \"0.5638304\",\n \"text\": \"async def log_transaction(self, **params):\\n\\t\\tif params.get(\\\"message\\\"):\\n\\t\\t\\tparams = json.loads(params.get(\\\"message\\\", \\\"{}\\\"))\\n\\t\\t\\n\\t\\tif not params:\\n\\t\\t\\treturn {\\\"error\\\":400, \\\"reason\\\":\\\"Missed required fields\\\"}\\n\\n\\t\\tcoinid = params.get(\\\"coinid\\\")\\n\\n\\t\\tif not coinid in [\\\"QTUM\\\", \\\"PUT\\\"]:\\n\\t\\t\\treturn {\\\"error\\\":400, \\\"reason\\\": \\\"Missed or invalid coinid\\\"}\\n\\n\\t\\tdatabase = client[settings.TXS]\\n\\t\\tsource_collection = database[coinid]\\n\\n\\t\\tawait source_collection.find_one_and_update({\\\"txid\\\":params.get(\\\"txid\\\")},{\\\"$set\\\":{\\n\\t\\t\\t\\t\\\"blocknumber\\\":params.get(\\\"blocknumber\\\"),\\n\\t\\t\\t\\t\\\"blockhash\\\":params.get(\\\"blockhash\\\"),\\n\\t\\t\\t\\t\\\"gasLimit\\\":params.get(\\\"gasLimit\\\"),\\n\\t\\t\\t\\t\\\"gasPrice\\\":params.get(\\\"gasPrice\\\"),\\n\\t\\t\\t}})\\n\\t\\treturn {\\\"success\\\":True}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58dab38bf11228796415acd0d27a8a7a\",\n \"score\": \"0.5636962\",\n \"text\": \"def _perform_database_action(action, *args):\\r\\n session = _get_session()\\r\\n getattr(session, action)(*args)\\r\\n session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a485601fc154e53fd0dd2b03b6b5da\",\n \"score\": \"0.5634563\",\n \"text\": \"def commit(self):\\n if self.in_transaction:\\n self.__private_cursor__.execute(\\\"COMMIT\\\")\\n self.in_transaction = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bfacea4fd541c9e71f019d5757cffd5\",\n \"score\": \"0.56310683\",\n \"text\": \"async def send(self, transaction: Any, sync: bool = True, **kwargs: Any) -> None:\\n await self._send(transaction, None, None, sync=sync, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3def92f430044d5807e461dfa65287dd\",\n \"score\": \"0.5627344\",\n \"text\": \"def commit(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48945318c3604e454718ae9ff61c71d\",\n \"score\": \"0.5625734\",\n \"text\": \"def execute(self, *args, **kwargs):\\n return self._operate(Connection.execute, args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed191e54103d8e7867ba47725a57bf0\",\n \"score\": \"0.56246305\",\n \"text\": \"def execute(self, context):\\n logging.info(f\\\"Running SQL :{self.sql}\\\")\\n self.hook = TrinoHook()\\n query = self.hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters)\\n if self.xcom_push:\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50046687c4334b965a8536f72755317f\",\n \"score\": \"0.5621117\",\n \"text\": \"def transaction(session):\\n def wrapper(fn):\\n @wraps(fn)\\n def decorated_view(*args, **kwargs):\\n start = time.time()\\n stmp_id = id_gen()\\n try:\\n logger.debug('Transaction %s - START' % stmp_id)\\n res = fn(session, *args, **kwargs)\\n session.commit()\\n elapsed = round(time.time() - start, 4)\\n logger.debug('Transaction %s - STOP - %s' % (stmp_id, elapsed))\\n return res \\n except IntegrityError as ex:\\n elapsed = round(time.time() - start, 4)\\n logger.error('Transaction %s - ERROR: %s - %s' % (stmp_id, ex, elapsed))\\n session.rollback()\\n raise TransactionError(ex)\\n except DBAPIError as ex:\\n elapsed = round(time.time() - start, 4)\\n logger.error('Transaction %s - ERROR: %s - %s' % (stmp_id, ex, elapsed))\\n session.rollback()\\n raise TransactionError(ex)\\n \\n return decorated_view\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69941950f0aa029249f09e28fbc7ee90\",\n \"score\": \"0.56188357\",\n \"text\": \"def transaction(self, command, data=''):\\n self.write(command, data)\\n time.sleep(0.1)\\n response_command, status, data = self.read()\\n if response_command != command:\\n raise RuntimeError(\\\"Response to invalid command in transaction\\\")\\n return status, data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af716a54491b6f0cf93000acc79fc920\",\n \"score\": \"0.5608859\",\n \"text\": \"def tx_subtask_invoke(tst, wrapper):\\n return tst.execute(wrapper, *tst.save_args, **tst.save_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9430ae6c2431915a34abf320cd7fe1\",\n \"score\": \"0.5602103\",\n \"text\": \"def executePendingTransactions(self):\\n self.__fragmentActivity.frame.update_idletasks()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8a88d3c2647ed4158a49dce382a7af\",\n \"score\": \"0.5591583\",\n \"text\": \"def transactionally(transactionCreator):\\n def thunk(operation):\\n return inTransaction(transactionCreator, operation)\\n return thunk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a086270d1db51d2d5311f1c0f43763d7\",\n \"score\": \"0.55832964\",\n \"text\": \"def execute_query(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422aa225bf14b843defc41bdd0c7087b\",\n \"score\": \"0.55783856\",\n \"text\": \"def zsa_savepoints(conn):\\n from transaction.interfaces import ISynchronizer\\n from zope.interface import implementer\\n\\n @implementer(ISynchronizer)\\n class Savepoints(object):\\n def __init__(self, conn):\\n self.conn = conn\\n self.sp = None\\n self.state = None\\n\\n def beforeCompletion(self, transaction):\\n pass\\n\\n def afterCompletion(self, transaction):\\n # txn be aborted a second time in manager.begin()\\n if self.sp is None:\\n return\\n if self.state == 'commit':\\n self.state = 'completion'\\n self.sp.commit()\\n else:\\n self.state = 'abort'\\n self.sp.rollback()\\n self.sp = None\\n self.state = 'done'\\n\\n def newTransaction(self, transaction):\\n self.state = 'new'\\n self.sp = self.conn.begin_nested()\\n self.state = 'begun'\\n transaction.addBeforeCommitHook(self._registerCommit)\\n\\n def _registerCommit(self):\\n self.state = 'commit'\\n\\n zsa_savepoints = Savepoints(conn)\\n\\n import transaction\\n transaction.manager.registerSynch(zsa_savepoints)\\n\\n yield zsa_savepoints\\n transaction.manager.unregisterSynch(zsa_savepoints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdde10c744902c313ece4b4b4cbf9555\",\n \"score\": \"0.557831\",\n \"text\": \"def autonested_transaction(sess):\\n if sess.is_active:\\n session_context = sess.begin(nested=True)\\n else:\\n session_context = sess.begin(subtransactions=True)\\n with session_context as tx:\\n yield tx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d43b291d94445ed319ac67089e2db2aa\",\n \"score\": \"0.5577309\",\n \"text\": \"def commit(self):\\n self._transaction_strategy(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbb3128002356aa2dad89eaaf18012e\",\n \"score\": \"0.55714905\",\n \"text\": \"def commit(self):\\n\\n if self.transaction is None:\\n if self.transactional:\\n self.begin()\\n else:\\n raise exceptions.InvalidRequestError(\\\"No transaction is begun.\\\")\\n\\n self.transaction.commit()\\n if self.transaction is None and self.transactional:\\n self.begin()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c89a304f9c1e9f5302aff25823e5aa4\",\n \"score\": \"0.5563152\",\n \"text\": \"def start_transaction(self):\\n if self._transaction:\\n raise Exception('Transaction already running!')\\n self._transaction = True\\n self._tid = self._txn.Begin(Empty())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb143854b9a80c942432d2fdb6da38a\",\n \"score\": \"0.55472463\",\n \"text\": \"def test_commit_transaction_wont_commit_if_commit_param_false(auth_client, single_transaction):\\n r = auth_client.commit_transaction('DEFAULT', single_transaction, {'commit': False})\\n assert r.json()['status'] == 'Saved'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be95cd074bd261d44a7b3ff64c117e2f\",\n \"score\": \"0.554703\",\n \"text\": \"def send_prepared(self, txn_hash):\\n\\n params = {\\\"transactionHash\\\": txn_hash}\\n\\n return self._post_request('transactions/send/prepared', params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1015b37abe50e51166b966f291b126\",\n \"score\": \"0.55434597\",\n \"text\": \"def runInTransaction(cls, fn):\\r\\n try:\\r\\n transaction = cls.ensureGet()\\r\\n except IllegalStateException:\\r\\n transaction = cls._transactions.local = LockingTransaction()\\r\\n\\r\\n if transaction._info:\\r\\n # Already running transaction... execute current transaction in it. No nested transactions in the same thread\\r\\n return apply(fn)\\r\\n\\r\\n return transaction.run(fn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fa70ac98a4bdc080a07dbebb235f62\",\n \"score\": \"0.5541767\",\n \"text\": \"def commit_db_transaction(state: State) -> None:\\n if state._tx_stack[-1] is None:\\n raise Exception(\\\"Current transaction is not a db transaction\\\")\\n state_root(state)\\n state._tx_stack.pop().commit()\\n if state._tx_stack != []:\\n state._current_tx = state._tx_stack[-1]\\n else:\\n state._current_tx = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec1fcbbc857262b0c9a74bf84a4a398\",\n \"score\": \"0.5533833\",\n \"text\": \"def session_commit():\\r\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc9564cdb16290f8df84367a42cecdf\",\n \"score\": \"0.55310845\",\n \"text\": \"def transaction(self,\\n statements,\\n cursor_factory=None,\\n auto_rollback=True):\\n cursors = []\\n transaction_future = Future()\\n\\n queue = self._statement_generator(statements)\\n\\n def exec_statements(future):\\n try:\\n cursor = future.result()\\n cursors.append(cursor)\\n except Exception as error:\\n if auto_rollback and not self.closed:\\n self._rollback(transaction_future, error)\\n else:\\n future_set_exc_info(transaction_future, sys.exc_info())\\n return\\n\\n try:\\n operation, parameters = next(queue)\\n except StopIteration:\\n transaction_future.set_result(cursors[1:-1])\\n return\\n\\n f = self.execute(operation, parameters, cursor_factory)\\n self.ioloop.add_future(f, exec_statements)\\n\\n self.ioloop.add_future(self.execute(\\\"BEGIN;\\\"), exec_statements)\\n return transaction_future\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb8c9278a7b06e632930591c57de7a5\",\n \"score\": \"0.5512913\",\n \"text\": \"def transaction_commit(user=None, note=''):\\n transaction_note(user, note)\\n import transaction\\n \\n # new semantics as of 3.3.0c1: if an exception occurs while committing, the current\\n # transaction must be aborted explicitly. \\n try:\\n transaction.commit()\\n except:\\n transaction.abort()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5092a88a94f7d9573d1bebc7fff9ae03\",\n \"score\": \"0.5511806\",\n \"text\": \"def savepoint_commit_sql(self, sid):\\r\\n return \\\"COMMIT TRANSACTION %s\\\" % sid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e45425ef3c75453a17c7bdd45f24315\",\n \"score\": \"0.5492369\",\n \"text\": \"def transactional(operation):\\n def transactional_operation(*args, **kwargs):\\n return datastore.RunInTransaction(operation, *args, **kwargs)\\n return transactional_operation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97737bc7d39d58d85a215dd1581c1ae\",\n \"score\": \"0.54916525\",\n \"text\": \"def do_begin(conn):\\n conn.execute(\\\"BEGIN\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260acba5c64edc7127cf302981c919f6\",\n \"score\": \"0.5485624\",\n \"text\": \"def commit(self):\\n #print \\\"--> commit\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d27c4e16f498b1fd427bf7ab3651c3\",\n \"score\": \"0.54802924\",\n \"text\": \"def commit(self):\\n self._local.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4091f8a6b82c0577d743bd555d28586\",\n \"score\": \"0.5479733\",\n \"text\": \"def sync_transaction(self):\\n sock = self.connect_and_send('sync:')\\n try:\\n yield sock\\n finally:\\n try: sync_send_req(sock, 'QUIT', '')\\n except socket.error: pass\\n try: sock.close()\\n except socket.error: pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e6b69fc93d33022dc9e8ffa8a63317\",\n \"score\": \"0.54613274\",\n \"text\": \"def commit(self):\\n self.CheckOpen()\\n request = sql_pb2.ExecOpRequest()\\n request.op.type = client_pb2.OpProto.COMMIT\\n self.MakeRequest('ExecOp', request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77cb8ca8d3ddec5e0b697d600b46e5f2\",\n \"score\": \"0.5456491\",\n \"text\": \"def create_transaction(self, *args, **kwargs):\\n return self.get_vm().create_transaction(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35bebf7c6a3ee02f124b4c55647720ce\",\n \"score\": \"0.5450554\",\n \"text\": \"def commit(self):\\n return CSQLRelay.commit(self.connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26dcec96362c20cd939930623554548f\",\n \"score\": \"0.5444752\",\n \"text\": \"def execute(self):\\n self.bitshares.blocking = \\\"head\\\"\\n r = self.bitshares.txbuffer.broadcast()\\n self.bitshares.blocking = False\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92098a7754c503e3471d239e9a6f632\",\n \"score\": \"0.5443431\",\n \"text\": \"def commit(self):\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5cf500a1f23487a08eb8355af2a431\",\n \"score\": \"0.54395956\",\n \"text\": \"def commit(self):\\n fut = self._execute(self._conn.commit)\\n return fut\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0\",\n \"score\": \"0.5437911\",\n \"text\": \"def commit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0\",\n \"score\": \"0.5437911\",\n \"text\": \"def commit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7f689bfa2c86c4a644cbbffb007d58\",\n \"score\": \"0.54288435\",\n \"text\": \"def transaction_after_request(response, base_status_code_error=500):\\n if view_has_annotation(NO_AUTO_TRANSACTION_ATTR):\\n return response\\n if response.status_code >= base_status_code_error:\\n try:\\n commands.rollback()\\n except OperationFailure:\\n logger.exception('Transaction rollback failed after request')\\n else:\\n try:\\n commands.commit()\\n except OperationFailure as error:\\n # transaction commit failed, log and reraise\\n message = utils.get_error_message(error)\\n if messages.LOCK_ERROR in message:\\n commands.rollback()\\n return utils.handle_error(LOCK_ERROR_CODE)\\n raise\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded2ffc3598110189aabea98e1ca8b73\",\n \"score\": \"0.5421346\",\n \"text\": \"def create(params):\\r\\n return Configuration.gateway().transaction.create(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55663d7304146779dade101de20ec01\",\n \"score\": \"0.5419232\",\n \"text\": \"def commit(self):\\n self.__parent._trans_commit(self)\\n self.__isactive = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b23b1a5e21662dfaf38d96b9a2aca50f\",\n \"score\": \"0.54062235\",\n \"text\": \"def execute(self, parameters=None, messages=None):\\n self._is_executing = True\\n try:\\n self._commit_temporaries()\\n except:\\n pass \\n self.tool.main(self.par, parameters, messages)\\n try:\\n self.clear_temporary_dbs()\\n except:\\n pass\\n self._is_executing = False\\n self.show_outputs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2cd039407c982ffdd398e6c5ec0b3d\",\n \"score\": \"0.5404173\",\n \"text\": \"async def execute_transaction(\\n self, tx: TypedTransaction, from_address, block_height: Optional[int]\\n ) -> Optional[bytes]:\\n evm_tx = tx.tx.to_evm_tx()\\n evm_tx.set_quark_chain_config(self.env.quark_chain_config)\\n branch = Branch(evm_tx.from_full_shard_id)\\n if branch.value not in self.branch_to_slaves:\\n return None\\n\\n futures = []\\n for slave in self.branch_to_slaves[branch.value]:\\n futures.append(slave.execute_transaction(tx, from_address, block_height))\\n responses = await asyncio.gather(*futures)\\n # failed response will return as None\\n success = all(r is not None for r in responses) and len(set(responses)) == 1\\n if not success:\\n return None\\n\\n check(len(responses) >= 1)\\n return responses[0]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1483,"cells":{"query_id":{"kind":"string","value":"13466d15f5f4824688018c31f9057ed5"},"query":{"kind":"string","value":"Fetch messages up to the maximum, return email.Message objects."},"positive_passages":{"kind":"list like","value":[{"docid":"5942301071fd7181362ee182b6626593","score":"0.7981605","text":"def fetch_messages(self, max_messages=15):\n # Connect to the IMAP4 mailbox\n mailbox = imaplib.IMAP4(self.host, int(self.port))\n mailbox.login(self.user, self.passwd)\n mailbox.select()\n \n # Look up undeleted messages, establish a window for fetching\n nums = mailbox.search(None, \"UNDELETED\")[1][0].split()\n end_pos = len(nums)\n start_pos = max(0, end_pos - max_messages)\n \n # Fetch and accumulate Messages\n msgs = []\n for i in nums[start_pos:end_pos]:\n try:\n msg_txt = mailbox.fetch(str(i), \"RFC822\")[1][0][1]\n msg = email.message_from_string(msg_txt)\n msgs.append(msg)\n except KeyboardInterrupt:\n raise\n except:\n pass\n \n # Log out of mailbox\n mailbox.close()\n mailbox.logout()\n\n return msgs","title":""}],"string":"[\n {\n \"docid\": \"5942301071fd7181362ee182b6626593\",\n \"score\": \"0.7981605\",\n \"text\": \"def fetch_messages(self, max_messages=15):\\n # Connect to the IMAP4 mailbox\\n mailbox = imaplib.IMAP4(self.host, int(self.port))\\n mailbox.login(self.user, self.passwd)\\n mailbox.select()\\n \\n # Look up undeleted messages, establish a window for fetching\\n nums = mailbox.search(None, \\\"UNDELETED\\\")[1][0].split()\\n end_pos = len(nums)\\n start_pos = max(0, end_pos - max_messages)\\n \\n # Fetch and accumulate Messages\\n msgs = []\\n for i in nums[start_pos:end_pos]:\\n try:\\n msg_txt = mailbox.fetch(str(i), \\\"RFC822\\\")[1][0][1]\\n msg = email.message_from_string(msg_txt)\\n msgs.append(msg)\\n except KeyboardInterrupt:\\n raise\\n except:\\n pass\\n \\n # Log out of mailbox\\n mailbox.close()\\n mailbox.logout()\\n\\n return msgs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3388dfd7de27679aac6ba6880a661420","score":"0.79869056","text":"def fetch_messages(self, max_messages=15):\n # Connect to the POP3 mailbox\n mailbox = poplib.POP3(self.host, self.port)\n mailbox.user(self.user)\n mailbox.pass_(self.passwd)\n\n # Look up messages, establish a window for fetching\n nums = range(len(mailbox.list()[1]))\n end_pos = len(nums)\n start_pos = max(0, end_pos - max_messages)\n \n # Fetch and accumulate Messages\n msgs = []\n for i in nums[start_pos:end_pos]:\n try:\n msg_txt = \"\\n\".join(mailbox.retr(i+1)[1])\n msg = email.message_from_string(msg_txt)\n msgs.append(msg)\n except KeyboardInterrupt:\n raise\n except:\n pass\n\n # Log out of mailbox\n mailbox.quit()\n\n return msgs","title":""},{"docid":"5556203646d16ec9b0ca4b8f83a43b8b","score":"0.72879046","text":"def get_messages(delete=True, max_get=1000):\n messages = []\n if BUGMAIL_HOST:\n conn = poplib.POP3_SSL(BUGMAIL_HOST)\n conn.user(BUGMAIL_USER)\n conn.pass_(BUGMAIL_PASS)\n (num_messages, total_size) = conn.stat()\n num_get = min(num_messages, max_get)\n print 'Getting %d bugmails' % num_get\n for msgid in range(1, num_get + 1):\n msg_str = '\\n'.join(conn.retr(msgid)[1])\n msg = Parser().parsestr(msg_str)\n if is_bugmail(msg):\n if is_interesting(msg):\n yield msg\n if delete:\n conn.dele(msgid)\n conn.quit()","title":""},{"docid":"4082e7f07f8c07814c25afd206d58c66","score":"0.72795755","text":"def get_latest_messages(number_of_messages=50):\n # JOIN see https://www.w3schools.com/sql/sql_join.asp\n stuff = execute_sql(\n MySQL_DATABASE_CHAT_APP,\n \"\"\"\n SELECT users.UserName, messages.MessageContent\n FROM messages INNER JOIN users\n ON users.UserId = messages.SenderId\n ORDER BY messages.MessageId DESC\n LIMIT %s\n ;\n \"\"\",\n (int(number_of_messages),)\n )\n return stuff","title":""},{"docid":"92f64efae1bca0e333f1242817d12e2a","score":"0.69429094","text":"async def peek_messages(self, max_messages=None, **kwargs):\n # type: (Optional[int], Optional[Any]) -> List[QueueMessage]\n if max_messages and not 1 <= max_messages <= 32:\n raise ValueError(\"Number of messages to peek should be between 1 and 32\")\n\n timeout = kwargs.pop('timeout', None)\n if self.key_encryption_key or self.key_resolver_function:\n modify_user_agent_for_encryption(\n self._config.user_agent_policy.user_agent,\n self._sdk_moniker,\n self.encryption_version,\n kwargs)\n\n self._config.message_decode_policy.configure(\n require_encryption=self.require_encryption,\n key_encryption_key=self.key_encryption_key,\n resolver=self.key_resolver_function\n )\n try:\n messages = await self._client.messages.peek(\n number_of_messages=max_messages, timeout=timeout, cls=self._config.message_decode_policy, **kwargs\n )\n wrapped_messages = []\n for peeked in messages:\n wrapped_messages.append(QueueMessage._from_generated(peeked)) # pylint: disable=protected-access\n return wrapped_messages\n except HttpResponseError as error:\n process_storage_error(error)","title":""},{"docid":"aa1db03e80b9ff5dfef9fac411085f5e","score":"0.6831436","text":"def get_messages(self, limit=25, *, query=None, order_by=None, batch=None,\n download_attachments=False):\n\n if self.root:\n url = self.build_url(self._endpoints.get('root_messages'))\n else:\n url = self.build_url(self._endpoints.get('folder_messages').format(\n id=self.folder_id))\n\n if limit is None or limit > self.protocol.max_top_value:\n batch = self.protocol.max_top_value\n\n params = {'$top': batch if batch else limit}\n\n if order_by:\n params['$orderby'] = order_by\n\n if query:\n if isinstance(query, str):\n params['$filter'] = query\n else:\n params.update(query.as_params())\n\n response = self.con.get(url, params=params)\n if not response:\n return iter(())\n\n data = response.json()\n\n # Everything received from cloud must be passed as self._cloud_data_key\n messages = (self.message_constructor(\n parent=self,\n download_attachments=download_attachments,\n **{self._cloud_data_key: message})\n for message in data.get('value', []))\n\n next_link = data.get(NEXT_LINK_KEYWORD, None)\n if batch and next_link:\n return Pagination(parent=self, data=messages,\n constructor=self.message_constructor,\n next_link=next_link, limit=limit,\n download_attachments=download_attachments)\n else:\n return messages","title":""},{"docid":"5aca343a48e122db38d54466c9823a05","score":"0.67001694","text":"def get_group_message_chunk(self, group_id, max_messages=100, before_id=None, since_id=None):\n\t\turl = API_BASE + \"groups/{}/messages\".format(group_id)\n\n\t\tparams = {\"token\" : self.auth_token, \"limit\" : 100}\n\t\tif before_id:\n\t\t\tparams[\"before_id\"] = before_id\n\t\tif since_id:\n\t\t\tparams[\"since_id\"] = since_id\n\n\t\tr = requests.get(url, params=params)\n\t\tif r and r.status_code == 200:\n\t\t\tans = r.json()\n\t\t\tif ans and ans[\"response\"]:\n\t\t\t\tans = ans[\"response\"][\"messages\"]\n\t\t\t\t# print ans\n\t\t\t\treturn ans","title":""},{"docid":"bcaa7acb01cf87838de74202087e1ccd","score":"0.6662036","text":"def get_all_group_messages(self, group_id, before_id=None, max_messages=None):\n\t\tall_messages = list()\n\t\tfor chunk in self.group_message_generator(group_id, before_id):\n\t\t\tall_messages += chunk\n\t\t\tif max_messages and len(all_messages) > max_messages:\n\t\t\t\tbreak;\n\t\treturn all_messages","title":""},{"docid":"539826c068ed0e9c510ebe6a2a6712eb","score":"0.66313386","text":"def get_limited_number_of_messages_from_qs(qs, limit): # pragma: no cover\n count = 0\n results = []\n for item in qs:\n if count == limit:\n break\n if isinstance(item, Message):\n count += 1\n results.append(item)\n return results","title":""},{"docid":"becd65eb9334d86ff6dd43d5773a6b37","score":"0.6609195","text":"def get_messages(self, **params):\n all_args = ['limit', 'offset']\n params = Resource.sanitize_params(params, all_args)\n\n return [Message(self.parent, obj) for obj in self._request_uri(\n 'messages', params=params\n )]","title":""},{"docid":"fe30d5b234fe584560efefb3bd517f0c","score":"0.65774363","text":"def get_messages(self, limit=100):\n if self.verbose:\n self.logger.debug('get_messages called')\n # get messages from local buffer\n msg_list = []\n for j in range(limit):\n msg_obj = self.msg_buffer.get()\n if msg_obj is None:\n break\n msg_list.append(msg_obj)\n if self.verbose:\n self.logger.debug('got {n} messages'.format(n=len(msg_list)))\n return msg_list","title":""},{"docid":"62feec4af8c60572b8aac6768d6e2352","score":"0.6525903","text":"def retrieve_latest_email(self, timeout=36):\n count = 0\n while count < timeout:\n id_list = self.get_all_email_ids()\n if id_list:\n return self.get_message(id_list[-1])\n time.sleep(5)\n count += 1","title":""},{"docid":"ccb8924738d7cba34930ecfd6cbfb488","score":"0.6520283","text":"def get_messages():\n kiasufoodies = client.get_entity(\"kiasufoodies\")\n return client.iter_messages(kiasufoodies, limit=30)","title":""},{"docid":"360c194c20257da66faed5669c4414b1","score":"0.6448578","text":"def raw_longest_messages(self, num=None) -> list:\n assert num is None or isinstance(num, int), (\n \"num must be None or an integer representing the number of messages desired\"\n )\n num = min(num, len(self)) if num is not None else len(self)\n\n order = Counter({index: len(self._convo[index][1]) for index in range(len(self))})\n return order.most_common(num)","title":""},{"docid":"56afed0fbaac4efc0c36ded598b4e57b","score":"0.64294106","text":"def getRecentChannelMessages(self):\n\n async def main():\n async for message in Channel.client.iter_messages(self.username, offset_date=calcDateOffset(\n Channel.config.get(\"scrape_offset\")), reverse=True):\n if type(message) == telethon.tl.types.Message:\n await self.parseMessage(message)\n\n with Channel.client:\n try:\n Channel.client.loop.run_until_complete(main())\n except telethon.errors.ServerError:\n logging.info(\"Server error: Passed\")\n pass\n except telethon.errors.FloodWaitError as e:\n logging.info(\"FloodWaitError: Sleep for \" + str(e.seconds))\n time.sleep(e.seconds)\n\n self.messages = list(reversed(self.messages))","title":""},{"docid":"fe3cf4a9948b45401ba2bc3725fd2021","score":"0.6400792","text":"def test_request_messages_with_smaller_limit_than_messages(self):\n\n lim = 2\n timestamp = 10 ** 11\n\n for i in range(100):\n create_message(\n text='Message' + str(i),\n timestamp=timestamp + i,\n username='vitsalis',\n channel=self.channel\n )\n\n messages = json.loads(self.client.get(\n reverse('chat:message', args=('channel', self.channel.name,)),\n {'lim': lim}\n ).content)\n\n self.assertEqual(len(messages), 2)\n\n self.assertEqual(messages[0]['text'], 'Message99')\n self.assertEqual(messages[1]['text'], 'Message98')","title":""},{"docid":"217b7d4abd029b2dde50043a8280e292","score":"0.6377988","text":"def GetMessages(self,organisation,flow):\n # prepare the target URL\n url=self.MakeURL(organisation,flow,\"messages\")\n\n # Pull messages until we've reached the requested time offset\n reachedDesiredDate=False\n last_id=-1\n new_messages={}\n thread_re=re.compile(r\"influx:(\\d+)\")\n while(not reachedDesiredDate):\n # Request a set of messages\n params={'limit':100}\n if last_id != -1: params['until_id']=last_id\n objects=self.Request(url,**params)\n if len(objects) == 0:\n # No more messages in the flow\n break\n if type(objects) is not list:\n reachedDesiredDate=True\n break\n\n # Reverse iterate to check if we've got all that we need to reach\n # our requested time limit\n for obj in reversed(objects):\n created_at=datetime.strptime(obj[\"created_at\"],TIME_FORMAT)\n last_id=int(obj[\"id\"])\n if created_at < self.stop_time:\n reachedDesiredDate=True\n break\n # Valid new message so keep it\n new_messages[obj[\"id\"]]=(obj)\n self.GetThreadHeads([ thread_re.match(tag).group(1) for tag in obj[\"tags\"] if thread_re.match(tag) ],\n organisation,flow)\n \n self.last_id[flow]=last_id\n return new_messages","title":""},{"docid":"47f1ec69c636a7f328c8345ebf15548e","score":"0.62303907","text":"def get_messages(self, **params):\n all_args = ['include_thread_size', 'include_body', \n 'body_type', 'include_headers', 'include_flags', 'flag_seen',\n 'limit', 'offset'\n ]\n params = Resource.sanitize_params(params, all_args)\n\n return [Message(self.parent.parent, obj) for obj in self._request_uri(\n 'messages', params=params\n )]","title":""},{"docid":"d20a73556add37b80b52bd60d1a75d45","score":"0.6174438","text":"def test_request_messages_with_bigger_limit_than_messages(self):\n lim = 100\n timestamp = 10 ** 11\n\n create_message(\n text='Message1',\n timestamp=timestamp,\n username='vitsalis',\n channel=self.channel\n )\n\n create_message(\n text='Message2',\n timestamp=timestamp + 60 * 60,\n username='pkakelas',\n channel=self.channel\n )\n\n messages = json.loads(self.client.get(\n reverse('chat:message', args=('channel', self.channel.name,)),\n {'lim': lim}\n ).content)\n\n self.assertEqual(len(messages), 2)","title":""},{"docid":"e85f8209d3145ca677a9640732d786a1","score":"0.6146502","text":"def get_next(self) -> 'MessageGroup':\n # prevent more fetching\n if self._limit != -1 and self._total_count >= self._limit:\n raise IndexError\n\n popped = self.groups.popleft()\n self._total_count += len(popped.msgs)\n return popped","title":""},{"docid":"bb3cb4a6eee76f058d12288f522a10d9","score":"0.61252254","text":"def get_messages():\n uid = get_jwt_identity()\n page = request.args.get(\"page\", default=1, type=int)\n # autoMarkAsRead = request.args.get('autoMarkAsRead', default=True, type=bool)\n\n msg = misc.get_messages_inbox(page, uid=uid)\n\n return jsonify(messages=[message_fields_for_api(m) for m in msg])","title":""},{"docid":"ae78676cf8cc8434d0c05aafb1fb32ae","score":"0.6120805","text":"def fetchmany(self, size=None):\n self.messages=[] \n if size is None:\n size = self.arraysize\n return self._fetch(size)","title":""},{"docid":"d4398374ff17ed1fadfe02640dc0f2b7","score":"0.61146027","text":"async def get_messages(\n db: AsyncIOMotorDatabase\n ):\n\n # collection.objects returns a lazy QuerySet\n messages = await db.messages.find().sort(\n [('time', 1)]\n ).to_list(length=None)\n\n return messages","title":""},{"docid":"9dba1527c2c600fe37ca1fe6ac83f7e9","score":"0.6109274","text":"def receive_messages(self, **kwargs):\n # type: (Optional[Any]) -> AsyncItemPaged[QueueMessage]\n messages_per_page = kwargs.pop('messages_per_page', None)\n visibility_timeout = kwargs.pop('visibility_timeout', None)\n timeout = kwargs.pop('timeout', None)\n max_messages = kwargs.pop('max_messages', None)\n if self.key_encryption_key or self.key_resolver_function:\n modify_user_agent_for_encryption(\n self._config.user_agent_policy.user_agent,\n self._sdk_moniker,\n self.encryption_version,\n kwargs)\n\n self._config.message_decode_policy.configure(\n require_encryption=self.require_encryption,\n key_encryption_key=self.key_encryption_key,\n resolver=self.key_resolver_function\n )\n try:\n command = functools.partial(\n self._client.messages.dequeue,\n visibilitytimeout=visibility_timeout,\n timeout=timeout,\n cls=self._config.message_decode_policy,\n **kwargs\n )\n if max_messages is not None and messages_per_page is not None:\n if max_messages < messages_per_page:\n raise ValueError(\"max_messages must be greater or equal to messages_per_page\")\n return AsyncItemPaged(command, results_per_page=messages_per_page,\n page_iterator_class=MessagesPaged, max_messages=max_messages)\n except HttpResponseError as error:\n process_storage_error(error)","title":""},{"docid":"4a5cbd231d1fbcc605342d4a2b0e00ab","score":"0.6077979","text":"def produce_entries(self):\n msgs = self.client.fetch_messages(max_messages=self.max_messages)\n filtered_msgs = self.filter_messages(msgs)\n return self.entries_from_messages(filtered_msgs)","title":""},{"docid":"e72f1ee6d024056f8b6e3fc99d20e194","score":"0.60757256","text":"def get_messages(target):\n\n # Get the first page\n data = get_page(1, 'unknown')\n for message in data['raw_messages']:\n if message['msg_id'] in blacklist:\n continue\n if target in json.dumps(message):\n yield message\n\n more = functools.partial(get_page, pages=data['pages'])\n\n # Get all subsequent pages (if there are any...)\n for page in range(1, data['pages']):\n data = more(page + 1)\n\n for message in data['raw_messages']:\n if message['msg_id'] in blacklist:\n continue\n if target in json.dumps(message):\n yield message","title":""},{"docid":"8eb12a3de7a3669b4d40cdb361f2862f","score":"0.6047707","text":"def fetchall(self):\n self.messages=[] \n return self._fetch()","title":""},{"docid":"6ccc3864fbc4035d6f3beccf2acf509c","score":"0.604666","text":"def get_messages(self, **params):\n all_args = ['subject', 'email', 'to',\n 'sender', 'from_', 'cc', 'bcc', 'folder', 'date_before', 'date_after',\n 'indexed_before', 'indexed_after', 'include_thread_size', 'include_body', 'source', \n 'file_name', 'file_size_min', 'file_size_max', 'include_source',\n 'include_headers', 'include_flags', 'body_type', 'sort_order',\n 'limit', 'offset'\n ]\n\n params = Resource.sanitize_params(params, all_args)\n\n # workaround to send param \"from\" even though it's a reserved keyword\n # in python\n if 'sender' in params:\n params['from'] = params['sender']\n del params['sender']\n\n if 'from_' in params:\n params['from'] = params['from_']\n del params['from_']\n\n return [Message(self, obj) for obj in self._request_uri(\n 'messages', params=params\n )]","title":""},{"docid":"53fca5ea2bbd243867bc709a1d6b9400","score":"0.60074055","text":"def gmail_messages():\n return __gmail_v1__().messages()","title":""},{"docid":"a1781492a9df26f4095de7c0fb2deece","score":"0.5974734","text":"def fetchUnreadEmails(profile, since=None, markRead=False, limit=None):\n conn = imaplib.IMAP4_SSL('imap.gmail.com')\n conn.debug = 0\n conn.login(profile['gmail_address'], profile['gmail_password'])\n conn.select(readonly=(not markRead))\n\n msgs = []\n (retcode, messages) = conn.search(None, '(UNSEEN)')\n\n if retcode == 'OK' and messages != ['']:\n numUnread = len(messages[0].split(' '))\n if limit and numUnread > limit:\n return numUnread\n\n for num in messages[0].split(' '):\n # parse email RFC822 format\n ret, data = conn.fetch(num, '(RFC822)')\n msg = email.message_from_string(data[0][1])\n\n if not since or getDate(msg) > since:\n msgs.append(msg)\n conn.close()\n conn.logout()\n\n return msgs","title":""},{"docid":"f22429401069bcf52a077a2abc643d55","score":"0.5956753","text":"def receive_messages(self, WaitTimeSeconds=\"0\", MaxNumberOfMessages=\"10\", **kwargs):\n wait_seconds = int(WaitTimeSeconds)\n max_messages = int(MaxNumberOfMessages)\n\n ready_messages = []\n push_back_messages = []\n\n threshold = datetime.datetime.utcnow() + datetime.timedelta(\n seconds=wait_seconds\n )\n for message in self.messages:\n if message.execute_at > threshold or len(ready_messages) >= max_messages:\n push_back_messages.append(message)\n else:\n ready_messages.append(message)\n self.messages[:] = push_back_messages\n return ready_messages","title":""},{"docid":"23ca633cd19d386ebfe49a77fef6904f","score":"0.59555346","text":"async def _get_messages(self):\n client = build_client()\n queue = client.get_queue_by_name(QueueName=\"TestQueue\")\n\n all_messages=[]\n i=1\n\n while True:\n messages = queue.receive_messages(MaxNumberOfMessages=10)\n for msg in messages:\n all_messages.append(msg.body)\n msg.delete()\n if len(messages) == 0:\n break\n print(all_messages)\n return all_messages","title":""},{"docid":"d0044b74aafd921beb96e6ef9491c0a5","score":"0.5918131","text":"def _get_maximum_entries(self):\n return self.__maximum_entries","title":""},{"docid":"d0044b74aafd921beb96e6ef9491c0a5","score":"0.5918131","text":"def _get_maximum_entries(self):\n return self.__maximum_entries","title":""},{"docid":"d0044b74aafd921beb96e6ef9491c0a5","score":"0.5918131","text":"def _get_maximum_entries(self):\n return self.__maximum_entries","title":""},{"docid":"d0044b74aafd921beb96e6ef9491c0a5","score":"0.5918131","text":"def _get_maximum_entries(self):\n return self.__maximum_entries","title":""},{"docid":"d5f993b467cd4e964f870d5b1fa8dc41","score":"0.59144247","text":"def get_messages(access_token, user_email, last_processed_date_str_iso8601=None):\n get_messages_url = outlook_api_endpoint.format('/me/mailfolders/inbox/messages')\n\n # '$filter': 'ReceivedDateTime gt ' + timestamp + ' and ' + 'from eq \\'venmo@venmo.co\\'',\n # The query parameter '$filter' is not supported with '$search'.\" therefore need to manually filter all email from venmo\n\n select_expr = 'receivedDateTime,subject,body,from'\n filter_expr = '(From/EmailAddress/Address eq \\'venmo@venmo.com\\')'\n if last_processed_date_str_iso8601 is None:\n last_processed_date_str_iso8601 = '1970-01-01T00:00:00Z'\n # Note the insertion order of the new expression. Fields in 'orderby' must come first in 'filter' and must be in the same order\n # For details: https://stackoverflow.com/a/41026120/1387710\n\n # filter_expr = '(ReceivedDateTime gt {}) and '.format(last_processed_date_str_iso8601) + filter_expr\n filter_expr = '(ReceivedDateTime gt {})'.format(last_processed_date_str_iso8601) # TODO: delete this line\n order_by_expr = 'receivedDateTime DESC'\n\n query_parameters = {'$filter': filter_expr,\n '$select': select_expr,\n '$orderby': order_by_expr\n }\n\n r = make_api_call('GET', get_messages_url, access_token, user_email, parameters=query_parameters)\n\n if r.status_code == requests.codes.unauthorized:\n raise yeti_exceptions.YetiAuthInvalidTokenException(\"Invalid authorization when trying to get messages. Response: {}\".format(r))\n\n try:\n response = r.json()\n except Exception as e:\n raise yeti_exceptions.OutlookApiErrorException(\"Failed to transform response text to JSON: {}, raw response: {}\".format(e, r))\n\n if r.status_code != requests.codes.ok or ('error' in response and response['error']):\n if 'code' in response['error'] and response['error']['code'] == 'InvalidAuthenticationToken':\n raise yeti_exceptions.YetiAuthTokenExpiredException()\n else:\n raise yeti_exceptions.OutlookApiErrorException(\"Failed to get email messages. Response: {}\".format(response))\n if 'value' not in response:\n raise yeti_exceptions.OutlookApiErrorException(\"Failed to get email messages. 'value' is empty. Response: {}\".format(response))\n new_emails = response['value']\n return new_emails","title":""},{"docid":"409e9e55028e0c8bd5fa41504c115494","score":"0.59016675","text":"def _fetch(self) -> typing.List[MessageContainer]:\n messages = []\n # select mailbox\n logger.debug(self, 'Fetch messages from folder {}'.format(\n self.folder,\n ))\n rv, data = self._connection.select(self.folder)\n logger.debug(self, 'Response status {}'.format(\n rv,\n ))\n if rv == 'OK':\n # get mails\n # TODO - G.M - 2017-11-15 Which files to select as new file ?\n # Unseen file or All file from a directory (old one should be\n # moved/ deleted from mailbox during this process) ?\n logger.debug(self, 'Fetch unseen messages')\n\n rv, data = self._connection.search(None, \"(UNSEEN)\")\n logger.debug(self, 'Response status {}'.format(\n rv,\n ))\n if rv == 'OK':\n # get mail content\n logger.debug(self, 'Found {} unseen mails'.format(\n len(data[0].split()),\n ))\n for uid in data[0].split():\n # INFO - G.M - 2017-12-08 - Fetch BODY.PEEK[]\n # Retrieve all mail(body and header) but don't set mail\n # as seen because of PEEK\n # see rfc3501\n logger.debug(self, 'Fetch mail \"{}\"'.format(\n uid,\n ))\n rv, data = self._connection.fetch(uid, 'BODY.PEEK[]')\n logger.debug(self, 'Response status {}'.format(\n rv,\n ))\n if rv == 'OK':\n msg = message_from_bytes(data[0][1])\n msg_container = MessageContainer(msg, uid)\n messages.append(msg_container)\n self._set_flag(uid, IMAP_SEEN_FLAG)\n else:\n log = 'IMAP : Unable to get mail : {}'\n logger.error(self, log.format(str(rv)))\n else:\n log = 'IMAP : Unable to get unseen mail : {}'\n logger.error(self, log.format(str(rv)))\n else:\n log = 'IMAP : Unable to open mailbox : {}'\n logger.error(self, log.format(str(rv)))\n return messages","title":""},{"docid":"ad73e7e76024599648bb70d12b8875da","score":"0.5898244","text":"def getMessages():\n\n def emailLogin():\n \"\"\"\n Log into gmail using IMAP, return imaplib object\n \"\"\"\n\n # Login information\n USER = loginValues['user']\n PW = loginValues['pw']\n SERVER = loginValues['server']\n MAIN_FOLDER = 'inbox'\n\n # Login and return mail object\n mail = imaplib.IMAP4_SSL(SERVER)\n mail.login(USER, PW)\n mail.list()\n mail.select(MAIN_FOLDER)\n return mail\n\n def getLastMsgCore(y, mail):\n \"\"\"\n Pulls the last unread email and returns a processed core tokenized message\n \"\"\"\n # Get number of unread emails\n latest_email_uid = y[0].split()[0]\n result, data = mail.uid('fetch', latest_email_uid, \"(RFC822)\")\n raw_email = data[0][1]\n\n # Generate email message object from raw message\n email_message = email.message_from_string(raw_email)\n\n # Convert to text block\n block = get_text_block(email_message)\n\n # Get rid of header and footer\n block = block.split('StreetAccount')[1:3]\n block = \"\".join(block)\n\n # Get rid of potential \"Reference Link in footer\"\n block = block.split('Reference Link')[0]\n\n # Get rid of =, /r, /n\n block = block.translate(None, '=\\r\\\\')\n block = block.translate(None, '\\n')\n\n # Special case--wierd norweigan char\n block = block.replace('\\xf8', 'o')\n block = block.replace('\\xe5', 'a')\n block = block.replace('\\xe9', 'e')\n block = block.replace('\\xeb', 'e')\n block = block.replace('\\xfc', 'u')\n block = block.replace('\\xe4', 'a')\n block = block.replace('\\xf6', 'o')\n block = block.replace('\\xd8', 'O')\n block = block.replace('\\xdf', 'ss')\n block = block.replace('\\xc5', 'A')\n block = block.replace('\\xd6', 'O')\n\n #letters w/ acsents\n block = block.replace('\\xe1', 'a')\n block = block.replace('\\xe8', 'e')\n block = block.replace('\\xe7', 'c')\n block = block.replace('\\xea', 'e')\n\n #Remove ' from names\n block = block.replace('\\x92','')\n\n \n\n # Replace EUR\n block = block.replace('\\x80', 'EUR')\n\n # Tokenize using NLTK\n tokenized_block = nltk.word_tokenize(block)\n\n # Isolate message from email and return\n index = tokenized_block.index('ET')\n msg = tokenized_block[index-1:]\n return msg\n\n def getDate(msg):\n \"\"\"\n Process date from message\n \"\"\"\n # Sometimes (e.g. in case of revision), chars stuck in date, remove\n sub = re.sub('[^a-zA-Z_]+', '', msg[0])\n\n # Get Date\n if not sub: dt = msg[2] + ' ' + msg[0]\n else: dt = msg[2] + ' ' + msg[0].replace(sub, '')\n\n # Special case-- remove '.'\n dt = dt.replace('.', '')\n return dt\n\n def getFirm(msg, typ='single'):\n \"\"\"\n Cross reference with firm database and return firm name, if nothing found add firm to database\n \"\"\"\n\n # If in multiple event message, find firm in beginning\n if typ == 'multiple':\n multiple_msg_tokens = ['upgrades', 'downgrades', 'initiates', 'resumes', 'reinstates', 'assumes', 'raises', 'lowers', 'initiates', 'initiates/resumes']\n ix = 10000\n\n # Look for token, assign index\n for token in multiple_msg_tokens:\n try:\n if (msg.index(token) < ix): ix = msg.index(token)\n except: pass\n\n st = ix\n while (st > 0):\n st -= 1\n if msg[st] == ';' or msg[st] == ':':\n if (ix-st == 1): continue\n else: break\n if msg[st] == ']': break\n\n if ix != 0: name = ' '.join(msg[st+1:ix])\n else: name = ''\n\n # If single message / in \"notable\"\n else:\n # Look for 'at'\n try:\n ix = msg.index('at')\n\n # If no 'at', look after msg token\n except:\n single_msg_tokens = ['upgraded', 'downgraded', 'initiated', 'resumed', 'reinstated', 'reinitiated', 'assumed', 're-initiated', 're-instated']\n for token in single_msg_tokens:\n try:\n ix = msg.index(token) + 4\n except: pass\n\n # Look for '(' or '--' or '*' or ';', whichever comes first after 'at'\n try: ix1 = msg[ix+1:].index('(')\n except: ix1 = 10000\n try: ix2 = msg[ix+1:].index('--')\n except: ix2 = 10000\n try: ix3 = msg[ix+1:].index('*')\n except: ix3 = 10000\n try: ix4 = msg[ix+1:].index(';')\n except: ix4 = 10000\n\n end = min(ix1, ix2, ix3, ix4)\n sub = msg[ix+1:ix+1+end]\n\n # Get rid of \"we're told\"\n try:\n sub = sub[:sub.index(\"'re\")-2]\n except:\n pass\n\n # Get firm name\n name = ' '.join(sub)\n\n return name\n\n def getAnalyst(msg):\n \"\"\"\n Extract analyst from message and return (assumed that message ends right after analyst or has another bullet)\n If nothing found, return empty string\n \"\"\"\n # If \"Analyst\" is found in message\n if \"Analyst\" in msg:\n\n # Get index of occurence\n ix = msg.index('Analyst')\n\n # Get index of beginning of analyst name (is there \"is\" in between?)\n if msg[ix+1] != 'is': startix = ix+1\n else: startix = ix+2\n\n # Flatten rest of message\n msg_substring = ' '.join(msg[startix:])\n\n # If another bullet, set analyst name equal to rest of text before bullet\n if '*' in msg_substring: return msg_substring[:msg_substring.index('*')]\n\n # If no more bullets, assume end of message and set analyst to rest of message\n else: return msg_substring\n\n # If \"Analyst\" not found, return empty string\n else:\n return ''\n\n def get_text_block(email_message_instance):\n \"\"\"\n Process raw email string, returns decoded message string\n \"\"\"\n maintype = email_message_instance.get_content_maintype()\n if maintype == 'multipart':\n for part in email_message_instance.get_payload():\n if part.get_content_maintype() == 'text':\n return part.get_payload(decode=True)\n elif maintype == 'text':\n return email_message_instance.get_payload(decode=True)\n\n def getCurrency(PT, msg, PTix):\n \"\"\"\n Process currecy identifiers for PT subsection of msg, returns currency string\n \"\"\"\n # If currency is not directly attached to PT field (e.g. ['SEK','25.7'] vs. ['SEK25.7']\n if PT == msg[PTix]:\n\n # Set currency equal to field immediately prior to PT field\n curr = msg[PTix-1]\n\n # If prior field is '$'\n if curr == '$':\n\n # If 'C' precedes $, return 'CAD'\n if msg[PTix-2] == 'C': return 'CAD'\n\n # If 'A' precedes $, return 'AUD'\n if msg[PTix-2] == 'A': return 'AUD'\n\n # If 'HK' preceds $, return 'HKD'\n if msg[PTix-2] == 'HK': return 'HKD'\n\n # If nothing precedes $, return 'USD'\n else: return 'USD'\n\n # If prior field is not '$', return string (in these cases the field is the currency string)\n else: return curr\n\n # If currency is directly attached to PT field (e.g. 'SEK25.7')\n else:\n\n # Seperate non digits from PT field\n curr = ''.join(i for i in msg[PTix] if not i.isdigit())\n\n # Get rid of potential \".\" and \",\" left in string (e.g. '.SEK')\n curr = curr.replace('.', '')\n curr = curr.replace(',', '')\n\n # If curr = 'R', return 'ZAR'\n if curr == 'R': return 'ZAR'\n\n # If 'p' or '\\xa3' is identifier, return 'GBP' (e.g. '257p')\n if ('p' in curr) or ('\\xa3' in curr): return 'GBP'\n\n # If '\\x80' (Euro symbol) is identifier, return 'EUR' (e.g. '\\x80270')\n if '\\x80' in curr: return 'EUR'\n\n # If '\\xa5' (Yen symbol) is identifier, return 'JPY'\n if '\\xa5' in curr: return 'JPY'\n\n # If not 'GBP' or 'EUR', return extracted string (e.g. 'NOK', 'SEK', 'TWD')\n return curr\n\n def getPT(msg, typ='single'):\n \"\"\"\n Get price target from message and return\n Behaves differently given whether single or multiple messages\n\n -- If 'single' will look for target after occurrence of 'Target'\n -- If 'multiple' will look for target after occurrence of ')'\n \"\"\"\n # Look for occurrence of 'target' if single message\n if typ == 'single':\n if \"Target\" in msg: ix = msg.index('Target')\n elif 'target' in msg: ix = msg.index('target')\n else: return '',''\n\n # Look for occurrence of ')' if not single message\n else:\n if ')' in msg: ix = msg.index(')')\n else: return '',''\n\n # If valuation range, return empty strings\n if 'range' in msg: return '',''\n\n # Loop through message after occurrence of \"target\"/\"Target\" until PT found\n for i, x in enumerate(msg[ix:]):\n\n # If a float is found\n if len(re.findall('\\d+.\\d+', x)) > 0:\n\n # Strip float (PT) from field\n PT = re.findall('\\d+.\\d+', x)[0]\n\n # Get currency\n curr = getCurrency(PT, msg, ix+i)\n\n # Convert string PT to float\n PT = float(PT.replace(',', ''))\n\n # Stop looking and return\n return PT, curr\n\n # If an int is found\n elif len(re.findall('\\d+', x)) > 0:\n\n # Strip int (PT) from field\n PT = re.findall('\\d+', x)[0]\n\n # Get currency\n curr = getCurrency(PT, msg, ix+i)\n\n # Convert string PT to float\n PT = float(PT.replace(',', ''))\n\n # Stop looking and return\n return PT, curr\n\n # If nothing found (misc. format), return empty strings\n return '', ''\n\n def getMsgType(msg):\n \"\"\"\n Returns three values:\n --- boolean: whether message type was recognized\n --- string: message type\n --- string: rating\n \"\"\"\n # If rating starts with one of the following, it is a two-word rating (e.g. \"sector perform\")\n two_word_rating_list = ['sector', 'market', 'strong']\n\n # Key/value dictionary for other message types in headline\n key_dict = {'upgraded': 'upgrade',\n 'downgraded': 'downgrade',\n 'initiated': 'initiation',\n 'resumed': 'resumption',\n 'reinstated': 'resumption',\n 're-instated': 'resumption',\n 'reinitiated': 'resumption',\n 're-initiated': 'resumption',\n 'assumed': 'resumption',\n }\n\n # Message type recognized instantiated to False\n msgtype_recognized = False\n\n # Search dictionary for keywords\n for key, value in key_dict.iteritems():\n\n # If keyword found\n if key in msg:\n\n # Message recognized\n msgtype_recognized = True\n\n # Set type according to dictionary\n msgtype = value\n\n # Get index of keyword occurrence\n ix = msg.index(key)\n\n # If not followed by something like 'to', decrement index\n if len(msg[ix+1]) != 2: ix -= 1\n\n # Check if two-word rating\n if msg[ix+2] in two_word_rating_list:\n\n # Return two-word rating\n rating = msg[ix+2] + ' ' + msg[ix+3]\n\n # If keyword not in two_word_rating list\n else:\n\n # Return single-word rating\n rating = msg[ix+2]\n\n if msgtype_recognized:\n return msgtype_recognized, msgtype, rating\n else: return msgtype_recognized, None, None\n\n\n def getSingle(msg):\n \"\"\"\n Check if single message and, if so, process message and return event values\n\n Returns a tuple: (boolean, list), where:\n --boolean: \"True\" if single event, otherwise \"False\"\n --list: [ticker, msgtype, rating, PT, curr, analyst]\n\n Will return \"True, None\" if message type not recognized\n Will return \"False, None\" if more than one event in message\n \"\"\"\n # If msg contains any of the following, there are multiple events per message\n multiple_msg_tokens = ['upgrades', 'downgrades', 'initiates', 'resumes', 'reinstates', 'assumes', 'notable']\n\n # Look for header (before first bullet)\n try:\n header = msg[:msg.index('*')]\n except:\n header = msg\n\n # If no value in multiple_msg_tokens found in msg, assume single event\n if not any([e in header for e in multiple_msg_tokens]):\n\n # Get ticker by looking at standard index in message\n SINGLE_MESSAGE_TICKER_INDEX = 5\n ticker = msg[SINGLE_MESSAGE_TICKER_INDEX]\n\n # Check if target price increase or decrease\n # Assumed format 'target in/decreased to x' before first bullet or +5 spaces after ticker (headline)\n try:\n stpindx = msg.index('*')\n except:\n stpindx = 5\n\n # If 'target' found in msg headline\n if 'target' in msg[SINGLE_MESSAGE_TICKER_INDEX:stpindx]:\n\n # Get index of occurence\n ix = msg.index('target')\n\n # If followed by 'increased' or 'raises'\n if msg[ix+1] == 'increased' or msg[ix-1] == 'raises':\n\n # Set type to 'PT increased'\n msgtype = 'PT increased'\n\n # Get PT and currency\n PT, curr = getPT(msg)\n\n # Set rating to empty string\n rating = ''\n\n # Get firm\n if msg[ix+1] == 'increased':\n firm = getFirm(msg)\n else:\n firm = getFirm(msg, typ='multiple')\n\n # If followed by 'decreased' or 'lowers'\n elif msg[ix+1] == 'decreased' or msg[ix-1] == 'lowers':\n\n # Set type to 'PT decreased'\n msgtype = 'PT decreased'\n\n # Get PT and currency\n PT, curr = getPT(msg)\n\n # Set rating to empty string\n rating = ''\n\n # Get firm\n if msg[ix+1] == 'decreased':\n firm = getFirm(msg)\n else:\n firm = getFirm(msg, typ='multiple')\n\n # If not followed by one of the above, msg type not understood\n else:\n return True, None\n\n # Return True for single event, return values\n return True, [firm, ticker, msgtype, rating, PT, curr]\n\n # If not PT change, check for other msg types\n else:\n\n # Get msg type and rating\n msgtype_recognized, msgtype, rating = getMsgType(msg)\n\n # If msg type not recognized, exit\n if not msgtype_recognized: return True, None\n\n # Get Price Target and currency\n PT, curr = getPT(msg)\n\n # Get firm\n firm = getFirm(msg)\n\n # Return True and values\n return True, [firm, ticker, msgtype, rating, PT, curr]\n\n # More than one event in msg\n return False, None\n\n def getMultiple(msg, date, analyst):\n \"\"\"\n Process core with multiple events, will return generator of lists:\n List looks like this [ticker, msgtype, rating, PT, curr, analyst]\n \"\"\"\n # Get firm\n firm = getFirm(msg, typ='multiple')\n\n # Get index of first bullet, otherwise quit\n try:\n ix = msg.index('*')\n anotherBullet = True\n except:\n anotherBullet = False\n\n # Loop through multiple messages\n while anotherBullet:\n\n # Get index of next bullet if possible\n try:\n nextix = msg[ix+1:].index('*')\n\n # If next bullet immediately follows, this is the real bullet\n # E.g. Don't want the \"Upgrade\" bullet, want actual msg bullet\n if (nextix < 5):\n ix += (nextix + 1)\n\n # Again get index of next bullet\n try:\n nextix = msg[ix+1:].index('*')\n\n # Current msg is until next bullet\n currentMsg = msg[(ix+1):(ix+nextix+1)]\n\n # If no more bullets, current msg is until end of msg\n except:\n currentMsg = msg[ix+1:]\n anotherBullet = False\n\n # If no more bullets, current msg is until end of msg\n except:\n currentMsg = msg[ix+1:]\n anotherBullet = False\n\n # If bullet is followed immediately by \"Analyst\", exit\n if (msg[ix+1] == 'Analyst'): break\n\n # Get index of '(', if not found, move to next bullet / exit\n try: indx = currentMsg.index('(')\n except:\n if not anotherBullet: continue\n else:\n ix += (nextix + 1)\n continue\n\n # Ticker follows '('\n ticker = currentMsg[indx+1]\n\n # Get msg type and rating\n msgtype_recognized, msgtype, rating = getMsgType(currentMsg)\n\n # Get PT and curr\n PT, curr = getPT(currentMsg, typ='multiple')\n\n # Increment bullet index to next bullet\n ix += (nextix + 1)\n\n # If msgtype not recognized print out \n if not msgtype_recognized:\n print '------------------------------'\n print ' NEW MESSAGE '\n print '------------------------------'\n print ' Can\\'t process message type '\n print ''\n continue\n\n yield [date, firm, ticker, msgtype, rating, PT, curr, analyst]\n\n def getNotable(msg, date):\n \"\"\"\n Get messages for email with \"Other notable research\", will return generator of events\n List looks like this [ticker, msgtype, rating, PT, curr, analyst]\n \"\"\"\n # Look for first bullet (before first '(')\n ix = msg.index('(')\n\n # If followed by (pre-open), look for next '('\n if msg[ix+1] == 'pre-open':\n ix += msg[ix+1:].index('(') + 1\n\n # Loop back from '(' until first bullet found, get index\n while msg[ix] != '*': ix -= 1\n\n anotherBullet = True\n\n while anotherBullet:\n\n # Get index of next bullet if possible\n try:\n nextix = msg[ix+1:].index('*')\n\n # Current msg is until next bullet\n currentMsg = msg[(ix+1):(ix+nextix+1)]\n\n except:\n anotherBullet = False\n\n # Current msg is until end of msg\n currentMsg = msg[ix+1:]\n\n # Check if current message contains PT, if so continue to next bullet\n if ('target' in currentMsg) or ('Target' in currentMsg):\n ix += (nextix + 1)\n continue\n\n # Check if current message contains PT, if so continue to next bullet\n if ('analyst' in currentMsg) or ('Analyst' in currentMsg):\n ix += (nextix + 1)\n continue\n\n # Get index of '(', if not found, move to next bullet\n try: indx = currentMsg.index('(')\n except:\n ix += (nextix + 1)\n continue\n\n # Ticker follows '('\n ticker = currentMsg[indx+1]\n\n # Get message type and rating\n msgtype_recognized, msgtype, rating = getMsgType(currentMsg)\n\n # Assume no PT / currency / analyst\n PT = ''\n curr = ''\n analyst = ''\n\n # Get firm\n firm = getFirm(currentMsg)\n\n # Check if followed by price target\n if anotherBullet:\n try:\n tgtix = msg[ix+nextix+2:].index('*')\n\n # Next msg is until next bullet\n nextMsg = msg[(ix+nextix+2):(ix+nextix+tgtix+2)]\n\n except:\n # Next msg is until end of msg\n nextMsg = msg[(ix+nextix+2):]\n\n # If next message contains target\n if ('Target' in nextMsg) or ('target' in nextMsg):\n\n # Get PT and curr\n PT, curr = getPT(nextMsg)\n\n # If next message contains analyst\n if ('Analyst' in nextMsg) or ('analyst' in nextMsg):\n\n # Get analyst name\n analyst = getAnalyst(nextMsg)\n\n # Increment bullet index to next bullet\n ix += (nextix + 1)\n\n # If msgtype not recognized print out\n if not msgtype_recognized:\n print '------------------------------'\n print ' NEW MESSAGE '\n print '------------------------------'\n print ' Can\\'t process message type '\n print ''\n continue\n\n yield [date, firm, ticker, msgtype, rating, PT, curr, analyst]\n\n def processMsg(msg):\n \"\"\"\n Process message, if successful, write to database, if not print why\n \"\"\"\n # Look for date\n date = getDate(msg)\n\n # Check to see if \"Other notable research calls\"\n if msg[6] == 'notable':\n events = getNotable(msg, date)\n\n for e in events:\n\n firm = e[1]\n ticker = e[2]\n msgType = e[3]\n newRating = e[4]\n PT = e[5]\n fx = e[6]\n analyst = e[7]\n \n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\n\n # If not \"Other notable research calls\"\n else:\n\n # Look for analyst name\n analyst = getAnalyst(msg)\n\n # Check if email only contains one event (one ticker)\n isSingle, vals = getSingle(msg)\n\n # If contains only one event\n if isSingle:\n\n # If doesn't recognize type print following\n if vals is None:\n print '------------------------------'\n print ' NEW MESSAGE '\n print '------------------------------'\n print ' Can\\'t process message type '\n print ''\n\n # If type recognized, write event to database\n else:\n firm = vals[0]\n ticker = vals[1]\n msgType = vals[2]\n newRating = vals[3]\n PT = vals[4]\n fx = vals[5]\n\n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\n\n # If more than one event, print following\n else:\n events = getMultiple(msg, date, analyst)\n for e in events:\n\n firm = e[1]\n ticker = e[2]\n msgType = e[3]\n newRating = e[4]\n PT = e[5]\n fx = e[6]\n \n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\n\n \n # Login to gmail and get mail object\n mail = emailLogin()\n\n # Get unread message ids\n x, y = mail.uid('search', None, \"(UNSEEN)\")\n\n # Check if there are unread emails\n unreadMails = len(y[0].split()) > 0\n\n # While there are unread emails\n while(unreadMails):\n\n # Get first unread email and get tokenized message\n msg = getLastMsgCore(y, mail)\n\n # Process message\n processMsg(msg)\n\n # Update unread email boolean\n x, y = mail.uid('search', None, \"(UNSEEN)\")\n unreadMails = len(y[0].split()) > 0","title":""},{"docid":"f803e42b5c1bb0bf32956623eab1fb9f","score":"0.5876127","text":"def _query_messages(client, page=1):\n if DEBUG:\n print \"requesting page {}\".format(page)\n\n return client.api_call('search.messages', query=MESSAGE_QUERY, count=MESSAGE_PAGE_SIZE, page=page)","title":""},{"docid":"0d0d3d53c08b728f6e61727cd4d261fa","score":"0.5864518","text":"def receive_messages(queue, max_number, wait_time):\n try:\n messages = queue.receive_messages(\n MessageAttributeNames=['All'],\n MaxNumberOfMessages=max_number,\n WaitTimeSeconds=wait_time\n )\n for msg in messages:\n logger.info(\"Received message: %s: %s\", msg.message_id, msg.body)\n except ClientError as error:\n logger.exception(\"Couldn't receive messages from queue: %s\", queue)\n raise error\n else:\n return messages","title":""},{"docid":"f0ac26cdc4b9bb5bb049d96751778ad2","score":"0.58494055","text":"def get_all_messages():\n query_ = select([Message.message])\n all_messages = IRC_CONNECTION.execute(query_).fetchall()\n return all_messages","title":""},{"docid":"e18531210a8de168df1a18b8c14d3cf4","score":"0.5845751","text":"def get_messages(self, request, last_message=0, read=True, old=False):\n\n user = request.user\n\n if old:\n messages_qs = self.filter(player=user.player, id__gt=last_message)\n else:\n messages_qs = self.filter(\n player=user.player, id__gt=last_message, read=False\n )\n messages_qs = messages_qs.order_by('-id')\n messages = list(messages_qs)[: settings.MAX_SESSION_MESSAGES]\n for message in messages:\n message.new = not message.read\n if read:\n messages_qs.update(read=True)\n\n return messages","title":""},{"docid":"10fa7b0f477c4b75b018fca410e069ae","score":"0.5819849","text":"def max(self):\n return self.msg.max","title":""},{"docid":"b197d39431e37d37a0c0bc4b8c6b3509","score":"0.5804066","text":"def get_messages(room_id=0, offset=0, size=50):\n room_key = f\"room:{room_id}\"\n room_exists = redis_client.exists(room_key)\n if not room_exists:\n return []\n else:\n values = redis_client.zrevrange(room_key, offset, offset + size)\n return list(map(lambda x: json.loads(x.decode(\"utf-8\")), values))","title":""},{"docid":"891d903008979f5036780d9afa2741de","score":"0.57971257","text":"def limit(self, count):\r\n return self.all().limit(count)","title":""},{"docid":"fe23e55861e716dad009c4a978f88737","score":"0.5790616","text":"def get_messages(queue_url):\n while True:\n resp = sqs_client.receive_message(\n QueueUrl=queue_url,\n AttributeNames=['All'],\n MaxNumberOfMessages=10\n )\n\n try:\n yield from resp['Messages']\n except KeyError:\n return\n\n entries = [\n {'Id': msg['MessageId'], 'ReceiptHandle': msg['ReceiptHandle']}\n for msg in resp['Messages']\n ]\n\n resp = sqs_client.delete_message_batch(\n QueueUrl=queue_url, Entries=entries\n )\n\n if len(resp['Successful']) != len(entries):\n raise RuntimeError(\n \"Failed to delete messages: entries={!r} resp={!r}\"\n .format(entries, resp)\n )","title":""},{"docid":"7445c0caf8d117cd10ed5a1a6359267e","score":"0.578687","text":"def message_get_all(context, filters=None, limit=None, offset=None,\n sort_key=None, sort_dir=None):\n return IMPL.message_get_all(context, filters=filters, limit=limit,\n offset=offset, sort_key=sort_key,\n sort_dir=sort_dir)","title":""},{"docid":"529db5bd3669f87efe478e9eada67c12","score":"0.57843995","text":"def _fetch(self, imapc: imapclient.IMAPClient) -> typing.List[MessageContainer]:\n messages = []\n\n logger.debug(self, \"Fetch unflagged messages\")\n uids = imapc.search([\"UNFLAGGED\"])\n logger.debug(self, \"Found {} unflagged mails\".format(len(uids)))\n for msgid, data in imapc.fetch(uids, [\"BODY.PEEK[]\"]).items():\n # INFO - G.M - 2017-12-08 - Fetch BODY.PEEK[]\n # Retrieve all mail(body and header) but don't set mail\n # as seen because of PEEK\n # see rfc3501\n logger.debug(self, 'Fetch mail \"{}\"'.format(msgid))\n\n try:\n msg = message_from_bytes(data[b\"BODY[]\"])\n except KeyError as e:\n # INFO - G.M - 12-01-2018 - Fetch may return events response\n # In some specific case, fetch command may return events\n # response unrelated to fetch request.\n # This should happen only when someone-else use the mailbox\n # at the same time of the fetcher.\n # see https://github.com/mjs/imapclient/issues/334\n except_msg = \"fetch response : {}\".format(str(data))\n raise BadIMAPFetchResponse(except_msg) from e\n\n msg_container = MessageContainer(msg, msgid)\n messages.append(msg_container)\n\n return messages","title":""},{"docid":"18ac877394b93cb341cfb108971ba5d9","score":"0.57789975","text":"def get_all_msgs(self):\n msgs = []\n while True:\n msg = self.conn.recv_match()\n if msg != None:\n msgs.append(msg)\n else:\n break\n return msgs","title":""},{"docid":"224b49c2323ada5fe50fd9e91d57fb6b","score":"0.57684296","text":"def _get_max_results(max_results, limit, count_so_far):\n # limit is None means we get every record (no limit).\n if limit is None:\n return max_results\n\n return min(max_results, limit - count_so_far)","title":""},{"docid":"cc004e3d16b3db8af66567816e6754be","score":"0.57667977","text":"def get_all(self, select: Optional[Select] = None) -> Sequence[Message]:\n return [entry.msg for entry in self.queue.get_all(select)]","title":""},{"docid":"a662933a0f8fcdc70280083b018f0fc3","score":"0.57499135","text":"def get_recent_messages(self, channel_oid: ObjectId, *, limit: Optional[int] = None, skip: Optional[int] = None) \\\n -> ExtendedCursor[MessageRecordModel]:\n addl_kwargs = {}\n\n # Both `limit` and `skip` cannot accept ``None``, so only attach them if needed\n\n if limit:\n addl_kwargs[\"limit\"] = limit\n\n if skip:\n addl_kwargs[\"skip\"] = skip\n\n return self.find_cursor_with_count({MessageRecordModel.ChannelOid.key: channel_oid},\n sort=[(OID_KEY, pymongo.DESCENDING)], **addl_kwargs)","title":""},{"docid":"cf0b76ca9dc7560d98f8e86bc34f8c9f","score":"0.574574","text":"async def fetch_next_page(self):\n if self._limit != -1 and self._total_count >= self._limit:\n return\n\n results = await self.sq.execute(page=self.page)\n\n # add a new messagegroup to the end\n for r in results:\n self.groups.append(MessageGroup(r))\n\n self.page += 1","title":""},{"docid":"e6486d17a61d189822ae71908e0ab588","score":"0.57332814","text":"def get_messages_list(self):\r\n results = ResponsesREST.SERVER_ERROR.value\r\n query = \"SELECT idMessage, message, idChat, dateTime, memberType FROM Message \" \\\r\n \"WHERE idChat = %s;\"\r\n param = [self.id_chat]\r\n list_messages = self.connect.select(query, param)\r\n if list_messages:\r\n message_list = []\r\n for messages in list_messages:\r\n message = Message()\r\n message.id_message = messages[\"idMessage\"]\r\n message.message = messages[\"message\"]\r\n message.id_chat = messages[\"idChat\"]\r\n message.date_time = messages[\"dateTime\"]\r\n message.date_time = message.date_time.strftime(\"%Y/%m/%d %H:%M:%S\")\r\n message.member_type = messages[\"memberType\"]\r\n message_list.append(message)\r\n results = message_list\r\n else:\r\n results = ResponsesREST.NOT_FOUND.value\r\n return results","title":""},{"docid":"dcee8071482aebcd0b850f391654d0ba","score":"0.5725758","text":"def get_recent_emails(account, folder_name, count):\n # Get the folder object\n folder = account.root / 'Top of Information Store' / folder_name\n # Get emails\n return folder.all().order_by('-datetime_received')[:count]","title":""},{"docid":"09abeefa5f159ac3ddce91c23b26516e","score":"0.5718951","text":"def getMessages(self, date):\r\n messages = self.client.messages.list(\r\n date_sent_after=date,\r\n to=self.phone_num,\r\n )\r\n return messages","title":""},{"docid":"da1f1344d536e203360ffdb149662ca2","score":"0.5713777","text":"def limit(self, count):\r\n return self.find(Document.limit(count))","title":""},{"docid":"dda30b10dc94ecacfc7c08e0522351fc","score":"0.57061654","text":"async def prune(self, ctx, *, max_messages:str=None):\n\t\tif ctx.message.server.me.permissions_in(ctx.message.channel).manage_messages == False:\n\t\t\tawait self.bot.say(\"Sorry, this doesn't work on this server (No manage_messages Permission)!\")\n\t\t\treturn\n\t\tusers = []\n\t\tnone = False\n\t\tif max_messages:\n\t\t\tfor u in ctx.message.mentions:\n\t\t\t\tusers.append(u)\n\t\t\t\tmax_messages = max_messages.replace(u.mention, '')\n\t\t\tif max_messages.replace(' ', '').isdigit():\n\t\t\t\tmax_messages = int(max_messages)\n\t\tif max_messages is None:\n\t\t\tnone = True\n\t\t\tmax_messages = 100\n\t\ttry:\n\t\t\tmax_messages = int(max_messages)\n\t\texcept:\n\t\t\tmax_messages = 100\n\t\tif max_messages > 6000:\n\t\t\tawait self.bot.say(\"2 many messages (<= 6000)\")\n\t\t\treturn\n\t\tcheck = lambda m: m.author in users\n\t\tdeleted = await self.bot.purge_from(ctx.message.channel, limit=max_messages, before=ctx.message, after=datetime.datetime.now() - datetime.timedelta(minutes=5) if none else None, check=check if users else None)\n\t\tself.bot.pruned_messages.append(deleted)\n\t\tif users and len(deleted) == 0:\n\t\t\tx = await self.bot.send_message(ctx.message.channel, \":warning: No messages found by `{0}` within `{1}` searched messages!\".format(', '.join(users), max_messages))\n\t\telse:\n\t\t\tx = await self.bot.send_message(ctx.message.channel, \"ok, removed **{0}** messages.\".format(len(deleted)))\n\t\tawait asyncio.sleep(10)\n\t\ttry:\n\t\t\tself.bot.pruned_messages.append(ctx.message)\n\t\t\tawait self.bot.delete_message(ctx.message)\n\t\texcept:\n\t\t\tpass\n\t\ttry:\n\t\t\tawait self.bot.delete_message(x)\n\t\texcept:\n\t\t\tpass","title":""},{"docid":"bb3b38896749f9173ddd1dccdc25fabc","score":"0.5695202","text":"def get_messages_from_queue(url):\n\n while True:\n resp = sqs_client.receive_message(\n QueueUrl=queue_url,\n AttributeNames=['All'],\n MaxNumberOfMessages=10\n )\n\n try:\n yield from resp['Messages']\n except KeyError:\n return\n\n entries = [\n {'Id': msg['MessageId'], 'ReceiptHandle': msg['ReceiptHandle']}\n for msg in resp['Messages']\n ]\n\n resp = sqs_client.delete_message_batch(\n QueueUrl=queue_url, Entries=entries\n )\n\n if len(resp['Successful']) != len(entries):\n raise RuntimeError(\n f\"Failed to delete messages: entries={entries!r} resp={resp!r}\"\n )\n else:\n break","title":""},{"docid":"5140b4bcfe3daf2dd85be47c5c0b2816","score":"0.56937915","text":"def test_request_messages_without_lim(self):\n timestamp = 10 ** 11\n\n for i in range(200):\n create_message(\n text='Message' + str(i),\n timestamp=timestamp + i,\n username='vitsalis',\n channel=self.channel\n )\n\n messages = json.loads(self.client.get(\n reverse('chat:message', args=('channel', self.channel.name,)),\n ).content)\n\n self.assertEqual(len(messages), 100)","title":""},{"docid":"4519364d142b7334e80c11691781fa53","score":"0.5679032","text":"def fetchMail(self):\n\n fetcher = FetchMail(self.FETCHMAILSERVER, self.BOTACCOUNT, self.BOTPASSWORD, self.leaveunread, download_folder=self.unprocessedpath)\n if self.printstatus:\n print(\"Successfully connected to imap server.\")\n emails = fetcher.fetch_unread_messages()\n numattachments = len(emails)\n if self.printstatus:\n print(\"Successfully fetched %s emails.\" % numattachments) # probably want to log this message\n if numattachments:\n\n for entry in emails:\n self.skippedemailfiles.extend(fetcher.save_attachment(entry))\n if self.printstatus:\n print(self.skippedemailfiles )\n if self.printstatus and emails:\n print(\"Successfully saved attachments.\")\n fetcher.close_connection()\n if self.printstatus:\n print(\"Successfully received mail.\")\n\n return numattachments","title":""},{"docid":"928050344a144f83a13e1a32695ea886","score":"0.56714356","text":"def limit(cls, limit=5):\n return cls.query.limit(limit).all()","title":""},{"docid":"e30412e3864e8d278e9a0b64a7bcd5e0","score":"0.567061","text":"def get_all(self, context, search_opts=None, limit=None,\n offset=None, sort_key=None, sort_dir=None):\n LOG.debug(\"Searching for messages by: %s\", search_opts)\n\n search_opts = search_opts or {}\n messages = self.db.message_get_all(context, filters=search_opts,\n limit=limit, offset=offset,\n sort_key=sort_key,\n sort_dir=sort_dir)\n\n return messages","title":""},{"docid":"e54915691e17c3e51e10fd685e43fbf8","score":"0.566538","text":"async def get_messages(self, page: int = 0) -> 'SearchResults':\n res = SearchResults(self)\n res.page = page\n await res.fetch_next_page()\n return res","title":""},{"docid":"b537f738355fe87e76447c9099fdcb0d","score":"0.56494975","text":"def potential_message_targets(page_size: int = 2000):\n with connection.cursor() as cursor:\n cursor.execute(MESSAGE_TARGET_QUERY)\n while page := cursor.fetchmany(page_size):\n yield from starmap(MessageTarget, page)","title":""},{"docid":"730c741ce25c5e18d8c894b3cfa48de1","score":"0.5634011","text":"def consume(self):\n return [\n self.sqs_envelope.parse_raw_message(self, raw_message)\n for raw_message in self.sqs_client.receive_message(\n AttributeNames=[\n \"ApproximateReceiveCount\",\n ],\n MaxNumberOfMessages=self.limit,\n QueueUrl=self.sqs_queue_url,\n WaitTimeSeconds=self.wait_seconds,\n ).get(\"Messages\", [])\n ]","title":""},{"docid":"2034be2da6779f0f09e0ba0395aae93e","score":"0.5623556","text":"def get_messages(self, conv_id=0, msg_type=0, page=0):\r\n \r\n # FIXME: this should be sanitized.\r\n conditions = []\r\n if conv_id > 0:\r\n conditions.append(\"msg_conv_id = {}\".format(conv_id))\r\n if msg_type > 0:\r\n conditions.append(\"msg_type = {}\".format(msg_type))\r\n\r\n where = \" WHERE \" + \" AND \".join(conditions) if conditions else \"\"\r\n \r\n if page > 0:\r\n page_lbound = page * self.messages_per_page\r\n page_ubound = page_lbound + self.messages_per_page\r\n limit = \" LIMIT {},{}\".format(page_lbound, page_ubound)\r\n else:\r\n limit = \"\"\r\n\r\n query = \"SELECT * FROM [Messages]\" + where + \" ORDER BY [msg_timestamp] DESC\" + limit\r\n return self.cursor.execute(query)","title":""},{"docid":"72cecde362a00fba3dbe64db7412d00f","score":"0.5618267","text":"def get_last_10_CAN_messages():\n try:\n # finding last 10 can messages from excavator_canstatus collection based on sorting mongo Object Id _id in descending order\n records = db.excavator_canstatus.find({},{'id':1, 'message':1, 'timestamp':1, '_id':0}).sort('timestamp',-1).limit(10)\n\n # converting cursor to list \n canList = list(records)\n\n # formatting timestamp from DB to local time\n for item in canList:\n item['timestamp'] = item['timestamp'].strftime(\"%c\") \n\n return jsonify(canList)\n except Exception as e:\n logger.error(\"Error \" + str(e))\n return f\"Error {str(e)}\"","title":""},{"docid":"ee79644d89c4bb0f59214da7184bfd20","score":"0.56042546","text":"def messages(self, user_id='me', query=''):\n try:\n if self.page_token():\n response = self._service.users().messages() \\\n .list(userId=user_id, q=query,\n pageToken=self.page_token()).execute()\n else:\n response = self._service.users().messages() \\\n .list(userId=user_id, q=query).execute()\n\n messages = []\n if 'messages' in response:\n messages.extend(response['messages'])\n\n self._page_token = response.get('nextPageToken')\n return messages\n\n except errors.HttpError as e:\n print(e) \n return []","title":""},{"docid":"4f0cdcab76a50c143dcf6fd3992c8675","score":"0.5601558","text":"def last(self):\r\n current_max = None\r\n for message in self.messages:\r\n if current_max is None:\r\n current_max = message.time\r\n\r\n if message.time > current_max:\r\n current_max = message.time\r\n return current_max","title":""},{"docid":"6ef303892dd75f4743c2ce782a7453d2","score":"0.55999434","text":"def fetch_messages(channel_id, latest_ts=None):\n messages = {} \n if latest_ts:\n messages = client.api_call(\n \"im.history\",\n channel=channel_id,\n latest=latest_ts,\n count=PAGE_COUNT\n )\n else:\n messages = client.api_call(\n \"im.history\",\n channel=channel_id,\n count=PAGE_COUNT\n )\n return messages","title":""},{"docid":"4cfe082fd1352a60180914e639f805df","score":"0.55982536","text":"def _get_last_imap_emails(self):\n email = self._read_config_file('email')\n password = self._read_config_file('password')\n imap_url = self._read_config_file('imap_url')\n with MailBox(imap_url).login(email, password, 'INBOX') as mailbox:\n mails = [msg for msg in mailbox.fetch(Q(all=True))]\n latest_email = mails[-1]\n return latest_email.html","title":""},{"docid":"ea6234223e2d1e8f78d341d0bc62eab8","score":"0.5592901","text":"def get_messages_nowait(self) -> List[Message]:\n messages = []\n while not self._message_queue.empty():\n messages.append(self._message_queue.get_nowait())\n\n self._new_message_event.clear()\n return messages","title":""},{"docid":"cb20a629860e52f6a0b84204c231a989","score":"0.5567465","text":"def get_message(self):\n\n # Select inbox and get all messages\n self.mail.select('INBOX')\n status, messages = self.mail.search(None, self.email_state)\n\n if status == 'OK':\n for msg_num in messages[0].split():\n typ, data = self.mail.fetch(msg_num, '(RFC822)')\n for raw_msg in data:\n if isinstance(raw_msg, tuple):\n new_email = email.message_from_bytes(raw_msg[1])\n if self.from_condition in new_email['From']:\n if new_email['Subject'][0:2] == self.subject_condition:\n msg = new_email.get_payload()\n self.mail.close()\n return msg\n\n self.mail.close()","title":""},{"docid":"33514c3b183310b5677933dc05937664","score":"0.5554532","text":"def takeSomeFromQueue(self):\n def yield_max_datapoints():\n for count in range(settings.MAX_DATAPOINTS_PER_MESSAGE):\n try:\n yield self.queue.popleft()\n except IndexError:\n raise StopIteration\n return list(yield_max_datapoints())","title":""},{"docid":"a2f054d7a82a5d9f72f4e08f717575df","score":"0.55515677","text":"def list_messages(\n self,\n folder_name: Optional[str] = None,\n criterion: Optional[str] = None,\n contains: Optional[bool] = False, # pylint: disable=unused-argument\n count: Optional[int] = 100,\n save_dir: Optional[str] = None,\n items_only: Optional[bool] = False,\n ) -> list:\n # pylint: disable=no-member\n messages = []\n source_folder = self._get_folder_object(folder_name)\n if criterion:\n filter_dict = self._get_filter_key_value(criterion)\n items = source_folder.filter(**filter_dict)\n else:\n items = source_folder.all()\n if items_only:\n return items\n for item in items.order_by(\"-datetime_received\")[:count]:\n attachments = []\n if save_dir and len(item.attachments) > 0:\n # NOTE(cmin764): Default `overwrite` param is assumed here.\n attachments = self._save_attachments(item, save_dir)\n messages.append(self._get_email_details(item, attachments))\n return messages","title":""},{"docid":"18a31ab50daa71029d12fc92ec885c7c","score":"0.55415785","text":"def getNewMessages(self):\n\t\trv = self.__recv_messages\n\t\tself.__recv_messages = []\n\t\treturn rv","title":""},{"docid":"8c624364183209a8ddc3b75bd945ef2a","score":"0.5525904","text":"def get_messages(self):\n\n request_response = self.getv2Inbox()\n actual_responses = self.LastJson\n\n inbox = actual_responses[\"inbox\"]\n threads = inbox[\"threads\"]\n\n for thread in threads:\n thread_users = thread[\"users\"]\n usernames = list()\n for thread_user in thread_users:\n usernames.append(thread_user[\"username\"])\n usernames.append(thread_user[\"full_name\"])\n\n print(\"Group chat with: \" + \", \".join(usernames))\n\n sender = thread[\"inviter\"]\n print(\"From: \" + sender[\"username\"] + \" \" + sender[\"full_name\"])\n\n messages = thread[\"items\"]\n for message in messages:\n if \"text\" in message:\n print(message[\"text\"])\n\n elif \"reel_share\" in message:\n print(message[\"reel_share\"][\"text\"])\n\n elif \"items\" in thread:\n descriptions = list()\n for item in thread[\"items\"]:\n if \"action_log\" in item:\n descriptions.append(item[\"action_log\"])\n elif \"media_share\" in item:\n media_url = 'https://instagram.com/p/' + item['media_share']['code']\n descriptions.append(media_url)\n\n print(' \\t'.join(str(description) for description in descriptions))\n\n else:\n print(\"Unable to find: \" + json.dumps(thread, indent=4))","title":""},{"docid":"7f00984fc63b76a4084ad9ebeec209c4","score":"0.5517321","text":"def messages(self, request, pk=None):\n try:\n chat = self.get_object()\n except Chat.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n queryset = chat.messages.select_related('chat', 'user').prefetch_related('images', 'user__images')\n page = self.paginate_queryset(queryset)\n if page is not None and request.GET.get('page') is not None:\n return self.get_paginated_response(MessageSerializer(page, many=True).data)\n\n return Response(MessageSerializer(queryset, many=True).data)","title":""},{"docid":"fbe3e041950c43ed59a76f285e5d75d2","score":"0.551348","text":"def GetMessage(service, user_id, msg_id):\n\n\tfor n in range(0,7):\n\t\ttry:\n\t\t\tmessage = service.users().messages().get(userId=user_id, id=msg_id).execute()\n\t\t\t# print 'Message snippet: %s' % message['snippet']\n\t\t\treturn message\n\n\t\texcept (socket.error,errors.HttpError) as error:\n\t\t\t#print 'An error occurred: %s' % error\n\t\t\t#print 'Message ID:', msg_id\n\n\t\t\tif \"User Rate Limit Exceeded\" in str(error):\n\t\t\t\tprint \"User rate limit exceeded - Request delayed \" + str(2**n) + \" sec\"\n\t\t\t\ttime.sleep((2**n) + random.random())\n\t\n\t\t\tmessage = {\n\t\t\t\t'payload': {\n\t\t\t\t\t'headers': []\n\t\t\t\t}\n\t\t\t} \n\n\treturn message","title":""},{"docid":"9f8da04561229d7460303546adacdfc3","score":"0.55084485","text":"def limit_number_of_results(self):\n self.response.debug(f\"Limiting Number of Results\")\n self.response.info(f\"Filtering excess results above max result limit\")\n params = self.parameters\n try:\n n = params['max_results']\n self.message.results = self.message.results[:n]\n except:\n tb = traceback.format_exc()\n error_type, error, _ = sys.exc_info()\n self.response.error(tb, error_code = error_type.__name__)\n self.response.error(f\"Something went wrong when limiting results\")\n else:\n self.response.info(f\"Successfully limited the number of results\")\n\n return self.response","title":""},{"docid":"058b00b48f130f56b8c1fd7d259cd728","score":"0.5498757","text":"def fetch(self):\n logging.info(\"Fetching emails for %s\" % self.name)\n\n end = datetime.now() + timedelta(minutes=self._limit)\n p = Popen(self._command)\n\n while True:\n if p.poll() is not None:\n return\n\n if datetime.now() > end:\n os.killpg(p.pid, signal.SIGTERM)\n self._log_kill(p.pid)\n return\n\n sleep(1)","title":""},{"docid":"0a86028474cb5a3da70bbef34bab6e3c","score":"0.54944074","text":"def getAllChannelMessages(self):\n\n async def main():\n async for message in Channel.client.iter_messages(self.username):\n if type(message) == telethon.tl.types.Message:\n await self.parseMessage(message)\n\n with Channel.client:\n try:\n Channel.client.loop.run_until_complete(main())\n except telethon.errors.ServerError:\n logging.info(\"Server error: Passed\")\n pass\n except telethon.errors.FloodWaitError as e:\n logging.info(\"FloodWaitError: Sleep for \" + str(e.seconds))\n time.sleep(e.seconds)","title":""},{"docid":"ea65956834c4d48e046573c9537a1c85","score":"0.5489506","text":"def get_messages(self):\n\t\treturn","title":""},{"docid":"f50bd3bc2b7212d5aab01e0b203f28e1","score":"0.54876417","text":"def chat_messages():\n messages = reversed(Message.query(ancestor=thread_key())\n .order(-Message.creation_date).fetch(10))\n return \"\\n\".join(re.sub(r'\\r\\n|\\r|\\n', ' ', cgi.escape(m.text))\n for m in messages)","title":""},{"docid":"d04ef56c89c0e8cd1f1727e8f138a469","score":"0.5479722","text":"def ListMessagesMatchingQuery(service, user_id, query):\n\n\tfor n in range(0,10):\n\t\ttry:\n\t\t\tresponse = service.users().messages().list(userId=user_id,q=query).execute()\n\t\t\tmessages = []\n\t\t\tif 'messages' in response:\n\t\t\t\tmessages.extend(response['messages'])\n\n\t\t\twhile 'nextPageToken' in response:\n\t\t\t\tpage_token = response['nextPageToken']\n\t\t\t\tresponse = service.users().messages().list(userId=user_id, q=query, pageToken=page_token).execute()\n\t\t\t\t\n\t\t\t\tif 'messages' in response.keys():\n\t\t\t\t\tmessages.extend(response['messages'])\n\n\t\t\treturn messages\n\n\t\texcept errors.HttpError, error:\n\t\t\t#print 'An error occurred: %s' % error\n\n\t\t\tif \"User Rate Limit Exceeded\" in str(error):\n\t\t\t\tprint \"User rate limit exceeded - Request delayed \" + str(2**n) + \" sec\"\n\t\t\t\ttime.sleep((2**n) + random.random())\n\n\traise Exception, \"Failed to load message lists from Gmail (in ListMessagesMatchingQuery)\"","title":""},{"docid":"b19c23b793d71dd5d7036e5dddfcb067","score":"0.5478784","text":"def iter_messages(self):\n\t\treturn self.__messages.iter_messages()","title":""},{"docid":"80def62f8336fd3ef3fd044ed80a9d5d","score":"0.5477561","text":"def latest(self):\n\n if not self.messages: # pragma: nocover\n return None\n return self.messages[-1]","title":""},{"docid":"b40866f2732a2047c5a5fabf1acf454b","score":"0.5475289","text":"def get_latest_news(self, limit=10):\n news = []\n params = { 'limit': limit}\n items = self.__db_worker.execute(\n \"select id, title, url, published from news order by id desc \" \\\n \"limit :limit\", params\n )\n for item in items:\n news.append(item)\n return news","title":""},{"docid":"545d57ae3259c7d21f0b2c4a18d43bf4","score":"0.5464635","text":"def get_message_ids(imap):\n package = dict()\n\n mailboxes = get_mailboxes(imap)\n total = get_mail_count(imap, mailboxes)\n remaining = total\n for mailbox in mailboxes:\n package[mailbox] = list()\n\n imap.select(mailbox)\n uid_list = imap.uid('search', None, 'ALL')[1][0].split()\n\n for uid in uid_list:\n print('Remaining: %d ' % remaining, end='\\r')\n data = imap.uid('fetch', uid, '(BODY.PEEK[HEADER])')[1]\n try:\n message_id = email.message_from_bytes(data[0][1])['Message-ID']\n if message_id == None:\n remaining -= 1\n continue\n except TypeError:\n # unable to get message-id\n # to-do: implement logging here\n remaining -= 1\n continue\n\n data = (uid, message_id)\n package[mailbox].append(data)\n remaining -= 1\n print('Remaining: %d ' % remaining)\n \n return package","title":""},{"docid":"2c2688d19f22cbfaa7ce4cc094369530","score":"0.54632854","text":"def getEntries(maximum=None, offset=0):","title":""},{"docid":"e0b21f9785d0cdea8a9b693564c56b2e","score":"0.5461734","text":"def unread_messages(self):\n return sum(map(lambda l: l.unread_messages_seller(), self.requests.all()))","title":""},{"docid":"f4f2e2694a56648e22897ec5fb9d7036","score":"0.5458467","text":"def list_messages(self, filter_by=None):\n to_be_listed = self.message_store\n if filter_by:\n events = [msg.generate_events()\n for msg in to_be_listed if msg.to in filter_by]\n else:\n events = [msg.generate_events() for msg in to_be_listed]\n return {\n \"items\": events,\n \"paging\": {\n \"next\": \"http://i-am-a-fake-link-to-nothing\",\n \"last\": \"http://i-am-a-fake-link-to-nothing\",\n \"first\": \"http://i-am-a-fake-link-to-nothing=\",\n \"previous\": \"http://i-am-a-fake-link-to-nothing==\"\n }}","title":""},{"docid":"20cc62bf103c2fc2bec0fb8a434c00f6","score":"0.54464346","text":"def limit(self, limit):\n return self[:limit]","title":""},{"docid":"e3e8e55e4608c24c5c3b08f103767a19","score":"0.544258","text":"def list_messages(service, query=''):\n\n response = service.users().messages().list(userId='me', q=query).execute()\n messages = []\n if 'messages' in response:\n messages.extend(response['messages'])\n\n while 'nextPageToken' in response:\n page_token = response['nextPageToken']\n response = service.users().messages().list(userId='me', q=query,\n pageToken=page_token).execute()\n messages.extend(response['messages'])\n\n return messages","title":""},{"docid":"e5eb722cab3d593f12034563fb210396","score":"0.5439611","text":"def gather(self):\n\n self.messages = []\n\n self.msg(0, \"Gathering messages.\")\n\n if not self.pump:\n return []\n\n outbox_major = []\n\n try:\n outbox_major = self.me.outbox.major[:20]\n except:\n if self.debug:\n print(self.me)\n self.msg(0, \"An error occurred while gathering recent messages.\")\n return []\n \n for activity in outbox_major:\n pump_obj = activity.obj\n\n pump_obj_id = activity.id\n\n try:\n pump_obj_url = pump_obj.url\n except AttributeError:\n continue\n pass\n\n message = Message()\n\n message.source = \"Pump.io\"\n message.id = self.generate_id(pump_obj_id)\n\n # get the text into decent shape\n text = \"\"\n\n try:\n if not pump_obj.content:\n text = \"\"\n else:\n text = pump_obj.content\n pass\n except AttributeError:\n continue\n \n\n message.SetContent(text)\n\n message.link = pump_obj_url\n \n try:\n if pump_obj.deleted:\n continue\n except AttributeError:\n print(\"The arrtribute \\\"deleted\\\" does not exist . . . continuing anyway . . . \")\n pass\n\n # Determine if this message was directed to someone on Pump.io and thus\n # isn't intended for sharing outside the network.\n to_list = getattr(activity, \"to\", [])\n skip_this_message = True\n if len(to_list) > 0:\n # was \"Public\" among the \"To:\" recipients? Then we can go on; otherwise,\n # skip this message\n is_direct = False\n is_public = False\n for person in to_list:\n if isinstance(person, Person):\n is_direct = True\n elif isinstance(person, Collection):\n if person.id.find(\"public\") != -1:\n is_public = True\n skip_this_message = False\n break\n\n if person.id.find(\"followers\") != -1:\n skip_this_message = False\n\n pass\n pass \n\n if is_direct:\n message.direct = 1\n if is_public:\n message.public = 1\n pass\n\n pass\n\n\n cc_list = getattr(activity, \"cc\", [])\n if len(cc_list) > 0:\n for person in cc_list:\n if isinstance(person, Collection):\n if person.id.find(\"followers\") != -1:\n skip_this_message = False\n pass\n pass\n pass\n pass\n\n if skip_this_message:\n continue;\n \n if isinstance( pump_obj, Image):\n img_url = pump_obj.original.url\n\n message.link = img_url\n\n # construct a request to grab the image\n endpoint = \"{0}\".format('/'.join(img_url.split('/')[3:]))\n # remove \"_thumb\" from the image name to get the original image\n endpoint = endpoint.replace('_thumb','')\n local_img_name = endpoint.split('/')[-1]\n client = self.pump.setup_oauth_client(endpoint)\n params = {} \n headers = {\"Content-Type\": \"application/json\"}\n request = {\n \"params\": params,\n \"auth\": client,\n \"headers\": headers,\n }\n \n image_raw = self.pump._requester(requests.get, endpoint, raw=False, **request)\n \n fout = open(\"/tmp/{0}\".format(local_img_name), \"w\")\n fout.write(image_raw.content)\n fout.close()\n \n text = \"\"\n if not pump_obj.content:\n message.SetContent(\"\")\n else:\n message.SetContent(pump_obj.content)\n pass\n\n if message.content == '':\n message.content = 'An image...'\n pass\n message.attachments.append(\"/tmp/{0}\".format(local_img_name) )\n\n if not pump_obj.updated:\n continue\n t = datetime.datetime.strptime(str(pump_obj.updated), \"%Y-%m-%d %H:%M:%S+00:00\")\n message.date = calendar.timegm(t.timetuple())\n\n message.author = pump_obj.author.display_name\n message.author_url = pump_obj.author.url\n\n\n #if str(message.author) != str(self.me):\n if message.author != self.me.display_name:\n message.repost = True\n message.SetContent( \"RT from <a href=\\\"%s\\\">%s</a>: \" % (message.author_url,message.author) + message.content )\n pass\n\n self.append_message(message)\n\n pass\n\n self.messages = sorted(self.messages, key=lambda msg: msg.date, reverse=False)\n\n if self.debug:\n print(\"********************** Pump.io Handler **********************\\n\")\n print(\"Here are the messages I gathered from the pump.io server:\\n\")\n for message in self.messages:\n print(message.Printable())\n pass\n \n return self.messages","title":""},{"docid":"93658c1d133f29ca0a2cfc7595366848","score":"0.5437911","text":"def _fetch_and_parse_messages(self, mailing_list, from_date):\n\n from_date = datetime_to_utc(from_date)\n\n nmsgs, imsgs, tmsgs = (0, 0, 0)\n\n for mbox in mailing_list.mboxes:\n try:\n tmp_path = self._copy_mbox(mbox)\n\n for message in self.parse_mbox(tmp_path):\n tmsgs += 1\n\n if not self._validate_message(message):\n imsgs += 1\n continue\n\n # Ignore those messages sent before the given date\n dt = str_to_datetime(message[MBox.DATE_FIELD])\n\n if dt < from_date:\n logger.debug(\"Message %s sent before %s; skipped\",\n message['unixfrom'], str(from_date))\n tmsgs -= 1\n continue\n\n # Convert 'CaseInsensitiveDict' to dict\n message = self._casedict_to_dict(message)\n\n nmsgs += 1\n logger.debug(\"Message %s parsed\", message['unixfrom'])\n\n yield message\n except OSError as e:\n logger.warning(\"Ignoring %s mbox due to: %s\", mbox.filepath, str(e))\n except Exception as e:\n if os.path.exists(tmp_path):\n os.remove(tmp_path)\n raise e\n finally:\n if os.path.exists(tmp_path):\n os.remove(tmp_path)\n\n logger.info(\"Done. %s/%s messages fetched; %s ignored\",\n nmsgs, tmsgs, imsgs)","title":""},{"docid":"db57ddc2316a199749c94ab4234aa345","score":"0.54357165","text":"def test_orders_paging_max_limit(self):\n resp = self.orders_client.get_orders(limit=101, offset=0)\n self._check_list_of_orders(resp=resp, limit=100)","title":""},{"docid":"9629d05ccf7d82f186e030d4682d2ea9","score":"0.5430537","text":"def APIgetAllEmails(self, formatted_labels: str = None, date_first: datetime.datetime = None,\n date_last: datetime.datetime = None,\n filter: str = None, metadata_limit: int = None, user: str = None, output: bool = False):\n self.messages = []\n\n if not date_first:\n date_first = aa_globals.todayLocalUser() - datetime.timedelta(days=7)\n\n elif not isinstance(date_first, datetime.date):\n date_first = aa_helper_methods.build_tz_aware_datetime(date_first, to_utc=False).date()\n\n if not date_last:\n date_last = aa_globals.todayLocalUser()\n\n elif not isinstance(date_last, datetime.date):\n date_last = aa_helper_methods.build_tz_aware_datetime(date_last, to_utc=False).date()\n\n date_last += datetime.timedelta(days=1) # include the current day\n\n ms_token = self.ms_auth_client.get_or_refresh_token()\n graph_client = OAuth2Session(token=ms_token)\n\n endpoint = '{}/me/messages'.format(ms_settings.get('graph_url'))\n\n # Get emails\n query_params = {\n '$count': 'true',\n '$orderby': 'receivedDateTime DESC',\n '$filter': 'receivedDateTime ge {} AND receivedDateTime le {}'.format(date_first, date_last)\n }\n\n self.messages = self.graph_generator(graph_client=graph_client, endpoint=endpoint, params=query_params)\n\n if output:\n return self.parseAllEmailAPIResponses()","title":""},{"docid":"a6cc86167dd89b76079ee20684f2f687","score":"0.54304403","text":"def get_next_msg(self):\n\n if self.msg_list: # a message is already available\n return\n\n self.socket.settimeout(5)\n retry = 5\n grablen = 5\n while retry and (grablen == 5 or len(self.remain) < grablen):\n if grablen == 5 and len(self.remain) >= 5:\n grablen = struct.unpack('!H', self.remain[3:5])[0] + 5\n if grablen == len(self.remain):\n break\n\n try:\n tmp = self.socket.recv(grablen - len(self.remain))\n if not tmp:\n retry -= 1\n else:\n self.remain += tmp\n except:\n retry -= 1\n\n if self.remain < 5 or len(self.remain) != grablen:\n # Remote peer is not willing to respond\n return\n\n # Instantiate TLS packet (record header only, at this point)\n p = TLS(self.remain, tls_session=self.cur_session)\n self.cur_session = p.tls_session\n self.remain = \"\"\n self.msg_list += p.msg\n\n while p.payload:\n if isinstance(p.payload, Raw):\n self.remain += p.payload.load\n p = p.payload\n elif isinstance(p.payload, TLS):\n p = p.payload\n self.msg_list += p.msg","title":""}],"string":"[\n {\n \"docid\": \"3388dfd7de27679aac6ba6880a661420\",\n \"score\": \"0.79869056\",\n \"text\": \"def fetch_messages(self, max_messages=15):\\n # Connect to the POP3 mailbox\\n mailbox = poplib.POP3(self.host, self.port)\\n mailbox.user(self.user)\\n mailbox.pass_(self.passwd)\\n\\n # Look up messages, establish a window for fetching\\n nums = range(len(mailbox.list()[1]))\\n end_pos = len(nums)\\n start_pos = max(0, end_pos - max_messages)\\n \\n # Fetch and accumulate Messages\\n msgs = []\\n for i in nums[start_pos:end_pos]:\\n try:\\n msg_txt = \\\"\\\\n\\\".join(mailbox.retr(i+1)[1])\\n msg = email.message_from_string(msg_txt)\\n msgs.append(msg)\\n except KeyboardInterrupt:\\n raise\\n except:\\n pass\\n\\n # Log out of mailbox\\n mailbox.quit()\\n\\n return msgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5556203646d16ec9b0ca4b8f83a43b8b\",\n \"score\": \"0.72879046\",\n \"text\": \"def get_messages(delete=True, max_get=1000):\\n messages = []\\n if BUGMAIL_HOST:\\n conn = poplib.POP3_SSL(BUGMAIL_HOST)\\n conn.user(BUGMAIL_USER)\\n conn.pass_(BUGMAIL_PASS)\\n (num_messages, total_size) = conn.stat()\\n num_get = min(num_messages, max_get)\\n print 'Getting %d bugmails' % num_get\\n for msgid in range(1, num_get + 1):\\n msg_str = '\\\\n'.join(conn.retr(msgid)[1])\\n msg = Parser().parsestr(msg_str)\\n if is_bugmail(msg):\\n if is_interesting(msg):\\n yield msg\\n if delete:\\n conn.dele(msgid)\\n conn.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4082e7f07f8c07814c25afd206d58c66\",\n \"score\": \"0.72795755\",\n \"text\": \"def get_latest_messages(number_of_messages=50):\\n # JOIN see https://www.w3schools.com/sql/sql_join.asp\\n stuff = execute_sql(\\n MySQL_DATABASE_CHAT_APP,\\n \\\"\\\"\\\"\\n SELECT users.UserName, messages.MessageContent\\n FROM messages INNER JOIN users\\n ON users.UserId = messages.SenderId\\n ORDER BY messages.MessageId DESC\\n LIMIT %s\\n ;\\n \\\"\\\"\\\",\\n (int(number_of_messages),)\\n )\\n return stuff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f64efae1bca0e333f1242817d12e2a\",\n \"score\": \"0.69429094\",\n \"text\": \"async def peek_messages(self, max_messages=None, **kwargs):\\n # type: (Optional[int], Optional[Any]) -> List[QueueMessage]\\n if max_messages and not 1 <= max_messages <= 32:\\n raise ValueError(\\\"Number of messages to peek should be between 1 and 32\\\")\\n\\n timeout = kwargs.pop('timeout', None)\\n if self.key_encryption_key or self.key_resolver_function:\\n modify_user_agent_for_encryption(\\n self._config.user_agent_policy.user_agent,\\n self._sdk_moniker,\\n self.encryption_version,\\n kwargs)\\n\\n self._config.message_decode_policy.configure(\\n require_encryption=self.require_encryption,\\n key_encryption_key=self.key_encryption_key,\\n resolver=self.key_resolver_function\\n )\\n try:\\n messages = await self._client.messages.peek(\\n number_of_messages=max_messages, timeout=timeout, cls=self._config.message_decode_policy, **kwargs\\n )\\n wrapped_messages = []\\n for peeked in messages:\\n wrapped_messages.append(QueueMessage._from_generated(peeked)) # pylint: disable=protected-access\\n return wrapped_messages\\n except HttpResponseError as error:\\n process_storage_error(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1db03e80b9ff5dfef9fac411085f5e\",\n \"score\": \"0.6831436\",\n \"text\": \"def get_messages(self, limit=25, *, query=None, order_by=None, batch=None,\\n download_attachments=False):\\n\\n if self.root:\\n url = self.build_url(self._endpoints.get('root_messages'))\\n else:\\n url = self.build_url(self._endpoints.get('folder_messages').format(\\n id=self.folder_id))\\n\\n if limit is None or limit > self.protocol.max_top_value:\\n batch = self.protocol.max_top_value\\n\\n params = {'$top': batch if batch else limit}\\n\\n if order_by:\\n params['$orderby'] = order_by\\n\\n if query:\\n if isinstance(query, str):\\n params['$filter'] = query\\n else:\\n params.update(query.as_params())\\n\\n response = self.con.get(url, params=params)\\n if not response:\\n return iter(())\\n\\n data = response.json()\\n\\n # Everything received from cloud must be passed as self._cloud_data_key\\n messages = (self.message_constructor(\\n parent=self,\\n download_attachments=download_attachments,\\n **{self._cloud_data_key: message})\\n for message in data.get('value', []))\\n\\n next_link = data.get(NEXT_LINK_KEYWORD, None)\\n if batch and next_link:\\n return Pagination(parent=self, data=messages,\\n constructor=self.message_constructor,\\n next_link=next_link, limit=limit,\\n download_attachments=download_attachments)\\n else:\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aca343a48e122db38d54466c9823a05\",\n \"score\": \"0.67001694\",\n \"text\": \"def get_group_message_chunk(self, group_id, max_messages=100, before_id=None, since_id=None):\\n\\t\\turl = API_BASE + \\\"groups/{}/messages\\\".format(group_id)\\n\\n\\t\\tparams = {\\\"token\\\" : self.auth_token, \\\"limit\\\" : 100}\\n\\t\\tif before_id:\\n\\t\\t\\tparams[\\\"before_id\\\"] = before_id\\n\\t\\tif since_id:\\n\\t\\t\\tparams[\\\"since_id\\\"] = since_id\\n\\n\\t\\tr = requests.get(url, params=params)\\n\\t\\tif r and r.status_code == 200:\\n\\t\\t\\tans = r.json()\\n\\t\\t\\tif ans and ans[\\\"response\\\"]:\\n\\t\\t\\t\\tans = ans[\\\"response\\\"][\\\"messages\\\"]\\n\\t\\t\\t\\t# print ans\\n\\t\\t\\t\\treturn ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcaa7acb01cf87838de74202087e1ccd\",\n \"score\": \"0.6662036\",\n \"text\": \"def get_all_group_messages(self, group_id, before_id=None, max_messages=None):\\n\\t\\tall_messages = list()\\n\\t\\tfor chunk in self.group_message_generator(group_id, before_id):\\n\\t\\t\\tall_messages += chunk\\n\\t\\t\\tif max_messages and len(all_messages) > max_messages:\\n\\t\\t\\t\\tbreak;\\n\\t\\treturn all_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"539826c068ed0e9c510ebe6a2a6712eb\",\n \"score\": \"0.66313386\",\n \"text\": \"def get_limited_number_of_messages_from_qs(qs, limit): # pragma: no cover\\n count = 0\\n results = []\\n for item in qs:\\n if count == limit:\\n break\\n if isinstance(item, Message):\\n count += 1\\n results.append(item)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"becd65eb9334d86ff6dd43d5773a6b37\",\n \"score\": \"0.6609195\",\n \"text\": \"def get_messages(self, **params):\\n all_args = ['limit', 'offset']\\n params = Resource.sanitize_params(params, all_args)\\n\\n return [Message(self.parent, obj) for obj in self._request_uri(\\n 'messages', params=params\\n )]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe30d5b234fe584560efefb3bd517f0c\",\n \"score\": \"0.65774363\",\n \"text\": \"def get_messages(self, limit=100):\\n if self.verbose:\\n self.logger.debug('get_messages called')\\n # get messages from local buffer\\n msg_list = []\\n for j in range(limit):\\n msg_obj = self.msg_buffer.get()\\n if msg_obj is None:\\n break\\n msg_list.append(msg_obj)\\n if self.verbose:\\n self.logger.debug('got {n} messages'.format(n=len(msg_list)))\\n return msg_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62feec4af8c60572b8aac6768d6e2352\",\n \"score\": \"0.6525903\",\n \"text\": \"def retrieve_latest_email(self, timeout=36):\\n count = 0\\n while count < timeout:\\n id_list = self.get_all_email_ids()\\n if id_list:\\n return self.get_message(id_list[-1])\\n time.sleep(5)\\n count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb8924738d7cba34930ecfd6cbfb488\",\n \"score\": \"0.6520283\",\n \"text\": \"def get_messages():\\n kiasufoodies = client.get_entity(\\\"kiasufoodies\\\")\\n return client.iter_messages(kiasufoodies, limit=30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360c194c20257da66faed5669c4414b1\",\n \"score\": \"0.6448578\",\n \"text\": \"def raw_longest_messages(self, num=None) -> list:\\n assert num is None or isinstance(num, int), (\\n \\\"num must be None or an integer representing the number of messages desired\\\"\\n )\\n num = min(num, len(self)) if num is not None else len(self)\\n\\n order = Counter({index: len(self._convo[index][1]) for index in range(len(self))})\\n return order.most_common(num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56afed0fbaac4efc0c36ded598b4e57b\",\n \"score\": \"0.64294106\",\n \"text\": \"def getRecentChannelMessages(self):\\n\\n async def main():\\n async for message in Channel.client.iter_messages(self.username, offset_date=calcDateOffset(\\n Channel.config.get(\\\"scrape_offset\\\")), reverse=True):\\n if type(message) == telethon.tl.types.Message:\\n await self.parseMessage(message)\\n\\n with Channel.client:\\n try:\\n Channel.client.loop.run_until_complete(main())\\n except telethon.errors.ServerError:\\n logging.info(\\\"Server error: Passed\\\")\\n pass\\n except telethon.errors.FloodWaitError as e:\\n logging.info(\\\"FloodWaitError: Sleep for \\\" + str(e.seconds))\\n time.sleep(e.seconds)\\n\\n self.messages = list(reversed(self.messages))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3cf4a9948b45401ba2bc3725fd2021\",\n \"score\": \"0.6400792\",\n \"text\": \"def test_request_messages_with_smaller_limit_than_messages(self):\\n\\n lim = 2\\n timestamp = 10 ** 11\\n\\n for i in range(100):\\n create_message(\\n text='Message' + str(i),\\n timestamp=timestamp + i,\\n username='vitsalis',\\n channel=self.channel\\n )\\n\\n messages = json.loads(self.client.get(\\n reverse('chat:message', args=('channel', self.channel.name,)),\\n {'lim': lim}\\n ).content)\\n\\n self.assertEqual(len(messages), 2)\\n\\n self.assertEqual(messages[0]['text'], 'Message99')\\n self.assertEqual(messages[1]['text'], 'Message98')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"217b7d4abd029b2dde50043a8280e292\",\n \"score\": \"0.6377988\",\n \"text\": \"def GetMessages(self,organisation,flow):\\n # prepare the target URL\\n url=self.MakeURL(organisation,flow,\\\"messages\\\")\\n\\n # Pull messages until we've reached the requested time offset\\n reachedDesiredDate=False\\n last_id=-1\\n new_messages={}\\n thread_re=re.compile(r\\\"influx:(\\\\d+)\\\")\\n while(not reachedDesiredDate):\\n # Request a set of messages\\n params={'limit':100}\\n if last_id != -1: params['until_id']=last_id\\n objects=self.Request(url,**params)\\n if len(objects) == 0:\\n # No more messages in the flow\\n break\\n if type(objects) is not list:\\n reachedDesiredDate=True\\n break\\n\\n # Reverse iterate to check if we've got all that we need to reach\\n # our requested time limit\\n for obj in reversed(objects):\\n created_at=datetime.strptime(obj[\\\"created_at\\\"],TIME_FORMAT)\\n last_id=int(obj[\\\"id\\\"])\\n if created_at < self.stop_time:\\n reachedDesiredDate=True\\n break\\n # Valid new message so keep it\\n new_messages[obj[\\\"id\\\"]]=(obj)\\n self.GetThreadHeads([ thread_re.match(tag).group(1) for tag in obj[\\\"tags\\\"] if thread_re.match(tag) ],\\n organisation,flow)\\n \\n self.last_id[flow]=last_id\\n return new_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f1ec69c636a7f328c8345ebf15548e\",\n \"score\": \"0.62303907\",\n \"text\": \"def get_messages(self, **params):\\n all_args = ['include_thread_size', 'include_body', \\n 'body_type', 'include_headers', 'include_flags', 'flag_seen',\\n 'limit', 'offset'\\n ]\\n params = Resource.sanitize_params(params, all_args)\\n\\n return [Message(self.parent.parent, obj) for obj in self._request_uri(\\n 'messages', params=params\\n )]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20a73556add37b80b52bd60d1a75d45\",\n \"score\": \"0.6174438\",\n \"text\": \"def test_request_messages_with_bigger_limit_than_messages(self):\\n lim = 100\\n timestamp = 10 ** 11\\n\\n create_message(\\n text='Message1',\\n timestamp=timestamp,\\n username='vitsalis',\\n channel=self.channel\\n )\\n\\n create_message(\\n text='Message2',\\n timestamp=timestamp + 60 * 60,\\n username='pkakelas',\\n channel=self.channel\\n )\\n\\n messages = json.loads(self.client.get(\\n reverse('chat:message', args=('channel', self.channel.name,)),\\n {'lim': lim}\\n ).content)\\n\\n self.assertEqual(len(messages), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85f8209d3145ca677a9640732d786a1\",\n \"score\": \"0.6146502\",\n \"text\": \"def get_next(self) -> 'MessageGroup':\\n # prevent more fetching\\n if self._limit != -1 and self._total_count >= self._limit:\\n raise IndexError\\n\\n popped = self.groups.popleft()\\n self._total_count += len(popped.msgs)\\n return popped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3cb4a6eee76f058d12288f522a10d9\",\n \"score\": \"0.61252254\",\n \"text\": \"def get_messages():\\n uid = get_jwt_identity()\\n page = request.args.get(\\\"page\\\", default=1, type=int)\\n # autoMarkAsRead = request.args.get('autoMarkAsRead', default=True, type=bool)\\n\\n msg = misc.get_messages_inbox(page, uid=uid)\\n\\n return jsonify(messages=[message_fields_for_api(m) for m in msg])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae78676cf8cc8434d0c05aafb1fb32ae\",\n \"score\": \"0.6120805\",\n \"text\": \"def fetchmany(self, size=None):\\n self.messages=[] \\n if size is None:\\n size = self.arraysize\\n return self._fetch(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4398374ff17ed1fadfe02640dc0f2b7\",\n \"score\": \"0.61146027\",\n \"text\": \"async def get_messages(\\n db: AsyncIOMotorDatabase\\n ):\\n\\n # collection.objects returns a lazy QuerySet\\n messages = await db.messages.find().sort(\\n [('time', 1)]\\n ).to_list(length=None)\\n\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dba1527c2c600fe37ca1fe6ac83f7e9\",\n \"score\": \"0.6109274\",\n \"text\": \"def receive_messages(self, **kwargs):\\n # type: (Optional[Any]) -> AsyncItemPaged[QueueMessage]\\n messages_per_page = kwargs.pop('messages_per_page', None)\\n visibility_timeout = kwargs.pop('visibility_timeout', None)\\n timeout = kwargs.pop('timeout', None)\\n max_messages = kwargs.pop('max_messages', None)\\n if self.key_encryption_key or self.key_resolver_function:\\n modify_user_agent_for_encryption(\\n self._config.user_agent_policy.user_agent,\\n self._sdk_moniker,\\n self.encryption_version,\\n kwargs)\\n\\n self._config.message_decode_policy.configure(\\n require_encryption=self.require_encryption,\\n key_encryption_key=self.key_encryption_key,\\n resolver=self.key_resolver_function\\n )\\n try:\\n command = functools.partial(\\n self._client.messages.dequeue,\\n visibilitytimeout=visibility_timeout,\\n timeout=timeout,\\n cls=self._config.message_decode_policy,\\n **kwargs\\n )\\n if max_messages is not None and messages_per_page is not None:\\n if max_messages < messages_per_page:\\n raise ValueError(\\\"max_messages must be greater or equal to messages_per_page\\\")\\n return AsyncItemPaged(command, results_per_page=messages_per_page,\\n page_iterator_class=MessagesPaged, max_messages=max_messages)\\n except HttpResponseError as error:\\n process_storage_error(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5cbd231d1fbcc605342d4a2b0e00ab\",\n \"score\": \"0.6077979\",\n \"text\": \"def produce_entries(self):\\n msgs = self.client.fetch_messages(max_messages=self.max_messages)\\n filtered_msgs = self.filter_messages(msgs)\\n return self.entries_from_messages(filtered_msgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72f1ee6d024056f8b6e3fc99d20e194\",\n \"score\": \"0.60757256\",\n \"text\": \"def get_messages(target):\\n\\n # Get the first page\\n data = get_page(1, 'unknown')\\n for message in data['raw_messages']:\\n if message['msg_id'] in blacklist:\\n continue\\n if target in json.dumps(message):\\n yield message\\n\\n more = functools.partial(get_page, pages=data['pages'])\\n\\n # Get all subsequent pages (if there are any...)\\n for page in range(1, data['pages']):\\n data = more(page + 1)\\n\\n for message in data['raw_messages']:\\n if message['msg_id'] in blacklist:\\n continue\\n if target in json.dumps(message):\\n yield message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eb12a3de7a3669b4d40cdb361f2862f\",\n \"score\": \"0.6047707\",\n \"text\": \"def fetchall(self):\\n self.messages=[] \\n return self._fetch()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ccc3864fbc4035d6f3beccf2acf509c\",\n \"score\": \"0.604666\",\n \"text\": \"def get_messages(self, **params):\\n all_args = ['subject', 'email', 'to',\\n 'sender', 'from_', 'cc', 'bcc', 'folder', 'date_before', 'date_after',\\n 'indexed_before', 'indexed_after', 'include_thread_size', 'include_body', 'source', \\n 'file_name', 'file_size_min', 'file_size_max', 'include_source',\\n 'include_headers', 'include_flags', 'body_type', 'sort_order',\\n 'limit', 'offset'\\n ]\\n\\n params = Resource.sanitize_params(params, all_args)\\n\\n # workaround to send param \\\"from\\\" even though it's a reserved keyword\\n # in python\\n if 'sender' in params:\\n params['from'] = params['sender']\\n del params['sender']\\n\\n if 'from_' in params:\\n params['from'] = params['from_']\\n del params['from_']\\n\\n return [Message(self, obj) for obj in self._request_uri(\\n 'messages', params=params\\n )]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fca5ea2bbd243867bc709a1d6b9400\",\n \"score\": \"0.60074055\",\n \"text\": \"def gmail_messages():\\n return __gmail_v1__().messages()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1781492a9df26f4095de7c0fb2deece\",\n \"score\": \"0.5974734\",\n \"text\": \"def fetchUnreadEmails(profile, since=None, markRead=False, limit=None):\\n conn = imaplib.IMAP4_SSL('imap.gmail.com')\\n conn.debug = 0\\n conn.login(profile['gmail_address'], profile['gmail_password'])\\n conn.select(readonly=(not markRead))\\n\\n msgs = []\\n (retcode, messages) = conn.search(None, '(UNSEEN)')\\n\\n if retcode == 'OK' and messages != ['']:\\n numUnread = len(messages[0].split(' '))\\n if limit and numUnread > limit:\\n return numUnread\\n\\n for num in messages[0].split(' '):\\n # parse email RFC822 format\\n ret, data = conn.fetch(num, '(RFC822)')\\n msg = email.message_from_string(data[0][1])\\n\\n if not since or getDate(msg) > since:\\n msgs.append(msg)\\n conn.close()\\n conn.logout()\\n\\n return msgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22429401069bcf52a077a2abc643d55\",\n \"score\": \"0.5956753\",\n \"text\": \"def receive_messages(self, WaitTimeSeconds=\\\"0\\\", MaxNumberOfMessages=\\\"10\\\", **kwargs):\\n wait_seconds = int(WaitTimeSeconds)\\n max_messages = int(MaxNumberOfMessages)\\n\\n ready_messages = []\\n push_back_messages = []\\n\\n threshold = datetime.datetime.utcnow() + datetime.timedelta(\\n seconds=wait_seconds\\n )\\n for message in self.messages:\\n if message.execute_at > threshold or len(ready_messages) >= max_messages:\\n push_back_messages.append(message)\\n else:\\n ready_messages.append(message)\\n self.messages[:] = push_back_messages\\n return ready_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ca633cd19d386ebfe49a77fef6904f\",\n \"score\": \"0.59555346\",\n \"text\": \"async def _get_messages(self):\\n client = build_client()\\n queue = client.get_queue_by_name(QueueName=\\\"TestQueue\\\")\\n\\n all_messages=[]\\n i=1\\n\\n while True:\\n messages = queue.receive_messages(MaxNumberOfMessages=10)\\n for msg in messages:\\n all_messages.append(msg.body)\\n msg.delete()\\n if len(messages) == 0:\\n break\\n print(all_messages)\\n return all_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0044b74aafd921beb96e6ef9491c0a5\",\n \"score\": \"0.5918131\",\n \"text\": \"def _get_maximum_entries(self):\\n return self.__maximum_entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0044b74aafd921beb96e6ef9491c0a5\",\n \"score\": \"0.5918131\",\n \"text\": \"def _get_maximum_entries(self):\\n return self.__maximum_entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0044b74aafd921beb96e6ef9491c0a5\",\n \"score\": \"0.5918131\",\n \"text\": \"def _get_maximum_entries(self):\\n return self.__maximum_entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0044b74aafd921beb96e6ef9491c0a5\",\n \"score\": \"0.5918131\",\n \"text\": \"def _get_maximum_entries(self):\\n return self.__maximum_entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f993b467cd4e964f870d5b1fa8dc41\",\n \"score\": \"0.59144247\",\n \"text\": \"def get_messages(access_token, user_email, last_processed_date_str_iso8601=None):\\n get_messages_url = outlook_api_endpoint.format('/me/mailfolders/inbox/messages')\\n\\n # '$filter': 'ReceivedDateTime gt ' + timestamp + ' and ' + 'from eq \\\\'venmo@venmo.co\\\\'',\\n # The query parameter '$filter' is not supported with '$search'.\\\" therefore need to manually filter all email from venmo\\n\\n select_expr = 'receivedDateTime,subject,body,from'\\n filter_expr = '(From/EmailAddress/Address eq \\\\'venmo@venmo.com\\\\')'\\n if last_processed_date_str_iso8601 is None:\\n last_processed_date_str_iso8601 = '1970-01-01T00:00:00Z'\\n # Note the insertion order of the new expression. Fields in 'orderby' must come first in 'filter' and must be in the same order\\n # For details: https://stackoverflow.com/a/41026120/1387710\\n\\n # filter_expr = '(ReceivedDateTime gt {}) and '.format(last_processed_date_str_iso8601) + filter_expr\\n filter_expr = '(ReceivedDateTime gt {})'.format(last_processed_date_str_iso8601) # TODO: delete this line\\n order_by_expr = 'receivedDateTime DESC'\\n\\n query_parameters = {'$filter': filter_expr,\\n '$select': select_expr,\\n '$orderby': order_by_expr\\n }\\n\\n r = make_api_call('GET', get_messages_url, access_token, user_email, parameters=query_parameters)\\n\\n if r.status_code == requests.codes.unauthorized:\\n raise yeti_exceptions.YetiAuthInvalidTokenException(\\\"Invalid authorization when trying to get messages. Response: {}\\\".format(r))\\n\\n try:\\n response = r.json()\\n except Exception as e:\\n raise yeti_exceptions.OutlookApiErrorException(\\\"Failed to transform response text to JSON: {}, raw response: {}\\\".format(e, r))\\n\\n if r.status_code != requests.codes.ok or ('error' in response and response['error']):\\n if 'code' in response['error'] and response['error']['code'] == 'InvalidAuthenticationToken':\\n raise yeti_exceptions.YetiAuthTokenExpiredException()\\n else:\\n raise yeti_exceptions.OutlookApiErrorException(\\\"Failed to get email messages. Response: {}\\\".format(response))\\n if 'value' not in response:\\n raise yeti_exceptions.OutlookApiErrorException(\\\"Failed to get email messages. 'value' is empty. Response: {}\\\".format(response))\\n new_emails = response['value']\\n return new_emails\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409e9e55028e0c8bd5fa41504c115494\",\n \"score\": \"0.59016675\",\n \"text\": \"def _fetch(self) -> typing.List[MessageContainer]:\\n messages = []\\n # select mailbox\\n logger.debug(self, 'Fetch messages from folder {}'.format(\\n self.folder,\\n ))\\n rv, data = self._connection.select(self.folder)\\n logger.debug(self, 'Response status {}'.format(\\n rv,\\n ))\\n if rv == 'OK':\\n # get mails\\n # TODO - G.M - 2017-11-15 Which files to select as new file ?\\n # Unseen file or All file from a directory (old one should be\\n # moved/ deleted from mailbox during this process) ?\\n logger.debug(self, 'Fetch unseen messages')\\n\\n rv, data = self._connection.search(None, \\\"(UNSEEN)\\\")\\n logger.debug(self, 'Response status {}'.format(\\n rv,\\n ))\\n if rv == 'OK':\\n # get mail content\\n logger.debug(self, 'Found {} unseen mails'.format(\\n len(data[0].split()),\\n ))\\n for uid in data[0].split():\\n # INFO - G.M - 2017-12-08 - Fetch BODY.PEEK[]\\n # Retrieve all mail(body and header) but don't set mail\\n # as seen because of PEEK\\n # see rfc3501\\n logger.debug(self, 'Fetch mail \\\"{}\\\"'.format(\\n uid,\\n ))\\n rv, data = self._connection.fetch(uid, 'BODY.PEEK[]')\\n logger.debug(self, 'Response status {}'.format(\\n rv,\\n ))\\n if rv == 'OK':\\n msg = message_from_bytes(data[0][1])\\n msg_container = MessageContainer(msg, uid)\\n messages.append(msg_container)\\n self._set_flag(uid, IMAP_SEEN_FLAG)\\n else:\\n log = 'IMAP : Unable to get mail : {}'\\n logger.error(self, log.format(str(rv)))\\n else:\\n log = 'IMAP : Unable to get unseen mail : {}'\\n logger.error(self, log.format(str(rv)))\\n else:\\n log = 'IMAP : Unable to open mailbox : {}'\\n logger.error(self, log.format(str(rv)))\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad73e7e76024599648bb70d12b8875da\",\n \"score\": \"0.5898244\",\n \"text\": \"def getMessages():\\n\\n def emailLogin():\\n \\\"\\\"\\\"\\n Log into gmail using IMAP, return imaplib object\\n \\\"\\\"\\\"\\n\\n # Login information\\n USER = loginValues['user']\\n PW = loginValues['pw']\\n SERVER = loginValues['server']\\n MAIN_FOLDER = 'inbox'\\n\\n # Login and return mail object\\n mail = imaplib.IMAP4_SSL(SERVER)\\n mail.login(USER, PW)\\n mail.list()\\n mail.select(MAIN_FOLDER)\\n return mail\\n\\n def getLastMsgCore(y, mail):\\n \\\"\\\"\\\"\\n Pulls the last unread email and returns a processed core tokenized message\\n \\\"\\\"\\\"\\n # Get number of unread emails\\n latest_email_uid = y[0].split()[0]\\n result, data = mail.uid('fetch', latest_email_uid, \\\"(RFC822)\\\")\\n raw_email = data[0][1]\\n\\n # Generate email message object from raw message\\n email_message = email.message_from_string(raw_email)\\n\\n # Convert to text block\\n block = get_text_block(email_message)\\n\\n # Get rid of header and footer\\n block = block.split('StreetAccount')[1:3]\\n block = \\\"\\\".join(block)\\n\\n # Get rid of potential \\\"Reference Link in footer\\\"\\n block = block.split('Reference Link')[0]\\n\\n # Get rid of =, /r, /n\\n block = block.translate(None, '=\\\\r\\\\\\\\')\\n block = block.translate(None, '\\\\n')\\n\\n # Special case--wierd norweigan char\\n block = block.replace('\\\\xf8', 'o')\\n block = block.replace('\\\\xe5', 'a')\\n block = block.replace('\\\\xe9', 'e')\\n block = block.replace('\\\\xeb', 'e')\\n block = block.replace('\\\\xfc', 'u')\\n block = block.replace('\\\\xe4', 'a')\\n block = block.replace('\\\\xf6', 'o')\\n block = block.replace('\\\\xd8', 'O')\\n block = block.replace('\\\\xdf', 'ss')\\n block = block.replace('\\\\xc5', 'A')\\n block = block.replace('\\\\xd6', 'O')\\n\\n #letters w/ acsents\\n block = block.replace('\\\\xe1', 'a')\\n block = block.replace('\\\\xe8', 'e')\\n block = block.replace('\\\\xe7', 'c')\\n block = block.replace('\\\\xea', 'e')\\n\\n #Remove ' from names\\n block = block.replace('\\\\x92','')\\n\\n \\n\\n # Replace EUR\\n block = block.replace('\\\\x80', 'EUR')\\n\\n # Tokenize using NLTK\\n tokenized_block = nltk.word_tokenize(block)\\n\\n # Isolate message from email and return\\n index = tokenized_block.index('ET')\\n msg = tokenized_block[index-1:]\\n return msg\\n\\n def getDate(msg):\\n \\\"\\\"\\\"\\n Process date from message\\n \\\"\\\"\\\"\\n # Sometimes (e.g. in case of revision), chars stuck in date, remove\\n sub = re.sub('[^a-zA-Z_]+', '', msg[0])\\n\\n # Get Date\\n if not sub: dt = msg[2] + ' ' + msg[0]\\n else: dt = msg[2] + ' ' + msg[0].replace(sub, '')\\n\\n # Special case-- remove '.'\\n dt = dt.replace('.', '')\\n return dt\\n\\n def getFirm(msg, typ='single'):\\n \\\"\\\"\\\"\\n Cross reference with firm database and return firm name, if nothing found add firm to database\\n \\\"\\\"\\\"\\n\\n # If in multiple event message, find firm in beginning\\n if typ == 'multiple':\\n multiple_msg_tokens = ['upgrades', 'downgrades', 'initiates', 'resumes', 'reinstates', 'assumes', 'raises', 'lowers', 'initiates', 'initiates/resumes']\\n ix = 10000\\n\\n # Look for token, assign index\\n for token in multiple_msg_tokens:\\n try:\\n if (msg.index(token) < ix): ix = msg.index(token)\\n except: pass\\n\\n st = ix\\n while (st > 0):\\n st -= 1\\n if msg[st] == ';' or msg[st] == ':':\\n if (ix-st == 1): continue\\n else: break\\n if msg[st] == ']': break\\n\\n if ix != 0: name = ' '.join(msg[st+1:ix])\\n else: name = ''\\n\\n # If single message / in \\\"notable\\\"\\n else:\\n # Look for 'at'\\n try:\\n ix = msg.index('at')\\n\\n # If no 'at', look after msg token\\n except:\\n single_msg_tokens = ['upgraded', 'downgraded', 'initiated', 'resumed', 'reinstated', 'reinitiated', 'assumed', 're-initiated', 're-instated']\\n for token in single_msg_tokens:\\n try:\\n ix = msg.index(token) + 4\\n except: pass\\n\\n # Look for '(' or '--' or '*' or ';', whichever comes first after 'at'\\n try: ix1 = msg[ix+1:].index('(')\\n except: ix1 = 10000\\n try: ix2 = msg[ix+1:].index('--')\\n except: ix2 = 10000\\n try: ix3 = msg[ix+1:].index('*')\\n except: ix3 = 10000\\n try: ix4 = msg[ix+1:].index(';')\\n except: ix4 = 10000\\n\\n end = min(ix1, ix2, ix3, ix4)\\n sub = msg[ix+1:ix+1+end]\\n\\n # Get rid of \\\"we're told\\\"\\n try:\\n sub = sub[:sub.index(\\\"'re\\\")-2]\\n except:\\n pass\\n\\n # Get firm name\\n name = ' '.join(sub)\\n\\n return name\\n\\n def getAnalyst(msg):\\n \\\"\\\"\\\"\\n Extract analyst from message and return (assumed that message ends right after analyst or has another bullet)\\n If nothing found, return empty string\\n \\\"\\\"\\\"\\n # If \\\"Analyst\\\" is found in message\\n if \\\"Analyst\\\" in msg:\\n\\n # Get index of occurence\\n ix = msg.index('Analyst')\\n\\n # Get index of beginning of analyst name (is there \\\"is\\\" in between?)\\n if msg[ix+1] != 'is': startix = ix+1\\n else: startix = ix+2\\n\\n # Flatten rest of message\\n msg_substring = ' '.join(msg[startix:])\\n\\n # If another bullet, set analyst name equal to rest of text before bullet\\n if '*' in msg_substring: return msg_substring[:msg_substring.index('*')]\\n\\n # If no more bullets, assume end of message and set analyst to rest of message\\n else: return msg_substring\\n\\n # If \\\"Analyst\\\" not found, return empty string\\n else:\\n return ''\\n\\n def get_text_block(email_message_instance):\\n \\\"\\\"\\\"\\n Process raw email string, returns decoded message string\\n \\\"\\\"\\\"\\n maintype = email_message_instance.get_content_maintype()\\n if maintype == 'multipart':\\n for part in email_message_instance.get_payload():\\n if part.get_content_maintype() == 'text':\\n return part.get_payload(decode=True)\\n elif maintype == 'text':\\n return email_message_instance.get_payload(decode=True)\\n\\n def getCurrency(PT, msg, PTix):\\n \\\"\\\"\\\"\\n Process currecy identifiers for PT subsection of msg, returns currency string\\n \\\"\\\"\\\"\\n # If currency is not directly attached to PT field (e.g. ['SEK','25.7'] vs. ['SEK25.7']\\n if PT == msg[PTix]:\\n\\n # Set currency equal to field immediately prior to PT field\\n curr = msg[PTix-1]\\n\\n # If prior field is '$'\\n if curr == '$':\\n\\n # If 'C' precedes $, return 'CAD'\\n if msg[PTix-2] == 'C': return 'CAD'\\n\\n # If 'A' precedes $, return 'AUD'\\n if msg[PTix-2] == 'A': return 'AUD'\\n\\n # If 'HK' preceds $, return 'HKD'\\n if msg[PTix-2] == 'HK': return 'HKD'\\n\\n # If nothing precedes $, return 'USD'\\n else: return 'USD'\\n\\n # If prior field is not '$', return string (in these cases the field is the currency string)\\n else: return curr\\n\\n # If currency is directly attached to PT field (e.g. 'SEK25.7')\\n else:\\n\\n # Seperate non digits from PT field\\n curr = ''.join(i for i in msg[PTix] if not i.isdigit())\\n\\n # Get rid of potential \\\".\\\" and \\\",\\\" left in string (e.g. '.SEK')\\n curr = curr.replace('.', '')\\n curr = curr.replace(',', '')\\n\\n # If curr = 'R', return 'ZAR'\\n if curr == 'R': return 'ZAR'\\n\\n # If 'p' or '\\\\xa3' is identifier, return 'GBP' (e.g. '257p')\\n if ('p' in curr) or ('\\\\xa3' in curr): return 'GBP'\\n\\n # If '\\\\x80' (Euro symbol) is identifier, return 'EUR' (e.g. '\\\\x80270')\\n if '\\\\x80' in curr: return 'EUR'\\n\\n # If '\\\\xa5' (Yen symbol) is identifier, return 'JPY'\\n if '\\\\xa5' in curr: return 'JPY'\\n\\n # If not 'GBP' or 'EUR', return extracted string (e.g. 'NOK', 'SEK', 'TWD')\\n return curr\\n\\n def getPT(msg, typ='single'):\\n \\\"\\\"\\\"\\n Get price target from message and return\\n Behaves differently given whether single or multiple messages\\n\\n -- If 'single' will look for target after occurrence of 'Target'\\n -- If 'multiple' will look for target after occurrence of ')'\\n \\\"\\\"\\\"\\n # Look for occurrence of 'target' if single message\\n if typ == 'single':\\n if \\\"Target\\\" in msg: ix = msg.index('Target')\\n elif 'target' in msg: ix = msg.index('target')\\n else: return '',''\\n\\n # Look for occurrence of ')' if not single message\\n else:\\n if ')' in msg: ix = msg.index(')')\\n else: return '',''\\n\\n # If valuation range, return empty strings\\n if 'range' in msg: return '',''\\n\\n # Loop through message after occurrence of \\\"target\\\"/\\\"Target\\\" until PT found\\n for i, x in enumerate(msg[ix:]):\\n\\n # If a float is found\\n if len(re.findall('\\\\d+.\\\\d+', x)) > 0:\\n\\n # Strip float (PT) from field\\n PT = re.findall('\\\\d+.\\\\d+', x)[0]\\n\\n # Get currency\\n curr = getCurrency(PT, msg, ix+i)\\n\\n # Convert string PT to float\\n PT = float(PT.replace(',', ''))\\n\\n # Stop looking and return\\n return PT, curr\\n\\n # If an int is found\\n elif len(re.findall('\\\\d+', x)) > 0:\\n\\n # Strip int (PT) from field\\n PT = re.findall('\\\\d+', x)[0]\\n\\n # Get currency\\n curr = getCurrency(PT, msg, ix+i)\\n\\n # Convert string PT to float\\n PT = float(PT.replace(',', ''))\\n\\n # Stop looking and return\\n return PT, curr\\n\\n # If nothing found (misc. format), return empty strings\\n return '', ''\\n\\n def getMsgType(msg):\\n \\\"\\\"\\\"\\n Returns three values:\\n --- boolean: whether message type was recognized\\n --- string: message type\\n --- string: rating\\n \\\"\\\"\\\"\\n # If rating starts with one of the following, it is a two-word rating (e.g. \\\"sector perform\\\")\\n two_word_rating_list = ['sector', 'market', 'strong']\\n\\n # Key/value dictionary for other message types in headline\\n key_dict = {'upgraded': 'upgrade',\\n 'downgraded': 'downgrade',\\n 'initiated': 'initiation',\\n 'resumed': 'resumption',\\n 'reinstated': 'resumption',\\n 're-instated': 'resumption',\\n 'reinitiated': 'resumption',\\n 're-initiated': 'resumption',\\n 'assumed': 'resumption',\\n }\\n\\n # Message type recognized instantiated to False\\n msgtype_recognized = False\\n\\n # Search dictionary for keywords\\n for key, value in key_dict.iteritems():\\n\\n # If keyword found\\n if key in msg:\\n\\n # Message recognized\\n msgtype_recognized = True\\n\\n # Set type according to dictionary\\n msgtype = value\\n\\n # Get index of keyword occurrence\\n ix = msg.index(key)\\n\\n # If not followed by something like 'to', decrement index\\n if len(msg[ix+1]) != 2: ix -= 1\\n\\n # Check if two-word rating\\n if msg[ix+2] in two_word_rating_list:\\n\\n # Return two-word rating\\n rating = msg[ix+2] + ' ' + msg[ix+3]\\n\\n # If keyword not in two_word_rating list\\n else:\\n\\n # Return single-word rating\\n rating = msg[ix+2]\\n\\n if msgtype_recognized:\\n return msgtype_recognized, msgtype, rating\\n else: return msgtype_recognized, None, None\\n\\n\\n def getSingle(msg):\\n \\\"\\\"\\\"\\n Check if single message and, if so, process message and return event values\\n\\n Returns a tuple: (boolean, list), where:\\n --boolean: \\\"True\\\" if single event, otherwise \\\"False\\\"\\n --list: [ticker, msgtype, rating, PT, curr, analyst]\\n\\n Will return \\\"True, None\\\" if message type not recognized\\n Will return \\\"False, None\\\" if more than one event in message\\n \\\"\\\"\\\"\\n # If msg contains any of the following, there are multiple events per message\\n multiple_msg_tokens = ['upgrades', 'downgrades', 'initiates', 'resumes', 'reinstates', 'assumes', 'notable']\\n\\n # Look for header (before first bullet)\\n try:\\n header = msg[:msg.index('*')]\\n except:\\n header = msg\\n\\n # If no value in multiple_msg_tokens found in msg, assume single event\\n if not any([e in header for e in multiple_msg_tokens]):\\n\\n # Get ticker by looking at standard index in message\\n SINGLE_MESSAGE_TICKER_INDEX = 5\\n ticker = msg[SINGLE_MESSAGE_TICKER_INDEX]\\n\\n # Check if target price increase or decrease\\n # Assumed format 'target in/decreased to x' before first bullet or +5 spaces after ticker (headline)\\n try:\\n stpindx = msg.index('*')\\n except:\\n stpindx = 5\\n\\n # If 'target' found in msg headline\\n if 'target' in msg[SINGLE_MESSAGE_TICKER_INDEX:stpindx]:\\n\\n # Get index of occurence\\n ix = msg.index('target')\\n\\n # If followed by 'increased' or 'raises'\\n if msg[ix+1] == 'increased' or msg[ix-1] == 'raises':\\n\\n # Set type to 'PT increased'\\n msgtype = 'PT increased'\\n\\n # Get PT and currency\\n PT, curr = getPT(msg)\\n\\n # Set rating to empty string\\n rating = ''\\n\\n # Get firm\\n if msg[ix+1] == 'increased':\\n firm = getFirm(msg)\\n else:\\n firm = getFirm(msg, typ='multiple')\\n\\n # If followed by 'decreased' or 'lowers'\\n elif msg[ix+1] == 'decreased' or msg[ix-1] == 'lowers':\\n\\n # Set type to 'PT decreased'\\n msgtype = 'PT decreased'\\n\\n # Get PT and currency\\n PT, curr = getPT(msg)\\n\\n # Set rating to empty string\\n rating = ''\\n\\n # Get firm\\n if msg[ix+1] == 'decreased':\\n firm = getFirm(msg)\\n else:\\n firm = getFirm(msg, typ='multiple')\\n\\n # If not followed by one of the above, msg type not understood\\n else:\\n return True, None\\n\\n # Return True for single event, return values\\n return True, [firm, ticker, msgtype, rating, PT, curr]\\n\\n # If not PT change, check for other msg types\\n else:\\n\\n # Get msg type and rating\\n msgtype_recognized, msgtype, rating = getMsgType(msg)\\n\\n # If msg type not recognized, exit\\n if not msgtype_recognized: return True, None\\n\\n # Get Price Target and currency\\n PT, curr = getPT(msg)\\n\\n # Get firm\\n firm = getFirm(msg)\\n\\n # Return True and values\\n return True, [firm, ticker, msgtype, rating, PT, curr]\\n\\n # More than one event in msg\\n return False, None\\n\\n def getMultiple(msg, date, analyst):\\n \\\"\\\"\\\"\\n Process core with multiple events, will return generator of lists:\\n List looks like this [ticker, msgtype, rating, PT, curr, analyst]\\n \\\"\\\"\\\"\\n # Get firm\\n firm = getFirm(msg, typ='multiple')\\n\\n # Get index of first bullet, otherwise quit\\n try:\\n ix = msg.index('*')\\n anotherBullet = True\\n except:\\n anotherBullet = False\\n\\n # Loop through multiple messages\\n while anotherBullet:\\n\\n # Get index of next bullet if possible\\n try:\\n nextix = msg[ix+1:].index('*')\\n\\n # If next bullet immediately follows, this is the real bullet\\n # E.g. Don't want the \\\"Upgrade\\\" bullet, want actual msg bullet\\n if (nextix < 5):\\n ix += (nextix + 1)\\n\\n # Again get index of next bullet\\n try:\\n nextix = msg[ix+1:].index('*')\\n\\n # Current msg is until next bullet\\n currentMsg = msg[(ix+1):(ix+nextix+1)]\\n\\n # If no more bullets, current msg is until end of msg\\n except:\\n currentMsg = msg[ix+1:]\\n anotherBullet = False\\n\\n # If no more bullets, current msg is until end of msg\\n except:\\n currentMsg = msg[ix+1:]\\n anotherBullet = False\\n\\n # If bullet is followed immediately by \\\"Analyst\\\", exit\\n if (msg[ix+1] == 'Analyst'): break\\n\\n # Get index of '(', if not found, move to next bullet / exit\\n try: indx = currentMsg.index('(')\\n except:\\n if not anotherBullet: continue\\n else:\\n ix += (nextix + 1)\\n continue\\n\\n # Ticker follows '('\\n ticker = currentMsg[indx+1]\\n\\n # Get msg type and rating\\n msgtype_recognized, msgtype, rating = getMsgType(currentMsg)\\n\\n # Get PT and curr\\n PT, curr = getPT(currentMsg, typ='multiple')\\n\\n # Increment bullet index to next bullet\\n ix += (nextix + 1)\\n\\n # If msgtype not recognized print out \\n if not msgtype_recognized:\\n print '------------------------------'\\n print ' NEW MESSAGE '\\n print '------------------------------'\\n print ' Can\\\\'t process message type '\\n print ''\\n continue\\n\\n yield [date, firm, ticker, msgtype, rating, PT, curr, analyst]\\n\\n def getNotable(msg, date):\\n \\\"\\\"\\\"\\n Get messages for email with \\\"Other notable research\\\", will return generator of events\\n List looks like this [ticker, msgtype, rating, PT, curr, analyst]\\n \\\"\\\"\\\"\\n # Look for first bullet (before first '(')\\n ix = msg.index('(')\\n\\n # If followed by (pre-open), look for next '('\\n if msg[ix+1] == 'pre-open':\\n ix += msg[ix+1:].index('(') + 1\\n\\n # Loop back from '(' until first bullet found, get index\\n while msg[ix] != '*': ix -= 1\\n\\n anotherBullet = True\\n\\n while anotherBullet:\\n\\n # Get index of next bullet if possible\\n try:\\n nextix = msg[ix+1:].index('*')\\n\\n # Current msg is until next bullet\\n currentMsg = msg[(ix+1):(ix+nextix+1)]\\n\\n except:\\n anotherBullet = False\\n\\n # Current msg is until end of msg\\n currentMsg = msg[ix+1:]\\n\\n # Check if current message contains PT, if so continue to next bullet\\n if ('target' in currentMsg) or ('Target' in currentMsg):\\n ix += (nextix + 1)\\n continue\\n\\n # Check if current message contains PT, if so continue to next bullet\\n if ('analyst' in currentMsg) or ('Analyst' in currentMsg):\\n ix += (nextix + 1)\\n continue\\n\\n # Get index of '(', if not found, move to next bullet\\n try: indx = currentMsg.index('(')\\n except:\\n ix += (nextix + 1)\\n continue\\n\\n # Ticker follows '('\\n ticker = currentMsg[indx+1]\\n\\n # Get message type and rating\\n msgtype_recognized, msgtype, rating = getMsgType(currentMsg)\\n\\n # Assume no PT / currency / analyst\\n PT = ''\\n curr = ''\\n analyst = ''\\n\\n # Get firm\\n firm = getFirm(currentMsg)\\n\\n # Check if followed by price target\\n if anotherBullet:\\n try:\\n tgtix = msg[ix+nextix+2:].index('*')\\n\\n # Next msg is until next bullet\\n nextMsg = msg[(ix+nextix+2):(ix+nextix+tgtix+2)]\\n\\n except:\\n # Next msg is until end of msg\\n nextMsg = msg[(ix+nextix+2):]\\n\\n # If next message contains target\\n if ('Target' in nextMsg) or ('target' in nextMsg):\\n\\n # Get PT and curr\\n PT, curr = getPT(nextMsg)\\n\\n # If next message contains analyst\\n if ('Analyst' in nextMsg) or ('analyst' in nextMsg):\\n\\n # Get analyst name\\n analyst = getAnalyst(nextMsg)\\n\\n # Increment bullet index to next bullet\\n ix += (nextix + 1)\\n\\n # If msgtype not recognized print out\\n if not msgtype_recognized:\\n print '------------------------------'\\n print ' NEW MESSAGE '\\n print '------------------------------'\\n print ' Can\\\\'t process message type '\\n print ''\\n continue\\n\\n yield [date, firm, ticker, msgtype, rating, PT, curr, analyst]\\n\\n def processMsg(msg):\\n \\\"\\\"\\\"\\n Process message, if successful, write to database, if not print why\\n \\\"\\\"\\\"\\n # Look for date\\n date = getDate(msg)\\n\\n # Check to see if \\\"Other notable research calls\\\"\\n if msg[6] == 'notable':\\n events = getNotable(msg, date)\\n\\n for e in events:\\n\\n firm = e[1]\\n ticker = e[2]\\n msgType = e[3]\\n newRating = e[4]\\n PT = e[5]\\n fx = e[6]\\n analyst = e[7]\\n \\n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\\n\\n # If not \\\"Other notable research calls\\\"\\n else:\\n\\n # Look for analyst name\\n analyst = getAnalyst(msg)\\n\\n # Check if email only contains one event (one ticker)\\n isSingle, vals = getSingle(msg)\\n\\n # If contains only one event\\n if isSingle:\\n\\n # If doesn't recognize type print following\\n if vals is None:\\n print '------------------------------'\\n print ' NEW MESSAGE '\\n print '------------------------------'\\n print ' Can\\\\'t process message type '\\n print ''\\n\\n # If type recognized, write event to database\\n else:\\n firm = vals[0]\\n ticker = vals[1]\\n msgType = vals[2]\\n newRating = vals[3]\\n PT = vals[4]\\n fx = vals[5]\\n\\n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\\n\\n # If more than one event, print following\\n else:\\n events = getMultiple(msg, date, analyst)\\n for e in events:\\n\\n firm = e[1]\\n ticker = e[2]\\n msgType = e[3]\\n newRating = e[4]\\n PT = e[5]\\n fx = e[6]\\n \\n processEvent(date, firm, ticker, msgType, newRating, PT, fx, analyst)\\n\\n \\n # Login to gmail and get mail object\\n mail = emailLogin()\\n\\n # Get unread message ids\\n x, y = mail.uid('search', None, \\\"(UNSEEN)\\\")\\n\\n # Check if there are unread emails\\n unreadMails = len(y[0].split()) > 0\\n\\n # While there are unread emails\\n while(unreadMails):\\n\\n # Get first unread email and get tokenized message\\n msg = getLastMsgCore(y, mail)\\n\\n # Process message\\n processMsg(msg)\\n\\n # Update unread email boolean\\n x, y = mail.uid('search', None, \\\"(UNSEEN)\\\")\\n unreadMails = len(y[0].split()) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f803e42b5c1bb0bf32956623eab1fb9f\",\n \"score\": \"0.5876127\",\n \"text\": \"def _query_messages(client, page=1):\\n if DEBUG:\\n print \\\"requesting page {}\\\".format(page)\\n\\n return client.api_call('search.messages', query=MESSAGE_QUERY, count=MESSAGE_PAGE_SIZE, page=page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d0d3d53c08b728f6e61727cd4d261fa\",\n \"score\": \"0.5864518\",\n \"text\": \"def receive_messages(queue, max_number, wait_time):\\n try:\\n messages = queue.receive_messages(\\n MessageAttributeNames=['All'],\\n MaxNumberOfMessages=max_number,\\n WaitTimeSeconds=wait_time\\n )\\n for msg in messages:\\n logger.info(\\\"Received message: %s: %s\\\", msg.message_id, msg.body)\\n except ClientError as error:\\n logger.exception(\\\"Couldn't receive messages from queue: %s\\\", queue)\\n raise error\\n else:\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ac26cdc4b9bb5bb049d96751778ad2\",\n \"score\": \"0.58494055\",\n \"text\": \"def get_all_messages():\\n query_ = select([Message.message])\\n all_messages = IRC_CONNECTION.execute(query_).fetchall()\\n return all_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18531210a8de168df1a18b8c14d3cf4\",\n \"score\": \"0.5845751\",\n \"text\": \"def get_messages(self, request, last_message=0, read=True, old=False):\\n\\n user = request.user\\n\\n if old:\\n messages_qs = self.filter(player=user.player, id__gt=last_message)\\n else:\\n messages_qs = self.filter(\\n player=user.player, id__gt=last_message, read=False\\n )\\n messages_qs = messages_qs.order_by('-id')\\n messages = list(messages_qs)[: settings.MAX_SESSION_MESSAGES]\\n for message in messages:\\n message.new = not message.read\\n if read:\\n messages_qs.update(read=True)\\n\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10fa7b0f477c4b75b018fca410e069ae\",\n \"score\": \"0.5819849\",\n \"text\": \"def max(self):\\n return self.msg.max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b197d39431e37d37a0c0bc4b8c6b3509\",\n \"score\": \"0.5804066\",\n \"text\": \"def get_messages(room_id=0, offset=0, size=50):\\n room_key = f\\\"room:{room_id}\\\"\\n room_exists = redis_client.exists(room_key)\\n if not room_exists:\\n return []\\n else:\\n values = redis_client.zrevrange(room_key, offset, offset + size)\\n return list(map(lambda x: json.loads(x.decode(\\\"utf-8\\\")), values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891d903008979f5036780d9afa2741de\",\n \"score\": \"0.57971257\",\n \"text\": \"def limit(self, count):\\r\\n return self.all().limit(count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe23e55861e716dad009c4a978f88737\",\n \"score\": \"0.5790616\",\n \"text\": \"def get_messages(queue_url):\\n while True:\\n resp = sqs_client.receive_message(\\n QueueUrl=queue_url,\\n AttributeNames=['All'],\\n MaxNumberOfMessages=10\\n )\\n\\n try:\\n yield from resp['Messages']\\n except KeyError:\\n return\\n\\n entries = [\\n {'Id': msg['MessageId'], 'ReceiptHandle': msg['ReceiptHandle']}\\n for msg in resp['Messages']\\n ]\\n\\n resp = sqs_client.delete_message_batch(\\n QueueUrl=queue_url, Entries=entries\\n )\\n\\n if len(resp['Successful']) != len(entries):\\n raise RuntimeError(\\n \\\"Failed to delete messages: entries={!r} resp={!r}\\\"\\n .format(entries, resp)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7445c0caf8d117cd10ed5a1a6359267e\",\n \"score\": \"0.578687\",\n \"text\": \"def message_get_all(context, filters=None, limit=None, offset=None,\\n sort_key=None, sort_dir=None):\\n return IMPL.message_get_all(context, filters=filters, limit=limit,\\n offset=offset, sort_key=sort_key,\\n sort_dir=sort_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"529db5bd3669f87efe478e9eada67c12\",\n \"score\": \"0.57843995\",\n \"text\": \"def _fetch(self, imapc: imapclient.IMAPClient) -> typing.List[MessageContainer]:\\n messages = []\\n\\n logger.debug(self, \\\"Fetch unflagged messages\\\")\\n uids = imapc.search([\\\"UNFLAGGED\\\"])\\n logger.debug(self, \\\"Found {} unflagged mails\\\".format(len(uids)))\\n for msgid, data in imapc.fetch(uids, [\\\"BODY.PEEK[]\\\"]).items():\\n # INFO - G.M - 2017-12-08 - Fetch BODY.PEEK[]\\n # Retrieve all mail(body and header) but don't set mail\\n # as seen because of PEEK\\n # see rfc3501\\n logger.debug(self, 'Fetch mail \\\"{}\\\"'.format(msgid))\\n\\n try:\\n msg = message_from_bytes(data[b\\\"BODY[]\\\"])\\n except KeyError as e:\\n # INFO - G.M - 12-01-2018 - Fetch may return events response\\n # In some specific case, fetch command may return events\\n # response unrelated to fetch request.\\n # This should happen only when someone-else use the mailbox\\n # at the same time of the fetcher.\\n # see https://github.com/mjs/imapclient/issues/334\\n except_msg = \\\"fetch response : {}\\\".format(str(data))\\n raise BadIMAPFetchResponse(except_msg) from e\\n\\n msg_container = MessageContainer(msg, msgid)\\n messages.append(msg_container)\\n\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ac877394b93cb341cfb108971ba5d9\",\n \"score\": \"0.57789975\",\n \"text\": \"def get_all_msgs(self):\\n msgs = []\\n while True:\\n msg = self.conn.recv_match()\\n if msg != None:\\n msgs.append(msg)\\n else:\\n break\\n return msgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"224b49c2323ada5fe50fd9e91d57fb6b\",\n \"score\": \"0.57684296\",\n \"text\": \"def _get_max_results(max_results, limit, count_so_far):\\n # limit is None means we get every record (no limit).\\n if limit is None:\\n return max_results\\n\\n return min(max_results, limit - count_so_far)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc004e3d16b3db8af66567816e6754be\",\n \"score\": \"0.57667977\",\n \"text\": \"def get_all(self, select: Optional[Select] = None) -> Sequence[Message]:\\n return [entry.msg for entry in self.queue.get_all(select)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a662933a0f8fcdc70280083b018f0fc3\",\n \"score\": \"0.57499135\",\n \"text\": \"def get_recent_messages(self, channel_oid: ObjectId, *, limit: Optional[int] = None, skip: Optional[int] = None) \\\\\\n -> ExtendedCursor[MessageRecordModel]:\\n addl_kwargs = {}\\n\\n # Both `limit` and `skip` cannot accept ``None``, so only attach them if needed\\n\\n if limit:\\n addl_kwargs[\\\"limit\\\"] = limit\\n\\n if skip:\\n addl_kwargs[\\\"skip\\\"] = skip\\n\\n return self.find_cursor_with_count({MessageRecordModel.ChannelOid.key: channel_oid},\\n sort=[(OID_KEY, pymongo.DESCENDING)], **addl_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf0b76ca9dc7560d98f8e86bc34f8c9f\",\n \"score\": \"0.574574\",\n \"text\": \"async def fetch_next_page(self):\\n if self._limit != -1 and self._total_count >= self._limit:\\n return\\n\\n results = await self.sq.execute(page=self.page)\\n\\n # add a new messagegroup to the end\\n for r in results:\\n self.groups.append(MessageGroup(r))\\n\\n self.page += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6486d17a61d189822ae71908e0ab588\",\n \"score\": \"0.57332814\",\n \"text\": \"def get_messages_list(self):\\r\\n results = ResponsesREST.SERVER_ERROR.value\\r\\n query = \\\"SELECT idMessage, message, idChat, dateTime, memberType FROM Message \\\" \\\\\\r\\n \\\"WHERE idChat = %s;\\\"\\r\\n param = [self.id_chat]\\r\\n list_messages = self.connect.select(query, param)\\r\\n if list_messages:\\r\\n message_list = []\\r\\n for messages in list_messages:\\r\\n message = Message()\\r\\n message.id_message = messages[\\\"idMessage\\\"]\\r\\n message.message = messages[\\\"message\\\"]\\r\\n message.id_chat = messages[\\\"idChat\\\"]\\r\\n message.date_time = messages[\\\"dateTime\\\"]\\r\\n message.date_time = message.date_time.strftime(\\\"%Y/%m/%d %H:%M:%S\\\")\\r\\n message.member_type = messages[\\\"memberType\\\"]\\r\\n message_list.append(message)\\r\\n results = message_list\\r\\n else:\\r\\n results = ResponsesREST.NOT_FOUND.value\\r\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcee8071482aebcd0b850f391654d0ba\",\n \"score\": \"0.5725758\",\n \"text\": \"def get_recent_emails(account, folder_name, count):\\n # Get the folder object\\n folder = account.root / 'Top of Information Store' / folder_name\\n # Get emails\\n return folder.all().order_by('-datetime_received')[:count]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09abeefa5f159ac3ddce91c23b26516e\",\n \"score\": \"0.5718951\",\n \"text\": \"def getMessages(self, date):\\r\\n messages = self.client.messages.list(\\r\\n date_sent_after=date,\\r\\n to=self.phone_num,\\r\\n )\\r\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1f1344d536e203360ffdb149662ca2\",\n \"score\": \"0.5713777\",\n \"text\": \"def limit(self, count):\\r\\n return self.find(Document.limit(count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda30b10dc94ecacfc7c08e0522351fc\",\n \"score\": \"0.57061654\",\n \"text\": \"async def prune(self, ctx, *, max_messages:str=None):\\n\\t\\tif ctx.message.server.me.permissions_in(ctx.message.channel).manage_messages == False:\\n\\t\\t\\tawait self.bot.say(\\\"Sorry, this doesn't work on this server (No manage_messages Permission)!\\\")\\n\\t\\t\\treturn\\n\\t\\tusers = []\\n\\t\\tnone = False\\n\\t\\tif max_messages:\\n\\t\\t\\tfor u in ctx.message.mentions:\\n\\t\\t\\t\\tusers.append(u)\\n\\t\\t\\t\\tmax_messages = max_messages.replace(u.mention, '')\\n\\t\\t\\tif max_messages.replace(' ', '').isdigit():\\n\\t\\t\\t\\tmax_messages = int(max_messages)\\n\\t\\tif max_messages is None:\\n\\t\\t\\tnone = True\\n\\t\\t\\tmax_messages = 100\\n\\t\\ttry:\\n\\t\\t\\tmax_messages = int(max_messages)\\n\\t\\texcept:\\n\\t\\t\\tmax_messages = 100\\n\\t\\tif max_messages > 6000:\\n\\t\\t\\tawait self.bot.say(\\\"2 many messages (<= 6000)\\\")\\n\\t\\t\\treturn\\n\\t\\tcheck = lambda m: m.author in users\\n\\t\\tdeleted = await self.bot.purge_from(ctx.message.channel, limit=max_messages, before=ctx.message, after=datetime.datetime.now() - datetime.timedelta(minutes=5) if none else None, check=check if users else None)\\n\\t\\tself.bot.pruned_messages.append(deleted)\\n\\t\\tif users and len(deleted) == 0:\\n\\t\\t\\tx = await self.bot.send_message(ctx.message.channel, \\\":warning: No messages found by `{0}` within `{1}` searched messages!\\\".format(', '.join(users), max_messages))\\n\\t\\telse:\\n\\t\\t\\tx = await self.bot.send_message(ctx.message.channel, \\\"ok, removed **{0}** messages.\\\".format(len(deleted)))\\n\\t\\tawait asyncio.sleep(10)\\n\\t\\ttry:\\n\\t\\t\\tself.bot.pruned_messages.append(ctx.message)\\n\\t\\t\\tawait self.bot.delete_message(ctx.message)\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\t\\ttry:\\n\\t\\t\\tawait self.bot.delete_message(x)\\n\\t\\texcept:\\n\\t\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3b38896749f9173ddd1dccdc25fabc\",\n \"score\": \"0.5695202\",\n \"text\": \"def get_messages_from_queue(url):\\n\\n while True:\\n resp = sqs_client.receive_message(\\n QueueUrl=queue_url,\\n AttributeNames=['All'],\\n MaxNumberOfMessages=10\\n )\\n\\n try:\\n yield from resp['Messages']\\n except KeyError:\\n return\\n\\n entries = [\\n {'Id': msg['MessageId'], 'ReceiptHandle': msg['ReceiptHandle']}\\n for msg in resp['Messages']\\n ]\\n\\n resp = sqs_client.delete_message_batch(\\n QueueUrl=queue_url, Entries=entries\\n )\\n\\n if len(resp['Successful']) != len(entries):\\n raise RuntimeError(\\n f\\\"Failed to delete messages: entries={entries!r} resp={resp!r}\\\"\\n )\\n else:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5140b4bcfe3daf2dd85be47c5c0b2816\",\n \"score\": \"0.56937915\",\n \"text\": \"def test_request_messages_without_lim(self):\\n timestamp = 10 ** 11\\n\\n for i in range(200):\\n create_message(\\n text='Message' + str(i),\\n timestamp=timestamp + i,\\n username='vitsalis',\\n channel=self.channel\\n )\\n\\n messages = json.loads(self.client.get(\\n reverse('chat:message', args=('channel', self.channel.name,)),\\n ).content)\\n\\n self.assertEqual(len(messages), 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4519364d142b7334e80c11691781fa53\",\n \"score\": \"0.5679032\",\n \"text\": \"def fetchMail(self):\\n\\n fetcher = FetchMail(self.FETCHMAILSERVER, self.BOTACCOUNT, self.BOTPASSWORD, self.leaveunread, download_folder=self.unprocessedpath)\\n if self.printstatus:\\n print(\\\"Successfully connected to imap server.\\\")\\n emails = fetcher.fetch_unread_messages()\\n numattachments = len(emails)\\n if self.printstatus:\\n print(\\\"Successfully fetched %s emails.\\\" % numattachments) # probably want to log this message\\n if numattachments:\\n\\n for entry in emails:\\n self.skippedemailfiles.extend(fetcher.save_attachment(entry))\\n if self.printstatus:\\n print(self.skippedemailfiles )\\n if self.printstatus and emails:\\n print(\\\"Successfully saved attachments.\\\")\\n fetcher.close_connection()\\n if self.printstatus:\\n print(\\\"Successfully received mail.\\\")\\n\\n return numattachments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928050344a144f83a13e1a32695ea886\",\n \"score\": \"0.56714356\",\n \"text\": \"def limit(cls, limit=5):\\n return cls.query.limit(limit).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30412e3864e8d278e9a0b64a7bcd5e0\",\n \"score\": \"0.567061\",\n \"text\": \"def get_all(self, context, search_opts=None, limit=None,\\n offset=None, sort_key=None, sort_dir=None):\\n LOG.debug(\\\"Searching for messages by: %s\\\", search_opts)\\n\\n search_opts = search_opts or {}\\n messages = self.db.message_get_all(context, filters=search_opts,\\n limit=limit, offset=offset,\\n sort_key=sort_key,\\n sort_dir=sort_dir)\\n\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54915691e17c3e51e10fd685e43fbf8\",\n \"score\": \"0.566538\",\n \"text\": \"async def get_messages(self, page: int = 0) -> 'SearchResults':\\n res = SearchResults(self)\\n res.page = page\\n await res.fetch_next_page()\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b537f738355fe87e76447c9099fdcb0d\",\n \"score\": \"0.56494975\",\n \"text\": \"def potential_message_targets(page_size: int = 2000):\\n with connection.cursor() as cursor:\\n cursor.execute(MESSAGE_TARGET_QUERY)\\n while page := cursor.fetchmany(page_size):\\n yield from starmap(MessageTarget, page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730c741ce25c5e18d8c894b3cfa48de1\",\n \"score\": \"0.5634011\",\n \"text\": \"def consume(self):\\n return [\\n self.sqs_envelope.parse_raw_message(self, raw_message)\\n for raw_message in self.sqs_client.receive_message(\\n AttributeNames=[\\n \\\"ApproximateReceiveCount\\\",\\n ],\\n MaxNumberOfMessages=self.limit,\\n QueueUrl=self.sqs_queue_url,\\n WaitTimeSeconds=self.wait_seconds,\\n ).get(\\\"Messages\\\", [])\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2034be2da6779f0f09e0ba0395aae93e\",\n \"score\": \"0.5623556\",\n \"text\": \"def get_messages(self, conv_id=0, msg_type=0, page=0):\\r\\n \\r\\n # FIXME: this should be sanitized.\\r\\n conditions = []\\r\\n if conv_id > 0:\\r\\n conditions.append(\\\"msg_conv_id = {}\\\".format(conv_id))\\r\\n if msg_type > 0:\\r\\n conditions.append(\\\"msg_type = {}\\\".format(msg_type))\\r\\n\\r\\n where = \\\" WHERE \\\" + \\\" AND \\\".join(conditions) if conditions else \\\"\\\"\\r\\n \\r\\n if page > 0:\\r\\n page_lbound = page * self.messages_per_page\\r\\n page_ubound = page_lbound + self.messages_per_page\\r\\n limit = \\\" LIMIT {},{}\\\".format(page_lbound, page_ubound)\\r\\n else:\\r\\n limit = \\\"\\\"\\r\\n\\r\\n query = \\\"SELECT * FROM [Messages]\\\" + where + \\\" ORDER BY [msg_timestamp] DESC\\\" + limit\\r\\n return self.cursor.execute(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72cecde362a00fba3dbe64db7412d00f\",\n \"score\": \"0.5618267\",\n \"text\": \"def get_last_10_CAN_messages():\\n try:\\n # finding last 10 can messages from excavator_canstatus collection based on sorting mongo Object Id _id in descending order\\n records = db.excavator_canstatus.find({},{'id':1, 'message':1, 'timestamp':1, '_id':0}).sort('timestamp',-1).limit(10)\\n\\n # converting cursor to list \\n canList = list(records)\\n\\n # formatting timestamp from DB to local time\\n for item in canList:\\n item['timestamp'] = item['timestamp'].strftime(\\\"%c\\\") \\n\\n return jsonify(canList)\\n except Exception as e:\\n logger.error(\\\"Error \\\" + str(e))\\n return f\\\"Error {str(e)}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee79644d89c4bb0f59214da7184bfd20\",\n \"score\": \"0.56042546\",\n \"text\": \"def messages(self, user_id='me', query=''):\\n try:\\n if self.page_token():\\n response = self._service.users().messages() \\\\\\n .list(userId=user_id, q=query,\\n pageToken=self.page_token()).execute()\\n else:\\n response = self._service.users().messages() \\\\\\n .list(userId=user_id, q=query).execute()\\n\\n messages = []\\n if 'messages' in response:\\n messages.extend(response['messages'])\\n\\n self._page_token = response.get('nextPageToken')\\n return messages\\n\\n except errors.HttpError as e:\\n print(e) \\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f0cdcab76a50c143dcf6fd3992c8675\",\n \"score\": \"0.5601558\",\n \"text\": \"def last(self):\\r\\n current_max = None\\r\\n for message in self.messages:\\r\\n if current_max is None:\\r\\n current_max = message.time\\r\\n\\r\\n if message.time > current_max:\\r\\n current_max = message.time\\r\\n return current_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ef303892dd75f4743c2ce782a7453d2\",\n \"score\": \"0.55999434\",\n \"text\": \"def fetch_messages(channel_id, latest_ts=None):\\n messages = {} \\n if latest_ts:\\n messages = client.api_call(\\n \\\"im.history\\\",\\n channel=channel_id,\\n latest=latest_ts,\\n count=PAGE_COUNT\\n )\\n else:\\n messages = client.api_call(\\n \\\"im.history\\\",\\n channel=channel_id,\\n count=PAGE_COUNT\\n )\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfe082fd1352a60180914e639f805df\",\n \"score\": \"0.55982536\",\n \"text\": \"def _get_last_imap_emails(self):\\n email = self._read_config_file('email')\\n password = self._read_config_file('password')\\n imap_url = self._read_config_file('imap_url')\\n with MailBox(imap_url).login(email, password, 'INBOX') as mailbox:\\n mails = [msg for msg in mailbox.fetch(Q(all=True))]\\n latest_email = mails[-1]\\n return latest_email.html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6234223e2d1e8f78d341d0bc62eab8\",\n \"score\": \"0.5592901\",\n \"text\": \"def get_messages_nowait(self) -> List[Message]:\\n messages = []\\n while not self._message_queue.empty():\\n messages.append(self._message_queue.get_nowait())\\n\\n self._new_message_event.clear()\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb20a629860e52f6a0b84204c231a989\",\n \"score\": \"0.5567465\",\n \"text\": \"def get_message(self):\\n\\n # Select inbox and get all messages\\n self.mail.select('INBOX')\\n status, messages = self.mail.search(None, self.email_state)\\n\\n if status == 'OK':\\n for msg_num in messages[0].split():\\n typ, data = self.mail.fetch(msg_num, '(RFC822)')\\n for raw_msg in data:\\n if isinstance(raw_msg, tuple):\\n new_email = email.message_from_bytes(raw_msg[1])\\n if self.from_condition in new_email['From']:\\n if new_email['Subject'][0:2] == self.subject_condition:\\n msg = new_email.get_payload()\\n self.mail.close()\\n return msg\\n\\n self.mail.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33514c3b183310b5677933dc05937664\",\n \"score\": \"0.5554532\",\n \"text\": \"def takeSomeFromQueue(self):\\n def yield_max_datapoints():\\n for count in range(settings.MAX_DATAPOINTS_PER_MESSAGE):\\n try:\\n yield self.queue.popleft()\\n except IndexError:\\n raise StopIteration\\n return list(yield_max_datapoints())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f054d7a82a5d9f72f4e08f717575df\",\n \"score\": \"0.55515677\",\n \"text\": \"def list_messages(\\n self,\\n folder_name: Optional[str] = None,\\n criterion: Optional[str] = None,\\n contains: Optional[bool] = False, # pylint: disable=unused-argument\\n count: Optional[int] = 100,\\n save_dir: Optional[str] = None,\\n items_only: Optional[bool] = False,\\n ) -> list:\\n # pylint: disable=no-member\\n messages = []\\n source_folder = self._get_folder_object(folder_name)\\n if criterion:\\n filter_dict = self._get_filter_key_value(criterion)\\n items = source_folder.filter(**filter_dict)\\n else:\\n items = source_folder.all()\\n if items_only:\\n return items\\n for item in items.order_by(\\\"-datetime_received\\\")[:count]:\\n attachments = []\\n if save_dir and len(item.attachments) > 0:\\n # NOTE(cmin764): Default `overwrite` param is assumed here.\\n attachments = self._save_attachments(item, save_dir)\\n messages.append(self._get_email_details(item, attachments))\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a31ab50daa71029d12fc92ec885c7c\",\n \"score\": \"0.55415785\",\n \"text\": \"def getNewMessages(self):\\n\\t\\trv = self.__recv_messages\\n\\t\\tself.__recv_messages = []\\n\\t\\treturn rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c624364183209a8ddc3b75bd945ef2a\",\n \"score\": \"0.5525904\",\n \"text\": \"def get_messages(self):\\n\\n request_response = self.getv2Inbox()\\n actual_responses = self.LastJson\\n\\n inbox = actual_responses[\\\"inbox\\\"]\\n threads = inbox[\\\"threads\\\"]\\n\\n for thread in threads:\\n thread_users = thread[\\\"users\\\"]\\n usernames = list()\\n for thread_user in thread_users:\\n usernames.append(thread_user[\\\"username\\\"])\\n usernames.append(thread_user[\\\"full_name\\\"])\\n\\n print(\\\"Group chat with: \\\" + \\\", \\\".join(usernames))\\n\\n sender = thread[\\\"inviter\\\"]\\n print(\\\"From: \\\" + sender[\\\"username\\\"] + \\\" \\\" + sender[\\\"full_name\\\"])\\n\\n messages = thread[\\\"items\\\"]\\n for message in messages:\\n if \\\"text\\\" in message:\\n print(message[\\\"text\\\"])\\n\\n elif \\\"reel_share\\\" in message:\\n print(message[\\\"reel_share\\\"][\\\"text\\\"])\\n\\n elif \\\"items\\\" in thread:\\n descriptions = list()\\n for item in thread[\\\"items\\\"]:\\n if \\\"action_log\\\" in item:\\n descriptions.append(item[\\\"action_log\\\"])\\n elif \\\"media_share\\\" in item:\\n media_url = 'https://instagram.com/p/' + item['media_share']['code']\\n descriptions.append(media_url)\\n\\n print(' \\\\t'.join(str(description) for description in descriptions))\\n\\n else:\\n print(\\\"Unable to find: \\\" + json.dumps(thread, indent=4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f00984fc63b76a4084ad9ebeec209c4\",\n \"score\": \"0.5517321\",\n \"text\": \"def messages(self, request, pk=None):\\n try:\\n chat = self.get_object()\\n except Chat.DoesNotExist:\\n return Response(status=status.HTTP_404_NOT_FOUND)\\n queryset = chat.messages.select_related('chat', 'user').prefetch_related('images', 'user__images')\\n page = self.paginate_queryset(queryset)\\n if page is not None and request.GET.get('page') is not None:\\n return self.get_paginated_response(MessageSerializer(page, many=True).data)\\n\\n return Response(MessageSerializer(queryset, many=True).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe3e041950c43ed59a76f285e5d75d2\",\n \"score\": \"0.551348\",\n \"text\": \"def GetMessage(service, user_id, msg_id):\\n\\n\\tfor n in range(0,7):\\n\\t\\ttry:\\n\\t\\t\\tmessage = service.users().messages().get(userId=user_id, id=msg_id).execute()\\n\\t\\t\\t# print 'Message snippet: %s' % message['snippet']\\n\\t\\t\\treturn message\\n\\n\\t\\texcept (socket.error,errors.HttpError) as error:\\n\\t\\t\\t#print 'An error occurred: %s' % error\\n\\t\\t\\t#print 'Message ID:', msg_id\\n\\n\\t\\t\\tif \\\"User Rate Limit Exceeded\\\" in str(error):\\n\\t\\t\\t\\tprint \\\"User rate limit exceeded - Request delayed \\\" + str(2**n) + \\\" sec\\\"\\n\\t\\t\\t\\ttime.sleep((2**n) + random.random())\\n\\t\\n\\t\\t\\tmessage = {\\n\\t\\t\\t\\t'payload': {\\n\\t\\t\\t\\t\\t'headers': []\\n\\t\\t\\t\\t}\\n\\t\\t\\t} \\n\\n\\treturn message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8da04561229d7460303546adacdfc3\",\n \"score\": \"0.55084485\",\n \"text\": \"def limit_number_of_results(self):\\n self.response.debug(f\\\"Limiting Number of Results\\\")\\n self.response.info(f\\\"Filtering excess results above max result limit\\\")\\n params = self.parameters\\n try:\\n n = params['max_results']\\n self.message.results = self.message.results[:n]\\n except:\\n tb = traceback.format_exc()\\n error_type, error, _ = sys.exc_info()\\n self.response.error(tb, error_code = error_type.__name__)\\n self.response.error(f\\\"Something went wrong when limiting results\\\")\\n else:\\n self.response.info(f\\\"Successfully limited the number of results\\\")\\n\\n return self.response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058b00b48f130f56b8c1fd7d259cd728\",\n \"score\": \"0.5498757\",\n \"text\": \"def fetch(self):\\n logging.info(\\\"Fetching emails for %s\\\" % self.name)\\n\\n end = datetime.now() + timedelta(minutes=self._limit)\\n p = Popen(self._command)\\n\\n while True:\\n if p.poll() is not None:\\n return\\n\\n if datetime.now() > end:\\n os.killpg(p.pid, signal.SIGTERM)\\n self._log_kill(p.pid)\\n return\\n\\n sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a86028474cb5a3da70bbef34bab6e3c\",\n \"score\": \"0.54944074\",\n \"text\": \"def getAllChannelMessages(self):\\n\\n async def main():\\n async for message in Channel.client.iter_messages(self.username):\\n if type(message) == telethon.tl.types.Message:\\n await self.parseMessage(message)\\n\\n with Channel.client:\\n try:\\n Channel.client.loop.run_until_complete(main())\\n except telethon.errors.ServerError:\\n logging.info(\\\"Server error: Passed\\\")\\n pass\\n except telethon.errors.FloodWaitError as e:\\n logging.info(\\\"FloodWaitError: Sleep for \\\" + str(e.seconds))\\n time.sleep(e.seconds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea65956834c4d48e046573c9537a1c85\",\n \"score\": \"0.5489506\",\n \"text\": \"def get_messages(self):\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50bd3bc2b7212d5aab01e0b203f28e1\",\n \"score\": \"0.54876417\",\n \"text\": \"def chat_messages():\\n messages = reversed(Message.query(ancestor=thread_key())\\n .order(-Message.creation_date).fetch(10))\\n return \\\"\\\\n\\\".join(re.sub(r'\\\\r\\\\n|\\\\r|\\\\n', ' ', cgi.escape(m.text))\\n for m in messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04ef56c89c0e8cd1f1727e8f138a469\",\n \"score\": \"0.5479722\",\n \"text\": \"def ListMessagesMatchingQuery(service, user_id, query):\\n\\n\\tfor n in range(0,10):\\n\\t\\ttry:\\n\\t\\t\\tresponse = service.users().messages().list(userId=user_id,q=query).execute()\\n\\t\\t\\tmessages = []\\n\\t\\t\\tif 'messages' in response:\\n\\t\\t\\t\\tmessages.extend(response['messages'])\\n\\n\\t\\t\\twhile 'nextPageToken' in response:\\n\\t\\t\\t\\tpage_token = response['nextPageToken']\\n\\t\\t\\t\\tresponse = service.users().messages().list(userId=user_id, q=query, pageToken=page_token).execute()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif 'messages' in response.keys():\\n\\t\\t\\t\\t\\tmessages.extend(response['messages'])\\n\\n\\t\\t\\treturn messages\\n\\n\\t\\texcept errors.HttpError, error:\\n\\t\\t\\t#print 'An error occurred: %s' % error\\n\\n\\t\\t\\tif \\\"User Rate Limit Exceeded\\\" in str(error):\\n\\t\\t\\t\\tprint \\\"User rate limit exceeded - Request delayed \\\" + str(2**n) + \\\" sec\\\"\\n\\t\\t\\t\\ttime.sleep((2**n) + random.random())\\n\\n\\traise Exception, \\\"Failed to load message lists from Gmail (in ListMessagesMatchingQuery)\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19c23b793d71dd5d7036e5dddfcb067\",\n \"score\": \"0.5478784\",\n \"text\": \"def iter_messages(self):\\n\\t\\treturn self.__messages.iter_messages()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80def62f8336fd3ef3fd044ed80a9d5d\",\n \"score\": \"0.5477561\",\n \"text\": \"def latest(self):\\n\\n if not self.messages: # pragma: nocover\\n return None\\n return self.messages[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40866f2732a2047c5a5fabf1acf454b\",\n \"score\": \"0.5475289\",\n \"text\": \"def get_latest_news(self, limit=10):\\n news = []\\n params = { 'limit': limit}\\n items = self.__db_worker.execute(\\n \\\"select id, title, url, published from news order by id desc \\\" \\\\\\n \\\"limit :limit\\\", params\\n )\\n for item in items:\\n news.append(item)\\n return news\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545d57ae3259c7d21f0b2c4a18d43bf4\",\n \"score\": \"0.5464635\",\n \"text\": \"def get_message_ids(imap):\\n package = dict()\\n\\n mailboxes = get_mailboxes(imap)\\n total = get_mail_count(imap, mailboxes)\\n remaining = total\\n for mailbox in mailboxes:\\n package[mailbox] = list()\\n\\n imap.select(mailbox)\\n uid_list = imap.uid('search', None, 'ALL')[1][0].split()\\n\\n for uid in uid_list:\\n print('Remaining: %d ' % remaining, end='\\\\r')\\n data = imap.uid('fetch', uid, '(BODY.PEEK[HEADER])')[1]\\n try:\\n message_id = email.message_from_bytes(data[0][1])['Message-ID']\\n if message_id == None:\\n remaining -= 1\\n continue\\n except TypeError:\\n # unable to get message-id\\n # to-do: implement logging here\\n remaining -= 1\\n continue\\n\\n data = (uid, message_id)\\n package[mailbox].append(data)\\n remaining -= 1\\n print('Remaining: %d ' % remaining)\\n \\n return package\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c2688d19f22cbfaa7ce4cc094369530\",\n \"score\": \"0.54632854\",\n \"text\": \"def getEntries(maximum=None, offset=0):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b21f9785d0cdea8a9b693564c56b2e\",\n \"score\": \"0.5461734\",\n \"text\": \"def unread_messages(self):\\n return sum(map(lambda l: l.unread_messages_seller(), self.requests.all()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f2e2694a56648e22897ec5fb9d7036\",\n \"score\": \"0.5458467\",\n \"text\": \"def list_messages(self, filter_by=None):\\n to_be_listed = self.message_store\\n if filter_by:\\n events = [msg.generate_events()\\n for msg in to_be_listed if msg.to in filter_by]\\n else:\\n events = [msg.generate_events() for msg in to_be_listed]\\n return {\\n \\\"items\\\": events,\\n \\\"paging\\\": {\\n \\\"next\\\": \\\"http://i-am-a-fake-link-to-nothing\\\",\\n \\\"last\\\": \\\"http://i-am-a-fake-link-to-nothing\\\",\\n \\\"first\\\": \\\"http://i-am-a-fake-link-to-nothing=\\\",\\n \\\"previous\\\": \\\"http://i-am-a-fake-link-to-nothing==\\\"\\n }}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cc62bf103c2fc2bec0fb8a434c00f6\",\n \"score\": \"0.54464346\",\n \"text\": \"def limit(self, limit):\\n return self[:limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e8e55e4608c24c5c3b08f103767a19\",\n \"score\": \"0.544258\",\n \"text\": \"def list_messages(service, query=''):\\n\\n response = service.users().messages().list(userId='me', q=query).execute()\\n messages = []\\n if 'messages' in response:\\n messages.extend(response['messages'])\\n\\n while 'nextPageToken' in response:\\n page_token = response['nextPageToken']\\n response = service.users().messages().list(userId='me', q=query,\\n pageToken=page_token).execute()\\n messages.extend(response['messages'])\\n\\n return messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5eb722cab3d593f12034563fb210396\",\n \"score\": \"0.5439611\",\n \"text\": \"def gather(self):\\n\\n self.messages = []\\n\\n self.msg(0, \\\"Gathering messages.\\\")\\n\\n if not self.pump:\\n return []\\n\\n outbox_major = []\\n\\n try:\\n outbox_major = self.me.outbox.major[:20]\\n except:\\n if self.debug:\\n print(self.me)\\n self.msg(0, \\\"An error occurred while gathering recent messages.\\\")\\n return []\\n \\n for activity in outbox_major:\\n pump_obj = activity.obj\\n\\n pump_obj_id = activity.id\\n\\n try:\\n pump_obj_url = pump_obj.url\\n except AttributeError:\\n continue\\n pass\\n\\n message = Message()\\n\\n message.source = \\\"Pump.io\\\"\\n message.id = self.generate_id(pump_obj_id)\\n\\n # get the text into decent shape\\n text = \\\"\\\"\\n\\n try:\\n if not pump_obj.content:\\n text = \\\"\\\"\\n else:\\n text = pump_obj.content\\n pass\\n except AttributeError:\\n continue\\n \\n\\n message.SetContent(text)\\n\\n message.link = pump_obj_url\\n \\n try:\\n if pump_obj.deleted:\\n continue\\n except AttributeError:\\n print(\\\"The arrtribute \\\\\\\"deleted\\\\\\\" does not exist . . . continuing anyway . . . \\\")\\n pass\\n\\n # Determine if this message was directed to someone on Pump.io and thus\\n # isn't intended for sharing outside the network.\\n to_list = getattr(activity, \\\"to\\\", [])\\n skip_this_message = True\\n if len(to_list) > 0:\\n # was \\\"Public\\\" among the \\\"To:\\\" recipients? Then we can go on; otherwise,\\n # skip this message\\n is_direct = False\\n is_public = False\\n for person in to_list:\\n if isinstance(person, Person):\\n is_direct = True\\n elif isinstance(person, Collection):\\n if person.id.find(\\\"public\\\") != -1:\\n is_public = True\\n skip_this_message = False\\n break\\n\\n if person.id.find(\\\"followers\\\") != -1:\\n skip_this_message = False\\n\\n pass\\n pass \\n\\n if is_direct:\\n message.direct = 1\\n if is_public:\\n message.public = 1\\n pass\\n\\n pass\\n\\n\\n cc_list = getattr(activity, \\\"cc\\\", [])\\n if len(cc_list) > 0:\\n for person in cc_list:\\n if isinstance(person, Collection):\\n if person.id.find(\\\"followers\\\") != -1:\\n skip_this_message = False\\n pass\\n pass\\n pass\\n pass\\n\\n if skip_this_message:\\n continue;\\n \\n if isinstance( pump_obj, Image):\\n img_url = pump_obj.original.url\\n\\n message.link = img_url\\n\\n # construct a request to grab the image\\n endpoint = \\\"{0}\\\".format('/'.join(img_url.split('/')[3:]))\\n # remove \\\"_thumb\\\" from the image name to get the original image\\n endpoint = endpoint.replace('_thumb','')\\n local_img_name = endpoint.split('/')[-1]\\n client = self.pump.setup_oauth_client(endpoint)\\n params = {} \\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n request = {\\n \\\"params\\\": params,\\n \\\"auth\\\": client,\\n \\\"headers\\\": headers,\\n }\\n \\n image_raw = self.pump._requester(requests.get, endpoint, raw=False, **request)\\n \\n fout = open(\\\"/tmp/{0}\\\".format(local_img_name), \\\"w\\\")\\n fout.write(image_raw.content)\\n fout.close()\\n \\n text = \\\"\\\"\\n if not pump_obj.content:\\n message.SetContent(\\\"\\\")\\n else:\\n message.SetContent(pump_obj.content)\\n pass\\n\\n if message.content == '':\\n message.content = 'An image...'\\n pass\\n message.attachments.append(\\\"/tmp/{0}\\\".format(local_img_name) )\\n\\n if not pump_obj.updated:\\n continue\\n t = datetime.datetime.strptime(str(pump_obj.updated), \\\"%Y-%m-%d %H:%M:%S+00:00\\\")\\n message.date = calendar.timegm(t.timetuple())\\n\\n message.author = pump_obj.author.display_name\\n message.author_url = pump_obj.author.url\\n\\n\\n #if str(message.author) != str(self.me):\\n if message.author != self.me.display_name:\\n message.repost = True\\n message.SetContent( \\\"RT from <a href=\\\\\\\"%s\\\\\\\">%s</a>: \\\" % (message.author_url,message.author) + message.content )\\n pass\\n\\n self.append_message(message)\\n\\n pass\\n\\n self.messages = sorted(self.messages, key=lambda msg: msg.date, reverse=False)\\n\\n if self.debug:\\n print(\\\"********************** Pump.io Handler **********************\\\\n\\\")\\n print(\\\"Here are the messages I gathered from the pump.io server:\\\\n\\\")\\n for message in self.messages:\\n print(message.Printable())\\n pass\\n \\n return self.messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93658c1d133f29ca0a2cfc7595366848\",\n \"score\": \"0.5437911\",\n \"text\": \"def _fetch_and_parse_messages(self, mailing_list, from_date):\\n\\n from_date = datetime_to_utc(from_date)\\n\\n nmsgs, imsgs, tmsgs = (0, 0, 0)\\n\\n for mbox in mailing_list.mboxes:\\n try:\\n tmp_path = self._copy_mbox(mbox)\\n\\n for message in self.parse_mbox(tmp_path):\\n tmsgs += 1\\n\\n if not self._validate_message(message):\\n imsgs += 1\\n continue\\n\\n # Ignore those messages sent before the given date\\n dt = str_to_datetime(message[MBox.DATE_FIELD])\\n\\n if dt < from_date:\\n logger.debug(\\\"Message %s sent before %s; skipped\\\",\\n message['unixfrom'], str(from_date))\\n tmsgs -= 1\\n continue\\n\\n # Convert 'CaseInsensitiveDict' to dict\\n message = self._casedict_to_dict(message)\\n\\n nmsgs += 1\\n logger.debug(\\\"Message %s parsed\\\", message['unixfrom'])\\n\\n yield message\\n except OSError as e:\\n logger.warning(\\\"Ignoring %s mbox due to: %s\\\", mbox.filepath, str(e))\\n except Exception as e:\\n if os.path.exists(tmp_path):\\n os.remove(tmp_path)\\n raise e\\n finally:\\n if os.path.exists(tmp_path):\\n os.remove(tmp_path)\\n\\n logger.info(\\\"Done. %s/%s messages fetched; %s ignored\\\",\\n nmsgs, tmsgs, imsgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db57ddc2316a199749c94ab4234aa345\",\n \"score\": \"0.54357165\",\n \"text\": \"def test_orders_paging_max_limit(self):\\n resp = self.orders_client.get_orders(limit=101, offset=0)\\n self._check_list_of_orders(resp=resp, limit=100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9629d05ccf7d82f186e030d4682d2ea9\",\n \"score\": \"0.5430537\",\n \"text\": \"def APIgetAllEmails(self, formatted_labels: str = None, date_first: datetime.datetime = None,\\n date_last: datetime.datetime = None,\\n filter: str = None, metadata_limit: int = None, user: str = None, output: bool = False):\\n self.messages = []\\n\\n if not date_first:\\n date_first = aa_globals.todayLocalUser() - datetime.timedelta(days=7)\\n\\n elif not isinstance(date_first, datetime.date):\\n date_first = aa_helper_methods.build_tz_aware_datetime(date_first, to_utc=False).date()\\n\\n if not date_last:\\n date_last = aa_globals.todayLocalUser()\\n\\n elif not isinstance(date_last, datetime.date):\\n date_last = aa_helper_methods.build_tz_aware_datetime(date_last, to_utc=False).date()\\n\\n date_last += datetime.timedelta(days=1) # include the current day\\n\\n ms_token = self.ms_auth_client.get_or_refresh_token()\\n graph_client = OAuth2Session(token=ms_token)\\n\\n endpoint = '{}/me/messages'.format(ms_settings.get('graph_url'))\\n\\n # Get emails\\n query_params = {\\n '$count': 'true',\\n '$orderby': 'receivedDateTime DESC',\\n '$filter': 'receivedDateTime ge {} AND receivedDateTime le {}'.format(date_first, date_last)\\n }\\n\\n self.messages = self.graph_generator(graph_client=graph_client, endpoint=endpoint, params=query_params)\\n\\n if output:\\n return self.parseAllEmailAPIResponses()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cc86167dd89b76079ee20684f2f687\",\n \"score\": \"0.54304403\",\n \"text\": \"def get_next_msg(self):\\n\\n if self.msg_list: # a message is already available\\n return\\n\\n self.socket.settimeout(5)\\n retry = 5\\n grablen = 5\\n while retry and (grablen == 5 or len(self.remain) < grablen):\\n if grablen == 5 and len(self.remain) >= 5:\\n grablen = struct.unpack('!H', self.remain[3:5])[0] + 5\\n if grablen == len(self.remain):\\n break\\n\\n try:\\n tmp = self.socket.recv(grablen - len(self.remain))\\n if not tmp:\\n retry -= 1\\n else:\\n self.remain += tmp\\n except:\\n retry -= 1\\n\\n if self.remain < 5 or len(self.remain) != grablen:\\n # Remote peer is not willing to respond\\n return\\n\\n # Instantiate TLS packet (record header only, at this point)\\n p = TLS(self.remain, tls_session=self.cur_session)\\n self.cur_session = p.tls_session\\n self.remain = \\\"\\\"\\n self.msg_list += p.msg\\n\\n while p.payload:\\n if isinstance(p.payload, Raw):\\n self.remain += p.payload.load\\n p = p.payload\\n elif isinstance(p.payload, TLS):\\n p = p.payload\\n self.msg_list += p.msg\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1484,"cells":{"query_id":{"kind":"string","value":"09b5d53d36a23680222292030a612cd8"},"query":{"kind":"string","value":"This function goes through all the points in the data set and returns the indices of the samples to put into the training set. X is the data set to split while k is the number of points that should be put into the training set."},"positive_passages":{"kind":"list like","value":[{"docid":"58b0f5ab32ed2344cfb887e83fa446e4","score":"0.6394534","text":"def fft_idx(X, k):\n\n # Creating the matrix of the distances\n dist_mat_glob = np.zeros(shape=(X.shape[0], X.shape[0]))\n\n for i in range(X.shape[0] - 1):\n for j in range(i + 1, X.shape[0]):\n distvec = X[j, :] - X[i, :]\n dist_mat_glob[i, j] = np.dot(distvec, distvec)\n dist_mat_glob[j, i] = np.dot(distvec, distvec)\n\n # print \"Generated \" + str(X.shape[0]) + \" by \" + str(X.shape[0]) + \" distance matrix.\"\n\n n_samples = X.shape[0]\n\n train_set = []\n\n idx = np.int32(np.random.uniform(n_samples))\n train_set.append(idx)\n\n for i in range(1, k):\n dist_list = []\n for index in train_set:\n dist_list.append(dist_mat_glob[index, :])\n dist_set = np.amin(dist_list, axis=0)\n dist_idx = np.argmax(dist_set)\n train_set.append(dist_idx)\n\n # np.save(\"train_idx.npy\",train_set)\n return train_set","title":""}],"string":"[\n {\n \"docid\": \"58b0f5ab32ed2344cfb887e83fa446e4\",\n \"score\": \"0.6394534\",\n \"text\": \"def fft_idx(X, k):\\n\\n # Creating the matrix of the distances\\n dist_mat_glob = np.zeros(shape=(X.shape[0], X.shape[0]))\\n\\n for i in range(X.shape[0] - 1):\\n for j in range(i + 1, X.shape[0]):\\n distvec = X[j, :] - X[i, :]\\n dist_mat_glob[i, j] = np.dot(distvec, distvec)\\n dist_mat_glob[j, i] = np.dot(distvec, distvec)\\n\\n # print \\\"Generated \\\" + str(X.shape[0]) + \\\" by \\\" + str(X.shape[0]) + \\\" distance matrix.\\\"\\n\\n n_samples = X.shape[0]\\n\\n train_set = []\\n\\n idx = np.int32(np.random.uniform(n_samples))\\n train_set.append(idx)\\n\\n for i in range(1, k):\\n dist_list = []\\n for index in train_set:\\n dist_list.append(dist_mat_glob[index, :])\\n dist_set = np.amin(dist_list, axis=0)\\n dist_idx = np.argmax(dist_set)\\n train_set.append(dist_idx)\\n\\n # np.save(\\\"train_idx.npy\\\",train_set)\\n return train_set\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"88ab12ff6a2547a51bc03c813ace4c65","score":"0.7543154","text":"def k_split(dataset, k):\n\n\n index = np.random.permutation(dataset[0].shape[0])\n fold_index= np.split(index, k)\n\n return fold_index","title":""},{"docid":"cdd15b0fabbd27eecfdfd8f7ba95f9cd","score":"0.72427803","text":"def split_indexes(data_size, k, seed=1):\n np.random.seed(seed)\n indexes = np.random.choice(data_size, data_size, replace=False)\n\n average_size = data_size / float(k)\n fold_indexes = []\n last = 0.0\n\n while last < len(indexes):\n fold_indexes.append(indexes[int(last) : int(last + average_size)])\n last += average_size\n\n train_indices = []\n test_indices = []\n\n for i in range(k):\n test_indices.append(fold_indexes[i])\n train_indices_intermediate = []\n for j in range(k - 1):\n train_indices_intermediate.append(fold_indexes[(i + j + 1) % k])\n train_indices.append(np.concatenate(train_indices_intermediate))\n\n return np.array(test_indices), np.array(train_indices)","title":""},{"docid":"1baccd5915eb59a784e4e15124c66b3c","score":"0.7221375","text":"def training_sample_selection(self, k):\n\n idxs_train = np.empty(0)\n idxs_test = np.empty(0)\n for class_idx in xrange(self.labels.max()):\n idxs_class = np.where(self.labels == class_idx)[0]\n idxs_train = np.append(idxs_train, np.random.choice(idxs_class, k, replace=False)).astype(dtype=np.int32)\n idxs_test = np.setdiff1d(idxs_class, idxs_train)\n idxs_test = np.append(idxs_test,\n np.random.choice(idxs_test, np.min([idxs_test.shape[0], 50]), replace=False))\n\n return idxs_train, idxs_test","title":""},{"docid":"ab25e6e7943c3123b988854ab710eeeb","score":"0.7219873","text":"def split_data(X, k=3, shuffle=True, random_state=None):\n\n all_indices = np.arange(X.shape[0])\n n_samples = X.shape[0]\n\n ## Should initialize what the output will be here.. based on the k there should be indices for X and y rows,\n ## therefore could have a list of tuples??\n\n # In order to align with scikit learn - cross referenced: https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/model_selection/_split.py#L357\n # The first n_samples % n_splits folds have size n_samples // n_splits + 1,\n # other folds have size n_samples // n_splits, where n_samples is the number of samples.\n\n fold_extra = n_samples % k\n num_folds = 0\n\n fold_sizes = (n_samples // k) * np.ones(k, dtype=np.int)\n fold_sizes[0:fold_extra] += 1\n\n val_indicies = []\n\n if shuffle:\n check_random_state(random_state).shuffle(all_indices)\n\n current = 0\n for fold in fold_sizes:\n start, stop = current, current + fold\n val_indicies.append(all_indices[start:stop])\n current = stop\n\n for fold in val_indicies:\n val = fold\n other = np.setdiff1d(all_indices, val)\n yield (val, other)","title":""},{"docid":"6ec3f5b6b1bbe3d2cea31fb2dd8b63d5","score":"0.71851474","text":"def split_data_K(x, y, K, seed=1):\n # set seed\n np.random.seed(seed)\n # generate random indices\n num_row = len(y)\n indices = np.random.permutation(num_row)\n index_split = int(np.floor(num_row/K))\n x_K = []\n y_K = []\n for i in range(K):\n if i!=K:\n index = indices[: i*index_split]\n else:\n index = indices[i*index_split:]\n # create split\n x_K.append(x[index])\n y_K.append(y[index])\n return np.asarray(x_K), np.asarray(y_K)","title":""},{"docid":"a7e6c30b7fd8c5803b965dae9c60bbe0","score":"0.7120892","text":"def split_data_k_fold(x, y, begin, k):\n test_indeces = np.arange(begin*int(np.shape(x)[0]/k), (begin+1)*int(np.shape(x)[0]/k))\n train_indeces = np.asarray(list(range(0, begin*int(np.shape(x)[0]/k))) \\\n + list(range((begin+1)*int(np.shape(x)[0]/k), np.shape(x)[0])))\n train_x, train_y = x[train_indeces], y[train_indeces]\n test_x, test_y = x[test_indeces], y[test_indeces]\n return (train_x, train_y), (test_x, test_y)","title":""},{"docid":"7d6bfe1a1256772b83434d76bef31166","score":"0.7102071","text":"def k_fold_split(X, Y, k=5):\n splits = np.linspace(0, X.shape[0], k + 1).astype(int)\n intervals = zip(splits[:-1], splits[1:]) # the intervals for the test data\n\n for start, stop in intervals:\n # the training set is the entire set excluding test\n X_train = np.vstack([X[:start], X[stop:]])\n Y_train = np.concatenate([Y[:start], Y[stop:]])\n X_test = X[start:stop]\n Y_test = Y[start:stop]\n yield (X_train, Y_train, X_test, Y_test)","title":""},{"docid":"474fedf4aa5dc10c651973859a0b19e6","score":"0.70264345","text":"def split_data(y, x, k_fold, seed, k):\n # K group in train, other in test\n k_indices = build_k_indices(y, k_fold, seed)\n te_ind = k_indices[k]\n\n # k-1 split for training\n tr_ind = [i for i in range(k_indices.shape[0]) if i is not k]\n tr_ind = k_indices[tr_ind,:].reshape(-1)\n\n #Build the x and y train and test set\n y_tr = y[tr_ind]\n y_te = y[te_ind]\n\n x_tr = x[tr_ind, :]\n x_te = x[te_ind, :]\n\n return y_tr, x_tr, y_te, x_te","title":""},{"docid":"f4d57f4eba996fa920bca465e9f0decd","score":"0.6768413","text":"def get_kfold_splits(n_samples, k):\n indices = np.arange(n_samples)\n test_mask = np.empty(n_samples, dtype=bool)\n splits = []\n start = 0\n for i in range(k):\n n_test = n_samples // k\n if i < n_samples % k:\n n_test += 1\n stop = start + n_test\n test_mask[:] = False\n test_mask[start:stop] = True\n splits.append((indices[np.logical_not(test_mask)], indices[test_mask]))\n start = stop\n return splits","title":""},{"docid":"51d8631868f517aab73687b4d3f870fd","score":"0.6755415","text":"def foldIndex(dataset,i, k):\n n = len(dataset)\n indices = np.arange(n)\n a = n*(i-1)//k\n b = (n*(i)//k)\n test_index = indices[a:b]\n size_test = test_index.size\n size_train = int(np.abs(n - size_test))\n train_index = np.zeros(int(np.abs(n - size_test)), dtype=int)\n train_index[:a] = indices[:a]\n train_index[a:] = indices[b:]\n return train_index, test_index","title":""},{"docid":"877d3d873f0b601e05146362a4b6bbd1","score":"0.6670171","text":"def splitDataKFolds(unsplitFeatures, unsplitLabels, k):\r\n if k <= 1:\r\n return unsplitFeatures, unsplitLabels\r\n features, labels = [], []\r\n split = int(len(unsplitFeatures) / k)\r\n for i in range(0,k):\r\n features.append(unsplitFeatures[(split*i):(split*(i+1))])\r\n labels.append(unsplitLabels[(split*i):(split*(i+1))])\r\n return features, labels","title":""},{"docid":"ad108a1189ac462488a3d4f9bf0d8c79","score":"0.65592694","text":"def p_y_x_knn(y, k):\n # print(y.shape)\n #print(y)\n # print(k)\n categories = set(y[0])\n # print(categories)\n # print(number_of_categories)\n result = []\n for row in y:\n result_row = []\n first_k_indexes = row[0:k]\n for i in range(len(categories)):\n result_row.append(np.count_nonzero(first_k_indexes == i+1))\n # print(result_row)\n result.append(result_row)\n result = np.array(result) / k\n #print(result)\n return result","title":""},{"docid":"29eb2eb83272b942afe04017666bfdb1","score":"0.6519975","text":"def splitTrainAndTest(self, num_line, k=10):\n start = 0\n end = 0\n users = []\n for i, line in enumerate(num_line):\n if i == len(num_line) - 1:\n users.append(self.splitFromEachUser(k, newSet[start:]))\n elif num_line[end][0] == line[0]:\n end = i\n else:\n users.append(self.splitFromEachUser(k, newSet[start:i]))\n start = i\n end = i\n dataset =[]\n for i in range(k):\n train = []\n test = []\n for user in users:\n for j in range(k):\n if i!=j:\n train += user[j]\n test += user[i]\n dataset.append((train,test))\n return dataset","title":""},{"docid":"21072028a03ce8f2afe94c1c094701fc","score":"0.6516137","text":"def split(self, X):\n number_folds = self.n_folds\n data_size = len(X)\n len_ = int(data_size / number_folds)\n index_ = np.arange(data_size)\n split_indexes = []\n\n for i in range(number_folds):\n i1 = i * len_\n i2 = (i + 1) * len_\n test_indexes = index_[i1: i2]\n train_indexes = np.concatenate((index_[:i1], index_[i2:]))\n split_indexes.append((train_indexes, test_indexes))\n return split_indexes","title":""},{"docid":"2a701b6c3d5e8a814c5695024bb9e851","score":"0.649697","text":"def skeleton_points(self, k=1):\n if k >= self.dim():\n return range(self.npoints())\n skeleton = set([])\n for face in self.faces_lp(dim=k):\n skeleton.update(face.ambient_point_indices())\n skeleton = sorted(skeleton)\n return skeleton","title":""},{"docid":"17e624009e622270394e14f836d020cd","score":"0.6494762","text":"def fit(self, x_train, y_train, k=1):\n self.k = k\n\n train = []\n for i in range(len(x_train)):\n train.append(x_train[i])\n train[i].append(y_train[i])\n\n self.train = train","title":""},{"docid":"bd124286abea4caa95469d1ad28bfc4b","score":"0.6473322","text":"def get_k_fold_partition(samples, labels, k = 5): \n if k < 2: # k must be at least 2\n k = 2\n \n # samples = dataset[:,:-1] \n # labels = dataset[:,-1] \n folds = StratifiedKFold(labels, k) # Generate the fold indexes.\n \n # Build the 2 sets:\n for train_index, test_index in folds:\n X_train, X_test = samples[train_index], samples[test_index]\n y_train, y_test = labels[train_index], labels[test_index]\n \n train_set = np.concatenate((X_train, y_train[:,newaxis]), axis = 1)\n test_set = np.concatenate((X_test, y_test[:,newaxis]), axis = 1)\n \n return train_set, test_set","title":""},{"docid":"ad7400b1543d8ec9ae08b4658eb1cdd8","score":"0.64665055","text":"def spatial_cv(points, k=10):\n left = points.longitude.min()\n right = points.longitude.max() + 0.01\n top = points.latitude.max()\n bottom = points.latitude.min() - 0.01\n width = right - left\n height = top - bottom\n size_x = width / 10\n size_y = -height / 10\n centroid_x = []\n centroid_y = []\n for i in range(10):\n centroid_x.append(left + size_x / 2 + size_x * i)\n centroid_y.append(top + size_y / 2 + size_y * i)\n cell = []\n for y in range(10):\n for x in range(10):\n cell.append([x, y])\n id_chosen = np.random.choice(range(len(cell)), k, replace=False)\n cell_chosen = [cell[i] for i in id_chosen]\n train_n = int(len(points) / k) * (k - 1)\n test_n = len(points) - train_n\n train_test_split = []\n for c in cell_chosen:\n x, y = centroid_x[c[0]], centroid_y[c[1]]\n points_c= points.copy()\n points_c['x_diff'] = points_c['longitude'] - x\n points_c['y_diff'] = points_c['latitude'] - y\n points_c['dis'] = (points_c['x_diff'] ** 2 + points_c['y_diff'] ** 2) ** .5\n points_c = points_c.sort_values(by='dis')\n id_order = points_c.index.tolist()\n test_id = id_order[:test_n]\n train_id = id_order[test_n:]\n train_test_split.append((train_id, test_id))\n yield (train_id, test_id)","title":""},{"docid":"6ca296aced32ab1aa30a97139440ef9a","score":"0.64552474","text":"def do_split_data(X, y, k = 10):\n return np.array_split(X, k), np.array_split(y, k)","title":""},{"docid":"d59b1f434385c405c7938fc85bc0cf9b","score":"0.64530706","text":"def generate_k(dataset, k):\n # create a list of number 0 - len(dataset)\n # chooses k random numbers\n nums = list(range(0, len(dataset)))\n points = random.choices(nums, k=k)\n return [dataset[i] for i in points]","title":""},{"docid":"aaa8d85e73a15fc92147c62a4d2da2d2","score":"0.64482844","text":"def divide(data, k, i):\r\n\r\n new_data = np.array_split(data, k)\r\n l_sample = new_data[i]\r\n t_sample = []\r\n t_sample_hold = np.delete(new_data, i, axis=0)\r\n for elements in t_sample_hold:\r\n t_sample.extend(elements.tolist())\r\n\r\n np.asarray(t_sample)\r\n return l_sample, t_sample","title":""},{"docid":"003883c0d9b7c534386ebdb7b254a2f9","score":"0.6414206","text":"def kmeans(X, k):\n # 1. Select K points as initial centroids\n idx = np.random.choice(X.shape[0], size=k)\n M = X[idx, :]\n\n # 2. repeat until Centroid do not change:\n ml = np.zeros((k))\n lsse = 0\n S = []\n while True:\n # Form K clusters by assigning each point to its closest centroid\n S = []\n for i in range(k):\n t = [(M[i, 0], M[i, 1])]\n S.append(t)\n S = kmstep(X, M, k, S)\n\n # Recompute the centroid of each cluster\n M = kestep(M, S)\n\n sse = ksse(S)\n if lsse != 0 and lsse - sse < MINDELTA:\n break\n lsse = sse\n\n return M, S, sse","title":""},{"docid":"a19b544fd17ee02ce35972cf496f1680","score":"0.64137846","text":"def data_split(samples,target,test_size = 0.2, k = 10, shuffle = True):\n #\n # convert samples, target to numpy array.\n #\n X = samples\n y = target\n\n #\n # train, test split.\n #\n\n\n X_train, X_test, Y_train, Y_test = train_test_split( X, y, test_size = test_size, shuffle = shuffle)\n\n # train set\n train_set = {}\n train_set['X_train'] = X_train\n train_set['y_train'] = Y_train\n\n # test set\n test_set = {}\n test_set['X_test'] = X_test\n test_set['y_test'] = Y_test\n\n #\n # K-fold cross-validation.\n # https://medium.com/datadriveninvestor/k-fold-cross-validation-6b8518070833\n #\n cv = KFold(n_splits = k, random_state = 42, shuffle = shuffle)\n\n train_crs_val = []\n test_crs_val =[]\n\n for train_index, test_index in cv.split(X):\n x_train, x_test, y_train, y_test = X[train_index], X[test_index], y[train_index], y[test_index]\n # add a fold to the lists\n train_crs_val.append((x_train,y_train))\n test_crs_val.append((x_test,y_test))\n\n return train_set, test_set, train_crs_val, test_crs_val","title":""},{"docid":"45bfd2d45190bcd9ebb9678e47aa859c","score":"0.64032257","text":"def getNeighbors(trainingSet, testPoint, k):\n\tdistances = []\n\tlength = len(testPoint)-1\n\tfor x in range(len(trainingSet)):\n\t\tdist = calculateDistance(testPoint, trainingSet[x], length)\n\t\tdistances.append((trainingSet[x], dist))\n\tdistances.sort(key=operator.itemgetter(1))\n\tneighbors = []\n\tfor x in range(k):\n\t\tneighbors.append(distances[x][0])\n\treturn neighbors","title":""},{"docid":"3f80250d53658de7cee3a7b4683cc0a2","score":"0.63833976","text":"def get_datasets(self, k):\n assert k >= 0, 'Fold index k = {} must be positive'.format(k)\n assert k < self.k, 'Fold index k = {} can not be greater than number of folds {}'.format(k, self.k)\n\n where = self._df.Fold == k\n test_set = self._df[where].copy()\n train_set = self._df[~where].copy()\n\n return train_set, test_set","title":""},{"docid":"7f52bf955a96393342e150e7ddcba0f5","score":"0.6365757","text":"def get_k_nearest_neighbours(sample, training_set, k):\r\n neighbours_list = training_set\r\n # Calculate the distance from each node to the sample node.\r\n for neighbour in training_set:\r\n neighbour.set_distance_to(sample)\r\n neighbours_list.sort()\r\n # Return the k nearest neighbours of the sample node.\r\n return neighbours_list[:k]","title":""},{"docid":"dc9744a0c2798d3ece5c470ba9a7ab7c","score":"0.6360685","text":"def generate_k_folds(dataset, k):\n n_features = len(dataset[0][0])\n dataset = np.concatenate((dataset[0], dataset[1].reshape(len(dataset[0]),1)), axis=1)\n np.random.shuffle(dataset)\n\n output = []\n for i in range(0, k):\n test_start = i * (dataset.shape[0]//k)\n test_end = (i+1) * (dataset.shape[0]//k)\n\n test_features = dataset[test_start:test_end, :n_features]\n test_classes = dataset[test_start:test_end, -1]\n\n train_features = np.concatenate((dataset[0:test_start, :n_features], dataset[test_end:, :n_features]), axis=0)\n train_classes = np.concatenate((dataset[0:test_start, -1], dataset[test_end:, -1]), axis=0)\n \n output.append(([train_features,train_classes],[test_features,test_classes]))\n \n return output","title":""},{"docid":"ba678d9d77018c2ed8866367c3d4eba1","score":"0.6342283","text":"def split_data(x: np.array, y: np.array, k: int, i: int) -> (np.array, np.array, np.array, np.array):\n assert(x.shape[0] == y.shape[0])\n assert(0 <= i < k <= x.shape[0])\n\n size = int(x.shape[0] / k)\n xt = np.concatenate((x[0:size*i, :], x[size*(i+1):size*k, :]), axis=0)\n yt = np.concatenate((y[0:size*i], y[size*(i+1):size*k]), axis=0)\n xv = x[size*i:size*(i+1), :]\n yv = y[size*i:size*(i+1)]\n\n return xt, yt, xv, yv","title":""},{"docid":"af74229215cfff9a1876217569a8febd","score":"0.63304156","text":"def partition_data(dataframe, k=0.2):\n\tmixed_rows = dataframe.reindex(np.random.permutation(dataframe.index))\n\trows = dataframe.shape[0]\n\tnr_trows = 0.2*rows\n\tnr_valrows = nr_trows + 0.2*rows \n\tt_data = mixed_rows[:int(nr_trows)]\n\tval_data = mixed_rows[int(nr_trows):int(nr_valrows)]\n\ttrain_data = mixed_rows[int(nr_valrows):]\n\treturn [t_data, val_data, train_data]","title":""},{"docid":"e7a7e55d5a50b46ff22bb9b280f1692e","score":"0.6312328","text":"def create_dataset_split_indices(data, train_size):\n dataset_split_indices_holder = {}\n # NOTE: in matlab the index starts from 1 in python its starts from 0\n for session_id in range(data.shape[0]):\n num_spike_trials = data[session_id]['spks']\n # get trails for the neurons\n session_id_trial_indices = np.random.permutation(num_spike_trials.shape[1])\n # NOTE: split done for only train and test sets, I think validation set is also useful can easily be modified later\n #training_idx, temp_valid_idx = np.split(session_id_indices, [int(train_per * session_id_indices.shape[0])])\n training_trials_idx, test_trails_idx = np.split(session_id_trial_indices, [int(train_size * session_id_trial_indices.shape[0])])\n assert session_id_trial_indices.shape[0] == (training_trials_idx.shape[0] + test_trails_idx.shape[0])\n dataset_split_indices_holder[session_id] = {'train': training_trials_idx, 'test': test_trails_idx}\n\n return dataset_split_indices_holder","title":""},{"docid":"514ca2f0accf574465d7e3da6ed86b3d","score":"0.63025355","text":"def k_fold_cross_validation_sets(X, y, k, shuffle=True):\n if shuffle:\n X, y = shuffle_data(X, y)\n\n n_samples = len(y)\n left_overs = {}\n n_left_overs = (n_samples % k)\n if n_left_overs != 0:\n left_overs[\"X\"] = X[-n_left_overs:]\n left_overs[\"y\"] = y[-n_left_overs:]\n X = X[:-n_left_overs]\n y = y[:-n_left_overs]\n\n X_split = np.split(X, k)\n y_split = np.split(y, k)\n sets = []\n for i in range(k):\n X_test, y_test = X_split[i], y_split[i]\n X_train = np.concatenate(X_split[:i] + X_split[i + 1:], axis=0)\n y_train = np.concatenate(y_split[:i] + y_split[i + 1:], axis=0)\n sets.append([X_train, X_test, y_train, y_test])\n\n # Add left over samples to last set as training samples\n if n_left_overs != 0:\n np.append(sets[-1][0], left_overs[\"X\"], axis=0)\n np.append(sets[-1][2], left_overs[\"y\"], axis=0)\n\n return np.array(sets)","title":""},{"docid":"f77a7e3d258a913d405486fe360739a3","score":"0.6287668","text":"def get_neighbors(train, test_data, k):\n distances = list() # Initialize list of distances\n\n for train_data in train: \n dist = get_distance(test_data, train_data) # Find distance between inputted test_data and every train_data in train\n distances.append((train_data, dist)) # Add this distance to our list of distances\n\n distances.sort(key=lambda tup: tup[1]) # Sort list of distances in non-decreasing fashion\n neighbors = list() # Initialize list of neigboring points to test_data\n \n for i in range(k):\n neighbors.append(distances[i][0]) # Add first k training data points according to their distances to list of neighboring points\n return neighbors # Return the k closest training data points (already classfified points) to the test data point","title":""},{"docid":"9f218b95d4cb307fcae9bde09301c294","score":"0.6276425","text":"def split_data_for_cv(self, k=10):\n cv_data_dict = {}\n cv_ys_dict = {}\n\n data = list(zip(self.xs, self.ys))\n random.shuffle(data)\n\n total_length = self.data_shape[0]\n\n k_length = round(total_length / k)\n\n for i in range(k-1):\n fold_data = data[i*k_length:(i+1)*k_length]\n fold_xs, fold_ys = list(zip(*fold_data))\n cv_data_dict[i+1] = np.array(fold_xs)\n cv_ys_dict[i+1] = np.array(fold_ys)\n final_fold_data = data[(k-1)*k_length:]\n final_xs, final_ys = list(zip(*final_fold_data))\n cv_data_dict[k] = np.array(final_xs)\n cv_ys_dict[k] = np.array(final_ys)\n\n return cv_data_dict, cv_ys_dict","title":""},{"docid":"89146ade849b61998d7aa2d17a67177f","score":"0.62750894","text":"def knn(x, k):\r\n inner = -2 * torch.matmul(x.transpose(2, 1), x)\r\n xx = torch.sum(x**2, dim=1, keepdim=True)\r\n pairwise_distance = -xx - inner - xx.transpose(2, 1)\r\n\r\n idx = pairwise_distance.topk(k=k, dim=-1)[1] # [batch_size, num_points, k]\r\n return idx","title":""},{"docid":"9e4aa4bbd49dbbaa55fa0cc54dd6ea17","score":"0.62715167","text":"def build_k_indices(y, K, seed):\n num_row = y.shape[0]\n interval = int(num_row / K)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n K_indices = [indices[k * interval: (k + 1) * interval] for k in range(K)]\n return np.array(K_indices)","title":""},{"docid":"847b177913553c3078de1ea1d0e12aa3","score":"0.62685627","text":"def split_into_k_folds(links, k=5, strategy='random', sample_seed=2017):\n # print(links[:10])\n if strategy == 'random':\n kf = KFold(n_splits=k, random_state=sample_seed, shuffle=True)\n split_indices = kf.split(links)\n return split_indices\n elif strategy == 'random_per_source':\n pass","title":""},{"docid":"f9b8dfb4a8c066918eafdfcce8bfa207","score":"0.62584865","text":"def myKmeans(data, k):\n centers = np.zeros((k, data.shape[1]))\n index = np.zeros(data.shape[0], dtype=int)\n clusters = [[] for i in range(k)]\n\n # random centers initialization\n for i in range(k):\n centers[i] = data[random.randint(0, data.shape[0] - 1)]\n\n convergence = False\n iterationNo = 0\n while not convergence:\n convergence = True\n for i in range(k):\n clusters[i] = []\n\n # assign each point to the cluster of closest center\n for i in range(data.shape[0]):\n min_dist = np.inf\n closest_center = 0\n for j in range(k):\n dist = np.linalg.norm(data[i] - centers[j])\n if dist < min_dist:\n min_dist = dist\n closest_center = j\n clusters[closest_center].append(data[i])\n index[i] = closest_center\n # update clusters' centers\n for i in range(k):\n if len(clusters[i]) != 0:\n c = sum(clusters[i]) / float(len(clusters[i]))\n if np.linalg.norm(c - centers[i]) > 2:\n convergence = False\n centers[i] = c\n iterationNo += 1\n print('iterationNo = ', iterationNo)\n\n return index, centers","title":""},{"docid":"cd4da4b0de94fdb02ccd7ca0649e8c89","score":"0.6244749","text":"def query_train(self, data, k):","title":""},{"docid":"09660fade005ea6cd5692c69996fc49b","score":"0.62347436","text":"def fit(self):\n num_train_points = self.train_x.shape[0] # num sample points\n centroid_ids = np.random.choice(num_train_points, (self.k,), replace=False)\n self.centroids = self.train_x[centroid_ids, :]","title":""},{"docid":"a0b9966a56bac662e3bd2999a9d70b70","score":"0.6230166","text":"def kmember_partition(data, k=20, verbose=False):\n clusters = []\n # randomly choose seed and find k-1 nearest records to form cluster with size k\n r_pos = random.randrange(len(data)) # select a single point\n r_i = data[r_pos]\n while len(data) >= k:\n t = time.time()\n r_pos = m_find_furthest_record(r_i, data)\n r_i = data.pop(r_pos)\n cluster = [r_i]\n while len(cluster) < k:\n #t = time.time()\n r_pos = m_find_best_record_iloss_increase(cluster, data)\n #print('Time for finding best record: ', time.time() - t)\n r_j = data.pop(r_pos)\n #print('r_j ', r_j)\n cluster.append(r_j)\n #print(cluster)\n clusters.append(cluster)\n if verbose==True:\n print('Time for finding this cluster: ', time.time() - t)\n print('Current remaining datapoints: ', len(data))\n # residual assignment\n while len(data) > 0:\n t = data.pop()\n cluster_index = m_find_best_cluster_iloss_increase(t, clusters)\n clusters[cluster_index].append(t)\n return clusters","title":""},{"docid":"2657c3fe0b2ee0ed452ca9b602bd3890","score":"0.6227386","text":"def find_clusters(point_lst, k):\n assert len({len(x) for x in point_lst}) == 1, \\\n \"not all points same # of dimensions\"\n new_centroids = init_centroids(point_lst, k)\n old_centroids = None\n i = 0\n while new_centroids != old_centroids:\n i += 1\n # in case it keeps flipflopping\n if i > 500:\n x = None\n while x not in ['y', 'n']:\n x = input('500 iterations have passed, do you want to break'\n ' (y/n)?')\n if x == 'y':\n break\n else:\n i = 0\n continue\n cluster_lst = associate_cluster(point_lst, new_centroids)\n # sorts out loss of clusters\n if len(set(cluster_lst)) != k:\n missing_k_s = list(set(range(1, k+1)) - set(cluster_lst))\n # print('clusters added:', missing_k_s)\n # getting indices in the for loop gave the same result both loops\n # also, random choices gave me the same 2 options at the beginning\n # solved by adding more samples + set\n indices = list(set(random.choices(list(range(len(cluster_lst))),\n k=len(missing_k_s)+20)))\n for i, new_clust in enumerate(missing_k_s):\n cluster_lst[indices[i]] = new_clust\n\n old_centroids = new_centroids\n new_centroids = make_centroids(point_lst, cluster_lst)\n\n return cluster_lst, new_centroids, i","title":""},{"docid":"33a831459d9f42ab546beb8c0baae264","score":"0.62239933","text":"def knn(p, data, k=3):\t\r\n\tdistances = np.zeros(data.shape[0])\r\n\tfor i in range(len(data)):\r\n\t\tdistances[i] = distance(p, data[i])\r\n\tind = np.argsort(distances)\r\n\treturn ind[:k]","title":""},{"docid":"6fe441eba6cca72524a1f7eb943c7b3c","score":"0.6223551","text":"def mean_k(data, k):\n n = len(data)\n mean_k_data = list()\n for i, datapoint in enumerate(data):\n a = max(0, int(np.ceil(i - k/2)))\n b = min(n, int(np.floor(i + k/2)))\n mean_k_datapoint = np.mean(data[a:b+1])\n mean_k_data.append(mean_k_datapoint)\n\n return mean_k_data","title":""},{"docid":"bfb3ef8e9bedca2aa1da18e74402b63b","score":"0.6221975","text":"def k_closet_points(points, k):\n\n distances = map(lambda point: sum(map(lambda x: x**2, point)), points)\n k_closet_indexs = sorted(range(len(points)), key=lambda i: distances[i])[:k]\n return [points[i] for i in k_closet_indexs]","title":""},{"docid":"d87edc87067f442f9453811d0f51a399","score":"0.6218206","text":"def build_k_indices(y, k_fold, seed=1):\n\n # Get the number of rows of the data\n num_row = y.shape[0]\n # Get the number of rows for each interval depending on k\n interval = int(num_row / k_fold)\n\n np.random.seed(seed)\n\n # Shuffle the indeces of the data\n indices = np.random.permutation(num_row)\n\n # Get an array of the indeces that will go in each k interval\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n\n return np.array(k_indices)","title":""},{"docid":"260138d6eab4afada61c9aa7b73fb226","score":"0.6200755","text":"def k_folds(data: tdata.Dataset, n_splits: int):\n n = len(data)\n indices = np.arange(n).astype(int)\n for val_idx in get_indices(n_splits, n):\n train_idx = np.setdiff1d(indices, val_idx)\n yield tdata.Subset(data, train_idx), tdata.Subset(data, val_idx)","title":""},{"docid":"71a1da5bdb2feacc19083ae5ec9e649c","score":"0.61944693","text":"def get_kNN(self, test_points, k):\n \n # calculate all distances between training and 'test_points'\n dists = self.calc_distance(test_points)\n \n # sort array to find k-nearest neighbours\n NNs_idx = np.argsort(dists, axis=1)[:,:k]\n\n # find labels of k-nearest neighbours\n NNs_labels = self.train_set.labels[NNs_idx]\n \n return NNs_labels","title":""},{"docid":"2e6a9a92ad70985a7b8d76b169967030","score":"0.6191881","text":"def growing_sequential_kfold(dataset, k):\n if k < 2:\n raise ValueError(\"Parameter k has to be greater than 1.\")\n folds = []\n\n for i in range(1, k + 1):\n proportions = [i, 1, 1, k - i] # trainset, validset, testset, leftover\n folds.append(split_dataset(dataset, proportions)[:3])\n\n return folds","title":""},{"docid":"1c34d89776b36a537219dbfdcecdc0b6","score":"0.6190533","text":"def nearestNeighbors(trainData, k=3):\n\n length = trainData.shape[0]\n # Label each one with indices\n responses = np.arange(0, length).astype(np.float32)\n knn = cv2.ml.KNearest_create()\n knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)\n\n neighbor_array = []\n for newcomer in trainData:\n query = np.reshape(newcomer, (1, 2))\n ret, results, neighbors, dist = knn.findNearest(query, k+1)\n neighbor_array.append(list(map(int, neighbors[0, 1:]))) # exclude itself.\n return neighbor_array","title":""},{"docid":"5892a8f11daa370c65e4bab67fe6ec96","score":"0.6169984","text":"def divide_train_test(y, tx, k_indices, k):\n tr_indice = k_indices[~(np.arange(k_indices.shape[0]) == k)].reshape(-1)\n test_tx = tx[k_indices[k]]\n test_y = y[k_indices[k]]\n train_tx = tx[tr_indice]\n train_y = y[tr_indice]\n \n return test_tx, test_y, train_tx, train_y","title":""},{"docid":"903dcc6ca8323af56cca4cd03664079c","score":"0.61693597","text":"def initialize_clusters(points, k):\n return points[np.random.randint(points.shape[0], size=k)]","title":""},{"docid":"6884f5935d673d3a4a0958cb8db0ad4d","score":"0.61665565","text":"def sampling_points(mask, N, k=3, beta=0.75, training=True):\n\n assert mask.dim() == 5, \"Dim must be N(Batch)CDHW\"\n device = mask.device\n B, _, D, H, W = mask.shape\n mask, _ = mask.sort(1, descending=True)\n\n if not training:\n D_step, H_step, W_step = 1 / D, 1 / H, 1 / W\n N = min(D * H * W, N)\n uncertainty_map = -1 * (mask[:, 0] - mask[:, 1])\n _, idx = uncertainty_map.view(B, -1).topk(N, dim=1) # idx:N个最不确定的点的索引\n\n points = torch.zeros(B, N, 3, dtype=torch.float, device=device)\n # 因为推理时候没用使用rand生成[0,1]的随机索引，所以需要从idx反推回去属于[0,1]的索引\n points[:, :, 0] = W_step / 2.0 + (idx % W).to(torch.float) * W_step # 表示列索引号\n points[:, :, 1] = H_step / 2.0 + (idx % (H * W) // W).to(torch.float) * H_step # 表示行索引号\n points[:, :, 2] = D_step / 2.0 + (idx // (H * W)).to(torch.float) * D_step # 表示层索引号\n\n return idx, points\n\n # Official Comment : point_features.py#92\n # It is crucial to calculate uncertanty based on the sampled prediction value for the points.\n # Calculating uncertainties of the coarse predictions first and sampling them for points leads\n # to worse results. To illustrate the difference: a sampled point between two coarse predictions\n # with -1 and 1 logits has 0 logit prediction and therefore 0 uncertainty value, however, if one\n # calculates uncertainties for the coarse predictions first (-1 and -1) and sampe it for the\n # center point, they will get -1 unceratinty.\n\n over_generation = torch.rand(B, k * N, 3, device=device) # 随机选取一堆点的坐标\n over_generation_map = point_sample(mask, over_generation,\n align_corners=False) # 获取mask上这些坐标位置上的值（可以理解为概率值）(N,C,P) C为类别数\n\n uncertainty_map = -1 * (over_generation_map[:, 0] - over_generation_map[:, 1])\n _, idx = uncertainty_map.topk(int(beta * N), -1) # idx:over_generation 上的最重要的beta*N个点的位置\n\n shift = (k * N) * torch.arange(B, dtype=torch.long, device=device)\n\n idx += shift[:, None] # batch_size=1的话相当于没变\n\n importance = over_generation.view(-1, 3)[idx.view(-1), :].view(B, int(beta * N), 3)\n coverage = torch.rand(B, N - int(beta * N), 3, device=device)\n return torch.cat([importance, coverage], 1).to(device)","title":""},{"docid":"106c74f64e180d15718d6c7796d4dcc3","score":"0.6144333","text":"def k_fold_cross_validation(k: int, num_data: int, shuffle: bool = True) -> tuple:\r\n\r\n assert num_data >= k, \"Total amount of data needs to be larger or equal to the number of folds!\"\r\n\r\n # create indices, corresponding to the data points, and shuffle them, if required\r\n indices = list(range(num_data))\r\n if shuffle:\r\n random.shuffle(indices)\r\n\r\n # compute the sizes of the folds and the remaining number of data\r\n fold_size = int(num_data / k)\r\n remaining_data = num_data % k\r\n\r\n # compute the splits\r\n fold_list = []\r\n for i in range(k):\r\n fold_list.append(indices[i*fold_size:(i+1)*fold_size])\r\n\r\n # append the remaining data points to the folds\r\n for i in range(remaining_data):\r\n fold_list[i].append(indices[k*fold_size+i])\r\n split = tuple(fold_list)\r\n return split","title":""},{"docid":"d33282b19b9f716118c2194e84a88f56","score":"0.6140482","text":"def k_means(dataset, k, maxit=300):\n\tcentroids = dataset[np.random.permutation(dataset.shape[0])[:k]]\n\tfor i in range(maxit):\n\t\tprevCentroids = centroids\n\t\tlabels = setClusters(dataset, prevCentroids)\n\t\tcentroids = setCetroids(dataset, labels, k)\n\t\terror = calcError(dataset, labels, centroids)\n\t\tif np.all(centroids == prevCentroids):\n\t\t\t# print(i)\n\t\t\treturn labels, centroids, error\n\treturn labels, centroids, error","title":""},{"docid":"6e4d35660e0b6076ac5f224e0cc00511","score":"0.613471","text":"def getNeighbors(X_train, X_test_i, k):\r\n dists = []\r\n \r\n for h in range(len(X_train)): # For each data element,\r\n dist = euclideanDist(X_train[h], X_test_i) # get distance to X_test_i\r\n dists.append((h, dist)) # Add it to a list\r\n \r\n dists.sort(key=operator.itemgetter(1)) # Sort the list by distance\r\n\r\n plt = knnPlot(dists, k)\r\n \r\n # Uncomment the following line if you want to see the euclidean distances\r\n # for every row, rather than just one example row.\r\n #plt.show()\r\n \r\n neighbors = []\r\n \r\n for n in range(k): # For the k nearest neighbors\r\n neighbors.append(dists[n][0]) # Add them to a list\r\n \r\n return neighbors, plt","title":""},{"docid":"efb379d1eb0e8b7f5ca3edde07a8b25b","score":"0.61294323","text":"def k_nn(train_x, train_y, test_x, k):\n # Calculate the distance of current point from all other points.\n # Complexity: O(nf), where f is number of features\n dist = numpy.sqrt(sum(numpy.square(train_x - test_x).T)).T\n # Add labels with corresponding distances\n dist_with_label = numpy.hstack([train_y, dist]).T\n # Get index of smallest k elements. Complexity: O(n + klog(k))\n dist_with_label_sorted = numpy.argpartition(dist_with_label, -k).T[:k]\n # Arrange 'dist_with_label' according to sorted index of distance\n inter = dist_with_label[:, dist_with_label_sorted[:, 1]].reshape([2, k])\n # Get the top labels in 1D array\n top_labels = numpy.asarray(inter[0]).reshape([k])\n # Get the most common neighbor. Complexity: O(k)\n k_nn_label = collections.Counter(top_labels).most_common(1)[0][0]\n return k_nn_label","title":""},{"docid":"f4d111b91072156275dac7f4948dd7df","score":"0.611971","text":"def build_k_indices(y, k_fold, seed):\n np.random.seed(seed)\n\n num_row = len(y)\n interval = int(num_row/k_fold)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n \n return np.array(k_indices)","title":""},{"docid":"2947c1ec8413ada710717c142047d756","score":"0.6109455","text":"def calc_k_nearest_neighbors(data, k):\r\n # Calculate the squared euclidean pairwise distances.\r\n squared_distance_matrix = calc_squared_euclidean_distance_matrix(data)\r\n\r\n # The i-th row contains the indices of the k nearest neighbors of the i-th point.\r\n nearest_neighbors = np.argpartition(squared_distance_matrix, k, axis=1)[:, :k]\r\n\r\n return nearest_neighbors","title":""},{"docid":"bff6b534b804de135572ed3d3113569b","score":"0.6101243","text":"def kmeans(X: Union[tf.Tensor, np.ndarray], \n k: int, \n n_iter: int = 10) -> (tf.Tensor, tf.Tensor):\n\n X = tf.convert_to_tensor(X)\n X = tf.cast(X, tf.float32)\n assert len(tf.shape(X)) == 2, \"Training data X must be represented as 2D array only\"\n m = tf.shape(X)[0]\n\n random_select = tf.random.shuffle(X)\n init_centroids = random_select[:k]\n\n clusters = tf.zeros([m, ], dtype=tf.int64)\n centroids = init_centroids\n prev_centroids = centroids\n\n for _ in tf.range(n_iter + 1):\n squared_diffs = tf.square(X[None, :, :] - centroids[:, None, :])\n euclidean_dists = tf.reduce_sum(squared_diffs, axis=-1) ** 0.5\n clusters = tf.argmin(euclidean_dists, axis=0)\n\n selector = tf.range(k, dtype=tf.int64)[:, None] == clusters[None, :]\n centroids_array = tf.TensorArray(tf.float32, size=k)\n prev_centroids = centroids\n\n for c in tf.range(k):\n select = selector[c]\n points = X[select]\n mean_points = tf.reduce_mean(points, axis=0)\n centroids_array = centroids_array.write(c, mean_points)\n \n centroids = centroids_array.stack()\n\n return prev_centroids, clusters","title":""},{"docid":"0ee36d82a7610269c3b6e8a6e85b710e","score":"0.609205","text":"def find_centers(X, k):\n estimator = KMeans(init='k-means++', n_clusters=k, n_init=10)\n estimator.fit(X)\n mu = estimator.cluster_centers_\n labels = estimator.labels_\n return mu, labels","title":""},{"docid":"c73da6341fc83aad030f15f491ac254e","score":"0.6064342","text":"def k_means(points, k, iterations=10):\n idx = np.random.randint(len(points),size=k)\n\n centroids = points[idx,:]\n clusters = get_clusters(points,centroids)\n\n for i in range(iterations):\n\n # Use get_clusters() and update_centroids() for each iteration.\n\n return clusters,centroids","title":""},{"docid":"2c043a13dfd705b4742319d9093d1d47","score":"0.60559326","text":"def farthest_point_sampling(point_set, k):\n num_pts = point_set.shape[0]\n start_idx = np.random.randint(num_pts)\n existing_set = np.expand_dims(point_set[start_idx], axis=0)\n rest_set = np.copy(point_set)\n np.delete(rest_set, start_idx, 0)\n\n existing_indices = [start_idx]\n rest_indices = np.arange(num_pts)\n np.delete(rest_indices, start_idx)\n\n for _ in range(k - 1):\n dist = (\n np.sum(np.square(existing_set), axis=1, keepdims=True) +\n np.sum(np.square(rest_set.T), axis=0, keepdims=True) -\n np.dot(existing_set, rest_set.T) * 2)\n min_dist = dist.min(axis=0)\n max_idx = min_dist.argmax()\n existing_set = np.concatenate(\n [existing_set, np.expand_dims(rest_set[max_idx], axis=0)], axis=0)\n existing_indices.append(rest_indices[max_idx])\n np.delete(rest_set, max_idx, 0)\n np.delete(rest_indices, max_idx)\n\n return existing_set, existing_indices","title":""},{"docid":"1fe2f1a8bb24a2ce48cf3b66007c6fa4","score":"0.6048238","text":"def initialize_centroids(points, k):\n centers_id = random.sample(range(points.shape[0]), k)\n return points[centers_id]","title":""},{"docid":"edff4477083c4ef81ff1c393b9514716","score":"0.60462356","text":"def kmeans_from_data_in_some_space(X, k, n_init=3):\n\n # validating the X data\n if X.ndim != 2 or X.shape[0] < 1 or X.shape[1] < 1:\n raise ValueError('Data must be a valid 2D matrix.')\n\n # validating the amount of clusters\n if k <= 0:\n raise ValueError('The amount of clusters must be positive.')\n\n # creating an instance of KMeans clustering algorithm\n kmeans = KMeans(n_clusters=k, n_init=n_init, random_state=RND_MAGIC_SALT)\n\n # performing clustering\n kmeans.fit(X)\n\n # returning computed partition\n return kmeans.labels_","title":""},{"docid":"06767de26f3683137f8aa857062a9e49","score":"0.60252905","text":"def get_split_points(data):\n train_split_points, val_split_points = [], []\n min_block_num = data.date_block_num.min()\n max_block_num = data.date_block_num.max()\n \n for date_block_num in range(min_block_num, min_block_num + 5):\n begin_val = data.date_block_num.searchsorted(max_block_num - 4 + date_block_num - min_block_num)\n begin_train = 0 #data.date_block_num.searchsorted(date_block_num)\n end_train = begin_val\n if date_block_num == min_block_num + 4:\n end_val = len(data)\n else:\n end_val = data.date_block_num.searchsorted(max_block_num - 4 + date_block_num - min_block_num + 1)\n val_split_points.append((begin_val, end_val))\n train_split_points.append((begin_train, end_train))\n return train_split_points, val_split_points","title":""},{"docid":"26d2d7261a3edf2750ae835b358b08d8","score":"0.60249066","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"1af2a595ade396ff6cf85f9e23252161","score":"0.6021595","text":"def kmeans(X, k, max_iter=100):\n\n centers = None\n assign = None\n i=0\n \n start = time.time()\n\n\n #######################################################################\n # TODO: #\n # Perfom k-means clustering of the input data X and store the #\n # resulting cluster-centers as well as cluster assignments. #\n # #\n #######################################################################\n\n centers = X[np.random.randint(X.shape[0], size=k)]\n assign = np.zeros(X.shape[0], dtype=np.float64)\n distances = np.zeros([X.shape[0], k], dtype=np.float64)\n \n for i in range(max_iter):\n for i, c in enumerate(centers):\n distances[:, i] = np.linalg.norm(X - c, axis=1)\n assign = np.argmin(distances, axis=1)\n for c in range(k):\n centers[c] = np.mean(X[assign==c], 0)\n \n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n \n exec_time = time.time()-start\n print('Number of iterations: {}, Execution time: {}s'.format(i+1, exec_time))\n \n return centers, assign","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.6020689","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"8510d2a385e963218ce03c0c36b1adc1","score":"0.60188293","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"8510d2a385e963218ce03c0c36b1adc1","score":"0.60188293","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"188084aa844ff27c527ac6aff1886226","score":"0.6013784","text":"def _get_train_idx(self):\n len_list = [len(df) for df in self.df]\n \n bz_t = self.bz//len(len_list)\n batch_num = [x//bz_t for x in len_list]\n\n batch_nth = [0] * len(len_list)\n\n select = [list(range(x)) for x in len_list]\n\n for s in select:\n random.shuffle(s)\n\n while True:\n idxs = []\n for i in range(len(len_list)):\n if batch_nth[i] >= batch_num[i]:\n batch_nth[i] = 0\n random.shuffle(select[i])\n idx = select[i][batch_nth[i]*bz_t:(batch_nth[i]+1)*bz_t]\n batch_nth[i] += 1\n idxs.append(idx)\n\n yield idxs","title":""},{"docid":"cd6aab0dd92d74fff39a8685a7201c33","score":"0.6010272","text":"def train_knn(training_data, label_data, test_data): \r\n predict_labels = [] \r\n for x_test in test_data:\r\n distances = np.sqrt(np.sum((training_data - x_test) ** 2, axis = 1))\r\n min_index = np.argmin(distances)\r\n predict_labels.append(label_data[min_index])\r\n return predict_labels","title":""},{"docid":"4c50495deba095f677c64395f617276e","score":"0.6005331","text":"def split_training_set(self, size: int, start: int = 0):\n return self.training_set.split(size=size, start=start)","title":""},{"docid":"949db4f7961ff6509afb916072ce7466","score":"0.6005121","text":"def get_set_from_partitions(partitioned_samples, partition_num = 0):\n\n k = partitioned_samples.shape[2]\n N = partitioned_samples.shape[0]\n\n test_set = partitioned_samples[:, :, partition_num]\n\n train_set = np.zeros((N*(k-1), partitioned_samples.shape[1]))\n\n j = 0\n for i in range(k):\n if i != partition_num:\n train_set[j * N:(j + 1) * N, :] = partitioned_samples[:, :, i]\n j += 1\n\n return train_set, test_set","title":""},{"docid":"64144d1e57f39091394b346a5ee8a50b","score":"0.5998632","text":"def __knnsearch_self(self,seeds,points,K):\n nseeds = seeds.shape[0]\n npoints = points.shape[0]\n #dim = seeds.shape[1]\n ind_dis = np.zeros([npoints,2,K])\n distmatrix = pairwise_distances(points, seeds,metric = 'euclidean')\n for kidx in range(K):\n for pind in range(0,npoints):\n leastInd = self.__quickSelect(distmatrix[pind,:], range(nseeds),kidx)\n ind_dis[pind,:,kidx]=np.array([leastInd, distmatrix[pind,leastInd]])\n return ind_dis[:,0,:], ind_dis[:,1,:]","title":""},{"docid":"29bdb193d3376a064f0f8886dec5795e","score":"0.59973997","text":"def k_plus_proche(p_test):\n #Création de la liste avec tout les distances, ainsi que la liste l_indices contenant les indices associés aux distances, elle même assosocié aux points \n l = list()\n for p_train in points_train:\n l.append(distance(p_test, p_train))\n l_indice = list(range(len(points_train)))\n\n for j in range(len(points_train)):\n for i in range(len(points_train)-1):\n if l[i+1]<l[i]:\n #on actualise la liste de distance, normal \n tmp = l[i]\n l[i] = l[i+1]\n l[i+1]= tmp\n\n #MAIS AUSSI la l_indice, pour garder une trace de des indices et continuer de les associer aux points\n tmp = l_indice[i]\n l_indice[i] = l_indice[i+1]\n l_indice[i+1]= tmp\n return l_indice[:k]","title":""},{"docid":"4c802448acd3883278b0bf5c7ee24ad5","score":"0.59970415","text":"def k_fold_CV(k, X, y):\r\n \r\n n, m = np.shape(X)\r\n ind = np.arange(0, n, k)\r\n for i in ind:\r\n X_test, y_test = X[i:(i + k),:], y[i:(i + k)]\r\n if i == 0:\r\n X_train, y_train = X[i + k:, :], y[i + k:]\r\n elif i == (n - k):\r\n X_train, y_train = X[:i, :], y[:i]\r\n else:\r\n X_train = np.vstack([X[:i, :], X[i + k:, :]])\r\n y_train = np.hstack([y[:i], y[i + k:]])\r\n yield X_train, X_test, y_train, y_test","title":""},{"docid":"beb9cbaed7852008ee9fefd6d2fd8536","score":"0.59962285","text":"def kmeans_pp_init(X, k, metric):\r\n centers = np.zeros((k, X.shape[1]))\r\n indices_centers = np.zeros(k).astype(int)\r\n\r\n # choose first center uniformly random from data\r\n idx_chosen = np.random.choice(np.arange(X.shape[0]))\r\n indices_centers[0] = idx_chosen\r\n centers[0] = X[idx_chosen]\r\n centers_chosen = 1\r\n\r\n while centers_chosen < k:\r\n\r\n # choose center from remaining data points\r\n remaining_data = np.delete(X, indices_centers, axis=0)\r\n # compute distance of each point in data set from centers\r\n distances = metric(remaining_data, centers[:centers_chosen]) # Nxk\r\n\r\n # extract minimal distance for each point (for closest center)\r\n min_distances = np.min(distances, axis=1)[:, None] # Nx1\r\n D_squared = np.square(min_distances) # Nx1\r\n\r\n # choose center randomly with weighted probability ~ D_squared\r\n probabilities = (D_squared / np.sum(D_squared)).flatten()\r\n indices = np.arange(probabilities.size)\r\n indices_centers[centers_chosen] = np.random.choice(indices, p=probabilities)\r\n centers[centers_chosen] = remaining_data[indices_centers[centers_chosen]]\r\n centers_chosen += 1\r\n\r\n return centers","title":""},{"docid":"4e58be2d99295520c95582b070de5308","score":"0.59911495","text":"def build_k_indices(y, k_fold, seed = 1):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"d82b42b1f3b69ce47740a2557e8905ab","score":"0.5988685","text":"def build_k_indices(y, k_fold, seed):\n \"\"\"build k indices for k-fold.\"\"\"\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"ba1d5c3f7ac206297c008e3fafbcc452","score":"0.5980611","text":"def get_clusters_Kmeans(K, MU, X):\n \n # getting square pairwise distances\n pair_dist = tf.Session().run(distanceFunc_Kmeans(X, MU))\n \n # getting most likely cluster by index\n S_indices = np.argmin(pair_dist, axis=1)\n \n # getting list of actual data grouped by cluster\n S = [None]*K\n for i in range(K):\n S[i] = X[S_indices == i] \n \n return S","title":""},{"docid":"8360b654b2b9b7af29fd23433fb30ee4","score":"0.5979377","text":"def _k_nearest_indices(self, array):\n \n # Calculate the Euclidean distances between the new data point and \n # the training data.\n euclidean_distances = self._euclidean_distances(array)\n # Sort the indices from smallest Euclidean distance from the new\n # data point to the largest.\n sorted_indices = np.argsort(euclidean_distances, \n axis=None, \n kind=\"quicksort\")\n # Save the indices of the k nearest points.\n k_nearest_indices = sorted_indices[0:self.k]\n # Return the indicies of the k nearest points.\n return k_nearest_indices","title":""},{"docid":"f22ac42024ec7240bd5418636ffaf4df","score":"0.5977788","text":"def build_k_indices(y, k_fold, seed=1):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"46670d218b296aed126f037ed1b85be7","score":"0.5977213","text":"def p_y_x_knn(y, k):\n result = np.zeros(shape=(y.shape[0],4))\n y=np.delete(y,range(k,y.shape[1]), axis=1) #take k closest\n for i in range(y.shape[0]):\n result[i] = np.bincount(y[i],minlength=4) #minlength = 4 -> 4 classess\n return np.divide(result,k)\n #pass","title":""},{"docid":"e66181bd317136a0ae2c74aa3c44c4d2","score":"0.59653527","text":"def get_neighbours_index(X_train, X_test_individual, k): \n distances = [] # list for the distances\n neighbors_index = [] # list for the neighbors\n for i in range(0, X_train.shape[0]):\n dist = distance(X_train[i], X_test_individual) #calculates the distance between the x_test point and all the x_train points\n dist = np.absolute(dist) #makes sure it is not negative\n distances.append((i, dist))\n distances.sort(key=operator.itemgetter(1)) #sorted the list of distances, wherein the distance and INDEX is listed\n for x in range(k):\n neighbors_index.append(distances[x][0]) #list only the index of the nearest neighbours and not their distance\n\n return neighbors_index","title":""}],"string":"[\n {\n \"docid\": \"88ab12ff6a2547a51bc03c813ace4c65\",\n \"score\": \"0.7543154\",\n \"text\": \"def k_split(dataset, k):\\n\\n\\n index = np.random.permutation(dataset[0].shape[0])\\n fold_index= np.split(index, k)\\n\\n return fold_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd15b0fabbd27eecfdfd8f7ba95f9cd\",\n \"score\": \"0.72427803\",\n \"text\": \"def split_indexes(data_size, k, seed=1):\\n np.random.seed(seed)\\n indexes = np.random.choice(data_size, data_size, replace=False)\\n\\n average_size = data_size / float(k)\\n fold_indexes = []\\n last = 0.0\\n\\n while last < len(indexes):\\n fold_indexes.append(indexes[int(last) : int(last + average_size)])\\n last += average_size\\n\\n train_indices = []\\n test_indices = []\\n\\n for i in range(k):\\n test_indices.append(fold_indexes[i])\\n train_indices_intermediate = []\\n for j in range(k - 1):\\n train_indices_intermediate.append(fold_indexes[(i + j + 1) % k])\\n train_indices.append(np.concatenate(train_indices_intermediate))\\n\\n return np.array(test_indices), np.array(train_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1baccd5915eb59a784e4e15124c66b3c\",\n \"score\": \"0.7221375\",\n \"text\": \"def training_sample_selection(self, k):\\n\\n idxs_train = np.empty(0)\\n idxs_test = np.empty(0)\\n for class_idx in xrange(self.labels.max()):\\n idxs_class = np.where(self.labels == class_idx)[0]\\n idxs_train = np.append(idxs_train, np.random.choice(idxs_class, k, replace=False)).astype(dtype=np.int32)\\n idxs_test = np.setdiff1d(idxs_class, idxs_train)\\n idxs_test = np.append(idxs_test,\\n np.random.choice(idxs_test, np.min([idxs_test.shape[0], 50]), replace=False))\\n\\n return idxs_train, idxs_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab25e6e7943c3123b988854ab710eeeb\",\n \"score\": \"0.7219873\",\n \"text\": \"def split_data(X, k=3, shuffle=True, random_state=None):\\n\\n all_indices = np.arange(X.shape[0])\\n n_samples = X.shape[0]\\n\\n ## Should initialize what the output will be here.. based on the k there should be indices for X and y rows,\\n ## therefore could have a list of tuples??\\n\\n # In order to align with scikit learn - cross referenced: https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/model_selection/_split.py#L357\\n # The first n_samples % n_splits folds have size n_samples // n_splits + 1,\\n # other folds have size n_samples // n_splits, where n_samples is the number of samples.\\n\\n fold_extra = n_samples % k\\n num_folds = 0\\n\\n fold_sizes = (n_samples // k) * np.ones(k, dtype=np.int)\\n fold_sizes[0:fold_extra] += 1\\n\\n val_indicies = []\\n\\n if shuffle:\\n check_random_state(random_state).shuffle(all_indices)\\n\\n current = 0\\n for fold in fold_sizes:\\n start, stop = current, current + fold\\n val_indicies.append(all_indices[start:stop])\\n current = stop\\n\\n for fold in val_indicies:\\n val = fold\\n other = np.setdiff1d(all_indices, val)\\n yield (val, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec3f5b6b1bbe3d2cea31fb2dd8b63d5\",\n \"score\": \"0.71851474\",\n \"text\": \"def split_data_K(x, y, K, seed=1):\\n # set seed\\n np.random.seed(seed)\\n # generate random indices\\n num_row = len(y)\\n indices = np.random.permutation(num_row)\\n index_split = int(np.floor(num_row/K))\\n x_K = []\\n y_K = []\\n for i in range(K):\\n if i!=K:\\n index = indices[: i*index_split]\\n else:\\n index = indices[i*index_split:]\\n # create split\\n x_K.append(x[index])\\n y_K.append(y[index])\\n return np.asarray(x_K), np.asarray(y_K)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e6c30b7fd8c5803b965dae9c60bbe0\",\n \"score\": \"0.7120892\",\n \"text\": \"def split_data_k_fold(x, y, begin, k):\\n test_indeces = np.arange(begin*int(np.shape(x)[0]/k), (begin+1)*int(np.shape(x)[0]/k))\\n train_indeces = np.asarray(list(range(0, begin*int(np.shape(x)[0]/k))) \\\\\\n + list(range((begin+1)*int(np.shape(x)[0]/k), np.shape(x)[0])))\\n train_x, train_y = x[train_indeces], y[train_indeces]\\n test_x, test_y = x[test_indeces], y[test_indeces]\\n return (train_x, train_y), (test_x, test_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6bfe1a1256772b83434d76bef31166\",\n \"score\": \"0.7102071\",\n \"text\": \"def k_fold_split(X, Y, k=5):\\n splits = np.linspace(0, X.shape[0], k + 1).astype(int)\\n intervals = zip(splits[:-1], splits[1:]) # the intervals for the test data\\n\\n for start, stop in intervals:\\n # the training set is the entire set excluding test\\n X_train = np.vstack([X[:start], X[stop:]])\\n Y_train = np.concatenate([Y[:start], Y[stop:]])\\n X_test = X[start:stop]\\n Y_test = Y[start:stop]\\n yield (X_train, Y_train, X_test, Y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474fedf4aa5dc10c651973859a0b19e6\",\n \"score\": \"0.70264345\",\n \"text\": \"def split_data(y, x, k_fold, seed, k):\\n # K group in train, other in test\\n k_indices = build_k_indices(y, k_fold, seed)\\n te_ind = k_indices[k]\\n\\n # k-1 split for training\\n tr_ind = [i for i in range(k_indices.shape[0]) if i is not k]\\n tr_ind = k_indices[tr_ind,:].reshape(-1)\\n\\n #Build the x and y train and test set\\n y_tr = y[tr_ind]\\n y_te = y[te_ind]\\n\\n x_tr = x[tr_ind, :]\\n x_te = x[te_ind, :]\\n\\n return y_tr, x_tr, y_te, x_te\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d57f4eba996fa920bca465e9f0decd\",\n \"score\": \"0.6768413\",\n \"text\": \"def get_kfold_splits(n_samples, k):\\n indices = np.arange(n_samples)\\n test_mask = np.empty(n_samples, dtype=bool)\\n splits = []\\n start = 0\\n for i in range(k):\\n n_test = n_samples // k\\n if i < n_samples % k:\\n n_test += 1\\n stop = start + n_test\\n test_mask[:] = False\\n test_mask[start:stop] = True\\n splits.append((indices[np.logical_not(test_mask)], indices[test_mask]))\\n start = stop\\n return splits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d8631868f517aab73687b4d3f870fd\",\n \"score\": \"0.6755415\",\n \"text\": \"def foldIndex(dataset,i, k):\\n n = len(dataset)\\n indices = np.arange(n)\\n a = n*(i-1)//k\\n b = (n*(i)//k)\\n test_index = indices[a:b]\\n size_test = test_index.size\\n size_train = int(np.abs(n - size_test))\\n train_index = np.zeros(int(np.abs(n - size_test)), dtype=int)\\n train_index[:a] = indices[:a]\\n train_index[a:] = indices[b:]\\n return train_index, test_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877d3d873f0b601e05146362a4b6bbd1\",\n \"score\": \"0.6670171\",\n \"text\": \"def splitDataKFolds(unsplitFeatures, unsplitLabels, k):\\r\\n if k <= 1:\\r\\n return unsplitFeatures, unsplitLabels\\r\\n features, labels = [], []\\r\\n split = int(len(unsplitFeatures) / k)\\r\\n for i in range(0,k):\\r\\n features.append(unsplitFeatures[(split*i):(split*(i+1))])\\r\\n labels.append(unsplitLabels[(split*i):(split*(i+1))])\\r\\n return features, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad108a1189ac462488a3d4f9bf0d8c79\",\n \"score\": \"0.65592694\",\n \"text\": \"def p_y_x_knn(y, k):\\n # print(y.shape)\\n #print(y)\\n # print(k)\\n categories = set(y[0])\\n # print(categories)\\n # print(number_of_categories)\\n result = []\\n for row in y:\\n result_row = []\\n first_k_indexes = row[0:k]\\n for i in range(len(categories)):\\n result_row.append(np.count_nonzero(first_k_indexes == i+1))\\n # print(result_row)\\n result.append(result_row)\\n result = np.array(result) / k\\n #print(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29eb2eb83272b942afe04017666bfdb1\",\n \"score\": \"0.6519975\",\n \"text\": \"def splitTrainAndTest(self, num_line, k=10):\\n start = 0\\n end = 0\\n users = []\\n for i, line in enumerate(num_line):\\n if i == len(num_line) - 1:\\n users.append(self.splitFromEachUser(k, newSet[start:]))\\n elif num_line[end][0] == line[0]:\\n end = i\\n else:\\n users.append(self.splitFromEachUser(k, newSet[start:i]))\\n start = i\\n end = i\\n dataset =[]\\n for i in range(k):\\n train = []\\n test = []\\n for user in users:\\n for j in range(k):\\n if i!=j:\\n train += user[j]\\n test += user[i]\\n dataset.append((train,test))\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21072028a03ce8f2afe94c1c094701fc\",\n \"score\": \"0.6516137\",\n \"text\": \"def split(self, X):\\n number_folds = self.n_folds\\n data_size = len(X)\\n len_ = int(data_size / number_folds)\\n index_ = np.arange(data_size)\\n split_indexes = []\\n\\n for i in range(number_folds):\\n i1 = i * len_\\n i2 = (i + 1) * len_\\n test_indexes = index_[i1: i2]\\n train_indexes = np.concatenate((index_[:i1], index_[i2:]))\\n split_indexes.append((train_indexes, test_indexes))\\n return split_indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a701b6c3d5e8a814c5695024bb9e851\",\n \"score\": \"0.649697\",\n \"text\": \"def skeleton_points(self, k=1):\\n if k >= self.dim():\\n return range(self.npoints())\\n skeleton = set([])\\n for face in self.faces_lp(dim=k):\\n skeleton.update(face.ambient_point_indices())\\n skeleton = sorted(skeleton)\\n return skeleton\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e624009e622270394e14f836d020cd\",\n \"score\": \"0.6494762\",\n \"text\": \"def fit(self, x_train, y_train, k=1):\\n self.k = k\\n\\n train = []\\n for i in range(len(x_train)):\\n train.append(x_train[i])\\n train[i].append(y_train[i])\\n\\n self.train = train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd124286abea4caa95469d1ad28bfc4b\",\n \"score\": \"0.6473322\",\n \"text\": \"def get_k_fold_partition(samples, labels, k = 5): \\n if k < 2: # k must be at least 2\\n k = 2\\n \\n # samples = dataset[:,:-1] \\n # labels = dataset[:,-1] \\n folds = StratifiedKFold(labels, k) # Generate the fold indexes.\\n \\n # Build the 2 sets:\\n for train_index, test_index in folds:\\n X_train, X_test = samples[train_index], samples[test_index]\\n y_train, y_test = labels[train_index], labels[test_index]\\n \\n train_set = np.concatenate((X_train, y_train[:,newaxis]), axis = 1)\\n test_set = np.concatenate((X_test, y_test[:,newaxis]), axis = 1)\\n \\n return train_set, test_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7400b1543d8ec9ae08b4658eb1cdd8\",\n \"score\": \"0.64665055\",\n \"text\": \"def spatial_cv(points, k=10):\\n left = points.longitude.min()\\n right = points.longitude.max() + 0.01\\n top = points.latitude.max()\\n bottom = points.latitude.min() - 0.01\\n width = right - left\\n height = top - bottom\\n size_x = width / 10\\n size_y = -height / 10\\n centroid_x = []\\n centroid_y = []\\n for i in range(10):\\n centroid_x.append(left + size_x / 2 + size_x * i)\\n centroid_y.append(top + size_y / 2 + size_y * i)\\n cell = []\\n for y in range(10):\\n for x in range(10):\\n cell.append([x, y])\\n id_chosen = np.random.choice(range(len(cell)), k, replace=False)\\n cell_chosen = [cell[i] for i in id_chosen]\\n train_n = int(len(points) / k) * (k - 1)\\n test_n = len(points) - train_n\\n train_test_split = []\\n for c in cell_chosen:\\n x, y = centroid_x[c[0]], centroid_y[c[1]]\\n points_c= points.copy()\\n points_c['x_diff'] = points_c['longitude'] - x\\n points_c['y_diff'] = points_c['latitude'] - y\\n points_c['dis'] = (points_c['x_diff'] ** 2 + points_c['y_diff'] ** 2) ** .5\\n points_c = points_c.sort_values(by='dis')\\n id_order = points_c.index.tolist()\\n test_id = id_order[:test_n]\\n train_id = id_order[test_n:]\\n train_test_split.append((train_id, test_id))\\n yield (train_id, test_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca296aced32ab1aa30a97139440ef9a\",\n \"score\": \"0.64552474\",\n \"text\": \"def do_split_data(X, y, k = 10):\\n return np.array_split(X, k), np.array_split(y, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59b1f434385c405c7938fc85bc0cf9b\",\n \"score\": \"0.64530706\",\n \"text\": \"def generate_k(dataset, k):\\n # create a list of number 0 - len(dataset)\\n # chooses k random numbers\\n nums = list(range(0, len(dataset)))\\n points = random.choices(nums, k=k)\\n return [dataset[i] for i in points]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa8d85e73a15fc92147c62a4d2da2d2\",\n \"score\": \"0.64482844\",\n \"text\": \"def divide(data, k, i):\\r\\n\\r\\n new_data = np.array_split(data, k)\\r\\n l_sample = new_data[i]\\r\\n t_sample = []\\r\\n t_sample_hold = np.delete(new_data, i, axis=0)\\r\\n for elements in t_sample_hold:\\r\\n t_sample.extend(elements.tolist())\\r\\n\\r\\n np.asarray(t_sample)\\r\\n return l_sample, t_sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"003883c0d9b7c534386ebdb7b254a2f9\",\n \"score\": \"0.6414206\",\n \"text\": \"def kmeans(X, k):\\n # 1. Select K points as initial centroids\\n idx = np.random.choice(X.shape[0], size=k)\\n M = X[idx, :]\\n\\n # 2. repeat until Centroid do not change:\\n ml = np.zeros((k))\\n lsse = 0\\n S = []\\n while True:\\n # Form K clusters by assigning each point to its closest centroid\\n S = []\\n for i in range(k):\\n t = [(M[i, 0], M[i, 1])]\\n S.append(t)\\n S = kmstep(X, M, k, S)\\n\\n # Recompute the centroid of each cluster\\n M = kestep(M, S)\\n\\n sse = ksse(S)\\n if lsse != 0 and lsse - sse < MINDELTA:\\n break\\n lsse = sse\\n\\n return M, S, sse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19b544fd17ee02ce35972cf496f1680\",\n \"score\": \"0.64137846\",\n \"text\": \"def data_split(samples,target,test_size = 0.2, k = 10, shuffle = True):\\n #\\n # convert samples, target to numpy array.\\n #\\n X = samples\\n y = target\\n\\n #\\n # train, test split.\\n #\\n\\n\\n X_train, X_test, Y_train, Y_test = train_test_split( X, y, test_size = test_size, shuffle = shuffle)\\n\\n # train set\\n train_set = {}\\n train_set['X_train'] = X_train\\n train_set['y_train'] = Y_train\\n\\n # test set\\n test_set = {}\\n test_set['X_test'] = X_test\\n test_set['y_test'] = Y_test\\n\\n #\\n # K-fold cross-validation.\\n # https://medium.com/datadriveninvestor/k-fold-cross-validation-6b8518070833\\n #\\n cv = KFold(n_splits = k, random_state = 42, shuffle = shuffle)\\n\\n train_crs_val = []\\n test_crs_val =[]\\n\\n for train_index, test_index in cv.split(X):\\n x_train, x_test, y_train, y_test = X[train_index], X[test_index], y[train_index], y[test_index]\\n # add a fold to the lists\\n train_crs_val.append((x_train,y_train))\\n test_crs_val.append((x_test,y_test))\\n\\n return train_set, test_set, train_crs_val, test_crs_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45bfd2d45190bcd9ebb9678e47aa859c\",\n \"score\": \"0.64032257\",\n \"text\": \"def getNeighbors(trainingSet, testPoint, k):\\n\\tdistances = []\\n\\tlength = len(testPoint)-1\\n\\tfor x in range(len(trainingSet)):\\n\\t\\tdist = calculateDistance(testPoint, trainingSet[x], length)\\n\\t\\tdistances.append((trainingSet[x], dist))\\n\\tdistances.sort(key=operator.itemgetter(1))\\n\\tneighbors = []\\n\\tfor x in range(k):\\n\\t\\tneighbors.append(distances[x][0])\\n\\treturn neighbors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f80250d53658de7cee3a7b4683cc0a2\",\n \"score\": \"0.63833976\",\n \"text\": \"def get_datasets(self, k):\\n assert k >= 0, 'Fold index k = {} must be positive'.format(k)\\n assert k < self.k, 'Fold index k = {} can not be greater than number of folds {}'.format(k, self.k)\\n\\n where = self._df.Fold == k\\n test_set = self._df[where].copy()\\n train_set = self._df[~where].copy()\\n\\n return train_set, test_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f52bf955a96393342e150e7ddcba0f5\",\n \"score\": \"0.6365757\",\n \"text\": \"def get_k_nearest_neighbours(sample, training_set, k):\\r\\n neighbours_list = training_set\\r\\n # Calculate the distance from each node to the sample node.\\r\\n for neighbour in training_set:\\r\\n neighbour.set_distance_to(sample)\\r\\n neighbours_list.sort()\\r\\n # Return the k nearest neighbours of the sample node.\\r\\n return neighbours_list[:k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9744a0c2798d3ece5c470ba9a7ab7c\",\n \"score\": \"0.6360685\",\n \"text\": \"def generate_k_folds(dataset, k):\\n n_features = len(dataset[0][0])\\n dataset = np.concatenate((dataset[0], dataset[1].reshape(len(dataset[0]),1)), axis=1)\\n np.random.shuffle(dataset)\\n\\n output = []\\n for i in range(0, k):\\n test_start = i * (dataset.shape[0]//k)\\n test_end = (i+1) * (dataset.shape[0]//k)\\n\\n test_features = dataset[test_start:test_end, :n_features]\\n test_classes = dataset[test_start:test_end, -1]\\n\\n train_features = np.concatenate((dataset[0:test_start, :n_features], dataset[test_end:, :n_features]), axis=0)\\n train_classes = np.concatenate((dataset[0:test_start, -1], dataset[test_end:, -1]), axis=0)\\n \\n output.append(([train_features,train_classes],[test_features,test_classes]))\\n \\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba678d9d77018c2ed8866367c3d4eba1\",\n \"score\": \"0.6342283\",\n \"text\": \"def split_data(x: np.array, y: np.array, k: int, i: int) -> (np.array, np.array, np.array, np.array):\\n assert(x.shape[0] == y.shape[0])\\n assert(0 <= i < k <= x.shape[0])\\n\\n size = int(x.shape[0] / k)\\n xt = np.concatenate((x[0:size*i, :], x[size*(i+1):size*k, :]), axis=0)\\n yt = np.concatenate((y[0:size*i], y[size*(i+1):size*k]), axis=0)\\n xv = x[size*i:size*(i+1), :]\\n yv = y[size*i:size*(i+1)]\\n\\n return xt, yt, xv, yv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af74229215cfff9a1876217569a8febd\",\n \"score\": \"0.63304156\",\n \"text\": \"def partition_data(dataframe, k=0.2):\\n\\tmixed_rows = dataframe.reindex(np.random.permutation(dataframe.index))\\n\\trows = dataframe.shape[0]\\n\\tnr_trows = 0.2*rows\\n\\tnr_valrows = nr_trows + 0.2*rows \\n\\tt_data = mixed_rows[:int(nr_trows)]\\n\\tval_data = mixed_rows[int(nr_trows):int(nr_valrows)]\\n\\ttrain_data = mixed_rows[int(nr_valrows):]\\n\\treturn [t_data, val_data, train_data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a7e55d5a50b46ff22bb9b280f1692e\",\n \"score\": \"0.6312328\",\n \"text\": \"def create_dataset_split_indices(data, train_size):\\n dataset_split_indices_holder = {}\\n # NOTE: in matlab the index starts from 1 in python its starts from 0\\n for session_id in range(data.shape[0]):\\n num_spike_trials = data[session_id]['spks']\\n # get trails for the neurons\\n session_id_trial_indices = np.random.permutation(num_spike_trials.shape[1])\\n # NOTE: split done for only train and test sets, I think validation set is also useful can easily be modified later\\n #training_idx, temp_valid_idx = np.split(session_id_indices, [int(train_per * session_id_indices.shape[0])])\\n training_trials_idx, test_trails_idx = np.split(session_id_trial_indices, [int(train_size * session_id_trial_indices.shape[0])])\\n assert session_id_trial_indices.shape[0] == (training_trials_idx.shape[0] + test_trails_idx.shape[0])\\n dataset_split_indices_holder[session_id] = {'train': training_trials_idx, 'test': test_trails_idx}\\n\\n return dataset_split_indices_holder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514ca2f0accf574465d7e3da6ed86b3d\",\n \"score\": \"0.63025355\",\n \"text\": \"def k_fold_cross_validation_sets(X, y, k, shuffle=True):\\n if shuffle:\\n X, y = shuffle_data(X, y)\\n\\n n_samples = len(y)\\n left_overs = {}\\n n_left_overs = (n_samples % k)\\n if n_left_overs != 0:\\n left_overs[\\\"X\\\"] = X[-n_left_overs:]\\n left_overs[\\\"y\\\"] = y[-n_left_overs:]\\n X = X[:-n_left_overs]\\n y = y[:-n_left_overs]\\n\\n X_split = np.split(X, k)\\n y_split = np.split(y, k)\\n sets = []\\n for i in range(k):\\n X_test, y_test = X_split[i], y_split[i]\\n X_train = np.concatenate(X_split[:i] + X_split[i + 1:], axis=0)\\n y_train = np.concatenate(y_split[:i] + y_split[i + 1:], axis=0)\\n sets.append([X_train, X_test, y_train, y_test])\\n\\n # Add left over samples to last set as training samples\\n if n_left_overs != 0:\\n np.append(sets[-1][0], left_overs[\\\"X\\\"], axis=0)\\n np.append(sets[-1][2], left_overs[\\\"y\\\"], axis=0)\\n\\n return np.array(sets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77a7e3d258a913d405486fe360739a3\",\n \"score\": \"0.6287668\",\n \"text\": \"def get_neighbors(train, test_data, k):\\n distances = list() # Initialize list of distances\\n\\n for train_data in train: \\n dist = get_distance(test_data, train_data) # Find distance between inputted test_data and every train_data in train\\n distances.append((train_data, dist)) # Add this distance to our list of distances\\n\\n distances.sort(key=lambda tup: tup[1]) # Sort list of distances in non-decreasing fashion\\n neighbors = list() # Initialize list of neigboring points to test_data\\n \\n for i in range(k):\\n neighbors.append(distances[i][0]) # Add first k training data points according to their distances to list of neighboring points\\n return neighbors # Return the k closest training data points (already classfified points) to the test data point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f218b95d4cb307fcae9bde09301c294\",\n \"score\": \"0.6276425\",\n \"text\": \"def split_data_for_cv(self, k=10):\\n cv_data_dict = {}\\n cv_ys_dict = {}\\n\\n data = list(zip(self.xs, self.ys))\\n random.shuffle(data)\\n\\n total_length = self.data_shape[0]\\n\\n k_length = round(total_length / k)\\n\\n for i in range(k-1):\\n fold_data = data[i*k_length:(i+1)*k_length]\\n fold_xs, fold_ys = list(zip(*fold_data))\\n cv_data_dict[i+1] = np.array(fold_xs)\\n cv_ys_dict[i+1] = np.array(fold_ys)\\n final_fold_data = data[(k-1)*k_length:]\\n final_xs, final_ys = list(zip(*final_fold_data))\\n cv_data_dict[k] = np.array(final_xs)\\n cv_ys_dict[k] = np.array(final_ys)\\n\\n return cv_data_dict, cv_ys_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89146ade849b61998d7aa2d17a67177f\",\n \"score\": \"0.62750894\",\n \"text\": \"def knn(x, k):\\r\\n inner = -2 * torch.matmul(x.transpose(2, 1), x)\\r\\n xx = torch.sum(x**2, dim=1, keepdim=True)\\r\\n pairwise_distance = -xx - inner - xx.transpose(2, 1)\\r\\n\\r\\n idx = pairwise_distance.topk(k=k, dim=-1)[1] # [batch_size, num_points, k]\\r\\n return idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e4aa4bbd49dbbaa55fa0cc54dd6ea17\",\n \"score\": \"0.62715167\",\n \"text\": \"def build_k_indices(y, K, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / K)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n K_indices = [indices[k * interval: (k + 1) * interval] for k in range(K)]\\n return np.array(K_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847b177913553c3078de1ea1d0e12aa3\",\n \"score\": \"0.62685627\",\n \"text\": \"def split_into_k_folds(links, k=5, strategy='random', sample_seed=2017):\\n # print(links[:10])\\n if strategy == 'random':\\n kf = KFold(n_splits=k, random_state=sample_seed, shuffle=True)\\n split_indices = kf.split(links)\\n return split_indices\\n elif strategy == 'random_per_source':\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b8dfb4a8c066918eafdfcce8bfa207\",\n \"score\": \"0.62584865\",\n \"text\": \"def myKmeans(data, k):\\n centers = np.zeros((k, data.shape[1]))\\n index = np.zeros(data.shape[0], dtype=int)\\n clusters = [[] for i in range(k)]\\n\\n # random centers initialization\\n for i in range(k):\\n centers[i] = data[random.randint(0, data.shape[0] - 1)]\\n\\n convergence = False\\n iterationNo = 0\\n while not convergence:\\n convergence = True\\n for i in range(k):\\n clusters[i] = []\\n\\n # assign each point to the cluster of closest center\\n for i in range(data.shape[0]):\\n min_dist = np.inf\\n closest_center = 0\\n for j in range(k):\\n dist = np.linalg.norm(data[i] - centers[j])\\n if dist < min_dist:\\n min_dist = dist\\n closest_center = j\\n clusters[closest_center].append(data[i])\\n index[i] = closest_center\\n # update clusters' centers\\n for i in range(k):\\n if len(clusters[i]) != 0:\\n c = sum(clusters[i]) / float(len(clusters[i]))\\n if np.linalg.norm(c - centers[i]) > 2:\\n convergence = False\\n centers[i] = c\\n iterationNo += 1\\n print('iterationNo = ', iterationNo)\\n\\n return index, centers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd4da4b0de94fdb02ccd7ca0649e8c89\",\n \"score\": \"0.6244749\",\n \"text\": \"def query_train(self, data, k):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09660fade005ea6cd5692c69996fc49b\",\n \"score\": \"0.62347436\",\n \"text\": \"def fit(self):\\n num_train_points = self.train_x.shape[0] # num sample points\\n centroid_ids = np.random.choice(num_train_points, (self.k,), replace=False)\\n self.centroids = self.train_x[centroid_ids, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b9966a56bac662e3bd2999a9d70b70\",\n \"score\": \"0.6230166\",\n \"text\": \"def kmember_partition(data, k=20, verbose=False):\\n clusters = []\\n # randomly choose seed and find k-1 nearest records to form cluster with size k\\n r_pos = random.randrange(len(data)) # select a single point\\n r_i = data[r_pos]\\n while len(data) >= k:\\n t = time.time()\\n r_pos = m_find_furthest_record(r_i, data)\\n r_i = data.pop(r_pos)\\n cluster = [r_i]\\n while len(cluster) < k:\\n #t = time.time()\\n r_pos = m_find_best_record_iloss_increase(cluster, data)\\n #print('Time for finding best record: ', time.time() - t)\\n r_j = data.pop(r_pos)\\n #print('r_j ', r_j)\\n cluster.append(r_j)\\n #print(cluster)\\n clusters.append(cluster)\\n if verbose==True:\\n print('Time for finding this cluster: ', time.time() - t)\\n print('Current remaining datapoints: ', len(data))\\n # residual assignment\\n while len(data) > 0:\\n t = data.pop()\\n cluster_index = m_find_best_cluster_iloss_increase(t, clusters)\\n clusters[cluster_index].append(t)\\n return clusters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2657c3fe0b2ee0ed452ca9b602bd3890\",\n \"score\": \"0.6227386\",\n \"text\": \"def find_clusters(point_lst, k):\\n assert len({len(x) for x in point_lst}) == 1, \\\\\\n \\\"not all points same # of dimensions\\\"\\n new_centroids = init_centroids(point_lst, k)\\n old_centroids = None\\n i = 0\\n while new_centroids != old_centroids:\\n i += 1\\n # in case it keeps flipflopping\\n if i > 500:\\n x = None\\n while x not in ['y', 'n']:\\n x = input('500 iterations have passed, do you want to break'\\n ' (y/n)?')\\n if x == 'y':\\n break\\n else:\\n i = 0\\n continue\\n cluster_lst = associate_cluster(point_lst, new_centroids)\\n # sorts out loss of clusters\\n if len(set(cluster_lst)) != k:\\n missing_k_s = list(set(range(1, k+1)) - set(cluster_lst))\\n # print('clusters added:', missing_k_s)\\n # getting indices in the for loop gave the same result both loops\\n # also, random choices gave me the same 2 options at the beginning\\n # solved by adding more samples + set\\n indices = list(set(random.choices(list(range(len(cluster_lst))),\\n k=len(missing_k_s)+20)))\\n for i, new_clust in enumerate(missing_k_s):\\n cluster_lst[indices[i]] = new_clust\\n\\n old_centroids = new_centroids\\n new_centroids = make_centroids(point_lst, cluster_lst)\\n\\n return cluster_lst, new_centroids, i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33a831459d9f42ab546beb8c0baae264\",\n \"score\": \"0.62239933\",\n \"text\": \"def knn(p, data, k=3):\\t\\r\\n\\tdistances = np.zeros(data.shape[0])\\r\\n\\tfor i in range(len(data)):\\r\\n\\t\\tdistances[i] = distance(p, data[i])\\r\\n\\tind = np.argsort(distances)\\r\\n\\treturn ind[:k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe441eba6cca72524a1f7eb943c7b3c\",\n \"score\": \"0.6223551\",\n \"text\": \"def mean_k(data, k):\\n n = len(data)\\n mean_k_data = list()\\n for i, datapoint in enumerate(data):\\n a = max(0, int(np.ceil(i - k/2)))\\n b = min(n, int(np.floor(i + k/2)))\\n mean_k_datapoint = np.mean(data[a:b+1])\\n mean_k_data.append(mean_k_datapoint)\\n\\n return mean_k_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb3ef8e9bedca2aa1da18e74402b63b\",\n \"score\": \"0.6221975\",\n \"text\": \"def k_closet_points(points, k):\\n\\n distances = map(lambda point: sum(map(lambda x: x**2, point)), points)\\n k_closet_indexs = sorted(range(len(points)), key=lambda i: distances[i])[:k]\\n return [points[i] for i in k_closet_indexs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87edc87067f442f9453811d0f51a399\",\n \"score\": \"0.6218206\",\n \"text\": \"def build_k_indices(y, k_fold, seed=1):\\n\\n # Get the number of rows of the data\\n num_row = y.shape[0]\\n # Get the number of rows for each interval depending on k\\n interval = int(num_row / k_fold)\\n\\n np.random.seed(seed)\\n\\n # Shuffle the indeces of the data\\n indices = np.random.permutation(num_row)\\n\\n # Get an array of the indeces that will go in each k interval\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260138d6eab4afada61c9aa7b73fb226\",\n \"score\": \"0.6200755\",\n \"text\": \"def k_folds(data: tdata.Dataset, n_splits: int):\\n n = len(data)\\n indices = np.arange(n).astype(int)\\n for val_idx in get_indices(n_splits, n):\\n train_idx = np.setdiff1d(indices, val_idx)\\n yield tdata.Subset(data, train_idx), tdata.Subset(data, val_idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a1da5bdb2feacc19083ae5ec9e649c\",\n \"score\": \"0.61944693\",\n \"text\": \"def get_kNN(self, test_points, k):\\n \\n # calculate all distances between training and 'test_points'\\n dists = self.calc_distance(test_points)\\n \\n # sort array to find k-nearest neighbours\\n NNs_idx = np.argsort(dists, axis=1)[:,:k]\\n\\n # find labels of k-nearest neighbours\\n NNs_labels = self.train_set.labels[NNs_idx]\\n \\n return NNs_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e6a9a92ad70985a7b8d76b169967030\",\n \"score\": \"0.6191881\",\n \"text\": \"def growing_sequential_kfold(dataset, k):\\n if k < 2:\\n raise ValueError(\\\"Parameter k has to be greater than 1.\\\")\\n folds = []\\n\\n for i in range(1, k + 1):\\n proportions = [i, 1, 1, k - i] # trainset, validset, testset, leftover\\n folds.append(split_dataset(dataset, proportions)[:3])\\n\\n return folds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c34d89776b36a537219dbfdcecdc0b6\",\n \"score\": \"0.6190533\",\n \"text\": \"def nearestNeighbors(trainData, k=3):\\n\\n length = trainData.shape[0]\\n # Label each one with indices\\n responses = np.arange(0, length).astype(np.float32)\\n knn = cv2.ml.KNearest_create()\\n knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)\\n\\n neighbor_array = []\\n for newcomer in trainData:\\n query = np.reshape(newcomer, (1, 2))\\n ret, results, neighbors, dist = knn.findNearest(query, k+1)\\n neighbor_array.append(list(map(int, neighbors[0, 1:]))) # exclude itself.\\n return neighbor_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5892a8f11daa370c65e4bab67fe6ec96\",\n \"score\": \"0.6169984\",\n \"text\": \"def divide_train_test(y, tx, k_indices, k):\\n tr_indice = k_indices[~(np.arange(k_indices.shape[0]) == k)].reshape(-1)\\n test_tx = tx[k_indices[k]]\\n test_y = y[k_indices[k]]\\n train_tx = tx[tr_indice]\\n train_y = y[tr_indice]\\n \\n return test_tx, test_y, train_tx, train_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903dcc6ca8323af56cca4cd03664079c\",\n \"score\": \"0.61693597\",\n \"text\": \"def initialize_clusters(points, k):\\n return points[np.random.randint(points.shape[0], size=k)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6884f5935d673d3a4a0958cb8db0ad4d\",\n \"score\": \"0.61665565\",\n \"text\": \"def sampling_points(mask, N, k=3, beta=0.75, training=True):\\n\\n assert mask.dim() == 5, \\\"Dim must be N(Batch)CDHW\\\"\\n device = mask.device\\n B, _, D, H, W = mask.shape\\n mask, _ = mask.sort(1, descending=True)\\n\\n if not training:\\n D_step, H_step, W_step = 1 / D, 1 / H, 1 / W\\n N = min(D * H * W, N)\\n uncertainty_map = -1 * (mask[:, 0] - mask[:, 1])\\n _, idx = uncertainty_map.view(B, -1).topk(N, dim=1) # idx:N个最不确定的点的索引\\n\\n points = torch.zeros(B, N, 3, dtype=torch.float, device=device)\\n # 因为推理时候没用使用rand生成[0,1]的随机索引，所以需要从idx反推回去属于[0,1]的索引\\n points[:, :, 0] = W_step / 2.0 + (idx % W).to(torch.float) * W_step # 表示列索引号\\n points[:, :, 1] = H_step / 2.0 + (idx % (H * W) // W).to(torch.float) * H_step # 表示行索引号\\n points[:, :, 2] = D_step / 2.0 + (idx // (H * W)).to(torch.float) * D_step # 表示层索引号\\n\\n return idx, points\\n\\n # Official Comment : point_features.py#92\\n # It is crucial to calculate uncertanty based on the sampled prediction value for the points.\\n # Calculating uncertainties of the coarse predictions first and sampling them for points leads\\n # to worse results. To illustrate the difference: a sampled point between two coarse predictions\\n # with -1 and 1 logits has 0 logit prediction and therefore 0 uncertainty value, however, if one\\n # calculates uncertainties for the coarse predictions first (-1 and -1) and sampe it for the\\n # center point, they will get -1 unceratinty.\\n\\n over_generation = torch.rand(B, k * N, 3, device=device) # 随机选取一堆点的坐标\\n over_generation_map = point_sample(mask, over_generation,\\n align_corners=False) # 获取mask上这些坐标位置上的值（可以理解为概率值）(N,C,P) C为类别数\\n\\n uncertainty_map = -1 * (over_generation_map[:, 0] - over_generation_map[:, 1])\\n _, idx = uncertainty_map.topk(int(beta * N), -1) # idx:over_generation 上的最重要的beta*N个点的位置\\n\\n shift = (k * N) * torch.arange(B, dtype=torch.long, device=device)\\n\\n idx += shift[:, None] # batch_size=1的话相当于没变\\n\\n importance = over_generation.view(-1, 3)[idx.view(-1), :].view(B, int(beta * N), 3)\\n coverage = torch.rand(B, N - int(beta * N), 3, device=device)\\n return torch.cat([importance, coverage], 1).to(device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106c74f64e180d15718d6c7796d4dcc3\",\n \"score\": \"0.6144333\",\n \"text\": \"def k_fold_cross_validation(k: int, num_data: int, shuffle: bool = True) -> tuple:\\r\\n\\r\\n assert num_data >= k, \\\"Total amount of data needs to be larger or equal to the number of folds!\\\"\\r\\n\\r\\n # create indices, corresponding to the data points, and shuffle them, if required\\r\\n indices = list(range(num_data))\\r\\n if shuffle:\\r\\n random.shuffle(indices)\\r\\n\\r\\n # compute the sizes of the folds and the remaining number of data\\r\\n fold_size = int(num_data / k)\\r\\n remaining_data = num_data % k\\r\\n\\r\\n # compute the splits\\r\\n fold_list = []\\r\\n for i in range(k):\\r\\n fold_list.append(indices[i*fold_size:(i+1)*fold_size])\\r\\n\\r\\n # append the remaining data points to the folds\\r\\n for i in range(remaining_data):\\r\\n fold_list[i].append(indices[k*fold_size+i])\\r\\n split = tuple(fold_list)\\r\\n return split\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33282b19b9f716118c2194e84a88f56\",\n \"score\": \"0.6140482\",\n \"text\": \"def k_means(dataset, k, maxit=300):\\n\\tcentroids = dataset[np.random.permutation(dataset.shape[0])[:k]]\\n\\tfor i in range(maxit):\\n\\t\\tprevCentroids = centroids\\n\\t\\tlabels = setClusters(dataset, prevCentroids)\\n\\t\\tcentroids = setCetroids(dataset, labels, k)\\n\\t\\terror = calcError(dataset, labels, centroids)\\n\\t\\tif np.all(centroids == prevCentroids):\\n\\t\\t\\t# print(i)\\n\\t\\t\\treturn labels, centroids, error\\n\\treturn labels, centroids, error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e4d35660e0b6076ac5f224e0cc00511\",\n \"score\": \"0.613471\",\n \"text\": \"def getNeighbors(X_train, X_test_i, k):\\r\\n dists = []\\r\\n \\r\\n for h in range(len(X_train)): # For each data element,\\r\\n dist = euclideanDist(X_train[h], X_test_i) # get distance to X_test_i\\r\\n dists.append((h, dist)) # Add it to a list\\r\\n \\r\\n dists.sort(key=operator.itemgetter(1)) # Sort the list by distance\\r\\n\\r\\n plt = knnPlot(dists, k)\\r\\n \\r\\n # Uncomment the following line if you want to see the euclidean distances\\r\\n # for every row, rather than just one example row.\\r\\n #plt.show()\\r\\n \\r\\n neighbors = []\\r\\n \\r\\n for n in range(k): # For the k nearest neighbors\\r\\n neighbors.append(dists[n][0]) # Add them to a list\\r\\n \\r\\n return neighbors, plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb379d1eb0e8b7f5ca3edde07a8b25b\",\n \"score\": \"0.61294323\",\n \"text\": \"def k_nn(train_x, train_y, test_x, k):\\n # Calculate the distance of current point from all other points.\\n # Complexity: O(nf), where f is number of features\\n dist = numpy.sqrt(sum(numpy.square(train_x - test_x).T)).T\\n # Add labels with corresponding distances\\n dist_with_label = numpy.hstack([train_y, dist]).T\\n # Get index of smallest k elements. Complexity: O(n + klog(k))\\n dist_with_label_sorted = numpy.argpartition(dist_with_label, -k).T[:k]\\n # Arrange 'dist_with_label' according to sorted index of distance\\n inter = dist_with_label[:, dist_with_label_sorted[:, 1]].reshape([2, k])\\n # Get the top labels in 1D array\\n top_labels = numpy.asarray(inter[0]).reshape([k])\\n # Get the most common neighbor. Complexity: O(k)\\n k_nn_label = collections.Counter(top_labels).most_common(1)[0][0]\\n return k_nn_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d111b91072156275dac7f4948dd7df\",\n \"score\": \"0.611971\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n np.random.seed(seed)\\n\\n num_row = len(y)\\n interval = int(num_row/k_fold)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n \\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2947c1ec8413ada710717c142047d756\",\n \"score\": \"0.6109455\",\n \"text\": \"def calc_k_nearest_neighbors(data, k):\\r\\n # Calculate the squared euclidean pairwise distances.\\r\\n squared_distance_matrix = calc_squared_euclidean_distance_matrix(data)\\r\\n\\r\\n # The i-th row contains the indices of the k nearest neighbors of the i-th point.\\r\\n nearest_neighbors = np.argpartition(squared_distance_matrix, k, axis=1)[:, :k]\\r\\n\\r\\n return nearest_neighbors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff6b534b804de135572ed3d3113569b\",\n \"score\": \"0.6101243\",\n \"text\": \"def kmeans(X: Union[tf.Tensor, np.ndarray], \\n k: int, \\n n_iter: int = 10) -> (tf.Tensor, tf.Tensor):\\n\\n X = tf.convert_to_tensor(X)\\n X = tf.cast(X, tf.float32)\\n assert len(tf.shape(X)) == 2, \\\"Training data X must be represented as 2D array only\\\"\\n m = tf.shape(X)[0]\\n\\n random_select = tf.random.shuffle(X)\\n init_centroids = random_select[:k]\\n\\n clusters = tf.zeros([m, ], dtype=tf.int64)\\n centroids = init_centroids\\n prev_centroids = centroids\\n\\n for _ in tf.range(n_iter + 1):\\n squared_diffs = tf.square(X[None, :, :] - centroids[:, None, :])\\n euclidean_dists = tf.reduce_sum(squared_diffs, axis=-1) ** 0.5\\n clusters = tf.argmin(euclidean_dists, axis=0)\\n\\n selector = tf.range(k, dtype=tf.int64)[:, None] == clusters[None, :]\\n centroids_array = tf.TensorArray(tf.float32, size=k)\\n prev_centroids = centroids\\n\\n for c in tf.range(k):\\n select = selector[c]\\n points = X[select]\\n mean_points = tf.reduce_mean(points, axis=0)\\n centroids_array = centroids_array.write(c, mean_points)\\n \\n centroids = centroids_array.stack()\\n\\n return prev_centroids, clusters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee36d82a7610269c3b6e8a6e85b710e\",\n \"score\": \"0.609205\",\n \"text\": \"def find_centers(X, k):\\n estimator = KMeans(init='k-means++', n_clusters=k, n_init=10)\\n estimator.fit(X)\\n mu = estimator.cluster_centers_\\n labels = estimator.labels_\\n return mu, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c73da6341fc83aad030f15f491ac254e\",\n \"score\": \"0.6064342\",\n \"text\": \"def k_means(points, k, iterations=10):\\n idx = np.random.randint(len(points),size=k)\\n\\n centroids = points[idx,:]\\n clusters = get_clusters(points,centroids)\\n\\n for i in range(iterations):\\n\\n # Use get_clusters() and update_centroids() for each iteration.\\n\\n return clusters,centroids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c043a13dfd705b4742319d9093d1d47\",\n \"score\": \"0.60559326\",\n \"text\": \"def farthest_point_sampling(point_set, k):\\n num_pts = point_set.shape[0]\\n start_idx = np.random.randint(num_pts)\\n existing_set = np.expand_dims(point_set[start_idx], axis=0)\\n rest_set = np.copy(point_set)\\n np.delete(rest_set, start_idx, 0)\\n\\n existing_indices = [start_idx]\\n rest_indices = np.arange(num_pts)\\n np.delete(rest_indices, start_idx)\\n\\n for _ in range(k - 1):\\n dist = (\\n np.sum(np.square(existing_set), axis=1, keepdims=True) +\\n np.sum(np.square(rest_set.T), axis=0, keepdims=True) -\\n np.dot(existing_set, rest_set.T) * 2)\\n min_dist = dist.min(axis=0)\\n max_idx = min_dist.argmax()\\n existing_set = np.concatenate(\\n [existing_set, np.expand_dims(rest_set[max_idx], axis=0)], axis=0)\\n existing_indices.append(rest_indices[max_idx])\\n np.delete(rest_set, max_idx, 0)\\n np.delete(rest_indices, max_idx)\\n\\n return existing_set, existing_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe2f1a8bb24a2ce48cf3b66007c6fa4\",\n \"score\": \"0.6048238\",\n \"text\": \"def initialize_centroids(points, k):\\n centers_id = random.sample(range(points.shape[0]), k)\\n return points[centers_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edff4477083c4ef81ff1c393b9514716\",\n \"score\": \"0.60462356\",\n \"text\": \"def kmeans_from_data_in_some_space(X, k, n_init=3):\\n\\n # validating the X data\\n if X.ndim != 2 or X.shape[0] < 1 or X.shape[1] < 1:\\n raise ValueError('Data must be a valid 2D matrix.')\\n\\n # validating the amount of clusters\\n if k <= 0:\\n raise ValueError('The amount of clusters must be positive.')\\n\\n # creating an instance of KMeans clustering algorithm\\n kmeans = KMeans(n_clusters=k, n_init=n_init, random_state=RND_MAGIC_SALT)\\n\\n # performing clustering\\n kmeans.fit(X)\\n\\n # returning computed partition\\n return kmeans.labels_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06767de26f3683137f8aa857062a9e49\",\n \"score\": \"0.60252905\",\n \"text\": \"def get_split_points(data):\\n train_split_points, val_split_points = [], []\\n min_block_num = data.date_block_num.min()\\n max_block_num = data.date_block_num.max()\\n \\n for date_block_num in range(min_block_num, min_block_num + 5):\\n begin_val = data.date_block_num.searchsorted(max_block_num - 4 + date_block_num - min_block_num)\\n begin_train = 0 #data.date_block_num.searchsorted(date_block_num)\\n end_train = begin_val\\n if date_block_num == min_block_num + 4:\\n end_val = len(data)\\n else:\\n end_val = data.date_block_num.searchsorted(max_block_num - 4 + date_block_num - min_block_num + 1)\\n val_split_points.append((begin_val, end_val))\\n train_split_points.append((begin_train, end_train))\\n return train_split_points, val_split_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d2d7261a3edf2750ae835b358b08d8\",\n \"score\": \"0.60249066\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af2a595ade396ff6cf85f9e23252161\",\n \"score\": \"0.6021595\",\n \"text\": \"def kmeans(X, k, max_iter=100):\\n\\n centers = None\\n assign = None\\n i=0\\n \\n start = time.time()\\n\\n\\n #######################################################################\\n # TODO: #\\n # Perfom k-means clustering of the input data X and store the #\\n # resulting cluster-centers as well as cluster assignments. #\\n # #\\n #######################################################################\\n\\n centers = X[np.random.randint(X.shape[0], size=k)]\\n assign = np.zeros(X.shape[0], dtype=np.float64)\\n distances = np.zeros([X.shape[0], k], dtype=np.float64)\\n \\n for i in range(max_iter):\\n for i, c in enumerate(centers):\\n distances[:, i] = np.linalg.norm(X - c, axis=1)\\n assign = np.argmin(distances, axis=1)\\n for c in range(k):\\n centers[c] = np.mean(X[assign==c], 0)\\n \\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n \\n exec_time = time.time()-start\\n print('Number of iterations: {}, Execution time: {}s'.format(i+1, exec_time))\\n \\n return centers, assign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.6020689\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8510d2a385e963218ce03c0c36b1adc1\",\n \"score\": \"0.60188293\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8510d2a385e963218ce03c0c36b1adc1\",\n \"score\": \"0.60188293\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188084aa844ff27c527ac6aff1886226\",\n \"score\": \"0.6013784\",\n \"text\": \"def _get_train_idx(self):\\n len_list = [len(df) for df in self.df]\\n \\n bz_t = self.bz//len(len_list)\\n batch_num = [x//bz_t for x in len_list]\\n\\n batch_nth = [0] * len(len_list)\\n\\n select = [list(range(x)) for x in len_list]\\n\\n for s in select:\\n random.shuffle(s)\\n\\n while True:\\n idxs = []\\n for i in range(len(len_list)):\\n if batch_nth[i] >= batch_num[i]:\\n batch_nth[i] = 0\\n random.shuffle(select[i])\\n idx = select[i][batch_nth[i]*bz_t:(batch_nth[i]+1)*bz_t]\\n batch_nth[i] += 1\\n idxs.append(idx)\\n\\n yield idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6aab0dd92d74fff39a8685a7201c33\",\n \"score\": \"0.6010272\",\n \"text\": \"def train_knn(training_data, label_data, test_data): \\r\\n predict_labels = [] \\r\\n for x_test in test_data:\\r\\n distances = np.sqrt(np.sum((training_data - x_test) ** 2, axis = 1))\\r\\n min_index = np.argmin(distances)\\r\\n predict_labels.append(label_data[min_index])\\r\\n return predict_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c50495deba095f677c64395f617276e\",\n \"score\": \"0.6005331\",\n \"text\": \"def split_training_set(self, size: int, start: int = 0):\\n return self.training_set.split(size=size, start=start)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949db4f7961ff6509afb916072ce7466\",\n \"score\": \"0.6005121\",\n \"text\": \"def get_set_from_partitions(partitioned_samples, partition_num = 0):\\n\\n k = partitioned_samples.shape[2]\\n N = partitioned_samples.shape[0]\\n\\n test_set = partitioned_samples[:, :, partition_num]\\n\\n train_set = np.zeros((N*(k-1), partitioned_samples.shape[1]))\\n\\n j = 0\\n for i in range(k):\\n if i != partition_num:\\n train_set[j * N:(j + 1) * N, :] = partitioned_samples[:, :, i]\\n j += 1\\n\\n return train_set, test_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64144d1e57f39091394b346a5ee8a50b\",\n \"score\": \"0.5998632\",\n \"text\": \"def __knnsearch_self(self,seeds,points,K):\\n nseeds = seeds.shape[0]\\n npoints = points.shape[0]\\n #dim = seeds.shape[1]\\n ind_dis = np.zeros([npoints,2,K])\\n distmatrix = pairwise_distances(points, seeds,metric = 'euclidean')\\n for kidx in range(K):\\n for pind in range(0,npoints):\\n leastInd = self.__quickSelect(distmatrix[pind,:], range(nseeds),kidx)\\n ind_dis[pind,:,kidx]=np.array([leastInd, distmatrix[pind,leastInd]])\\n return ind_dis[:,0,:], ind_dis[:,1,:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29bdb193d3376a064f0f8886dec5795e\",\n \"score\": \"0.59973997\",\n \"text\": \"def k_plus_proche(p_test):\\n #Création de la liste avec tout les distances, ainsi que la liste l_indices contenant les indices associés aux distances, elle même assosocié aux points \\n l = list()\\n for p_train in points_train:\\n l.append(distance(p_test, p_train))\\n l_indice = list(range(len(points_train)))\\n\\n for j in range(len(points_train)):\\n for i in range(len(points_train)-1):\\n if l[i+1]<l[i]:\\n #on actualise la liste de distance, normal \\n tmp = l[i]\\n l[i] = l[i+1]\\n l[i+1]= tmp\\n\\n #MAIS AUSSI la l_indice, pour garder une trace de des indices et continuer de les associer aux points\\n tmp = l_indice[i]\\n l_indice[i] = l_indice[i+1]\\n l_indice[i+1]= tmp\\n return l_indice[:k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c802448acd3883278b0bf5c7ee24ad5\",\n \"score\": \"0.59970415\",\n \"text\": \"def k_fold_CV(k, X, y):\\r\\n \\r\\n n, m = np.shape(X)\\r\\n ind = np.arange(0, n, k)\\r\\n for i in ind:\\r\\n X_test, y_test = X[i:(i + k),:], y[i:(i + k)]\\r\\n if i == 0:\\r\\n X_train, y_train = X[i + k:, :], y[i + k:]\\r\\n elif i == (n - k):\\r\\n X_train, y_train = X[:i, :], y[:i]\\r\\n else:\\r\\n X_train = np.vstack([X[:i, :], X[i + k:, :]])\\r\\n y_train = np.hstack([y[:i], y[i + k:]])\\r\\n yield X_train, X_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb9cbaed7852008ee9fefd6d2fd8536\",\n \"score\": \"0.59962285\",\n \"text\": \"def kmeans_pp_init(X, k, metric):\\r\\n centers = np.zeros((k, X.shape[1]))\\r\\n indices_centers = np.zeros(k).astype(int)\\r\\n\\r\\n # choose first center uniformly random from data\\r\\n idx_chosen = np.random.choice(np.arange(X.shape[0]))\\r\\n indices_centers[0] = idx_chosen\\r\\n centers[0] = X[idx_chosen]\\r\\n centers_chosen = 1\\r\\n\\r\\n while centers_chosen < k:\\r\\n\\r\\n # choose center from remaining data points\\r\\n remaining_data = np.delete(X, indices_centers, axis=0)\\r\\n # compute distance of each point in data set from centers\\r\\n distances = metric(remaining_data, centers[:centers_chosen]) # Nxk\\r\\n\\r\\n # extract minimal distance for each point (for closest center)\\r\\n min_distances = np.min(distances, axis=1)[:, None] # Nx1\\r\\n D_squared = np.square(min_distances) # Nx1\\r\\n\\r\\n # choose center randomly with weighted probability ~ D_squared\\r\\n probabilities = (D_squared / np.sum(D_squared)).flatten()\\r\\n indices = np.arange(probabilities.size)\\r\\n indices_centers[centers_chosen] = np.random.choice(indices, p=probabilities)\\r\\n centers[centers_chosen] = remaining_data[indices_centers[centers_chosen]]\\r\\n centers_chosen += 1\\r\\n\\r\\n return centers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e58be2d99295520c95582b070de5308\",\n \"score\": \"0.59911495\",\n \"text\": \"def build_k_indices(y, k_fold, seed = 1):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82b42b1f3b69ce47740a2557e8905ab\",\n \"score\": \"0.5988685\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n \\\"\\\"\\\"build k indices for k-fold.\\\"\\\"\\\"\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1d5c3f7ac206297c008e3fafbcc452\",\n \"score\": \"0.5980611\",\n \"text\": \"def get_clusters_Kmeans(K, MU, X):\\n \\n # getting square pairwise distances\\n pair_dist = tf.Session().run(distanceFunc_Kmeans(X, MU))\\n \\n # getting most likely cluster by index\\n S_indices = np.argmin(pair_dist, axis=1)\\n \\n # getting list of actual data grouped by cluster\\n S = [None]*K\\n for i in range(K):\\n S[i] = X[S_indices == i] \\n \\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8360b654b2b9b7af29fd23433fb30ee4\",\n \"score\": \"0.5979377\",\n \"text\": \"def _k_nearest_indices(self, array):\\n \\n # Calculate the Euclidean distances between the new data point and \\n # the training data.\\n euclidean_distances = self._euclidean_distances(array)\\n # Sort the indices from smallest Euclidean distance from the new\\n # data point to the largest.\\n sorted_indices = np.argsort(euclidean_distances, \\n axis=None, \\n kind=\\\"quicksort\\\")\\n # Save the indices of the k nearest points.\\n k_nearest_indices = sorted_indices[0:self.k]\\n # Return the indicies of the k nearest points.\\n return k_nearest_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ac42024ec7240bd5418636ffaf4df\",\n \"score\": \"0.5977788\",\n \"text\": \"def build_k_indices(y, k_fold, seed=1):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46670d218b296aed126f037ed1b85be7\",\n \"score\": \"0.5977213\",\n \"text\": \"def p_y_x_knn(y, k):\\n result = np.zeros(shape=(y.shape[0],4))\\n y=np.delete(y,range(k,y.shape[1]), axis=1) #take k closest\\n for i in range(y.shape[0]):\\n result[i] = np.bincount(y[i],minlength=4) #minlength = 4 -> 4 classess\\n return np.divide(result,k)\\n #pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66181bd317136a0ae2c74aa3c44c4d2\",\n \"score\": \"0.59653527\",\n \"text\": \"def get_neighbours_index(X_train, X_test_individual, k): \\n distances = [] # list for the distances\\n neighbors_index = [] # list for the neighbors\\n for i in range(0, X_train.shape[0]):\\n dist = distance(X_train[i], X_test_individual) #calculates the distance between the x_test point and all the x_train points\\n dist = np.absolute(dist) #makes sure it is not negative\\n distances.append((i, dist))\\n distances.sort(key=operator.itemgetter(1)) #sorted the list of distances, wherein the distance and INDEX is listed\\n for x in range(k):\\n neighbors_index.append(distances[x][0]) #list only the index of the nearest neighbours and not their distance\\n\\n return neighbors_index\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1485,"cells":{"query_id":{"kind":"string","value":"4dbccca105258ecee5465e2cf45445a6"},"query":{"kind":"string","value":"EfficientNetB1 AdvProp. Tensorflow compatible variant"},"positive_passages":{"kind":"list like","value":[{"docid":"2683435426b061f5b465f905f5964349","score":"0.6500853","text":"def tf_efficientnet_b1_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b1_ap\",\n channel_multiplier=1.0,\n depth_multiplier=1.1,\n pretrained=pretrained,\n **kwargs)\n return model","title":""}],"string":"[\n {\n \"docid\": \"2683435426b061f5b465f905f5964349\",\n \"score\": \"0.6500853\",\n \"text\": \"def tf_efficientnet_b1_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b1_ap\\\",\\n channel_multiplier=1.0,\\n depth_multiplier=1.1,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"61f985ccd4b9affc2bdd78c1c09e8bfd","score":"0.5862529","text":"def task_metalearn(inp, reuse=True):\n inputa, inputb, labela, labelb = inp # input = (NK,latent_dim) label = (NK, num of au, N), N = num of class\n #inputa = tf.reshape(inputa, [int(inputa.shape[0]), int(inputa.shape[1]), 1]) # (NK,2000,1)\n #inputb = tf.reshape(inputb, [int(inputb.shape[0]), int(inputb.shape[1]), 1])\n inputa = tf.cast(inputa, tf.float32)\n inputb = tf.cast(inputb, tf.float32)\n\n labela_ce = tf.one_hot(labela, self.num_classes) # (NK,8,2)\n labela_ce = tf.cast(labela_ce, tf.float32)[:, self.au_idx, :] # (NK,1)\n labela_ce = tf.reshape(labela_ce, [int(labela_ce.shape[0]), 1, self.num_classes]) # (NK,1,N)\n\n labelb_ce = tf.one_hot(labelb, self.num_classes) # (NK,2)\n labelb_ce = tf.cast(labelb_ce, tf.float32)[:, self.au_idx, :]\n labelb_ce = tf.reshape(labelb_ce, [int(labelb_ce.shape[0]), 1, self.num_classes]) # (NK,1,N)\n\n this_w = weights['w1'][:, self.au_idx, :] # weights['w1'] = (300, 8,2) this_w = (300,2)\n this_b = weights['b1'][self.au_idx, :]\n this_w = tf.reshape(this_w, [int(this_w.shape[0]), 1, int(this_w.shape[1])]) # (300,1,2)\n this_b = tf.reshape(this_b, [1, int(this_b.shape[0])])\n this_weight = {'w1': this_w, 'b1': this_b, 'w2': weights['w2'], 'b2': weights['b2'], 'w3': weights['w3'], 'b3': weights['b3']}\n # only reuse on the first iter: <<<previously meta-updated weight * input a>>>\n task_outputa = self.forward(inputa, this_weight) # (NK, 1, 2)\n\n ##### for cross-entropy loss ####\n task_ce_lossa = self.loss_func(task_outputa, labela_ce)[:, 0] # 2,1\n\n ##### for co-occur loss ####\n def predict_other_au(i, input, label):\n other_w = weights['w1'][:, i, :] # weights['w1'] = (300, 8,2) this_w = (300,2)\n other_b = weights['b1'][i, :]\n other_w = tf.reshape(other_w, [int(other_w.shape[0]), 1, int(other_w.shape[1])]) # (300,1,2)\n other_b = tf.reshape(other_b, [1, int(other_b.shape[0])])\n other_weight = {'w1': other_w, 'b1': other_b, 'w2': weights['w2'], 'b2': weights['b2'], 'w3': weights['w3'], 'b3': weights['b3']}\n pred_other_au = self.forward(input, other_weight)\n pred_other_au = tf.nn.softmax(pred_other_au)\n pred_other_au = pred_other_au[:, 0, 1]\n label_other_au = tf.cast(label[:, i], tf.float32)\n return [pred_other_au, label_other_au]\n\n pred_this_au = tf.nn.softmax(task_outputa)\n pred_this_au = pred_this_au[:, 0, 1]\n labela_this_au = tf.cast(labela[:, self.au_idx], tf.float32) # (NK,)\n\n task_co_lossa = []\n for i in range(self.total_num_au):\n pred_other_au, label_other_au = predict_other_au(i, inputa, labela)\n loss = self.loss_func2(pred_this_au * pred_other_au,\n labela_this_au * label_other_au) # (num of samples=NK,1=num of au,2=N)\n # losses 는 현재 주어진 subject이, between 현재 주어진 au and 다른 모든 au간 이룬 loss들의 모임.\n task_co_lossa.append(loss)\n task_co_lossa = tf.reduce_sum(task_co_lossa, 0) / self.total_num_au\n task_lossa = task_ce_lossa + self.LAMBDA2 * task_co_lossa\n\n grads = tf.gradients(task_lossa, list(this_weight.values())) # 300,1,2\n if FLAGS.stop_grad:\n grads = [tf.stop_gradient(grad) for grad in grads]\n gradients = dict(zip(this_weight.keys(), grads))\n fast_weights = dict(\n zip(this_weight.keys(),\n [this_weight[key] - self.update_lr * gradients[key] for key in this_weight.keys()]))\n \n #### make evaluation with inputb ####\n task_lossb = []\n outputb = self.forward(inputb, fast_weights) # (2,1,2) = (2*k, # of au, onehot label)\n task_ce_lossb = self.loss_func(outputb, labelb_ce)[:, 0]\n ### for co-occur loss ###\n labelb_this_au = tf.cast(labelb[:, self.au_idx], tf.float32) # (NK,)\n predb_this_au = tf.nn.softmax(outputb)\n predb_this_au = predb_this_au[:, 0, 1]\n task_co_lossb = []\n for i in range(self.total_num_au):\n predb_other_au, labelb_other_au = predict_other_au(i, inputb, labelb)\n task_co_lossb.append(self.loss_func2(predb_this_au * predb_other_au,\n labelb_this_au * labelb_other_au))\n # test_other_au.append(labelb_other_au)\n # test_other_au = tf.convert_to_tensor(test_other_au)\n task_co_lossb = tf.reduce_sum(task_co_lossb, 0) / self.total_num_au\n task_lossb.append(task_ce_lossb + self.LAMBDA2 * task_co_lossb)\n\n\n ### for more inner update ###\n for j in range(num_updates - 1):\n\n task_outputa = self.forward(inputa, fast_weights)\n task_ce_loss = self.loss_func(task_outputa, labela_ce)[:, 0]\n ### co-occur ###\n task_co_loss = []\n pred_this_au = tf.nn.softmax(task_outputa)\n pred_this_au = pred_this_au[:, 0, 1]\n\n for i in range(self.total_num_au):\n pred_other_au, label_other_au = predict_other_au(i, inputa, labela)\n task_co_loss.append(self.loss_func2(pred_this_au * pred_other_au,\n labela_this_au * label_other_au))\n\n task_co_loss = tf.reduce_sum(task_co_loss, 0) / self.total_num_au\n task_loss = task_ce_loss + self.LAMBDA2 * task_co_loss\n # compute gradients based on the previous fast weights\n grads = tf.gradients(task_loss, list(fast_weights.values()))\n if FLAGS.stop_grad:\n grads = [tf.stop_gradient(grad) for grad in grads]\n gradients = dict(zip(fast_weights.keys(), grads))\n # fast_weights are updated\n fast_weights = dict(zip(fast_weights.keys(),\n [fast_weights[key] - self.update_lr * gradients[key] for key in\n fast_weights.keys()]))\n \n #### make evaluation with inputb ####\n outputb = self.forward(inputb, fast_weights) # (2,1,2) = (2*k, # of au, onehot label)\n task_ce_lossb = self.loss_func(outputb, labelb_ce)[:, 0]\n ### for co-occur loss ###\n labelb_this_au = tf.cast(labelb[:, self.au_idx], tf.float32) # (NK,)\n predb_this_au = tf.nn.softmax(outputb)\n predb_this_au = predb_this_au[:, 0, 1]\n task_co_lossb = []\n for i in range(self.total_num_au):\n predb_other_au, labelb_other_au = predict_other_au(i, inputb, labelb)\n task_co_lossb.append(self.loss_func2(predb_this_au * predb_other_au,\n labelb_this_au * labelb_other_au))\n # test_other_au.append(labelb_other_au)\n # test_other_au = tf.convert_to_tensor(test_other_au)\n task_co_lossb = tf.reduce_sum(task_co_lossb, 0) / self.total_num_au\n task_lossb.append(task_ce_lossb + self.LAMBDA2 * task_co_lossb)\n #task_lossb = tf.reduce_sum(task_lossb)\n task_lossb = task_lossb[-1]\n\n ### return output ###\n if FLAGS.adaptation:\n task_output = [fast_weights['w1'], fast_weights['b1'], task_ce_lossb, task_co_lossb, task_lossa,\n predb_this_au]\n else:\n task_output = [fast_weights['w1'], fast_weights['b1'], task_ce_lossb, task_co_lossb, task_lossb,\n predb_this_au]\n return task_output","title":""},{"docid":"87cfe2fc86c92b2df994c44d8ae095e9","score":"0.5802021","text":"def adversarial_NN(DNN,adv_inputs):\n Dx = DNN(adv_inputs)\n Dx = Dense(10, activation=\"linear\")(adv_inputs)\n Dx = Dense(10, activation=\"tanh\")(Dx)\n Dx = Dense(2, activation=\"softmax\", name='adversary_output')(Dx)\n adv_NN = Model(input=[adv_inputs], output=[Dx])\n adv_NN.name = 'adv_NN'\n return adv_NN","title":""},{"docid":"7af2f1352b3fbceb3e49a889cd12e14a","score":"0.57253855","text":"def adversarial_example(self, inputs):\n pass","title":""},{"docid":"921177fb91a63ad4f1d7ca422ac35796","score":"0.5700022","text":"def learn(self, batch_exp):\n raise NotImplementedError","title":""},{"docid":"3023e6d63c37c1e0b4aa25f784030054","score":"0.5691788","text":"def saliency():\n\n buho = 'images/great_grey_owl.jpg'\n di1 = 'images/1.png'\n image = load_image(buho)\n # image = load_image(buho)\n\n # plt.imshow(image)\n # plt.title('Original image'+str(type(image)))\n # plt.axis('off');\n # plt.show()\n \"\"\"### 2. Load a pre-trained Model\"\"\"\n\n model = models.alexnet(pretrained=True)\n # model = torch.load('/content/alexnet-frames-Finetuned:False-1di-tempMaxPool-OnPlateau.tar')\n\n \"\"\"### 3. Create an instance of Backprop with the model\"\"\"\n\n backprop = Backprop(model)\n\n \"\"\"### 4. Visualize saliency maps\"\"\"\n\n # Transform the input image to a tensor\n\n owl = apply_transforms(image)\n # print(owl.size()) #torch.Size([/1, 3, 224, 224])\n # input_size = 224\n # data_transforms = createTransforms(input_size)\n # owl = data_transforms['test'](image)\n # owl = owl.unsqueeze(dim=0)\n # owl = owl.unsqueeze(dim=0)\n # owl = owl.permute(1, 0, 2, 3, 4)\n # print(owl.size())\n\n # Set a target class from ImageNet task: 24 in case of great gray owl\n\n target_class = 24\n\n # Ready to roll!\n\n backprop.visualize(owl, target_class=target_class, guided=True, use_gpu=True)\n\n\n\n # \"\"\"### 5. What about other birds?\n\n # What makes peacock a peacock...?\n # \"\"\"\n\n # peacock = apply_transforms(load_image('/content/images/peacock.jpg'))\n # backprop.visualize(peacock, 84, guided=True, use_gpu=True)\n\n # \"\"\"Or a toucan?\"\"\"\n\n # toucan = apply_transforms(load_image('/content/images/toucan.jpg'))\n # backprop.visualize(toucan, 96, guided=True, use_gpu=True)\n\n # \"\"\"Please try out other models/images too!\"\"\"","title":""},{"docid":"93f073666fba5679aba5ef6ce5e0a764","score":"0.56886184","text":"def tf_efficientnet_b0_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b0_ap\",\n channel_multiplier=1.0,\n depth_multiplier=1.0,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"2080f2393e7a798f3e0ea65e319c77f1","score":"0.56620723","text":"def tf_efficientnet_b1(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b1\",\n channel_multiplier=1.0,\n depth_multiplier=1.1,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"2c858a3a262f2e344429778995a8a91f","score":"0.5618886","text":"def train(self, v0, vk, ph0, phk):\n \n # Update weights and biases\n self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()\n self.b += torch.sum((v0 - vk), 0) # ----> 0 is added to keep the shape of the tensor b\n self.a += torch.sum((ph0 - phk), 0)","title":""},{"docid":"fe371336cf3b08a015754425887b61ed","score":"0.55788666","text":"def adverarial_train_natural(model, train_set, test_set, save_path=None, **kwargs):\n assert save_path is not None\n print('start training ... ')\n val_acc_history = []\n val_loss_history = []\n epoch_num = kwargs[\"epoch_num\"]\n advtrain_start_num = kwargs[\"advtrain_start_num\"]\n batch_size = kwargs[\"batch_size\"]\n adversarial_trans = kwargs[\"adversarial_trans\"]\n opt = kwargs[\"optimizer\"]\n model_para_name = kwargs[\"model_para_name\"]\n model_opt_para_name = kwargs[\"model_opt_para_name\"]\n\n train_loader = paddle.io.DataLoader(train_set, shuffle=True, batch_size=batch_size)\n valid_loader = paddle.io.DataLoader(test_set, batch_size=batch_size)\n max_acc = 0\n for epoch in range(epoch_num):\n for batch_id, data in enumerate(train_loader()):\n x_data = data[0]\n y_data = paddle.unsqueeze(data[1], 1)\n\n # adversarial training late start\n if epoch >= advtrain_start_num and adversarial_trans is not None:\n x_data_augmented, y_data_augmented = adversarial_trans(x_data.numpy(), y_data.numpy())\n else:\n x_data_augmented, y_data_augmented = x_data, y_data\n # turn model into training mode\n model.train()\n # make sure gradient flow for model parameter\n for param in model.parameters():\n param.stop_gradient = False\n\n # numpy to paddle.Tensor\n x_data_augmented = paddle.to_tensor(x_data_augmented, dtype='float32', place=USE_GPU)\n y_data_augmented = paddle.to_tensor(y_data_augmented, dtype='int64', place=USE_GPU)\n y_data_augmented = paddle.unsqueeze(y_data_augmented, 1)\n\n logits = model(x_data_augmented)\n loss = F.cross_entropy(logits, y_data_augmented)\n acc = paddle.metric.accuracy(logits, y_data_augmented)\n acc = acc.numpy()\n acc = round(acc[0], 3)\n if batch_id % 10 == 0:\n print(\"epoch:{}, batch_id:{}, loss:{}, acc:{}\".format(epoch, batch_id, loss.numpy(), acc))\n loss.backward()\n opt.step()\n opt.clear_grad()\n # evaluate model after one epoch\n model.eval()\n accuracies = []\n losses = []\n with paddle.no_grad():\n for batch_id, data in enumerate(valid_loader()):\n x_data = data[0]\n y_data = paddle.unsqueeze(data[1], 1)\n logits = model(x_data)\n loss = F.cross_entropy(logits, y_data)\n acc = paddle.metric.accuracy(logits, y_data)\n accuracies.append(acc.numpy())\n losses.append(loss.numpy())\n avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)\n avg_acc = round(avg_acc, 6)\n avg_loss = round(avg_loss, 6)\n if avg_acc > max_acc:\n max_acc = avg_acc\n paddle.save(model.state_dict(), save_path + model_para_name + str(max_acc) + '.pdparams')\n paddle.save(opt.state_dict(), save_path + model_opt_para_name + str(max_acc) + '.pdopt')\n print(\"best saved at: \", save_path)\n else:\n pass\n print(\"[validation] accuracy/loss:{}/{}, max_acc:{}\".format(avg_acc, avg_loss, max_acc))\n val_acc_history.append(avg_acc)\n val_loss_history.append(avg_loss)\n model.train()\n\n return val_acc_history, val_loss_history","title":""},{"docid":"a7683ca8797688f0dda0e626ce50df26","score":"0.5561336","text":"def __init__(self, n_latent=1024):\n super(MLPAdjustment, self).__init__()\n self.n_latent = n_latent\n\n self.conv1_bn = tf_utils.Conv1DBatchNorm(n_latent, 1,\n scope=\"MLPAdjust_Conv1D_1\")\n self.conv2_bn = tf_utils.Conv1DBatchNorm(n_latent // 2, 1,\n scope=\"MLPAdjust_Conv1D_2\")\n self.conv3_bn = tf_utils.Conv1DBatchNorm(n_latent // 4, 1,\n scope=\"MLPAdjust_Conv1D_3\")\n self.conv4 = layers.Conv1D(3, 1, activation=\"tanh\")","title":""},{"docid":"9738aa99a2ea5c360f874a83df0dbd49","score":"0.5546009","text":"def __activate(self, preact, layer_name, skip_af=False, batch_norm=False):\n assert not (self.NetSettings.learn_layer_stats and batch_norm), 'batch normalization and learned normalization prohibited to run in parralel.'\n assert self.NetSettings.ABU_normalization in ['unrestricted','normalized','posnormed','absnormed','softmaxed'], 'specified ABU normalization unknown.'\n\n # NORMALIZATION\n self.__variable_summaries(preact, 'preact', stats=['mean', 'std', 'hist'])\n if batch_norm:\n preact = tf.layers.batch_normalization(preact, name='batchnorm', training=True)\n if self.NetSettings.learn_layer_stats:\n alpha_initializer = tf.fill([1], 1.0)\n beta_initializer = tf.fill([1], 0.0)\n alpha = tf.get_variable(\"alpha\", initializer=alpha_initializer)\n beta = tf.get_variable(\"beta\", initializer=beta_initializer)\n self.__variable_summaries(alpha, 'alpha', stats=['mean'])\n self.__variable_summaries(beta, 'beta', stats=['mean'])\n preact = alpha * (preact + beta)\n\n # ACTIVATION FUNCTIONS\n if skip_af:\n activation = preact\n elif self.NetSettings.activation_function == 'relu':\n activation = tf.nn.relu(preact)\n elif self.NetSettings.activation_function == 'elu':\n activation = tf.nn.elu(preact)\n elif self.NetSettings.activation_function == 'tanh':\n activation = tf.nn.tanh(preact)\n elif self.NetSettings.activation_function == 'swish':\n if self.NetSettings.af_weights_pretrained:\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\n else:\n swish_beta_init = tf.fill([1], 1.0)\n swish_beta = tf.get_variable(\"swish_beta\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\n activation = preact * tf.nn.sigmoid(swish_beta*preact)\n elif self.NetSettings.activation_function == 'id':\n activation = preact\n elif self.NetSettings.activation_function == 'selu':\n activation = tf.nn.selu(preact)\n elif self.NetSettings.activation_function == 'ABU':\n # INITIALIZE BLENDING WEIGHTS & SWISH BETA\n if self.NetSettings.af_weights_pretrained:\n blending_weights_initializer = self.__get_predefined_af_weights(layer_name, w_type='blend')\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\n else:\n blending_weights_initializer = tf.fill([5], 1/5.0)\n swish_beta_init = tf.fill([1], 1.0)\n blending_weights_raw = tf.get_variable(\"blending_weights_raw\", trainable=self.NetSettings.ABU_trainable, initializer=blending_weights_initializer)\n swish_beta = tf.get_variable(\"swish_beta\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\n self.__variable_summaries(blending_weights_raw, 'blending_weights', stats=['mean', 'min', 'max'])\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\n # NORMALIZE\n if self.NetSettings.ABU_normalization == 'unrestricted':\n blending_weights = blending_weights_raw\n elif self.NetSettings.ABU_normalization == 'normalized':\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'absnormed':\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(tf.abs(blending_weights_raw), keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'posnormed':\n blending_weights = tf.divide(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), tf.reduce_sum(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), keep_dims=True))\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'softmaxed':\n blending_weights = tf.exp(blending_weights_raw)\n blending_weights = tf.divide(blending_weights, tf.reduce_sum(blending_weights, keep_dims=True))\n # ACTIVATE\n act_relu = tf.nn.relu(preact)\n act_elu = tf.nn.elu(preact)\n act_tanh = tf.nn.tanh(preact)\n act_swish = preact * tf.nn.sigmoid(swish_beta*preact)\n act_id = preact\n activation = tf.add_n([blending_weights[0] * act_relu,\n blending_weights[1] * act_elu,\n blending_weights[2] * act_tanh,\n blending_weights[3] * act_swish,\n blending_weights[4] * act_id])\n # custom ABU\n elif self.NetSettings.activation_function.startswith('ABU-'):\n # ABUs with different sets of AFs. e.g. \"ABU-relu-tanh-swish\"\n n_AFs = len(self.NetSettings.activation_function.split('-'))-1\n AF_list = self.NetSettings.activation_function.split('-')[1:]\n # INITIALIZE SWISH BETA IF APPLICABLE\n if 'swish' in AF_list:\n if self.NetSettings.af_weights_pretrained:\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\n else:\n swish_beta_init = tf.fill([1], 1.0)\n swish_beta = tf.get_variable(\"swish_beta\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\n # INITIALIZE BLENDING WEIGHTS\n if self.NetSettings.af_weights_pretrained:\n blending_weights_initializer = self.__get_predefined_af_weights(layer_name, w_type='blend')\n else:\n blending_weights_initializer = tf.fill([n_AFs], 1/float(n_AFs))\n blending_weights_raw = tf.get_variable(\"blending_weights_raw\", trainable=self.NetSettings.ABU_trainable, initializer=blending_weights_initializer)\n self.__variable_summaries(blending_weights_raw, 'blending_weights', stats=['mean', 'min', 'max'])\n # NORMALIZE\n if self.NetSettings.ABU_normalization == 'unrestricted':\n blending_weights = blending_weights_raw\n elif self.NetSettings.ABU_normalization == 'normalized':\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'absnormed':\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(tf.abs(blending_weights_raw), keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'posnormed':\n blending_weights = tf.divide(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), tf.reduce_sum(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), keep_dims=True))\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\n elif self.NetSettings.ABU_normalization == 'softmaxed':\n blending_weights = tf.exp(blending_weights_raw)\n blending_weights = tf.divide(blending_weights, tf.reduce_sum(blending_weights, keep_dims=True))\n # ACTIVATE\n activations = []\n for i, AF in enumerate(AF_list):\n assert AF in ['id', 'tanh', 'relu', 'elu', 'swish'], 'requested activation function unknown: %s' %(AF)\n if AF == 'id':\n activations.append(blending_weights[i] * preact)\n elif AF == 'tanh':\n activations.append(blending_weights[i] * tf.nn.tanh(preact))\n elif AF == 'relu':\n activations.append(blending_weights[i] * tf.nn.relu(preact))\n elif AF == 'elu':\n activations.append(blending_weights[i] * tf.nn.elu(preact))\n elif AF == 'swish':\n activations.append(blending_weights[i] * preact * tf.nn.sigmoid(swish_beta*preact))\n activation = tf.add_n(activations)\n self.__variable_summaries(activation, 'activation', stats=['mean', 'std', 'hist'])\n return activation","title":""},{"docid":"8995caded15c986993024e51cf1a055a","score":"0.55321515","text":"def tf_efficientnet_b2_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b2_ap\",\n channel_multiplier=1.1,\n depth_multiplier=1.2,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"c154933240faafaeec6a453006364e3a","score":"0.5531579","text":"def generate_adversaries(model, baseImage, delta, classIdx, defense, steps=50):\n # Iterate over the number of steps\n for step in range(0, steps):\n # Record the gradients\n with tf.GradientTape() as tape:\n # Explicitly indicate that the perturbation vector should be tracked for gradient updates\n tape.watch(delta)\n # Add the perturbation vector to the base image and preprocess the resulting image\n #adversary = preprocess_input(baseImage + delta)\n adversary = baseImage + delta\n # Run this newly constructed image tensor through the base model and calculate the loss with respect to the\n # original class index\n predictions = model(adversary, defense = defense)\n\n #print(predictions)\n loss = -sccLoss(tf.convert_to_tensor([classIdx]), predictions)\n # Check the loss value and display it in the terminal\n #if step % 10 == 0:\n # print(\"[INFO] Step: {}, Loss: {}...\".format(step, loss.numpy()))\n # Calculate the gradients of loss with respect to the perturbation vector\n gradients = tape.gradient(loss, delta)\n # Update the weights, clip the perturbation vector and update its value\n optimizer.apply_gradients([(gradients, delta)])\n delta.assign_add(clip_eps(delta, eps=EPS))\n #deltaImage = delta.numpy().squeeze()\n #deltaImage = cv2.cvtColor(deltaImage, cv2.COLOR_RGB2BGR)\n #cv2.imshow(\"Output1\", deltaImage)\n #print(delta)\n # Return the perturbation vector\n return delta","title":""},{"docid":"18db48e3800e184d3ca7df7d74b7a4bd","score":"0.55061907","text":"def layer1(input, name=\"pretrainedL1\"):\n w = tf.Variable(tf.convert_to_tensor(wts1, dtype=tf.float32), name=\"W\")\n b = tf.Variable(tf.convert_to_tensor(bias1, dtype=tf.float32), name=\"B\")\n h = tf.matmul(w, input) + b\n h = tf.nn.relu(h)\n tf.summary.histogram(\"weights\", w)\n tf.summary.histogram(\"biases\", b)\n tf.summary.histogram(\"activations\", h)\n return h","title":""},{"docid":"3a8e02994086b1725883b91bfa78b268","score":"0.54683954","text":"def model_fn(features,labels,mode,params=None): \n params = params or {}\n loss, acc, train_op, export_outputs = None, None, None, None\n is_training = (mode==tf.estimator.ModeKeys.TRAIN)\n\n cls_model = Alexnet(is_training=is_training,num_classes=FLAGS.num_classes)\n #preprocessed_inputs = cls_model.preprocess(features['images']) \n preprocessed_inputs = features['images']\n prediction_dict = cls_model.predict(preprocessed_inputs)\n postprocessed_dict = cls_model.postprocess(prediction_dict)\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n if FLAGS.checkpoint_path:\n init_variables_from_checkpoint()\n if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):\n loss_dict = cls_model.loss(postprocessed_dict,labels)\n loss = loss_dict['loss']\n pre_classes = postprocessed_dict['classes']\n\n acc = tf.reduce_mean(tf.cast(tf.equal(pre_classes,labels),dtype=tf.float32))\n tf.summary.scalar('loss',loss)\n tf.summary.scalar('accuracy',acc)\n\n scaffold = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n global_step = tf.train.get_or_create_global_step()\n learning_rate = configure_learning_rate(FLAGS.decay_steps,global_step)\n optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=0.9)\n train_op = slim.learning.create_train_op(loss, optimizer,\n summarize_gradients=True)\n keep_checkpoint_every_n_hours = FLAGS.keep_checkpoint_every_n_hours\n saver = tf.train.Saver(\n sharded=True,\n keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,\n save_relative_paths=True) \n tf.add_to_collection(tf.GraphKeys.SAVERS, saver)\n scaffold = tf.train.Scaffold(saver=saver)\n \n #return tf.estimator.EstimatorSpec(mode=mode,loss=loss,train_op=train_op,scaffold=scaffold)\n\n \n eval_metric_ops = None\n if mode == tf.estimator.ModeKeys.EVAL:\n accuracy = tf.metrics.accuracy(labels=labels, predictions=pre_classes)\n eval_metric_ops = {'Accuracy': accuracy}\n\n # if mode == tf.estimator.ModeKeys.PREDICT:\n # export_output = exporter._add_output_tensor_nodes(postprocessed_dict)\n # export_outputs = {\n # tf.saved_model.signature_constants.PREDICT_METHOD_NAME:\n # tf.estimator.export.PredictOutput(export_output)}\n return tf.estimator.EstimatorSpec(mode=mode,\n predictions=prediction_dict,\n loss=loss,\n train_op=train_op,\n eval_metric_ops=eval_metric_ops,\n scaffold=scaffold)","title":""},{"docid":"cf7b05b05b051a3e751db7122f886737","score":"0.5459812","text":"def __init__(self, kernel_size=1, decay=_DECAY, name='attention'):\n super(AttentionModel, self).__init__(name=name)\n\n # First convolutional layer (called with relu activation).\n self.conv1 = layers.Conv2D(\n 512,\n kernel_size,\n kernel_regularizer=reg.l2(decay),\n padding='same',\n name='attn_conv1')\n self.bn_conv1 = layers.BatchNormalization(axis=3, name='bn_conv1')\n\n # Second convolutional layer, with softplus activation.\n self.conv2 = layers.Conv2D(\n 1,\n kernel_size,\n kernel_regularizer=reg.l2(decay),\n padding='same',\n name='attn_conv2')\n self.activation_layer = layers.Activation('softplus')","title":""},{"docid":"aaf9aabed34a5525ed93c443b17087c7","score":"0.5455983","text":"def forward(self, inputs, **unused_params):\n anchor_weights = tf.get_variable(\"anchor_weights{}\".format(\"\" if self.scope_id is None else str(self.scope_id)),\n [self.feature_size, self.num_anchors],\n initializer=tf.random_normal_initializer(\n stddev=1 / math.sqrt(self.num_anchors)),\n dtype=tf.float32)\n tf.summary.histogram(\"anchor_weights{}\".format(\"\" if self.scope_id is None else str(self.scope_id)),\n anchor_weights)\n\n anchor_weights = tf.transpose(anchor_weights)\n anchor_weights = tf.reshape(anchor_weights, [1, self.feature_size * self.num_anchors])\n\n # Tile inputs to subtract all anchors.\n tiled_inputs = tf.tile(inputs, [1, self.num_anchors])\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\n t_emb = tf.subtract(tiled_inputs, anchor_weights)\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\n\n t_emb = tf.reshape(t_emb, [-1, self.num_anchors, self.feature_size])\n # -> (batch_size * num_descriptors) x num_anchors x feature_size; Keep normalized residuals.\n t_emb = tf.nn.l2_normalize(t_emb, 2)\n t_emb = tf.reshape(t_emb, [-1, self.feature_size * self.num_anchors])\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\n\n if self.add_batch_norm:\n t_emb = slim.batch_norm(\n t_emb,\n center=True,\n scale=True,\n is_training=self.is_training,\n scope=\"t_emb_bn\")\n\n activation = tf.reshape(t_emb, [-1, self.num_descriptors, self.feature_size * self.num_anchors])\n\n return activation","title":""},{"docid":"521bce2086d8424ead8dfc58006ae7f9","score":"0.5450276","text":"def __call__(self, image_input, training=False, keep_prob=1.0):\r\n def leaky_relu(x, leak=0.2, name=''):\r\n return tf.maximum(x, x * leak, name=name)\r\n with tf.variable_scope('RelationModule', reuse=self.reuse):\r\n with tf.variable_scope('conv_layers'):\r\n with tf.variable_scope('RelationModule_conv1'):\r\n g_conv1_encoder = tf.layers.conv1d(image_input, 64, 9, strides=1, padding='VALID')\r\n g_conv1_encoder = leaky_relu(g_conv1_encoder, name='outputs')\r\n g_conv1_encoder = tf.layers.max_pooling1d(g_conv1_encoder, 2, 2, padding='SAME')\r\n g_conv1_encoder = tf.nn.dropout(g_conv1_encoder, keep_prob=keep_prob)\r\n with tf.variable_scope('RelationModule_conv2'):\r\n g_conv2_encoder = tf.layers.conv1d(g_conv1_encoder, 64, 9, strides=1, padding='VALID')\r\n g_conv2_encoder = leaky_relu(g_conv2_encoder, name='outputs')\r\n g_conv2_encoder = tf.layers.max_pooling1d(g_conv2_encoder, 2, 2, padding='SAME')\r\n g_conv2_encoder = tf.nn.dropout(g_conv2_encoder, keep_prob=keep_prob)\r\n with tf.variable_scope('fully_connected_relu'):\r\n g_fc1_encoder = tf.contrib.layers.flatten(g_conv2_encoder)\r\n g_fc1_encoder = tf.contrib.layers.fully_connected(g_fc1_encoder, 8, trainable=True, scope='fc_relu')\r\n with tf.variable_scope('fully_connected_sigmoid'):\r\n g_fc2_encoder = tf.contrib.layers.fully_connected(g_fc1_encoder, 1, activation_fn=tf.nn.sigmoid,\r\n trainable=True, scope='fc_sigmoid')\r\n g_conv_encoder = g_fc2_encoder\r\n\r\n self.reuse = True\r\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='RelationModule')\r\n return g_conv_encoder","title":""},{"docid":"caf0c53cfb4b01c3e00ba82de98cd694","score":"0.5447174","text":"def test_adversarial_example(learning_rate=0.001, L1_reg=0.00, L2_reg=0.0001, n_epochs=100,\n batch_size=128, n_hidden=500, n_hiddenLayers=3,\n verbose=False, smaller_set=True):\n\n \"\"\"\n # load the dataset; download the dataset if it is not present\n if smaller_set:\n datasets = load_data(ds_rate=5)\n else:\n datasets = load_data()\n \"\"\"\n \n # Load the smaller dataset in raw format, since we need to preprocess it\n train_set, valid_set, test_set = load_data(ds_rate=5, theano_shared=False)\n \n # Convert raw dataset to Theano shared variables.\n test_set_x, test_set_y = shared_dataset(test_set)\n valid_set_x, valid_set_y = shared_dataset(valid_set)\n train_set_x, train_set_y = shared_dataset(train_set)\n \n \n # compute number of minibatches for training, validation and testing\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size\n\n ######################\n # BUILD ACTUAL MODEL #\n ######################\n print('... building the model')\n\n # allocate symbolic variables for the data\n index = T.lscalar() # index to a [mini]batch\n x = T.matrix('x') # the data is presented as rasterized images\n y = T.ivector('y') # the labels are presented as 1D vector of\n # [int] labels\n\n rng = numpy.random.RandomState(1234)\n\n # TODO: construct a neural network, either MLP or CNN.\n classifier = myMLP(\n rng=rng,\n input=x,\n n_in=32*32*3,\n n_hidden=n_hidden,\n n_out=10,\n n_hiddenLayers=n_hiddenLayers \n )\n\n # the cost we minimize during training is the negative log likelihood of\n # the model plus the regularization terms (L1 and L2); cost is expressed\n # here symbolically\n cost = (\n classifier.negative_log_likelihood(y)\n + L1_reg * classifier.L1\n + L2_reg * classifier.L2_sqr\n )\n\n # compiling a Theano function that computes the mistakes that are made\n # by the model on a minibatch\n test_model = theano.function(\n inputs=[index],\n outputs=classifier.errors(y),\n givens={\n x: test_set_x[index * batch_size:(index + 1) * batch_size],\n y: test_set_y[index * batch_size:(index + 1) * batch_size]\n }\n )\n\n validate_model = theano.function(\n inputs=[index],\n outputs=classifier.errors(y),\n givens={\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]\n }\n )\n\n # compute the gradient of cost with respect to theta (sotred in params)\n # the resulting gradients will be stored in a list gparams\n gparams = [T.grad(cost, param) for param in classifier.params]\n \n \n # specify how to update the parameters of the model as a list of\n # (variable, update expression) pairs\n\n # given two lists of the same length, A = [a1, a2, a3, a4] and\n # B = [b1, b2, b3, b4], zip generates a list C of same size, where each\n # element is a pair formed from the two lists :\n # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]\n updates = [\n (param, param - learning_rate * gparam)\n for param, gparam in zip(classifier.params, gparams)\n ]\n\n # compiling a Theano function `train_model` that returns the cost, but\n # in the same time updates the parameter of the model based on the rules\n # defined in `updates`\n train_model = theano.function(\n inputs=[index],\n outputs=cost,\n updates=updates,\n givens={\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\n }\n )\n \n get_grad = theano.function(\n inputs=[index],\n outputs=T.grad(cost,x),\n givens={\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\n }\n )\n \n pred = classifier.logRegressionLayer.p_y_given_x\n get_probability = theano.function(\n inputs=[index],\n outputs=pred,\n givens={\n x: train_set_x[index * batch_size: (index + 1) * batch_size]\n }\n )\n\n label = classifier.logRegressionLayer.y_pred\n get_label = theano.function(\n inputs=[index],\n outputs=label,\n givens={\n x: train_set_x[index * batch_size: (index + 1) * batch_size]\n }\n ) \n\n \n ###############\n # TRAIN MODEL #\n ###############\n print('... training')\n\n train_nn(train_model, validate_model, test_model,\n n_train_batches, n_valid_batches, n_test_batches, n_epochs, verbose)\n \n \n \n \n advers = train_set\n \n examp_grad = get_grad(0)\n examp_prob = get_probability(0)\n examp_label = get_label(0)\n \n \n advers_x = advers[0]\n advers_x[:batch_size,:] = advers_x[:batch_size,:] + (numpy.random.rand(batch_size,3072)*numpy.sign(examp_grad))/20\n advers[0] = advers_x\n shared_advers_x, shared_advers_y = shared_dataset(train_set)\n train_set_x2, train_set_y2 = shared_dataset(advers)\n \n \n get_probability_advers = theano.function(\n inputs=[index],\n outputs=pred,\n givens={\n x: train_set_x2[index * batch_size: (index + 1) * batch_size]\n }\n )\n \n advers_prob = get_probability_advers(0)\n \n \n return advers_x[:batch_size,:], examp_prob, advers_prob, examp_label","title":""},{"docid":"43bdd748fa5bd3e600d7dfa04813b544","score":"0.5438254","text":"def __init__(\n self,\n learning_rate: Union[FloatTensorLike, Callable, Dict] = 0.001,\n beta_1: FloatTensorLike = 0.9,\n beta_2: FloatTensorLike = 0.999,\n epsilon: FloatTensorLike = 1e-7,\n weight_decay: Union[FloatTensorLike, Callable, Dict] = 0.0,\n amsgrad: bool = False,\n sma_threshold: FloatTensorLike = 5.0,\n total_steps: int = 0,\n warmup_proportion: FloatTensorLike = 0.1,\n min_lr: FloatTensorLike = 0.0,\n name: str = \"RectifiedAdam\",\n **kwargs,\n ):\n super().__init__(name, **kwargs)\n\n if isinstance(learning_rate, Dict):\n learning_rate = tf.keras.optimizers.schedules.deserialize(learning_rate)\n\n if isinstance(weight_decay, Dict):\n weight_decay = tf.keras.optimizers.schedules.deserialize(weight_decay)\n\n self._set_hyper(\"learning_rate\", kwargs.get(\"lr\", learning_rate))\n self._set_hyper(\"beta_1\", beta_1)\n self._set_hyper(\"beta_2\", beta_2)\n self._set_hyper(\"decay\", self._initial_decay)\n self._set_hyper(\"weight_decay\", weight_decay)\n self._set_hyper(\"sma_threshold\", sma_threshold)\n self._set_hyper(\"total_steps\", float(total_steps))\n self._set_hyper(\"warmup_proportion\", warmup_proportion)\n self._set_hyper(\"min_lr\", min_lr)\n self.epsilon = epsilon or tf.keras.backend.epsilon()\n self.amsgrad = amsgrad\n self._has_weight_decay = weight_decay != 0.0\n self._initial_total_steps = total_steps","title":""},{"docid":"05479f32f44fb05a9dee6ccbad0d7355","score":"0.5424882","text":"def _train(self, experience, weights):","title":""},{"docid":"709ea2287d8820cade09ca87eb1b8652","score":"0.54195005","text":"def build_model(self, reset_graph=False):\n \n if self.data is None:\n raise Exception('a dataset must be specified')\n \n if (self.adv and (self.classifier is None)):\n raise Exception('A classifier must be specified to train in adversarial mode')\n \n if(reset_graph):\n tf.reset_default_graph()\n \n self.dataset_is_tf = self.data.is_tf\n \n self.classifier.load_model()\n \n shape_x, shape_y = self.data.shape()\n shape_x[0] = None\n \n z_dims = [None] + self.z_dim\n \n shape_y[0] = None\n \n if self.dataset_is_tf:\n if(self.use_mask):\n self.X_real , self.labels, _, self.mask = self.data.next_batch()\n probs = self.classifier.predict(self.X_real)[:,:,:,4] \n labels_masked = tf.cast(self.labels * self.mask, tf.float32)\n pm = tf.cast(tf.expand_dims(probs, -1)*labels_masked, tf.float32)\n self.classifier_proba = tf.cast(1.0-(tf.reduce_sum(pm, [1,2,3]) / tf.reduce_sum(labels_masked, [1,2,3])), tf.float32)\n self.mask.set_shape(shape_y)\n elif(self.use_labels):\n self.X_real , self.labels, _, _ = self.data.next_batch()\n probs = self.classifier.predict(self.X_real)[:,:,:,4] \n pm = tf.cast(tf.expand_dims(probs, -1)*self.labels, tf.float32)\n self.classifier_proba = tf.cast(1.0-(tf.reduce_sum(pm, [1,2,3]) / tf.reduce_sum(self.labels, [1,2,3])), tf.float32) \n else:\n self.X_real , self.labels, self.classifier_proba = self.data.next_batch()\n \n self.X_real.set_shape(shape_x)\n self.labels.set_shape(shape_y)\n \n if(self.use_ceil):\n if self.ceil_value is not None:\n print('meh')\n self.classifier_proba = tf.minimum(self.classifier_proba, self.ceil_value)\n\n self.classifier_proba = tf.nn.softmax(self.classifier_proba * self.temperature)\n self.classifier_proba = tf.expand_dims(self.classifier_proba, -1)\n \n else:\n self.X_real = tf.placeholder(\"float\", shape_x, name=\"X_real\")\n self.labels = tf.placeholder(\"float\", shape_y, name=\"labels\")\n self.classifier_proba = tf.placeholder(\"float\", [None, 1], name=\"classifier_proba\")\n self.adv_map = tf.placeholder(\"float\", shape_y, name=\"adv_map\")\n\n self.z = tf.placeholder(\"float\", z_dims, name=\"z_input\")\n self.is_training = tf.placeholder(tf.bool, shape=())\n if self.use_clf:\n self.label_used = tf.constant(self.label_used, dtype='float')\n\n\n \n #train with real and classifier\n \n if(self.use_labels):\n self.pred_real = self.discriminator(self.X_real, self.labels, reuse=False)\n elif(self.use_mask):\n self.pred_real = self.discriminator(self.X_real, self.mask, reuse=False)\n else:\n self.pred_real = self.discriminator(self.X_real, reuse=False) \n \n if(self.adv):\n self.pred_real *= self.classifier_proba\n \n #train with fake\n if(self.use_labels):\n self.X_fake = self.generator(self.X_real, self.labels, self.z, train=self.is_training)\n elif(self.use_mask):\n self.X_fake = self.generator(self.X_real, self.mask, self.z, train=self.is_training)\n else:\n self.X_fake = self.generator(self.z, train=self.is_training)\n \n self.X_fake = tf.identity(self.X_fake, name=\"X_fake\")\n \n shape_x[0] = -1\n \n self.X_fake = tf.reshape(self.X_fake, shape_x)\n \n if(self.use_labels):\n self.pred_fake = self.discriminator(self.X_fake, self.labels)\n elif(self.use_mask):\n self.pred_fake = self.discriminator(self.X_fake, self.mask)\n else:\n self.pred_fake = self.discriminator(self.X_fake)\n \n if(self.adv and not self.use_softmax):\n self.pred_fake *= 1/len(self.data.shape()[0])\n \n self.g_loss = - tf.reduce_mean(self.pred_fake)\n \n \n if(self.adv and not self.use_softmax):\n self.d_loss = - (tf.reduce_sum(self.pred_real) - tf.reduce_sum(self.pred_fake))\n elif(self.adv):\n self.d_loss = - (tf.reduce_sum(self.pred_real) - tf.reduce_mean(self.pred_fake))\n else:\n self.d_loss = - (tf.reduce_mean(self.pred_real) - tf.reduce_mean(self.pred_fake))\n \n if self.reg_losses :\n self.d_loss = self.reg_term * self.d_loss\n self.g_loss = self.reg_term * self.g_loss\n \n if self.use_recon :\n if(self.use_mask):\n X_fake_masked = (1-self.mask) * self.X_fake\n X_real_masked = (1-self.mask) * self.X_real \n elif(self.use_labels):\n X_fake_masked = (1-self.labels) * self.X_fake\n X_real_masked = (1-self.labels) * self.X_real \n \n self.g_loss += self.recon_beta * (tf.reduce_mean(tf.reduce_sum(tf.square(X_fake_masked - X_real_masked), axis=np.arange(1, len(X_fake_masked.shape)))))\n \n if self.use_clf :\n y_t = (self.mask) * self.label_used\n preds = self.classifier.predict(self.X_fake)\n preds = preds * (self.mask)\n preds = 1-preds\n self.loss_clf = self.clf_alpha * tf.reduce_mean(-tf.reduce_sum(y_t * tf.log(tf.clip_by_value(preds ,1e-10,1.0)), reduction_indices=[1]))\n self.g_loss += self.loss_clf\n \n self.neg_loss = - self.d_loss\n \n if(self.use_labels):\n labels = self.labels\n elif(self.use_mask):\n labels = self.mask\n else:\n labels = None\n \n if self.loss == 'gp' :\n self.d_loss += losses.gp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n \n if self.loss == 'lp' :\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n \n if self.loss == 'ct':\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n self.d_loss += losses.ct_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n \n D_vars = [var for var in tf.trainable_variables() if \"discriminator\" in var.name]\n G_vars = [var for var in tf.trainable_variables() if \"generator\" in var.name] \n \n if self.accumulate_grd:\n # initialized with 0s\n \n self.accum_vars_D = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False ) for tv in D_vars] \n self.accumulation_counter_D = tf.Variable(0.0, trainable=False)\n \n self.accum_vars_G = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in G_vars] \n self.accumulation_counter_G = tf.Variable(0.0, trainable=False)\n \n self.D_sum = tf.summary.scalar('D_loss', self.d_loss)\n self.G_sum = tf.summary.scalar('G_loss', self.g_loss)\n\n with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n \n if self.accumulate_grd:\n\n gvs_G = (self.optimizer_G).compute_gradients(self.g_loss, G_vars)\n gvs_D = (self.optimizer_D).compute_gradients(self.d_loss, D_vars)\n \n self.accum_ops_G = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in zip(self.accum_vars_G, gvs_G)] \n self.accum_ops_G.append(self.accumulation_counter_G.assign_add(1.0)) \n \n self.accum_ops_D = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in zip(self.accum_vars_D, gvs_D)] \n self.accum_ops_D.append(self.accumulation_counter_D.assign_add(1.0)) \n \n self.G_step = (self.optimizer_G).apply_gradients([(accumulator / self.accumulation_counter_G, var) for (accumulator, (grad, var)) in zip(self.accum_vars_G, gvs_G)])\n\n self.D_step = (self.optimizer_D).apply_gradients([(accumulator / self.accumulation_counter_D, var) for (accumulator, (grad, var)) in zip(self.accum_vars_D, gvs_D)])\n \n self.zero_ops_G = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in zip(self.accum_vars_G, G_vars)]\n self.zero_ops_D = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in zip(self.accum_vars_D, D_vars)]\n \n self.zero_ops_G.append(self.accumulation_counter_G.assign(0.0))\n self.zero_ops_D.append(self.accumulation_counter_D.assign(0.0))\n \n else:\n self.G_step = (self.optimizer_G).minimize(self.g_loss, var_list=G_vars)\n self.D_step = (self.optimizer_D).minimize(self.d_loss, var_list=D_vars)\n \n if self.loss == 'clip' :\n self.D_clipping = [w.assign(tf.clip_by_value(w, -self.clip_value, self.clip_value)) for w in D_vars]\n\n \n self.saver = tf.train.Saver()\n self.builded = True","title":""},{"docid":"3cf448bb9546fbe318886d58d33a7591","score":"0.5416056","text":"def index_adam_input(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\n weight_decay: float, eps: float, indices: List[Tensor]):\n\n for i, param in enumerate(params):\n\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\n param_tmp, ind_tmp = torch.sort(param.view(-1))\n indices[i].view(-1)[torch.argsort(param_new.view(-1))] = ind_tmp\n param.data.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp","title":""},{"docid":"4b79ba1dd9f979defd167a5ee90c711a","score":"0.5413545","text":"def get_adversarial(self, batch_size, mode):\n # Restart at beggining if iteration if complete\n if self.count[mode] >= self.adv_examples[mode].size(0):\n self.count[mode] = 0\n \n x_batch = self.adv_examples[mode][(self.count[mode]):(self.count[mode] + batch_size)]\n y_batch = self.adv_labels[mode][(self.count[mode]):(self.count[mode] + batch_size)]\n self.count[mode] = self.count[mode] + batch_size\n \n return x_batch.cuda(), y_batch.cuda()","title":""},{"docid":"af8451e7709fe8b9b2f57cb95270cdf9","score":"0.53965044","text":"def __init__(self, params, **kwargs):\n super().__init__(name=\"AdaGrad\")\n self._accu = [\n shared(aet.zeros_like(p()).eval()) for p in params if p.status != 1\n ]","title":""},{"docid":"afc1939e01226ee1f60eb5235b414b01","score":"0.53934616","text":"def index_adam(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\n weight_decay: float, eps: float):\n\n for i, param in enumerate(params):\n\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\n param_tmp, _ = torch.sort(param.view(-1))\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp","title":""},{"docid":"2ec57db0599b9203cc1f11d5583fd173","score":"0.53554124","text":"def tf_efficientnet_b1_ns(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b1_ns\",\n channel_multiplier=1.0,\n depth_multiplier=1.1,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"f8220e5f2760478d2ca0b91ba8deffd9","score":"0.53538835","text":"def __mlp__(self):\n # Define the operations\n\n # Network input\n vec_inputs = []\n\n # Distributional component\n if self.hparams.input in ['dist', 'dist-nc', 'integrated', 'integrated-nc']:\n for emb_id in [self.x_embedding_id, self.y_embedding_id]:\n vec_inputs.append(tf.nn.embedding_lookup(self.relata_lookup, emb_id))\n\n # Noun compound component\n if self.hparams.input in ['dist-nc', 'integrated-nc']:\n vec = tf.nn.embedding_lookup(self.nc_lookup, self.nc_embedding_id)\n vec_inputs.append(vec)\n\n # Path-based component\n if self.hparams.input in ['path', 'integrated', 'integrated-nc']:\n\n # Get the current paths for each batch instance\n self.path_embeddings = tf.nn.embedding_lookup(self.path_lookup,\n self.path_embedding_id)\n\n # self.path_embeddings is of shape\n # [batch_size, max_path_per_instance, output_dim]\n # We need to multiply it by path counts\n # ([batch_size, max_path_per_instance]).\n # Start by duplicating path_counts along the output_dim axis.\n self.path_freq = tf.tile(tf.expand_dims(self.path_counts, -1),\n [1, 1, self.path_dim])\n\n # Compute the averaged path vector for each instance.\n # First, multiply the path embeddings and frequencies element-wise.\n self.weighted = tf.multiply(self.path_freq, self.path_embeddings)\n\n # Second, take the sum to get a tensor of shape [batch_size, output_dim].\n self.pair_path_embeddings = tf.reduce_sum(self.weighted, 1)\n\n # Finally, divide by the total number of paths.\n # The number of paths for each pair has a shape [batch_size, 1],\n # We duplicate it output_dim times along the second axis.\n self.num_paths = tf.clip_by_value(\n tf.reduce_sum(self.path_counts, 1), 1, np.inf)\n self.num_paths = tf.tile(tf.expand_dims(self.num_paths, -1),\n [1, self.path_dim])\n\n # And finally, divide pair_path_embeddings by num_paths element-wise.\n self.pair_path_embeddings = tf.div(\n self.pair_path_embeddings, self.num_paths)\n vec_inputs.append(self.pair_path_embeddings)\n\n # Concatenate the inputs and feed to the MLP\n self.input_vec = tf.nn.dropout(\n tf.concat(vec_inputs, 1),\n keep_prob=self.hparams.input_keep_prob)\n\n h = tf.matmul(self.input_vec, self.weights1)\n self.output = h\n\n if self.hparams.hidden_layers == 1:\n self.output = tf.matmul(tf.nn.tanh(h), self.weights2)\n\n self.scores = self.output\n self.predictions = tf.argmax(self.scores, axis=1)\n\n # Define the loss function and the optimization algorithm\n self.cross_entropies = tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=self.scores, labels=self.labels)\n self.cost = tf.reduce_sum(self.cross_entropies, name='cost')\n self.global_step = tf.Variable(0, name='global_step', trainable=False)\n self.optimizer = tf.train.AdamOptimizer()\n self.train_op = self.optimizer.minimize(\n self.cost, global_step=self.global_step)","title":""},{"docid":"f5a3c846ec9038dfe0189e669abbb781","score":"0.5348033","text":"def __init__(self, filter_sizes,num_filters,num_classes, learning_rate, batch_size, decay_steps, decay_rate,sequence_length,vocab_size,embed_size,\n is_training,initializer=tf.random_normal_initializer(stddev=0.1),multi_label_flag=False,clip_gradients=5.0,decay_rate_big=0.50):\n # set hyperparamter\n self.num_classes = num_classes\n self.batch_size = batch_size\n self.sequence_length=sequence_length\n self.vocab_size=vocab_size\n self.embed_size=embed_size\n self.is_training=is_training\n self.learning_rate = tf.Variable(learning_rate, trainable=False, name=\"learning_rate\")#ADD learning_rate\n self.learning_rate_decay_half_op = tf.assign(self.learning_rate, self.learning_rate * decay_rate_big)\n \n\n self.instantiate_weights()\n self.logits = self.inference()\n if not is_training:\n return\n if multi_label_flag:\n print(\"going to use multi label loss.\")\n self.loss_val = self.loss_multilabel()\n else:\n print(\"going to use single label loss.\")\n self.loss_val = self.loss()\n self.train_op = self.train()\n self.predictions = tf.argmax(self.logits, 1, name=\"predictions\")\n\n if not self.multi_label_flag:\n correct_prediction = tf.equal(tf.cast(self.predictions,tf.int32), self.input_y) #tf.argmax(self.logits, 1)-->[batch_size]\n self.accuracy =tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name=\"Accuracy\") # shape=()\n else:\n self.accuracy = tf.constant(0.5)","title":""},{"docid":"2806a813b4b5a897f3a6a1ad87049622","score":"0.53439796","text":"def __init__(self, conf):\n\n super(PolicyVNetwork, self).__init__(conf)\n\n self.entropy_regularisation_strength = conf['entropy_regularisation_strength']\n self.softmax_temp = conf['softmax_temp']\n self.op = Operations(conf)\n self.total_repetitions = conf['nb_choices']\n\n with tf.device(conf['device']):\n with tf.name_scope('Training'):\n\n with tf.name_scope('Critic'):\n self.critic_target_ph = tf.placeholder(\"float32\", [None], name='target')\n # Final critic layer\n _, _, self.output_layer_v = self.op.fc('critic_output', self.output, 1, activation=\"linear\")\n self.output_layer_v = tf.reshape(self.output_layer_v, [-1])\n # Advantage critic\n self.critic_loss = tf.subtract(self.critic_target_ph, self.output_layer_v)\n self.critic_loss_mean = tf.reduce_mean(tf.scalar_mul(0.25, tf.pow(self.critic_loss, 2)), name='mean_critic_loss')\n\n\n with tf.name_scope('Actor'):\n # Final actor layer\n _, _, self.output_layer_pi = self.op.softmax('actor_output', self.output, self.num_actions, self.softmax_temp)\n # Avoiding log(0) by adding a very small quantity (1e-30) to output.\n self.log_output_layer_pi = tf.log(tf.add(self.output_layer_pi, tf.constant(1e-30)),\n name='actor_output_log_policy')\n # Entropy: sum_a (-p_a ln p_a)\n self.output_layer_entropy_actor = tf.reduce_sum(tf.multiply(\n tf.constant(-1.0),\n tf.multiply(self.output_layer_pi, self.log_output_layer_pi)), reduction_indices=1)\n self.log_output_selected_action = tf.reduce_sum(\n tf.multiply(self.log_output_layer_pi, self.selected_action_ph),\n reduction_indices=1)\n self.actor_objective_entropy_term = tf.multiply(self.entropy_regularisation_strength, self.output_layer_entropy_actor)\n\n\n with tf.name_scope('Repetition'):\n # Final repetition layer\n _, _, self.output_layer_rep = self.op.softmax('repetition_output', self.output, self.total_repetitions, self.softmax_temp)\n self.log_output_layer_rep = tf.log(tf.add(self.output_layer_rep, tf.constant(1e-30)),\n name='repetition_output_log_policy')\n self.output_layer_entropy_rep = tf.reduce_sum(tf.multiply(\n tf.constant(-1.0),\n tf.multiply(self.output_layer_rep, self.log_output_layer_rep)), reduction_indices=1, name='entropy_rep')\n self.log_output_selected_repetition = tf.reduce_sum(\n tf.multiply(self.log_output_layer_rep, self.selected_repetition_ph),\n reduction_indices=1)\n self.log_repetition_mean = tf.reduce_mean(self.log_output_selected_repetition)\n self.rep_objective_entropy_term = tf.multiply(self.entropy_regularisation_strength, self.output_layer_entropy_rep, name='rep_entropy_term')\n\n with tf.name_scope('ComputeLoss'):\n self.adv_actor_ph = tf.placeholder(\"float\", [None], name='advantage')\n self.actor_objective_advantage_term = tf.multiply(\n tf.add(self.log_output_selected_action, self.log_output_selected_repetition),\n self.adv_actor_ph, name='advantage')\n self.actor_advantage_mean = tf.reduce_mean(self.actor_objective_advantage_term)\n\n self.entropy_term = tf.add(self.actor_objective_entropy_term, self.rep_objective_entropy_term, name='entropy_term')\n\n self.actor_objective_mean = tf.reduce_mean(tf.multiply(tf.constant(-1.0),\n tf.add(self.actor_objective_advantage_term, self.entropy_term)),\n name='mean_actor_objective')\n\n # Loss scaling is used because the learning rate was initially runed tuned to be used with\n # max_local_steps = 5 and summing over timesteps, which is now replaced with the mean.\n self.loss = tf.scalar_mul(self.loss_scaling, self.actor_objective_mean + self.critic_loss_mean)","title":""},{"docid":"26f99e95b62bf4d35c0f77f0891e3a04","score":"0.5342701","text":"def create(self):\n # (self.feed('data')\n # .conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')\n # .lrn(2, 2e-05, 0.75, name='norm1')\n # .max_pool(3, 3, 2, 2, padding='VALID', name='pool1')\n # .conv(5, 5, 256, 1, 1, group=2, name='conv2')\n # .lrn(2, 2e-05, 0.75, name='norm2')\n # .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')\n # .conv(3, 3, 384, 1, 1, name='conv3')\n # .conv(3, 3, 384, 1, 1, group=2, name='conv4')\n # .conv(3, 3, 256, 1, 1, group=2, name='conv5')\n # .fc(4096, name='fc6')\n # .fc(4096, name='fc7')\n # .fc(1000, relu=False, name='fc8')\n # .softmax(name='prob'))\n\n #conv1\n #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')\n k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4\n conv1W = tf.Variable(self.WEIGHTS[\"conv1\"][\"weights\"], name='conv1/weights')\n conv1b = tf.Variable(self.WEIGHTS[\"conv1\"][\"biases\"], name='conv1/biases')\n conv1_in = conv(self.X, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding=\"SAME\", group=1)\n conv1 = tf.nn.relu(conv1_in)\n\n #lrn1\n #lrn(2, 2e-05, 0.75, name='norm1')\n radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0\n lrn1 = tf.nn.local_response_normalization(conv1,\n depth_radius=radius,\n alpha=alpha,\n beta=beta,\n bias=bias)\n\n #maxpool1\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool1')\n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\n maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n\n #conv2\n #conv(5, 5, 256, 1, 1, group=2, name='conv2')\n k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2\n conv2W = tf.Variable(self.WEIGHTS[\"conv2\"][\"weights\"], name='conv2/weights')\n conv2b = tf.Variable(self.WEIGHTS[\"conv2\"][\"biases\"], name='conv2/biases')\n conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding=\"SAME\", group=group)\n conv2 = tf.nn.relu(conv2_in)\n\n #lrn2\n #lrn(2, 2e-05, 0.75, name='norm2')\n radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0\n lrn2 = tf.nn.local_response_normalization(conv2,\n depth_radius=radius,\n alpha=alpha,\n beta=beta,\n bias=bias)\n\n #maxpool2\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool2') \n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\n maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n\n #conv3\n #conv(3, 3, 384, 1, 1, name='conv3')\n k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1\n conv3W = tf.Variable(self.WEIGHTS[\"conv3\"][\"weights\"], name='conv3/weights')\n conv3b = tf.Variable(self.WEIGHTS[\"conv3\"][\"biases\"], name='conv3/biases')\n conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding=\"SAME\", group=group)\n conv3 = tf.nn.relu(conv3_in)\n\n #conv4\n #conv(3, 3, 384, 1, 1, group=2, name='conv4')\n k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2\n conv4W = tf.Variable(self.WEIGHTS[\"conv4\"][\"weights\"], name='conv4/weights')\n conv4b = tf.Variable(self.WEIGHTS[\"conv4\"][\"biases\"], name='conv4/biases')\n conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding=\"SAME\", group=group)\n conv4 = tf.nn.relu(conv4_in)\n\n #conv5\n #conv(3, 3, 256, 1, 1, group=2, name='conv5')\n k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2\n conv5W = tf.Variable(self.WEIGHTS[\"conv5\"][\"weights\"], name='conv5/weights')\n conv5b = tf.Variable(self.WEIGHTS[\"conv5\"][\"biases\"], name='conv5/biases')\n conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding=\"SAME\", group=group)\n conv5 = tf.nn.relu(conv5_in)\n\n #maxpool5\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool5')\n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\n maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n\n #fc6\n #fc(4096, name='fc6')\n fc6W = tf.Variable(self.WEIGHTS[\"fc6\"][\"weights\"], name='fc6/weights')\n fc6b = tf.Variable(self.WEIGHTS[\"fc6\"][\"biases\"], name='fc6/biases')\n fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(np.prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)\n\n #Add a dropout layer here\n dropout6 = tf.nn.dropout(fc6, self.KEEP_PROB)\n \n #fc7\n #fc(4096, name='fc7')\n fc7W = tf.Variable(self.WEIGHTS[\"fc7\"][\"weights\"], name='fc7/weights')\n fc7b = tf.Variable(self.WEIGHTS[\"fc7\"][\"biases\"], name='fc7/biases')\n fc7 = tf.nn.relu_layer(dropout6, fc7W, fc7b)\n\n #Add a dropout layer here\n dropout7 = tf.nn.dropout(fc7, self.KEEP_PROB)\n \n #fc8\n #fc(1000, relu=False, name='fc8')\n fc8W = tf.get_variable(\"fc8/weights\", [4096, self.NUM_CLASSES])\n fc8b = tf.get_variable(\"fc8/biases\", [self.NUM_CLASSES])\n self.fc8 = tf.nn.xw_plus_b(dropout7, fc8W, fc8b)","title":""},{"docid":"bcf0d3936e441a4509d7388cf96133e2","score":"0.5338003","text":"def neural_net_predict_gen(params, inputs):\r\n inpW, inpb = params[0]\r\n inputs = leaky_relu(np.dot(inputs, inpW) + inpb)\r\n for W, b in params[1:-1]:\r\n outputs = np.dot(inputs, W) + b\r\n outputs = batch_normalize(np.dot(inputs, W) + b)\r\n inputs = leaky_relu(outputs)\r\n outW, outb = params[-1]\r\n outputs = np.dot(inputs, outW) + outb\r\n return outputs","title":""},{"docid":"238e036f1e83b9151ac54e48ec653bf0","score":"0.5337877","text":"def adam(params: List[Tensor],\n grads: List[Tensor],\n exp_avgs: List[Tensor],\n exp_avg_sqs: List[Tensor],\n max_exp_avg_sqs: List[Tensor],\n state_steps: List[int],\n amsgrad: bool,\n beta1: float,\n beta2: float,\n lr: float,\n weight_decay: float,\n eps: float):\n\n for i, param in enumerate(params):\n\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n if amsgrad:\n max_exp_avg_sq = max_exp_avg_sqs[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # expavg=exp_avg*beta1+grad*(1-beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # exp_avg_sq = exp_avg_sq*beta2+ grad*grad*(1-beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n\n param.addcdiv_(exp_avg, denom, value=-step_size)","title":""},{"docid":"e0053f7cbb12b4326437a7fe183db262","score":"0.53284603","text":"def vgg16(self):\n x = tflearn.input_data(shape=[None, self.width, self.height, 1], name='input')\n\n x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_1')\n x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')\n\n x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')\n x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')\n\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')\n\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')\n\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')\n\n x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')\n x = tflearn.dropout(x, 0.5, name='dropout1')\n\n x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')\n x = tflearn.dropout(x, 0.5, name='dropout2')\n\n x = tflearn.fully_connected(x, self.output, activation='softmax', scope='fc8')\n\n network = regression(x, optimizer='momentum', loss='categorical_crossentropy',\n learning_rate=self.learning_rate)\n model = tflearn.DNN(network, checkpoint_path='vgg16',\n max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')\n return model","title":""},{"docid":"c5e9b980d51e65268987aebda68090ff","score":"0.53265566","text":"def _learn_advantage_network(self, player):\n for data in self._get_advantage_dataset(\n player, self._advantage_network_train_steps):\n (self._params_adv_network[player], self._opt_adv_state[player],\n main_loss) = self._jitted_adv_update(self._params_adv_network[player],\n self._opt_adv_state[player],\n *data, jnp.array(self._iteration))\n\n return main_loss","title":""},{"docid":"4508193264bafa7e74fbf6398041c55e","score":"0.53227943","text":"def train(self):\n\n if not os.path.isdir(os.path.join(self.args.save_dir, \"checkpoints\")):\n os.mkdir(os.path.join(self.args.save_dir, \"checkpoints\"))\n\n lr1, lr2, lr3 = self.args.learning_rates\n if self.args.use_cons_optim:\n cons_thre1, cons_thre2, cons_thre3 = self.args.cons_thres\n global_emb_optim = optim.AdamCons([self.model.global_embs],\n lr=lr1,\n eps=1.0e-3,\n cons_thre=cons_thre1)\n graph_module_optim = optim.AdamCons(\n list(self.model.fc_out.parameters()) +\n list(self.model.fc_cat.parameters()),\n lr=lr2,\n eps=1.0e-3,\n cons_thre=cons_thre2)\n ts_module_optim = optim.AdamCons(\n list(self.model.encoder.parameters()) +\n list(self.model.decoder.parameters()),\n lr=lr3,\n eps=1.0e-3,\n cons_thre=cons_thre3)\n else:\n global_emb_optim = Adam([self.model.global_embs], lr=lr1, eps=1.0e-3)\n graph_module_optim = Adam(\n list(self.model.fc_out.parameters()) +\n list(self.model.fc_cat.parameters()),\n lr=lr2,\n eps=1.0e-3)\n ts_module_optim = Adam(\n list(self.model.encoder.parameters()) +\n list(self.model.decoder.parameters()),\n lr=lr3,\n eps=1.0e-3)\n optims = [global_emb_optim, graph_module_optim, ts_module_optim]\n\n edit_edge_idxs, old_adj_pred, adj_label, weights = self._get_error_edges_new_labels(\n )\n adj_label = adj_label.to(self.device)\n\n for iter_num in range(self.args.num_max_fine_tune_iters):\n self._train_iter(optims, adj_label, weights)\n\n if (iter_num + 1) % 100 == 0:\n test_results = self.test(edit_edge_idxs, old_adj_pred, adj_label)\n\n with open(os.path.join(self.args.save_dir, \"val_test.txt\"), \"w\") as f:\n f.write(\n \"testing mae: {}, adj accuracy: {}, sparsity: {}, preserve: {}, edit edge accuracy: {}\\n\"\n .format(test_results[0], test_results[1], test_results[2],\n test_results[3], test_results[4]))\n\n if self.args.save_model:\n torch.save(\n self.model.state_dict(),\n os.path.join(self.args.save_dir, \"checkpoints\",\n \"iter_{}.ckpt\".format(iter_num)))","title":""},{"docid":"f27f4c885a68209c17393c8a3a95ec78","score":"0.5312277","text":"def __init__(\n self,\n size,\n self_attn,\n src_attn,\n feed_forward,\n dropout_rate,\n normalize_before=True,\n concat_after=False,\n compute_type=mstype.float32,\n ):\n super().__init__()\n self.size = size\n self.self_attn = self_attn\n self.src_attn = src_attn\n self.feed_forward = feed_forward\n self.norm1 = CustomLayerNorm(size, epsilon=1e-12)\n self.norm2 = CustomLayerNorm(size, epsilon=1e-12)\n self.norm3 = CustomLayerNorm(size, epsilon=1e-12)\n self.dropout = nn.Dropout(p=dropout_rate)\n self.normalize_before = normalize_before\n self.concat_after = concat_after\n if self.concat_after:\n self.concat_linear1 = Dense(\n size + size, size).to_float(compute_type)\n self.concat_linear2 = Dense(\n size + size, size).to_float(compute_type)\n self.cat1 = ops.Concat(axis=-1)\n self.cat2 = ops.Concat(axis=1)\n self.expand_dims = ops.ExpandDims()","title":""},{"docid":"d5e4a5b988f37e26e0c4805e94abe755","score":"0.53115296","text":"def __init__(self, input0, input1, model='net-lin', net='alex', version=0.1):\n # flatten the leading dimensions\n batch_shape = tf.shape(input0)[:-3]\n input0 = tf.reshape(input0, tf.concat([[-1], tf.shape(input0)[-3:]], axis=0))\n input1 = tf.reshape(input1, tf.concat([[-1], tf.shape(input1)[-3:]], axis=0))\n input0 = tf.transpose(input0, [0, 3, 1, 2])\n input1 = tf.transpose(input1, [0, 3, 1, 2])\n\n input0_name, input1_name = '0:0', '1:0'\n\n default_graph = tf.get_default_graph()\n producer_version = default_graph.graph_def_versions.producer\n producer_version = 27 # 38\n\n cache_dir = 'lpips_pb'\n pb_fname = '%s_%s_v%s_%d.pb' % (model, net, version, producer_version)\n\n with open(os.path.join(cache_dir, pb_fname), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def,\n input_map={input0_name: input0, input1_name: input1})\n distance, = default_graph.get_operations()[-1].outputs\n assert distance.name[-12:] == 'Reshape_10:0', distance.name\n\n if distance.shape.ndims == 4:\n distance = tf.squeeze(distance, axis=[-3, -2, -1])\n distance = tf.reshape(distance, batch_shape)\n self.distance = tf.reduce_mean(distance)","title":""},{"docid":"2c0ab55c39ea4c9e168749a2e4239a9f","score":"0.53050554","text":"def fprop(input_batch, word_embedding_weights, embed_to_hid_weights,\n\t\thid_to_output_weights, hid_bias, output_bias):\n\n#[numwords, batchsize] = size(input_batch);\n\tnumwords, batchsize = input_batch.shape # (3, 100)\n#[vocab_size, numhid1] = size(word_embedding_weights);\n\tvocab_size, numhid1 = word_embedding_weights.shape # (250, 50)\n#numhid2 = size(embed_to_hid_weights, 2);\n\tnumhid2 = embed_to_hid_weights.shape[1] # 200\n\n#%% COMPUTE STATE OF WORD EMBEDDING LAYER.\n#% Look up the inputs word indices in the word_embedding_weights matrix.\n#embedding_layer_state = reshape(...\n# word_embedding_weights(reshape(input_batch, 1, []),:)',...\n# numhid1 * numwords, []);\n\tembedding_layer_state = word_embedding_weights[ravel(\n\t\tinput_batch, order='F')-1,:].T.reshape(\n\t\tnumhid1 * numwords, -1, order='F')\n\n#%% COMPUTE STATE OF HIDDEN LAYER.\n#% Compute inputs to hidden units.\n#inputs_to_hidden_units = embed_to_hid_weights' * embedding_layer_state + ...\n# repmat(hid_bias, 1, batchsize);\n\tinputs_to_hidden_units = dot(embed_to_hid_weights.T,\n\t\tembedding_layer_state) + tile(hid_bias, (1, batchsize))\n\t\n#% Apply logistic activation function.\n#% FILL IN CODE. Replace the line below by one of the options.\n#hidden_layer_state = zeros(numhid2, batchsize);\n#% Options\n#% (a) hidden_layer_state = 1 ./ (1 + exp(inputs_to_hidden_units));\n#% (b) hidden_layer_state = 1 ./ (1 - exp(-inputs_to_hidden_units));\n#% (c) hidden_layer_state = 1 ./ (1 + exp(-inputs_to_hidden_units));\n\thidden_layer_state = 1 / (1 + exp(-inputs_to_hidden_units))\n#% (d) hidden_layer_state = -1 ./ (1 + exp(-inputs_to_hidden_units));\n\n#%% COMPUTE STATE OF OUTPUT LAYER.\n#% Compute inputs to softmax.\n#% FILL IN CODE. Replace the line below by one of the options.\n#inputs_to_softmax = zeros(vocab_size, batchsize);\n#% Options\n#% (a) inputs_to_softmax = hid_to_output_weights' * hidden_layer_state + repmat(output_bias, 1, batchsize);\n\tinputs_to_softmax = dot(hid_to_output_weights.T,\n\t\thidden_layer_state) + tile(output_bias, (1, batchsize))\n#% (b) inputs_to_softmax = hid_to_output_weights' * hidden_layer_state + repmat(output_bias, batchsize, 1);\n#% (c) inputs_to_softmax = hidden_layer_state * hid_to_output_weights' + repmat(output_bias, 1, batchsize);\n#% (d) inputs_to_softmax = hid_to_output_weights * hidden_layer_state + repmat(output_bias, batchsize, 1);\n\n#% Subtract maximum. \n#% Remember that adding or subtracting the same constant from each input to a\n#% softmax unit does not affect the outputs. Here we are subtracting maximum to\n#% make all inputs <= 0. This prevents overflows when computing their\n#% exponents.\n#inputs_to_softmax = inputs_to_softmax...\n# - repmat(max(inputs_to_softmax), vocab_size, 1);\n\tinputs_to_softmax = inputs_to_softmax - tile(inputs_to_softmax.max(0), (vocab_size, 1))\n\n#% Compute exp.\n\toutput_layer_state = exp(inputs_to_softmax);\n\n#% Normalize to get probability distribution.\n#output_layer_state = output_layer_state ./ repmat(...\n# sum(output_layer_state, 1), vocab_size, 1);\n\toutput_layer_state = output_layer_state / tile(\n\t\toutput_layer_state.sum(axis=0), (vocab_size, 1))\n\n\treturn embedding_layer_state, hidden_layer_state, output_layer_state","title":""},{"docid":"ff71055a4ddb64faa50ca7c2e498655e","score":"0.5304338","text":"def __init__(self, BATCH_SIZE, EMBEDDING_SIZE, HIDDEN_UNITS, VOCAB_SIZE):\r\n super(RNN_Vanila, self).__init__()\r\n w_init = tf.random_normal_initializer()\r\n b_init = tf.zeros_initializer()\r\n self.BATCH_SIZE = BATCH_SIZE\r\n self.EMBEDDING_SIZE = EMBEDDING_SIZE\r\n self.HIDDEN_UNITS = HIDDEN_UNITS\r\n self.VOCAB_SIZE = VOCAB_SIZE\r\n \r\n self.w_ip = tf.Variable(initial_value=w_init(shape=(self.EMBEDDING_SIZE, self.HIDDEN_UNITS), dtype='float32'), trainable=True)\r\n self.w_hidden = tf.Variable(initial_value=w_init(shape=(self.HIDDEN_UNITS, self.HIDDEN_UNITS), dtype='float32'), trainable=True)\r\n self.w_op = tf.Variable(initial_value=w_init(shape=(self.HIDDEN_UNITS, self.VOCAB_SIZE), dtype='float32'), trainable=True)","title":""},{"docid":"98d61f99646e0a5646a4c8198156cfe6","score":"0.5300648","text":"def tf_efficientnet_b3_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b3_ap\",\n channel_multiplier=1.2,\n depth_multiplier=1.4,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"e79c2519f2affd389278a2935917cd53","score":"0.5297878","text":"def alex_net(self):\n tweet_length = 140\n tweet_height = 5\n input_size = 8 * 140 # make binary power * max characters\n\n # Sizes for the layers\n filters_0 = 20\n filters_1_1 = 10\n filters_1_2 = 10\n filters_2_1 = 20\n filters_2_2 = 20\n filters_3_1 = 10\n filters_3_2 = 10\n\n\n\n # Arbitrary number of vectors with length tweetLength\n x = tf.placeholder(tf.float32, [None, input_size], name='x')\n # Reshapes x to be 3d so that convolution can be applied\n x_tweet = tf.reshape(x, [-1, tweet_length, tweet_height, 1])\n # Tensor storing all the true labels for each tweet in our training batch\n y_true = tf.placeholder(tf.float32, shape=[None, self.numberOfClasses], name='yTrue')\n\n yTrueClass = tf.argmax(y_true, dimesnion=1)\n\n # Similarly the number of input channels will be updated\n numberOfInputChannels = 1\n\n # Creates the convolutional layers\n for i in range(numberOfConvolutionalLayers):\n convolutionalLayers[i], convolutionalWeights[i] = \\\n self.newConvolutionalLayer(input=input, numberOfInputChannels=numberOfInputChannels,\n filterHight=filterHeights[i], filterLength=filterLengths[i],\n numberOfFilters=numberOfFilters[i], use2x2pooling=False)\n\n # Adds the output from the current layer to the tensor of all the previous layers' outputs\n input = tf.concat(input, convolutionalLayers[i])\n\n # Updates the number of input channels so that all previous layers' inputs are taken into account\n numberOfInputChannels = 0\n for j in range(i):\n numberOfInputChannels += numberOfFilters[j]\n\n fullyConnectedLayers = [numberOfFullyConnectedLayers]\n numberOfFeautres = [numberOfFullyConnectedLayers]\n\n # Reshapes the input tensor to 1D so that it can be run through a fully connected layer\n flattenedInput = self.flattenLayer(input)\n\n # Creates a fully connected layer\n fullyConnectedLayers[0] = self.newFullyConnectedLayer(input=flattenedInput, numberOfInputs=numberOfFeautres,\n numberOfOutputs=fullyConnectedSize, useRelu=True)\n\n # Updates the input and number of inputs\n flattenedInput = tf.concat(flattenedInput, fullyConnectedLayers[0])\n numberOfFeautres += fullyConnectedSize\n\n # Creates the last layer of the network\n fullyConnectedLayers[1] = self.newFullyConnectedLayer(input=flattenedInput, numberOfInputs=numberOfFeautres,\n numberOfOutputs=self.numberOfClasses, useRelu=True)","title":""},{"docid":"3f1a08800b15c601252575fe0409b25e","score":"0.52905256","text":"def create_adversarial_img(path, org_img, true_label):\r\n\r\n # Load trained model\r\n trained_net = models.SimpleModel()\r\n trained_net.load(path=path)\r\n trained_net.eval()\r\n\r\n # show original image\r\n visualizations.imshow(org_img)\r\n\r\n # Adversarial method 1\r\n\r\n # Copy the model so the original trained network wont change while we creating\r\n # the adversarial image\r\n model_copy = copy.deepcopy(trained_net)\r\n model_copy.eval()\r\n\r\n noise, adv_label = adversarial_optimizing_noise(model_copy, org_img, true_label=0, target_label=2, regularization=\"l1\")\r\n\r\n visualizations.imshow(noise) # show noise\r\n visualizations.imshow(org_img+noise) # show adversarial image\r\n out = trained_net((org_img+noise).unsqueeze(0))\r\n print(\"true label:\", true_label, \"adv_label:\", adv_label, \"trained_net label:\", out)\r\n\r\n\r\n # Adversarial method 2\r\n\r\n model_copy2 = copy.deepcopy(trained_net)\r\n adver_img, noise2, adv_label_2 = FGSM(model_copy2, org_img, true_label=0)\r\n\r\n visualizations.imshow(noise2) # show noise\r\n visualizations.imshow(adver_img) # show adversarial image\r\n out = trained_net(adver_img.unsqueeze(0))\r\n print(\"true label:\", true_label, \"adv_label:\", adv_label_2, \"trained_ned label:\", out)","title":""},{"docid":"95c708c6afbb42a83a61650d20d59e5a","score":"0.52858627","text":"def learn_from_batch(self, batch):\n pass","title":""},{"docid":"6af821645bb3ef4f7807713765d7f8af","score":"0.5278808","text":"def optimizer(self):\n \n #Placeholders\n state_pl = self.model.input\n action_onehot_pl = K.placeholder(name='action_onehot', shape=(None,self.output_dim))\n adv_pl = K.placeholder(name='advantage', shape=(None,))\n \n #Set up loss\n pi_pl = self.model.output\n pi_vec = K.sum(action_onehot_pl*pi_pl, axis=1)\n loss_vec = -K.log(pi_vec)*K.stop_gradient(adv_pl)\n loss = K.mean(loss_vec)\n \n #Get updates\n opt = Adam(self.lr)\n pars = self.model.trainable_weights\n updates = opt.get_updates(loss = loss, params = pars)\n \n return K.function(inputs=[state_pl, action_onehot_pl, adv_pl], outputs = [], updates = updates)","title":""},{"docid":"81957e3c037252dda27e78b9556e0db5","score":"0.5276922","text":"def mnasnet_b1(pretrained=False, **kwargs):\n return mnasnet_100(pretrained, **kwargs)","title":""},{"docid":"93a8fa9e155162d3273c38703f8b0411","score":"0.52604926","text":"def inference(self, image, keep_prob, istraining):\n with tf.variable_scope('AlexNet') as scope:\n with tf.variable_scope('conv1') as scope:\n temp_conv = self.conv2d(\"conv1\", image, kernel_size=[11, 11, 3, 96], stride=4,\n istraining=istraining, stop_grad=True, padding='VALID')\n\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\n\n with tf.variable_scope('conv2') as scope:\n temp_conv = self.conv2d(\"conv2\", temp_pool, kernel_size=[5, 5, 96, 256], stride=1,\n istraining=istraining, stop_grad=True)\n\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\n\n with tf.variable_scope('conv3') as scope:\n temp_conv = self.conv2d(\"conv3\", temp_pool, kernel_size=[3, 3, 256, 384], stride=1,\n istraining=istraining, stop_grad=True)\n\n with tf.variable_scope('conv4') as scope:\n temp_conv = self.conv2d(\"conv4\", temp_conv, kernel_size=[3, 3, 384, 384], stride=1,\n istraining=istraining, stop_grad=True)\n\n with tf.variable_scope('conv5') as scope:\n temp_conv = self.conv2d(\"conv5\", temp_conv, kernel_size=[3, 3, 384, 256], stride=1,\n istraining=istraining, stop_grad=True)\n\n temp_pool = self.max_pool(temp_conv, [2, 2], 2, padding='VALID')\n # fc to conv\n fc6 = self.local('fc6', temp_pool, kernel_size=[6, 6, 256, 4096], stride=1, pretrain=True, train=True,\n istraining=istraining, stop_grad=True)\n fc6_dropout = tf.nn.dropout(fc6, keep_prob=keep_prob)\n\n fc7 = self.local('fc7', fc6_dropout, kernel_size=[1, 1, 4096, 4096], stride=1, pretrain=True,\n train=True, istraining=istraining, stop_grad=True)\n fc7_dropout = tf.nn.dropout(fc7, keep_prob=keep_prob)\n\n embedding = self.local('fc8', fc7_dropout, kernel_size=[1, 1, 4096, self.embedding], stride=1,\n pretrain=False, train=True, istraining=True)\n embedding_squeeze = tf.squeeze(embedding)\n return embedding_squeeze","title":""},{"docid":"74b6352a54de0b54bd3d4951ae35734b","score":"0.5254964","text":"def __init__(self, model, optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), name=\"ReptileMetaLearner\"):\n self.model = model\n self.optimizer = optimizer","title":""},{"docid":"4c529d6014e71bc7edd15463608a17b1","score":"0.5253789","text":"def test_tensor_replication():\n anchorDict = {\n \"ReplicationFactor\": 2,\n # Accl factor must divide batch size\n \"AccumulationFactor\": 4,\n \"Pipelining\": False, # True\n \"ReturnType\": \"ALL\",\n }\n label_array = np.ones([BATCH_SIZE]).astype(np.int32)\n\n micro_batch_size = BATCH_SIZE // (\n anchorDict[\"AccumulationFactor\"] * anchorDict[\"ReplicationFactor\"]\n )\n\n builder = popart.Builder()\n input_shape = [micro_batch_size, CHANNELS, DATA_LEN, DATA_LEN]\n\n data_shape = popart.TensorInfo(\"FLOAT\", input_shape)\n lbl_shape = popart.TensorInfo(\"INT32\", [micro_batch_size])\n w = builder.addInitializedInputTensor(\n np.random.random_sample(input_shape).astype(np.float32)\n )\n\n settings = popart.InputSettings(popart.ReplicatedStreamMode.Broadcast)\n\n ip = builder.addInputTensor(data_shape, settings)\n lb = builder.addInputTensor(lbl_shape)\n\n a = builder.aiOnnx.matmul([ip, w])\n o = builder.reshape_const(\n builder.aiOnnx, [a], [micro_batch_size, CHANNELS * DATA_LEN * DATA_LEN]\n )\n o = builder.aiOnnx.relu([o])\n o = builder.aiOnnx.softmax([o])\n nll = builder.aiGraphcore.nllloss([o, lb])\n\n GRAD = popart.reservedGradientPrefix() + w\n ACCL = popart.reservedAccumPrefix() + w\n art = popart.AnchorReturnType(\"All\")\n data_flow = popart.DataFlow(\n BATCHES_PER_STEP, {o: art, a: art, ip: art, w: art, GRAD: art, ACCL: art}\n )\n\n opts, device = return_options(anchorDict)\n with device as device:\n if device is None:\n pytest.skip(\"Test needs to run on IPU, but none are available\")\n\n session = popart.TrainingSession(\n fnModel=builder.getModelProto(),\n dataFlow=data_flow,\n loss=nll,\n optimizer=popart.ConstSGD(LEARNING_RATE),\n userOptions=opts,\n deviceInfo=device,\n )\n\n session.prepareDevice()\n\n if (\n anchorDict[\"ReplicationFactor\"] > 1\n and settings.replicatedStreamMode() != popart.ReplicatedStreamMode.Broadcast\n ):\n input_shape = [anchorDict[\"ReplicationFactor\"]] + input_shape\n label_array = label_array.reshape([anchorDict[\"ReplicationFactor\"], -1])\n if anchorDict[\"AccumulationFactor\"] > 1:\n input_shape = [anchorDict[\"AccumulationFactor\"]] + input_shape\n label_array = label_array.reshape([anchorDict[\"AccumulationFactor\"], -1])\n if BATCHES_PER_STEP > 1:\n input_shape = [BATCHES_PER_STEP] + input_shape\n label_array = np.repeat(label_array[np.newaxis], BATCHES_PER_STEP, 0)\n\n anchors = session.initAnchorArrays()\n in_array = np.random.random_sample(input_shape).astype(np.float32)\n\n stepio = popart.PyStepIO({ip: in_array, lb: label_array}, anchors)\n session.weightsFromHost()\n\n session.run(stepio)\n\n # Compare the inputs\n for batch in range(anchors[ip].shape[0]):\n in_0 = anchors[ip][batch, -1, 0, :, :, :, :]\n in_1 = anchors[ip][batch, -1, 1, :, :, :, :]\n\n mx = anchors[ip].shape[len(anchors[ip].shape) - 1]\n _ = mx if mx <= 5 else 5\n assert np.allclose(in_0, in_1, equal_nan=False)","title":""},{"docid":"cbc33819982983b03fbb56ec09e277d5","score":"0.52514184","text":"def _adversary_model(self, pred_logits, true_labels):\n with tf.variable_scope(\"adversary_model\"):\n c = tf.get_variable('c', initializer=tf.constant(1.0))\n s = tf.sigmoid((1 + tf.abs(c)) * pred_logits)\n\n W2 = tf.get_variable('W2', [3, 1],\n initializer=tf.contrib.layers.xavier_initializer())\n b2 = tf.Variable(tf.zeros(shape=[1]), name='b2')\n\n pred_protected_attribute_logit = tf.matmul(tf.concat([s, s * true_labels, s * (1.0 - true_labels)], axis=1), W2) + b2\n pred_protected_attribute_label = tf.sigmoid(pred_protected_attribute_logit)\n\n return pred_protected_attribute_label, pred_protected_attribute_logit","title":""},{"docid":"fa86381149bc10320c7337f53f7922fa","score":"0.52485394","text":"def get_model(\n input_shape: List[int],\n weight_array: np.array,\n batches_per_step: int,\n replication_factor: int,\n batch_size: int,\n channels: int,\n data_len: int,\n synthetic_data: bool,\n buffer_streams: bool,\n device,\n) -> Tuple:\n\n micro_batch_size = batch_size // (replication_factor)\n\n builder = popart.Builder()\n\n data_shape = popart.TensorInfo(\"FLOAT\", input_shape)\n lbl_shape = popart.TensorInfo(\"INT32\", [micro_batch_size])\n w = builder.addInitializedInputTensor(weight_array)\n\n ip = builder.addInputTensor(data_shape, \"main_input_123\")\n lb = builder.addInputTensor(lbl_shape, \"label_input_456\")\n\n a = builder.aiOnnx.matmul([ip, w])\n o = builder.reshape_const(\n builder.aiOnnx, [a], [micro_batch_size, channels * data_len * data_len]\n )\n relu = builder.aiOnnx.relu([o])\n sm = builder.aiOnnx.softmax([relu], axis=0, debugContext=\"output\")\n builder.addOutputTensor(sm)\n o = builder.aiGraphcore.nllloss([sm, lb], reduction=popart.ReductionType.Mean)\n\n art = popart.AnchorReturnType(\"All\")\n data_flow = popart.DataFlow(\n batches_per_step, {ip: art, lb: art, o: art, sm: art, a: art, relu: art}\n )\n\n opts = popart.SessionOptions()\n opts.useHostCopyOps = buffer_streams\n # TODO: Fix outlining\n opts.enableOutlining = False\n\n ipus = 1\n\n if replication_factor > 1:\n opts.replicatedGraphCount = replication_factor\n opts.enableReplicatedGraphs = True\n ipus *= replication_factor\n\n assert device\n\n patterns = (\n popart.Patterns(popart.PatternsLevel.Minimal)\n .enablePattern(\"MatMulLhsGradOp\", True)\n .enablePattern(\"MatMulRhsGradOp\", True)\n )\n patterns.InPlace = False\n if synthetic_data:\n opts.syntheticDataMode = popart.SyntheticDataMode.Zeros\n\n session = popart.TrainingSession(\n fnModel=builder.getModelProto(),\n dataFlow=data_flow,\n loss=o,\n optimizer=popart.ConstSGD(LR),\n userOptions=opts,\n deviceInfo=device,\n patterns=patterns,\n )\n\n session.setRandomSeed(0)\n session.prepareDevice()\n\n label_shape = [micro_batch_size]\n\n if replication_factor > 1:\n input_shape = [replication_factor] + input_shape\n label_shape = [replication_factor] + label_shape\n if batches_per_step > 1:\n input_shape = [batches_per_step] + input_shape\n label_shape = [batches_per_step] + label_shape\n\n anchors = session.initAnchorArrays()\n\n return session, anchors, label_shape","title":""},{"docid":"67bc33af08ecb847b557172977c46e3f","score":"0.5246301","text":"def build_model(is_training, inputs, params):\n images = inputs['images']\n\n assert images.get_shape().as_list() == [None, params.image_size, params.image_size, 3]\n\n\n # TO DO : vgg input for images are 224 * 224 * 3\n\n # VGG RGB -> BGR\n VGG_MEAN = [103.939, 116.779, 123.68]\n red, green, blue = tf.split(axis=3, num_or_size_splits=3, value= images)\n out = tf.concat(axis=3, values=[blue - VGG_MEAN[0], green - VGG_MEAN[1],red - VGG_MEAN[2]])\n\n\n ##### modification - Add VGG ########\n\n # TO DO : get the vgg pretrained weight and name it vgg_weight.npy, name of weight in the file \n weights = np.load(\"vgg_weights.npy\", encoding = 'latin1')\n pretrainedWeights = weights.item()\n trainedWeights = collections.defaultdict()\n\n num_conv_layers = 5 # pls change later\n num_repeat_times_1 = 2\n num_repeat_times_2 = 3\n repear_layers1 = [1,2]\n repear_layers2 = [3,4,5]\n\n for i in range(1, num_conv_layers + 1):\n if i in repear_layers1:\n for j in range(1,num_repeat_times_1 + 1):\n trainedWeights['conv' + str(i) + '_' + str(j) + '_weights'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_weights', initializer=pretrainedWeights['conv' + str(i) + \"_\" + str(j) ][0], trainable = False)\n trainedWeights['conv' + str(i) + '_' + str(j) + '_biases'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_biases', initializer=pretrainedWeights['conv' + str(i) + \"_\" + str(j) ][1], trainable = False)\n if i in repear_layers2:\n for j in range(1,num_repeat_times_2 + 1):\n trainedWeights['conv' + str(i) + '_' + str(j) + '_weights'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_weights', initializer=pretrainedWeights['conv' + str(i) + \"_\" + str(j) ][0], trainable = False)\n trainedWeights['conv' + str(i) + '_' + str(j) + '_biases'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_biases', initializer=pretrainedWeights['conv' + str(i) + \"_\" + str(j) ][1], trainable = False)\n\n num_fc_layers = 2 # pls change later\n for i in range(1, num_fc_layers + 1):\n trainedWeights['fc' + str(i) + '_weights'] = tf.get_variable('fc' + str(i) + '_weights', initializer=pretrainedWeights['fc' + str(i + num_conv_layers)][0], trainable = False) # i + num_conv_layers might not hold for other weights file\n trainedWeights['fc' + str(i) + '_biases'] = tf.get_variable('fc' + str(i) + '_biases', initializer=pretrainedWeights['fc' + str(i + num_conv_layers)][1], trainable = False) # i + num_conv_layers might not hold for other weights file\n\n\n # block 1 -- outputs 112x112x64\n # conv1_1\n k = 3; c = 64; s_h = 1; s_w = 1\n conv1_1_out = convLayer(out, k, k, s_h, s_w, c, \"conv1_1\", \"SAME\", trainedWeights[\"conv1_1_weights\"], trainedWeights[\"conv1_1_biases\"])\n\n # conv1_2\n k = 3; c = 64; s_h = 1; s_w = 1\n conv1_2_out = convLayer(conv1_1_out, k, k, s_h, s_w, c, \"conv1_2\", \"SAME\", trainedWeights[\"conv1_2_weights\"], trainedWeights[\"conv1_2_biases\"])\n \n # maxpool1\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\n padding = 'SAME'\n maxpool1 = tf.nn.max_pool(conv1_2_out, ksize=[1, k_h, k_w , 1],strides=[1, s_h, s_w, 1], padding=padding)\n\n #block 2 -- outputs 56x56x128\n # conv2_1\n k = 3; c = 128; s_h = 1; s_w = 1\n conv2_1_out = convLayer(maxpool1, k, k, s_h, s_w, c, \"conv2_1\", \"SAME\", trainedWeights[\"conv2_1_weights\"], trainedWeights[\"conv2_1_biases\"])\n\n # conv2_2\n k = 3; c = 64; s_h = 1; s_w = 1\n conv2_2_out = convLayer(conv2_1_out, k, k, s_h, s_w, c, \"conv2_2\", \"SAME\", trainedWeights[\"conv2_2_weights\"], trainedWeights[\"conv2_2_biases\"])\n \n # maxpool2\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\n padding = 'SAME'\n maxpool2 = tf.nn.max_pool(conv2_2_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n \n # block 3 -- outputs 28x28x256\n # conv3_1\n k = 3; c = 256; s_h = 1; s_w = 1\n conv3_1_out = convLayer(maxpool2, k, k, s_h, s_w, c, \"conv3_1\", \"SAME\", trainedWeights[\"conv3_1_weights\"], trainedWeights[\"conv3_1_biases\"])\n\n # conv3_2\n k = 3; c = 256; s_h = 1; s_w = 1\n conv3_2_out = convLayer(conv3_1_out, k, k, s_h, s_w, c, \"conv3_2\", \"SAME\", trainedWeights[\"conv3_2_weights\"], trainedWeights[\"conv3_2_biases\"])\n \n # conv3_3\n k = 3; c = 256; s_h = 1; s_w = 1\n conv3_3_out = convLayer(conv3_2_out, k, k, s_h, s_w, c, \"conv3_3\", \"SAME\", trainedWeights[\"conv3_3_weights\"], trainedWeights[\"conv3_3_biases\"])\n\n # maxpool3\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\n padding = 'SAME'\n maxpool3 = tf.nn.max_pool(conv3_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n\n # block 4 -- outputs 14x14x512\n # COV 4 \n # conv4_1\n k = 3; c = 512; s_h = 1; s_w = 1\n conv4_1_out = convLayer(maxpool3, k, k, s_h, s_w, c, \"conv4_1\", \"SAME\", trainedWeights[\"conv4_1_weights\"], trainedWeights[\"conv4_1_biases\"])\n\n # conv4_2\n k = 3; c = 512; s_h = 1; s_w = 1\n conv4_2_out = convLayer(conv4_1_out, k, k, s_h, s_w, c, \"conv4_2\", \"SAME\", trainedWeights[\"conv4_2_weights\"], trainedWeights[\"conv4_2_biases\"])\n \n # conv4_3\n k = 3; c = 512; s_h = 1; s_w = 1\n conv4_3_out = convLayer(conv4_2_out, k, k, s_h, s_w, c, \"conv4_3\", \"SAME\", trainedWeights[\"conv4_3_weights\"], trainedWeights[\"conv4_3_biases\"])\n\n # maxpool4\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\n padding = 'SAME'\n maxpool4 = tf.nn.max_pool(conv4_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n\n # block 5 -- outputs 7x7x512\n # COV 5\n # conv5_1\n k = 3; c = 512; s_h = 1; s_w = 1\n conv5_1_out = convLayer(maxpool4, k, k, s_h, s_w, c, \"conv5_1\", \"SAME\", trainedWeights[\"conv5_1_weights\"], trainedWeights[\"conv5_1_biases\"])\n\n # conv5_2\n k = 3; c = 512; s_h = 1; s_w = 1\n conv5_2_out = convLayer(conv5_1_out, k, k, s_h, s_w, c, \"conv5_2\", \"SAME\", trainedWeights[\"conv5_2_weights\"], trainedWeights[\"conv5_2_biases\"])\n \n # conv5_3\n k = 3; c = 512; s_h = 1; s_w = 1\n# conv5_3_out = convLayer(conv5_2_out, k, k, s_h, s_w, c, \"conv5_3\", \"SAME\", trainedWeights[\"conv5_3_weights\"], trainedWeights[\"conv5_3_biases\"])\n conv5_3_out = tf.layers.conv2d(conv5_2_out, filters=c, kernel_size=k, strides=(s_h, s_w), padding='same')\n\n # maxpool5\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\n padding = 'SAME'\n maxpool5 = tf.nn.max_pool(conv5_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\n \n # fullly connected layer\n fc1_in = tf.reshape(maxpool5, [-1, 7*7*512])\n\n with tf.variable_scope('fc_1'):\n # w = fc1_weights, shape = [4096, 4096], dtype = \"float\")\n # b = tf.get_variable(fc1_biases, shape = [4096], dtype = \"float\")\n# out = tf.nn.xw_plus_b(fc1_in,trainedWeights[\"fc1_weights\"], trainedWeights[\"fc1_biases\"])\n out = tf.layers.dense(fc1_in,4096)\n fc2_in = tf.nn.relu(out)\n\n with tf.variable_scope('fc_2'):\n #w = tf.get_variable(fc2_weights, shape = [4096, 4096], dtype = \"float\")\n #b = tf.get_variable(fc2_biases, shape = [4096], dtype = \"float\")\n# out = tf.nn.xw_plus_b(fc2_in, trainedWeights[\"fc2_weights\"], trainedWeights[\"fc2_biases\"])\n out = tf.layers.dense(fc2_in,4096)\n fc3_in = tf.nn.relu(out)\n\n ### trainable layer #####\n with tf.variable_scope('fc_3'):\n logits = tf.layers.dense(fc3_in, params.num_labels)\n return logits\n\n\n ########### END ##########\n\n # assert out.get_shape().as_list() == [None, 4, 4, num_channels * 8]\n #\n # out = tf.reshape(out, [-1, 4 * 4 * num_channels * 8])\n # with tf.variable_scope('fc_1'):\n # out = tf.layers.dense(out, num_channels * 8)\n # if params.use_batch_norm:\n # out = tf.layers.batch_normalization(out, momentum=bn_momentum, training=is_training)\n # out = tf.nn.relu(out)\n # with tf.variable_scope('fc_2'):\n # logits = tf.layers.dense(out, params.num_labels)\n #\n # return logits\n #","title":""},{"docid":"3623ca34023e3b61ab60f8bdbcf35729","score":"0.52379715","text":"def independent_avg_modular_deeptrack_L1(# bad name\n layer_size,\n train_generator,\n input_shape=(51,51,1),\n output_shape=3,\n nbr_nodes_added=1,\n sample_sizes=(8, 32, 128, 512, 1024),\n iteration_numbers=(401, 301, 201, 101, 51),\n verbose=0.01,\n save_networks=False,\n mp_training=False, # use multiprocessing training\n translation_distance=5, # parameters for multiprocessing training\n SN_limits=[10,100], # parameters for multiprocessing training\n model_path=\"\", # determines the type of layer used for combining the\n # predictions. Addition and average only options at the moment\n ):\n import deeptrack\n import keras\n from keras import Input,models,layers\n from keras.models import Model\n from feature_by_feature import freeze_all_layers\n #import deeptrackelli_mod_mproc as multiprocess_training # Needed for fast parallelized image generator\n\n input_tensor = Input(input_shape)\n conv_list = [] # List of convolutional neruons in L1, needed for subsequent layers\n flattened_list = [] # List of flattened layers\n output_list = [] # List of the output layers\n\n # Loop thorugh the neoruns and add them one by one\n for i in range(round(layer_size/nbr_nodes_added)):\n next_node = layers.Conv2D(nbr_nodes_added,(3,3),activation='relu')(input_tensor)\n next_node = layers.MaxPooling2D((2,2))(next_node)\n if(i==0):\n # i = 0 special case. No addition needed\n next_flattened = layers.Flatten()(next_node)\n next_output = layers.Dense(3)(next_flattened)\n final_output = next_output\n output_list.append(next_output)\n flattened_list.append(next_flattened)\n else:\n # Construct the next output node\n next_flattened = layers.Flatten()(next_node)\n flattened_list.append(next_flattened)\n # Can't concatenate a single layer\n if(len(flattened_list)>1):\n next_output = layers.Concatenate(axis=-1)(flattened_list)\n\n next_output = layers.Dense(3)(next_output)\n output_list.append(next_output)\n # Construct and compile network\n network = models.Model(input_tensor,next_output)\n network.compile(optimizer='rmsprop', loss='mse', metrics=['mse', 'mae'])\n network.summary()\n\n # Train and freeze layers in network\n if mp_training:\n deeptrack.train_deep_learning_network_mp(\n network,\n sample_sizes = sample_sizes,\n iteration_numbers = iteration_numbers,\n verbose=verbose,\n SN_limits=SN_limits,\n translation_distance=translation_distance,\n )\n else:\n deeptrack.train_deep_learning_network(\n network,\n train_generator,\n sample_sizes = sample_sizes,\n iteration_numbers = iteration_numbers,\n verbose=verbose)\n\n freeze_all_layers(network)\n conv_list.append(next_node)\n if(save_networks):\n network.save(model_path+\"L1_\"+str((i+1)*nbr_nodes_added)+\"F.h5\")\n # Create final output using all the output layers and averaging them\n if(len(output_list)>1):\n avg_out = layers.average(output_list)\n else:\n avg_out = output_list[0]\n network = models.Model(input_tensor,avg_out)\n network.compile(optimizer='rmsprop', loss='mse', metrics=['mse', 'mae'])\n print('final network architecture')\n network.summary()\n if(save_networks):\n network.save(model_path+\"final_L\"+str(1)+\"_F.h5\")\n return network,conv_list,output_list,flattened_list,input_tensor","title":""},{"docid":"c4f6b8f67b338e9461ac7fbc177c610d","score":"0.5233395","text":"def experiment(opt, path):\n x = Input((1,), name=\"x\")\n player_a = Dense(3, activation='softmax', name=\"player_a\", bias=False, W_regularizer=l2(1e-2))\n player_b = Dense(3, activation='softmax', name=\"player_b\", bias=False, W_regularizer=l2(1e-2))\n\n action_a = player_a(x)\n action_b = player_b(x)\n\n def rps(z):\n u = z[0]\n v = z[1]\n return u[:, 0] * v[:, 2] + u[:, 1] * v[:, 0] + u[:, 2] * v[:, 1]\n\n model_a = Model(x, merge([action_a, action_b], mode=rps, output_shape=lambda z: (z[0][0], 1)))\n model_b = Model(x, merge([action_b, action_a], mode=rps, output_shape=lambda z: (z[0][0], 1)))\n\n adversarial_model = AdversarialModel(player_models=[model_a, model_b],\n player_params=[[player_a.W], [player_b.W]],\n player_names=[\"a\", \"b\"])\n adversarial_model.adversarial_compile(opt,\n player_optimizers=[SGD(1), SGD(1)],\n loss=\"mean_absolute_error\")\n param_model = Model(x, [action_a, action_b])\n\n def print_params(epoch, logs):\n params = param_model.predict(np.ones((1, 1)))\n a = params[0].ravel()\n b = params[1].ravel()\n print(\"Epoch: {}, A: {}, B: {}\".format(epoch, a, b))\n imgpath = os.path.join(path, \"epoch-{:03d}.png\".format(epoch))\n rps_chart(imgpath, a, b)\n\n cb = LambdaCallback(on_epoch_begin=print_params)\n batch_count = 5\n adversarial_model.fit(np.ones((batch_count, 1)),\n [np.ones((batch_count, 1)), np.ones((batch_count, 1))],\n nb_epoch=120, callbacks=[cb], verbose=0, batch_size=1)","title":""},{"docid":"511ddf25f189c01d1c83a0d430eb3f6e","score":"0.5231497","text":"def train():\n args = arguments_mt_train()\n \n h, w = map(int, args.input_size.split(','))\n input_size = (h, w)\n\n init_weights = map(float, args.init_weights.split(','))\n \n if args.use_random_seed:\n tf.set_random_seed(args.random_seed)\n \n # Create queue coordinator.\n coord = tf.train.Coordinator()\n \n # Load reader.\n with tf.name_scope(\"create_inputs\"):\n reader = ImageReader(\n args.data_dir,\n args.data_list_1,\n args.data_list_2,\n input_size=input_size,\n random_scale=args.random_scale,\n random_mirror=args.random_mirror,\n random_crop=args.random_crop,\n ignore_label=args.ignore_label,\n img_mean=IMG_MEAN,\n coord=coord,\n task_1=args.task_1,\n task_2=args.task_2)\n image_batch, label_batch_1, label_batch_2 = reader.dequeue(args.batch_size)\n \n # Create network.\n with slim.arg_scope(vgg_arg_scope(weight_decay=args.weight_decay, use_batch_norm=True, is_training=True)):\n if args.network == 'vgg_16_deeplab_nddr':\n _, _, end_points_1, end_points_2, _ = vgg_16_deeplab_nddr(image_batch,\n num_classes_1=args.num_classes_1,\n num_classes_2=args.num_classes_2,\n is_training=True,\n dropout_keep_prob=args.keep_prob,\n init_method=args.init_method,\n init_weights=init_weights)\n elif args.network == 'vgg_16_shortcut_deeplab_nddr':\n _, _, end_points_1, end_points_2, _ = vgg_16_shortcut_deeplab_nddr(image_batch,\n num_classes_1=args.num_classes_1,\n num_classes_2=args.num_classes_2,\n is_training=True,\n dropout_keep_prob=args.keep_prob,\n init_method=args.init_method,\n init_weights=init_weights)\n elif args.network == 'vgg_16_deeplab_mt':\n _, _, end_points_1, end_points_2, _ = vgg_16_deeplab_mt(image_batch,\n num_classes_1=args.num_classes_1,\n num_classes_2=args.num_classes_2,\n is_training=True,\n dropout_keep_prob=args.keep_prob)\n elif args.network == 'vgg_16_shortcut_deeplab_mt':\n _, _, end_points_1, end_points_2, _ = vgg_16_shortcut_deeplab_mt(image_batch,\n num_classes_1=args.num_classes_1,\n num_classes_2=args.num_classes_2,\n is_training=True,\n dropout_keep_prob=args.keep_prob)\n else:\n raise Exception('network name is not recognized!')\n \n # Predictions.\n raw_output_1 = end_points_1['vgg_16_1_6/fc8_voc12']\n raw_output_2 = end_points_2['vgg_16_2_6/fc8_voc12']\n\n if args.task_1 == 'seg' and args.task_2 == 'normal':\n seg_output = raw_output_1\n seg_classes = args.num_classes_1\n seg_label = prepare_label(label_batch_1, tf.stack(raw_output_1.get_shape()[1:3]), num_classes=seg_classes,\n one_hot=False, task='seg')\n seg_label_batch = label_batch_1\n seg_loss_scale = args.loss_scale_1\n\n normal_output = raw_output_2\n normal_classes = args.num_classes_2\n normal_label = prepare_label(label_batch_2, tf.stack(raw_output_2.get_shape()[1:3]), num_classes=normal_classes,\n one_hot=False, task='normal')\n normal_label_batch = label_batch_2\n normal_loss_scale = args.loss_scale_2\n\n elif args.task_1 == 'normal' and args.task2 == 'seg':\n seg_output = raw_output_2\n seg_classes = args.num_classes_2\n seg_label = prepare_label(label_batch_2, tf.stack(raw_output_2.get_shape()[1:3]), num_classes=seg_classes,\n one_hot=False, task='seg')\n seg_label_batch = label_batch_2\n seg_loss_scale = args.loss_scale_2\n\n normal_output = raw_output_1\n normal_classes = args.num_classes_1\n normal_label = prepare_label(label_batch_1, tf.stack(raw_output_1.get_shape()[1:3]), num_classes=normal_classes,\n one_hot=False, task='normal')\n normal_label_batch = label_batch_1\n normal_loss_scale = args.loss_scale_1\n\n else:\n raise Exception('check the tasks!')\n\n seg_loss = get_seg_loss(seg_output, seg_label, seg_classes, args.ignore_label)\n normal_loss = get_normal_loss(normal_output, normal_label, normal_classes, args.ignore_label)\n l2_losses = tf.add_n([args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name])\n reduced_loss = tf.reduce_mean(seg_loss) * seg_loss_scale + tf.reduce_mean(normal_loss) * normal_loss_scale + l2_losses * 0.5\n \n # Train Ops\n train_op, step_ph, learning_rate = create_train_ops_mt(reduced_loss, args)\n\n # Summaries\n seg_pred = tf.image.resize_bilinear(seg_output, tf.shape(image_batch)[1:3, ])\n seg_pred = tf.argmax(seg_pred, axis=3)\n seg_pred = tf.expand_dims(seg_pred, dim=3)\n\n normal_pred = tf.image.resize_bilinear(normal_output, tf.shape(image_batch)[1:3, ])\n\n if 'nddr' in args.network:\n nddr_l2_losses = tf.add_n([args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name and 'nddr' in v.name])\n else:\n nddr_l2_losses = tf.constant(0.0)\n\n summary_op = create_summary_op_mt(image_batch, seg_label_batch, normal_label_batch, seg_pred, normal_pred, IMG_MEAN,\n reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses,\n learning_rate, seg_classes, normal_classes, args.save_num_images, args.network)\n\n summary_writer = tf.summary.FileWriter(args.snapshot_dir,\n graph=tf.get_default_graph())\n \n # Set up tf session and initialize variables. \n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n init = tf.global_variables_initializer()\n \n sess.run(init)\n \n # Saver for storing checkpoints of the model.\n saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)\n \n # Load variables if the checkpoint is provided.\n load_mt(sess, args)\n \n # Start queue threads.\n threads = tf.train.start_queue_runners(coord=coord, sess=sess)\n\n # Iterate over training steps.\n for step in range(args.num_steps):\n start_time = time.time()\n feed_dict = {step_ph: step}\n\n if step % args.save_pred_every == 0:\n loss_value, seg_loss_value, normal_loss_value, loss_weight_value, loss_nddr_value, images, labels_1, \\\n labels_2, seg_pred_value, normal_pred_value, summary, _ = \\\n sess.run([reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses, image_batch, label_batch_1,\n label_batch_2, seg_pred, normal_pred, summary_op, train_op], feed_dict=feed_dict)\n save(saver, sess, args.snapshot_dir, step)\n else:\n loss_value, seg_loss_value, normal_loss_value, loss_weight_value, loss_nddr_value, summary, _ = \\\n sess.run([reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses, summary_op, train_op],\n feed_dict=feed_dict)\n\n summary_writer.add_summary(summary, step)\n duration = time.time() - start_time\n print('step {:d} \\t loss = {:.3f} \\t seg_loss = {:.3f}, seg_loss_scale = {:.1f} \\t normal_loss = {:.3f}, '\n 'normal_loss_scale = {:.1f} \\t loss_w = {:.3f}, loss_nddr_w = {:.3f} \\t nddr_mult = {:.1f}'\n .format(step, loss_value, seg_loss_value, seg_loss_scale, normal_loss_value, normal_loss_scale,\n loss_weight_value, loss_nddr_value, args.nddr_mult))\n\n coord.request_stop()\n coord.join(threads)","title":""},{"docid":"f8c56875886f40a35b1e2ec7f9cb27ef","score":"0.5229802","text":"def __init__(self,\n attention_states: tf.Tensor,\n scope: str,\n attention_state_size: int = None,\n input_weights: tf.Tensor=None,\n attention_fertility: int = None) -> None:\n super().__init__(\n scope, attention_states, attention_state_size, input_weights)\n\n self.attn_size = attention_states.get_shape()[2].value\n\n if self.attention_state_size is None:\n self.attention_state_size = self.attn_size\n\n with tf.variable_scope(scope):\n # To calculate W1 * h_t we use a 1-by-1 convolution, need to\n # reshape before.\n self.att_states_reshaped = tf.expand_dims(self.attention_states, 2)\n\n # This variable corresponds to Bahdanau's U_a in the paper\n k = tf.get_variable(\n \"AttnW\", [1, 1, self.attn_size, self.attention_state_size],\n initializer=tf.random_normal_initializer(stddev=0.001))\n\n self.hidden_features = tf.nn.conv2d(self.att_states_reshaped, k,\n [1, 1, 1, 1], \"SAME\")\n\n # pylint: disable=invalid-name\n # see comments on disabling invalid names below\n self.v = tf.get_variable(\n name=\"AttnV\",\n shape=[self.attention_state_size],\n initializer=tf.random_normal_initializer(stddev=.001))\n self.v_bias = tf.get_variable(\n \"AttnV_b\", [], initializer=tf.constant_initializer(0))","title":""},{"docid":"84b687813146fefc9fcaefbd641fba8d","score":"0.5226202","text":"def apply_model(state, images, labels):\n\n def loss_fn(params):\n logits = state.apply_fn({\"params\": params}, images)\n one_hot = jax.nn.one_hot(labels, 10)\n loss = jnp.mean(optax.softmax_cross_entropy(logits=logits, labels=one_hot))\n return loss, logits\n\n grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n (loss, logits), grads = grad_fn(state.params)\n accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)\n return grads, loss, accuracy","title":""},{"docid":"fe337cff2e3741c07bc4d2083023ed29","score":"0.522599","text":"def revnet_base():\n hparams = common_hparams.basic_params1()\n hparams.add_hparam('num_channels', [64, 128, 256, 416])\n hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])\n hparams.add_hparam('bottleneck', True)\n hparams.add_hparam('first_batch_norm', [False, True, True, True])\n hparams.add_hparam('init_stride', 2)\n hparams.add_hparam('init_kernel_size', 7)\n hparams.add_hparam('init_maxpool', True)\n hparams.add_hparam('strides', [1, 2, 2, 2])\n hparams.add_hparam('num_channels_init_block', 64)\n hparams.add_hparam('dim', '2d')\n\n # Variable init\n hparams.initializer = 'normal_unit_scaling'\n hparams.initializer_gain = 2.\n\n # Optimization\n hparams.optimizer = 'Momentum'\n hparams.optimizer_momentum_momentum = 0.9\n hparams.optimizer_momentum_nesterov = True\n hparams.weight_decay = 1e-4\n hparams.clip_grad_norm = 0.0\n # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)\n hparams.learning_rate = 0.4\n hparams.learning_rate_decay_scheme = 'cosine'\n # For image_imagenet224, 120k training steps, which effectively makes this a\n # cosine decay (i.e. no cycles).\n hparams.learning_rate_cosine_cycle_steps = 120000\n\n # Can run with a batch size of 128 with Problem ImageImagenet224\n hparams.batch_size = 128\n return hparams","title":""},{"docid":"49e566f5ce0da7e495f243582de00912","score":"0.5219748","text":"def AdaBnn(x_train,y_train,conf,classes=2,verbose=1):\n activation = conf['network']['activation']\n output_activation = conf['network']['output_activation']\n optimizer = conf['network']['optimizer']\n loss = conf['network']['loss']\n \n batch_size = conf['training']['batch_size']\n epochs = conf['training']['epochs']\n \n B = conf['adabnn']['B']\n T = conf['adabnn']['T']\n delta = conf['adabnn']['delta']\n seed = conf['adabnn']['seed']\n\n if classes == 2:\n output_shape = 1\n if classes > 2:\n output_shape = classes\n\n H = {}\n perf = []\n input_shape = x_train.shape[1::]\n inp = Input(shape=input_shape,name = 'input_layer')\n if len(input_shape) > 1:\n raise ValueError('The input data features'\n 'will be flatten into one dimension'\n 'next to the input layer')\n prep = Flatten(name='flat')(inp)\n h11 = Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name = 'h11')(prep)\n H[0] = ['h11']\n else:\n h11 = Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name = 'h11')(inp)\n H[0] = ['h11']\n out = Dense(output_shape,\n activation=output_activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name = 'output_layer')(h11)\n model = Model(inp,out)\n depth = 1\n for t in range(0,T):\n H1 = H.copy()\n H2 = H.copy()\n if verbose in [1,2]: print('[INFO] Iteration: ',t + 1,'\\n[INFO] Preparing H')\n for depth_h1 in range(1,depth+1):\n if depth_h1 == 1:\n h11_name = 'h' + str(depth_h1) + str(t+2)\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h11_name)\n h1_layers = [Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n use_bias=False,\n name=h11_name)(model.get_layer('input_layer').output)]\n H1[t+1] = [h11_name]\n else:\n conc = [model.get_layer(h).output for h in [h[depth_h1-2] for n,h in H.items() if len(h) >= (depth_h1-1)] ]\n conc.append(h1_layers[depth_h1-2])\n h22_name = 'h' + str(depth_h1) + str(t+2)\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h22_name)\n h1_layers.append(Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name=h22_name)(concatenate(conc)))\n H1[t+1].append(h22_name)\n conc1 = [model.get_layer(y[-1]).output for (k,y) in H.items()]\n conc1.append(h1_layers[-1])\n out = Dense(output_shape,\n activation=output_activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name='output_layer')(concatenate(conc1))\n mo1 = Model(inputs=inp,\n outputs=out)\n if verbose == 2:\n t1 = time.time()\n print('[TIMING] Defining layers and stacking them', t1 - t0)\n mo1.compile(optimizer=optimizer,\n loss=loss,\n metrics=['accuracy'])\n if verbose == 2:\n t2 = time.time()\n print('[TIMING] Compilation of the model', t2-t1)\n mo1.fit(x_train,\n y_train,\n batch_size=batch_size,\n epochs=epochs,\n shuffle=True,\n verbose=0)\n if verbose == 2:\n t3 = time.time()\n print('[TIMING] Fitting of the model', t3-t2)\n loss_h1,acc_h1 = mo1.evaluate(x_train,\n y_train,\n batch_size=batch_size,\n verbose=0)\n if verbose == 2:\n t4 = time.time()\n print('[TIMING] Evaluation of the model', t4-t3)\n\n if verbose in [1,2]: print('[INFO] Preparing Hprime')\n for depth_h2 in range(1,depth+2):\n if depth_h2 == 1:\n h11_name = 'h' + str(depth_h2) + str(t+2)\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h11_name)\n h2_layers = [Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n use_bias=False,\n name=h11_name)(model.get_layer('input_layer').output)]\n H2[t+1] = [h11_name]\n else:\n conc = [model.get_layer(h).output for h in [h[depth_h1-2] for n,h in H.items() if len(h) >= (depth_h1-1)] ]\n conc.append(h2_layers[depth_h2-2])\n h22_name = 'h' + str(depth_h2) + str(t+2)\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h22_name)\n h2_layers.append(Dense(B,\n activation=activation,\n kernel_regularizer=RademacherComplexity,\n use_bias=False,\n name=h22_name)(concatenate(conc)))\n H2[t+1].append(h22_name)\n conc2 = [model.get_layer(y[-1]).output for (k,y) in H.items()]\n conc2.append(h2_layers[-1])\n out = Dense(output_shape,\n activation=output_activation,\n kernel_regularizer=RademacherComplexity,\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\n use_bias=False,\n name='output_layer')(concatenate(conc2))\n mo2 = Model(inputs=inp,\n outputs=out)\n if verbose == 2:\n t5 = time.time()\n print('[TIMING] Defining layers and stacking them', t5 - t4)\n mo2.compile(optimizer=optimizer,\n loss=loss,\n metrics=['accuracy'])\n if verbose == 2:\n t6 = time.time()\n print('[TIMING] Compilation of the model', t6 - t5)\n mo2.fit(x_train,\n y_train,\n batch_size=batch_size,\n epochs=epochs,\n shuffle=True,\n verbose=0)\n if verbose == 2:\n t7 = time.time()\n print('[TIMING] Fitting of the model', t7 - t6)\n loss_h2,acc_h2 = mo2.evaluate(x_train,\n y_train,\n batch_size=batch_size,\n verbose=0)\n if verbose == 2:\n t8 = time.time()\n print('[TIMING] Evaluation of the model', t8 - t7)\n if loss_h1 < loss_h2:\n model = mo1\n H = H1\n loss_selected = loss_h1\n acc_selected = acc_h1\n if verbose in [1,2]: print('[INFO] H was selected')\n elif loss_h1 > loss_h2:\n if verbose in [1,2]: print('[INFO] Hprime was selected')\n model = mo2\n H = H2\n loss_selected = loss_h2\n acc_selected = acc_h2\n depth = depth + 1\n else:\n model = mo1\n H = H1\n loss_selected = loss_h1\n acc_selected = acc_h1\n if verbose in [1,2]: print('[INFO] Choosing H, without improving the loss function')\n break\n perf.append({\n \"iteration\": t + 1,\n \"accuracy\": acc_selected,\n \"cost function\": loss_selected\n })\n return model,perf","title":""},{"docid":"751d7b9af71af6725c3561e71d74efee","score":"0.52159697","text":"def adapt(self, original_inputs, adversarial_inputs, labels):\n return adversarial_inputs","title":""},{"docid":"73bbdab84c2979d8134fd9fe7b3970c8","score":"0.5214423","text":"def get_adadelta_trainer(self):\n batch_x = T.fmatrix('batch_x')\n batch_y = T.ivector('batch_y')\n # compute the gradients with respect to the model parameters\n gparams = T.grad(self.mean_cost, self.params)\n # compute list of weights updates\n updates = OrderedDict()\n for accugrad, accudelta, param, gparam in zip(self._accugrads,\n self._accudeltas, self.params, gparams):\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\n agrad = self._rho * accugrad + (1 - self._rho) * gparam * gparam\n agrad=T.cast(agrad, 'float32')\n dx = - T.sqrt((accudelta + self._eps)\n / (agrad + self._eps)) * gparam\n dx=T.cast(dx, 'float32')\n updates[accudelta] = T.cast((self._rho * accudelta\n + (1 - self._rho) * dx * dx),'float32')\n if self.max_norm:\n W = T.cast(param + dx,'float32')\n col_norms = T.cast(W.norm(2, axis=0),'float32')\n desired_norms = T.cast(T.clip(col_norms, 0, self.max_norm),'float32')\n updates[param] = T.cast(W * (desired_norms / (1e-6 + col_norms)),'float32')\n else:\n updates[param] = T.cast(param + dx,'float32')\n updates[accugrad] = T.cast(agrad,'float32')\n \n train_fn = theano.function(inputs=[theano.Param(batch_x),\n theano.Param(batch_y)],\n outputs=self.mean_cost,\n updates=updates,\n givens={self.x: batch_x, self.y: batch_y},\n allow_input_downcast=True)\n \n return train_fn","title":""},{"docid":"73bbdab84c2979d8134fd9fe7b3970c8","score":"0.5214423","text":"def get_adadelta_trainer(self):\n batch_x = T.fmatrix('batch_x')\n batch_y = T.ivector('batch_y')\n # compute the gradients with respect to the model parameters\n gparams = T.grad(self.mean_cost, self.params)\n # compute list of weights updates\n updates = OrderedDict()\n for accugrad, accudelta, param, gparam in zip(self._accugrads,\n self._accudeltas, self.params, gparams):\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\n agrad = self._rho * accugrad + (1 - self._rho) * gparam * gparam\n agrad=T.cast(agrad, 'float32')\n dx = - T.sqrt((accudelta + self._eps)\n / (agrad + self._eps)) * gparam\n dx=T.cast(dx, 'float32')\n updates[accudelta] = T.cast((self._rho * accudelta\n + (1 - self._rho) * dx * dx),'float32')\n if self.max_norm:\n W = T.cast(param + dx,'float32')\n col_norms = T.cast(W.norm(2, axis=0),'float32')\n desired_norms = T.cast(T.clip(col_norms, 0, self.max_norm),'float32')\n updates[param] = T.cast(W * (desired_norms / (1e-6 + col_norms)),'float32')\n else:\n updates[param] = T.cast(param + dx,'float32')\n updates[accugrad] = T.cast(agrad,'float32')\n \n train_fn = theano.function(inputs=[theano.Param(batch_x),\n theano.Param(batch_y)],\n outputs=self.mean_cost,\n updates=updates,\n givens={self.x: batch_x, self.y: batch_y},\n allow_input_downcast=True)\n \n return train_fn","title":""},{"docid":"1e89749fc6c776a5d4d7b1a8391381c9","score":"0.52032626","text":"def __init__(self, inp_len, inp_alpha, outp_alpha, nhidden, cascade = 0):\n self.nn1 = ml.NN(inp_len * len(inp_alpha), nhidden, len(outp_alpha)) # neural net\n self.nn2 = None\n self.cascade = cascade\n if cascade > 0:\n self.nn2 = ml.NN(cascade * len(outp_alpha), cascade, len(outp_alpha)) # cascaded neural net\n self.inp_len = inp_len\n self.inp_alpha = inp_alpha\n self.outp_alpha = outp_alpha","title":""},{"docid":"3cbaf2412bfc7edbf4fa35ca6172cb4e","score":"0.52013725","text":"def index_adam_full(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\n weight_decay: float, eps: float, ste: bool, params_prime: List[Tensor], granularity_channel: bool, granularity_kernel: bool):\n if ste:\n for i, (param, param_prime) in enumerate(zip(params, params_prime)):\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\n param_prime.addcdiv_(exp_avg, denom, value=-step_size)\n if granularity_kernel and len(param.shape) > 2: # nn.Conv2D weight\n for idx_dim0, param_dim0 in enumerate(param):\n for idx_dim1, param_dim1 in enumerate(param_dim0):\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\n param_dim1.view(-1)[torch.argsort(param_prime[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\n elif (granularity_channel or granularity_kernel) and len(param.shape) > 1: # nn.Linear or nn.Conv2D weight\n for idx_dim0, param_dim0 in enumerate(param):\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\n param_dim0.view(-1)[torch.argsort(param_prime[idx_dim0].view(-1))] = param_dim0_tmp\n else: # bias\n param_tmp, _ = torch.sort(param.view(-1))\n param.view(-1)[torch.argsort(param_prime.view(-1))] = param_tmp\n else:\n for i, param in enumerate(params):\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\n if granularity_kernel and len(param.shape) > 2: # nn.Conv2D weight\n for idx_dim0, param_dim0 in enumerate(param):\n for idx_dim1, param_dim1 in enumerate(param_dim0):\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\n param_dim1.view(-1)[torch.argsort(param_new[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\n elif (granularity_channel or granularity_kernel) and len(param.shape) > 1: # nn.Linear or nn.Conv2D weight\n for idx_dim0, param_dim0 in enumerate(param):\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\n param_dim0.view(-1)[torch.argsort(param_new[idx_dim0].view(-1))] = param_dim0_tmp\n else: # bias\n param_tmp, _ = torch.sort(param.view(-1))\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp","title":""},{"docid":"f5bfd89353afefdef76b50e2d0213a7e","score":"0.51992154","text":"def __call__(self, x, z_dim, y_dim, reuse=None, batch_size=100, x_width=28,train_phase = True):\n\n #self.hidden_dims = [self.hidden_dim] * 2\n net = tf.reshape(x, [batch_size, x_width, x_width, -1])\n net = slim.conv2d(net, 28, [5,5], stride=2, reuse=reuse, scope='enc_conv0',\n normalizer_fn=slim.batch_norm,\n normalizer_params={'reuse':reuse,'is_training':train_phase,\n 'scale':True,\n 'updates_collections':None,\n 'scope':'enc_bn0'})\n net = slim.conv2d(net, 56, [5,5], stride=2, reuse=reuse,scope='enc_conv1',\n normalizer_fn=slim.batch_norm,\n normalizer_params={'reuse':reuse,'is_training':train_phase,\n 'scale':True,\n 'updates_collections':None,\n 'scope':'enc_bn1'})\n net = slim.conv2d(net, 108, [5,5], stride=2, reuse=reuse, scope='enc_conv2',\n normalizer_fn=slim.batch_norm,\n normalizer_params={'reuse':reuse,'is_training':train_phase,\n 'scale':True,\n 'updates_collections':None,\n 'scope':'enc_bn2'})\n net = tf.reshape(net, [batch_size, -1])\n h1 = net\n #net = tf.concat(1, (net,y))\n #z_mean = slim.fully_connected(net, z_dim, scope='z_mean', activation_fn=None)\n z_log_sigma_sq = slim.fully_connected(net, z_dim, scope='z_log_sigma', activation_fn=None, reuse=reuse)\n\n y_prob = slim.fully_connected(h1, y_dim, scope='y_prob',\n activation_fn=tf.nn.softmax, reuse=reuse,\n normalizer_fn=slim.batch_norm,\n normalizer_params={'reuse':reuse,'is_training':train_phase,\n 'scale':True,\n 'updates_collections':None,\n 'scope':'y_prob_bn'})\n\n \"\"\"\n\n h0 = tf.nn.softplus(linear(x, self.hidden_dim, scope = 'en_fc0'))\n\n h1 = tf.nn.softplus(linear(h0, self.hidden_dim, scope = 'en_fc1'))\n\n z_mean = linear(h1, z_dim, scope = 'z_mean')\n z_log_sigma_sq = linear(h1, z_dim, scope = 'z_log_sigma')\n \"\"\"\n return (z_log_sigma_sq, y_prob, h1)","title":""},{"docid":"c24d29e9801c6d798100f27fdb121316","score":"0.5197575","text":"def yolo3_vgg16_body(inputs, num_anchors, num_classes):\n '''\n Layer Name input_1 Output: Tensor(\"input_1:0\", shape=(?, 416, 416, 3), dtype=float32)\n Layer Name block1_conv1 Output: Tensor(\"block1_conv1/Relu:0\", shape=(?, 416, 416, 64), dtype=float32)\n Layer Name block1_conv2 Output: Tensor(\"block1_conv2/Relu:0\", shape=(?, 416, 416, 64), dtype=float32)\n Layer Name block1_pool Output: Tensor(\"block1_pool/MaxPool:0\", shape=(?, 208, 208, 64), dtype=float32)\n Layer Name block2_conv1 Output: Tensor(\"block2_conv1/Relu:0\", shape=(?, 208, 208, 128), dtype=float32)\n Layer Name block2_conv2 Output: Tensor(\"block2_conv2/Relu:0\", shape=(?, 208, 208, 128), dtype=float32)\n Layer Name block2_pool Output: Tensor(\"block2_pool/MaxPool:0\", shape=(?, 104, 104, 128), dtype=float32)\n Layer Name block3_conv1 Output: Tensor(\"block3_conv1/Relu:0\", shape=(?, 104, 104, 256), dtype=float32)\n Layer Name block3_conv2 Output: Tensor(\"block3_conv2/Relu:0\", shape=(?, 104, 104, 256), dtype=float32)\n Layer Name block3_conv3 Output: Tensor(\"block3_conv3/Relu:0\", shape=(?, 104, 104, 256), dtype=float32)\n Layer Name block3_pool Output: Tensor(\"block3_pool/MaxPool:0\", shape=(?, 52, 52, 256), dtype=float32)\n Layer Name block4_conv1 Output: Tensor(\"block4_conv1/Relu:0\", shape=(?, 52, 52, 512), dtype=float32)\n Layer Name block4_conv2 Output: Tensor(\"block4_conv2/Relu:0\", shape=(?, 52, 52, 512), dtype=float32)\n Layer Name block4_conv3 Output: Tensor(\"block4_conv3/Relu:0\", shape=(?, 52, 52, 512), dtype=float32)\n Layer Name block4_pool Output: Tensor(\"block4_pool/MaxPool:0\", shape=(?, 26, 26, 512), dtype=float32)\n Layer Name block5_conv1 Output: Tensor(\"block5_conv1/Relu:0\", shape=(?, 26, 26, 512), dtype=float32)\n Layer Name block5_conv2 Output: Tensor(\"block5_conv2/Relu:0\", shape=(?, 26, 26, 512), dtype=float32)\n Layer Name block5_conv3 Output: Tensor(\"block5_conv3/Relu:0\", shape=(?, 26, 26, 512), dtype=float32)\n Layer Name block5_pool Output: Tensor(\"block5_pool/MaxPool:0\", shape=(?, 13, 13, 512), dtype=float32)\n '''\n\n #net, endpoint = inception_v2.inception_v2(inputs)\n vgg16 = VGG16(input_tensor=inputs,weights='imagenet',include_top=False)\n x = vgg16.get_layer('block5_pool').output\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv1')(x)\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv2')(x)\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv3')(x)\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv4')(x)\n\n # input: 416 x 416 x 3\n # block6_conv3 :13 x 13 x 512\n # block5_conv3 :26 x 26 x 512\n # block4_conv3 : 52 x 52 x 512\n\n\n # f1 :13 x 13 x 1024 13 x 13 x 512\n x, y1 = make_last_layers(x, 512, num_anchors * (num_classes + 5), predict_id='1')\n\n x = compose(\n DarknetConv2D_BN_Leaky(256, (1,1)),\n UpSampling2D(2))(x)\n\n f2 = vgg16.get_layer('block5_conv3').output\n # f2: 26 x 26 x 512\n x = Concatenate()([x,f2])\n\n x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5), predict_id='2')\n\n x = compose(\n DarknetConv2D_BN_Leaky(128, (1,1)),\n UpSampling2D(2))(x)\n\n f3 = vgg16.get_layer('block4_conv3').output\n # f3 : 52 x 52 x 256\n x = Concatenate()([x, f3])\n x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5), predict_id='3')\n\n return Model(inputs = inputs, outputs=[y1,y2,y3])","title":""},{"docid":"e23258f091ee5d75646f0ad6c10d2886","score":"0.51935583","text":"def __init__(self,\n name,\n num_units,\n memory,\n memory_projection='independent',\n memory_sequence_length=None,\n score_scale=True,\n probability_fn=None,\n dtype=None):\n print('INFO: Using {}.'.format(self.__class__.__name__))\n if probability_fn is None:\n probability_fn = tf.nn.softmax\n if dtype is None:\n dtype = tf.float32\n wrapped_probability_fn = lambda score, _: probability_fn(score)\n assert memory_projection in ['independent', 'tied']\n \n assert len(_shape(memory)) == 3, \\\n 'The CNN feature maps must be a rank-3 tensor of NTC.'\n \n proj_kwargs = dict(\n units=num_units,\n use_bias=True,\n activation=None,\n dtype=dtype)\n with tf.variable_scope(name):\n super(BahdanauAttentionV1, self).__init__(\n query_layer=Dense(name='query_layer', **proj_kwargs),\n memory_layer=Dense(name='memory_layer', **proj_kwargs),\n memory=memory,\n probability_fn=wrapped_probability_fn,\n memory_sequence_length=memory_sequence_length,\n score_mask_value=None,\n name=name)\n self._num_units = num_units\n self._memory_projection = memory_projection\n self._score_scale = score_scale\n self._name = name\n \n if self._memory_projection == 'tied':\n self._values = tf.identity(self._keys)\n elif self._memory_projection == 'independent':\n # Project memory\n self._values = Dense(\n name='value_layer',\n **proj_kwargs)(self._values)\n else:\n raise ValueError('Undefined.')","title":""},{"docid":"9cb8ca43b2aaec3cca0aeaf719137f4e","score":"0.5192833","text":"def define_vanilla_model_MLP(input_shape,\n num_classes,\n hidden_units = 32):\n\n # IoT Node (input image)\n img_input_1 = Input(shape = input_shape) \n img_input_2 = Input(shape = input_shape)\n img_input_3 = Input(shape = input_shape)\n img_input_4 = Input(shape = input_shape) \n img_input_5 = Input(shape = input_shape) \n img_input_6 = Input(shape = input_shape) \n \n input_edge1 = add([img_input_1,img_input_2])\n input_edge2 = img_input_3\n input_edge3 = add([img_input_4,img_input_5])\n input_edge4 = img_input_6\n\n # edge nodes\n edge1 = define_MLP_architecture_edge(input_edge1, hidden_units, \"edge1_output_layer\")\n edge2 = define_MLP_architecture_edge(input_edge2, hidden_units, \"edge2_output_layer\")\n edge3 = define_MLP_architecture_edge(input_edge3, hidden_units, \"edge3_output_layer\")\n edge4 = define_MLP_architecture_edge(input_edge4, hidden_units, \"edge4_output_layer\")\n\n # fog node 4\n fog4_input = layers.add([edge2,edge3, edge4], name = \"node5_input\")\n fog4 = define_MLP_architecture_fog_with_two_layers(fog4_input, hidden_units,\"fog4_output_layer\",\"fog4_input_layer\")\n\n # fog node 3\n fog3 = Lambda(lambda x: x * 1,name=\"node4_input\")(edge1)\n fog3 = define_MLP_architecture_fog_with_one_layer(fog3, hidden_units, \"fog3_output_layer\")\n\n # fog node 2\n fog2_input = layers.add([fog3, fog4], name = \"node3_input\")\n fog2 = define_MLP_architecture_fog_with_two_layers(fog2_input, hidden_units, \"fog2_output_layer\", \"fog2_input_layer\")\n\n # fog node 1\n fog1 = Lambda(lambda x: x * 1,name=\"node2_input\")(fog2)\n fog1 = define_MLP_architecture_fog_with_two_layers(fog1, hidden_units, \"fog1_output_layer\", \"fog1_input_layer\")\n \n\n # cloud node\n cloud = Lambda(lambda x: x * 1,name=\"node1_input\")(fog1)\n cloud = define_MLP_architecture_cloud(cloud, hidden_units, num_classes)\n\n model = Model(inputs=[img_input_1,img_input_2,img_input_3,img_input_4,img_input_5,img_input_6], outputs=cloud)\n model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n return model","title":""},{"docid":"5a4ae8de5a4d0321100e7055d62a83b2","score":"0.51927143","text":"def g_vae_model_fn(mode, inputs, params, reuse=False):\n\n #params.cat_distr = params.n_latent//params.k\n #params.n_latent = params.cat_distr*params.k\n\n if mode == 'train':\n is_training = True\n else:\n is_training = False\n # -----------------------------------------------------------\n # MODEL: define the layers of the model\n with tf.variable_scope('g_vae_model', reuse=reuse):\n # Compute the output distribution of the model and the predictions\n loss_likelihood, kl_loss, sampled, reconstructed_mean, sigma_placeholder = build_model(inputs, is_training,\n params)\n\n # Define the Loss\n loss = tf.reduce_mean(loss_likelihood + kl_loss)\n\n # Define training step that minimizes the loss with the Adam optimizer\n learning_rate_ph = tf.placeholder(tf.float32, [], name=\"learning_rate\")\n if mode == 'train':\n optimizer = tf.train.AdamOptimizer(learning_rate_ph)\n global_step = tf.train.get_or_create_global_step()\n with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n train_op = optimizer.minimize(loss, global_step=global_step)\n\n # -----------------------------------------------------------\n # METRICS AND SUMMARIES\n # Metrics for evaluation using tf.metrics (average over whole dataset)\n with tf.variable_scope(\"g_vae_metrics\"):\n metrics = {\n 'loss': tf.metrics.mean(loss),\n 'kl_loss': tf.metrics.mean(kl_loss),\n 'neg_log_likelihood': tf.metrics.mean(loss_likelihood)\n }\n\n # Group the update ops for the tf.metrics\n update_metrics_op = tf.group(*[op for _, op in metrics.values()])\n\n # Get the op to reset the local variables used in tf.metrics\n metric_variables = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=\"g_vae_metrics\")\n metrics_init_op = tf.variables_initializer(metric_variables)\n\n # Summaries for training\n tf.summary.scalar('loss', loss)\n tf.summary.scalar('kl_loss', kl_loss)\n tf.summary.scalar('neg_log_likelihood', loss_likelihood)\n # Summary for reconstruction and original image with max_outpus images\n tf.summary.image('Original Image', inputs['img'], max_outputs=6, collections=None, family=None)\n latent_img = tf.reshape(sampled, [-1, 1, params.n_latent, 1])\n tf.summary.image('Latent Space', latent_img, max_outputs=6, collections=None, family=None)\n tf.summary.image('Reconstructions', tf.sigmoid(reconstructed_mean), max_outputs=6, collections=None, family=None)\n\n # -----------------------------------------------------------\n # -----------------------------------------------------------\n # MODEL SPECIFICATION\n # Create the model specification and return it\n # It contains nodes or operations in the graph that will be used for training and evaluation\n model_spec = inputs\n variable_init_op = tf.group(*[tf.global_variables_initializer(), tf.tables_initializer()])\n model_spec['variable_init_op'] = variable_init_op\n model_spec['loss'] = loss\n model_spec['metrics_init_op'] = metrics_init_op\n model_spec['metrics'] = metrics\n model_spec['update_metrics'] = update_metrics_op\n model_spec['summary_op'] = tf.summary.merge_all()\n model_spec['reconstructions'] = tf.sigmoid(reconstructed_mean)\n model_spec['sigma_placeholder'] = sigma_placeholder\n model_spec['learning_rate_placeholder'] = learning_rate_ph\n model_spec['lambda_r_placeholder'] = tf.placeholder(tf.float32, shape=[], name='Reconstruction_regularization')\n model_spec['lambda_c_placeholder'] = tf.placeholder(tf.float32, shape=[], name='center_sim_regularization')\n model_spec['lambda_d_placeholder'] = tf.placeholder(tf.float32, shape=[], name='discriminative_regularization')\n model_spec['lambda_b_placeholder'] = tf.placeholder(tf.float32, shape=[], name='inter_cluster_sim_regularization')\n model_spec['lambda_w_placeholder'] = tf.placeholder(tf.float32, shape=[], name='intra_cluster_sim_regularization')\n\n if mode == 'train':\n model_spec['train_op'] = train_op\n elif mode == 'cluster':\n model_spec['sample'] = sampled\n\n return model_spec","title":""},{"docid":"c534cbcefec0a4cea5836abc0deb1799","score":"0.51907414","text":"def nnObjFunction(params, *args):\n \n n_input, n_hidden, n_class, training_data, training_label, lambdaval = args\n# params = np.linspace(-5,5, num=26)\n\n w1 = params[0:n_hidden * (n_input + 1)].reshape( (n_hidden, (n_input + 1)))\n w2 = params[(n_hidden * (n_input + 1)):].reshape((n_class, (n_hidden + 1)))\n \n #Your code here\n input_bias = np.ones((training_data.shape[0],1))\n input_data = np.concatenate((training_data,input_bias),axis=1)\n hidden_out =sigmoid((input_data.dot((np.transpose(w1)))))\n \n hidden_bias = np.ones((training_data.shape[0],1))\n outer_in=np.concatenate((hidden_out,hidden_bias),axis=1)\n oil=sigmoid(outer_in.dot(np.transpose(w2)))\n \n ones_mat = np.ones((training_data.shape[0],n_class))\n zeros_mat = np.zeros((training_data.shape[0],n_class))\n \n for i in range(training_label.shape[0]):\n t = training_label[i,0]\n zeros_mat[i,t] = 1\n \n training_label = zeros_mat\n \n obj_val = np.zeros((training_data.shape[0],10))\n obj_val = (training_label*(np.log(oil))+(((ones_mat-training_label)*(np.log(ones_mat-oil)))))\n obj_val = -(obj_val/training_data.shape[0])\n \n w1sqr=w1**2\n w2sqr=w2**2\n wsum = w1sqr.sum() + w2sqr.sum()\n \n obj_val=obj_val.sum()+(lambdaval/(2*training_data.shape[0]))*(wsum)\n \n grad_w2 = np.zeros((n_class,n_hidden+1))\n grad_w2 = (((np.transpose(oil-training_label)).dot(outer_in)) + (lambdaval*w2))/training_data.shape[0]\n \n grad_w1 = np.zeros((n_hidden,n_input+1)) \n hidden_val = (np.ones((training_data.shape[0],n_hidden+1))) - outer_in\n \n grad_w1 = np.dot(np.transpose((np.multiply((np.multiply(hidden_val,outer_in)),(np.dot((oil-training_label),w2))))),input_data) \n grad_w1 = ((np.delete(grad_w1,n_hidden,0)) + (lambdaval*w1))/training_data.shape[0]\n \n obj_grad = np.concatenate((grad_w1.flatten(), grad_w2.flatten()),0) \n return (obj_val,obj_grad)","title":""},{"docid":"2381318310a3afde9b04fe530ff44fd7","score":"0.51900274","text":"def att_original_unet_model(input_shape, pool_size=(2, 2, 2), n_labels=1, initial_learning_rate=0.00001,\n deconvolution=False,depth=4, n_base_filters=32, metrics=dice_coefficient,\n batch_normalization=False, activation_name=\"sigmoid\"):\n inputs = Input(input_shape)\n\n current_layer = inputs\n\n levels = list()\n level_filters = list()\n for level_number in range(depth): # depth=4; [0,1,2,3]\n\n n_level_filters = list()\n n_level_filters.append((2**level_number) * n_base_filters) # 2^level_num * 16\n n_level_filters.append(n_level_filters[0] * 2)\n level_filters.append(n_level_filters)\n\n # add levels with max pooling\n layer1 = create_convolution_block(input_layer=current_layer, n_filters=n_level_filters[0],\n batch_normalization=batch_normalization)\n layer2 = create_convolution_block(input_layer=layer1, n_filters=n_level_filters[1],\n batch_normalization=batch_normalization)\n if level_number < depth - 1:\n current_layer = MaxPooling3D(pool_size=pool_size)(layer2)\n levels.append([layer1, layer2, current_layer])\n else: # 最下一层\n current_layer = layer2\n levels.append([layer1, layer2])\n\n for level_number in range(depth - 2, -1, -1): # [2,1,0]\n\n gating = gating_signal(levels[level_number + 1][1], level_filters[level_number][1], False)\n att = attention_block(levels[level_number][1], gating, level_filters[level_number][1])\n\n up_convolution = get_up_convolution(pool_size=pool_size, deconvolution=deconvolution,\n n_filters=level_filters[level_number + 1][1])(current_layer)\n concat = concatenate([up_convolution, att], axis=1)\n current_layer = create_convolution_block(n_filters=level_filters[level_number][1],\n input_layer=concat,\n batch_normalization=batch_normalization)\n current_layer = create_convolution_block(n_filters=level_filters[level_number][1],\n input_layer=current_layer,\n batch_normalization=batch_normalization)\n\n # sigmoid激活输出\n final_convolution = Conv3D(n_labels, (1, 1, 1))(current_layer)\n act = Activation(activation_name)(final_convolution)\n model = Model(inputs=inputs, outputs=act)\n model.compile(optimizer=Adam(lr=initial_learning_rate), loss=dice_coefficient_loss)\n # 返回模型\n return model","title":""},{"docid":"1e09162bdc34c53f17048185c69c3747","score":"0.5188103","text":"def __init__(self, num_conv_in_channel, num_conv_out_channel, num_primary_unit, primary_unit_size,\n num_classes, output_unit_size, num_routing,\n use_reconstruction_loss, regularization_scale, cuda_enabled,caps_channels = 32, kernel_size = 9, stride = 2):\n super(Net, self).__init__()\n\n self.cuda_enabled = cuda_enabled\n\n # Configurations used for image reconstruction.\n self.use_reconstruction_loss = use_reconstruction_loss\n self.image_width = 224 # MNIST digit image width\n self.image_height = 224 # MNIST digit image height\n self.image_channel = 3 # MNIST digit image channel\n self.regularization_scale = regularization_scale\n\n # Layer 1: Conventional Conv2d layer\n # self.conv1 = ConvLayer(in_channel=num_conv_in_channel,\n # out_channel=num_conv_out_channel,\n # kernel_size=9)\n\n\n features = list(models.alexnet(pretrained=True).features)[:-3]\n # features.pop()\n # features.pop()\n # features.pop()\n self.conv1 = nn.Sequential(*features)\n \n\n\n # ConvLayer(in_channel=num_conv_in_channel,\n # out_channel=num_conv_out_channel,\n # kernel_size=9)\n\n # PrimaryCaps\n # Layer 2: Conv2D layer with `squash` activation\n # print ('n_channel=',num_conv_out_channel)\n # print ('num_unit=',num_primary_unit)\n # print ('unit_size=',primary_unit_size)\n # print ('use_routing=',False)\n # print ('num_routing=',num_routing)\n # print ('cuda_enabled=',cuda_enabled)\n self.primary = CapsuleLayer(in_unit=0,\n in_channel=num_conv_out_channel,\n num_unit=num_primary_unit,\n unit_size=primary_unit_size, # capsule outputs\n use_routing=False,\n num_routing=num_routing,\n cuda_enabled=cuda_enabled,caps_channels = caps_channels,kernel_size=kernel_size, stride = stride)\n\n # DigitCaps\n # Final layer: Capsule layer where the routing algorithm is.\n # print ('SECOND')\n # print ('in_unit=',num_primary_unit)\n # print ('in_channel=',primary_unit_size)\n # print ('num_unit=',num_classes)\n # print ('unit_size=',output_unit_size)\n # print ('use_routing=',True)\n # print ('num_routing=',num_routing)\n # print ('cuda_enabled=',cuda_enabled)\n \n\n self.digits = CapsuleLayer(in_unit=num_primary_unit,\n in_channel=primary_unit_size,\n num_unit=num_classes,\n unit_size=output_unit_size, # 16D capsule per digit class\n use_routing=True,\n num_routing=num_routing,\n cuda_enabled=cuda_enabled)\n\n # Reconstruction network\n if use_reconstruction_loss:\n # The output of the digit capsule is fed into a decoder consisting of\n # 3 fully connected layers that model the pixel intensities.\n fc1_output_size = 512\n fc2_output_size = 1024\n self.fc1 = nn.Linear(num_classes * output_unit_size, fc1_output_size)\n self.fc2 = nn.Linear(fc1_output_size, fc2_output_size)\n self.fc3 = nn.Linear(fc2_output_size, 784)\n # Activation functions\n self.relu = nn.ReLU(inplace=True)\n self.sigmoid = nn.Sigmoid()","title":""},{"docid":"cf91acb37977c4463e687ef864abc582","score":"0.51855826","text":"def __init__(self, keep_prob, input_x, input_y_first, input_y_second, input_product, input_brand, input_tag, fc_size, attention_unit_size, alpha, threshold, num_classes_first, num_classes_second, num_classes_third, num_classes_product, num_classes_brand, num_tags, learning_rate, batch_size, decay_steps, decay_rate, sequence_length,\n vocab_size, embed_size, hidden_size, is_training, initializer=initializers.xavier_initializer(), clip_gradients=5.0):\n # set hyperparamter\n self.fc_size = fc_size\n self.attention_unit_size = attention_unit_size\n self.alpha = alpha\n self.threshold = threshold\n self.num_classes_first = num_classes_first\n self.num_classes_second = num_classes_second\n self.num_classes_third = num_classes_third\n self.num_classes_product = num_classes_product\n self.num_classes_brand = num_classes_brand\n self.num_tags = num_tags\n self.batch_size = batch_size\n self.sequence_length = sequence_length\n self.vocab_size = vocab_size\n self.embed_size = embed_size\n self.hidden_size = hidden_size\n self.is_training = is_training\n self.learning_rate = tf.Variable(learning_rate, trainable=False, name=\"learning_rate\")\n self.learning_rate_decay_half_op = tf.assign(self.learning_rate, self.learning_rate * 0.95)\n self.initializer = initializer\n self.clip_gradients=clip_gradients\n\n #self.input_x = tf.placeholder(tf.int32, [None, None], name=\"input_x\")\n self.input_x = input_x\n # sequence true length\n self.length = tf.reduce_sum(tf.sign(self.input_x), reduction_indices=1)\n self.length = tf.cast(self.length, tf.int32)\n\n self.input_mask = tf.cast(tf.not_equal(self.input_x, 0), tf.float32)\n\n self.input_y_first = input_y_first\n self.input_y_second = input_y_second\n self.input_product = input_product\n self.input_brand = input_brand\n self.input_tag = input_tag\n \n self.dropout_keep_prob = keep_prob\n self.decay_steps, self.decay_rate = decay_steps, decay_rate\n # init model\n self.instantiate_weights()\n \n # lstm output\n self.gru_out, self.gru_out_pool = self.BiGRU()\n\n # for tag head\n self.tag_logits, self.tag_logits_ = self.Ner(self.gru_out)\n tag_predictions = tf.nn.softmax(self.tag_logits, axis=-1) \n tag_predictionss = tf.nn.softmax(self.tag_logits_, axis=-1)\n self.tag_softmax = tf.identity(tag_predictionss, name='tag_predicitions') \n self.tag_predictions = tf.argmax(tag_predictions, axis=-1) \n \n # first level\n self.first_att_weight, self.first_att_out = self.attention(self.gru_out, self.num_classes_first, name=\"first-\")\n self.first_local_input = tf.concat([self.gru_out_pool, self.first_att_out], axis=1)\n self.first_local_fc_out = self.fc_layer(self.first_local_input, name=\"first-local-\")\n self.first_logits, self.first_scores, self.first_visual = self.local_layer(\n self.first_local_fc_out, self.first_att_weight, self.num_classes_first, name=\"first-\")\n\n # second level\n self.second_att_input = tf.multiply(self.gru_out, tf.expand_dims(self.first_visual, -1))\n self.second_local_input = tf.reduce_sum(self.second_att_input, axis=1)\n\n self.fc_out = self.fc_layer(self.second_local_input)\n \n # dropout \n with tf.name_scope(\"dropout\"):\n self.h_drop = tf.nn.dropout(self.fc_out, self.dropout_keep_prob)\n # global output\n self.second_logits, self.second_scores = self.global_layer(self.num_classes_second, 'cid_class')\n self.product_logits, self.product_scores = self.global_layer(self.num_classes_product, 'product_class')\n self.brand_logits, self.brand_scores = self.global_layer(self.num_classes_brand, 'brand_class')\n \n self.predictions = tf.reshape(self.second_scores,[-1, self.num_classes_second], name='prediction')\n self.product_predictions = tf.reshape(self.product_scores,[-1, self.num_classes_product], name='product_prediction')\n self.brand_predictions = tf.reshape(self.brand_scores,[-1, self.num_classes_brand], name='brand_prediction')\n \n self.cid_precision, self.cid_recall, self.cid_f1 = self.metric(self.predictions, self.input_y_second, 'cid_') \n self.product_precision, self.product_recall, self.product_f1 = self.metric(self.product_predictions, self.input_product, 'product_') \n self.brand_precision, self.brand_recall, self.brand_f1 = self.metric(self.brand_predictions, self.input_brand, 'brand_') \n \n\n # for tag acc\n correct = tf.cast(tf.equal(tf.cast(self.tag_predictions, tf.int64) - self.input_tag, 0), tf.float32)\n correct = tf.reduce_sum(self.input_mask * correct) \n self.tag_acc = tf.div(correct, tf.cast(tf.reduce_sum(self.length), tf.float32))\n \n \n # multi task loss \n self.loss_val = self.multi_task_loss()\n\n if not is_training:\n return\n\n # optimizer\n self.train_op = self.train()","title":""},{"docid":"2c5974e5fb78c77f27de9d9b1e688e67","score":"0.5177354","text":"def _add_train_op(self):\n hps = self._hps\n '''\n self._lr_rate = tf.maximum(\n hps.min_lr, # min_lr_rate.\n tf.train.exponential_decay(hps.lr, self.global_step, 30000, 0.98))\n '''\n tvars = tf.trainable_variables()\n with tf.device('/gpu:0'):\n grads, global_norm = tf.clip_by_global_norm(tf.gradients(self._loss_total, tvars), hps.max_grad_norm)\n tf.summary.scalar('global_norm', global_norm)\n #optimizer = tf.train.GradientDescentOptimizer(self._lr_rate)\n optimizer = tf.train.AdagradOptimizer(hps.lr, initial_accumulator_value=0.1)\n #tf.summary.scalar('learning rate', self._lr_rate)\n with tf.device('/gpu:0'):\n self._train_op = optimizer.apply_gradients(\n zip(grads, tvars), global_step=self.global_step, name='train_step')","title":""},{"docid":"f3ca8b13c5bd1267f9f5c7d50007829e","score":"0.5173547","text":"def tf_efficientnet_b5_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b5_ap\",\n channel_multiplier=1.6,\n depth_multiplier=2.2,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"ef699ac60c76b31c9c1fb56605105fe1","score":"0.5173306","text":"def get_model(point_cloud, img, is_training, num_cls, feature_num, bn_decay=None):\n # b x n x 10\n \n #regularizer = tf.contrib.layers.l2_regularizer(scale=0.1)\n regularizer = tf.keras.regularizers.l2(l=0.1)\n batch_size = point_cloud.get_shape()[0].value\n num_point = point_cloud.get_shape()[1].value\n #image pixel size\n num_pix = img.get_shape()[1].value\n input_image = tf.expand_dims(point_cloud, -1)\n #CONV for RGB images\n conv1 = tf.layers.conv2d(inputs=img,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu) \n print (conv1.shape)\n pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)\n conv2 = tf.layers.conv2d(inputs=pool1,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)\n conv3 = tf.layers.conv2d(inputs=pool2,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n #conv = tf.layers.conv2d(inputs=pool,filters=32,kernel_size=[3, 3],padding=\"valid\",activation=tf.nn.relu)\n #pool = tf.layers.max_pooling2d(inputs=conv, pool_size=[2, 2], strides=2)\n conv4 = tf.layers.conv2d(inputs=conv3,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n #pool = tf.layers.max_pooling2d(inputs=conv, pool_size=[2, 2], strides=2)\n print( conv4.shape)\n \n Up1 = tf.keras.layers.UpSampling2D(size=(2,2))(conv4)\n print (Up1.shape)\n dconv1 = tf.layers.conv2d(inputs=Up1,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n con1 =tf.concat(axis=-1, values=[dconv1, conv2])\n dconv2 = tf.layers.conv2d(inputs=con1,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n Up2 = tf.keras.layers.UpSampling2D(size=(2,2))(dconv2)\n dconv3 = tf.layers.conv2d(inputs=Up2,filters=64,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n con2 =tf.concat(axis=-1, values=[dconv3, conv1])\n feature_img = tf.layers.conv2d(inputs=con2,filters=128,kernel_size=[3, 3],padding=\"same\",activation=tf.nn.relu)\n print (feature_img.shape)\n\n\n # CONV pointcloud\n print (\"input_image.shape: \"+ str(input_image.shape))\n #net = tf_util.conv2d(input_image, 64, [1,feature_num], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv0', bn_decay=bn_decay)\n\n net = tf.keras.layers.Convolution2D(filters=64, kernel_size=[1,feature_num], strides=(1, 1), padding='valid', activation='elu', use_bias=True, kernel_initializer='random_uniform')(input_image)\n\n net = tf.keras.layers.Convolution2D(filters=64, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\n print(\"second_layer pointcloud: \" + str(net.shape))\n\n #net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay)\n\n print(net.shape)\n\n #net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay)\n\n net_test = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\n\n #net_test= tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay)\n\n #points_feat1 = tf_util.conv2d(net_test, 1024, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay)\n\n points_feat1 = tf.keras.layers.Convolution2D(filters=1024, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net_test)\n\n print(\"points_feat\" + str(points_feat1.shape))\n\n pc_feat1 = tf_util.max_pool2d(points_feat1, [num_point,1], padding='VALID', scope='maxpool1')\n print(\"mapool1: \" + str(pc_feat1.shape))\n \n pc_feat1 = tf.reshape(pc_feat1, [batch_size, -1])\n print(pc_feat1.shape)\n \n print (pc_feat1.shape)\n pc_feat1_expand = tf.tile(tf.reshape(pc_feat1, [batch_size, 1, 1, -1]), [1, num_point, 1, 1])\n print (points_feat1.shape)\n print(\"pc_feeat1: \" + str(pc_feat1_expand.shape))\n\n #dense = tf.tile(tf.reshape(dense, [batch_size, 1, 1, -1]), [1, num_point, 1, 1])\n #print(dense.shape)\n\n\n points_feat1_concat = tf.concat(axis=-1, values=[net_test,pc_feat1_expand])\n print(points_feat1_concat.shape)\n\n #net = tf_util.conv2d(points_feat1_concat, 128, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv7')\n\n net = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(points_feat1_concat)\n\n # point-wise concatanation of features between image and pointcloud from the first layer\n #point_corres_featur1 = get_point_corres_features(box_size=50, pixel_size=0.16,feature_img=conv1, point_cloud=point_cloud)\n\n # point-wise concatanation of features between image and pointcloud final layer\n point_corres_featur = get_point_corres_features(box_size=15.6, pixel_size=0.05,feature_img=feature_img, point_cloud=point_cloud)\n point_corres_featur = tf.concat(axis=-1, values=[point_corres_featur,net])\n print (\"point_corres_featur\" + str(point_corres_featur.shape) )\n\n\n #net = tf_util.conv2d(point_corres_featur, 256, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv8')\n\n\n net = tf.keras.layers.Convolution2D(filters=256, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(point_corres_featur)\n #net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1],\n # bn=True, is_training=is_training, scope='conv9')\n net_print = tf.squeeze(net, [2])\n net = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\n\n print(net.shape)\n net = tf_util.dropout(net, keep_prob=0.6, is_training=is_training, scope='dp1')\n \n #net = tf_util.conv2d(net, num_cls , [1,1], padding='VALID', stride=[1,1],\n # activation_fn=None, scope='conv10')\n\n net = tf.keras.layers.Convolution2D(filters=num_cls, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\n print(net.shape)\n net = tf.squeeze(net, [2])\n print(net.shape)\n model_vars = tf.trainable_variables()\n print (slim.model_analyzer.analyze_vars(model_vars, print_info=True))\n return (net, net_print)","title":""},{"docid":"38af1d9029e26ce0199778e25f93dee0","score":"0.5167507","text":"def pred_from_arr():\n\n global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n\n with tf.name_scope(\"Input\"):\n x = tf.placeholder(tf.float32, [None,156,156,100])\n y = tf.placeholder(tf.int32, [None,156,156,100])\n keepprob = tf.placeholder(tf.float32)\n is_training = tf.placeholder(tf.bool)\n loss, logits, conv1, upsamp1, conv_decode1, conv2, conv4, upsamp2, upsamp4, conv_decode2, conv_decode4 = model5.inference(x,y, keepprob, is_training)\n\n learning_rate = 0.05\n\n\n pred = tf.cast(tf.argmax(tf.nn.softmax(logits), -1), tf.int32)\n\n\n\n with tf.name_scope(\"Train\"):\n grad_op = tf.train.AdamOptimizer(learning_rate)\n train_op = grad_op.minimize(loss, global_step=global_step)\n\n tf.summary.scalar(\"cost\", loss)\n tf.summary.histogram('histogram_loss', loss)\n summary_op = tf.summary.merge_all()\n saver=tf.train.Saver()\n init_op=tf.global_variables_initializer()\n def parse_image(image, y_size, x_size, z_size):\n image = image[0:x_size, 0:y_size, 0:z_size]\n num_patches_per_x = image.shape[0] // 156\n num_patches_per_y = image.shape[1] // 156\n num_patches_per_z = image.shape[2] // 100\n x_size_crop = num_patches_per_x * 156\n y_size_crop = num_patches_per_y * 156\n z_size_crop = num_patches_per_z * 100\n image = image[0:x_size_crop, 0:y_size_crop, 0:z_size_crop]\n patches = image.reshape(num_patches_per_x, 156,\n num_patches_per_y, 156, num_patches_per_z, 100)\n patches = patches.transpose(0,2,4, 1,3,5)\n patches = patches.reshape(num_patches_per_x * num_patches_per_y * num_patches_per_z,\n 156, 156, 100)\n return patches\n sess = tf.Session()\n init_op.run(session=sess) \n ckpt = tf.train.get_checkpoint_state(os.path.dirname('~/pytorch-geo-intro/checkpoint'))\n # if that checkpoint exists, restore from checkpoint\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path)\n\n \n test = pickle.load( open( \"dw.p\", \"rb\" ) )\n m=8.529666e-05\n s=0.0065450026\n \n test=(test-m)/s\n test=parse_image(test, 312, 312, 100)\n test_data=test\n\n test_images = np.zeros([4, 156, 156, 100])\n\n null_labels_test = np.zeros_like(test_images).astype(np.int32)\n output_test=None\n for i in range(4):\n test_images[i,:,:,:]=test_data[i,:,:,:]\n\n num_sample_generate = 300\n pred_tot = []\n var_tot = []\n prob_tot=[]\n\n test_output = sess.run(tf.nn.softmax(logits), feed_dict={x: test_images, y: null_labels_test, keepprob:1, is_training:True})\n if output_test is not None:\n output_test = np.concatenate((output_test, test_output), axis=0)\n else:\n output_test = test_output\n\n\n for image_batch, label_batch in zip(test_images,null_labels_test):\n image_batch = np.reshape(image_batch,[1,156,156,100])\n label_batch = np.reshape(label_batch,[1,156,156,100])\n prob_iter_tot = []\n pred_iter_tot = []\n \n for iter_step in range(num_sample_generate):\n prob_iter_step = sess.run(tf.nn.softmax(logits), feed_dict = {x: image_batch, y: label_batch, keepprob:0.7, is_training:True}) \n prob_iter_tot.append(prob_iter_step)\n pred_iter_tot.append(np.reshape(np.argmax(prob_iter_step,axis = -1),[-1]))\n\n \n prob_mean = np.nanmean(prob_iter_tot,axis = 0)\n prob_variance = np.var(prob_iter_tot, axis = 0)\n pred = np.reshape(np.argmax(prob_mean,axis = -1),[-1]) \n pred = np.reshape(pred,[156,156,100])\n prob=prob_mean[0,:,:,:,1]\n chan_var=prob_variance[0,:,:,:,1]\n prob_variance=np.reshape(prob_variance,[156*156*100,2])\n variance=np.nanmean(prob_variance,axis=1)\n variance=np.reshape(variance,(156,156,100))\n pred_tot.append(prob)\n prob_tot.append(pred)\n var_tot.append(chan_var)\n pred_tot=np.array(pred_tot)\n\n var_tot=np.array(var_tot)\n prob=np.concatenate((np.concatenate((pred_tot[0,:,:,:],pred_tot[1,:,:,:]),axis=1),np.concatenate((pred_tot[2,:,:,:],pred_tot[3,:,:,:]),axis=1)),axis=0)\n var=np.concatenate((np.concatenate((var_tot[0,:,:,:],var_tot[1,:,:,:]),axis=1),np.concatenate((var_tot[2,:,:,:],var_tot[3,:,:,:]),axis=1)),axis=0)\n return 'Hello'","title":""},{"docid":"e4c47fe71ea1258b050521d2ef351cab","score":"0.51613176","text":"def __init__(self, env, params):\n super(AgentA2CVae, self).__init__(params)\n\n self.best_avg_reward = -sys.float_info.max\n\n num_actions = env.action_space.n\n\n global_step = tf.train.get_or_create_global_step()\n\n input_shape = list(env.observation_space.shape)\n input_shape = [None] + input_shape # add batch dimension\n self.observations = tf.placeholder(tf.float32, shape=input_shape)\n\n regularizer = tf.contrib.layers.l2_regularizer(scale=1e-6)\n\n self.memory = ReplayMemory()\n\n if self.params.use_vae:\n self.autoencoder = VAE(self.observations, params.num_latent)\n else:\n self.autoencoder = Autoencoder(self.observations, params.num_latent)\n\n self.sampled_embeddings = tf.placeholder(tf.float32, [None, self.autoencoder.num_latent])\n\n self.policy = Policy(self.sampled_embeddings, num_actions, regularizer)\n\n self.selected_actions = tf.placeholder(tf.int32, [None]) # action selected by the policy\n self.value_estimates = tf.placeholder(tf.float32, [None])\n self.discounted_rewards = tf.placeholder(tf.float32, [None]) # estimate of total reward (rollout + value)\n\n advantages = self.discounted_rewards - self.value_estimates\n\n # negative logarithms of the probabilities of actions\n # softmax turns action logits into a probability distribution over actions\n # cross entropy with true distribution (one-hot vector with 1 for action taken and 0 everywhere else)\n # will give h(p,q) = -sum_over_actions(p(action) * log q(action)) = -p(taken) * log q(taken) = -log q(taken)\n neglogp_actions = tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=self.policy.actions, labels=self.selected_actions,\n )\n\n # maximize probabilities of actions that give high advantage\n action_loss = tf.reduce_mean(advantages * neglogp_actions)\n\n # penalize for inaccurate value estimation\n value_loss = tf.losses.mean_squared_error(self.discounted_rewards, self.policy.value)\n value_loss = self.params.value_loss_coeff * value_loss\n\n # penalize the agent for being \"too sure\" about it's actions (to prevent converging to the suboptimal local\n # minimum too soon)\n entropy_loss = -tf.reduce_mean(self.policy.actions_prob_distribution.entropy())\n entropy_loss_coeff = tf.train.exponential_decay(\n self.params.initial_entropy_loss_coeff, tf.cast(global_step, tf.float32), 50.0, 0.95, staircase=True,\n )\n entropy_loss_coeff = tf.maximum(entropy_loss_coeff, 0.02)\n entropy_loss = entropy_loss_coeff * entropy_loss\n\n a2c_loss = action_loss + entropy_loss + value_loss\n regularization_loss = tf.losses.get_regularization_loss()\n loss = regularization_loss + a2c_loss\n\n # training\n autoenc_optimizer = tf.train.AdamOptimizer(learning_rate=self.params.enc_learning_rate)\n self.train_enc = autoenc_optimizer.minimize(self.autoencoder.loss)\n a2c_optimizer = tf.train.AdamOptimizer(learning_rate=self.params.a2c_learning_rate)\n self.train_a2c = a2c_optimizer.minimize(loss, global_step=global_step)\n\n # summaries for the agent and the training process\n with tf.name_scope('agent_summary'):\n tf.summary.histogram('actions', self.policy.actions)\n tf.summary.scalar('action_avg', tf.reduce_mean(tf.to_float(self.policy.act)))\n tf.summary.scalar('advantages', tf.reduce_mean(advantages))\n tf.summary.scalar('value', tf.reduce_mean(self.policy.value))\n\n tf.summary.histogram('selected_actions', self.selected_actions)\n tf.summary.scalar('selected_action_avg', tf.reduce_mean(tf.to_float(self.selected_actions)))\n\n tf.summary.scalar('policy_entropy', tf.reduce_mean(self.policy.actions_prob_distribution.entropy()))\n tf.summary.scalar('entropy_coeff', entropy_loss_coeff)\n\n tf.summary.scalar('action_loss', action_loss)\n tf.summary.scalar('value_loss', value_loss)\n tf.summary.scalar('entropy_loss', entropy_loss)\n tf.summary.scalar('a2c_loss', a2c_loss)\n tf.summary.scalar('regularization_loss', regularization_loss)\n tf.summary.scalar('loss', loss)\n\n self.a2c_summaries = tf.summary.merge_all()\n\n logger.info('Total parameters in the model: %d', count_total_parameters())\n\n summary_dir = ensure_dir_exists(join(summaries_dir(), self.params.experiment_name))\n self.summary_writer = tf.summary.FileWriter(summary_dir)\n self.saver = tf.train.Saver(max_to_keep=3)","title":""},{"docid":"1a71cf48bca6b5c8663bf9c7e08d44b6","score":"0.5158724","text":"def __init__(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7,\n hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0,\n dtype=np.float32):\n self.params = {}\n self.reg = reg\n self.dtype = dtype\n \n ############################################################################\n # TODO: Initialize weights and biases for the three-layer convolutional #\n # network. Weights should be initialized from a Gaussian with standard #\n # deviation equal to weight_scale; biases should be initialized to zero. #\n # All weights and biases should be stored in the dictionary self.params. #\n # Store weights and biases for the convolutional layer using the keys 'W1' #\n # and 'b1'; use keys 'W2' and 'b2' for the weights and biases of the #\n # hidden affine layer, and keys 'W3' and 'b3' for the weights and biases #\n # of the output affine layer. #\n ############################################################################\n C, H, W = input_dim\n #used for cnn_relu_maxpool\n self.params['W1']=np.random.normal(0,weight_scale,(num_filters,C,filter_size,filter_size))\n self.params['b1']=np.zeros(num_filters)\n #used for affine\n self.params['W2']=np.random.normal(0,weight_scale,(num_filters*H/2*W/2,hidden_dim))\n self.params['b2']=np.zeros(hidden_dim)\n #used for classfication\n self.params['W3']=np.random.normal(0,weight_scale,(hidden_dim,num_classes))\n self.params['b3']=np.zeros(num_classes)\n #used for nomarlbatch\n self.params['beta1']=np.zeros(num_filters)\n self.params['gamma1']=np.ones(num_filters)\n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n\n\n self.bn_params = [{'mode': 'train'} for i in xrange(2)]\n\n\n for k, v in self.params.iteritems():\n self.params[k] = v.astype(dtype)","title":""},{"docid":"10572ce1c936bb07ccb9ae68c7648a2f","score":"0.5157911","text":"def __init__(self, output_stride, input_shape):\n\n super(ASPP, self).__init__()\n\n avg_pool_kernel = tuple(np.array(input_shape) / 16)\n\n if output_stride == 16:\n dilations = [1, 6 , 12, 18]\n elif output_stride == 8:\n dilations = [1, 12, 24, 36]\n else:\n raise NotImplementedError()\n\n self.assp1 = _ASPPModule(256, 1, dilation=dilations[0])\n self.assp2 = _ASPPModule(256, 3, dilation=dilations[1])\n self.assp3 = _ASPPModule(256, 3, dilation=dilations[2])\n self.assp4 = _ASPPModule(256, 3, dilation=dilations[3])\n\n self.global_avg_pool = tf.keras.Sequential([tf.keras.layers.AvgPool2D(avg_pool_kernel),\n tf.keras.layers.Conv2D(256, 1, use_bias=False),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.ReLU()])\n\n self.upsampling = tf.keras.layers.UpSampling2D(avg_pool_kernel, interpolation='bilinear')\n\n\n self.conv = tf.keras.layers.Conv2D(256, 1, use_bias=False)\n self.norm = tf.keras.layers.BatchNormalization()\n self.relu = tf.keras.layers.ReLU()\n self.dropout = tf.keras.layers.SpatialDropout2D(rate=0.5)","title":""},{"docid":"a43da7f38fdb886995ce90f1c6ff4f19","score":"0.51577115","text":"def tf_efficientnet_b4_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b4_ap\",\n channel_multiplier=1.4,\n depth_multiplier=1.8,\n pretrained=pretrained,\n **kwargs)\n return model","title":""},{"docid":"3d8899aa7bc858bd82cb08f698cc0967","score":"0.5154683","text":"def test_conv_layer_bprop(layer_class, do_cross_correlation=False):\n inputs = np.arange(96).reshape((2, 3, 4, 4))\n kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n if do_cross_correlation:\n kernels = kernels[:, :, ::-1, ::-1]\n biases = np.arange(2)\n grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\n outputs = np.array(\n [[[[-958., -1036., -1114.],\n [-1270., -1348., -1426.],\n [-1582., -1660., -1738.]],\n [[1707., 1773., 1839.],\n [1971., 2037., 2103.],\n [2235., 2301., 2367.]]],\n [[[-4702., -4780., -4858.],\n [-5014., -5092., -5170.],\n [-5326., -5404., -5482.]],\n [[4875., 4941., 5007.],\n [5139., 5205., 5271.],\n [5403., 5469., 5535.]]]]\n )\n true_grads_wrt_inputs = np.array(\n [[[[147., 319., 305., 162.],\n [338., 716., 680., 354.],\n [290., 608., 572., 294.],\n [149., 307., 285., 144.]],\n [[23., 79., 81., 54.],\n [114., 284., 280., 162.],\n [114., 272., 268., 150.],\n [73., 163., 157., 84.]],\n [[-101., -161., -143., -54.],\n [-110., -148., -120., -30.],\n [-62., -64., -36., 6.],\n [-3., 19., 29., 24.]]],\n [[[39., 67., 53., 18.],\n [50., 68., 32., -6.],\n [2., -40., -76., -66.],\n [-31., -89., -111., -72.]],\n [[59., 115., 117., 54.],\n [114., 212., 208., 90.],\n [114., 200., 196., 78.],\n [37., 55., 49., 12.]],\n [[79., 163., 181., 90.],\n [178., 356., 384., 186.],\n [226., 440., 468., 222.],\n [105., 199., 209., 96.]]]])\n layer = layer_class(\n num_input_channels=kernels.shape[1],\n num_output_channels=kernels.shape[0],\n input_height=inputs.shape[2],\n input_width=inputs.shape[3],\n kernel_height=kernels.shape[2],\n kernel_width=kernels.shape[3]\n )\n layer.params = [kernels, biases]\n layer_grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)\n assert layer_grads_wrt_inputs.shape == true_grads_wrt_inputs.shape, (\n 'Layer bprop returns incorrect shaped array. '\n 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n .format(true_grads_wrt_inputs.shape, layer_grads_wrt_inputs.shape)\n )\n assert np.allclose(layer_grads_wrt_inputs, true_grads_wrt_inputs), (\n 'Layer bprop does not return correct values. '\n 'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n .format(true_grads_wrt_inputs, layer_grads_wrt_inputs,\n layer_grads_wrt_inputs - true_grads_wrt_inputs)\n )\n return True","title":""},{"docid":"18a0d2a4065dffffbc12f9eb98d94ed7","score":"0.5152247","text":"def index_adam_small_range(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\n weight_decay: float, eps: float, extra_small: bool):\n\n for i, param in enumerate(params):\n\n grad = grads[i]\n exp_avg = exp_avgs[i]\n exp_avg_sq = exp_avg_sqs[i]\n step = state_steps[i]\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n\n if weight_decay != 0:\n grad = grad.add(param, alpha=weight_decay)\n\n # Decay the first and second moment running average coefficient\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\n # Use the max. for normalizing running avg. of gradient\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\n else:\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n step_size = lr / bias_correction1\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\n if extra_small and len(param.shape) > 2: # Conv2D weight\n for idx_dim0, param_dim0 in enumerate(param):\n for idx_dim1, param_dim1 in enumerate(param_dim0):\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\n param_dim1.view(-1)[torch.argsort(param_new[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\n elif len(param.shape) > 1: # weight\n for idx_dim0, param_dim0 in enumerate(param):\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\n param_dim0.view(-1)[torch.argsort(param_new[idx_dim0].view(-1))] = param_dim0_tmp\n else: # bias\n param_tmp, _ = torch.sort(param.view(-1))\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp","title":""},{"docid":"a8670f2ecfc20fb1e1bbad12bf240ef0","score":"0.51521254","text":"def __init__(self, input_dim, output_dim, growth_factor=2):\n super().__init__() \n self.attn = nn.Linear((input_dim * 3 * growth_factor), output_dim)\n self.v = nn.Parameter(torch.rand(output_dim))","title":""},{"docid":"fea7e33fec43ee551722525546909b51","score":"0.5148238","text":"def get_model(point_cloud, point_cloud_kernel,point_cloud_all, \n Weights_f1_1, Weights_f1_2, Weights_f1_3, biases_f1_1, biases_f1_2, biases_f1_3,\n Weights_f2_1, Weights_f2_2, Weights_f2_3, biases_f2_1, biases_f2_2, biases_f2_3,\n Weights_f3_1, Weights_f3_2, Weights_f3_3, biases_f3_1, biases_f3_2, biases_f3_3,\n Weights_all1_1, Weights_all1_2, Weights_all1_3, biases_all1_1, biases_all1_2, biases_all1_3,\n is_training = True):\n if is_training == True:\n dropout_prob_conv = 0.1\n dropout_prob_f = 0.2\n else:\n dropout_prob_conv = 0.0\n dropout_prob_f = 0.0\n \n\n batch_size = point_cloud.get_shape()[0].value\n PGK = point_cloud.get_shape()[1].value\n PGN = point_cloud.get_shape()[2].value\n ##allnet1\n point_cloud_all = tf.reshape(point_cloud_all, [batch_size, 1, PGK, -1])\n allnet1_x = tf.slice(point_cloud, [0, 0, 0, 0], [-1, -1,-1, 2])\n allnet1_y = tf.slice(point_cloud, [0, 0, 0, 2], [-1, -1,-1, 1])\n allf1net = get_feature( \"all_feature1\", allnet1_x, allnet1_y, Weights_all1_1, Weights_all1_2, Weights_all1_3, biases_all1_1, biases_all1_2, biases_all1_3)\n allf1net = tf.reshape(allf1net, [batch_size, PGK, -1])\n feature_all = tf.reduce_sum(allf1net, 1, keep_dims=False)\n feature_all = tf.cast(feature_all, tf.float32)\n \n ##net1\n net1_x = tf.slice(point_cloud, [0, 0, 0, 0], [-1, -1,-1, 2])\n net1_y = tf.slice(point_cloud, [0, 0, 0, 2], [-1, -1,-1, 1])\n \n f1net = get_feature( \"feature1\", net1_x, net1_y, Weights_f1_1, Weights_f1_2, Weights_f1_3, biases_f1_1, biases_f1_2, biases_f1_3)\n f1net = tf.reshape(f1net, [batch_size, PGK, -1])\n feature_1 = tf.reduce_sum(f1net, 1, keep_dims=False)\n feature_1 = tf.cast(feature_1, tf.float32)\n \n ##net2\n f2net_input, _ = point_group(\"point group2\", point_cloud_kernel,f1net,32,PGN)\n \n net2_x = tf.slice(f2net_input, [0, 0, 0, 0], [-1, -1,-1, 63])\n net2_y = tf.slice(f2net_input, [0, 0, 0, 63], [-1, -1,-1, 1])\n \n f2net = get_feature( \"feature2\", net2_x, net2_y, Weights_f2_1, Weights_f2_2, Weights_f2_3, biases_f2_1, biases_f2_2, biases_f2_3)\n \n f2net = tf.reshape(f2net, [batch_size, 32, -1])\n feature_2 = tf.reduce_sum(f2net, 1, keep_dims=False)\n feature_2 = tf.cast(feature_2, tf.float32)\n \n ##net3\n f3net_input = tf.reshape(f2net, [batch_size, 1, 32, -1])\n net3_x = tf.slice(f3net_input, [0, 0, 0, 0], [-1, -1,-1, 63])\n net3_y = tf.slice(f3net_input, [0, 0, 0, 63], [-1, -1,-1, 1])\n \n f3net = get_feature( \"feature3\", net3_x, net3_y, Weights_f3_1, Weights_f3_2, Weights_f3_3, biases_f3_1, biases_f3_2, biases_f3_3)\n \n f3net = tf.reshape(f3net, [batch_size, 1, -1])\n feature_3 = tf.reduce_sum(f3net, 1, keep_dims=False)\n feature_3 = tf.cast(feature_3, tf.float32)\n '''\n for y_num in range (64): \n if y_num == 0:\n net1_y_num = net1_y\n else : \n net1_y_num = tf.concat([net1_y_num,net1_y],axis=3)\n\n net1 = conv2d(\"conv1_1\", input=net1_x, activation=selu, ksize=1, f_in=2, f_out=128)\n net1 = conv2d(\"conv1_2\", input=net1, activation=selu, ksize=1, f_in=128, f_out=64)\n #norm\n net1_norm = tf.sqrt(tf.reduce_sum(tf.square(net1), axis=2)) \n net1_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net1_y_num), axis=2)) \n #cos\n net1cos = tf.reduce_sum(tf.multiply(net1, net1_y_num), axis=2) \n net1cos_norm = net1cos / (net1_norm * net1_y_norm) \n net1cos_norm = selu(net1cos_norm)\n #feature1 \n Feature1 = tf.reduce_mean(net1cos_norm, 1, keep_dims=False)\n Feature1 = tf.reshape(Feature1, [batch_size, -1])\n \n net1_f = tf.reshape(net1cos_norm, [batch_size, PGK, -1])\n net2_2, net_idx2 = point_group(\"group1\", point_cloud_kernel,net1_f,32,PGN)\n \n ##net2\n net2_x = tf.slice(net2_2, [0, 0, 0, 0], [-1, -1,-1, 63])\n net2_y = tf.slice(net2_2, [0, 0, 0, 63], [-1, -1,-1, 1])\n for y_num in range (64): \n if y_num == 0:\n net2_y_num = net2_y\n else : \n net2_y_num = tf.concat([net2_y_num,net2_y],axis=3)\n\n net2 = conv2d(\"conv2_1\", input=net2_x, activation=selu, ksize=1, f_in=63, f_out=128)\n net2 = conv2d(\"conv2_2\", input=net2, activation=selu, ksize=1, f_in=128, f_out=64)\n #norm\n net2_norm = tf.sqrt(tf.reduce_sum(tf.square(net2), axis=2)) \n net2_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net2_y_num), axis=2)) \n #cos\n net2cos = tf.reduce_sum(tf.multiply(net2, net2_y_num), axis=2) \n net2cos_norm = net2cos / (net2_norm * net2_y_norm) \n net2cos_norm = selu(net2cos_norm)\n #feature2 \n Feature2 = tf.reduce_mean(net2cos_norm, 1, keep_dims=False)\n Feature2 = tf.reshape(Feature2, [batch_size, -1]) \n \n net2_f = tf.reshape(net2cos_norm, [batch_size, 1, 32, -1])\n \n ##net3\n net3_x = tf.slice(net2_f, [0, 0, 0, 0], [-1, -1,-1, 63])\n net3_y = tf.slice(net2_f, [0, 0, 0, 63], [-1, -1,-1, 1])\n for y_num in range (64): \n if y_num == 0:\n net3_y_num = net3_y\n else : \n net3_y_num = tf.concat([net3_y_num,net3_y],axis=3)\n\n net3 = conv2d(\"conv3_1\", input=net3_x, activation=selu, ksize=1, f_in=63, f_out=128)\n net3 = conv2d(\"conv3_2\", input=net3, activation=selu, ksize=1, f_in=128, f_out=64)\n #norm\n net3_norm = tf.sqrt(tf.reduce_sum(tf.square(net3), axis=2)) \n net3_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net3_y_num), axis=2)) \n #cos\n net3cos = tf.reduce_sum(tf.multiply(net3, net3_y_num), axis=2) \n net3cos_norm = net3cos / (net3_norm * net3_y_norm)\n net3cos_norm = selu(net3cos_norm)\n #feature3\n Feature3 = tf.reduce_mean(net3cos_norm, 1, keep_dims=False)\n Feature3 = tf.reshape(Feature3, [batch_size, -1])\n '''\n net = tf.concat([feature_all,feature_1,feature_2,feature_3],axis=1)\n dim = net.get_shape()[1].value\n\n net = fc('fully_connected1', input=net, activation=selu, n_in=dim, n_out=256, stddev=0.04, bias_init=0.1,\n weight_decay=0.004)\n net = dropout_selu(net, dropout_prob_f,training=is_training)\n net = fc('fully_connected2', input=net, activation=selu, n_in=256, n_out=128, stddev=0.04, bias_init=0.1,\n weight_decay=0.004)\n net = dropout_selu(net, dropout_prob_f,training=is_training)\n\n weights = _variable_with_weight_decay('weights', [128, 40], stddev=1 / 128.0, activation=\"selu\", wd=0.0)\n biases = tf.get_variable(name='biases', shape=[40], initializer=tf.constant_initializer(0.0), dtype=tf.float32)\n net = tf.add(tf.matmul(net, weights), biases, name='output')\n \n return net, f2net_input, f3net_input","title":""},{"docid":"7e1d87b640941fd8030c7c2b77a8ae62","score":"0.514711","text":"def AE_model(init_lr=0.1,\n decay_rate=0.95,\n batchsize=1,\n epochs=1,\n display_steps=10000,\n num_hidden=20,\n data={},\n key='users'):\n \n # Some variables/parameters declaration\n learning_rate = tf.placeholder(tf.float32, shape=[])\n steps = int(settings.num_data[key]/batchsize) + 1\n \n with tf.name_scope('Parameters'):\n X = tf.placeholder('float', [None, settings.num_feat[key]])\n weights = tf.Variable(tf.random_normal([settings.num_feat[key], num_hidden],mean=0,stddev=0.5))\n \n # Predictions and labels definition\n with tf.name_scope('Model'):\n predictions = decoder(encoder(X,weights),weights)\n preds = tf.round(predictions) # round to integers the predictions (-> accuracy calc.)\n with tf.name_scope('Target'):\n labels = X\n\n # Define accuracy and objective\n with tf.name_scope('Loss'):\n loss = tf.reduce_mean(tf.reduce_max(tf.abs(labels - predictions), keep_dims=True, axis=1))\n with tf.name_scope('Accuracy'):\n results = tf.reduce_sum(tf.cast(tf.equal(labels,preds), tf.int32), keep_dims=True, axis=1)\n accuracy = tf.reduce_sum(tf.cast(tf.equal(results, settings.num_feat[key]), tf.int32), axis=0) / tf.size(results)\n\n # Define optimization\n with tf.name_scope('SGD'):\n optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n \n # Create a Saver to save NN weights\n saver = tf.train.Saver([weights])\n\n # Add variables to be written to a summary\n tf.summary.scalar('loss', loss)\n summary = tf.summary.merge_all()\n \n # Create a Python dict to hold various TensorFlow objects, and Training parameters\n tfdict = {'loss': loss,\n 'accuracy': accuracy,\n 'optimizer': optimizer,\n 'summary': summary,\n 'saver': saver,\n 'X': X,\n 'learning_rate': learning_rate,\n 'weights': weights}\n \n params = {'init_lr': init_lr,\n 'decay_rate': decay_rate,\n 'batchsize': batchsize,\n 'epochs': epochs,\n 'display_steps': display_steps,\n 'steps': steps}\n \n # train the auto-encoder to generate weights\n if settings.ML_mode['AE'] == True:\n print('Start training Auto-Encoder for %s set' % key)\n AE_training(params = params,\n data = data,\n tfobjects = tfdict,\n key = key)\n # Load weights from disk\n else:\n print('Use weights for %s A-E from the disk' % key)\n AE_load(tfobjects = tfdict,\n key = key)\n \n return None","title":""},{"docid":"85c6ef6b5c616e01c5bce44c7b924331","score":"0.51464427","text":"def inference(self, image, keep_prob, istraining):\n with tf.variable_scope('AlexNet') as scope:\n with tf.variable_scope('conv1') as scope:\n temp_conv = self.conv2d(\"conv1\", image, kernel_size=[11, 11, 3, 96], stride=4,\n istraining=istraining, padding='VALID')\n\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\n\n with tf.variable_scope('conv2') as scope:\n temp_conv = self.conv2d(\"conv2\", temp_pool, kernel_size=[5, 5, 96, 256], stride=1,\n istraining=istraining)\n\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\n\n with tf.variable_scope('conv3') as scope:\n temp_conv = self.conv2d(\"conv3\", temp_pool, kernel_size=[3, 3, 256, 384], stride=1,\n istraining=istraining)\n\n with tf.variable_scope('conv4') as scope:\n temp_conv = self.conv2d(\"conv4\", temp_conv, kernel_size=[3, 3, 384, 384], stride=1,\n istraining=istraining)\n\n with tf.variable_scope('conv5') as scope:\n temp_conv = self.conv2d(\"conv5\", temp_conv, kernel_size=[3, 3, 384, 256], stride=1,\n istraining=istraining)\n\n temp_pool = self.max_pool(temp_conv, [2, 2], 2, padding='VALID')\n # fc to conv\n fc6 = self.local('fc6', temp_pool, kernel_size=[6, 6, 256, 4096], stride=1, istraining=istraining)\n fc6_dropout = tf.nn.dropout(fc6, keep_prob=keep_prob)\n\n fc7 = self.local('fc7', fc6_dropout, kernel_size=[1, 1, 4096, 4096], stride=1, istraining=istraining)\n fc7_dropout = tf.nn.dropout(fc7, keep_prob=keep_prob)\n\n fc8 = self.local('fc8', fc7_dropout, kernel_size=[1, 1, 4096, self.num_classes], stride=1,\n istraining=istraining)\n fc8_squeeze = tf.squeeze(fc8)\n return fc8_squeeze","title":""},{"docid":"72b3e2da9c8c18ab8afb572598248560","score":"0.5141961","text":"def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability})","title":""},{"docid":"79a75bdb7934836138a9f8468076af10","score":"0.5141666","text":"def neural_net_predict_dsc(params, inputs):\r\n inpW, inpb = params[0]\r\n inputs = relu(np.dot(inputs, inpW) + inpb)\r\n for W, b in params[1:-1]:\r\n outputs = np.dot(inputs, W) + b\r\n # outputs = batch_normalize(np.dot(inputs, W) + b)\r\n inputs = leaky_relu(outputs)\r\n # inputs = relu(outputs)\r\n outW, outb = params[-1]\r\n outputs = np.dot(inputs, outW) + outb\r\n return outputs","title":""},{"docid":"c7e4234b98ecb63071f9325295795948","score":"0.51411444","text":"def __init__(self, feat_dim, hid_dim, att_head_num=1):\n super(StructAttention, self).__init__()\n self.W1 = torch.nn.Linear(feat_dim, hid_dim, bias=False)\n nn.init.xavier_normal_(self.W1.weight)\n\n self.W2 = torch.nn.Linear(hid_dim, att_head_num, bias=False)\n nn.init.xavier_normal_(self.W2.weight)\n\n self.att_head_num = att_head_num","title":""},{"docid":"e0fe30f65f071856f693be598abc6f0e","score":"0.51366657","text":"def forward_backward_prop(data, labels, params, dimensions):","title":""},{"docid":"823e6efe3d519dfac6e466c02c74af00","score":"0.5129472","text":"def model(images):\n net = tf.layers.dense(images, 500, activation=tf.nn.relu)\n net = tf.layers.dense(net, 500, activation=tf.nn.relu)\n net = tf.layers.dense(net, 10, activation=None)\n return net","title":""},{"docid":"b388a0b5ee56e6fff18af6fda40c4054","score":"0.5118143","text":"def deepnn(x, is_training):\n # Reshape to use within a convolutional neural net.\n # Last dimension is for \"features\" - there is only one here, since images are\n # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.\n with tf.name_scope('reshape'):\n x_image = tf.reshape(x, [-1, 1024,1, 1])\n\n # First convolutional layer - maps one grayscale image to 5 feature maps.\n # The third dimension of W is number of input channels, the last is the \n # number of output channels\n with tf.name_scope('conv1'):\n num_feature1 = 20\n W_conv1 = weight_variable([16, 1, 1, num_feature1])\n b_conv1 = bias_variable([num_feature1])\n BN_conv1 = batch_normalization(\n tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1],\n padding='SAME') + b_conv1, is_training=is_training)\n h_conv1 = tf.nn.relu(BN_conv1)\n\n # Pooling layer - downsamples by 2X.\n with tf.name_scope('pool1'):\n h_pool1 = max_pool(h_conv1)\n\n # Second convolutional layer -- maps 5 feature maps to 5\n with tf.name_scope('conv2'):\n num_feature2 = 40\n W_conv2 = weight_variable([5, 1, num_feature1, num_feature2])\n b_conv2 = bias_variable([num_feature2])\n BN_conv2 = batch_normalization(\n conv2d(h_pool1, W_conv2) + b_conv2, is_training=is_training)\n h_conv2 = tf.nn.relu(BN_conv2)\n\n # Second pooling layer.\n with tf.name_scope('pool2'):\n h_pool2 = max_pool(h_conv2)\n \n with tf.name_scope('conv3'):\n num_feature3 = 40\n W_conv3 = weight_variable([5, 1, num_feature2, num_feature3])\n b_conv3 = bias_variable([num_feature3])\n BN_conv3 = batch_normalization(\n conv2d(h_pool2, W_conv3) + b_conv3, is_training=is_training)\n h_conv3 = tf.nn.relu(BN_conv3)\n\n # Second pooling layer.\n with tf.name_scope('pool3'):\n h_pool3 = max_pool(h_conv3)\n \n # Fully connected layer 1 -- after 2 round of downsampling, our 300x200 image\n # is down to 75x50x64 feature maps -- maps this to 256 features.\n with tf.name_scope('fc1'):\n out_size = 2048\n W_fc1 = weight_variable([128 * num_feature3, out_size])\n b_fc1 = bias_variable([out_size])\n \n h_pool2_flat = tf.reshape(h_pool3, [-1, 128*num_feature3])\n BN_fc1 = batch_normalization(\n tf.matmul(h_pool2_flat, W_fc1) + b_fc1, is_training=is_training)\n h_fc1 = tf.nn.relu(BN_fc1)\n\n # Dropout - controls the complexity of the model, prevents co-adaptation of\n # features.\n with tf.name_scope('dropout'):\n keep_prob = tf.placeholder(tf.float32)\n h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n\n # Map the 256 features to 3 classes, one for each digit\n with tf.name_scope('fc2'):\n W_fc2 = weight_variable([out_size, 3])\n b_fc2 = bias_variable([3])\n \n y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n return y_conv, keep_prob","title":""},{"docid":"02181222373f2658dae82ae67e07a1b6","score":"0.5117104","text":"def forward(self, state):\n x = F.relu(self.fc1(state))\n value = F.relu(self.fc_value(x))\n adv = F.relu(self.fc_adv(x))\n\n value = self.value(value)\n adv = self.adv(adv)\n\n advAverage = torch.mean(adv, dim=1, keepdim=True)\n return value + adv - advAverage","title":""},{"docid":"08580f2c201a10f57567a1b780e914f4","score":"0.5117088","text":"def train(self):\n\n def weight_var(shape):\n return tf.Variable(tf.truncated_normal(shape, stddev=0.1))\n\n def bias_var(shape):\n return tf.Variable(tf.constant(0.1, shape=shape))\n\n def conv3d(input, weights):\n return tf.nn.conv3d(input, weights, strides=[1,1,1,1,1], padding='SAME')\n\n def max_pool3d(input, k, s):\n return tf.nn.max_pool3d(input, ksize=[1,k,k,k,1], strides=[1,s,s,s,1], padding='SAME')\n\n def batch_norm(tensor):\n #m, v = tf.nn.moments(tensor, [0])\n m, v = tf.nn.moments(tensor, [0,1,2,3])\n return tf.nn.batch_normalization(tensor, m, v, None, None, 1e-12)\n \n graph = tf.Graph()\n\n with graph.as_default():\n # Input data.\n #tf_train_dataset = tf.placeholder(tf.float32,\n # shape=(batch_size, self.image_size_x,\n # self.image_size_y, self.image_size_z,1))\n train_shape = self.train_dataset.shape[1:]\n tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, train_shape[0], train_shape[1],\n train_shape[2], train_shape[3]))\n tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n tf_valid_dataset = tf.constant(self.valid_dataset, dtype=tf.float32)\n tf_test_dataset = tf.constant(self.test_dataset, dtype=tf.float32)\n\n print tf_train_dataset.get_shape(), tf_valid_dataset.get_shape(), tf_test_dataset.get_shape()\n \n # Variables.\n # Layer 1 (conv)\n weights1 = weight_var([patch_size, patch_size, patch_size, 1, out_features1])\n biases1 = bias_var([out_features1])\n # Layer 2 (conv)\n weights2 = weight_var([patch_size, patch_size, patch_size, out_features1, out_features2])\n biases2 = bias_var([out_features2])\n # Layer 3 (fully connected)\n #weights3 = weight_var([self.image_size_x // 4 * self.image_size_y // 4\n # * self.image_size_z // 4 * out_features2, num_hidden])\n weights3 = weight_var([4032, num_hidden])\n biases3 = bias_var([num_hidden])\n # Layer 4 (output)\n weights4 = weight_var([num_hidden, num_labels])\n biases4 = bias_var([num_labels])\n\n # Model.\n def model(data, dropout=False):\n # do dropout on the input data itself i.e. blankout \n #data = tf.nn.dropout(data, 0.5)\n\n # batch_normalization\n #data = batch_norm(data)\n \n # Layer 1 (conv): data = `patch_size` x `patch_size` x `image_size_z`\n print \"conv1.shape (before conv)\", data.get_shape()\n #pdb.set_trace()\n conv1 = conv3d(data, weights1) # conv = `patch_size` x `patch_size` x `out_features1`\n conv1 = batch_norm(conv1)\n conv1 = tf.nn.relu(conv1 + biases1)\n print \"conv1.shape (after conv)\", conv1.get_shape()\n #conv1 = max_pool_kxk(conv1, 2) # pool = `patch_size`/2 x `patch_size`/2 x `out_features1`\n conv1 = max_pool3d(conv1, 3,3)\n print \"conv1.shape (after pool)\", conv1.get_shape()\n if dropout:\n conv1 = tf.nn.dropout(conv1, dropout)\n\n # Layer 2 (conv): data = `patch_size`/2 x `patch_size`/2 x `out_features1`\n print \"conv2.shape (before conv)\", conv1.get_shape()\n conv2 = conv3d(conv1, weights2) # conv = `patch_size` x `patch_size` x `out_features2`\n conv2 = batch_norm(conv2)\n conv2 = tf.nn.relu(conv2 + biases2)\n print \"conv2.shape (after conv)\", conv2.get_shape()\n conv2 = max_pool3d(conv2, 3, 3) # pool = `patch_size`/2/2 x `patch_size`/2/2 x `out_features2`\n print \"conv2.shape (after pool)\", conv2.get_shape()\n if dropout:\n conv2 = tf.nn.dropout(conv2, dropout)\n\n # Layer 3 (fully connected): data = `patch_size`/2/2 x `patch_size`/2/2 x `out_features2`\n shape = conv2.get_shape().as_list()\n reshape = tf.reshape(conv2, [-1, shape[1] * shape[2] * shape[3] * shape[4]]) # reshape to fit dense layer input\n print \"dense1.shape (input)\", reshape.get_shape()\n print \"dense1.shape (weights)\", weights3.get_shape()\n dense1 = tf.nn.relu(tf.matmul(reshape, weights3) + biases3)\n #dense1 = batch_norm(dense1)\n if dropout:\n dense1 = tf.nn.dropout(dense1, dropout)\n\n # Layer 4 (output)\n return tf.matmul(dense1, weights4) + biases4\n\n # Training computation.\n logits = model(tf_train_dataset)\n self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n\n # Optimizer.\n #self.optimizer = tf.train.GradientDescentOptimizer(0.00001).minimize(self.loss)\n self.optimizer = tf.train.AdamOptimizer(0.00005).minimize(self.loss)\n \n # Predictions for the training, validation, and test data.\n self.train_prediction = tf.nn.softmax(logits)\n correct_prediction = tf.equal(self.train_prediction, tf_train_labels)\n train_acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.scalar_summary('minibatch accuracy', train_acc)\n merged = tf.merge_all_summaries()\n \n self.valid_prediction = tf.nn.softmax(model(tf_valid_dataset, False))\n self.test_prediction = tf.nn.softmax(model(tf_test_dataset, False))\n\n with tf.Session(graph=graph) as session:\n #f_log = open('cropping.log' , 'wb')\n train_writer = tf.train.SummaryWriter('/home/ycho1/Desktop/', session.graph)\n tf.initialize_all_variables().run()\n print 'initialization done'\n for step in range(num_steps):\n offset = (step * batch_size) % (self.train_labels.shape[0] - batch_size)\n batch_data = self.train_dataset[offset:(offset + batch_size), :, :, :]\n #batch_data = preprocess(batch_data)\n batch_labels = self.train_labels[offset:(offset + batch_size), :]\n feed_dict = {tf_train_dataset: batch_data, tf_train_labels: batch_labels}\n #_ , l, predictions = session.run([self.optimizer, self.loss, self.train_prediction], feed_dict=feed_dict)\n summary, l, predictions = session.run([merged, self.loss, self.train_prediction], feed_dict=feed_dict)\n if (step % 50 == 0):\n train_writer.add_summary(summary, step)\n print('Minibatch loss at step %d: %f' % (step, l))\n minibatch_acc = accuracy(predictions, batch_labels)\n print('Minibatch accuracy: %.1f%%' % minibatch_acc)\n val_acc = accuracy(self.valid_prediction.eval(), self.valid_labels)\n print('Validation accuracy: %.1f%%' % val_acc)\n #test_acc = accuracy(self.test_prediction.eval(), self.test_labels)\n #print('Test accuracy: %.1f%%' % test_acc)\n #f_log.write(('%d, %f, %.1f%% , %.1f%%, %.1f%% \\n' % (step, l, minibatch_acc, val_acc, test_acc)))","title":""},{"docid":"015e968060e818382274988bdf85e046","score":"0.511292","text":"def get_adagrad_trainer(self):\n batch_x = T.fmatrix('batch_x')\n #batch_x= T.matrix('batch_x', dtype=theano.config.floatX)\n batch_y = T.ivector('batch_y')\n learning_rate = T.fscalar('lr') # learning rate to use\n # compute the gradients with respect to the model parameters\n gparams = T.grad(self.mean_cost, self.params)\n \n # compute list of weights updates\n updates = OrderedDict()\n for accugrad, param, gparam in zip(self._accugrads, self.params, gparams):\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\n agrad = accugrad + gparam * gparam\n dx = - (learning_rate / T.sqrt(agrad + self._eps)) * gparam\n if self.max_norm:\n W = param + dx\n col_norms = W.norm(2, axis=0)\n desired_norms = T.clip(col_norms, 0, self.max_norm)\n updates[param] = W * (desired_norms / (1e-6 + col_norms))\n else:\n updates[param] = param + dx\n updates[accugrad] = agrad\n \n train_fn = theano.function(inputs=[theano.Param(batch_x), \n theano.Param(batch_y),\n theano.Param(learning_rate)],\n outputs=self.mean_cost,\n updates=updates,\n givens={self.x: batch_x, self.y: batch_y})\n \n return train_fn","title":""},{"docid":"015e968060e818382274988bdf85e046","score":"0.511292","text":"def get_adagrad_trainer(self):\n batch_x = T.fmatrix('batch_x')\n #batch_x= T.matrix('batch_x', dtype=theano.config.floatX)\n batch_y = T.ivector('batch_y')\n learning_rate = T.fscalar('lr') # learning rate to use\n # compute the gradients with respect to the model parameters\n gparams = T.grad(self.mean_cost, self.params)\n \n # compute list of weights updates\n updates = OrderedDict()\n for accugrad, param, gparam in zip(self._accugrads, self.params, gparams):\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\n agrad = accugrad + gparam * gparam\n dx = - (learning_rate / T.sqrt(agrad + self._eps)) * gparam\n if self.max_norm:\n W = param + dx\n col_norms = W.norm(2, axis=0)\n desired_norms = T.clip(col_norms, 0, self.max_norm)\n updates[param] = W * (desired_norms / (1e-6 + col_norms))\n else:\n updates[param] = param + dx\n updates[accugrad] = agrad\n \n train_fn = theano.function(inputs=[theano.Param(batch_x), \n theano.Param(batch_y),\n theano.Param(learning_rate)],\n outputs=self.mean_cost,\n updates=updates,\n givens={self.x: batch_x, self.y: batch_y})\n \n return train_fn","title":""},{"docid":"f041c36f80f11600af2c2d31fb74985d","score":"0.51126796","text":"def __init__(self, dataset, batch_size, name, logger, version, base_dir, num_gpus):\n Network.__init__(self, dataset, logger, version, base_dir, name)\n\n self.dataset = dataset\n self.batch_size = batch_size\n self.data_dims = dataset.data_dims\n self.LOG = logger\n self.num_gpus = num_gpus\n\n # VLAE parameters - assumes input image is square and at least a multiple of 16 (usually power of 2)\n self.vlae_levels = 4\n self.vlae_latent_dims = [3, 3, 3, 3]\n self.image_sizes = [self.data_dims[0], self.data_dims[0] // 2, \n self.data_dims[0] // 4, self.data_dims[0] // 8,\n self.data_dims[0] // 16]\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\n\n # PixelCNN parameters (most of these are hiddent in picel_cnn.pixelvae.py)\n self.two_step_pixelvae = False\n\n # SeqVAE parameters \n self.share_theta_weights = False\n self.share_phi_weights = False\n self.inference_first_step = self.inference_ladder \n self.generator_first_step = self.generator_ladder \n self.inference = self.inference_ladder \n self.generator = self.generator_ladder \n self.mc_steps = 8\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.intermediate_reconstruction = True\n self.early_stopping_mc = False\n self.early_stopping_threshold = 0.0000000001\n self.regularized_steps = range(self.mc_steps)\n self.predict_latent_code = False\n self.first_step_loss_coeff = 1.0\n self.add_improvement_maximization_loss = False\n self.add_revamped_improvement_maximization_loss = False\n self.latent_mean_clip = np.inf\n self.predict_latent_code_with_regularization = False # TEMPORARY VARIABLE FOR SOME TESTS\n self.latent_prior_stddev = 1.0\n self.use_uniform_prior = False \n self.add_noise_to_chain = False\n self.predict_generator_noise = False\n self.predict_generator_noise_as_scalar = False\n self.predict_generator_stddev_max = 1.0\n self.predict_generator_stddev_conv_layers = 5 # with 5x5 convs, this gives a receptive field of 1+4*5 = 21\n self.predict_generator_stddev_filter_sizes = [5, 5, 5, 5, 5]\n self.noise_stddevs = [0.5 ** 1, 0.5 ** 2, 0.5 ** 3, 0.5 ** 4, 0.5 ** 5, 0.5 ** 6, 0.5 ** 7, 0]\n self.max_highway_ratio = 1.0\n self.min_highway_ratio = 0.0\n self.combine_noise_method = \"concat\"\n\n # \"Flat Conv Infusion\" parameters\n self.flat_conv_layers = 6\n self.flat_conv_filter_sizes = [self.data_dims[-1], self.data_dims[-1]*2, self.data_dims[-1]*4, self.data_dims[-1]*8,\n self.data_dims[-1]*4, self.data_dims[-1]*2, self.data_dims[-1]]\n\n # Hyperparams and training params\n self.learning_rate_decay = 1.0\n self.learning_rate = 0.0002\n self.reg_coeff_rate = 5000.0 # 1 epoch = 1000\n self.latent_pred_loss_coeff = 0.001 # 0.5, and reduce mean did really well, but very slow...\n self.save_freq = 2000\n self.tb_summary_freq = 10\n self.add_debug_tb_variables = True\n self.clip_grads = True\n self.clip_grad_value = 10.0\n\n\n # Config for different netnames, where customization is needed.\n # add overides for any of the above parameters here\n\n ####\n # 1: c_pixelvae \n # 2: ^^^^^\n # 3: c_v2_diag_noise_abl_constrained_highway 11\n # 4: c_v2_revamped_imp_max_abl 2\n ####\n # rerun revamped imp max, with the coefficient change\n # rerun one step homog (with samew number of filters)\n # run homog + longer generation\n # run one step on all datasets\n # run old homog on all datasets\n # run final model on all datasets\n\n #####################\n # Single step VLAEs #\n #####################\n ## TODO ##\n if self.name == \"c_homog_one_step\": #v3\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n # self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512] # v2\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 1\n\n ##TODO4##\n elif self.name == \"m_homog_one_step\":\n self.vlae_levels = 4\n self.vlae_latent_dims = [8, 8, 8, 8]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 1\n\n elif self.name == \"s_homog_one_step\": # v1\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 1\n\n ##TODO##\n elif self.name == \"l_homog_one_step\":\n pass\n\n ##############\n # V1 - Homog #\n ##############\n elif self.name == \"c_homog_v1\": #v1\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n \n ##TODO## \n elif self.name == \"m_homog_v1\":\n pass\n\n ##TODO## \n elif self.name == \"s_homog_v1\":\n pass\n \n ##TODO## \n elif self.name == \"l_homog_v1\":\n pass\n\n\n\n ################\n # V1 - Inhomog #\n ################\n ##TODO##\n elif self.name == \"c_inhomog_v1\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n\n\n ##TODO## \n elif self.name == \"m_inhomog_v1\":\n pass\n \n ##TODO## \n elif self.name == \"s_inhomog_v1\":\n pass\n \n ##TODO## \n elif self.name == \"l_inhomog_v1\":\n pass\n\n\n\n ####################################\n # Ablations (v2) (just celeba?) #\n # - latent regularization vs none #\n # - coeff for log likelihood of x1 #\n # - improvement maximization loss #\n # - sampling through the chain #\n ####################################\n # regularized (this is v1, but with a new dependency model)\n elif self.name == \"c_homog_reg_pred_latent\": #v3 # \"c_v2_reg\"\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n \n\n # no regularization\n elif self.name == \"c_homog_no_reg_pred_latent\": # v5 # \"c_v2_no_reg_abl\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n\n\n # no reg + changed coeff regularization\n elif self.name == \"c_v2_coeff_change_abl\": #1\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.first_step_loss_coeff = 2.0\n\n # no reg + improvement max\n elif self.name == \"c_homog_no_reg_imp_max\": #v2 #\"c_v2_imp_max_abl\"\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n\n\n #NEXT\n # no reg + changed imp max loss to be an actual L2 norm\n elif self.name == \"c_v2_revamped_imp_max_abl\": #v2\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.add_improvement_maximization_loss = True\n self.add_revamped_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.latent_pred_loss_coeff = 0.01\n\n # no reg + changed imp max loss to be an actual L2 norm + changed coeff of first step, because it's important now!!\n elif self.name == \"c_v2_revamped_imp_max_abl_coeff_change\": #v1\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.add_improvement_maximization_loss = True\n self.add_revamped_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.latent_pred_loss_coeff = 0.01\n self.first_step_loss_coeff = 2.0\n\n\n\n # no reg + improvement max + changed coeff reg\n elif self.name == \"c_v2_coeff_change_and_imp_max_abl\": #v1\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_improvement_maximization_loss = True\n self.first_step_loss_coeff = 2.0\n\n # no reg + improvement max + diagonal noise\n elif self.name == \"c_v2_diag_noise_abl\": # v2\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n self.predict_generator_stddev_max = 1.0\n\n\n # same as c_v2_diag_noise_abl, but added a coeff to highway connection to for the generative model to work\n elif self.name == \"c_v2_diag_noise_abl_constrained_highway\": # v11\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n self.predict_generator_stddev_max = 1.0\n self.max_highway_ratio = 1.0\n self.min_highway_ratio = 0.3\n\n\n # no reg + improvement max + scalar noise\n elif self.name == \"c_v2_scalar_noise_abl\": # v4\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n self.predict_generator_stddev_max = 1.0\n self.predict_generator_noise_as_scalar = True\n\n\n\n ################\n # Random tests #\n ################\n\n # TODO1: test where generative samples are like 20 long rather than 8 long\n\n\n #NEXT\n elif self.name == \"c_pixelvae\":\n self.latent_mean_clip = 4.0\n self.max_highway_ratio = 0.8\n self.min_highway_ratio = 0.2\n self.two_step_pixelvae = 2\n self.mc_steps = 2\n self.generator = self.generator_pixelcnn\n self.learning_rate_decay = 0.99999\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.regularized_steps = [0]\n self.first_step_loss_coeff = 2.0\n\n ###############################\n # Best model one all datasets #\n ###############################\n ##TODO##\n elif self.name == \"c_final\":\n pass\n\n ##TODO##\n elif self.name == \"m_final\":\n pass\n\n ##TODO##\n elif self.name == \"s_final\":\n pass\n\n ##TODO##\n elif self.name == \"l_final\":\n pass\n\n\n\n #######\n # Old #\n #######\n\n elif self.name == \"c_homog_imp_max_var_pred\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n\n elif self.name == \"c_homog_no_reg_pred_latent_var_pred\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n\n elif self.name == \"c_homog_no_reg_pred_latent_var_pred_more_noise\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.regularized_steps = [0]\n self.latent_mean_clip = 32.0\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n self.predict_generator_stddev_max = 1.0\n\n # three\n elif self.name == \"s_homog_imp_max\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n\n # four\n elif self.name == \"l_homog_imp_max\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n\n # six\n elif self.name == \"c_homog_imp_max\":\n self.vlae_latent_dims = [12, 12, 12, 12]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 8\n self.predict_latent_code = True\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n\n\n # five + seven\n elif self.name == \"m_homog_imp_max_var_pred\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [8, 8, 8]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 6\n self.predict_latent_code = True\n self.add_improvement_maximization_loss = True\n self.latent_mean_clip = 32.0\n self.predict_latent_code_with_regularization = True\n self.add_noise_to_chain = True\n self.predict_generator_noise = True\n\n\n\n elif self.name == \"sequential_vae_celebA_inhomog\":\n # nothing\n pass\n\n elif self.name == \"sequential_vae_celebA_homog\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n\n elif self.name == \"sequential_vae_celebA_homog_early_stopping\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.early_stopping_mc = True\n self.mc_steps = 15\n\n elif self.name == \"sequential_vae_celebA_inhomog_inf_max\":\n self.predict_latent_code = True\n\n elif self.name == \"sequential_vae_celebA_homog_inf_max\":\n self.share_theta_weights = True \n self.share_phi_weights = True\n self.predict_latent_code = True\n\n elif self.name == \"sequential_vae_celebA_homog_inf_max_early_stopping\":\n self.share_theta_weights = True \n self.share_phi_weights = True\n self.predict_latent_code = True\n self.mc_steps = 15\n self.early_stopping_mc = True\n\n elif self.name == \"sequential_vae_celebA_inhomog_inf_max_uniform\":\n self.predict_latent_code = True\n self.use_uniform_prior = True\n\n elif self.name == \"vlae_celebA\":\n self.mc_steps = 1\n self.vlae_latent_dims = [16, 16, 16, 16] # same latent size as 8 stage markov chain\n self.latent_dim = np.sum(self.vlae_latent_dims)\n\n elif self.name == \"sequential_vae_celebA_homog_fixed_length\":\n self.share_theta_weights = True \n\n elif self.name == \"sequential_vae_lsun\":\n self.vlae_latent_dims = [20, 30, 30, 30]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n\n elif self.name == \"sequential_vae_lsun_single\":\n self.vlae_latent_dims = [160, 240, 240, 240]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.mc_steps = 1\n\n elif self.name == \"sequential_vae_lsun_final\":\n self.vlae_latent_dims = [20, 30, 30, 30]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\n self.intermediate_reconstruction = False\n\n elif self.name == \"c_inhomog\":\n pass\n\n elif self.name == \"c_homog\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.mc_steps = 25\n\n elif self.name == \"c_early_stop\":\n self.mc_steps = 15\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.early_stopping_mc = True\n\n elif self.name == \"c_inhomog_inf_max\":\n self.predict_latent_code = True\n self.latent_mean_clip = 8.0\n\n elif self.name == \"c_homog_inf_max_clipped\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.predict_latent_code = True\n self.latent_mean_clip = 8.0\n\n elif self.name == \"c_inhomog_inf_max_clipped\":\n self.predict_latent_code = True\n self.latent_mean_clip = 8.0\n\n elif self.name == \"c_homog_inf_max_regularized\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.predict_latent_code = True\n self.predict_latent_code_with_regularization = True\n\n elif self.name == \"c_sample_images\":\n self.add_noise_to_chain = True\n\n elif self.name == \"c_homog_sample_images\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.add_noise_to_chain = True\n\n elif self.name == \"c_infusion_test\":\n self.share_theta_weights = False\n self.share_phi_weights = False\n self.generator = self.generator_flat\n self.add_noise_to_chain = True\n\n elif self.name == \"c_homog_infusion_test\":\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.generator = self.generator_flat\n self.add_noise_to_chain = True\n \n elif self.name == \"m_infusion_test\":\n self.share_theta_weights = False\n self.share_phi_weights = False\n self.generator = self.generator_flat\n self.add_noise_to_chain = True\n self.vlae_levels = 3\n self.vlae_latent_dims = [2, 2, 2]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n\n elif self.name == \"c_homog_infusion_test\": \n self.share_theta_weights = False\n self.share_phi_weights = False\n self.generator = self.generator_flat\n self.add_noise_to_chain = True\n self.share_theta_weights = True\n self.share_phi_weights = True\n\n\n elif self.name == \"sequential_vae_mnist_homog_early_stopping\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [2, 2, 2]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 15\n self.share_theta_weights = True\n self.share_phi_weights = True\n self.early_stopping_mc = True\n\n elif self.name == \"sequential_vae_mnist_share_all\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [8, 8, 8]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n self.share_theta_weights = True\n self.share_phi_weights = True\n\n elif self.name == \"sequential_vae_mnist_share_encoders\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [8, 8, 8]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n self.share_encoder_params = True\n\n elif self.name == \"sequential_vae_mnist_share_inference\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [8, 8, 8]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n self.share_phi_weights = True\n\n # python main.py --dataset=mnist --plot_reconstruction --netname=m_inhomog --version=1\n elif self.name == \"m_inhomog\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [2, 2, 2]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n\n elif self.name == \"m_noise\":\n self.vlae_levels = 3\n self.vlae_latent_dims = [4, 4, 4]\n self.latent_dim = np.sum(self.vlae_latent_dims)\n self.image_sizes = [32, 16, 8, 4] \n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\n self.mc_steps = 5\n self.add_noise_to_chain = True\n self.noise_stddevs = [8.0, 4.0, 1.0, 0.25, 0]\n\n else:\n self.LOG.error(\"Unknown network name %s\" % self.name)\n exit(-1)\n \n # Construct initialize and print network\n self.construct_network()\n self.init_network()\n self.print_network()\n self.log_tf_variables()\n\n # stuff for figures\n self.mc_fig, self.mc_ax = None, None","title":""},{"docid":"cd82ac3cd8c2d70c70283d69ba2aa0a1","score":"0.51125777","text":"def forward_backward_prop(dimensions, data, labels, params):\n ###################################################################\n # Compute the forward propagation and for the cross entropy cost, #\n # and backward propagation for the gradients for all parameters. #\n ###################################################################\n\n ### Unpack network parameters (do not modify)\n t = 0\n W1 = np.reshape(params[t:t+dimensions[0]*dimensions[1]], (dimensions[0], dimensions[1]))\n t += dimensions[0]*dimensions[1]\n b1 = np.reshape(params[t:t+dimensions[1]], (1, dimensions[1]))\n t += dimensions[1]\n W2 = np.reshape(params[t:t+dimensions[1]*dimensions[2]], (dimensions[1], dimensions[2]))\n t += dimensions[1]*dimensions[2]\n b2 = np.reshape(params[t:t+dimensions[2]], (1, dimensions[2]))\n\n ### YOUR CODE HERE: forward propagation\n\n # cost = ...\n # labels is (20, 10) (20 1-hot vectors) - this is y\n # data is (20, 10) - this is x\n # W1 is (10, 5)\n # W2 is (5, 10)\n # b1 is (1, 5)\n # b2 is (1, 10)\n\n a = data.dot(W1) + b1\n h = sigmoid(a) # hidden layer\n y_hat = softmax(h.dot(W2) + b2) # Top classifier layer\n N, D = data.shape\n (Dx, H) = W1.shape\n\n # TODO: may need to change this to sum over rows and then sum up rows?\n # cost = np.sum(-np.sum(np.multiply(labels, np.log(y_hat)), axis=1).reshape((N, 1)))\n cost_per_datapoint = -np.sum(labels * np.log(y_hat), axis=1).reshape((N, 1)) # sum over rows\n cost = np.sum(cost_per_datapoint)\n\n ### END YOUR CODE\n\n ### YOUR CODE HERE: backward propagation\n\n #gradW1 = ...\n #gradb1 = ...\n #gradW2 = ...\n #gradb2 = ...\n\n # d_y_hat/d_W2\n J_theta = y_hat - labels\n # theta_W2 = h\n # theta_h = W2\n h_a = h * (1.0 - h)\n a_W1 = data\n y_hathw = J_theta.dot(W2.T)*h_a\n\n gradW2 = h.T.dot(J_theta)\n gradW1 = data.T.dot(y_hathw)\n # gradW1 = np.dot(data.T, np.dot(J_theta, theta_h.T) * h_a)\n gradb1 = np.sum(y_hathw, axis=0).reshape((1, H))\n gradb2 = np.sum(J_theta, axis=0).reshape((1, D))\n\n\n assert gradW1.shape == W1.shape\n assert gradb1.shape == b1.shape\n assert W2.shape == gradW2.shape\n assert gradb2.shape == b2.shape\n\n ### END YOUR CODE\n\n ### Stack gradients (do not modify)\n grad = np.concatenate((gradW1.flatten(), gradb1.flatten(), gradW2.flatten(), gradb2.flatten()))\n\n return cost, grad","title":""},{"docid":"b9e0953c03499f06cf439e75dc968c89","score":"0.51104575","text":"def tf_policy_forward(x): # x ~ [1,D]\n extras = list()\n\n # First conv layer\n W_conv1 = weight_variable([20, 20, 2, 64])\n b_conv1 = bias_variable([64])\n x_image = tf.reshape(tf_x, [-1, image_d1, image_d2, 2])\n # extras.append(x_image)\n h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, strides=[1, 20, 20, 1]) + b_conv1)\n h_pool1 = h_conv1\n h_pool1 = max_pool_2x2(h_pool1)\n\n # Second conv layer\n W_conv2 = weight_variable([1, 1 , 64, 64])\n b_conv2 = bias_variable([64])\n h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n # h_pool2 = max_pool_2x2(h_conv2)\n h_pool2 = h_conv2\n\n conv_out = h_pool2\n vsize = conv_out._shape[1]._value * conv_out._shape[2]._value * conv_out._shape[3]._value\n\n # Densely connected layer:\n\n W_fc1 = weight_variable([vsize, 256])\n b_fc1 = bias_variable([256])\n\n h_pool2_flat = tf.reshape(conv_out, [-1,vsize])\n h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n\n \"\"\"\n # Dropout layer\n keep_prob = tf.placeholder(tf.float32)\n h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n \"\"\"\n\n # Readout layer\n W_fc2 = weight_variable([256, n_actions])\n b_fc2 = bias_variable([n_actions])\n\n y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2\n\n p = tf.nn.softmax(y_conv)\n #p = y_conv\n\n return [p, extras]","title":""}],"string":"[\n {\n \"docid\": \"61f985ccd4b9affc2bdd78c1c09e8bfd\",\n \"score\": \"0.5862529\",\n \"text\": \"def task_metalearn(inp, reuse=True):\\n inputa, inputb, labela, labelb = inp # input = (NK,latent_dim) label = (NK, num of au, N), N = num of class\\n #inputa = tf.reshape(inputa, [int(inputa.shape[0]), int(inputa.shape[1]), 1]) # (NK,2000,1)\\n #inputb = tf.reshape(inputb, [int(inputb.shape[0]), int(inputb.shape[1]), 1])\\n inputa = tf.cast(inputa, tf.float32)\\n inputb = tf.cast(inputb, tf.float32)\\n\\n labela_ce = tf.one_hot(labela, self.num_classes) # (NK,8,2)\\n labela_ce = tf.cast(labela_ce, tf.float32)[:, self.au_idx, :] # (NK,1)\\n labela_ce = tf.reshape(labela_ce, [int(labela_ce.shape[0]), 1, self.num_classes]) # (NK,1,N)\\n\\n labelb_ce = tf.one_hot(labelb, self.num_classes) # (NK,2)\\n labelb_ce = tf.cast(labelb_ce, tf.float32)[:, self.au_idx, :]\\n labelb_ce = tf.reshape(labelb_ce, [int(labelb_ce.shape[0]), 1, self.num_classes]) # (NK,1,N)\\n\\n this_w = weights['w1'][:, self.au_idx, :] # weights['w1'] = (300, 8,2) this_w = (300,2)\\n this_b = weights['b1'][self.au_idx, :]\\n this_w = tf.reshape(this_w, [int(this_w.shape[0]), 1, int(this_w.shape[1])]) # (300,1,2)\\n this_b = tf.reshape(this_b, [1, int(this_b.shape[0])])\\n this_weight = {'w1': this_w, 'b1': this_b, 'w2': weights['w2'], 'b2': weights['b2'], 'w3': weights['w3'], 'b3': weights['b3']}\\n # only reuse on the first iter: <<<previously meta-updated weight * input a>>>\\n task_outputa = self.forward(inputa, this_weight) # (NK, 1, 2)\\n\\n ##### for cross-entropy loss ####\\n task_ce_lossa = self.loss_func(task_outputa, labela_ce)[:, 0] # 2,1\\n\\n ##### for co-occur loss ####\\n def predict_other_au(i, input, label):\\n other_w = weights['w1'][:, i, :] # weights['w1'] = (300, 8,2) this_w = (300,2)\\n other_b = weights['b1'][i, :]\\n other_w = tf.reshape(other_w, [int(other_w.shape[0]), 1, int(other_w.shape[1])]) # (300,1,2)\\n other_b = tf.reshape(other_b, [1, int(other_b.shape[0])])\\n other_weight = {'w1': other_w, 'b1': other_b, 'w2': weights['w2'], 'b2': weights['b2'], 'w3': weights['w3'], 'b3': weights['b3']}\\n pred_other_au = self.forward(input, other_weight)\\n pred_other_au = tf.nn.softmax(pred_other_au)\\n pred_other_au = pred_other_au[:, 0, 1]\\n label_other_au = tf.cast(label[:, i], tf.float32)\\n return [pred_other_au, label_other_au]\\n\\n pred_this_au = tf.nn.softmax(task_outputa)\\n pred_this_au = pred_this_au[:, 0, 1]\\n labela_this_au = tf.cast(labela[:, self.au_idx], tf.float32) # (NK,)\\n\\n task_co_lossa = []\\n for i in range(self.total_num_au):\\n pred_other_au, label_other_au = predict_other_au(i, inputa, labela)\\n loss = self.loss_func2(pred_this_au * pred_other_au,\\n labela_this_au * label_other_au) # (num of samples=NK,1=num of au,2=N)\\n # losses 는 현재 주어진 subject이, between 현재 주어진 au and 다른 모든 au간 이룬 loss들의 모임.\\n task_co_lossa.append(loss)\\n task_co_lossa = tf.reduce_sum(task_co_lossa, 0) / self.total_num_au\\n task_lossa = task_ce_lossa + self.LAMBDA2 * task_co_lossa\\n\\n grads = tf.gradients(task_lossa, list(this_weight.values())) # 300,1,2\\n if FLAGS.stop_grad:\\n grads = [tf.stop_gradient(grad) for grad in grads]\\n gradients = dict(zip(this_weight.keys(), grads))\\n fast_weights = dict(\\n zip(this_weight.keys(),\\n [this_weight[key] - self.update_lr * gradients[key] for key in this_weight.keys()]))\\n \\n #### make evaluation with inputb ####\\n task_lossb = []\\n outputb = self.forward(inputb, fast_weights) # (2,1,2) = (2*k, # of au, onehot label)\\n task_ce_lossb = self.loss_func(outputb, labelb_ce)[:, 0]\\n ### for co-occur loss ###\\n labelb_this_au = tf.cast(labelb[:, self.au_idx], tf.float32) # (NK,)\\n predb_this_au = tf.nn.softmax(outputb)\\n predb_this_au = predb_this_au[:, 0, 1]\\n task_co_lossb = []\\n for i in range(self.total_num_au):\\n predb_other_au, labelb_other_au = predict_other_au(i, inputb, labelb)\\n task_co_lossb.append(self.loss_func2(predb_this_au * predb_other_au,\\n labelb_this_au * labelb_other_au))\\n # test_other_au.append(labelb_other_au)\\n # test_other_au = tf.convert_to_tensor(test_other_au)\\n task_co_lossb = tf.reduce_sum(task_co_lossb, 0) / self.total_num_au\\n task_lossb.append(task_ce_lossb + self.LAMBDA2 * task_co_lossb)\\n\\n\\n ### for more inner update ###\\n for j in range(num_updates - 1):\\n\\n task_outputa = self.forward(inputa, fast_weights)\\n task_ce_loss = self.loss_func(task_outputa, labela_ce)[:, 0]\\n ### co-occur ###\\n task_co_loss = []\\n pred_this_au = tf.nn.softmax(task_outputa)\\n pred_this_au = pred_this_au[:, 0, 1]\\n\\n for i in range(self.total_num_au):\\n pred_other_au, label_other_au = predict_other_au(i, inputa, labela)\\n task_co_loss.append(self.loss_func2(pred_this_au * pred_other_au,\\n labela_this_au * label_other_au))\\n\\n task_co_loss = tf.reduce_sum(task_co_loss, 0) / self.total_num_au\\n task_loss = task_ce_loss + self.LAMBDA2 * task_co_loss\\n # compute gradients based on the previous fast weights\\n grads = tf.gradients(task_loss, list(fast_weights.values()))\\n if FLAGS.stop_grad:\\n grads = [tf.stop_gradient(grad) for grad in grads]\\n gradients = dict(zip(fast_weights.keys(), grads))\\n # fast_weights are updated\\n fast_weights = dict(zip(fast_weights.keys(),\\n [fast_weights[key] - self.update_lr * gradients[key] for key in\\n fast_weights.keys()]))\\n \\n #### make evaluation with inputb ####\\n outputb = self.forward(inputb, fast_weights) # (2,1,2) = (2*k, # of au, onehot label)\\n task_ce_lossb = self.loss_func(outputb, labelb_ce)[:, 0]\\n ### for co-occur loss ###\\n labelb_this_au = tf.cast(labelb[:, self.au_idx], tf.float32) # (NK,)\\n predb_this_au = tf.nn.softmax(outputb)\\n predb_this_au = predb_this_au[:, 0, 1]\\n task_co_lossb = []\\n for i in range(self.total_num_au):\\n predb_other_au, labelb_other_au = predict_other_au(i, inputb, labelb)\\n task_co_lossb.append(self.loss_func2(predb_this_au * predb_other_au,\\n labelb_this_au * labelb_other_au))\\n # test_other_au.append(labelb_other_au)\\n # test_other_au = tf.convert_to_tensor(test_other_au)\\n task_co_lossb = tf.reduce_sum(task_co_lossb, 0) / self.total_num_au\\n task_lossb.append(task_ce_lossb + self.LAMBDA2 * task_co_lossb)\\n #task_lossb = tf.reduce_sum(task_lossb)\\n task_lossb = task_lossb[-1]\\n\\n ### return output ###\\n if FLAGS.adaptation:\\n task_output = [fast_weights['w1'], fast_weights['b1'], task_ce_lossb, task_co_lossb, task_lossa,\\n predb_this_au]\\n else:\\n task_output = [fast_weights['w1'], fast_weights['b1'], task_ce_lossb, task_co_lossb, task_lossb,\\n predb_this_au]\\n return task_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87cfe2fc86c92b2df994c44d8ae095e9\",\n \"score\": \"0.5802021\",\n \"text\": \"def adversarial_NN(DNN,adv_inputs):\\n Dx = DNN(adv_inputs)\\n Dx = Dense(10, activation=\\\"linear\\\")(adv_inputs)\\n Dx = Dense(10, activation=\\\"tanh\\\")(Dx)\\n Dx = Dense(2, activation=\\\"softmax\\\", name='adversary_output')(Dx)\\n adv_NN = Model(input=[adv_inputs], output=[Dx])\\n adv_NN.name = 'adv_NN'\\n return adv_NN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af2f1352b3fbceb3e49a889cd12e14a\",\n \"score\": \"0.57253855\",\n \"text\": \"def adversarial_example(self, inputs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921177fb91a63ad4f1d7ca422ac35796\",\n \"score\": \"0.5700022\",\n \"text\": \"def learn(self, batch_exp):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3023e6d63c37c1e0b4aa25f784030054\",\n \"score\": \"0.5691788\",\n \"text\": \"def saliency():\\n\\n buho = 'images/great_grey_owl.jpg'\\n di1 = 'images/1.png'\\n image = load_image(buho)\\n # image = load_image(buho)\\n\\n # plt.imshow(image)\\n # plt.title('Original image'+str(type(image)))\\n # plt.axis('off');\\n # plt.show()\\n \\\"\\\"\\\"### 2. Load a pre-trained Model\\\"\\\"\\\"\\n\\n model = models.alexnet(pretrained=True)\\n # model = torch.load('/content/alexnet-frames-Finetuned:False-1di-tempMaxPool-OnPlateau.tar')\\n\\n \\\"\\\"\\\"### 3. Create an instance of Backprop with the model\\\"\\\"\\\"\\n\\n backprop = Backprop(model)\\n\\n \\\"\\\"\\\"### 4. Visualize saliency maps\\\"\\\"\\\"\\n\\n # Transform the input image to a tensor\\n\\n owl = apply_transforms(image)\\n # print(owl.size()) #torch.Size([/1, 3, 224, 224])\\n # input_size = 224\\n # data_transforms = createTransforms(input_size)\\n # owl = data_transforms['test'](image)\\n # owl = owl.unsqueeze(dim=0)\\n # owl = owl.unsqueeze(dim=0)\\n # owl = owl.permute(1, 0, 2, 3, 4)\\n # print(owl.size())\\n\\n # Set a target class from ImageNet task: 24 in case of great gray owl\\n\\n target_class = 24\\n\\n # Ready to roll!\\n\\n backprop.visualize(owl, target_class=target_class, guided=True, use_gpu=True)\\n\\n\\n\\n # \\\"\\\"\\\"### 5. What about other birds?\\n\\n # What makes peacock a peacock...?\\n # \\\"\\\"\\\"\\n\\n # peacock = apply_transforms(load_image('/content/images/peacock.jpg'))\\n # backprop.visualize(peacock, 84, guided=True, use_gpu=True)\\n\\n # \\\"\\\"\\\"Or a toucan?\\\"\\\"\\\"\\n\\n # toucan = apply_transforms(load_image('/content/images/toucan.jpg'))\\n # backprop.visualize(toucan, 96, guided=True, use_gpu=True)\\n\\n # \\\"\\\"\\\"Please try out other models/images too!\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f073666fba5679aba5ef6ce5e0a764\",\n \"score\": \"0.56886184\",\n \"text\": \"def tf_efficientnet_b0_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b0_ap\\\",\\n channel_multiplier=1.0,\\n depth_multiplier=1.0,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2080f2393e7a798f3e0ea65e319c77f1\",\n \"score\": \"0.56620723\",\n \"text\": \"def tf_efficientnet_b1(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b1\\\",\\n channel_multiplier=1.0,\\n depth_multiplier=1.1,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c858a3a262f2e344429778995a8a91f\",\n \"score\": \"0.5618886\",\n \"text\": \"def train(self, v0, vk, ph0, phk):\\n \\n # Update weights and biases\\n self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()\\n self.b += torch.sum((v0 - vk), 0) # ----> 0 is added to keep the shape of the tensor b\\n self.a += torch.sum((ph0 - phk), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe371336cf3b08a015754425887b61ed\",\n \"score\": \"0.55788666\",\n \"text\": \"def adverarial_train_natural(model, train_set, test_set, save_path=None, **kwargs):\\n assert save_path is not None\\n print('start training ... ')\\n val_acc_history = []\\n val_loss_history = []\\n epoch_num = kwargs[\\\"epoch_num\\\"]\\n advtrain_start_num = kwargs[\\\"advtrain_start_num\\\"]\\n batch_size = kwargs[\\\"batch_size\\\"]\\n adversarial_trans = kwargs[\\\"adversarial_trans\\\"]\\n opt = kwargs[\\\"optimizer\\\"]\\n model_para_name = kwargs[\\\"model_para_name\\\"]\\n model_opt_para_name = kwargs[\\\"model_opt_para_name\\\"]\\n\\n train_loader = paddle.io.DataLoader(train_set, shuffle=True, batch_size=batch_size)\\n valid_loader = paddle.io.DataLoader(test_set, batch_size=batch_size)\\n max_acc = 0\\n for epoch in range(epoch_num):\\n for batch_id, data in enumerate(train_loader()):\\n x_data = data[0]\\n y_data = paddle.unsqueeze(data[1], 1)\\n\\n # adversarial training late start\\n if epoch >= advtrain_start_num and adversarial_trans is not None:\\n x_data_augmented, y_data_augmented = adversarial_trans(x_data.numpy(), y_data.numpy())\\n else:\\n x_data_augmented, y_data_augmented = x_data, y_data\\n # turn model into training mode\\n model.train()\\n # make sure gradient flow for model parameter\\n for param in model.parameters():\\n param.stop_gradient = False\\n\\n # numpy to paddle.Tensor\\n x_data_augmented = paddle.to_tensor(x_data_augmented, dtype='float32', place=USE_GPU)\\n y_data_augmented = paddle.to_tensor(y_data_augmented, dtype='int64', place=USE_GPU)\\n y_data_augmented = paddle.unsqueeze(y_data_augmented, 1)\\n\\n logits = model(x_data_augmented)\\n loss = F.cross_entropy(logits, y_data_augmented)\\n acc = paddle.metric.accuracy(logits, y_data_augmented)\\n acc = acc.numpy()\\n acc = round(acc[0], 3)\\n if batch_id % 10 == 0:\\n print(\\\"epoch:{}, batch_id:{}, loss:{}, acc:{}\\\".format(epoch, batch_id, loss.numpy(), acc))\\n loss.backward()\\n opt.step()\\n opt.clear_grad()\\n # evaluate model after one epoch\\n model.eval()\\n accuracies = []\\n losses = []\\n with paddle.no_grad():\\n for batch_id, data in enumerate(valid_loader()):\\n x_data = data[0]\\n y_data = paddle.unsqueeze(data[1], 1)\\n logits = model(x_data)\\n loss = F.cross_entropy(logits, y_data)\\n acc = paddle.metric.accuracy(logits, y_data)\\n accuracies.append(acc.numpy())\\n losses.append(loss.numpy())\\n avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)\\n avg_acc = round(avg_acc, 6)\\n avg_loss = round(avg_loss, 6)\\n if avg_acc > max_acc:\\n max_acc = avg_acc\\n paddle.save(model.state_dict(), save_path + model_para_name + str(max_acc) + '.pdparams')\\n paddle.save(opt.state_dict(), save_path + model_opt_para_name + str(max_acc) + '.pdopt')\\n print(\\\"best saved at: \\\", save_path)\\n else:\\n pass\\n print(\\\"[validation] accuracy/loss:{}/{}, max_acc:{}\\\".format(avg_acc, avg_loss, max_acc))\\n val_acc_history.append(avg_acc)\\n val_loss_history.append(avg_loss)\\n model.train()\\n\\n return val_acc_history, val_loss_history\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7683ca8797688f0dda0e626ce50df26\",\n \"score\": \"0.5561336\",\n \"text\": \"def __init__(self, n_latent=1024):\\n super(MLPAdjustment, self).__init__()\\n self.n_latent = n_latent\\n\\n self.conv1_bn = tf_utils.Conv1DBatchNorm(n_latent, 1,\\n scope=\\\"MLPAdjust_Conv1D_1\\\")\\n self.conv2_bn = tf_utils.Conv1DBatchNorm(n_latent // 2, 1,\\n scope=\\\"MLPAdjust_Conv1D_2\\\")\\n self.conv3_bn = tf_utils.Conv1DBatchNorm(n_latent // 4, 1,\\n scope=\\\"MLPAdjust_Conv1D_3\\\")\\n self.conv4 = layers.Conv1D(3, 1, activation=\\\"tanh\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9738aa99a2ea5c360f874a83df0dbd49\",\n \"score\": \"0.5546009\",\n \"text\": \"def __activate(self, preact, layer_name, skip_af=False, batch_norm=False):\\n assert not (self.NetSettings.learn_layer_stats and batch_norm), 'batch normalization and learned normalization prohibited to run in parralel.'\\n assert self.NetSettings.ABU_normalization in ['unrestricted','normalized','posnormed','absnormed','softmaxed'], 'specified ABU normalization unknown.'\\n\\n # NORMALIZATION\\n self.__variable_summaries(preact, 'preact', stats=['mean', 'std', 'hist'])\\n if batch_norm:\\n preact = tf.layers.batch_normalization(preact, name='batchnorm', training=True)\\n if self.NetSettings.learn_layer_stats:\\n alpha_initializer = tf.fill([1], 1.0)\\n beta_initializer = tf.fill([1], 0.0)\\n alpha = tf.get_variable(\\\"alpha\\\", initializer=alpha_initializer)\\n beta = tf.get_variable(\\\"beta\\\", initializer=beta_initializer)\\n self.__variable_summaries(alpha, 'alpha', stats=['mean'])\\n self.__variable_summaries(beta, 'beta', stats=['mean'])\\n preact = alpha * (preact + beta)\\n\\n # ACTIVATION FUNCTIONS\\n if skip_af:\\n activation = preact\\n elif self.NetSettings.activation_function == 'relu':\\n activation = tf.nn.relu(preact)\\n elif self.NetSettings.activation_function == 'elu':\\n activation = tf.nn.elu(preact)\\n elif self.NetSettings.activation_function == 'tanh':\\n activation = tf.nn.tanh(preact)\\n elif self.NetSettings.activation_function == 'swish':\\n if self.NetSettings.af_weights_pretrained:\\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\\n else:\\n swish_beta_init = tf.fill([1], 1.0)\\n swish_beta = tf.get_variable(\\\"swish_beta\\\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\\n activation = preact * tf.nn.sigmoid(swish_beta*preact)\\n elif self.NetSettings.activation_function == 'id':\\n activation = preact\\n elif self.NetSettings.activation_function == 'selu':\\n activation = tf.nn.selu(preact)\\n elif self.NetSettings.activation_function == 'ABU':\\n # INITIALIZE BLENDING WEIGHTS & SWISH BETA\\n if self.NetSettings.af_weights_pretrained:\\n blending_weights_initializer = self.__get_predefined_af_weights(layer_name, w_type='blend')\\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\\n else:\\n blending_weights_initializer = tf.fill([5], 1/5.0)\\n swish_beta_init = tf.fill([1], 1.0)\\n blending_weights_raw = tf.get_variable(\\\"blending_weights_raw\\\", trainable=self.NetSettings.ABU_trainable, initializer=blending_weights_initializer)\\n swish_beta = tf.get_variable(\\\"swish_beta\\\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\\n self.__variable_summaries(blending_weights_raw, 'blending_weights', stats=['mean', 'min', 'max'])\\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\\n # NORMALIZE\\n if self.NetSettings.ABU_normalization == 'unrestricted':\\n blending_weights = blending_weights_raw\\n elif self.NetSettings.ABU_normalization == 'normalized':\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'absnormed':\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(tf.abs(blending_weights_raw), keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'posnormed':\\n blending_weights = tf.divide(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), tf.reduce_sum(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), keep_dims=True))\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'softmaxed':\\n blending_weights = tf.exp(blending_weights_raw)\\n blending_weights = tf.divide(blending_weights, tf.reduce_sum(blending_weights, keep_dims=True))\\n # ACTIVATE\\n act_relu = tf.nn.relu(preact)\\n act_elu = tf.nn.elu(preact)\\n act_tanh = tf.nn.tanh(preact)\\n act_swish = preact * tf.nn.sigmoid(swish_beta*preact)\\n act_id = preact\\n activation = tf.add_n([blending_weights[0] * act_relu,\\n blending_weights[1] * act_elu,\\n blending_weights[2] * act_tanh,\\n blending_weights[3] * act_swish,\\n blending_weights[4] * act_id])\\n # custom ABU\\n elif self.NetSettings.activation_function.startswith('ABU-'):\\n # ABUs with different sets of AFs. e.g. \\\"ABU-relu-tanh-swish\\\"\\n n_AFs = len(self.NetSettings.activation_function.split('-'))-1\\n AF_list = self.NetSettings.activation_function.split('-')[1:]\\n # INITIALIZE SWISH BETA IF APPLICABLE\\n if 'swish' in AF_list:\\n if self.NetSettings.af_weights_pretrained:\\n swish_beta_init = self.__get_predefined_af_weights(layer_name, w_type='swish_beta')\\n else:\\n swish_beta_init = tf.fill([1], 1.0)\\n swish_beta = tf.get_variable(\\\"swish_beta\\\", trainable=self.NetSettings.swish_beta_trainable, initializer=swish_beta_init)\\n self.__variable_summaries(swish_beta, 'swish_beta', stats=['mean'])\\n # INITIALIZE BLENDING WEIGHTS\\n if self.NetSettings.af_weights_pretrained:\\n blending_weights_initializer = self.__get_predefined_af_weights(layer_name, w_type='blend')\\n else:\\n blending_weights_initializer = tf.fill([n_AFs], 1/float(n_AFs))\\n blending_weights_raw = tf.get_variable(\\\"blending_weights_raw\\\", trainable=self.NetSettings.ABU_trainable, initializer=blending_weights_initializer)\\n self.__variable_summaries(blending_weights_raw, 'blending_weights', stats=['mean', 'min', 'max'])\\n # NORMALIZE\\n if self.NetSettings.ABU_normalization == 'unrestricted':\\n blending_weights = blending_weights_raw\\n elif self.NetSettings.ABU_normalization == 'normalized':\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'absnormed':\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(tf.abs(blending_weights_raw), keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'posnormed':\\n blending_weights = tf.divide(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), tf.reduce_sum(tf.clip_by_value(blending_weights_raw, 0.0001, 1000.0), keep_dims=True))\\n blending_weights = tf.divide(blending_weights_raw, tf.reduce_sum(blending_weights_raw, keep_dims=True))\\n elif self.NetSettings.ABU_normalization == 'softmaxed':\\n blending_weights = tf.exp(blending_weights_raw)\\n blending_weights = tf.divide(blending_weights, tf.reduce_sum(blending_weights, keep_dims=True))\\n # ACTIVATE\\n activations = []\\n for i, AF in enumerate(AF_list):\\n assert AF in ['id', 'tanh', 'relu', 'elu', 'swish'], 'requested activation function unknown: %s' %(AF)\\n if AF == 'id':\\n activations.append(blending_weights[i] * preact)\\n elif AF == 'tanh':\\n activations.append(blending_weights[i] * tf.nn.tanh(preact))\\n elif AF == 'relu':\\n activations.append(blending_weights[i] * tf.nn.relu(preact))\\n elif AF == 'elu':\\n activations.append(blending_weights[i] * tf.nn.elu(preact))\\n elif AF == 'swish':\\n activations.append(blending_weights[i] * preact * tf.nn.sigmoid(swish_beta*preact))\\n activation = tf.add_n(activations)\\n self.__variable_summaries(activation, 'activation', stats=['mean', 'std', 'hist'])\\n return activation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8995caded15c986993024e51cf1a055a\",\n \"score\": \"0.55321515\",\n \"text\": \"def tf_efficientnet_b2_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b2_ap\\\",\\n channel_multiplier=1.1,\\n depth_multiplier=1.2,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c154933240faafaeec6a453006364e3a\",\n \"score\": \"0.5531579\",\n \"text\": \"def generate_adversaries(model, baseImage, delta, classIdx, defense, steps=50):\\n # Iterate over the number of steps\\n for step in range(0, steps):\\n # Record the gradients\\n with tf.GradientTape() as tape:\\n # Explicitly indicate that the perturbation vector should be tracked for gradient updates\\n tape.watch(delta)\\n # Add the perturbation vector to the base image and preprocess the resulting image\\n #adversary = preprocess_input(baseImage + delta)\\n adversary = baseImage + delta\\n # Run this newly constructed image tensor through the base model and calculate the loss with respect to the\\n # original class index\\n predictions = model(adversary, defense = defense)\\n\\n #print(predictions)\\n loss = -sccLoss(tf.convert_to_tensor([classIdx]), predictions)\\n # Check the loss value and display it in the terminal\\n #if step % 10 == 0:\\n # print(\\\"[INFO] Step: {}, Loss: {}...\\\".format(step, loss.numpy()))\\n # Calculate the gradients of loss with respect to the perturbation vector\\n gradients = tape.gradient(loss, delta)\\n # Update the weights, clip the perturbation vector and update its value\\n optimizer.apply_gradients([(gradients, delta)])\\n delta.assign_add(clip_eps(delta, eps=EPS))\\n #deltaImage = delta.numpy().squeeze()\\n #deltaImage = cv2.cvtColor(deltaImage, cv2.COLOR_RGB2BGR)\\n #cv2.imshow(\\\"Output1\\\", deltaImage)\\n #print(delta)\\n # Return the perturbation vector\\n return delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18db48e3800e184d3ca7df7d74b7a4bd\",\n \"score\": \"0.55061907\",\n \"text\": \"def layer1(input, name=\\\"pretrainedL1\\\"):\\n w = tf.Variable(tf.convert_to_tensor(wts1, dtype=tf.float32), name=\\\"W\\\")\\n b = tf.Variable(tf.convert_to_tensor(bias1, dtype=tf.float32), name=\\\"B\\\")\\n h = tf.matmul(w, input) + b\\n h = tf.nn.relu(h)\\n tf.summary.histogram(\\\"weights\\\", w)\\n tf.summary.histogram(\\\"biases\\\", b)\\n tf.summary.histogram(\\\"activations\\\", h)\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8e02994086b1725883b91bfa78b268\",\n \"score\": \"0.54683954\",\n \"text\": \"def model_fn(features,labels,mode,params=None): \\n params = params or {}\\n loss, acc, train_op, export_outputs = None, None, None, None\\n is_training = (mode==tf.estimator.ModeKeys.TRAIN)\\n\\n cls_model = Alexnet(is_training=is_training,num_classes=FLAGS.num_classes)\\n #preprocessed_inputs = cls_model.preprocess(features['images']) \\n preprocessed_inputs = features['images']\\n prediction_dict = cls_model.predict(preprocessed_inputs)\\n postprocessed_dict = cls_model.postprocess(prediction_dict)\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n if FLAGS.checkpoint_path:\\n init_variables_from_checkpoint()\\n if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):\\n loss_dict = cls_model.loss(postprocessed_dict,labels)\\n loss = loss_dict['loss']\\n pre_classes = postprocessed_dict['classes']\\n\\n acc = tf.reduce_mean(tf.cast(tf.equal(pre_classes,labels),dtype=tf.float32))\\n tf.summary.scalar('loss',loss)\\n tf.summary.scalar('accuracy',acc)\\n\\n scaffold = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n global_step = tf.train.get_or_create_global_step()\\n learning_rate = configure_learning_rate(FLAGS.decay_steps,global_step)\\n optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=0.9)\\n train_op = slim.learning.create_train_op(loss, optimizer,\\n summarize_gradients=True)\\n keep_checkpoint_every_n_hours = FLAGS.keep_checkpoint_every_n_hours\\n saver = tf.train.Saver(\\n sharded=True,\\n keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,\\n save_relative_paths=True) \\n tf.add_to_collection(tf.GraphKeys.SAVERS, saver)\\n scaffold = tf.train.Scaffold(saver=saver)\\n \\n #return tf.estimator.EstimatorSpec(mode=mode,loss=loss,train_op=train_op,scaffold=scaffold)\\n\\n \\n eval_metric_ops = None\\n if mode == tf.estimator.ModeKeys.EVAL:\\n accuracy = tf.metrics.accuracy(labels=labels, predictions=pre_classes)\\n eval_metric_ops = {'Accuracy': accuracy}\\n\\n # if mode == tf.estimator.ModeKeys.PREDICT:\\n # export_output = exporter._add_output_tensor_nodes(postprocessed_dict)\\n # export_outputs = {\\n # tf.saved_model.signature_constants.PREDICT_METHOD_NAME:\\n # tf.estimator.export.PredictOutput(export_output)}\\n return tf.estimator.EstimatorSpec(mode=mode,\\n predictions=prediction_dict,\\n loss=loss,\\n train_op=train_op,\\n eval_metric_ops=eval_metric_ops,\\n scaffold=scaffold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7b05b05b051a3e751db7122f886737\",\n \"score\": \"0.5459812\",\n \"text\": \"def __init__(self, kernel_size=1, decay=_DECAY, name='attention'):\\n super(AttentionModel, self).__init__(name=name)\\n\\n # First convolutional layer (called with relu activation).\\n self.conv1 = layers.Conv2D(\\n 512,\\n kernel_size,\\n kernel_regularizer=reg.l2(decay),\\n padding='same',\\n name='attn_conv1')\\n self.bn_conv1 = layers.BatchNormalization(axis=3, name='bn_conv1')\\n\\n # Second convolutional layer, with softplus activation.\\n self.conv2 = layers.Conv2D(\\n 1,\\n kernel_size,\\n kernel_regularizer=reg.l2(decay),\\n padding='same',\\n name='attn_conv2')\\n self.activation_layer = layers.Activation('softplus')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf9aabed34a5525ed93c443b17087c7\",\n \"score\": \"0.5455983\",\n \"text\": \"def forward(self, inputs, **unused_params):\\n anchor_weights = tf.get_variable(\\\"anchor_weights{}\\\".format(\\\"\\\" if self.scope_id is None else str(self.scope_id)),\\n [self.feature_size, self.num_anchors],\\n initializer=tf.random_normal_initializer(\\n stddev=1 / math.sqrt(self.num_anchors)),\\n dtype=tf.float32)\\n tf.summary.histogram(\\\"anchor_weights{}\\\".format(\\\"\\\" if self.scope_id is None else str(self.scope_id)),\\n anchor_weights)\\n\\n anchor_weights = tf.transpose(anchor_weights)\\n anchor_weights = tf.reshape(anchor_weights, [1, self.feature_size * self.num_anchors])\\n\\n # Tile inputs to subtract all anchors.\\n tiled_inputs = tf.tile(inputs, [1, self.num_anchors])\\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\\n t_emb = tf.subtract(tiled_inputs, anchor_weights)\\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\\n\\n t_emb = tf.reshape(t_emb, [-1, self.num_anchors, self.feature_size])\\n # -> (batch_size * num_descriptors) x num_anchors x feature_size; Keep normalized residuals.\\n t_emb = tf.nn.l2_normalize(t_emb, 2)\\n t_emb = tf.reshape(t_emb, [-1, self.feature_size * self.num_anchors])\\n # -> (batch_size * num_descriptors) x (feature_size * num_anchors)\\n\\n if self.add_batch_norm:\\n t_emb = slim.batch_norm(\\n t_emb,\\n center=True,\\n scale=True,\\n is_training=self.is_training,\\n scope=\\\"t_emb_bn\\\")\\n\\n activation = tf.reshape(t_emb, [-1, self.num_descriptors, self.feature_size * self.num_anchors])\\n\\n return activation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521bce2086d8424ead8dfc58006ae7f9\",\n \"score\": \"0.5450276\",\n \"text\": \"def __call__(self, image_input, training=False, keep_prob=1.0):\\r\\n def leaky_relu(x, leak=0.2, name=''):\\r\\n return tf.maximum(x, x * leak, name=name)\\r\\n with tf.variable_scope('RelationModule', reuse=self.reuse):\\r\\n with tf.variable_scope('conv_layers'):\\r\\n with tf.variable_scope('RelationModule_conv1'):\\r\\n g_conv1_encoder = tf.layers.conv1d(image_input, 64, 9, strides=1, padding='VALID')\\r\\n g_conv1_encoder = leaky_relu(g_conv1_encoder, name='outputs')\\r\\n g_conv1_encoder = tf.layers.max_pooling1d(g_conv1_encoder, 2, 2, padding='SAME')\\r\\n g_conv1_encoder = tf.nn.dropout(g_conv1_encoder, keep_prob=keep_prob)\\r\\n with tf.variable_scope('RelationModule_conv2'):\\r\\n g_conv2_encoder = tf.layers.conv1d(g_conv1_encoder, 64, 9, strides=1, padding='VALID')\\r\\n g_conv2_encoder = leaky_relu(g_conv2_encoder, name='outputs')\\r\\n g_conv2_encoder = tf.layers.max_pooling1d(g_conv2_encoder, 2, 2, padding='SAME')\\r\\n g_conv2_encoder = tf.nn.dropout(g_conv2_encoder, keep_prob=keep_prob)\\r\\n with tf.variable_scope('fully_connected_relu'):\\r\\n g_fc1_encoder = tf.contrib.layers.flatten(g_conv2_encoder)\\r\\n g_fc1_encoder = tf.contrib.layers.fully_connected(g_fc1_encoder, 8, trainable=True, scope='fc_relu')\\r\\n with tf.variable_scope('fully_connected_sigmoid'):\\r\\n g_fc2_encoder = tf.contrib.layers.fully_connected(g_fc1_encoder, 1, activation_fn=tf.nn.sigmoid,\\r\\n trainable=True, scope='fc_sigmoid')\\r\\n g_conv_encoder = g_fc2_encoder\\r\\n\\r\\n self.reuse = True\\r\\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='RelationModule')\\r\\n return g_conv_encoder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf0c53cfb4b01c3e00ba82de98cd694\",\n \"score\": \"0.5447174\",\n \"text\": \"def test_adversarial_example(learning_rate=0.001, L1_reg=0.00, L2_reg=0.0001, n_epochs=100,\\n batch_size=128, n_hidden=500, n_hiddenLayers=3,\\n verbose=False, smaller_set=True):\\n\\n \\\"\\\"\\\"\\n # load the dataset; download the dataset if it is not present\\n if smaller_set:\\n datasets = load_data(ds_rate=5)\\n else:\\n datasets = load_data()\\n \\\"\\\"\\\"\\n \\n # Load the smaller dataset in raw format, since we need to preprocess it\\n train_set, valid_set, test_set = load_data(ds_rate=5, theano_shared=False)\\n \\n # Convert raw dataset to Theano shared variables.\\n test_set_x, test_set_y = shared_dataset(test_set)\\n valid_set_x, valid_set_y = shared_dataset(valid_set)\\n train_set_x, train_set_y = shared_dataset(train_set)\\n \\n \\n # compute number of minibatches for training, validation and testing\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size\\n\\n ######################\\n # BUILD ACTUAL MODEL #\\n ######################\\n print('... building the model')\\n\\n # allocate symbolic variables for the data\\n index = T.lscalar() # index to a [mini]batch\\n x = T.matrix('x') # the data is presented as rasterized images\\n y = T.ivector('y') # the labels are presented as 1D vector of\\n # [int] labels\\n\\n rng = numpy.random.RandomState(1234)\\n\\n # TODO: construct a neural network, either MLP or CNN.\\n classifier = myMLP(\\n rng=rng,\\n input=x,\\n n_in=32*32*3,\\n n_hidden=n_hidden,\\n n_out=10,\\n n_hiddenLayers=n_hiddenLayers \\n )\\n\\n # the cost we minimize during training is the negative log likelihood of\\n # the model plus the regularization terms (L1 and L2); cost is expressed\\n # here symbolically\\n cost = (\\n classifier.negative_log_likelihood(y)\\n + L1_reg * classifier.L1\\n + L2_reg * classifier.L2_sqr\\n )\\n\\n # compiling a Theano function that computes the mistakes that are made\\n # by the model on a minibatch\\n test_model = theano.function(\\n inputs=[index],\\n outputs=classifier.errors(y),\\n givens={\\n x: test_set_x[index * batch_size:(index + 1) * batch_size],\\n y: test_set_y[index * batch_size:(index + 1) * batch_size]\\n }\\n )\\n\\n validate_model = theano.function(\\n inputs=[index],\\n outputs=classifier.errors(y),\\n givens={\\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]\\n }\\n )\\n\\n # compute the gradient of cost with respect to theta (sotred in params)\\n # the resulting gradients will be stored in a list gparams\\n gparams = [T.grad(cost, param) for param in classifier.params]\\n \\n \\n # specify how to update the parameters of the model as a list of\\n # (variable, update expression) pairs\\n\\n # given two lists of the same length, A = [a1, a2, a3, a4] and\\n # B = [b1, b2, b3, b4], zip generates a list C of same size, where each\\n # element is a pair formed from the two lists :\\n # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]\\n updates = [\\n (param, param - learning_rate * gparam)\\n for param, gparam in zip(classifier.params, gparams)\\n ]\\n\\n # compiling a Theano function `train_model` that returns the cost, but\\n # in the same time updates the parameter of the model based on the rules\\n # defined in `updates`\\n train_model = theano.function(\\n inputs=[index],\\n outputs=cost,\\n updates=updates,\\n givens={\\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\\n }\\n )\\n \\n get_grad = theano.function(\\n inputs=[index],\\n outputs=T.grad(cost,x),\\n givens={\\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\\n }\\n )\\n \\n pred = classifier.logRegressionLayer.p_y_given_x\\n get_probability = theano.function(\\n inputs=[index],\\n outputs=pred,\\n givens={\\n x: train_set_x[index * batch_size: (index + 1) * batch_size]\\n }\\n )\\n\\n label = classifier.logRegressionLayer.y_pred\\n get_label = theano.function(\\n inputs=[index],\\n outputs=label,\\n givens={\\n x: train_set_x[index * batch_size: (index + 1) * batch_size]\\n }\\n ) \\n\\n \\n ###############\\n # TRAIN MODEL #\\n ###############\\n print('... training')\\n\\n train_nn(train_model, validate_model, test_model,\\n n_train_batches, n_valid_batches, n_test_batches, n_epochs, verbose)\\n \\n \\n \\n \\n advers = train_set\\n \\n examp_grad = get_grad(0)\\n examp_prob = get_probability(0)\\n examp_label = get_label(0)\\n \\n \\n advers_x = advers[0]\\n advers_x[:batch_size,:] = advers_x[:batch_size,:] + (numpy.random.rand(batch_size,3072)*numpy.sign(examp_grad))/20\\n advers[0] = advers_x\\n shared_advers_x, shared_advers_y = shared_dataset(train_set)\\n train_set_x2, train_set_y2 = shared_dataset(advers)\\n \\n \\n get_probability_advers = theano.function(\\n inputs=[index],\\n outputs=pred,\\n givens={\\n x: train_set_x2[index * batch_size: (index + 1) * batch_size]\\n }\\n )\\n \\n advers_prob = get_probability_advers(0)\\n \\n \\n return advers_x[:batch_size,:], examp_prob, advers_prob, examp_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43bdd748fa5bd3e600d7dfa04813b544\",\n \"score\": \"0.5438254\",\n \"text\": \"def __init__(\\n self,\\n learning_rate: Union[FloatTensorLike, Callable, Dict] = 0.001,\\n beta_1: FloatTensorLike = 0.9,\\n beta_2: FloatTensorLike = 0.999,\\n epsilon: FloatTensorLike = 1e-7,\\n weight_decay: Union[FloatTensorLike, Callable, Dict] = 0.0,\\n amsgrad: bool = False,\\n sma_threshold: FloatTensorLike = 5.0,\\n total_steps: int = 0,\\n warmup_proportion: FloatTensorLike = 0.1,\\n min_lr: FloatTensorLike = 0.0,\\n name: str = \\\"RectifiedAdam\\\",\\n **kwargs,\\n ):\\n super().__init__(name, **kwargs)\\n\\n if isinstance(learning_rate, Dict):\\n learning_rate = tf.keras.optimizers.schedules.deserialize(learning_rate)\\n\\n if isinstance(weight_decay, Dict):\\n weight_decay = tf.keras.optimizers.schedules.deserialize(weight_decay)\\n\\n self._set_hyper(\\\"learning_rate\\\", kwargs.get(\\\"lr\\\", learning_rate))\\n self._set_hyper(\\\"beta_1\\\", beta_1)\\n self._set_hyper(\\\"beta_2\\\", beta_2)\\n self._set_hyper(\\\"decay\\\", self._initial_decay)\\n self._set_hyper(\\\"weight_decay\\\", weight_decay)\\n self._set_hyper(\\\"sma_threshold\\\", sma_threshold)\\n self._set_hyper(\\\"total_steps\\\", float(total_steps))\\n self._set_hyper(\\\"warmup_proportion\\\", warmup_proportion)\\n self._set_hyper(\\\"min_lr\\\", min_lr)\\n self.epsilon = epsilon or tf.keras.backend.epsilon()\\n self.amsgrad = amsgrad\\n self._has_weight_decay = weight_decay != 0.0\\n self._initial_total_steps = total_steps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05479f32f44fb05a9dee6ccbad0d7355\",\n \"score\": \"0.5424882\",\n \"text\": \"def _train(self, experience, weights):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"709ea2287d8820cade09ca87eb1b8652\",\n \"score\": \"0.54195005\",\n \"text\": \"def build_model(self, reset_graph=False):\\n \\n if self.data is None:\\n raise Exception('a dataset must be specified')\\n \\n if (self.adv and (self.classifier is None)):\\n raise Exception('A classifier must be specified to train in adversarial mode')\\n \\n if(reset_graph):\\n tf.reset_default_graph()\\n \\n self.dataset_is_tf = self.data.is_tf\\n \\n self.classifier.load_model()\\n \\n shape_x, shape_y = self.data.shape()\\n shape_x[0] = None\\n \\n z_dims = [None] + self.z_dim\\n \\n shape_y[0] = None\\n \\n if self.dataset_is_tf:\\n if(self.use_mask):\\n self.X_real , self.labels, _, self.mask = self.data.next_batch()\\n probs = self.classifier.predict(self.X_real)[:,:,:,4] \\n labels_masked = tf.cast(self.labels * self.mask, tf.float32)\\n pm = tf.cast(tf.expand_dims(probs, -1)*labels_masked, tf.float32)\\n self.classifier_proba = tf.cast(1.0-(tf.reduce_sum(pm, [1,2,3]) / tf.reduce_sum(labels_masked, [1,2,3])), tf.float32)\\n self.mask.set_shape(shape_y)\\n elif(self.use_labels):\\n self.X_real , self.labels, _, _ = self.data.next_batch()\\n probs = self.classifier.predict(self.X_real)[:,:,:,4] \\n pm = tf.cast(tf.expand_dims(probs, -1)*self.labels, tf.float32)\\n self.classifier_proba = tf.cast(1.0-(tf.reduce_sum(pm, [1,2,3]) / tf.reduce_sum(self.labels, [1,2,3])), tf.float32) \\n else:\\n self.X_real , self.labels, self.classifier_proba = self.data.next_batch()\\n \\n self.X_real.set_shape(shape_x)\\n self.labels.set_shape(shape_y)\\n \\n if(self.use_ceil):\\n if self.ceil_value is not None:\\n print('meh')\\n self.classifier_proba = tf.minimum(self.classifier_proba, self.ceil_value)\\n\\n self.classifier_proba = tf.nn.softmax(self.classifier_proba * self.temperature)\\n self.classifier_proba = tf.expand_dims(self.classifier_proba, -1)\\n \\n else:\\n self.X_real = tf.placeholder(\\\"float\\\", shape_x, name=\\\"X_real\\\")\\n self.labels = tf.placeholder(\\\"float\\\", shape_y, name=\\\"labels\\\")\\n self.classifier_proba = tf.placeholder(\\\"float\\\", [None, 1], name=\\\"classifier_proba\\\")\\n self.adv_map = tf.placeholder(\\\"float\\\", shape_y, name=\\\"adv_map\\\")\\n\\n self.z = tf.placeholder(\\\"float\\\", z_dims, name=\\\"z_input\\\")\\n self.is_training = tf.placeholder(tf.bool, shape=())\\n if self.use_clf:\\n self.label_used = tf.constant(self.label_used, dtype='float')\\n\\n\\n \\n #train with real and classifier\\n \\n if(self.use_labels):\\n self.pred_real = self.discriminator(self.X_real, self.labels, reuse=False)\\n elif(self.use_mask):\\n self.pred_real = self.discriminator(self.X_real, self.mask, reuse=False)\\n else:\\n self.pred_real = self.discriminator(self.X_real, reuse=False) \\n \\n if(self.adv):\\n self.pred_real *= self.classifier_proba\\n \\n #train with fake\\n if(self.use_labels):\\n self.X_fake = self.generator(self.X_real, self.labels, self.z, train=self.is_training)\\n elif(self.use_mask):\\n self.X_fake = self.generator(self.X_real, self.mask, self.z, train=self.is_training)\\n else:\\n self.X_fake = self.generator(self.z, train=self.is_training)\\n \\n self.X_fake = tf.identity(self.X_fake, name=\\\"X_fake\\\")\\n \\n shape_x[0] = -1\\n \\n self.X_fake = tf.reshape(self.X_fake, shape_x)\\n \\n if(self.use_labels):\\n self.pred_fake = self.discriminator(self.X_fake, self.labels)\\n elif(self.use_mask):\\n self.pred_fake = self.discriminator(self.X_fake, self.mask)\\n else:\\n self.pred_fake = self.discriminator(self.X_fake)\\n \\n if(self.adv and not self.use_softmax):\\n self.pred_fake *= 1/len(self.data.shape()[0])\\n \\n self.g_loss = - tf.reduce_mean(self.pred_fake)\\n \\n \\n if(self.adv and not self.use_softmax):\\n self.d_loss = - (tf.reduce_sum(self.pred_real) - tf.reduce_sum(self.pred_fake))\\n elif(self.adv):\\n self.d_loss = - (tf.reduce_sum(self.pred_real) - tf.reduce_mean(self.pred_fake))\\n else:\\n self.d_loss = - (tf.reduce_mean(self.pred_real) - tf.reduce_mean(self.pred_fake))\\n \\n if self.reg_losses :\\n self.d_loss = self.reg_term * self.d_loss\\n self.g_loss = self.reg_term * self.g_loss\\n \\n if self.use_recon :\\n if(self.use_mask):\\n X_fake_masked = (1-self.mask) * self.X_fake\\n X_real_masked = (1-self.mask) * self.X_real \\n elif(self.use_labels):\\n X_fake_masked = (1-self.labels) * self.X_fake\\n X_real_masked = (1-self.labels) * self.X_real \\n \\n self.g_loss += self.recon_beta * (tf.reduce_mean(tf.reduce_sum(tf.square(X_fake_masked - X_real_masked), axis=np.arange(1, len(X_fake_masked.shape)))))\\n \\n if self.use_clf :\\n y_t = (self.mask) * self.label_used\\n preds = self.classifier.predict(self.X_fake)\\n preds = preds * (self.mask)\\n preds = 1-preds\\n self.loss_clf = self.clf_alpha * tf.reduce_mean(-tf.reduce_sum(y_t * tf.log(tf.clip_by_value(preds ,1e-10,1.0)), reduction_indices=[1]))\\n self.g_loss += self.loss_clf\\n \\n self.neg_loss = - self.d_loss\\n \\n if(self.use_labels):\\n labels = self.labels\\n elif(self.use_mask):\\n labels = self.mask\\n else:\\n labels = None\\n \\n if self.loss == 'gp' :\\n self.d_loss += losses.gp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n \\n if self.loss == 'lp' :\\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n \\n if self.loss == 'ct':\\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n self.d_loss += losses.ct_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n \\n D_vars = [var for var in tf.trainable_variables() if \\\"discriminator\\\" in var.name]\\n G_vars = [var for var in tf.trainable_variables() if \\\"generator\\\" in var.name] \\n \\n if self.accumulate_grd:\\n # initialized with 0s\\n \\n self.accum_vars_D = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False ) for tv in D_vars] \\n self.accumulation_counter_D = tf.Variable(0.0, trainable=False)\\n \\n self.accum_vars_G = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in G_vars] \\n self.accumulation_counter_G = tf.Variable(0.0, trainable=False)\\n \\n self.D_sum = tf.summary.scalar('D_loss', self.d_loss)\\n self.G_sum = tf.summary.scalar('G_loss', self.g_loss)\\n\\n with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\\n \\n if self.accumulate_grd:\\n\\n gvs_G = (self.optimizer_G).compute_gradients(self.g_loss, G_vars)\\n gvs_D = (self.optimizer_D).compute_gradients(self.d_loss, D_vars)\\n \\n self.accum_ops_G = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in zip(self.accum_vars_G, gvs_G)] \\n self.accum_ops_G.append(self.accumulation_counter_G.assign_add(1.0)) \\n \\n self.accum_ops_D = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in zip(self.accum_vars_D, gvs_D)] \\n self.accum_ops_D.append(self.accumulation_counter_D.assign_add(1.0)) \\n \\n self.G_step = (self.optimizer_G).apply_gradients([(accumulator / self.accumulation_counter_G, var) for (accumulator, (grad, var)) in zip(self.accum_vars_G, gvs_G)])\\n\\n self.D_step = (self.optimizer_D).apply_gradients([(accumulator / self.accumulation_counter_D, var) for (accumulator, (grad, var)) in zip(self.accum_vars_D, gvs_D)])\\n \\n self.zero_ops_G = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in zip(self.accum_vars_G, G_vars)]\\n self.zero_ops_D = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in zip(self.accum_vars_D, D_vars)]\\n \\n self.zero_ops_G.append(self.accumulation_counter_G.assign(0.0))\\n self.zero_ops_D.append(self.accumulation_counter_D.assign(0.0))\\n \\n else:\\n self.G_step = (self.optimizer_G).minimize(self.g_loss, var_list=G_vars)\\n self.D_step = (self.optimizer_D).minimize(self.d_loss, var_list=D_vars)\\n \\n if self.loss == 'clip' :\\n self.D_clipping = [w.assign(tf.clip_by_value(w, -self.clip_value, self.clip_value)) for w in D_vars]\\n\\n \\n self.saver = tf.train.Saver()\\n self.builded = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf448bb9546fbe318886d58d33a7591\",\n \"score\": \"0.5416056\",\n \"text\": \"def index_adam_input(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\\n weight_decay: float, eps: float, indices: List[Tensor]):\\n\\n for i, param in enumerate(params):\\n\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\\n param_tmp, ind_tmp = torch.sort(param.view(-1))\\n indices[i].view(-1)[torch.argsort(param_new.view(-1))] = ind_tmp\\n param.data.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b79ba1dd9f979defd167a5ee90c711a\",\n \"score\": \"0.5413545\",\n \"text\": \"def get_adversarial(self, batch_size, mode):\\n # Restart at beggining if iteration if complete\\n if self.count[mode] >= self.adv_examples[mode].size(0):\\n self.count[mode] = 0\\n \\n x_batch = self.adv_examples[mode][(self.count[mode]):(self.count[mode] + batch_size)]\\n y_batch = self.adv_labels[mode][(self.count[mode]):(self.count[mode] + batch_size)]\\n self.count[mode] = self.count[mode] + batch_size\\n \\n return x_batch.cuda(), y_batch.cuda()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8451e7709fe8b9b2f57cb95270cdf9\",\n \"score\": \"0.53965044\",\n \"text\": \"def __init__(self, params, **kwargs):\\n super().__init__(name=\\\"AdaGrad\\\")\\n self._accu = [\\n shared(aet.zeros_like(p()).eval()) for p in params if p.status != 1\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc1939e01226ee1f60eb5235b414b01\",\n \"score\": \"0.53934616\",\n \"text\": \"def index_adam(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\\n weight_decay: float, eps: float):\\n\\n for i, param in enumerate(params):\\n\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\\n param_tmp, _ = torch.sort(param.view(-1))\\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ec57db0599b9203cc1f11d5583fd173\",\n \"score\": \"0.53554124\",\n \"text\": \"def tf_efficientnet_b1_ns(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b1_ns\\\",\\n channel_multiplier=1.0,\\n depth_multiplier=1.1,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8220e5f2760478d2ca0b91ba8deffd9\",\n \"score\": \"0.53538835\",\n \"text\": \"def __mlp__(self):\\n # Define the operations\\n\\n # Network input\\n vec_inputs = []\\n\\n # Distributional component\\n if self.hparams.input in ['dist', 'dist-nc', 'integrated', 'integrated-nc']:\\n for emb_id in [self.x_embedding_id, self.y_embedding_id]:\\n vec_inputs.append(tf.nn.embedding_lookup(self.relata_lookup, emb_id))\\n\\n # Noun compound component\\n if self.hparams.input in ['dist-nc', 'integrated-nc']:\\n vec = tf.nn.embedding_lookup(self.nc_lookup, self.nc_embedding_id)\\n vec_inputs.append(vec)\\n\\n # Path-based component\\n if self.hparams.input in ['path', 'integrated', 'integrated-nc']:\\n\\n # Get the current paths for each batch instance\\n self.path_embeddings = tf.nn.embedding_lookup(self.path_lookup,\\n self.path_embedding_id)\\n\\n # self.path_embeddings is of shape\\n # [batch_size, max_path_per_instance, output_dim]\\n # We need to multiply it by path counts\\n # ([batch_size, max_path_per_instance]).\\n # Start by duplicating path_counts along the output_dim axis.\\n self.path_freq = tf.tile(tf.expand_dims(self.path_counts, -1),\\n [1, 1, self.path_dim])\\n\\n # Compute the averaged path vector for each instance.\\n # First, multiply the path embeddings and frequencies element-wise.\\n self.weighted = tf.multiply(self.path_freq, self.path_embeddings)\\n\\n # Second, take the sum to get a tensor of shape [batch_size, output_dim].\\n self.pair_path_embeddings = tf.reduce_sum(self.weighted, 1)\\n\\n # Finally, divide by the total number of paths.\\n # The number of paths for each pair has a shape [batch_size, 1],\\n # We duplicate it output_dim times along the second axis.\\n self.num_paths = tf.clip_by_value(\\n tf.reduce_sum(self.path_counts, 1), 1, np.inf)\\n self.num_paths = tf.tile(tf.expand_dims(self.num_paths, -1),\\n [1, self.path_dim])\\n\\n # And finally, divide pair_path_embeddings by num_paths element-wise.\\n self.pair_path_embeddings = tf.div(\\n self.pair_path_embeddings, self.num_paths)\\n vec_inputs.append(self.pair_path_embeddings)\\n\\n # Concatenate the inputs and feed to the MLP\\n self.input_vec = tf.nn.dropout(\\n tf.concat(vec_inputs, 1),\\n keep_prob=self.hparams.input_keep_prob)\\n\\n h = tf.matmul(self.input_vec, self.weights1)\\n self.output = h\\n\\n if self.hparams.hidden_layers == 1:\\n self.output = tf.matmul(tf.nn.tanh(h), self.weights2)\\n\\n self.scores = self.output\\n self.predictions = tf.argmax(self.scores, axis=1)\\n\\n # Define the loss function and the optimization algorithm\\n self.cross_entropies = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n logits=self.scores, labels=self.labels)\\n self.cost = tf.reduce_sum(self.cross_entropies, name='cost')\\n self.global_step = tf.Variable(0, name='global_step', trainable=False)\\n self.optimizer = tf.train.AdamOptimizer()\\n self.train_op = self.optimizer.minimize(\\n self.cost, global_step=self.global_step)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a3c846ec9038dfe0189e669abbb781\",\n \"score\": \"0.5348033\",\n \"text\": \"def __init__(self, filter_sizes,num_filters,num_classes, learning_rate, batch_size, decay_steps, decay_rate,sequence_length,vocab_size,embed_size,\\n is_training,initializer=tf.random_normal_initializer(stddev=0.1),multi_label_flag=False,clip_gradients=5.0,decay_rate_big=0.50):\\n # set hyperparamter\\n self.num_classes = num_classes\\n self.batch_size = batch_size\\n self.sequence_length=sequence_length\\n self.vocab_size=vocab_size\\n self.embed_size=embed_size\\n self.is_training=is_training\\n self.learning_rate = tf.Variable(learning_rate, trainable=False, name=\\\"learning_rate\\\")#ADD learning_rate\\n self.learning_rate_decay_half_op = tf.assign(self.learning_rate, self.learning_rate * decay_rate_big)\\n \\n\\n self.instantiate_weights()\\n self.logits = self.inference()\\n if not is_training:\\n return\\n if multi_label_flag:\\n print(\\\"going to use multi label loss.\\\")\\n self.loss_val = self.loss_multilabel()\\n else:\\n print(\\\"going to use single label loss.\\\")\\n self.loss_val = self.loss()\\n self.train_op = self.train()\\n self.predictions = tf.argmax(self.logits, 1, name=\\\"predictions\\\")\\n\\n if not self.multi_label_flag:\\n correct_prediction = tf.equal(tf.cast(self.predictions,tf.int32), self.input_y) #tf.argmax(self.logits, 1)-->[batch_size]\\n self.accuracy =tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name=\\\"Accuracy\\\") # shape=()\\n else:\\n self.accuracy = tf.constant(0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2806a813b4b5a897f3a6a1ad87049622\",\n \"score\": \"0.53439796\",\n \"text\": \"def __init__(self, conf):\\n\\n super(PolicyVNetwork, self).__init__(conf)\\n\\n self.entropy_regularisation_strength = conf['entropy_regularisation_strength']\\n self.softmax_temp = conf['softmax_temp']\\n self.op = Operations(conf)\\n self.total_repetitions = conf['nb_choices']\\n\\n with tf.device(conf['device']):\\n with tf.name_scope('Training'):\\n\\n with tf.name_scope('Critic'):\\n self.critic_target_ph = tf.placeholder(\\\"float32\\\", [None], name='target')\\n # Final critic layer\\n _, _, self.output_layer_v = self.op.fc('critic_output', self.output, 1, activation=\\\"linear\\\")\\n self.output_layer_v = tf.reshape(self.output_layer_v, [-1])\\n # Advantage critic\\n self.critic_loss = tf.subtract(self.critic_target_ph, self.output_layer_v)\\n self.critic_loss_mean = tf.reduce_mean(tf.scalar_mul(0.25, tf.pow(self.critic_loss, 2)), name='mean_critic_loss')\\n\\n\\n with tf.name_scope('Actor'):\\n # Final actor layer\\n _, _, self.output_layer_pi = self.op.softmax('actor_output', self.output, self.num_actions, self.softmax_temp)\\n # Avoiding log(0) by adding a very small quantity (1e-30) to output.\\n self.log_output_layer_pi = tf.log(tf.add(self.output_layer_pi, tf.constant(1e-30)),\\n name='actor_output_log_policy')\\n # Entropy: sum_a (-p_a ln p_a)\\n self.output_layer_entropy_actor = tf.reduce_sum(tf.multiply(\\n tf.constant(-1.0),\\n tf.multiply(self.output_layer_pi, self.log_output_layer_pi)), reduction_indices=1)\\n self.log_output_selected_action = tf.reduce_sum(\\n tf.multiply(self.log_output_layer_pi, self.selected_action_ph),\\n reduction_indices=1)\\n self.actor_objective_entropy_term = tf.multiply(self.entropy_regularisation_strength, self.output_layer_entropy_actor)\\n\\n\\n with tf.name_scope('Repetition'):\\n # Final repetition layer\\n _, _, self.output_layer_rep = self.op.softmax('repetition_output', self.output, self.total_repetitions, self.softmax_temp)\\n self.log_output_layer_rep = tf.log(tf.add(self.output_layer_rep, tf.constant(1e-30)),\\n name='repetition_output_log_policy')\\n self.output_layer_entropy_rep = tf.reduce_sum(tf.multiply(\\n tf.constant(-1.0),\\n tf.multiply(self.output_layer_rep, self.log_output_layer_rep)), reduction_indices=1, name='entropy_rep')\\n self.log_output_selected_repetition = tf.reduce_sum(\\n tf.multiply(self.log_output_layer_rep, self.selected_repetition_ph),\\n reduction_indices=1)\\n self.log_repetition_mean = tf.reduce_mean(self.log_output_selected_repetition)\\n self.rep_objective_entropy_term = tf.multiply(self.entropy_regularisation_strength, self.output_layer_entropy_rep, name='rep_entropy_term')\\n\\n with tf.name_scope('ComputeLoss'):\\n self.adv_actor_ph = tf.placeholder(\\\"float\\\", [None], name='advantage')\\n self.actor_objective_advantage_term = tf.multiply(\\n tf.add(self.log_output_selected_action, self.log_output_selected_repetition),\\n self.adv_actor_ph, name='advantage')\\n self.actor_advantage_mean = tf.reduce_mean(self.actor_objective_advantage_term)\\n\\n self.entropy_term = tf.add(self.actor_objective_entropy_term, self.rep_objective_entropy_term, name='entropy_term')\\n\\n self.actor_objective_mean = tf.reduce_mean(tf.multiply(tf.constant(-1.0),\\n tf.add(self.actor_objective_advantage_term, self.entropy_term)),\\n name='mean_actor_objective')\\n\\n # Loss scaling is used because the learning rate was initially runed tuned to be used with\\n # max_local_steps = 5 and summing over timesteps, which is now replaced with the mean.\\n self.loss = tf.scalar_mul(self.loss_scaling, self.actor_objective_mean + self.critic_loss_mean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f99e95b62bf4d35c0f77f0891e3a04\",\n \"score\": \"0.5342701\",\n \"text\": \"def create(self):\\n # (self.feed('data')\\n # .conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')\\n # .lrn(2, 2e-05, 0.75, name='norm1')\\n # .max_pool(3, 3, 2, 2, padding='VALID', name='pool1')\\n # .conv(5, 5, 256, 1, 1, group=2, name='conv2')\\n # .lrn(2, 2e-05, 0.75, name='norm2')\\n # .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')\\n # .conv(3, 3, 384, 1, 1, name='conv3')\\n # .conv(3, 3, 384, 1, 1, group=2, name='conv4')\\n # .conv(3, 3, 256, 1, 1, group=2, name='conv5')\\n # .fc(4096, name='fc6')\\n # .fc(4096, name='fc7')\\n # .fc(1000, relu=False, name='fc8')\\n # .softmax(name='prob'))\\n\\n #conv1\\n #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')\\n k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4\\n conv1W = tf.Variable(self.WEIGHTS[\\\"conv1\\\"][\\\"weights\\\"], name='conv1/weights')\\n conv1b = tf.Variable(self.WEIGHTS[\\\"conv1\\\"][\\\"biases\\\"], name='conv1/biases')\\n conv1_in = conv(self.X, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding=\\\"SAME\\\", group=1)\\n conv1 = tf.nn.relu(conv1_in)\\n\\n #lrn1\\n #lrn(2, 2e-05, 0.75, name='norm1')\\n radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0\\n lrn1 = tf.nn.local_response_normalization(conv1,\\n depth_radius=radius,\\n alpha=alpha,\\n beta=beta,\\n bias=bias)\\n\\n #maxpool1\\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool1')\\n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\\n maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n\\n #conv2\\n #conv(5, 5, 256, 1, 1, group=2, name='conv2')\\n k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2\\n conv2W = tf.Variable(self.WEIGHTS[\\\"conv2\\\"][\\\"weights\\\"], name='conv2/weights')\\n conv2b = tf.Variable(self.WEIGHTS[\\\"conv2\\\"][\\\"biases\\\"], name='conv2/biases')\\n conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding=\\\"SAME\\\", group=group)\\n conv2 = tf.nn.relu(conv2_in)\\n\\n #lrn2\\n #lrn(2, 2e-05, 0.75, name='norm2')\\n radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0\\n lrn2 = tf.nn.local_response_normalization(conv2,\\n depth_radius=radius,\\n alpha=alpha,\\n beta=beta,\\n bias=bias)\\n\\n #maxpool2\\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool2') \\n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\\n maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n\\n #conv3\\n #conv(3, 3, 384, 1, 1, name='conv3')\\n k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1\\n conv3W = tf.Variable(self.WEIGHTS[\\\"conv3\\\"][\\\"weights\\\"], name='conv3/weights')\\n conv3b = tf.Variable(self.WEIGHTS[\\\"conv3\\\"][\\\"biases\\\"], name='conv3/biases')\\n conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding=\\\"SAME\\\", group=group)\\n conv3 = tf.nn.relu(conv3_in)\\n\\n #conv4\\n #conv(3, 3, 384, 1, 1, group=2, name='conv4')\\n k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2\\n conv4W = tf.Variable(self.WEIGHTS[\\\"conv4\\\"][\\\"weights\\\"], name='conv4/weights')\\n conv4b = tf.Variable(self.WEIGHTS[\\\"conv4\\\"][\\\"biases\\\"], name='conv4/biases')\\n conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding=\\\"SAME\\\", group=group)\\n conv4 = tf.nn.relu(conv4_in)\\n\\n #conv5\\n #conv(3, 3, 256, 1, 1, group=2, name='conv5')\\n k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2\\n conv5W = tf.Variable(self.WEIGHTS[\\\"conv5\\\"][\\\"weights\\\"], name='conv5/weights')\\n conv5b = tf.Variable(self.WEIGHTS[\\\"conv5\\\"][\\\"biases\\\"], name='conv5/biases')\\n conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding=\\\"SAME\\\", group=group)\\n conv5 = tf.nn.relu(conv5_in)\\n\\n #maxpool5\\n #max_pool(3, 3, 2, 2, padding='VALID', name='pool5')\\n k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'\\n maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n\\n #fc6\\n #fc(4096, name='fc6')\\n fc6W = tf.Variable(self.WEIGHTS[\\\"fc6\\\"][\\\"weights\\\"], name='fc6/weights')\\n fc6b = tf.Variable(self.WEIGHTS[\\\"fc6\\\"][\\\"biases\\\"], name='fc6/biases')\\n fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(np.prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)\\n\\n #Add a dropout layer here\\n dropout6 = tf.nn.dropout(fc6, self.KEEP_PROB)\\n \\n #fc7\\n #fc(4096, name='fc7')\\n fc7W = tf.Variable(self.WEIGHTS[\\\"fc7\\\"][\\\"weights\\\"], name='fc7/weights')\\n fc7b = tf.Variable(self.WEIGHTS[\\\"fc7\\\"][\\\"biases\\\"], name='fc7/biases')\\n fc7 = tf.nn.relu_layer(dropout6, fc7W, fc7b)\\n\\n #Add a dropout layer here\\n dropout7 = tf.nn.dropout(fc7, self.KEEP_PROB)\\n \\n #fc8\\n #fc(1000, relu=False, name='fc8')\\n fc8W = tf.get_variable(\\\"fc8/weights\\\", [4096, self.NUM_CLASSES])\\n fc8b = tf.get_variable(\\\"fc8/biases\\\", [self.NUM_CLASSES])\\n self.fc8 = tf.nn.xw_plus_b(dropout7, fc8W, fc8b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf0d3936e441a4509d7388cf96133e2\",\n \"score\": \"0.5338003\",\n \"text\": \"def neural_net_predict_gen(params, inputs):\\r\\n inpW, inpb = params[0]\\r\\n inputs = leaky_relu(np.dot(inputs, inpW) + inpb)\\r\\n for W, b in params[1:-1]:\\r\\n outputs = np.dot(inputs, W) + b\\r\\n outputs = batch_normalize(np.dot(inputs, W) + b)\\r\\n inputs = leaky_relu(outputs)\\r\\n outW, outb = params[-1]\\r\\n outputs = np.dot(inputs, outW) + outb\\r\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238e036f1e83b9151ac54e48ec653bf0\",\n \"score\": \"0.5337877\",\n \"text\": \"def adam(params: List[Tensor],\\n grads: List[Tensor],\\n exp_avgs: List[Tensor],\\n exp_avg_sqs: List[Tensor],\\n max_exp_avg_sqs: List[Tensor],\\n state_steps: List[int],\\n amsgrad: bool,\\n beta1: float,\\n beta2: float,\\n lr: float,\\n weight_decay: float,\\n eps: float):\\n\\n for i, param in enumerate(params):\\n\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n if amsgrad:\\n max_exp_avg_sq = max_exp_avg_sqs[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # expavg=exp_avg*beta1+grad*(1-beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # exp_avg_sq = exp_avg_sq*beta2+ grad*grad*(1-beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n\\n param.addcdiv_(exp_avg, denom, value=-step_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0053f7cbb12b4326437a7fe183db262\",\n \"score\": \"0.53284603\",\n \"text\": \"def vgg16(self):\\n x = tflearn.input_data(shape=[None, self.width, self.height, 1], name='input')\\n\\n x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_1')\\n x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')\\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')\\n\\n x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')\\n x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')\\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')\\n\\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')\\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')\\n x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')\\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')\\n\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')\\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')\\n\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')\\n x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')\\n x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')\\n\\n x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')\\n x = tflearn.dropout(x, 0.5, name='dropout1')\\n\\n x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')\\n x = tflearn.dropout(x, 0.5, name='dropout2')\\n\\n x = tflearn.fully_connected(x, self.output, activation='softmax', scope='fc8')\\n\\n network = regression(x, optimizer='momentum', loss='categorical_crossentropy',\\n learning_rate=self.learning_rate)\\n model = tflearn.DNN(network, checkpoint_path='vgg16',\\n max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e9b980d51e65268987aebda68090ff\",\n \"score\": \"0.53265566\",\n \"text\": \"def _learn_advantage_network(self, player):\\n for data in self._get_advantage_dataset(\\n player, self._advantage_network_train_steps):\\n (self._params_adv_network[player], self._opt_adv_state[player],\\n main_loss) = self._jitted_adv_update(self._params_adv_network[player],\\n self._opt_adv_state[player],\\n *data, jnp.array(self._iteration))\\n\\n return main_loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4508193264bafa7e74fbf6398041c55e\",\n \"score\": \"0.53227943\",\n \"text\": \"def train(self):\\n\\n if not os.path.isdir(os.path.join(self.args.save_dir, \\\"checkpoints\\\")):\\n os.mkdir(os.path.join(self.args.save_dir, \\\"checkpoints\\\"))\\n\\n lr1, lr2, lr3 = self.args.learning_rates\\n if self.args.use_cons_optim:\\n cons_thre1, cons_thre2, cons_thre3 = self.args.cons_thres\\n global_emb_optim = optim.AdamCons([self.model.global_embs],\\n lr=lr1,\\n eps=1.0e-3,\\n cons_thre=cons_thre1)\\n graph_module_optim = optim.AdamCons(\\n list(self.model.fc_out.parameters()) +\\n list(self.model.fc_cat.parameters()),\\n lr=lr2,\\n eps=1.0e-3,\\n cons_thre=cons_thre2)\\n ts_module_optim = optim.AdamCons(\\n list(self.model.encoder.parameters()) +\\n list(self.model.decoder.parameters()),\\n lr=lr3,\\n eps=1.0e-3,\\n cons_thre=cons_thre3)\\n else:\\n global_emb_optim = Adam([self.model.global_embs], lr=lr1, eps=1.0e-3)\\n graph_module_optim = Adam(\\n list(self.model.fc_out.parameters()) +\\n list(self.model.fc_cat.parameters()),\\n lr=lr2,\\n eps=1.0e-3)\\n ts_module_optim = Adam(\\n list(self.model.encoder.parameters()) +\\n list(self.model.decoder.parameters()),\\n lr=lr3,\\n eps=1.0e-3)\\n optims = [global_emb_optim, graph_module_optim, ts_module_optim]\\n\\n edit_edge_idxs, old_adj_pred, adj_label, weights = self._get_error_edges_new_labels(\\n )\\n adj_label = adj_label.to(self.device)\\n\\n for iter_num in range(self.args.num_max_fine_tune_iters):\\n self._train_iter(optims, adj_label, weights)\\n\\n if (iter_num + 1) % 100 == 0:\\n test_results = self.test(edit_edge_idxs, old_adj_pred, adj_label)\\n\\n with open(os.path.join(self.args.save_dir, \\\"val_test.txt\\\"), \\\"w\\\") as f:\\n f.write(\\n \\\"testing mae: {}, adj accuracy: {}, sparsity: {}, preserve: {}, edit edge accuracy: {}\\\\n\\\"\\n .format(test_results[0], test_results[1], test_results[2],\\n test_results[3], test_results[4]))\\n\\n if self.args.save_model:\\n torch.save(\\n self.model.state_dict(),\\n os.path.join(self.args.save_dir, \\\"checkpoints\\\",\\n \\\"iter_{}.ckpt\\\".format(iter_num)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27f4c885a68209c17393c8a3a95ec78\",\n \"score\": \"0.5312277\",\n \"text\": \"def __init__(\\n self,\\n size,\\n self_attn,\\n src_attn,\\n feed_forward,\\n dropout_rate,\\n normalize_before=True,\\n concat_after=False,\\n compute_type=mstype.float32,\\n ):\\n super().__init__()\\n self.size = size\\n self.self_attn = self_attn\\n self.src_attn = src_attn\\n self.feed_forward = feed_forward\\n self.norm1 = CustomLayerNorm(size, epsilon=1e-12)\\n self.norm2 = CustomLayerNorm(size, epsilon=1e-12)\\n self.norm3 = CustomLayerNorm(size, epsilon=1e-12)\\n self.dropout = nn.Dropout(p=dropout_rate)\\n self.normalize_before = normalize_before\\n self.concat_after = concat_after\\n if self.concat_after:\\n self.concat_linear1 = Dense(\\n size + size, size).to_float(compute_type)\\n self.concat_linear2 = Dense(\\n size + size, size).to_float(compute_type)\\n self.cat1 = ops.Concat(axis=-1)\\n self.cat2 = ops.Concat(axis=1)\\n self.expand_dims = ops.ExpandDims()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e4a5b988f37e26e0c4805e94abe755\",\n \"score\": \"0.53115296\",\n \"text\": \"def __init__(self, input0, input1, model='net-lin', net='alex', version=0.1):\\n # flatten the leading dimensions\\n batch_shape = tf.shape(input0)[:-3]\\n input0 = tf.reshape(input0, tf.concat([[-1], tf.shape(input0)[-3:]], axis=0))\\n input1 = tf.reshape(input1, tf.concat([[-1], tf.shape(input1)[-3:]], axis=0))\\n input0 = tf.transpose(input0, [0, 3, 1, 2])\\n input1 = tf.transpose(input1, [0, 3, 1, 2])\\n\\n input0_name, input1_name = '0:0', '1:0'\\n\\n default_graph = tf.get_default_graph()\\n producer_version = default_graph.graph_def_versions.producer\\n producer_version = 27 # 38\\n\\n cache_dir = 'lpips_pb'\\n pb_fname = '%s_%s_v%s_%d.pb' % (model, net, version, producer_version)\\n\\n with open(os.path.join(cache_dir, pb_fname), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def,\\n input_map={input0_name: input0, input1_name: input1})\\n distance, = default_graph.get_operations()[-1].outputs\\n assert distance.name[-12:] == 'Reshape_10:0', distance.name\\n\\n if distance.shape.ndims == 4:\\n distance = tf.squeeze(distance, axis=[-3, -2, -1])\\n distance = tf.reshape(distance, batch_shape)\\n self.distance = tf.reduce_mean(distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0ab55c39ea4c9e168749a2e4239a9f\",\n \"score\": \"0.53050554\",\n \"text\": \"def fprop(input_batch, word_embedding_weights, embed_to_hid_weights,\\n\\t\\thid_to_output_weights, hid_bias, output_bias):\\n\\n#[numwords, batchsize] = size(input_batch);\\n\\tnumwords, batchsize = input_batch.shape # (3, 100)\\n#[vocab_size, numhid1] = size(word_embedding_weights);\\n\\tvocab_size, numhid1 = word_embedding_weights.shape # (250, 50)\\n#numhid2 = size(embed_to_hid_weights, 2);\\n\\tnumhid2 = embed_to_hid_weights.shape[1] # 200\\n\\n#%% COMPUTE STATE OF WORD EMBEDDING LAYER.\\n#% Look up the inputs word indices in the word_embedding_weights matrix.\\n#embedding_layer_state = reshape(...\\n# word_embedding_weights(reshape(input_batch, 1, []),:)',...\\n# numhid1 * numwords, []);\\n\\tembedding_layer_state = word_embedding_weights[ravel(\\n\\t\\tinput_batch, order='F')-1,:].T.reshape(\\n\\t\\tnumhid1 * numwords, -1, order='F')\\n\\n#%% COMPUTE STATE OF HIDDEN LAYER.\\n#% Compute inputs to hidden units.\\n#inputs_to_hidden_units = embed_to_hid_weights' * embedding_layer_state + ...\\n# repmat(hid_bias, 1, batchsize);\\n\\tinputs_to_hidden_units = dot(embed_to_hid_weights.T,\\n\\t\\tembedding_layer_state) + tile(hid_bias, (1, batchsize))\\n\\t\\n#% Apply logistic activation function.\\n#% FILL IN CODE. Replace the line below by one of the options.\\n#hidden_layer_state = zeros(numhid2, batchsize);\\n#% Options\\n#% (a) hidden_layer_state = 1 ./ (1 + exp(inputs_to_hidden_units));\\n#% (b) hidden_layer_state = 1 ./ (1 - exp(-inputs_to_hidden_units));\\n#% (c) hidden_layer_state = 1 ./ (1 + exp(-inputs_to_hidden_units));\\n\\thidden_layer_state = 1 / (1 + exp(-inputs_to_hidden_units))\\n#% (d) hidden_layer_state = -1 ./ (1 + exp(-inputs_to_hidden_units));\\n\\n#%% COMPUTE STATE OF OUTPUT LAYER.\\n#% Compute inputs to softmax.\\n#% FILL IN CODE. Replace the line below by one of the options.\\n#inputs_to_softmax = zeros(vocab_size, batchsize);\\n#% Options\\n#% (a) inputs_to_softmax = hid_to_output_weights' * hidden_layer_state + repmat(output_bias, 1, batchsize);\\n\\tinputs_to_softmax = dot(hid_to_output_weights.T,\\n\\t\\thidden_layer_state) + tile(output_bias, (1, batchsize))\\n#% (b) inputs_to_softmax = hid_to_output_weights' * hidden_layer_state + repmat(output_bias, batchsize, 1);\\n#% (c) inputs_to_softmax = hidden_layer_state * hid_to_output_weights' + repmat(output_bias, 1, batchsize);\\n#% (d) inputs_to_softmax = hid_to_output_weights * hidden_layer_state + repmat(output_bias, batchsize, 1);\\n\\n#% Subtract maximum. \\n#% Remember that adding or subtracting the same constant from each input to a\\n#% softmax unit does not affect the outputs. Here we are subtracting maximum to\\n#% make all inputs <= 0. This prevents overflows when computing their\\n#% exponents.\\n#inputs_to_softmax = inputs_to_softmax...\\n# - repmat(max(inputs_to_softmax), vocab_size, 1);\\n\\tinputs_to_softmax = inputs_to_softmax - tile(inputs_to_softmax.max(0), (vocab_size, 1))\\n\\n#% Compute exp.\\n\\toutput_layer_state = exp(inputs_to_softmax);\\n\\n#% Normalize to get probability distribution.\\n#output_layer_state = output_layer_state ./ repmat(...\\n# sum(output_layer_state, 1), vocab_size, 1);\\n\\toutput_layer_state = output_layer_state / tile(\\n\\t\\toutput_layer_state.sum(axis=0), (vocab_size, 1))\\n\\n\\treturn embedding_layer_state, hidden_layer_state, output_layer_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff71055a4ddb64faa50ca7c2e498655e\",\n \"score\": \"0.5304338\",\n \"text\": \"def __init__(self, BATCH_SIZE, EMBEDDING_SIZE, HIDDEN_UNITS, VOCAB_SIZE):\\r\\n super(RNN_Vanila, self).__init__()\\r\\n w_init = tf.random_normal_initializer()\\r\\n b_init = tf.zeros_initializer()\\r\\n self.BATCH_SIZE = BATCH_SIZE\\r\\n self.EMBEDDING_SIZE = EMBEDDING_SIZE\\r\\n self.HIDDEN_UNITS = HIDDEN_UNITS\\r\\n self.VOCAB_SIZE = VOCAB_SIZE\\r\\n \\r\\n self.w_ip = tf.Variable(initial_value=w_init(shape=(self.EMBEDDING_SIZE, self.HIDDEN_UNITS), dtype='float32'), trainable=True)\\r\\n self.w_hidden = tf.Variable(initial_value=w_init(shape=(self.HIDDEN_UNITS, self.HIDDEN_UNITS), dtype='float32'), trainable=True)\\r\\n self.w_op = tf.Variable(initial_value=w_init(shape=(self.HIDDEN_UNITS, self.VOCAB_SIZE), dtype='float32'), trainable=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d61f99646e0a5646a4c8198156cfe6\",\n \"score\": \"0.5300648\",\n \"text\": \"def tf_efficientnet_b3_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b3_ap\\\",\\n channel_multiplier=1.2,\\n depth_multiplier=1.4,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e79c2519f2affd389278a2935917cd53\",\n \"score\": \"0.5297878\",\n \"text\": \"def alex_net(self):\\n tweet_length = 140\\n tweet_height = 5\\n input_size = 8 * 140 # make binary power * max characters\\n\\n # Sizes for the layers\\n filters_0 = 20\\n filters_1_1 = 10\\n filters_1_2 = 10\\n filters_2_1 = 20\\n filters_2_2 = 20\\n filters_3_1 = 10\\n filters_3_2 = 10\\n\\n\\n\\n # Arbitrary number of vectors with length tweetLength\\n x = tf.placeholder(tf.float32, [None, input_size], name='x')\\n # Reshapes x to be 3d so that convolution can be applied\\n x_tweet = tf.reshape(x, [-1, tweet_length, tweet_height, 1])\\n # Tensor storing all the true labels for each tweet in our training batch\\n y_true = tf.placeholder(tf.float32, shape=[None, self.numberOfClasses], name='yTrue')\\n\\n yTrueClass = tf.argmax(y_true, dimesnion=1)\\n\\n # Similarly the number of input channels will be updated\\n numberOfInputChannels = 1\\n\\n # Creates the convolutional layers\\n for i in range(numberOfConvolutionalLayers):\\n convolutionalLayers[i], convolutionalWeights[i] = \\\\\\n self.newConvolutionalLayer(input=input, numberOfInputChannels=numberOfInputChannels,\\n filterHight=filterHeights[i], filterLength=filterLengths[i],\\n numberOfFilters=numberOfFilters[i], use2x2pooling=False)\\n\\n # Adds the output from the current layer to the tensor of all the previous layers' outputs\\n input = tf.concat(input, convolutionalLayers[i])\\n\\n # Updates the number of input channels so that all previous layers' inputs are taken into account\\n numberOfInputChannels = 0\\n for j in range(i):\\n numberOfInputChannels += numberOfFilters[j]\\n\\n fullyConnectedLayers = [numberOfFullyConnectedLayers]\\n numberOfFeautres = [numberOfFullyConnectedLayers]\\n\\n # Reshapes the input tensor to 1D so that it can be run through a fully connected layer\\n flattenedInput = self.flattenLayer(input)\\n\\n # Creates a fully connected layer\\n fullyConnectedLayers[0] = self.newFullyConnectedLayer(input=flattenedInput, numberOfInputs=numberOfFeautres,\\n numberOfOutputs=fullyConnectedSize, useRelu=True)\\n\\n # Updates the input and number of inputs\\n flattenedInput = tf.concat(flattenedInput, fullyConnectedLayers[0])\\n numberOfFeautres += fullyConnectedSize\\n\\n # Creates the last layer of the network\\n fullyConnectedLayers[1] = self.newFullyConnectedLayer(input=flattenedInput, numberOfInputs=numberOfFeautres,\\n numberOfOutputs=self.numberOfClasses, useRelu=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1a08800b15c601252575fe0409b25e\",\n \"score\": \"0.52905256\",\n \"text\": \"def create_adversarial_img(path, org_img, true_label):\\r\\n\\r\\n # Load trained model\\r\\n trained_net = models.SimpleModel()\\r\\n trained_net.load(path=path)\\r\\n trained_net.eval()\\r\\n\\r\\n # show original image\\r\\n visualizations.imshow(org_img)\\r\\n\\r\\n # Adversarial method 1\\r\\n\\r\\n # Copy the model so the original trained network wont change while we creating\\r\\n # the adversarial image\\r\\n model_copy = copy.deepcopy(trained_net)\\r\\n model_copy.eval()\\r\\n\\r\\n noise, adv_label = adversarial_optimizing_noise(model_copy, org_img, true_label=0, target_label=2, regularization=\\\"l1\\\")\\r\\n\\r\\n visualizations.imshow(noise) # show noise\\r\\n visualizations.imshow(org_img+noise) # show adversarial image\\r\\n out = trained_net((org_img+noise).unsqueeze(0))\\r\\n print(\\\"true label:\\\", true_label, \\\"adv_label:\\\", adv_label, \\\"trained_net label:\\\", out)\\r\\n\\r\\n\\r\\n # Adversarial method 2\\r\\n\\r\\n model_copy2 = copy.deepcopy(trained_net)\\r\\n adver_img, noise2, adv_label_2 = FGSM(model_copy2, org_img, true_label=0)\\r\\n\\r\\n visualizations.imshow(noise2) # show noise\\r\\n visualizations.imshow(adver_img) # show adversarial image\\r\\n out = trained_net(adver_img.unsqueeze(0))\\r\\n print(\\\"true label:\\\", true_label, \\\"adv_label:\\\", adv_label_2, \\\"trained_ned label:\\\", out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c708c6afbb42a83a61650d20d59e5a\",\n \"score\": \"0.52858627\",\n \"text\": \"def learn_from_batch(self, batch):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af821645bb3ef4f7807713765d7f8af\",\n \"score\": \"0.5278808\",\n \"text\": \"def optimizer(self):\\n \\n #Placeholders\\n state_pl = self.model.input\\n action_onehot_pl = K.placeholder(name='action_onehot', shape=(None,self.output_dim))\\n adv_pl = K.placeholder(name='advantage', shape=(None,))\\n \\n #Set up loss\\n pi_pl = self.model.output\\n pi_vec = K.sum(action_onehot_pl*pi_pl, axis=1)\\n loss_vec = -K.log(pi_vec)*K.stop_gradient(adv_pl)\\n loss = K.mean(loss_vec)\\n \\n #Get updates\\n opt = Adam(self.lr)\\n pars = self.model.trainable_weights\\n updates = opt.get_updates(loss = loss, params = pars)\\n \\n return K.function(inputs=[state_pl, action_onehot_pl, adv_pl], outputs = [], updates = updates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81957e3c037252dda27e78b9556e0db5\",\n \"score\": \"0.5276922\",\n \"text\": \"def mnasnet_b1(pretrained=False, **kwargs):\\n return mnasnet_100(pretrained, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a8fa9e155162d3273c38703f8b0411\",\n \"score\": \"0.52604926\",\n \"text\": \"def inference(self, image, keep_prob, istraining):\\n with tf.variable_scope('AlexNet') as scope:\\n with tf.variable_scope('conv1') as scope:\\n temp_conv = self.conv2d(\\\"conv1\\\", image, kernel_size=[11, 11, 3, 96], stride=4,\\n istraining=istraining, stop_grad=True, padding='VALID')\\n\\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\\n\\n with tf.variable_scope('conv2') as scope:\\n temp_conv = self.conv2d(\\\"conv2\\\", temp_pool, kernel_size=[5, 5, 96, 256], stride=1,\\n istraining=istraining, stop_grad=True)\\n\\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\\n\\n with tf.variable_scope('conv3') as scope:\\n temp_conv = self.conv2d(\\\"conv3\\\", temp_pool, kernel_size=[3, 3, 256, 384], stride=1,\\n istraining=istraining, stop_grad=True)\\n\\n with tf.variable_scope('conv4') as scope:\\n temp_conv = self.conv2d(\\\"conv4\\\", temp_conv, kernel_size=[3, 3, 384, 384], stride=1,\\n istraining=istraining, stop_grad=True)\\n\\n with tf.variable_scope('conv5') as scope:\\n temp_conv = self.conv2d(\\\"conv5\\\", temp_conv, kernel_size=[3, 3, 384, 256], stride=1,\\n istraining=istraining, stop_grad=True)\\n\\n temp_pool = self.max_pool(temp_conv, [2, 2], 2, padding='VALID')\\n # fc to conv\\n fc6 = self.local('fc6', temp_pool, kernel_size=[6, 6, 256, 4096], stride=1, pretrain=True, train=True,\\n istraining=istraining, stop_grad=True)\\n fc6_dropout = tf.nn.dropout(fc6, keep_prob=keep_prob)\\n\\n fc7 = self.local('fc7', fc6_dropout, kernel_size=[1, 1, 4096, 4096], stride=1, pretrain=True,\\n train=True, istraining=istraining, stop_grad=True)\\n fc7_dropout = tf.nn.dropout(fc7, keep_prob=keep_prob)\\n\\n embedding = self.local('fc8', fc7_dropout, kernel_size=[1, 1, 4096, self.embedding], stride=1,\\n pretrain=False, train=True, istraining=True)\\n embedding_squeeze = tf.squeeze(embedding)\\n return embedding_squeeze\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b6352a54de0b54bd3d4951ae35734b\",\n \"score\": \"0.5254964\",\n \"text\": \"def __init__(self, model, optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), name=\\\"ReptileMetaLearner\\\"):\\n self.model = model\\n self.optimizer = optimizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c529d6014e71bc7edd15463608a17b1\",\n \"score\": \"0.5253789\",\n \"text\": \"def test_tensor_replication():\\n anchorDict = {\\n \\\"ReplicationFactor\\\": 2,\\n # Accl factor must divide batch size\\n \\\"AccumulationFactor\\\": 4,\\n \\\"Pipelining\\\": False, # True\\n \\\"ReturnType\\\": \\\"ALL\\\",\\n }\\n label_array = np.ones([BATCH_SIZE]).astype(np.int32)\\n\\n micro_batch_size = BATCH_SIZE // (\\n anchorDict[\\\"AccumulationFactor\\\"] * anchorDict[\\\"ReplicationFactor\\\"]\\n )\\n\\n builder = popart.Builder()\\n input_shape = [micro_batch_size, CHANNELS, DATA_LEN, DATA_LEN]\\n\\n data_shape = popart.TensorInfo(\\\"FLOAT\\\", input_shape)\\n lbl_shape = popart.TensorInfo(\\\"INT32\\\", [micro_batch_size])\\n w = builder.addInitializedInputTensor(\\n np.random.random_sample(input_shape).astype(np.float32)\\n )\\n\\n settings = popart.InputSettings(popart.ReplicatedStreamMode.Broadcast)\\n\\n ip = builder.addInputTensor(data_shape, settings)\\n lb = builder.addInputTensor(lbl_shape)\\n\\n a = builder.aiOnnx.matmul([ip, w])\\n o = builder.reshape_const(\\n builder.aiOnnx, [a], [micro_batch_size, CHANNELS * DATA_LEN * DATA_LEN]\\n )\\n o = builder.aiOnnx.relu([o])\\n o = builder.aiOnnx.softmax([o])\\n nll = builder.aiGraphcore.nllloss([o, lb])\\n\\n GRAD = popart.reservedGradientPrefix() + w\\n ACCL = popart.reservedAccumPrefix() + w\\n art = popart.AnchorReturnType(\\\"All\\\")\\n data_flow = popart.DataFlow(\\n BATCHES_PER_STEP, {o: art, a: art, ip: art, w: art, GRAD: art, ACCL: art}\\n )\\n\\n opts, device = return_options(anchorDict)\\n with device as device:\\n if device is None:\\n pytest.skip(\\\"Test needs to run on IPU, but none are available\\\")\\n\\n session = popart.TrainingSession(\\n fnModel=builder.getModelProto(),\\n dataFlow=data_flow,\\n loss=nll,\\n optimizer=popart.ConstSGD(LEARNING_RATE),\\n userOptions=opts,\\n deviceInfo=device,\\n )\\n\\n session.prepareDevice()\\n\\n if (\\n anchorDict[\\\"ReplicationFactor\\\"] > 1\\n and settings.replicatedStreamMode() != popart.ReplicatedStreamMode.Broadcast\\n ):\\n input_shape = [anchorDict[\\\"ReplicationFactor\\\"]] + input_shape\\n label_array = label_array.reshape([anchorDict[\\\"ReplicationFactor\\\"], -1])\\n if anchorDict[\\\"AccumulationFactor\\\"] > 1:\\n input_shape = [anchorDict[\\\"AccumulationFactor\\\"]] + input_shape\\n label_array = label_array.reshape([anchorDict[\\\"AccumulationFactor\\\"], -1])\\n if BATCHES_PER_STEP > 1:\\n input_shape = [BATCHES_PER_STEP] + input_shape\\n label_array = np.repeat(label_array[np.newaxis], BATCHES_PER_STEP, 0)\\n\\n anchors = session.initAnchorArrays()\\n in_array = np.random.random_sample(input_shape).astype(np.float32)\\n\\n stepio = popart.PyStepIO({ip: in_array, lb: label_array}, anchors)\\n session.weightsFromHost()\\n\\n session.run(stepio)\\n\\n # Compare the inputs\\n for batch in range(anchors[ip].shape[0]):\\n in_0 = anchors[ip][batch, -1, 0, :, :, :, :]\\n in_1 = anchors[ip][batch, -1, 1, :, :, :, :]\\n\\n mx = anchors[ip].shape[len(anchors[ip].shape) - 1]\\n _ = mx if mx <= 5 else 5\\n assert np.allclose(in_0, in_1, equal_nan=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbc33819982983b03fbb56ec09e277d5\",\n \"score\": \"0.52514184\",\n \"text\": \"def _adversary_model(self, pred_logits, true_labels):\\n with tf.variable_scope(\\\"adversary_model\\\"):\\n c = tf.get_variable('c', initializer=tf.constant(1.0))\\n s = tf.sigmoid((1 + tf.abs(c)) * pred_logits)\\n\\n W2 = tf.get_variable('W2', [3, 1],\\n initializer=tf.contrib.layers.xavier_initializer())\\n b2 = tf.Variable(tf.zeros(shape=[1]), name='b2')\\n\\n pred_protected_attribute_logit = tf.matmul(tf.concat([s, s * true_labels, s * (1.0 - true_labels)], axis=1), W2) + b2\\n pred_protected_attribute_label = tf.sigmoid(pred_protected_attribute_logit)\\n\\n return pred_protected_attribute_label, pred_protected_attribute_logit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa86381149bc10320c7337f53f7922fa\",\n \"score\": \"0.52485394\",\n \"text\": \"def get_model(\\n input_shape: List[int],\\n weight_array: np.array,\\n batches_per_step: int,\\n replication_factor: int,\\n batch_size: int,\\n channels: int,\\n data_len: int,\\n synthetic_data: bool,\\n buffer_streams: bool,\\n device,\\n) -> Tuple:\\n\\n micro_batch_size = batch_size // (replication_factor)\\n\\n builder = popart.Builder()\\n\\n data_shape = popart.TensorInfo(\\\"FLOAT\\\", input_shape)\\n lbl_shape = popart.TensorInfo(\\\"INT32\\\", [micro_batch_size])\\n w = builder.addInitializedInputTensor(weight_array)\\n\\n ip = builder.addInputTensor(data_shape, \\\"main_input_123\\\")\\n lb = builder.addInputTensor(lbl_shape, \\\"label_input_456\\\")\\n\\n a = builder.aiOnnx.matmul([ip, w])\\n o = builder.reshape_const(\\n builder.aiOnnx, [a], [micro_batch_size, channels * data_len * data_len]\\n )\\n relu = builder.aiOnnx.relu([o])\\n sm = builder.aiOnnx.softmax([relu], axis=0, debugContext=\\\"output\\\")\\n builder.addOutputTensor(sm)\\n o = builder.aiGraphcore.nllloss([sm, lb], reduction=popart.ReductionType.Mean)\\n\\n art = popart.AnchorReturnType(\\\"All\\\")\\n data_flow = popart.DataFlow(\\n batches_per_step, {ip: art, lb: art, o: art, sm: art, a: art, relu: art}\\n )\\n\\n opts = popart.SessionOptions()\\n opts.useHostCopyOps = buffer_streams\\n # TODO: Fix outlining\\n opts.enableOutlining = False\\n\\n ipus = 1\\n\\n if replication_factor > 1:\\n opts.replicatedGraphCount = replication_factor\\n opts.enableReplicatedGraphs = True\\n ipus *= replication_factor\\n\\n assert device\\n\\n patterns = (\\n popart.Patterns(popart.PatternsLevel.Minimal)\\n .enablePattern(\\\"MatMulLhsGradOp\\\", True)\\n .enablePattern(\\\"MatMulRhsGradOp\\\", True)\\n )\\n patterns.InPlace = False\\n if synthetic_data:\\n opts.syntheticDataMode = popart.SyntheticDataMode.Zeros\\n\\n session = popart.TrainingSession(\\n fnModel=builder.getModelProto(),\\n dataFlow=data_flow,\\n loss=o,\\n optimizer=popart.ConstSGD(LR),\\n userOptions=opts,\\n deviceInfo=device,\\n patterns=patterns,\\n )\\n\\n session.setRandomSeed(0)\\n session.prepareDevice()\\n\\n label_shape = [micro_batch_size]\\n\\n if replication_factor > 1:\\n input_shape = [replication_factor] + input_shape\\n label_shape = [replication_factor] + label_shape\\n if batches_per_step > 1:\\n input_shape = [batches_per_step] + input_shape\\n label_shape = [batches_per_step] + label_shape\\n\\n anchors = session.initAnchorArrays()\\n\\n return session, anchors, label_shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67bc33af08ecb847b557172977c46e3f\",\n \"score\": \"0.5246301\",\n \"text\": \"def build_model(is_training, inputs, params):\\n images = inputs['images']\\n\\n assert images.get_shape().as_list() == [None, params.image_size, params.image_size, 3]\\n\\n\\n # TO DO : vgg input for images are 224 * 224 * 3\\n\\n # VGG RGB -> BGR\\n VGG_MEAN = [103.939, 116.779, 123.68]\\n red, green, blue = tf.split(axis=3, num_or_size_splits=3, value= images)\\n out = tf.concat(axis=3, values=[blue - VGG_MEAN[0], green - VGG_MEAN[1],red - VGG_MEAN[2]])\\n\\n\\n ##### modification - Add VGG ########\\n\\n # TO DO : get the vgg pretrained weight and name it vgg_weight.npy, name of weight in the file \\n weights = np.load(\\\"vgg_weights.npy\\\", encoding = 'latin1')\\n pretrainedWeights = weights.item()\\n trainedWeights = collections.defaultdict()\\n\\n num_conv_layers = 5 # pls change later\\n num_repeat_times_1 = 2\\n num_repeat_times_2 = 3\\n repear_layers1 = [1,2]\\n repear_layers2 = [3,4,5]\\n\\n for i in range(1, num_conv_layers + 1):\\n if i in repear_layers1:\\n for j in range(1,num_repeat_times_1 + 1):\\n trainedWeights['conv' + str(i) + '_' + str(j) + '_weights'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_weights', initializer=pretrainedWeights['conv' + str(i) + \\\"_\\\" + str(j) ][0], trainable = False)\\n trainedWeights['conv' + str(i) + '_' + str(j) + '_biases'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_biases', initializer=pretrainedWeights['conv' + str(i) + \\\"_\\\" + str(j) ][1], trainable = False)\\n if i in repear_layers2:\\n for j in range(1,num_repeat_times_2 + 1):\\n trainedWeights['conv' + str(i) + '_' + str(j) + '_weights'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_weights', initializer=pretrainedWeights['conv' + str(i) + \\\"_\\\" + str(j) ][0], trainable = False)\\n trainedWeights['conv' + str(i) + '_' + str(j) + '_biases'] = tf.get_variable('conv' + str(i) + '_' + str(j) + '_biases', initializer=pretrainedWeights['conv' + str(i) + \\\"_\\\" + str(j) ][1], trainable = False)\\n\\n num_fc_layers = 2 # pls change later\\n for i in range(1, num_fc_layers + 1):\\n trainedWeights['fc' + str(i) + '_weights'] = tf.get_variable('fc' + str(i) + '_weights', initializer=pretrainedWeights['fc' + str(i + num_conv_layers)][0], trainable = False) # i + num_conv_layers might not hold for other weights file\\n trainedWeights['fc' + str(i) + '_biases'] = tf.get_variable('fc' + str(i) + '_biases', initializer=pretrainedWeights['fc' + str(i + num_conv_layers)][1], trainable = False) # i + num_conv_layers might not hold for other weights file\\n\\n\\n # block 1 -- outputs 112x112x64\\n # conv1_1\\n k = 3; c = 64; s_h = 1; s_w = 1\\n conv1_1_out = convLayer(out, k, k, s_h, s_w, c, \\\"conv1_1\\\", \\\"SAME\\\", trainedWeights[\\\"conv1_1_weights\\\"], trainedWeights[\\\"conv1_1_biases\\\"])\\n\\n # conv1_2\\n k = 3; c = 64; s_h = 1; s_w = 1\\n conv1_2_out = convLayer(conv1_1_out, k, k, s_h, s_w, c, \\\"conv1_2\\\", \\\"SAME\\\", trainedWeights[\\\"conv1_2_weights\\\"], trainedWeights[\\\"conv1_2_biases\\\"])\\n \\n # maxpool1\\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\\n padding = 'SAME'\\n maxpool1 = tf.nn.max_pool(conv1_2_out, ksize=[1, k_h, k_w , 1],strides=[1, s_h, s_w, 1], padding=padding)\\n\\n #block 2 -- outputs 56x56x128\\n # conv2_1\\n k = 3; c = 128; s_h = 1; s_w = 1\\n conv2_1_out = convLayer(maxpool1, k, k, s_h, s_w, c, \\\"conv2_1\\\", \\\"SAME\\\", trainedWeights[\\\"conv2_1_weights\\\"], trainedWeights[\\\"conv2_1_biases\\\"])\\n\\n # conv2_2\\n k = 3; c = 64; s_h = 1; s_w = 1\\n conv2_2_out = convLayer(conv2_1_out, k, k, s_h, s_w, c, \\\"conv2_2\\\", \\\"SAME\\\", trainedWeights[\\\"conv2_2_weights\\\"], trainedWeights[\\\"conv2_2_biases\\\"])\\n \\n # maxpool2\\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\\n padding = 'SAME'\\n maxpool2 = tf.nn.max_pool(conv2_2_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n \\n # block 3 -- outputs 28x28x256\\n # conv3_1\\n k = 3; c = 256; s_h = 1; s_w = 1\\n conv3_1_out = convLayer(maxpool2, k, k, s_h, s_w, c, \\\"conv3_1\\\", \\\"SAME\\\", trainedWeights[\\\"conv3_1_weights\\\"], trainedWeights[\\\"conv3_1_biases\\\"])\\n\\n # conv3_2\\n k = 3; c = 256; s_h = 1; s_w = 1\\n conv3_2_out = convLayer(conv3_1_out, k, k, s_h, s_w, c, \\\"conv3_2\\\", \\\"SAME\\\", trainedWeights[\\\"conv3_2_weights\\\"], trainedWeights[\\\"conv3_2_biases\\\"])\\n \\n # conv3_3\\n k = 3; c = 256; s_h = 1; s_w = 1\\n conv3_3_out = convLayer(conv3_2_out, k, k, s_h, s_w, c, \\\"conv3_3\\\", \\\"SAME\\\", trainedWeights[\\\"conv3_3_weights\\\"], trainedWeights[\\\"conv3_3_biases\\\"])\\n\\n # maxpool3\\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\\n padding = 'SAME'\\n maxpool3 = tf.nn.max_pool(conv3_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n\\n # block 4 -- outputs 14x14x512\\n # COV 4 \\n # conv4_1\\n k = 3; c = 512; s_h = 1; s_w = 1\\n conv4_1_out = convLayer(maxpool3, k, k, s_h, s_w, c, \\\"conv4_1\\\", \\\"SAME\\\", trainedWeights[\\\"conv4_1_weights\\\"], trainedWeights[\\\"conv4_1_biases\\\"])\\n\\n # conv4_2\\n k = 3; c = 512; s_h = 1; s_w = 1\\n conv4_2_out = convLayer(conv4_1_out, k, k, s_h, s_w, c, \\\"conv4_2\\\", \\\"SAME\\\", trainedWeights[\\\"conv4_2_weights\\\"], trainedWeights[\\\"conv4_2_biases\\\"])\\n \\n # conv4_3\\n k = 3; c = 512; s_h = 1; s_w = 1\\n conv4_3_out = convLayer(conv4_2_out, k, k, s_h, s_w, c, \\\"conv4_3\\\", \\\"SAME\\\", trainedWeights[\\\"conv4_3_weights\\\"], trainedWeights[\\\"conv4_3_biases\\\"])\\n\\n # maxpool4\\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\\n padding = 'SAME'\\n maxpool4 = tf.nn.max_pool(conv4_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n\\n # block 5 -- outputs 7x7x512\\n # COV 5\\n # conv5_1\\n k = 3; c = 512; s_h = 1; s_w = 1\\n conv5_1_out = convLayer(maxpool4, k, k, s_h, s_w, c, \\\"conv5_1\\\", \\\"SAME\\\", trainedWeights[\\\"conv5_1_weights\\\"], trainedWeights[\\\"conv5_1_biases\\\"])\\n\\n # conv5_2\\n k = 3; c = 512; s_h = 1; s_w = 1\\n conv5_2_out = convLayer(conv5_1_out, k, k, s_h, s_w, c, \\\"conv5_2\\\", \\\"SAME\\\", trainedWeights[\\\"conv5_2_weights\\\"], trainedWeights[\\\"conv5_2_biases\\\"])\\n \\n # conv5_3\\n k = 3; c = 512; s_h = 1; s_w = 1\\n# conv5_3_out = convLayer(conv5_2_out, k, k, s_h, s_w, c, \\\"conv5_3\\\", \\\"SAME\\\", trainedWeights[\\\"conv5_3_weights\\\"], trainedWeights[\\\"conv5_3_biases\\\"])\\n conv5_3_out = tf.layers.conv2d(conv5_2_out, filters=c, kernel_size=k, strides=(s_h, s_w), padding='same')\\n\\n # maxpool5\\n k_h = 2; k_w = 2; s_h = 2; s_w = 2\\n padding = 'SAME'\\n maxpool5 = tf.nn.max_pool(conv5_3_out, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)\\n \\n # fullly connected layer\\n fc1_in = tf.reshape(maxpool5, [-1, 7*7*512])\\n\\n with tf.variable_scope('fc_1'):\\n # w = fc1_weights, shape = [4096, 4096], dtype = \\\"float\\\")\\n # b = tf.get_variable(fc1_biases, shape = [4096], dtype = \\\"float\\\")\\n# out = tf.nn.xw_plus_b(fc1_in,trainedWeights[\\\"fc1_weights\\\"], trainedWeights[\\\"fc1_biases\\\"])\\n out = tf.layers.dense(fc1_in,4096)\\n fc2_in = tf.nn.relu(out)\\n\\n with tf.variable_scope('fc_2'):\\n #w = tf.get_variable(fc2_weights, shape = [4096, 4096], dtype = \\\"float\\\")\\n #b = tf.get_variable(fc2_biases, shape = [4096], dtype = \\\"float\\\")\\n# out = tf.nn.xw_plus_b(fc2_in, trainedWeights[\\\"fc2_weights\\\"], trainedWeights[\\\"fc2_biases\\\"])\\n out = tf.layers.dense(fc2_in,4096)\\n fc3_in = tf.nn.relu(out)\\n\\n ### trainable layer #####\\n with tf.variable_scope('fc_3'):\\n logits = tf.layers.dense(fc3_in, params.num_labels)\\n return logits\\n\\n\\n ########### END ##########\\n\\n # assert out.get_shape().as_list() == [None, 4, 4, num_channels * 8]\\n #\\n # out = tf.reshape(out, [-1, 4 * 4 * num_channels * 8])\\n # with tf.variable_scope('fc_1'):\\n # out = tf.layers.dense(out, num_channels * 8)\\n # if params.use_batch_norm:\\n # out = tf.layers.batch_normalization(out, momentum=bn_momentum, training=is_training)\\n # out = tf.nn.relu(out)\\n # with tf.variable_scope('fc_2'):\\n # logits = tf.layers.dense(out, params.num_labels)\\n #\\n # return logits\\n #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3623ca34023e3b61ab60f8bdbcf35729\",\n \"score\": \"0.52379715\",\n \"text\": \"def independent_avg_modular_deeptrack_L1(# bad name\\n layer_size,\\n train_generator,\\n input_shape=(51,51,1),\\n output_shape=3,\\n nbr_nodes_added=1,\\n sample_sizes=(8, 32, 128, 512, 1024),\\n iteration_numbers=(401, 301, 201, 101, 51),\\n verbose=0.01,\\n save_networks=False,\\n mp_training=False, # use multiprocessing training\\n translation_distance=5, # parameters for multiprocessing training\\n SN_limits=[10,100], # parameters for multiprocessing training\\n model_path=\\\"\\\", # determines the type of layer used for combining the\\n # predictions. Addition and average only options at the moment\\n ):\\n import deeptrack\\n import keras\\n from keras import Input,models,layers\\n from keras.models import Model\\n from feature_by_feature import freeze_all_layers\\n #import deeptrackelli_mod_mproc as multiprocess_training # Needed for fast parallelized image generator\\n\\n input_tensor = Input(input_shape)\\n conv_list = [] # List of convolutional neruons in L1, needed for subsequent layers\\n flattened_list = [] # List of flattened layers\\n output_list = [] # List of the output layers\\n\\n # Loop thorugh the neoruns and add them one by one\\n for i in range(round(layer_size/nbr_nodes_added)):\\n next_node = layers.Conv2D(nbr_nodes_added,(3,3),activation='relu')(input_tensor)\\n next_node = layers.MaxPooling2D((2,2))(next_node)\\n if(i==0):\\n # i = 0 special case. No addition needed\\n next_flattened = layers.Flatten()(next_node)\\n next_output = layers.Dense(3)(next_flattened)\\n final_output = next_output\\n output_list.append(next_output)\\n flattened_list.append(next_flattened)\\n else:\\n # Construct the next output node\\n next_flattened = layers.Flatten()(next_node)\\n flattened_list.append(next_flattened)\\n # Can't concatenate a single layer\\n if(len(flattened_list)>1):\\n next_output = layers.Concatenate(axis=-1)(flattened_list)\\n\\n next_output = layers.Dense(3)(next_output)\\n output_list.append(next_output)\\n # Construct and compile network\\n network = models.Model(input_tensor,next_output)\\n network.compile(optimizer='rmsprop', loss='mse', metrics=['mse', 'mae'])\\n network.summary()\\n\\n # Train and freeze layers in network\\n if mp_training:\\n deeptrack.train_deep_learning_network_mp(\\n network,\\n sample_sizes = sample_sizes,\\n iteration_numbers = iteration_numbers,\\n verbose=verbose,\\n SN_limits=SN_limits,\\n translation_distance=translation_distance,\\n )\\n else:\\n deeptrack.train_deep_learning_network(\\n network,\\n train_generator,\\n sample_sizes = sample_sizes,\\n iteration_numbers = iteration_numbers,\\n verbose=verbose)\\n\\n freeze_all_layers(network)\\n conv_list.append(next_node)\\n if(save_networks):\\n network.save(model_path+\\\"L1_\\\"+str((i+1)*nbr_nodes_added)+\\\"F.h5\\\")\\n # Create final output using all the output layers and averaging them\\n if(len(output_list)>1):\\n avg_out = layers.average(output_list)\\n else:\\n avg_out = output_list[0]\\n network = models.Model(input_tensor,avg_out)\\n network.compile(optimizer='rmsprop', loss='mse', metrics=['mse', 'mae'])\\n print('final network architecture')\\n network.summary()\\n if(save_networks):\\n network.save(model_path+\\\"final_L\\\"+str(1)+\\\"_F.h5\\\")\\n return network,conv_list,output_list,flattened_list,input_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f6b8f67b338e9461ac7fbc177c610d\",\n \"score\": \"0.5233395\",\n \"text\": \"def experiment(opt, path):\\n x = Input((1,), name=\\\"x\\\")\\n player_a = Dense(3, activation='softmax', name=\\\"player_a\\\", bias=False, W_regularizer=l2(1e-2))\\n player_b = Dense(3, activation='softmax', name=\\\"player_b\\\", bias=False, W_regularizer=l2(1e-2))\\n\\n action_a = player_a(x)\\n action_b = player_b(x)\\n\\n def rps(z):\\n u = z[0]\\n v = z[1]\\n return u[:, 0] * v[:, 2] + u[:, 1] * v[:, 0] + u[:, 2] * v[:, 1]\\n\\n model_a = Model(x, merge([action_a, action_b], mode=rps, output_shape=lambda z: (z[0][0], 1)))\\n model_b = Model(x, merge([action_b, action_a], mode=rps, output_shape=lambda z: (z[0][0], 1)))\\n\\n adversarial_model = AdversarialModel(player_models=[model_a, model_b],\\n player_params=[[player_a.W], [player_b.W]],\\n player_names=[\\\"a\\\", \\\"b\\\"])\\n adversarial_model.adversarial_compile(opt,\\n player_optimizers=[SGD(1), SGD(1)],\\n loss=\\\"mean_absolute_error\\\")\\n param_model = Model(x, [action_a, action_b])\\n\\n def print_params(epoch, logs):\\n params = param_model.predict(np.ones((1, 1)))\\n a = params[0].ravel()\\n b = params[1].ravel()\\n print(\\\"Epoch: {}, A: {}, B: {}\\\".format(epoch, a, b))\\n imgpath = os.path.join(path, \\\"epoch-{:03d}.png\\\".format(epoch))\\n rps_chart(imgpath, a, b)\\n\\n cb = LambdaCallback(on_epoch_begin=print_params)\\n batch_count = 5\\n adversarial_model.fit(np.ones((batch_count, 1)),\\n [np.ones((batch_count, 1)), np.ones((batch_count, 1))],\\n nb_epoch=120, callbacks=[cb], verbose=0, batch_size=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"511ddf25f189c01d1c83a0d430eb3f6e\",\n \"score\": \"0.5231497\",\n \"text\": \"def train():\\n args = arguments_mt_train()\\n \\n h, w = map(int, args.input_size.split(','))\\n input_size = (h, w)\\n\\n init_weights = map(float, args.init_weights.split(','))\\n \\n if args.use_random_seed:\\n tf.set_random_seed(args.random_seed)\\n \\n # Create queue coordinator.\\n coord = tf.train.Coordinator()\\n \\n # Load reader.\\n with tf.name_scope(\\\"create_inputs\\\"):\\n reader = ImageReader(\\n args.data_dir,\\n args.data_list_1,\\n args.data_list_2,\\n input_size=input_size,\\n random_scale=args.random_scale,\\n random_mirror=args.random_mirror,\\n random_crop=args.random_crop,\\n ignore_label=args.ignore_label,\\n img_mean=IMG_MEAN,\\n coord=coord,\\n task_1=args.task_1,\\n task_2=args.task_2)\\n image_batch, label_batch_1, label_batch_2 = reader.dequeue(args.batch_size)\\n \\n # Create network.\\n with slim.arg_scope(vgg_arg_scope(weight_decay=args.weight_decay, use_batch_norm=True, is_training=True)):\\n if args.network == 'vgg_16_deeplab_nddr':\\n _, _, end_points_1, end_points_2, _ = vgg_16_deeplab_nddr(image_batch,\\n num_classes_1=args.num_classes_1,\\n num_classes_2=args.num_classes_2,\\n is_training=True,\\n dropout_keep_prob=args.keep_prob,\\n init_method=args.init_method,\\n init_weights=init_weights)\\n elif args.network == 'vgg_16_shortcut_deeplab_nddr':\\n _, _, end_points_1, end_points_2, _ = vgg_16_shortcut_deeplab_nddr(image_batch,\\n num_classes_1=args.num_classes_1,\\n num_classes_2=args.num_classes_2,\\n is_training=True,\\n dropout_keep_prob=args.keep_prob,\\n init_method=args.init_method,\\n init_weights=init_weights)\\n elif args.network == 'vgg_16_deeplab_mt':\\n _, _, end_points_1, end_points_2, _ = vgg_16_deeplab_mt(image_batch,\\n num_classes_1=args.num_classes_1,\\n num_classes_2=args.num_classes_2,\\n is_training=True,\\n dropout_keep_prob=args.keep_prob)\\n elif args.network == 'vgg_16_shortcut_deeplab_mt':\\n _, _, end_points_1, end_points_2, _ = vgg_16_shortcut_deeplab_mt(image_batch,\\n num_classes_1=args.num_classes_1,\\n num_classes_2=args.num_classes_2,\\n is_training=True,\\n dropout_keep_prob=args.keep_prob)\\n else:\\n raise Exception('network name is not recognized!')\\n \\n # Predictions.\\n raw_output_1 = end_points_1['vgg_16_1_6/fc8_voc12']\\n raw_output_2 = end_points_2['vgg_16_2_6/fc8_voc12']\\n\\n if args.task_1 == 'seg' and args.task_2 == 'normal':\\n seg_output = raw_output_1\\n seg_classes = args.num_classes_1\\n seg_label = prepare_label(label_batch_1, tf.stack(raw_output_1.get_shape()[1:3]), num_classes=seg_classes,\\n one_hot=False, task='seg')\\n seg_label_batch = label_batch_1\\n seg_loss_scale = args.loss_scale_1\\n\\n normal_output = raw_output_2\\n normal_classes = args.num_classes_2\\n normal_label = prepare_label(label_batch_2, tf.stack(raw_output_2.get_shape()[1:3]), num_classes=normal_classes,\\n one_hot=False, task='normal')\\n normal_label_batch = label_batch_2\\n normal_loss_scale = args.loss_scale_2\\n\\n elif args.task_1 == 'normal' and args.task2 == 'seg':\\n seg_output = raw_output_2\\n seg_classes = args.num_classes_2\\n seg_label = prepare_label(label_batch_2, tf.stack(raw_output_2.get_shape()[1:3]), num_classes=seg_classes,\\n one_hot=False, task='seg')\\n seg_label_batch = label_batch_2\\n seg_loss_scale = args.loss_scale_2\\n\\n normal_output = raw_output_1\\n normal_classes = args.num_classes_1\\n normal_label = prepare_label(label_batch_1, tf.stack(raw_output_1.get_shape()[1:3]), num_classes=normal_classes,\\n one_hot=False, task='normal')\\n normal_label_batch = label_batch_1\\n normal_loss_scale = args.loss_scale_1\\n\\n else:\\n raise Exception('check the tasks!')\\n\\n seg_loss = get_seg_loss(seg_output, seg_label, seg_classes, args.ignore_label)\\n normal_loss = get_normal_loss(normal_output, normal_label, normal_classes, args.ignore_label)\\n l2_losses = tf.add_n([args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name])\\n reduced_loss = tf.reduce_mean(seg_loss) * seg_loss_scale + tf.reduce_mean(normal_loss) * normal_loss_scale + l2_losses * 0.5\\n \\n # Train Ops\\n train_op, step_ph, learning_rate = create_train_ops_mt(reduced_loss, args)\\n\\n # Summaries\\n seg_pred = tf.image.resize_bilinear(seg_output, tf.shape(image_batch)[1:3, ])\\n seg_pred = tf.argmax(seg_pred, axis=3)\\n seg_pred = tf.expand_dims(seg_pred, dim=3)\\n\\n normal_pred = tf.image.resize_bilinear(normal_output, tf.shape(image_batch)[1:3, ])\\n\\n if 'nddr' in args.network:\\n nddr_l2_losses = tf.add_n([args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name and 'nddr' in v.name])\\n else:\\n nddr_l2_losses = tf.constant(0.0)\\n\\n summary_op = create_summary_op_mt(image_batch, seg_label_batch, normal_label_batch, seg_pred, normal_pred, IMG_MEAN,\\n reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses,\\n learning_rate, seg_classes, normal_classes, args.save_num_images, args.network)\\n\\n summary_writer = tf.summary.FileWriter(args.snapshot_dir,\\n graph=tf.get_default_graph())\\n \\n # Set up tf session and initialize variables. \\n config = tf.ConfigProto()\\n config.gpu_options.allow_growth = True\\n sess = tf.Session(config=config)\\n init = tf.global_variables_initializer()\\n \\n sess.run(init)\\n \\n # Saver for storing checkpoints of the model.\\n saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)\\n \\n # Load variables if the checkpoint is provided.\\n load_mt(sess, args)\\n \\n # Start queue threads.\\n threads = tf.train.start_queue_runners(coord=coord, sess=sess)\\n\\n # Iterate over training steps.\\n for step in range(args.num_steps):\\n start_time = time.time()\\n feed_dict = {step_ph: step}\\n\\n if step % args.save_pred_every == 0:\\n loss_value, seg_loss_value, normal_loss_value, loss_weight_value, loss_nddr_value, images, labels_1, \\\\\\n labels_2, seg_pred_value, normal_pred_value, summary, _ = \\\\\\n sess.run([reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses, image_batch, label_batch_1,\\n label_batch_2, seg_pred, normal_pred, summary_op, train_op], feed_dict=feed_dict)\\n save(saver, sess, args.snapshot_dir, step)\\n else:\\n loss_value, seg_loss_value, normal_loss_value, loss_weight_value, loss_nddr_value, summary, _ = \\\\\\n sess.run([reduced_loss, seg_loss, normal_loss, l2_losses, nddr_l2_losses, summary_op, train_op],\\n feed_dict=feed_dict)\\n\\n summary_writer.add_summary(summary, step)\\n duration = time.time() - start_time\\n print('step {:d} \\\\t loss = {:.3f} \\\\t seg_loss = {:.3f}, seg_loss_scale = {:.1f} \\\\t normal_loss = {:.3f}, '\\n 'normal_loss_scale = {:.1f} \\\\t loss_w = {:.3f}, loss_nddr_w = {:.3f} \\\\t nddr_mult = {:.1f}'\\n .format(step, loss_value, seg_loss_value, seg_loss_scale, normal_loss_value, normal_loss_scale,\\n loss_weight_value, loss_nddr_value, args.nddr_mult))\\n\\n coord.request_stop()\\n coord.join(threads)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c56875886f40a35b1e2ec7f9cb27ef\",\n \"score\": \"0.5229802\",\n \"text\": \"def __init__(self,\\n attention_states: tf.Tensor,\\n scope: str,\\n attention_state_size: int = None,\\n input_weights: tf.Tensor=None,\\n attention_fertility: int = None) -> None:\\n super().__init__(\\n scope, attention_states, attention_state_size, input_weights)\\n\\n self.attn_size = attention_states.get_shape()[2].value\\n\\n if self.attention_state_size is None:\\n self.attention_state_size = self.attn_size\\n\\n with tf.variable_scope(scope):\\n # To calculate W1 * h_t we use a 1-by-1 convolution, need to\\n # reshape before.\\n self.att_states_reshaped = tf.expand_dims(self.attention_states, 2)\\n\\n # This variable corresponds to Bahdanau's U_a in the paper\\n k = tf.get_variable(\\n \\\"AttnW\\\", [1, 1, self.attn_size, self.attention_state_size],\\n initializer=tf.random_normal_initializer(stddev=0.001))\\n\\n self.hidden_features = tf.nn.conv2d(self.att_states_reshaped, k,\\n [1, 1, 1, 1], \\\"SAME\\\")\\n\\n # pylint: disable=invalid-name\\n # see comments on disabling invalid names below\\n self.v = tf.get_variable(\\n name=\\\"AttnV\\\",\\n shape=[self.attention_state_size],\\n initializer=tf.random_normal_initializer(stddev=.001))\\n self.v_bias = tf.get_variable(\\n \\\"AttnV_b\\\", [], initializer=tf.constant_initializer(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b687813146fefc9fcaefbd641fba8d\",\n \"score\": \"0.5226202\",\n \"text\": \"def apply_model(state, images, labels):\\n\\n def loss_fn(params):\\n logits = state.apply_fn({\\\"params\\\": params}, images)\\n one_hot = jax.nn.one_hot(labels, 10)\\n loss = jnp.mean(optax.softmax_cross_entropy(logits=logits, labels=one_hot))\\n return loss, logits\\n\\n grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\\n (loss, logits), grads = grad_fn(state.params)\\n accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)\\n return grads, loss, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe337cff2e3741c07bc4d2083023ed29\",\n \"score\": \"0.522599\",\n \"text\": \"def revnet_base():\\n hparams = common_hparams.basic_params1()\\n hparams.add_hparam('num_channels', [64, 128, 256, 416])\\n hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])\\n hparams.add_hparam('bottleneck', True)\\n hparams.add_hparam('first_batch_norm', [False, True, True, True])\\n hparams.add_hparam('init_stride', 2)\\n hparams.add_hparam('init_kernel_size', 7)\\n hparams.add_hparam('init_maxpool', True)\\n hparams.add_hparam('strides', [1, 2, 2, 2])\\n hparams.add_hparam('num_channels_init_block', 64)\\n hparams.add_hparam('dim', '2d')\\n\\n # Variable init\\n hparams.initializer = 'normal_unit_scaling'\\n hparams.initializer_gain = 2.\\n\\n # Optimization\\n hparams.optimizer = 'Momentum'\\n hparams.optimizer_momentum_momentum = 0.9\\n hparams.optimizer_momentum_nesterov = True\\n hparams.weight_decay = 1e-4\\n hparams.clip_grad_norm = 0.0\\n # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)\\n hparams.learning_rate = 0.4\\n hparams.learning_rate_decay_scheme = 'cosine'\\n # For image_imagenet224, 120k training steps, which effectively makes this a\\n # cosine decay (i.e. no cycles).\\n hparams.learning_rate_cosine_cycle_steps = 120000\\n\\n # Can run with a batch size of 128 with Problem ImageImagenet224\\n hparams.batch_size = 128\\n return hparams\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e566f5ce0da7e495f243582de00912\",\n \"score\": \"0.5219748\",\n \"text\": \"def AdaBnn(x_train,y_train,conf,classes=2,verbose=1):\\n activation = conf['network']['activation']\\n output_activation = conf['network']['output_activation']\\n optimizer = conf['network']['optimizer']\\n loss = conf['network']['loss']\\n \\n batch_size = conf['training']['batch_size']\\n epochs = conf['training']['epochs']\\n \\n B = conf['adabnn']['B']\\n T = conf['adabnn']['T']\\n delta = conf['adabnn']['delta']\\n seed = conf['adabnn']['seed']\\n\\n if classes == 2:\\n output_shape = 1\\n if classes > 2:\\n output_shape = classes\\n\\n H = {}\\n perf = []\\n input_shape = x_train.shape[1::]\\n inp = Input(shape=input_shape,name = 'input_layer')\\n if len(input_shape) > 1:\\n raise ValueError('The input data features'\\n 'will be flatten into one dimension'\\n 'next to the input layer')\\n prep = Flatten(name='flat')(inp)\\n h11 = Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name = 'h11')(prep)\\n H[0] = ['h11']\\n else:\\n h11 = Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name = 'h11')(inp)\\n H[0] = ['h11']\\n out = Dense(output_shape,\\n activation=output_activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name = 'output_layer')(h11)\\n model = Model(inp,out)\\n depth = 1\\n for t in range(0,T):\\n H1 = H.copy()\\n H2 = H.copy()\\n if verbose in [1,2]: print('[INFO] Iteration: ',t + 1,'\\\\n[INFO] Preparing H')\\n for depth_h1 in range(1,depth+1):\\n if depth_h1 == 1:\\n h11_name = 'h' + str(depth_h1) + str(t+2)\\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h11_name)\\n h1_layers = [Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n use_bias=False,\\n name=h11_name)(model.get_layer('input_layer').output)]\\n H1[t+1] = [h11_name]\\n else:\\n conc = [model.get_layer(h).output for h in [h[depth_h1-2] for n,h in H.items() if len(h) >= (depth_h1-1)] ]\\n conc.append(h1_layers[depth_h1-2])\\n h22_name = 'h' + str(depth_h1) + str(t+2)\\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h22_name)\\n h1_layers.append(Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name=h22_name)(concatenate(conc)))\\n H1[t+1].append(h22_name)\\n conc1 = [model.get_layer(y[-1]).output for (k,y) in H.items()]\\n conc1.append(h1_layers[-1])\\n out = Dense(output_shape,\\n activation=output_activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name='output_layer')(concatenate(conc1))\\n mo1 = Model(inputs=inp,\\n outputs=out)\\n if verbose == 2:\\n t1 = time.time()\\n print('[TIMING] Defining layers and stacking them', t1 - t0)\\n mo1.compile(optimizer=optimizer,\\n loss=loss,\\n metrics=['accuracy'])\\n if verbose == 2:\\n t2 = time.time()\\n print('[TIMING] Compilation of the model', t2-t1)\\n mo1.fit(x_train,\\n y_train,\\n batch_size=batch_size,\\n epochs=epochs,\\n shuffle=True,\\n verbose=0)\\n if verbose == 2:\\n t3 = time.time()\\n print('[TIMING] Fitting of the model', t3-t2)\\n loss_h1,acc_h1 = mo1.evaluate(x_train,\\n y_train,\\n batch_size=batch_size,\\n verbose=0)\\n if verbose == 2:\\n t4 = time.time()\\n print('[TIMING] Evaluation of the model', t4-t3)\\n\\n if verbose in [1,2]: print('[INFO] Preparing Hprime')\\n for depth_h2 in range(1,depth+2):\\n if depth_h2 == 1:\\n h11_name = 'h' + str(depth_h2) + str(t+2)\\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h11_name)\\n h2_layers = [Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n use_bias=False,\\n name=h11_name)(model.get_layer('input_layer').output)]\\n H2[t+1] = [h11_name]\\n else:\\n conc = [model.get_layer(h).output for h in [h[depth_h1-2] for n,h in H.items() if len(h) >= (depth_h1-1)] ]\\n conc.append(h2_layers[depth_h2-2])\\n h22_name = 'h' + str(depth_h2) + str(t+2)\\n if verbose in [1,2]: print('[INFO] Adding layer node: ',h22_name)\\n h2_layers.append(Dense(B,\\n activation=activation,\\n kernel_regularizer=RademacherComplexity,\\n use_bias=False,\\n name=h22_name)(concatenate(conc)))\\n H2[t+1].append(h22_name)\\n conc2 = [model.get_layer(y[-1]).output for (k,y) in H.items()]\\n conc2.append(h2_layers[-1])\\n out = Dense(output_shape,\\n activation=output_activation,\\n kernel_regularizer=RademacherComplexity,\\n kernel_initializer=BinaryGlorot_uniform(seed=seed),\\n use_bias=False,\\n name='output_layer')(concatenate(conc2))\\n mo2 = Model(inputs=inp,\\n outputs=out)\\n if verbose == 2:\\n t5 = time.time()\\n print('[TIMING] Defining layers and stacking them', t5 - t4)\\n mo2.compile(optimizer=optimizer,\\n loss=loss,\\n metrics=['accuracy'])\\n if verbose == 2:\\n t6 = time.time()\\n print('[TIMING] Compilation of the model', t6 - t5)\\n mo2.fit(x_train,\\n y_train,\\n batch_size=batch_size,\\n epochs=epochs,\\n shuffle=True,\\n verbose=0)\\n if verbose == 2:\\n t7 = time.time()\\n print('[TIMING] Fitting of the model', t7 - t6)\\n loss_h2,acc_h2 = mo2.evaluate(x_train,\\n y_train,\\n batch_size=batch_size,\\n verbose=0)\\n if verbose == 2:\\n t8 = time.time()\\n print('[TIMING] Evaluation of the model', t8 - t7)\\n if loss_h1 < loss_h2:\\n model = mo1\\n H = H1\\n loss_selected = loss_h1\\n acc_selected = acc_h1\\n if verbose in [1,2]: print('[INFO] H was selected')\\n elif loss_h1 > loss_h2:\\n if verbose in [1,2]: print('[INFO] Hprime was selected')\\n model = mo2\\n H = H2\\n loss_selected = loss_h2\\n acc_selected = acc_h2\\n depth = depth + 1\\n else:\\n model = mo1\\n H = H1\\n loss_selected = loss_h1\\n acc_selected = acc_h1\\n if verbose in [1,2]: print('[INFO] Choosing H, without improving the loss function')\\n break\\n perf.append({\\n \\\"iteration\\\": t + 1,\\n \\\"accuracy\\\": acc_selected,\\n \\\"cost function\\\": loss_selected\\n })\\n return model,perf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751d7b9af71af6725c3561e71d74efee\",\n \"score\": \"0.52159697\",\n \"text\": \"def adapt(self, original_inputs, adversarial_inputs, labels):\\n return adversarial_inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73bbdab84c2979d8134fd9fe7b3970c8\",\n \"score\": \"0.5214423\",\n \"text\": \"def get_adadelta_trainer(self):\\n batch_x = T.fmatrix('batch_x')\\n batch_y = T.ivector('batch_y')\\n # compute the gradients with respect to the model parameters\\n gparams = T.grad(self.mean_cost, self.params)\\n # compute list of weights updates\\n updates = OrderedDict()\\n for accugrad, accudelta, param, gparam in zip(self._accugrads,\\n self._accudeltas, self.params, gparams):\\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\\n agrad = self._rho * accugrad + (1 - self._rho) * gparam * gparam\\n agrad=T.cast(agrad, 'float32')\\n dx = - T.sqrt((accudelta + self._eps)\\n / (agrad + self._eps)) * gparam\\n dx=T.cast(dx, 'float32')\\n updates[accudelta] = T.cast((self._rho * accudelta\\n + (1 - self._rho) * dx * dx),'float32')\\n if self.max_norm:\\n W = T.cast(param + dx,'float32')\\n col_norms = T.cast(W.norm(2, axis=0),'float32')\\n desired_norms = T.cast(T.clip(col_norms, 0, self.max_norm),'float32')\\n updates[param] = T.cast(W * (desired_norms / (1e-6 + col_norms)),'float32')\\n else:\\n updates[param] = T.cast(param + dx,'float32')\\n updates[accugrad] = T.cast(agrad,'float32')\\n \\n train_fn = theano.function(inputs=[theano.Param(batch_x),\\n theano.Param(batch_y)],\\n outputs=self.mean_cost,\\n updates=updates,\\n givens={self.x: batch_x, self.y: batch_y},\\n allow_input_downcast=True)\\n \\n return train_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73bbdab84c2979d8134fd9fe7b3970c8\",\n \"score\": \"0.5214423\",\n \"text\": \"def get_adadelta_trainer(self):\\n batch_x = T.fmatrix('batch_x')\\n batch_y = T.ivector('batch_y')\\n # compute the gradients with respect to the model parameters\\n gparams = T.grad(self.mean_cost, self.params)\\n # compute list of weights updates\\n updates = OrderedDict()\\n for accugrad, accudelta, param, gparam in zip(self._accugrads,\\n self._accudeltas, self.params, gparams):\\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\\n agrad = self._rho * accugrad + (1 - self._rho) * gparam * gparam\\n agrad=T.cast(agrad, 'float32')\\n dx = - T.sqrt((accudelta + self._eps)\\n / (agrad + self._eps)) * gparam\\n dx=T.cast(dx, 'float32')\\n updates[accudelta] = T.cast((self._rho * accudelta\\n + (1 - self._rho) * dx * dx),'float32')\\n if self.max_norm:\\n W = T.cast(param + dx,'float32')\\n col_norms = T.cast(W.norm(2, axis=0),'float32')\\n desired_norms = T.cast(T.clip(col_norms, 0, self.max_norm),'float32')\\n updates[param] = T.cast(W * (desired_norms / (1e-6 + col_norms)),'float32')\\n else:\\n updates[param] = T.cast(param + dx,'float32')\\n updates[accugrad] = T.cast(agrad,'float32')\\n \\n train_fn = theano.function(inputs=[theano.Param(batch_x),\\n theano.Param(batch_y)],\\n outputs=self.mean_cost,\\n updates=updates,\\n givens={self.x: batch_x, self.y: batch_y},\\n allow_input_downcast=True)\\n \\n return train_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e89749fc6c776a5d4d7b1a8391381c9\",\n \"score\": \"0.52032626\",\n \"text\": \"def __init__(self, inp_len, inp_alpha, outp_alpha, nhidden, cascade = 0):\\n self.nn1 = ml.NN(inp_len * len(inp_alpha), nhidden, len(outp_alpha)) # neural net\\n self.nn2 = None\\n self.cascade = cascade\\n if cascade > 0:\\n self.nn2 = ml.NN(cascade * len(outp_alpha), cascade, len(outp_alpha)) # cascaded neural net\\n self.inp_len = inp_len\\n self.inp_alpha = inp_alpha\\n self.outp_alpha = outp_alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbaf2412bfc7edbf4fa35ca6172cb4e\",\n \"score\": \"0.52013725\",\n \"text\": \"def index_adam_full(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\\n weight_decay: float, eps: float, ste: bool, params_prime: List[Tensor], granularity_channel: bool, granularity_kernel: bool):\\n if ste:\\n for i, (param, param_prime) in enumerate(zip(params, params_prime)):\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\\n param_prime.addcdiv_(exp_avg, denom, value=-step_size)\\n if granularity_kernel and len(param.shape) > 2: # nn.Conv2D weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n for idx_dim1, param_dim1 in enumerate(param_dim0):\\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\\n param_dim1.view(-1)[torch.argsort(param_prime[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\\n elif (granularity_channel or granularity_kernel) and len(param.shape) > 1: # nn.Linear or nn.Conv2D weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\\n param_dim0.view(-1)[torch.argsort(param_prime[idx_dim0].view(-1))] = param_dim0_tmp\\n else: # bias\\n param_tmp, _ = torch.sort(param.view(-1))\\n param.view(-1)[torch.argsort(param_prime.view(-1))] = param_tmp\\n else:\\n for i, param in enumerate(params):\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\\n if granularity_kernel and len(param.shape) > 2: # nn.Conv2D weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n for idx_dim1, param_dim1 in enumerate(param_dim0):\\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\\n param_dim1.view(-1)[torch.argsort(param_new[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\\n elif (granularity_channel or granularity_kernel) and len(param.shape) > 1: # nn.Linear or nn.Conv2D weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\\n param_dim0.view(-1)[torch.argsort(param_new[idx_dim0].view(-1))] = param_dim0_tmp\\n else: # bias\\n param_tmp, _ = torch.sort(param.view(-1))\\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5bfd89353afefdef76b50e2d0213a7e\",\n \"score\": \"0.51992154\",\n \"text\": \"def __call__(self, x, z_dim, y_dim, reuse=None, batch_size=100, x_width=28,train_phase = True):\\n\\n #self.hidden_dims = [self.hidden_dim] * 2\\n net = tf.reshape(x, [batch_size, x_width, x_width, -1])\\n net = slim.conv2d(net, 28, [5,5], stride=2, reuse=reuse, scope='enc_conv0',\\n normalizer_fn=slim.batch_norm,\\n normalizer_params={'reuse':reuse,'is_training':train_phase,\\n 'scale':True,\\n 'updates_collections':None,\\n 'scope':'enc_bn0'})\\n net = slim.conv2d(net, 56, [5,5], stride=2, reuse=reuse,scope='enc_conv1',\\n normalizer_fn=slim.batch_norm,\\n normalizer_params={'reuse':reuse,'is_training':train_phase,\\n 'scale':True,\\n 'updates_collections':None,\\n 'scope':'enc_bn1'})\\n net = slim.conv2d(net, 108, [5,5], stride=2, reuse=reuse, scope='enc_conv2',\\n normalizer_fn=slim.batch_norm,\\n normalizer_params={'reuse':reuse,'is_training':train_phase,\\n 'scale':True,\\n 'updates_collections':None,\\n 'scope':'enc_bn2'})\\n net = tf.reshape(net, [batch_size, -1])\\n h1 = net\\n #net = tf.concat(1, (net,y))\\n #z_mean = slim.fully_connected(net, z_dim, scope='z_mean', activation_fn=None)\\n z_log_sigma_sq = slim.fully_connected(net, z_dim, scope='z_log_sigma', activation_fn=None, reuse=reuse)\\n\\n y_prob = slim.fully_connected(h1, y_dim, scope='y_prob',\\n activation_fn=tf.nn.softmax, reuse=reuse,\\n normalizer_fn=slim.batch_norm,\\n normalizer_params={'reuse':reuse,'is_training':train_phase,\\n 'scale':True,\\n 'updates_collections':None,\\n 'scope':'y_prob_bn'})\\n\\n \\\"\\\"\\\"\\n\\n h0 = tf.nn.softplus(linear(x, self.hidden_dim, scope = 'en_fc0'))\\n\\n h1 = tf.nn.softplus(linear(h0, self.hidden_dim, scope = 'en_fc1'))\\n\\n z_mean = linear(h1, z_dim, scope = 'z_mean')\\n z_log_sigma_sq = linear(h1, z_dim, scope = 'z_log_sigma')\\n \\\"\\\"\\\"\\n return (z_log_sigma_sq, y_prob, h1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24d29e9801c6d798100f27fdb121316\",\n \"score\": \"0.5197575\",\n \"text\": \"def yolo3_vgg16_body(inputs, num_anchors, num_classes):\\n '''\\n Layer Name input_1 Output: Tensor(\\\"input_1:0\\\", shape=(?, 416, 416, 3), dtype=float32)\\n Layer Name block1_conv1 Output: Tensor(\\\"block1_conv1/Relu:0\\\", shape=(?, 416, 416, 64), dtype=float32)\\n Layer Name block1_conv2 Output: Tensor(\\\"block1_conv2/Relu:0\\\", shape=(?, 416, 416, 64), dtype=float32)\\n Layer Name block1_pool Output: Tensor(\\\"block1_pool/MaxPool:0\\\", shape=(?, 208, 208, 64), dtype=float32)\\n Layer Name block2_conv1 Output: Tensor(\\\"block2_conv1/Relu:0\\\", shape=(?, 208, 208, 128), dtype=float32)\\n Layer Name block2_conv2 Output: Tensor(\\\"block2_conv2/Relu:0\\\", shape=(?, 208, 208, 128), dtype=float32)\\n Layer Name block2_pool Output: Tensor(\\\"block2_pool/MaxPool:0\\\", shape=(?, 104, 104, 128), dtype=float32)\\n Layer Name block3_conv1 Output: Tensor(\\\"block3_conv1/Relu:0\\\", shape=(?, 104, 104, 256), dtype=float32)\\n Layer Name block3_conv2 Output: Tensor(\\\"block3_conv2/Relu:0\\\", shape=(?, 104, 104, 256), dtype=float32)\\n Layer Name block3_conv3 Output: Tensor(\\\"block3_conv3/Relu:0\\\", shape=(?, 104, 104, 256), dtype=float32)\\n Layer Name block3_pool Output: Tensor(\\\"block3_pool/MaxPool:0\\\", shape=(?, 52, 52, 256), dtype=float32)\\n Layer Name block4_conv1 Output: Tensor(\\\"block4_conv1/Relu:0\\\", shape=(?, 52, 52, 512), dtype=float32)\\n Layer Name block4_conv2 Output: Tensor(\\\"block4_conv2/Relu:0\\\", shape=(?, 52, 52, 512), dtype=float32)\\n Layer Name block4_conv3 Output: Tensor(\\\"block4_conv3/Relu:0\\\", shape=(?, 52, 52, 512), dtype=float32)\\n Layer Name block4_pool Output: Tensor(\\\"block4_pool/MaxPool:0\\\", shape=(?, 26, 26, 512), dtype=float32)\\n Layer Name block5_conv1 Output: Tensor(\\\"block5_conv1/Relu:0\\\", shape=(?, 26, 26, 512), dtype=float32)\\n Layer Name block5_conv2 Output: Tensor(\\\"block5_conv2/Relu:0\\\", shape=(?, 26, 26, 512), dtype=float32)\\n Layer Name block5_conv3 Output: Tensor(\\\"block5_conv3/Relu:0\\\", shape=(?, 26, 26, 512), dtype=float32)\\n Layer Name block5_pool Output: Tensor(\\\"block5_pool/MaxPool:0\\\", shape=(?, 13, 13, 512), dtype=float32)\\n '''\\n\\n #net, endpoint = inception_v2.inception_v2(inputs)\\n vgg16 = VGG16(input_tensor=inputs,weights='imagenet',include_top=False)\\n x = vgg16.get_layer('block5_pool').output\\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv1')(x)\\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv2')(x)\\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv3')(x)\\n x = YoloConv2D(512, (3, 3), activation='relu', padding='same', name='block6_conv4')(x)\\n\\n # input: 416 x 416 x 3\\n # block6_conv3 :13 x 13 x 512\\n # block5_conv3 :26 x 26 x 512\\n # block4_conv3 : 52 x 52 x 512\\n\\n\\n # f1 :13 x 13 x 1024 13 x 13 x 512\\n x, y1 = make_last_layers(x, 512, num_anchors * (num_classes + 5), predict_id='1')\\n\\n x = compose(\\n DarknetConv2D_BN_Leaky(256, (1,1)),\\n UpSampling2D(2))(x)\\n\\n f2 = vgg16.get_layer('block5_conv3').output\\n # f2: 26 x 26 x 512\\n x = Concatenate()([x,f2])\\n\\n x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5), predict_id='2')\\n\\n x = compose(\\n DarknetConv2D_BN_Leaky(128, (1,1)),\\n UpSampling2D(2))(x)\\n\\n f3 = vgg16.get_layer('block4_conv3').output\\n # f3 : 52 x 52 x 256\\n x = Concatenate()([x, f3])\\n x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5), predict_id='3')\\n\\n return Model(inputs = inputs, outputs=[y1,y2,y3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e23258f091ee5d75646f0ad6c10d2886\",\n \"score\": \"0.51935583\",\n \"text\": \"def __init__(self,\\n name,\\n num_units,\\n memory,\\n memory_projection='independent',\\n memory_sequence_length=None,\\n score_scale=True,\\n probability_fn=None,\\n dtype=None):\\n print('INFO: Using {}.'.format(self.__class__.__name__))\\n if probability_fn is None:\\n probability_fn = tf.nn.softmax\\n if dtype is None:\\n dtype = tf.float32\\n wrapped_probability_fn = lambda score, _: probability_fn(score)\\n assert memory_projection in ['independent', 'tied']\\n \\n assert len(_shape(memory)) == 3, \\\\\\n 'The CNN feature maps must be a rank-3 tensor of NTC.'\\n \\n proj_kwargs = dict(\\n units=num_units,\\n use_bias=True,\\n activation=None,\\n dtype=dtype)\\n with tf.variable_scope(name):\\n super(BahdanauAttentionV1, self).__init__(\\n query_layer=Dense(name='query_layer', **proj_kwargs),\\n memory_layer=Dense(name='memory_layer', **proj_kwargs),\\n memory=memory,\\n probability_fn=wrapped_probability_fn,\\n memory_sequence_length=memory_sequence_length,\\n score_mask_value=None,\\n name=name)\\n self._num_units = num_units\\n self._memory_projection = memory_projection\\n self._score_scale = score_scale\\n self._name = name\\n \\n if self._memory_projection == 'tied':\\n self._values = tf.identity(self._keys)\\n elif self._memory_projection == 'independent':\\n # Project memory\\n self._values = Dense(\\n name='value_layer',\\n **proj_kwargs)(self._values)\\n else:\\n raise ValueError('Undefined.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb8ca43b2aaec3cca0aeaf719137f4e\",\n \"score\": \"0.5192833\",\n \"text\": \"def define_vanilla_model_MLP(input_shape,\\n num_classes,\\n hidden_units = 32):\\n\\n # IoT Node (input image)\\n img_input_1 = Input(shape = input_shape) \\n img_input_2 = Input(shape = input_shape)\\n img_input_3 = Input(shape = input_shape)\\n img_input_4 = Input(shape = input_shape) \\n img_input_5 = Input(shape = input_shape) \\n img_input_6 = Input(shape = input_shape) \\n \\n input_edge1 = add([img_input_1,img_input_2])\\n input_edge2 = img_input_3\\n input_edge3 = add([img_input_4,img_input_5])\\n input_edge4 = img_input_6\\n\\n # edge nodes\\n edge1 = define_MLP_architecture_edge(input_edge1, hidden_units, \\\"edge1_output_layer\\\")\\n edge2 = define_MLP_architecture_edge(input_edge2, hidden_units, \\\"edge2_output_layer\\\")\\n edge3 = define_MLP_architecture_edge(input_edge3, hidden_units, \\\"edge3_output_layer\\\")\\n edge4 = define_MLP_architecture_edge(input_edge4, hidden_units, \\\"edge4_output_layer\\\")\\n\\n # fog node 4\\n fog4_input = layers.add([edge2,edge3, edge4], name = \\\"node5_input\\\")\\n fog4 = define_MLP_architecture_fog_with_two_layers(fog4_input, hidden_units,\\\"fog4_output_layer\\\",\\\"fog4_input_layer\\\")\\n\\n # fog node 3\\n fog3 = Lambda(lambda x: x * 1,name=\\\"node4_input\\\")(edge1)\\n fog3 = define_MLP_architecture_fog_with_one_layer(fog3, hidden_units, \\\"fog3_output_layer\\\")\\n\\n # fog node 2\\n fog2_input = layers.add([fog3, fog4], name = \\\"node3_input\\\")\\n fog2 = define_MLP_architecture_fog_with_two_layers(fog2_input, hidden_units, \\\"fog2_output_layer\\\", \\\"fog2_input_layer\\\")\\n\\n # fog node 1\\n fog1 = Lambda(lambda x: x * 1,name=\\\"node2_input\\\")(fog2)\\n fog1 = define_MLP_architecture_fog_with_two_layers(fog1, hidden_units, \\\"fog1_output_layer\\\", \\\"fog1_input_layer\\\")\\n \\n\\n # cloud node\\n cloud = Lambda(lambda x: x * 1,name=\\\"node1_input\\\")(fog1)\\n cloud = define_MLP_architecture_cloud(cloud, hidden_units, num_classes)\\n\\n model = Model(inputs=[img_input_1,img_input_2,img_input_3,img_input_4,img_input_5,img_input_6], outputs=cloud)\\n model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4ae8de5a4d0321100e7055d62a83b2\",\n \"score\": \"0.51927143\",\n \"text\": \"def g_vae_model_fn(mode, inputs, params, reuse=False):\\n\\n #params.cat_distr = params.n_latent//params.k\\n #params.n_latent = params.cat_distr*params.k\\n\\n if mode == 'train':\\n is_training = True\\n else:\\n is_training = False\\n # -----------------------------------------------------------\\n # MODEL: define the layers of the model\\n with tf.variable_scope('g_vae_model', reuse=reuse):\\n # Compute the output distribution of the model and the predictions\\n loss_likelihood, kl_loss, sampled, reconstructed_mean, sigma_placeholder = build_model(inputs, is_training,\\n params)\\n\\n # Define the Loss\\n loss = tf.reduce_mean(loss_likelihood + kl_loss)\\n\\n # Define training step that minimizes the loss with the Adam optimizer\\n learning_rate_ph = tf.placeholder(tf.float32, [], name=\\\"learning_rate\\\")\\n if mode == 'train':\\n optimizer = tf.train.AdamOptimizer(learning_rate_ph)\\n global_step = tf.train.get_or_create_global_step()\\n with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\\n train_op = optimizer.minimize(loss, global_step=global_step)\\n\\n # -----------------------------------------------------------\\n # METRICS AND SUMMARIES\\n # Metrics for evaluation using tf.metrics (average over whole dataset)\\n with tf.variable_scope(\\\"g_vae_metrics\\\"):\\n metrics = {\\n 'loss': tf.metrics.mean(loss),\\n 'kl_loss': tf.metrics.mean(kl_loss),\\n 'neg_log_likelihood': tf.metrics.mean(loss_likelihood)\\n }\\n\\n # Group the update ops for the tf.metrics\\n update_metrics_op = tf.group(*[op for _, op in metrics.values()])\\n\\n # Get the op to reset the local variables used in tf.metrics\\n metric_variables = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=\\\"g_vae_metrics\\\")\\n metrics_init_op = tf.variables_initializer(metric_variables)\\n\\n # Summaries for training\\n tf.summary.scalar('loss', loss)\\n tf.summary.scalar('kl_loss', kl_loss)\\n tf.summary.scalar('neg_log_likelihood', loss_likelihood)\\n # Summary for reconstruction and original image with max_outpus images\\n tf.summary.image('Original Image', inputs['img'], max_outputs=6, collections=None, family=None)\\n latent_img = tf.reshape(sampled, [-1, 1, params.n_latent, 1])\\n tf.summary.image('Latent Space', latent_img, max_outputs=6, collections=None, family=None)\\n tf.summary.image('Reconstructions', tf.sigmoid(reconstructed_mean), max_outputs=6, collections=None, family=None)\\n\\n # -----------------------------------------------------------\\n # -----------------------------------------------------------\\n # MODEL SPECIFICATION\\n # Create the model specification and return it\\n # It contains nodes or operations in the graph that will be used for training and evaluation\\n model_spec = inputs\\n variable_init_op = tf.group(*[tf.global_variables_initializer(), tf.tables_initializer()])\\n model_spec['variable_init_op'] = variable_init_op\\n model_spec['loss'] = loss\\n model_spec['metrics_init_op'] = metrics_init_op\\n model_spec['metrics'] = metrics\\n model_spec['update_metrics'] = update_metrics_op\\n model_spec['summary_op'] = tf.summary.merge_all()\\n model_spec['reconstructions'] = tf.sigmoid(reconstructed_mean)\\n model_spec['sigma_placeholder'] = sigma_placeholder\\n model_spec['learning_rate_placeholder'] = learning_rate_ph\\n model_spec['lambda_r_placeholder'] = tf.placeholder(tf.float32, shape=[], name='Reconstruction_regularization')\\n model_spec['lambda_c_placeholder'] = tf.placeholder(tf.float32, shape=[], name='center_sim_regularization')\\n model_spec['lambda_d_placeholder'] = tf.placeholder(tf.float32, shape=[], name='discriminative_regularization')\\n model_spec['lambda_b_placeholder'] = tf.placeholder(tf.float32, shape=[], name='inter_cluster_sim_regularization')\\n model_spec['lambda_w_placeholder'] = tf.placeholder(tf.float32, shape=[], name='intra_cluster_sim_regularization')\\n\\n if mode == 'train':\\n model_spec['train_op'] = train_op\\n elif mode == 'cluster':\\n model_spec['sample'] = sampled\\n\\n return model_spec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c534cbcefec0a4cea5836abc0deb1799\",\n \"score\": \"0.51907414\",\n \"text\": \"def nnObjFunction(params, *args):\\n \\n n_input, n_hidden, n_class, training_data, training_label, lambdaval = args\\n# params = np.linspace(-5,5, num=26)\\n\\n w1 = params[0:n_hidden * (n_input + 1)].reshape( (n_hidden, (n_input + 1)))\\n w2 = params[(n_hidden * (n_input + 1)):].reshape((n_class, (n_hidden + 1)))\\n \\n #Your code here\\n input_bias = np.ones((training_data.shape[0],1))\\n input_data = np.concatenate((training_data,input_bias),axis=1)\\n hidden_out =sigmoid((input_data.dot((np.transpose(w1)))))\\n \\n hidden_bias = np.ones((training_data.shape[0],1))\\n outer_in=np.concatenate((hidden_out,hidden_bias),axis=1)\\n oil=sigmoid(outer_in.dot(np.transpose(w2)))\\n \\n ones_mat = np.ones((training_data.shape[0],n_class))\\n zeros_mat = np.zeros((training_data.shape[0],n_class))\\n \\n for i in range(training_label.shape[0]):\\n t = training_label[i,0]\\n zeros_mat[i,t] = 1\\n \\n training_label = zeros_mat\\n \\n obj_val = np.zeros((training_data.shape[0],10))\\n obj_val = (training_label*(np.log(oil))+(((ones_mat-training_label)*(np.log(ones_mat-oil)))))\\n obj_val = -(obj_val/training_data.shape[0])\\n \\n w1sqr=w1**2\\n w2sqr=w2**2\\n wsum = w1sqr.sum() + w2sqr.sum()\\n \\n obj_val=obj_val.sum()+(lambdaval/(2*training_data.shape[0]))*(wsum)\\n \\n grad_w2 = np.zeros((n_class,n_hidden+1))\\n grad_w2 = (((np.transpose(oil-training_label)).dot(outer_in)) + (lambdaval*w2))/training_data.shape[0]\\n \\n grad_w1 = np.zeros((n_hidden,n_input+1)) \\n hidden_val = (np.ones((training_data.shape[0],n_hidden+1))) - outer_in\\n \\n grad_w1 = np.dot(np.transpose((np.multiply((np.multiply(hidden_val,outer_in)),(np.dot((oil-training_label),w2))))),input_data) \\n grad_w1 = ((np.delete(grad_w1,n_hidden,0)) + (lambdaval*w1))/training_data.shape[0]\\n \\n obj_grad = np.concatenate((grad_w1.flatten(), grad_w2.flatten()),0) \\n return (obj_val,obj_grad)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2381318310a3afde9b04fe530ff44fd7\",\n \"score\": \"0.51900274\",\n \"text\": \"def att_original_unet_model(input_shape, pool_size=(2, 2, 2), n_labels=1, initial_learning_rate=0.00001,\\n deconvolution=False,depth=4, n_base_filters=32, metrics=dice_coefficient,\\n batch_normalization=False, activation_name=\\\"sigmoid\\\"):\\n inputs = Input(input_shape)\\n\\n current_layer = inputs\\n\\n levels = list()\\n level_filters = list()\\n for level_number in range(depth): # depth=4; [0,1,2,3]\\n\\n n_level_filters = list()\\n n_level_filters.append((2**level_number) * n_base_filters) # 2^level_num * 16\\n n_level_filters.append(n_level_filters[0] * 2)\\n level_filters.append(n_level_filters)\\n\\n # add levels with max pooling\\n layer1 = create_convolution_block(input_layer=current_layer, n_filters=n_level_filters[0],\\n batch_normalization=batch_normalization)\\n layer2 = create_convolution_block(input_layer=layer1, n_filters=n_level_filters[1],\\n batch_normalization=batch_normalization)\\n if level_number < depth - 1:\\n current_layer = MaxPooling3D(pool_size=pool_size)(layer2)\\n levels.append([layer1, layer2, current_layer])\\n else: # 最下一层\\n current_layer = layer2\\n levels.append([layer1, layer2])\\n\\n for level_number in range(depth - 2, -1, -1): # [2,1,0]\\n\\n gating = gating_signal(levels[level_number + 1][1], level_filters[level_number][1], False)\\n att = attention_block(levels[level_number][1], gating, level_filters[level_number][1])\\n\\n up_convolution = get_up_convolution(pool_size=pool_size, deconvolution=deconvolution,\\n n_filters=level_filters[level_number + 1][1])(current_layer)\\n concat = concatenate([up_convolution, att], axis=1)\\n current_layer = create_convolution_block(n_filters=level_filters[level_number][1],\\n input_layer=concat,\\n batch_normalization=batch_normalization)\\n current_layer = create_convolution_block(n_filters=level_filters[level_number][1],\\n input_layer=current_layer,\\n batch_normalization=batch_normalization)\\n\\n # sigmoid激活输出\\n final_convolution = Conv3D(n_labels, (1, 1, 1))(current_layer)\\n act = Activation(activation_name)(final_convolution)\\n model = Model(inputs=inputs, outputs=act)\\n model.compile(optimizer=Adam(lr=initial_learning_rate), loss=dice_coefficient_loss)\\n # 返回模型\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e09162bdc34c53f17048185c69c3747\",\n \"score\": \"0.5188103\",\n \"text\": \"def __init__(self, num_conv_in_channel, num_conv_out_channel, num_primary_unit, primary_unit_size,\\n num_classes, output_unit_size, num_routing,\\n use_reconstruction_loss, regularization_scale, cuda_enabled,caps_channels = 32, kernel_size = 9, stride = 2):\\n super(Net, self).__init__()\\n\\n self.cuda_enabled = cuda_enabled\\n\\n # Configurations used for image reconstruction.\\n self.use_reconstruction_loss = use_reconstruction_loss\\n self.image_width = 224 # MNIST digit image width\\n self.image_height = 224 # MNIST digit image height\\n self.image_channel = 3 # MNIST digit image channel\\n self.regularization_scale = regularization_scale\\n\\n # Layer 1: Conventional Conv2d layer\\n # self.conv1 = ConvLayer(in_channel=num_conv_in_channel,\\n # out_channel=num_conv_out_channel,\\n # kernel_size=9)\\n\\n\\n features = list(models.alexnet(pretrained=True).features)[:-3]\\n # features.pop()\\n # features.pop()\\n # features.pop()\\n self.conv1 = nn.Sequential(*features)\\n \\n\\n\\n # ConvLayer(in_channel=num_conv_in_channel,\\n # out_channel=num_conv_out_channel,\\n # kernel_size=9)\\n\\n # PrimaryCaps\\n # Layer 2: Conv2D layer with `squash` activation\\n # print ('n_channel=',num_conv_out_channel)\\n # print ('num_unit=',num_primary_unit)\\n # print ('unit_size=',primary_unit_size)\\n # print ('use_routing=',False)\\n # print ('num_routing=',num_routing)\\n # print ('cuda_enabled=',cuda_enabled)\\n self.primary = CapsuleLayer(in_unit=0,\\n in_channel=num_conv_out_channel,\\n num_unit=num_primary_unit,\\n unit_size=primary_unit_size, # capsule outputs\\n use_routing=False,\\n num_routing=num_routing,\\n cuda_enabled=cuda_enabled,caps_channels = caps_channels,kernel_size=kernel_size, stride = stride)\\n\\n # DigitCaps\\n # Final layer: Capsule layer where the routing algorithm is.\\n # print ('SECOND')\\n # print ('in_unit=',num_primary_unit)\\n # print ('in_channel=',primary_unit_size)\\n # print ('num_unit=',num_classes)\\n # print ('unit_size=',output_unit_size)\\n # print ('use_routing=',True)\\n # print ('num_routing=',num_routing)\\n # print ('cuda_enabled=',cuda_enabled)\\n \\n\\n self.digits = CapsuleLayer(in_unit=num_primary_unit,\\n in_channel=primary_unit_size,\\n num_unit=num_classes,\\n unit_size=output_unit_size, # 16D capsule per digit class\\n use_routing=True,\\n num_routing=num_routing,\\n cuda_enabled=cuda_enabled)\\n\\n # Reconstruction network\\n if use_reconstruction_loss:\\n # The output of the digit capsule is fed into a decoder consisting of\\n # 3 fully connected layers that model the pixel intensities.\\n fc1_output_size = 512\\n fc2_output_size = 1024\\n self.fc1 = nn.Linear(num_classes * output_unit_size, fc1_output_size)\\n self.fc2 = nn.Linear(fc1_output_size, fc2_output_size)\\n self.fc3 = nn.Linear(fc2_output_size, 784)\\n # Activation functions\\n self.relu = nn.ReLU(inplace=True)\\n self.sigmoid = nn.Sigmoid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf91acb37977c4463e687ef864abc582\",\n \"score\": \"0.51855826\",\n \"text\": \"def __init__(self, keep_prob, input_x, input_y_first, input_y_second, input_product, input_brand, input_tag, fc_size, attention_unit_size, alpha, threshold, num_classes_first, num_classes_second, num_classes_third, num_classes_product, num_classes_brand, num_tags, learning_rate, batch_size, decay_steps, decay_rate, sequence_length,\\n vocab_size, embed_size, hidden_size, is_training, initializer=initializers.xavier_initializer(), clip_gradients=5.0):\\n # set hyperparamter\\n self.fc_size = fc_size\\n self.attention_unit_size = attention_unit_size\\n self.alpha = alpha\\n self.threshold = threshold\\n self.num_classes_first = num_classes_first\\n self.num_classes_second = num_classes_second\\n self.num_classes_third = num_classes_third\\n self.num_classes_product = num_classes_product\\n self.num_classes_brand = num_classes_brand\\n self.num_tags = num_tags\\n self.batch_size = batch_size\\n self.sequence_length = sequence_length\\n self.vocab_size = vocab_size\\n self.embed_size = embed_size\\n self.hidden_size = hidden_size\\n self.is_training = is_training\\n self.learning_rate = tf.Variable(learning_rate, trainable=False, name=\\\"learning_rate\\\")\\n self.learning_rate_decay_half_op = tf.assign(self.learning_rate, self.learning_rate * 0.95)\\n self.initializer = initializer\\n self.clip_gradients=clip_gradients\\n\\n #self.input_x = tf.placeholder(tf.int32, [None, None], name=\\\"input_x\\\")\\n self.input_x = input_x\\n # sequence true length\\n self.length = tf.reduce_sum(tf.sign(self.input_x), reduction_indices=1)\\n self.length = tf.cast(self.length, tf.int32)\\n\\n self.input_mask = tf.cast(tf.not_equal(self.input_x, 0), tf.float32)\\n\\n self.input_y_first = input_y_first\\n self.input_y_second = input_y_second\\n self.input_product = input_product\\n self.input_brand = input_brand\\n self.input_tag = input_tag\\n \\n self.dropout_keep_prob = keep_prob\\n self.decay_steps, self.decay_rate = decay_steps, decay_rate\\n # init model\\n self.instantiate_weights()\\n \\n # lstm output\\n self.gru_out, self.gru_out_pool = self.BiGRU()\\n\\n # for tag head\\n self.tag_logits, self.tag_logits_ = self.Ner(self.gru_out)\\n tag_predictions = tf.nn.softmax(self.tag_logits, axis=-1) \\n tag_predictionss = tf.nn.softmax(self.tag_logits_, axis=-1)\\n self.tag_softmax = tf.identity(tag_predictionss, name='tag_predicitions') \\n self.tag_predictions = tf.argmax(tag_predictions, axis=-1) \\n \\n # first level\\n self.first_att_weight, self.first_att_out = self.attention(self.gru_out, self.num_classes_first, name=\\\"first-\\\")\\n self.first_local_input = tf.concat([self.gru_out_pool, self.first_att_out], axis=1)\\n self.first_local_fc_out = self.fc_layer(self.first_local_input, name=\\\"first-local-\\\")\\n self.first_logits, self.first_scores, self.first_visual = self.local_layer(\\n self.first_local_fc_out, self.first_att_weight, self.num_classes_first, name=\\\"first-\\\")\\n\\n # second level\\n self.second_att_input = tf.multiply(self.gru_out, tf.expand_dims(self.first_visual, -1))\\n self.second_local_input = tf.reduce_sum(self.second_att_input, axis=1)\\n\\n self.fc_out = self.fc_layer(self.second_local_input)\\n \\n # dropout \\n with tf.name_scope(\\\"dropout\\\"):\\n self.h_drop = tf.nn.dropout(self.fc_out, self.dropout_keep_prob)\\n # global output\\n self.second_logits, self.second_scores = self.global_layer(self.num_classes_second, 'cid_class')\\n self.product_logits, self.product_scores = self.global_layer(self.num_classes_product, 'product_class')\\n self.brand_logits, self.brand_scores = self.global_layer(self.num_classes_brand, 'brand_class')\\n \\n self.predictions = tf.reshape(self.second_scores,[-1, self.num_classes_second], name='prediction')\\n self.product_predictions = tf.reshape(self.product_scores,[-1, self.num_classes_product], name='product_prediction')\\n self.brand_predictions = tf.reshape(self.brand_scores,[-1, self.num_classes_brand], name='brand_prediction')\\n \\n self.cid_precision, self.cid_recall, self.cid_f1 = self.metric(self.predictions, self.input_y_second, 'cid_') \\n self.product_precision, self.product_recall, self.product_f1 = self.metric(self.product_predictions, self.input_product, 'product_') \\n self.brand_precision, self.brand_recall, self.brand_f1 = self.metric(self.brand_predictions, self.input_brand, 'brand_') \\n \\n\\n # for tag acc\\n correct = tf.cast(tf.equal(tf.cast(self.tag_predictions, tf.int64) - self.input_tag, 0), tf.float32)\\n correct = tf.reduce_sum(self.input_mask * correct) \\n self.tag_acc = tf.div(correct, tf.cast(tf.reduce_sum(self.length), tf.float32))\\n \\n \\n # multi task loss \\n self.loss_val = self.multi_task_loss()\\n\\n if not is_training:\\n return\\n\\n # optimizer\\n self.train_op = self.train()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c5974e5fb78c77f27de9d9b1e688e67\",\n \"score\": \"0.5177354\",\n \"text\": \"def _add_train_op(self):\\n hps = self._hps\\n '''\\n self._lr_rate = tf.maximum(\\n hps.min_lr, # min_lr_rate.\\n tf.train.exponential_decay(hps.lr, self.global_step, 30000, 0.98))\\n '''\\n tvars = tf.trainable_variables()\\n with tf.device('/gpu:0'):\\n grads, global_norm = tf.clip_by_global_norm(tf.gradients(self._loss_total, tvars), hps.max_grad_norm)\\n tf.summary.scalar('global_norm', global_norm)\\n #optimizer = tf.train.GradientDescentOptimizer(self._lr_rate)\\n optimizer = tf.train.AdagradOptimizer(hps.lr, initial_accumulator_value=0.1)\\n #tf.summary.scalar('learning rate', self._lr_rate)\\n with tf.device('/gpu:0'):\\n self._train_op = optimizer.apply_gradients(\\n zip(grads, tvars), global_step=self.global_step, name='train_step')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ca8b13c5bd1267f9f5c7d50007829e\",\n \"score\": \"0.5173547\",\n \"text\": \"def tf_efficientnet_b5_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b5_ap\\\",\\n channel_multiplier=1.6,\\n depth_multiplier=2.2,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef699ac60c76b31c9c1fb56605105fe1\",\n \"score\": \"0.5173306\",\n \"text\": \"def get_model(point_cloud, img, is_training, num_cls, feature_num, bn_decay=None):\\n # b x n x 10\\n \\n #regularizer = tf.contrib.layers.l2_regularizer(scale=0.1)\\n regularizer = tf.keras.regularizers.l2(l=0.1)\\n batch_size = point_cloud.get_shape()[0].value\\n num_point = point_cloud.get_shape()[1].value\\n #image pixel size\\n num_pix = img.get_shape()[1].value\\n input_image = tf.expand_dims(point_cloud, -1)\\n #CONV for RGB images\\n conv1 = tf.layers.conv2d(inputs=img,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu) \\n print (conv1.shape)\\n pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)\\n conv2 = tf.layers.conv2d(inputs=pool1,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)\\n conv3 = tf.layers.conv2d(inputs=pool2,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n #conv = tf.layers.conv2d(inputs=pool,filters=32,kernel_size=[3, 3],padding=\\\"valid\\\",activation=tf.nn.relu)\\n #pool = tf.layers.max_pooling2d(inputs=conv, pool_size=[2, 2], strides=2)\\n conv4 = tf.layers.conv2d(inputs=conv3,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n #pool = tf.layers.max_pooling2d(inputs=conv, pool_size=[2, 2], strides=2)\\n print( conv4.shape)\\n \\n Up1 = tf.keras.layers.UpSampling2D(size=(2,2))(conv4)\\n print (Up1.shape)\\n dconv1 = tf.layers.conv2d(inputs=Up1,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n con1 =tf.concat(axis=-1, values=[dconv1, conv2])\\n dconv2 = tf.layers.conv2d(inputs=con1,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n Up2 = tf.keras.layers.UpSampling2D(size=(2,2))(dconv2)\\n dconv3 = tf.layers.conv2d(inputs=Up2,filters=64,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n con2 =tf.concat(axis=-1, values=[dconv3, conv1])\\n feature_img = tf.layers.conv2d(inputs=con2,filters=128,kernel_size=[3, 3],padding=\\\"same\\\",activation=tf.nn.relu)\\n print (feature_img.shape)\\n\\n\\n # CONV pointcloud\\n print (\\\"input_image.shape: \\\"+ str(input_image.shape))\\n #net = tf_util.conv2d(input_image, 64, [1,feature_num], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv0', bn_decay=bn_decay)\\n\\n net = tf.keras.layers.Convolution2D(filters=64, kernel_size=[1,feature_num], strides=(1, 1), padding='valid', activation='elu', use_bias=True, kernel_initializer='random_uniform')(input_image)\\n\\n net = tf.keras.layers.Convolution2D(filters=64, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\\n print(\\\"second_layer pointcloud: \\\" + str(net.shape))\\n\\n #net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay)\\n\\n print(net.shape)\\n\\n #net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay)\\n\\n net_test = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\\n\\n #net_test= tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay)\\n\\n #points_feat1 = tf_util.conv2d(net_test, 1024, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay)\\n\\n points_feat1 = tf.keras.layers.Convolution2D(filters=1024, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net_test)\\n\\n print(\\\"points_feat\\\" + str(points_feat1.shape))\\n\\n pc_feat1 = tf_util.max_pool2d(points_feat1, [num_point,1], padding='VALID', scope='maxpool1')\\n print(\\\"mapool1: \\\" + str(pc_feat1.shape))\\n \\n pc_feat1 = tf.reshape(pc_feat1, [batch_size, -1])\\n print(pc_feat1.shape)\\n \\n print (pc_feat1.shape)\\n pc_feat1_expand = tf.tile(tf.reshape(pc_feat1, [batch_size, 1, 1, -1]), [1, num_point, 1, 1])\\n print (points_feat1.shape)\\n print(\\\"pc_feeat1: \\\" + str(pc_feat1_expand.shape))\\n\\n #dense = tf.tile(tf.reshape(dense, [batch_size, 1, 1, -1]), [1, num_point, 1, 1])\\n #print(dense.shape)\\n\\n\\n points_feat1_concat = tf.concat(axis=-1, values=[net_test,pc_feat1_expand])\\n print(points_feat1_concat.shape)\\n\\n #net = tf_util.conv2d(points_feat1_concat, 128, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv7')\\n\\n net = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(points_feat1_concat)\\n\\n # point-wise concatanation of features between image and pointcloud from the first layer\\n #point_corres_featur1 = get_point_corres_features(box_size=50, pixel_size=0.16,feature_img=conv1, point_cloud=point_cloud)\\n\\n # point-wise concatanation of features between image and pointcloud final layer\\n point_corres_featur = get_point_corres_features(box_size=15.6, pixel_size=0.05,feature_img=feature_img, point_cloud=point_cloud)\\n point_corres_featur = tf.concat(axis=-1, values=[point_corres_featur,net])\\n print (\\\"point_corres_featur\\\" + str(point_corres_featur.shape) )\\n\\n\\n #net = tf_util.conv2d(point_corres_featur, 256, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv8')\\n\\n\\n net = tf.keras.layers.Convolution2D(filters=256, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(point_corres_featur)\\n #net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1],\\n # bn=True, is_training=is_training, scope='conv9')\\n net_print = tf.squeeze(net, [2])\\n net = tf.keras.layers.Convolution2D(filters=128, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\\n\\n print(net.shape)\\n net = tf_util.dropout(net, keep_prob=0.6, is_training=is_training, scope='dp1')\\n \\n #net = tf_util.conv2d(net, num_cls , [1,1], padding='VALID', stride=[1,1],\\n # activation_fn=None, scope='conv10')\\n\\n net = tf.keras.layers.Convolution2D(filters=num_cls, kernel_size=[1,1], strides=(1, 1), padding='valid', activation='elu', use_bias=True)(net)\\n print(net.shape)\\n net = tf.squeeze(net, [2])\\n print(net.shape)\\n model_vars = tf.trainable_variables()\\n print (slim.model_analyzer.analyze_vars(model_vars, print_info=True))\\n return (net, net_print)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38af1d9029e26ce0199778e25f93dee0\",\n \"score\": \"0.5167507\",\n \"text\": \"def pred_from_arr():\\n\\n global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\\n with tf.name_scope(\\\"Input\\\"):\\n x = tf.placeholder(tf.float32, [None,156,156,100])\\n y = tf.placeholder(tf.int32, [None,156,156,100])\\n keepprob = tf.placeholder(tf.float32)\\n is_training = tf.placeholder(tf.bool)\\n loss, logits, conv1, upsamp1, conv_decode1, conv2, conv4, upsamp2, upsamp4, conv_decode2, conv_decode4 = model5.inference(x,y, keepprob, is_training)\\n\\n learning_rate = 0.05\\n\\n\\n pred = tf.cast(tf.argmax(tf.nn.softmax(logits), -1), tf.int32)\\n\\n\\n\\n with tf.name_scope(\\\"Train\\\"):\\n grad_op = tf.train.AdamOptimizer(learning_rate)\\n train_op = grad_op.minimize(loss, global_step=global_step)\\n\\n tf.summary.scalar(\\\"cost\\\", loss)\\n tf.summary.histogram('histogram_loss', loss)\\n summary_op = tf.summary.merge_all()\\n saver=tf.train.Saver()\\n init_op=tf.global_variables_initializer()\\n def parse_image(image, y_size, x_size, z_size):\\n image = image[0:x_size, 0:y_size, 0:z_size]\\n num_patches_per_x = image.shape[0] // 156\\n num_patches_per_y = image.shape[1] // 156\\n num_patches_per_z = image.shape[2] // 100\\n x_size_crop = num_patches_per_x * 156\\n y_size_crop = num_patches_per_y * 156\\n z_size_crop = num_patches_per_z * 100\\n image = image[0:x_size_crop, 0:y_size_crop, 0:z_size_crop]\\n patches = image.reshape(num_patches_per_x, 156,\\n num_patches_per_y, 156, num_patches_per_z, 100)\\n patches = patches.transpose(0,2,4, 1,3,5)\\n patches = patches.reshape(num_patches_per_x * num_patches_per_y * num_patches_per_z,\\n 156, 156, 100)\\n return patches\\n sess = tf.Session()\\n init_op.run(session=sess) \\n ckpt = tf.train.get_checkpoint_state(os.path.dirname('~/pytorch-geo-intro/checkpoint'))\\n # if that checkpoint exists, restore from checkpoint\\n if ckpt and ckpt.model_checkpoint_path:\\n saver.restore(sess, ckpt.model_checkpoint_path)\\n\\n \\n test = pickle.load( open( \\\"dw.p\\\", \\\"rb\\\" ) )\\n m=8.529666e-05\\n s=0.0065450026\\n \\n test=(test-m)/s\\n test=parse_image(test, 312, 312, 100)\\n test_data=test\\n\\n test_images = np.zeros([4, 156, 156, 100])\\n\\n null_labels_test = np.zeros_like(test_images).astype(np.int32)\\n output_test=None\\n for i in range(4):\\n test_images[i,:,:,:]=test_data[i,:,:,:]\\n\\n num_sample_generate = 300\\n pred_tot = []\\n var_tot = []\\n prob_tot=[]\\n\\n test_output = sess.run(tf.nn.softmax(logits), feed_dict={x: test_images, y: null_labels_test, keepprob:1, is_training:True})\\n if output_test is not None:\\n output_test = np.concatenate((output_test, test_output), axis=0)\\n else:\\n output_test = test_output\\n\\n\\n for image_batch, label_batch in zip(test_images,null_labels_test):\\n image_batch = np.reshape(image_batch,[1,156,156,100])\\n label_batch = np.reshape(label_batch,[1,156,156,100])\\n prob_iter_tot = []\\n pred_iter_tot = []\\n \\n for iter_step in range(num_sample_generate):\\n prob_iter_step = sess.run(tf.nn.softmax(logits), feed_dict = {x: image_batch, y: label_batch, keepprob:0.7, is_training:True}) \\n prob_iter_tot.append(prob_iter_step)\\n pred_iter_tot.append(np.reshape(np.argmax(prob_iter_step,axis = -1),[-1]))\\n\\n \\n prob_mean = np.nanmean(prob_iter_tot,axis = 0)\\n prob_variance = np.var(prob_iter_tot, axis = 0)\\n pred = np.reshape(np.argmax(prob_mean,axis = -1),[-1]) \\n pred = np.reshape(pred,[156,156,100])\\n prob=prob_mean[0,:,:,:,1]\\n chan_var=prob_variance[0,:,:,:,1]\\n prob_variance=np.reshape(prob_variance,[156*156*100,2])\\n variance=np.nanmean(prob_variance,axis=1)\\n variance=np.reshape(variance,(156,156,100))\\n pred_tot.append(prob)\\n prob_tot.append(pred)\\n var_tot.append(chan_var)\\n pred_tot=np.array(pred_tot)\\n\\n var_tot=np.array(var_tot)\\n prob=np.concatenate((np.concatenate((pred_tot[0,:,:,:],pred_tot[1,:,:,:]),axis=1),np.concatenate((pred_tot[2,:,:,:],pred_tot[3,:,:,:]),axis=1)),axis=0)\\n var=np.concatenate((np.concatenate((var_tot[0,:,:,:],var_tot[1,:,:,:]),axis=1),np.concatenate((var_tot[2,:,:,:],var_tot[3,:,:,:]),axis=1)),axis=0)\\n return 'Hello'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c47fe71ea1258b050521d2ef351cab\",\n \"score\": \"0.51613176\",\n \"text\": \"def __init__(self, env, params):\\n super(AgentA2CVae, self).__init__(params)\\n\\n self.best_avg_reward = -sys.float_info.max\\n\\n num_actions = env.action_space.n\\n\\n global_step = tf.train.get_or_create_global_step()\\n\\n input_shape = list(env.observation_space.shape)\\n input_shape = [None] + input_shape # add batch dimension\\n self.observations = tf.placeholder(tf.float32, shape=input_shape)\\n\\n regularizer = tf.contrib.layers.l2_regularizer(scale=1e-6)\\n\\n self.memory = ReplayMemory()\\n\\n if self.params.use_vae:\\n self.autoencoder = VAE(self.observations, params.num_latent)\\n else:\\n self.autoencoder = Autoencoder(self.observations, params.num_latent)\\n\\n self.sampled_embeddings = tf.placeholder(tf.float32, [None, self.autoencoder.num_latent])\\n\\n self.policy = Policy(self.sampled_embeddings, num_actions, regularizer)\\n\\n self.selected_actions = tf.placeholder(tf.int32, [None]) # action selected by the policy\\n self.value_estimates = tf.placeholder(tf.float32, [None])\\n self.discounted_rewards = tf.placeholder(tf.float32, [None]) # estimate of total reward (rollout + value)\\n\\n advantages = self.discounted_rewards - self.value_estimates\\n\\n # negative logarithms of the probabilities of actions\\n # softmax turns action logits into a probability distribution over actions\\n # cross entropy with true distribution (one-hot vector with 1 for action taken and 0 everywhere else)\\n # will give h(p,q) = -sum_over_actions(p(action) * log q(action)) = -p(taken) * log q(taken) = -log q(taken)\\n neglogp_actions = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n logits=self.policy.actions, labels=self.selected_actions,\\n )\\n\\n # maximize probabilities of actions that give high advantage\\n action_loss = tf.reduce_mean(advantages * neglogp_actions)\\n\\n # penalize for inaccurate value estimation\\n value_loss = tf.losses.mean_squared_error(self.discounted_rewards, self.policy.value)\\n value_loss = self.params.value_loss_coeff * value_loss\\n\\n # penalize the agent for being \\\"too sure\\\" about it's actions (to prevent converging to the suboptimal local\\n # minimum too soon)\\n entropy_loss = -tf.reduce_mean(self.policy.actions_prob_distribution.entropy())\\n entropy_loss_coeff = tf.train.exponential_decay(\\n self.params.initial_entropy_loss_coeff, tf.cast(global_step, tf.float32), 50.0, 0.95, staircase=True,\\n )\\n entropy_loss_coeff = tf.maximum(entropy_loss_coeff, 0.02)\\n entropy_loss = entropy_loss_coeff * entropy_loss\\n\\n a2c_loss = action_loss + entropy_loss + value_loss\\n regularization_loss = tf.losses.get_regularization_loss()\\n loss = regularization_loss + a2c_loss\\n\\n # training\\n autoenc_optimizer = tf.train.AdamOptimizer(learning_rate=self.params.enc_learning_rate)\\n self.train_enc = autoenc_optimizer.minimize(self.autoencoder.loss)\\n a2c_optimizer = tf.train.AdamOptimizer(learning_rate=self.params.a2c_learning_rate)\\n self.train_a2c = a2c_optimizer.minimize(loss, global_step=global_step)\\n\\n # summaries for the agent and the training process\\n with tf.name_scope('agent_summary'):\\n tf.summary.histogram('actions', self.policy.actions)\\n tf.summary.scalar('action_avg', tf.reduce_mean(tf.to_float(self.policy.act)))\\n tf.summary.scalar('advantages', tf.reduce_mean(advantages))\\n tf.summary.scalar('value', tf.reduce_mean(self.policy.value))\\n\\n tf.summary.histogram('selected_actions', self.selected_actions)\\n tf.summary.scalar('selected_action_avg', tf.reduce_mean(tf.to_float(self.selected_actions)))\\n\\n tf.summary.scalar('policy_entropy', tf.reduce_mean(self.policy.actions_prob_distribution.entropy()))\\n tf.summary.scalar('entropy_coeff', entropy_loss_coeff)\\n\\n tf.summary.scalar('action_loss', action_loss)\\n tf.summary.scalar('value_loss', value_loss)\\n tf.summary.scalar('entropy_loss', entropy_loss)\\n tf.summary.scalar('a2c_loss', a2c_loss)\\n tf.summary.scalar('regularization_loss', regularization_loss)\\n tf.summary.scalar('loss', loss)\\n\\n self.a2c_summaries = tf.summary.merge_all()\\n\\n logger.info('Total parameters in the model: %d', count_total_parameters())\\n\\n summary_dir = ensure_dir_exists(join(summaries_dir(), self.params.experiment_name))\\n self.summary_writer = tf.summary.FileWriter(summary_dir)\\n self.saver = tf.train.Saver(max_to_keep=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a71cf48bca6b5c8663bf9c7e08d44b6\",\n \"score\": \"0.5158724\",\n \"text\": \"def __init__(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7,\\n hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0,\\n dtype=np.float32):\\n self.params = {}\\n self.reg = reg\\n self.dtype = dtype\\n \\n ############################################################################\\n # TODO: Initialize weights and biases for the three-layer convolutional #\\n # network. Weights should be initialized from a Gaussian with standard #\\n # deviation equal to weight_scale; biases should be initialized to zero. #\\n # All weights and biases should be stored in the dictionary self.params. #\\n # Store weights and biases for the convolutional layer using the keys 'W1' #\\n # and 'b1'; use keys 'W2' and 'b2' for the weights and biases of the #\\n # hidden affine layer, and keys 'W3' and 'b3' for the weights and biases #\\n # of the output affine layer. #\\n ############################################################################\\n C, H, W = input_dim\\n #used for cnn_relu_maxpool\\n self.params['W1']=np.random.normal(0,weight_scale,(num_filters,C,filter_size,filter_size))\\n self.params['b1']=np.zeros(num_filters)\\n #used for affine\\n self.params['W2']=np.random.normal(0,weight_scale,(num_filters*H/2*W/2,hidden_dim))\\n self.params['b2']=np.zeros(hidden_dim)\\n #used for classfication\\n self.params['W3']=np.random.normal(0,weight_scale,(hidden_dim,num_classes))\\n self.params['b3']=np.zeros(num_classes)\\n #used for nomarlbatch\\n self.params['beta1']=np.zeros(num_filters)\\n self.params['gamma1']=np.ones(num_filters)\\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n\\n\\n self.bn_params = [{'mode': 'train'} for i in xrange(2)]\\n\\n\\n for k, v in self.params.iteritems():\\n self.params[k] = v.astype(dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10572ce1c936bb07ccb9ae68c7648a2f\",\n \"score\": \"0.5157911\",\n \"text\": \"def __init__(self, output_stride, input_shape):\\n\\n super(ASPP, self).__init__()\\n\\n avg_pool_kernel = tuple(np.array(input_shape) / 16)\\n\\n if output_stride == 16:\\n dilations = [1, 6 , 12, 18]\\n elif output_stride == 8:\\n dilations = [1, 12, 24, 36]\\n else:\\n raise NotImplementedError()\\n\\n self.assp1 = _ASPPModule(256, 1, dilation=dilations[0])\\n self.assp2 = _ASPPModule(256, 3, dilation=dilations[1])\\n self.assp3 = _ASPPModule(256, 3, dilation=dilations[2])\\n self.assp4 = _ASPPModule(256, 3, dilation=dilations[3])\\n\\n self.global_avg_pool = tf.keras.Sequential([tf.keras.layers.AvgPool2D(avg_pool_kernel),\\n tf.keras.layers.Conv2D(256, 1, use_bias=False),\\n tf.keras.layers.BatchNormalization(),\\n tf.keras.layers.ReLU()])\\n\\n self.upsampling = tf.keras.layers.UpSampling2D(avg_pool_kernel, interpolation='bilinear')\\n\\n\\n self.conv = tf.keras.layers.Conv2D(256, 1, use_bias=False)\\n self.norm = tf.keras.layers.BatchNormalization()\\n self.relu = tf.keras.layers.ReLU()\\n self.dropout = tf.keras.layers.SpatialDropout2D(rate=0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43da7f38fdb886995ce90f1c6ff4f19\",\n \"score\": \"0.51577115\",\n \"text\": \"def tf_efficientnet_b4_ap(pretrained=False, **kwargs):\\n kwargs[\\\"bn_eps\\\"] = BN_EPS_TF_DEFAULT\\n kwargs[\\\"pad_type\\\"] = \\\"same\\\"\\n model = _gen_efficientnet(\\\"tf_efficientnet_b4_ap\\\",\\n channel_multiplier=1.4,\\n depth_multiplier=1.8,\\n pretrained=pretrained,\\n **kwargs)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8899aa7bc858bd82cb08f698cc0967\",\n \"score\": \"0.5154683\",\n \"text\": \"def test_conv_layer_bprop(layer_class, do_cross_correlation=False):\\n inputs = np.arange(96).reshape((2, 3, 4, 4))\\n kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\\n if do_cross_correlation:\\n kernels = kernels[:, :, ::-1, ::-1]\\n biases = np.arange(2)\\n grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\\n outputs = np.array(\\n [[[[-958., -1036., -1114.],\\n [-1270., -1348., -1426.],\\n [-1582., -1660., -1738.]],\\n [[1707., 1773., 1839.],\\n [1971., 2037., 2103.],\\n [2235., 2301., 2367.]]],\\n [[[-4702., -4780., -4858.],\\n [-5014., -5092., -5170.],\\n [-5326., -5404., -5482.]],\\n [[4875., 4941., 5007.],\\n [5139., 5205., 5271.],\\n [5403., 5469., 5535.]]]]\\n )\\n true_grads_wrt_inputs = np.array(\\n [[[[147., 319., 305., 162.],\\n [338., 716., 680., 354.],\\n [290., 608., 572., 294.],\\n [149., 307., 285., 144.]],\\n [[23., 79., 81., 54.],\\n [114., 284., 280., 162.],\\n [114., 272., 268., 150.],\\n [73., 163., 157., 84.]],\\n [[-101., -161., -143., -54.],\\n [-110., -148., -120., -30.],\\n [-62., -64., -36., 6.],\\n [-3., 19., 29., 24.]]],\\n [[[39., 67., 53., 18.],\\n [50., 68., 32., -6.],\\n [2., -40., -76., -66.],\\n [-31., -89., -111., -72.]],\\n [[59., 115., 117., 54.],\\n [114., 212., 208., 90.],\\n [114., 200., 196., 78.],\\n [37., 55., 49., 12.]],\\n [[79., 163., 181., 90.],\\n [178., 356., 384., 186.],\\n [226., 440., 468., 222.],\\n [105., 199., 209., 96.]]]])\\n layer = layer_class(\\n num_input_channels=kernels.shape[1],\\n num_output_channels=kernels.shape[0],\\n input_height=inputs.shape[2],\\n input_width=inputs.shape[3],\\n kernel_height=kernels.shape[2],\\n kernel_width=kernels.shape[3]\\n )\\n layer.params = [kernels, biases]\\n layer_grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)\\n assert layer_grads_wrt_inputs.shape == true_grads_wrt_inputs.shape, (\\n 'Layer bprop returns incorrect shaped array. '\\n 'Correct shape is \\\\n\\\\n{0}\\\\n\\\\n but returned shape is \\\\n\\\\n{1}.'\\n .format(true_grads_wrt_inputs.shape, layer_grads_wrt_inputs.shape)\\n )\\n assert np.allclose(layer_grads_wrt_inputs, true_grads_wrt_inputs), (\\n 'Layer bprop does not return correct values. '\\n 'Correct output is \\\\n\\\\n{0}\\\\n\\\\n but returned output is \\\\n\\\\n{1}\\\\n\\\\n difference is \\\\n\\\\n{2}'\\n .format(true_grads_wrt_inputs, layer_grads_wrt_inputs,\\n layer_grads_wrt_inputs - true_grads_wrt_inputs)\\n )\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a0d2a4065dffffbc12f9eb98d94ed7\",\n \"score\": \"0.5152247\",\n \"text\": \"def index_adam_small_range(params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],\\n max_exp_avg_sqs: List[Tensor], state_steps: List[int], amsgrad: bool, beta1: float, beta2: float, lr: float,\\n weight_decay: float, eps: float, extra_small: bool):\\n\\n for i, param in enumerate(params):\\n\\n grad = grads[i]\\n exp_avg = exp_avgs[i]\\n exp_avg_sq = exp_avg_sqs[i]\\n step = state_steps[i]\\n\\n bias_correction1 = 1 - beta1 ** step\\n bias_correction2 = 1 - beta2 ** step\\n\\n if weight_decay != 0:\\n grad = grad.add(param, alpha=weight_decay)\\n\\n # Decay the first and second moment running average coefficient\\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\\n if amsgrad:\\n # Maintains the maximum of all 2nd moment running avg. till now\\n torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])\\n # Use the max. for normalizing running avg. of gradient\\n denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n else:\\n denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\\n\\n step_size = lr / bias_correction1\\n # param.addcdiv_(exp_avg, denom, value=-step_size) # adam, from pytorch original code\\n param_new = param.addcdiv(exp_avg, denom, value=-step_size)\\n if extra_small and len(param.shape) > 2: # Conv2D weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n for idx_dim1, param_dim1 in enumerate(param_dim0):\\n param_dim1_tmp, _ = torch.sort(param_dim1.view(-1))\\n param_dim1.view(-1)[torch.argsort(param_new[idx_dim0, idx_dim1].view(-1))] = param_dim1_tmp\\n elif len(param.shape) > 1: # weight\\n for idx_dim0, param_dim0 in enumerate(param):\\n param_dim0_tmp, _ = torch.sort(param_dim0.view(-1))\\n param_dim0.view(-1)[torch.argsort(param_new[idx_dim0].view(-1))] = param_dim0_tmp\\n else: # bias\\n param_tmp, _ = torch.sort(param.view(-1))\\n param.view(-1)[torch.argsort(param_new.view(-1))] = param_tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8670f2ecfc20fb1e1bbad12bf240ef0\",\n \"score\": \"0.51521254\",\n \"text\": \"def __init__(self, input_dim, output_dim, growth_factor=2):\\n super().__init__() \\n self.attn = nn.Linear((input_dim * 3 * growth_factor), output_dim)\\n self.v = nn.Parameter(torch.rand(output_dim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea7e33fec43ee551722525546909b51\",\n \"score\": \"0.5148238\",\n \"text\": \"def get_model(point_cloud, point_cloud_kernel,point_cloud_all, \\n Weights_f1_1, Weights_f1_2, Weights_f1_3, biases_f1_1, biases_f1_2, biases_f1_3,\\n Weights_f2_1, Weights_f2_2, Weights_f2_3, biases_f2_1, biases_f2_2, biases_f2_3,\\n Weights_f3_1, Weights_f3_2, Weights_f3_3, biases_f3_1, biases_f3_2, biases_f3_3,\\n Weights_all1_1, Weights_all1_2, Weights_all1_3, biases_all1_1, biases_all1_2, biases_all1_3,\\n is_training = True):\\n if is_training == True:\\n dropout_prob_conv = 0.1\\n dropout_prob_f = 0.2\\n else:\\n dropout_prob_conv = 0.0\\n dropout_prob_f = 0.0\\n \\n\\n batch_size = point_cloud.get_shape()[0].value\\n PGK = point_cloud.get_shape()[1].value\\n PGN = point_cloud.get_shape()[2].value\\n ##allnet1\\n point_cloud_all = tf.reshape(point_cloud_all, [batch_size, 1, PGK, -1])\\n allnet1_x = tf.slice(point_cloud, [0, 0, 0, 0], [-1, -1,-1, 2])\\n allnet1_y = tf.slice(point_cloud, [0, 0, 0, 2], [-1, -1,-1, 1])\\n allf1net = get_feature( \\\"all_feature1\\\", allnet1_x, allnet1_y, Weights_all1_1, Weights_all1_2, Weights_all1_3, biases_all1_1, biases_all1_2, biases_all1_3)\\n allf1net = tf.reshape(allf1net, [batch_size, PGK, -1])\\n feature_all = tf.reduce_sum(allf1net, 1, keep_dims=False)\\n feature_all = tf.cast(feature_all, tf.float32)\\n \\n ##net1\\n net1_x = tf.slice(point_cloud, [0, 0, 0, 0], [-1, -1,-1, 2])\\n net1_y = tf.slice(point_cloud, [0, 0, 0, 2], [-1, -1,-1, 1])\\n \\n f1net = get_feature( \\\"feature1\\\", net1_x, net1_y, Weights_f1_1, Weights_f1_2, Weights_f1_3, biases_f1_1, biases_f1_2, biases_f1_3)\\n f1net = tf.reshape(f1net, [batch_size, PGK, -1])\\n feature_1 = tf.reduce_sum(f1net, 1, keep_dims=False)\\n feature_1 = tf.cast(feature_1, tf.float32)\\n \\n ##net2\\n f2net_input, _ = point_group(\\\"point group2\\\", point_cloud_kernel,f1net,32,PGN)\\n \\n net2_x = tf.slice(f2net_input, [0, 0, 0, 0], [-1, -1,-1, 63])\\n net2_y = tf.slice(f2net_input, [0, 0, 0, 63], [-1, -1,-1, 1])\\n \\n f2net = get_feature( \\\"feature2\\\", net2_x, net2_y, Weights_f2_1, Weights_f2_2, Weights_f2_3, biases_f2_1, biases_f2_2, biases_f2_3)\\n \\n f2net = tf.reshape(f2net, [batch_size, 32, -1])\\n feature_2 = tf.reduce_sum(f2net, 1, keep_dims=False)\\n feature_2 = tf.cast(feature_2, tf.float32)\\n \\n ##net3\\n f3net_input = tf.reshape(f2net, [batch_size, 1, 32, -1])\\n net3_x = tf.slice(f3net_input, [0, 0, 0, 0], [-1, -1,-1, 63])\\n net3_y = tf.slice(f3net_input, [0, 0, 0, 63], [-1, -1,-1, 1])\\n \\n f3net = get_feature( \\\"feature3\\\", net3_x, net3_y, Weights_f3_1, Weights_f3_2, Weights_f3_3, biases_f3_1, biases_f3_2, biases_f3_3)\\n \\n f3net = tf.reshape(f3net, [batch_size, 1, -1])\\n feature_3 = tf.reduce_sum(f3net, 1, keep_dims=False)\\n feature_3 = tf.cast(feature_3, tf.float32)\\n '''\\n for y_num in range (64): \\n if y_num == 0:\\n net1_y_num = net1_y\\n else : \\n net1_y_num = tf.concat([net1_y_num,net1_y],axis=3)\\n\\n net1 = conv2d(\\\"conv1_1\\\", input=net1_x, activation=selu, ksize=1, f_in=2, f_out=128)\\n net1 = conv2d(\\\"conv1_2\\\", input=net1, activation=selu, ksize=1, f_in=128, f_out=64)\\n #norm\\n net1_norm = tf.sqrt(tf.reduce_sum(tf.square(net1), axis=2)) \\n net1_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net1_y_num), axis=2)) \\n #cos\\n net1cos = tf.reduce_sum(tf.multiply(net1, net1_y_num), axis=2) \\n net1cos_norm = net1cos / (net1_norm * net1_y_norm) \\n net1cos_norm = selu(net1cos_norm)\\n #feature1 \\n Feature1 = tf.reduce_mean(net1cos_norm, 1, keep_dims=False)\\n Feature1 = tf.reshape(Feature1, [batch_size, -1])\\n \\n net1_f = tf.reshape(net1cos_norm, [batch_size, PGK, -1])\\n net2_2, net_idx2 = point_group(\\\"group1\\\", point_cloud_kernel,net1_f,32,PGN)\\n \\n ##net2\\n net2_x = tf.slice(net2_2, [0, 0, 0, 0], [-1, -1,-1, 63])\\n net2_y = tf.slice(net2_2, [0, 0, 0, 63], [-1, -1,-1, 1])\\n for y_num in range (64): \\n if y_num == 0:\\n net2_y_num = net2_y\\n else : \\n net2_y_num = tf.concat([net2_y_num,net2_y],axis=3)\\n\\n net2 = conv2d(\\\"conv2_1\\\", input=net2_x, activation=selu, ksize=1, f_in=63, f_out=128)\\n net2 = conv2d(\\\"conv2_2\\\", input=net2, activation=selu, ksize=1, f_in=128, f_out=64)\\n #norm\\n net2_norm = tf.sqrt(tf.reduce_sum(tf.square(net2), axis=2)) \\n net2_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net2_y_num), axis=2)) \\n #cos\\n net2cos = tf.reduce_sum(tf.multiply(net2, net2_y_num), axis=2) \\n net2cos_norm = net2cos / (net2_norm * net2_y_norm) \\n net2cos_norm = selu(net2cos_norm)\\n #feature2 \\n Feature2 = tf.reduce_mean(net2cos_norm, 1, keep_dims=False)\\n Feature2 = tf.reshape(Feature2, [batch_size, -1]) \\n \\n net2_f = tf.reshape(net2cos_norm, [batch_size, 1, 32, -1])\\n \\n ##net3\\n net3_x = tf.slice(net2_f, [0, 0, 0, 0], [-1, -1,-1, 63])\\n net3_y = tf.slice(net2_f, [0, 0, 0, 63], [-1, -1,-1, 1])\\n for y_num in range (64): \\n if y_num == 0:\\n net3_y_num = net3_y\\n else : \\n net3_y_num = tf.concat([net3_y_num,net3_y],axis=3)\\n\\n net3 = conv2d(\\\"conv3_1\\\", input=net3_x, activation=selu, ksize=1, f_in=63, f_out=128)\\n net3 = conv2d(\\\"conv3_2\\\", input=net3, activation=selu, ksize=1, f_in=128, f_out=64)\\n #norm\\n net3_norm = tf.sqrt(tf.reduce_sum(tf.square(net3), axis=2)) \\n net3_y_norm = tf.sqrt(tf.reduce_sum(tf.square(net3_y_num), axis=2)) \\n #cos\\n net3cos = tf.reduce_sum(tf.multiply(net3, net3_y_num), axis=2) \\n net3cos_norm = net3cos / (net3_norm * net3_y_norm)\\n net3cos_norm = selu(net3cos_norm)\\n #feature3\\n Feature3 = tf.reduce_mean(net3cos_norm, 1, keep_dims=False)\\n Feature3 = tf.reshape(Feature3, [batch_size, -1])\\n '''\\n net = tf.concat([feature_all,feature_1,feature_2,feature_3],axis=1)\\n dim = net.get_shape()[1].value\\n\\n net = fc('fully_connected1', input=net, activation=selu, n_in=dim, n_out=256, stddev=0.04, bias_init=0.1,\\n weight_decay=0.004)\\n net = dropout_selu(net, dropout_prob_f,training=is_training)\\n net = fc('fully_connected2', input=net, activation=selu, n_in=256, n_out=128, stddev=0.04, bias_init=0.1,\\n weight_decay=0.004)\\n net = dropout_selu(net, dropout_prob_f,training=is_training)\\n\\n weights = _variable_with_weight_decay('weights', [128, 40], stddev=1 / 128.0, activation=\\\"selu\\\", wd=0.0)\\n biases = tf.get_variable(name='biases', shape=[40], initializer=tf.constant_initializer(0.0), dtype=tf.float32)\\n net = tf.add(tf.matmul(net, weights), biases, name='output')\\n \\n return net, f2net_input, f3net_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e1d87b640941fd8030c7c2b77a8ae62\",\n \"score\": \"0.514711\",\n \"text\": \"def AE_model(init_lr=0.1,\\n decay_rate=0.95,\\n batchsize=1,\\n epochs=1,\\n display_steps=10000,\\n num_hidden=20,\\n data={},\\n key='users'):\\n \\n # Some variables/parameters declaration\\n learning_rate = tf.placeholder(tf.float32, shape=[])\\n steps = int(settings.num_data[key]/batchsize) + 1\\n \\n with tf.name_scope('Parameters'):\\n X = tf.placeholder('float', [None, settings.num_feat[key]])\\n weights = tf.Variable(tf.random_normal([settings.num_feat[key], num_hidden],mean=0,stddev=0.5))\\n \\n # Predictions and labels definition\\n with tf.name_scope('Model'):\\n predictions = decoder(encoder(X,weights),weights)\\n preds = tf.round(predictions) # round to integers the predictions (-> accuracy calc.)\\n with tf.name_scope('Target'):\\n labels = X\\n\\n # Define accuracy and objective\\n with tf.name_scope('Loss'):\\n loss = tf.reduce_mean(tf.reduce_max(tf.abs(labels - predictions), keep_dims=True, axis=1))\\n with tf.name_scope('Accuracy'):\\n results = tf.reduce_sum(tf.cast(tf.equal(labels,preds), tf.int32), keep_dims=True, axis=1)\\n accuracy = tf.reduce_sum(tf.cast(tf.equal(results, settings.num_feat[key]), tf.int32), axis=0) / tf.size(results)\\n\\n # Define optimization\\n with tf.name_scope('SGD'):\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\\n \\n # Create a Saver to save NN weights\\n saver = tf.train.Saver([weights])\\n\\n # Add variables to be written to a summary\\n tf.summary.scalar('loss', loss)\\n summary = tf.summary.merge_all()\\n \\n # Create a Python dict to hold various TensorFlow objects, and Training parameters\\n tfdict = {'loss': loss,\\n 'accuracy': accuracy,\\n 'optimizer': optimizer,\\n 'summary': summary,\\n 'saver': saver,\\n 'X': X,\\n 'learning_rate': learning_rate,\\n 'weights': weights}\\n \\n params = {'init_lr': init_lr,\\n 'decay_rate': decay_rate,\\n 'batchsize': batchsize,\\n 'epochs': epochs,\\n 'display_steps': display_steps,\\n 'steps': steps}\\n \\n # train the auto-encoder to generate weights\\n if settings.ML_mode['AE'] == True:\\n print('Start training Auto-Encoder for %s set' % key)\\n AE_training(params = params,\\n data = data,\\n tfobjects = tfdict,\\n key = key)\\n # Load weights from disk\\n else:\\n print('Use weights for %s A-E from the disk' % key)\\n AE_load(tfobjects = tfdict,\\n key = key)\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c6ef6b5c616e01c5bce44c7b924331\",\n \"score\": \"0.51464427\",\n \"text\": \"def inference(self, image, keep_prob, istraining):\\n with tf.variable_scope('AlexNet') as scope:\\n with tf.variable_scope('conv1') as scope:\\n temp_conv = self.conv2d(\\\"conv1\\\", image, kernel_size=[11, 11, 3, 96], stride=4,\\n istraining=istraining, padding='VALID')\\n\\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\\n\\n with tf.variable_scope('conv2') as scope:\\n temp_conv = self.conv2d(\\\"conv2\\\", temp_pool, kernel_size=[5, 5, 96, 256], stride=1,\\n istraining=istraining)\\n\\n temp_pool = self.max_pool(temp_conv, [3, 3], 2, padding='VALID')\\n\\n with tf.variable_scope('conv3') as scope:\\n temp_conv = self.conv2d(\\\"conv3\\\", temp_pool, kernel_size=[3, 3, 256, 384], stride=1,\\n istraining=istraining)\\n\\n with tf.variable_scope('conv4') as scope:\\n temp_conv = self.conv2d(\\\"conv4\\\", temp_conv, kernel_size=[3, 3, 384, 384], stride=1,\\n istraining=istraining)\\n\\n with tf.variable_scope('conv5') as scope:\\n temp_conv = self.conv2d(\\\"conv5\\\", temp_conv, kernel_size=[3, 3, 384, 256], stride=1,\\n istraining=istraining)\\n\\n temp_pool = self.max_pool(temp_conv, [2, 2], 2, padding='VALID')\\n # fc to conv\\n fc6 = self.local('fc6', temp_pool, kernel_size=[6, 6, 256, 4096], stride=1, istraining=istraining)\\n fc6_dropout = tf.nn.dropout(fc6, keep_prob=keep_prob)\\n\\n fc7 = self.local('fc7', fc6_dropout, kernel_size=[1, 1, 4096, 4096], stride=1, istraining=istraining)\\n fc7_dropout = tf.nn.dropout(fc7, keep_prob=keep_prob)\\n\\n fc8 = self.local('fc8', fc7_dropout, kernel_size=[1, 1, 4096, self.num_classes], stride=1,\\n istraining=istraining)\\n fc8_squeeze = tf.squeeze(fc8)\\n return fc8_squeeze\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b3e2da9c8c18ab8afb572598248560\",\n \"score\": \"0.5141961\",\n \"text\": \"def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\\n session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a75bdb7934836138a9f8468076af10\",\n \"score\": \"0.5141666\",\n \"text\": \"def neural_net_predict_dsc(params, inputs):\\r\\n inpW, inpb = params[0]\\r\\n inputs = relu(np.dot(inputs, inpW) + inpb)\\r\\n for W, b in params[1:-1]:\\r\\n outputs = np.dot(inputs, W) + b\\r\\n # outputs = batch_normalize(np.dot(inputs, W) + b)\\r\\n inputs = leaky_relu(outputs)\\r\\n # inputs = relu(outputs)\\r\\n outW, outb = params[-1]\\r\\n outputs = np.dot(inputs, outW) + outb\\r\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e4234b98ecb63071f9325295795948\",\n \"score\": \"0.51411444\",\n \"text\": \"def __init__(self, feat_dim, hid_dim, att_head_num=1):\\n super(StructAttention, self).__init__()\\n self.W1 = torch.nn.Linear(feat_dim, hid_dim, bias=False)\\n nn.init.xavier_normal_(self.W1.weight)\\n\\n self.W2 = torch.nn.Linear(hid_dim, att_head_num, bias=False)\\n nn.init.xavier_normal_(self.W2.weight)\\n\\n self.att_head_num = att_head_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0fe30f65f071856f693be598abc6f0e\",\n \"score\": \"0.51366657\",\n \"text\": \"def forward_backward_prop(data, labels, params, dimensions):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"823e6efe3d519dfac6e466c02c74af00\",\n \"score\": \"0.5129472\",\n \"text\": \"def model(images):\\n net = tf.layers.dense(images, 500, activation=tf.nn.relu)\\n net = tf.layers.dense(net, 500, activation=tf.nn.relu)\\n net = tf.layers.dense(net, 10, activation=None)\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b388a0b5ee56e6fff18af6fda40c4054\",\n \"score\": \"0.5118143\",\n \"text\": \"def deepnn(x, is_training):\\n # Reshape to use within a convolutional neural net.\\n # Last dimension is for \\\"features\\\" - there is only one here, since images are\\n # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.\\n with tf.name_scope('reshape'):\\n x_image = tf.reshape(x, [-1, 1024,1, 1])\\n\\n # First convolutional layer - maps one grayscale image to 5 feature maps.\\n # The third dimension of W is number of input channels, the last is the \\n # number of output channels\\n with tf.name_scope('conv1'):\\n num_feature1 = 20\\n W_conv1 = weight_variable([16, 1, 1, num_feature1])\\n b_conv1 = bias_variable([num_feature1])\\n BN_conv1 = batch_normalization(\\n tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1],\\n padding='SAME') + b_conv1, is_training=is_training)\\n h_conv1 = tf.nn.relu(BN_conv1)\\n\\n # Pooling layer - downsamples by 2X.\\n with tf.name_scope('pool1'):\\n h_pool1 = max_pool(h_conv1)\\n\\n # Second convolutional layer -- maps 5 feature maps to 5\\n with tf.name_scope('conv2'):\\n num_feature2 = 40\\n W_conv2 = weight_variable([5, 1, num_feature1, num_feature2])\\n b_conv2 = bias_variable([num_feature2])\\n BN_conv2 = batch_normalization(\\n conv2d(h_pool1, W_conv2) + b_conv2, is_training=is_training)\\n h_conv2 = tf.nn.relu(BN_conv2)\\n\\n # Second pooling layer.\\n with tf.name_scope('pool2'):\\n h_pool2 = max_pool(h_conv2)\\n \\n with tf.name_scope('conv3'):\\n num_feature3 = 40\\n W_conv3 = weight_variable([5, 1, num_feature2, num_feature3])\\n b_conv3 = bias_variable([num_feature3])\\n BN_conv3 = batch_normalization(\\n conv2d(h_pool2, W_conv3) + b_conv3, is_training=is_training)\\n h_conv3 = tf.nn.relu(BN_conv3)\\n\\n # Second pooling layer.\\n with tf.name_scope('pool3'):\\n h_pool3 = max_pool(h_conv3)\\n \\n # Fully connected layer 1 -- after 2 round of downsampling, our 300x200 image\\n # is down to 75x50x64 feature maps -- maps this to 256 features.\\n with tf.name_scope('fc1'):\\n out_size = 2048\\n W_fc1 = weight_variable([128 * num_feature3, out_size])\\n b_fc1 = bias_variable([out_size])\\n \\n h_pool2_flat = tf.reshape(h_pool3, [-1, 128*num_feature3])\\n BN_fc1 = batch_normalization(\\n tf.matmul(h_pool2_flat, W_fc1) + b_fc1, is_training=is_training)\\n h_fc1 = tf.nn.relu(BN_fc1)\\n\\n # Dropout - controls the complexity of the model, prevents co-adaptation of\\n # features.\\n with tf.name_scope('dropout'):\\n keep_prob = tf.placeholder(tf.float32)\\n h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\\n\\n # Map the 256 features to 3 classes, one for each digit\\n with tf.name_scope('fc2'):\\n W_fc2 = weight_variable([out_size, 3])\\n b_fc2 = bias_variable([3])\\n \\n y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\\n return y_conv, keep_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02181222373f2658dae82ae67e07a1b6\",\n \"score\": \"0.5117104\",\n \"text\": \"def forward(self, state):\\n x = F.relu(self.fc1(state))\\n value = F.relu(self.fc_value(x))\\n adv = F.relu(self.fc_adv(x))\\n\\n value = self.value(value)\\n adv = self.adv(adv)\\n\\n advAverage = torch.mean(adv, dim=1, keepdim=True)\\n return value + adv - advAverage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08580f2c201a10f57567a1b780e914f4\",\n \"score\": \"0.5117088\",\n \"text\": \"def train(self):\\n\\n def weight_var(shape):\\n return tf.Variable(tf.truncated_normal(shape, stddev=0.1))\\n\\n def bias_var(shape):\\n return tf.Variable(tf.constant(0.1, shape=shape))\\n\\n def conv3d(input, weights):\\n return tf.nn.conv3d(input, weights, strides=[1,1,1,1,1], padding='SAME')\\n\\n def max_pool3d(input, k, s):\\n return tf.nn.max_pool3d(input, ksize=[1,k,k,k,1], strides=[1,s,s,s,1], padding='SAME')\\n\\n def batch_norm(tensor):\\n #m, v = tf.nn.moments(tensor, [0])\\n m, v = tf.nn.moments(tensor, [0,1,2,3])\\n return tf.nn.batch_normalization(tensor, m, v, None, None, 1e-12)\\n \\n graph = tf.Graph()\\n\\n with graph.as_default():\\n # Input data.\\n #tf_train_dataset = tf.placeholder(tf.float32,\\n # shape=(batch_size, self.image_size_x,\\n # self.image_size_y, self.image_size_z,1))\\n train_shape = self.train_dataset.shape[1:]\\n tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, train_shape[0], train_shape[1],\\n train_shape[2], train_shape[3]))\\n tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\\n tf_valid_dataset = tf.constant(self.valid_dataset, dtype=tf.float32)\\n tf_test_dataset = tf.constant(self.test_dataset, dtype=tf.float32)\\n\\n print tf_train_dataset.get_shape(), tf_valid_dataset.get_shape(), tf_test_dataset.get_shape()\\n \\n # Variables.\\n # Layer 1 (conv)\\n weights1 = weight_var([patch_size, patch_size, patch_size, 1, out_features1])\\n biases1 = bias_var([out_features1])\\n # Layer 2 (conv)\\n weights2 = weight_var([patch_size, patch_size, patch_size, out_features1, out_features2])\\n biases2 = bias_var([out_features2])\\n # Layer 3 (fully connected)\\n #weights3 = weight_var([self.image_size_x // 4 * self.image_size_y // 4\\n # * self.image_size_z // 4 * out_features2, num_hidden])\\n weights3 = weight_var([4032, num_hidden])\\n biases3 = bias_var([num_hidden])\\n # Layer 4 (output)\\n weights4 = weight_var([num_hidden, num_labels])\\n biases4 = bias_var([num_labels])\\n\\n # Model.\\n def model(data, dropout=False):\\n # do dropout on the input data itself i.e. blankout \\n #data = tf.nn.dropout(data, 0.5)\\n\\n # batch_normalization\\n #data = batch_norm(data)\\n \\n # Layer 1 (conv): data = `patch_size` x `patch_size` x `image_size_z`\\n print \\\"conv1.shape (before conv)\\\", data.get_shape()\\n #pdb.set_trace()\\n conv1 = conv3d(data, weights1) # conv = `patch_size` x `patch_size` x `out_features1`\\n conv1 = batch_norm(conv1)\\n conv1 = tf.nn.relu(conv1 + biases1)\\n print \\\"conv1.shape (after conv)\\\", conv1.get_shape()\\n #conv1 = max_pool_kxk(conv1, 2) # pool = `patch_size`/2 x `patch_size`/2 x `out_features1`\\n conv1 = max_pool3d(conv1, 3,3)\\n print \\\"conv1.shape (after pool)\\\", conv1.get_shape()\\n if dropout:\\n conv1 = tf.nn.dropout(conv1, dropout)\\n\\n # Layer 2 (conv): data = `patch_size`/2 x `patch_size`/2 x `out_features1`\\n print \\\"conv2.shape (before conv)\\\", conv1.get_shape()\\n conv2 = conv3d(conv1, weights2) # conv = `patch_size` x `patch_size` x `out_features2`\\n conv2 = batch_norm(conv2)\\n conv2 = tf.nn.relu(conv2 + biases2)\\n print \\\"conv2.shape (after conv)\\\", conv2.get_shape()\\n conv2 = max_pool3d(conv2, 3, 3) # pool = `patch_size`/2/2 x `patch_size`/2/2 x `out_features2`\\n print \\\"conv2.shape (after pool)\\\", conv2.get_shape()\\n if dropout:\\n conv2 = tf.nn.dropout(conv2, dropout)\\n\\n # Layer 3 (fully connected): data = `patch_size`/2/2 x `patch_size`/2/2 x `out_features2`\\n shape = conv2.get_shape().as_list()\\n reshape = tf.reshape(conv2, [-1, shape[1] * shape[2] * shape[3] * shape[4]]) # reshape to fit dense layer input\\n print \\\"dense1.shape (input)\\\", reshape.get_shape()\\n print \\\"dense1.shape (weights)\\\", weights3.get_shape()\\n dense1 = tf.nn.relu(tf.matmul(reshape, weights3) + biases3)\\n #dense1 = batch_norm(dense1)\\n if dropout:\\n dense1 = tf.nn.dropout(dense1, dropout)\\n\\n # Layer 4 (output)\\n return tf.matmul(dense1, weights4) + biases4\\n\\n # Training computation.\\n logits = model(tf_train_dataset)\\n self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\\n\\n # Optimizer.\\n #self.optimizer = tf.train.GradientDescentOptimizer(0.00001).minimize(self.loss)\\n self.optimizer = tf.train.AdamOptimizer(0.00005).minimize(self.loss)\\n \\n # Predictions for the training, validation, and test data.\\n self.train_prediction = tf.nn.softmax(logits)\\n correct_prediction = tf.equal(self.train_prediction, tf_train_labels)\\n train_acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\\n tf.scalar_summary('minibatch accuracy', train_acc)\\n merged = tf.merge_all_summaries()\\n \\n self.valid_prediction = tf.nn.softmax(model(tf_valid_dataset, False))\\n self.test_prediction = tf.nn.softmax(model(tf_test_dataset, False))\\n\\n with tf.Session(graph=graph) as session:\\n #f_log = open('cropping.log' , 'wb')\\n train_writer = tf.train.SummaryWriter('/home/ycho1/Desktop/', session.graph)\\n tf.initialize_all_variables().run()\\n print 'initialization done'\\n for step in range(num_steps):\\n offset = (step * batch_size) % (self.train_labels.shape[0] - batch_size)\\n batch_data = self.train_dataset[offset:(offset + batch_size), :, :, :]\\n #batch_data = preprocess(batch_data)\\n batch_labels = self.train_labels[offset:(offset + batch_size), :]\\n feed_dict = {tf_train_dataset: batch_data, tf_train_labels: batch_labels}\\n #_ , l, predictions = session.run([self.optimizer, self.loss, self.train_prediction], feed_dict=feed_dict)\\n summary, l, predictions = session.run([merged, self.loss, self.train_prediction], feed_dict=feed_dict)\\n if (step % 50 == 0):\\n train_writer.add_summary(summary, step)\\n print('Minibatch loss at step %d: %f' % (step, l))\\n minibatch_acc = accuracy(predictions, batch_labels)\\n print('Minibatch accuracy: %.1f%%' % minibatch_acc)\\n val_acc = accuracy(self.valid_prediction.eval(), self.valid_labels)\\n print('Validation accuracy: %.1f%%' % val_acc)\\n #test_acc = accuracy(self.test_prediction.eval(), self.test_labels)\\n #print('Test accuracy: %.1f%%' % test_acc)\\n #f_log.write(('%d, %f, %.1f%% , %.1f%%, %.1f%% \\\\n' % (step, l, minibatch_acc, val_acc, test_acc)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015e968060e818382274988bdf85e046\",\n \"score\": \"0.511292\",\n \"text\": \"def get_adagrad_trainer(self):\\n batch_x = T.fmatrix('batch_x')\\n #batch_x= T.matrix('batch_x', dtype=theano.config.floatX)\\n batch_y = T.ivector('batch_y')\\n learning_rate = T.fscalar('lr') # learning rate to use\\n # compute the gradients with respect to the model parameters\\n gparams = T.grad(self.mean_cost, self.params)\\n \\n # compute list of weights updates\\n updates = OrderedDict()\\n for accugrad, param, gparam in zip(self._accugrads, self.params, gparams):\\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\\n agrad = accugrad + gparam * gparam\\n dx = - (learning_rate / T.sqrt(agrad + self._eps)) * gparam\\n if self.max_norm:\\n W = param + dx\\n col_norms = W.norm(2, axis=0)\\n desired_norms = T.clip(col_norms, 0, self.max_norm)\\n updates[param] = W * (desired_norms / (1e-6 + col_norms))\\n else:\\n updates[param] = param + dx\\n updates[accugrad] = agrad\\n \\n train_fn = theano.function(inputs=[theano.Param(batch_x), \\n theano.Param(batch_y),\\n theano.Param(learning_rate)],\\n outputs=self.mean_cost,\\n updates=updates,\\n givens={self.x: batch_x, self.y: batch_y})\\n \\n return train_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015e968060e818382274988bdf85e046\",\n \"score\": \"0.511292\",\n \"text\": \"def get_adagrad_trainer(self):\\n batch_x = T.fmatrix('batch_x')\\n #batch_x= T.matrix('batch_x', dtype=theano.config.floatX)\\n batch_y = T.ivector('batch_y')\\n learning_rate = T.fscalar('lr') # learning rate to use\\n # compute the gradients with respect to the model parameters\\n gparams = T.grad(self.mean_cost, self.params)\\n \\n # compute list of weights updates\\n updates = OrderedDict()\\n for accugrad, param, gparam in zip(self._accugrads, self.params, gparams):\\n # c.f. Algorithm 1 in the Adadelta paper (Zeiler 2012)\\n agrad = accugrad + gparam * gparam\\n dx = - (learning_rate / T.sqrt(agrad + self._eps)) * gparam\\n if self.max_norm:\\n W = param + dx\\n col_norms = W.norm(2, axis=0)\\n desired_norms = T.clip(col_norms, 0, self.max_norm)\\n updates[param] = W * (desired_norms / (1e-6 + col_norms))\\n else:\\n updates[param] = param + dx\\n updates[accugrad] = agrad\\n \\n train_fn = theano.function(inputs=[theano.Param(batch_x), \\n theano.Param(batch_y),\\n theano.Param(learning_rate)],\\n outputs=self.mean_cost,\\n updates=updates,\\n givens={self.x: batch_x, self.y: batch_y})\\n \\n return train_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f041c36f80f11600af2c2d31fb74985d\",\n \"score\": \"0.51126796\",\n \"text\": \"def __init__(self, dataset, batch_size, name, logger, version, base_dir, num_gpus):\\n Network.__init__(self, dataset, logger, version, base_dir, name)\\n\\n self.dataset = dataset\\n self.batch_size = batch_size\\n self.data_dims = dataset.data_dims\\n self.LOG = logger\\n self.num_gpus = num_gpus\\n\\n # VLAE parameters - assumes input image is square and at least a multiple of 16 (usually power of 2)\\n self.vlae_levels = 4\\n self.vlae_latent_dims = [3, 3, 3, 3]\\n self.image_sizes = [self.data_dims[0], self.data_dims[0] // 2, \\n self.data_dims[0] // 4, self.data_dims[0] // 8,\\n self.data_dims[0] // 16]\\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\\n\\n # PixelCNN parameters (most of these are hiddent in picel_cnn.pixelvae.py)\\n self.two_step_pixelvae = False\\n\\n # SeqVAE parameters \\n self.share_theta_weights = False\\n self.share_phi_weights = False\\n self.inference_first_step = self.inference_ladder \\n self.generator_first_step = self.generator_ladder \\n self.inference = self.inference_ladder \\n self.generator = self.generator_ladder \\n self.mc_steps = 8\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.intermediate_reconstruction = True\\n self.early_stopping_mc = False\\n self.early_stopping_threshold = 0.0000000001\\n self.regularized_steps = range(self.mc_steps)\\n self.predict_latent_code = False\\n self.first_step_loss_coeff = 1.0\\n self.add_improvement_maximization_loss = False\\n self.add_revamped_improvement_maximization_loss = False\\n self.latent_mean_clip = np.inf\\n self.predict_latent_code_with_regularization = False # TEMPORARY VARIABLE FOR SOME TESTS\\n self.latent_prior_stddev = 1.0\\n self.use_uniform_prior = False \\n self.add_noise_to_chain = False\\n self.predict_generator_noise = False\\n self.predict_generator_noise_as_scalar = False\\n self.predict_generator_stddev_max = 1.0\\n self.predict_generator_stddev_conv_layers = 5 # with 5x5 convs, this gives a receptive field of 1+4*5 = 21\\n self.predict_generator_stddev_filter_sizes = [5, 5, 5, 5, 5]\\n self.noise_stddevs = [0.5 ** 1, 0.5 ** 2, 0.5 ** 3, 0.5 ** 4, 0.5 ** 5, 0.5 ** 6, 0.5 ** 7, 0]\\n self.max_highway_ratio = 1.0\\n self.min_highway_ratio = 0.0\\n self.combine_noise_method = \\\"concat\\\"\\n\\n # \\\"Flat Conv Infusion\\\" parameters\\n self.flat_conv_layers = 6\\n self.flat_conv_filter_sizes = [self.data_dims[-1], self.data_dims[-1]*2, self.data_dims[-1]*4, self.data_dims[-1]*8,\\n self.data_dims[-1]*4, self.data_dims[-1]*2, self.data_dims[-1]]\\n\\n # Hyperparams and training params\\n self.learning_rate_decay = 1.0\\n self.learning_rate = 0.0002\\n self.reg_coeff_rate = 5000.0 # 1 epoch = 1000\\n self.latent_pred_loss_coeff = 0.001 # 0.5, and reduce mean did really well, but very slow...\\n self.save_freq = 2000\\n self.tb_summary_freq = 10\\n self.add_debug_tb_variables = True\\n self.clip_grads = True\\n self.clip_grad_value = 10.0\\n\\n\\n # Config for different netnames, where customization is needed.\\n # add overides for any of the above parameters here\\n\\n ####\\n # 1: c_pixelvae \\n # 2: ^^^^^\\n # 3: c_v2_diag_noise_abl_constrained_highway 11\\n # 4: c_v2_revamped_imp_max_abl 2\\n ####\\n # rerun revamped imp max, with the coefficient change\\n # rerun one step homog (with samew number of filters)\\n # run homog + longer generation\\n # run one step on all datasets\\n # run old homog on all datasets\\n # run final model on all datasets\\n\\n #####################\\n # Single step VLAEs #\\n #####################\\n ## TODO ##\\n if self.name == \\\"c_homog_one_step\\\": #v3\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n # self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512] # v2\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 1\\n\\n ##TODO4##\\n elif self.name == \\\"m_homog_one_step\\\":\\n self.vlae_levels = 4\\n self.vlae_latent_dims = [8, 8, 8, 8]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 1\\n\\n elif self.name == \\\"s_homog_one_step\\\": # v1\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 1\\n\\n ##TODO##\\n elif self.name == \\\"l_homog_one_step\\\":\\n pass\\n\\n ##############\\n # V1 - Homog #\\n ##############\\n elif self.name == \\\"c_homog_v1\\\": #v1\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n \\n ##TODO## \\n elif self.name == \\\"m_homog_v1\\\":\\n pass\\n\\n ##TODO## \\n elif self.name == \\\"s_homog_v1\\\":\\n pass\\n \\n ##TODO## \\n elif self.name == \\\"l_homog_v1\\\":\\n pass\\n\\n\\n\\n ################\\n # V1 - Inhomog #\\n ################\\n ##TODO##\\n elif self.name == \\\"c_inhomog_v1\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n\\n\\n ##TODO## \\n elif self.name == \\\"m_inhomog_v1\\\":\\n pass\\n \\n ##TODO## \\n elif self.name == \\\"s_inhomog_v1\\\":\\n pass\\n \\n ##TODO## \\n elif self.name == \\\"l_inhomog_v1\\\":\\n pass\\n\\n\\n\\n ####################################\\n # Ablations (v2) (just celeba?) #\\n # - latent regularization vs none #\\n # - coeff for log likelihood of x1 #\\n # - improvement maximization loss #\\n # - sampling through the chain #\\n ####################################\\n # regularized (this is v1, but with a new dependency model)\\n elif self.name == \\\"c_homog_reg_pred_latent\\\": #v3 # \\\"c_v2_reg\\\"\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n \\n\\n # no regularization\\n elif self.name == \\\"c_homog_no_reg_pred_latent\\\": # v5 # \\\"c_v2_no_reg_abl\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n\\n\\n # no reg + changed coeff regularization\\n elif self.name == \\\"c_v2_coeff_change_abl\\\": #1\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.first_step_loss_coeff = 2.0\\n\\n # no reg + improvement max\\n elif self.name == \\\"c_homog_no_reg_imp_max\\\": #v2 #\\\"c_v2_imp_max_abl\\\"\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n\\n\\n #NEXT\\n # no reg + changed imp max loss to be an actual L2 norm\\n elif self.name == \\\"c_v2_revamped_imp_max_abl\\\": #v2\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.add_improvement_maximization_loss = True\\n self.add_revamped_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.latent_pred_loss_coeff = 0.01\\n\\n # no reg + changed imp max loss to be an actual L2 norm + changed coeff of first step, because it's important now!!\\n elif self.name == \\\"c_v2_revamped_imp_max_abl_coeff_change\\\": #v1\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.add_improvement_maximization_loss = True\\n self.add_revamped_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.latent_pred_loss_coeff = 0.01\\n self.first_step_loss_coeff = 2.0\\n\\n\\n\\n # no reg + improvement max + changed coeff reg\\n elif self.name == \\\"c_v2_coeff_change_and_imp_max_abl\\\": #v1\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_improvement_maximization_loss = True\\n self.first_step_loss_coeff = 2.0\\n\\n # no reg + improvement max + diagonal noise\\n elif self.name == \\\"c_v2_diag_noise_abl\\\": # v2\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n self.predict_generator_stddev_max = 1.0\\n\\n\\n # same as c_v2_diag_noise_abl, but added a coeff to highway connection to for the generative model to work\\n elif self.name == \\\"c_v2_diag_noise_abl_constrained_highway\\\": # v11\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n self.predict_generator_stddev_max = 1.0\\n self.max_highway_ratio = 1.0\\n self.min_highway_ratio = 0.3\\n\\n\\n # no reg + improvement max + scalar noise\\n elif self.name == \\\"c_v2_scalar_noise_abl\\\": # v4\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n self.predict_generator_stddev_max = 1.0\\n self.predict_generator_noise_as_scalar = True\\n\\n\\n\\n ################\\n # Random tests #\\n ################\\n\\n # TODO1: test where generative samples are like 20 long rather than 8 long\\n\\n\\n #NEXT\\n elif self.name == \\\"c_pixelvae\\\":\\n self.latent_mean_clip = 4.0\\n self.max_highway_ratio = 0.8\\n self.min_highway_ratio = 0.2\\n self.two_step_pixelvae = 2\\n self.mc_steps = 2\\n self.generator = self.generator_pixelcnn\\n self.learning_rate_decay = 0.99999\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.regularized_steps = [0]\\n self.first_step_loss_coeff = 2.0\\n\\n ###############################\\n # Best model one all datasets #\\n ###############################\\n ##TODO##\\n elif self.name == \\\"c_final\\\":\\n pass\\n\\n ##TODO##\\n elif self.name == \\\"m_final\\\":\\n pass\\n\\n ##TODO##\\n elif self.name == \\\"s_final\\\":\\n pass\\n\\n ##TODO##\\n elif self.name == \\\"l_final\\\":\\n pass\\n\\n\\n\\n #######\\n # Old #\\n #######\\n\\n elif self.name == \\\"c_homog_imp_max_var_pred\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n\\n elif self.name == \\\"c_homog_no_reg_pred_latent_var_pred\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n\\n elif self.name == \\\"c_homog_no_reg_pred_latent_var_pred_more_noise\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.regularized_steps = [0]\\n self.latent_mean_clip = 32.0\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n self.predict_generator_stddev_max = 1.0\\n\\n # three\\n elif self.name == \\\"s_homog_imp_max\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n\\n # four\\n elif self.name == \\\"l_homog_imp_max\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n\\n # six\\n elif self.name == \\\"c_homog_imp_max\\\":\\n self.vlae_latent_dims = [12, 12, 12, 12]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 16, 32, 64, 128, 384]\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 8\\n self.predict_latent_code = True\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n\\n\\n # five + seven\\n elif self.name == \\\"m_homog_imp_max_var_pred\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [8, 8, 8]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 6\\n self.predict_latent_code = True\\n self.add_improvement_maximization_loss = True\\n self.latent_mean_clip = 32.0\\n self.predict_latent_code_with_regularization = True\\n self.add_noise_to_chain = True\\n self.predict_generator_noise = True\\n\\n\\n\\n elif self.name == \\\"sequential_vae_celebA_inhomog\\\":\\n # nothing\\n pass\\n\\n elif self.name == \\\"sequential_vae_celebA_homog\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n\\n elif self.name == \\\"sequential_vae_celebA_homog_early_stopping\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.early_stopping_mc = True\\n self.mc_steps = 15\\n\\n elif self.name == \\\"sequential_vae_celebA_inhomog_inf_max\\\":\\n self.predict_latent_code = True\\n\\n elif self.name == \\\"sequential_vae_celebA_homog_inf_max\\\":\\n self.share_theta_weights = True \\n self.share_phi_weights = True\\n self.predict_latent_code = True\\n\\n elif self.name == \\\"sequential_vae_celebA_homog_inf_max_early_stopping\\\":\\n self.share_theta_weights = True \\n self.share_phi_weights = True\\n self.predict_latent_code = True\\n self.mc_steps = 15\\n self.early_stopping_mc = True\\n\\n elif self.name == \\\"sequential_vae_celebA_inhomog_inf_max_uniform\\\":\\n self.predict_latent_code = True\\n self.use_uniform_prior = True\\n\\n elif self.name == \\\"vlae_celebA\\\":\\n self.mc_steps = 1\\n self.vlae_latent_dims = [16, 16, 16, 16] # same latent size as 8 stage markov chain\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n\\n elif self.name == \\\"sequential_vae_celebA_homog_fixed_length\\\":\\n self.share_theta_weights = True \\n\\n elif self.name == \\\"sequential_vae_lsun\\\":\\n self.vlae_latent_dims = [20, 30, 30, 30]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n\\n elif self.name == \\\"sequential_vae_lsun_single\\\":\\n self.vlae_latent_dims = [160, 240, 240, 240]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.mc_steps = 1\\n\\n elif self.name == \\\"sequential_vae_lsun_final\\\":\\n self.vlae_latent_dims = [20, 30, 30, 30]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.filter_sizes = [self.data_dims[-1], 32, 64, 128, 384, 512]\\n self.intermediate_reconstruction = False\\n\\n elif self.name == \\\"c_inhomog\\\":\\n pass\\n\\n elif self.name == \\\"c_homog\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.mc_steps = 25\\n\\n elif self.name == \\\"c_early_stop\\\":\\n self.mc_steps = 15\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.early_stopping_mc = True\\n\\n elif self.name == \\\"c_inhomog_inf_max\\\":\\n self.predict_latent_code = True\\n self.latent_mean_clip = 8.0\\n\\n elif self.name == \\\"c_homog_inf_max_clipped\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.predict_latent_code = True\\n self.latent_mean_clip = 8.0\\n\\n elif self.name == \\\"c_inhomog_inf_max_clipped\\\":\\n self.predict_latent_code = True\\n self.latent_mean_clip = 8.0\\n\\n elif self.name == \\\"c_homog_inf_max_regularized\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.predict_latent_code = True\\n self.predict_latent_code_with_regularization = True\\n\\n elif self.name == \\\"c_sample_images\\\":\\n self.add_noise_to_chain = True\\n\\n elif self.name == \\\"c_homog_sample_images\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.add_noise_to_chain = True\\n\\n elif self.name == \\\"c_infusion_test\\\":\\n self.share_theta_weights = False\\n self.share_phi_weights = False\\n self.generator = self.generator_flat\\n self.add_noise_to_chain = True\\n\\n elif self.name == \\\"c_homog_infusion_test\\\":\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.generator = self.generator_flat\\n self.add_noise_to_chain = True\\n \\n elif self.name == \\\"m_infusion_test\\\":\\n self.share_theta_weights = False\\n self.share_phi_weights = False\\n self.generator = self.generator_flat\\n self.add_noise_to_chain = True\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [2, 2, 2]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n\\n elif self.name == \\\"c_homog_infusion_test\\\": \\n self.share_theta_weights = False\\n self.share_phi_weights = False\\n self.generator = self.generator_flat\\n self.add_noise_to_chain = True\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n\\n\\n elif self.name == \\\"sequential_vae_mnist_homog_early_stopping\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [2, 2, 2]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 15\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n self.early_stopping_mc = True\\n\\n elif self.name == \\\"sequential_vae_mnist_share_all\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [8, 8, 8]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n self.share_theta_weights = True\\n self.share_phi_weights = True\\n\\n elif self.name == \\\"sequential_vae_mnist_share_encoders\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [8, 8, 8]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n self.share_encoder_params = True\\n\\n elif self.name == \\\"sequential_vae_mnist_share_inference\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [8, 8, 8]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n self.share_phi_weights = True\\n\\n # python main.py --dataset=mnist --plot_reconstruction --netname=m_inhomog --version=1\\n elif self.name == \\\"m_inhomog\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [2, 2, 2]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n\\n elif self.name == \\\"m_noise\\\":\\n self.vlae_levels = 3\\n self.vlae_latent_dims = [4, 4, 4]\\n self.latent_dim = np.sum(self.vlae_latent_dims)\\n self.image_sizes = [32, 16, 8, 4] \\n self.filter_sizes = [self.data_dims[-1], 64, 128, 192, 256]\\n self.mc_steps = 5\\n self.add_noise_to_chain = True\\n self.noise_stddevs = [8.0, 4.0, 1.0, 0.25, 0]\\n\\n else:\\n self.LOG.error(\\\"Unknown network name %s\\\" % self.name)\\n exit(-1)\\n \\n # Construct initialize and print network\\n self.construct_network()\\n self.init_network()\\n self.print_network()\\n self.log_tf_variables()\\n\\n # stuff for figures\\n self.mc_fig, self.mc_ax = None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd82ac3cd8c2d70c70283d69ba2aa0a1\",\n \"score\": \"0.51125777\",\n \"text\": \"def forward_backward_prop(dimensions, data, labels, params):\\n ###################################################################\\n # Compute the forward propagation and for the cross entropy cost, #\\n # and backward propagation for the gradients for all parameters. #\\n ###################################################################\\n\\n ### Unpack network parameters (do not modify)\\n t = 0\\n W1 = np.reshape(params[t:t+dimensions[0]*dimensions[1]], (dimensions[0], dimensions[1]))\\n t += dimensions[0]*dimensions[1]\\n b1 = np.reshape(params[t:t+dimensions[1]], (1, dimensions[1]))\\n t += dimensions[1]\\n W2 = np.reshape(params[t:t+dimensions[1]*dimensions[2]], (dimensions[1], dimensions[2]))\\n t += dimensions[1]*dimensions[2]\\n b2 = np.reshape(params[t:t+dimensions[2]], (1, dimensions[2]))\\n\\n ### YOUR CODE HERE: forward propagation\\n\\n # cost = ...\\n # labels is (20, 10) (20 1-hot vectors) - this is y\\n # data is (20, 10) - this is x\\n # W1 is (10, 5)\\n # W2 is (5, 10)\\n # b1 is (1, 5)\\n # b2 is (1, 10)\\n\\n a = data.dot(W1) + b1\\n h = sigmoid(a) # hidden layer\\n y_hat = softmax(h.dot(W2) + b2) # Top classifier layer\\n N, D = data.shape\\n (Dx, H) = W1.shape\\n\\n # TODO: may need to change this to sum over rows and then sum up rows?\\n # cost = np.sum(-np.sum(np.multiply(labels, np.log(y_hat)), axis=1).reshape((N, 1)))\\n cost_per_datapoint = -np.sum(labels * np.log(y_hat), axis=1).reshape((N, 1)) # sum over rows\\n cost = np.sum(cost_per_datapoint)\\n\\n ### END YOUR CODE\\n\\n ### YOUR CODE HERE: backward propagation\\n\\n #gradW1 = ...\\n #gradb1 = ...\\n #gradW2 = ...\\n #gradb2 = ...\\n\\n # d_y_hat/d_W2\\n J_theta = y_hat - labels\\n # theta_W2 = h\\n # theta_h = W2\\n h_a = h * (1.0 - h)\\n a_W1 = data\\n y_hathw = J_theta.dot(W2.T)*h_a\\n\\n gradW2 = h.T.dot(J_theta)\\n gradW1 = data.T.dot(y_hathw)\\n # gradW1 = np.dot(data.T, np.dot(J_theta, theta_h.T) * h_a)\\n gradb1 = np.sum(y_hathw, axis=0).reshape((1, H))\\n gradb2 = np.sum(J_theta, axis=0).reshape((1, D))\\n\\n\\n assert gradW1.shape == W1.shape\\n assert gradb1.shape == b1.shape\\n assert W2.shape == gradW2.shape\\n assert gradb2.shape == b2.shape\\n\\n ### END YOUR CODE\\n\\n ### Stack gradients (do not modify)\\n grad = np.concatenate((gradW1.flatten(), gradb1.flatten(), gradW2.flatten(), gradb2.flatten()))\\n\\n return cost, grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e0953c03499f06cf439e75dc968c89\",\n \"score\": \"0.51104575\",\n \"text\": \"def tf_policy_forward(x): # x ~ [1,D]\\n extras = list()\\n\\n # First conv layer\\n W_conv1 = weight_variable([20, 20, 2, 64])\\n b_conv1 = bias_variable([64])\\n x_image = tf.reshape(tf_x, [-1, image_d1, image_d2, 2])\\n # extras.append(x_image)\\n h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, strides=[1, 20, 20, 1]) + b_conv1)\\n h_pool1 = h_conv1\\n h_pool1 = max_pool_2x2(h_pool1)\\n\\n # Second conv layer\\n W_conv2 = weight_variable([1, 1 , 64, 64])\\n b_conv2 = bias_variable([64])\\n h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\\n # h_pool2 = max_pool_2x2(h_conv2)\\n h_pool2 = h_conv2\\n\\n conv_out = h_pool2\\n vsize = conv_out._shape[1]._value * conv_out._shape[2]._value * conv_out._shape[3]._value\\n\\n # Densely connected layer:\\n\\n W_fc1 = weight_variable([vsize, 256])\\n b_fc1 = bias_variable([256])\\n\\n h_pool2_flat = tf.reshape(conv_out, [-1,vsize])\\n h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\\n\\n \\\"\\\"\\\"\\n # Dropout layer\\n keep_prob = tf.placeholder(tf.float32)\\n h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\\n \\\"\\\"\\\"\\n\\n # Readout layer\\n W_fc2 = weight_variable([256, n_actions])\\n b_fc2 = bias_variable([n_actions])\\n\\n y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2\\n\\n p = tf.nn.softmax(y_conv)\\n #p = y_conv\\n\\n return [p, extras]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1486,"cells":{"query_id":{"kind":"string","value":"66654227fe48409f5632140195afb885"},"query":{"kind":"string","value":"Simulate the given `move' on the current game to see if it is worth making. This does not modify the game passed in, it returns a modified copy."},"positive_passages":{"kind":"list like","value":[{"docid":"a544d23f0e32584a8c635fe1f84a69c4","score":"0.75738066","text":"def simulate(game, move):\n\n def play_one_move(game, current_player, memory, user_args):\n return move\n\n # Make a copy\n new_game = deepcopy(game)\n new_game = play_one_turn(new_game,\n new_game[\"current_player\"],\n play_one_move,\n None,\n None,\n False)\n\n return new_game","title":""}],"string":"[\n {\n \"docid\": \"a544d23f0e32584a8c635fe1f84a69c4\",\n \"score\": \"0.75738066\",\n \"text\": \"def simulate(game, move):\\n\\n def play_one_move(game, current_player, memory, user_args):\\n return move\\n\\n # Make a copy\\n new_game = deepcopy(game)\\n new_game = play_one_turn(new_game,\\n new_game[\\\"current_player\\\"],\\n play_one_move,\\n None,\\n None,\\n False)\\n\\n return new_game\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5e4c62077b64c1a05e5257a263e86b6b","score":"0.71304905","text":"def make_move(self, game):\n cell_map = game.field.get_cell_mapping()\n\n # if random.random() < 0.5:\n return self.make_random_birth_move(game, cell_map)\n\n # return self.make_random_kill_move(game, cell_map)","title":""},{"docid":"299a76fe44fd169b283e1640fcee0854","score":"0.7019477","text":"def simulate_move(piece, move, board, game, player):\r\n board.direct_move(piece, move, player)\r\n return board","title":""},{"docid":"f3e7697f430f7b646b7d1f8b92b133c3","score":"0.69555765","text":"def simulate(self, _move):\n self.legalMoves = None","title":""},{"docid":"691988082dfb2723e5e746d4841c7dbc","score":"0.69014937","text":"def make_move(self, game_id, move):\n path = f'api/bot/game/{game_id}/move/{move}'\n return self._r.post(path)['ok']","title":""},{"docid":"0f29b0395e435319f97b98f214024c92","score":"0.6771747","text":"def make_move(self, game: a2_minichess.MinichessGame, previous_move: Optional[str]) -> str: \n \n ran_num = random.uniform(0.0, 1.0) \n \n if self._game_tree is not None and previous_move is not None: \n self._game_tree = self._game_tree.find_subtree_by_move(previous_move) \n \n if self._game_tree is None or self._game_tree.get_subtrees() == []: \n return random.choice(game.get_valid_moves()) \n \n if ran_num < self._exploration_probability: \n self._game_tree = None \n return random.choice(game.get_valid_moves()) \n \n else: \n if game.is_white_move(): \n temp_subtree = self._game_tree.get_subtrees()[0] \n for subtree in self._game_tree.get_subtrees(): \n if subtree.white_win_probability > temp_subtree.white_win_probability: \n temp_subtree = subtree \n self._game_tree = temp_subtree \n return temp_subtree.move \n \n else: \n temp_subtree = self._game_tree.get_subtrees()[0] \n for subtree in self._game_tree.get_subtrees(): \n if subtree.white_win_probability < temp_subtree.white_win_probability: \n temp_subtree = subtree \n self._game_tree = temp_subtree \n \n return temp_subtree.move","title":""},{"docid":"59fbb4e267d1ef9b9dc8d310c888ae6f","score":"0.6763375","text":"def make_move(self, move: Any) -> 'GameState':\n raise NotImplementedError","title":""},{"docid":"98c15d505eb3f7a119554832fdb0e9aa","score":"0.6683921","text":"def make_move(self, move):\n # print(move)\n if self.board.make_move(move, self.board.current_player):\n # print(self.board)\n self.master.status.set(\"{} | {}\".format(\n self.board.turn_string(), self.board.score_string()))\n self.draw_discs()\n else:\n self.master.status.set(\"{} | Invalid move... try again.\".format(\n self.board.turn_string()))\n\n if self.board.terminal_test():\n self.master.status.set(\"Game Over!!! | {}\".format(self.board.score_string()))","title":""},{"docid":"d3288f23936a994dddaee12a004b5517","score":"0.6655375","text":"def make_move(self, game_id, move):\n path = f'api/board/game/{game_id}/move/{move}'\n return self._r.post(path)['ok']","title":""},{"docid":"dcd803328731193fe5349f98a26ca1c1","score":"0.6609868","text":"def copy_and_make_move(self, move: str) -> MinichessGame:\n return MinichessGame(board=self._board_after_move(move),\n white_active=not self._is_white_active,\n move_count=self._move_count + 1)","title":""},{"docid":"e2c94600e9b6bc5b3622be91b545714e","score":"0.65781206","text":"def move(self, game=None):\n return self._move(game)","title":""},{"docid":"77aa5447e3abb1e0b76d01ad9bab68e9","score":"0.6540235","text":"def make_move(self, args):\n if not len(args) == 1:\n return False\n move = args[0]\n\n global games\n global connections\n\n with self.lock:\n game = games[self.user.game_room]\n if game[WHITE].name == self.user.username: # the user is white\n opponent = game[BLACK]\n player = game[WHITE]\n else:\n opponent = game[WHITE]\n player = game[BLACK]\n\n if opponent and opponent.color != game.turn and not game.game_over:\n # The player has the turn. First check if move is legal\n if move in game.board.possible_moves:\n game.toggle_turn(move)\n my_time = round(opponent.time_left, 3)\n opponent_time = round(player.time_left, 3)\n connections[opponent.name].send(\"MOVED \" + move + \" \" + str(my_time) + \" \" + str(opponent_time))\n # Check if someone lost\n if game.game_over:\n if game.is_draw():\n connections[opponent.name].send(\"DRAW\")\n self.client.send(\"DRAW\")\n del games[self.user.game_room]\n check_if_to_save_game(game, \"\") # Empty winner name because no one won\n else:\n loser = game.turn\n connections[game[loser].name].send(\"LOST\")\n connections[game[not bool(loser)].name].send(\"WON\")\n del games[self.user.game_room]\n check_if_to_save_game(game, game[not bool(loser)].name)","title":""},{"docid":"a0d60eb3f74a737763e6d3c272ac70e0","score":"0.6463725","text":"def ai_find_move(self):\r\n # creates a copy of the game instance and sends it to the AI.\r\n sim_game = Game()\r\n board, register, cell_set, counter = self.__game.get_attr_for_sim()\r\n sim_game.set_attr_for_sim(board, register, cell_set, counter)\r\n\r\n try:\r\n self.__ai.find_legal_move(sim_game, self.play_my_move)\r\n except:\r\n self.__screen.print_to_screen(self.__ai.NO_AI_MOVE, self.__my_color)","title":""},{"docid":"87851950fe1a51ae865959d4794e01a0","score":"0.6379014","text":"def move_heuristic(self, move, board_state):\n return self.rulebook.score_move(move, board_state)","title":""},{"docid":"706ab038d255a7563ca95c8137d1f1a2","score":"0.6372193","text":"def isMoveLegal(self, move):","title":""},{"docid":"108e19b3413ff9135f10766789ea14dd","score":"0.63085663","text":"def simulate(self, move):\n self.last_move = move\n # Note: simulate modifies the given state (in this case, self)\n simulation.simulate(self, move)","title":""},{"docid":"a2ac7f6d6216b72fb9ead88761ee58ba","score":"0.6292397","text":"def play(self, random_moves=False):\n self.logged_random_moves.clear()\n turn = 1\n while self.check_game_win() == 0 and not self.check_game_draw_3_1() and not self.check_game_draw_repeat():\n end_move_flag = False\n self.table_obj.table_print()\n all_moves, must_eat_flag = self.scan_all_moves('white') if turn == 1 else self.scan_all_moves('black')\n while not end_move_flag:\n print(Template(\"Turn is $color\").substitute(color='white' if turn == 1 else 'black'))\n move = self.take_move() if not random_moves else self.take_random_move(all_moves, must_eat_flag)\n if move == 1:\n continue\n elif move == -1:\n return -1\n if not must_eat_flag:\n if move not in all_moves:\n print('Illegal move, first break, make new')\n self.write_state('N---')\n continue\n else:\n try:\n self.make_move('white' if turn == 1 else 'black', move)\n except CheckerException as cherr:\n print(cherr, 'Something Wrong with that', sep='\\n')\n self.write_state('N---')\n return -1\n else:\n self.write_state('YN--')\n end_move_flag = True\n else:\n short_moves = [(from_pos, to_pos) for from_pos, to_pos, eaten, in all_moves]\n if move not in short_moves:\n self.write_state('N---')\n print('Illegal move, make new')\n continue\n try:\n last_checker = self.make_move('white' if turn == 1 else 'black',\n all_moves[short_moves.index(move)])\n except CheckerException as cherr:\n self.write_state('N---')\n print(cherr, 'Something went wrong...', sep='\\n')\n return -1\n if self.scan_checker(last_checker, True)[0] is None:\n self.write_state('YN' + all_moves[short_moves.index(move)][2])\n end_move_flag = True\n else:\n self.write_state('YS' + all_moves[short_moves.index(move)][2])\n all_moves, must_eat_flag = self.scan_checker(last_checker, True)\n turn = 0 if turn == 1 else 1\n else:\n self.table_obj.table_print()\n if self.three_vs_one == 15:\n # Print pipe Draw\n print('Draw!')\n elif self.check_game_win() == 1:\n # Print pipe Draw\n print('White won')\n elif self.check_game_win() == -1:\n # Print pipe Draw\n print('Black won')\n else:\n print('Draw by the repeat')\n return 0","title":""},{"docid":"f01dbd573601bacd8dc6e0e7fb56ef71","score":"0.626742","text":"def checkMove(move: Card, game) -> bool:\n print(\"Checking move\")\n lastMove = game.lastMove\n\n if move.number == lastMove.number:\n return True\n\n elif move.color == lastMove.color: \n return True\n\n elif move.wild: \n return True\n\n return False","title":""},{"docid":"c8fc3f8cb84a4d84761069299178221f","score":"0.6247149","text":"def move(self, movement):\n made_movement = False\n # If agent moves first (whites and first move)\n if self.agent.color and len(self.board.move_stack) == 0:\n agents_best_move = self.agent.best_move(self, real_game=True)\n self.board.push(chess.Move.from_uci(agents_best_move))\n made_movement = True\n else:\n made_movement = super().move(movement)\n if made_movement and self.get_result() is None:\n agents_best_move = self.agent.best_move(self, real_game=True)\n self.board.push(chess.Move.from_uci(agents_best_move))\n return made_movement","title":""},{"docid":"eada8f7934ee0156fd1bda02cda24a77","score":"0.6234029","text":"def act(state, action, player): # TODO : Wondering if I should make the random move here.\n if SeegaRules.is_legal_move(state, action, player):\n return SeegaRules.make_move(state, action, player)\n else:\n return False","title":""},{"docid":"c8b08c0d4db1b86cc3fb13e1c027cdc9","score":"0.6228436","text":"def computer_move_random(possible_moves):\n move = random.choice(possible_moves)\n return(move)","title":""},{"docid":"8e00ad104c45fa51dc896b7562836695","score":"0.6186098","text":"def play_move(board, move):\n islegal = True\n if board[move] == 0 and board[move+27] == 0:\n board[move] = 1\n else:\n islegal = False\n return board, islegal","title":""},{"docid":"a3c8688d9da78a4c8f86fc40559802fd","score":"0.6181433","text":"def make_move(self, request):\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\n\n if not game:\n raise endpoints.NotFoundException('Game not found')\n\n # ensure the game is not already over.\n if game.game_over:\n raise endpoints.NotFoundException('Game already over')\n\n # ensure that ships have been inserted onto both grids prior to a move.\n if game.total_ships(grid=1) == 0 or game.total_ships(grid=2) == 0:\n raise endpoints.BadRequestException('Both users must have inserted ships '\n 'prior to beginning the game.')\n\n # ensure the correct user is making a move.\n user = User.query(User.name == request.user_name).get()\n if user.key != game.next_move:\n raise endpoints.BadRequestException('It\\'s not your turn!')\n\n # User signifier. True if current user is player 1, otherwise False.\n user_1 = True if user.key == game.user_1 else False\n\n # set target grid to 2 if current user is user 1, and vice versa.\n target_grid = 2 if user_1 else 1\n\n row_loc, col_loc = int(request.target_row), int(request.target_col)\n\n # Verify move is valid\n if row_loc < 0 or row_loc > 9 or col_loc < 0 or col_loc > 9:\n raise endpoints.BadRequestException('Invalid move! Rows and columns must be '\n 'between 0 and 9')\n\n # check if grid cell is already destroyed.\n if game.return_grid_status(row_loc, col_loc, target_grid) == 'X':\n raise endpoints.BadRequestException('That grid cell is already destroyed! '\n 'Try picking another!')\n\n # check whether the move was a hit or miss.\n target_hit = game.destroy_cell(row_loc, col_loc, grid=target_grid)\n\n # Append a move to the relevant history dict key dependent on user and hit status.\n history_msg = 'Ship hit!' if target_hit else 'No ship hit!'\n history_entry = (row_loc, col_loc, history_msg)\n game.history['grid_2' if user_1 else 'grid_1'].append(history_entry)\n\n # set next move within the game.\n game.next_move = game.user_2 if user_1 else game.user_1\n\n # check for any winners, end game and report winner if true.\n winner_p1, winner_p2 = game.check_winner()\n if winner_p1:\n game.end_game(game.user_1)\n game.put()\n return StringMessage(message='The game is over! {0} has won the match!'.\n format(game.user_1.get().name))\n\n if winner_p2:\n game.end_game(game.user_2)\n game.put()\n return StringMessage(message='The game is over! {0} has won the match!'.\n format(game.user_2.get().name))\n\n else:\n # Send reminder email\n taskqueue.add(url='/tasks/send_move_email',\n params=dict(user_key=game.next_move.urlsafe(), game_key=game.key.urlsafe()))\n\n # update memcache with user ships remaining\n taskqueue.add(url='/tasks/cache_ships_remaining',\n params=None)\n\n target_hit_msg = 'You hit a ship! Well done!'\n target_miss_msg = 'No ship hit! Better luck next time!'\n\n # create a message to notify the player that they hit or missed.\n ret_msg = (\"{0} You have now made the following moves: {1}. \"\n \"{2} is up next!\".format(target_hit_msg if target_hit else target_miss_msg,\n game.history['grid_2' if user_1 else 'grid_1'],\n game.next_move.get().name))\n\n game.put()\n return StringMessage(message=ret_msg)","title":""},{"docid":"afce34b98e64b7e35a8bcca936bfc820","score":"0.61792415","text":"def wouldwin(self,move,who):\n (r,c) = move\n self[r,c] = who\n result = self.win(who)\n self[r,c] = ' '\n return result","title":""},{"docid":"b8a00df6f1dbcfc69d5f7be637755e02","score":"0.61695015","text":"def try_move(self, move):\n move = int(move) - 1\n success = False\n # can't make moves outside the range, or in an occupied space\n if move in self.possible_moves and self.state[move] == ' ':\n cur_p = self.players[self.current_player]\n self.state[move] = cur_p\n self.current_player = (self.current_player + 1) % 2\n self.find_connections(move, cur_p)\n success = True\n return success","title":""},{"docid":"337ef157eb07a51735cf513768e7bd49","score":"0.61680573","text":"def DoMove(self, move):\r\n x, y, plant = move\r\n if self.farm[x][y] == option.EMPTY.value:\r\n self.farm[x][y] = plant\r\n return True\r\n return False","title":""},{"docid":"099ec83ae434c0a4e3099798b783512b","score":"0.61470234","text":"def illegal_move(move, game):\n for player in game:\n if make_move(move, game[player]) == game[player]:\n return player","title":""},{"docid":"6f55649c8cc63ca639fca1222add4d15","score":"0.61352473","text":"def test_generate_random_move():\n from agents.agent_random import generate_move\n from agents.common import NO_PLAYER, PLAYER1, PLAYER2, BoardPiece\n\n test_board = np.full((6, 7), NO_PLAYER)\n action, _ = generate_move(test_board, PLAYER1, None)\n\n assert (float(action))\n test_board = np.full((6, 7), PLAYER2)\n test_board[0, 0] = NO_PLAYER\n action, _ = generate_move(test_board, PLAYER1, None)\n assert (action == 0)","title":""},{"docid":"6806c6c07d64e94a60eb628338fe30fb","score":"0.61132026","text":"def ai_move_if_not_won(game: ttt.GameState) -> None:\n if not Config.WIN_STATUS:\n player_next = Config.GAME_OBJS[game.next_player]\n if player_next != \"human\":\n # obtain the player's move, place the game piece, and draw it on the UI\n dom['game_status'].text = dom['game_status'].text + \"..\"\n timer.set_timeout(ai_make_move, 0, player_next, game, game.move_history[-1])\n\n # check for winners again\n timer.set_timeout(check_winner, 0, game)","title":""},{"docid":"a33705ec85d84c5e2624490f0f3a2474","score":"0.6102181","text":"def make_move(self, game: MinichessGame, previous_move: Optional[str]) -> str:\n possible_moves = game.get_valid_moves()\n return random.choice(possible_moves)","title":""},{"docid":"69d0769ffa6d40488f7afce183a71aec","score":"0.6100382","text":"def make_move(self, move):\n result = self._recycle_card(move) if move.type else self._add_card(move)\n if result.success:\n self._moves.append(move)\n self.last_moved = move\n self._num_moves += 1\n return result","title":""},{"docid":"37cc580946397ad27ee1146ee2f8ec03","score":"0.60941744","text":"def make_move(self, move: Any) -> 'StonghengeState':\n new_state = StonehengeState(not self.p1_turn, self.size,\n self.grid.copy())\n new_state.grid.update_grid(self.get_current_player_name(), move)\n new_state.p1, new_state.p2 = new_state.grid.get_score(1),\\\n new_state.grid.get_score(2)\n return new_state","title":""},{"docid":"20c2b797a78e9eabfdf0273ffe32c326","score":"0.6085027","text":"def simulate_move(self, board, move, symbol):\n if move == None:\n return board\n r = move[0]\n c = move[1]\n board_copy = deepcopy(board)\n board_copy[r][c] = symbol\n\n for v in [-1, 0, 1]:\n for h in [-1, 0, 1]:\n stones = []\n if v == 0 == h:\n continue\n row = r\n col = c\n row += v\n col += h\n while is_field_legit(row, col):\n # if it is other symbol\n if board_copy[row][col] != symbol and board_copy[row][col] != self.space:\n stones.append((row, col))\n # if space found - oon t flip this line\n elif board_copy[row][col] == self.space:\n break\n # same symbol found flip all stones\n elif board_copy[row][col] == symbol:\n for a, b in stones:\n board_copy[a][b] = symbol\n break\n row += v\n col += h\n return board_copy","title":""},{"docid":"ef9c7279a0ff0f386665707b6efd6e37","score":"0.60723364","text":"def DoMove(self, move):\n assert self.game.players[0].hero.health > 0 and self.game.players[1].hero.health > 0 and not self.game.game_ended\n\n def _choose_target(targets):\n if move[4] is None:\n return None\n else:\n return targets[move[4]]\n\n def _choose_index(targets, player):\n return move[5]\n\n def _choose_option(*options):\n return options[move[4]]\n\n if self.game.current_player.name == \"one\":\n self.playerJustMoved = 1\n else:\n self.playerJustMoved = 2\n\n # print(str(self.game.current_player.mana) + \"/\" + str(self.game.current_player.max_mana))\n if move[0] == MOVE.PRE_GAME:\n self.replay = record(self.game)\n self.game.current_player = self.game.players[1]\n self.game.other_player = self.game.players[0]\n self.game.pre_game()\n #self.game._start_turn()\n elif move[0] == MOVE.START_TURN:\n self.game._start_turn()\n elif move[0] == MOVE.PICK_CLASS:\n self.game.current_player.deck = Deck([], hero_for_class(move[1]))\n self.game.current_player.hero = self.game.current_player.deck.hero.create_hero(self.game.current_player)\n self.game.current_player.hero.card = self.game.current_player.deck.hero\n elif move[0] == MOVE.PICK_CARD:\n card = move[1] #copy.deepcopy(move[1])\n card.drawn = False\n self.game.current_player.deck.cards.append(card)\n \n if len(self.game.current_player.deck.cards) == 30:\n self.game.current_player = self.game.players[1]\n self.game.other_player = self.game.players[0]\n elif move[0] == MOVE.END_TURN:\n try:\n self.game._end_turn()\n self.game._start_turn()\n except:\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.HERO_POWER:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.current_player.hero.power.use()\n except:\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.SUMMON_MINION:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.current_player.agent.choose_index = _choose_index\n self.game.play_card(self.game.current_player.hand[move[3]])\n except:\n print(move)\n print(self.game.current_player.hand)\n print(self.game.other_player.hand)\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.EQUIP_WEAPON:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.play_card(self.game.current_player.hand[move[3]])\n except:\n print(move)\n print(self.game.current_player.hand)\n print(self.game.other_player.hand)\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[2] is None: # Passing index rather than object, hopefully the game copy fix will help with this\n try:\n self.game.play_card(self.game.current_player.hand[move[3]])\n except:\n print(move)\n print(self.game.current_player.hand)\n print(self.game.other_player.hand)\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.MINION_ATTACK:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.current_player.minions[move[3]].attack()\n except:\n print(move)\n print(self.game.current_player.minions)\n print(self.game.other_player.minions)\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.HERO_ATTACK:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.current_player.hero.attack()\n except:\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n elif move[0] == MOVE.TARGETED_SPELL:\n try:\n self.game.current_player.agent.choose_target = _choose_target\n self.game.play_card(self.game.current_player.hand[move[3]])\n except:\n print(move)\n print(self.game.current_player.hand)\n print(self.game.other_player.hand)\n print(self.game.players[0].deck.__str__())\n print(self.game.players[1].deck.__str__())\n self.replay.write_json(\"ragowit_ai.hsreplay\")\n traceback.print_exc()\n sys.exit()\n else:\n raise NameError(\"DoMove ran into unclassified card\", move)","title":""},{"docid":"9b7eea6d730d91135f37016bfe7df719","score":"0.60347205","text":"def validmove(move):\n if move==0:\n return True\n else:\n return False","title":""},{"docid":"4f3de8ae4876ddcff5321f3ef8348b82","score":"0.6028611","text":"def DoMove(self, move):\n board = GameState.Board(move)\n position = GameState.Position(move)\n\n self.playerJustMoved = self.NextPlayer()\n self.pieces[board][position] = self.playerJustMoved\n self.activeBoard = position\n\n if self.GetWinner(board) == self.playerJustMoved:\n self.winnerOfBoard[board] = self.playerJustMoved\n for (x, y, z) in GameState.WINNING_COMBINATIONS:\n if self.winnerOfBoard[x] == self.winnerOfBoard[y] == self.winnerOfBoard[z] != 0:\n self.winnerOfGame = self.winnerOfBoard[z]","title":""},{"docid":"5f02b4e9f2af76acd6033195de8a1953","score":"0.60150576","text":"def make_move(self, move, state):\n pass","title":""},{"docid":"da0d6d729d407c9d61ff3f7075b51ce6","score":"0.6004809","text":"def makeMove(board, move):\n new_board = board\n if isPosEmpty(board, move[0:2]):\n raise IndexError('no piece in source position!')\n if not isPosEmpty(board, move[2:4]):\n raise IndexError('Target position is not empty!')\n else:\n piece = new_board[move[1], move[0]]\n if VERBOSE:\n if piece == WHITE:\n print(\"white plays!\")\n else:\n print(\"black plays!\")\n\n new_board[move[1], move[0]] = EMPTY\n new_board[move[3], move[2]] = piece\n if isCapture(board, move):\n cap_coords = getCaptureCoords(board, move)\n if VERBOSE:\n print(f'{piece} captured a piece in pos ' + str(cap_coords))\n\n new_board[cap_coords[1], cap_coords[0]] = EMPTY\n return new_board","title":""},{"docid":"c59c4fc78b216b168583c44a3e0825c4","score":"0.59979814","text":"def make_move(self):\n move = input(\"Enter move: \").lower()\n valid = self.board.make_move(move)\n if not valid:\n print(\"Invalid move supplied\")","title":""},{"docid":"bb7240f9dab58f0a4211f078fcfa34c4","score":"0.5994267","text":"def make_move(self, request):\n # fetch game data and check if it is valid\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\n if not game:\n raise endpoints.NotFoundException('Game not found')\n if game.game_over:\n raise endpoints.NotFoundException('Game is over')\n # fetch user data and check if valid\n user = Game.query(Game.user == request.user_name).get()\n if not user:\n raise endpoints.NotFoundException('The user does not exist!')\n # validate user input\n if not re.match(\"^[A-Za-z]$\", request.move):\n raise endpoints.BadRequestException('You can only enter 1 letter!')\n # validate move played\n if request.move in game.check_answer:\n return game.to_form('You already played {}'.format(request.move))\n # wrong move attemp logic\n if request.move.lower() not in game.answer.lower():\n game.attempts_remaining -= 1\n if game.attempts_remaining < 1:\n user.total_played += 1\n game.end_game(False)\n msg = 'Game Over, you lose!'\n if game.attempts_remaining == 1:\n msg = 'Try again, last move!'\n if game.attempts_remaining > 1:\n msg = 'Try again, {} moves left!'.format(game.attempts_remaining)\n # game in progress logic\n # use check_answer to update user progress\n else:\n for i, x in enumerate(game.answer):\n if x == request.move:\n game.check_answer[i] = request.move\n if game.check_answer == game.answer:\n user.wins += 1\n user.total_played += 1\n game.end_game(True)\n msg = 'You win!'\n else:\n msg = 'Correct! You have: {}'.format(game.check_answer)\n # log moves to generate history\n game.move_histories.append(\n [\"{},\".format(request.move.lower()) + \"{}\".format(msg)]\n )\n game.put()\n user.put()\n return game.to_form(msg)","title":""},{"docid":"282c3acb6cbb3fda4be4ab596763649f","score":"0.59742534","text":"def make_move(self, request):\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\n if game.game_over:\n raise endpoints.ForbiddenException('Wait, what? Game is already over!')\n game.history.append({'player_move':request.guess,'computer_move':game.target})\n if request.guess == game.target:\n game.points += 1\n msg = '%s vs %s! Tie! Take a point and try again!' % (request.guess , game.target)\n elif request.guess == 'rock':\n if game.target == 'paper':\n game.end_game(False)\n return game.to_form('You lose! Paper beats rock')\n else:\n game.end_game(True)\n return game.to_form('You win! rock beats scissors!')\n elif request.guess == 'paper':\n if game.target == 'scissors':\n game.end_game(False)\n return game.to_form('You lose! Scissors beats paper')\n else:\n game.end_game(True)\n return game.to_form('You win! Paper beats rock!')\n elif request.guess == 'scissors':\n if game.target == 'rock':\n game.end_game(False)\n return game.to_form('You lose! Rock beats scissors')\n else:\n game.end_game(True)\n return game.to_form('You win! Paper beats rock!')\n else:\n msg = 'That\\'s not a valid play. Try rock, paper, or scissors.'\n # re-set computer to random rock, paper, or scissors\n game.target = Game.rando_select()\n print game\n game.put()\n return game.to_form(msg)","title":""},{"docid":"ca2bcab634df9f2f939fd56b0458be35","score":"0.5970347","text":"def computer_move(possible_moves, mode):\n if mode == 'easy':\n move = computer_move_random(possible_moves)\n elif mode == 'hard':\n move = computer_move_hard(possible_moves, board_state)\n else:#medium mode is combination of good and random moves\n move = random.choice([computer_move_random(possible_moves), computer_move_hard(possible_moves, board_state)])\n return(move)","title":""},{"docid":"dd51656ea22b8711cd585b912bebdd3f","score":"0.5961737","text":"def test_move(m, i):\n\n test_board = board.copy()\n test_board[i] = m\n return check_win(test_board, m)","title":""},{"docid":"274d7e06982d9dde07a58265419aed67","score":"0.59344506","text":"def result(self, game_id, move):\n game = self.game_states[game_id]\n next_game = simulate(game, move)\n self.game_states.append(next_game)\n return (len(self.game_states) - 1)","title":""},{"docid":"a6cfdecf90bcb59b5ee61723f7bcb251","score":"0.5930572","text":"def learn_from_move(self, game, move):\n game.make_move(move)\n r = self.__reward(game)\n td_target = r\n next_state_value = 0.0\n selected_next_move = None\n if game.playable():\n best_next_move, selected_next_move = self.learn_select_move(game)\n next_state_value = self.state_value(best_next_move)\n\n current_state_value = self.state_value(move)\n td_target = r + next_state_value\n self.V[move] = current_state_value + self.alpha * (td_target - current_state_value)\n return selected_next_move","title":""},{"docid":"3d70bafb2c39075265672bf32feaa432","score":"0.59262985","text":"def apply_move(self, move):\n next_board = copy.deepcopy(self.board)\n if move is not None:\n next_board.place(self.next_player, move.point_a, move.point_b)\n return GameState(next_board, self.next_player.other, move)","title":""},{"docid":"30faa9c7afd84dcc513eb118ae1679d4","score":"0.5905913","text":"def is_legal(self, move: int) -> bool:\n return self.board_state[move] is None","title":""},{"docid":"4cdb9cbc22e34bf6e189346538562405","score":"0.5901934","text":"def result(self, state, move):\n moves = state.moves.copy()\n moves.remove(move)\n player = state.to_move\n knights = state.knights.copy()\n knights[player]=move\n return GameState(to_move=(2 if player == 1 else 1),\n knights=knights, moves=moves)","title":""},{"docid":"75facd803f78511ee8845b5f62c17ee7","score":"0.5901085","text":"def forecast_move(self, move):\n state = deepcopy(self.state)\n\n if move not in self.get_legal_moves():\n print(\"Illegal move {}\".format(move))\n return self.__class__(state)\n\n state['board'][move[0]][move[1]] = 1\n state['positions'][state['turn']] = move\n state['turn'] = 1 - state['turn']\n\n return self.__class__(state)","title":""},{"docid":"d7a732f7ac77cafc1f46875e0e088019","score":"0.58877766","text":"def make_move(self, start, finish):\n print(\"make_move(\", start, \",\", finish, \")\")\n # check for game status\n if self.get_game_state() != 'UNFINISHED':\n return False\n\n # check to make sure player is moving the correct piece\n if not self._board.is_player_piece(self, start):\n return False\n\n # find game piece\n row, col = self._board.algebraic_to_index(start)\n piece = self._board.get_piece(row, col)\n\n # finish location\n fut_row, fut_col = self._board.algebraic_to_index(finish)\n\n # check if General is in check then find move to get out of check\n team = piece.get_team()\n generals_in_check = self._board.get_generals_in_check()\n for general in generals_in_check:\n # check to see if your general is in the list of checked generals if so pass the general to the neutralize\n # threat method\n if general.get_team() == team:\n if self._board.neutralize_threats(self, general, piece, fut_row, fut_col):\n self._board.move_piece(piece, fut_row, fut_col)\n self._board.print_game_board()\n self.set_whose_turn()\n return True\n # check for game status\n if self.get_game_state() != 'UNFINISHED':\n return False\n\n elif team == \"blue\":\n self._game_state = \"BLUE_WON\"\n else:\n self._game_state = \"RED_WON\"\n return True\n\n # check piece rules\n if not piece.move_rules(self._board, fut_row, fut_col):\n return False\n\n # make move\n if not self._board.move_piece(piece, fut_row, fut_col):\n return False\n\n self._board.print_game_board()\n\n # change turn\n self.set_whose_turn()\n\n return True","title":""},{"docid":"9dcdecaba12e90ca6edc8ddd96f37941","score":"0.58842903","text":"def _move(self, game):\n raise NotImplementedError(\"Class {} doesn't implement _move()\".format(\n self.__class__.__name__))","title":""},{"docid":"aa85b788858a1304574ed3ce65398571","score":"0.58776176","text":"def evaluatemove(self,move):\n (r,c) = move\n self[r,c] = self.tomove()\n result = self.evaluate()\n self[r,c] = ' '\n return result","title":""},{"docid":"4cb9919b0eb81aca2a86a32807778132","score":"0.5870437","text":"def move(self, move):\n valid_words = self._possible_words()\n if move in valid_words:\n new_game = WordLadderPuzzle(self._start, self._target)\n new_game._moves += self._moves[1:]\n new_game._moves.append(move)\n return new_game\n else:\n raise ValueError","title":""},{"docid":"f735d04de644efc91521374b15177670","score":"0.5858091","text":"def play_game(self, moves):\n \n c = W\n\n for m in moves:\n self.assertEqual(self.g.move(c, m.src, m.dest), m.type_)\n if(c == W):\n c = B\n else:\n c = W","title":""},{"docid":"7945bb6f3ede777304ff04da944ead1c","score":"0.5843221","text":"def make_safe_move(self):\n\n # If the move is safe and hasn't already being taken, then return it\n for x in self.safes:\n if x not in self.moves_made:\n return x\n # Else return it and make a random move\n return None","title":""},{"docid":"ee0d09de0ad2b57185e2a1c1553cfd4e","score":"0.58129907","text":"def make_move(self, board):\n if self._bot:\n # Minimax algorithm\n _, moves = minimax_soft_alpha_beta(board, get_depth(board), True, -inf, +inf)\n move = random.choice(moves)\n else:\n # Prompt the user to select a move\n index = input(\"Enter move: \")\n move = Player.convert_index_to_move(int(index))\n\n return move","title":""},{"docid":"532ea40ce80be10b728481241ca7814c","score":"0.58126426","text":"def humanMove(board, human) :\n legal = legalMove(board)\n move = None\n while move not in legal:\n move = getNumber(\"Where would you like to make your move? (0-8)\", 0, NUMSQUARES)\n if move not in legal:\n print(\"\\nThat square is already occupied, foolish human. Choose another.\\n\")\n print(\"Fine. . .\")\n return move","title":""},{"docid":"2769e4265e33b609253ca411a33c5454","score":"0.5811516","text":"def opponent_move(self, move):\n # validate input\n if len(move) > 1:\n source_row = move[1][0]\n source_col = move[1][1]\n if source_row != None and self.board[source_row][source_col] != self.opp:\n self.print_board()\n print(move)\n raise Exception(\"You don't have a piece there!\")\n if abs(source_row - move[0][0]) > 1 or abs(source_col - move[0][1]) > 1:\n self.print_board()\n print(move)\n raise Exception('Illegal move: Can only move to an adjacent space')\n if self.board[move[0][0]][move[0][1]] != ' ':\n raise Exception(\"Illegal move detected\")\n # make move\n self.place_piece(move, self.opp)","title":""},{"docid":"45e830b80d38ab26a991bf7c1ff9fb6e","score":"0.58062017","text":"def make_move(self, move: str) -> None:\n if move not in self._valid_moves:\n raise ValueError(f'Move \"{move}\" is not valid')\n\n self._board = self._board_after_move(move)\n\n self._is_white_active = not self._is_white_active\n self._move_count += 1\n\n self._recalculate_valid_moves()","title":""},{"docid":"869165e32753344f1956717e7be1b20c","score":"0.5783674","text":"def random_move(self):\n rand_number = random.random()\n if rand_number <= self.gamma:\n disc = random.choice(Game.DISCS_LST)\n col = self.find_random_free_col()\n\n row = self.check_column(col)[1]\n self.make_random_move(row, col, disc)\n self.players[self.get_current_player()] += 1\n return True\n self.players[self.get_current_player()] += 1\n return False","title":""},{"docid":"f3a5a29607377a128fdfa8af7bcda8f1","score":"0.57768023","text":"def simulate_move(self, move, sym, grid):\n\t\tgrid[move[0]][move[1]] = sym\n\t\treturn grid","title":""},{"docid":"f0872ae2b0e5c61ba3a99db95d96848b","score":"0.57689226","text":"def is_valid_move(self, move: Any) -> bool:\n return move in self.get_possible_moves()","title":""},{"docid":"da0bb83a5b1815376f32b1f2c20ec13b","score":"0.57658607","text":"def is_move_legal(self, move):\n if move is None:\n return True\n if self.board[move] != EMPTY:\n return False\n if move == self.ko:\n return False\n if self.is_move_suicidal(move):\n return False\n\n return True","title":""},{"docid":"1efa0155554584ab9955bba5a1d35dd1","score":"0.57565314","text":"def simulate(self, ann, move_callback=None, turn_wall_reward=20):\n\n fitness = 0\n\n for i in range(self.simulate_steps):\n # check if the callback was set\n if move_callback:\n move_callback(self)\n\n if self.wall_hit and self.turn_wall_rewards_num:\n self.turn_wall_rewards_num -= 1\n self.wall_hit = False\n fitness += turn_wall_reward\n self.object_captured = False\n self.large_object_hit = False\n\n # handle the case when object hits the tracker\n is_last_floor = next(iter(self.object_position))[0] == self.world_height - 1\n if is_last_floor:\n fitness = self.recompute_fitness(fitness)\n self.object_position = self.generate_object()\n\n else:\n self.lower_object()\n\n # use ANN to calculate move direction and magnitude\n ann_input = self.compute_tracker_shadowing()\n\n if not self.wraparound:\n self.deploy_wall_sensors()\n ann_input += [self.wall_on_left, self.wall_on_right]\n ann_result = ann.compute(ann_input)\n direction, speed, pull = self.interpret_ann_result(ann_result)\n self.move(direction, speed)\n\n if pull and not is_last_floor:\n self.pull_object()\n self.object_pulled = True\n\n # normalize fitness to interval [0, 1]\n min_value = (self.simulate_steps/self.world_height) * max(self.fitness_params.capture_punishment, self.fitness_params.avoidance_punishment) * -1\n max_value = (self.simulate_steps/self.world_height) * max(self.fitness_params.avoidance_reward, self.fitness_params.capture_reward)\n fitness = (fitness - min_value) / (max_value - min_value)\n\n self.initialize_world()\n\n return fitness","title":""},{"docid":"4b95895193ab61b214c915dccadcbc0d","score":"0.5746591","text":"def isWin(board, move, current_player):\n return False","title":""},{"docid":"8c300c1f931a01d7cc73e53961bab59d","score":"0.57442856","text":"def make_random_move(self):\n cutter = 0\n clear = list()\n temp = [0] * 2\n # Loops through whole board and adds any cells that aren't mines or are already filled\n for x in range(self.width):\n for y in range(self.height):\n temp[0] = x\n temp[1] = y\n wtf = tuple(temp)\n\n if wtf not in self.moves_made and wtf not in self.mines:\n cutter += 1\n clear.append(wtf)\n # If no moves can be taken, then player has won\n if cutter == 0:\n return None\n # Else randomly pick a cell from the one's available\n else:\n return random.choice(clear)","title":""},{"docid":"327f146623d09d3bc5c70a8fa75369d4","score":"0.5743668","text":"def make_human_move(self, move):\n\n i, j = move\n if self._board[i][j] == 'x':\n return\n self._cells_lbls[i][j].configure(text='O')\n self._cells_lbls[i][j].pack(ipadx=32, ipady=10)\n self._board[i][j] = 'o'\n self._player = 'x'\n self._moveN += 1","title":""},{"docid":"21885849ae2815760d360de4c0e5a27c","score":"0.57330453","text":"def test_find_legal_move3(self):\n self.game.make_move(0)\n self.game.make_move(1)\n self.game.make_move(1)\n self.game.make_move(2)\n self.game.make_move(2)\n self.game.make_move(5)\n self.game.make_move(2)\n self.game.make_move(3)\n self.game.make_move(3)\n self.game.make_move(3)\n self.game.make_move(6)\n self.game.make_move(0)\n # self.game.make_move(3) The move I want the computer to find\n ai = AI(self.game, Board.WHITE)\n column_cal = ai.find_legal_move(AI.FAST_ALGORITHM_TIMEOUT)\n self.assertEqual(3, column_cal)","title":""},{"docid":"dcdad9d901999b5cf785ca9fbaa22679","score":"0.57285094","text":"def is_move_check(board, move):\n board.push(move)\n checked = board.is_check()\n board.pop()\n return checked","title":""},{"docid":"875546b25d2bbfd37cdc4253480179ed","score":"0.5724579","text":"def updateGame(self, move, player):\n if self.isValidMove(move):\n self.board[move[0]][move[1]]=player\n else:\n print(\"That is not a valid move\")","title":""},{"docid":"9bbbad092038ac3dc6b35af8d33c176c","score":"0.5720912","text":"def make_move(symbol, board, move_co_ords):\n valid_move = False\n while not valid_move:\n # TODO\n # Use an if statement to check if the position is valid.\n pass","title":""},{"docid":"0b6161f7c92eb30d1d654a4642db5e73","score":"0.5710088","text":"def chooseMove(self,moves):\n\n\t\treturn random.choice(list(moves))","title":""},{"docid":"4d11b45ed8d242301d2771ad726468ce","score":"0.5709726","text":"def process_move(player, board):\r\n print(str(player) + \"'s turn\")\r\n col = player.next_move(board)\r\n board.add_checker(player.checker, col)\r\n print()\r\n print(board)\r\n if board.is_win_for(player.checker) == True:\r\n print(player, 'wins in', player.num_moves, 'moves.')\r\n print('Congratulations!')\r\n return True\r\n elif board.is_full() == True:\r\n print(\"It's a tie!\")\r\n return True\r\n else:\r\n return False","title":""},{"docid":"a15a1f98c360c45a10a10d309b0dc257","score":"0.570442","text":"def make_move(move, board):\n\n # a map of how to check in each direction\n dir_map = {'U': -8, 'D': 8, 'L': -1, 'R': 1, 'DL': 7, 'DR': 9, 'UL': -7, 'UR': -9}\n\n # gets x and y from column and row\n x = move['column'] - 1\n y = move['row'] - 1\n\n # finds the starting index and places a piece there\n start = x + (8 * (y % 8))\n board[start] = move['player']\n\n # runs make_move_helper for every direction\n for key in dir_map:\n make_move_help(start, board, key, dir_map, start, move['player'])","title":""},{"docid":"6dbe57fabc5b46e2d0a5d835ff0d61a7","score":"0.5692183","text":"def select_move(self, game_state: GameState):\n candidates = []\n for r in range(1, game_state.board.num_rows + 1):\n for c in range(1, game_state.board.num_cols + 1):\n candidate = Point(row = r, col = c)\n if game_state.is_valid_move(Move.play(candidate)) and not is_point_an_eye(game_state.board, candidate, game_state.next_player):\n candidates.append(candidate)\n\n if not candidates:\n return Move.pass_turn()\n\n return Move.play(random.choice(candidates))","title":""},{"docid":"8859fa10ebd1b4d93bca5b756e00410e","score":"0.5683324","text":"def human_move(self, square):\n if self.is_computer_turn():\n return (None, False, None)\n \n square, self.board = self._apply_move(square, self.board, self.turn+1)\n winner = None\n if square is not None:\n self._set_turn()\n self.game_over, winner = self._game_over_validation(self.board)\n return (square, self.game_over, winner)","title":""},{"docid":"a9d42c20b58fb77b3a809f539066ee2c","score":"0.5671319","text":"def step(self, action_dict, check_for_human_move=True, check_for_bot_move=True, allow_piece_oscillation=False):\n\n # action should only be for current player\n assert self.player in action_dict\n assert self.player * -1 not in action_dict\n\n action = action_dict[self.player]\n action = self.base_env.get_action_1d_index_from_player_perspective(action_index=action, player=self.player)\n\n self.state, self.player = self.base_env.get_next_state(self.state, self.player, action,\n allow_piece_oscillation=allow_piece_oscillation)\n\n # if self.vs_bot:\n # debug_action_pos = self.base_env.get_action_positions_from_1d_index(action)\n # print(f\"action by player {self.player*-1}: {debug_action_pos}\")\n # self.base_env.print_fully_observable_board_to_console(state=self.state)\n\n reward = self.base_env.get_game_ended(self.state, self.player)\n\n if reward == 0:\n\n if self.vs_human and check_for_human_move and self.player == self.human_player_num:\n action_positions = self.gui_server.get_action_by_position(state=self.state, player=self.player)\n action = self.base_env.get_action_1d_index_from_positions(*action_positions)\n return self.step(action_dict={self.human_player_num: action})\n\n if self.vs_bot and check_for_bot_move and self.player == self.bot_player_num:\n action_positions_to_bot = self.base_env.get_action_positions_from_1d_index(action_index=action)\n socket_pickle.pickle_send(self.bot_controller_socket, data=(\"new_move\", action_positions_to_bot))\n\n try:\n cmd, bot_move = socket_pickle.pickle_recv(self.bot_controller_socket)\n except ValueError:\n # TODO HACK, fix this, assumes the bot won ####################\n dones = {1: True, -1: True, \"__all__\": True}\n obs = {1: self._get_current_obs(player=1), -1: self._get_current_obs(player=-1)}\n\n infos = {1: {}, -1: {}}\n\n infos[1]['game_result_was_invalid'] = False\n infos[-1]['game_result_was_invalid'] = False\n\n player_1_reward = 1 if self.bot_player_num == 1 else -1\n player_2_reward = 1 if self.bot_player_num == -1 else -1\n\n if player_1_reward == 1:\n infos[1]['game_result'] = 'won'\n infos[-1]['game_result'] = 'lost'\n elif player_1_reward == -1:\n infos[1]['game_result'] = 'lost'\n infos[-1]['game_result'] = 'won'\n else:\n infos[1]['game_result'] = 'tied'\n infos[-1]['game_result'] = 'tied'\n\n rewards = {1: player_1_reward,\n -1: player_2_reward}\n\n return obs, rewards, dones, infos\n\n #####################################################\n\n\n assert cmd == 'bot made move'\n # print(f\"Step(): Got bot move: {bot_move}\")\n assert self.base_env.is_move_valid_by_position(self.state, self.player, *bot_move, allow_piece_oscillation=True)\n bot_move = self.base_env.get_action_positions_from_player_perspective(self.bot_player_num, *bot_move)\n bot_move_action_index = self.base_env.get_action_1d_index_from_positions(*bot_move)\n return self.step(action_dict={self.bot_player_num: bot_move_action_index}, allow_piece_oscillation=True)\n\n dones = {self.player: False, \"__all__\": False}\n obs = {self.player: self._get_current_obs(player=self.player)}\n rewards = {self.player: 0}\n infos = {}\n else:\n dones = {1: True, -1: True, \"__all__\": True}\n obs = {1: self._get_current_obs(player=1), -1: self._get_current_obs(player=-1)}\n\n infos = {1: {}, -1: {}}\n\n if self.base_env.get_game_result_is_invalid(self.state):\n rewards = {1: 0, -1: 0}\n infos[1]['game_result_was_invalid'] = True\n infos[-1]['game_result_was_invalid'] = True\n infos[1]['game_result'] = 'tied'\n infos[-1]['game_result'] = 'tied'\n else:\n infos[1]['game_result_was_invalid'] = False\n infos[-1]['game_result_was_invalid'] = False\n\n player_1_reward = self.base_env.get_game_ended(self.state, player=1)\n player_2_reward = self.base_env.get_game_ended(self.state, player=-1)\n\n if player_1_reward == 1:\n infos[1]['game_result'] = 'won'\n infos[-1]['game_result'] = 'lost'\n elif player_1_reward == -1:\n infos[1]['game_result'] = 'lost'\n infos[-1]['game_result'] = 'won'\n else:\n infos[1]['game_result'] = 'tied'\n infos[-1]['game_result'] = 'tied'\n\n rewards = {1: player_1_reward,\n -1: player_2_reward}\n\n if self.vs_human:\n obs.pop(self.human_player_num)\n rewards.pop(self.human_player_num)\n dones.pop(self.human_player_num)\n infos.pop(self.human_player_num)\n\n return obs, rewards, dones, infos","title":""},{"docid":"e1b058d4333be9106b842d5c36b56601","score":"0.5664976","text":"def make_move(self, game: MinichessGame, previous_move: Optional[str]) -> str:\n raise NotImplementedError","title":""},{"docid":"44898663025817d49b4b0f0fb47ad678","score":"0.56623054","text":"def make_move(self, move):\n self.preprev_matrix = deepcopy(self.prev_matrix)\n self.prev_matrix = deepcopy(self.matrix) # in case undo is needed\n moving_piece = self.get_space(move.from_coords)\n direc = move.direc\n dist = abs((move.from_coords[0] - move.to_coords[0]) +\n (move.from_coords[1] - move.to_coords[1]))\n curr_space = move.from_coords\n for i in range(dist):\n self.change_space(curr_space, BLANK_SPACE) # noms\n curr_space = (curr_space[0] + MOVE_DICT[direc][0],\n curr_space[1] + MOVE_DICT[direc][1])\n self.change_space(move.to_coords, moving_piece)","title":""},{"docid":"18d37eb8cca918b2875eceebe91e12e2","score":"0.56545174","text":"def decide_move(self, hashed_game_state):\n raise NotImplementedError","title":""},{"docid":"a291111f5b0e94dc8f372baadd25d4eb","score":"0.56541026","text":"def request_move(self, status: AI_GameStatus, move: AI_Move, pid: int):\n self.set_state(HI_State.GRIDMODE)\n self.action_specified = MoveState.WAIT\n self.movement_specified = MoveState.WAIT\n self.move = move\n self.move.from_human_interaction = True\n self.game_status = status","title":""},{"docid":"0d57e9d241a71484c129d48d971cd8af","score":"0.5652121","text":"def make_a_move(self, board):\n \n # board.get_coords(string) returns either None or a tuple\n # board.update_board(start,end) is expecting two tuples (coord of piece that is going to move)(coord of destination)\n\n self.update_initial_options(board)\n self.update_checkless_options(board)\n end_game = board.assess_end_game(self.color)\n\n if not end_game:\n valid_piece = None\n while not valid_piece:\n piece_space = input(\"Select the piece that you wish to move by providing the space it currently occupies: \")\n piece_tup = board.get_coords(piece_space)\n if piece_tup != None and board.board[piece_tup[0]][piece_tup[1]].piece != None and board.board[piece_tup[0]][piece_tup[1]].piece.color == self.color and len(board.board[piece_tup[0]][piece_tup[1]].piece.options) > 0:\n valid_piece = board.board[piece_tup[0]][piece_tup[1]].piece\n\n valid_moves = valid_piece.options\n move_choice = None\n while move_choice not in valid_moves:\n potential_move = input(\"Select the space that you would like to move your piece to.\\nYou can enter 'help' to see a list of possible moves or 'change' to select a different piece to move: \")\n if potential_move == \"help\":\n self.print_move_options(piece_tup, board)\n if potential_move == \"change\":\n valid_piece = None\n break\n move_choice = board.get_coords(potential_move)\n \n board.update_board(piece_tup, move_choice)\n return end_game","title":""},{"docid":"6eb90fc23b54b46474b325f8910d7db4","score":"0.5651445","text":"def move_pieces(self, move, player):\n beans = self.mat.grab_pit(move)\n loc = move\n last = -1\n while True:\n loc = self.mat.next_pit(player, loc)\n last = self.mat.put(loc, 1)\n beans -= 1\n if beans <= 0:\n break\n if last == 1 and loc in self.mat.get_move_options(player):\n opp = self.mat.get_opposite(loc)\n if self.mat.board[opp] != 0:\n jackpot = self.mat.grab_pit(opp) + self.mat.grab_pit(loc)\n print \"Super Grab! %s gets %d points!\" % (self.players[player], jackpot)\n self.mat.put(self.mat.get_stash_loc(player),jackpot)\n if loc == self.mat.get_stash_loc(player):\n return 1\n else:\n return 0","title":""},{"docid":"bca18986dec125432d0f78fd1caa063f","score":"0.5638063","text":"def human_move(board_state, possible_moves, side):\n print('Where would you like to move? (enter the abbreviation)')\n move = input().upper()\n if not move in possible_moves:\n print('Sorry, that is not a possible move.')\n human_move(board_state, possible_moves, side)\n else:\n board_state[move] = side","title":""},{"docid":"62454462466ce040f1289701a76fec24","score":"0.5637819","text":"def getMove(self, current_state: GameState) -> Move:\n\n # Increment the amount of moves.\n self.current_gene.total_moves_across_games += 1\n\n # Randomly select a move.\n moves = listAllLegalMoves(current_state)\n selected_move = moves[random.randint(0, len(moves) - 1)]\n\n # Don't do a build move if there are already 3+ ants\n num_ants = len(current_state.inventories[current_state.whoseTurn].ants)\n while selected_move.moveType == BUILD and num_ants >= 3:\n selected_move = moves[random.randint(0, len(moves) - 1)]\n\n # Set the current state to an instance variable (if currently playing a game).\n if current_state.phase == PLAY_PHASE:\n self.setup_state = current_state\n\n return selected_move","title":""},{"docid":"93119b50faa591cc0b2d9ffeab81107c","score":"0.56363195","text":"def get_random_move(ship, game_map, avoid_moves):\n options = [ Direction.North, Direction.South, Direction.East, Direction.West ]\n while True:\n move = random.choice(options)\n options.remove(move)\n new_position = ship.position.directional_offset(move)\n if new_position not in avoid_moves:\n avoid_moves.append(new_position)\n move = game_map.naive_navigate(ship, new_position)\n return move\n if len(options) == 0:\n return ship.position","title":""},{"docid":"c532ebef06065ab051fd7fc22f529a34","score":"0.5634934","text":"def apply_move(self, move, check_valid=True):\n if not check_valid or move in self.valid_moves():\n self.turn += 1\n self.merged = []\n self.field = self.update_field(move)\n self.spawn(CREATE_TURN)\n score = self.calculate_score()\n self.score += score\n return score\n else:\n print(\"INVALID MOVE\")","title":""},{"docid":"2f00d64b9f04f35a3d5922c7bd836bd6","score":"0.5625707","text":"def is_move_recapture(board, move):\n last_move = board.move_stack[-1]\n board.pop()\n if is_move_takes(board, last_move):\n board.push(last_move)\n return move.to_square == last_move.to_square\n return False","title":""},{"docid":"2d810997d9fb12c7c7904770306fb88e","score":"0.56238484","text":"def make_move(self, move, state):\n\n ## return the same state if the move suggested is not part of the legal moves for the state\n if move not in self.legal_moves(state):\n return state\n\n \n return Struct(to_move=if_(state.to_move=='P1','P2','P1'), board=move)","title":""},{"docid":"72049bd20586f9fc345c1292fddc9d51","score":"0.56205136","text":"def execute_move(self, move):\n b = move.beginning\n e = move.end\n self.board[e[0]][e[1]] = self.board[b[0]][b[1]] # new territory, conquered!!\n self.board[b[0]][b[1]] = 1 # deserted land left in the wake of battle\n self.board[e[0]][e[1]].location = e # update the piece instance's location attribute\n\n if isinstance(move, moves.Jump):\n for piece in move.eliminated: # removed jumped pieces\n row, col = piece.location\n self.board[row][col] = 1\n self.draw_counter = 0\n else:\n self.draw_counter += 1\n\n self._check_king(self.board[e[0]][e[1]])","title":""},{"docid":"8405a2d3e020f910956a7030f5bf4ea0","score":"0.5605117","text":"def _verify_add(self, move):\n result_move = self._verify_move(move)\n if not result_move.success:\n return result_move\n\n result = {\n 'cards left': self._num_moves < MAX_CARDS,\n 'space available': self._space_avail(move.x, move.y, move.placement),\n }\n result.update(result_move.conditions)\n return Result(result)","title":""},{"docid":"cc72b6e5e7143d9398fb3a248cdf9d24","score":"0.5600151","text":"def is_move_possible( board, player, fromPoint, toPoint ):\n ## temporary:\n return True","title":""},{"docid":"78c584c856c6628e7ed94f9a351ed673","score":"0.5599904","text":"def move(self, game):\r\n if not self.player:\r\n raise CpuError('Can not move before being assigned a player.')\r\n \r\n self.board = game.unpack_board()\r\n self.index = game.players.index(self.player.key()) + 1\r\n\r\n rules = game.rule_set\r\n self.board_width = rules.m\r\n self.board_height = rules.n\r\n self.win_length = rules.k\r\n self.per_turn = rules.p\r\n self.turns_left = rules.turns_left(game.turn)\r\n self.force = []\r\n self.moves = []\r\n\r\n loc = None\r\n ox = self.board_width - 1\r\n\r\n try:\r\n # Horizontal checks.\r\n for y in xrange(0, self.board_height):\r\n cp1, rl1 = 0, 0\r\n cp2, rl2 = 0, 0\r\n cp3, rl3 = 0, 0\r\n for x in xrange(0, self.board_width + 1):\r\n cp1, rl1 = self.check(cp1, rl1, x, y, 1, 0)\r\n\r\n # Skip checks that will be made by vertical checks.\r\n if y == 0: continue\r\n\r\n cp2, rl2 = self.check(cp2, rl2, x, y + x, 1, 1)\r\n cp3, rl3 = self.check(cp3, rl3, ox - x, y + x, -1, 1)\r\n\r\n # Vertical checks.\r\n for x in xrange(0, self.board_width):\r\n cp1, rl1 = 0, 0\r\n cp2, rl2 = 0, 0\r\n cp3, rl3 = 0, 0\r\n for y in xrange(0, self.board_height + 1):\r\n cp1, rl1 = self.check(cp1, rl1, x, y, 0, 1)\r\n cp2, rl2 = self.check(cp2, rl2, y + x, y, 1, 1)\r\n cp3, rl3 = self.check(cp3, rl3, -y + x, y, -1, 1)\r\n\r\n m = self.moves\r\n if len(m) > 0:\r\n # Merge moves to the same location.\r\n for a in xrange(len(m)):\r\n try:\r\n # Value, coordinate\r\n av, ac = m[a][0], m[a][1]\r\n \r\n # Looping backwards so that index is not affected by\r\n # deleting items in the list.\r\n for b in xrange(len(m) - 1, a):\r\n bv, bc = m[b][0], m[b][1]\r\n \r\n # Same coordinates?\r\n if ac == bc:\r\n mn, mx = (av, bv) if av < bv else (bv, av)\r\n av = mx + mn / 2.0\r\n del m[b]\r\n\r\n m[a][0] = av\r\n except KeyError:\r\n # Reached the end of the list; stop the loop.\r\n break\r\n\r\n # Order moves by score.\r\n def o(x, y):\r\n s = cmp(int(y[0] * self.cleverness),\r\n int(x[0] * self.cleverness))\r\n return random.randint(-1, 1) if not s else s\r\n\r\n m.sort(o)\r\n\r\n # Forcing is done after merging and sorting so that the best\r\n # block can be chosen.\r\n if len(self.force) > 0:\r\n for move in m:\r\n if move[1] in self.force:\r\n raise ForcedMove(move[1])\r\n\r\n # Perform best move.\r\n loc = m[0][1]\r\n except ForcedMove, (c,):\r\n loc = c\r\n\r\n if not loc:\r\n # Crazy, inefficient method of getting all positions, in order of\r\n # closeness to center.\r\n locs = [(x, y)\r\n for y in xrange(self.board_height)\r\n for x in xrange(self.board_width)]\r\n\r\n cx, cy = int(self.board_width / 2), int(self.board_height / 2)\r\n def closer(x, y):\r\n return cmp((x[0] - cx) ** 2 + (x[1] - cy) ** 2,\r\n (y[0] - cx) ** 2 + (y[1] - cy) ** 2)\r\n\r\n locs.sort(closer)\r\n\r\n for loc in locs:\r\n if not self.board[loc[0]][loc[1]]: break\r\n\r\n game.move(self.player, loc[0], loc[1])","title":""},{"docid":"2cf54f21ea0c3d599f552206526485ee","score":"0.55860156","text":"def make_move(self, move):\n # print(move)\n if move is None:\n return 1, -1\n if len(move) == 0:\n return 1, -1\n self.current_position = move[1]\n for line in move[0]:\n self.board[line[0], line[1]] = 1\n done = 1\n if move[2] == 0:\n done = 0\n return done, move[2]","title":""},{"docid":"956a162f850df1e743d2bdbab21edbd8","score":"0.55787104","text":"def heuristic(self, move):\n # Only apply heuristics to AI's move\n if self.game_state.current_player != self.player_id:\n return 0, 0, 0, 0\n\n # Make a move\n game_state_copy = deepcopy(self.game_state)\n game_state_copy.make_move(move[0], move[1])\n\n # Check win\n if game_state_copy.winner is not None and game_state_copy.winner == self.player_id:\n return 1, math.inf, 1, math.inf\n\n # Check immediate loss\n legal_moves = game_state_copy.get_legal_moves()\n for move_2 in legal_moves:\n game_state_copy_2 = deepcopy(game_state_copy)\n game_state_copy_2.make_move(move_2[0], move_2[1])\n if game_state_copy_2.winner is not None and game_state_copy_2.winner != self.player_id:\n return 1, -math.inf, 1, -math.inf\n\n # Wins sub-board\n if game_state_copy.sub_board_values[move[0][0]][move[0][1]] == self.player_id:\n return 1, 1, 1, 10\n\n # # Blocks opponent winner that sub board\n # game_state_copy_opponent = deepcopy(self.game_state)\n # game_state_copy_opponent.current_player = int(2 / game_state_copy_opponent.current_player)\n # game_state_copy_opponent.make_move(move[0], move[1])\n # if game_state_copy_opponent.sub_board_values[move[0][0]][move[0][1]] == int(2 / self.player_id):\n # return 1, 1, 1, 8\n\n # # Center\n # if move[1] == (1, 1):\n # return 1, 1, 1, 2\n\n return 0, 0, 0, 0","title":""},{"docid":"e1f9f5303179431b5ef903f9d504759b","score":"0.5575543","text":"def move(self, move):\r\n assert isinstance(move, Move)\r\n\r\n old_piece_end = self.at(move.end)\r\n new_piece_end = piece_start = self.at(move.start)\r\n new_score = self.cur_score\r\n\r\n # pawn promotion\r\n if new_piece_end == 'p' and move.end[1] == 1:\r\n assert self.turn == 'B'\r\n new_piece_end = 'q'\r\n new_score -= Board.piece_values['p']\r\n new_score += Board.piece_values['q']\r\n elif new_piece_end == 'P' and move.end[1] == 6:\r\n assert self.turn == 'W'\r\n new_piece_end = 'Q'\r\n new_score -= Board.piece_values['P']\r\n new_score += Board.piece_values['Q']\r\n\r\n new_score -= Board.piece_values[old_piece_end]\r\n self.set(move.end, new_piece_end)\r\n self.set(move.start, '.')\r\n\r\n # remove bonus score of start piece\r\n new_score -= self._bonus_score(move.start, piece_start)\r\n\r\n # remove bonus score of captured piece\r\n new_score -= self._bonus_score(move.end, old_piece_end)\r\n\r\n # add bonus score of end piece\r\n new_score += self._bonus_score(move.end, new_piece_end)\r\n\r\n if self.turn == \"W\":\r\n self.turn = \"B\"\r\n else:\r\n self.move_num += 1\r\n self.turn = \"W\"\r\n\r\n # king capture and result\r\n if old_piece_end == 'k':\r\n result = 'W'\r\n new_score = 100000\r\n elif old_piece_end == 'K':\r\n result = 'B'\r\n new_score = -100000\r\n elif self.move_num == 41:\r\n result = '='\r\n new_score = 0\r\n else:\r\n result = '?'\r\n\r\n # save old state to be able to undo it later\r\n # ((startpos, startpiece), (endpos, endpiece), score)\r\n # if self._calc_score() != new_score:\r\n # print(\"calc_score:\", self._calc_score(), \"new_score:\")\r\n # print(\"Wtf\")\r\n self.history.append(((move.start, piece_start), (move.end, old_piece_end), self.cur_score))\r\n\r\n self.cur_score = new_score\r\n return result","title":""},{"docid":"1571d93cd409e3ab3031f7c485c34770","score":"0.5573586","text":"def validate_move(self, move):\r\n\t\treturn move in range(1, 10)","title":""},{"docid":"a1fdbd6a501ecc6bacaac9bb802c0991","score":"0.5572056","text":"def test_adviser(self):\n\n game2 = XiangqiGame()\n\n # Valid move down/right\n move = game2.make_move('d1','e2')\n self.assertTrue(move) \n\n # Valid move up/right\n move = game2.make_move('d10','e9')\n self.assertTrue(move) \n\n # Valid move up/left\n move = game2.make_move('e2','d1')\n self.assertTrue(move) \n\n # Valid move down/left\n move = game2.make_move('e9','d10')\n self.assertTrue(move) \n\n # Invalid move down/left \n move = game2.make_move('f1','d3')\n self.assertFalse(move) \n\n # Invalid move down\n move = game2.make_move('f1','f2')\n self.assertFalse(move)","title":""},{"docid":"aba29672e4c129f21b601a28bc92b9dc","score":"0.5571676","text":"def apply_move(game_state: connectfour.ConnectFourGameState, move: str) -> connectfour.ConnectFourGameState:\r\n col = int(move[move.find(\" \") + 1:])\r\n \r\n if move.lower().startswith(\"drop\"):\r\n print()\r\n return connectfour.drop_piece(game_state, col - 1)\r\n elif move.lower().startswith(\"pop\"):\r\n print()\r\n return connectfour.pop_piece(game_state, col - 1)\r\n else:\r\n print(\"INVALID MOVE\")","title":""},{"docid":"06b1183cdf4384c04217bb01b494e790","score":"0.55713856","text":"def is_move_takes(board, move):\n victim = board.piece_at(move.to_square)\n aggressor = board.piece_at(move.from_square)\n if victim is None:\n return False, 0\n return True, mvv_lva(aggressor, victim)","title":""},{"docid":"15b1301f35e096c1dd81bea62aeb6934","score":"0.556823","text":"def choose_move(self) -> GameState:\r\n possible_moves = [child.root for child in self.game_tree.children]\r\n\r\n return random.choice(possible_moves)","title":""}],"string":"[\n {\n \"docid\": \"5e4c62077b64c1a05e5257a263e86b6b\",\n \"score\": \"0.71304905\",\n \"text\": \"def make_move(self, game):\\n cell_map = game.field.get_cell_mapping()\\n\\n # if random.random() < 0.5:\\n return self.make_random_birth_move(game, cell_map)\\n\\n # return self.make_random_kill_move(game, cell_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"299a76fe44fd169b283e1640fcee0854\",\n \"score\": \"0.7019477\",\n \"text\": \"def simulate_move(piece, move, board, game, player):\\r\\n board.direct_move(piece, move, player)\\r\\n return board\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3e7697f430f7b646b7d1f8b92b133c3\",\n \"score\": \"0.69555765\",\n \"text\": \"def simulate(self, _move):\\n self.legalMoves = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"691988082dfb2723e5e746d4841c7dbc\",\n \"score\": \"0.69014937\",\n \"text\": \"def make_move(self, game_id, move):\\n path = f'api/bot/game/{game_id}/move/{move}'\\n return self._r.post(path)['ok']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f29b0395e435319f97b98f214024c92\",\n \"score\": \"0.6771747\",\n \"text\": \"def make_move(self, game: a2_minichess.MinichessGame, previous_move: Optional[str]) -> str: \\n \\n ran_num = random.uniform(0.0, 1.0) \\n \\n if self._game_tree is not None and previous_move is not None: \\n self._game_tree = self._game_tree.find_subtree_by_move(previous_move) \\n \\n if self._game_tree is None or self._game_tree.get_subtrees() == []: \\n return random.choice(game.get_valid_moves()) \\n \\n if ran_num < self._exploration_probability: \\n self._game_tree = None \\n return random.choice(game.get_valid_moves()) \\n \\n else: \\n if game.is_white_move(): \\n temp_subtree = self._game_tree.get_subtrees()[0] \\n for subtree in self._game_tree.get_subtrees(): \\n if subtree.white_win_probability > temp_subtree.white_win_probability: \\n temp_subtree = subtree \\n self._game_tree = temp_subtree \\n return temp_subtree.move \\n \\n else: \\n temp_subtree = self._game_tree.get_subtrees()[0] \\n for subtree in self._game_tree.get_subtrees(): \\n if subtree.white_win_probability < temp_subtree.white_win_probability: \\n temp_subtree = subtree \\n self._game_tree = temp_subtree \\n \\n return temp_subtree.move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fbb4e267d1ef9b9dc8d310c888ae6f\",\n \"score\": \"0.6763375\",\n \"text\": \"def make_move(self, move: Any) -> 'GameState':\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c15d505eb3f7a119554832fdb0e9aa\",\n \"score\": \"0.6683921\",\n \"text\": \"def make_move(self, move):\\n # print(move)\\n if self.board.make_move(move, self.board.current_player):\\n # print(self.board)\\n self.master.status.set(\\\"{} | {}\\\".format(\\n self.board.turn_string(), self.board.score_string()))\\n self.draw_discs()\\n else:\\n self.master.status.set(\\\"{} | Invalid move... try again.\\\".format(\\n self.board.turn_string()))\\n\\n if self.board.terminal_test():\\n self.master.status.set(\\\"Game Over!!! | {}\\\".format(self.board.score_string()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3288f23936a994dddaee12a004b5517\",\n \"score\": \"0.6655375\",\n \"text\": \"def make_move(self, game_id, move):\\n path = f'api/board/game/{game_id}/move/{move}'\\n return self._r.post(path)['ok']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd803328731193fe5349f98a26ca1c1\",\n \"score\": \"0.6609868\",\n \"text\": \"def copy_and_make_move(self, move: str) -> MinichessGame:\\n return MinichessGame(board=self._board_after_move(move),\\n white_active=not self._is_white_active,\\n move_count=self._move_count + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c94600e9b6bc5b3622be91b545714e\",\n \"score\": \"0.65781206\",\n \"text\": \"def move(self, game=None):\\n return self._move(game)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77aa5447e3abb1e0b76d01ad9bab68e9\",\n \"score\": \"0.6540235\",\n \"text\": \"def make_move(self, args):\\n if not len(args) == 1:\\n return False\\n move = args[0]\\n\\n global games\\n global connections\\n\\n with self.lock:\\n game = games[self.user.game_room]\\n if game[WHITE].name == self.user.username: # the user is white\\n opponent = game[BLACK]\\n player = game[WHITE]\\n else:\\n opponent = game[WHITE]\\n player = game[BLACK]\\n\\n if opponent and opponent.color != game.turn and not game.game_over:\\n # The player has the turn. First check if move is legal\\n if move in game.board.possible_moves:\\n game.toggle_turn(move)\\n my_time = round(opponent.time_left, 3)\\n opponent_time = round(player.time_left, 3)\\n connections[opponent.name].send(\\\"MOVED \\\" + move + \\\" \\\" + str(my_time) + \\\" \\\" + str(opponent_time))\\n # Check if someone lost\\n if game.game_over:\\n if game.is_draw():\\n connections[opponent.name].send(\\\"DRAW\\\")\\n self.client.send(\\\"DRAW\\\")\\n del games[self.user.game_room]\\n check_if_to_save_game(game, \\\"\\\") # Empty winner name because no one won\\n else:\\n loser = game.turn\\n connections[game[loser].name].send(\\\"LOST\\\")\\n connections[game[not bool(loser)].name].send(\\\"WON\\\")\\n del games[self.user.game_room]\\n check_if_to_save_game(game, game[not bool(loser)].name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d60eb3f74a737763e6d3c272ac70e0\",\n \"score\": \"0.6463725\",\n \"text\": \"def ai_find_move(self):\\r\\n # creates a copy of the game instance and sends it to the AI.\\r\\n sim_game = Game()\\r\\n board, register, cell_set, counter = self.__game.get_attr_for_sim()\\r\\n sim_game.set_attr_for_sim(board, register, cell_set, counter)\\r\\n\\r\\n try:\\r\\n self.__ai.find_legal_move(sim_game, self.play_my_move)\\r\\n except:\\r\\n self.__screen.print_to_screen(self.__ai.NO_AI_MOVE, self.__my_color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87851950fe1a51ae865959d4794e01a0\",\n \"score\": \"0.6379014\",\n \"text\": \"def move_heuristic(self, move, board_state):\\n return self.rulebook.score_move(move, board_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706ab038d255a7563ca95c8137d1f1a2\",\n \"score\": \"0.6372193\",\n \"text\": \"def isMoveLegal(self, move):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108e19b3413ff9135f10766789ea14dd\",\n \"score\": \"0.63085663\",\n \"text\": \"def simulate(self, move):\\n self.last_move = move\\n # Note: simulate modifies the given state (in this case, self)\\n simulation.simulate(self, move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ac7f6d6216b72fb9ead88761ee58ba\",\n \"score\": \"0.6292397\",\n \"text\": \"def play(self, random_moves=False):\\n self.logged_random_moves.clear()\\n turn = 1\\n while self.check_game_win() == 0 and not self.check_game_draw_3_1() and not self.check_game_draw_repeat():\\n end_move_flag = False\\n self.table_obj.table_print()\\n all_moves, must_eat_flag = self.scan_all_moves('white') if turn == 1 else self.scan_all_moves('black')\\n while not end_move_flag:\\n print(Template(\\\"Turn is $color\\\").substitute(color='white' if turn == 1 else 'black'))\\n move = self.take_move() if not random_moves else self.take_random_move(all_moves, must_eat_flag)\\n if move == 1:\\n continue\\n elif move == -1:\\n return -1\\n if not must_eat_flag:\\n if move not in all_moves:\\n print('Illegal move, first break, make new')\\n self.write_state('N---')\\n continue\\n else:\\n try:\\n self.make_move('white' if turn == 1 else 'black', move)\\n except CheckerException as cherr:\\n print(cherr, 'Something Wrong with that', sep='\\\\n')\\n self.write_state('N---')\\n return -1\\n else:\\n self.write_state('YN--')\\n end_move_flag = True\\n else:\\n short_moves = [(from_pos, to_pos) for from_pos, to_pos, eaten, in all_moves]\\n if move not in short_moves:\\n self.write_state('N---')\\n print('Illegal move, make new')\\n continue\\n try:\\n last_checker = self.make_move('white' if turn == 1 else 'black',\\n all_moves[short_moves.index(move)])\\n except CheckerException as cherr:\\n self.write_state('N---')\\n print(cherr, 'Something went wrong...', sep='\\\\n')\\n return -1\\n if self.scan_checker(last_checker, True)[0] is None:\\n self.write_state('YN' + all_moves[short_moves.index(move)][2])\\n end_move_flag = True\\n else:\\n self.write_state('YS' + all_moves[short_moves.index(move)][2])\\n all_moves, must_eat_flag = self.scan_checker(last_checker, True)\\n turn = 0 if turn == 1 else 1\\n else:\\n self.table_obj.table_print()\\n if self.three_vs_one == 15:\\n # Print pipe Draw\\n print('Draw!')\\n elif self.check_game_win() == 1:\\n # Print pipe Draw\\n print('White won')\\n elif self.check_game_win() == -1:\\n # Print pipe Draw\\n print('Black won')\\n else:\\n print('Draw by the repeat')\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01dbd573601bacd8dc6e0e7fb56ef71\",\n \"score\": \"0.626742\",\n \"text\": \"def checkMove(move: Card, game) -> bool:\\n print(\\\"Checking move\\\")\\n lastMove = game.lastMove\\n\\n if move.number == lastMove.number:\\n return True\\n\\n elif move.color == lastMove.color: \\n return True\\n\\n elif move.wild: \\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8fc3f8cb84a4d84761069299178221f\",\n \"score\": \"0.6247149\",\n \"text\": \"def move(self, movement):\\n made_movement = False\\n # If agent moves first (whites and first move)\\n if self.agent.color and len(self.board.move_stack) == 0:\\n agents_best_move = self.agent.best_move(self, real_game=True)\\n self.board.push(chess.Move.from_uci(agents_best_move))\\n made_movement = True\\n else:\\n made_movement = super().move(movement)\\n if made_movement and self.get_result() is None:\\n agents_best_move = self.agent.best_move(self, real_game=True)\\n self.board.push(chess.Move.from_uci(agents_best_move))\\n return made_movement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eada8f7934ee0156fd1bda02cda24a77\",\n \"score\": \"0.6234029\",\n \"text\": \"def act(state, action, player): # TODO : Wondering if I should make the random move here.\\n if SeegaRules.is_legal_move(state, action, player):\\n return SeegaRules.make_move(state, action, player)\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b08c0d4db1b86cc3fb13e1c027cdc9\",\n \"score\": \"0.6228436\",\n \"text\": \"def computer_move_random(possible_moves):\\n move = random.choice(possible_moves)\\n return(move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e00ad104c45fa51dc896b7562836695\",\n \"score\": \"0.6186098\",\n \"text\": \"def play_move(board, move):\\n islegal = True\\n if board[move] == 0 and board[move+27] == 0:\\n board[move] = 1\\n else:\\n islegal = False\\n return board, islegal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c8688d9da78a4c8f86fc40559802fd\",\n \"score\": \"0.6181433\",\n \"text\": \"def make_move(self, request):\\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\\n\\n if not game:\\n raise endpoints.NotFoundException('Game not found')\\n\\n # ensure the game is not already over.\\n if game.game_over:\\n raise endpoints.NotFoundException('Game already over')\\n\\n # ensure that ships have been inserted onto both grids prior to a move.\\n if game.total_ships(grid=1) == 0 or game.total_ships(grid=2) == 0:\\n raise endpoints.BadRequestException('Both users must have inserted ships '\\n 'prior to beginning the game.')\\n\\n # ensure the correct user is making a move.\\n user = User.query(User.name == request.user_name).get()\\n if user.key != game.next_move:\\n raise endpoints.BadRequestException('It\\\\'s not your turn!')\\n\\n # User signifier. True if current user is player 1, otherwise False.\\n user_1 = True if user.key == game.user_1 else False\\n\\n # set target grid to 2 if current user is user 1, and vice versa.\\n target_grid = 2 if user_1 else 1\\n\\n row_loc, col_loc = int(request.target_row), int(request.target_col)\\n\\n # Verify move is valid\\n if row_loc < 0 or row_loc > 9 or col_loc < 0 or col_loc > 9:\\n raise endpoints.BadRequestException('Invalid move! Rows and columns must be '\\n 'between 0 and 9')\\n\\n # check if grid cell is already destroyed.\\n if game.return_grid_status(row_loc, col_loc, target_grid) == 'X':\\n raise endpoints.BadRequestException('That grid cell is already destroyed! '\\n 'Try picking another!')\\n\\n # check whether the move was a hit or miss.\\n target_hit = game.destroy_cell(row_loc, col_loc, grid=target_grid)\\n\\n # Append a move to the relevant history dict key dependent on user and hit status.\\n history_msg = 'Ship hit!' if target_hit else 'No ship hit!'\\n history_entry = (row_loc, col_loc, history_msg)\\n game.history['grid_2' if user_1 else 'grid_1'].append(history_entry)\\n\\n # set next move within the game.\\n game.next_move = game.user_2 if user_1 else game.user_1\\n\\n # check for any winners, end game and report winner if true.\\n winner_p1, winner_p2 = game.check_winner()\\n if winner_p1:\\n game.end_game(game.user_1)\\n game.put()\\n return StringMessage(message='The game is over! {0} has won the match!'.\\n format(game.user_1.get().name))\\n\\n if winner_p2:\\n game.end_game(game.user_2)\\n game.put()\\n return StringMessage(message='The game is over! {0} has won the match!'.\\n format(game.user_2.get().name))\\n\\n else:\\n # Send reminder email\\n taskqueue.add(url='/tasks/send_move_email',\\n params=dict(user_key=game.next_move.urlsafe(), game_key=game.key.urlsafe()))\\n\\n # update memcache with user ships remaining\\n taskqueue.add(url='/tasks/cache_ships_remaining',\\n params=None)\\n\\n target_hit_msg = 'You hit a ship! Well done!'\\n target_miss_msg = 'No ship hit! Better luck next time!'\\n\\n # create a message to notify the player that they hit or missed.\\n ret_msg = (\\\"{0} You have now made the following moves: {1}. \\\"\\n \\\"{2} is up next!\\\".format(target_hit_msg if target_hit else target_miss_msg,\\n game.history['grid_2' if user_1 else 'grid_1'],\\n game.next_move.get().name))\\n\\n game.put()\\n return StringMessage(message=ret_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afce34b98e64b7e35a8bcca936bfc820\",\n \"score\": \"0.61792415\",\n \"text\": \"def wouldwin(self,move,who):\\n (r,c) = move\\n self[r,c] = who\\n result = self.win(who)\\n self[r,c] = ' '\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a00df6f1dbcfc69d5f7be637755e02\",\n \"score\": \"0.61695015\",\n \"text\": \"def try_move(self, move):\\n move = int(move) - 1\\n success = False\\n # can't make moves outside the range, or in an occupied space\\n if move in self.possible_moves and self.state[move] == ' ':\\n cur_p = self.players[self.current_player]\\n self.state[move] = cur_p\\n self.current_player = (self.current_player + 1) % 2\\n self.find_connections(move, cur_p)\\n success = True\\n return success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"337ef157eb07a51735cf513768e7bd49\",\n \"score\": \"0.61680573\",\n \"text\": \"def DoMove(self, move):\\r\\n x, y, plant = move\\r\\n if self.farm[x][y] == option.EMPTY.value:\\r\\n self.farm[x][y] = plant\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099ec83ae434c0a4e3099798b783512b\",\n \"score\": \"0.61470234\",\n \"text\": \"def illegal_move(move, game):\\n for player in game:\\n if make_move(move, game[player]) == game[player]:\\n return player\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f55649c8cc63ca639fca1222add4d15\",\n \"score\": \"0.61352473\",\n \"text\": \"def test_generate_random_move():\\n from agents.agent_random import generate_move\\n from agents.common import NO_PLAYER, PLAYER1, PLAYER2, BoardPiece\\n\\n test_board = np.full((6, 7), NO_PLAYER)\\n action, _ = generate_move(test_board, PLAYER1, None)\\n\\n assert (float(action))\\n test_board = np.full((6, 7), PLAYER2)\\n test_board[0, 0] = NO_PLAYER\\n action, _ = generate_move(test_board, PLAYER1, None)\\n assert (action == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6806c6c07d64e94a60eb628338fe30fb\",\n \"score\": \"0.61132026\",\n \"text\": \"def ai_move_if_not_won(game: ttt.GameState) -> None:\\n if not Config.WIN_STATUS:\\n player_next = Config.GAME_OBJS[game.next_player]\\n if player_next != \\\"human\\\":\\n # obtain the player's move, place the game piece, and draw it on the UI\\n dom['game_status'].text = dom['game_status'].text + \\\"..\\\"\\n timer.set_timeout(ai_make_move, 0, player_next, game, game.move_history[-1])\\n\\n # check for winners again\\n timer.set_timeout(check_winner, 0, game)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33705ec85d84c5e2624490f0f3a2474\",\n \"score\": \"0.6102181\",\n \"text\": \"def make_move(self, game: MinichessGame, previous_move: Optional[str]) -> str:\\n possible_moves = game.get_valid_moves()\\n return random.choice(possible_moves)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d0769ffa6d40488f7afce183a71aec\",\n \"score\": \"0.6100382\",\n \"text\": \"def make_move(self, move):\\n result = self._recycle_card(move) if move.type else self._add_card(move)\\n if result.success:\\n self._moves.append(move)\\n self.last_moved = move\\n self._num_moves += 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37cc580946397ad27ee1146ee2f8ec03\",\n \"score\": \"0.60941744\",\n \"text\": \"def make_move(self, move: Any) -> 'StonghengeState':\\n new_state = StonehengeState(not self.p1_turn, self.size,\\n self.grid.copy())\\n new_state.grid.update_grid(self.get_current_player_name(), move)\\n new_state.p1, new_state.p2 = new_state.grid.get_score(1),\\\\\\n new_state.grid.get_score(2)\\n return new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c2b797a78e9eabfdf0273ffe32c326\",\n \"score\": \"0.6085027\",\n \"text\": \"def simulate_move(self, board, move, symbol):\\n if move == None:\\n return board\\n r = move[0]\\n c = move[1]\\n board_copy = deepcopy(board)\\n board_copy[r][c] = symbol\\n\\n for v in [-1, 0, 1]:\\n for h in [-1, 0, 1]:\\n stones = []\\n if v == 0 == h:\\n continue\\n row = r\\n col = c\\n row += v\\n col += h\\n while is_field_legit(row, col):\\n # if it is other symbol\\n if board_copy[row][col] != symbol and board_copy[row][col] != self.space:\\n stones.append((row, col))\\n # if space found - oon t flip this line\\n elif board_copy[row][col] == self.space:\\n break\\n # same symbol found flip all stones\\n elif board_copy[row][col] == symbol:\\n for a, b in stones:\\n board_copy[a][b] = symbol\\n break\\n row += v\\n col += h\\n return board_copy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9c7279a0ff0f386665707b6efd6e37\",\n \"score\": \"0.60723364\",\n \"text\": \"def DoMove(self, move):\\n assert self.game.players[0].hero.health > 0 and self.game.players[1].hero.health > 0 and not self.game.game_ended\\n\\n def _choose_target(targets):\\n if move[4] is None:\\n return None\\n else:\\n return targets[move[4]]\\n\\n def _choose_index(targets, player):\\n return move[5]\\n\\n def _choose_option(*options):\\n return options[move[4]]\\n\\n if self.game.current_player.name == \\\"one\\\":\\n self.playerJustMoved = 1\\n else:\\n self.playerJustMoved = 2\\n\\n # print(str(self.game.current_player.mana) + \\\"/\\\" + str(self.game.current_player.max_mana))\\n if move[0] == MOVE.PRE_GAME:\\n self.replay = record(self.game)\\n self.game.current_player = self.game.players[1]\\n self.game.other_player = self.game.players[0]\\n self.game.pre_game()\\n #self.game._start_turn()\\n elif move[0] == MOVE.START_TURN:\\n self.game._start_turn()\\n elif move[0] == MOVE.PICK_CLASS:\\n self.game.current_player.deck = Deck([], hero_for_class(move[1]))\\n self.game.current_player.hero = self.game.current_player.deck.hero.create_hero(self.game.current_player)\\n self.game.current_player.hero.card = self.game.current_player.deck.hero\\n elif move[0] == MOVE.PICK_CARD:\\n card = move[1] #copy.deepcopy(move[1])\\n card.drawn = False\\n self.game.current_player.deck.cards.append(card)\\n \\n if len(self.game.current_player.deck.cards) == 30:\\n self.game.current_player = self.game.players[1]\\n self.game.other_player = self.game.players[0]\\n elif move[0] == MOVE.END_TURN:\\n try:\\n self.game._end_turn()\\n self.game._start_turn()\\n except:\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.HERO_POWER:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.current_player.hero.power.use()\\n except:\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.SUMMON_MINION:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.current_player.agent.choose_index = _choose_index\\n self.game.play_card(self.game.current_player.hand[move[3]])\\n except:\\n print(move)\\n print(self.game.current_player.hand)\\n print(self.game.other_player.hand)\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.EQUIP_WEAPON:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.play_card(self.game.current_player.hand[move[3]])\\n except:\\n print(move)\\n print(self.game.current_player.hand)\\n print(self.game.other_player.hand)\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[2] is None: # Passing index rather than object, hopefully the game copy fix will help with this\\n try:\\n self.game.play_card(self.game.current_player.hand[move[3]])\\n except:\\n print(move)\\n print(self.game.current_player.hand)\\n print(self.game.other_player.hand)\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.MINION_ATTACK:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.current_player.minions[move[3]].attack()\\n except:\\n print(move)\\n print(self.game.current_player.minions)\\n print(self.game.other_player.minions)\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.HERO_ATTACK:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.current_player.hero.attack()\\n except:\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n elif move[0] == MOVE.TARGETED_SPELL:\\n try:\\n self.game.current_player.agent.choose_target = _choose_target\\n self.game.play_card(self.game.current_player.hand[move[3]])\\n except:\\n print(move)\\n print(self.game.current_player.hand)\\n print(self.game.other_player.hand)\\n print(self.game.players[0].deck.__str__())\\n print(self.game.players[1].deck.__str__())\\n self.replay.write_json(\\\"ragowit_ai.hsreplay\\\")\\n traceback.print_exc()\\n sys.exit()\\n else:\\n raise NameError(\\\"DoMove ran into unclassified card\\\", move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7eea6d730d91135f37016bfe7df719\",\n \"score\": \"0.60347205\",\n \"text\": \"def validmove(move):\\n if move==0:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3de8ae4876ddcff5321f3ef8348b82\",\n \"score\": \"0.6028611\",\n \"text\": \"def DoMove(self, move):\\n board = GameState.Board(move)\\n position = GameState.Position(move)\\n\\n self.playerJustMoved = self.NextPlayer()\\n self.pieces[board][position] = self.playerJustMoved\\n self.activeBoard = position\\n\\n if self.GetWinner(board) == self.playerJustMoved:\\n self.winnerOfBoard[board] = self.playerJustMoved\\n for (x, y, z) in GameState.WINNING_COMBINATIONS:\\n if self.winnerOfBoard[x] == self.winnerOfBoard[y] == self.winnerOfBoard[z] != 0:\\n self.winnerOfGame = self.winnerOfBoard[z]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f02b4e9f2af76acd6033195de8a1953\",\n \"score\": \"0.60150576\",\n \"text\": \"def make_move(self, move, state):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0d6d729d407c9d61ff3f7075b51ce6\",\n \"score\": \"0.6004809\",\n \"text\": \"def makeMove(board, move):\\n new_board = board\\n if isPosEmpty(board, move[0:2]):\\n raise IndexError('no piece in source position!')\\n if not isPosEmpty(board, move[2:4]):\\n raise IndexError('Target position is not empty!')\\n else:\\n piece = new_board[move[1], move[0]]\\n if VERBOSE:\\n if piece == WHITE:\\n print(\\\"white plays!\\\")\\n else:\\n print(\\\"black plays!\\\")\\n\\n new_board[move[1], move[0]] = EMPTY\\n new_board[move[3], move[2]] = piece\\n if isCapture(board, move):\\n cap_coords = getCaptureCoords(board, move)\\n if VERBOSE:\\n print(f'{piece} captured a piece in pos ' + str(cap_coords))\\n\\n new_board[cap_coords[1], cap_coords[0]] = EMPTY\\n return new_board\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59c4fc78b216b168583c44a3e0825c4\",\n \"score\": \"0.59979814\",\n \"text\": \"def make_move(self):\\n move = input(\\\"Enter move: \\\").lower()\\n valid = self.board.make_move(move)\\n if not valid:\\n print(\\\"Invalid move supplied\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7240f9dab58f0a4211f078fcfa34c4\",\n \"score\": \"0.5994267\",\n \"text\": \"def make_move(self, request):\\n # fetch game data and check if it is valid\\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\\n if not game:\\n raise endpoints.NotFoundException('Game not found')\\n if game.game_over:\\n raise endpoints.NotFoundException('Game is over')\\n # fetch user data and check if valid\\n user = Game.query(Game.user == request.user_name).get()\\n if not user:\\n raise endpoints.NotFoundException('The user does not exist!')\\n # validate user input\\n if not re.match(\\\"^[A-Za-z]$\\\", request.move):\\n raise endpoints.BadRequestException('You can only enter 1 letter!')\\n # validate move played\\n if request.move in game.check_answer:\\n return game.to_form('You already played {}'.format(request.move))\\n # wrong move attemp logic\\n if request.move.lower() not in game.answer.lower():\\n game.attempts_remaining -= 1\\n if game.attempts_remaining < 1:\\n user.total_played += 1\\n game.end_game(False)\\n msg = 'Game Over, you lose!'\\n if game.attempts_remaining == 1:\\n msg = 'Try again, last move!'\\n if game.attempts_remaining > 1:\\n msg = 'Try again, {} moves left!'.format(game.attempts_remaining)\\n # game in progress logic\\n # use check_answer to update user progress\\n else:\\n for i, x in enumerate(game.answer):\\n if x == request.move:\\n game.check_answer[i] = request.move\\n if game.check_answer == game.answer:\\n user.wins += 1\\n user.total_played += 1\\n game.end_game(True)\\n msg = 'You win!'\\n else:\\n msg = 'Correct! You have: {}'.format(game.check_answer)\\n # log moves to generate history\\n game.move_histories.append(\\n [\\\"{},\\\".format(request.move.lower()) + \\\"{}\\\".format(msg)]\\n )\\n game.put()\\n user.put()\\n return game.to_form(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282c3acb6cbb3fda4be4ab596763649f\",\n \"score\": \"0.59742534\",\n \"text\": \"def make_move(self, request):\\n game = get_by_urlsafe(request.urlsafe_game_key, Game)\\n if game.game_over:\\n raise endpoints.ForbiddenException('Wait, what? Game is already over!')\\n game.history.append({'player_move':request.guess,'computer_move':game.target})\\n if request.guess == game.target:\\n game.points += 1\\n msg = '%s vs %s! Tie! Take a point and try again!' % (request.guess , game.target)\\n elif request.guess == 'rock':\\n if game.target == 'paper':\\n game.end_game(False)\\n return game.to_form('You lose! Paper beats rock')\\n else:\\n game.end_game(True)\\n return game.to_form('You win! rock beats scissors!')\\n elif request.guess == 'paper':\\n if game.target == 'scissors':\\n game.end_game(False)\\n return game.to_form('You lose! Scissors beats paper')\\n else:\\n game.end_game(True)\\n return game.to_form('You win! Paper beats rock!')\\n elif request.guess == 'scissors':\\n if game.target == 'rock':\\n game.end_game(False)\\n return game.to_form('You lose! Rock beats scissors')\\n else:\\n game.end_game(True)\\n return game.to_form('You win! Paper beats rock!')\\n else:\\n msg = 'That\\\\'s not a valid play. Try rock, paper, or scissors.'\\n # re-set computer to random rock, paper, or scissors\\n game.target = Game.rando_select()\\n print game\\n game.put()\\n return game.to_form(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca2bcab634df9f2f939fd56b0458be35\",\n \"score\": \"0.5970347\",\n \"text\": \"def computer_move(possible_moves, mode):\\n if mode == 'easy':\\n move = computer_move_random(possible_moves)\\n elif mode == 'hard':\\n move = computer_move_hard(possible_moves, board_state)\\n else:#medium mode is combination of good and random moves\\n move = random.choice([computer_move_random(possible_moves), computer_move_hard(possible_moves, board_state)])\\n return(move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd51656ea22b8711cd585b912bebdd3f\",\n \"score\": \"0.5961737\",\n \"text\": \"def test_move(m, i):\\n\\n test_board = board.copy()\\n test_board[i] = m\\n return check_win(test_board, m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274d7e06982d9dde07a58265419aed67\",\n \"score\": \"0.59344506\",\n \"text\": \"def result(self, game_id, move):\\n game = self.game_states[game_id]\\n next_game = simulate(game, move)\\n self.game_states.append(next_game)\\n return (len(self.game_states) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cfdecf90bcb59b5ee61723f7bcb251\",\n \"score\": \"0.5930572\",\n \"text\": \"def learn_from_move(self, game, move):\\n game.make_move(move)\\n r = self.__reward(game)\\n td_target = r\\n next_state_value = 0.0\\n selected_next_move = None\\n if game.playable():\\n best_next_move, selected_next_move = self.learn_select_move(game)\\n next_state_value = self.state_value(best_next_move)\\n\\n current_state_value = self.state_value(move)\\n td_target = r + next_state_value\\n self.V[move] = current_state_value + self.alpha * (td_target - current_state_value)\\n return selected_next_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d70bafb2c39075265672bf32feaa432\",\n \"score\": \"0.59262985\",\n \"text\": \"def apply_move(self, move):\\n next_board = copy.deepcopy(self.board)\\n if move is not None:\\n next_board.place(self.next_player, move.point_a, move.point_b)\\n return GameState(next_board, self.next_player.other, move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30faa9c7afd84dcc513eb118ae1679d4\",\n \"score\": \"0.5905913\",\n \"text\": \"def is_legal(self, move: int) -> bool:\\n return self.board_state[move] is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdb9cbc22e34bf6e189346538562405\",\n \"score\": \"0.5901934\",\n \"text\": \"def result(self, state, move):\\n moves = state.moves.copy()\\n moves.remove(move)\\n player = state.to_move\\n knights = state.knights.copy()\\n knights[player]=move\\n return GameState(to_move=(2 if player == 1 else 1),\\n knights=knights, moves=moves)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75facd803f78511ee8845b5f62c17ee7\",\n \"score\": \"0.5901085\",\n \"text\": \"def forecast_move(self, move):\\n state = deepcopy(self.state)\\n\\n if move not in self.get_legal_moves():\\n print(\\\"Illegal move {}\\\".format(move))\\n return self.__class__(state)\\n\\n state['board'][move[0]][move[1]] = 1\\n state['positions'][state['turn']] = move\\n state['turn'] = 1 - state['turn']\\n\\n return self.__class__(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a732f7ac77cafc1f46875e0e088019\",\n \"score\": \"0.58877766\",\n \"text\": \"def make_move(self, start, finish):\\n print(\\\"make_move(\\\", start, \\\",\\\", finish, \\\")\\\")\\n # check for game status\\n if self.get_game_state() != 'UNFINISHED':\\n return False\\n\\n # check to make sure player is moving the correct piece\\n if not self._board.is_player_piece(self, start):\\n return False\\n\\n # find game piece\\n row, col = self._board.algebraic_to_index(start)\\n piece = self._board.get_piece(row, col)\\n\\n # finish location\\n fut_row, fut_col = self._board.algebraic_to_index(finish)\\n\\n # check if General is in check then find move to get out of check\\n team = piece.get_team()\\n generals_in_check = self._board.get_generals_in_check()\\n for general in generals_in_check:\\n # check to see if your general is in the list of checked generals if so pass the general to the neutralize\\n # threat method\\n if general.get_team() == team:\\n if self._board.neutralize_threats(self, general, piece, fut_row, fut_col):\\n self._board.move_piece(piece, fut_row, fut_col)\\n self._board.print_game_board()\\n self.set_whose_turn()\\n return True\\n # check for game status\\n if self.get_game_state() != 'UNFINISHED':\\n return False\\n\\n elif team == \\\"blue\\\":\\n self._game_state = \\\"BLUE_WON\\\"\\n else:\\n self._game_state = \\\"RED_WON\\\"\\n return True\\n\\n # check piece rules\\n if not piece.move_rules(self._board, fut_row, fut_col):\\n return False\\n\\n # make move\\n if not self._board.move_piece(piece, fut_row, fut_col):\\n return False\\n\\n self._board.print_game_board()\\n\\n # change turn\\n self.set_whose_turn()\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dcdecaba12e90ca6edc8ddd96f37941\",\n \"score\": \"0.58842903\",\n \"text\": \"def _move(self, game):\\n raise NotImplementedError(\\\"Class {} doesn't implement _move()\\\".format(\\n self.__class__.__name__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa85b788858a1304574ed3ce65398571\",\n \"score\": \"0.58776176\",\n \"text\": \"def evaluatemove(self,move):\\n (r,c) = move\\n self[r,c] = self.tomove()\\n result = self.evaluate()\\n self[r,c] = ' '\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb9919b0eb81aca2a86a32807778132\",\n \"score\": \"0.5870437\",\n \"text\": \"def move(self, move):\\n valid_words = self._possible_words()\\n if move in valid_words:\\n new_game = WordLadderPuzzle(self._start, self._target)\\n new_game._moves += self._moves[1:]\\n new_game._moves.append(move)\\n return new_game\\n else:\\n raise ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f735d04de644efc91521374b15177670\",\n \"score\": \"0.5858091\",\n \"text\": \"def play_game(self, moves):\\n \\n c = W\\n\\n for m in moves:\\n self.assertEqual(self.g.move(c, m.src, m.dest), m.type_)\\n if(c == W):\\n c = B\\n else:\\n c = W\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7945bb6f3ede777304ff04da944ead1c\",\n \"score\": \"0.5843221\",\n \"text\": \"def make_safe_move(self):\\n\\n # If the move is safe and hasn't already being taken, then return it\\n for x in self.safes:\\n if x not in self.moves_made:\\n return x\\n # Else return it and make a random move\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0d09de0ad2b57185e2a1c1553cfd4e\",\n \"score\": \"0.58129907\",\n \"text\": \"def make_move(self, board):\\n if self._bot:\\n # Minimax algorithm\\n _, moves = minimax_soft_alpha_beta(board, get_depth(board), True, -inf, +inf)\\n move = random.choice(moves)\\n else:\\n # Prompt the user to select a move\\n index = input(\\\"Enter move: \\\")\\n move = Player.convert_index_to_move(int(index))\\n\\n return move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"532ea40ce80be10b728481241ca7814c\",\n \"score\": \"0.58126426\",\n \"text\": \"def humanMove(board, human) :\\n legal = legalMove(board)\\n move = None\\n while move not in legal:\\n move = getNumber(\\\"Where would you like to make your move? (0-8)\\\", 0, NUMSQUARES)\\n if move not in legal:\\n print(\\\"\\\\nThat square is already occupied, foolish human. Choose another.\\\\n\\\")\\n print(\\\"Fine. . .\\\")\\n return move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2769e4265e33b609253ca411a33c5454\",\n \"score\": \"0.5811516\",\n \"text\": \"def opponent_move(self, move):\\n # validate input\\n if len(move) > 1:\\n source_row = move[1][0]\\n source_col = move[1][1]\\n if source_row != None and self.board[source_row][source_col] != self.opp:\\n self.print_board()\\n print(move)\\n raise Exception(\\\"You don't have a piece there!\\\")\\n if abs(source_row - move[0][0]) > 1 or abs(source_col - move[0][1]) > 1:\\n self.print_board()\\n print(move)\\n raise Exception('Illegal move: Can only move to an adjacent space')\\n if self.board[move[0][0]][move[0][1]] != ' ':\\n raise Exception(\\\"Illegal move detected\\\")\\n # make move\\n self.place_piece(move, self.opp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e830b80d38ab26a991bf7c1ff9fb6e\",\n \"score\": \"0.58062017\",\n \"text\": \"def make_move(self, move: str) -> None:\\n if move not in self._valid_moves:\\n raise ValueError(f'Move \\\"{move}\\\" is not valid')\\n\\n self._board = self._board_after_move(move)\\n\\n self._is_white_active = not self._is_white_active\\n self._move_count += 1\\n\\n self._recalculate_valid_moves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869165e32753344f1956717e7be1b20c\",\n \"score\": \"0.5783674\",\n \"text\": \"def random_move(self):\\n rand_number = random.random()\\n if rand_number <= self.gamma:\\n disc = random.choice(Game.DISCS_LST)\\n col = self.find_random_free_col()\\n\\n row = self.check_column(col)[1]\\n self.make_random_move(row, col, disc)\\n self.players[self.get_current_player()] += 1\\n return True\\n self.players[self.get_current_player()] += 1\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a5a29607377a128fdfa8af7bcda8f1\",\n \"score\": \"0.57768023\",\n \"text\": \"def simulate_move(self, move, sym, grid):\\n\\t\\tgrid[move[0]][move[1]] = sym\\n\\t\\treturn grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0872ae2b0e5c61ba3a99db95d96848b\",\n \"score\": \"0.57689226\",\n \"text\": \"def is_valid_move(self, move: Any) -> bool:\\n return move in self.get_possible_moves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0bb83a5b1815376f32b1f2c20ec13b\",\n \"score\": \"0.57658607\",\n \"text\": \"def is_move_legal(self, move):\\n if move is None:\\n return True\\n if self.board[move] != EMPTY:\\n return False\\n if move == self.ko:\\n return False\\n if self.is_move_suicidal(move):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efa0155554584ab9955bba5a1d35dd1\",\n \"score\": \"0.57565314\",\n \"text\": \"def simulate(self, ann, move_callback=None, turn_wall_reward=20):\\n\\n fitness = 0\\n\\n for i in range(self.simulate_steps):\\n # check if the callback was set\\n if move_callback:\\n move_callback(self)\\n\\n if self.wall_hit and self.turn_wall_rewards_num:\\n self.turn_wall_rewards_num -= 1\\n self.wall_hit = False\\n fitness += turn_wall_reward\\n self.object_captured = False\\n self.large_object_hit = False\\n\\n # handle the case when object hits the tracker\\n is_last_floor = next(iter(self.object_position))[0] == self.world_height - 1\\n if is_last_floor:\\n fitness = self.recompute_fitness(fitness)\\n self.object_position = self.generate_object()\\n\\n else:\\n self.lower_object()\\n\\n # use ANN to calculate move direction and magnitude\\n ann_input = self.compute_tracker_shadowing()\\n\\n if not self.wraparound:\\n self.deploy_wall_sensors()\\n ann_input += [self.wall_on_left, self.wall_on_right]\\n ann_result = ann.compute(ann_input)\\n direction, speed, pull = self.interpret_ann_result(ann_result)\\n self.move(direction, speed)\\n\\n if pull and not is_last_floor:\\n self.pull_object()\\n self.object_pulled = True\\n\\n # normalize fitness to interval [0, 1]\\n min_value = (self.simulate_steps/self.world_height) * max(self.fitness_params.capture_punishment, self.fitness_params.avoidance_punishment) * -1\\n max_value = (self.simulate_steps/self.world_height) * max(self.fitness_params.avoidance_reward, self.fitness_params.capture_reward)\\n fitness = (fitness - min_value) / (max_value - min_value)\\n\\n self.initialize_world()\\n\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b95895193ab61b214c915dccadcbc0d\",\n \"score\": \"0.5746591\",\n \"text\": \"def isWin(board, move, current_player):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c300c1f931a01d7cc73e53961bab59d\",\n \"score\": \"0.57442856\",\n \"text\": \"def make_random_move(self):\\n cutter = 0\\n clear = list()\\n temp = [0] * 2\\n # Loops through whole board and adds any cells that aren't mines or are already filled\\n for x in range(self.width):\\n for y in range(self.height):\\n temp[0] = x\\n temp[1] = y\\n wtf = tuple(temp)\\n\\n if wtf not in self.moves_made and wtf not in self.mines:\\n cutter += 1\\n clear.append(wtf)\\n # If no moves can be taken, then player has won\\n if cutter == 0:\\n return None\\n # Else randomly pick a cell from the one's available\\n else:\\n return random.choice(clear)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"327f146623d09d3bc5c70a8fa75369d4\",\n \"score\": \"0.5743668\",\n \"text\": \"def make_human_move(self, move):\\n\\n i, j = move\\n if self._board[i][j] == 'x':\\n return\\n self._cells_lbls[i][j].configure(text='O')\\n self._cells_lbls[i][j].pack(ipadx=32, ipady=10)\\n self._board[i][j] = 'o'\\n self._player = 'x'\\n self._moveN += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21885849ae2815760d360de4c0e5a27c\",\n \"score\": \"0.57330453\",\n \"text\": \"def test_find_legal_move3(self):\\n self.game.make_move(0)\\n self.game.make_move(1)\\n self.game.make_move(1)\\n self.game.make_move(2)\\n self.game.make_move(2)\\n self.game.make_move(5)\\n self.game.make_move(2)\\n self.game.make_move(3)\\n self.game.make_move(3)\\n self.game.make_move(3)\\n self.game.make_move(6)\\n self.game.make_move(0)\\n # self.game.make_move(3) The move I want the computer to find\\n ai = AI(self.game, Board.WHITE)\\n column_cal = ai.find_legal_move(AI.FAST_ALGORITHM_TIMEOUT)\\n self.assertEqual(3, column_cal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcdad9d901999b5cf785ca9fbaa22679\",\n \"score\": \"0.57285094\",\n \"text\": \"def is_move_check(board, move):\\n board.push(move)\\n checked = board.is_check()\\n board.pop()\\n return checked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875546b25d2bbfd37cdc4253480179ed\",\n \"score\": \"0.5724579\",\n \"text\": \"def updateGame(self, move, player):\\n if self.isValidMove(move):\\n self.board[move[0]][move[1]]=player\\n else:\\n print(\\\"That is not a valid move\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbbad092038ac3dc6b35af8d33c176c\",\n \"score\": \"0.5720912\",\n \"text\": \"def make_move(symbol, board, move_co_ords):\\n valid_move = False\\n while not valid_move:\\n # TODO\\n # Use an if statement to check if the position is valid.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b6161f7c92eb30d1d654a4642db5e73\",\n \"score\": \"0.5710088\",\n \"text\": \"def chooseMove(self,moves):\\n\\n\\t\\treturn random.choice(list(moves))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d11b45ed8d242301d2771ad726468ce\",\n \"score\": \"0.5709726\",\n \"text\": \"def process_move(player, board):\\r\\n print(str(player) + \\\"'s turn\\\")\\r\\n col = player.next_move(board)\\r\\n board.add_checker(player.checker, col)\\r\\n print()\\r\\n print(board)\\r\\n if board.is_win_for(player.checker) == True:\\r\\n print(player, 'wins in', player.num_moves, 'moves.')\\r\\n print('Congratulations!')\\r\\n return True\\r\\n elif board.is_full() == True:\\r\\n print(\\\"It's a tie!\\\")\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15a1f98c360c45a10a10d309b0dc257\",\n \"score\": \"0.570442\",\n \"text\": \"def make_move(move, board):\\n\\n # a map of how to check in each direction\\n dir_map = {'U': -8, 'D': 8, 'L': -1, 'R': 1, 'DL': 7, 'DR': 9, 'UL': -7, 'UR': -9}\\n\\n # gets x and y from column and row\\n x = move['column'] - 1\\n y = move['row'] - 1\\n\\n # finds the starting index and places a piece there\\n start = x + (8 * (y % 8))\\n board[start] = move['player']\\n\\n # runs make_move_helper for every direction\\n for key in dir_map:\\n make_move_help(start, board, key, dir_map, start, move['player'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dbe57fabc5b46e2d0a5d835ff0d61a7\",\n \"score\": \"0.5692183\",\n \"text\": \"def select_move(self, game_state: GameState):\\n candidates = []\\n for r in range(1, game_state.board.num_rows + 1):\\n for c in range(1, game_state.board.num_cols + 1):\\n candidate = Point(row = r, col = c)\\n if game_state.is_valid_move(Move.play(candidate)) and not is_point_an_eye(game_state.board, candidate, game_state.next_player):\\n candidates.append(candidate)\\n\\n if not candidates:\\n return Move.pass_turn()\\n\\n return Move.play(random.choice(candidates))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8859fa10ebd1b4d93bca5b756e00410e\",\n \"score\": \"0.5683324\",\n \"text\": \"def human_move(self, square):\\n if self.is_computer_turn():\\n return (None, False, None)\\n \\n square, self.board = self._apply_move(square, self.board, self.turn+1)\\n winner = None\\n if square is not None:\\n self._set_turn()\\n self.game_over, winner = self._game_over_validation(self.board)\\n return (square, self.game_over, winner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9d42c20b58fb77b3a809f539066ee2c\",\n \"score\": \"0.5671319\",\n \"text\": \"def step(self, action_dict, check_for_human_move=True, check_for_bot_move=True, allow_piece_oscillation=False):\\n\\n # action should only be for current player\\n assert self.player in action_dict\\n assert self.player * -1 not in action_dict\\n\\n action = action_dict[self.player]\\n action = self.base_env.get_action_1d_index_from_player_perspective(action_index=action, player=self.player)\\n\\n self.state, self.player = self.base_env.get_next_state(self.state, self.player, action,\\n allow_piece_oscillation=allow_piece_oscillation)\\n\\n # if self.vs_bot:\\n # debug_action_pos = self.base_env.get_action_positions_from_1d_index(action)\\n # print(f\\\"action by player {self.player*-1}: {debug_action_pos}\\\")\\n # self.base_env.print_fully_observable_board_to_console(state=self.state)\\n\\n reward = self.base_env.get_game_ended(self.state, self.player)\\n\\n if reward == 0:\\n\\n if self.vs_human and check_for_human_move and self.player == self.human_player_num:\\n action_positions = self.gui_server.get_action_by_position(state=self.state, player=self.player)\\n action = self.base_env.get_action_1d_index_from_positions(*action_positions)\\n return self.step(action_dict={self.human_player_num: action})\\n\\n if self.vs_bot and check_for_bot_move and self.player == self.bot_player_num:\\n action_positions_to_bot = self.base_env.get_action_positions_from_1d_index(action_index=action)\\n socket_pickle.pickle_send(self.bot_controller_socket, data=(\\\"new_move\\\", action_positions_to_bot))\\n\\n try:\\n cmd, bot_move = socket_pickle.pickle_recv(self.bot_controller_socket)\\n except ValueError:\\n # TODO HACK, fix this, assumes the bot won ####################\\n dones = {1: True, -1: True, \\\"__all__\\\": True}\\n obs = {1: self._get_current_obs(player=1), -1: self._get_current_obs(player=-1)}\\n\\n infos = {1: {}, -1: {}}\\n\\n infos[1]['game_result_was_invalid'] = False\\n infos[-1]['game_result_was_invalid'] = False\\n\\n player_1_reward = 1 if self.bot_player_num == 1 else -1\\n player_2_reward = 1 if self.bot_player_num == -1 else -1\\n\\n if player_1_reward == 1:\\n infos[1]['game_result'] = 'won'\\n infos[-1]['game_result'] = 'lost'\\n elif player_1_reward == -1:\\n infos[1]['game_result'] = 'lost'\\n infos[-1]['game_result'] = 'won'\\n else:\\n infos[1]['game_result'] = 'tied'\\n infos[-1]['game_result'] = 'tied'\\n\\n rewards = {1: player_1_reward,\\n -1: player_2_reward}\\n\\n return obs, rewards, dones, infos\\n\\n #####################################################\\n\\n\\n assert cmd == 'bot made move'\\n # print(f\\\"Step(): Got bot move: {bot_move}\\\")\\n assert self.base_env.is_move_valid_by_position(self.state, self.player, *bot_move, allow_piece_oscillation=True)\\n bot_move = self.base_env.get_action_positions_from_player_perspective(self.bot_player_num, *bot_move)\\n bot_move_action_index = self.base_env.get_action_1d_index_from_positions(*bot_move)\\n return self.step(action_dict={self.bot_player_num: bot_move_action_index}, allow_piece_oscillation=True)\\n\\n dones = {self.player: False, \\\"__all__\\\": False}\\n obs = {self.player: self._get_current_obs(player=self.player)}\\n rewards = {self.player: 0}\\n infos = {}\\n else:\\n dones = {1: True, -1: True, \\\"__all__\\\": True}\\n obs = {1: self._get_current_obs(player=1), -1: self._get_current_obs(player=-1)}\\n\\n infos = {1: {}, -1: {}}\\n\\n if self.base_env.get_game_result_is_invalid(self.state):\\n rewards = {1: 0, -1: 0}\\n infos[1]['game_result_was_invalid'] = True\\n infos[-1]['game_result_was_invalid'] = True\\n infos[1]['game_result'] = 'tied'\\n infos[-1]['game_result'] = 'tied'\\n else:\\n infos[1]['game_result_was_invalid'] = False\\n infos[-1]['game_result_was_invalid'] = False\\n\\n player_1_reward = self.base_env.get_game_ended(self.state, player=1)\\n player_2_reward = self.base_env.get_game_ended(self.state, player=-1)\\n\\n if player_1_reward == 1:\\n infos[1]['game_result'] = 'won'\\n infos[-1]['game_result'] = 'lost'\\n elif player_1_reward == -1:\\n infos[1]['game_result'] = 'lost'\\n infos[-1]['game_result'] = 'won'\\n else:\\n infos[1]['game_result'] = 'tied'\\n infos[-1]['game_result'] = 'tied'\\n\\n rewards = {1: player_1_reward,\\n -1: player_2_reward}\\n\\n if self.vs_human:\\n obs.pop(self.human_player_num)\\n rewards.pop(self.human_player_num)\\n dones.pop(self.human_player_num)\\n infos.pop(self.human_player_num)\\n\\n return obs, rewards, dones, infos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b058d4333be9106b842d5c36b56601\",\n \"score\": \"0.5664976\",\n \"text\": \"def make_move(self, game: MinichessGame, previous_move: Optional[str]) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44898663025817d49b4b0f0fb47ad678\",\n \"score\": \"0.56623054\",\n \"text\": \"def make_move(self, move):\\n self.preprev_matrix = deepcopy(self.prev_matrix)\\n self.prev_matrix = deepcopy(self.matrix) # in case undo is needed\\n moving_piece = self.get_space(move.from_coords)\\n direc = move.direc\\n dist = abs((move.from_coords[0] - move.to_coords[0]) +\\n (move.from_coords[1] - move.to_coords[1]))\\n curr_space = move.from_coords\\n for i in range(dist):\\n self.change_space(curr_space, BLANK_SPACE) # noms\\n curr_space = (curr_space[0] + MOVE_DICT[direc][0],\\n curr_space[1] + MOVE_DICT[direc][1])\\n self.change_space(move.to_coords, moving_piece)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d37eb8cca918b2875eceebe91e12e2\",\n \"score\": \"0.56545174\",\n \"text\": \"def decide_move(self, hashed_game_state):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a291111f5b0e94dc8f372baadd25d4eb\",\n \"score\": \"0.56541026\",\n \"text\": \"def request_move(self, status: AI_GameStatus, move: AI_Move, pid: int):\\n self.set_state(HI_State.GRIDMODE)\\n self.action_specified = MoveState.WAIT\\n self.movement_specified = MoveState.WAIT\\n self.move = move\\n self.move.from_human_interaction = True\\n self.game_status = status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d57e9d241a71484c129d48d971cd8af\",\n \"score\": \"0.5652121\",\n \"text\": \"def make_a_move(self, board):\\n \\n # board.get_coords(string) returns either None or a tuple\\n # board.update_board(start,end) is expecting two tuples (coord of piece that is going to move)(coord of destination)\\n\\n self.update_initial_options(board)\\n self.update_checkless_options(board)\\n end_game = board.assess_end_game(self.color)\\n\\n if not end_game:\\n valid_piece = None\\n while not valid_piece:\\n piece_space = input(\\\"Select the piece that you wish to move by providing the space it currently occupies: \\\")\\n piece_tup = board.get_coords(piece_space)\\n if piece_tup != None and board.board[piece_tup[0]][piece_tup[1]].piece != None and board.board[piece_tup[0]][piece_tup[1]].piece.color == self.color and len(board.board[piece_tup[0]][piece_tup[1]].piece.options) > 0:\\n valid_piece = board.board[piece_tup[0]][piece_tup[1]].piece\\n\\n valid_moves = valid_piece.options\\n move_choice = None\\n while move_choice not in valid_moves:\\n potential_move = input(\\\"Select the space that you would like to move your piece to.\\\\nYou can enter 'help' to see a list of possible moves or 'change' to select a different piece to move: \\\")\\n if potential_move == \\\"help\\\":\\n self.print_move_options(piece_tup, board)\\n if potential_move == \\\"change\\\":\\n valid_piece = None\\n break\\n move_choice = board.get_coords(potential_move)\\n \\n board.update_board(piece_tup, move_choice)\\n return end_game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eb90fc23b54b46474b325f8910d7db4\",\n \"score\": \"0.5651445\",\n \"text\": \"def move_pieces(self, move, player):\\n beans = self.mat.grab_pit(move)\\n loc = move\\n last = -1\\n while True:\\n loc = self.mat.next_pit(player, loc)\\n last = self.mat.put(loc, 1)\\n beans -= 1\\n if beans <= 0:\\n break\\n if last == 1 and loc in self.mat.get_move_options(player):\\n opp = self.mat.get_opposite(loc)\\n if self.mat.board[opp] != 0:\\n jackpot = self.mat.grab_pit(opp) + self.mat.grab_pit(loc)\\n print \\\"Super Grab! %s gets %d points!\\\" % (self.players[player], jackpot)\\n self.mat.put(self.mat.get_stash_loc(player),jackpot)\\n if loc == self.mat.get_stash_loc(player):\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca18986dec125432d0f78fd1caa063f\",\n \"score\": \"0.5638063\",\n \"text\": \"def human_move(board_state, possible_moves, side):\\n print('Where would you like to move? (enter the abbreviation)')\\n move = input().upper()\\n if not move in possible_moves:\\n print('Sorry, that is not a possible move.')\\n human_move(board_state, possible_moves, side)\\n else:\\n board_state[move] = side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62454462466ce040f1289701a76fec24\",\n \"score\": \"0.5637819\",\n \"text\": \"def getMove(self, current_state: GameState) -> Move:\\n\\n # Increment the amount of moves.\\n self.current_gene.total_moves_across_games += 1\\n\\n # Randomly select a move.\\n moves = listAllLegalMoves(current_state)\\n selected_move = moves[random.randint(0, len(moves) - 1)]\\n\\n # Don't do a build move if there are already 3+ ants\\n num_ants = len(current_state.inventories[current_state.whoseTurn].ants)\\n while selected_move.moveType == BUILD and num_ants >= 3:\\n selected_move = moves[random.randint(0, len(moves) - 1)]\\n\\n # Set the current state to an instance variable (if currently playing a game).\\n if current_state.phase == PLAY_PHASE:\\n self.setup_state = current_state\\n\\n return selected_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93119b50faa591cc0b2d9ffeab81107c\",\n \"score\": \"0.56363195\",\n \"text\": \"def get_random_move(ship, game_map, avoid_moves):\\n options = [ Direction.North, Direction.South, Direction.East, Direction.West ]\\n while True:\\n move = random.choice(options)\\n options.remove(move)\\n new_position = ship.position.directional_offset(move)\\n if new_position not in avoid_moves:\\n avoid_moves.append(new_position)\\n move = game_map.naive_navigate(ship, new_position)\\n return move\\n if len(options) == 0:\\n return ship.position\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c532ebef06065ab051fd7fc22f529a34\",\n \"score\": \"0.5634934\",\n \"text\": \"def apply_move(self, move, check_valid=True):\\n if not check_valid or move in self.valid_moves():\\n self.turn += 1\\n self.merged = []\\n self.field = self.update_field(move)\\n self.spawn(CREATE_TURN)\\n score = self.calculate_score()\\n self.score += score\\n return score\\n else:\\n print(\\\"INVALID MOVE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f00d64b9f04f35a3d5922c7bd836bd6\",\n \"score\": \"0.5625707\",\n \"text\": \"def is_move_recapture(board, move):\\n last_move = board.move_stack[-1]\\n board.pop()\\n if is_move_takes(board, last_move):\\n board.push(last_move)\\n return move.to_square == last_move.to_square\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d810997d9fb12c7c7904770306fb88e\",\n \"score\": \"0.56238484\",\n \"text\": \"def make_move(self, move, state):\\n\\n ## return the same state if the move suggested is not part of the legal moves for the state\\n if move not in self.legal_moves(state):\\n return state\\n\\n \\n return Struct(to_move=if_(state.to_move=='P1','P2','P1'), board=move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72049bd20586f9fc345c1292fddc9d51\",\n \"score\": \"0.56205136\",\n \"text\": \"def execute_move(self, move):\\n b = move.beginning\\n e = move.end\\n self.board[e[0]][e[1]] = self.board[b[0]][b[1]] # new territory, conquered!!\\n self.board[b[0]][b[1]] = 1 # deserted land left in the wake of battle\\n self.board[e[0]][e[1]].location = e # update the piece instance's location attribute\\n\\n if isinstance(move, moves.Jump):\\n for piece in move.eliminated: # removed jumped pieces\\n row, col = piece.location\\n self.board[row][col] = 1\\n self.draw_counter = 0\\n else:\\n self.draw_counter += 1\\n\\n self._check_king(self.board[e[0]][e[1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8405a2d3e020f910956a7030f5bf4ea0\",\n \"score\": \"0.5605117\",\n \"text\": \"def _verify_add(self, move):\\n result_move = self._verify_move(move)\\n if not result_move.success:\\n return result_move\\n\\n result = {\\n 'cards left': self._num_moves < MAX_CARDS,\\n 'space available': self._space_avail(move.x, move.y, move.placement),\\n }\\n result.update(result_move.conditions)\\n return Result(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc72b6e5e7143d9398fb3a248cdf9d24\",\n \"score\": \"0.5600151\",\n \"text\": \"def is_move_possible( board, player, fromPoint, toPoint ):\\n ## temporary:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c584c856c6628e7ed94f9a351ed673\",\n \"score\": \"0.5599904\",\n \"text\": \"def move(self, game):\\r\\n if not self.player:\\r\\n raise CpuError('Can not move before being assigned a player.')\\r\\n \\r\\n self.board = game.unpack_board()\\r\\n self.index = game.players.index(self.player.key()) + 1\\r\\n\\r\\n rules = game.rule_set\\r\\n self.board_width = rules.m\\r\\n self.board_height = rules.n\\r\\n self.win_length = rules.k\\r\\n self.per_turn = rules.p\\r\\n self.turns_left = rules.turns_left(game.turn)\\r\\n self.force = []\\r\\n self.moves = []\\r\\n\\r\\n loc = None\\r\\n ox = self.board_width - 1\\r\\n\\r\\n try:\\r\\n # Horizontal checks.\\r\\n for y in xrange(0, self.board_height):\\r\\n cp1, rl1 = 0, 0\\r\\n cp2, rl2 = 0, 0\\r\\n cp3, rl3 = 0, 0\\r\\n for x in xrange(0, self.board_width + 1):\\r\\n cp1, rl1 = self.check(cp1, rl1, x, y, 1, 0)\\r\\n\\r\\n # Skip checks that will be made by vertical checks.\\r\\n if y == 0: continue\\r\\n\\r\\n cp2, rl2 = self.check(cp2, rl2, x, y + x, 1, 1)\\r\\n cp3, rl3 = self.check(cp3, rl3, ox - x, y + x, -1, 1)\\r\\n\\r\\n # Vertical checks.\\r\\n for x in xrange(0, self.board_width):\\r\\n cp1, rl1 = 0, 0\\r\\n cp2, rl2 = 0, 0\\r\\n cp3, rl3 = 0, 0\\r\\n for y in xrange(0, self.board_height + 1):\\r\\n cp1, rl1 = self.check(cp1, rl1, x, y, 0, 1)\\r\\n cp2, rl2 = self.check(cp2, rl2, y + x, y, 1, 1)\\r\\n cp3, rl3 = self.check(cp3, rl3, -y + x, y, -1, 1)\\r\\n\\r\\n m = self.moves\\r\\n if len(m) > 0:\\r\\n # Merge moves to the same location.\\r\\n for a in xrange(len(m)):\\r\\n try:\\r\\n # Value, coordinate\\r\\n av, ac = m[a][0], m[a][1]\\r\\n \\r\\n # Looping backwards so that index is not affected by\\r\\n # deleting items in the list.\\r\\n for b in xrange(len(m) - 1, a):\\r\\n bv, bc = m[b][0], m[b][1]\\r\\n \\r\\n # Same coordinates?\\r\\n if ac == bc:\\r\\n mn, mx = (av, bv) if av < bv else (bv, av)\\r\\n av = mx + mn / 2.0\\r\\n del m[b]\\r\\n\\r\\n m[a][0] = av\\r\\n except KeyError:\\r\\n # Reached the end of the list; stop the loop.\\r\\n break\\r\\n\\r\\n # Order moves by score.\\r\\n def o(x, y):\\r\\n s = cmp(int(y[0] * self.cleverness),\\r\\n int(x[0] * self.cleverness))\\r\\n return random.randint(-1, 1) if not s else s\\r\\n\\r\\n m.sort(o)\\r\\n\\r\\n # Forcing is done after merging and sorting so that the best\\r\\n # block can be chosen.\\r\\n if len(self.force) > 0:\\r\\n for move in m:\\r\\n if move[1] in self.force:\\r\\n raise ForcedMove(move[1])\\r\\n\\r\\n # Perform best move.\\r\\n loc = m[0][1]\\r\\n except ForcedMove, (c,):\\r\\n loc = c\\r\\n\\r\\n if not loc:\\r\\n # Crazy, inefficient method of getting all positions, in order of\\r\\n # closeness to center.\\r\\n locs = [(x, y)\\r\\n for y in xrange(self.board_height)\\r\\n for x in xrange(self.board_width)]\\r\\n\\r\\n cx, cy = int(self.board_width / 2), int(self.board_height / 2)\\r\\n def closer(x, y):\\r\\n return cmp((x[0] - cx) ** 2 + (x[1] - cy) ** 2,\\r\\n (y[0] - cx) ** 2 + (y[1] - cy) ** 2)\\r\\n\\r\\n locs.sort(closer)\\r\\n\\r\\n for loc in locs:\\r\\n if not self.board[loc[0]][loc[1]]: break\\r\\n\\r\\n game.move(self.player, loc[0], loc[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cf54f21ea0c3d599f552206526485ee\",\n \"score\": \"0.55860156\",\n \"text\": \"def make_move(self, move):\\n # print(move)\\n if move is None:\\n return 1, -1\\n if len(move) == 0:\\n return 1, -1\\n self.current_position = move[1]\\n for line in move[0]:\\n self.board[line[0], line[1]] = 1\\n done = 1\\n if move[2] == 0:\\n done = 0\\n return done, move[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956a162f850df1e743d2bdbab21edbd8\",\n \"score\": \"0.55787104\",\n \"text\": \"def heuristic(self, move):\\n # Only apply heuristics to AI's move\\n if self.game_state.current_player != self.player_id:\\n return 0, 0, 0, 0\\n\\n # Make a move\\n game_state_copy = deepcopy(self.game_state)\\n game_state_copy.make_move(move[0], move[1])\\n\\n # Check win\\n if game_state_copy.winner is not None and game_state_copy.winner == self.player_id:\\n return 1, math.inf, 1, math.inf\\n\\n # Check immediate loss\\n legal_moves = game_state_copy.get_legal_moves()\\n for move_2 in legal_moves:\\n game_state_copy_2 = deepcopy(game_state_copy)\\n game_state_copy_2.make_move(move_2[0], move_2[1])\\n if game_state_copy_2.winner is not None and game_state_copy_2.winner != self.player_id:\\n return 1, -math.inf, 1, -math.inf\\n\\n # Wins sub-board\\n if game_state_copy.sub_board_values[move[0][0]][move[0][1]] == self.player_id:\\n return 1, 1, 1, 10\\n\\n # # Blocks opponent winner that sub board\\n # game_state_copy_opponent = deepcopy(self.game_state)\\n # game_state_copy_opponent.current_player = int(2 / game_state_copy_opponent.current_player)\\n # game_state_copy_opponent.make_move(move[0], move[1])\\n # if game_state_copy_opponent.sub_board_values[move[0][0]][move[0][1]] == int(2 / self.player_id):\\n # return 1, 1, 1, 8\\n\\n # # Center\\n # if move[1] == (1, 1):\\n # return 1, 1, 1, 2\\n\\n return 0, 0, 0, 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f9f5303179431b5ef903f9d504759b\",\n \"score\": \"0.5575543\",\n \"text\": \"def move(self, move):\\r\\n assert isinstance(move, Move)\\r\\n\\r\\n old_piece_end = self.at(move.end)\\r\\n new_piece_end = piece_start = self.at(move.start)\\r\\n new_score = self.cur_score\\r\\n\\r\\n # pawn promotion\\r\\n if new_piece_end == 'p' and move.end[1] == 1:\\r\\n assert self.turn == 'B'\\r\\n new_piece_end = 'q'\\r\\n new_score -= Board.piece_values['p']\\r\\n new_score += Board.piece_values['q']\\r\\n elif new_piece_end == 'P' and move.end[1] == 6:\\r\\n assert self.turn == 'W'\\r\\n new_piece_end = 'Q'\\r\\n new_score -= Board.piece_values['P']\\r\\n new_score += Board.piece_values['Q']\\r\\n\\r\\n new_score -= Board.piece_values[old_piece_end]\\r\\n self.set(move.end, new_piece_end)\\r\\n self.set(move.start, '.')\\r\\n\\r\\n # remove bonus score of start piece\\r\\n new_score -= self._bonus_score(move.start, piece_start)\\r\\n\\r\\n # remove bonus score of captured piece\\r\\n new_score -= self._bonus_score(move.end, old_piece_end)\\r\\n\\r\\n # add bonus score of end piece\\r\\n new_score += self._bonus_score(move.end, new_piece_end)\\r\\n\\r\\n if self.turn == \\\"W\\\":\\r\\n self.turn = \\\"B\\\"\\r\\n else:\\r\\n self.move_num += 1\\r\\n self.turn = \\\"W\\\"\\r\\n\\r\\n # king capture and result\\r\\n if old_piece_end == 'k':\\r\\n result = 'W'\\r\\n new_score = 100000\\r\\n elif old_piece_end == 'K':\\r\\n result = 'B'\\r\\n new_score = -100000\\r\\n elif self.move_num == 41:\\r\\n result = '='\\r\\n new_score = 0\\r\\n else:\\r\\n result = '?'\\r\\n\\r\\n # save old state to be able to undo it later\\r\\n # ((startpos, startpiece), (endpos, endpiece), score)\\r\\n # if self._calc_score() != new_score:\\r\\n # print(\\\"calc_score:\\\", self._calc_score(), \\\"new_score:\\\")\\r\\n # print(\\\"Wtf\\\")\\r\\n self.history.append(((move.start, piece_start), (move.end, old_piece_end), self.cur_score))\\r\\n\\r\\n self.cur_score = new_score\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1571d93cd409e3ab3031f7c485c34770\",\n \"score\": \"0.5573586\",\n \"text\": \"def validate_move(self, move):\\r\\n\\t\\treturn move in range(1, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fdbd6a501ecc6bacaac9bb802c0991\",\n \"score\": \"0.5572056\",\n \"text\": \"def test_adviser(self):\\n\\n game2 = XiangqiGame()\\n\\n # Valid move down/right\\n move = game2.make_move('d1','e2')\\n self.assertTrue(move) \\n\\n # Valid move up/right\\n move = game2.make_move('d10','e9')\\n self.assertTrue(move) \\n\\n # Valid move up/left\\n move = game2.make_move('e2','d1')\\n self.assertTrue(move) \\n\\n # Valid move down/left\\n move = game2.make_move('e9','d10')\\n self.assertTrue(move) \\n\\n # Invalid move down/left \\n move = game2.make_move('f1','d3')\\n self.assertFalse(move) \\n\\n # Invalid move down\\n move = game2.make_move('f1','f2')\\n self.assertFalse(move)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba29672e4c129f21b601a28bc92b9dc\",\n \"score\": \"0.5571676\",\n \"text\": \"def apply_move(game_state: connectfour.ConnectFourGameState, move: str) -> connectfour.ConnectFourGameState:\\r\\n col = int(move[move.find(\\\" \\\") + 1:])\\r\\n \\r\\n if move.lower().startswith(\\\"drop\\\"):\\r\\n print()\\r\\n return connectfour.drop_piece(game_state, col - 1)\\r\\n elif move.lower().startswith(\\\"pop\\\"):\\r\\n print()\\r\\n return connectfour.pop_piece(game_state, col - 1)\\r\\n else:\\r\\n print(\\\"INVALID MOVE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b1183cdf4384c04217bb01b494e790\",\n \"score\": \"0.55713856\",\n \"text\": \"def is_move_takes(board, move):\\n victim = board.piece_at(move.to_square)\\n aggressor = board.piece_at(move.from_square)\\n if victim is None:\\n return False, 0\\n return True, mvv_lva(aggressor, victim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b1301f35e096c1dd81bea62aeb6934\",\n \"score\": \"0.556823\",\n \"text\": \"def choose_move(self) -> GameState:\\r\\n possible_moves = [child.root for child in self.game_tree.children]\\r\\n\\r\\n return random.choice(possible_moves)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1487,"cells":{"query_id":{"kind":"string","value":"9b4661bdc794e3f86f83595908714d28"},"query":{"kind":"string","value":"Get MySQL config via config file or command line."},"positive_passages":{"kind":"list like","value":[{"docid":"1828b595138a6ad40c43e966d3bdd3d4","score":"0.75491196","text":"def get_config(args):\n config = {\n 'mysql_host': '127.0.0.1',\n 'mysql_port': 3306,\n 'mysql_user': 'root',\n 'mysql_password': '',\n 'tcpproxy_mgmt_port': args.tcpproxy_mgmt_port,\n 'kimo_server_port': args.kimo_server_port,\n }\n if os.path.exists(args.mysql_config_file):\n load_mysql_config(args.mysql_config_file, config)\n\n if args.host:\n config['mysql_host'] = args.host\n if args.user:\n config['mysql_user'] = args.user\n if args.password:\n config['mysql_password'] = args.password\n\n return config","title":""}],"string":"[\n {\n \"docid\": \"1828b595138a6ad40c43e966d3bdd3d4\",\n \"score\": \"0.75491196\",\n \"text\": \"def get_config(args):\\n config = {\\n 'mysql_host': '127.0.0.1',\\n 'mysql_port': 3306,\\n 'mysql_user': 'root',\\n 'mysql_password': '',\\n 'tcpproxy_mgmt_port': args.tcpproxy_mgmt_port,\\n 'kimo_server_port': args.kimo_server_port,\\n }\\n if os.path.exists(args.mysql_config_file):\\n load_mysql_config(args.mysql_config_file, config)\\n\\n if args.host:\\n config['mysql_host'] = args.host\\n if args.user:\\n config['mysql_user'] = args.user\\n if args.password:\\n config['mysql_password'] = args.password\\n\\n return config\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d874b506a34823a8e1687f474869fe88","score":"0.68761575","text":"def load_mysql_config(mysql_config_path, config):\n\n parser = SafeConfigParser()\n try:\n parser.read(mysql_config_path)\n except Exception as e:\n logger.warning(\"cannot read config from %s: %s\", mysql_config_path, e)\n return\n\n update_config(config, 'mysql_host', parser, 'host')\n update_config(config, 'mysql_port', parser, 'port')\n update_config(config, 'mysql_user', parser, 'user')\n update_config(config, 'mysql_password', parser, 'password')","title":""},{"docid":"4e8ae586e660c53c3dd79f1b6f88ee07","score":"0.68471897","text":"def get_config():\n return run('config list')","title":""},{"docid":"8f368e1b98d0937d65cff4723fa1982e","score":"0.6753627","text":"def get_config(args):\n # So, ConfigParser doesn't correctly handle in-line comments in multi-line\n # values. That's why we do this song & dance with the StringIO\n # instance. We strip out the in-line comments, loading the result into a\n # StringIO instance, then pass that to ConfigParser's readfp method, which\n # reads a file-like object like StringIO.\n stripped = StringIO()\n with open(args.config, 'r') as handle:\n for line in handle:\n if ';' in line:\n stripped.write(line[:line.index(';')] + '\\n')\n else:\n stripped.write(line)\n stripped.seek(0)\n config = ConfigParser.SafeConfigParser()\n config.readfp(stripped)\n ### Get the regions. Prefer regions specified on the command line, then\n ### regions specified in the config file. If neither is provided, use a\n ### sensible default.\n if args.region is None:\n ### No regions were specified on the command line, so try loading them\n ### from the config file.\n try:\n config.get('CONFIG', 'regions')\n except (ConfigParser.NoOptionError, ConfigParser.NoSectionError):\n ### No regions on the command line, and none in the config file,\n ### so set a sane default.\n if not config.has_section('CONFIG'):\n config.add_section('CONFIG')\n config.set('CONFIG', 'regions', 'us-east-1')\n else:\n config.set('CONFIG', 'regions', ','.join(args.region))\n return config","title":""},{"docid":"49129d2bcda3b8db51ef796c72813a7b","score":"0.6670259","text":"def getConfig():\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', default='./importMariaDBdmpFile/config.ini', )\n\n args = parser.parse_args()\n\n if os.path.exists(args.config):\n return args\n\n raise Exception('Configuration file ' + args.config + ' does not exist')","title":""},{"docid":"f6140699bc9dcdf4f316929619ac2ee1","score":"0.6637672","text":"def read_db_config(filename='config.ini', section='mysql'):\n # create parser and read ini configuration file\n parser = ConfigParser()\n parser.read(filename)\n\n # get section, default to mysql\n db = {}\n if parser.has_section(section):\n items = parser.items(section)\n for item in items:\n db[item[0]] = item[1]\n else:\n raise Exception('{0} not found in the {1} file'.format(section, filename))\n\n return db","title":""},{"docid":"7a6f233121eb90c551af3e870e33316d","score":"0.6566089","text":"def read_config(self):\n cfg = {}\n cfg['db_host'] = 'localhost'\n cfg['port'] = 28015\n cfg['db_name'] = 'Banzai'\n cfg['auth_key'] = ''\n try:\n with open(os.path.expanduser('~/')+'.BanzaiDB.cfg') as fin:\n # sys.stderr.write(\"Using a BanzaiDB config file\\n\")\n for line in fin:\n if (line.startswith('db_host') or\n line.startswith('port') or\n line.startswith('db_name') or\n line.startswith('auth_key')):\n option, val = line.split('=')\n option, val = option.strip(), val.strip()\n if option == 'port':\n val = int(val)\n cfg[option] = val\n except IOError:\n sys.stderr.write(\"Using RethinkDB defaults\\n\")\n return cfg","title":""},{"docid":"0207682c30721cd3672bbc2e548a7c95","score":"0.6557553","text":"def get_config():\n config = os.path.join(\n '/etc/cfssl',\n os.environ.get('DB_CONFIG'),\n )\n with open(config, 'r') as fh:\n return json.load(fh)","title":""},{"docid":"23d38f222761f2b41f54e28c5a0f4bfc","score":"0.64770365","text":"def get_conf_file():\n return get_option(\"config\") or get_conf_file_path()","title":""},{"docid":"f7b168fa740fec6bf7f09267ec544098","score":"0.6445694","text":"def get_config():\n cfg = configparser.ConfigParser()\n if os.path.exists(_DEFAULT_CONFIG):\n cfg.read(_DEFAULT_CONFIG)\n\n if not cfg.read([os.path.join(os.getenv('HOME'), '.kattisrc'),\n os.path.join(os.path.dirname(sys.argv[0]), '.kattisrc')]):\n raise configparser.Error('''\\\nI failed to read in a config file from your home directory or from the\nsame directory as this script. To download a .kattisrc file please visit\nhttps://<kattis>/download/kattisrc\nThe file should look something like this:\n[user]\nusername: yourusername\ntoken: *********\n[kattis]\nhostname: <kattis>\nloginurl: https://<kattis>/login\nsubmissionurl: https://<kattis>/submit\nsubmissionsurl: https://<kattis>/submissions''')\n return cfg","title":""},{"docid":"4fb498abedba3973def4143de9ead5c5","score":"0.6445182","text":"def get_config():\n with open('config.yml', 'r') as file:\n return yaml.load(file)","title":""},{"docid":"87136c980649e603f537c229505bfe29","score":"0.64423394","text":"def read_config(filename='config.ini', section='bot'):\n # create parser and read ini configuration file\n parser = ConfigParser()\n parser.read(filename)\n\n # get section, default to mysql\n cf = {}\n if parser.has_section(section):\n items = parser.items(section)\n for item in items:\n cf[item[0]] = item[1]\n else:\n raise Exception('{0} not found in the {1} file'.format(section, filename))\n\n return cf","title":""},{"docid":"daa76f69532dd141363f6cc0d3f11375","score":"0.6408929","text":"def get_config():","title":""},{"docid":"e71100b85cc5869ce71435a716d05afc","score":"0.6407183","text":"def get_config():\n with open(CONFIG_FILE) as fin:\n return yaml.load(fin)","title":""},{"docid":"4c3a31c5c9255b9160061fe0debc071c","score":"0.6401122","text":"def get_config():\n config = ConfigParser()\n raw_config = get_data(__package__, 'config.ini').decode('utf-8')\n config.read_file(StringIO(raw_config))\n return config","title":""},{"docid":"39deaad17db23ee08880fc890291acfd","score":"0.6399153","text":"def get_config(args: Namespace = None) -> ConfigParser:\n global CONFIG\n\n if args:\n CONFIG = ConfigParser()\n CONFIG.read(args.config_file)\n return CONFIG","title":""},{"docid":"20e6073a402096da9bd4dbff5bc8a77d","score":"0.63506263","text":"def get_config(self):\r\n cmd = {\r\n \"cmd\": \"get_config\"\r\n }\r\n\r\n reply = self.send(cmd)\r\n if self.is_valid_reply(reply):\r\n return reply\r\n else:\r\n return None","title":""},{"docid":"31dd01064317b5e1676a0c03e323771c","score":"0.63352937","text":"def get_config(filename):\n pathname = locate_config(filename)\n if pathname:\n return Config(pathname)","title":""},{"docid":"13a0a0342737d55c263dc1f9ce4fc09a","score":"0.6302416","text":"def get_conf(path_to_config):\n config = ConfigParser()\n config.read(path_to_config)\n conf = {'host': config.get('database', 'host'),\n 'user': config.get('database', 'user'),\n 'passwd': config.get('database', 'passwd'),\n 'db': config.get('database', 'db'),\n 'path_archive': config.get('workspaces', 'end_storage'),\n 'path_gridftp': config.get('workspaces', 'receiving')}\n conf_named = namedtuple(\"Config\", conf.keys())(*conf.values())\n return conf_named","title":""},{"docid":"311db4f0386e548ea53edac4559f2d6b","score":"0.6298438","text":"def get_db_config(profile):\n config = configparser.ConfigParser()\n configs = config.read(expanduser(\"~/.databrickscfg\"))\n if not configs:\n print_error(\"Cannot read ~/.databrickscfg\")\n bye(1)\n\n profiles = config.sections()\n if not profile in profiles:\n print(\" The profile '%s' is not available in ~/.databrickscfg:\" % profile)\n for p in profiles:\n print(\"- %s\" % p)\n bye(1)\n else:\n host = config[profile][\"host\"]\n token = config[profile][\"token\"]\n return host, token","title":""},{"docid":"e325d185124c7ac42fa47fd129d1880a","score":"0.62774044","text":"def get_config():\n _write_msg(type='get_config')\n return dict_to_object(json.loads(sys.stdin.readline()))","title":""},{"docid":"fc6a488ea491ecb6491355b5224217ec","score":"0.6264164","text":"def get_config():\n if not config_file_present():\n raise SwiggyCliConfigError(\"No config file present\")\n\n Config = configparser.RawConfigParser()\n Config.read(CONFIG_FILEPATH)\n return Config.get('Auth', 'Username')","title":""},{"docid":"90fa1cfd823318f88acea205a8038db7","score":"0.62609017","text":"def getConfig():\n config = configparser.ConfigParser()\n user_config_dir = os.path.expanduser(\"~\") + \"/.config/delugeClient\"\n\n config_dir = os.path.join(user_config_dir, 'config.ini')\n if not os.path.isfile(config_dir):\n defaultConfig = os.path.join(BASE_DIR, 'default_config.ini')\n logger.error('Missing config! Moved default.config.ini to {}.\\nOpen this file and set all varaibles!'.format(config_dir))\n os.makedirs(user_config_dir, exist_ok=True)\n shutil.copyfile(defaultConfig, config_dir)\n\n config.read(config_dir)\n\n requiredParameters = [('deluge host', config['deluge']['host']), ('deluge port', config['deluge']['port']),\n ('deluge user', config['deluge']['user']), ('deluge password', config['deluge']['password']),\n ('ssh password', config['ssh']['user'])]\n for key, value in requiredParameters:\n if value == '':\n logger.error('Missing value for variable: \"{}\" in config: \\\n\"{}.'.format(key, user_config_dir))\n exit(1)\n\n return config","title":""},{"docid":"650215b3428ca983eb563d5e747dfe1f","score":"0.6247317","text":"def get_config_db() -> tuple:\n with open('Application/config.json') as config:\n json_str = config.read()\n json_str = json.loads(json_str)\n host = json_str['server']['host']\n port = json_str['server']['port']\n return (host, port)","title":""},{"docid":"bb2670de9f8523e80a70758a3c3e428a","score":"0.62392586","text":"def get_config():\n\n config_file_name = 'ldap_target_ctl.conf'\n # Look in current working directory for config file.\n if os.path.isfile(os.path.join(os.getcwd(), config_file_name)):\n config_file = os.path.join(os.getcwd(), config_file_name)\n\n # Look in user home dir\n elif os.path.isfile(os.path.join(os.path.expanduser('~'),\n '.{}'.format(config_file_name))):\n config_file = os.path.join(os.path.expanduser('~'),\n '.{}'.format(config_file_name))\n # Look in /etc/ldap_target_ctl\n elif os.path.isfile(os.path.join('/etc/ldap_target_ctl',\n config_file_name)):\n config_file = os.path.join('/etc/ldap_target_ctl', config_file_name)\n\n # Last... look for the default config in the module\n elif os.path.isfile(os.path.join(os.path.dirname(__file__),\n config_file_name)):\n config_file = os.path.join(os.path.dirname(__file__), config_file_name)\n\n # Or fail\n else:\n print 'ERROR: No acceptable {} file found!'.format(config_file_name)\n return 1\n\n config = ConfigParser.ConfigParser()\n config.read(config_file)\n\n # Return config.get(section, name)\n return config","title":""},{"docid":"6897bb2cecbcf5691c6487a45f6085f1","score":"0.6232985","text":"def get_config(config_path):\n\n parser = configparser.ConfigParser()\n parser.read(config_path)\n return parser","title":""},{"docid":"6b6a01b5614360567481e6ec4f1916d2","score":"0.62049556","text":"def readConfigFile():\n # Either way, we need $MOPS_HOME. We do not necessarily need \n # $MOPS_DBINSTANCE, but is that is not defined, it is a problem.\n try:\n mopsHome = os.environ['MOPS_HOME']\n except:\n raise(IOError('Unable to read %s: $MOPS_HOME not defined.' %(CFG_NAME)))\n try:\n instanceName = os.environ['MOPS_DBINSTANCE']\n except:\n raise(IOError('Unable to read %s: $MOPS_DBINSTANCE not defined.' \\\n %(CFG_NAME)))\n \n # The two possible config file locations.\n localCF = os.path.join(mopsHome, 'var', instanceName, 'config', CFG_NAME)\n globalCF = os.path.join(mopsHome, 'config', CFG_NAME)\n \n # If localCF is there, read that one; otherwise go for globalCF.\n if(not os.path.exists(localCF)):\n return(parseConfigFile(globalCF))\n return(parseConfigFile(localCF))","title":""},{"docid":"c444f8b2044c01d69933a264bdb76af1","score":"0.6201997","text":"def parseConfig(self):\r\n\t\twith open(self.sqlConfig,\"r\") as config:\r\n\t\t\tfor line in config:\r\n\t\t\t\tif \"user\" in line:\r\n\t\t\t\t\tself.config['user'] = line[line.find(\"=\") + 1:].strip()\r\n\t\t\t\telif \"pass\" in line:\r\n\t\t\t\t\tself.config['password']= line[line.find(\"=\") + 1:].strip()\r\n\t\t\t\telif \"host\" in line:\r\n\t\t\t\t\tself.config['host'] = line[line.find(\"=\") + 1:].strip()\r\n\t\t\t\telif \"db\" in line:\r\n\t\t\t\t\tself.config['database']= line[line.find(\"=\") + 1:].strip()","title":""},{"docid":"02046a9ebec960182fdef2880cf6cfbf","score":"0.62007076","text":"def get_config():\n try:\n cfg_file = read_yaml_file(CFG_PATH)\n return cfg_file\n except FileNotFoundError:\n return DEFAULT_CONFIG","title":""},{"docid":"e3a314cde0fd4af54aa15e2e737deb5c","score":"0.619561","text":"def get_alembic_cfg(stdout=sys.stdout):\n try:\n db_url = CONF.get_config().get('db')['url']\n except (TypeError, KeyError):\n raise RuntimeError('No database configuration found')\n\n alembic_cfg = Config(ALEMBIC_CFG, stdout=stdout)\n alembic_cfg.set_main_option(\"sqlalchemy.url\", db_url)\n return alembic_cfg","title":""},{"docid":"9de9736d11b5f9f081ccacd09c31246f","score":"0.61945343","text":"def fetch_config(ctx, fail_if_missing=True):\n config_file = ctx.obj and ctx.obj.get('config_file', None)\n\n if config_file:\n return config_file\n elif fail_if_missing:\n ctx_, param = fetch_param(ctx, 'config_file')\n msg = ('Must specify configuration file. Pass it to the program as '\n 'an option to the main program (`cedar -C CONFIG_FILE ...`) '\n 'or using the environment variable '\n f'(`{defaults.ENVVAR_CONFIG_FILE}=CONFIG_FILE cedar ...`)')\n raise click.BadParameter(msg, ctx=ctx_, param=param)\n else:\n return {}","title":""},{"docid":"6e798ca9dbb379d73d08b40eece46358","score":"0.6191493","text":"def readconfig():\n config = configparser.ConfigParser()\n config.read(CONFIG_FILENAME)\n conf_dict = {section: dict(config.items(section)) for section in config.sections()}\n\n conf = conf_dict.get(\"options\", {})\n conf[\"aliases\"] = conf_dict.get(\"aliases\", {})\n\n return conf","title":""},{"docid":"af8441e354f5057a0a6c4b17e875b819","score":"0.61707515","text":"def get_config():\n import bob.extension\n return bob.extension.get_config(__name__, version.externals)","title":""},{"docid":"a52b5024357076e6a5f66b777e4e0a1c","score":"0.6165017","text":"def read_config():\n return doto.simpleconf.Config(os.getenv(\"DOTO_CONFIG\", CONFIG_FILE), CONF_DEF)","title":""},{"docid":"364a18290f920ab303d9c9fcb76355b8","score":"0.6145475","text":"def read_config():\n\n executing_path = os.path.dirname(sys.argv[0])\n config_filename = os.path.join(executing_path, 'config.json')\n with open(config_filename) as conf:\n config = json.load(conf)\n return config","title":""},{"docid":"0b2e36994d0eb54da0e9aa9e76e03a5b","score":"0.614501","text":"def get_config(config_type):\n c = ConfigFile()\n c.load_from_file(DEFAULT_CONFIG_FILE)\n db = c.get_cdb()\n db.update_path(config_type)\n\n names = []\n for item in db.get_all():\n names.append(item['name'])\n\n m = ConfigGet()\n m.set_items(names)\n return m.GetMsg()","title":""},{"docid":"9fdfaf40ee80739abe73d67a9cb5c032","score":"0.61442906","text":"def _handle_getconfig(self,cfg_file,*args,**options):\n if args:\n raise CommandError(\"supervisord getconfig takes no arguments\")\n print cfg_file.getvalue()\n return 0","title":""},{"docid":"586fe4d2741fd7c062de6c52715640d2","score":"0.613794","text":"def _config(filename='database.ini', section='postgresql'):\n\n # create a parser\n parser = ConfigParser()\n\n # read config file\n parser.read(filename)\n \n # get section, default to postgresql\n db = {}\n if parser.has_section(section):\n params = parser.items(section)\n for param in params:\n db[param[0]] = param[1]\n else:\n raise Exception('Section {0} not found in the {1} file'.format(section, filename))\n \n return db","title":""},{"docid":"46dbfcebc6169231e3677d62f3587e6e","score":"0.61315125","text":"def getConfig():\n if not os.path.exists(CONFIG):\n raise IOError('Configuration File \"{}\" does not exist!'.format(CONFIG))\n return munch.munchify(json.load(open(CONFIG, 'r'), 'utf-8'))","title":""},{"docid":"771f4517efe7b3044a617e52f8894ae7","score":"0.6129717","text":"def get_config():\n import bob.extension\n\n return bob.extension.get_config(__name__)","title":""},{"docid":"4af838f425b003c6c08156512b3c7b20","score":"0.6126344","text":"def initDBParams(self):\n try:\n if not os.path.isfile(self.__filename):\n print(\"Error file '%s' dosen't exists\" % self.__filename)\n cnf = configparser.ConfigParser()\n cnf.read(self.__filename)\n sections = cnf.sections()\n options = cnf.options(sections[sections.index(\"database\")])\n for option in options:\n val = cnf.get(sections[sections.index(\"database\")], option)\n self.__connection_mysql[option] = val\n except Exception as exc:\n raise exc\n return self.__connection_mysql","title":""},{"docid":"96c3ddbd4a52d4e53ea912cc4e2aa582","score":"0.6107669","text":"def _get_config_data():\n try:\n config = ConfigLoader(CONFIG_FILE)()\n except IOError:\n config = Config()\n\n host = config.get('opentsdbreporter-host', 'localhost')\n port = config.getint('opentsdbreporter-port', 4242)\n interval = config.getint('opentsdbreporter-interval', 60)\n\n return host, port, interval","title":""},{"docid":"30f86a0412ddd58943d386ead1fbdf73","score":"0.60783696","text":"def get_configs():\n conf_files = [f for f in os.listdir(\".\") if f.endswith(\".conf\")]\n if conf_files == []:\n print(\"\\nError: Can't find configuration file: bank2ynab.conf\")\n config = configparser.ConfigParser()\n if __PY2:\n config.read(conf_files)\n else:\n config.read(conf_files, encoding=\"utf-8\")\n return config","title":""},{"docid":"025caa4f58849aa86bda81d0c3cd39ba","score":"0.6064538","text":"def get_config(path):\n if not os.path.exists(path):\n create_config(path)\n \n config = ConfigParser.ConfigParser()\n config.read(path)\n return config","title":""},{"docid":"3c5c11b4bc9415fff77fc1e135e9bf63","score":"0.6063834","text":"def _read_config(configfile):\n config = configparser.ConfigParser()\n config.read(configfile)\n return config","title":""},{"docid":"c69c082dea788fd4ee216689d8039c7d","score":"0.6061321","text":"def _load_config(self):\n try:\n self.logger.warning(\"starting get the config file in ./conf/db.ini\")\n config = ConfigParser()\n config.read(self._config_path)\n return config\n except ParsingError as err:\n self.logger.error(\"Can not find the config file in ./conf/db.ini\")\n raise FileNotFoundError(\"Can not find config file in ./conf/db.ini\")","title":""},{"docid":"666e663a78f8735a1f555c59a364daf3","score":"0.6047573","text":"def getConfig():\n with open(os.path.join(util.root(), \"etc\", \"config.json\"), \"r\") as configFile:\n return json.loads(configFile.read())","title":""},{"docid":"ce24e9a184de99ca47e4e507892530a1","score":"0.60423696","text":"def get_config(self):\n config = ConfigParser.ConfigParser()\n self.config_file.open()\n config.read(self.config_file.path)\n return config","title":""},{"docid":"3f8a9ffbc87e98e8e93ce17899ea5e4f","score":"0.6035277","text":"def config(filename='database_moml.ini', section='postgresql'):\n # create a parser\n parser = ConfigParser()\n # read config file\n parser.read(filename)\n # get section, default to postgresql\n db = {}\n if parser.has_section(section):\n params = parser.items(section)\n for param in params:\n db[param[0]] = param[1]\n else:\n raise Exception(\n 'Section {0} not found in the {1} file'.format(section, filename))\n return db","title":""},{"docid":"00a12bf7054931a7a0892f075958aaad","score":"0.60299236","text":"def get_config_parser():\n config_parser = ConfigParser()\n\n if getattr(sys, 'frozen', False):\n application_path = os.path.dirname(sys.executable)\n else:\n application_path = os.path.dirname(__file__)\n\n config_file = os.path.join(os.path.abspath(application_path), \"config.cfg\")\n\n try:\n files_read = config_parser.read(config_file)\n except Error as e:\n Logger.error(str(e))\n raise SystemExit(1)\n\n if len(files_read) == 0:\n Logger.error(\"ScreenManager: Cannot read config file for hosts information : \" + config_file)\n raise SystemExit(1)\n\n return config_parser","title":""},{"docid":"ab0ac66765357166554664d8213fb6b2","score":"0.6021975","text":"def get_db_url(config_file):\n\n parser = configparser.ConfigParser()\n try:\n parser.read(config_file)\n db_url = parser.get('database', 'connection', raw=True)\n except:\n print( \"ERROR: Check Cinder configuration file.\")\n sys.exit(2)\n return db_url","title":""},{"docid":"3fc7dfadb9c1953730709d65c1b0fd69","score":"0.60182244","text":"def _get_config(log):\n config_file = os.environ['XDS_COMMIT_EDITOR_BUILD_INI']\n log.info(' _get_config: Read config file: %r' % config_file)\n return patcher_config.Config(config_path=config_file)","title":""},{"docid":"121b9b4adc69319749e2b9067eb37206","score":"0.6016428","text":"def read_config(self):\n try:\n config = ConfigParser.ConfigParser()\n config.read(os.path.dirname(__file__) + \"\\\\pastelogger.config\")\n return config\n except Exception, err:\n servicemanager.LogInfoMsg(\"Error while getting pastelogger.config\")\n self.SvcStop()","title":""},{"docid":"df1d7777fe15cfafed03fe37b6c6155e","score":"0.60150886","text":"def get_config():\n from frites.config import CONFIG\n return CONFIG","title":""},{"docid":"4bbd544e32c2b2b94d94341b85a22b2a","score":"0.6011097","text":"def get_config(key_value=None, path=None):\n config = configparser.ConfigParser()\n if path is None:\n # default assumption is that the config is in the same folder\n directory = os.path.dirname(__file__)\n path = os.path.join(directory, 'global_config.ini')\n config.read(path)\n if key_value is None:\n return config\n elif key_value in config:\n return config[key_value]\n return None","title":""},{"docid":"7da9f635c616677bed297ee1804e7c71","score":"0.5995045","text":"def _config_reader(filename):\n # Get the configuration directory\n # Expand linux ~ notation for home directories if provided.\n _config_directory = log.check_environment()\n config_directory = os.path.expanduser(_config_directory)\n config_file = '{}{}{}'.format(config_directory, os.sep, filename)\n config_dict = files.read_yaml_file(config_file)\n return config_dict","title":""},{"docid":"034b280e2c47cc9ce683ff3a62f7ce84","score":"0.59902686","text":"def read_config() -> configparser.ConfigParser:\n config = configparser.ConfigParser()\n config.read(DEFAULT_CONFIG_FILE)\n return config","title":""},{"docid":"269ec496a9a60f0d424f56b02e8a0b72","score":"0.5986545","text":"def handle_config(self):\n if self._args['--get']:\n print(config.get(self._args['--get']))\n elif self._args['--set']:\n config.set(self._args['--set'], self._args['<value>'])\n elif self._args['--default']:\n print(config.get_default(self._args['--default']))\n elif self._args['--config']:\n config.load(self._args['--config'])","title":""},{"docid":"d8ece8acd37d1217889eaba1e72df734","score":"0.5984816","text":"def get_config(config_file):\n try:\n with open(config_file, 'r') as f:\n config_yml = f.read()\n return yaml.load(config_yml)\n except FileNotFoundError:\n print('Could not find configuration file: {}'.format(config_file))\n print('Sopping')\n sys.exit(1)\n except yaml.parser.ParserError as e:\n print(\"Yaml parse error\")\n print(\"{}\".format(e))\n sys.exit(1)","title":""},{"docid":"da7420bd5ff707397d7187e8319539d7","score":"0.5981786","text":"def get_config(config_file):\n with open(config_file) as f:\n config = yaml.load(f, Loader=yaml.FullLoader)\n return config","title":""},{"docid":"1da4807ee0c7ea8218ac426cef9d535c","score":"0.5961345","text":"def __ReadConfig(filename='app.ini', section='app_config'):\n parser = ConfigParser()\n parser.read(filename)\n\n if parser.has_section(section):\n params = parser.items(section)\n for param in params:\n if param[0] == \"app_secret\":\n return param[1]\n\n else:\n return None","title":""},{"docid":"699a29cfce63da33a567e656a8289948","score":"0.5957388","text":"def get_db_url(self):\n try:\n parser = configparser.ConfigParser()\n parser.read(self.config_file)\n db_url = parser.get('database', 'connection', raw=True)\n except Exception as e:\n print(f'ERROR: Parse {self.config_file}: ' + str(e))\n sys.exit(2)\n return db_url","title":""},{"docid":"cbca48581c9388a224e6692791e80e15","score":"0.59554243","text":"def config_file_reader():\n config = configparser.ConfigParser()\n config.read(\"config.ini\")\n return config","title":""},{"docid":"a861c3835039cd95028aa6844e9de4c7","score":"0.5952229","text":"def database_config():\n cfg = None\n # Configuration errors are handled by django, because of this every not\n # given option will be set to the empty string.\n if config.get('database', 'type', '') == 'sqlite3':\n db_path = config.get('database', 'name', '')\n db_path = os.path.abspath(db_path)\n cfg = {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': db_path,\n }\n elif config.get('database', 'type', '') == 'mysql':\n cfg = {\n 'ENGINE': 'django.db.backends.mysql',\n 'NAME': config.get('database', 'name', ''),\n 'USER': config.get('database', 'user', ''),\n 'PASSWORD': config.get('database', 'password', ''),\n 'HOST': config.get('database', 'host', 'localhost'),\n 'PORT': config.get('database', 'port', '3306'),\n }\n else:\n print('Datenbank konfiguration konnte nicht geladen werden.')\n return cfg","title":""},{"docid":"8c65f195304bca6773da4ae8b7472c09","score":"0.5948511","text":"def getDbConfig(param):\n field = {'admin' : 3, 'host' : 0, 'port' : 1}\n if param not in field.keys():\n return False\n\n inDbConfigSection = False\n if (os.path.exists(basedefs.DB_PASS_FILE)):\n logging.debug(\"found existing pgpass file, fetching DB %s value\" % param)\n with open (basedefs.DB_PASS_FILE) as pgPassFile:\n for line in pgPassFile:\n if \"oVirt-engine DB ADMIN settings section\" in line:\n inDbConfigSection = True\n continue\n\n if inDbConfigSection:\n # Means we're on DB ADMIN user line, as it's for all DBs\n dbcreds = line.split(\":\")\n pgPassFile.close()\n return dbcreds[field[param]]\n\n return False","title":""},{"docid":"5e1d77e8c80112785e4963ab0b96d77f","score":"0.5947661","text":"def read_config(ctx, param, value):\n cfg = ctx.ensure_object(Config)\n if value is None:\n value = os.path.join(os.path.dirname(__file__), 'aliases.ini')\n cfg.read_config(value)\n return value","title":""},{"docid":"2d30683b7fd896be6a23fe7458800047","score":"0.5944982","text":"def getcfg(filename):\n\tif os.path.isfile(filename):\t\t\n\t\tconfig = ConfigParser.RawConfigParser()\n\t\tconfig.read(filename)\n\t\treturn config.get(\"Mail\", \"To\"), config.get(\"Mail\", \"Subject\"), config.get(\"Mail\", \"Body\"), config.get(\"Mail\", \"MailType\"), config.get(\"Mail\", \"Attachment\"), config.get(\"Mail\", \"Cc\"), config.get(\"Mail\", \"Bcc\")","title":""},{"docid":"c04072ab4e378933622a37041a2d6eaa","score":"0.59332836","text":"def get_config() -> configparser.RawConfigParser:\n if not getattr(get_config, \"config\", None):\n # Use RawConfigParser, so we can also use it as the logging config\n config = configparser.RawConfigParser()\n # TODO - use logger and be sure that all variables have a\n # propper default, and the sections are initialized\n if not any(os.path.exists(c) for c in CONFIG_FILES):\n print(f\"Config file not found in {CONFIG_FILES}. Please set \"\n f\"environment variable KEYLIME_CONFIG or see {__file__} \"\n \"for more details.\")\n if _CONFIG_FALLBACK:\n print(\"Falling back on package provided configuration\")\n file = resources.files(__package__).joinpath(\"keylime.conf\")\n config.read_string(file.read_text(\"utf-8\"))\n else:\n # Validate that at least one config file is present\n config_files = config.read(CONFIG_FILES)\n # TODO - use the logger\n print(f\"Reading configuration from {config_files}\")\n get_config.config = config\n return get_config.config","title":""},{"docid":"bf3cd15a1b9112e3d5bd948230f52314","score":"0.5931263","text":"def GetConfig(user_config):\n return configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME)","title":""},{"docid":"2868277c77816c5d071e9cdd2357724c","score":"0.5925271","text":"def GetConfiguration(filename='config/namebench.cfg'):\n (options, args) = DefineAndParseOptions(filename=filename)\n (configured_options, global_ns, regional_ns) = ProcessConfigurationFile(options)\n supplied_ns = addr_util.ExtractIPTuplesFromString(' '.join(args))\n return (configured_options, supplied_ns, global_ns, regional_ns)","title":""},{"docid":"157b5f2cf3eeebb98ae02c43d2267257","score":"0.5922523","text":"def _read_db_config(configfile):\n if exists(configfile):\n with open(configfile, 'r') as cfg:\n db = json.load(cfg)\n\n if not db['password']:\n db['password'] = getpass()\n \n return db\n else:\n raise IOError('Path to config file is wrong or file does not exist!\\n%s' % configfile)","title":""},{"docid":"6070a6a93a6500383f0dff77d51970ef","score":"0.5919658","text":"def load_config():\n parser = argparse.ArgumentParser(prog='OSG_autoconf')\n parser.add_argument('config', nargs=1, help='The configuration file')\n args = parser.parse_args()\n\n config = get_yaml_file_info(args.config[0])\n\n return config","title":""},{"docid":"bc3b6b0bb2bbe118aa84da6d52b48ceb","score":"0.59144765","text":"def _get_config(name):\n # from env\n value = os.environ.get(name, getattr(settings, name, None))\n if value:\n return value\n else:\n raise ImproperlyConfigured('{} should be set in env or settings.py'.format(name))","title":""},{"docid":"4ab71fe6c18b17baa90ca44144736ef4","score":"0.5910909","text":"def _getConfig():\n\t\treturn Config.get()","title":""},{"docid":"e34d1ac3ee9c8a3c1cf541a128adb6bd","score":"0.5903957","text":"def loadconfig():\n homedir = os.environ['HOME']\n\n import configparser\n config = configparser.ConfigParser()\n\n config.read(os.path.join(homedir, \".pleskrc\"))\n\n return config","title":""},{"docid":"f19d255ccda1b5593644577d6f6901fa","score":"0.58958095","text":"def get_config(section=\"Pyhole\"):\n return config.Config(get_conf_file(), section)","title":""},{"docid":"19fe993644409bb91a6c1b36a7c13ded","score":"0.58897936","text":"def read_config(self, config):\n cp = cfg_parser.config_parser()\n return cp.read(config)","title":""},{"docid":"82efaa2720c9a8eb09e2a0bd229b674b","score":"0.5889205","text":"def read_configfile():\n configtp = namedtuple(\n \"Config\",\n [\n \"lb_user\",\n \"lb_pwd\",\n \"lb1\",\n \"lb2\",\n \"lb_dg\",\n \"lb_dg_partition\",\n \"ca\",\n \"ca_proxy\",\n \"cm_account\",\n \"cm_renewal_days\",\n \"cm_delayed_days\",\n \"plugin\",\n ],\n )\n config = configparser.ConfigParser()\n config.read(CONFIG_FILE)\n if config.getboolean(\"Certificate Authority\", \"use proxy\"):\n ca_proxy = config.get(\"Certificate Authority\", \"proxy\")\n else:\n ca_proxy = False\n\n if config.getboolean(\"Load Balancer\", \"cluster\"):\n bigip1 = config.get(\"Load Balancer\", \"host 1\")\n bigip2 = config.get(\"Load Balancer\", \"host 2\")\n else:\n bigip1 = config.get(\"Load Balancer\", \"host 1\")\n bigip2 = None\n\n try:\n plugin_section = config.items(\"Plugin\")\n except configparser.NoSectionError:\n plugin_section = None\n\n the_config = configtp(\n lb1=bigip1,\n lb2=bigip2,\n lb_user=config.get(\"Load Balancer\", \"username\"),\n lb_pwd=config.get(\"Load Balancer\", \"password\"),\n lb_dg=config.get(\"Load Balancer\", \"datagroup\"),\n lb_dg_partition=config.get(\"Load Balancer\", \"datagroup partition\"),\n ca=config.get(\"Certificate Authority\", \"directory url\"),\n ca_proxy=ca_proxy,\n cm_account=config.get(\"Common\", \"account config\"),\n cm_renewal_days=int(config.get(\"Common\", \"renewal days\")),\n cm_delayed_days=int(config.get(\"Common\", \"delayed installation days\")),\n plugin=plugin_section,\n )\n return the_config","title":""},{"docid":"e49aa0308c998532269b74006eed5c5c","score":"0.58858067","text":"def get_config(ini):\n config = configparser.ConfigParser()\n config.read(ini)\n return config","title":""},{"docid":"efb32787ae22c006ea57fdd9ff86b3a2","score":"0.5880701","text":"def _get_config(default=None):\n config = Config(default) if default is not None else Config()\n app = NbConvertApp()\n app.load_config_file()\n _update_config(config, app.config)\n return config","title":""},{"docid":"2c37b4bbeac8c4f435bf31addc0954a7","score":"0.58648986","text":"def parse_config():\n parser = argparse.ArgumentParser(conflict_handler='resolve')\n parser.add_argument('-c', '--config', metavar='<filename>', required=False, help='Path to the config file')\n arg_pattern = re.compile('^-|--')\n\n # Prep parse_args for potential command line arguments\n for arg in sys.argv:\n if arg == '-c' or arg == '--config':\n continue\n if arg_pattern.match(arg):\n parser.add_argument(arg)\n args = parser.parse_args()\n args_dict = vars(args)\n\n # Determine which file to use\n if args.config:\n path = args.config\n if not os.path.exists(path):\n raise Exception(\"File %s does not exist\" % path)\n elif os.path.exists('./local.cfg'):\n path = './local.cfg'\n elif os.path.exists('/etc/otto/local.cfg'):\n path = '/etc/otto/local.cfg'\n else:\n raise Exception(\"No configuration supplied and no default configuration could be found.\")\n\n # Parse config file to dictionary\n config = file_to_dict(path)\n\n # Overwrite/append command line config entries in config dictionary.\n for key in args_dict.keys():\n try:\n res = re.match('(\\w+):(\\w+)', key)\n try:\n config[res.group(1)][res.group(2)] = ast.literal_eval(args_dict[key])\n except:\n config[res.group(1)][res.group(2)] = args_dict[key]\n except AttributeError:\n pass\n\n return Namespace(config)","title":""},{"docid":"d2fbeaa5edab92a12522d4d9d5865bcc","score":"0.5863434","text":"def parse_config(config_path=\"~/.pg.cnf\"):\n _, ext = os.path.splitext(config_path)\n if ext == \".cnf\":\n with open(os.path.expanduser(config_path), \"r\") as f:\n config = f.readlines()\n config_dict = [x.strip().split(\" = \") for x in config]\n config_dict = [x for x in config_dict if len(x) > 1]\n config_dict = {x[0]: x[1] for x in config_dict}\n elif ext == \".yaml\":\n config_dict = yaml_read(os.path.expanduser(config_path))\n else:\n raise ValueError(\"Only .cnf and .yaml database config files supported\")\n\n return config_dict","title":""},{"docid":"6f1fc1b37af0f7bd07df3e27fcfa3066","score":"0.5861524","text":"def init_config():\n global db_host, db_port\n\n config = ConfigParser.SafeConfigParser()\n config.read(\"settings.cfg\")\n\n db_host = config.get(\"mongo\", \"db_host\")\n db_port = config.getint(\"mongo\", \"db_port\")","title":""},{"docid":"4caca2299354eac898f76e68e9185c3d","score":"0.5857844","text":"def parse_config_file(self):\n self.logger.debug('self.args.config_file: %s', self.args.config_file)\n\n # find external configuration if none is specified\n if self.args.config_file is None:\n for config_path in CONFIG_PATHS:\n config_file = os.path.join(config_path, CONFIG_FILE)\n self.logger.debug('Looking for config file: %s', config_file)\n if os.path.isfile(config_file):\n self.logger.info('Found config file: %s', config_file)\n self.args.config_file = config_file\n break\n\n if self.args.config_file is None:\n self.logger.info('No config file found.')\n\n # read external configuration if specified and found\n if self.args.config_file is not None:\n if os.path.isfile(self.args.config_file):\n config = configparser.ConfigParser()\n config.read(self.args.config_file)\n self.defaults.update(dict(config.items(\"dns\")))\n self.defaults.update(dict(config.items(\"local\")))\n self.defaults.update(dict(config.items(\"remote\")))\n self.defaults.update(dict(config.items(\"docker\")))\n self.defaults['prepend_www'] = config['dns'].getboolean('prepend_www')\n\n self.logger.debug('Args from config file: %s', json.dumps(self.defaults, indent=4))\n else:\n self.logger.error('Config file (%s) does not exist.',\n self.args.config_file)","title":""},{"docid":"52ba9acdb995942586a18b5d97a5b78f","score":"0.5854959","text":"def configuration():\n config_yaml = open(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../',\"config.yaml\")), 'r')\n config = yaml.load(config_yaml)\n config_yaml.close()\n #Config is returned as a dictionary, which you can navigate through later to get\n #a specific setting\n return config","title":""},{"docid":"9afb3a86bd79b810d2826245987b30d8","score":"0.5835572","text":"def get_config():\n\n filepath = os.path.dirname(\n os.path.abspath(inspect.getfile(inspect.currentframe()))) + r\"\"\"/config/config.json\"\"\"\n\n try:\n data = json.load(open(filepath))\n return data\n except FileNotFoundError:\n print(\"Config File Not Found\")\n return None","title":""},{"docid":"e78369a4d9cc3eb74c4895f4c85e8520","score":"0.583405","text":"def _read_config():\n config = configparser.ConfigParser()\n if os.path.exists(SETUP_FILE_NAME):\n config.read(SETUP_FILE_NAME)\n return config","title":""},{"docid":"4a98d6f1d44134da54c1e7e87bbc9328","score":"0.5833993","text":"def get_config(config_path):\n if not os.path.exists(config_path):\n logger.error('Configuration file %s does not exist.' % config_path)\n sys.exit(3)\n with open(config_path, 'r') as config_file:\n return yaml.safe_load(config_file)","title":""},{"docid":"f19557b960ab1923dadc9d9e49fdf2d8","score":"0.58336735","text":"def read_dbconf(conf_file, db_adapter, schema_name):\n\n try:\n with open(conf_file, 'r') as yml_conf:\n conf = yaml.load(yml_conf)\n except(TypeError, FileNotFoundError, yaml.YAMLError) as err:\n logger.debug(err)\n raise\n\n return conf.get(db_adapter).get(schema_name)","title":""},{"docid":"6d0124eb4cdf11bd3dffbabd50964cb3","score":"0.58299214","text":"def read_configuration(config_file):\n cfg = configparser.ConfigParser()\n debug(f\"Reading configuration: {config_file}\")\n if not os.path.exists(config_file):\n error(f\"Failed to find configuration file {config_file}\")\n sys.exit(os.EX_CONFIG)\n with open(config_file) as fd:\n cfg.read_file(fd)\n return cfg","title":""},{"docid":"0d215001fadd51024a7a0b202ae97356","score":"0.58267343","text":"def _get_config(self, host, port, unix_socket, auth, config_key):\n\n client = self._client(host, port, unix_socket, auth)\n if client is None:\n return None\n\n config_value = client.config_get(config_key)\n del client\n return config_value","title":""},{"docid":"4acdb7d686a4b8364bf17abb2ca39074","score":"0.5820745","text":"def _load_config(filename='recon_helper.config'):\n\tpath = get_current_path()\n\tconfig_file = path + '\\\\' + filename\n\t# print(config_file)\n\tcfg = configparser.ConfigParser()\n\tcfg.read(config_file)\n\treturn cfg","title":""},{"docid":"581608b8360edfb7bf4f3d036cf24e85","score":"0.58168983","text":"def get_conf(path):\n config = ConfigParser()\n config.read(path)\n\n conf = {'host': config.get('database', 'host'),\n 'user': config.get('database', 'user'),\n 'passwd': config.get('database', 'passwd'),\n 'db': config.get('database', 'db'),\n 'file': config.get('files', 'f_mutations')}\n\n conf_named = namedtuple(\"Config\", conf.keys())(*conf.values())\n\n return conf_named","title":""},{"docid":"438d1052eb5f768e9466ed796d456100","score":"0.58135283","text":"def read_configuration(args):\n config = {'chefRepo': os.path.abspath('./chef-repo'),\n 'keyName': args.application,\n 'application': args.application,\n 'operation': args.operation,\n 'accessKey': os.getenv('AWS_ACCESS_KEY'),\n 'secretKey': os.getenv('AWS_SECRET_KEY'),\n 'ec2Endpoint': os.getenv('EC2_URL'),\n 'iamEndpoint': os.getenv('AWS_IAM_URL')\n }\n # Add in config file\n with open(args.config) as config_file:\n config_file = yaml.load(config_file)\n # Add in profile\n profile = config_file['profiles'][args.profile]\n config.update(profile)\n if not config['accessKey'] or not config['secretKey']:\n print 'Unable to find access or secret key.'\n print 'Please set the AWS_ACCESS_KEY and AWS_SECRET_KEY env variables.'\n exit(1)\n # Add in creds if they are in the profile\n if 'endpoints' in profile:\n config.update(config_file['endpoints'][profile['endpoints']])\n return config","title":""},{"docid":"9fa16ac3c674489f9cb587aea4256dc6","score":"0.5811347","text":"def load_config():\n config = configparser.ConfigParser()\n config.add_section('ssh')\n config.set('ssh', 'user', getpass.getuser())\n config.set('ssh', 'hostname', 'localhost')\n config.set('ssh', 'port', '')\n config.set('ssh', 'passenv', '')\n config.set('ssh', 'folder', 'hash')\n config.set('ssh', 'python', 'python')\n\n dir = os.getcwd()\n while dir:\n f = os.path.join(dir, '.rtox.cfg')\n if os.path.isfile(f):\n break\n dir = os.path.dirname(dir)\n if not dir:\n f = os.path.expanduser('~/.rtox.cfg')\n logging.info(\"Loading config from %s\" % f)\n config.read(f)\n\n if config.get('ssh', 'folder') not in ['hash', 'repo']:\n raise SystemExit(\"Invalid config value found for 'folder', \"\n \"possible values: hash, repo.\")\n return config","title":""},{"docid":"d59dbcc9b94d6ff0471d1d7a0795c7e9","score":"0.5804033","text":"def configure():\n\n try:\n config_file = open('/usr/local/etc/check_disk_stats.yaml', 'r')\n except IOError:\n print 'Unable to open configuration file, check permissions'\n sys.exit(1)\n\n config = yaml.load(config_file.read())\n config_file.close()\n\n return config","title":""},{"docid":"161aacd9e9e9ab23a5b5985b0a73e0a8","score":"0.58026546","text":"def load_config_file():\n\n p = configparser.ConfigParser()\n\n path0 = os.getenv(\"DYSON_CONFIG\", None)\n if path0 is not None:\n path0 = os.path.expanduser(path0)\n if os.path.isdir(path0):\n path0 += \"/dyson.cfg\"\n try:\n path1 = os.path.join(BASE_DIR, \"dyson.cfg\")\n except OSError:\n path1 = None\n path2 = os.path.expanduser(\"~/.dyson.cfg\")\n\n for path in [path0, path1, path2]:\n if path is not None and os.path.exists(path):\n try:\n p.read(path)\n except configparser.Error as e:\n raise DysonError(\"Error reading config file: \\n{0}\".format(e))\n return p, path\n return None, ''","title":""},{"docid":"e6e15227bdc7960f416cea1385145ad3","score":"0.57958543","text":"def Get_db_config(self, section='dbConnection'):\n # create parser and read ini configuration file\n self.cfg.read(self.ConfigFileName)\n\n # get section, default to mysql\n db = {}\n if self.cfg.has_section(section):\n items = self.cfg.items(section)\n for item in items:\n for itm in self.encryptedconfig:\n sec, key = itm.split(':')\n \n if section == sec and item[0] == key:\n db[item[0]] = zx.GetKey()\n else:\n db[item[0]] = item[1]\n else:\n raise Exception('{0} not found in the {1} file'.format(section,self.ConfigFileName))\n \n return db","title":""},{"docid":"2c1f7a7fd6d23dc32e5075c5c482e6d8","score":"0.5795735","text":"def get_config(self):\n return self._client.get('config')","title":""},{"docid":"c308bc74db79afc5b3adb82b7537dde8","score":"0.5794504","text":"def get_config(name):\n config = os.environ.get(name, getattr(settings, name, None))\n if config:\n return config\n else:\n raise ImproperlyConfigured(\"Can't find config for '%s' either in environment\"\n \"variable or in settings.py\" % name)","title":""},{"docid":"35b03c9bea58fd48da6f50a27725b78d","score":"0.57943225","text":"def read_config_file():\n with open(get_config_file()) as stream:\n config = yaml.load(stream.read())\n return config","title":""}],"string":"[\n {\n \"docid\": \"d874b506a34823a8e1687f474869fe88\",\n \"score\": \"0.68761575\",\n \"text\": \"def load_mysql_config(mysql_config_path, config):\\n\\n parser = SafeConfigParser()\\n try:\\n parser.read(mysql_config_path)\\n except Exception as e:\\n logger.warning(\\\"cannot read config from %s: %s\\\", mysql_config_path, e)\\n return\\n\\n update_config(config, 'mysql_host', parser, 'host')\\n update_config(config, 'mysql_port', parser, 'port')\\n update_config(config, 'mysql_user', parser, 'user')\\n update_config(config, 'mysql_password', parser, 'password')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8ae586e660c53c3dd79f1b6f88ee07\",\n \"score\": \"0.68471897\",\n \"text\": \"def get_config():\\n return run('config list')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f368e1b98d0937d65cff4723fa1982e\",\n \"score\": \"0.6753627\",\n \"text\": \"def get_config(args):\\n # So, ConfigParser doesn't correctly handle in-line comments in multi-line\\n # values. That's why we do this song & dance with the StringIO\\n # instance. We strip out the in-line comments, loading the result into a\\n # StringIO instance, then pass that to ConfigParser's readfp method, which\\n # reads a file-like object like StringIO.\\n stripped = StringIO()\\n with open(args.config, 'r') as handle:\\n for line in handle:\\n if ';' in line:\\n stripped.write(line[:line.index(';')] + '\\\\n')\\n else:\\n stripped.write(line)\\n stripped.seek(0)\\n config = ConfigParser.SafeConfigParser()\\n config.readfp(stripped)\\n ### Get the regions. Prefer regions specified on the command line, then\\n ### regions specified in the config file. If neither is provided, use a\\n ### sensible default.\\n if args.region is None:\\n ### No regions were specified on the command line, so try loading them\\n ### from the config file.\\n try:\\n config.get('CONFIG', 'regions')\\n except (ConfigParser.NoOptionError, ConfigParser.NoSectionError):\\n ### No regions on the command line, and none in the config file,\\n ### so set a sane default.\\n if not config.has_section('CONFIG'):\\n config.add_section('CONFIG')\\n config.set('CONFIG', 'regions', 'us-east-1')\\n else:\\n config.set('CONFIG', 'regions', ','.join(args.region))\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49129d2bcda3b8db51ef796c72813a7b\",\n \"score\": \"0.6670259\",\n \"text\": \"def getConfig():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-c', '--config', default='./importMariaDBdmpFile/config.ini', )\\n\\n args = parser.parse_args()\\n\\n if os.path.exists(args.config):\\n return args\\n\\n raise Exception('Configuration file ' + args.config + ' does not exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6140699bc9dcdf4f316929619ac2ee1\",\n \"score\": \"0.6637672\",\n \"text\": \"def read_db_config(filename='config.ini', section='mysql'):\\n # create parser and read ini configuration file\\n parser = ConfigParser()\\n parser.read(filename)\\n\\n # get section, default to mysql\\n db = {}\\n if parser.has_section(section):\\n items = parser.items(section)\\n for item in items:\\n db[item[0]] = item[1]\\n else:\\n raise Exception('{0} not found in the {1} file'.format(section, filename))\\n\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6f233121eb90c551af3e870e33316d\",\n \"score\": \"0.6566089\",\n \"text\": \"def read_config(self):\\n cfg = {}\\n cfg['db_host'] = 'localhost'\\n cfg['port'] = 28015\\n cfg['db_name'] = 'Banzai'\\n cfg['auth_key'] = ''\\n try:\\n with open(os.path.expanduser('~/')+'.BanzaiDB.cfg') as fin:\\n # sys.stderr.write(\\\"Using a BanzaiDB config file\\\\n\\\")\\n for line in fin:\\n if (line.startswith('db_host') or\\n line.startswith('port') or\\n line.startswith('db_name') or\\n line.startswith('auth_key')):\\n option, val = line.split('=')\\n option, val = option.strip(), val.strip()\\n if option == 'port':\\n val = int(val)\\n cfg[option] = val\\n except IOError:\\n sys.stderr.write(\\\"Using RethinkDB defaults\\\\n\\\")\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0207682c30721cd3672bbc2e548a7c95\",\n \"score\": \"0.6557553\",\n \"text\": \"def get_config():\\n config = os.path.join(\\n '/etc/cfssl',\\n os.environ.get('DB_CONFIG'),\\n )\\n with open(config, 'r') as fh:\\n return json.load(fh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d38f222761f2b41f54e28c5a0f4bfc\",\n \"score\": \"0.64770365\",\n \"text\": \"def get_conf_file():\\n return get_option(\\\"config\\\") or get_conf_file_path()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b168fa740fec6bf7f09267ec544098\",\n \"score\": \"0.6445694\",\n \"text\": \"def get_config():\\n cfg = configparser.ConfigParser()\\n if os.path.exists(_DEFAULT_CONFIG):\\n cfg.read(_DEFAULT_CONFIG)\\n\\n if not cfg.read([os.path.join(os.getenv('HOME'), '.kattisrc'),\\n os.path.join(os.path.dirname(sys.argv[0]), '.kattisrc')]):\\n raise configparser.Error('''\\\\\\nI failed to read in a config file from your home directory or from the\\nsame directory as this script. To download a .kattisrc file please visit\\nhttps://<kattis>/download/kattisrc\\nThe file should look something like this:\\n[user]\\nusername: yourusername\\ntoken: *********\\n[kattis]\\nhostname: <kattis>\\nloginurl: https://<kattis>/login\\nsubmissionurl: https://<kattis>/submit\\nsubmissionsurl: https://<kattis>/submissions''')\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb498abedba3973def4143de9ead5c5\",\n \"score\": \"0.6445182\",\n \"text\": \"def get_config():\\n with open('config.yml', 'r') as file:\\n return yaml.load(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87136c980649e603f537c229505bfe29\",\n \"score\": \"0.64423394\",\n \"text\": \"def read_config(filename='config.ini', section='bot'):\\n # create parser and read ini configuration file\\n parser = ConfigParser()\\n parser.read(filename)\\n\\n # get section, default to mysql\\n cf = {}\\n if parser.has_section(section):\\n items = parser.items(section)\\n for item in items:\\n cf[item[0]] = item[1]\\n else:\\n raise Exception('{0} not found in the {1} file'.format(section, filename))\\n\\n return cf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa76f69532dd141363f6cc0d3f11375\",\n \"score\": \"0.6408929\",\n \"text\": \"def get_config():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e71100b85cc5869ce71435a716d05afc\",\n \"score\": \"0.6407183\",\n \"text\": \"def get_config():\\n with open(CONFIG_FILE) as fin:\\n return yaml.load(fin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3a31c5c9255b9160061fe0debc071c\",\n \"score\": \"0.6401122\",\n \"text\": \"def get_config():\\n config = ConfigParser()\\n raw_config = get_data(__package__, 'config.ini').decode('utf-8')\\n config.read_file(StringIO(raw_config))\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39deaad17db23ee08880fc890291acfd\",\n \"score\": \"0.6399153\",\n \"text\": \"def get_config(args: Namespace = None) -> ConfigParser:\\n global CONFIG\\n\\n if args:\\n CONFIG = ConfigParser()\\n CONFIG.read(args.config_file)\\n return CONFIG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e6073a402096da9bd4dbff5bc8a77d\",\n \"score\": \"0.63506263\",\n \"text\": \"def get_config(self):\\r\\n cmd = {\\r\\n \\\"cmd\\\": \\\"get_config\\\"\\r\\n }\\r\\n\\r\\n reply = self.send(cmd)\\r\\n if self.is_valid_reply(reply):\\r\\n return reply\\r\\n else:\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31dd01064317b5e1676a0c03e323771c\",\n \"score\": \"0.63352937\",\n \"text\": \"def get_config(filename):\\n pathname = locate_config(filename)\\n if pathname:\\n return Config(pathname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a0a0342737d55c263dc1f9ce4fc09a\",\n \"score\": \"0.6302416\",\n \"text\": \"def get_conf(path_to_config):\\n config = ConfigParser()\\n config.read(path_to_config)\\n conf = {'host': config.get('database', 'host'),\\n 'user': config.get('database', 'user'),\\n 'passwd': config.get('database', 'passwd'),\\n 'db': config.get('database', 'db'),\\n 'path_archive': config.get('workspaces', 'end_storage'),\\n 'path_gridftp': config.get('workspaces', 'receiving')}\\n conf_named = namedtuple(\\\"Config\\\", conf.keys())(*conf.values())\\n return conf_named\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311db4f0386e548ea53edac4559f2d6b\",\n \"score\": \"0.6298438\",\n \"text\": \"def get_db_config(profile):\\n config = configparser.ConfigParser()\\n configs = config.read(expanduser(\\\"~/.databrickscfg\\\"))\\n if not configs:\\n print_error(\\\"Cannot read ~/.databrickscfg\\\")\\n bye(1)\\n\\n profiles = config.sections()\\n if not profile in profiles:\\n print(\\\" The profile '%s' is not available in ~/.databrickscfg:\\\" % profile)\\n for p in profiles:\\n print(\\\"- %s\\\" % p)\\n bye(1)\\n else:\\n host = config[profile][\\\"host\\\"]\\n token = config[profile][\\\"token\\\"]\\n return host, token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e325d185124c7ac42fa47fd129d1880a\",\n \"score\": \"0.62774044\",\n \"text\": \"def get_config():\\n _write_msg(type='get_config')\\n return dict_to_object(json.loads(sys.stdin.readline()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc6a488ea491ecb6491355b5224217ec\",\n \"score\": \"0.6264164\",\n \"text\": \"def get_config():\\n if not config_file_present():\\n raise SwiggyCliConfigError(\\\"No config file present\\\")\\n\\n Config = configparser.RawConfigParser()\\n Config.read(CONFIG_FILEPATH)\\n return Config.get('Auth', 'Username')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90fa1cfd823318f88acea205a8038db7\",\n \"score\": \"0.62609017\",\n \"text\": \"def getConfig():\\n config = configparser.ConfigParser()\\n user_config_dir = os.path.expanduser(\\\"~\\\") + \\\"/.config/delugeClient\\\"\\n\\n config_dir = os.path.join(user_config_dir, 'config.ini')\\n if not os.path.isfile(config_dir):\\n defaultConfig = os.path.join(BASE_DIR, 'default_config.ini')\\n logger.error('Missing config! Moved default.config.ini to {}.\\\\nOpen this file and set all varaibles!'.format(config_dir))\\n os.makedirs(user_config_dir, exist_ok=True)\\n shutil.copyfile(defaultConfig, config_dir)\\n\\n config.read(config_dir)\\n\\n requiredParameters = [('deluge host', config['deluge']['host']), ('deluge port', config['deluge']['port']),\\n ('deluge user', config['deluge']['user']), ('deluge password', config['deluge']['password']),\\n ('ssh password', config['ssh']['user'])]\\n for key, value in requiredParameters:\\n if value == '':\\n logger.error('Missing value for variable: \\\"{}\\\" in config: \\\\\\n\\\"{}.'.format(key, user_config_dir))\\n exit(1)\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650215b3428ca983eb563d5e747dfe1f\",\n \"score\": \"0.6247317\",\n \"text\": \"def get_config_db() -> tuple:\\n with open('Application/config.json') as config:\\n json_str = config.read()\\n json_str = json.loads(json_str)\\n host = json_str['server']['host']\\n port = json_str['server']['port']\\n return (host, port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2670de9f8523e80a70758a3c3e428a\",\n \"score\": \"0.62392586\",\n \"text\": \"def get_config():\\n\\n config_file_name = 'ldap_target_ctl.conf'\\n # Look in current working directory for config file.\\n if os.path.isfile(os.path.join(os.getcwd(), config_file_name)):\\n config_file = os.path.join(os.getcwd(), config_file_name)\\n\\n # Look in user home dir\\n elif os.path.isfile(os.path.join(os.path.expanduser('~'),\\n '.{}'.format(config_file_name))):\\n config_file = os.path.join(os.path.expanduser('~'),\\n '.{}'.format(config_file_name))\\n # Look in /etc/ldap_target_ctl\\n elif os.path.isfile(os.path.join('/etc/ldap_target_ctl',\\n config_file_name)):\\n config_file = os.path.join('/etc/ldap_target_ctl', config_file_name)\\n\\n # Last... look for the default config in the module\\n elif os.path.isfile(os.path.join(os.path.dirname(__file__),\\n config_file_name)):\\n config_file = os.path.join(os.path.dirname(__file__), config_file_name)\\n\\n # Or fail\\n else:\\n print 'ERROR: No acceptable {} file found!'.format(config_file_name)\\n return 1\\n\\n config = ConfigParser.ConfigParser()\\n config.read(config_file)\\n\\n # Return config.get(section, name)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6897bb2cecbcf5691c6487a45f6085f1\",\n \"score\": \"0.6232985\",\n \"text\": \"def get_config(config_path):\\n\\n parser = configparser.ConfigParser()\\n parser.read(config_path)\\n return parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6a01b5614360567481e6ec4f1916d2\",\n \"score\": \"0.62049556\",\n \"text\": \"def readConfigFile():\\n # Either way, we need $MOPS_HOME. We do not necessarily need \\n # $MOPS_DBINSTANCE, but is that is not defined, it is a problem.\\n try:\\n mopsHome = os.environ['MOPS_HOME']\\n except:\\n raise(IOError('Unable to read %s: $MOPS_HOME not defined.' %(CFG_NAME)))\\n try:\\n instanceName = os.environ['MOPS_DBINSTANCE']\\n except:\\n raise(IOError('Unable to read %s: $MOPS_DBINSTANCE not defined.' \\\\\\n %(CFG_NAME)))\\n \\n # The two possible config file locations.\\n localCF = os.path.join(mopsHome, 'var', instanceName, 'config', CFG_NAME)\\n globalCF = os.path.join(mopsHome, 'config', CFG_NAME)\\n \\n # If localCF is there, read that one; otherwise go for globalCF.\\n if(not os.path.exists(localCF)):\\n return(parseConfigFile(globalCF))\\n return(parseConfigFile(localCF))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c444f8b2044c01d69933a264bdb76af1\",\n \"score\": \"0.6201997\",\n \"text\": \"def parseConfig(self):\\r\\n\\t\\twith open(self.sqlConfig,\\\"r\\\") as config:\\r\\n\\t\\t\\tfor line in config:\\r\\n\\t\\t\\t\\tif \\\"user\\\" in line:\\r\\n\\t\\t\\t\\t\\tself.config['user'] = line[line.find(\\\"=\\\") + 1:].strip()\\r\\n\\t\\t\\t\\telif \\\"pass\\\" in line:\\r\\n\\t\\t\\t\\t\\tself.config['password']= line[line.find(\\\"=\\\") + 1:].strip()\\r\\n\\t\\t\\t\\telif \\\"host\\\" in line:\\r\\n\\t\\t\\t\\t\\tself.config['host'] = line[line.find(\\\"=\\\") + 1:].strip()\\r\\n\\t\\t\\t\\telif \\\"db\\\" in line:\\r\\n\\t\\t\\t\\t\\tself.config['database']= line[line.find(\\\"=\\\") + 1:].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02046a9ebec960182fdef2880cf6cfbf\",\n \"score\": \"0.62007076\",\n \"text\": \"def get_config():\\n try:\\n cfg_file = read_yaml_file(CFG_PATH)\\n return cfg_file\\n except FileNotFoundError:\\n return DEFAULT_CONFIG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a314cde0fd4af54aa15e2e737deb5c\",\n \"score\": \"0.619561\",\n \"text\": \"def get_alembic_cfg(stdout=sys.stdout):\\n try:\\n db_url = CONF.get_config().get('db')['url']\\n except (TypeError, KeyError):\\n raise RuntimeError('No database configuration found')\\n\\n alembic_cfg = Config(ALEMBIC_CFG, stdout=stdout)\\n alembic_cfg.set_main_option(\\\"sqlalchemy.url\\\", db_url)\\n return alembic_cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de9736d11b5f9f081ccacd09c31246f\",\n \"score\": \"0.61945343\",\n \"text\": \"def fetch_config(ctx, fail_if_missing=True):\\n config_file = ctx.obj and ctx.obj.get('config_file', None)\\n\\n if config_file:\\n return config_file\\n elif fail_if_missing:\\n ctx_, param = fetch_param(ctx, 'config_file')\\n msg = ('Must specify configuration file. Pass it to the program as '\\n 'an option to the main program (`cedar -C CONFIG_FILE ...`) '\\n 'or using the environment variable '\\n f'(`{defaults.ENVVAR_CONFIG_FILE}=CONFIG_FILE cedar ...`)')\\n raise click.BadParameter(msg, ctx=ctx_, param=param)\\n else:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e798ca9dbb379d73d08b40eece46358\",\n \"score\": \"0.6191493\",\n \"text\": \"def readconfig():\\n config = configparser.ConfigParser()\\n config.read(CONFIG_FILENAME)\\n conf_dict = {section: dict(config.items(section)) for section in config.sections()}\\n\\n conf = conf_dict.get(\\\"options\\\", {})\\n conf[\\\"aliases\\\"] = conf_dict.get(\\\"aliases\\\", {})\\n\\n return conf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8441e354f5057a0a6c4b17e875b819\",\n \"score\": \"0.61707515\",\n \"text\": \"def get_config():\\n import bob.extension\\n return bob.extension.get_config(__name__, version.externals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a52b5024357076e6a5f66b777e4e0a1c\",\n \"score\": \"0.6165017\",\n \"text\": \"def read_config():\\n return doto.simpleconf.Config(os.getenv(\\\"DOTO_CONFIG\\\", CONFIG_FILE), CONF_DEF)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364a18290f920ab303d9c9fcb76355b8\",\n \"score\": \"0.6145475\",\n \"text\": \"def read_config():\\n\\n executing_path = os.path.dirname(sys.argv[0])\\n config_filename = os.path.join(executing_path, 'config.json')\\n with open(config_filename) as conf:\\n config = json.load(conf)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b2e36994d0eb54da0e9aa9e76e03a5b\",\n \"score\": \"0.614501\",\n \"text\": \"def get_config(config_type):\\n c = ConfigFile()\\n c.load_from_file(DEFAULT_CONFIG_FILE)\\n db = c.get_cdb()\\n db.update_path(config_type)\\n\\n names = []\\n for item in db.get_all():\\n names.append(item['name'])\\n\\n m = ConfigGet()\\n m.set_items(names)\\n return m.GetMsg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fdfaf40ee80739abe73d67a9cb5c032\",\n \"score\": \"0.61442906\",\n \"text\": \"def _handle_getconfig(self,cfg_file,*args,**options):\\n if args:\\n raise CommandError(\\\"supervisord getconfig takes no arguments\\\")\\n print cfg_file.getvalue()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586fe4d2741fd7c062de6c52715640d2\",\n \"score\": \"0.613794\",\n \"text\": \"def _config(filename='database.ini', section='postgresql'):\\n\\n # create a parser\\n parser = ConfigParser()\\n\\n # read config file\\n parser.read(filename)\\n \\n # get section, default to postgresql\\n db = {}\\n if parser.has_section(section):\\n params = parser.items(section)\\n for param in params:\\n db[param[0]] = param[1]\\n else:\\n raise Exception('Section {0} not found in the {1} file'.format(section, filename))\\n \\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46dbfcebc6169231e3677d62f3587e6e\",\n \"score\": \"0.61315125\",\n \"text\": \"def getConfig():\\n if not os.path.exists(CONFIG):\\n raise IOError('Configuration File \\\"{}\\\" does not exist!'.format(CONFIG))\\n return munch.munchify(json.load(open(CONFIG, 'r'), 'utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771f4517efe7b3044a617e52f8894ae7\",\n \"score\": \"0.6129717\",\n \"text\": \"def get_config():\\n import bob.extension\\n\\n return bob.extension.get_config(__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4af838f425b003c6c08156512b3c7b20\",\n \"score\": \"0.6126344\",\n \"text\": \"def initDBParams(self):\\n try:\\n if not os.path.isfile(self.__filename):\\n print(\\\"Error file '%s' dosen't exists\\\" % self.__filename)\\n cnf = configparser.ConfigParser()\\n cnf.read(self.__filename)\\n sections = cnf.sections()\\n options = cnf.options(sections[sections.index(\\\"database\\\")])\\n for option in options:\\n val = cnf.get(sections[sections.index(\\\"database\\\")], option)\\n self.__connection_mysql[option] = val\\n except Exception as exc:\\n raise exc\\n return self.__connection_mysql\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c3ddbd4a52d4e53ea912cc4e2aa582\",\n \"score\": \"0.6107669\",\n \"text\": \"def _get_config_data():\\n try:\\n config = ConfigLoader(CONFIG_FILE)()\\n except IOError:\\n config = Config()\\n\\n host = config.get('opentsdbreporter-host', 'localhost')\\n port = config.getint('opentsdbreporter-port', 4242)\\n interval = config.getint('opentsdbreporter-interval', 60)\\n\\n return host, port, interval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f86a0412ddd58943d386ead1fbdf73\",\n \"score\": \"0.60783696\",\n \"text\": \"def get_configs():\\n conf_files = [f for f in os.listdir(\\\".\\\") if f.endswith(\\\".conf\\\")]\\n if conf_files == []:\\n print(\\\"\\\\nError: Can't find configuration file: bank2ynab.conf\\\")\\n config = configparser.ConfigParser()\\n if __PY2:\\n config.read(conf_files)\\n else:\\n config.read(conf_files, encoding=\\\"utf-8\\\")\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025caa4f58849aa86bda81d0c3cd39ba\",\n \"score\": \"0.6064538\",\n \"text\": \"def get_config(path):\\n if not os.path.exists(path):\\n create_config(path)\\n \\n config = ConfigParser.ConfigParser()\\n config.read(path)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5c11b4bc9415fff77fc1e135e9bf63\",\n \"score\": \"0.6063834\",\n \"text\": \"def _read_config(configfile):\\n config = configparser.ConfigParser()\\n config.read(configfile)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69c082dea788fd4ee216689d8039c7d\",\n \"score\": \"0.6061321\",\n \"text\": \"def _load_config(self):\\n try:\\n self.logger.warning(\\\"starting get the config file in ./conf/db.ini\\\")\\n config = ConfigParser()\\n config.read(self._config_path)\\n return config\\n except ParsingError as err:\\n self.logger.error(\\\"Can not find the config file in ./conf/db.ini\\\")\\n raise FileNotFoundError(\\\"Can not find config file in ./conf/db.ini\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666e663a78f8735a1f555c59a364daf3\",\n \"score\": \"0.6047573\",\n \"text\": \"def getConfig():\\n with open(os.path.join(util.root(), \\\"etc\\\", \\\"config.json\\\"), \\\"r\\\") as configFile:\\n return json.loads(configFile.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce24e9a184de99ca47e4e507892530a1\",\n \"score\": \"0.60423696\",\n \"text\": \"def get_config(self):\\n config = ConfigParser.ConfigParser()\\n self.config_file.open()\\n config.read(self.config_file.path)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8a9ffbc87e98e8e93ce17899ea5e4f\",\n \"score\": \"0.6035277\",\n \"text\": \"def config(filename='database_moml.ini', section='postgresql'):\\n # create a parser\\n parser = ConfigParser()\\n # read config file\\n parser.read(filename)\\n # get section, default to postgresql\\n db = {}\\n if parser.has_section(section):\\n params = parser.items(section)\\n for param in params:\\n db[param[0]] = param[1]\\n else:\\n raise Exception(\\n 'Section {0} not found in the {1} file'.format(section, filename))\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a12bf7054931a7a0892f075958aaad\",\n \"score\": \"0.60299236\",\n \"text\": \"def get_config_parser():\\n config_parser = ConfigParser()\\n\\n if getattr(sys, 'frozen', False):\\n application_path = os.path.dirname(sys.executable)\\n else:\\n application_path = os.path.dirname(__file__)\\n\\n config_file = os.path.join(os.path.abspath(application_path), \\\"config.cfg\\\")\\n\\n try:\\n files_read = config_parser.read(config_file)\\n except Error as e:\\n Logger.error(str(e))\\n raise SystemExit(1)\\n\\n if len(files_read) == 0:\\n Logger.error(\\\"ScreenManager: Cannot read config file for hosts information : \\\" + config_file)\\n raise SystemExit(1)\\n\\n return config_parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0ac66765357166554664d8213fb6b2\",\n \"score\": \"0.6021975\",\n \"text\": \"def get_db_url(config_file):\\n\\n parser = configparser.ConfigParser()\\n try:\\n parser.read(config_file)\\n db_url = parser.get('database', 'connection', raw=True)\\n except:\\n print( \\\"ERROR: Check Cinder configuration file.\\\")\\n sys.exit(2)\\n return db_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc7dfadb9c1953730709d65c1b0fd69\",\n \"score\": \"0.60182244\",\n \"text\": \"def _get_config(log):\\n config_file = os.environ['XDS_COMMIT_EDITOR_BUILD_INI']\\n log.info(' _get_config: Read config file: %r' % config_file)\\n return patcher_config.Config(config_path=config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121b9b4adc69319749e2b9067eb37206\",\n \"score\": \"0.6016428\",\n \"text\": \"def read_config(self):\\n try:\\n config = ConfigParser.ConfigParser()\\n config.read(os.path.dirname(__file__) + \\\"\\\\\\\\pastelogger.config\\\")\\n return config\\n except Exception, err:\\n servicemanager.LogInfoMsg(\\\"Error while getting pastelogger.config\\\")\\n self.SvcStop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1d7777fe15cfafed03fe37b6c6155e\",\n \"score\": \"0.60150886\",\n \"text\": \"def get_config():\\n from frites.config import CONFIG\\n return CONFIG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bbd544e32c2b2b94d94341b85a22b2a\",\n \"score\": \"0.6011097\",\n \"text\": \"def get_config(key_value=None, path=None):\\n config = configparser.ConfigParser()\\n if path is None:\\n # default assumption is that the config is in the same folder\\n directory = os.path.dirname(__file__)\\n path = os.path.join(directory, 'global_config.ini')\\n config.read(path)\\n if key_value is None:\\n return config\\n elif key_value in config:\\n return config[key_value]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da9f635c616677bed297ee1804e7c71\",\n \"score\": \"0.5995045\",\n \"text\": \"def _config_reader(filename):\\n # Get the configuration directory\\n # Expand linux ~ notation for home directories if provided.\\n _config_directory = log.check_environment()\\n config_directory = os.path.expanduser(_config_directory)\\n config_file = '{}{}{}'.format(config_directory, os.sep, filename)\\n config_dict = files.read_yaml_file(config_file)\\n return config_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034b280e2c47cc9ce683ff3a62f7ce84\",\n \"score\": \"0.59902686\",\n \"text\": \"def read_config() -> configparser.ConfigParser:\\n config = configparser.ConfigParser()\\n config.read(DEFAULT_CONFIG_FILE)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269ec496a9a60f0d424f56b02e8a0b72\",\n \"score\": \"0.5986545\",\n \"text\": \"def handle_config(self):\\n if self._args['--get']:\\n print(config.get(self._args['--get']))\\n elif self._args['--set']:\\n config.set(self._args['--set'], self._args['<value>'])\\n elif self._args['--default']:\\n print(config.get_default(self._args['--default']))\\n elif self._args['--config']:\\n config.load(self._args['--config'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ece8acd37d1217889eaba1e72df734\",\n \"score\": \"0.5984816\",\n \"text\": \"def get_config(config_file):\\n try:\\n with open(config_file, 'r') as f:\\n config_yml = f.read()\\n return yaml.load(config_yml)\\n except FileNotFoundError:\\n print('Could not find configuration file: {}'.format(config_file))\\n print('Sopping')\\n sys.exit(1)\\n except yaml.parser.ParserError as e:\\n print(\\\"Yaml parse error\\\")\\n print(\\\"{}\\\".format(e))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7420bd5ff707397d7187e8319539d7\",\n \"score\": \"0.5981786\",\n \"text\": \"def get_config(config_file):\\n with open(config_file) as f:\\n config = yaml.load(f, Loader=yaml.FullLoader)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da4807ee0c7ea8218ac426cef9d535c\",\n \"score\": \"0.5961345\",\n \"text\": \"def __ReadConfig(filename='app.ini', section='app_config'):\\n parser = ConfigParser()\\n parser.read(filename)\\n\\n if parser.has_section(section):\\n params = parser.items(section)\\n for param in params:\\n if param[0] == \\\"app_secret\\\":\\n return param[1]\\n\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699a29cfce63da33a567e656a8289948\",\n \"score\": \"0.5957388\",\n \"text\": \"def get_db_url(self):\\n try:\\n parser = configparser.ConfigParser()\\n parser.read(self.config_file)\\n db_url = parser.get('database', 'connection', raw=True)\\n except Exception as e:\\n print(f'ERROR: Parse {self.config_file}: ' + str(e))\\n sys.exit(2)\\n return db_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbca48581c9388a224e6692791e80e15\",\n \"score\": \"0.59554243\",\n \"text\": \"def config_file_reader():\\n config = configparser.ConfigParser()\\n config.read(\\\"config.ini\\\")\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a861c3835039cd95028aa6844e9de4c7\",\n \"score\": \"0.5952229\",\n \"text\": \"def database_config():\\n cfg = None\\n # Configuration errors are handled by django, because of this every not\\n # given option will be set to the empty string.\\n if config.get('database', 'type', '') == 'sqlite3':\\n db_path = config.get('database', 'name', '')\\n db_path = os.path.abspath(db_path)\\n cfg = {\\n 'ENGINE': 'django.db.backends.sqlite3',\\n 'NAME': db_path,\\n }\\n elif config.get('database', 'type', '') == 'mysql':\\n cfg = {\\n 'ENGINE': 'django.db.backends.mysql',\\n 'NAME': config.get('database', 'name', ''),\\n 'USER': config.get('database', 'user', ''),\\n 'PASSWORD': config.get('database', 'password', ''),\\n 'HOST': config.get('database', 'host', 'localhost'),\\n 'PORT': config.get('database', 'port', '3306'),\\n }\\n else:\\n print('Datenbank konfiguration konnte nicht geladen werden.')\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c65f195304bca6773da4ae8b7472c09\",\n \"score\": \"0.5948511\",\n \"text\": \"def getDbConfig(param):\\n field = {'admin' : 3, 'host' : 0, 'port' : 1}\\n if param not in field.keys():\\n return False\\n\\n inDbConfigSection = False\\n if (os.path.exists(basedefs.DB_PASS_FILE)):\\n logging.debug(\\\"found existing pgpass file, fetching DB %s value\\\" % param)\\n with open (basedefs.DB_PASS_FILE) as pgPassFile:\\n for line in pgPassFile:\\n if \\\"oVirt-engine DB ADMIN settings section\\\" in line:\\n inDbConfigSection = True\\n continue\\n\\n if inDbConfigSection:\\n # Means we're on DB ADMIN user line, as it's for all DBs\\n dbcreds = line.split(\\\":\\\")\\n pgPassFile.close()\\n return dbcreds[field[param]]\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1d77e8c80112785e4963ab0b96d77f\",\n \"score\": \"0.5947661\",\n \"text\": \"def read_config(ctx, param, value):\\n cfg = ctx.ensure_object(Config)\\n if value is None:\\n value = os.path.join(os.path.dirname(__file__), 'aliases.ini')\\n cfg.read_config(value)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d30683b7fd896be6a23fe7458800047\",\n \"score\": \"0.5944982\",\n \"text\": \"def getcfg(filename):\\n\\tif os.path.isfile(filename):\\t\\t\\n\\t\\tconfig = ConfigParser.RawConfigParser()\\n\\t\\tconfig.read(filename)\\n\\t\\treturn config.get(\\\"Mail\\\", \\\"To\\\"), config.get(\\\"Mail\\\", \\\"Subject\\\"), config.get(\\\"Mail\\\", \\\"Body\\\"), config.get(\\\"Mail\\\", \\\"MailType\\\"), config.get(\\\"Mail\\\", \\\"Attachment\\\"), config.get(\\\"Mail\\\", \\\"Cc\\\"), config.get(\\\"Mail\\\", \\\"Bcc\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04072ab4e378933622a37041a2d6eaa\",\n \"score\": \"0.59332836\",\n \"text\": \"def get_config() -> configparser.RawConfigParser:\\n if not getattr(get_config, \\\"config\\\", None):\\n # Use RawConfigParser, so we can also use it as the logging config\\n config = configparser.RawConfigParser()\\n # TODO - use logger and be sure that all variables have a\\n # propper default, and the sections are initialized\\n if not any(os.path.exists(c) for c in CONFIG_FILES):\\n print(f\\\"Config file not found in {CONFIG_FILES}. Please set \\\"\\n f\\\"environment variable KEYLIME_CONFIG or see {__file__} \\\"\\n \\\"for more details.\\\")\\n if _CONFIG_FALLBACK:\\n print(\\\"Falling back on package provided configuration\\\")\\n file = resources.files(__package__).joinpath(\\\"keylime.conf\\\")\\n config.read_string(file.read_text(\\\"utf-8\\\"))\\n else:\\n # Validate that at least one config file is present\\n config_files = config.read(CONFIG_FILES)\\n # TODO - use the logger\\n print(f\\\"Reading configuration from {config_files}\\\")\\n get_config.config = config\\n return get_config.config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3cd15a1b9112e3d5bd948230f52314\",\n \"score\": \"0.5931263\",\n \"text\": \"def GetConfig(user_config):\\n return configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2868277c77816c5d071e9cdd2357724c\",\n \"score\": \"0.5925271\",\n \"text\": \"def GetConfiguration(filename='config/namebench.cfg'):\\n (options, args) = DefineAndParseOptions(filename=filename)\\n (configured_options, global_ns, regional_ns) = ProcessConfigurationFile(options)\\n supplied_ns = addr_util.ExtractIPTuplesFromString(' '.join(args))\\n return (configured_options, supplied_ns, global_ns, regional_ns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157b5f2cf3eeebb98ae02c43d2267257\",\n \"score\": \"0.5922523\",\n \"text\": \"def _read_db_config(configfile):\\n if exists(configfile):\\n with open(configfile, 'r') as cfg:\\n db = json.load(cfg)\\n\\n if not db['password']:\\n db['password'] = getpass()\\n \\n return db\\n else:\\n raise IOError('Path to config file is wrong or file does not exist!\\\\n%s' % configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6070a6a93a6500383f0dff77d51970ef\",\n \"score\": \"0.5919658\",\n \"text\": \"def load_config():\\n parser = argparse.ArgumentParser(prog='OSG_autoconf')\\n parser.add_argument('config', nargs=1, help='The configuration file')\\n args = parser.parse_args()\\n\\n config = get_yaml_file_info(args.config[0])\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3b6b0bb2bbe118aa84da6d52b48ceb\",\n \"score\": \"0.59144765\",\n \"text\": \"def _get_config(name):\\n # from env\\n value = os.environ.get(name, getattr(settings, name, None))\\n if value:\\n return value\\n else:\\n raise ImproperlyConfigured('{} should be set in env or settings.py'.format(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab71fe6c18b17baa90ca44144736ef4\",\n \"score\": \"0.5910909\",\n \"text\": \"def _getConfig():\\n\\t\\treturn Config.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34d1ac3ee9c8a3c1cf541a128adb6bd\",\n \"score\": \"0.5903957\",\n \"text\": \"def loadconfig():\\n homedir = os.environ['HOME']\\n\\n import configparser\\n config = configparser.ConfigParser()\\n\\n config.read(os.path.join(homedir, \\\".pleskrc\\\"))\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19d255ccda1b5593644577d6f6901fa\",\n \"score\": \"0.58958095\",\n \"text\": \"def get_config(section=\\\"Pyhole\\\"):\\n return config.Config(get_conf_file(), section)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fe993644409bb91a6c1b36a7c13ded\",\n \"score\": \"0.58897936\",\n \"text\": \"def read_config(self, config):\\n cp = cfg_parser.config_parser()\\n return cp.read(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82efaa2720c9a8eb09e2a0bd229b674b\",\n \"score\": \"0.5889205\",\n \"text\": \"def read_configfile():\\n configtp = namedtuple(\\n \\\"Config\\\",\\n [\\n \\\"lb_user\\\",\\n \\\"lb_pwd\\\",\\n \\\"lb1\\\",\\n \\\"lb2\\\",\\n \\\"lb_dg\\\",\\n \\\"lb_dg_partition\\\",\\n \\\"ca\\\",\\n \\\"ca_proxy\\\",\\n \\\"cm_account\\\",\\n \\\"cm_renewal_days\\\",\\n \\\"cm_delayed_days\\\",\\n \\\"plugin\\\",\\n ],\\n )\\n config = configparser.ConfigParser()\\n config.read(CONFIG_FILE)\\n if config.getboolean(\\\"Certificate Authority\\\", \\\"use proxy\\\"):\\n ca_proxy = config.get(\\\"Certificate Authority\\\", \\\"proxy\\\")\\n else:\\n ca_proxy = False\\n\\n if config.getboolean(\\\"Load Balancer\\\", \\\"cluster\\\"):\\n bigip1 = config.get(\\\"Load Balancer\\\", \\\"host 1\\\")\\n bigip2 = config.get(\\\"Load Balancer\\\", \\\"host 2\\\")\\n else:\\n bigip1 = config.get(\\\"Load Balancer\\\", \\\"host 1\\\")\\n bigip2 = None\\n\\n try:\\n plugin_section = config.items(\\\"Plugin\\\")\\n except configparser.NoSectionError:\\n plugin_section = None\\n\\n the_config = configtp(\\n lb1=bigip1,\\n lb2=bigip2,\\n lb_user=config.get(\\\"Load Balancer\\\", \\\"username\\\"),\\n lb_pwd=config.get(\\\"Load Balancer\\\", \\\"password\\\"),\\n lb_dg=config.get(\\\"Load Balancer\\\", \\\"datagroup\\\"),\\n lb_dg_partition=config.get(\\\"Load Balancer\\\", \\\"datagroup partition\\\"),\\n ca=config.get(\\\"Certificate Authority\\\", \\\"directory url\\\"),\\n ca_proxy=ca_proxy,\\n cm_account=config.get(\\\"Common\\\", \\\"account config\\\"),\\n cm_renewal_days=int(config.get(\\\"Common\\\", \\\"renewal days\\\")),\\n cm_delayed_days=int(config.get(\\\"Common\\\", \\\"delayed installation days\\\")),\\n plugin=plugin_section,\\n )\\n return the_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e49aa0308c998532269b74006eed5c5c\",\n \"score\": \"0.58858067\",\n \"text\": \"def get_config(ini):\\n config = configparser.ConfigParser()\\n config.read(ini)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb32787ae22c006ea57fdd9ff86b3a2\",\n \"score\": \"0.5880701\",\n \"text\": \"def _get_config(default=None):\\n config = Config(default) if default is not None else Config()\\n app = NbConvertApp()\\n app.load_config_file()\\n _update_config(config, app.config)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c37b4bbeac8c4f435bf31addc0954a7\",\n \"score\": \"0.58648986\",\n \"text\": \"def parse_config():\\n parser = argparse.ArgumentParser(conflict_handler='resolve')\\n parser.add_argument('-c', '--config', metavar='<filename>', required=False, help='Path to the config file')\\n arg_pattern = re.compile('^-|--')\\n\\n # Prep parse_args for potential command line arguments\\n for arg in sys.argv:\\n if arg == '-c' or arg == '--config':\\n continue\\n if arg_pattern.match(arg):\\n parser.add_argument(arg)\\n args = parser.parse_args()\\n args_dict = vars(args)\\n\\n # Determine which file to use\\n if args.config:\\n path = args.config\\n if not os.path.exists(path):\\n raise Exception(\\\"File %s does not exist\\\" % path)\\n elif os.path.exists('./local.cfg'):\\n path = './local.cfg'\\n elif os.path.exists('/etc/otto/local.cfg'):\\n path = '/etc/otto/local.cfg'\\n else:\\n raise Exception(\\\"No configuration supplied and no default configuration could be found.\\\")\\n\\n # Parse config file to dictionary\\n config = file_to_dict(path)\\n\\n # Overwrite/append command line config entries in config dictionary.\\n for key in args_dict.keys():\\n try:\\n res = re.match('(\\\\w+):(\\\\w+)', key)\\n try:\\n config[res.group(1)][res.group(2)] = ast.literal_eval(args_dict[key])\\n except:\\n config[res.group(1)][res.group(2)] = args_dict[key]\\n except AttributeError:\\n pass\\n\\n return Namespace(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2fbeaa5edab92a12522d4d9d5865bcc\",\n \"score\": \"0.5863434\",\n \"text\": \"def parse_config(config_path=\\\"~/.pg.cnf\\\"):\\n _, ext = os.path.splitext(config_path)\\n if ext == \\\".cnf\\\":\\n with open(os.path.expanduser(config_path), \\\"r\\\") as f:\\n config = f.readlines()\\n config_dict = [x.strip().split(\\\" = \\\") for x in config]\\n config_dict = [x for x in config_dict if len(x) > 1]\\n config_dict = {x[0]: x[1] for x in config_dict}\\n elif ext == \\\".yaml\\\":\\n config_dict = yaml_read(os.path.expanduser(config_path))\\n else:\\n raise ValueError(\\\"Only .cnf and .yaml database config files supported\\\")\\n\\n return config_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f1fc1b37af0f7bd07df3e27fcfa3066\",\n \"score\": \"0.5861524\",\n \"text\": \"def init_config():\\n global db_host, db_port\\n\\n config = ConfigParser.SafeConfigParser()\\n config.read(\\\"settings.cfg\\\")\\n\\n db_host = config.get(\\\"mongo\\\", \\\"db_host\\\")\\n db_port = config.getint(\\\"mongo\\\", \\\"db_port\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4caca2299354eac898f76e68e9185c3d\",\n \"score\": \"0.5857844\",\n \"text\": \"def parse_config_file(self):\\n self.logger.debug('self.args.config_file: %s', self.args.config_file)\\n\\n # find external configuration if none is specified\\n if self.args.config_file is None:\\n for config_path in CONFIG_PATHS:\\n config_file = os.path.join(config_path, CONFIG_FILE)\\n self.logger.debug('Looking for config file: %s', config_file)\\n if os.path.isfile(config_file):\\n self.logger.info('Found config file: %s', config_file)\\n self.args.config_file = config_file\\n break\\n\\n if self.args.config_file is None:\\n self.logger.info('No config file found.')\\n\\n # read external configuration if specified and found\\n if self.args.config_file is not None:\\n if os.path.isfile(self.args.config_file):\\n config = configparser.ConfigParser()\\n config.read(self.args.config_file)\\n self.defaults.update(dict(config.items(\\\"dns\\\")))\\n self.defaults.update(dict(config.items(\\\"local\\\")))\\n self.defaults.update(dict(config.items(\\\"remote\\\")))\\n self.defaults.update(dict(config.items(\\\"docker\\\")))\\n self.defaults['prepend_www'] = config['dns'].getboolean('prepend_www')\\n\\n self.logger.debug('Args from config file: %s', json.dumps(self.defaults, indent=4))\\n else:\\n self.logger.error('Config file (%s) does not exist.',\\n self.args.config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ba9acdb995942586a18b5d97a5b78f\",\n \"score\": \"0.5854959\",\n \"text\": \"def configuration():\\n config_yaml = open(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../',\\\"config.yaml\\\")), 'r')\\n config = yaml.load(config_yaml)\\n config_yaml.close()\\n #Config is returned as a dictionary, which you can navigate through later to get\\n #a specific setting\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9afb3a86bd79b810d2826245987b30d8\",\n \"score\": \"0.5835572\",\n \"text\": \"def get_config():\\n\\n filepath = os.path.dirname(\\n os.path.abspath(inspect.getfile(inspect.currentframe()))) + r\\\"\\\"\\\"/config/config.json\\\"\\\"\\\"\\n\\n try:\\n data = json.load(open(filepath))\\n return data\\n except FileNotFoundError:\\n print(\\\"Config File Not Found\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78369a4d9cc3eb74c4895f4c85e8520\",\n \"score\": \"0.583405\",\n \"text\": \"def _read_config():\\n config = configparser.ConfigParser()\\n if os.path.exists(SETUP_FILE_NAME):\\n config.read(SETUP_FILE_NAME)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a98d6f1d44134da54c1e7e87bbc9328\",\n \"score\": \"0.5833993\",\n \"text\": \"def get_config(config_path):\\n if not os.path.exists(config_path):\\n logger.error('Configuration file %s does not exist.' % config_path)\\n sys.exit(3)\\n with open(config_path, 'r') as config_file:\\n return yaml.safe_load(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19557b960ab1923dadc9d9e49fdf2d8\",\n \"score\": \"0.58336735\",\n \"text\": \"def read_dbconf(conf_file, db_adapter, schema_name):\\n\\n try:\\n with open(conf_file, 'r') as yml_conf:\\n conf = yaml.load(yml_conf)\\n except(TypeError, FileNotFoundError, yaml.YAMLError) as err:\\n logger.debug(err)\\n raise\\n\\n return conf.get(db_adapter).get(schema_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0124eb4cdf11bd3dffbabd50964cb3\",\n \"score\": \"0.58299214\",\n \"text\": \"def read_configuration(config_file):\\n cfg = configparser.ConfigParser()\\n debug(f\\\"Reading configuration: {config_file}\\\")\\n if not os.path.exists(config_file):\\n error(f\\\"Failed to find configuration file {config_file}\\\")\\n sys.exit(os.EX_CONFIG)\\n with open(config_file) as fd:\\n cfg.read_file(fd)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d215001fadd51024a7a0b202ae97356\",\n \"score\": \"0.58267343\",\n \"text\": \"def _get_config(self, host, port, unix_socket, auth, config_key):\\n\\n client = self._client(host, port, unix_socket, auth)\\n if client is None:\\n return None\\n\\n config_value = client.config_get(config_key)\\n del client\\n return config_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acdb7d686a4b8364bf17abb2ca39074\",\n \"score\": \"0.5820745\",\n \"text\": \"def _load_config(filename='recon_helper.config'):\\n\\tpath = get_current_path()\\n\\tconfig_file = path + '\\\\\\\\' + filename\\n\\t# print(config_file)\\n\\tcfg = configparser.ConfigParser()\\n\\tcfg.read(config_file)\\n\\treturn cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581608b8360edfb7bf4f3d036cf24e85\",\n \"score\": \"0.58168983\",\n \"text\": \"def get_conf(path):\\n config = ConfigParser()\\n config.read(path)\\n\\n conf = {'host': config.get('database', 'host'),\\n 'user': config.get('database', 'user'),\\n 'passwd': config.get('database', 'passwd'),\\n 'db': config.get('database', 'db'),\\n 'file': config.get('files', 'f_mutations')}\\n\\n conf_named = namedtuple(\\\"Config\\\", conf.keys())(*conf.values())\\n\\n return conf_named\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438d1052eb5f768e9466ed796d456100\",\n \"score\": \"0.58135283\",\n \"text\": \"def read_configuration(args):\\n config = {'chefRepo': os.path.abspath('./chef-repo'),\\n 'keyName': args.application,\\n 'application': args.application,\\n 'operation': args.operation,\\n 'accessKey': os.getenv('AWS_ACCESS_KEY'),\\n 'secretKey': os.getenv('AWS_SECRET_KEY'),\\n 'ec2Endpoint': os.getenv('EC2_URL'),\\n 'iamEndpoint': os.getenv('AWS_IAM_URL')\\n }\\n # Add in config file\\n with open(args.config) as config_file:\\n config_file = yaml.load(config_file)\\n # Add in profile\\n profile = config_file['profiles'][args.profile]\\n config.update(profile)\\n if not config['accessKey'] or not config['secretKey']:\\n print 'Unable to find access or secret key.'\\n print 'Please set the AWS_ACCESS_KEY and AWS_SECRET_KEY env variables.'\\n exit(1)\\n # Add in creds if they are in the profile\\n if 'endpoints' in profile:\\n config.update(config_file['endpoints'][profile['endpoints']])\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa16ac3c674489f9cb587aea4256dc6\",\n \"score\": \"0.5811347\",\n \"text\": \"def load_config():\\n config = configparser.ConfigParser()\\n config.add_section('ssh')\\n config.set('ssh', 'user', getpass.getuser())\\n config.set('ssh', 'hostname', 'localhost')\\n config.set('ssh', 'port', '')\\n config.set('ssh', 'passenv', '')\\n config.set('ssh', 'folder', 'hash')\\n config.set('ssh', 'python', 'python')\\n\\n dir = os.getcwd()\\n while dir:\\n f = os.path.join(dir, '.rtox.cfg')\\n if os.path.isfile(f):\\n break\\n dir = os.path.dirname(dir)\\n if not dir:\\n f = os.path.expanduser('~/.rtox.cfg')\\n logging.info(\\\"Loading config from %s\\\" % f)\\n config.read(f)\\n\\n if config.get('ssh', 'folder') not in ['hash', 'repo']:\\n raise SystemExit(\\\"Invalid config value found for 'folder', \\\"\\n \\\"possible values: hash, repo.\\\")\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59dbcc9b94d6ff0471d1d7a0795c7e9\",\n \"score\": \"0.5804033\",\n \"text\": \"def configure():\\n\\n try:\\n config_file = open('/usr/local/etc/check_disk_stats.yaml', 'r')\\n except IOError:\\n print 'Unable to open configuration file, check permissions'\\n sys.exit(1)\\n\\n config = yaml.load(config_file.read())\\n config_file.close()\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"161aacd9e9e9ab23a5b5985b0a73e0a8\",\n \"score\": \"0.58026546\",\n \"text\": \"def load_config_file():\\n\\n p = configparser.ConfigParser()\\n\\n path0 = os.getenv(\\\"DYSON_CONFIG\\\", None)\\n if path0 is not None:\\n path0 = os.path.expanduser(path0)\\n if os.path.isdir(path0):\\n path0 += \\\"/dyson.cfg\\\"\\n try:\\n path1 = os.path.join(BASE_DIR, \\\"dyson.cfg\\\")\\n except OSError:\\n path1 = None\\n path2 = os.path.expanduser(\\\"~/.dyson.cfg\\\")\\n\\n for path in [path0, path1, path2]:\\n if path is not None and os.path.exists(path):\\n try:\\n p.read(path)\\n except configparser.Error as e:\\n raise DysonError(\\\"Error reading config file: \\\\n{0}\\\".format(e))\\n return p, path\\n return None, ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6e15227bdc7960f416cea1385145ad3\",\n \"score\": \"0.57958543\",\n \"text\": \"def Get_db_config(self, section='dbConnection'):\\n # create parser and read ini configuration file\\n self.cfg.read(self.ConfigFileName)\\n\\n # get section, default to mysql\\n db = {}\\n if self.cfg.has_section(section):\\n items = self.cfg.items(section)\\n for item in items:\\n for itm in self.encryptedconfig:\\n sec, key = itm.split(':')\\n \\n if section == sec and item[0] == key:\\n db[item[0]] = zx.GetKey()\\n else:\\n db[item[0]] = item[1]\\n else:\\n raise Exception('{0} not found in the {1} file'.format(section,self.ConfigFileName))\\n \\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1f7a7fd6d23dc32e5075c5c482e6d8\",\n \"score\": \"0.5795735\",\n \"text\": \"def get_config(self):\\n return self._client.get('config')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c308bc74db79afc5b3adb82b7537dde8\",\n \"score\": \"0.5794504\",\n \"text\": \"def get_config(name):\\n config = os.environ.get(name, getattr(settings, name, None))\\n if config:\\n return config\\n else:\\n raise ImproperlyConfigured(\\\"Can't find config for '%s' either in environment\\\"\\n \\\"variable or in settings.py\\\" % name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b03c9bea58fd48da6f50a27725b78d\",\n \"score\": \"0.57943225\",\n \"text\": \"def read_config_file():\\n with open(get_config_file()) as stream:\\n config = yaml.load(stream.read())\\n return config\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1488,"cells":{"query_id":{"kind":"string","value":"e86cabd8a25cb1c663355bfcf3083d53"},"query":{"kind":"string","value":"Run the optimization algorithm."},"positive_passages":{"kind":"list like","value":[{"docid":"498e1f0984a2bba66c03fc845c00e36d","score":"0.5889775","text":"def optimize(self, fw_spec, manager_id):\n x = list(fw_spec[\"_x\"])\n y = fw_spec[\"_y\"]\n if isinstance(y, (list, tuple)):\n if len(y) == 1:\n y = y[0]\n self.n_objs = len(y)\n if self.acq not in (\"maximin\", None):\n raise ValueError(\n \"{} is not a valid acquisition function for multiobjective \"\n \"optimization\".format(self.acq)\n )\n else:\n if self.acq == \"maximin\":\n raise ValueError(\n \"Maximin is not a valid acquisition function for single \"\n \"objective optimization.\"\n )\n self.n_objs = 1\n\n # If process A suggests a certain guess and runs it, process B may\n # suggest the same guess while process A is running its new workflow.\n # Therefore, process A must reserve the guess. Line below releases\n # reservation on this document in case of workflow failure or end of\n # workflow.\n self.c.delete_one({\"x\": x, \"y\": \"reserved\"})\n\n # fetch additional attributes for constructing ML model\n z = self.get_z(x, *self.get_z_args, **self.get_z_kwargs)\n\n # use all possible training points as default\n n_completed = self.c.count_documents(self._completed)\n if not self.n_train_pts or self.n_train_pts > n_completed:\n self.n_train_pts = n_completed\n\n # check if optimization should be done, if in batch mode\n batch_mode = False if self.batch_size == 1 else True\n batch_ready = (\n n_completed not in (0, 1) and (n_completed + 1) % self.batch_size == 0\n )\n\n x = convert_native(x)\n y = convert_native(y)\n z = convert_native(z)\n\n if batch_mode and not batch_ready:\n # 'None' predictor means this job was not used for\n # an optimization run.\n if self.c.find_one({\"x\": x}):\n if self.c.find_one({\"x\": x, \"y\": \"reserved\"}):\n # For reserved guesses: update everything\n self.c.find_one_and_update(\n {\"x\": x, \"y\": \"reserved\"},\n {\n \"$set\": {\n \"y\": y,\n \"z\": z,\n \"z_new\": [],\n \"x_new\": [],\n \"predictor\": None,\n \"index\": n_completed + 1,\n }\n },\n )\n else:\n # For completed guesses (ie, this workflow\n # is a forced duplicate), do not update\n # index, but update everything else\n self.c.find_one_and_update(\n {\"x\": x},\n {\n \"$set\": {\n \"y\": y,\n \"z\": z,\n \"z_new\": [],\n \"x_new\": [],\n \"predictor\": None,\n }\n },\n )\n else:\n # For new guesses: insert x, y, z, index,\n # predictor, and dummy new guesses\n self.c.insert_one(\n {\n \"x\": x,\n \"y\": y,\n \"z\": z,\n \"x_new\": [],\n \"z_new\": [],\n \"predictor\": None,\n \"index\": n_completed + 1,\n }\n )\n self.pop_lock(manager_id)\n raise BatchNotReadyError\n\n # Mongo aggregation framework may give duplicate documents, so we cannot\n # use $sample to randomize the training points used\n searched_indices = random.sample(range(1, n_completed + 1), self.n_train_pts)\n searched_docs = self.c.find(\n {\"index\": {\"$in\": searched_indices}}, batch_size=10000\n )\n reserved_docs = self.c.find({\"y\": \"reserved\"}, batch_size=10000)\n reserved = []\n for doc in reserved_docs:\n reserved.append(doc[\"x\"])\n all_y = [None] * n_completed\n all_y.append(y)\n all_x_searched = [None] * n_completed\n all_x_searched.append(x)\n z = list(z)\n all_xz_searched = [None] * n_completed\n all_xz_searched.append(x + z)\n for i, doc in enumerate(searched_docs):\n all_x_searched[i] = doc[\"x\"]\n all_xz_searched[i] = doc[\"x\"] + doc[\"z\"]\n all_y[i] = doc[\"y\"]\n\n all_x_space = self._discretize_space(self.x_dims)\n all_x_space = list(all_x_space) if self.z_file else all_x_space\n all_x_unsearched = []\n for xi in all_x_space:\n xj = list(xi)\n if xj not in all_x_searched and xj not in reserved:\n all_x_unsearched.append(xj)\n if len(all_x_unsearched) == self.n_search_pts:\n break\n\n if self.z_file:\n if path.exists(self.z_file):\n with open(self.z_file, \"rb\") as f:\n xz_map = pickle.load(f)\n else:\n xz_map = {\n tuple(xi): self.get_z(xi, *self.get_z_args, **self.get_z_kwargs)\n for xi in all_x_space\n }\n with open(self.z_file, \"wb\") as f:\n pickle.dump(xz_map, f)\n\n all_xz_unsearched = [xi + xz_map[tuple(xi)] for xi in all_x_unsearched]\n else:\n all_xz_unsearched = [\n xi + self.get_z(xi, *self.get_z_args, **self.get_z_kwargs)\n for xi in all_x_unsearched\n ]\n\n # if there are no more unsearched points in the entire\n # space, either they have been searched (ie have x, y,\n # and z) or have been reserved.\n if len(all_xz_unsearched) < 1:\n if self.is_discrete_all:\n raise ExhaustedSpaceError(\n \"The discrete space has been searched exhaustively.\"\n )\n else:\n raise TypeError(\n \"A comprehensive list of points was exhausted \"\n \"but the dimensions are not discrete.\"\n )\n z_dims = self._z_dims(all_xz_unsearched, all_xz_searched)\n xz_dims = self.x_dims + z_dims\n\n # run machine learner on Z or X features\n if self.predictor in self.builtin_predictors:\n model = self.builtin_predictors[self.predictor]\n all_xz_searched = self._encode(all_xz_searched, xz_dims)\n all_xz_unsearched = self._encode(all_xz_unsearched, xz_dims)\n all_xz_new_onehot = []\n\n if self.batch_size > 1:\n iterator_obj = tqdm.tqdm(\n range(self.batch_size), desc=\"Batch predictions\"\n )\n else:\n iterator_obj = range(self.batch_size)\n for _ in iterator_obj:\n xz1h = self._predict(\n all_xz_searched,\n all_y,\n all_xz_unsearched,\n model(*self.predictor_args, **self.predictor_kwargs),\n self.maximize,\n scaling=True,\n )\n ix = all_xz_unsearched.index(xz1h)\n all_xz_unsearched.pop(ix)\n all_xz_new_onehot.append(xz1h)\n all_xz_new = [\n self._decode(xz_onehot, xz_dims) for xz_onehot in all_xz_new_onehot\n ]\n\n elif self.predictor == \"random\":\n all_xz_new = random.sample(all_xz_unsearched, self.batch_size)\n\n else:\n # If using a custom predictor, automatically convert\n # categorical info to one-hot encoded ints.\n # Used when a custom predictor cannot natively use\n # categorical info\n if self.onehot_categorical:\n all_xz_searched = self._encode(all_xz_searched, xz_dims)\n all_xz_unsearched = self._encode(all_xz_unsearched, xz_dims)\n\n try:\n predictor_fun = deserialize(self.predictor)\n except Exception as E:\n raise NameError(\n \"The custom predictor {} didnt import \"\n \"correctly!\\n{}\".format(self.predictor, E)\n )\n\n all_xz_new = predictor_fun(\n all_xz_searched,\n all_y,\n self.x_dims,\n all_xz_unsearched,\n *self.predictor_args,\n **self.predictor_kwargs,\n )\n if self.onehot_categorical:\n all_xz_new = self._decode(all_xz_new, xz_dims)\n\n if not isinstance(all_xz_new[0], (list, tuple, np.ndarray)):\n all_xz_new = [all_xz_new]\n\n # duplicate checking for custom optimizer functions\n if self.duplicate_check:\n\n if not self.enforce_sequential:\n raise ValueError(\n \"Duplicate checking cannot work when \"\n \"optimizations are not enforced sequentially.\"\n )\n if (\n self.predictor not in self.builtin_predictors\n and self.predictor != \"random\"\n ):\n all_x_new = [\n split_xz(xz_new, self.x_dims, x_only=True)\n for xz_new in all_xz_new\n ]\n all_x_searched = [\n split_xz(xz, self.x_dims, x_only=True) for xz in all_xz_searched\n ]\n if self.tolerances:\n for n, x_new in enumerate(all_x_new):\n if is_duplicate_by_tolerance(\n x_new, all_x_searched, tolerances=self.tolerances\n ):\n all_xz_new[n] = random.choice(all_xz_unsearched)\n else:\n if self.is_discrete_all:\n # test only for x, not xz because custom predicted z\n # may not be accounted for\n for n, x_new in enumerate(all_x_new):\n if x_new in all_x_searched or x_new == x:\n all_xz_new[n] = random.choice(all_xz_unsearched)\n else:\n raise ValueError(\n \"Define tolerances parameter to \"\n \"duplicate check floats.\"\n )\n return x, y, z, all_xz_new, n_completed","title":""}],"string":"[\n {\n \"docid\": \"498e1f0984a2bba66c03fc845c00e36d\",\n \"score\": \"0.5889775\",\n \"text\": \"def optimize(self, fw_spec, manager_id):\\n x = list(fw_spec[\\\"_x\\\"])\\n y = fw_spec[\\\"_y\\\"]\\n if isinstance(y, (list, tuple)):\\n if len(y) == 1:\\n y = y[0]\\n self.n_objs = len(y)\\n if self.acq not in (\\\"maximin\\\", None):\\n raise ValueError(\\n \\\"{} is not a valid acquisition function for multiobjective \\\"\\n \\\"optimization\\\".format(self.acq)\\n )\\n else:\\n if self.acq == \\\"maximin\\\":\\n raise ValueError(\\n \\\"Maximin is not a valid acquisition function for single \\\"\\n \\\"objective optimization.\\\"\\n )\\n self.n_objs = 1\\n\\n # If process A suggests a certain guess and runs it, process B may\\n # suggest the same guess while process A is running its new workflow.\\n # Therefore, process A must reserve the guess. Line below releases\\n # reservation on this document in case of workflow failure or end of\\n # workflow.\\n self.c.delete_one({\\\"x\\\": x, \\\"y\\\": \\\"reserved\\\"})\\n\\n # fetch additional attributes for constructing ML model\\n z = self.get_z(x, *self.get_z_args, **self.get_z_kwargs)\\n\\n # use all possible training points as default\\n n_completed = self.c.count_documents(self._completed)\\n if not self.n_train_pts or self.n_train_pts > n_completed:\\n self.n_train_pts = n_completed\\n\\n # check if optimization should be done, if in batch mode\\n batch_mode = False if self.batch_size == 1 else True\\n batch_ready = (\\n n_completed not in (0, 1) and (n_completed + 1) % self.batch_size == 0\\n )\\n\\n x = convert_native(x)\\n y = convert_native(y)\\n z = convert_native(z)\\n\\n if batch_mode and not batch_ready:\\n # 'None' predictor means this job was not used for\\n # an optimization run.\\n if self.c.find_one({\\\"x\\\": x}):\\n if self.c.find_one({\\\"x\\\": x, \\\"y\\\": \\\"reserved\\\"}):\\n # For reserved guesses: update everything\\n self.c.find_one_and_update(\\n {\\\"x\\\": x, \\\"y\\\": \\\"reserved\\\"},\\n {\\n \\\"$set\\\": {\\n \\\"y\\\": y,\\n \\\"z\\\": z,\\n \\\"z_new\\\": [],\\n \\\"x_new\\\": [],\\n \\\"predictor\\\": None,\\n \\\"index\\\": n_completed + 1,\\n }\\n },\\n )\\n else:\\n # For completed guesses (ie, this workflow\\n # is a forced duplicate), do not update\\n # index, but update everything else\\n self.c.find_one_and_update(\\n {\\\"x\\\": x},\\n {\\n \\\"$set\\\": {\\n \\\"y\\\": y,\\n \\\"z\\\": z,\\n \\\"z_new\\\": [],\\n \\\"x_new\\\": [],\\n \\\"predictor\\\": None,\\n }\\n },\\n )\\n else:\\n # For new guesses: insert x, y, z, index,\\n # predictor, and dummy new guesses\\n self.c.insert_one(\\n {\\n \\\"x\\\": x,\\n \\\"y\\\": y,\\n \\\"z\\\": z,\\n \\\"x_new\\\": [],\\n \\\"z_new\\\": [],\\n \\\"predictor\\\": None,\\n \\\"index\\\": n_completed + 1,\\n }\\n )\\n self.pop_lock(manager_id)\\n raise BatchNotReadyError\\n\\n # Mongo aggregation framework may give duplicate documents, so we cannot\\n # use $sample to randomize the training points used\\n searched_indices = random.sample(range(1, n_completed + 1), self.n_train_pts)\\n searched_docs = self.c.find(\\n {\\\"index\\\": {\\\"$in\\\": searched_indices}}, batch_size=10000\\n )\\n reserved_docs = self.c.find({\\\"y\\\": \\\"reserved\\\"}, batch_size=10000)\\n reserved = []\\n for doc in reserved_docs:\\n reserved.append(doc[\\\"x\\\"])\\n all_y = [None] * n_completed\\n all_y.append(y)\\n all_x_searched = [None] * n_completed\\n all_x_searched.append(x)\\n z = list(z)\\n all_xz_searched = [None] * n_completed\\n all_xz_searched.append(x + z)\\n for i, doc in enumerate(searched_docs):\\n all_x_searched[i] = doc[\\\"x\\\"]\\n all_xz_searched[i] = doc[\\\"x\\\"] + doc[\\\"z\\\"]\\n all_y[i] = doc[\\\"y\\\"]\\n\\n all_x_space = self._discretize_space(self.x_dims)\\n all_x_space = list(all_x_space) if self.z_file else all_x_space\\n all_x_unsearched = []\\n for xi in all_x_space:\\n xj = list(xi)\\n if xj not in all_x_searched and xj not in reserved:\\n all_x_unsearched.append(xj)\\n if len(all_x_unsearched) == self.n_search_pts:\\n break\\n\\n if self.z_file:\\n if path.exists(self.z_file):\\n with open(self.z_file, \\\"rb\\\") as f:\\n xz_map = pickle.load(f)\\n else:\\n xz_map = {\\n tuple(xi): self.get_z(xi, *self.get_z_args, **self.get_z_kwargs)\\n for xi in all_x_space\\n }\\n with open(self.z_file, \\\"wb\\\") as f:\\n pickle.dump(xz_map, f)\\n\\n all_xz_unsearched = [xi + xz_map[tuple(xi)] for xi in all_x_unsearched]\\n else:\\n all_xz_unsearched = [\\n xi + self.get_z(xi, *self.get_z_args, **self.get_z_kwargs)\\n for xi in all_x_unsearched\\n ]\\n\\n # if there are no more unsearched points in the entire\\n # space, either they have been searched (ie have x, y,\\n # and z) or have been reserved.\\n if len(all_xz_unsearched) < 1:\\n if self.is_discrete_all:\\n raise ExhaustedSpaceError(\\n \\\"The discrete space has been searched exhaustively.\\\"\\n )\\n else:\\n raise TypeError(\\n \\\"A comprehensive list of points was exhausted \\\"\\n \\\"but the dimensions are not discrete.\\\"\\n )\\n z_dims = self._z_dims(all_xz_unsearched, all_xz_searched)\\n xz_dims = self.x_dims + z_dims\\n\\n # run machine learner on Z or X features\\n if self.predictor in self.builtin_predictors:\\n model = self.builtin_predictors[self.predictor]\\n all_xz_searched = self._encode(all_xz_searched, xz_dims)\\n all_xz_unsearched = self._encode(all_xz_unsearched, xz_dims)\\n all_xz_new_onehot = []\\n\\n if self.batch_size > 1:\\n iterator_obj = tqdm.tqdm(\\n range(self.batch_size), desc=\\\"Batch predictions\\\"\\n )\\n else:\\n iterator_obj = range(self.batch_size)\\n for _ in iterator_obj:\\n xz1h = self._predict(\\n all_xz_searched,\\n all_y,\\n all_xz_unsearched,\\n model(*self.predictor_args, **self.predictor_kwargs),\\n self.maximize,\\n scaling=True,\\n )\\n ix = all_xz_unsearched.index(xz1h)\\n all_xz_unsearched.pop(ix)\\n all_xz_new_onehot.append(xz1h)\\n all_xz_new = [\\n self._decode(xz_onehot, xz_dims) for xz_onehot in all_xz_new_onehot\\n ]\\n\\n elif self.predictor == \\\"random\\\":\\n all_xz_new = random.sample(all_xz_unsearched, self.batch_size)\\n\\n else:\\n # If using a custom predictor, automatically convert\\n # categorical info to one-hot encoded ints.\\n # Used when a custom predictor cannot natively use\\n # categorical info\\n if self.onehot_categorical:\\n all_xz_searched = self._encode(all_xz_searched, xz_dims)\\n all_xz_unsearched = self._encode(all_xz_unsearched, xz_dims)\\n\\n try:\\n predictor_fun = deserialize(self.predictor)\\n except Exception as E:\\n raise NameError(\\n \\\"The custom predictor {} didnt import \\\"\\n \\\"correctly!\\\\n{}\\\".format(self.predictor, E)\\n )\\n\\n all_xz_new = predictor_fun(\\n all_xz_searched,\\n all_y,\\n self.x_dims,\\n all_xz_unsearched,\\n *self.predictor_args,\\n **self.predictor_kwargs,\\n )\\n if self.onehot_categorical:\\n all_xz_new = self._decode(all_xz_new, xz_dims)\\n\\n if not isinstance(all_xz_new[0], (list, tuple, np.ndarray)):\\n all_xz_new = [all_xz_new]\\n\\n # duplicate checking for custom optimizer functions\\n if self.duplicate_check:\\n\\n if not self.enforce_sequential:\\n raise ValueError(\\n \\\"Duplicate checking cannot work when \\\"\\n \\\"optimizations are not enforced sequentially.\\\"\\n )\\n if (\\n self.predictor not in self.builtin_predictors\\n and self.predictor != \\\"random\\\"\\n ):\\n all_x_new = [\\n split_xz(xz_new, self.x_dims, x_only=True)\\n for xz_new in all_xz_new\\n ]\\n all_x_searched = [\\n split_xz(xz, self.x_dims, x_only=True) for xz in all_xz_searched\\n ]\\n if self.tolerances:\\n for n, x_new in enumerate(all_x_new):\\n if is_duplicate_by_tolerance(\\n x_new, all_x_searched, tolerances=self.tolerances\\n ):\\n all_xz_new[n] = random.choice(all_xz_unsearched)\\n else:\\n if self.is_discrete_all:\\n # test only for x, not xz because custom predicted z\\n # may not be accounted for\\n for n, x_new in enumerate(all_x_new):\\n if x_new in all_x_searched or x_new == x:\\n all_xz_new[n] = random.choice(all_xz_unsearched)\\n else:\\n raise ValueError(\\n \\\"Define tolerances parameter to \\\"\\n \\\"duplicate check floats.\\\"\\n )\\n return x, y, z, all_xz_new, n_completed\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2e4784ec13198e30e578db97a463bfa4","score":"0.7996382","text":"def run(self):\n # set up logging, write params\n self.setup_optimization()\n\n # set up pool of workers\n self.setup_pool()\n\n with self.open_data('w'):\n # run optimization\n self.optimize()\n\n if self['postopt', 'run_postopt']:\n with self.open_data('r'):\n # evaluate individuals in hall of fame\n self.post_optimization()\n if self.pool is not None:\n logging.info('Closing pool')\n self.pool.close()","title":""},{"docid":"f5c5980ce4b3fcbf91b458a41a768c42","score":"0.79808754","text":"def run(self,optimizer):\n\t\toptimizer.optimize()","title":""},{"docid":"287ba9734b8ee1e43f062d8c18fc7530","score":"0.7589167","text":"def optimize(self):\n self.opt_input = optimize(self.molsys, self.computer)\n # update molecular system\n # set E, g_x, and hessian to have their last values\n # set step_number\n # set self.history to match history.","title":""},{"docid":"8a038b1e379ae68f7c391f62ff3abbf4","score":"0.747118","text":"def run(self):\n self._reconstruct()\n self._invert()\n if 0:\n self._optimize()","title":""},{"docid":"b05dbbb99230897e8df78e09a125872f","score":"0.7422692","text":"def optimize():","title":""},{"docid":"e1ab5fcd5db587aae2a2e53dfa331d5e","score":"0.7254709","text":"def run(self):\n problem = self._problem()\n model = problem.model\n relevant = model._relevant\n self.pyopt_solution = None\n self._total_jac = None\n self.iter_count = 0\n fwd = problem._mode == 'fwd'\n optimizer = self.options['optimizer']\n self._quantities = []\n\n self._check_for_missing_objective()\n self._check_jac = self.options['singular_jac_behavior'] in ['error', 'warn']\n\n # Only need initial run if we have linear constraints or if we are using an optimizer that\n # doesn't perform one initially.\n con_meta = self._cons\n model_ran = False\n if optimizer in run_required or np.any([con['linear'] for con in self._cons.values()]):\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\n # Initial Run\n model.run_solve_nonlinear()\n rec.abs = 0.0\n rec.rel = 0.0\n model_ran = True\n self.iter_count += 1\n\n # compute dynamic simul deriv coloring or just sparsity if option is set\n if c_mod._use_total_sparsity:\n coloring = None\n if self._coloring_info['coloring'] is None and self._coloring_info['dynamic']:\n coloring = c_mod.dynamic_total_coloring(self, run_model=not model_ran,\n fname=self._get_total_coloring_fname())\n\n if coloring is not None:\n # if the improvement wasn't large enough, don't use coloring\n pct = coloring._solves_info()[-1]\n info = self._coloring_info\n if info['min_improve_pct'] > pct:\n info['coloring'] = info['static'] = None\n msg = f\"Coloring was deactivated. Improvement of {pct:.1f}% was less \" \\\n f\"than min allowed ({info['min_improve_pct']:.1f}%).\"\n issue_warning(msg, prefix=self.msginfo, category=DerivativesWarning)\n\n comm = None if isinstance(problem.comm, FakeComm) else problem.comm\n opt_prob = Optimization(self.options['title'], weak_method_wrapper(self, '_objfunc'),\n comm=comm)\n\n # Add all design variables\n input_meta = self._designvars\n self._indep_list = indep_list = list(input_meta)\n input_vals = self.get_design_var_values()\n\n for name, meta in input_meta.items():\n size = meta['global_size'] if meta['distributed'] else meta['size']\n opt_prob.addVarGroup(name, size, type='c',\n value=input_vals[name],\n lower=meta['lower'], upper=meta['upper'])\n\n if not hasattr(pyoptsparse, '__version__') or \\\n LooseVersion(pyoptsparse.__version__) < LooseVersion('2.5.1'):\n opt_prob.finalizeDesignVariables()\n else:\n opt_prob.finalize()\n\n # Add all objectives\n objs = self.get_objective_values()\n for name in objs:\n opt_prob.addObj(name)\n self._quantities.append(name)\n\n # Calculate and save derivatives for any linear constraints.\n lcons = [key for (key, con) in con_meta.items() if con['linear']]\n if len(lcons) > 0:\n _lin_jacs = self._compute_totals(of=lcons, wrt=indep_list, return_format='dict')\n # convert all of our linear constraint jacs to COO format. Otherwise pyoptsparse will\n # do it for us and we'll end up with a fully dense COO matrix and very slow evaluation\n # of linear constraints!\n to_remove = []\n for jacdct in _lin_jacs.values():\n for n, subjac in jacdct.items():\n if isinstance(subjac, np.ndarray):\n # we can safely use coo_matrix to automatically convert the ndarray\n # since our linear constraint jacs are constant, so zeros won't become\n # nonzero during the optimization.\n mat = coo_matrix(subjac)\n if mat.row.size > 0:\n # convert to 'coo' format here to avoid an emphatic warning\n # by pyoptsparse.\n jacdct[n] = {'coo': [mat.row, mat.col, mat.data], 'shape': mat.shape}\n\n # Add all equality constraints\n for name, meta in con_meta.items():\n if meta['equals'] is None:\n continue\n size = meta['global_size'] if meta['distributed'] else meta['size']\n lower = upper = meta['equals']\n if fwd:\n wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]]\n else:\n rels = relevant[name]\n wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels]\n\n if meta['linear']:\n jac = {w: _lin_jacs[name][w] for w in wrt}\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\n linear=True, wrt=wrt, jac=jac)\n else:\n if name in self._res_jacs:\n resjac = self._res_jacs[name]\n jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt}\n else:\n jac = None\n opt_prob.addConGroup(name, size, lower=lower, upper=upper, wrt=wrt, jac=jac)\n self._quantities.append(name)\n\n # Add all inequality constraints\n for name, meta in con_meta.items():\n if meta['equals'] is not None:\n continue\n size = meta['global_size'] if meta['distributed'] else meta['size']\n\n # Bounds - double sided is supported\n lower = meta['lower']\n upper = meta['upper']\n\n if fwd:\n wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]]\n else:\n rels = relevant[name]\n wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels]\n\n if meta['linear']:\n jac = {w: _lin_jacs[name][w] for w in wrt}\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\n linear=True, wrt=wrt, jac=jac)\n else:\n if name in self._res_jacs:\n resjac = self._res_jacs[name]\n jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt}\n else:\n jac = None\n opt_prob.addConGroup(name, size, upper=upper, lower=lower, wrt=wrt, jac=jac)\n self._quantities.append(name)\n\n # Instantiate the requested optimizer\n try:\n _tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)\n opt = getattr(_tmp, optimizer)()\n\n except Exception as err:\n # Change whatever pyopt gives us to an ImportError, give it a readable message,\n # but raise with the original traceback.\n msg = \"Optimizer %s is not available in this installation.\" % optimizer\n raise ImportError(msg)\n\n # Process any default optimizer-specific settings.\n if optimizer in DEFAULT_OPT_SETTINGS:\n for name, value in DEFAULT_OPT_SETTINGS[optimizer].items():\n if name not in self.opt_settings:\n self.opt_settings[name] = value\n\n # Set optimization options\n for option, value in self.opt_settings.items():\n opt.setOption(option, value)\n\n self._exc_info = None\n try:\n\n # Execute the optimization problem\n if self.options['gradient method'] == 'pyopt_fd':\n\n # Use pyOpt's internal finite difference\n # TODO: Need to get this from OpenMDAO\n # fd_step = problem.model.deriv_options['step_size']\n fd_step = 1e-6\n sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file,\n hotStart=self.hotstart_file)\n\n elif self.options['gradient method'] == 'snopt_fd':\n if self.options['optimizer'] == 'SNOPT':\n\n # Use SNOPT's internal finite difference\n # TODO: Need to get this from OpenMDAO\n # fd_step = problem.model.deriv_options['step_size']\n fd_step = 1e-6\n sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file,\n hotStart=self.hotstart_file)\n\n else:\n msg = \"SNOPT's internal finite difference can only be used with SNOPT\"\n self._exc_info = Exception(msg)\n else:\n\n # Use OpenMDAO's differentiator for the gradient\n sol = opt(opt_prob, sens=weak_method_wrapper(self, '_gradfunc'),\n storeHistory=self.hist_file, hotStart=self.hotstart_file)\n\n except Exception as _:\n if not self._exc_info:\n raise()\n\n if self._exc_info:\n raise self._exc_info\n\n # Print results\n if self.options['print_results']:\n print(sol)\n\n # Pull optimal parameters back into framework and re-run, so that\n # framework is left in the right final state\n dv_dict = sol.getDVs()\n for name in indep_list:\n self.set_design_var(name, dv_dict[name])\n\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\n try:\n model.run_solve_nonlinear()\n except AnalysisError:\n model._clear_iprint()\n\n rec.abs = 0.0\n rec.rel = 0.0\n self.iter_count += 1\n\n # Save the most recent solution.\n self.pyopt_solution = sol\n\n try:\n exit_status = sol.optInform['value']\n self.fail = False\n\n # These are various failed statuses.\n if optimizer == 'IPOPT':\n if exit_status not in {0, 1}:\n self.fail = True\n elif exit_status > 2:\n self.fail = True\n\n except KeyError:\n # optimizers other than pySNOPT may not populate this dict\n pass\n\n # revert signal handler to cached version\n sigusr = self.options['user_terminate_signal']\n if sigusr is not None:\n signal.signal(sigusr, self._signal_cache)\n self._signal_cache = None # to prevent memory leak test from failing\n\n return self.fail","title":""},{"docid":"0828d7991c90098a8d8f6b506e5497cb","score":"0.7117354","text":"def Optimizer():\n\n\n pass","title":""},{"docid":"35c6373b926da083d00dfce1675a5223","score":"0.7092209","text":"def solve(self):\r\n self.start_learning_time = time.time()\r\n self.learn_models()\r\n self.end_learning_time = time.time()\r\n\r\n self.start_optimization_time = time.time()\r\n self.optimize()\r\n self.end_optimization_time = time.time()\r\n\r\n self.end_time = time.time()","title":""},{"docid":"f772018b8208cedbc2b3a76bd623640e","score":"0.7049566","text":"def optimize(self):\n raise NotImplementedError()","title":""},{"docid":"fe363e6ac9de347a8a3ae161a17fffbe","score":"0.703108","text":"def optimize(self) -> None:\n self.base_gp.optimize()","title":""},{"docid":"21c20787781a9824f5f4131895bced38","score":"0.69480073","text":"def optimize():\n sleep(DELAY * 10)\n print('Optimization finished.')","title":""},{"docid":"21c20787781a9824f5f4131895bced38","score":"0.69480073","text":"def optimize():\n sleep(DELAY * 10)\n print('Optimization finished.')","title":""},{"docid":"8453bdf13da0fdef966beef95ad0b060","score":"0.69443613","text":"def optimizeWorkload(self):\n pass","title":""},{"docid":"bd780923047a365b13926313378d9a42","score":"0.6830286","text":"def run(self):\n\n # Uncomment this to use finite differences over the entire model\n # self.prob.root.deriv_options['type'] = 'cs'\n\n # Record optimization history to a database\n # Data saved here can be examined using `plot_all.py`\n recorder = SqliteRecorder(self.prob_dict['prob_name']+\".db\")\n recorder.options['record_params'] = True\n recorder.options['record_derivs'] = True\n self.prob.driver.add_recorder(recorder)\n\n # Profile (time) the problem\n # profile.setup(self.prob)\n # profile.start()\n\n # Set up the problem\n self.prob.setup()\n\n # Uncomment this line to have more verbose output about convergence\n # self.prob.print_all_convergence()\n\n # Save an N2 diagram for the problem\n view_model(self.prob, outfile=self.prob_dict['prob_name']+\".html\", show_browser=False)\n\n # Run a single analysis loop to populate uninitialized values\n self.prob.run_once()\n\n # If `optimize` == True in prob_dict, perform optimization. Otherwise,\n # simply pass the problem since analysis has already been run.\n if not self.prob_dict['optimize']: # run analysis once\n pass\n else: # perform optimization\n self.prob.run()\n\n # Uncomment this to check the partial derivatives of each component\n # self.prob.check_partial_derivatives(compact_print=True)","title":""},{"docid":"9c6e0b395ed76f8e38e1847306baa609","score":"0.6767848","text":"def optimize(self):\n if not self.ball_tree:\n print('Optimizing search...')\n self.ball_tree = BallTree(self.vectors, metric=cosine)\n print('Finished.')\n else:\n print('Already optimized.')","title":""},{"docid":"46b273b0c3c17de5657c2ed6e7716d08","score":"0.6716359","text":"def optimize(self):\n if self.termination_strategy():\n if self.models is None:\n self._log(f'{\"Res\" if self.g > 0 else \"S\"}tarting {self.run_name}')\n self.models = self._init_models()\n self._log(f'Models init')\n\n ret = self._evolve_iter()\n self.g += 1\n self._save_checkpoint(ret)\n self._log(f\"Gen {self.g}: elite_average: {ret['elite_avg']}\")\n\n return ret\n else:\n self._log('end')\n return False","title":""},{"docid":"648a77b7f36de6e09bd54de17c423963","score":"0.6685609","text":"def compute(self, inputs, outputs):\n global counter\n counter += 1\n cpacs_path = mif.get_tooloutput_file_path(Rt.modules[-1])\n\n # Save the CPACS file for this iteration\n if counter%Rt.save_iter == 0:\n loc = optim_dir_path+'/Geometry/'+'iter_{}.xml'.format(counter)\n shutil.copy(cpacs_path, loc)\n\n # Add new variables to dictionnary\n tixi = cpsf.open_tixi(cpacs_path)\n dct.update_dict(tixi, optim_var_dict)\n\n # Change local wkdir for the next iteration\n tixi.updateTextElement(opf.WKDIR_XPATH,ceaf.create_new_wkdir(Rt.date,Rt.type))\n\n for obj in Rt.objective:\n var_list = splt('[+*/-]', obj)\n for v in var_list:\n if not v.isdigit() and v != '':\n exec('{} = inputs[\"{}\"]'.format(v, v))\n\n result = eval(obj)\n if Rt.minmax == 'min':\n outputs['Objective function '+obj] = -result\n else:\n outputs['Objective function '+obj] = result\n\n cpsf.close_tixi(tixi, cpacs_path)","title":""},{"docid":"e4a659566f91666d16ce3ca7d1bb20e2","score":"0.6643353","text":"def optimize( self ):\n nVars = self.nVars\n #find radius\n k = math.floor(self.D * self.Nf) # Neighborhood size\n L =np.zeros([self.D,nVars])\n g =np.zeros([self.D,nVars])\n y =np.zeros([self.D,nVars])\n self.u = np.zeros([self.D, nVars])\n AdaptiveNeighborhoodSize = 3 # range [i − k, i + k]\n\n #find radius\n k = round(self.D * self.Nf) # Neighborhood size\n \n # start the iteration\n doStop = False\n \n while not doStop:\n self.Iteration += 1\n #Calculate weight w through eq. (6) => w = w min + (w max − w min ) *(t − 1) / (I max − 1)\n \n w = self.w_min+(self.w_max-self.w_min)*(self.Iteration-1)/(self.MaxIterations-1) \n \n #-------------------start of loop through ensemble------------------------\n #for i = 1 to D\n for i in range(0, self.D): \n \n # find neighbors\n neighbors = np.array([w for w in range((i-k),(i+k+1))])\n neighbors[neighbors < 0] += neighbors[neighbors < 0] + self.D; \n neighbors[neighbors > self.D-1] =neighbors[neighbors > self.D-1] - self.D\n \n #print(neighbors)\n neighbors_idx = np.random.choice( neighbors, size=AdaptiveNeighborhoodSize, replace=False)\n neighbors_idx[neighbors_idx == i] = neighbors_idx[2]; # do not select itself, i.e., index i\n p=neighbors_idx[0]\n q=neighbors_idx[1]\n \n\n bInd = self.PreviousBestFitness[neighbors].argmin()\n bestNeighbor = neighbors[bInd]\n z_best = self.z[bestNeighbor]\n \n # Mutate using eq. (3)–(5) → new vector yi\n # Li = zi + α · (zbesti − zi) + β · (zp − zq), (3)\n L[i,] = self.z[i,] + self.a * (z_best - self.z[i,]) + self.b * (self.z[p,] - self.z[q,])\n \n # mutated solution by the whole population\n idx = np.random.choice(self.D, AdaptiveNeighborhoodSize, replace = False)\n idx[idx==i] = idx[2] # do not select itself, i.e., index i\n \n # r1 and r2 random indices in space [1, D] so that r1 ̸ = r2 ̸ = i and zbest the best\n # chromosome of the whole population of the previous generation\n r1 = idx[0]\n r2 = idx[1]\n \n bInd = self.PreviousBestFitness[idx].argmin()\n z_best = self.z[bInd]\n \n # gi = zi + α · (zbest − zi) + β · (zr1 − zr2), (4)\n g[i,] = self.z[i,] + self.a * (z_best - self.z[i,]) + self.b * (self.z[r1,] - self.z[r2,])\n \n # yi = w · gi + (1 − w) · Li, (5)\n y[i,] = w * g[i,] + (1 - w) * L[i,]\n \n # Crossover using eq. (7) → new vector ui\n Cr = 0.8 \n j_rand = randint(0,self.D)\n for c in range(0,self.nVars):\n self.u[i,c] = y[i,c] if (random.randrange(0,1) < Cr or k==j_rand) else self.z[i,c] \n \n #Ensure (saturate) => u(i,j) ∈ [z(min,j) , z(max,j)]\n # check bounds violation\n posInvalid = self.u[i,:] < self.LowerBounds\n self.u[i,posInvalid] = self.LowerBounds[posInvalid]\n \n posInvalid = self.u[i,:] > self.UpperBounds\n self.u[i,posInvalid] = self.UpperBounds[posInvalid]\n \n \n #calculate fitness\n self.__evaluateDEu()\n # find chromosomes tha has been improved and replace the old values with the new\n genProgressed = self.CurrentGenFitness < self.PreviousBestFitness\n self.PreviousBestPosition[genProgressed, :] = self.u[genProgressed, :]\n self.z[genProgressed, :] = self.u[genProgressed, :]\n self.PreviousBestFitness[genProgressed] = self.CurrentGenFitness[genProgressed]\n \n # update global best, adaptive neighborhood size and stall counter\n newBestInd = self.CurrentGenFitness.argmin()\n newBestFit = self.CurrentGenFitness[newBestInd]\n \n if newBestFit < self.GlobalBestFitness:\n self.GlobalBestFitness = newBestFit\n self.GlobalBestPosition = self.z[newBestInd, :].copy()\n \n self.StallCounter = max(0, self.StallCounter-1)\n # calculate remaining only once when fitness is improved to save some time\n # useful for the plots created\n [self.newOrder,self.remaining] = get_Data(self)\n else:\n self.StallCounter += 1\n \n # first element of self.GlobalBestSoFarFitnesses is for self.Iteration == -1\n self.GlobalBestSoFarFitnesses[self.Iteration+1] = self.GlobalBestFitness\n \n # run output function and stop if necessary\n if self.OutputFcn and self.OutputFcn(self):\n self.StopReason = 'OutputFcn requested to stop.'\n doStop = True\n continue\n \n # stop if max iterations\n if self.Iteration >= self.MaxIterations-1:\n self.StopReason = 'MaxIterations reached.'\n doStop = True\n continue\n \n # stop if insignificant improvement\n if self.Iteration > self.MaxStallIterations:\n # The minimum global best fitness is the one stored in self.GlobalBestSoFarFitnesses[self.Iteration+1]\n # (only updated if newBestFit is less than the previously stored). The maximum (may be equal to the \n # current) is the one in self.GlobalBestSoFarFitnesses MaxStallIterations before.\n minBestFitness = self.GlobalBestSoFarFitnesses[self.Iteration+1]\n maxPastBestFit = self.GlobalBestSoFarFitnesses[self.Iteration+1-self.MaxStallIterations]\n if (maxPastBestFit == 0.0) and (minBestFitness < maxPastBestFit):\n windowProgress = np.inf # don't stop\n elif (maxPastBestFit == 0.0) and (minBestFitness == 0.0):\n windowProgress = 0.0 # not progressed\n else:\n windowProgress = abs(minBestFitness - maxPastBestFit) / abs(maxPastBestFit)\n if windowProgress <= self.FunctionTolerance:\n self.StopReason = 'Population did not improve significantly the last MaxStallIterations.'\n doStop = True\n \n \n # print stop message\n print('Algorithm stopped after {} iterations. Best fitness attained: {}'.format(\n self.Iteration+1,self.GlobalBestFitness))\n print(f'Stop reason: {self.StopReason}')","title":""},{"docid":"82d24fa5cc5c4d29ab2cfc82350500e5","score":"0.65597224","text":"def test_optimization(self):\n pass","title":""},{"docid":"ffea8f98408e9b2654e78da561037373","score":"0.64937925","text":"def step(self):\n params = self.params\n if np.isnan(self.G).any():\n raise RuntimeError(\"Gradient contains nan - check output and temp-files for possible errors\")\n if np.isnan(self.H).any():\n raise RuntimeError(\"Hessian contains nan - check output and temp-files for possible errors\")\n self.Iteration += 1\n if (self.Iteration%5) == 0:\n self.engine.clearCalcs()\n self.IC.clearCache()\n\n # At the start of the loop, the optimization variables, function value, gradient and Hessian are known.\n # (i.e. self.Y, self.E, self.G, self.H)\n if params.verbose: self.IC.printRotations(self.X)\n Eig = self.SortedEigenvalues(self.H)\n Emin = Eig[0].real\n if params.transition:\n v0 = 1.0\n elif Emin < params.epsilon:\n v0 = params.epsilon-Emin\n else:\n v0 = 0.0\n # Are we far from constraint satisfaction?\n self.farConstraints = self.IC.haveConstraints() and self.IC.maxConstraintViolation(self.X) > 1e-1\n ### OBTAIN AN OPTIMIZATION STEP ###\n # The trust radius is to be computed in Cartesian coordinates.\n # First take a full-size optimization step\n if params.verbose: logger.info(\" Optimizer.step : Attempting full-size optimization step\\n\")\n dy, _, __ = self.get_delta_prime(v0, verbose=self.params.verbose)\n # Internal coordinate step size\n inorm = np.linalg.norm(dy)\n # Cartesian coordinate step size\n self.cnorm = self.get_cartesian_norm(dy)\n # If the full-size step is within the trust radius, then call get_delta_prime again with diagnostic messages if needed\n if (self.params.verbose >= 2 and self.params.verbose < 4 and self.cnorm <= 1.1*self.trust):\n self.get_delta_prime(v0, verbose=self.params.verbose+2)\n if params.verbose: logger.info(\" Optimizer.step : Internal-step: %.4f Cartesian-step: %.4f Trust-radius: %.4f\\n\" % (inorm, self.cnorm, self.trust))\n # If the step is above the trust radius in Cartesian coordinates, then\n # do the following to reduce the step length:\n if self.cnorm > 1.1 * self.trust:\n # This is the function f(inorm) = cnorm-target that we find a root\n # for obtaining a step with the desired Cartesian step size.\n froot = self.createFroot(v0)\n froot.stores[inorm] = self.cnorm\n ### Find the internal coordinate norm that matches the desired Cartesian coordinate norm\n if params.verbose: logger.info(\" Optimizer.step : Using Brent algorithm to target Cartesian trust radius\\n\")\n iopt = brent_wiki(froot.evaluate, 0.0, inorm, self.trust, cvg=0.1, obj=froot, verbose=params.verbose)\n if froot.brentFailed and froot.stored_arg is not None:\n # If Brent fails but we obtained an IC step that is smaller than the Cartesian trust radius, use it\n if params.verbose: logger.info(\" Optimizer.step : \\x1b[93mUsing stored solution at %.3e\\x1b[0m\\n\" % froot.stored_val)\n iopt = froot.stored_arg\n elif self.IC.bork:\n # Decrease the target Cartesian step size and try again\n for i in range(3):\n froot.target /= 2\n if params.verbose: logger.info(\" Optimizer.step : \\x1b[93mReducing target to %.3e\\x1b[0m\\n\" % froot.target)\n froot.above_flag = True # Stop at any valid step between current target step size and trust radius\n iopt = brent_wiki(froot.evaluate, 0.0, iopt, froot.target, cvg=0.1, verbose=params.verbose)\n if not self.IC.bork: break\n LastForce = self.ForceRebuild\n self.ForceRebuild = False\n if self.IC.bork:\n logger.info(\"\\x1b[91mInverse iteration for Cartesians failed\\x1b[0m\\n\")\n # This variable is added because IC.bork is unset later.\n self.ForceRebuild = True\n else:\n if params.verbose: logger.info(\" Optimizer.step : \\x1b[93mBrent algorithm requires %i evaluations\\x1b[0m\\n\" % froot.counter)\n ##### If IC failed to produce valid Cartesian step, it is \"borked\" and we need to rebuild it.\n if self.ForceRebuild:\n # Force a rebuild of the coordinate system and skip the energy / gradient and evaluation steps.\n if LastForce:\n logger.warning(\"\\x1b[1;91mFailed twice in a row to rebuild the coordinate system; continuing in Cartesian coordinates\\x1b[0m\\n\")\n self.checkCoordinateSystem(recover=True, cartesian=LastForce)\n logger.info(\"\\x1b[1;93mSkipping optimization step\\x1b[0m\\n\")\n self.Iteration -= 1\n self.state = OPT_STATE.SKIP_EVALUATION\n return\n ##### End Rebuild\n # Finally, take an internal coordinate step of the desired length.\n dy, _ = self.trust_step(iopt, v0, verbose=(self.params.verbose+1 if self.params.verbose >= 2 else 0))\n self.cnorm = self.get_cartesian_norm(dy)\n ### DONE OBTAINING THE STEP ###\n if isinstance(self.IC, PrimitiveInternalCoordinates):\n idx = np.argmax(np.abs(dy))\n iunit = np.zeros_like(dy)\n iunit[idx] = 1.0\n self.prim_msg = \"Along %s %.3f\" % (self.IC.Internals[idx], np.dot(dy/np.linalg.norm(dy), iunit))\n ### These quantities, computed previously, are no longer used.\n # Dot product of the gradient with the step direction\n # Dot = -np.dot(dy/np.linalg.norm(dy), self.G/np.linalg.norm(self.G))\n # Whether the Cartesian norm comes close to the trust radius\n # bump = cnorm > 0.8 * self.trust\n ### Before updating any of our variables, copy current variables to \"previous\"\n self.Yprev = self.Y.copy()\n self.Xprev = self.X.copy()\n self.Gxprev = self.gradx.copy()\n self.Gprev = self.G.copy()\n self.Eprev = self.E\n ### Update the Internal Coordinates ###\n X0 = self.X.copy()\n self.newCartesian(dy)\n ## The \"actual\" dy may be different from the one passed to newCartesian(),\n ## for example if we enforce constraints or don't get the step we expect.\n dy = self.IC.calcDiff(self.X, X0)\n # dyp = self.IC.Prims.calcDiff(self.X, X0)\n # print(\"Actual dy:\", dy)\n self.Y += dy\n self.expect = flat(0.5*multi_dot([row(dy),self.H,col(dy)]))[0] + np.dot(dy,self.G)\n # self.expectdG = np.dot(self.H, col(dy).flatten())\n self.state = OPT_STATE.NEEDS_EVALUATION","title":""},{"docid":"a4bfffb68092daef07bbfcb33e404aca","score":"0.6486699","text":"def run_optimization(self, optimization_setting, max_iteration=100000000):\n # Get optimization setting and target\n settings = optimization_setting.generate_setting_ga()\n self.target_name = optimization_setting.target_name\n\n total_size = len(settings)\n\n # Run ga optimization\n logging.info(f\"total search space size：{total_size}\")\n score = dict()\n counter = 0\n for combination in settings:\n score[self._evaluate(combination)] = combination\n counter += 1\n if counter > max_iteration:\n logging.warning(\"Max number of iteration reached. Search stopped.. \")\n break\n\n results = []\n for key in sorted(score.keys()):\n results.append((score[key], key))\n\n # Return result list\n\n self.results = results\n logging.info(f\"Best signal combination {results[-1]} \")\n return results","title":""},{"docid":"74c1a5724af884f1a48718c3e67f20be","score":"0.6449846","text":"def run(self):\r\n loop = True\r\n iterations = 0\r\n self.print_stats(iterations, True)\r\n while loop:\r\n self.expectation_step()\r\n self.maximization_step()\r\n if self.check_convergence():\r\n loop = False\r\n for i in xrange(self.K):\r\n self.alpha[i] = self.alpha_new[i]\r\n iterations += 1\r\n logging.info(\"iterations = \" + str(iterations))\r\n\r\n self.print_stats(iterations)","title":""},{"docid":"4497f71e7189a87294719223dc993a55","score":"0.644211","text":"def run_algo(self):\n #p_angulo = self.run_proc(['batch_angulo.py'])\n #self.wait_proc(p_angulo, timeout=self.timeout)\n\n #p_refinement = self.run_proc(['/bin/bash','batch_refinement.sh', self.key])\n\n p = self.run_proc(['/bin/bash','run_sgm.sh'])\n self.wait_proc(p, timeout=self.timeout)\n\n if self.cfg['param']['ground_truth'] != '':\n p_stat = self.run_proc(['/bin/bash','compute_statistics.sh'])\n self.wait_proc(p_stat, timeout=self.timeout)","title":""},{"docid":"eaa3a57d769ae98523b4b240a74d98e1","score":"0.64235586","text":"def optimize(self):\n\n time=self.Params.TimeLimit\n seed=self.Params.RandomSeed\n LOG=self.Params.OutputFlag\n\n f = self.update()\n\n f3 = open(\"scop_input.txt\",\"w\")\n f3.write(f)\n f3.close()\n\n if LOG>=100:\n print(\"scop input: \\n\")\n print(f)\n print(\"\\n\")\n if LOG:\n print(\"solving using parameters: \\n \")\n print(\" TimeLimit =%s second \\n\"%time)\n print(\" RandomSeed= %s \\n\"%seed)\n print(\" OutputFlag= %s \\n\"%LOG)\n import subprocess\n# if platform.system() == \"Windows\":\n# cmd = \"scop -time \"+str(time)+\" -seed \"+str(seed) #solver call for win\n# elif platform.system()== \"Darwin\":\n# cmd = \"./scop -time \"+str(time)+\" -seed \"+str(seed) #solver call for mac\n# elif platform.system() == \"Linux\":\n# cmd = \"./scop-linux -time \"+str(time)+\" -seed \"+str(seed) #solver call for linux\n\n# トライアル版の場合は以下を生かす\n if platform.system() == \"Windows\":\n cmd = \"scop-win -time \"+str(time)+\" -seed \"+str(seed) #solver call for win\n elif platform.system()== \"Darwin\":\n cmd = \"./scop-mac -time \"+str(time)+\" -seed \"+str(seed) #solver call for mac\n elif platform.system() == \"Linux\":\n cmd = \"./scop-linux -time \"+str(time)+\" -seed \"+str(seed) #solver call for linux\n\n\n if self.Params.Initial:\n cmd += \" -initsolfile scop_best_data.txt\"\n\n try:\n if platform.system() == \"Windows\": #Winの場合にはコマンドをsplit!\n pipe = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stdin=subprocess.PIPE, shell=True)\n else:\n pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stdin=subprocess.PIPE, shell=True)\n print(\"\\n ================ Now solving the problem ================ \\n\")\n #pipe = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE,stdin=subprocess.PIPE)\n except OSError:\n print(\"error: could not execute command '%s'\" % cmd)\n print(\"please check that the solver is in the path\")\n self.Status = 7 #execution falied\n return None, None\n\n out, err = pipe.communicate(f.encode()) #get the result\n if err!=None:\n if int(sys.version_info[0])>=3:\n err = str(err, encoding='utf-8')\n f2 = open(\"scop_error.txt\",\"w\")\n f2.write(err)\n f2.close()\n\n if int(sys.version_info[0])>=3:\n out = str(out, encoding='utf-8')\n\n if LOG:\n print (out, '\\n')\n #print (\"out=\",out)\n #print (\"err=\",err)\n #print(\"Return Code=\",pipe.returncode)\n\n f = open(\"scop_out.txt\",\"w\")\n f.write(out)\n f.close()\n\n #check the return code\n self.Status = pipe.returncode\n if self.Status !=0: #if the return code is not \"optimal\", then return\n print(\"Status=\",self.Status)\n print(\"Output=\",out)\n return None, None\n\n #extract the solution and the violated constraints\n s0 = \"[best solution]\"\n s1 = \"penalty\"\n s2 = \"[Violated constraints]\"\n i0 = out.find(s0) + len(s0)\n i1 = out.find(s1, i0)\n i2 = out.find(s2, i1) + len(s2)\n\n data = out[i0:i1].strip()\n\n #save the best solution\n f3 = open(\"scop_best_data.txt\",\"w\")\n f3.write(data.lstrip())\n f3.close()\n\n sol = {}\n if data != \"\":\n for s in data.split(\"\\n\"):\n name, value = s.split(\":\")\n sol[name]=value.strip() #remove redunant string\n\n data = out[i2:].strip()\n violated = {}\n if data != \"\":\n for s in data.split(\"\\n\"):\n try:\n name, value = s.split(\":\")\n except:\n print(\"Error String=\",s)\n\n try:\n temp=int(value)\n except:\n violated[name] = value\n else:\n violated[name] = int(value)\n\n #set the optimal solution to the variable\n for name in sol:\n if name in self.varDict:\n self.varDict[name].value = sol[name]\n else:\n raise NameError(\"Solution {0} is not in variable list\".format(name))\n\n #evaluate the left hand sides of the constraints\n for con in self.constraints:\n\n if isinstance(con,Linear):\n lhs=0\n for (coeff,var,domain) in con.terms:\n if var.value==domain:\n lhs+=coeff\n\n con.lhs=lhs\n if isinstance(con,Quadratic):\n lhs=0\n #print con.terms\n for (coeff,var1,domain1,var2,domain2) in con.terms:\n if var1.value==domain1 and var2.value==domain2:\n lhs+=coeff\n\n con.lhs=lhs\n if isinstance(con,Alldiff):\n VarSet=set([])\n lhs=0\n for v in con.variables:\n index=v.domain.index(v.value)\n #print v,index\n if index in VarSet:\n lhs+=1\n VarSet.add(index)\n #print VarSet\n con.lhs=lhs\n #return dictionaries containing the solution and the violated constraints\n return sol,violated","title":""},{"docid":"462f278c5224abcd036debe348c6be34","score":"0.63882387","text":"def run(self):\n f_names = self.get_f_names()\n global_best = None\n global_best_dist = float('Inf')\n\n for file in f_names:\n if (file[-4:] != 'json'):\n pass\n else:\n self.output_file.write(\"\\nFile name: {}\\n\".format(file))\n data = self.get_raw_solutions(file)\n self.output_file.write(\"# of Solutions: {}\\n\".format(len(data)))\n self.summarize(data)\n avg_dist, best_sol = self.get_avg_dist(data)\n avg_dist_output = \"Average dist: {:0.2f}\\n\".format(avg_dist)\n self.output_file.write(avg_dist_output)\n\n if self.calc_dist(best_sol) < global_best_dist:\n global_best = best_sol\n\n best_sol['position'] = [round(num, 2) for num in best_sol['position']]\n best_sol['objectives'] = [round(num, 2) for num in best_sol['objectives']]\n \n best_sol_output = \"Min distance Solution:\\n{}\\n\".format(best_sol)\n self.output_file.write(best_sol_output)\n\n global_best['position'] = [round(num, 2) for num in global_best['position']]\n global_best['objectives'] = [round(num, 2) for num in global_best['objectives']]\n \n global_best_sol_output = \"\\nGlobal Min Distance Solution:\\n{}\\n\".format(global_best)\n self.output_file.write(best_sol_output)\n\n\n \n\n return 0","title":""},{"docid":"beee14a579b94ecfa9cf6fb320839491","score":"0.6383306","text":"def search(self) -> SearchReturnType:\n\n self.io.write_line(True, self.full_output_file, '\\n*******Optimization Beginning*******')\n\n # evaluate starting point\n base_values = {dv.var_name: dv.x_base for dv in self.dvs}\n obj_base = self.f_of_x(base_values)\n j_base = obj_base.value\n if obj_base.return_state == ReturnStateEnum.UserAborted: # pragma: no cover -- stop file may be deprecated\n self.io.write_line(True, self.full_output_file,\n 'User aborted simulation via stop signal file...')\n r = SearchReturnType(False, ReturnStateEnum.UserAborted)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n elif not obj_base.return_state == ReturnStateEnum.Successful:\n self.io.write_line(True, self.full_output_file,\n 'Initial point is infeasible or invalid, cannot begin iterations. Aborting...')\n r = SearchReturnType(False, ReturnStateEnum.InvalidInitialPoint)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n\n # begin iteration loop\n for iteration in range(1, self.project.max_iterations + 1):\n\n self.io.write_line(self.project.verbose, self.full_output_file, 'iter = ' + str(iteration))\n\n if os.path.exists(self.io.stopFile): # pragma: no cover -- not covering stop file stuff\n self.io.write_line(True, self.full_output_file,\n 'Found stop signal file in run directory; stopping now...')\n r = SearchReturnType(False, ReturnStateEnum.UserAborted)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n\n # begin DV loop\n for dv in self.dvs:\n\n # set up a new point\n dv.x_new = dv.x_base + dv.delta_x\n new_values = {dv.var_name: dv.x_new for dv in self.dvs}\n\n if dv.x_new > dv.value_maximum or dv.x_new < dv.value_minimum: # pragma: no cover\n # arranging the unit test to cover this condition is too much for now\n # if we wanted to do it, we could have the objective function be a generator that yields a bad value\n self.io.write_line(True, self.full_output_file,\n 'infeasible DV, name=' + dv.var_name)\n r = SearchReturnType(False, ReturnStateEnum.InfeasibleDV)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n\n # then evaluate the new point\n obj_new = self.f_of_x(new_values)\n j_new = obj_new.value\n\n w = self.io.write_line\n w(self.project.verbose, self.full_output_file, 'iter=' + str(iteration))\n w(self.project.verbose, self.full_output_file, 'var=' + str(dv))\n w(self.project.verbose, self.full_output_file, 'x_base=' + str([x.x_base for x in self.dvs]))\n w(self.project.verbose, self.full_output_file, 'j_base=' + str(j_base))\n w(self.project.verbose, self.full_output_file, 'x_new=' + str([x.x_new for x in self.dvs]))\n w(self.project.verbose, self.full_output_file, 'j_new=' + str(j_new))\n\n if obj_new.return_state == ReturnStateEnum.UnsuccessfulOther: # pragma: no cover\n # not covering this either, this is kind of a dumping ground for unexpected errors to fail nicely\n self.io.write_line(True, self.full_output_file,\n 'Optimization ended unexpectedly, check all inputs and outputs')\n self.io.write_line(True, self.full_output_file,\n 'Error message: ' + str(obj_new.message))\n r = SearchReturnType(False, ReturnStateEnum.UnsuccessfulOther)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n elif (not obj_new.return_state == ReturnStateEnum.Successful) or (j_new > j_base):\n dv.delta_x = -self.project.coefficient_contract * dv.delta_x\n dv.x_new = dv.x_base\n self.io.write_line(self.project.verbose, self.full_output_file,\n '## Unsuccessful objective evaluation, or worse result, going back ##')\n else:\n j_base = j_new\n dv.x_base = dv.x_new\n dv.delta_x = self.project.coefficient_expand * dv.delta_x\n self.io.write_line(self.project.verbose, self.full_output_file,\n '## Improved result, accepting and continuing forward ##')\n\n converged = True\n for dv in self.dvs:\n if abs(dv.delta_x) > dv.convergence_criteria:\n converged = False\n break\n\n if converged:\n self.io.write_line(True, self.full_output_file, '*******Converged*******')\n converged_values = {x.var_name: x.x_new for x in self.dvs}\n r = SearchReturnType(True, ReturnStateEnum.Successful, converged_values)\n if self.callback_completed:\n self.callback_completed(r)\n self.full_output_file.close()\n return r\n\n if self.callback_progress:\n self.callback_progress(iteration, j_base)","title":""},{"docid":"eb95f1c58fa06ee5f03ab294a81f0df5","score":"0.63015676","text":"def solver(self):\r\n\r\n # define the problem parameters \r\n self.initProblemParameters()\r\n\r\n # define the ACO parameters\r\n self.initACOParameters()\r\n\r\n self.initSolverParameters()\r\n\r\n self.best_solution = None\r\n object_val_per_iter = []\r\n start_time = datetime.now()\r\n for iter in tqdm(range(self.iterations)):\r\n # for every iterations defined \r\n\r\n self.nodes_visited_by_ant = {}\r\n self.reloading_points = {}\r\n self.solutions_per_ant = {}\r\n\r\n for ant in self.ants: # define the type of ant here / most probably is a range(NO_OF_ANTS)\r\n # for every ants taken (cumulative most probably)\r\n # print(\"Iteration {} Ant {}\".format(iter,ant))\r\n sub_fleets = self.getNoOfSubFleets()\r\n\r\n while(True):\r\n # print(\"start\")\r\n truck_i_j_DV = None\r\n bike_i_j_DV = None\r\n truck_decision = None\r\n bike_decision = None\r\n add_bike_i_j_DV = np.zeros(shape=(self.node_num, self.node_num), dtype=np.int)\r\n add_bike_decision = {}\r\n add_bike_decision['add_bike_routes']=None\r\n add_bike_decision['add_bike_freight']=None\r\n add_bike_curr = [self.DEPOT]\r\n add_bike_curr_freight = []\r\n truck_i_j_DV, bike_i_j_DV, truck_decision, bike_decision = self.solveForNFleet(sub_fleets, ant)\r\n if np.sum(self.demand_satisfied) == self.node_num:\r\n break\r\n else:\r\n demand_left = np.sum(self.customer_demand.take(np.where(self.demand_satisfied)))\r\n if demand_left <= self.bike_capacity:\r\n # bring in additional bike\r\n originAdd = self.DEPOT\r\n bike_freight = min(demand_left, self.bike_capacity)\r\n add_bike_curr_freight.append(bike_freight)\r\n while(bike_freight>0):\r\n # print(\"add_bike\")\r\n destinationAdd = self.getNextStop(ant, originAdd)\r\n # see if other constraints persists\r\n if self.checkFeasibility1(originAdd, destinationAdd, ant, bike_freight):\r\n add_bike_i_j_DV[originAdd][destinationAdd] = 1\r\n add_bike_curr.append(destinationAdd)\r\n self.nodes_visited_by_ant[ant].append(destinationAdd)\r\n bike_freight -= self.customer_demand[destinationAdd]\r\n add_bike_curr_freight.append(bike_freight)\r\n self.demand_satisfied[destinationAdd] = 1\r\n originAdd = destinationAdd\r\n # bike_working_time += self.customer_service_time[destinationB]\r\n destinationAdd = self.DEPOT\r\n add_bike_i_j_DV[originAdd][destinationAdd] = 1\r\n add_bike_curr.append(self.DEPOT)\r\n add_bike_decision['add_bike_routes'] = add_bike_curr\r\n add_bike_decision['add_bike_freight'] = add_bike_curr_freight\r\n\r\n else:\r\n sub_fleets += 1\r\n\r\n self.saveSolution(ant, truck_i_j_DV, bike_i_j_DV, add_bike_i_j_DV, truck_decision, bike_decision, add_bike_decision)\r\n self.localPheremoneUpdate()\r\n\r\n # perform global pheromone updates \r\n # after the iteration is over perform the global pheromone update\r\n # Tij(t+1) = (1-rho_1) * Tij(t) + rho_1 * delta(Tij)(t)\r\n # delta(Tij)(t) = 1 / func(global - last_ob_val(t))\r\n print(self.best_solution['objective_value'])\r\n object_val_per_iter.append(str(self.best_solution['objective_value']))\r\n self.globalPheromoneUpdate()\r\n # import pdb; pdb.set_trace()\r\n\r\n # print the arrays into a file\r\n print(\"\\n\")\r\n print(\"*\"*20)\r\n print(\"Time Taken : {}\".format(datetime.now() - start_time))\r\n\r\n print(\"Truck Routes : \")\r\n for trucks in self.best_solution['truck_decision']:\r\n print(trucks['fleet_no'])\r\n print(trucks['truck_routes'])\r\n print(trucks['truck_freight'])\r\n\r\n print(\"\\n\")\r\n print(\"*\"*20)\r\n print(\"Bike Routes : \")\r\n for bike in self.best_solution['bike_decision']:\r\n print(bike['fleet_no'])\r\n print(bike['bike_routes'])\r\n print(bike['bike_freight']) \r\n\r\n print(\"\\n\")\r\n print(\"*\"*20)\r\n print(\"Add Bike Routes : \")\r\n print(self.best_solution['add_bike_decision']['add_bike_routes'])\r\n print(self.best_solution['add_bike_decision']['add_bike_freight'])\r\n\r\n # with open(\"obj_val_per_iter.txt\", \"w\") as fp:\r\n # fp.write(\"\\n\".join(object_val_per_iter))\r\n\r\n # import pdb; pdb.set_trace()\r\n # with open(\"results_truck.txt\",\"w\") as fp:\r\n # for slice2d in self.best_solution['truck_route']:\r\n # np.savetxt(fp, slice2d, fmt='%5d')\r\n # fp.write(\"\\n\")\r\n\r\n\r\n # with open(\"results_bike.txt\",\"w\") as fp:\r\n # for slice2d in self.best_solution['bike_route']:\r\n # np.savetxt(fp, slice2d, fmt='%5d')\r\n # fp.write(\"\\n\")\r\n\r\n\r\n # with open(\"results_addbike.txt\") as fp:\r\n # np.savetxt(fp, self.best_solution['additional_bike_route'], fmt='%5d')\r\n \r\n # self.printSolution()\r\n\r\n\r\n\r\n return","title":""},{"docid":"8ca69c492545f9575f5823acce20027e","score":"0.6287877","text":"def optimize(self):\n\t\tself.forwardpropagate(self.getLastVariable()) # forward propagation\n\t\tself.backpropagate(self.getLastVariable(), first = 1) # backward propagation \n\t\tself.updateweights(self.getLastVariable())\t\t\t # update values of parameters ","title":""},{"docid":"ee2f0baf0222b6471834378ef47c98a6","score":"0.62825644","text":"def test_optimisation_single(self):\n self.report('')\n self.report('Testing optimisation on a single worker.')\n for idx, (dcf, raw_prob_funcs) in enumerate(self.opt_problems):\n self.report('[%d/%d] Testing optimisation with 1 worker on %s.'%(\n idx + 1, len(self.opt_problems), dcf), 'test_result')\n self.worker_manager_1.reset()\n opt_val, opt_point, history = self._run_optimiser(raw_prob_funcs, dcf,\n self.worker_manager_1, self.max_capital, 'asy')\n self._test_optimiser_results(opt_val, opt_point, history, dcf)\n self.report('')","title":""},{"docid":"66a03bf473167eed8d7ac521ac5e1e38","score":"0.62807673","text":"def runRegression(self,maxiter=100000000000):\n\n self.fit_result = minimize(fun=self._objective,\n x0=self.param_guess,\n bounds=self.bounds,\n options={\"maxiter\":maxiter,\"maxfun\":maxiter})","title":""},{"docid":"0a2feb73baae455586b924acaf277700","score":"0.6275972","text":"def run_routine():\n # sInitialize dictionnaries\n # init_dict()\n\n # Build the model\n prob = om.Problem()\n model = prob.model\n\n # Build model components\n indeps = model.add_subsystem('indeps', om.IndepVarComp())\n model.add_subsystem('objective', objective_function())\n model.add_subsystem('const', constraint())\n\n # Choose between optimizer or driver\n if Rt.type == 'DoE':\n if Rt.doetype == 'uniform':\n driver = prob.driver = om.DOEDriver(om.UniformGenerator(num_samples=Rt.samplesnb))\n elif Rt.doetype == 'fullfact':\n # 2->9 3->81\n driver = prob.driver = om.DOEDriver(om.FullFactorialGenerator(Rt.samplesnb))\n elif Rt.type == 'Optim':\n driver = prob.driver = om.ScipyOptimizeDriver()\n # SLSQP,COBYLA,shgo,TNC\n driver.options['optimizer'] = Rt.driver\n # driver.options['maxiter'] = 20\n driver.options['tol'] = 1e-2\n if Rt.driver == 'COBYLA':\n driver.opt_settings['catol'] = 0.06\n\n\n # Connect problem components to model components\n\n # Design variable\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\n norm = int(np.log10(abs(listval[0])+1)+1)\n indeps.add_output(key, listval[0], ref=norm, ref0=0)\n model.connect('indeps.'+key, 'objective.'+key)\n model.add_design_var('indeps.'+key, lower=minval, upper=maxval)\n\n # Constraints\n for key, (name, listval, minval, maxval, getcommand) in res_var_dict.items():\n # Select only one constrain\n if name in Rt.constraints:\n norm = int(np.log10(abs(listval[0])+1)+1)\n model.add_constraint('const.'+name, ref=norm, lower=-0.25, upper=0.25)\n\n # Objective function\n model.add_objective('objective.{}'.format(Rt.objective))\n\n # Recorder\n path = optim_dir_path\n driver.add_recorder(om.SqliteRecorder(path + '/Driver_recorder.sql'))\n\n # Run\n prob.setup()\n prob.run_driver()\n prob.cleanup()\n\n # Results\n log.info('=========================================')\n log.info('min = ' + str(prob['objective.{}'.format(Rt.objective)]))\n\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\n log.info(name + ' = ' + str(prob['indeps.'+key]) + '\\n Min :' + str(minval) + ' Max : ' + str(maxval))\n\n log.info('Variable history')\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\n log.info(name + ' => ' + str(listval))\n\n log.info('=========================================')\n\n # Generate plots, maybe make a dynamic plot\n opf.read_results(optim_dir_path, Rt.type)","title":""},{"docid":"c53fba9b0d84c54f542b6ad337277e2c","score":"0.6266641","text":"def run_solver():\n # start timer\n start_time = time.perf_counter()\n # find files\n num_graphs = []\n # we can just do a subset of graphs if the parameter full is set to False\n sizes = []\n if RUN_LIST_SMALL:\n sizes.append(\"small\")\n num_graphs.append(304)\n if RUN_LIST_MEDIUM:\n sizes.append(\"medium\")\n num_graphs.append(304)\n if RUN_LIST_LARGE:\n sizes.append(\"large\")\n num_graphs.append(401)\n # loop through all inputs and create outputs\n num_cores = multiprocessing.cpu_count()\n outputs = []\n\n def solver(size=\"\", index=\"\", filename=\"\"):\n if not size and not index:\n filepath = filename\n else:\n filepath = size + \"-\" + str(index)\n G = read_input_file(\"inputs/\"+ filepath +\".in\")\n print(\"analyzing \"+filepath)\n EXISTING_T = read_output_file(\"outputs/\"+filepath+\".out\", G)\n # outputs.append((solve(G, EXISTING_T), filepath))\n # um = solve(G, EXISTING_T, filename=filepath)\n if PSEUDO_SOLVER:\n write_output_file(psuedo_solver(G, EXISTING_T, filename=filepath), \"outputs/\"+filepath+\".out\")\n else:\n write_output_file(solve(G, EXISTING_T, filepath, EXISTING_T), \"outputs/\"+filepath+\".out\")\n\n if not file and ONLY_RUN_IMPROVABLE:\n if RUN_LIST_SMALL:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_small)\n if RUN_LIST_MEDIUM:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_medium)\n if RUN_LIST_LARGE:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large)\n else:\n if RUN_LIST_LARGE_1:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_1)\n if RUN_LIST_LARGE_2:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_2)\n if RUN_LIST_LARGE_3:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_3)\n if RUN_LIST_LARGE_4:\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_4)\n\n elif not file and not ONLY_RUN_IMPROVABLE:\n for i in range(len(sizes)):\n size = sizes[i]\n GRAPH_RANGE = range(START, num_graphs[i])\n Parallel(n_jobs=num_cores)(delayed(solver)(size, j) for j in GRAPH_RANGE)\n #for j in GRAPH_RANGE:\n # if j != 58:\n # solver(size, j)\n elif file: # file-specific running\n T = solver(filename=file)\n for output in outputs:\n # write_output_file(output[0], \"outputs/\"+output[1]+\".out\")\n pass\n end_time = time.perf_counter()\n print(f\"Process complete. Total time {end_time - start_time:0.4f} seconds\")","title":""},{"docid":"4518826a630f8f3ac5a1675da2e54465","score":"0.6259849","text":"def run(self):\r\n\t\t# reset total time\r\n\t\tself.setTotalTime(0)\r\n\t\tstart = timeit.default_timer()\r\n\t\t# tell puzzle to run the selected algorithm\r\n\t\tself.puzzle.run(self.alg.get())\r\n\t\tend = timeit.default_timer()\r\n\t\t# Set total time to end-start time\r\n\t\tself.setTotalTime((end - start)*1000)\r\n\t\t# print the solved final puzzle\r\n\t\tprint self.puzzle\r\n\t\tself.puzzle.setPuzzle(self.puzzle.grid)","title":""},{"docid":"18701bd40bba52df90c82632b88ae1d8","score":"0.62485784","text":"def work(self):\n self.blocklen = int(self.defset(self.args.blocklen, 128))\n deg = int(self.defset(self.args.degree, 3))\n tvsize_orig = int(self.defset(self.process_size(self.args.tvsize), 1024*256))\n zscore_thresh = float(self.args.conf)\n rounds = int(self.args.rounds) if self.args.rounds is not None else None\n top_k = int(self.args.topk) if self.args.topk is not None else None\n top_comb = int(self.defset(self.args.combdeg, 2))\n reffile = self.defset(self.args.reffile)\n all_deg = self.args.alldeg\n\n # Load input polynomials\n self.load_input_poly()\n self.load_input_objects()\n\n logger.info('Basic settings, deg: %s, blocklen: %s, TV size: %s, rounds: %s'\n % (deg, self.blocklen, tvsize_orig, rounds))\n\n # specific polynomial testing\n logger.info('Initialising')\n time_test_start = time.time()\n\n # read file by file\n for iobj in self.input_objects:\n tvsize = tvsize_orig\n\n iobj.check()\n size = iobj.size()\n logger.info('Testing input object: %s, size: %d kB' % (iobj, size/1024.0))\n\n # size smaller than TV? Adapt tv then\n if size >= 0 and size < tvsize:\n logger.info('File size is smaller than TV, updating TV to %d' % size)\n tvsize = size\n\n if tvsize*8 % self.blocklen != 0:\n rem = tvsize*8 % self.blocklen\n logger.warning('Input data size not aligned to the block size. '\n 'Input bytes: %d, block bits: %d, rem: %d' % (tvsize, self.blocklen, rem))\n tvsize -= rem//8\n logger.info('Updating TV to %d' % tvsize)\n\n hwanalysis = HWAnalysis()\n hwanalysis.deg = deg\n hwanalysis.blocklen = self.blocklen\n hwanalysis.top_comb = top_comb\n hwanalysis.comb_random = self.args.comb_random\n hwanalysis.top_k = top_k\n hwanalysis.combine_all_deg = all_deg\n hwanalysis.zscore_thresh = zscore_thresh\n hwanalysis.do_ref = reffile is not None\n hwanalysis.input_poly = self.input_poly\n hwanalysis.no_comb_and = self.args.no_comb_and\n hwanalysis.no_comb_xor = self.args.no_comb_xor\n hwanalysis.prob_comb = self.args.prob_comb\n hwanalysis.do_only_top_comb = self.args.only_top_comb\n hwanalysis.do_only_top_deg = self.args.only_top_deg\n hwanalysis.no_term_map = self.args.no_term_map\n hwanalysis.use_zscore_heap = self.args.topterm_heap\n hwanalysis.sort_best_zscores = max(common.replace_none([self.args.topterm_heap_k, top_k, 100]))\n hwanalysis.best_x_combinations = self.args.best_x_combinations\n\n # compute classical analysis only if there are no input polynomials\n hwanalysis.all_deg_compute = len(self.input_poly) == 0\n logger.info('Initializing test')\n hwanalysis.init()\n\n total_terms = int(scipy.misc.comb(self.blocklen, deg, True))\n logger.info('BlockLength: %d, deg: %d, terms: %d' % (self.blocklen, deg, total_terms))\n\n # Reference data stream reading\n # Read the file until there is no data.\n fref = None\n if reffile is not None:\n fref = open(reffile, 'r')\n\n with iobj:\n data_read = 0\n cur_round = 0\n\n while size < 0 or data_read < size:\n if rounds is not None and cur_round > rounds:\n break\n\n data = iobj.read(tvsize)\n bits = common.to_bitarray(data)\n if len(bits) == 0:\n logger.info('File read completely')\n break\n\n ref_bits = None\n if fref is not None:\n ref_data = fref.read(tvsize)\n ref_bits = common.to_bitarray(ref_data)\n\n logger.info('Pre-computing with TV, deg: %d, blocklen: %04d, tvsize: %08d = %8.2f kB = %8.2f MB, '\n 'round: %d, avail: %d' %\n (deg, self.blocklen, tvsize, tvsize/1024.0, tvsize/1024.0/1024.0, cur_round, len(bits)))\n\n hwanalysis.proces_chunk(bits, ref_bits)\n cur_round += 1\n pass\n\n logger.info('Finished processing %s ' % iobj)\n logger.info('Data read %s ' % iobj.data_read)\n logger.info('Read data hash %s ' % iobj.sha1.hexdigest())\n\n if fref is not None:\n fref.close()\n logger.info('Processing finished')","title":""},{"docid":"501a67bb077eaec59e938e1f7111921d","score":"0.62383354","text":"def run(self):\n\n print(\"Run NMOEA algorithm!\")\n\n # Random initialization of population\n population = []\n for _ in range(self.population_size):\n new_individual = list(range(self.node_num))\n for i in range(int(0.4 * self.node_num)):\n v = random.choice(self.nodes)\n\n if v in self.graph.adj:\n for neighbor in self.graph.adj[v].keys():\n new_individual[neighbor - 1] = new_individual[v - 1]\n\n population.append(new_individual)\n\n with open(\"../output/results/iter{}.json\".format(0), \"w\") as f:\n json.dump(population, f)\n it = 1\n update_count = self.population_size\n\n while update_count > 0:\n for i in range(self.population_size // 2):\n # Except the first loop, population is sorted by fast-non-dominated-sort.\n # Select from first 1/3\n a, b = get_two_random(0, (self.population_size - 1) // 3)\n population.append(self.crossover(population[a], population[b]))\n population.append(self.mutation(population[a]))\n # print(\"Iter:{} updating: {}/{}\".format(it,\n # i + 1, self.population_size // 2))\n\n population, fitness_list, update_count = self.fast_non_dominated_sort(\n population)\n\n print(\"Iter:{}, population is updated by {}\".format(it, update_count))\n\n community_lens = [len(self.decode_communities(indiv))\n for indiv in population[:10]]\n\n _fitness = []\n for x in fitness_list[:10]:\n _fitness.append((round(x[0], 4), round(x[1], 4)))\n print(community_lens)\n print(_fitness)\n with open(\"../output/results/report.txt\", \"a+\") as f:\n f.write(\"Iter:{}, population is updated by {}\\n\".format(\n it, update_count))\n f.write(str(community_lens) + \"\\n\")\n f.write(str(_fitness) + \"\\n\")\n with open(\"../output/results/iter{}.json\".format(it), \"w\") as f:\n json.dump(population, f)\n it += 1","title":""},{"docid":"d70e5beb7708b919e48e95b16d9b0044","score":"0.623162","text":"def run_all(self, max_passes):\n if max_passes == 0:\n debug('optimizer disabled')\n return\n self.run_opt(self.del_unused_results) # no need to run this one multiple times\n for count in xrange(max_passes):\n debug('------------- global optimizer pass %d (of max %d) -------------' % (\n count+1, max_passes))\n sum_counters = sum(self.opt_counters.values())\n self.run_opt(self.del_jumps_to_next, max_passes=self.INF_PASSES)\n self.run_opt(self.del_unused_labels)\n self.run_opt(self.reduce_push_pop, max_passes=self.INF_PASSES)\n self.run_opt(self.propagate_constants)\n #self.run_opt(self.clear_deleted_codes)\n if sum(self.opt_counters.values()) == sum_counters:\n debug('------------------ all optimizations returned finish -----------------')\n break\n # TODO don't assign dead vars\n # TODO free string memory\n # TODO [mov mem regA, mov regA regB]\n # TODO [mov regA memX, mov memX regB]\n if Flags.optimizer_summary:\n Status.add_note(LatteError('optimizer case counters:'))\n for name, count in self.opt_counters.iteritems():\n Status.add_note(LatteError(name + ': ' + str(count)))","title":""},{"docid":"479a9e90cdb00c55f9be5669ab50aac3","score":"0.62308276","text":"def run_optimizer(**kwargs):\n #==============================#\n #| Log file configuration |#\n #==============================#\n # By default, output should be written to <args.prefix>.log and also printed to the terminal.\n # This behavior may be changed by editing the log.ini file.\n # Output will only be written to log files after the 'logConfig' line is called!\n logIni = 'log.ini'\n if kwargs.get('logIni') is None:\n import geometric.optimize\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, logIni)\n else:\n logIni = kwargs.get('logIni')\n logfilename = kwargs.get('prefix')\n # Input file for optimization; QC input file or OpenMM .xml file\n inputf = kwargs.get('input')\n # Get calculation prefix and temporary directory name\n arg_prefix = kwargs.get('prefix', None) #prefix for output file and temporary directory\n prefix = arg_prefix if arg_prefix is not None else os.path.splitext(inputf)[0]\n logfilename = prefix + \".log\"\n # Create a backup if the log file already exists\n backed_up = bak(logfilename)\n import logging.config\n logging.config.fileConfig(logIni,defaults={'logfilename': logfilename},disable_existing_loggers=False)\n #==============================#\n #| End log file configuration |#\n #==============================#\n\n import geometric\n printLogo(logger)\n logger.info('-=# \\x1b[1;94m geomeTRIC started. Version: %s \\x1b[0m #=-\\n' % geometric.__version__)\n logger.info('geometric-optimize called with the following command line:\\n')\n logger.info(' '.join(sys.argv)+'\\n')\n if backed_up:\n logger.info('Backed up existing log file: %s -> %s\\n' % (logfilename, os.path.basename(backed_up)))\n\n t0 = time.time()\n # Custom convergence criteria\n criteria = kwargs.get('converge', [])\n if len(criteria)%2 != 0:\n raise RuntimeError('Please pass an even number of options to --converge')\n for i in range(int(len(criteria)/2)):\n key = 'convergence_' + criteria[2*i].lower()\n try:\n val = float(criteria[2*i+1])\n logger.info('Using convergence criteria: %s %.2e\\n' % (key, val))\n except ValueError:\n # This must be a set\n val = str(criteria[2*i+1])\n logger.info('Using convergence criteria set: %s %s\\n' % (key, val))\n kwargs[key] = val\n\n # LPW 2019-04-20: Changed the default behavior of \"reset\" to True.\n # When users pass '--no_reset' via the CLI, that will result in\n # passing OptParams(reset=False) to the constructor.\n # After a suitable amount of time, remove '--reset' from the CLI.\n if kwargs.get('no_reset', False):\n if kwargs.get('reset', False):\n raise RuntimeError('Do not provide both --reset and --no_reset')\n kwargs['reset'] = False\n else:\n if kwargs.get('reset', False):\n logger.info('The --reset option defaults to True and will be removed soon from the CLI.')\n kwargs['reset'] = True\n if 'no_reset' in kwargs: del kwargs['no_reset']\n\n params = OptParams(**kwargs)\n\n # Get the Molecule and engine objects needed for optimization\n M, engine = get_molecule_engine(**kwargs)\n\n #============================================#\n #| Temporary file and folder management |#\n #============================================#\n # LPW: Should this be refactored and moved into get_molecule_engine?\n # Probably not .. get_molecule_engine is independent of dirname.\n # We may implement Engine.initialize(dirname) in the future.\n dirname = prefix+\".tmp\"\n if not os.path.exists(dirname):\n # LPW: Some engines do not need the tmp-folder, but the\n # auxiliary functions might (such as writing out displacements)\n os.makedirs(dirname)\n else:\n logger.info(\"%s exists ; make sure nothing else is writing to the folder\\n\" % dirname)\n # TC-specific: Remove existing scratch files in ./run.tmp/scr to avoid confusion\n for f in ['c0', 'ca0', 'cb0']:\n if os.path.exists(os.path.join(dirname, 'scr', f)):\n os.remove(os.path.join(dirname, 'scr', f))\n\n # QC-specific scratch folder\n qcdir = kwargs.get('qcdir', None) # Provide an initial qchem scratch folder (e.g. supplied initial guess\n qchem = kwargs.get('qchem', False)\n if qcdir is not None:\n if not qchem:\n raise RuntimeError(\"--qcdir only valid if --qchem is specified\")\n if not os.path.exists(qcdir):\n raise RuntimeError(\"--qcdir points to a folder that doesn't exist\")\n shutil.copytree(qcdir, os.path.join(dirname, \"run.d\"))\n engine.M.edit_qcrems({'scf_guess':'read'})\n engine.qcdir = True\n #============================================#\n #| End temporary file and folder management |#\n #============================================#\n\n # Get initial coordinates in bohr\n coords = M.xyzs[0].flatten() * ang2bohr\n\n # Read in the constraints\n constraints = kwargs.get('constraints', None) #Constraint input file (optional)\n\n if constraints is not None:\n Cons, CVals = ParseConstraints(M, open(constraints).read())\n else:\n Cons = None\n CVals = None\n\n #=========================================#\n #| Set up the internal coordinate system |#\n #=========================================#\n # First item in tuple: The class to be initialized\n # Second item in tuple: Whether to connect nonbonded fragments\n # Third item in tuple: Whether to throw in all Cartesians (no effect if second item is True)\n CoordSysDict = {'cart':(CartesianCoordinates, False, False),\n 'prim':(PrimitiveInternalCoordinates, True, False),\n 'dlc':(DelocalizedInternalCoordinates, True, False),\n 'hdlc':(DelocalizedInternalCoordinates, False, True),\n 'tric':(DelocalizedInternalCoordinates, False, False)}\n coordsys = kwargs.get('coordsys', 'tric')\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\n\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVals[0] if CVals is not None else None,\n conmethod=params.conmethod)\n #========================================#\n #| End internal coordinate system setup |#\n #========================================#\n\n # Auxiliary functions (will not do optimization):\n verbose = kwargs.get('verbose', False)\n displace = kwargs.get('displace', False) # Write out the displacements of the coordinates.\n if displace:\n WriteDisplacements(coords, M, IC, dirname, verbose)\n return\n\n fdcheck = kwargs.get('fdcheck', False) # Check internal coordinate gradients using finite difference..\n if fdcheck:\n IC.Prims.checkFiniteDifferenceGrad(coords)\n IC.Prims.checkFiniteDifferenceHess(coords)\n CheckInternalGrad(coords, M, IC.Prims, engine, dirname, verbose)\n CheckInternalHess(coords, M, IC.Prims, engine, dirname, verbose)\n return\n\n # Print out information about the coordinate system\n if isinstance(IC, CartesianCoordinates):\n logger.info(\"%i Cartesian coordinates being used\\n\" % (3*M.na))\n else:\n logger.info(\"%i internal coordinates being used (instead of %i Cartesians)\\n\" % (len(IC.Internals), 3*M.na))\n logger.info(IC)\n logger.info(\"\\n\")\n\n if Cons is None:\n # Run a standard geometry optimization\n if prefix == os.path.splitext(inputf)[0]:\n params.xyzout = prefix+\"_optim.xyz\"\n else:\n params.xyzout = prefix+\".xyz\"\n progress = Optimize(coords, M, IC, engine, dirname, params)\n else:\n # Run a single constrained geometry optimization or scan over a grid of values\n if isinstance(IC, (CartesianCoordinates, PrimitiveInternalCoordinates)):\n raise RuntimeError(\"Constraints only work with delocalized internal coordinates\")\n Mfinal = None\n for ic, CVal in enumerate(CVals):\n if len(CVals) > 1:\n logger.info(\"---=== Scan %i/%i : Constrained Optimization ===---\\n\" % (ic+1, len(CVals)))\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal, conmethod=params.conmethod)\n IC.printConstraints(coords, thre=-1)\n if len(CVals) > 1:\n params.xyzout = prefix+\"_scan-%03i.xyz\" % ic\n # In the special case of a constraint scan, we write out multiple qdata.txt files\n if params.qdata is not None: params.qdata = 'qdata_scan-%03i.txt' % ic\n elif prefix == os.path.splitext(kwargs['input'])[0]:\n params.xyzout = prefix+\"_optim.xyz\"\n else:\n params.xyzout = prefix+\".xyz\"\n if ic == 0:\n progress = Optimize(coords, M, IC, engine, dirname, params)\n else:\n progress += Optimize(coords, M, IC, engine, dirname, params)\n # update the structure for next optimization in SCAN (by CNH)\n M.xyzs[0] = progress.xyzs[-1]\n coords = progress.xyzs[-1].flatten() * ang2bohr\n if Mfinal:\n Mfinal += progress[-1]\n else:\n Mfinal = progress[-1]\n cNames, cVals = IC.getConstraintTargetVals()\n comment = ', '.join([\"%s = %.2f\" % (cName, cVal) for cName, cVal in zip(cNames, cVals)])\n Mfinal.comms[-1] = \"Scan Cycle %i/%i ; %s ; %s\" % (ic+1, len(CVals), comment, progress.comms[-1])\n #print\n if len(CVals) > 1:\n Mfinal.write('scan-final.xyz')\n if params.qdata is not None: Mfinal.write('qdata-final.txt')\n print_msg()\n logger.info(\"Time elapsed since start of run_optimizer: %.3f seconds\\n\" % (time.time()-t0))\n return progress","title":""},{"docid":"7667ba4918edddc4a8580aac899826f1","score":"0.6212641","text":"def main():\n\n ocp = prepare_ocp(long_optim=False)\n ocp.add_plot_penalty(CostType.ALL)\n\n # --- Solve the program --- #\n sol = ocp.solve(Solver.IPOPT(show_online_optim=platform.system() == \"Linux\"))\n sol.graphs(show_bounds=True)\n\n # --- Show results --- #\n sol.print_cost()\n sol.animate()","title":""},{"docid":"422204d3665e43c733003a6af1f87607","score":"0.62049055","text":"def run(self):\n\n self.generate_population()\n print('-----------Init--------------')\n print(self.config)\n\n fitness = []\n best = 0\n for i in range(100):\n self.reproduce()\n self.select()\n\n sorted_population = sorted(self.population, \\\n key=lambda x: x['fitness_points'])\n worst = sorted_population[0]['fitness_points']\n best = sorted_population[-1]['fitness_points']\n mean = (sorted_population[0]['fitness_points'] + \\\n sorted_population[-1]['fitness_points']) / 2\n fitness.append((best, worst, mean))\n\n print(best)\n print(sorted_population[-1]['solution'])\n self.plot(fitness, best)","title":""},{"docid":"ea273060e80ac78f27171805ac1cae1f","score":"0.6198797","text":"def run_optimization(request, configure_with_daemon, wait_for): # pylint: disable=unused-argument\n def inner(engine, func_workchain, engine_kwargs, evaluate=None): # pylint: disable=missing-docstring,useless-suppression\n from aiida_optimize import OptimizationWorkChain\n from aiida.orm import load_node\n from aiida.orm import Dict\n from aiida.engine.launch import run_get_node, submit\n\n inputs = dict(\n engine=engine,\n engine_kwargs=Dict(dict=dict(engine_kwargs)),\n evaluate_process=func_workchain,\n evaluate=evaluate if evaluate is not None else {},\n )\n\n if request.param == 'run':\n _, result_node = run_get_node(OptimizationWorkChain, **inputs)\n else:\n assert request.param == 'submit'\n pk = submit(OptimizationWorkChain, **inputs).pk\n wait_for(pk)\n result_node = load_node(pk)\n return result_node\n\n return inner","title":""},{"docid":"82f5ab5262c2de9dd8c16074129011e6","score":"0.6180509","text":"def objective(self, args):\n num_layers, hidden_size, learning_rate, batch_size = args\n num_layers = int(num_layers)\n hidden_size = int(hidden_size)\n batch_size = int(batch_size)\n\n print('')\n print('Attempting run with:')\n print('num_layers=%d' % num_layers)\n print('hidden_size=%d' % hidden_size)\n print('learning_rate=%f' % learning_rate)\n print('batch_size=%d' % batch_size)\n print('')\n\n if self.model_type == 'actprop':\n model_args = [\n \"-m\", \"actprop\", \"-O\",\n \"num_layers=%d,hidden_size=%d\" % (num_layers, hidden_size)\n ]\n else:\n model_args = [\n \"-m\", \"simple\", \"-O\",\n \"num_layers=%d,hidden_size=%d\" % (num_layers, hidden_size)\n ]\n cmdline = [\n \"./fpg.py\", \"-p\", self.problem, \"-t\", str(self.time_limit), \"-l\",\n self.log_dir, '-L', str(self.turns), '-R', str(self.eval_rounds),\n \"-f\", \"-o\", \"vpg\", \"-A\",\n \"learning_rate=%f,batch_size=%d\" % (learning_rate, batch_size),\n *model_args, *self.pddl_paths\n ]\n try:\n proc_stdout = check_output(cmdline, universal_newlines=True)\n except CalledProcessError as e:\n print(\n 'Error running fpy.py on %s: %s\\n\\nFailed command: %s' %\n (self.problem, str(e), cmdline),\n file=stderr)\n # BUG: I think that when the first run fails, hyperopt crashes on\n # hyperopt/base.py:585 when it tries to get the minimum loss over\n # all runs. Presumably this does not matter for later runs.\n return {'loss': 1e100, 'status': hopt.STATUS_FAIL}\n results = get_results(self.log_dir, proc_stdout)\n opt_time = results['elapsed_opt_time']\n succ_rate = results['successes'] / float(results['trials'])\n fail_rate = 1 - succ_rate\n hp_loss = fail_rate * self.score_weight + opt_time\n print('Success rate from this round: %f' % succ_rate)\n return {'loss': hp_loss, 'status': hopt.STATUS_OK}","title":""},{"docid":"e3ad1dd9ba2699f941f66a8586d374e7","score":"0.61802757","text":"def perform_optimization(self):\n\n # Create an optimizer (tf.train.AdamOptimizer)\n self.optimizer = tf.train.AdamOptimizer(self.learning_rate_ph)\n ######### YOUR CODE HERE #############\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n\n # Gradient clipping (tf.clip_by_norm) for self.grads_and_vars\n # Pay attention that you need to apply this operation only for gradients\n # because self.grads_and_vars contains also variables.\n # list comprehension might be useful in this case.\n clip_norm = tf.cast(1.0, tf.float32)\n self.grads_and_vars = [(tf.clip_by_norm(grad, clip_norm), var) for grad, var in self.grads_and_vars]\n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars)","title":""},{"docid":"0b19d54cda4fdf13464d0b9ddc106ef4","score":"0.6178133","text":"def run_optimization(output_dir, optimization, design_file, library):\n output_dir = output_dir.replace(' ', '_')\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n output_design_file = output_dir + '/design.blif'\n \n abc_command = 'read ' + library + '; '\n abc_command += 'read ' + design_file + '; '\n abc_command += 'strash; '\n abc_command += optimization + '; '\n abc_command += 'write ' + output_design_file + '; '\n abc_command += 'map -D ' + str(clock_period) + '; '\n abc_command += 'print_stats; '\n \n proc = subprocess.check_output(['yosys-abc','-c', abc_command])\n d, a = extract_results(proc)\n return output_design_file, d, a","title":""},{"docid":"07a9d6f9005828e07aa5b0fab43befa3","score":"0.61596817","text":"def run_algorithms(self):\n # REVERSE ORDER OF POLYGONS SO NODES ARE INPUT TO ALGORITHMS IN CLOCKWISE DIRECTION\n # (AS LAYER NODES ARE STORED IN LEFT TO RIGHT ORDER IN THE NUMPY ARRAYS\n if self.calc_grav_switch is True or self.calc_mag_switch is True:\n if len(self.polygons) > 1:\n for i in range(0, len(self.polygons)):\n self.polygons[i] = self.polygons[i][::-1]\n else:\n self.polygons[0] = self.polygons[0][::-1]\n\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n # TOPOGRAPHY - :FUTURE: PREDICTED TOPOGRAPHY FROM ISOSTATIC FUNC\n self.pred_topo = np.zeros_like(self.xp)\n\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n # GRAVITY\n # ARRAY OF DENSITY CONTRASTS TO BE PASSED TO BOTT ALGORITHM\n self.density_contrasts = np.zeros(len(self.densities))\n for i in range(len(self.densities)):\n self.density_contrasts[i] = (self.densities[i] - self.reference_densities[i])\n\n # ZIP POLYGONS WITH DENSITY CONTRASTS AND PASS TO BOTT\n if self.polygons and self.calc_grav_switch is True:\n # SELECT ONLY THOSE LAYERS THAT ARE CHECKED\n polygons_to_use, densities_to_use = [], []\n for layer in range(len(self.densities)):\n if self.layers_calculation_switch[layer] == 1:\n\n polygons_to_use.append(self.polygons[layer])\n densities_to_use.append(self.density_contrasts[layer])\n\n # PASS TO BOTT ALGORITHM\n polys = []\n for p, d in zip(polygons_to_use, densities_to_use):\n polys.append(Polygon(1000 * np.array(p), {'density': d}))\n self.predgz = bott.Gz(self.xp, self.zp, polys)\n else:\n self.predgz = np.zeros_like(self.xp)\n self.predplot.set_data(self.xp * 0.001, self.predgz)\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n # MAGNETICS\n # ZIP POLYGONS WITH SUSCEPT/STRIKES AND PASS TO TALWANI AND HEIRTZLER ALGORITHM\n if self.polygons and self.earth_field != 0.0 and self.calc_mag_switch is True:\n # SELECT ONLY THOSE LAYERS THAT ARE CHECKED\n polygons_to_use, susceptibilities_to_use = [], []\n for layer in range(0, len(self.polygons)):\n if self.layers_calculation_switch[layer] == 1:\n polygons_to_use.append(self.polygons[layer])\n susceptibilities_to_use.append(self.susceptibilities[layer])\n\n # PASS TO TALWANI AND HEIRTZLER ALGORITHM\n self.polys = []\n for p, s, in zip(polygons_to_use, susceptibilities_to_use):\n self.polys.append(Polygon(1000. * np.array(p), {'susceptibility': s}))\n self.prednt = talwani_and_heirtzler.ntz(self.xp, self.zp, self.polys, self.model_azimuth, self.earth_field,\n self.angle_a, self.angle_b, self.mag_observation_elv)\n else:\n self.prednt = np.zeros_like(self.xp)\n\n # PLOT MAGNETIC PROFILE\n self.prednt_plot.set_data(self.xp * 0.001, self.prednt)\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n # UPDATE RMS VALUES\n\n # RUN RMS CALC CODE\n self.model_rms(self.xp)\n\n # SET GRAV RMS VALUES\n if self.obs_gravity_data_for_rms != [] and self.grav_obs_switch == True and self.calc_grav_switch == True \\\n and self.predgz != []:\n self.grav_rms_plot.set_data(self.grav_residuals[:, 0], self.grav_residuals[:, 1])\n elif self.obs_gravity_data_for_rms is None:\n self.grav_rms_plot.set_data(self.obs_gravity_data_for_rms[:, 0],\n np.zeros_like(self.obs_gravity_data_for_rms[:, 0]))\n else:\n pass\n\n # SET MAG RMS VALUES\n if self.obs_mag_data_for_rms != [] and self.mag_obs_switch == True and self.calc_mag_switch == True \\\n and self.prednt != []:\n self.mag_rms_plot.set_data(self.mag_residuals[:, 0], self.mag_residuals[:, 1])\n elif self.obs_mag_data_for_rms is None:\n self.mag_rms_plot.set_data(self.obs_mag_data_for_rms[:, 0],\n np.zeros_like(self.obs_mag_data_for_rms[:, 0]))\n else:\n pass\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n # SET FRAME X AND Y LIMITS\n self.set_frame_limits()\n\n # AFTER RUNNING ALGORITHMS, SET MODEL AS UNSAVED\n self.model_saved = False\n\n # UPDATE GMG GRAPHICS\n self.draw()","title":""},{"docid":"52225774d11db8466b77d872149b8863","score":"0.61242497","text":"def optimize(self) -> None:\n if not self._order:\n raise ValueError(\"No parameters to refine.\")\n md.optimize(self._recipe, self._order, validate=False, verbose=self._verbose, **self._options)\n rw = self.get_rw()\n if self._verbose > 0:\n print(\"Optimization result: Rw = {:.6f}.\".format(rw))","title":""},{"docid":"3516dc094a8298d62209184f87f57b9e","score":"0.60990447","text":"def main():\n # create a GridSearch object to find the best fitting model\n grid_search = new_grid_search()\n # run the search algorithm\n run_grid_search(grid_search)\n # save the best fitting model into FS\n save_search_results(grid_search)","title":""},{"docid":"47534fa3b2527d69d936144b430541ed","score":"0.6098178","text":"def check_optimization(\n configure, # pylint: disable=unused-argument\n run_optimization, # pylint: disable=redefined-outer-name\n):\n\n def inner( # pylint: disable=too-many-arguments,missing-docstring,useless-suppression\n engine,\n func_workchain_name,\n engine_kwargs,\n xtol,\n ftol,\n x_exact,\n f_exact,\n evaluate=None,\n input_key='x',\n ):\n\n from aiida.orm import load_node\n\n import sample_processes\n func_workchain = getattr(sample_processes, func_workchain_name)\n\n result_node = run_optimization(\n engine=engine,\n engine_kwargs=ChainMap(engine_kwargs, {'result_key': 'result'}),\n func_workchain=func_workchain,\n evaluate=evaluate\n )\n\n assert 'optimal_process_uuid' in result_node.outputs\n assert np.isclose(result_node.outputs.optimal_process_output.value, f_exact, atol=ftol)\n calc = load_node(result_node.outputs.optimal_process_uuid.value)\n assert np.allclose(type(x_exact)(getattr(calc.inputs, input_key)), x_exact, atol=xtol)\n\n return inner","title":""},{"docid":"b26848cf5dae392ecec0e9d3eb6b4330","score":"0.6094927","text":"def _before_optimize(self):\n if config.monitor_verbosity > 0:\n logger.info('Starting optimization of %s...', str(self))\n\n # reset monitor object in case of rerun\n self.monitor.reset()\n\n # init monitor dict for algorithm\n self.monitor.t = time.time()\n\n # init optimization time control\n self.monitor.optimize_start = time.time()","title":""},{"docid":"def921edc7b662cb905ecc7db2051645","score":"0.6091037","text":"def solve(self):\n for algo in self.algorithms:\n log.info(\"algorithm %s is being solved\" % algo.algo.__name__)\n a = algo.algo(self.graphs.graph, self.graphs.drivers_graph, *algo.args, **algo.kwargs)\n try:\n start = time.time()\n a.solve()\n running_time = time.time() - start\n self.values.setdefault(algo.algo.__name__, a.get_optimal_value())\n self.running_times.setdefault(algo.algo.__name__, running_time)\n self.status.setdefault(algo.algo.__name__, a.get_status())\n except Exception:\n self.values.setdefault(algo.algo.__name__, None)\n self.running_times.setdefault(algo.algo.__name__, None)\n self.status.setdefault(algo.algo.__name__, options.FAILED)","title":""},{"docid":"6b63727a23f774affb6df5591e26f3c7","score":"0.6083168","text":"def run_problem5(config_file):\n C = json.load(open(config_file))\n \n # Optimizer config inputs\n opt_config = C['opt']['config']\n opt_in = C['opt']['input']\n opt_out = C['opt']['output']\n CP = C['planet']\n CV = C['vehicle']\n CT = C['target']\n CO = C['opt']\n \n # Problem and optimization configuration\n udp = prob_3D_lander_multiphase(C)\n prob = pg.problem(udp)\n prob.c_tol = opt_config['c_tol']\n uda = pg.algorithm(pg.nlopt(opt_config['nlopt_alg']))\n uda.set_verbosity(opt_config['verbosity'])\n uda.extract(pg.nlopt).xtol_rel = opt_config['xtol_rel']\n uda.extract(pg.nlopt).ftol_rel = opt_config['ftol_rel']\n uda.extract(pg.nlopt).maxeval = opt_config['maxeval']\n \n # Initial guess from file.\n if opt_in['guess_from_file']:\n print('LOADING GUESS FROM FILE')\n \n # Load in the file state vector\n Xdata_in = json.load(open(opt_in['in_file']))\n file_npts = Xdata_in['npts']\n ns = file_npts\n P = len(file_npts)\n x = Xdata_in['X0']\n \n # Other important information\n npts = opt_config['npts']\n\n Rx = []\n Ry = []\n Rz = []\n Vx = []\n Vy = []\n Vz = []\n Ux = []\n Uy = []\n Uz = []\n m = []\n Eta = []\n nu = 0.0\n T = []\n\n # Split up the state vector into a more meaningful shape\n i0 = 0\n for ii in range(0,P):\n Rx = Rx + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Ry = Ry + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Rz = Rz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Vx = Vx + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Vy = Vy + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Vz = Vz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Ux = Ux + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Uy = Uy + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Uz = Uz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n m = m + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n for ii in range(0,P):\n Eta= Eta+ [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\n i0 = i0 + ns[ii]\n nu = [x[i0]]\n i0 = i0 + 1\n T = x[(i0):(i0 + P)]\n\n for pp in range(0,len(file_npts)):\n \n # If number of points in phase does not line up:\n if not (file_npts[pp] == npts[pp]):\n \n Rx[pp] = recast_pts(Rx[pp],npts[pp])\n Ry[pp] = recast_pts(Ry[pp],npts[pp])\n Rz[pp] = recast_pts(Rz[pp],npts[pp])\n Vx[pp] = recast_pts(Vx[pp],npts[pp])\n Vy[pp] = recast_pts(Vy[pp],npts[pp])\n Vz[pp] = recast_pts(Vz[pp],npts[pp])\n Ux[pp] = recast_pts(Ux[pp],npts[pp])\n Uy[pp] = recast_pts(Uy[pp],npts[pp])\n Uz[pp] = recast_pts(Uz[pp],npts[pp])\n m[pp] = recast_pts(m[pp],npts[pp])\n Eta[pp] = recast_pts(Eta[pp],npts[pp])\n \n Rx = list(chain.from_iterable(Rx))\n Ry = list(chain.from_iterable(Ry))\n Rz = list(chain.from_iterable(Rz))\n Vx = list(chain.from_iterable(Vx))\n Vy = list(chain.from_iterable(Vy))\n Vz = list(chain.from_iterable(Vz))\n Ux = list(chain.from_iterable(Ux))\n Uy = list(chain.from_iterable(Uy))\n Uz = list(chain.from_iterable(Uz))\n m = list(chain.from_iterable(m))\n Eta= list(chain.from_iterable(Eta))\n \n X0 = Rx + Ry + Rz + Vx + Vy + Vz + Ux + Uy + Uz + m + Eta + nu + T\n \n # Initial guess - linear profile\n elif opt_in['use_linear_guess']:\n print('CONSTRUCTING LINEAR INITIAL GUESS')\n \n opt_lg = opt_in['linear_guess']\n npts = sum(opt_config['npts'])\n ns = opt_config['npts']\n tof = sum(opt_lg['tof'])\n nu0 = opt_lg['nu0']\n mass0 = sum([CV['stages'][ii]['ms'] + CV['stages'][ii]['mp'] for ii in CV['phases'][0][0]])\n isp0 = CV['engines'][CV['phases'][0][1]]['isp']\n tmax0 = CV['engines'][CV['phases'][0][1]]['Tmax']\n \n # Initial state\n ra = CP['R_eq'] + CV['orbit']['alta']\n rp = CP['R_eq'] + CV['orbit']['altp']\n sma = 0.5*(ra+rp)\n ecc = (ra-rp)/(ra+rp)\n inc = CV['orbit']['inc']\n Om = CV['orbit']['raan']\n om = CV['orbit']['argper']\n nu = nu0\n r0_I,v0_I = elts_to_rv(sma,ecc,inc,Om,om,nu,CP['mu'],CV['orbit']['degrees'])\n \n # Target state\n R_PF_UEN = Rot_PF_UEN(CT['lon'], CT['lat'], degrees=True)\n R_UEN_PF = R_PF_UEN.inv()\n R_I_PF = Rot_I_PF(CP['Omega'], CP['Epoch'], tof)\n R_UEN_I = R_I_PF.inv() * R_UEN_PF\n rLS_I = R_UEN_I.apply([CP['R_eq'] + CT['alt'],0,0])\n rt_I = rLS_I + R_UEN_I.apply(CT['target_pos_UEN'])\n vt_I = R_UEN_I.apply(CT['target_vel_UEN']) + cross([0,0,CP['Omega']],rt_I)\n \n # Construct X vector\n dt = tof/npts\n R = linspace(r0_I, rt_I, npts).transpose()\n V = linspace(v0_I, vt_I, npts).transpose()\n Ux = [1.0] + [(V[0,k+1] - V[0,k])/dt for k in range(0,npts-1)]\n Uy = [1.0] + [(V[1,k+1] - V[1,k])/dt for k in range(0,npts-1)]\n Uz = [1.0] + [(V[2,k+1] - V[2,k])/dt for k in range(0,npts-1)]\n for kk in range(0,len(Ux)):\n Umag = (Ux[kk]**2.0 + Uy[kk]**2.0 + Uz[kk]**2.0 )**0.5\n Ux[kk] = Ux[kk]/Umag\n Uy[kk] = Uy[kk]/Umag\n Uz[kk] = Uz[kk]/Umag \n U = array([Ux, Uy, Uz])\n Eta = [0.7]*ns[0] + [0.0]*ns[1] + [0.7]*ns[2]\n m = [mass0]\n for k in range(1,npts):\n m = m + [m[k-1] - tmax0*Eta[k]/(9.81*isp0)]\n X0 = list(R.flatten()) + list(V.flatten()) + list(U.flatten()) + list(m) + list(Eta) + [nu0] + opt_lg['tof']\n\n # Create a population\n print(\"Creating population...\")\n pop = pg.population(prob)\n pop.push_back(X0)\n\n # Monotonic basin hopping option\n if opt_config['use_mbh_wrapper']:\n mbhuda = pg.mbh(uda, stop=opt_config['mbh']['stop'], perturb=opt_config['mbh']['perturb'])\n algo = pg.algorithm(mbhuda)\n else:\n algo = uda\n \n # Evolve\n if C['opt']['evolve']:\n print(\"Evolving...\")\n pop = algo.evolve(pop)\n\n # Check feasibility\n is_feas = prob.feasibility_x(pop.champion_x)\n print(\"Feasible trajectory? \", is_feas)\n \n # Produce summary\n udp.run_traj(pop.champion_x, plot_traj=opt_out['plot_traj'], write_sum=opt_out['write_sum'], write_csv=opt_out['write_csv'] )\n \n if opt_out['write_X']:\n print('WRITING TO FILE')\n outdict = { 'npts':list(opt_config['npts']), 'X0':list(pop.champion_x) }\n if (not opt_out['only_write_feasible']) or (opt_out['only_write_feasible'] and is_feas):\n json.dump(outdict, open(opt_out['file_X_out'],'w+'), indent=4)","title":""},{"docid":"f031057b826cf4a97f9808101e42151b","score":"0.6079285","text":"def optimizeOptimization():\n initDict = {'target': [], 'damping': [], 'friction': []}\n acquisitionFunctions = ['ei', 'ucb', 'ei', 'ucb', 'ei', 'ucb']\n\n for i in range(6):\n optimization = BO(runAll40ExperimentsAndGetCost, {'damping': (0, 1), 'friction': (0, 1)})\n try:\n optimization.initialize(initDict)\n optimization.maximize(n_iter=50, acq=acquisitionFunctions[i])\n res = optimization.res['max']\n\n if res['max_val'] not in initDict['target']:\n initDict['target'].append(res['max_val'])\n maxParams = res['max_params']\n initDict['damping'].append(maxParams['damping'])\n initDict['friction'].append(maxParams['friction'])\n print(initDict)\n\n with open('BO_sledge_params/6xBO_all40_60iters_0-8_0-4.txt', 'w') as file:\n file.write(json.dumps(initDict)) # use `json.loads` to do the reverse\n\n except Exception as ex:\n print(ex)\n continue","title":""},{"docid":"8ac85dee859be2e0d831a857b2a2d040","score":"0.6072187","text":"def run(self):\n\n i = 0\n for training, validation, testing in tqdm(self.samples):\n for key, value in tqdm(self.models.items()):\n # If evaluation for key, iteration pair already exists, skip this pair.\n if not self.results[key][i]:\n self.results[key][i] = self._evaluate_algorithm(\n algorithm=value['algorithms'], configurations=value['configurations'], training_set=training,\n validation_set=validation, testing_set=testing, metric=self.metric)\n # Serialize benchmark.\n benchmark_to_pickle(self)\n i += 1","title":""},{"docid":"626684458c91b6a349dba256f569ae86","score":"0.606212","text":"def run(self, x, print_result=False, print_progress=False, print_time=False):\n args = print_progress\n t1 = time.time()\n res = minimize(\n self._score,\n x,\n args,\n method=\"Nelder-Mead\",\n tol=10e-3,\n options={\"maxiter\": 1000},\n )\n self.equilibrium_pos = res.x\n t2 = time.time()\n\n if print_result:\n print(res)\n\n if print_time:\n print(f\"Time Spent: {t2-t1} seconds\")","title":""},{"docid":"00e160f3644d688a9ef3958de8a28198","score":"0.60592455","text":"def _optimize(self):\n z, rho = self.phase.optimize(self.invert.z, self.invert.rho)\n self.fitz, self.fitrho = z, rho","title":""},{"docid":"08d701625caa8dd32e663e37206decda","score":"0.60560405","text":"def optimise(self, log_to_console=True, output_flag=True):\n\t\tfor m_str, m in self.model_with_separate_variables.items():\n\t\t\tif not log_to_console:\n\t\t\t\tm.setParam(\"LogToConsole\", 0)\n\t\t\tif not output_flag:\n\t\t\t\tm.setParam(\"OutputFlag\", 0) \t\t\t\n\t\t\t\n\t\t\tt = time.time()\n\t\t\t\t\t\t\n\t\t\tm.optimize()\n\t\t\t\n\t\t\tif m.Status == grb.GRB.INFEASIBLE:\n\t\t\t\traise ValueError(\"Model infeasible. I will stop here as the setup will lead to a bunch of infeasible models. Please, review the setup or change input parameters before continuing.\")\n\t\t\tself.model_with_separate_variables_runtime[m_str] = time.time() - t\t\t\t\n\t\t\tself.runtimes.append({'model number': m_str,\n\t\t\t\t\t\t\t\t 'model name': m.ModelName,\n\t\t\t\t\t\t\t\t 'time taken to optimise (in seconds)': self.model_with_separate_variables_runtime[m_str],\n\t\t\t\t\t\t\t\t 'number of partitions': len(self.partitions),\n\t\t\t\t\t\t\t\t 'partition size': len(self.x_range)})\n\n\t\tfor m_str, m in self.model_with_pwl_costs.items():\n\t\t\tif not log_to_console:\n\t\t\t\tm.setParam(\"LogToConsole\", 0)\n\t\t\tif not output_flag:\n\t\t\t\tm.setParam(\"OutputFlag\", 0) \t\t\t\n\n\t\t\tt = time.time()\n\t\t\tm.optimize()\n\t\t\tif m.Status == grb.GRB.INFEASIBLE:\n\t\t\t\traise ValueError(\"Model infeasible. I will stop here as the setup will lead to a bunch to infeasible models. Please, review the setup or change input parameters before continuing.\")\t\t\t\n\t\t\tself.model_with_pwl_costs_runtime[m_str] = time.time() - t\n\t\t\tself.runtimes.append({'model number': m_str,\n\t\t\t\t\t\t\t\t 'model name': m.ModelName,\n\t\t\t\t\t\t\t\t 'time taken to optimise (in seconds)': self.model_with_pwl_costs_runtime[m_str],\n\t\t\t\t\t\t\t\t 'number of partitions': len(self.partitions),\n\t\t\t\t\t\t\t\t 'partition size': len(self.x_range)})","title":""},{"docid":"be2b6dccf24e48f9818f0ddde4d40fe5","score":"0.6047866","text":"def run_objective(self, objective, candidates):\n pass","title":""},{"docid":"31cf1fc0a3047f221e50e3aa6834bfbc","score":"0.6044882","text":"def train_model(self):\n\n optimizer_lgbm = BayesianOptimization(\n f=self.lgbm_cv,\n pbounds=self.lgbm_pounds,\n random_state=1234,\n verbose=2\n )\n\n optimizer_xgb = BayesianOptimization(\n f=self.xgb_cv,\n pbounds=self.xgb_pounds,\n random_state=1234,\n verbose=2\n )\n #\n optimizer_cat = BayesianOptimization(\n f=self.cat_cv,\n pbounds=self.cat_pounds,\n random_state=1234,\n verbose=2\n )\n\n optimizer_xgb.maximize(n_iter=500)\n optimizer_lgbm.maximize(n_iter=500)\n optimizer_cat.maximize(n_iter=500)","title":""},{"docid":"0ab949a9ca5fd3b88f34cd1f71463621","score":"0.6033554","text":"def optimize(self):\n\n\n\t\tcl = self.clopath_W_rnn * self.con_dict['EE_mask']\n\t\tg_scale = cp.mean(cp.abs(self.grad_dict['W_rnn']))\n\t\tc_scale = cp.mean(cp.abs(cl))\n\t\tself.clopath_W_rnn = cl * (g_scale/c_scale)\n\t\tself.grad_dict['W_rnn'] += self.clopath_W_rnn\n\n\t\tcl = self.clopath_W_in * self.con_dict['XE_mask']\n\t\tg_scale = cp.mean(cp.abs(self.grad_dict['W_in']))\n\t\tc_scale = cp.mean(cp.abs(cl))\n\t\tself.clopath_W_in = cl * (g_scale/c_scale)\n\t\tself.grad_dict['W_in'] += self.clopath_W_in\n\n\n\t\tself.grad_dict['W_in'] *= self.con_dict['W_in_mask']\n\t\tself.grad_dict['W_rnn'] *= self.con_dict['W_rnn_mask']\n\t\tself.grad_dict['W_out'] *= self.con_dict['W_out_mask']\n\n\t\t# Calculate task loss\n\t\tself.task_loss = cross_entropy(self.output_mask, self.output_data, self.y)\n\n\t\t# Apply gradient updates using the chosen optimizer\n\t\tself.var_dict = self.optimizer.apply_gradients(self.grad_dict)","title":""},{"docid":"89fb7b87cff1cb42626487b40740392e","score":"0.6031508","text":"def step(self):\n self.inner_optimizer.step()\n\n self._increment_global_var()\n params_grads = []\n for param in self._parameter_list:\n if not param.trainable:\n continue\n if param._grad_ivar() is not None:\n grad_var = param._grad_ivar()\n params_grads.append((param, grad_var))\n\n self._apply_optimize(\n loss=None, startup_program=None, params_grads=params_grads\n )","title":""},{"docid":"4b7492c09c3418f9f75b598c7eacfca2","score":"0.6025532","text":"def one_iteration():\n global counter\n counter += 1\n\n # Create the parameter in CPACS with 'CPACSUpdater' module\n cpacs_path = mi.get_toolinput_file_path('CPACSUpdater')\n cpacs_out_path = mi.get_tooloutput_file_path('CPACSUpdater')\n\n tixi = cpsf.open_tixi(cpacs_path)\n wkdir_path = ceaf.create_new_wkdir(Rt.date)\n WKDIR_XPATH = '/cpacs/toolspecific/CEASIOMpy/filesPath/wkdirPath'\n tixi.updateTextElement(WKDIR_XPATH, wkdir_path)\n\n # TODO: improve this part! (maybe move somewhere else)\n # To delete coef from previous iter\n if opf.get_aeromap_path(Rt.modules) != 'None':\n xpath = opf.get_aeromap_path(Rt.modules)\n aeromap_uid = cpsf.get_value(tixi, xpath + '/aeroMapUID')\n Coef = apmf.get_aeromap(tixi, aeromap_uid)\n apmf.delete_aeromap(tixi, aeromap_uid)\n apmf.create_empty_aeromap(tixi, aeromap_uid, 'test_optim')\n apmf.save_parameters(tixi, aeromap_uid, Coef)\n cpsf.close_tixi(tixi, cpacs_path)\n\n # Update the CPACS file with the parameters contained in design_var_dict\n update_cpacs_file(cpacs_path, cpacs_out_path, design_var_dict)\n\n # Save the CPACS file\n if counter % 1 == 0:\n file_copy_from = mi.get_tooloutput_file_path('CPACSUpdater')\n shutil.copy(file_copy_from, optim_dir_path+'/Geometry/'+'iter_{}.xml'.format(counter))\n\n # Run optimisation sub workflow\n wkf.copy_module_to_module('CPACSUpdater', 'out', Rt.modules[0], 'in')\n wkf.run_subworkflow(Rt.modules)\n wkf.copy_module_to_module(Rt.modules[-1], 'out', 'CPACSUpdater', 'in')\n\n # Extract results TODO: improve this part\n cpacs_results_path = mi.get_tooloutput_file_path(Rt.modules[-1])\n log.info('Results will be extracted from:' + cpacs_results_path)\n tixi = cpsf.open_tixi(cpacs_results_path)\n\n # Update the constraints values\n update_res_var_dict(tixi)\n return compute_obj()","title":""},{"docid":"37c5fe88a09030955fda7daf1c62b8c7","score":"0.60232717","text":"def do_optimization(n,m,k,tau,lower_bounds, upper_bounds, r, rN, \\\n\t\t max_normal, sorted_index, max_processes, multi_event, get_values):\n\tenum = Enumerator(n, m, k, tau, lower_bounds, upper_bounds, multi_event)\n\topt = Optimizer(r, rN, m, n,tau, upper_bound=max_normal)\n\tMAX_QUEUE_SIZE = int(10E6)\n\ttry:\n\t\tqueue = Queue(MAX_QUEUE_SIZE) #Task queue for the processes\n\texcept OSError:\n\t\tMAX_QUEUE_SIZE = 2**15-1\n\t\tqueue = Queue(MAX_QUEUE_SIZE)\n\n\treturnQueue = Queue(MAX_QUEUE_SIZE) #Shared queue for processes to return results\n\n\tprocesses = start_processes(max_processes, queue, opt, returnQueue, \\\n\t\t\t sorted_index, get_values)\n\t\n\tC = enum.generate_next_C()\n\tcount = 0\n\twhile C is not False:\n\t\tcount += 1\n\t\tqueue.put(C, True)\n\t\tC = enum.generate_next_C()\n\tif count == 0:\n\t\tprint \"Error: No valid Copy Number Profiles exist for these intervals within the bounds specified. Exiting...\"\n\t\tsys.exit(1)\n\n\t# Send STOP signal to all processes\n\tfor i in range(max_processes-1):\n\t\tqueue.put(0)\n\n\tbest = []\n\tfor i in range(len(processes)):\n\t\titem = returnQueue.get()\n\t\tbest.append(item)\n\n\tfor p in processes:\n\t\tp.join()\n\n\tbest = find_mins(best)\n\treturn best","title":""},{"docid":"4b588cc9d53e32c69afc2c534e88d998","score":"0.60173005","text":"def __call__(self):\n # set type of callback function\n CMPFUNC = CFUNCTYPE(None, c_int, c_int, POINTER(c_double), POINTER(c_double))\n # try to initialize callback function\n callback = CMPFUNC(self._callback)\n # allocate solutions array\n solutions = numpy.zeros([self.mu*self.n_var])\n # prepare bounds\n if self.bounds[0] is None:\n lbound = None\n else:\n lbound = numpy.ctypeslib.as_ctypes(self.bounds[0])\n if self.bounds[1] is None:\n ubound = None\n else:\n ubound = numpy.ctypeslib.as_ctypes(self.bounds[1])\n # start the optimizer\n check = self.sharkwrapper.mocmaes(callback, self.n_var, self.n_objectives,\n numpy.ctypeslib.as_ctypes(self.initials), self.maxiter,\n lbound, ubound, self.mu, c_double(self.sigma),\n numpy.ctypeslib.as_ctypes(solutions))\n solutions = solutions.reshape(self.mu,self.n_var)\n return solutions","title":""},{"docid":"a8a8abf2d52137a7e241f9f178d828e0","score":"0.60145336","text":"def main():\n #Loading benchmark instances\n data = load_data()\n #-- Running solver\n for c in data:\n distances_data = data[c][0]\n print(f\"Instance at {c} cities\")\n start = time.time()\n TSP_CE_Solver(distances_data,\n 0.1,\n 5*c**2,\n n_iter = 100,\n tag= f'{c}_cities_{data[c][1]}',\n cout_th = data[c][1])\n print(f'Running time : {time.time()-start}') # note that with current implementation, first run also counts compilation","title":""},{"docid":"41e19129dcbca71160473935cdeaee0d","score":"0.60136986","text":"def main():\n args = run_argparse( sys.argv )\n\n if args.alpha:\n a = read_alpha( args.alpha, )\n\n if args.beta_analysis:\n beta_analysis(args, basis = args.basis,\n dal = args.beta_dal, in_AA = args.in_AA,\n out_AA = args.out_AA,\n ncpu = args.Ncpu,\n N_waters = args.N_waters)\n\n if args.beta_analysis_par:\n run_beta_analysis_par( N_waters = args.N_waters,\n ncpu = args.Ncpu,\n model = args.model )\n\n if args.alpha_analysis:\n alpha_analysis(args)\n\n if args.qm_generation:\n qm_generation( \n qm_waters = args.qm_waters,\n basis = args.basis\n )\n\n if args.qmmm_generation:\n qmmm_generation( \n qm_waters = args.qm_waters,\n mm_waters = args.mm_waters,\n potfreqs = args.potfreqs,\n potstyle = args.potstyle,\n basis = args.basis)\n\n if args.qm_analysis:\n qm_analysis( in_AA = args.in_AA,\n out_AA = args.out_AA )\n\n if args.qmmm_analysis:\n qmmm_analysis( args )\n\n if args.write:\n write_related( args )","title":""},{"docid":"b2332fb43142d4f6e3e8117ae21f885b","score":"0.6011758","text":"def algo(self, objective, niter):\n self._algo()\n self.step = self.post_step(self.step, self.sol, objective, niter)\n self.sol = self.post_sol(self.step, self.sol, objective, niter)","title":""},{"docid":"ae59c3a66d28c35cabf8af9d7cc06d60","score":"0.6004668","text":"def perform_optimization(self):\n \n # Create an optimizer (tf.train.AdamOptimizer)\n self.optimizer = tf.train.AdamOptimizer(self.learning_rate_ph)\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n \n # Gradient clipping (tf.clip_by_norm) for self.grads_and_vars\n # Pay attention that you need to apply this operation only for gradients\n # because self.grads_and_vars contains also variables.\n # list comprehension might be useful in this case.\n clip_norm = tf.cast(1.0, tf.float32)\n self.grads_and_vars = [(tf.clip_by_norm(grad,clip_norm),var) for grad,var in self.grads_and_vars]\n \n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars)","title":""},{"docid":"ecd2d6a2209f4b175c86eb30aa3572fa","score":"0.5987957","text":"def run_step(self, report_steps=1):\n \n _, step = self.model.sess.run([self.optimize_step, \n self.model.increment_step])\n self.steps_ago +=1\n cost = self.model.sess.run(self.cost_op) \n assert not(np.isnan(cost)), \"Objective is nan\"\n objective = - cost\n\n #print objective each step\n #print(\"objective %f best %f\" %(objective, self.best_objective))\n if np.isnan(cost):\n sys.stdout.write('X')\n sys.stdout.flush()\n elif objective > self.best_objective:\n self.best_objective = objective\n self.best_const_spend = self.model.sess.run(model.const_spend_pct_op)\n self.best_var_spend = self.model.sess.run(model.var_spending_ops)\n self.best_stock_alloc = self.model.sess.run(model.allocation_ops[0])\n self.best_step = step\n self.steps_ago = 0\n sys.stdout.write('!')\n sys.stdout.flush()\n else:\n sys.stdout.write('.')\n sys.stdout.flush()\n \n if step % report_steps == 0:\n sys.stdout.write(\"\\n%s step %d objective %f best %f (%d steps ago)\\n\" %\n (strftime(\"%H:%M:%S\"),\n step,\n objective,\n self.best_objective,\n self.steps_ago))\n # print variables optimized and gradients for debugging\n # sys.stdout.write(\"\\n\")\n # var_vals = self.model.sess.run(self.model.all_var_ops) \n # print(\"%s Variables\" % strftime(\"%H:%M:%S\"))\n # print(var_vals)\n\n # grad_vals = self.model.sess.run([grad[0] for grad in self.grads])\n # print(\"%s Gradients\" % strftime(\"%H:%M:%S\"))\n # print(grad_vals)\n\n sys.stdout.flush()\n\n self.best_bond_alloc = pd.Series([1 - bsa for bsa in self.best_stock_alloc])\n pickle_list = [self.best_const_spend, self.best_var_spend, self.best_stock_alloc,\n self.best_bond_alloc]\n pickle.dump( pickle_list, open( picklefile, \"wb\" ) )\n\n # every 10 report_steps show current best\n if step % (report_steps * 10) == 0:\n print (\"\\n#Objective: %f\\n\" % self.best_objective)\n print (\"const_spend_pct = %.12f\" % self.best_const_spend)\n print (\"var_spend_pcts = pd.Series(%s)\" % str(self.best_var_spend))\n print (\"stock_allocations = pd.Series(%s)\\n\" %str(self.best_stock_alloc))","title":""},{"docid":"19fbf7c96827eb7dd964e9be5f6fffd8","score":"0.5987113","text":"def __call__(self, graph: Graph):\n # initialize best distance and best solution\n best_cost = float('inf')\n best_solution = []\n\n # perform optimisation\n print('Optimizing...')\n generator = tqdm(range(self.generations))\n for _ in generator:\n # initialise new population\n ants = [_Ant(self, graph) for _ in range(self.ant_count)]\n\n # update ants position\n for ant in ants:\n for i in range(graph.rank - 1):\n ant.select_next_node()\n ant.close_route()\n ant.total_cost += graph.cost_matrix[ant.visited_nodes[-1]][ant.visited_nodes[0]]\n if ant.total_cost < best_cost:\n best_cost = ant.total_cost\n best_solution = [] + ant.visited_nodes\n # update pheromone\n ant.update_pheromone_delta()\n graph.update_pheromone(ants, self.rho)\n generator.set_description_str('best cost: {}'.format(best_cost))\n return best_solution, best_cost","title":""},{"docid":"c42e160528ab6442d26161441d3f41f0","score":"0.5984175","text":"def fit(self, V, y=None):\n\n # Initialization phase\n self._init_all(V)\n\n start_time = time()\n\n # Execution phase\n actual_n_iterations = 0\n\n #self.initial_tau_ = self._compute_taus() \n\n while actual_n_iterations != self.n_iterations:\n logging.debug(\"[INFO] iteration {0}\".format(actual_n_iterations))\n \n # each iterations performs a move on rows and n_view moves on columns\n\n iter_start_time = time()\n logging.debug(\"[INFO] ##############################################\\n\" +\n \"\\t\\t### Iteration {0}\".format(actual_n_iterations))\n #logging.debug(\"[INFO] X assignment: {0}\".format(self._assignment[0]))\n #logging.debug(\"[INFO] Y assignment: {0}\".format(self._assignment[1]))\n #logging.debug(\"[INFO] Z assignment: {0}\".format(self._assignment[2]))\n\n\n # perform a move in each dimension\n for i in range(self._n_modes):\n logging.debug(\"[INFO] Dimension {0}\".format(i))\n \n if self._iterations_without_moves == self._n_modes * self.n_threshold:\n self._actual_item = [randint(1, self._n[j] * 100) % self._n[j] for j in range(self._n_modes)]\n self._special_case = 0\n elif self._iterations_without_moves > self._n_modes * self.n_threshold:\n self._special_case += 1\n else:\n self._special_case = -1\n \n if int(self._special_case / self._n_modes) < self._n[i]:\n self._perform_move(i)\n else:\n self._update_intermediate_values_after_move(i)\n \n if self.compute_tau_list:\n tau = self._compute_taus()\n self.tau_vector_.append(tau)\n \n iter_end_time = time()\n\n \n if actual_n_iterations % 1 == 0:\n logging.info(\"Iteration #{0}\".format(actual_n_iterations))\n logging.info(\"[INFO] # clusters: {0}\".format(self._n_clusters))\n tau = self._compute_taus()\n logging.info(\"[INFO] Taus: {0}\".format(tau))\n logging.info(\"[INFO] Iteration time: {0}\".format(iter_end_time - iter_start_time))\n \n\n if self.path != None:\n if actual_n_iterations % 1000 == 0:\n f = open(self.path + 'assignments_' + str(actual_n_iterations) + '.txt', 'w+')\n \n for j in range(self._n_modes):\n f.write(f'##### mode {j} #####\\n')\n for i in range(self._n[j]):\n f.write(f\"{i}\\t{self._assignment[j][i]}\\n\")\n f.close()\n \n actual_n_iterations += 1\n if int(self._special_case / self._n_modes) == np.max(self._n):\n logging.info(\"[INFO] All moves have been tried. Taus can't be improved by moving any element.\")\n self._last_iteration = actual_n_iterations\n actual_n_iterations = self.n_iterations\n\n end_time = time()\n\n execution_time = end_time - start_time\n self.final_tau_ = self._compute_taus()\n \n logging.info('#####################################################')\n logging.info(\"[INFO] Execution time: {0}\".format(execution_time))\n\n # clone cluster assignments and transform in lists\n #self.x_ = np.copy(self._assignment[0]).tolist()\n #self.y_ = np.copy(self._assignment[1]).tolist()\n #self.z_ = np.copy(self._assignment[2]).tolist()\n self._n_clusters_ = np.copy(self._assignment)\n self.execution_time_ = execution_time\n\n logging.info(\"[INFO] Number of clusters found: {0}\".format(self._n_clusters))\n for j in range(self._n_modes):\n logging.info(\"[INFO] Number of clusters found on mode {1}: {0}\".format(self._n_clusters[j],j))\n logging.info(\"[INFO] Number of moves performed on mode {1}: {0} / {2}\".format(self._performed_moves[j],j, self.n_iterations))\n\n return self","title":""},{"docid":"f95d322a31125a57791ec373315ad1e0","score":"0.59841555","text":"def optimize(y, train_data, column_names, GD, population_size=20, size=10, max_iters=40, log=None):\n # Openning a file for storing the results\n if log != None:\n f = open(log, 'a')\n\n # Generating population randomly\n max_alg_iters = 1000 if GD else 1000000\n population = make_population(population_size, column_names, max_alg_iters) \n\n # Dictionary that stores fitness of each hyperparameter configuration \n # Fitness as calculated as minimum of test and training accuracy\n # Keys of the dictionary are strings of hyperparameters\n fitness = {}\n\n # Dictionary that stores parameters obtained by training the model \n # for each hyperparameter configuration\n # Keys of the dictionary are strings of hyperparameters\n w = {}\n \n # Dictionary that stores information about metrics\n statistics = {}\n\n for i in range(max_iters):\n print(i)\n for param in population:\n print(i)\n if str(param) in fitness:\n continue\n # Training the model and setting fitness\n w[str(param)], test_evaluation, train_evaluation = run(y, train_data, column_names, param, GD)\n fitness[str(param)] = min(test_evaluation['accuracy'], train_evaluation['accuracy'])\n\n # Writing obtained results in a file\n if log != None:\n f.write(str(w[str(param)]))\n f.write('\\n')\n f.write(str(param))\n f.write('\\n')\n f.write(str(fitness[str(param)]))\n f.write(str(test_evaluation))\n f.write('\\n')\n f.write(str(train_evaluation))\n f.write('\\n-------------------------------------------------------------------\\n')\n statistics = update_statistics(statistics, train_evaluation, test_evaluation)\n \n \n # Chosing the best instances from population and pair them randomly\n best = take_best_from_population(population, fitness, size)\n np.random.shuffle(best)\n for i in range(len(best)//2):\n baby1, baby2 = make_offspring(best[2*i], best[2*i+1])\n best.append(baby1)\n best.append(baby2)\n \n population = best\n \n if log != None:\n f.close()\n \n return fitness, w, statistics","title":""},{"docid":"a1dc3b892172b8cc831598ea1fd96df3","score":"0.5980781","text":"def optimize(self) -> None:\n logger.info(\"Running the POT optimization\")\n optimized_model = ModelEntity(\n self.dataset,\n configuration=self.task_environment.get_model_configuration(),\n )\n\n self.openvino_task.optimize(\n optimization_type=OptimizationType.POT,\n dataset=self.dataset,\n output_model=optimized_model,\n optimization_parameters=OptimizationParameters(),\n )\n\n logger.info(\"Inferring the optimised model on the validation set.\")\n result_set = self.infer(task=self.openvino_task, output_model=optimized_model)\n\n logger.info(\"Evaluating the optimized model on the validation set.\")\n self.evaluate(task=self.openvino_task, result_set=result_set)\n self.results[\"pot_int8\"] = result_set.performance.score.value","title":""},{"docid":"63c784f6f9563d9a3b127f4373c342b9","score":"0.5974644","text":"def optimize(self):\n # Create initial points for each TR\n for i in range(self.n_trust_regions):\n X_init = latin_hypercube(self.n_init, self.dim)\n X_init = from_unit_cube(X_init, self.lb, self.ub)\n fX_init = np.array([[self.f(x)] for x in X_init])\n\n # Update budget and set as initial data for this TR\n self.X = np.vstack((self.X, X_init))\n self.fX = np.vstack((self.fX, fX_init))\n self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))\n self.n_evals += self.n_init\n\n if self.verbose:\n fbest = fX_init.min()\n print(f\"TR-{i} starting from: {fbest:.4}\")\n sys.stdout.flush()\n\n # Thompson sample to get next suggestions\n while self.n_evals < self.max_evals:\n\n # Generate candidates from each TR\n X_cand = np.zeros((self.n_trust_regions, self.n_cand, self.dim))\n y_cand = np.inf * np.ones((self.n_trust_regions, self.n_cand, self.batch_size))\n for i in range(self.n_trust_regions):\n idx = np.where(self._idx == i)[0] # Extract all \"active\" indices\n\n # Get the points, values the active values\n X = deepcopy(self.X[idx, :])\n X = to_unit_cube(X, self.lb, self.ub)\n\n # Get the values from the standardized data\n fX = deepcopy(self.fX[idx, 0].ravel())\n\n # Don't retrain the model if the training data hasn't changed\n n_training_steps = 0 if self.hypers[i] else self.n_training_steps\n\n # Create new candidates\n X_cand[i, :, :], y_cand[i, :, :], self.hypers[i] = self._create_candidates(\n X, fX, length=self.length[i], n_training_steps=n_training_steps, hypers=self.hypers[i]\n )\n\n # Select the next candidates\n X_next, idx_next = self._select_candidates(X_cand, y_cand)\n assert X_next.min() >= 0.0 and X_next.max() <= 1.0\n\n # Undo the warping\n X_next = from_unit_cube(X_next, self.lb, self.ub)\n\n # Evaluate batch\n fX_next = np.array([[self.f(x)] for x in X_next])\n\n # Update trust regions\n for i in range(self.n_trust_regions):\n idx_i = np.where(idx_next == i)[0]\n if len(idx_i) > 0:\n self.hypers[i] = {} # Remove model hypers\n fX_i = fX_next[idx_i]\n\n if self.verbose and fX_i.min() < self.fX.min() - 1e-3 * math.fabs(self.fX.min()):\n n_evals, fbest = self.n_evals, fX_i.min()\n print(f\"{n_evals}) New best @ TR-{i}: {fbest:.4}\")\n sys.stdout.flush()\n self._adjust_length(fX_i, i)\n\n # Update budget and append data\n self.n_evals += self.batch_size\n self.X = np.vstack((self.X, deepcopy(X_next)))\n self.fX = np.vstack((self.fX, deepcopy(fX_next)))\n self._idx = np.vstack((self._idx, deepcopy(idx_next)))\n\n # Check if any TR needs to be restarted\n for i in range(self.n_trust_regions):\n if self.length[i] < self.length_min: # Restart trust region if converged\n idx_i = self._idx[:, 0] == i\n\n if self.verbose:\n n_evals, fbest = self.n_evals, self.fX[idx_i, 0].min()\n print(f\"{n_evals}) TR-{i} converged to: : {fbest:.4}\")\n sys.stdout.flush()\n\n # Reset length and counters, remove old data from trust region\n self.length[i] = self.length_init\n self.succcount[i] = 0\n self.failcount[i] = 0\n self._idx[idx_i, 0] = -1 # Remove points from trust region\n self.hypers[i] = {} # Remove model hypers\n\n # Create a new initial design\n X_init = latin_hypercube(self.n_init, self.dim)\n X_init = from_unit_cube(X_init, self.lb, self.ub)\n fX_init = np.array([[self.f(x)] for x in X_init])\n\n # Print progress\n if self.verbose:\n n_evals, fbest = self.n_evals, fX_init.min()\n print(f\"{n_evals}) TR-{i} is restarting from: : {fbest:.4}\")\n sys.stdout.flush()\n\n # Append data to local history\n self.X = np.vstack((self.X, X_init))\n self.fX = np.vstack((self.fX, fX_init))\n self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))\n self.n_evals += self.n_init","title":""},{"docid":"ed9489c049064922bd6901b2965ad693","score":"0.597257","text":"def run_step(self):\n assert self.model.training, \"[SimpleTrainer] model was changed to eval mode!\"\n start = time.perf_counter()\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n loss_dict = self.model(data)\n losses = sum(loss_dict.values())\n\n metrics_dict = loss_dict\n metrics_dict[\"data_time\"] = data_time\n self._write_metrics(metrics_dict)\n\n # Optimize\n self.optimizer.zero_grad()\n losses.backward()\n self.optimizer.step()","title":""},{"docid":"2d0c1d0a12ed99a39d2578475333e7ef","score":"0.5954046","text":"def _run_optimiser(cls, raw_func, domain_config_file, worker_manager, max_capital, mode,\n *args, **kwargs):\n raise NotImplementedError('Implement in a child class.')","title":""},{"docid":"2d0c1d0a12ed99a39d2578475333e7ef","score":"0.5954046","text":"def _run_optimiser(cls, raw_func, domain_config_file, worker_manager, max_capital, mode,\n *args, **kwargs):\n raise NotImplementedError('Implement in a child class.')","title":""},{"docid":"23dc8aed26ab24eec5c70d215515b7e1","score":"0.5947441","text":"def do_iteration(self):\n\n indices = rng.randint(self.cost_f.num, size=self.minibatch_size)\n self.w -= self.step_size*self.cost_f.derivative(self.w, indices)","title":""},{"docid":"52a4179e4ee3a576673d825274cdea52","score":"0.59455395","text":"def genetic_algorithm():\n \n warnings.filterwarnings(\"ignore\",\".*GUI is implemented.*\") # filter out the plotting warning about deprecation\n \n print('You are running the evolutionary algorithm')\n \n #This function sets all of the values of the number of parents, children, and mutation amount\n (num_init_parents, num_init_children, init_voltage, filename, num_parents, num_children, \n mutation_percentage, data_acquisition_device, mirror_communication_device, fom_num, \n deformable_mirror, zernike_polynomial_mode, radial_order) = initialization_f.initialize()\n \n print('\\nNOTE: LabView must be open in order to run the program\\n')\n print('Here are the options you have while the program is running:')\n print('\\tPress the enter key (or s) to stop the program')\n print('\\tPress \"m\" if you would like to change the mutation percentage')\n print('\\tPress \"c\" if you would like to change the number of children')\n print('\\tPrees \"p\" if you would like to change the number of parents')\n print('Press \"r\" then enter key to run the program')\n \n while True: # run an infinite loop until a key is pressed\n \tkeyboard_input = input() # get input from the user\n \tif keyboard_input == '\\r' or keyboard_input == '\\n' or keyboard_input == 'r': # if the key pressed was the enter key\n \t\tbreak # break out of the infinite loop\n \n \n opt_device = optimization_devices.initialize_opt_device(deformable_mirror, zernike_polynomial_mode, \n radial_order) # open and initialize the optimization device being used\n daq_device = daq_devices.initialize_daq_device(data_acquisition_device, \n fom_num)\t# open and initialize the data acquisition device being used\n opt_comm_device = opt_com_devices.initialize_comm_device(mirror_communication_device) # open and initialize the optimization communication device being used\n\n print('Starting...')\n start_time = time.time() # determine the time when the algorithm starts\n\n iteration_number = 0\n \n parents = people.parent_group(num_init_parents, opt_device, init_voltage, \n filename) # create parents from initialization constraints\n children = people.child_group(num_init_children, parents, \n opt_device) # create children from the given parents and do crossover\n children.mutate(mutation_percentage, opt_device) # mutate the children\n \n all_people = people.person_group(parents, children) # combine all of the children and parents into a single object\n all_people.test_and_sort_people(opt_device, daq_device, \n opt_comm_device) # measure the figures of merits and sort the people so the highest figure of merit is 0th indexed\n \n best_person = all_people.people[0] # the best person is the 0th indexed person in all_people\n print('best_person\\n', best_person.figure_of_merit) # show the best person's genes and figure of merit\n \n best_people = np.empty(0, people.person) # initialize the array of best people\n best_people = np.append(best_people, best_person) # store best person\n\n past_figures_of_merit = all_people.best_figures_of_merit(num_parents)\n \n print('Time to run: ', time.time() - start_time, ' seconds') # print out the number of seconds since the algorithm started\n \n while True: # run an infinite loop until user says to stop\n if msvcrt.kbhit(): # if the keyboard was hit\n \tkeyboard_input = msvcrt.getwche() # determine what key was pressed\n \tif keyboard_input == '\\r' or keyboard_input == 's': # if the enter key was pressed\n \t\tbreak # get out of the while loop\n \telif keyboard_input == 'm': # if the m key was pressed\n \t\tprint('\\nThis is the current mutation percentage: ', mutation_percentage)\n \t\tmutation_percentage = initialization_f.change_value('float', 0, 100) # change the mutation percentage to what the user wants\n \telif keyboard_input == 'c': # if the c key was pressed\n \t\tprint('\\nThis is the current number of children: ', num_children) \n \t\tnum_children = initialization_f.change_value('int', num_parents-1) # change the number of children to what the user wants\n \telif keyboard_input == 'p': # if the p key was pressed\n \t\tprint('\\nThis is the current number of parents: ', num_parents)\n \t\tnum_parents = initialization_f.change_value('float', 0, num_children+1) # change the number of parents to what the user wants\n\n parents = people.parent_group(num_parents, opt_device, None, None, all_people) # create parents from the best performing children\n children = people.child_group(num_children, parents, opt_device) # create children from the just created parents\n children.mutate(mutation_percentage, opt_device) # mutate the children\n \n all_people = people.person_group(parents, children) # combine all of the children and parents into a single container\n all_people.test_and_sort_people(opt_device, daq_device, \n opt_comm_device) # measure the figures of merits and sort the people so the highest figure of merit is 0th indexed\n \n new_best_person = all_people.people[0] # the best person is the 0th indexed person in all_people\n\n best_people = np.append(best_people, new_best_person) # store best person\n\n if new_best_person.figure_of_merit > best_person.figure_of_merit: # if the current best FOM is better than the overall best FOM\n \tbest_person = new_best_person # if the new best person is better, they are the overall best person ever\n print('best_person\\n', best_person.figure_of_merit) # print out the best person ever made\n\n opt_device.plot_object(new_best_person.genes, best_person.genes, \n iteration_number) # plot the current best and overall best genes on the optimization device \n\n figures_of_merit = np.concatenate((past_figures_of_merit, all_people.best_figures_of_merit(num_parents)), \n axis=1) # concatenate the previous figure of merit matrix with the current figures of merit\n iteration_number, past_figures_of_merit = plot_f.plot_performance(iteration_number, figures_of_merit) # plot the progressions of figures of merit\n \n print('Time to run: ', time.time() - start_time, ' seconds') # print out the number of seconds since the algorithm started\n \n daq_device.shut_down()\t# shut off the data acquisition device\n \n print('\\nWhat would you like to save?') # once the loop has finished, the user decides what to do with the genes made\n while True: # create an infinite loop\n print(\"\\nFor writing the best person's actuator voltages to a file, input 'write'\")\n print('For saving graph data, input \"graph\"')\n print(\"For saving the best person from each iteration, input 'all_best'\")\n print(\"For saving everything, input 'everything'\")\n print('For doing nothing, input \"none\"')\n saving_option = input() # get user input for what they want to do\n directory_path = os.path.dirname(os.path.abspath(__file__)) # get the current directory's path\n if saving_option == 'write': # if they want to write the genes to a file\n directory_path = save_different_directory(opt_device.default_directory)\n print(\"Enter the file name you want to be saved (do not include the file extension):\\nNote: this will overwrite a file with the same name\")\n filename = input() # get user input from for what filename they want\n opt_device.write_genes(best_person.genes, directory_path + filename) # write the genes to the input file\n if anything_else() == 'n': # if they don't want to do anything else\n break # break out of the while loop\n elif saving_option == 'graph': # if the user wants to save the graph\n directory_path = save_different_directory(directory_path + file_f.FOM_GRAPH_FOLDER)\n print(\"Enter the file name you want to be saved without the file extension (for test.csv & test.png, input test):\",\n \"\\nNote: This saves a .csv of graph's values and a pdf of the python figure\\nAlso, this will overwrite a file with the same name\")\n filename = input() # get user input from for what filename they want\n file_f.write_figures_of_merit(figures_of_merit, directory_path + filename) # write the figures of merit to a comma separated value file\n plt.savefig( directory_path +'%s.png' % filename, dpi = 300, bbox_inches = 'tight')\n if anything_else() == 'n': # if they don't want to do anything else\n break # break out of the while loop\n elif saving_option == 'all_best': # if the user wants to save all of the best genes\n directory_path = save_different_directory(opt_device.default_directory)\n print(\"Enter the folder you want the best people to be saved into (to have everything saved in a\",\n \"folder named 'test', input 'test'.:\\nNote: this will overwrite a folder with the same name\",\n \"\\nNote: It will save each person according to its iteration number. The ith person will be saved as 'i.[file_extension]'\")\n folder = input()\n new_dir_path = directory_path + \"\\\\\" + folder + \"\\\\\" # determine the directory path to save the genes to\n if not os.path.exists(new_dir_path): # if that path doesn't exist, create it\n os.makedirs(new_dir_path)\n for i in range(best_people.size): # for each best person\n opt_device.write_genes(best_people[i].genes, new_dir_path + str(i)) # write each gene\n if anything_else() == 'n': # if they don't want to do anything else\n break # break out of the while loop\n elif saving_option == 'everything':\n print(\"The directory it is saving into is \" + directory_path + file_f.FOM_GRAPH_FOLDER + \" and \" + opt_device.default_directory)\n print(\"Enter the file name you want for all of these files (for 'test.csv', 'test.png', etc., input 'test'.\\tNote: this will overwrite a file with the same name\")\n filename = input()\n opt_device.write_genes(best_person.genes, opt_device.default_directory + filename) # write the genes to the input file\n file_f.write_figures_of_merit(figures_of_merit, directory_path + file_f.FOM_GRAPH_FOLDER + filename) # write the figures of merit to a comma separated value file\n plt.savefig( directory_path + file_f.FOM_GRAPH_FOLDER +'%s.png' % filename, dpi = 300, bbox_inches = 'tight')\n new_dir_path = opt_device.default_directory + \"\\\\\" + filename + \"\\\\\" # determine the path to save the best people\n if not os.path.exists(new_dir_path): # if the directory path doesn't exist, create it\n os.makedirs(new_dir_path)\n for i in range(best_people.size): # for each best person\n opt_device.write_genes(best_people[i].genes, new_dir_path + str(i)) # write each gene\n if anything_else() == 'n': # if they don't want to do anything else\n break # break out of the while loop\n elif saving_option == 'none': # if the user doesn't want to do anything with the data\n break # break out of the while loop\n else: # if they didn't enter one of the options\n print(\"You didn't enter a correct command\")","title":""},{"docid":"e555c9e54a9a964ee6a92947ecc7e2f6","score":"0.59451145","text":"def run_optimizer(**kwargs):\n #==============================#\n #| Log file configuration |#\n #==============================#\n # By default, output should be written to <args.prefix>.log and also printed to the terminal.\n # This behavior may be changed by editing the log.ini file.\n # Output will only be written to log files after the 'logConfig' line is called!\n if kwargs.get('logIni') is None:\n import geometric.optimize\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, r'config/log.ini')\n else:\n logIni = kwargs.get('logIni')\n logfilename = kwargs.get('prefix')\n # Input file for optimization; QC input file or OpenMM .xml file\n inputf = kwargs.get('input')\n verbose = kwargs.get('verbose', False)\n # Get calculation prefix and temporary directory name\n arg_prefix = kwargs.get('prefix', None) #prefix for output file and temporary directory\n prefix = arg_prefix if arg_prefix is not None else os.path.splitext(inputf)[0]\n logfilename = rf\"{prefix}.log\"\n # Create a backup if the log file already exists\n backed_up = bak(logfilename)\n import logging.config\n logging.config.fileConfig(logIni,defaults={'logfilename': logfilename},disable_existing_loggers=False)\n #==============================#\n #| End log file configuration |#\n #==============================#\n\n import geometric\n logger.info('geometric-optimize called with the following command line:\\n')\n argv_print = []\n # When geometric-optimize is called on the command line with an argument such as \n # --ase-kwargs='{\"method\":\"GFN2-xTB\"}'\n # the shell strips away the single quotes resulting in printing out\n # --ase-kwargs={\"method\":\"GFN2-xTB\"}\n # making the copy-pasted command invalid.\n # This is a dirty hack to put the single quotes back.\n for arg in sys.argv:\n arg = arg.replace('\\'{','{').replace('{','\\'{')\n arg = arg.replace('}\\'','}').replace('}','}\\'')\n argv_print.append(arg)\n logger.info(' '.join(argv_print)+'\\n')\n print_logo(logger)\n now = datetime.now()\n logger.info('-=# \\x1b[1;94m geomeTRIC started. Version: %s \\x1b[0m #=-\\n' % (geometric.__version__))\n logger.info('Current date and time: %s\\n' % now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n \n if backed_up:\n logger.info('Backed up existing log file: %s -> %s\\n' % (logfilename, os.path.basename(backed_up)))\n\n t0 = time.time()\n\n # Create the params object, containing data to be passed into the optimizer\n params = OptParams(**kwargs)\n params.printInfo()\n\n # Create \"dirname\" folder for writing\n dirname = prefix+\".tmp\"\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n kwargs['dirname'] = dirname\n \n # Get the Molecule and engine objects needed for optimization\n M, engine = get_molecule_engine(**kwargs)\n \n # Create Work Queue object\n if kwargs.get('port', 0):\n logger.info(\"Creating Work Queue object for distributed Hessian calculation\\n\")\n createWorkQueue(kwargs['port'], debug=verbose>1)\n\n # Get initial coordinates in bohr\n coords = M.xyzs[0].flatten() * ang2bohr\n\n # Read in the constraints\n constraints = kwargs.get('constraints', None) #Constraint input file (optional)\n\n if constraints is not None:\n Cons, CVals = parse_constraints(M, open(constraints).read())\n else:\n Cons = None\n CVals = None\n\n #=========================================#\n #| Set up the internal coordinate system |#\n #=========================================#\n # First item in tuple: The class to be initialized\n # Second item in tuple: Whether to connect nonbonded fragments\n # Third item in tuple: Whether to throw in all Cartesians (no effect if second item is True)\n CoordSysDict = {'cart':(CartesianCoordinates, False, False),\n 'prim':(PrimitiveInternalCoordinates, True, False),\n 'dlc':(DelocalizedInternalCoordinates, True, False),\n 'hdlc':(DelocalizedInternalCoordinates, False, True),\n 'tric-p':(PrimitiveInternalCoordinates, False, False),\n 'tric':(DelocalizedInternalCoordinates, False, False)}\n coordsys = kwargs.get('coordsys', 'tric')\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\n\n # Perform an initial single-point QM calculation to determine bonding & fragments, if using TRIC\n if hasattr(engine, 'calc_bondorder') and coordsys.lower() in ['hdlc', 'tric'] and params.bothre > 1e-3:\n bothre = params.bothre\n logger.info(\"Calculating QM bond order and forming bonds using criterion of %.2f\\n\" % bothre)\n qm_bo = engine.calc_bondorder(coords, dirname)\n M.qm_bondorder = [qm_bo]\n M.build_topology(bond_order=bothre)\n if len(M.molecules) == 1 and bothre < 0.75:\n logger.info(\"Only one fragment found; increasing threshold\\n\")\n # Increase the bond order threshold until there are at least 2 fragments of size >1\n while bothre < 0.75 and len([m for m in M.molecules if len(m.e()) > 1]) == 1:\n bothre += 0.01\n M.build_topology(bond_order=bothre)\n logger.info(\"Using threshold of %.2f, there are now %i molecules\\n\" % (bothre, len(M.molecules)))\n M.top_settings['read_bonds'] = True\n # Delete the QM bond order to avoid problems when more structures are added\n del M.Data['qm_bondorder']\n else:\n if coordsys.lower() in ['hdlc', 'tric'] and params.bothre > 1e-3:\n logger.info(\"Requested bond order-based connectivity but it is not available in the current engine\\n\")\n logger.info(\"Bonds will be generated from interatomic distances less than %.2f times sum of covalent radii\\n\" % M.top_settings['Fac'])\n\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVals[0] if CVals is not None else None,\n conmethod=params.conmethod)\n \n #========================================#\n #| End internal coordinate system setup |#\n #========================================#\n\n # Auxiliary functions (will not do optimization):\n displace = kwargs.get('displace', False) # Write out the displacements of the coordinates.\n if displace:\n write_displacements(coords, M, IC, dirname, verbose)\n return\n\n fdcheck = kwargs.get('fdcheck', False) # Check internal coordinate gradients using finite difference..\n if fdcheck:\n IC.Prims.checkFiniteDifferenceGrad(coords)\n IC.Prims.checkFiniteDifferenceHess(coords)\n check_internal_grad(coords, M, IC.Prims, engine, dirname, verbose)\n check_internal_hess(coords, M, IC.Prims, engine, dirname, verbose)\n return\n\n # Print out information about the coordinate system\n if isinstance(IC, CartesianCoordinates):\n logger.info(\"%i Cartesian coordinates being used\\n\" % (3*M.na))\n else:\n logger.info(\"%i internal coordinates being used (instead of %i Cartesians)\\n\" % (len(IC.Internals), 3*M.na))\n logger.info(IC)\n logger.info(\"\\n\")\n\n # Print out a note if DFT is used for non-fragmented systems; recommend --dlc and --subfrctor 2.\n if engine.detect_dft() and coordsys != \"dlc\" and len(IC.frags) == 1:\n logger.info(\"#===================================================================================#\\n\")\n logger.info(\"#| \\x1b[91mNote: Detected the use of DFT for a system containing only one fragment.\\x1b[0m |#\\n\")\n logger.info(\"#| |#\\n\")\n logger.info(\"#| DFT calculations that use small to medium-sized grids can sometimes result in |#\\n\")\n logger.info(\"#| energies and/or gradients that are not translationally/rotationally invariant, |#\\n\")\n logger.info(\"#| which varies depending on the QC program used. |#\\n\")\n logger.info(\"#| |#\\n\")\n logger.info(\"#| Spurious translation/rotation contributions to the energy and/or gradient |#\\n\")\n logger.info(\"#| may cause convergence failure that is observable as 'movement' or 'tumbling' |#\\n\")\n logger.info(\"#| of the molecule in the optimization output. If observed, rerun the calculation |#\\n\")\n logger.info(\"#| using --coordsys dlc and --subfrctor 2. It will project out overall translation |#\\n\")\n logger.info(\"#| and rotation from the optimization space. |#\\n\")\n logger.info(\"#===================================================================================#\\n\")\n\n if Cons is None:\n # Run a standard geometry optimization\n params.xyzout = prefix+\"_optim.xyz\"\n progress = Optimize(coords, M, IC, engine, dirname, params)\n else:\n # Run a single constrained geometry optimization or scan over a grid of values\n if isinstance(IC, (CartesianCoordinates, PrimitiveInternalCoordinates)):\n raise RuntimeError(\"Constraints only work with delocalized internal coordinates\")\n Mfinal = None\n for ic, CVal in enumerate(CVals):\n if len(CVals) > 1:\n logger.info(\"---=== Scan %i/%i : Constrained Optimization ===---\\n\" % (ic+1, len(CVals)))\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal, conmethod=params.conmethod)\n IC.printConstraints(coords, thre=-1)\n if len(CVals) > 1:\n params.xyzout = prefix+\"_scan-%03i.xyz\" % (ic+1)\n # In the special case of a constraint scan, we write out multiple qdata.txt files\n if params.qdata is not None: params.qdata = 'qdata_scan-%03i.txt' % (ic+1)\n else:\n params.xyzout = prefix+\"_optim.xyz\"\n if ic == 0:\n progress = Optimize(coords, M, IC, engine, dirname, params)\n else:\n progress += Optimize(coords, M, IC, engine, dirname, params, print_info=False)\n # update the structure for next optimization in SCAN (by CNH)\n M.xyzs[0] = progress.xyzs[-1]\n coords = progress.xyzs[-1].flatten() * ang2bohr\n if Mfinal:\n Mfinal += progress[-1]\n else:\n Mfinal = progress[-1]\n cNames = IC.getConstraintNames()\n cVals = IC.getConstraintTargetVals()\n comment = ', '.join([\"%s = %.2f\" % (cName, cVal) for cName, cVal in zip(cNames, cVals)])\n Mfinal.comms[-1] = \"Scan Cycle %i/%i ; %s ; %s\" % (ic+1, len(CVals), comment, progress.comms[-1])\n #print\n if len(CVals) > 1:\n Mfinal.write('scan-final.xyz')\n if params.qdata is not None: Mfinal.write('qdata-final.txt')\n print_citation(logger)\n logger.info(\"Time elapsed since start of run_optimizer: %.3f seconds\\n\" % (time.time()-t0))\n if kwargs.get('port', 0):\n destroyWorkQueue()\n return progress","title":""},{"docid":"40c5f35a62bd0368ca2938567d59c17f","score":"0.59367657","text":"def step(self):\n params = self.params\n if np.isnan(self.G).any():\n raise RuntimeError(\"Gradient contains nan - check output and temp-files for possible errors\")\n if np.isnan(self.H).any():\n raise RuntimeError(\"Hessian contains nan - check output and temp-files for possible errors\")\n self.Iteration += 1\n if (self.Iteration%5) == 0:\n self.engine.clearCalcs()\n self.IC.clearCache()\n # At the start of the loop, the optimization variables, function value, gradient and Hessian are known.\n # (i.e. self.Y, self.E, self.G, self.H)\n Eig = sorted(np.linalg.eigh(self.H)[0])\n Emin = min(Eig).real\n if params.rfo:\n v0 = 1.0\n elif Emin < params.epsilon:\n v0 = params.epsilon-Emin\n else:\n v0 = 0.0\n if params.verbose: self.IC.Prims.printRotations(self.X)\n if len(Eig) >= 6:\n logger.info(\"Hessian Eigenvalues: %.5e %.5e %.5e ... %.5e %.5e %.5e\\n\" % (Eig[0],Eig[1],Eig[2],Eig[-3],Eig[-2],Eig[-1]))\n else:\n logger.info(\"Hessian Eigenvalues: \" + ' '.join(\"%.5e\" % i for i in Eig) + '\\n')\n # Are we far from constraint satisfaction?\n self.farConstraints = self.IC.haveConstraints() and self.IC.getConstraintViolation(self.X) > 1e-1\n ### OBTAIN AN OPTIMIZATION STEP ###\n # The trust radius is to be computed in Cartesian coordinates.\n # First take a full-size Newton Raphson step\n dy, _, __ = self.get_delta_prime(v0)\n # Internal coordinate step size\n inorm = np.linalg.norm(dy)\n # Cartesian coordinate step size\n self.cnorm = self.getCartesianNorm(dy)\n if params.verbose: logger.info(\"dy(i): %.4f dy(c) -> target: %.4f -> %.4f\\n\" % (inorm, self.cnorm, self.trust))\n # If the step is above the trust radius in Cartesian coordinates, then\n # do the following to reduce the step length:\n if self.cnorm > 1.1 * self.trust:\n # This is the function f(inorm) = cnorm-target that we find a root\n # for obtaining a step with the desired Cartesian step size.\n froot = self.createFroot(v0)\n froot.stores[inorm] = self.cnorm\n ### Find the internal coordinate norm that matches the desired Cartesian coordinate norm\n iopt = brent_wiki(froot.evaluate, 0.0, inorm, self.trust, cvg=0.1, obj=froot, verbose=params.verbose)\n if froot.brentFailed and froot.stored_arg is not None:\n # If Brent fails but we obtained an IC step that is smaller than the Cartesian trust radius, use it\n if params.verbose: logger.info(\"\\x1b[93mUsing stored solution at %.3e\\x1b[0m\\n\" % froot.stored_val)\n iopt = froot.stored_arg\n elif self.IC.bork:\n # Decrease the target Cartesian step size and try again\n for i in range(3):\n froot.target /= 2\n if params.verbose: logger.info(\"\\x1b[93mReducing target to %.3e\\x1b[0m\\n\" % froot.target)\n froot.above_flag = True # Stop at any valid step between current target step size and trust radius\n iopt = brent_wiki(froot.evaluate, 0.0, iopt, froot.target, cvg=0.1, verbose=params.verbose)\n if not self.IC.bork: break\n LastForce = self.ForceRebuild\n self.ForceRebuild = False\n if self.IC.bork:\n logger.info(\"\\x1b[91mInverse iteration for Cartesians failed\\x1b[0m\\n\")\n # This variable is added because IC.bork is unset later.\n self.ForceRebuild = True\n else:\n if params.verbose: logger.info(\"\\x1b[93mBrent algorithm requires %i evaluations\\x1b[0m\\n\" % froot.counter)\n ##### If IC failed to produce valid Cartesian step, it is \"borked\" and we need to rebuild it.\n if self.ForceRebuild:\n # Force a rebuild of the coordinate system and skip the energy / gradient and evaluation steps.\n if LastForce:\n logger.warning(\"\\x1b[1;91mFailed twice in a row to rebuild the coordinate system; continuing in Cartesian coordinates\\x1b[0m\\n\")\n self.checkCoordinateSystem(recover=True, cartesian=LastForce)\n logger.info(\"\\x1b[1;93mSkipping optimization step\\x1b[0m\\n\")\n self.Iteration -= 1\n self.state = OPT_STATE.SKIP_EVALUATION\n return\n ##### End Rebuild\n # Finally, take an internal coordinate step of the desired length.\n dy, _ = self.trust_step(iopt, v0)\n self.cnorm = self.getCartesianNorm(dy)\n ### DONE OBTAINING THE STEP ###\n if isinstance(self.IC, PrimitiveInternalCoordinates):\n idx = np.argmax(np.abs(dy))\n iunit = np.zeros_like(dy)\n iunit[idx] = 1.0\n self.prim_msg = \"Along %s %.3f\" % (self.IC.Internals[idx], np.dot(dy/np.linalg.norm(dy), iunit))\n ### These quantities, computed previously, are no longer used.\n # Dot product of the gradient with the step direction\n # Dot = -np.dot(dy/np.linalg.norm(dy), self.G/np.linalg.norm(self.G))\n # Whether the Cartesian norm comes close to the trust radius\n # bump = cnorm > 0.8 * self.trust\n ### Before updating any of our variables, copy current variables to \"previous\"\n self.Yprev = self.Y.copy()\n self.Xprev = self.X.copy()\n self.Gprev = self.G.copy()\n self.Eprev = self.E\n ### Update the Internal Coordinates ###\n X0 = self.X.copy()\n self.newCartesian(dy)\n ## The \"actual\" dy may be different from the one passed to newCartesian(),\n ## for example if we enforce constraints or don't get the step we expect.\n dy = self.IC.calcDiff(self.X, X0)\n self.Y += dy\n self.expect = flat(0.5*multi_dot([row(dy),self.H,col(dy)]))[0] + np.dot(dy,self.G)\n self.state = OPT_STATE.NEEDS_EVALUATION","title":""},{"docid":"34974aa97299d11078d5375377bb34f4","score":"0.593551","text":"def print_results(self):\n\t\t# for m_str, m in self.model_with_separate_variables.items():\t\t\t\n\t\t# \ttry: \n\t\t# \t\tprint(\"\\nModel {}\".format(m.ModelName))\n\t\t# \t\tprint(\"The value of the objective function after optimisation is {}\".format(m.objVal))\n\t\t# \t\tprint(\"Variables are as follows:\")\n\t\t# \t\tfor partition in self.partitions:\n\t\t# \t\t\t# for c, (interval_start, interval_end) in enumerate(self.interval_ends):\n\t\t# \t\t\t# \t# x = round(self.model_with_separate_variables_vars[m_str, partition, interval_start, interval_end].X, 2)\n\t\t# \t\t\t# \t# x = self.model_with_separate_variables_vars[m_str, partition, interval_start, interval_end].X\n\t\t# \t\t\t# \t# print(\"X_{}_{}_{} = {}\".format(partition, interval_start, interval_end, x))\n\t\t# \t\t\t# \tx = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\n\t\t# \t\t\t# \tprint(\"X_{} = {}\".format(partition, x))\n\t\t# \t\t\tx = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\n\t\t# \t\t\tprint(\"X_{} = {}\".format(partition, x))\n\t\t# \texcept AttributeError:\n\t\t# \t\tprint(\"\\nModel {} is infeasible!\".format(m_str))\n\n\t\t# for m_str, m in self.model_with_pwl_costs.items():\t\t\t\n\t\t# \ttry: \n\t\t# \t\tprint(\"\\nModel {}\".format(m.ModelName))\n\t\t# \t\tprint(\"The value of the objective function after optimisation is {}\".format(m.objVal))\n\t\t# \t\tprint(\"Variables are as follows:\")\n\t\t# \t\tfor partition in self.partitions:\n\t\t# \t\t\tx = self.model_with_pwl_costs_vars[m_str, partition].X\n\t\t# \t\t\tprint(\"X_{} = {}\".format(partition, x))\n\t\t# \texcept AttributeError:\n\t\t# \t\tprint(\"\\nModel {} is infeasible!\".format(m_str))\n\n\t\tprint(\"\\n MODEL COMPARISON\")\n\t\tfor m_str in self.model_with_separate_variables:\n\t\t\tprint(\"\\n{} (objective {}) | {} (objective {})\".format(self.model_with_separate_variables[m_str].ModelName, self.model_with_separate_variables[m_str].objVal,\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \tself.model_with_pwl_costs[m_str].ModelName, self.model_with_pwl_costs[m_str].objVal))\n\t\t\tprint(\"runtimes (in seconds): {} | {}\".format(self.model_with_separate_variables_runtime[m_str], self.model_with_pwl_costs_runtime[m_str]))\n\t\t\tfor partition in self.partitions:\n\t\t\t\tx_model_with_separate_variables = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\n\t\t\t\tx_model_with_pwl_costs = self.model_with_pwl_costs_vars[m_str, partition].X\n\t\t\t\tprint(\"X_{}: {} | {}\".format(partition, x_model_with_separate_variables, x_model_with_pwl_costs))","title":""},{"docid":"92f9e3f9d8efae72b85e397ccb90970c","score":"0.5935174","text":"def run(self):\n logger.info('Starting voxel-wise HRF estimation ...')\n logger.info('nvox=%d, ncond=%d, nscans=%d', self.nbVoxels, self.M,\n self.ImagesNb)\n # initialization of the matrices that will store all voxel resuls\n logger.info(\"Init storage ...\")\n self.InitStorageMat()\n logger.info(\"Compute onset matrix ...\")\n self.Compute_onset_matrix3()\n\n self.stop_iterations = np.zeros(self.bold.shape[1], dtype=int)\n\n # voxelwise analysis. This loop handles the currently analyzed voxels.\n for POI in xrange(self.bold.shape[1]): # POI = point of interest\n t0 = time()\n logger.info(\"Point %s / %s\", str(POI), str(self.nbVoxels))\n self.ReadPointOfInterestData(POI)\n\n # initialize with zeros or ones all matrix and vector used in\n # the class\n logger.info(\"Init matrix and vectors ...\")\n self.InitMatrixAndVectors(POI)\n\n # compute onset matrix\n\n # compute low frequency basis\n logger.info('build low freq mat ...')\n self.buildLowFreqMat()\n\n # precompute usefull data\n logger.info('Compute inv R ...')\n self.Compute_INV_R_and_R_and_DET_R()\n\n # solve find response functions and hyperparameters\n logger.info('EM solver ...')\n self.EM_solver(POI)\n\n # store current results in matrices initialized in 'InitStoringMat'\n logger.info('Store res ...')\n self.StoreRes(POI)\n logger.info(\"Done in %s\", format_duration(time() - t0))\n\n self.clean_memory()\n\n logger.info('Nb of iterations to reach stop crit: %s',\n array_summary(self.stop_iterations))","title":""},{"docid":"73dee46e94da00d9bc253a0cb7cc232c","score":"0.59339744","text":"def main(data_dir, parent_limit, branchvars, optimalpath, callback):\n dataset = parse_dataset(data_dir)\n\n solver = Solver(dataset, parent_limit, branchvars, optimalpath, callback)\n model = solver.solve()\n # if model feasible you can print the final objval here\n return model","title":""},{"docid":"7107e651c5823a15d2061e385465f386","score":"0.59294665","text":"def run(): \n description = \"Compare results of two separate optimisations\"\n parser = argparse.ArgumentParser(description=description)\n # Might want to make the type of this 'FileType('r')'\n parser.add_argument('filenames', metavar='F', nargs='+',\n help=\"the input files, should be exactly two\")\n arguments = parser.parse_args()\n\n if len(arguments.filenames) != 2:\n print (\"Must provide at least two best params files\")\n sys.exit(1)\n\n best_params_lists = [ parameters.parse_param_file(f) \n for f in arguments.filenames ]\n\n # We can do better than simply comparing every other file to the\n # first parameter file but for now I'm going to do that.\n first_best_params = best_params_lists[0]\n for other_best_params in best_params_lists[1:]:\n compare_parameters(first_best_params, other_best_params)","title":""},{"docid":"fbc8f4ad95c55aaa424646ffb8f946e3","score":"0.5926365","text":"def startProcess(self):\n self.best_obj_value = self.obj.value(self.best_solution)\n self.recordLog()\n\n if self.msg:\n during_solver_message_header()\n self.during_solver_message('S')","title":""},{"docid":"3cd77bface54206e92de993c5d2598fa","score":"0.5923426","text":"def optimize(self):\n def obj(parameters):\n \"\"\"\n Evaluates the mean absolute error for a set of input\n parameters: kfactor, decay, regress.\n\n \"\"\"\n kfactor, regress = parameters\n\n rating = Rating(\n mode=self.mode,\n kfactor=kfactor,\n regress=regress\n )\n\n residuals = rating.model_accuracy()\n mean_abs_error = np.abs(residuals).mean()\n return mean_abs_error\n\n bounds = [(25., 85.), (0.4, 0.8)]\n res_gp = gp_minimize(obj, bounds, n_calls=100, verbose=True)\n\n print(\"Best score: {:.4f}\".format(res_gp.fun))\n print(\"Best parameters: {}\".format(res_gp.x))","title":""},{"docid":"88944fa6284f693ed593aab814748728","score":"0.59185916","text":"def optimize(self):\n velocity = np.zeros([self.dimension, self.n_agents])\n population = self.generate_random_population()\n self.best_by_iter = []\n self.mean_by_iter = []\n self.optimal_fitness_value = np.inf\n self.optimal_model = None\n\n for i in range(self.maxiter):\n fitness = self.evaluate_objective_function(population)\n\n column_of_best_solution = np.argmin(fitness)\n current_best_fitness = fitness[column_of_best_solution]\n\n if current_best_fitness < self.optimal_fitness_value:\n self.optimal_fitness_value = current_best_fitness\n self.optimal_model = population[:, column_of_best_solution].copy()\n\n self.best_by_iter.append(self.optimal_fitness_value)\n self.mean_by_iter.append(np.mean(fitness))\n agent_mass = self.mass_calculation(fitness)\n gravitational_constant = self.calculate_gravitational_constant(i)\n acceleration = self.calculate_acceleration(\n population, agent_mass, gravitational_constant, i\n )\n velocity, population = self.update_velocity_position(\n population,\n acceleration,\n velocity,\n i,\n )\n\n return self","title":""},{"docid":"41978b757b56ec6ff7e3a64314250052","score":"0.5913431","text":"def eval(settings):\n\n settings['batch_size'] = 2048\n\n print(\"start evaluation with settings\", settings)\n\n # create the experiment folders\n logdir = os.path.join(settings['logdir'], settings['method'], settings['dataset'], utils.get_runname(settings))\n pathlib.Path(logdir).mkdir(parents=True, exist_ok=True)\n\n\n # prepare\n train_set = utils.dataset_from_name(split='train', **settings)\n val_set = utils.dataset_from_name(split='val', **settings)\n test_set = utils.dataset_from_name(split='test', **settings)\n\n train_loader = data.DataLoader(train_set, settings['batch_size'], shuffle=True,num_workers=settings['num_workers'])\n val_loader = data.DataLoader(val_set, settings['batch_size'], shuffle=True,num_workers=settings['num_workers'])\n test_loader = data.DataLoader(test_set, settings['batch_size'], settings['num_workers'])\n\n objectives = from_name(settings.pop('objectives'), val_set.task_names())\n scores1 = from_objectives(objectives)\n scores2 = [mcr(o.label_name, o.logits_name) for o in objectives]\n\n solver = solver_from_name(objectives=objectives, **settings)\n\n train_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\n val_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\n test_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\n\n \n task_ids = settings['task_ids'] if settings['method'] == 'SingleTask' else [0]\n for j in task_ids:\n if settings['method'] == 'SingleTask':\n # we ran it in parallel\n checkpoints = pathlib.Path(CHECKPOINT_DIR).glob(f'**/*_{j:03d}/*/c_*.pth')\n else:\n checkpoints = pathlib.Path(CHECKPOINT_DIR).glob('**/c_*.pth')\n \n train_results[f\"start_{j}\"] = {}\n val_results[f\"start_{j}\"] = {}\n test_results[f\"start_{j}\"] = {}\n\n for c in sorted(checkpoints):\n\n #c = list(sorted(checkpoints))[-1]\n print(\"checkpoint\", c)\n _, e = c.stem.replace('c_', '').split('-')\n\n j = int(j)\n e = int(e)\n \n solver.model.load_state_dict(torch.load(c))\n\n # Validation results\n val_results = evaluate(j, e, solver, scores1, scores2, val_loader, logdir, \n reference_point=settings['reference_point'],\n split='val',\n result_dict=val_results)\n\n # Test results\n test_results = evaluate(j, e, solver, scores1, scores2, test_loader, logdir, \n reference_point=settings['reference_point'],\n split='test',\n result_dict=test_results)\n\n # Train results\n # train_results = evaluate(j, e, solver, scores1, scores2, train_loader, logdir, \n # reference_point=settings['reference_point'],\n # split='train',\n # result_dict=train_results)","title":""},{"docid":"be2f93e630e801ee382db37120ce049a","score":"0.5909987","text":"def compute(self, inputs, outputs):\n # Add new variable value to the list in the dictionnay\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\n listval.append(inputs[key][0])\n\n # Evaluate the function to optimize\n outputs[Rt.objective] = one_iteration()","title":""},{"docid":"b9c2966319c32696f061c80666c712e5","score":"0.59034103","text":"def optimize_worker(circuit, options, q, x0):\n _, xopt = options.inner_solver.solve_for_unitary(circuit, options, x0)\n q.put((distance_for_x(xopt, options, circuit), xopt))","title":""},{"docid":"3c1a43149e0714cbd36c1ddce69914d3","score":"0.58992606","text":"def run(self):\n\n self.best_score = 1\n\n # runs the program x times depending on the runamount\n for z in range(self.runamount):\n\n done = False\n\n # restarts the program if an error occurs\n while done == False:\n\n # sets first fold and adds to final\n protein = Protein(self.user_input)\n\n # splits protein sequence into chunks of three amino acids\n user_input_split = split_protein(self.user_input)\n\n # updates x,y coordinates based on most recently determined fold\n coordinate_update = CoordinateUpdate()\n coordinate_update.update_coordinates(protein.final_placement)\n\n for i in range(len(user_input_split)):\n\n current_x, current_y = coordinate_update.update_coordinates(protein.final_placement)\n current_fold = protein.final_placement[-1][1]\n current_amino = self.user_input[len(protein.final_placement)]\n best_option = three_fold(protein.final_placement, user_input_split, current_fold, current_x, current_y, current_amino, i)\n\n # checks if an error has occured\n if best_option == False:\n break\n\n # adds the amino's the final placement\n for i in range(len(best_option) - 1):\n protein.add_amino_info(best_option[i])\n\n # checks if the end of protein hasn't been reached\n if current_fold != 0:\n current_x, current_y = coordinate_update.update_coordinates(protein.final_placement)\n\n # checks if last amino of sequence is reached\n if len(protein.final_placement) == (len(self.user_input) - 1):\n protein.add_last_amino_of_chunk_without_score(current_x, current_y, self.user_input)\n\n # end of protein has been reached\n if protein.final_placement[-1][1] == 0:\n done = True\n self.stability = Stability()\n\n stability_score, stability_connections = self.stability.get_stability_score(protein.final_placement)\n self.stability_coordinates = stability_connections\n self.stability.stability_score_coordinates(self.stability_coordinates)\n amino_stability_x = self.stability.amino_stability_x\n amino_stability_y = self.stability.amino_stability_y\n\n # checks if current score is lower than the current lowest score\n if stability_score < self.best_score:\n self.best_score = stability_score\n self.best_protein = protein.final_placement\n self.best_stability = stability_score\n self.amino_stability_x = amino_stability_x\n self.amino_stability_y = amino_stability_y","title":""},{"docid":"3cacebf0e49a67d9d4db339e78cb52ee","score":"0.5894279","text":"def run(self):\n logger.info('Running model to determine optimal solution.')\n super(ModelBase, self).run()\n logger.debug(f'Model summary outputs:\\n\\n{self.get_summary_outputs()}\\n')\n logger.debug(f'Model time series outputs:\\n\\n{self.get_timeseries_outputs()}\\n')\n if not psm.utils.has_consistent_outputs(model=self): # pragma: no cover\n logger.critical('Model has inconsistent outputs. Check log files for details.')\n logger.info('Done running model.')","title":""},{"docid":"c6033c8d21755839f103afdf4945d143","score":"0.5881525","text":"def bayesian_optimization(self):\n # Set the parameters for the bayesian optimization\n function = ho.objective_function\n lower = np.array([self._learning_rate[0], self._batch_size[0], \n self._number_filters[0], self._number_filters[0],\n self._number_filters[0]])\n upper = np.array([self._learning_rate[1], self._batch_size[1], \n self._number_filters[1], self._number_filters[1],\n self._number_filters[1]])\n for i in range(RUNS):\n results = bayesian_optimization(function, lower, upper,\n num_iterations=ITERATIONS)\n self.bo_performance.append(results['incumbent_values'])\n self.bo_runtime.append(results['runtime'])\n # Mean over all the runs\n self.bo_performance = np.mean(self.bo_performance, axis=0)\n # Cumulative sum of the mean for all the runs\n self.bo_runtime = np.cumsum(np.mean(self.bo_runtime, axis=0), axis=0)","title":""},{"docid":"45a67ac8c145f3a587a2649aaa624666","score":"0.5868775","text":"def main():\n\n parameters = {\n \"batch_size\": [16, 32, 64, 128, 256],\n \"learner_lr\": [0.001, 0.01, 0.1, 1],\n \"adversary_lr\": [0.001, 0.01, 0.1, 1],\n }\n\n for dataset in [\"law_school\"]:\n start = time.time()\n print(\"\\n\" + 40 * \"-\")\n print(f\"tuning hyperparameters for {dataset}\")\n print(40 * \"-\" + \"\\n\")\n best_param, best_auc, step = optimize_parameters(\n parameters, dataset, train_steps=1000, pretrain_steps=250\n )\n stop = time.time()\n print(f\"\\ntuning took {stop - start:.0f} seconds for {dataset}\")\n print(\n f\"the best parameters for {dataset} are:\\n{best_param}\\n\"\n f\"found on step {step} with an AUC of {best_auc:.3f}\\n\",\n )\n\n best_param[\"train_steps\"] = step\n\n filename = check_filename(f\"hyperparameters/{dataset}.txt\")\n\n try:\n with open(filename, \"w\") as f:\n f.write(str(best_param))\n except:\n print(\"results file could not be created\")","title":""},{"docid":"97cdfdf753295a833474b09490809da5","score":"0.5867343","text":"def optimizeGeometry(self):\n self.calcEnergyForce()\n self.prepareFirstStep()\n while self.state not in [OPT_STATE.CONVERGED, OPT_STATE.FAILED]:\n self.step()\n if self.state == OPT_STATE.NEEDS_EVALUATION:\n self.calcEnergyForce()\n self.evaluateStep()\n if self.state == OPT_STATE.FAILED:\n raise GeomOptNotConvergedError(\"Optimizer.optimizeGeometry() failed to converge.\")\n # If we want to save the Hessian used by the optimizer (in Cartesian coordinates)\n if self.params.write_cart_hess:\n # One last Hessian update before writing it out\n self.UpdateHessian()\n logger.info(\"Saving current approximate Hessian (Cartesian coordinates) to %s\" % self.params.write_cart_hess)\n Hx = self.IC.calcHessCart(self.X, self.G, self.H)\n np.savetxt(self.params.write_cart_hess, Hx, fmt='% 14.10f')\n return self.progress","title":""},{"docid":"f100ed55b644b19f8bd8b64d421a741b","score":"0.58617616","text":"def execute(args):\n LOGGER.info(\"Starting Scenic Quality Model\")\n dem_raster_info = pygeoprocessing.get_raster_info(args['dem_path'])\n\n try:\n do_valuation = bool(args['do_valuation'])\n except KeyError:\n do_valuation = False\n\n if do_valuation:\n valuation_coefficients = {\n 'a': float(args['a_coef']),\n 'b': float(args['b_coef']),\n }\n if args['valuation_function'].startswith('linear'):\n valuation_method = 'linear'\n elif args['valuation_function'].startswith('logarithmic'):\n valuation_method = 'logarithmic'\n elif args['valuation_function'].startswith('exponential'):\n valuation_method = 'exponential'\n else:\n raise ValueError('Valuation function type %s not recognized' %\n args['valuation_function'])\n\n max_valuation_radius = float(args['max_valuation_radius'])\n\n # Create output and intermediate directory\n output_dir = os.path.join(args['workspace_dir'], 'output')\n intermediate_dir = os.path.join(args['workspace_dir'], 'intermediate')\n utils.make_directories([output_dir, intermediate_dir])\n\n file_suffix = utils.make_suffix_string(\n args, 'results_suffix')\n\n LOGGER.info('Building file registry')\n file_registry = utils.build_file_registry(\n [(_OUTPUT_BASE_FILES, output_dir),\n (_INTERMEDIATE_BASE_FILES, intermediate_dir)],\n file_suffix)\n\n work_token_dir = os.path.join(intermediate_dir, '_tmp_work_tokens')\n try:\n n_workers = int(args['n_workers'])\n except (KeyError, ValueError, TypeError):\n # KeyError when n_workers is not present in args\n # ValueError when n_workers is an empty string.\n # TypeError when n_workers is None.\n n_workers = -1 # Synchronous execution\n graph = taskgraph.TaskGraph(work_token_dir, n_workers)\n\n reprojected_aoi_task = graph.add_task(\n pygeoprocessing.reproject_vector,\n args=(args['aoi_path'],\n dem_raster_info['projection'],\n file_registry['aoi_reprojected']),\n target_path_list=[file_registry['aoi_reprojected']],\n task_name='reproject_aoi_to_dem')\n\n reprojected_viewpoints_task = graph.add_task(\n pygeoprocessing.reproject_vector,\n args=(args['structure_path'],\n dem_raster_info['projection'],\n file_registry['structures_reprojected']),\n target_path_list=[file_registry['structures_reprojected']],\n task_name='reproject_structures_to_dem')\n\n clipped_viewpoints_task = graph.add_task(\n _clip_vector,\n args=(file_registry['structures_reprojected'],\n file_registry['aoi_reprojected'],\n file_registry['structures_clipped']),\n target_path_list=[file_registry['structures_clipped']],\n dependent_task_list=[reprojected_aoi_task,\n reprojected_viewpoints_task],\n task_name='clip_reprojected_structures_to_aoi')\n\n clipped_dem_task = graph.add_task(\n _clip_and_mask_dem,\n args=(args['dem_path'],\n file_registry['aoi_reprojected'],\n file_registry['clipped_dem'],\n intermediate_dir),\n target_path_list=[file_registry['clipped_dem']],\n dependent_task_list=[reprojected_aoi_task],\n task_name='clip_dem_to_aoi')\n\n # viewshed calculation requires that the DEM and structures are all\n # finished.\n LOGGER.info('Waiting for clipping to finish')\n clipped_dem_task.join()\n clipped_viewpoints_task.join()\n\n # phase 2: calculate viewsheds.\n LOGGER.info('Setting up viewshed tasks')\n viewpoint_tuples = []\n structures_vector = gdal.OpenEx(file_registry['structures_reprojected'],\n gdal.OF_VECTOR)\n for structures_layer_index in range(structures_vector.GetLayerCount()):\n structures_layer = structures_vector.GetLayer(structures_layer_index)\n layer_name = structures_layer.GetName()\n LOGGER.info('Layer %s has %s features', layer_name,\n structures_layer.GetFeatureCount())\n\n for point in structures_layer:\n # Coordinates in map units to pass to viewshed algorithm\n geometry = point.GetGeometryRef()\n viewpoint = (geometry.GetX(), geometry.GetY())\n\n if not _viewpoint_within_raster(viewpoint, file_registry['clipped_dem']):\n LOGGER.info(\n ('Feature %s in layer %s is outside of the DEM bounding '\n 'box. Skipping.'), layer_name, point.GetFID())\n continue\n\n if _viewpoint_over_nodata(viewpoint, file_registry['clipped_dem']):\n LOGGER.info(\n 'Feature %s in layer %s is over nodata; skipping.',\n point.GetFID(), layer_name)\n continue\n\n # RADIUS is the suggested value for InVEST Scenic Quality\n # RADIUS2 is for users coming from ArcGIS's viewshed.\n # Assume positive infinity if neither field is provided.\n # Positive infinity is represented in our viewshed by None.\n max_radius = None\n for fieldname in ('RADIUS', 'RADIUS2'):\n try:\n max_radius = math.fabs(point.GetField(fieldname))\n break\n except ValueError:\n # When this field is not present.\n pass\n\n try:\n viewpoint_height = math.fabs(point.GetField('HEIGHT'))\n except ValueError:\n # When height field is not present, assume height of 0.0\n viewpoint_height = 0.0\n\n try:\n weight = float(point.GetField('WEIGHT'))\n except ValueError:\n # When no weight provided, set scale to 1\n weight = 1.0\n\n viewpoint_tuples.append((viewpoint, max_radius, weight,\n viewpoint_height))\n structures_vector = None\n\n if not viewpoint_tuples:\n raise ValueError('No valid viewpoints found. This may happen if '\n 'viewpoints are beyond the edge of the DEM or are '\n 'over nodata pixels.')\n\n # These are sorted outside the vector to ensure consistent ordering. This\n # helps avoid unnecesary recomputation in taskgraph for when an ESRI\n # Shapefile, for example, returns a different order of points because\n # someone decided to repack it.\n viewshed_files = []\n viewshed_tasks = []\n valuation_tasks = []\n valuation_filepaths = []\n weights = []\n feature_index = 0\n for viewpoint, max_radius, weight, viewpoint_height in sorted(\n viewpoint_tuples, key=lambda x: x[0]):\n weights.append(weight)\n visibility_filepath = file_registry['visibility_pattern'].format(\n id=feature_index)\n viewshed_files.append(visibility_filepath)\n viewshed_task = graph.add_task(\n viewshed,\n args=((file_registry['clipped_dem'], 1), # DEM\n viewpoint,\n visibility_filepath),\n kwargs={'curved_earth': True, # SQ model always assumes this.\n 'refraction_coeff': float(args['refraction']),\n 'max_distance': max_radius,\n 'viewpoint_height': viewpoint_height,\n 'aux_filepath': None}, # Remove aux filepath after run\n target_path_list=[visibility_filepath],\n dependent_task_list=[clipped_dem_task,\n clipped_viewpoints_task],\n task_name='calculate_visibility_%s' % feature_index)\n viewshed_tasks.append(viewshed_task)\n\n if do_valuation:\n # calculate valuation\n viewshed_valuation_path = file_registry['value_pattern'].format(\n id=feature_index)\n valuation_task = graph.add_task(\n _calculate_valuation,\n args=(visibility_filepath,\n viewpoint,\n weight, # user defined, from WEIGHT field in vector\n valuation_method,\n valuation_coefficients, # a, b from args, a dict.\n max_valuation_radius,\n viewshed_valuation_path),\n target_path_list=[viewshed_valuation_path],\n dependent_task_list=[viewshed_task],\n task_name='calculate_valuation_for_viewshed_%s' % feature_index)\n valuation_tasks.append(valuation_task)\n valuation_filepaths.append(viewshed_valuation_path)\n\n feature_index += 1\n\n # The weighted visible structures raster is a leaf node\n weighted_visible_structures_task = graph.add_task(\n _count_and_weight_visible_structures,\n args=(viewshed_files,\n weights,\n file_registry['clipped_dem'],\n file_registry['n_visible_structures']),\n target_path_list=[file_registry['n_visible_structures']],\n dependent_task_list=sorted(viewshed_tasks),\n task_name='sum_visibility_for_all_structures')\n\n # If we're not doing valuation, we can still compute visual quality,\n # we'll just use the weighted visible structures raster instead of the\n # sum of the valuation rasters.\n if not do_valuation:\n parent_visual_quality_task = weighted_visible_structures_task\n parent_visual_quality_raster_path = (\n file_registry['n_visible_structures'])\n else:\n parent_visual_quality_task = graph.add_task(\n _sum_valuation_rasters,\n args=(file_registry['clipped_dem'],\n valuation_filepaths,\n file_registry['viewshed_value']),\n target_path_list=[file_registry['viewshed_value']],\n dependent_task_list=sorted(valuation_tasks),\n task_name='add_up_valuation_rasters')\n parent_visual_quality_raster_path = file_registry['viewshed_value']\n\n # visual quality is one of the leaf nodes on the task graph.\n graph.add_task(\n _calculate_visual_quality,\n args=(parent_visual_quality_raster_path,\n intermediate_dir,\n file_registry['viewshed_quality']),\n dependent_task_list=[parent_visual_quality_task],\n target_path_list=[file_registry['viewshed_quality']],\n task_name='calculate_visual_quality'\n )\n\n LOGGER.info('Waiting for Scenic Quality tasks to complete.')\n graph.join()","title":""},{"docid":"2e0bdc81087000ae4b75a404ced2d8ae","score":"0.58589476","text":"def optimize(self):\n # get the initial guess, flatten arrays and scale inputs\n xinit = self.graph.input.getFlat(self.prob.v, scaled=True)\n # Display a little information to check that things are working\n self.msgQueue.put(\n \"Starting NLopt Optimization at {0}\".format(\n time.strftime(\"%a, %d %b %Y %H:%M:%S\", time.localtime())\n )\n )\n self.msgQueue.put(\"\\nDecision Variables\\n---------------------\")\n vals = self.graph.input.getFlat(self.prob.v, scaled=False)\n for i, xn in enumerate(self.prob.v):\n self.msgQueue.put(\"{0}: {1} scaled: {2}\".format(xn, vals[i], xinit[i]))\n self.msgQueue.put(\"----------------------\")\n n = len(xinit) # Get the number of inputs\n # self.msgQueue.put(\"n = {0}\".format(n))\n #\n # Read solver options and handle any special cases of options\n #\n upper = self.options[\"upper\"].value # These are for scaled vars\n lower = self.options[\"lower\"].value # usually 0 to 10 not usually\n # changed but could be\n # If upper and/or lower are scalar conver to array with the\n # number of variables.\n if type(upper) == float or type(upper) == int:\n upper = upper * numpy.ones(n)\n else:\n upper = numpy.array(upper)\n if type(lower) == float or type(lower) == int:\n lower = lower * numpy.ones(n)\n else:\n lower = numpy.array(lower)\n\n # Get solver string and convert to NLopt enum\n method = self.optMethod(self.options[\"Solver\"].value)\n\n # Get rest of options\n absFunTol = self.options[\"tolfunabs\"].value\n relFunTol = self.options[\"tolfunrel\"].value\n absXtol = self.options[\"tolxabs\"].value\n relXtol = self.options[\"tolxrel\"].value\n maxeval = self.options[\"maxeval\"].value\n maxtime = self.options[\"maxtime\"].value\n saveRes = self.options[\"Save results\"].value\n initStep = self.options[\"init step\"].value\n pop = self.options[\"pop\"].value\n setName = self.options[\"Set Name\"].value\n self.bkp_int = self.options[\"Backup Interval\"].value\n\n # The set name to use when saving evaluations in flowsheet results\n if saveRes:\n setName = self.dat.flowsheet.results.incrimentSetName(setName)\n #\n # Create optimization object and set options\n opt = nlopt.opt(method, n)\n opt.set_lower_bounds(lower)\n opt.set_upper_bounds(upper)\n opt.set_ftol_rel(relFunTol)\n opt.set_ftol_abs(absFunTol)\n opt.set_xtol_rel(relXtol)\n opt.set_xtol_abs(absXtol)\n opt.set_maxeval(maxeval)\n if initStep > 1e-11:\n opt.set_initial_step(initStep)\n if pop > 0:\n opt.set_population(pop)\n needLocalOpt = False # so far I have not added any method that\n # would need this\n if needLocalOpt:\n opt2 = nlopt.opt(nlopt.LN_BOBYQA, n)\n opt.set_local_optimizer(opt2)\n # The solver is all setup and ready to go\n start = time.time() # get start time\n self.userInterupt = False #\n self.bestSoFar = float(\"inf\") # set inital best values\n self.bestSoFarList = [] # List of all previous best so far values\n # self.bestSoFarList.append(self.bestSoFar)\n\n # self.prob is the optimzation problem. get it ready\n self.prob.iterationNumber = 0\n self.prob.initSolverParameters() #\n self.prob.solverStart = start\n self.prob.maxSolverTime = maxtime\n if saveRes:\n self.prob.storeResults = setName\n else:\n self.prob.storeResults = None\n #\n opt.set_min_objective(self.f) # set the solver objective function\n self.prob.prep(self) # get problem ready for solving\n self.bkp_timer = time.time() # timer for flowseet backup\n # Run the optimzation\n x = opt.optimize(xinit)\n # Print some final words now that optimzation is done.\n eltime = time.time() - start\n self.msgQueue.put(\n \"{0}, Total Elasped Time {1}s, Obj: {2}\".format(\n self.prob.iterationNumber, math.floor(eltime), self.bestSoFar\n )\n )\n self.resQueue.put([\"IT\", self.prob.iterationNumber, self.bestSoFar])\n self.resQueue.put([\"BEST\", [self.bestSoFar], x])\n # print(self.bestSoFarList)\n min_obj_value = min(self.bestSoFarList, key=lambda t: t[1])\n self.msgQueue.put(\"Best result found stored in graph\")\n self.msgQueue.put(\n \"The best value for objective function was obtained at iteration number {}\".format(\n min_obj_value[0]\n )\n )","title":""},{"docid":"8fc057b79c320e3dbcd9f0192fb52f99","score":"0.58526593","text":"def optimize(self):\r\n try:\r\n model = Model(self.model_name)\r\n\r\n for rv_index in range(len(self.__regularVariablesLong)):\r\n\r\n if self.__regularVariablesLong[rv_index].variable_type == \"discrete\":\r\n for domain_index in range(len(self.__regularVariablesLong[rv_index].discrete_domain)):\r\n self.__w[rv_index, domain_index] = model.addVar(vtype=GRB.BINARY,\r\n name=self.__regularVariablesLong[\r\n rv_index].name + \"_w[[\" + str(\r\n domain_index) + \"]]\")\r\n self.__x[rv_index] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, ub=GRB.INFINITY,\r\n name=self.__regularVariablesLong[rv_index].name)\r\n else:\r\n self.__x[rv_index] = model.addVar(vtype=GRB.CONTINUOUS,\r\n lb=self.__regularVariablesLong[rv_index].lower_bound,\r\n ub=self.__regularVariablesLong[rv_index].upper_bound,\r\n name=self.__regularVariablesLong[rv_index].name)\r\n\r\n for pv_index in range(len(self.__predictedVariablesLong)):\r\n self.__y[pv_index] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, ub=GRB.INFINITY,\r\n name=self.__predictedVariablesLong[pv_index].name)\r\n\r\n model.update()\r\n\r\n \"\"\"\r\n Associate binary w with continuous x\r\n only needed for discrete variables\r\n \"\"\"\r\n\r\n for rv_index in range(len(self.__regularVariablesLong)):\r\n if self.__regularVariablesLong[rv_index].variable_type == \"discrete\":\r\n projection_expr = LinExpr(0.0)\r\n\r\n one_w_expr = LinExpr(0.0)\r\n for domain_index in range(len(self.__regularVariablesLong[rv_index].discrete_domain)):\r\n projection_expr += self.__regularVariablesLong[rv_index].discrete_domain[domain_index] * \\\r\n self.__w[rv_index, domain_index]\r\n one_w_expr += self.__w[rv_index, domain_index]\r\n model.addConstr(self.__x[rv_index] == projection_expr)\r\n model.addConstr(one_w_expr == 1)\r\n\r\n \"\"\"\r\n Put constraints\r\n \"\"\"\r\n for con_index in range(len(self.__constraints)):\r\n\r\n con_expr = LinExpr(0.0)\r\n\r\n for term_index in range(len(self.__constraints[con_index].con_expr.expression_terms)):\r\n var_vector_index = self.__regularVariablesLong.index(\r\n self.__constraints[con_index].con_expr.expression_terms[term_index].regular_var)\r\n value_in_coeff = self.__constraints[con_index].con_expr.expression_terms[term_index].value\r\n con_expr += value_in_coeff * self.__x[var_vector_index]\r\n\r\n if self.__constraints[con_index].sense == 'less_equal':\r\n model.addConstr(con_expr <= self.__constraints[con_index].rhs)\r\n elif self.__constraints[con_index].sense == 'equal':\r\n model.addConstr(con_expr == self.__constraints[con_index].rhs)\r\n else:\r\n model.addConstr(con_expr >= self.__constraints[con_index].rhs)\r\n\r\n \"\"\"\r\n Connect regular variables with predictive variables\r\n \"\"\"\r\n for pv_index in range(len(self.__predictedVariablesLong)):\r\n\r\n \"\"\"\r\n Neural Network\r\n \"\"\"\r\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, MLPRegressor):\r\n this_pm = self.__predictedVariablesLong[pv_index].opm\r\n for node in range(this_pm.n_nodes):\r\n self.__pre_relu_value[pv_index, node] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY,\r\n ub=GRB.INFINITY,\r\n name=self.__predictedVariablesLong[\r\n pv_index].name + \"_pre[[\" + str(\r\n node) + \"]]\")\r\n self.__post_relu_value[pv_index, node] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY,\r\n ub=GRB.INFINITY,\r\n name=self.__predictedVariablesLong[\r\n pv_index].name + \"_post[[\" + str(\r\n node) + \"]]\")\r\n self.__need_activation_func[pv_index, node] = model.addVar(vtype=GRB.BINARY,\r\n name=self.__predictedVariablesLong[\r\n pv_index].name + \"_actFun[[\" + str(\r\n node) + \"]]\")\r\n model.update()\r\n\r\n \"\"\"\r\n obtain the detail of the predictive model\r\n \"\"\"\r\n predicted_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\r\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\r\n nn_coefs = predicted_model_for_var.coefs_\r\n nn_intercepts = predicted_model_for_var.intercepts_\r\n\r\n \"\"\"\r\n generate matrices W and B and A\r\n \"\"\"\r\n\r\n \"\"\"\r\n initialize the matrices W and A\r\n \"\"\"\r\n W = np.zeros((this_pm.n_nodes, this_pm.n_nodes), dtype=float)\r\n A = np.zeros((this_pm.n_nodes, this_pm.n_nodes), dtype=int)\r\n\r\n for layer in range(this_pm.n_layers - 1):\r\n for i in range(this_pm.layers_sizes[layer]):\r\n for j in range(this_pm.layers_sizes[layer + 1]):\r\n A[i + this_pm.offset[layer], j + this_pm.offset[layer + 1]] = 1\r\n W[i + this_pm.offset[layer], j + this_pm.offset[layer + 1]] = nn_coefs[layer][i][j]\r\n\r\n \"\"\"\r\n initialize vector B\r\n \"\"\"\r\n B = np.zeros(this_pm.n_inputs, dtype=float).tolist()\r\n for layer in range(this_pm.n_layers - 1):\r\n B += nn_intercepts[layer].tolist()\r\n\r\n W = np.around(W, decimals=6)\r\n B = np.around(B, decimals=6)\r\n\r\n \"\"\"\r\n Constructing matrices W, B and A\r\n \"\"\"\r\n\r\n for node in range(this_pm.n_nodes):\r\n\r\n if this_pm.nodes_locations[node] == 0:\r\n \"\"\"\r\n input layer\r\n \"\"\"\r\n model.addConstr(\r\n self.__pre_relu_value[pv_index, node] == self.__post_relu_value[pv_index, node])\r\n\r\n feature_index = node\r\n\r\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\r\n rv_index = self.__regularVariablesLong.index(\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]])\r\n model.addConstr(self.__pre_relu_value[pv_index, node] == self.__x[rv_index])\r\n else:\r\n model.addConstr(self.__pre_relu_value[pv_index, node] ==\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]])\r\n\r\n elif this_pm.nodes_locations[node] == this_pm.n_layers - 1:\r\n \"\"\"\r\n output layer\r\n \"\"\"\r\n model.addConstr(\r\n self.__pre_relu_value[pv_index, node] == self.__post_relu_value[pv_index, node])\r\n\r\n incoming_arcs = LinExpr(0.0)\r\n for j in range(node):\r\n if A[j][node] == 1:\r\n incoming_arcs += W[j][node] * self.__post_relu_value[pv_index, j]\r\n model.addConstr(self.__pre_relu_value[pv_index, node] == incoming_arcs + B[node])\r\n\r\n else:\r\n \"\"\"\r\n hidden layers\r\n \"\"\"\r\n\r\n incoming_arcs = LinExpr(0.0)\r\n for j in range(node):\r\n if A[j][node] == 1:\r\n incoming_arcs += W[j][node] * self.__post_relu_value[pv_index, j]\r\n model.addConstr(self.__pre_relu_value[pv_index, node] == incoming_arcs + B[node])\r\n\r\n model.addConstr(\r\n self.__post_relu_value[pv_index, node] <= JANOS.BIG_M * self.__need_activation_func[\r\n pv_index, node] + JANOS.EPSILON)\r\n model.addConstr(self.__post_relu_value[pv_index, node] >= 0.0)\r\n\r\n model.addConstr(\r\n self.__pre_relu_value[pv_index, node] <= JANOS.BIG_M * self.__need_activation_func[\r\n pv_index, node])\r\n model.addConstr(self.__pre_relu_value[pv_index, node] >= -1.0 * JANOS.BIG_M * (\r\n 1 - self.__need_activation_func[pv_index, node]) + JANOS.EPSILON)\r\n\r\n model.addConstr(self.__post_relu_value[pv_index, node] <= self.__pre_relu_value[\r\n pv_index, node] + JANOS.BIG_M * (1 - self.__need_activation_func[\r\n pv_index, node]) + JANOS.EPSILON)\r\n model.addConstr(self.__post_relu_value[pv_index, node] >= self.__pre_relu_value[\r\n pv_index, node] - JANOS.BIG_M * (1 - self.__need_activation_func[\r\n pv_index, node]) - JANOS.EPSILON)\r\n\r\n model.addConstr(self.__y[pv_index] == self.__post_relu_value[pv_index, this_pm.n_nodes - 1])\r\n\r\n \"\"\"\r\n Linear regression\r\n \"\"\"\r\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, LinearRegression):\r\n\r\n predictive_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\r\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\r\n\r\n lin_regr_expr = LinExpr(0.0)\r\n for feature_index in range(len(opt_pm_for_var.feature_names)):\r\n\r\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\r\n rv_index = self.__regularVariablesLong.index(\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]])\r\n lin_regr_expr += predictive_model_for_var.coef_[0][feature_index] * self.__x[rv_index]\r\n else:\r\n\r\n lin_regr_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]]\r\n\r\n lin_regr_expr += predictive_model_for_var.intercept_\r\n model.addConstr(lin_regr_expr == self.__y[pv_index])\r\n\r\n \"\"\"\r\n Logistic regression\r\n \"\"\"\r\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, LogisticRegression):\r\n\r\n n_intervals_discretization = self.__predictedVariablesLong[pv_index].opm.n_breakpoints\r\n\r\n predictive_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\r\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\r\n\r\n max_value_lin_expr = 0.0\r\n min_value_lin_expr = 0.0\r\n\r\n for feature_index in range(len(opt_pm_for_var.feature_names)):\r\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\r\n rv_index = self.__regularVariablesLong.index(\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]])\r\n if predictive_model_for_var.coef_[0][feature_index] > 0:\r\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__regularVariablesLong[rv_index].upper_bound\r\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__regularVariablesLong[rv_index].lower_bound\r\n else:\r\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__regularVariablesLong[rv_index].lower_bound\r\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__regularVariablesLong[rv_index].upper_bound\r\n else:\r\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]]\r\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]]\r\n\r\n b = max_value_lin_expr + predictive_model_for_var.intercept_\r\n a = min_value_lin_expr + predictive_model_for_var.intercept_\r\n\r\n breakpoints_for_probs = np.linspace(calc_sigmoid_value(a), calc_sigmoid_value(b),\r\n n_intervals_discretization)\r\n breakpoints_for_probs = np.minimum(breakpoints_for_probs, 1 - JANOS.EPSILON)\r\n breakpoints_for_probs = np.maximum(breakpoints_for_probs, JANOS.EPSILON)\r\n breakpoints_for_lin_expr = np.apply_along_axis(calc_inverse_sigmoid, 0, breakpoints_for_probs)\r\n\r\n self.__z[pv_index] = model.addVars(n_intervals_discretization - 1, vtype=GRB.BINARY,\r\n name=\"z[\" + str(pv_index) + \"]\")\r\n\r\n model.update()\r\n\r\n \"\"\"\r\n Pick at most one z\r\n \"\"\"\r\n one_z_expr = LinExpr(0.0)\r\n for interval_index in range(n_intervals_discretization - 1):\r\n one_z_expr += self.__z[pv_index][interval_index]\r\n model.addConstr(one_z_expr == 1)\r\n\r\n exp_argument_expr = LinExpr(0.0)\r\n for feature_index in range(len(opt_pm_for_var.feature_names)):\r\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\r\n rv_index = self.__regularVariablesLong.index(\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]])\r\n exp_argument_expr += predictive_model_for_var.coef_[0][feature_index] * self.__x[rv_index]\r\n else:\r\n exp_argument_expr += predictive_model_for_var.coef_[0][feature_index] * \\\r\n self.__predictedVariablesLong[pv_index].variable_mapping[\r\n opt_pm_for_var.feature_names[feature_index]]\r\n\r\n exp_argument_expr += predictive_model_for_var.intercept_\r\n\r\n \"\"\"\r\n link breakpoints with probs\r\n \"\"\"\r\n model.addConstr(0 <= self.__y[pv_index])\r\n model.addConstr(1 >= self.__y[pv_index])\r\n\r\n min_prob = min(breakpoints_for_probs)\r\n max_prob = max(breakpoints_for_probs)\r\n min_prob = float(min_prob)\r\n max_prob = float(max_prob)\r\n model.addConstr(min_prob <= self.__y[pv_index])\r\n model.addConstr(max_prob >= self.__y[pv_index])\r\n\r\n \"\"\"\r\n link linear expression with binaries\r\n \"\"\"\r\n for interval_index in range(n_intervals_discretization - 1):\r\n lb_prob = breakpoints_for_probs[interval_index]\r\n ub_prob = breakpoints_for_probs[interval_index + 1]\r\n\r\n first_constant = 0.0\r\n second_constant = 0.0\r\n prob_val = 0.0\r\n\r\n if lb_prob > JANOS.EPSILON and ub_prob < 1 - JANOS.EPSILON:\r\n \"\"\"\r\n case 1\r\n \"\"\"\r\n first_constant = breakpoints_for_lin_expr[interval_index]\r\n second_constant = breakpoints_for_lin_expr[interval_index + 1]\r\n prob_val = calc_average_sigmoid_value(breakpoints_for_lin_expr[interval_index],\r\n breakpoints_for_lin_expr[interval_index + 1])\r\n elif lb_prob == 1 - JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\r\n \"\"\"\r\n case 2\r\n \"\"\"\r\n first_constant = calc_inverse_sigmoid(1 - JANOS.EPSILON)\r\n second_constant = b[0]\r\n prob_val = 1 - JANOS.EPSILON\r\n elif lb_prob == JANOS.EPSILON and ub_prob == JANOS.EPSILON:\r\n \"\"\"\r\n case 3\r\n \"\"\"\r\n first_constant = a[0]\r\n second_constant = calc_inverse_sigmoid(JANOS.EPSILON)\r\n prob_val = JANOS.EPSILON\r\n elif lb_prob > JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\r\n \"\"\"\r\n case 4\r\n \"\"\"\r\n first_constant = breakpoints_for_lin_expr[interval_index]\r\n second_constant = b[0]\r\n prob_val = 1 - JANOS.EPSILON\r\n elif lb_prob == JANOS.EPSILON and ub_prob < 1 - JANOS.EPSILON:\r\n \"\"\"\r\n case 5\r\n \"\"\"\r\n first_constant = a[0]\r\n second_constant = breakpoints_for_lin_expr[interval_index + 1]\r\n prob_val = JANOS.EPSILON\r\n elif lb_prob == JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\r\n \"\"\"\r\n case 6\r\n \"\"\"\r\n first_constant = a[0]\r\n second_constant = b[0]\r\n prob_val = 0.5\r\n else:\r\n print(\"JANOS Error: didn't find matched case for discretization.\")\r\n sys.exit(1)\r\n\r\n model.addConstr(\r\n (first_constant - a[0]) * self.__z[pv_index][interval_index] + a[0] <= exp_argument_expr)\r\n model.addConstr(\r\n (second_constant - b[0]) * self.__z[pv_index][interval_index] + b[0] >= exp_argument_expr)\r\n\r\n model.addConstr(\r\n min_prob + (prob_val - min_prob - JANOS.EPSILON) * self.__z[pv_index][interval_index] <=\r\n self.__y[pv_index])\r\n model.addConstr(\r\n max_prob + (prob_val - max_prob + JANOS.EPSILON) * self.__z[pv_index][interval_index] >=\r\n self.__y[pv_index])\r\n\r\n model.update()\r\n\r\n \"\"\"\r\n Setting objective\r\n \"\"\"\r\n\r\n obj_expr = LinExpr(0.0)\r\n\r\n for rv_index in range(len(self.__regularVariablesLong)):\r\n obj_expr += self.__regularVariablesLong[rv_index].objective_coefficient * self.__x[rv_index]\r\n for pv_index in range(len(self.__predictedVariablesLong)):\r\n obj_expr += self.__predictedVariablesLong[pv_index].objective_coefficient * self.__y[pv_index]\r\n\r\n model.setObjective(obj_expr, GRB.MAXIMIZE)\r\n\r\n \"\"\"\r\n Associate\r\n \"\"\"\r\n\r\n if self.write_lp_model:\r\n if self.get_lp_model_filename() != \"\":\r\n model.write(self.get_lp_model_filename())\r\n else:\r\n model.write(\"ipmodel.lp\")\r\n model.setParam(\"TimeLimit\", self.get_time_limit()) # OR: model.setParam.TimeLimit = self.get_time_limit()\r\n model.setParam(\"OutputFlag\", self.get_output_flag())\r\n for key, value in self.gurobi_param_settings.items():\r\n model.setParam(key, value)\r\n\r\n model.optimize()\r\n self.gurobi_model = model\r\n\r\n except GurobiError as e:\r\n print('Gurobi error ' + str(e.errno) + \": \" + str(e.message))","title":""},{"docid":"f9c89f09177afb4b9634679dd13edb65","score":"0.58515215","text":"def run(self) -> Tuple[List[float], List[float]]:\n self.opt_steps = 0\n ln: int = self.num_steps\n remainder = ln % self.window_size\n if remainder != 0:\n if self.verbose:\n print(f'window_size must be a factor of len(energy_cost), trimming arrays from {ln} to {ln-remainder}')\n self.energy_cost = self.energy_cost[:-remainder]\n self.outdoor_temps = self.outdoor_temps[:-remainder]\n ln -= remainder\n self.num_steps -= remainder\n\n step_size = int(self.window_size / 2)\n for i in range(step_size, ln + 1, step_size):\n self.opt_steps += 1\n h = HVAC(\n self.energy_cost[i - step_size: i + step_size],\n self.outdoor_temps[i - step_size: i + step_size],\n low_temp_threshold=self.low_thresh,\n high_temp_threshold=self.high_thresh,\n initial_building_temperature=self.temperature,\n verbose=self.verbose,\n integer_opt_only=self.int_opt_only\n )\n states, temps = h.optimized_values()\n these_states = states[:step_size]\n self.control_regime.extend(these_states)\n self.house_temps.extend(temps[:step_size])\n self.temperature = temps[step_size - 1]\n\n self.compute_regime_cost()\n\n if self.plot:\n self.plot_controls('Optimized Control by Energy Cost :')\n\n return self.control_regime, self.house_temps","title":""},{"docid":"ad2bb3e31b7934abff201c4fd90a13d5","score":"0.5849908","text":"def main():\n # PARAMETROS QUE NECESITAMOS\n # los hiperparametros que queramos añadir al programa se tiene que pasar por linea de comandos. \n # Cualquier parámetro que queramos añadir para que el optimizador de hiperparámetros los pueda modificar\n parser = argparse.ArgumentParser()\n parser.add_argument('--batch-size', type=int, help='Batch size for the training')\n parser.add_argument('--epochs', type=int, help='Number of epochs for the training')\n # Para informar si vamos a hacer o no el hyperTuning\n parser.add_argument('--hypertune', action='store_true', help='This is a hypertunning job')\n parser.add_argument('--model-type', default='Dense', help='This is model type job')\n \n # Estos parámetros son opciones de para AI platform\n # '--job-dir' obligatorio\n # '--model-output-path' path donde escribiremos el binario del modelo después de entrenarlo y que utilizaremos en producción.\n parser.add_argument('--job-dir', default=None, required=False, help='Option for AI Platform')\n parser.add_argument('--model-output-path', help='Path to write the SaveModel format')\n\n args = parser.parse_args()\n batch_size = args.batch_size\n epochs = args.epochs\n is_hypertune = args.hypertune\n model_type = args.model_type\n \n job_dir = args.job_dir\n model_output_path = args.model_output_path\n\n train_and_evaluate(batch_size, epochs, is_hypertune, model_type, job_dir, model_output_path)\n pass","title":""},{"docid":"8c0d9bd97d35f7c642cdaa3cb29bf5e9","score":"0.5849762","text":"def main():\n subj_list = [s for s in range(1, 20)]\n subj_list.remove(3)\n subj_list.remove(11)\n decondir = os.path.join(os.environ['avp'], 'nii',\n 'deconvolve_outs_concat_dec')\n f_suffx = 'Powered.cleanEPI_REML_fnirted_MNI2mm.nii.gz'\n for vvar in ['coef', 'tstat']:\n cond_dict = make_subbrck_dict(vvar)\n\n for subj in subj_list:\n inpref = 'decon_out.mion.{}_concat'.format(subj)\n infile_a = os.path.join(decondir, '{}.{}[{}]'.format(\n inpref, f_suffx, cond_dict['AHighVLow']))\n infile_b = os.path.join(decondir, '{}.{}[{}]'.format(\n inpref, f_suffx, cond_dict['ALowVHigh']))\n fx_dir = os.path.join(os.environ['avp'], 'nii',\n 'ss{}_effects_dec'.format(subj))\n mean_outname = os.path.join(fx_dir,\n 'crossd_avg_ss{}_{}'.format(\n subj, vvar))\n logfile = setup_log(os.path.join(os.environ['avp'], 'logs',\n 'calc_mean'))\n logfile.info('Doing calc_mean.')\n calc_mean(logfile, infile_a, infile_b, mean_outname)\n\n logfile = setup_log(os.path.join(os.environ['avp'], 'logs',\n 'calc_subt'))\n logfile.info('Doing calc_subt.')\n hh_file = os.path.join(decondir, '{}.{}[{}]'.format(\n inpref, f_suffx, cond_dict['AHighVHigh']))\n hhout_contr = os.path.join(fx_dir, 'high_grad_ss{}_{}'.format(\n subj, vvar))\n calc_subt(logfile, hh_file, '{}+tlrc'.format(mean_outname),\n hhout_contr)\n\n ll_file = os.path.join(decondir, '{}.{}[{}]'.format(\n inpref, f_suffx, cond_dict['ALowVLow']))\n llout_contr = os.path.join(fx_dir, 'low_grad_ss{}_{}'.format(\n subj, vvar))\n calc_subt(logfile, '{}+tlrc'.format(mean_outname), ll_file,\n llout_contr)","title":""}],"string":"[\n {\n \"docid\": \"2e4784ec13198e30e578db97a463bfa4\",\n \"score\": \"0.7996382\",\n \"text\": \"def run(self):\\n # set up logging, write params\\n self.setup_optimization()\\n\\n # set up pool of workers\\n self.setup_pool()\\n\\n with self.open_data('w'):\\n # run optimization\\n self.optimize()\\n\\n if self['postopt', 'run_postopt']:\\n with self.open_data('r'):\\n # evaluate individuals in hall of fame\\n self.post_optimization()\\n if self.pool is not None:\\n logging.info('Closing pool')\\n self.pool.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c5980ce4b3fcbf91b458a41a768c42\",\n \"score\": \"0.79808754\",\n \"text\": \"def run(self,optimizer):\\n\\t\\toptimizer.optimize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287ba9734b8ee1e43f062d8c18fc7530\",\n \"score\": \"0.7589167\",\n \"text\": \"def optimize(self):\\n self.opt_input = optimize(self.molsys, self.computer)\\n # update molecular system\\n # set E, g_x, and hessian to have their last values\\n # set step_number\\n # set self.history to match history.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a038b1e379ae68f7c391f62ff3abbf4\",\n \"score\": \"0.747118\",\n \"text\": \"def run(self):\\n self._reconstruct()\\n self._invert()\\n if 0:\\n self._optimize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05dbbb99230897e8df78e09a125872f\",\n \"score\": \"0.7422692\",\n \"text\": \"def optimize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ab5fcd5db587aae2a2e53dfa331d5e\",\n \"score\": \"0.7254709\",\n \"text\": \"def run(self):\\n problem = self._problem()\\n model = problem.model\\n relevant = model._relevant\\n self.pyopt_solution = None\\n self._total_jac = None\\n self.iter_count = 0\\n fwd = problem._mode == 'fwd'\\n optimizer = self.options['optimizer']\\n self._quantities = []\\n\\n self._check_for_missing_objective()\\n self._check_jac = self.options['singular_jac_behavior'] in ['error', 'warn']\\n\\n # Only need initial run if we have linear constraints or if we are using an optimizer that\\n # doesn't perform one initially.\\n con_meta = self._cons\\n model_ran = False\\n if optimizer in run_required or np.any([con['linear'] for con in self._cons.values()]):\\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\\n # Initial Run\\n model.run_solve_nonlinear()\\n rec.abs = 0.0\\n rec.rel = 0.0\\n model_ran = True\\n self.iter_count += 1\\n\\n # compute dynamic simul deriv coloring or just sparsity if option is set\\n if c_mod._use_total_sparsity:\\n coloring = None\\n if self._coloring_info['coloring'] is None and self._coloring_info['dynamic']:\\n coloring = c_mod.dynamic_total_coloring(self, run_model=not model_ran,\\n fname=self._get_total_coloring_fname())\\n\\n if coloring is not None:\\n # if the improvement wasn't large enough, don't use coloring\\n pct = coloring._solves_info()[-1]\\n info = self._coloring_info\\n if info['min_improve_pct'] > pct:\\n info['coloring'] = info['static'] = None\\n msg = f\\\"Coloring was deactivated. Improvement of {pct:.1f}% was less \\\" \\\\\\n f\\\"than min allowed ({info['min_improve_pct']:.1f}%).\\\"\\n issue_warning(msg, prefix=self.msginfo, category=DerivativesWarning)\\n\\n comm = None if isinstance(problem.comm, FakeComm) else problem.comm\\n opt_prob = Optimization(self.options['title'], weak_method_wrapper(self, '_objfunc'),\\n comm=comm)\\n\\n # Add all design variables\\n input_meta = self._designvars\\n self._indep_list = indep_list = list(input_meta)\\n input_vals = self.get_design_var_values()\\n\\n for name, meta in input_meta.items():\\n size = meta['global_size'] if meta['distributed'] else meta['size']\\n opt_prob.addVarGroup(name, size, type='c',\\n value=input_vals[name],\\n lower=meta['lower'], upper=meta['upper'])\\n\\n if not hasattr(pyoptsparse, '__version__') or \\\\\\n LooseVersion(pyoptsparse.__version__) < LooseVersion('2.5.1'):\\n opt_prob.finalizeDesignVariables()\\n else:\\n opt_prob.finalize()\\n\\n # Add all objectives\\n objs = self.get_objective_values()\\n for name in objs:\\n opt_prob.addObj(name)\\n self._quantities.append(name)\\n\\n # Calculate and save derivatives for any linear constraints.\\n lcons = [key for (key, con) in con_meta.items() if con['linear']]\\n if len(lcons) > 0:\\n _lin_jacs = self._compute_totals(of=lcons, wrt=indep_list, return_format='dict')\\n # convert all of our linear constraint jacs to COO format. Otherwise pyoptsparse will\\n # do it for us and we'll end up with a fully dense COO matrix and very slow evaluation\\n # of linear constraints!\\n to_remove = []\\n for jacdct in _lin_jacs.values():\\n for n, subjac in jacdct.items():\\n if isinstance(subjac, np.ndarray):\\n # we can safely use coo_matrix to automatically convert the ndarray\\n # since our linear constraint jacs are constant, so zeros won't become\\n # nonzero during the optimization.\\n mat = coo_matrix(subjac)\\n if mat.row.size > 0:\\n # convert to 'coo' format here to avoid an emphatic warning\\n # by pyoptsparse.\\n jacdct[n] = {'coo': [mat.row, mat.col, mat.data], 'shape': mat.shape}\\n\\n # Add all equality constraints\\n for name, meta in con_meta.items():\\n if meta['equals'] is None:\\n continue\\n size = meta['global_size'] if meta['distributed'] else meta['size']\\n lower = upper = meta['equals']\\n if fwd:\\n wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]]\\n else:\\n rels = relevant[name]\\n wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels]\\n\\n if meta['linear']:\\n jac = {w: _lin_jacs[name][w] for w in wrt}\\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\\n linear=True, wrt=wrt, jac=jac)\\n else:\\n if name in self._res_jacs:\\n resjac = self._res_jacs[name]\\n jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt}\\n else:\\n jac = None\\n opt_prob.addConGroup(name, size, lower=lower, upper=upper, wrt=wrt, jac=jac)\\n self._quantities.append(name)\\n\\n # Add all inequality constraints\\n for name, meta in con_meta.items():\\n if meta['equals'] is not None:\\n continue\\n size = meta['global_size'] if meta['distributed'] else meta['size']\\n\\n # Bounds - double sided is supported\\n lower = meta['lower']\\n upper = meta['upper']\\n\\n if fwd:\\n wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]]\\n else:\\n rels = relevant[name]\\n wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels]\\n\\n if meta['linear']:\\n jac = {w: _lin_jacs[name][w] for w in wrt}\\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\\n linear=True, wrt=wrt, jac=jac)\\n else:\\n if name in self._res_jacs:\\n resjac = self._res_jacs[name]\\n jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt}\\n else:\\n jac = None\\n opt_prob.addConGroup(name, size, upper=upper, lower=lower, wrt=wrt, jac=jac)\\n self._quantities.append(name)\\n\\n # Instantiate the requested optimizer\\n try:\\n _tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)\\n opt = getattr(_tmp, optimizer)()\\n\\n except Exception as err:\\n # Change whatever pyopt gives us to an ImportError, give it a readable message,\\n # but raise with the original traceback.\\n msg = \\\"Optimizer %s is not available in this installation.\\\" % optimizer\\n raise ImportError(msg)\\n\\n # Process any default optimizer-specific settings.\\n if optimizer in DEFAULT_OPT_SETTINGS:\\n for name, value in DEFAULT_OPT_SETTINGS[optimizer].items():\\n if name not in self.opt_settings:\\n self.opt_settings[name] = value\\n\\n # Set optimization options\\n for option, value in self.opt_settings.items():\\n opt.setOption(option, value)\\n\\n self._exc_info = None\\n try:\\n\\n # Execute the optimization problem\\n if self.options['gradient method'] == 'pyopt_fd':\\n\\n # Use pyOpt's internal finite difference\\n # TODO: Need to get this from OpenMDAO\\n # fd_step = problem.model.deriv_options['step_size']\\n fd_step = 1e-6\\n sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file,\\n hotStart=self.hotstart_file)\\n\\n elif self.options['gradient method'] == 'snopt_fd':\\n if self.options['optimizer'] == 'SNOPT':\\n\\n # Use SNOPT's internal finite difference\\n # TODO: Need to get this from OpenMDAO\\n # fd_step = problem.model.deriv_options['step_size']\\n fd_step = 1e-6\\n sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file,\\n hotStart=self.hotstart_file)\\n\\n else:\\n msg = \\\"SNOPT's internal finite difference can only be used with SNOPT\\\"\\n self._exc_info = Exception(msg)\\n else:\\n\\n # Use OpenMDAO's differentiator for the gradient\\n sol = opt(opt_prob, sens=weak_method_wrapper(self, '_gradfunc'),\\n storeHistory=self.hist_file, hotStart=self.hotstart_file)\\n\\n except Exception as _:\\n if not self._exc_info:\\n raise()\\n\\n if self._exc_info:\\n raise self._exc_info\\n\\n # Print results\\n if self.options['print_results']:\\n print(sol)\\n\\n # Pull optimal parameters back into framework and re-run, so that\\n # framework is left in the right final state\\n dv_dict = sol.getDVs()\\n for name in indep_list:\\n self.set_design_var(name, dv_dict[name])\\n\\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\\n try:\\n model.run_solve_nonlinear()\\n except AnalysisError:\\n model._clear_iprint()\\n\\n rec.abs = 0.0\\n rec.rel = 0.0\\n self.iter_count += 1\\n\\n # Save the most recent solution.\\n self.pyopt_solution = sol\\n\\n try:\\n exit_status = sol.optInform['value']\\n self.fail = False\\n\\n # These are various failed statuses.\\n if optimizer == 'IPOPT':\\n if exit_status not in {0, 1}:\\n self.fail = True\\n elif exit_status > 2:\\n self.fail = True\\n\\n except KeyError:\\n # optimizers other than pySNOPT may not populate this dict\\n pass\\n\\n # revert signal handler to cached version\\n sigusr = self.options['user_terminate_signal']\\n if sigusr is not None:\\n signal.signal(sigusr, self._signal_cache)\\n self._signal_cache = None # to prevent memory leak test from failing\\n\\n return self.fail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0828d7991c90098a8d8f6b506e5497cb\",\n \"score\": \"0.7117354\",\n \"text\": \"def Optimizer():\\n\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c6373b926da083d00dfce1675a5223\",\n \"score\": \"0.7092209\",\n \"text\": \"def solve(self):\\r\\n self.start_learning_time = time.time()\\r\\n self.learn_models()\\r\\n self.end_learning_time = time.time()\\r\\n\\r\\n self.start_optimization_time = time.time()\\r\\n self.optimize()\\r\\n self.end_optimization_time = time.time()\\r\\n\\r\\n self.end_time = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f772018b8208cedbc2b3a76bd623640e\",\n \"score\": \"0.7049566\",\n \"text\": \"def optimize(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe363e6ac9de347a8a3ae161a17fffbe\",\n \"score\": \"0.703108\",\n \"text\": \"def optimize(self) -> None:\\n self.base_gp.optimize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c20787781a9824f5f4131895bced38\",\n \"score\": \"0.69480073\",\n \"text\": \"def optimize():\\n sleep(DELAY * 10)\\n print('Optimization finished.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c20787781a9824f5f4131895bced38\",\n \"score\": \"0.69480073\",\n \"text\": \"def optimize():\\n sleep(DELAY * 10)\\n print('Optimization finished.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8453bdf13da0fdef966beef95ad0b060\",\n \"score\": \"0.69443613\",\n \"text\": \"def optimizeWorkload(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd780923047a365b13926313378d9a42\",\n \"score\": \"0.6830286\",\n \"text\": \"def run(self):\\n\\n # Uncomment this to use finite differences over the entire model\\n # self.prob.root.deriv_options['type'] = 'cs'\\n\\n # Record optimization history to a database\\n # Data saved here can be examined using `plot_all.py`\\n recorder = SqliteRecorder(self.prob_dict['prob_name']+\\\".db\\\")\\n recorder.options['record_params'] = True\\n recorder.options['record_derivs'] = True\\n self.prob.driver.add_recorder(recorder)\\n\\n # Profile (time) the problem\\n # profile.setup(self.prob)\\n # profile.start()\\n\\n # Set up the problem\\n self.prob.setup()\\n\\n # Uncomment this line to have more verbose output about convergence\\n # self.prob.print_all_convergence()\\n\\n # Save an N2 diagram for the problem\\n view_model(self.prob, outfile=self.prob_dict['prob_name']+\\\".html\\\", show_browser=False)\\n\\n # Run a single analysis loop to populate uninitialized values\\n self.prob.run_once()\\n\\n # If `optimize` == True in prob_dict, perform optimization. Otherwise,\\n # simply pass the problem since analysis has already been run.\\n if not self.prob_dict['optimize']: # run analysis once\\n pass\\n else: # perform optimization\\n self.prob.run()\\n\\n # Uncomment this to check the partial derivatives of each component\\n # self.prob.check_partial_derivatives(compact_print=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6e0b395ed76f8e38e1847306baa609\",\n \"score\": \"0.6767848\",\n \"text\": \"def optimize(self):\\n if not self.ball_tree:\\n print('Optimizing search...')\\n self.ball_tree = BallTree(self.vectors, metric=cosine)\\n print('Finished.')\\n else:\\n print('Already optimized.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b273b0c3c17de5657c2ed6e7716d08\",\n \"score\": \"0.6716359\",\n \"text\": \"def optimize(self):\\n if self.termination_strategy():\\n if self.models is None:\\n self._log(f'{\\\"Res\\\" if self.g > 0 else \\\"S\\\"}tarting {self.run_name}')\\n self.models = self._init_models()\\n self._log(f'Models init')\\n\\n ret = self._evolve_iter()\\n self.g += 1\\n self._save_checkpoint(ret)\\n self._log(f\\\"Gen {self.g}: elite_average: {ret['elite_avg']}\\\")\\n\\n return ret\\n else:\\n self._log('end')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648a77b7f36de6e09bd54de17c423963\",\n \"score\": \"0.6685609\",\n \"text\": \"def compute(self, inputs, outputs):\\n global counter\\n counter += 1\\n cpacs_path = mif.get_tooloutput_file_path(Rt.modules[-1])\\n\\n # Save the CPACS file for this iteration\\n if counter%Rt.save_iter == 0:\\n loc = optim_dir_path+'/Geometry/'+'iter_{}.xml'.format(counter)\\n shutil.copy(cpacs_path, loc)\\n\\n # Add new variables to dictionnary\\n tixi = cpsf.open_tixi(cpacs_path)\\n dct.update_dict(tixi, optim_var_dict)\\n\\n # Change local wkdir for the next iteration\\n tixi.updateTextElement(opf.WKDIR_XPATH,ceaf.create_new_wkdir(Rt.date,Rt.type))\\n\\n for obj in Rt.objective:\\n var_list = splt('[+*/-]', obj)\\n for v in var_list:\\n if not v.isdigit() and v != '':\\n exec('{} = inputs[\\\"{}\\\"]'.format(v, v))\\n\\n result = eval(obj)\\n if Rt.minmax == 'min':\\n outputs['Objective function '+obj] = -result\\n else:\\n outputs['Objective function '+obj] = result\\n\\n cpsf.close_tixi(tixi, cpacs_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a659566f91666d16ce3ca7d1bb20e2\",\n \"score\": \"0.6643353\",\n \"text\": \"def optimize( self ):\\n nVars = self.nVars\\n #find radius\\n k = math.floor(self.D * self.Nf) # Neighborhood size\\n L =np.zeros([self.D,nVars])\\n g =np.zeros([self.D,nVars])\\n y =np.zeros([self.D,nVars])\\n self.u = np.zeros([self.D, nVars])\\n AdaptiveNeighborhoodSize = 3 # range [i − k, i + k]\\n\\n #find radius\\n k = round(self.D * self.Nf) # Neighborhood size\\n \\n # start the iteration\\n doStop = False\\n \\n while not doStop:\\n self.Iteration += 1\\n #Calculate weight w through eq. (6) => w = w min + (w max − w min ) *(t − 1) / (I max − 1)\\n \\n w = self.w_min+(self.w_max-self.w_min)*(self.Iteration-1)/(self.MaxIterations-1) \\n \\n #-------------------start of loop through ensemble------------------------\\n #for i = 1 to D\\n for i in range(0, self.D): \\n \\n # find neighbors\\n neighbors = np.array([w for w in range((i-k),(i+k+1))])\\n neighbors[neighbors < 0] += neighbors[neighbors < 0] + self.D; \\n neighbors[neighbors > self.D-1] =neighbors[neighbors > self.D-1] - self.D\\n \\n #print(neighbors)\\n neighbors_idx = np.random.choice( neighbors, size=AdaptiveNeighborhoodSize, replace=False)\\n neighbors_idx[neighbors_idx == i] = neighbors_idx[2]; # do not select itself, i.e., index i\\n p=neighbors_idx[0]\\n q=neighbors_idx[1]\\n \\n\\n bInd = self.PreviousBestFitness[neighbors].argmin()\\n bestNeighbor = neighbors[bInd]\\n z_best = self.z[bestNeighbor]\\n \\n # Mutate using eq. (3)–(5) → new vector yi\\n # Li = zi + α · (zbesti − zi) + β · (zp − zq), (3)\\n L[i,] = self.z[i,] + self.a * (z_best - self.z[i,]) + self.b * (self.z[p,] - self.z[q,])\\n \\n # mutated solution by the whole population\\n idx = np.random.choice(self.D, AdaptiveNeighborhoodSize, replace = False)\\n idx[idx==i] = idx[2] # do not select itself, i.e., index i\\n \\n # r1 and r2 random indices in space [1, D] so that r1 ̸ = r2 ̸ = i and zbest the best\\n # chromosome of the whole population of the previous generation\\n r1 = idx[0]\\n r2 = idx[1]\\n \\n bInd = self.PreviousBestFitness[idx].argmin()\\n z_best = self.z[bInd]\\n \\n # gi = zi + α · (zbest − zi) + β · (zr1 − zr2), (4)\\n g[i,] = self.z[i,] + self.a * (z_best - self.z[i,]) + self.b * (self.z[r1,] - self.z[r2,])\\n \\n # yi = w · gi + (1 − w) · Li, (5)\\n y[i,] = w * g[i,] + (1 - w) * L[i,]\\n \\n # Crossover using eq. (7) → new vector ui\\n Cr = 0.8 \\n j_rand = randint(0,self.D)\\n for c in range(0,self.nVars):\\n self.u[i,c] = y[i,c] if (random.randrange(0,1) < Cr or k==j_rand) else self.z[i,c] \\n \\n #Ensure (saturate) => u(i,j) ∈ [z(min,j) , z(max,j)]\\n # check bounds violation\\n posInvalid = self.u[i,:] < self.LowerBounds\\n self.u[i,posInvalid] = self.LowerBounds[posInvalid]\\n \\n posInvalid = self.u[i,:] > self.UpperBounds\\n self.u[i,posInvalid] = self.UpperBounds[posInvalid]\\n \\n \\n #calculate fitness\\n self.__evaluateDEu()\\n # find chromosomes tha has been improved and replace the old values with the new\\n genProgressed = self.CurrentGenFitness < self.PreviousBestFitness\\n self.PreviousBestPosition[genProgressed, :] = self.u[genProgressed, :]\\n self.z[genProgressed, :] = self.u[genProgressed, :]\\n self.PreviousBestFitness[genProgressed] = self.CurrentGenFitness[genProgressed]\\n \\n # update global best, adaptive neighborhood size and stall counter\\n newBestInd = self.CurrentGenFitness.argmin()\\n newBestFit = self.CurrentGenFitness[newBestInd]\\n \\n if newBestFit < self.GlobalBestFitness:\\n self.GlobalBestFitness = newBestFit\\n self.GlobalBestPosition = self.z[newBestInd, :].copy()\\n \\n self.StallCounter = max(0, self.StallCounter-1)\\n # calculate remaining only once when fitness is improved to save some time\\n # useful for the plots created\\n [self.newOrder,self.remaining] = get_Data(self)\\n else:\\n self.StallCounter += 1\\n \\n # first element of self.GlobalBestSoFarFitnesses is for self.Iteration == -1\\n self.GlobalBestSoFarFitnesses[self.Iteration+1] = self.GlobalBestFitness\\n \\n # run output function and stop if necessary\\n if self.OutputFcn and self.OutputFcn(self):\\n self.StopReason = 'OutputFcn requested to stop.'\\n doStop = True\\n continue\\n \\n # stop if max iterations\\n if self.Iteration >= self.MaxIterations-1:\\n self.StopReason = 'MaxIterations reached.'\\n doStop = True\\n continue\\n \\n # stop if insignificant improvement\\n if self.Iteration > self.MaxStallIterations:\\n # The minimum global best fitness is the one stored in self.GlobalBestSoFarFitnesses[self.Iteration+1]\\n # (only updated if newBestFit is less than the previously stored). The maximum (may be equal to the \\n # current) is the one in self.GlobalBestSoFarFitnesses MaxStallIterations before.\\n minBestFitness = self.GlobalBestSoFarFitnesses[self.Iteration+1]\\n maxPastBestFit = self.GlobalBestSoFarFitnesses[self.Iteration+1-self.MaxStallIterations]\\n if (maxPastBestFit == 0.0) and (minBestFitness < maxPastBestFit):\\n windowProgress = np.inf # don't stop\\n elif (maxPastBestFit == 0.0) and (minBestFitness == 0.0):\\n windowProgress = 0.0 # not progressed\\n else:\\n windowProgress = abs(minBestFitness - maxPastBestFit) / abs(maxPastBestFit)\\n if windowProgress <= self.FunctionTolerance:\\n self.StopReason = 'Population did not improve significantly the last MaxStallIterations.'\\n doStop = True\\n \\n \\n # print stop message\\n print('Algorithm stopped after {} iterations. Best fitness attained: {}'.format(\\n self.Iteration+1,self.GlobalBestFitness))\\n print(f'Stop reason: {self.StopReason}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d24fa5cc5c4d29ab2cfc82350500e5\",\n \"score\": \"0.65597224\",\n \"text\": \"def test_optimization(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffea8f98408e9b2654e78da561037373\",\n \"score\": \"0.64937925\",\n \"text\": \"def step(self):\\n params = self.params\\n if np.isnan(self.G).any():\\n raise RuntimeError(\\\"Gradient contains nan - check output and temp-files for possible errors\\\")\\n if np.isnan(self.H).any():\\n raise RuntimeError(\\\"Hessian contains nan - check output and temp-files for possible errors\\\")\\n self.Iteration += 1\\n if (self.Iteration%5) == 0:\\n self.engine.clearCalcs()\\n self.IC.clearCache()\\n\\n # At the start of the loop, the optimization variables, function value, gradient and Hessian are known.\\n # (i.e. self.Y, self.E, self.G, self.H)\\n if params.verbose: self.IC.printRotations(self.X)\\n Eig = self.SortedEigenvalues(self.H)\\n Emin = Eig[0].real\\n if params.transition:\\n v0 = 1.0\\n elif Emin < params.epsilon:\\n v0 = params.epsilon-Emin\\n else:\\n v0 = 0.0\\n # Are we far from constraint satisfaction?\\n self.farConstraints = self.IC.haveConstraints() and self.IC.maxConstraintViolation(self.X) > 1e-1\\n ### OBTAIN AN OPTIMIZATION STEP ###\\n # The trust radius is to be computed in Cartesian coordinates.\\n # First take a full-size optimization step\\n if params.verbose: logger.info(\\\" Optimizer.step : Attempting full-size optimization step\\\\n\\\")\\n dy, _, __ = self.get_delta_prime(v0, verbose=self.params.verbose)\\n # Internal coordinate step size\\n inorm = np.linalg.norm(dy)\\n # Cartesian coordinate step size\\n self.cnorm = self.get_cartesian_norm(dy)\\n # If the full-size step is within the trust radius, then call get_delta_prime again with diagnostic messages if needed\\n if (self.params.verbose >= 2 and self.params.verbose < 4 and self.cnorm <= 1.1*self.trust):\\n self.get_delta_prime(v0, verbose=self.params.verbose+2)\\n if params.verbose: logger.info(\\\" Optimizer.step : Internal-step: %.4f Cartesian-step: %.4f Trust-radius: %.4f\\\\n\\\" % (inorm, self.cnorm, self.trust))\\n # If the step is above the trust radius in Cartesian coordinates, then\\n # do the following to reduce the step length:\\n if self.cnorm > 1.1 * self.trust:\\n # This is the function f(inorm) = cnorm-target that we find a root\\n # for obtaining a step with the desired Cartesian step size.\\n froot = self.createFroot(v0)\\n froot.stores[inorm] = self.cnorm\\n ### Find the internal coordinate norm that matches the desired Cartesian coordinate norm\\n if params.verbose: logger.info(\\\" Optimizer.step : Using Brent algorithm to target Cartesian trust radius\\\\n\\\")\\n iopt = brent_wiki(froot.evaluate, 0.0, inorm, self.trust, cvg=0.1, obj=froot, verbose=params.verbose)\\n if froot.brentFailed and froot.stored_arg is not None:\\n # If Brent fails but we obtained an IC step that is smaller than the Cartesian trust radius, use it\\n if params.verbose: logger.info(\\\" Optimizer.step : \\\\x1b[93mUsing stored solution at %.3e\\\\x1b[0m\\\\n\\\" % froot.stored_val)\\n iopt = froot.stored_arg\\n elif self.IC.bork:\\n # Decrease the target Cartesian step size and try again\\n for i in range(3):\\n froot.target /= 2\\n if params.verbose: logger.info(\\\" Optimizer.step : \\\\x1b[93mReducing target to %.3e\\\\x1b[0m\\\\n\\\" % froot.target)\\n froot.above_flag = True # Stop at any valid step between current target step size and trust radius\\n iopt = brent_wiki(froot.evaluate, 0.0, iopt, froot.target, cvg=0.1, verbose=params.verbose)\\n if not self.IC.bork: break\\n LastForce = self.ForceRebuild\\n self.ForceRebuild = False\\n if self.IC.bork:\\n logger.info(\\\"\\\\x1b[91mInverse iteration for Cartesians failed\\\\x1b[0m\\\\n\\\")\\n # This variable is added because IC.bork is unset later.\\n self.ForceRebuild = True\\n else:\\n if params.verbose: logger.info(\\\" Optimizer.step : \\\\x1b[93mBrent algorithm requires %i evaluations\\\\x1b[0m\\\\n\\\" % froot.counter)\\n ##### If IC failed to produce valid Cartesian step, it is \\\"borked\\\" and we need to rebuild it.\\n if self.ForceRebuild:\\n # Force a rebuild of the coordinate system and skip the energy / gradient and evaluation steps.\\n if LastForce:\\n logger.warning(\\\"\\\\x1b[1;91mFailed twice in a row to rebuild the coordinate system; continuing in Cartesian coordinates\\\\x1b[0m\\\\n\\\")\\n self.checkCoordinateSystem(recover=True, cartesian=LastForce)\\n logger.info(\\\"\\\\x1b[1;93mSkipping optimization step\\\\x1b[0m\\\\n\\\")\\n self.Iteration -= 1\\n self.state = OPT_STATE.SKIP_EVALUATION\\n return\\n ##### End Rebuild\\n # Finally, take an internal coordinate step of the desired length.\\n dy, _ = self.trust_step(iopt, v0, verbose=(self.params.verbose+1 if self.params.verbose >= 2 else 0))\\n self.cnorm = self.get_cartesian_norm(dy)\\n ### DONE OBTAINING THE STEP ###\\n if isinstance(self.IC, PrimitiveInternalCoordinates):\\n idx = np.argmax(np.abs(dy))\\n iunit = np.zeros_like(dy)\\n iunit[idx] = 1.0\\n self.prim_msg = \\\"Along %s %.3f\\\" % (self.IC.Internals[idx], np.dot(dy/np.linalg.norm(dy), iunit))\\n ### These quantities, computed previously, are no longer used.\\n # Dot product of the gradient with the step direction\\n # Dot = -np.dot(dy/np.linalg.norm(dy), self.G/np.linalg.norm(self.G))\\n # Whether the Cartesian norm comes close to the trust radius\\n # bump = cnorm > 0.8 * self.trust\\n ### Before updating any of our variables, copy current variables to \\\"previous\\\"\\n self.Yprev = self.Y.copy()\\n self.Xprev = self.X.copy()\\n self.Gxprev = self.gradx.copy()\\n self.Gprev = self.G.copy()\\n self.Eprev = self.E\\n ### Update the Internal Coordinates ###\\n X0 = self.X.copy()\\n self.newCartesian(dy)\\n ## The \\\"actual\\\" dy may be different from the one passed to newCartesian(),\\n ## for example if we enforce constraints or don't get the step we expect.\\n dy = self.IC.calcDiff(self.X, X0)\\n # dyp = self.IC.Prims.calcDiff(self.X, X0)\\n # print(\\\"Actual dy:\\\", dy)\\n self.Y += dy\\n self.expect = flat(0.5*multi_dot([row(dy),self.H,col(dy)]))[0] + np.dot(dy,self.G)\\n # self.expectdG = np.dot(self.H, col(dy).flatten())\\n self.state = OPT_STATE.NEEDS_EVALUATION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bfffb68092daef07bbfcb33e404aca\",\n \"score\": \"0.6486699\",\n \"text\": \"def run_optimization(self, optimization_setting, max_iteration=100000000):\\n # Get optimization setting and target\\n settings = optimization_setting.generate_setting_ga()\\n self.target_name = optimization_setting.target_name\\n\\n total_size = len(settings)\\n\\n # Run ga optimization\\n logging.info(f\\\"total search space size：{total_size}\\\")\\n score = dict()\\n counter = 0\\n for combination in settings:\\n score[self._evaluate(combination)] = combination\\n counter += 1\\n if counter > max_iteration:\\n logging.warning(\\\"Max number of iteration reached. Search stopped.. \\\")\\n break\\n\\n results = []\\n for key in sorted(score.keys()):\\n results.append((score[key], key))\\n\\n # Return result list\\n\\n self.results = results\\n logging.info(f\\\"Best signal combination {results[-1]} \\\")\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c1a5724af884f1a48718c3e67f20be\",\n \"score\": \"0.6449846\",\n \"text\": \"def run(self):\\r\\n loop = True\\r\\n iterations = 0\\r\\n self.print_stats(iterations, True)\\r\\n while loop:\\r\\n self.expectation_step()\\r\\n self.maximization_step()\\r\\n if self.check_convergence():\\r\\n loop = False\\r\\n for i in xrange(self.K):\\r\\n self.alpha[i] = self.alpha_new[i]\\r\\n iterations += 1\\r\\n logging.info(\\\"iterations = \\\" + str(iterations))\\r\\n\\r\\n self.print_stats(iterations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4497f71e7189a87294719223dc993a55\",\n \"score\": \"0.644211\",\n \"text\": \"def run_algo(self):\\n #p_angulo = self.run_proc(['batch_angulo.py'])\\n #self.wait_proc(p_angulo, timeout=self.timeout)\\n\\n #p_refinement = self.run_proc(['/bin/bash','batch_refinement.sh', self.key])\\n\\n p = self.run_proc(['/bin/bash','run_sgm.sh'])\\n self.wait_proc(p, timeout=self.timeout)\\n\\n if self.cfg['param']['ground_truth'] != '':\\n p_stat = self.run_proc(['/bin/bash','compute_statistics.sh'])\\n self.wait_proc(p_stat, timeout=self.timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa3a57d769ae98523b4b240a74d98e1\",\n \"score\": \"0.64235586\",\n \"text\": \"def optimize(self):\\n\\n time=self.Params.TimeLimit\\n seed=self.Params.RandomSeed\\n LOG=self.Params.OutputFlag\\n\\n f = self.update()\\n\\n f3 = open(\\\"scop_input.txt\\\",\\\"w\\\")\\n f3.write(f)\\n f3.close()\\n\\n if LOG>=100:\\n print(\\\"scop input: \\\\n\\\")\\n print(f)\\n print(\\\"\\\\n\\\")\\n if LOG:\\n print(\\\"solving using parameters: \\\\n \\\")\\n print(\\\" TimeLimit =%s second \\\\n\\\"%time)\\n print(\\\" RandomSeed= %s \\\\n\\\"%seed)\\n print(\\\" OutputFlag= %s \\\\n\\\"%LOG)\\n import subprocess\\n# if platform.system() == \\\"Windows\\\":\\n# cmd = \\\"scop -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for win\\n# elif platform.system()== \\\"Darwin\\\":\\n# cmd = \\\"./scop -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for mac\\n# elif platform.system() == \\\"Linux\\\":\\n# cmd = \\\"./scop-linux -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for linux\\n\\n# トライアル版の場合は以下を生かす\\n if platform.system() == \\\"Windows\\\":\\n cmd = \\\"scop-win -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for win\\n elif platform.system()== \\\"Darwin\\\":\\n cmd = \\\"./scop-mac -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for mac\\n elif platform.system() == \\\"Linux\\\":\\n cmd = \\\"./scop-linux -time \\\"+str(time)+\\\" -seed \\\"+str(seed) #solver call for linux\\n\\n\\n if self.Params.Initial:\\n cmd += \\\" -initsolfile scop_best_data.txt\\\"\\n\\n try:\\n if platform.system() == \\\"Windows\\\": #Winの場合にはコマンドをsplit!\\n pipe = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stdin=subprocess.PIPE, shell=True)\\n else:\\n pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stdin=subprocess.PIPE, shell=True)\\n print(\\\"\\\\n ================ Now solving the problem ================ \\\\n\\\")\\n #pipe = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE,stdin=subprocess.PIPE)\\n except OSError:\\n print(\\\"error: could not execute command '%s'\\\" % cmd)\\n print(\\\"please check that the solver is in the path\\\")\\n self.Status = 7 #execution falied\\n return None, None\\n\\n out, err = pipe.communicate(f.encode()) #get the result\\n if err!=None:\\n if int(sys.version_info[0])>=3:\\n err = str(err, encoding='utf-8')\\n f2 = open(\\\"scop_error.txt\\\",\\\"w\\\")\\n f2.write(err)\\n f2.close()\\n\\n if int(sys.version_info[0])>=3:\\n out = str(out, encoding='utf-8')\\n\\n if LOG:\\n print (out, '\\\\n')\\n #print (\\\"out=\\\",out)\\n #print (\\\"err=\\\",err)\\n #print(\\\"Return Code=\\\",pipe.returncode)\\n\\n f = open(\\\"scop_out.txt\\\",\\\"w\\\")\\n f.write(out)\\n f.close()\\n\\n #check the return code\\n self.Status = pipe.returncode\\n if self.Status !=0: #if the return code is not \\\"optimal\\\", then return\\n print(\\\"Status=\\\",self.Status)\\n print(\\\"Output=\\\",out)\\n return None, None\\n\\n #extract the solution and the violated constraints\\n s0 = \\\"[best solution]\\\"\\n s1 = \\\"penalty\\\"\\n s2 = \\\"[Violated constraints]\\\"\\n i0 = out.find(s0) + len(s0)\\n i1 = out.find(s1, i0)\\n i2 = out.find(s2, i1) + len(s2)\\n\\n data = out[i0:i1].strip()\\n\\n #save the best solution\\n f3 = open(\\\"scop_best_data.txt\\\",\\\"w\\\")\\n f3.write(data.lstrip())\\n f3.close()\\n\\n sol = {}\\n if data != \\\"\\\":\\n for s in data.split(\\\"\\\\n\\\"):\\n name, value = s.split(\\\":\\\")\\n sol[name]=value.strip() #remove redunant string\\n\\n data = out[i2:].strip()\\n violated = {}\\n if data != \\\"\\\":\\n for s in data.split(\\\"\\\\n\\\"):\\n try:\\n name, value = s.split(\\\":\\\")\\n except:\\n print(\\\"Error String=\\\",s)\\n\\n try:\\n temp=int(value)\\n except:\\n violated[name] = value\\n else:\\n violated[name] = int(value)\\n\\n #set the optimal solution to the variable\\n for name in sol:\\n if name in self.varDict:\\n self.varDict[name].value = sol[name]\\n else:\\n raise NameError(\\\"Solution {0} is not in variable list\\\".format(name))\\n\\n #evaluate the left hand sides of the constraints\\n for con in self.constraints:\\n\\n if isinstance(con,Linear):\\n lhs=0\\n for (coeff,var,domain) in con.terms:\\n if var.value==domain:\\n lhs+=coeff\\n\\n con.lhs=lhs\\n if isinstance(con,Quadratic):\\n lhs=0\\n #print con.terms\\n for (coeff,var1,domain1,var2,domain2) in con.terms:\\n if var1.value==domain1 and var2.value==domain2:\\n lhs+=coeff\\n\\n con.lhs=lhs\\n if isinstance(con,Alldiff):\\n VarSet=set([])\\n lhs=0\\n for v in con.variables:\\n index=v.domain.index(v.value)\\n #print v,index\\n if index in VarSet:\\n lhs+=1\\n VarSet.add(index)\\n #print VarSet\\n con.lhs=lhs\\n #return dictionaries containing the solution and the violated constraints\\n return sol,violated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462f278c5224abcd036debe348c6be34\",\n \"score\": \"0.63882387\",\n \"text\": \"def run(self):\\n f_names = self.get_f_names()\\n global_best = None\\n global_best_dist = float('Inf')\\n\\n for file in f_names:\\n if (file[-4:] != 'json'):\\n pass\\n else:\\n self.output_file.write(\\\"\\\\nFile name: {}\\\\n\\\".format(file))\\n data = self.get_raw_solutions(file)\\n self.output_file.write(\\\"# of Solutions: {}\\\\n\\\".format(len(data)))\\n self.summarize(data)\\n avg_dist, best_sol = self.get_avg_dist(data)\\n avg_dist_output = \\\"Average dist: {:0.2f}\\\\n\\\".format(avg_dist)\\n self.output_file.write(avg_dist_output)\\n\\n if self.calc_dist(best_sol) < global_best_dist:\\n global_best = best_sol\\n\\n best_sol['position'] = [round(num, 2) for num in best_sol['position']]\\n best_sol['objectives'] = [round(num, 2) for num in best_sol['objectives']]\\n \\n best_sol_output = \\\"Min distance Solution:\\\\n{}\\\\n\\\".format(best_sol)\\n self.output_file.write(best_sol_output)\\n\\n global_best['position'] = [round(num, 2) for num in global_best['position']]\\n global_best['objectives'] = [round(num, 2) for num in global_best['objectives']]\\n \\n global_best_sol_output = \\\"\\\\nGlobal Min Distance Solution:\\\\n{}\\\\n\\\".format(global_best)\\n self.output_file.write(best_sol_output)\\n\\n\\n \\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beee14a579b94ecfa9cf6fb320839491\",\n \"score\": \"0.6383306\",\n \"text\": \"def search(self) -> SearchReturnType:\\n\\n self.io.write_line(True, self.full_output_file, '\\\\n*******Optimization Beginning*******')\\n\\n # evaluate starting point\\n base_values = {dv.var_name: dv.x_base for dv in self.dvs}\\n obj_base = self.f_of_x(base_values)\\n j_base = obj_base.value\\n if obj_base.return_state == ReturnStateEnum.UserAborted: # pragma: no cover -- stop file may be deprecated\\n self.io.write_line(True, self.full_output_file,\\n 'User aborted simulation via stop signal file...')\\n r = SearchReturnType(False, ReturnStateEnum.UserAborted)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n elif not obj_base.return_state == ReturnStateEnum.Successful:\\n self.io.write_line(True, self.full_output_file,\\n 'Initial point is infeasible or invalid, cannot begin iterations. Aborting...')\\n r = SearchReturnType(False, ReturnStateEnum.InvalidInitialPoint)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n\\n # begin iteration loop\\n for iteration in range(1, self.project.max_iterations + 1):\\n\\n self.io.write_line(self.project.verbose, self.full_output_file, 'iter = ' + str(iteration))\\n\\n if os.path.exists(self.io.stopFile): # pragma: no cover -- not covering stop file stuff\\n self.io.write_line(True, self.full_output_file,\\n 'Found stop signal file in run directory; stopping now...')\\n r = SearchReturnType(False, ReturnStateEnum.UserAborted)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n\\n # begin DV loop\\n for dv in self.dvs:\\n\\n # set up a new point\\n dv.x_new = dv.x_base + dv.delta_x\\n new_values = {dv.var_name: dv.x_new for dv in self.dvs}\\n\\n if dv.x_new > dv.value_maximum or dv.x_new < dv.value_minimum: # pragma: no cover\\n # arranging the unit test to cover this condition is too much for now\\n # if we wanted to do it, we could have the objective function be a generator that yields a bad value\\n self.io.write_line(True, self.full_output_file,\\n 'infeasible DV, name=' + dv.var_name)\\n r = SearchReturnType(False, ReturnStateEnum.InfeasibleDV)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n\\n # then evaluate the new point\\n obj_new = self.f_of_x(new_values)\\n j_new = obj_new.value\\n\\n w = self.io.write_line\\n w(self.project.verbose, self.full_output_file, 'iter=' + str(iteration))\\n w(self.project.verbose, self.full_output_file, 'var=' + str(dv))\\n w(self.project.verbose, self.full_output_file, 'x_base=' + str([x.x_base for x in self.dvs]))\\n w(self.project.verbose, self.full_output_file, 'j_base=' + str(j_base))\\n w(self.project.verbose, self.full_output_file, 'x_new=' + str([x.x_new for x in self.dvs]))\\n w(self.project.verbose, self.full_output_file, 'j_new=' + str(j_new))\\n\\n if obj_new.return_state == ReturnStateEnum.UnsuccessfulOther: # pragma: no cover\\n # not covering this either, this is kind of a dumping ground for unexpected errors to fail nicely\\n self.io.write_line(True, self.full_output_file,\\n 'Optimization ended unexpectedly, check all inputs and outputs')\\n self.io.write_line(True, self.full_output_file,\\n 'Error message: ' + str(obj_new.message))\\n r = SearchReturnType(False, ReturnStateEnum.UnsuccessfulOther)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n elif (not obj_new.return_state == ReturnStateEnum.Successful) or (j_new > j_base):\\n dv.delta_x = -self.project.coefficient_contract * dv.delta_x\\n dv.x_new = dv.x_base\\n self.io.write_line(self.project.verbose, self.full_output_file,\\n '## Unsuccessful objective evaluation, or worse result, going back ##')\\n else:\\n j_base = j_new\\n dv.x_base = dv.x_new\\n dv.delta_x = self.project.coefficient_expand * dv.delta_x\\n self.io.write_line(self.project.verbose, self.full_output_file,\\n '## Improved result, accepting and continuing forward ##')\\n\\n converged = True\\n for dv in self.dvs:\\n if abs(dv.delta_x) > dv.convergence_criteria:\\n converged = False\\n break\\n\\n if converged:\\n self.io.write_line(True, self.full_output_file, '*******Converged*******')\\n converged_values = {x.var_name: x.x_new for x in self.dvs}\\n r = SearchReturnType(True, ReturnStateEnum.Successful, converged_values)\\n if self.callback_completed:\\n self.callback_completed(r)\\n self.full_output_file.close()\\n return r\\n\\n if self.callback_progress:\\n self.callback_progress(iteration, j_base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb95f1c58fa06ee5f03ab294a81f0df5\",\n \"score\": \"0.63015676\",\n \"text\": \"def solver(self):\\r\\n\\r\\n # define the problem parameters \\r\\n self.initProblemParameters()\\r\\n\\r\\n # define the ACO parameters\\r\\n self.initACOParameters()\\r\\n\\r\\n self.initSolverParameters()\\r\\n\\r\\n self.best_solution = None\\r\\n object_val_per_iter = []\\r\\n start_time = datetime.now()\\r\\n for iter in tqdm(range(self.iterations)):\\r\\n # for every iterations defined \\r\\n\\r\\n self.nodes_visited_by_ant = {}\\r\\n self.reloading_points = {}\\r\\n self.solutions_per_ant = {}\\r\\n\\r\\n for ant in self.ants: # define the type of ant here / most probably is a range(NO_OF_ANTS)\\r\\n # for every ants taken (cumulative most probably)\\r\\n # print(\\\"Iteration {} Ant {}\\\".format(iter,ant))\\r\\n sub_fleets = self.getNoOfSubFleets()\\r\\n\\r\\n while(True):\\r\\n # print(\\\"start\\\")\\r\\n truck_i_j_DV = None\\r\\n bike_i_j_DV = None\\r\\n truck_decision = None\\r\\n bike_decision = None\\r\\n add_bike_i_j_DV = np.zeros(shape=(self.node_num, self.node_num), dtype=np.int)\\r\\n add_bike_decision = {}\\r\\n add_bike_decision['add_bike_routes']=None\\r\\n add_bike_decision['add_bike_freight']=None\\r\\n add_bike_curr = [self.DEPOT]\\r\\n add_bike_curr_freight = []\\r\\n truck_i_j_DV, bike_i_j_DV, truck_decision, bike_decision = self.solveForNFleet(sub_fleets, ant)\\r\\n if np.sum(self.demand_satisfied) == self.node_num:\\r\\n break\\r\\n else:\\r\\n demand_left = np.sum(self.customer_demand.take(np.where(self.demand_satisfied)))\\r\\n if demand_left <= self.bike_capacity:\\r\\n # bring in additional bike\\r\\n originAdd = self.DEPOT\\r\\n bike_freight = min(demand_left, self.bike_capacity)\\r\\n add_bike_curr_freight.append(bike_freight)\\r\\n while(bike_freight>0):\\r\\n # print(\\\"add_bike\\\")\\r\\n destinationAdd = self.getNextStop(ant, originAdd)\\r\\n # see if other constraints persists\\r\\n if self.checkFeasibility1(originAdd, destinationAdd, ant, bike_freight):\\r\\n add_bike_i_j_DV[originAdd][destinationAdd] = 1\\r\\n add_bike_curr.append(destinationAdd)\\r\\n self.nodes_visited_by_ant[ant].append(destinationAdd)\\r\\n bike_freight -= self.customer_demand[destinationAdd]\\r\\n add_bike_curr_freight.append(bike_freight)\\r\\n self.demand_satisfied[destinationAdd] = 1\\r\\n originAdd = destinationAdd\\r\\n # bike_working_time += self.customer_service_time[destinationB]\\r\\n destinationAdd = self.DEPOT\\r\\n add_bike_i_j_DV[originAdd][destinationAdd] = 1\\r\\n add_bike_curr.append(self.DEPOT)\\r\\n add_bike_decision['add_bike_routes'] = add_bike_curr\\r\\n add_bike_decision['add_bike_freight'] = add_bike_curr_freight\\r\\n\\r\\n else:\\r\\n sub_fleets += 1\\r\\n\\r\\n self.saveSolution(ant, truck_i_j_DV, bike_i_j_DV, add_bike_i_j_DV, truck_decision, bike_decision, add_bike_decision)\\r\\n self.localPheremoneUpdate()\\r\\n\\r\\n # perform global pheromone updates \\r\\n # after the iteration is over perform the global pheromone update\\r\\n # Tij(t+1) = (1-rho_1) * Tij(t) + rho_1 * delta(Tij)(t)\\r\\n # delta(Tij)(t) = 1 / func(global - last_ob_val(t))\\r\\n print(self.best_solution['objective_value'])\\r\\n object_val_per_iter.append(str(self.best_solution['objective_value']))\\r\\n self.globalPheromoneUpdate()\\r\\n # import pdb; pdb.set_trace()\\r\\n\\r\\n # print the arrays into a file\\r\\n print(\\\"\\\\n\\\")\\r\\n print(\\\"*\\\"*20)\\r\\n print(\\\"Time Taken : {}\\\".format(datetime.now() - start_time))\\r\\n\\r\\n print(\\\"Truck Routes : \\\")\\r\\n for trucks in self.best_solution['truck_decision']:\\r\\n print(trucks['fleet_no'])\\r\\n print(trucks['truck_routes'])\\r\\n print(trucks['truck_freight'])\\r\\n\\r\\n print(\\\"\\\\n\\\")\\r\\n print(\\\"*\\\"*20)\\r\\n print(\\\"Bike Routes : \\\")\\r\\n for bike in self.best_solution['bike_decision']:\\r\\n print(bike['fleet_no'])\\r\\n print(bike['bike_routes'])\\r\\n print(bike['bike_freight']) \\r\\n\\r\\n print(\\\"\\\\n\\\")\\r\\n print(\\\"*\\\"*20)\\r\\n print(\\\"Add Bike Routes : \\\")\\r\\n print(self.best_solution['add_bike_decision']['add_bike_routes'])\\r\\n print(self.best_solution['add_bike_decision']['add_bike_freight'])\\r\\n\\r\\n # with open(\\\"obj_val_per_iter.txt\\\", \\\"w\\\") as fp:\\r\\n # fp.write(\\\"\\\\n\\\".join(object_val_per_iter))\\r\\n\\r\\n # import pdb; pdb.set_trace()\\r\\n # with open(\\\"results_truck.txt\\\",\\\"w\\\") as fp:\\r\\n # for slice2d in self.best_solution['truck_route']:\\r\\n # np.savetxt(fp, slice2d, fmt='%5d')\\r\\n # fp.write(\\\"\\\\n\\\")\\r\\n\\r\\n\\r\\n # with open(\\\"results_bike.txt\\\",\\\"w\\\") as fp:\\r\\n # for slice2d in self.best_solution['bike_route']:\\r\\n # np.savetxt(fp, slice2d, fmt='%5d')\\r\\n # fp.write(\\\"\\\\n\\\")\\r\\n\\r\\n\\r\\n # with open(\\\"results_addbike.txt\\\") as fp:\\r\\n # np.savetxt(fp, self.best_solution['additional_bike_route'], fmt='%5d')\\r\\n \\r\\n # self.printSolution()\\r\\n\\r\\n\\r\\n\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca69c492545f9575f5823acce20027e\",\n \"score\": \"0.6287877\",\n \"text\": \"def optimize(self):\\n\\t\\tself.forwardpropagate(self.getLastVariable()) # forward propagation\\n\\t\\tself.backpropagate(self.getLastVariable(), first = 1) # backward propagation \\n\\t\\tself.updateweights(self.getLastVariable())\\t\\t\\t # update values of parameters \",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2f0baf0222b6471834378ef47c98a6\",\n \"score\": \"0.62825644\",\n \"text\": \"def test_optimisation_single(self):\\n self.report('')\\n self.report('Testing optimisation on a single worker.')\\n for idx, (dcf, raw_prob_funcs) in enumerate(self.opt_problems):\\n self.report('[%d/%d] Testing optimisation with 1 worker on %s.'%(\\n idx + 1, len(self.opt_problems), dcf), 'test_result')\\n self.worker_manager_1.reset()\\n opt_val, opt_point, history = self._run_optimiser(raw_prob_funcs, dcf,\\n self.worker_manager_1, self.max_capital, 'asy')\\n self._test_optimiser_results(opt_val, opt_point, history, dcf)\\n self.report('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a03bf473167eed8d7ac521ac5e1e38\",\n \"score\": \"0.62807673\",\n \"text\": \"def runRegression(self,maxiter=100000000000):\\n\\n self.fit_result = minimize(fun=self._objective,\\n x0=self.param_guess,\\n bounds=self.bounds,\\n options={\\\"maxiter\\\":maxiter,\\\"maxfun\\\":maxiter})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2feb73baae455586b924acaf277700\",\n \"score\": \"0.6275972\",\n \"text\": \"def run_routine():\\n # sInitialize dictionnaries\\n # init_dict()\\n\\n # Build the model\\n prob = om.Problem()\\n model = prob.model\\n\\n # Build model components\\n indeps = model.add_subsystem('indeps', om.IndepVarComp())\\n model.add_subsystem('objective', objective_function())\\n model.add_subsystem('const', constraint())\\n\\n # Choose between optimizer or driver\\n if Rt.type == 'DoE':\\n if Rt.doetype == 'uniform':\\n driver = prob.driver = om.DOEDriver(om.UniformGenerator(num_samples=Rt.samplesnb))\\n elif Rt.doetype == 'fullfact':\\n # 2->9 3->81\\n driver = prob.driver = om.DOEDriver(om.FullFactorialGenerator(Rt.samplesnb))\\n elif Rt.type == 'Optim':\\n driver = prob.driver = om.ScipyOptimizeDriver()\\n # SLSQP,COBYLA,shgo,TNC\\n driver.options['optimizer'] = Rt.driver\\n # driver.options['maxiter'] = 20\\n driver.options['tol'] = 1e-2\\n if Rt.driver == 'COBYLA':\\n driver.opt_settings['catol'] = 0.06\\n\\n\\n # Connect problem components to model components\\n\\n # Design variable\\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\\n norm = int(np.log10(abs(listval[0])+1)+1)\\n indeps.add_output(key, listval[0], ref=norm, ref0=0)\\n model.connect('indeps.'+key, 'objective.'+key)\\n model.add_design_var('indeps.'+key, lower=minval, upper=maxval)\\n\\n # Constraints\\n for key, (name, listval, minval, maxval, getcommand) in res_var_dict.items():\\n # Select only one constrain\\n if name in Rt.constraints:\\n norm = int(np.log10(abs(listval[0])+1)+1)\\n model.add_constraint('const.'+name, ref=norm, lower=-0.25, upper=0.25)\\n\\n # Objective function\\n model.add_objective('objective.{}'.format(Rt.objective))\\n\\n # Recorder\\n path = optim_dir_path\\n driver.add_recorder(om.SqliteRecorder(path + '/Driver_recorder.sql'))\\n\\n # Run\\n prob.setup()\\n prob.run_driver()\\n prob.cleanup()\\n\\n # Results\\n log.info('=========================================')\\n log.info('min = ' + str(prob['objective.{}'.format(Rt.objective)]))\\n\\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\\n log.info(name + ' = ' + str(prob['indeps.'+key]) + '\\\\n Min :' + str(minval) + ' Max : ' + str(maxval))\\n\\n log.info('Variable history')\\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\\n log.info(name + ' => ' + str(listval))\\n\\n log.info('=========================================')\\n\\n # Generate plots, maybe make a dynamic plot\\n opf.read_results(optim_dir_path, Rt.type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c53fba9b0d84c54f542b6ad337277e2c\",\n \"score\": \"0.6266641\",\n \"text\": \"def run_solver():\\n # start timer\\n start_time = time.perf_counter()\\n # find files\\n num_graphs = []\\n # we can just do a subset of graphs if the parameter full is set to False\\n sizes = []\\n if RUN_LIST_SMALL:\\n sizes.append(\\\"small\\\")\\n num_graphs.append(304)\\n if RUN_LIST_MEDIUM:\\n sizes.append(\\\"medium\\\")\\n num_graphs.append(304)\\n if RUN_LIST_LARGE:\\n sizes.append(\\\"large\\\")\\n num_graphs.append(401)\\n # loop through all inputs and create outputs\\n num_cores = multiprocessing.cpu_count()\\n outputs = []\\n\\n def solver(size=\\\"\\\", index=\\\"\\\", filename=\\\"\\\"):\\n if not size and not index:\\n filepath = filename\\n else:\\n filepath = size + \\\"-\\\" + str(index)\\n G = read_input_file(\\\"inputs/\\\"+ filepath +\\\".in\\\")\\n print(\\\"analyzing \\\"+filepath)\\n EXISTING_T = read_output_file(\\\"outputs/\\\"+filepath+\\\".out\\\", G)\\n # outputs.append((solve(G, EXISTING_T), filepath))\\n # um = solve(G, EXISTING_T, filename=filepath)\\n if PSEUDO_SOLVER:\\n write_output_file(psuedo_solver(G, EXISTING_T, filename=filepath), \\\"outputs/\\\"+filepath+\\\".out\\\")\\n else:\\n write_output_file(solve(G, EXISTING_T, filepath, EXISTING_T), \\\"outputs/\\\"+filepath+\\\".out\\\")\\n\\n if not file and ONLY_RUN_IMPROVABLE:\\n if RUN_LIST_SMALL:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_small)\\n if RUN_LIST_MEDIUM:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_medium)\\n if RUN_LIST_LARGE:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large)\\n else:\\n if RUN_LIST_LARGE_1:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_1)\\n if RUN_LIST_LARGE_2:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_2)\\n if RUN_LIST_LARGE_3:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_3)\\n if RUN_LIST_LARGE_4:\\n Parallel(n_jobs=num_cores)(delayed(solver)(filename=file) for file in bad_large_4)\\n\\n elif not file and not ONLY_RUN_IMPROVABLE:\\n for i in range(len(sizes)):\\n size = sizes[i]\\n GRAPH_RANGE = range(START, num_graphs[i])\\n Parallel(n_jobs=num_cores)(delayed(solver)(size, j) for j in GRAPH_RANGE)\\n #for j in GRAPH_RANGE:\\n # if j != 58:\\n # solver(size, j)\\n elif file: # file-specific running\\n T = solver(filename=file)\\n for output in outputs:\\n # write_output_file(output[0], \\\"outputs/\\\"+output[1]+\\\".out\\\")\\n pass\\n end_time = time.perf_counter()\\n print(f\\\"Process complete. Total time {end_time - start_time:0.4f} seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4518826a630f8f3ac5a1675da2e54465\",\n \"score\": \"0.6259849\",\n \"text\": \"def run(self):\\r\\n\\t\\t# reset total time\\r\\n\\t\\tself.setTotalTime(0)\\r\\n\\t\\tstart = timeit.default_timer()\\r\\n\\t\\t# tell puzzle to run the selected algorithm\\r\\n\\t\\tself.puzzle.run(self.alg.get())\\r\\n\\t\\tend = timeit.default_timer()\\r\\n\\t\\t# Set total time to end-start time\\r\\n\\t\\tself.setTotalTime((end - start)*1000)\\r\\n\\t\\t# print the solved final puzzle\\r\\n\\t\\tprint self.puzzle\\r\\n\\t\\tself.puzzle.setPuzzle(self.puzzle.grid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18701bd40bba52df90c82632b88ae1d8\",\n \"score\": \"0.62485784\",\n \"text\": \"def work(self):\\n self.blocklen = int(self.defset(self.args.blocklen, 128))\\n deg = int(self.defset(self.args.degree, 3))\\n tvsize_orig = int(self.defset(self.process_size(self.args.tvsize), 1024*256))\\n zscore_thresh = float(self.args.conf)\\n rounds = int(self.args.rounds) if self.args.rounds is not None else None\\n top_k = int(self.args.topk) if self.args.topk is not None else None\\n top_comb = int(self.defset(self.args.combdeg, 2))\\n reffile = self.defset(self.args.reffile)\\n all_deg = self.args.alldeg\\n\\n # Load input polynomials\\n self.load_input_poly()\\n self.load_input_objects()\\n\\n logger.info('Basic settings, deg: %s, blocklen: %s, TV size: %s, rounds: %s'\\n % (deg, self.blocklen, tvsize_orig, rounds))\\n\\n # specific polynomial testing\\n logger.info('Initialising')\\n time_test_start = time.time()\\n\\n # read file by file\\n for iobj in self.input_objects:\\n tvsize = tvsize_orig\\n\\n iobj.check()\\n size = iobj.size()\\n logger.info('Testing input object: %s, size: %d kB' % (iobj, size/1024.0))\\n\\n # size smaller than TV? Adapt tv then\\n if size >= 0 and size < tvsize:\\n logger.info('File size is smaller than TV, updating TV to %d' % size)\\n tvsize = size\\n\\n if tvsize*8 % self.blocklen != 0:\\n rem = tvsize*8 % self.blocklen\\n logger.warning('Input data size not aligned to the block size. '\\n 'Input bytes: %d, block bits: %d, rem: %d' % (tvsize, self.blocklen, rem))\\n tvsize -= rem//8\\n logger.info('Updating TV to %d' % tvsize)\\n\\n hwanalysis = HWAnalysis()\\n hwanalysis.deg = deg\\n hwanalysis.blocklen = self.blocklen\\n hwanalysis.top_comb = top_comb\\n hwanalysis.comb_random = self.args.comb_random\\n hwanalysis.top_k = top_k\\n hwanalysis.combine_all_deg = all_deg\\n hwanalysis.zscore_thresh = zscore_thresh\\n hwanalysis.do_ref = reffile is not None\\n hwanalysis.input_poly = self.input_poly\\n hwanalysis.no_comb_and = self.args.no_comb_and\\n hwanalysis.no_comb_xor = self.args.no_comb_xor\\n hwanalysis.prob_comb = self.args.prob_comb\\n hwanalysis.do_only_top_comb = self.args.only_top_comb\\n hwanalysis.do_only_top_deg = self.args.only_top_deg\\n hwanalysis.no_term_map = self.args.no_term_map\\n hwanalysis.use_zscore_heap = self.args.topterm_heap\\n hwanalysis.sort_best_zscores = max(common.replace_none([self.args.topterm_heap_k, top_k, 100]))\\n hwanalysis.best_x_combinations = self.args.best_x_combinations\\n\\n # compute classical analysis only if there are no input polynomials\\n hwanalysis.all_deg_compute = len(self.input_poly) == 0\\n logger.info('Initializing test')\\n hwanalysis.init()\\n\\n total_terms = int(scipy.misc.comb(self.blocklen, deg, True))\\n logger.info('BlockLength: %d, deg: %d, terms: %d' % (self.blocklen, deg, total_terms))\\n\\n # Reference data stream reading\\n # Read the file until there is no data.\\n fref = None\\n if reffile is not None:\\n fref = open(reffile, 'r')\\n\\n with iobj:\\n data_read = 0\\n cur_round = 0\\n\\n while size < 0 or data_read < size:\\n if rounds is not None and cur_round > rounds:\\n break\\n\\n data = iobj.read(tvsize)\\n bits = common.to_bitarray(data)\\n if len(bits) == 0:\\n logger.info('File read completely')\\n break\\n\\n ref_bits = None\\n if fref is not None:\\n ref_data = fref.read(tvsize)\\n ref_bits = common.to_bitarray(ref_data)\\n\\n logger.info('Pre-computing with TV, deg: %d, blocklen: %04d, tvsize: %08d = %8.2f kB = %8.2f MB, '\\n 'round: %d, avail: %d' %\\n (deg, self.blocklen, tvsize, tvsize/1024.0, tvsize/1024.0/1024.0, cur_round, len(bits)))\\n\\n hwanalysis.proces_chunk(bits, ref_bits)\\n cur_round += 1\\n pass\\n\\n logger.info('Finished processing %s ' % iobj)\\n logger.info('Data read %s ' % iobj.data_read)\\n logger.info('Read data hash %s ' % iobj.sha1.hexdigest())\\n\\n if fref is not None:\\n fref.close()\\n logger.info('Processing finished')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501a67bb077eaec59e938e1f7111921d\",\n \"score\": \"0.62383354\",\n \"text\": \"def run(self):\\n\\n print(\\\"Run NMOEA algorithm!\\\")\\n\\n # Random initialization of population\\n population = []\\n for _ in range(self.population_size):\\n new_individual = list(range(self.node_num))\\n for i in range(int(0.4 * self.node_num)):\\n v = random.choice(self.nodes)\\n\\n if v in self.graph.adj:\\n for neighbor in self.graph.adj[v].keys():\\n new_individual[neighbor - 1] = new_individual[v - 1]\\n\\n population.append(new_individual)\\n\\n with open(\\\"../output/results/iter{}.json\\\".format(0), \\\"w\\\") as f:\\n json.dump(population, f)\\n it = 1\\n update_count = self.population_size\\n\\n while update_count > 0:\\n for i in range(self.population_size // 2):\\n # Except the first loop, population is sorted by fast-non-dominated-sort.\\n # Select from first 1/3\\n a, b = get_two_random(0, (self.population_size - 1) // 3)\\n population.append(self.crossover(population[a], population[b]))\\n population.append(self.mutation(population[a]))\\n # print(\\\"Iter:{} updating: {}/{}\\\".format(it,\\n # i + 1, self.population_size // 2))\\n\\n population, fitness_list, update_count = self.fast_non_dominated_sort(\\n population)\\n\\n print(\\\"Iter:{}, population is updated by {}\\\".format(it, update_count))\\n\\n community_lens = [len(self.decode_communities(indiv))\\n for indiv in population[:10]]\\n\\n _fitness = []\\n for x in fitness_list[:10]:\\n _fitness.append((round(x[0], 4), round(x[1], 4)))\\n print(community_lens)\\n print(_fitness)\\n with open(\\\"../output/results/report.txt\\\", \\\"a+\\\") as f:\\n f.write(\\\"Iter:{}, population is updated by {}\\\\n\\\".format(\\n it, update_count))\\n f.write(str(community_lens) + \\\"\\\\n\\\")\\n f.write(str(_fitness) + \\\"\\\\n\\\")\\n with open(\\\"../output/results/iter{}.json\\\".format(it), \\\"w\\\") as f:\\n json.dump(population, f)\\n it += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d70e5beb7708b919e48e95b16d9b0044\",\n \"score\": \"0.623162\",\n \"text\": \"def run_all(self, max_passes):\\n if max_passes == 0:\\n debug('optimizer disabled')\\n return\\n self.run_opt(self.del_unused_results) # no need to run this one multiple times\\n for count in xrange(max_passes):\\n debug('------------- global optimizer pass %d (of max %d) -------------' % (\\n count+1, max_passes))\\n sum_counters = sum(self.opt_counters.values())\\n self.run_opt(self.del_jumps_to_next, max_passes=self.INF_PASSES)\\n self.run_opt(self.del_unused_labels)\\n self.run_opt(self.reduce_push_pop, max_passes=self.INF_PASSES)\\n self.run_opt(self.propagate_constants)\\n #self.run_opt(self.clear_deleted_codes)\\n if sum(self.opt_counters.values()) == sum_counters:\\n debug('------------------ all optimizations returned finish -----------------')\\n break\\n # TODO don't assign dead vars\\n # TODO free string memory\\n # TODO [mov mem regA, mov regA regB]\\n # TODO [mov regA memX, mov memX regB]\\n if Flags.optimizer_summary:\\n Status.add_note(LatteError('optimizer case counters:'))\\n for name, count in self.opt_counters.iteritems():\\n Status.add_note(LatteError(name + ': ' + str(count)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479a9e90cdb00c55f9be5669ab50aac3\",\n \"score\": \"0.62308276\",\n \"text\": \"def run_optimizer(**kwargs):\\n #==============================#\\n #| Log file configuration |#\\n #==============================#\\n # By default, output should be written to <args.prefix>.log and also printed to the terminal.\\n # This behavior may be changed by editing the log.ini file.\\n # Output will only be written to log files after the 'logConfig' line is called!\\n logIni = 'log.ini'\\n if kwargs.get('logIni') is None:\\n import geometric.optimize\\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, logIni)\\n else:\\n logIni = kwargs.get('logIni')\\n logfilename = kwargs.get('prefix')\\n # Input file for optimization; QC input file or OpenMM .xml file\\n inputf = kwargs.get('input')\\n # Get calculation prefix and temporary directory name\\n arg_prefix = kwargs.get('prefix', None) #prefix for output file and temporary directory\\n prefix = arg_prefix if arg_prefix is not None else os.path.splitext(inputf)[0]\\n logfilename = prefix + \\\".log\\\"\\n # Create a backup if the log file already exists\\n backed_up = bak(logfilename)\\n import logging.config\\n logging.config.fileConfig(logIni,defaults={'logfilename': logfilename},disable_existing_loggers=False)\\n #==============================#\\n #| End log file configuration |#\\n #==============================#\\n\\n import geometric\\n printLogo(logger)\\n logger.info('-=# \\\\x1b[1;94m geomeTRIC started. Version: %s \\\\x1b[0m #=-\\\\n' % geometric.__version__)\\n logger.info('geometric-optimize called with the following command line:\\\\n')\\n logger.info(' '.join(sys.argv)+'\\\\n')\\n if backed_up:\\n logger.info('Backed up existing log file: %s -> %s\\\\n' % (logfilename, os.path.basename(backed_up)))\\n\\n t0 = time.time()\\n # Custom convergence criteria\\n criteria = kwargs.get('converge', [])\\n if len(criteria)%2 != 0:\\n raise RuntimeError('Please pass an even number of options to --converge')\\n for i in range(int(len(criteria)/2)):\\n key = 'convergence_' + criteria[2*i].lower()\\n try:\\n val = float(criteria[2*i+1])\\n logger.info('Using convergence criteria: %s %.2e\\\\n' % (key, val))\\n except ValueError:\\n # This must be a set\\n val = str(criteria[2*i+1])\\n logger.info('Using convergence criteria set: %s %s\\\\n' % (key, val))\\n kwargs[key] = val\\n\\n # LPW 2019-04-20: Changed the default behavior of \\\"reset\\\" to True.\\n # When users pass '--no_reset' via the CLI, that will result in\\n # passing OptParams(reset=False) to the constructor.\\n # After a suitable amount of time, remove '--reset' from the CLI.\\n if kwargs.get('no_reset', False):\\n if kwargs.get('reset', False):\\n raise RuntimeError('Do not provide both --reset and --no_reset')\\n kwargs['reset'] = False\\n else:\\n if kwargs.get('reset', False):\\n logger.info('The --reset option defaults to True and will be removed soon from the CLI.')\\n kwargs['reset'] = True\\n if 'no_reset' in kwargs: del kwargs['no_reset']\\n\\n params = OptParams(**kwargs)\\n\\n # Get the Molecule and engine objects needed for optimization\\n M, engine = get_molecule_engine(**kwargs)\\n\\n #============================================#\\n #| Temporary file and folder management |#\\n #============================================#\\n # LPW: Should this be refactored and moved into get_molecule_engine?\\n # Probably not .. get_molecule_engine is independent of dirname.\\n # We may implement Engine.initialize(dirname) in the future.\\n dirname = prefix+\\\".tmp\\\"\\n if not os.path.exists(dirname):\\n # LPW: Some engines do not need the tmp-folder, but the\\n # auxiliary functions might (such as writing out displacements)\\n os.makedirs(dirname)\\n else:\\n logger.info(\\\"%s exists ; make sure nothing else is writing to the folder\\\\n\\\" % dirname)\\n # TC-specific: Remove existing scratch files in ./run.tmp/scr to avoid confusion\\n for f in ['c0', 'ca0', 'cb0']:\\n if os.path.exists(os.path.join(dirname, 'scr', f)):\\n os.remove(os.path.join(dirname, 'scr', f))\\n\\n # QC-specific scratch folder\\n qcdir = kwargs.get('qcdir', None) # Provide an initial qchem scratch folder (e.g. supplied initial guess\\n qchem = kwargs.get('qchem', False)\\n if qcdir is not None:\\n if not qchem:\\n raise RuntimeError(\\\"--qcdir only valid if --qchem is specified\\\")\\n if not os.path.exists(qcdir):\\n raise RuntimeError(\\\"--qcdir points to a folder that doesn't exist\\\")\\n shutil.copytree(qcdir, os.path.join(dirname, \\\"run.d\\\"))\\n engine.M.edit_qcrems({'scf_guess':'read'})\\n engine.qcdir = True\\n #============================================#\\n #| End temporary file and folder management |#\\n #============================================#\\n\\n # Get initial coordinates in bohr\\n coords = M.xyzs[0].flatten() * ang2bohr\\n\\n # Read in the constraints\\n constraints = kwargs.get('constraints', None) #Constraint input file (optional)\\n\\n if constraints is not None:\\n Cons, CVals = ParseConstraints(M, open(constraints).read())\\n else:\\n Cons = None\\n CVals = None\\n\\n #=========================================#\\n #| Set up the internal coordinate system |#\\n #=========================================#\\n # First item in tuple: The class to be initialized\\n # Second item in tuple: Whether to connect nonbonded fragments\\n # Third item in tuple: Whether to throw in all Cartesians (no effect if second item is True)\\n CoordSysDict = {'cart':(CartesianCoordinates, False, False),\\n 'prim':(PrimitiveInternalCoordinates, True, False),\\n 'dlc':(DelocalizedInternalCoordinates, True, False),\\n 'hdlc':(DelocalizedInternalCoordinates, False, True),\\n 'tric':(DelocalizedInternalCoordinates, False, False)}\\n coordsys = kwargs.get('coordsys', 'tric')\\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\\n\\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVals[0] if CVals is not None else None,\\n conmethod=params.conmethod)\\n #========================================#\\n #| End internal coordinate system setup |#\\n #========================================#\\n\\n # Auxiliary functions (will not do optimization):\\n verbose = kwargs.get('verbose', False)\\n displace = kwargs.get('displace', False) # Write out the displacements of the coordinates.\\n if displace:\\n WriteDisplacements(coords, M, IC, dirname, verbose)\\n return\\n\\n fdcheck = kwargs.get('fdcheck', False) # Check internal coordinate gradients using finite difference..\\n if fdcheck:\\n IC.Prims.checkFiniteDifferenceGrad(coords)\\n IC.Prims.checkFiniteDifferenceHess(coords)\\n CheckInternalGrad(coords, M, IC.Prims, engine, dirname, verbose)\\n CheckInternalHess(coords, M, IC.Prims, engine, dirname, verbose)\\n return\\n\\n # Print out information about the coordinate system\\n if isinstance(IC, CartesianCoordinates):\\n logger.info(\\\"%i Cartesian coordinates being used\\\\n\\\" % (3*M.na))\\n else:\\n logger.info(\\\"%i internal coordinates being used (instead of %i Cartesians)\\\\n\\\" % (len(IC.Internals), 3*M.na))\\n logger.info(IC)\\n logger.info(\\\"\\\\n\\\")\\n\\n if Cons is None:\\n # Run a standard geometry optimization\\n if prefix == os.path.splitext(inputf)[0]:\\n params.xyzout = prefix+\\\"_optim.xyz\\\"\\n else:\\n params.xyzout = prefix+\\\".xyz\\\"\\n progress = Optimize(coords, M, IC, engine, dirname, params)\\n else:\\n # Run a single constrained geometry optimization or scan over a grid of values\\n if isinstance(IC, (CartesianCoordinates, PrimitiveInternalCoordinates)):\\n raise RuntimeError(\\\"Constraints only work with delocalized internal coordinates\\\")\\n Mfinal = None\\n for ic, CVal in enumerate(CVals):\\n if len(CVals) > 1:\\n logger.info(\\\"---=== Scan %i/%i : Constrained Optimization ===---\\\\n\\\" % (ic+1, len(CVals)))\\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal, conmethod=params.conmethod)\\n IC.printConstraints(coords, thre=-1)\\n if len(CVals) > 1:\\n params.xyzout = prefix+\\\"_scan-%03i.xyz\\\" % ic\\n # In the special case of a constraint scan, we write out multiple qdata.txt files\\n if params.qdata is not None: params.qdata = 'qdata_scan-%03i.txt' % ic\\n elif prefix == os.path.splitext(kwargs['input'])[0]:\\n params.xyzout = prefix+\\\"_optim.xyz\\\"\\n else:\\n params.xyzout = prefix+\\\".xyz\\\"\\n if ic == 0:\\n progress = Optimize(coords, M, IC, engine, dirname, params)\\n else:\\n progress += Optimize(coords, M, IC, engine, dirname, params)\\n # update the structure for next optimization in SCAN (by CNH)\\n M.xyzs[0] = progress.xyzs[-1]\\n coords = progress.xyzs[-1].flatten() * ang2bohr\\n if Mfinal:\\n Mfinal += progress[-1]\\n else:\\n Mfinal = progress[-1]\\n cNames, cVals = IC.getConstraintTargetVals()\\n comment = ', '.join([\\\"%s = %.2f\\\" % (cName, cVal) for cName, cVal in zip(cNames, cVals)])\\n Mfinal.comms[-1] = \\\"Scan Cycle %i/%i ; %s ; %s\\\" % (ic+1, len(CVals), comment, progress.comms[-1])\\n #print\\n if len(CVals) > 1:\\n Mfinal.write('scan-final.xyz')\\n if params.qdata is not None: Mfinal.write('qdata-final.txt')\\n print_msg()\\n logger.info(\\\"Time elapsed since start of run_optimizer: %.3f seconds\\\\n\\\" % (time.time()-t0))\\n return progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7667ba4918edddc4a8580aac899826f1\",\n \"score\": \"0.6212641\",\n \"text\": \"def main():\\n\\n ocp = prepare_ocp(long_optim=False)\\n ocp.add_plot_penalty(CostType.ALL)\\n\\n # --- Solve the program --- #\\n sol = ocp.solve(Solver.IPOPT(show_online_optim=platform.system() == \\\"Linux\\\"))\\n sol.graphs(show_bounds=True)\\n\\n # --- Show results --- #\\n sol.print_cost()\\n sol.animate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422204d3665e43c733003a6af1f87607\",\n \"score\": \"0.62049055\",\n \"text\": \"def run(self):\\n\\n self.generate_population()\\n print('-----------Init--------------')\\n print(self.config)\\n\\n fitness = []\\n best = 0\\n for i in range(100):\\n self.reproduce()\\n self.select()\\n\\n sorted_population = sorted(self.population, \\\\\\n key=lambda x: x['fitness_points'])\\n worst = sorted_population[0]['fitness_points']\\n best = sorted_population[-1]['fitness_points']\\n mean = (sorted_population[0]['fitness_points'] + \\\\\\n sorted_population[-1]['fitness_points']) / 2\\n fitness.append((best, worst, mean))\\n\\n print(best)\\n print(sorted_population[-1]['solution'])\\n self.plot(fitness, best)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea273060e80ac78f27171805ac1cae1f\",\n \"score\": \"0.6198797\",\n \"text\": \"def run_optimization(request, configure_with_daemon, wait_for): # pylint: disable=unused-argument\\n def inner(engine, func_workchain, engine_kwargs, evaluate=None): # pylint: disable=missing-docstring,useless-suppression\\n from aiida_optimize import OptimizationWorkChain\\n from aiida.orm import load_node\\n from aiida.orm import Dict\\n from aiida.engine.launch import run_get_node, submit\\n\\n inputs = dict(\\n engine=engine,\\n engine_kwargs=Dict(dict=dict(engine_kwargs)),\\n evaluate_process=func_workchain,\\n evaluate=evaluate if evaluate is not None else {},\\n )\\n\\n if request.param == 'run':\\n _, result_node = run_get_node(OptimizationWorkChain, **inputs)\\n else:\\n assert request.param == 'submit'\\n pk = submit(OptimizationWorkChain, **inputs).pk\\n wait_for(pk)\\n result_node = load_node(pk)\\n return result_node\\n\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f5ab5262c2de9dd8c16074129011e6\",\n \"score\": \"0.6180509\",\n \"text\": \"def objective(self, args):\\n num_layers, hidden_size, learning_rate, batch_size = args\\n num_layers = int(num_layers)\\n hidden_size = int(hidden_size)\\n batch_size = int(batch_size)\\n\\n print('')\\n print('Attempting run with:')\\n print('num_layers=%d' % num_layers)\\n print('hidden_size=%d' % hidden_size)\\n print('learning_rate=%f' % learning_rate)\\n print('batch_size=%d' % batch_size)\\n print('')\\n\\n if self.model_type == 'actprop':\\n model_args = [\\n \\\"-m\\\", \\\"actprop\\\", \\\"-O\\\",\\n \\\"num_layers=%d,hidden_size=%d\\\" % (num_layers, hidden_size)\\n ]\\n else:\\n model_args = [\\n \\\"-m\\\", \\\"simple\\\", \\\"-O\\\",\\n \\\"num_layers=%d,hidden_size=%d\\\" % (num_layers, hidden_size)\\n ]\\n cmdline = [\\n \\\"./fpg.py\\\", \\\"-p\\\", self.problem, \\\"-t\\\", str(self.time_limit), \\\"-l\\\",\\n self.log_dir, '-L', str(self.turns), '-R', str(self.eval_rounds),\\n \\\"-f\\\", \\\"-o\\\", \\\"vpg\\\", \\\"-A\\\",\\n \\\"learning_rate=%f,batch_size=%d\\\" % (learning_rate, batch_size),\\n *model_args, *self.pddl_paths\\n ]\\n try:\\n proc_stdout = check_output(cmdline, universal_newlines=True)\\n except CalledProcessError as e:\\n print(\\n 'Error running fpy.py on %s: %s\\\\n\\\\nFailed command: %s' %\\n (self.problem, str(e), cmdline),\\n file=stderr)\\n # BUG: I think that when the first run fails, hyperopt crashes on\\n # hyperopt/base.py:585 when it tries to get the minimum loss over\\n # all runs. Presumably this does not matter for later runs.\\n return {'loss': 1e100, 'status': hopt.STATUS_FAIL}\\n results = get_results(self.log_dir, proc_stdout)\\n opt_time = results['elapsed_opt_time']\\n succ_rate = results['successes'] / float(results['trials'])\\n fail_rate = 1 - succ_rate\\n hp_loss = fail_rate * self.score_weight + opt_time\\n print('Success rate from this round: %f' % succ_rate)\\n return {'loss': hp_loss, 'status': hopt.STATUS_OK}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ad1dd9ba2699f941f66a8586d374e7\",\n \"score\": \"0.61802757\",\n \"text\": \"def perform_optimization(self):\\n\\n # Create an optimizer (tf.train.AdamOptimizer)\\n self.optimizer = tf.train.AdamOptimizer(self.learning_rate_ph)\\n ######### YOUR CODE HERE #############\\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\\n\\n # Gradient clipping (tf.clip_by_norm) for self.grads_and_vars\\n # Pay attention that you need to apply this operation only for gradients\\n # because self.grads_and_vars contains also variables.\\n # list comprehension might be useful in this case.\\n clip_norm = tf.cast(1.0, tf.float32)\\n self.grads_and_vars = [(tf.clip_by_norm(grad, clip_norm), var) for grad, var in self.grads_and_vars]\\n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b19d54cda4fdf13464d0b9ddc106ef4\",\n \"score\": \"0.6178133\",\n \"text\": \"def run_optimization(output_dir, optimization, design_file, library):\\n output_dir = output_dir.replace(' ', '_')\\n if not os.path.exists(output_dir):\\n os.makedirs(output_dir)\\n output_design_file = output_dir + '/design.blif'\\n \\n abc_command = 'read ' + library + '; '\\n abc_command += 'read ' + design_file + '; '\\n abc_command += 'strash; '\\n abc_command += optimization + '; '\\n abc_command += 'write ' + output_design_file + '; '\\n abc_command += 'map -D ' + str(clock_period) + '; '\\n abc_command += 'print_stats; '\\n \\n proc = subprocess.check_output(['yosys-abc','-c', abc_command])\\n d, a = extract_results(proc)\\n return output_design_file, d, a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a9d6f9005828e07aa5b0fab43befa3\",\n \"score\": \"0.61596817\",\n \"text\": \"def run_algorithms(self):\\n # REVERSE ORDER OF POLYGONS SO NODES ARE INPUT TO ALGORITHMS IN CLOCKWISE DIRECTION\\n # (AS LAYER NODES ARE STORED IN LEFT TO RIGHT ORDER IN THE NUMPY ARRAYS\\n if self.calc_grav_switch is True or self.calc_mag_switch is True:\\n if len(self.polygons) > 1:\\n for i in range(0, len(self.polygons)):\\n self.polygons[i] = self.polygons[i][::-1]\\n else:\\n self.polygons[0] = self.polygons[0][::-1]\\n\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n # TOPOGRAPHY - :FUTURE: PREDICTED TOPOGRAPHY FROM ISOSTATIC FUNC\\n self.pred_topo = np.zeros_like(self.xp)\\n\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n # GRAVITY\\n # ARRAY OF DENSITY CONTRASTS TO BE PASSED TO BOTT ALGORITHM\\n self.density_contrasts = np.zeros(len(self.densities))\\n for i in range(len(self.densities)):\\n self.density_contrasts[i] = (self.densities[i] - self.reference_densities[i])\\n\\n # ZIP POLYGONS WITH DENSITY CONTRASTS AND PASS TO BOTT\\n if self.polygons and self.calc_grav_switch is True:\\n # SELECT ONLY THOSE LAYERS THAT ARE CHECKED\\n polygons_to_use, densities_to_use = [], []\\n for layer in range(len(self.densities)):\\n if self.layers_calculation_switch[layer] == 1:\\n\\n polygons_to_use.append(self.polygons[layer])\\n densities_to_use.append(self.density_contrasts[layer])\\n\\n # PASS TO BOTT ALGORITHM\\n polys = []\\n for p, d in zip(polygons_to_use, densities_to_use):\\n polys.append(Polygon(1000 * np.array(p), {'density': d}))\\n self.predgz = bott.Gz(self.xp, self.zp, polys)\\n else:\\n self.predgz = np.zeros_like(self.xp)\\n self.predplot.set_data(self.xp * 0.001, self.predgz)\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n # MAGNETICS\\n # ZIP POLYGONS WITH SUSCEPT/STRIKES AND PASS TO TALWANI AND HEIRTZLER ALGORITHM\\n if self.polygons and self.earth_field != 0.0 and self.calc_mag_switch is True:\\n # SELECT ONLY THOSE LAYERS THAT ARE CHECKED\\n polygons_to_use, susceptibilities_to_use = [], []\\n for layer in range(0, len(self.polygons)):\\n if self.layers_calculation_switch[layer] == 1:\\n polygons_to_use.append(self.polygons[layer])\\n susceptibilities_to_use.append(self.susceptibilities[layer])\\n\\n # PASS TO TALWANI AND HEIRTZLER ALGORITHM\\n self.polys = []\\n for p, s, in zip(polygons_to_use, susceptibilities_to_use):\\n self.polys.append(Polygon(1000. * np.array(p), {'susceptibility': s}))\\n self.prednt = talwani_and_heirtzler.ntz(self.xp, self.zp, self.polys, self.model_azimuth, self.earth_field,\\n self.angle_a, self.angle_b, self.mag_observation_elv)\\n else:\\n self.prednt = np.zeros_like(self.xp)\\n\\n # PLOT MAGNETIC PROFILE\\n self.prednt_plot.set_data(self.xp * 0.001, self.prednt)\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n # UPDATE RMS VALUES\\n\\n # RUN RMS CALC CODE\\n self.model_rms(self.xp)\\n\\n # SET GRAV RMS VALUES\\n if self.obs_gravity_data_for_rms != [] and self.grav_obs_switch == True and self.calc_grav_switch == True \\\\\\n and self.predgz != []:\\n self.grav_rms_plot.set_data(self.grav_residuals[:, 0], self.grav_residuals[:, 1])\\n elif self.obs_gravity_data_for_rms is None:\\n self.grav_rms_plot.set_data(self.obs_gravity_data_for_rms[:, 0],\\n np.zeros_like(self.obs_gravity_data_for_rms[:, 0]))\\n else:\\n pass\\n\\n # SET MAG RMS VALUES\\n if self.obs_mag_data_for_rms != [] and self.mag_obs_switch == True and self.calc_mag_switch == True \\\\\\n and self.prednt != []:\\n self.mag_rms_plot.set_data(self.mag_residuals[:, 0], self.mag_residuals[:, 1])\\n elif self.obs_mag_data_for_rms is None:\\n self.mag_rms_plot.set_data(self.obs_mag_data_for_rms[:, 0],\\n np.zeros_like(self.obs_mag_data_for_rms[:, 0]))\\n else:\\n pass\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\\n # SET FRAME X AND Y LIMITS\\n self.set_frame_limits()\\n\\n # AFTER RUNNING ALGORITHMS, SET MODEL AS UNSAVED\\n self.model_saved = False\\n\\n # UPDATE GMG GRAPHICS\\n self.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52225774d11db8466b77d872149b8863\",\n \"score\": \"0.61242497\",\n \"text\": \"def optimize(self) -> None:\\n if not self._order:\\n raise ValueError(\\\"No parameters to refine.\\\")\\n md.optimize(self._recipe, self._order, validate=False, verbose=self._verbose, **self._options)\\n rw = self.get_rw()\\n if self._verbose > 0:\\n print(\\\"Optimization result: Rw = {:.6f}.\\\".format(rw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3516dc094a8298d62209184f87f57b9e\",\n \"score\": \"0.60990447\",\n \"text\": \"def main():\\n # create a GridSearch object to find the best fitting model\\n grid_search = new_grid_search()\\n # run the search algorithm\\n run_grid_search(grid_search)\\n # save the best fitting model into FS\\n save_search_results(grid_search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47534fa3b2527d69d936144b430541ed\",\n \"score\": \"0.6098178\",\n \"text\": \"def check_optimization(\\n configure, # pylint: disable=unused-argument\\n run_optimization, # pylint: disable=redefined-outer-name\\n):\\n\\n def inner( # pylint: disable=too-many-arguments,missing-docstring,useless-suppression\\n engine,\\n func_workchain_name,\\n engine_kwargs,\\n xtol,\\n ftol,\\n x_exact,\\n f_exact,\\n evaluate=None,\\n input_key='x',\\n ):\\n\\n from aiida.orm import load_node\\n\\n import sample_processes\\n func_workchain = getattr(sample_processes, func_workchain_name)\\n\\n result_node = run_optimization(\\n engine=engine,\\n engine_kwargs=ChainMap(engine_kwargs, {'result_key': 'result'}),\\n func_workchain=func_workchain,\\n evaluate=evaluate\\n )\\n\\n assert 'optimal_process_uuid' in result_node.outputs\\n assert np.isclose(result_node.outputs.optimal_process_output.value, f_exact, atol=ftol)\\n calc = load_node(result_node.outputs.optimal_process_uuid.value)\\n assert np.allclose(type(x_exact)(getattr(calc.inputs, input_key)), x_exact, atol=xtol)\\n\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b26848cf5dae392ecec0e9d3eb6b4330\",\n \"score\": \"0.6094927\",\n \"text\": \"def _before_optimize(self):\\n if config.monitor_verbosity > 0:\\n logger.info('Starting optimization of %s...', str(self))\\n\\n # reset monitor object in case of rerun\\n self.monitor.reset()\\n\\n # init monitor dict for algorithm\\n self.monitor.t = time.time()\\n\\n # init optimization time control\\n self.monitor.optimize_start = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"def921edc7b662cb905ecc7db2051645\",\n \"score\": \"0.6091037\",\n \"text\": \"def solve(self):\\n for algo in self.algorithms:\\n log.info(\\\"algorithm %s is being solved\\\" % algo.algo.__name__)\\n a = algo.algo(self.graphs.graph, self.graphs.drivers_graph, *algo.args, **algo.kwargs)\\n try:\\n start = time.time()\\n a.solve()\\n running_time = time.time() - start\\n self.values.setdefault(algo.algo.__name__, a.get_optimal_value())\\n self.running_times.setdefault(algo.algo.__name__, running_time)\\n self.status.setdefault(algo.algo.__name__, a.get_status())\\n except Exception:\\n self.values.setdefault(algo.algo.__name__, None)\\n self.running_times.setdefault(algo.algo.__name__, None)\\n self.status.setdefault(algo.algo.__name__, options.FAILED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b63727a23f774affb6df5591e26f3c7\",\n \"score\": \"0.6083168\",\n \"text\": \"def run_problem5(config_file):\\n C = json.load(open(config_file))\\n \\n # Optimizer config inputs\\n opt_config = C['opt']['config']\\n opt_in = C['opt']['input']\\n opt_out = C['opt']['output']\\n CP = C['planet']\\n CV = C['vehicle']\\n CT = C['target']\\n CO = C['opt']\\n \\n # Problem and optimization configuration\\n udp = prob_3D_lander_multiphase(C)\\n prob = pg.problem(udp)\\n prob.c_tol = opt_config['c_tol']\\n uda = pg.algorithm(pg.nlopt(opt_config['nlopt_alg']))\\n uda.set_verbosity(opt_config['verbosity'])\\n uda.extract(pg.nlopt).xtol_rel = opt_config['xtol_rel']\\n uda.extract(pg.nlopt).ftol_rel = opt_config['ftol_rel']\\n uda.extract(pg.nlopt).maxeval = opt_config['maxeval']\\n \\n # Initial guess from file.\\n if opt_in['guess_from_file']:\\n print('LOADING GUESS FROM FILE')\\n \\n # Load in the file state vector\\n Xdata_in = json.load(open(opt_in['in_file']))\\n file_npts = Xdata_in['npts']\\n ns = file_npts\\n P = len(file_npts)\\n x = Xdata_in['X0']\\n \\n # Other important information\\n npts = opt_config['npts']\\n\\n Rx = []\\n Ry = []\\n Rz = []\\n Vx = []\\n Vy = []\\n Vz = []\\n Ux = []\\n Uy = []\\n Uz = []\\n m = []\\n Eta = []\\n nu = 0.0\\n T = []\\n\\n # Split up the state vector into a more meaningful shape\\n i0 = 0\\n for ii in range(0,P):\\n Rx = Rx + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Ry = Ry + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Rz = Rz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Vx = Vx + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Vy = Vy + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Vz = Vz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Ux = Ux + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Uy = Uy + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Uz = Uz + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n m = m + [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n for ii in range(0,P):\\n Eta= Eta+ [ list ( x[ (i0):(i0+ns[ii]) ] ) ]\\n i0 = i0 + ns[ii]\\n nu = [x[i0]]\\n i0 = i0 + 1\\n T = x[(i0):(i0 + P)]\\n\\n for pp in range(0,len(file_npts)):\\n \\n # If number of points in phase does not line up:\\n if not (file_npts[pp] == npts[pp]):\\n \\n Rx[pp] = recast_pts(Rx[pp],npts[pp])\\n Ry[pp] = recast_pts(Ry[pp],npts[pp])\\n Rz[pp] = recast_pts(Rz[pp],npts[pp])\\n Vx[pp] = recast_pts(Vx[pp],npts[pp])\\n Vy[pp] = recast_pts(Vy[pp],npts[pp])\\n Vz[pp] = recast_pts(Vz[pp],npts[pp])\\n Ux[pp] = recast_pts(Ux[pp],npts[pp])\\n Uy[pp] = recast_pts(Uy[pp],npts[pp])\\n Uz[pp] = recast_pts(Uz[pp],npts[pp])\\n m[pp] = recast_pts(m[pp],npts[pp])\\n Eta[pp] = recast_pts(Eta[pp],npts[pp])\\n \\n Rx = list(chain.from_iterable(Rx))\\n Ry = list(chain.from_iterable(Ry))\\n Rz = list(chain.from_iterable(Rz))\\n Vx = list(chain.from_iterable(Vx))\\n Vy = list(chain.from_iterable(Vy))\\n Vz = list(chain.from_iterable(Vz))\\n Ux = list(chain.from_iterable(Ux))\\n Uy = list(chain.from_iterable(Uy))\\n Uz = list(chain.from_iterable(Uz))\\n m = list(chain.from_iterable(m))\\n Eta= list(chain.from_iterable(Eta))\\n \\n X0 = Rx + Ry + Rz + Vx + Vy + Vz + Ux + Uy + Uz + m + Eta + nu + T\\n \\n # Initial guess - linear profile\\n elif opt_in['use_linear_guess']:\\n print('CONSTRUCTING LINEAR INITIAL GUESS')\\n \\n opt_lg = opt_in['linear_guess']\\n npts = sum(opt_config['npts'])\\n ns = opt_config['npts']\\n tof = sum(opt_lg['tof'])\\n nu0 = opt_lg['nu0']\\n mass0 = sum([CV['stages'][ii]['ms'] + CV['stages'][ii]['mp'] for ii in CV['phases'][0][0]])\\n isp0 = CV['engines'][CV['phases'][0][1]]['isp']\\n tmax0 = CV['engines'][CV['phases'][0][1]]['Tmax']\\n \\n # Initial state\\n ra = CP['R_eq'] + CV['orbit']['alta']\\n rp = CP['R_eq'] + CV['orbit']['altp']\\n sma = 0.5*(ra+rp)\\n ecc = (ra-rp)/(ra+rp)\\n inc = CV['orbit']['inc']\\n Om = CV['orbit']['raan']\\n om = CV['orbit']['argper']\\n nu = nu0\\n r0_I,v0_I = elts_to_rv(sma,ecc,inc,Om,om,nu,CP['mu'],CV['orbit']['degrees'])\\n \\n # Target state\\n R_PF_UEN = Rot_PF_UEN(CT['lon'], CT['lat'], degrees=True)\\n R_UEN_PF = R_PF_UEN.inv()\\n R_I_PF = Rot_I_PF(CP['Omega'], CP['Epoch'], tof)\\n R_UEN_I = R_I_PF.inv() * R_UEN_PF\\n rLS_I = R_UEN_I.apply([CP['R_eq'] + CT['alt'],0,0])\\n rt_I = rLS_I + R_UEN_I.apply(CT['target_pos_UEN'])\\n vt_I = R_UEN_I.apply(CT['target_vel_UEN']) + cross([0,0,CP['Omega']],rt_I)\\n \\n # Construct X vector\\n dt = tof/npts\\n R = linspace(r0_I, rt_I, npts).transpose()\\n V = linspace(v0_I, vt_I, npts).transpose()\\n Ux = [1.0] + [(V[0,k+1] - V[0,k])/dt for k in range(0,npts-1)]\\n Uy = [1.0] + [(V[1,k+1] - V[1,k])/dt for k in range(0,npts-1)]\\n Uz = [1.0] + [(V[2,k+1] - V[2,k])/dt for k in range(0,npts-1)]\\n for kk in range(0,len(Ux)):\\n Umag = (Ux[kk]**2.0 + Uy[kk]**2.0 + Uz[kk]**2.0 )**0.5\\n Ux[kk] = Ux[kk]/Umag\\n Uy[kk] = Uy[kk]/Umag\\n Uz[kk] = Uz[kk]/Umag \\n U = array([Ux, Uy, Uz])\\n Eta = [0.7]*ns[0] + [0.0]*ns[1] + [0.7]*ns[2]\\n m = [mass0]\\n for k in range(1,npts):\\n m = m + [m[k-1] - tmax0*Eta[k]/(9.81*isp0)]\\n X0 = list(R.flatten()) + list(V.flatten()) + list(U.flatten()) + list(m) + list(Eta) + [nu0] + opt_lg['tof']\\n\\n # Create a population\\n print(\\\"Creating population...\\\")\\n pop = pg.population(prob)\\n pop.push_back(X0)\\n\\n # Monotonic basin hopping option\\n if opt_config['use_mbh_wrapper']:\\n mbhuda = pg.mbh(uda, stop=opt_config['mbh']['stop'], perturb=opt_config['mbh']['perturb'])\\n algo = pg.algorithm(mbhuda)\\n else:\\n algo = uda\\n \\n # Evolve\\n if C['opt']['evolve']:\\n print(\\\"Evolving...\\\")\\n pop = algo.evolve(pop)\\n\\n # Check feasibility\\n is_feas = prob.feasibility_x(pop.champion_x)\\n print(\\\"Feasible trajectory? \\\", is_feas)\\n \\n # Produce summary\\n udp.run_traj(pop.champion_x, plot_traj=opt_out['plot_traj'], write_sum=opt_out['write_sum'], write_csv=opt_out['write_csv'] )\\n \\n if opt_out['write_X']:\\n print('WRITING TO FILE')\\n outdict = { 'npts':list(opt_config['npts']), 'X0':list(pop.champion_x) }\\n if (not opt_out['only_write_feasible']) or (opt_out['only_write_feasible'] and is_feas):\\n json.dump(outdict, open(opt_out['file_X_out'],'w+'), indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f031057b826cf4a97f9808101e42151b\",\n \"score\": \"0.6079285\",\n \"text\": \"def optimizeOptimization():\\n initDict = {'target': [], 'damping': [], 'friction': []}\\n acquisitionFunctions = ['ei', 'ucb', 'ei', 'ucb', 'ei', 'ucb']\\n\\n for i in range(6):\\n optimization = BO(runAll40ExperimentsAndGetCost, {'damping': (0, 1), 'friction': (0, 1)})\\n try:\\n optimization.initialize(initDict)\\n optimization.maximize(n_iter=50, acq=acquisitionFunctions[i])\\n res = optimization.res['max']\\n\\n if res['max_val'] not in initDict['target']:\\n initDict['target'].append(res['max_val'])\\n maxParams = res['max_params']\\n initDict['damping'].append(maxParams['damping'])\\n initDict['friction'].append(maxParams['friction'])\\n print(initDict)\\n\\n with open('BO_sledge_params/6xBO_all40_60iters_0-8_0-4.txt', 'w') as file:\\n file.write(json.dumps(initDict)) # use `json.loads` to do the reverse\\n\\n except Exception as ex:\\n print(ex)\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac85dee859be2e0d831a857b2a2d040\",\n \"score\": \"0.6072187\",\n \"text\": \"def run(self):\\n\\n i = 0\\n for training, validation, testing in tqdm(self.samples):\\n for key, value in tqdm(self.models.items()):\\n # If evaluation for key, iteration pair already exists, skip this pair.\\n if not self.results[key][i]:\\n self.results[key][i] = self._evaluate_algorithm(\\n algorithm=value['algorithms'], configurations=value['configurations'], training_set=training,\\n validation_set=validation, testing_set=testing, metric=self.metric)\\n # Serialize benchmark.\\n benchmark_to_pickle(self)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626684458c91b6a349dba256f569ae86\",\n \"score\": \"0.606212\",\n \"text\": \"def run(self, x, print_result=False, print_progress=False, print_time=False):\\n args = print_progress\\n t1 = time.time()\\n res = minimize(\\n self._score,\\n x,\\n args,\\n method=\\\"Nelder-Mead\\\",\\n tol=10e-3,\\n options={\\\"maxiter\\\": 1000},\\n )\\n self.equilibrium_pos = res.x\\n t2 = time.time()\\n\\n if print_result:\\n print(res)\\n\\n if print_time:\\n print(f\\\"Time Spent: {t2-t1} seconds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e160f3644d688a9ef3958de8a28198\",\n \"score\": \"0.60592455\",\n \"text\": \"def _optimize(self):\\n z, rho = self.phase.optimize(self.invert.z, self.invert.rho)\\n self.fitz, self.fitrho = z, rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d701625caa8dd32e663e37206decda\",\n \"score\": \"0.60560405\",\n \"text\": \"def optimise(self, log_to_console=True, output_flag=True):\\n\\t\\tfor m_str, m in self.model_with_separate_variables.items():\\n\\t\\t\\tif not log_to_console:\\n\\t\\t\\t\\tm.setParam(\\\"LogToConsole\\\", 0)\\n\\t\\t\\tif not output_flag:\\n\\t\\t\\t\\tm.setParam(\\\"OutputFlag\\\", 0) \\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\tt = time.time()\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\tm.optimize()\\n\\t\\t\\t\\n\\t\\t\\tif m.Status == grb.GRB.INFEASIBLE:\\n\\t\\t\\t\\traise ValueError(\\\"Model infeasible. I will stop here as the setup will lead to a bunch of infeasible models. Please, review the setup or change input parameters before continuing.\\\")\\n\\t\\t\\tself.model_with_separate_variables_runtime[m_str] = time.time() - t\\t\\t\\t\\n\\t\\t\\tself.runtimes.append({'model number': m_str,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'model name': m.ModelName,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'time taken to optimise (in seconds)': self.model_with_separate_variables_runtime[m_str],\\n\\t\\t\\t\\t\\t\\t\\t\\t 'number of partitions': len(self.partitions),\\n\\t\\t\\t\\t\\t\\t\\t\\t 'partition size': len(self.x_range)})\\n\\n\\t\\tfor m_str, m in self.model_with_pwl_costs.items():\\n\\t\\t\\tif not log_to_console:\\n\\t\\t\\t\\tm.setParam(\\\"LogToConsole\\\", 0)\\n\\t\\t\\tif not output_flag:\\n\\t\\t\\t\\tm.setParam(\\\"OutputFlag\\\", 0) \\t\\t\\t\\n\\n\\t\\t\\tt = time.time()\\n\\t\\t\\tm.optimize()\\n\\t\\t\\tif m.Status == grb.GRB.INFEASIBLE:\\n\\t\\t\\t\\traise ValueError(\\\"Model infeasible. I will stop here as the setup will lead to a bunch to infeasible models. Please, review the setup or change input parameters before continuing.\\\")\\t\\t\\t\\n\\t\\t\\tself.model_with_pwl_costs_runtime[m_str] = time.time() - t\\n\\t\\t\\tself.runtimes.append({'model number': m_str,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'model name': m.ModelName,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'time taken to optimise (in seconds)': self.model_with_pwl_costs_runtime[m_str],\\n\\t\\t\\t\\t\\t\\t\\t\\t 'number of partitions': len(self.partitions),\\n\\t\\t\\t\\t\\t\\t\\t\\t 'partition size': len(self.x_range)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2b6dccf24e48f9818f0ddde4d40fe5\",\n \"score\": \"0.6047866\",\n \"text\": \"def run_objective(self, objective, candidates):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31cf1fc0a3047f221e50e3aa6834bfbc\",\n \"score\": \"0.6044882\",\n \"text\": \"def train_model(self):\\n\\n optimizer_lgbm = BayesianOptimization(\\n f=self.lgbm_cv,\\n pbounds=self.lgbm_pounds,\\n random_state=1234,\\n verbose=2\\n )\\n\\n optimizer_xgb = BayesianOptimization(\\n f=self.xgb_cv,\\n pbounds=self.xgb_pounds,\\n random_state=1234,\\n verbose=2\\n )\\n #\\n optimizer_cat = BayesianOptimization(\\n f=self.cat_cv,\\n pbounds=self.cat_pounds,\\n random_state=1234,\\n verbose=2\\n )\\n\\n optimizer_xgb.maximize(n_iter=500)\\n optimizer_lgbm.maximize(n_iter=500)\\n optimizer_cat.maximize(n_iter=500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab949a9ca5fd3b88f34cd1f71463621\",\n \"score\": \"0.6033554\",\n \"text\": \"def optimize(self):\\n\\n\\n\\t\\tcl = self.clopath_W_rnn * self.con_dict['EE_mask']\\n\\t\\tg_scale = cp.mean(cp.abs(self.grad_dict['W_rnn']))\\n\\t\\tc_scale = cp.mean(cp.abs(cl))\\n\\t\\tself.clopath_W_rnn = cl * (g_scale/c_scale)\\n\\t\\tself.grad_dict['W_rnn'] += self.clopath_W_rnn\\n\\n\\t\\tcl = self.clopath_W_in * self.con_dict['XE_mask']\\n\\t\\tg_scale = cp.mean(cp.abs(self.grad_dict['W_in']))\\n\\t\\tc_scale = cp.mean(cp.abs(cl))\\n\\t\\tself.clopath_W_in = cl * (g_scale/c_scale)\\n\\t\\tself.grad_dict['W_in'] += self.clopath_W_in\\n\\n\\n\\t\\tself.grad_dict['W_in'] *= self.con_dict['W_in_mask']\\n\\t\\tself.grad_dict['W_rnn'] *= self.con_dict['W_rnn_mask']\\n\\t\\tself.grad_dict['W_out'] *= self.con_dict['W_out_mask']\\n\\n\\t\\t# Calculate task loss\\n\\t\\tself.task_loss = cross_entropy(self.output_mask, self.output_data, self.y)\\n\\n\\t\\t# Apply gradient updates using the chosen optimizer\\n\\t\\tself.var_dict = self.optimizer.apply_gradients(self.grad_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89fb7b87cff1cb42626487b40740392e\",\n \"score\": \"0.6031508\",\n \"text\": \"def step(self):\\n self.inner_optimizer.step()\\n\\n self._increment_global_var()\\n params_grads = []\\n for param in self._parameter_list:\\n if not param.trainable:\\n continue\\n if param._grad_ivar() is not None:\\n grad_var = param._grad_ivar()\\n params_grads.append((param, grad_var))\\n\\n self._apply_optimize(\\n loss=None, startup_program=None, params_grads=params_grads\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7492c09c3418f9f75b598c7eacfca2\",\n \"score\": \"0.6025532\",\n \"text\": \"def one_iteration():\\n global counter\\n counter += 1\\n\\n # Create the parameter in CPACS with 'CPACSUpdater' module\\n cpacs_path = mi.get_toolinput_file_path('CPACSUpdater')\\n cpacs_out_path = mi.get_tooloutput_file_path('CPACSUpdater')\\n\\n tixi = cpsf.open_tixi(cpacs_path)\\n wkdir_path = ceaf.create_new_wkdir(Rt.date)\\n WKDIR_XPATH = '/cpacs/toolspecific/CEASIOMpy/filesPath/wkdirPath'\\n tixi.updateTextElement(WKDIR_XPATH, wkdir_path)\\n\\n # TODO: improve this part! (maybe move somewhere else)\\n # To delete coef from previous iter\\n if opf.get_aeromap_path(Rt.modules) != 'None':\\n xpath = opf.get_aeromap_path(Rt.modules)\\n aeromap_uid = cpsf.get_value(tixi, xpath + '/aeroMapUID')\\n Coef = apmf.get_aeromap(tixi, aeromap_uid)\\n apmf.delete_aeromap(tixi, aeromap_uid)\\n apmf.create_empty_aeromap(tixi, aeromap_uid, 'test_optim')\\n apmf.save_parameters(tixi, aeromap_uid, Coef)\\n cpsf.close_tixi(tixi, cpacs_path)\\n\\n # Update the CPACS file with the parameters contained in design_var_dict\\n update_cpacs_file(cpacs_path, cpacs_out_path, design_var_dict)\\n\\n # Save the CPACS file\\n if counter % 1 == 0:\\n file_copy_from = mi.get_tooloutput_file_path('CPACSUpdater')\\n shutil.copy(file_copy_from, optim_dir_path+'/Geometry/'+'iter_{}.xml'.format(counter))\\n\\n # Run optimisation sub workflow\\n wkf.copy_module_to_module('CPACSUpdater', 'out', Rt.modules[0], 'in')\\n wkf.run_subworkflow(Rt.modules)\\n wkf.copy_module_to_module(Rt.modules[-1], 'out', 'CPACSUpdater', 'in')\\n\\n # Extract results TODO: improve this part\\n cpacs_results_path = mi.get_tooloutput_file_path(Rt.modules[-1])\\n log.info('Results will be extracted from:' + cpacs_results_path)\\n tixi = cpsf.open_tixi(cpacs_results_path)\\n\\n # Update the constraints values\\n update_res_var_dict(tixi)\\n return compute_obj()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c5fe88a09030955fda7daf1c62b8c7\",\n \"score\": \"0.60232717\",\n \"text\": \"def do_optimization(n,m,k,tau,lower_bounds, upper_bounds, r, rN, \\\\\\n\\t\\t max_normal, sorted_index, max_processes, multi_event, get_values):\\n\\tenum = Enumerator(n, m, k, tau, lower_bounds, upper_bounds, multi_event)\\n\\topt = Optimizer(r, rN, m, n,tau, upper_bound=max_normal)\\n\\tMAX_QUEUE_SIZE = int(10E6)\\n\\ttry:\\n\\t\\tqueue = Queue(MAX_QUEUE_SIZE) #Task queue for the processes\\n\\texcept OSError:\\n\\t\\tMAX_QUEUE_SIZE = 2**15-1\\n\\t\\tqueue = Queue(MAX_QUEUE_SIZE)\\n\\n\\treturnQueue = Queue(MAX_QUEUE_SIZE) #Shared queue for processes to return results\\n\\n\\tprocesses = start_processes(max_processes, queue, opt, returnQueue, \\\\\\n\\t\\t\\t sorted_index, get_values)\\n\\t\\n\\tC = enum.generate_next_C()\\n\\tcount = 0\\n\\twhile C is not False:\\n\\t\\tcount += 1\\n\\t\\tqueue.put(C, True)\\n\\t\\tC = enum.generate_next_C()\\n\\tif count == 0:\\n\\t\\tprint \\\"Error: No valid Copy Number Profiles exist for these intervals within the bounds specified. Exiting...\\\"\\n\\t\\tsys.exit(1)\\n\\n\\t# Send STOP signal to all processes\\n\\tfor i in range(max_processes-1):\\n\\t\\tqueue.put(0)\\n\\n\\tbest = []\\n\\tfor i in range(len(processes)):\\n\\t\\titem = returnQueue.get()\\n\\t\\tbest.append(item)\\n\\n\\tfor p in processes:\\n\\t\\tp.join()\\n\\n\\tbest = find_mins(best)\\n\\treturn best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b588cc9d53e32c69afc2c534e88d998\",\n \"score\": \"0.60173005\",\n \"text\": \"def __call__(self):\\n # set type of callback function\\n CMPFUNC = CFUNCTYPE(None, c_int, c_int, POINTER(c_double), POINTER(c_double))\\n # try to initialize callback function\\n callback = CMPFUNC(self._callback)\\n # allocate solutions array\\n solutions = numpy.zeros([self.mu*self.n_var])\\n # prepare bounds\\n if self.bounds[0] is None:\\n lbound = None\\n else:\\n lbound = numpy.ctypeslib.as_ctypes(self.bounds[0])\\n if self.bounds[1] is None:\\n ubound = None\\n else:\\n ubound = numpy.ctypeslib.as_ctypes(self.bounds[1])\\n # start the optimizer\\n check = self.sharkwrapper.mocmaes(callback, self.n_var, self.n_objectives,\\n numpy.ctypeslib.as_ctypes(self.initials), self.maxiter,\\n lbound, ubound, self.mu, c_double(self.sigma),\\n numpy.ctypeslib.as_ctypes(solutions))\\n solutions = solutions.reshape(self.mu,self.n_var)\\n return solutions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a8abf2d52137a7e241f9f178d828e0\",\n \"score\": \"0.60145336\",\n \"text\": \"def main():\\n #Loading benchmark instances\\n data = load_data()\\n #-- Running solver\\n for c in data:\\n distances_data = data[c][0]\\n print(f\\\"Instance at {c} cities\\\")\\n start = time.time()\\n TSP_CE_Solver(distances_data,\\n 0.1,\\n 5*c**2,\\n n_iter = 100,\\n tag= f'{c}_cities_{data[c][1]}',\\n cout_th = data[c][1])\\n print(f'Running time : {time.time()-start}') # note that with current implementation, first run also counts compilation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e19129dcbca71160473935cdeaee0d\",\n \"score\": \"0.60136986\",\n \"text\": \"def main():\\n args = run_argparse( sys.argv )\\n\\n if args.alpha:\\n a = read_alpha( args.alpha, )\\n\\n if args.beta_analysis:\\n beta_analysis(args, basis = args.basis,\\n dal = args.beta_dal, in_AA = args.in_AA,\\n out_AA = args.out_AA,\\n ncpu = args.Ncpu,\\n N_waters = args.N_waters)\\n\\n if args.beta_analysis_par:\\n run_beta_analysis_par( N_waters = args.N_waters,\\n ncpu = args.Ncpu,\\n model = args.model )\\n\\n if args.alpha_analysis:\\n alpha_analysis(args)\\n\\n if args.qm_generation:\\n qm_generation( \\n qm_waters = args.qm_waters,\\n basis = args.basis\\n )\\n\\n if args.qmmm_generation:\\n qmmm_generation( \\n qm_waters = args.qm_waters,\\n mm_waters = args.mm_waters,\\n potfreqs = args.potfreqs,\\n potstyle = args.potstyle,\\n basis = args.basis)\\n\\n if args.qm_analysis:\\n qm_analysis( in_AA = args.in_AA,\\n out_AA = args.out_AA )\\n\\n if args.qmmm_analysis:\\n qmmm_analysis( args )\\n\\n if args.write:\\n write_related( args )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2332fb43142d4f6e3e8117ae21f885b\",\n \"score\": \"0.6011758\",\n \"text\": \"def algo(self, objective, niter):\\n self._algo()\\n self.step = self.post_step(self.step, self.sol, objective, niter)\\n self.sol = self.post_sol(self.step, self.sol, objective, niter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae59c3a66d28c35cabf8af9d7cc06d60\",\n \"score\": \"0.6004668\",\n \"text\": \"def perform_optimization(self):\\n \\n # Create an optimizer (tf.train.AdamOptimizer)\\n self.optimizer = tf.train.AdamOptimizer(self.learning_rate_ph)\\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\\n \\n # Gradient clipping (tf.clip_by_norm) for self.grads_and_vars\\n # Pay attention that you need to apply this operation only for gradients\\n # because self.grads_and_vars contains also variables.\\n # list comprehension might be useful in this case.\\n clip_norm = tf.cast(1.0, tf.float32)\\n self.grads_and_vars = [(tf.clip_by_norm(grad,clip_norm),var) for grad,var in self.grads_and_vars]\\n \\n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd2d6a2209f4b175c86eb30aa3572fa\",\n \"score\": \"0.5987957\",\n \"text\": \"def run_step(self, report_steps=1):\\n \\n _, step = self.model.sess.run([self.optimize_step, \\n self.model.increment_step])\\n self.steps_ago +=1\\n cost = self.model.sess.run(self.cost_op) \\n assert not(np.isnan(cost)), \\\"Objective is nan\\\"\\n objective = - cost\\n\\n #print objective each step\\n #print(\\\"objective %f best %f\\\" %(objective, self.best_objective))\\n if np.isnan(cost):\\n sys.stdout.write('X')\\n sys.stdout.flush()\\n elif objective > self.best_objective:\\n self.best_objective = objective\\n self.best_const_spend = self.model.sess.run(model.const_spend_pct_op)\\n self.best_var_spend = self.model.sess.run(model.var_spending_ops)\\n self.best_stock_alloc = self.model.sess.run(model.allocation_ops[0])\\n self.best_step = step\\n self.steps_ago = 0\\n sys.stdout.write('!')\\n sys.stdout.flush()\\n else:\\n sys.stdout.write('.')\\n sys.stdout.flush()\\n \\n if step % report_steps == 0:\\n sys.stdout.write(\\\"\\\\n%s step %d objective %f best %f (%d steps ago)\\\\n\\\" %\\n (strftime(\\\"%H:%M:%S\\\"),\\n step,\\n objective,\\n self.best_objective,\\n self.steps_ago))\\n # print variables optimized and gradients for debugging\\n # sys.stdout.write(\\\"\\\\n\\\")\\n # var_vals = self.model.sess.run(self.model.all_var_ops) \\n # print(\\\"%s Variables\\\" % strftime(\\\"%H:%M:%S\\\"))\\n # print(var_vals)\\n\\n # grad_vals = self.model.sess.run([grad[0] for grad in self.grads])\\n # print(\\\"%s Gradients\\\" % strftime(\\\"%H:%M:%S\\\"))\\n # print(grad_vals)\\n\\n sys.stdout.flush()\\n\\n self.best_bond_alloc = pd.Series([1 - bsa for bsa in self.best_stock_alloc])\\n pickle_list = [self.best_const_spend, self.best_var_spend, self.best_stock_alloc,\\n self.best_bond_alloc]\\n pickle.dump( pickle_list, open( picklefile, \\\"wb\\\" ) )\\n\\n # every 10 report_steps show current best\\n if step % (report_steps * 10) == 0:\\n print (\\\"\\\\n#Objective: %f\\\\n\\\" % self.best_objective)\\n print (\\\"const_spend_pct = %.12f\\\" % self.best_const_spend)\\n print (\\\"var_spend_pcts = pd.Series(%s)\\\" % str(self.best_var_spend))\\n print (\\\"stock_allocations = pd.Series(%s)\\\\n\\\" %str(self.best_stock_alloc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbf7c96827eb7dd964e9be5f6fffd8\",\n \"score\": \"0.5987113\",\n \"text\": \"def __call__(self, graph: Graph):\\n # initialize best distance and best solution\\n best_cost = float('inf')\\n best_solution = []\\n\\n # perform optimisation\\n print('Optimizing...')\\n generator = tqdm(range(self.generations))\\n for _ in generator:\\n # initialise new population\\n ants = [_Ant(self, graph) for _ in range(self.ant_count)]\\n\\n # update ants position\\n for ant in ants:\\n for i in range(graph.rank - 1):\\n ant.select_next_node()\\n ant.close_route()\\n ant.total_cost += graph.cost_matrix[ant.visited_nodes[-1]][ant.visited_nodes[0]]\\n if ant.total_cost < best_cost:\\n best_cost = ant.total_cost\\n best_solution = [] + ant.visited_nodes\\n # update pheromone\\n ant.update_pheromone_delta()\\n graph.update_pheromone(ants, self.rho)\\n generator.set_description_str('best cost: {}'.format(best_cost))\\n return best_solution, best_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c42e160528ab6442d26161441d3f41f0\",\n \"score\": \"0.5984175\",\n \"text\": \"def fit(self, V, y=None):\\n\\n # Initialization phase\\n self._init_all(V)\\n\\n start_time = time()\\n\\n # Execution phase\\n actual_n_iterations = 0\\n\\n #self.initial_tau_ = self._compute_taus() \\n\\n while actual_n_iterations != self.n_iterations:\\n logging.debug(\\\"[INFO] iteration {0}\\\".format(actual_n_iterations))\\n \\n # each iterations performs a move on rows and n_view moves on columns\\n\\n iter_start_time = time()\\n logging.debug(\\\"[INFO] ##############################################\\\\n\\\" +\\n \\\"\\\\t\\\\t### Iteration {0}\\\".format(actual_n_iterations))\\n #logging.debug(\\\"[INFO] X assignment: {0}\\\".format(self._assignment[0]))\\n #logging.debug(\\\"[INFO] Y assignment: {0}\\\".format(self._assignment[1]))\\n #logging.debug(\\\"[INFO] Z assignment: {0}\\\".format(self._assignment[2]))\\n\\n\\n # perform a move in each dimension\\n for i in range(self._n_modes):\\n logging.debug(\\\"[INFO] Dimension {0}\\\".format(i))\\n \\n if self._iterations_without_moves == self._n_modes * self.n_threshold:\\n self._actual_item = [randint(1, self._n[j] * 100) % self._n[j] for j in range(self._n_modes)]\\n self._special_case = 0\\n elif self._iterations_without_moves > self._n_modes * self.n_threshold:\\n self._special_case += 1\\n else:\\n self._special_case = -1\\n \\n if int(self._special_case / self._n_modes) < self._n[i]:\\n self._perform_move(i)\\n else:\\n self._update_intermediate_values_after_move(i)\\n \\n if self.compute_tau_list:\\n tau = self._compute_taus()\\n self.tau_vector_.append(tau)\\n \\n iter_end_time = time()\\n\\n \\n if actual_n_iterations % 1 == 0:\\n logging.info(\\\"Iteration #{0}\\\".format(actual_n_iterations))\\n logging.info(\\\"[INFO] # clusters: {0}\\\".format(self._n_clusters))\\n tau = self._compute_taus()\\n logging.info(\\\"[INFO] Taus: {0}\\\".format(tau))\\n logging.info(\\\"[INFO] Iteration time: {0}\\\".format(iter_end_time - iter_start_time))\\n \\n\\n if self.path != None:\\n if actual_n_iterations % 1000 == 0:\\n f = open(self.path + 'assignments_' + str(actual_n_iterations) + '.txt', 'w+')\\n \\n for j in range(self._n_modes):\\n f.write(f'##### mode {j} #####\\\\n')\\n for i in range(self._n[j]):\\n f.write(f\\\"{i}\\\\t{self._assignment[j][i]}\\\\n\\\")\\n f.close()\\n \\n actual_n_iterations += 1\\n if int(self._special_case / self._n_modes) == np.max(self._n):\\n logging.info(\\\"[INFO] All moves have been tried. Taus can't be improved by moving any element.\\\")\\n self._last_iteration = actual_n_iterations\\n actual_n_iterations = self.n_iterations\\n\\n end_time = time()\\n\\n execution_time = end_time - start_time\\n self.final_tau_ = self._compute_taus()\\n \\n logging.info('#####################################################')\\n logging.info(\\\"[INFO] Execution time: {0}\\\".format(execution_time))\\n\\n # clone cluster assignments and transform in lists\\n #self.x_ = np.copy(self._assignment[0]).tolist()\\n #self.y_ = np.copy(self._assignment[1]).tolist()\\n #self.z_ = np.copy(self._assignment[2]).tolist()\\n self._n_clusters_ = np.copy(self._assignment)\\n self.execution_time_ = execution_time\\n\\n logging.info(\\\"[INFO] Number of clusters found: {0}\\\".format(self._n_clusters))\\n for j in range(self._n_modes):\\n logging.info(\\\"[INFO] Number of clusters found on mode {1}: {0}\\\".format(self._n_clusters[j],j))\\n logging.info(\\\"[INFO] Number of moves performed on mode {1}: {0} / {2}\\\".format(self._performed_moves[j],j, self.n_iterations))\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95d322a31125a57791ec373315ad1e0\",\n \"score\": \"0.59841555\",\n \"text\": \"def optimize(y, train_data, column_names, GD, population_size=20, size=10, max_iters=40, log=None):\\n # Openning a file for storing the results\\n if log != None:\\n f = open(log, 'a')\\n\\n # Generating population randomly\\n max_alg_iters = 1000 if GD else 1000000\\n population = make_population(population_size, column_names, max_alg_iters) \\n\\n # Dictionary that stores fitness of each hyperparameter configuration \\n # Fitness as calculated as minimum of test and training accuracy\\n # Keys of the dictionary are strings of hyperparameters\\n fitness = {}\\n\\n # Dictionary that stores parameters obtained by training the model \\n # for each hyperparameter configuration\\n # Keys of the dictionary are strings of hyperparameters\\n w = {}\\n \\n # Dictionary that stores information about metrics\\n statistics = {}\\n\\n for i in range(max_iters):\\n print(i)\\n for param in population:\\n print(i)\\n if str(param) in fitness:\\n continue\\n # Training the model and setting fitness\\n w[str(param)], test_evaluation, train_evaluation = run(y, train_data, column_names, param, GD)\\n fitness[str(param)] = min(test_evaluation['accuracy'], train_evaluation['accuracy'])\\n\\n # Writing obtained results in a file\\n if log != None:\\n f.write(str(w[str(param)]))\\n f.write('\\\\n')\\n f.write(str(param))\\n f.write('\\\\n')\\n f.write(str(fitness[str(param)]))\\n f.write(str(test_evaluation))\\n f.write('\\\\n')\\n f.write(str(train_evaluation))\\n f.write('\\\\n-------------------------------------------------------------------\\\\n')\\n statistics = update_statistics(statistics, train_evaluation, test_evaluation)\\n \\n \\n # Chosing the best instances from population and pair them randomly\\n best = take_best_from_population(population, fitness, size)\\n np.random.shuffle(best)\\n for i in range(len(best)//2):\\n baby1, baby2 = make_offspring(best[2*i], best[2*i+1])\\n best.append(baby1)\\n best.append(baby2)\\n \\n population = best\\n \\n if log != None:\\n f.close()\\n \\n return fitness, w, statistics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1dc3b892172b8cc831598ea1fd96df3\",\n \"score\": \"0.5980781\",\n \"text\": \"def optimize(self) -> None:\\n logger.info(\\\"Running the POT optimization\\\")\\n optimized_model = ModelEntity(\\n self.dataset,\\n configuration=self.task_environment.get_model_configuration(),\\n )\\n\\n self.openvino_task.optimize(\\n optimization_type=OptimizationType.POT,\\n dataset=self.dataset,\\n output_model=optimized_model,\\n optimization_parameters=OptimizationParameters(),\\n )\\n\\n logger.info(\\\"Inferring the optimised model on the validation set.\\\")\\n result_set = self.infer(task=self.openvino_task, output_model=optimized_model)\\n\\n logger.info(\\\"Evaluating the optimized model on the validation set.\\\")\\n self.evaluate(task=self.openvino_task, result_set=result_set)\\n self.results[\\\"pot_int8\\\"] = result_set.performance.score.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c784f6f9563d9a3b127f4373c342b9\",\n \"score\": \"0.5974644\",\n \"text\": \"def optimize(self):\\n # Create initial points for each TR\\n for i in range(self.n_trust_regions):\\n X_init = latin_hypercube(self.n_init, self.dim)\\n X_init = from_unit_cube(X_init, self.lb, self.ub)\\n fX_init = np.array([[self.f(x)] for x in X_init])\\n\\n # Update budget and set as initial data for this TR\\n self.X = np.vstack((self.X, X_init))\\n self.fX = np.vstack((self.fX, fX_init))\\n self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))\\n self.n_evals += self.n_init\\n\\n if self.verbose:\\n fbest = fX_init.min()\\n print(f\\\"TR-{i} starting from: {fbest:.4}\\\")\\n sys.stdout.flush()\\n\\n # Thompson sample to get next suggestions\\n while self.n_evals < self.max_evals:\\n\\n # Generate candidates from each TR\\n X_cand = np.zeros((self.n_trust_regions, self.n_cand, self.dim))\\n y_cand = np.inf * np.ones((self.n_trust_regions, self.n_cand, self.batch_size))\\n for i in range(self.n_trust_regions):\\n idx = np.where(self._idx == i)[0] # Extract all \\\"active\\\" indices\\n\\n # Get the points, values the active values\\n X = deepcopy(self.X[idx, :])\\n X = to_unit_cube(X, self.lb, self.ub)\\n\\n # Get the values from the standardized data\\n fX = deepcopy(self.fX[idx, 0].ravel())\\n\\n # Don't retrain the model if the training data hasn't changed\\n n_training_steps = 0 if self.hypers[i] else self.n_training_steps\\n\\n # Create new candidates\\n X_cand[i, :, :], y_cand[i, :, :], self.hypers[i] = self._create_candidates(\\n X, fX, length=self.length[i], n_training_steps=n_training_steps, hypers=self.hypers[i]\\n )\\n\\n # Select the next candidates\\n X_next, idx_next = self._select_candidates(X_cand, y_cand)\\n assert X_next.min() >= 0.0 and X_next.max() <= 1.0\\n\\n # Undo the warping\\n X_next = from_unit_cube(X_next, self.lb, self.ub)\\n\\n # Evaluate batch\\n fX_next = np.array([[self.f(x)] for x in X_next])\\n\\n # Update trust regions\\n for i in range(self.n_trust_regions):\\n idx_i = np.where(idx_next == i)[0]\\n if len(idx_i) > 0:\\n self.hypers[i] = {} # Remove model hypers\\n fX_i = fX_next[idx_i]\\n\\n if self.verbose and fX_i.min() < self.fX.min() - 1e-3 * math.fabs(self.fX.min()):\\n n_evals, fbest = self.n_evals, fX_i.min()\\n print(f\\\"{n_evals}) New best @ TR-{i}: {fbest:.4}\\\")\\n sys.stdout.flush()\\n self._adjust_length(fX_i, i)\\n\\n # Update budget and append data\\n self.n_evals += self.batch_size\\n self.X = np.vstack((self.X, deepcopy(X_next)))\\n self.fX = np.vstack((self.fX, deepcopy(fX_next)))\\n self._idx = np.vstack((self._idx, deepcopy(idx_next)))\\n\\n # Check if any TR needs to be restarted\\n for i in range(self.n_trust_regions):\\n if self.length[i] < self.length_min: # Restart trust region if converged\\n idx_i = self._idx[:, 0] == i\\n\\n if self.verbose:\\n n_evals, fbest = self.n_evals, self.fX[idx_i, 0].min()\\n print(f\\\"{n_evals}) TR-{i} converged to: : {fbest:.4}\\\")\\n sys.stdout.flush()\\n\\n # Reset length and counters, remove old data from trust region\\n self.length[i] = self.length_init\\n self.succcount[i] = 0\\n self.failcount[i] = 0\\n self._idx[idx_i, 0] = -1 # Remove points from trust region\\n self.hypers[i] = {} # Remove model hypers\\n\\n # Create a new initial design\\n X_init = latin_hypercube(self.n_init, self.dim)\\n X_init = from_unit_cube(X_init, self.lb, self.ub)\\n fX_init = np.array([[self.f(x)] for x in X_init])\\n\\n # Print progress\\n if self.verbose:\\n n_evals, fbest = self.n_evals, fX_init.min()\\n print(f\\\"{n_evals}) TR-{i} is restarting from: : {fbest:.4}\\\")\\n sys.stdout.flush()\\n\\n # Append data to local history\\n self.X = np.vstack((self.X, X_init))\\n self.fX = np.vstack((self.fX, fX_init))\\n self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))\\n self.n_evals += self.n_init\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9489c049064922bd6901b2965ad693\",\n \"score\": \"0.597257\",\n \"text\": \"def run_step(self):\\n assert self.model.training, \\\"[SimpleTrainer] model was changed to eval mode!\\\"\\n start = time.perf_counter()\\n data = next(self._data_loader_iter)\\n data_time = time.perf_counter() - start\\n\\n loss_dict = self.model(data)\\n losses = sum(loss_dict.values())\\n\\n metrics_dict = loss_dict\\n metrics_dict[\\\"data_time\\\"] = data_time\\n self._write_metrics(metrics_dict)\\n\\n # Optimize\\n self.optimizer.zero_grad()\\n losses.backward()\\n self.optimizer.step()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0c1d0a12ed99a39d2578475333e7ef\",\n \"score\": \"0.5954046\",\n \"text\": \"def _run_optimiser(cls, raw_func, domain_config_file, worker_manager, max_capital, mode,\\n *args, **kwargs):\\n raise NotImplementedError('Implement in a child class.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0c1d0a12ed99a39d2578475333e7ef\",\n \"score\": \"0.5954046\",\n \"text\": \"def _run_optimiser(cls, raw_func, domain_config_file, worker_manager, max_capital, mode,\\n *args, **kwargs):\\n raise NotImplementedError('Implement in a child class.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dc8aed26ab24eec5c70d215515b7e1\",\n \"score\": \"0.5947441\",\n \"text\": \"def do_iteration(self):\\n\\n indices = rng.randint(self.cost_f.num, size=self.minibatch_size)\\n self.w -= self.step_size*self.cost_f.derivative(self.w, indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a4179e4ee3a576673d825274cdea52\",\n \"score\": \"0.59455395\",\n \"text\": \"def genetic_algorithm():\\n \\n warnings.filterwarnings(\\\"ignore\\\",\\\".*GUI is implemented.*\\\") # filter out the plotting warning about deprecation\\n \\n print('You are running the evolutionary algorithm')\\n \\n #This function sets all of the values of the number of parents, children, and mutation amount\\n (num_init_parents, num_init_children, init_voltage, filename, num_parents, num_children, \\n mutation_percentage, data_acquisition_device, mirror_communication_device, fom_num, \\n deformable_mirror, zernike_polynomial_mode, radial_order) = initialization_f.initialize()\\n \\n print('\\\\nNOTE: LabView must be open in order to run the program\\\\n')\\n print('Here are the options you have while the program is running:')\\n print('\\\\tPress the enter key (or s) to stop the program')\\n print('\\\\tPress \\\"m\\\" if you would like to change the mutation percentage')\\n print('\\\\tPress \\\"c\\\" if you would like to change the number of children')\\n print('\\\\tPrees \\\"p\\\" if you would like to change the number of parents')\\n print('Press \\\"r\\\" then enter key to run the program')\\n \\n while True: # run an infinite loop until a key is pressed\\n \\tkeyboard_input = input() # get input from the user\\n \\tif keyboard_input == '\\\\r' or keyboard_input == '\\\\n' or keyboard_input == 'r': # if the key pressed was the enter key\\n \\t\\tbreak # break out of the infinite loop\\n \\n \\n opt_device = optimization_devices.initialize_opt_device(deformable_mirror, zernike_polynomial_mode, \\n radial_order) # open and initialize the optimization device being used\\n daq_device = daq_devices.initialize_daq_device(data_acquisition_device, \\n fom_num)\\t# open and initialize the data acquisition device being used\\n opt_comm_device = opt_com_devices.initialize_comm_device(mirror_communication_device) # open and initialize the optimization communication device being used\\n\\n print('Starting...')\\n start_time = time.time() # determine the time when the algorithm starts\\n\\n iteration_number = 0\\n \\n parents = people.parent_group(num_init_parents, opt_device, init_voltage, \\n filename) # create parents from initialization constraints\\n children = people.child_group(num_init_children, parents, \\n opt_device) # create children from the given parents and do crossover\\n children.mutate(mutation_percentage, opt_device) # mutate the children\\n \\n all_people = people.person_group(parents, children) # combine all of the children and parents into a single object\\n all_people.test_and_sort_people(opt_device, daq_device, \\n opt_comm_device) # measure the figures of merits and sort the people so the highest figure of merit is 0th indexed\\n \\n best_person = all_people.people[0] # the best person is the 0th indexed person in all_people\\n print('best_person\\\\n', best_person.figure_of_merit) # show the best person's genes and figure of merit\\n \\n best_people = np.empty(0, people.person) # initialize the array of best people\\n best_people = np.append(best_people, best_person) # store best person\\n\\n past_figures_of_merit = all_people.best_figures_of_merit(num_parents)\\n \\n print('Time to run: ', time.time() - start_time, ' seconds') # print out the number of seconds since the algorithm started\\n \\n while True: # run an infinite loop until user says to stop\\n if msvcrt.kbhit(): # if the keyboard was hit\\n \\tkeyboard_input = msvcrt.getwche() # determine what key was pressed\\n \\tif keyboard_input == '\\\\r' or keyboard_input == 's': # if the enter key was pressed\\n \\t\\tbreak # get out of the while loop\\n \\telif keyboard_input == 'm': # if the m key was pressed\\n \\t\\tprint('\\\\nThis is the current mutation percentage: ', mutation_percentage)\\n \\t\\tmutation_percentage = initialization_f.change_value('float', 0, 100) # change the mutation percentage to what the user wants\\n \\telif keyboard_input == 'c': # if the c key was pressed\\n \\t\\tprint('\\\\nThis is the current number of children: ', num_children) \\n \\t\\tnum_children = initialization_f.change_value('int', num_parents-1) # change the number of children to what the user wants\\n \\telif keyboard_input == 'p': # if the p key was pressed\\n \\t\\tprint('\\\\nThis is the current number of parents: ', num_parents)\\n \\t\\tnum_parents = initialization_f.change_value('float', 0, num_children+1) # change the number of parents to what the user wants\\n\\n parents = people.parent_group(num_parents, opt_device, None, None, all_people) # create parents from the best performing children\\n children = people.child_group(num_children, parents, opt_device) # create children from the just created parents\\n children.mutate(mutation_percentage, opt_device) # mutate the children\\n \\n all_people = people.person_group(parents, children) # combine all of the children and parents into a single container\\n all_people.test_and_sort_people(opt_device, daq_device, \\n opt_comm_device) # measure the figures of merits and sort the people so the highest figure of merit is 0th indexed\\n \\n new_best_person = all_people.people[0] # the best person is the 0th indexed person in all_people\\n\\n best_people = np.append(best_people, new_best_person) # store best person\\n\\n if new_best_person.figure_of_merit > best_person.figure_of_merit: # if the current best FOM is better than the overall best FOM\\n \\tbest_person = new_best_person # if the new best person is better, they are the overall best person ever\\n print('best_person\\\\n', best_person.figure_of_merit) # print out the best person ever made\\n\\n opt_device.plot_object(new_best_person.genes, best_person.genes, \\n iteration_number) # plot the current best and overall best genes on the optimization device \\n\\n figures_of_merit = np.concatenate((past_figures_of_merit, all_people.best_figures_of_merit(num_parents)), \\n axis=1) # concatenate the previous figure of merit matrix with the current figures of merit\\n iteration_number, past_figures_of_merit = plot_f.plot_performance(iteration_number, figures_of_merit) # plot the progressions of figures of merit\\n \\n print('Time to run: ', time.time() - start_time, ' seconds') # print out the number of seconds since the algorithm started\\n \\n daq_device.shut_down()\\t# shut off the data acquisition device\\n \\n print('\\\\nWhat would you like to save?') # once the loop has finished, the user decides what to do with the genes made\\n while True: # create an infinite loop\\n print(\\\"\\\\nFor writing the best person's actuator voltages to a file, input 'write'\\\")\\n print('For saving graph data, input \\\"graph\\\"')\\n print(\\\"For saving the best person from each iteration, input 'all_best'\\\")\\n print(\\\"For saving everything, input 'everything'\\\")\\n print('For doing nothing, input \\\"none\\\"')\\n saving_option = input() # get user input for what they want to do\\n directory_path = os.path.dirname(os.path.abspath(__file__)) # get the current directory's path\\n if saving_option == 'write': # if they want to write the genes to a file\\n directory_path = save_different_directory(opt_device.default_directory)\\n print(\\\"Enter the file name you want to be saved (do not include the file extension):\\\\nNote: this will overwrite a file with the same name\\\")\\n filename = input() # get user input from for what filename they want\\n opt_device.write_genes(best_person.genes, directory_path + filename) # write the genes to the input file\\n if anything_else() == 'n': # if they don't want to do anything else\\n break # break out of the while loop\\n elif saving_option == 'graph': # if the user wants to save the graph\\n directory_path = save_different_directory(directory_path + file_f.FOM_GRAPH_FOLDER)\\n print(\\\"Enter the file name you want to be saved without the file extension (for test.csv & test.png, input test):\\\",\\n \\\"\\\\nNote: This saves a .csv of graph's values and a pdf of the python figure\\\\nAlso, this will overwrite a file with the same name\\\")\\n filename = input() # get user input from for what filename they want\\n file_f.write_figures_of_merit(figures_of_merit, directory_path + filename) # write the figures of merit to a comma separated value file\\n plt.savefig( directory_path +'%s.png' % filename, dpi = 300, bbox_inches = 'tight')\\n if anything_else() == 'n': # if they don't want to do anything else\\n break # break out of the while loop\\n elif saving_option == 'all_best': # if the user wants to save all of the best genes\\n directory_path = save_different_directory(opt_device.default_directory)\\n print(\\\"Enter the folder you want the best people to be saved into (to have everything saved in a\\\",\\n \\\"folder named 'test', input 'test'.:\\\\nNote: this will overwrite a folder with the same name\\\",\\n \\\"\\\\nNote: It will save each person according to its iteration number. The ith person will be saved as 'i.[file_extension]'\\\")\\n folder = input()\\n new_dir_path = directory_path + \\\"\\\\\\\\\\\" + folder + \\\"\\\\\\\\\\\" # determine the directory path to save the genes to\\n if not os.path.exists(new_dir_path): # if that path doesn't exist, create it\\n os.makedirs(new_dir_path)\\n for i in range(best_people.size): # for each best person\\n opt_device.write_genes(best_people[i].genes, new_dir_path + str(i)) # write each gene\\n if anything_else() == 'n': # if they don't want to do anything else\\n break # break out of the while loop\\n elif saving_option == 'everything':\\n print(\\\"The directory it is saving into is \\\" + directory_path + file_f.FOM_GRAPH_FOLDER + \\\" and \\\" + opt_device.default_directory)\\n print(\\\"Enter the file name you want for all of these files (for 'test.csv', 'test.png', etc., input 'test'.\\\\tNote: this will overwrite a file with the same name\\\")\\n filename = input()\\n opt_device.write_genes(best_person.genes, opt_device.default_directory + filename) # write the genes to the input file\\n file_f.write_figures_of_merit(figures_of_merit, directory_path + file_f.FOM_GRAPH_FOLDER + filename) # write the figures of merit to a comma separated value file\\n plt.savefig( directory_path + file_f.FOM_GRAPH_FOLDER +'%s.png' % filename, dpi = 300, bbox_inches = 'tight')\\n new_dir_path = opt_device.default_directory + \\\"\\\\\\\\\\\" + filename + \\\"\\\\\\\\\\\" # determine the path to save the best people\\n if not os.path.exists(new_dir_path): # if the directory path doesn't exist, create it\\n os.makedirs(new_dir_path)\\n for i in range(best_people.size): # for each best person\\n opt_device.write_genes(best_people[i].genes, new_dir_path + str(i)) # write each gene\\n if anything_else() == 'n': # if they don't want to do anything else\\n break # break out of the while loop\\n elif saving_option == 'none': # if the user doesn't want to do anything with the data\\n break # break out of the while loop\\n else: # if they didn't enter one of the options\\n print(\\\"You didn't enter a correct command\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e555c9e54a9a964ee6a92947ecc7e2f6\",\n \"score\": \"0.59451145\",\n \"text\": \"def run_optimizer(**kwargs):\\n #==============================#\\n #| Log file configuration |#\\n #==============================#\\n # By default, output should be written to <args.prefix>.log and also printed to the terminal.\\n # This behavior may be changed by editing the log.ini file.\\n # Output will only be written to log files after the 'logConfig' line is called!\\n if kwargs.get('logIni') is None:\\n import geometric.optimize\\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, r'config/log.ini')\\n else:\\n logIni = kwargs.get('logIni')\\n logfilename = kwargs.get('prefix')\\n # Input file for optimization; QC input file or OpenMM .xml file\\n inputf = kwargs.get('input')\\n verbose = kwargs.get('verbose', False)\\n # Get calculation prefix and temporary directory name\\n arg_prefix = kwargs.get('prefix', None) #prefix for output file and temporary directory\\n prefix = arg_prefix if arg_prefix is not None else os.path.splitext(inputf)[0]\\n logfilename = rf\\\"{prefix}.log\\\"\\n # Create a backup if the log file already exists\\n backed_up = bak(logfilename)\\n import logging.config\\n logging.config.fileConfig(logIni,defaults={'logfilename': logfilename},disable_existing_loggers=False)\\n #==============================#\\n #| End log file configuration |#\\n #==============================#\\n\\n import geometric\\n logger.info('geometric-optimize called with the following command line:\\\\n')\\n argv_print = []\\n # When geometric-optimize is called on the command line with an argument such as \\n # --ase-kwargs='{\\\"method\\\":\\\"GFN2-xTB\\\"}'\\n # the shell strips away the single quotes resulting in printing out\\n # --ase-kwargs={\\\"method\\\":\\\"GFN2-xTB\\\"}\\n # making the copy-pasted command invalid.\\n # This is a dirty hack to put the single quotes back.\\n for arg in sys.argv:\\n arg = arg.replace('\\\\'{','{').replace('{','\\\\'{')\\n arg = arg.replace('}\\\\'','}').replace('}','}\\\\'')\\n argv_print.append(arg)\\n logger.info(' '.join(argv_print)+'\\\\n')\\n print_logo(logger)\\n now = datetime.now()\\n logger.info('-=# \\\\x1b[1;94m geomeTRIC started. Version: %s \\\\x1b[0m #=-\\\\n' % (geometric.__version__))\\n logger.info('Current date and time: %s\\\\n' % now.strftime(\\\"%Y-%m-%d %H:%M:%S\\\"))\\n \\n if backed_up:\\n logger.info('Backed up existing log file: %s -> %s\\\\n' % (logfilename, os.path.basename(backed_up)))\\n\\n t0 = time.time()\\n\\n # Create the params object, containing data to be passed into the optimizer\\n params = OptParams(**kwargs)\\n params.printInfo()\\n\\n # Create \\\"dirname\\\" folder for writing\\n dirname = prefix+\\\".tmp\\\"\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\\n kwargs['dirname'] = dirname\\n \\n # Get the Molecule and engine objects needed for optimization\\n M, engine = get_molecule_engine(**kwargs)\\n \\n # Create Work Queue object\\n if kwargs.get('port', 0):\\n logger.info(\\\"Creating Work Queue object for distributed Hessian calculation\\\\n\\\")\\n createWorkQueue(kwargs['port'], debug=verbose>1)\\n\\n # Get initial coordinates in bohr\\n coords = M.xyzs[0].flatten() * ang2bohr\\n\\n # Read in the constraints\\n constraints = kwargs.get('constraints', None) #Constraint input file (optional)\\n\\n if constraints is not None:\\n Cons, CVals = parse_constraints(M, open(constraints).read())\\n else:\\n Cons = None\\n CVals = None\\n\\n #=========================================#\\n #| Set up the internal coordinate system |#\\n #=========================================#\\n # First item in tuple: The class to be initialized\\n # Second item in tuple: Whether to connect nonbonded fragments\\n # Third item in tuple: Whether to throw in all Cartesians (no effect if second item is True)\\n CoordSysDict = {'cart':(CartesianCoordinates, False, False),\\n 'prim':(PrimitiveInternalCoordinates, True, False),\\n 'dlc':(DelocalizedInternalCoordinates, True, False),\\n 'hdlc':(DelocalizedInternalCoordinates, False, True),\\n 'tric-p':(PrimitiveInternalCoordinates, False, False),\\n 'tric':(DelocalizedInternalCoordinates, False, False)}\\n coordsys = kwargs.get('coordsys', 'tric')\\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\\n\\n # Perform an initial single-point QM calculation to determine bonding & fragments, if using TRIC\\n if hasattr(engine, 'calc_bondorder') and coordsys.lower() in ['hdlc', 'tric'] and params.bothre > 1e-3:\\n bothre = params.bothre\\n logger.info(\\\"Calculating QM bond order and forming bonds using criterion of %.2f\\\\n\\\" % bothre)\\n qm_bo = engine.calc_bondorder(coords, dirname)\\n M.qm_bondorder = [qm_bo]\\n M.build_topology(bond_order=bothre)\\n if len(M.molecules) == 1 and bothre < 0.75:\\n logger.info(\\\"Only one fragment found; increasing threshold\\\\n\\\")\\n # Increase the bond order threshold until there are at least 2 fragments of size >1\\n while bothre < 0.75 and len([m for m in M.molecules if len(m.e()) > 1]) == 1:\\n bothre += 0.01\\n M.build_topology(bond_order=bothre)\\n logger.info(\\\"Using threshold of %.2f, there are now %i molecules\\\\n\\\" % (bothre, len(M.molecules)))\\n M.top_settings['read_bonds'] = True\\n # Delete the QM bond order to avoid problems when more structures are added\\n del M.Data['qm_bondorder']\\n else:\\n if coordsys.lower() in ['hdlc', 'tric'] and params.bothre > 1e-3:\\n logger.info(\\\"Requested bond order-based connectivity but it is not available in the current engine\\\\n\\\")\\n logger.info(\\\"Bonds will be generated from interatomic distances less than %.2f times sum of covalent radii\\\\n\\\" % M.top_settings['Fac'])\\n\\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVals[0] if CVals is not None else None,\\n conmethod=params.conmethod)\\n \\n #========================================#\\n #| End internal coordinate system setup |#\\n #========================================#\\n\\n # Auxiliary functions (will not do optimization):\\n displace = kwargs.get('displace', False) # Write out the displacements of the coordinates.\\n if displace:\\n write_displacements(coords, M, IC, dirname, verbose)\\n return\\n\\n fdcheck = kwargs.get('fdcheck', False) # Check internal coordinate gradients using finite difference..\\n if fdcheck:\\n IC.Prims.checkFiniteDifferenceGrad(coords)\\n IC.Prims.checkFiniteDifferenceHess(coords)\\n check_internal_grad(coords, M, IC.Prims, engine, dirname, verbose)\\n check_internal_hess(coords, M, IC.Prims, engine, dirname, verbose)\\n return\\n\\n # Print out information about the coordinate system\\n if isinstance(IC, CartesianCoordinates):\\n logger.info(\\\"%i Cartesian coordinates being used\\\\n\\\" % (3*M.na))\\n else:\\n logger.info(\\\"%i internal coordinates being used (instead of %i Cartesians)\\\\n\\\" % (len(IC.Internals), 3*M.na))\\n logger.info(IC)\\n logger.info(\\\"\\\\n\\\")\\n\\n # Print out a note if DFT is used for non-fragmented systems; recommend --dlc and --subfrctor 2.\\n if engine.detect_dft() and coordsys != \\\"dlc\\\" and len(IC.frags) == 1:\\n logger.info(\\\"#===================================================================================#\\\\n\\\")\\n logger.info(\\\"#| \\\\x1b[91mNote: Detected the use of DFT for a system containing only one fragment.\\\\x1b[0m |#\\\\n\\\")\\n logger.info(\\\"#| |#\\\\n\\\")\\n logger.info(\\\"#| DFT calculations that use small to medium-sized grids can sometimes result in |#\\\\n\\\")\\n logger.info(\\\"#| energies and/or gradients that are not translationally/rotationally invariant, |#\\\\n\\\")\\n logger.info(\\\"#| which varies depending on the QC program used. |#\\\\n\\\")\\n logger.info(\\\"#| |#\\\\n\\\")\\n logger.info(\\\"#| Spurious translation/rotation contributions to the energy and/or gradient |#\\\\n\\\")\\n logger.info(\\\"#| may cause convergence failure that is observable as 'movement' or 'tumbling' |#\\\\n\\\")\\n logger.info(\\\"#| of the molecule in the optimization output. If observed, rerun the calculation |#\\\\n\\\")\\n logger.info(\\\"#| using --coordsys dlc and --subfrctor 2. It will project out overall translation |#\\\\n\\\")\\n logger.info(\\\"#| and rotation from the optimization space. |#\\\\n\\\")\\n logger.info(\\\"#===================================================================================#\\\\n\\\")\\n\\n if Cons is None:\\n # Run a standard geometry optimization\\n params.xyzout = prefix+\\\"_optim.xyz\\\"\\n progress = Optimize(coords, M, IC, engine, dirname, params)\\n else:\\n # Run a single constrained geometry optimization or scan over a grid of values\\n if isinstance(IC, (CartesianCoordinates, PrimitiveInternalCoordinates)):\\n raise RuntimeError(\\\"Constraints only work with delocalized internal coordinates\\\")\\n Mfinal = None\\n for ic, CVal in enumerate(CVals):\\n if len(CVals) > 1:\\n logger.info(\\\"---=== Scan %i/%i : Constrained Optimization ===---\\\\n\\\" % (ic+1, len(CVals)))\\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal, conmethod=params.conmethod)\\n IC.printConstraints(coords, thre=-1)\\n if len(CVals) > 1:\\n params.xyzout = prefix+\\\"_scan-%03i.xyz\\\" % (ic+1)\\n # In the special case of a constraint scan, we write out multiple qdata.txt files\\n if params.qdata is not None: params.qdata = 'qdata_scan-%03i.txt' % (ic+1)\\n else:\\n params.xyzout = prefix+\\\"_optim.xyz\\\"\\n if ic == 0:\\n progress = Optimize(coords, M, IC, engine, dirname, params)\\n else:\\n progress += Optimize(coords, M, IC, engine, dirname, params, print_info=False)\\n # update the structure for next optimization in SCAN (by CNH)\\n M.xyzs[0] = progress.xyzs[-1]\\n coords = progress.xyzs[-1].flatten() * ang2bohr\\n if Mfinal:\\n Mfinal += progress[-1]\\n else:\\n Mfinal = progress[-1]\\n cNames = IC.getConstraintNames()\\n cVals = IC.getConstraintTargetVals()\\n comment = ', '.join([\\\"%s = %.2f\\\" % (cName, cVal) for cName, cVal in zip(cNames, cVals)])\\n Mfinal.comms[-1] = \\\"Scan Cycle %i/%i ; %s ; %s\\\" % (ic+1, len(CVals), comment, progress.comms[-1])\\n #print\\n if len(CVals) > 1:\\n Mfinal.write('scan-final.xyz')\\n if params.qdata is not None: Mfinal.write('qdata-final.txt')\\n print_citation(logger)\\n logger.info(\\\"Time elapsed since start of run_optimizer: %.3f seconds\\\\n\\\" % (time.time()-t0))\\n if kwargs.get('port', 0):\\n destroyWorkQueue()\\n return progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c5f35a62bd0368ca2938567d59c17f\",\n \"score\": \"0.59367657\",\n \"text\": \"def step(self):\\n params = self.params\\n if np.isnan(self.G).any():\\n raise RuntimeError(\\\"Gradient contains nan - check output and temp-files for possible errors\\\")\\n if np.isnan(self.H).any():\\n raise RuntimeError(\\\"Hessian contains nan - check output and temp-files for possible errors\\\")\\n self.Iteration += 1\\n if (self.Iteration%5) == 0:\\n self.engine.clearCalcs()\\n self.IC.clearCache()\\n # At the start of the loop, the optimization variables, function value, gradient and Hessian are known.\\n # (i.e. self.Y, self.E, self.G, self.H)\\n Eig = sorted(np.linalg.eigh(self.H)[0])\\n Emin = min(Eig).real\\n if params.rfo:\\n v0 = 1.0\\n elif Emin < params.epsilon:\\n v0 = params.epsilon-Emin\\n else:\\n v0 = 0.0\\n if params.verbose: self.IC.Prims.printRotations(self.X)\\n if len(Eig) >= 6:\\n logger.info(\\\"Hessian Eigenvalues: %.5e %.5e %.5e ... %.5e %.5e %.5e\\\\n\\\" % (Eig[0],Eig[1],Eig[2],Eig[-3],Eig[-2],Eig[-1]))\\n else:\\n logger.info(\\\"Hessian Eigenvalues: \\\" + ' '.join(\\\"%.5e\\\" % i for i in Eig) + '\\\\n')\\n # Are we far from constraint satisfaction?\\n self.farConstraints = self.IC.haveConstraints() and self.IC.getConstraintViolation(self.X) > 1e-1\\n ### OBTAIN AN OPTIMIZATION STEP ###\\n # The trust radius is to be computed in Cartesian coordinates.\\n # First take a full-size Newton Raphson step\\n dy, _, __ = self.get_delta_prime(v0)\\n # Internal coordinate step size\\n inorm = np.linalg.norm(dy)\\n # Cartesian coordinate step size\\n self.cnorm = self.getCartesianNorm(dy)\\n if params.verbose: logger.info(\\\"dy(i): %.4f dy(c) -> target: %.4f -> %.4f\\\\n\\\" % (inorm, self.cnorm, self.trust))\\n # If the step is above the trust radius in Cartesian coordinates, then\\n # do the following to reduce the step length:\\n if self.cnorm > 1.1 * self.trust:\\n # This is the function f(inorm) = cnorm-target that we find a root\\n # for obtaining a step with the desired Cartesian step size.\\n froot = self.createFroot(v0)\\n froot.stores[inorm] = self.cnorm\\n ### Find the internal coordinate norm that matches the desired Cartesian coordinate norm\\n iopt = brent_wiki(froot.evaluate, 0.0, inorm, self.trust, cvg=0.1, obj=froot, verbose=params.verbose)\\n if froot.brentFailed and froot.stored_arg is not None:\\n # If Brent fails but we obtained an IC step that is smaller than the Cartesian trust radius, use it\\n if params.verbose: logger.info(\\\"\\\\x1b[93mUsing stored solution at %.3e\\\\x1b[0m\\\\n\\\" % froot.stored_val)\\n iopt = froot.stored_arg\\n elif self.IC.bork:\\n # Decrease the target Cartesian step size and try again\\n for i in range(3):\\n froot.target /= 2\\n if params.verbose: logger.info(\\\"\\\\x1b[93mReducing target to %.3e\\\\x1b[0m\\\\n\\\" % froot.target)\\n froot.above_flag = True # Stop at any valid step between current target step size and trust radius\\n iopt = brent_wiki(froot.evaluate, 0.0, iopt, froot.target, cvg=0.1, verbose=params.verbose)\\n if not self.IC.bork: break\\n LastForce = self.ForceRebuild\\n self.ForceRebuild = False\\n if self.IC.bork:\\n logger.info(\\\"\\\\x1b[91mInverse iteration for Cartesians failed\\\\x1b[0m\\\\n\\\")\\n # This variable is added because IC.bork is unset later.\\n self.ForceRebuild = True\\n else:\\n if params.verbose: logger.info(\\\"\\\\x1b[93mBrent algorithm requires %i evaluations\\\\x1b[0m\\\\n\\\" % froot.counter)\\n ##### If IC failed to produce valid Cartesian step, it is \\\"borked\\\" and we need to rebuild it.\\n if self.ForceRebuild:\\n # Force a rebuild of the coordinate system and skip the energy / gradient and evaluation steps.\\n if LastForce:\\n logger.warning(\\\"\\\\x1b[1;91mFailed twice in a row to rebuild the coordinate system; continuing in Cartesian coordinates\\\\x1b[0m\\\\n\\\")\\n self.checkCoordinateSystem(recover=True, cartesian=LastForce)\\n logger.info(\\\"\\\\x1b[1;93mSkipping optimization step\\\\x1b[0m\\\\n\\\")\\n self.Iteration -= 1\\n self.state = OPT_STATE.SKIP_EVALUATION\\n return\\n ##### End Rebuild\\n # Finally, take an internal coordinate step of the desired length.\\n dy, _ = self.trust_step(iopt, v0)\\n self.cnorm = self.getCartesianNorm(dy)\\n ### DONE OBTAINING THE STEP ###\\n if isinstance(self.IC, PrimitiveInternalCoordinates):\\n idx = np.argmax(np.abs(dy))\\n iunit = np.zeros_like(dy)\\n iunit[idx] = 1.0\\n self.prim_msg = \\\"Along %s %.3f\\\" % (self.IC.Internals[idx], np.dot(dy/np.linalg.norm(dy), iunit))\\n ### These quantities, computed previously, are no longer used.\\n # Dot product of the gradient with the step direction\\n # Dot = -np.dot(dy/np.linalg.norm(dy), self.G/np.linalg.norm(self.G))\\n # Whether the Cartesian norm comes close to the trust radius\\n # bump = cnorm > 0.8 * self.trust\\n ### Before updating any of our variables, copy current variables to \\\"previous\\\"\\n self.Yprev = self.Y.copy()\\n self.Xprev = self.X.copy()\\n self.Gprev = self.G.copy()\\n self.Eprev = self.E\\n ### Update the Internal Coordinates ###\\n X0 = self.X.copy()\\n self.newCartesian(dy)\\n ## The \\\"actual\\\" dy may be different from the one passed to newCartesian(),\\n ## for example if we enforce constraints or don't get the step we expect.\\n dy = self.IC.calcDiff(self.X, X0)\\n self.Y += dy\\n self.expect = flat(0.5*multi_dot([row(dy),self.H,col(dy)]))[0] + np.dot(dy,self.G)\\n self.state = OPT_STATE.NEEDS_EVALUATION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34974aa97299d11078d5375377bb34f4\",\n \"score\": \"0.593551\",\n \"text\": \"def print_results(self):\\n\\t\\t# for m_str, m in self.model_with_separate_variables.items():\\t\\t\\t\\n\\t\\t# \\ttry: \\n\\t\\t# \\t\\tprint(\\\"\\\\nModel {}\\\".format(m.ModelName))\\n\\t\\t# \\t\\tprint(\\\"The value of the objective function after optimisation is {}\\\".format(m.objVal))\\n\\t\\t# \\t\\tprint(\\\"Variables are as follows:\\\")\\n\\t\\t# \\t\\tfor partition in self.partitions:\\n\\t\\t# \\t\\t\\t# for c, (interval_start, interval_end) in enumerate(self.interval_ends):\\n\\t\\t# \\t\\t\\t# \\t# x = round(self.model_with_separate_variables_vars[m_str, partition, interval_start, interval_end].X, 2)\\n\\t\\t# \\t\\t\\t# \\t# x = self.model_with_separate_variables_vars[m_str, partition, interval_start, interval_end].X\\n\\t\\t# \\t\\t\\t# \\t# print(\\\"X_{}_{}_{} = {}\\\".format(partition, interval_start, interval_end, x))\\n\\t\\t# \\t\\t\\t# \\tx = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\\n\\t\\t# \\t\\t\\t# \\tprint(\\\"X_{} = {}\\\".format(partition, x))\\n\\t\\t# \\t\\t\\tx = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\\n\\t\\t# \\t\\t\\tprint(\\\"X_{} = {}\\\".format(partition, x))\\n\\t\\t# \\texcept AttributeError:\\n\\t\\t# \\t\\tprint(\\\"\\\\nModel {} is infeasible!\\\".format(m_str))\\n\\n\\t\\t# for m_str, m in self.model_with_pwl_costs.items():\\t\\t\\t\\n\\t\\t# \\ttry: \\n\\t\\t# \\t\\tprint(\\\"\\\\nModel {}\\\".format(m.ModelName))\\n\\t\\t# \\t\\tprint(\\\"The value of the objective function after optimisation is {}\\\".format(m.objVal))\\n\\t\\t# \\t\\tprint(\\\"Variables are as follows:\\\")\\n\\t\\t# \\t\\tfor partition in self.partitions:\\n\\t\\t# \\t\\t\\tx = self.model_with_pwl_costs_vars[m_str, partition].X\\n\\t\\t# \\t\\t\\tprint(\\\"X_{} = {}\\\".format(partition, x))\\n\\t\\t# \\texcept AttributeError:\\n\\t\\t# \\t\\tprint(\\\"\\\\nModel {} is infeasible!\\\".format(m_str))\\n\\n\\t\\tprint(\\\"\\\\n MODEL COMPARISON\\\")\\n\\t\\tfor m_str in self.model_with_separate_variables:\\n\\t\\t\\tprint(\\\"\\\\n{} (objective {}) | {} (objective {})\\\".format(self.model_with_separate_variables[m_str].ModelName, self.model_with_separate_variables[m_str].objVal,\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\tself.model_with_pwl_costs[m_str].ModelName, self.model_with_pwl_costs[m_str].objVal))\\n\\t\\t\\tprint(\\\"runtimes (in seconds): {} | {}\\\".format(self.model_with_separate_variables_runtime[m_str], self.model_with_pwl_costs_runtime[m_str]))\\n\\t\\t\\tfor partition in self.partitions:\\n\\t\\t\\t\\tx_model_with_separate_variables = self.model_with_separate_variables_vars.sum(m_str, partition, '*', '*').getValue()\\n\\t\\t\\t\\tx_model_with_pwl_costs = self.model_with_pwl_costs_vars[m_str, partition].X\\n\\t\\t\\t\\tprint(\\\"X_{}: {} | {}\\\".format(partition, x_model_with_separate_variables, x_model_with_pwl_costs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f9e3f9d8efae72b85e397ccb90970c\",\n \"score\": \"0.5935174\",\n \"text\": \"def run(self):\\n logger.info('Starting voxel-wise HRF estimation ...')\\n logger.info('nvox=%d, ncond=%d, nscans=%d', self.nbVoxels, self.M,\\n self.ImagesNb)\\n # initialization of the matrices that will store all voxel resuls\\n logger.info(\\\"Init storage ...\\\")\\n self.InitStorageMat()\\n logger.info(\\\"Compute onset matrix ...\\\")\\n self.Compute_onset_matrix3()\\n\\n self.stop_iterations = np.zeros(self.bold.shape[1], dtype=int)\\n\\n # voxelwise analysis. This loop handles the currently analyzed voxels.\\n for POI in xrange(self.bold.shape[1]): # POI = point of interest\\n t0 = time()\\n logger.info(\\\"Point %s / %s\\\", str(POI), str(self.nbVoxels))\\n self.ReadPointOfInterestData(POI)\\n\\n # initialize with zeros or ones all matrix and vector used in\\n # the class\\n logger.info(\\\"Init matrix and vectors ...\\\")\\n self.InitMatrixAndVectors(POI)\\n\\n # compute onset matrix\\n\\n # compute low frequency basis\\n logger.info('build low freq mat ...')\\n self.buildLowFreqMat()\\n\\n # precompute usefull data\\n logger.info('Compute inv R ...')\\n self.Compute_INV_R_and_R_and_DET_R()\\n\\n # solve find response functions and hyperparameters\\n logger.info('EM solver ...')\\n self.EM_solver(POI)\\n\\n # store current results in matrices initialized in 'InitStoringMat'\\n logger.info('Store res ...')\\n self.StoreRes(POI)\\n logger.info(\\\"Done in %s\\\", format_duration(time() - t0))\\n\\n self.clean_memory()\\n\\n logger.info('Nb of iterations to reach stop crit: %s',\\n array_summary(self.stop_iterations))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73dee46e94da00d9bc253a0cb7cc232c\",\n \"score\": \"0.59339744\",\n \"text\": \"def main(data_dir, parent_limit, branchvars, optimalpath, callback):\\n dataset = parse_dataset(data_dir)\\n\\n solver = Solver(dataset, parent_limit, branchvars, optimalpath, callback)\\n model = solver.solve()\\n # if model feasible you can print the final objval here\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7107e651c5823a15d2061e385465f386\",\n \"score\": \"0.59294665\",\n \"text\": \"def run(): \\n description = \\\"Compare results of two separate optimisations\\\"\\n parser = argparse.ArgumentParser(description=description)\\n # Might want to make the type of this 'FileType('r')'\\n parser.add_argument('filenames', metavar='F', nargs='+',\\n help=\\\"the input files, should be exactly two\\\")\\n arguments = parser.parse_args()\\n\\n if len(arguments.filenames) != 2:\\n print (\\\"Must provide at least two best params files\\\")\\n sys.exit(1)\\n\\n best_params_lists = [ parameters.parse_param_file(f) \\n for f in arguments.filenames ]\\n\\n # We can do better than simply comparing every other file to the\\n # first parameter file but for now I'm going to do that.\\n first_best_params = best_params_lists[0]\\n for other_best_params in best_params_lists[1:]:\\n compare_parameters(first_best_params, other_best_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc8f4ad95c55aaa424646ffb8f946e3\",\n \"score\": \"0.5926365\",\n \"text\": \"def startProcess(self):\\n self.best_obj_value = self.obj.value(self.best_solution)\\n self.recordLog()\\n\\n if self.msg:\\n during_solver_message_header()\\n self.during_solver_message('S')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd77bface54206e92de993c5d2598fa\",\n \"score\": \"0.5923426\",\n \"text\": \"def optimize(self):\\n def obj(parameters):\\n \\\"\\\"\\\"\\n Evaluates the mean absolute error for a set of input\\n parameters: kfactor, decay, regress.\\n\\n \\\"\\\"\\\"\\n kfactor, regress = parameters\\n\\n rating = Rating(\\n mode=self.mode,\\n kfactor=kfactor,\\n regress=regress\\n )\\n\\n residuals = rating.model_accuracy()\\n mean_abs_error = np.abs(residuals).mean()\\n return mean_abs_error\\n\\n bounds = [(25., 85.), (0.4, 0.8)]\\n res_gp = gp_minimize(obj, bounds, n_calls=100, verbose=True)\\n\\n print(\\\"Best score: {:.4f}\\\".format(res_gp.fun))\\n print(\\\"Best parameters: {}\\\".format(res_gp.x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88944fa6284f693ed593aab814748728\",\n \"score\": \"0.59185916\",\n \"text\": \"def optimize(self):\\n velocity = np.zeros([self.dimension, self.n_agents])\\n population = self.generate_random_population()\\n self.best_by_iter = []\\n self.mean_by_iter = []\\n self.optimal_fitness_value = np.inf\\n self.optimal_model = None\\n\\n for i in range(self.maxiter):\\n fitness = self.evaluate_objective_function(population)\\n\\n column_of_best_solution = np.argmin(fitness)\\n current_best_fitness = fitness[column_of_best_solution]\\n\\n if current_best_fitness < self.optimal_fitness_value:\\n self.optimal_fitness_value = current_best_fitness\\n self.optimal_model = population[:, column_of_best_solution].copy()\\n\\n self.best_by_iter.append(self.optimal_fitness_value)\\n self.mean_by_iter.append(np.mean(fitness))\\n agent_mass = self.mass_calculation(fitness)\\n gravitational_constant = self.calculate_gravitational_constant(i)\\n acceleration = self.calculate_acceleration(\\n population, agent_mass, gravitational_constant, i\\n )\\n velocity, population = self.update_velocity_position(\\n population,\\n acceleration,\\n velocity,\\n i,\\n )\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41978b757b56ec6ff7e3a64314250052\",\n \"score\": \"0.5913431\",\n \"text\": \"def eval(settings):\\n\\n settings['batch_size'] = 2048\\n\\n print(\\\"start evaluation with settings\\\", settings)\\n\\n # create the experiment folders\\n logdir = os.path.join(settings['logdir'], settings['method'], settings['dataset'], utils.get_runname(settings))\\n pathlib.Path(logdir).mkdir(parents=True, exist_ok=True)\\n\\n\\n # prepare\\n train_set = utils.dataset_from_name(split='train', **settings)\\n val_set = utils.dataset_from_name(split='val', **settings)\\n test_set = utils.dataset_from_name(split='test', **settings)\\n\\n train_loader = data.DataLoader(train_set, settings['batch_size'], shuffle=True,num_workers=settings['num_workers'])\\n val_loader = data.DataLoader(val_set, settings['batch_size'], shuffle=True,num_workers=settings['num_workers'])\\n test_loader = data.DataLoader(test_set, settings['batch_size'], settings['num_workers'])\\n\\n objectives = from_name(settings.pop('objectives'), val_set.task_names())\\n scores1 = from_objectives(objectives)\\n scores2 = [mcr(o.label_name, o.logits_name) for o in objectives]\\n\\n solver = solver_from_name(objectives=objectives, **settings)\\n\\n train_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\\n val_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\\n test_results = dict(settings=settings, num_parameters=utils.num_parameters(solver.model_params()))\\n\\n \\n task_ids = settings['task_ids'] if settings['method'] == 'SingleTask' else [0]\\n for j in task_ids:\\n if settings['method'] == 'SingleTask':\\n # we ran it in parallel\\n checkpoints = pathlib.Path(CHECKPOINT_DIR).glob(f'**/*_{j:03d}/*/c_*.pth')\\n else:\\n checkpoints = pathlib.Path(CHECKPOINT_DIR).glob('**/c_*.pth')\\n \\n train_results[f\\\"start_{j}\\\"] = {}\\n val_results[f\\\"start_{j}\\\"] = {}\\n test_results[f\\\"start_{j}\\\"] = {}\\n\\n for c in sorted(checkpoints):\\n\\n #c = list(sorted(checkpoints))[-1]\\n print(\\\"checkpoint\\\", c)\\n _, e = c.stem.replace('c_', '').split('-')\\n\\n j = int(j)\\n e = int(e)\\n \\n solver.model.load_state_dict(torch.load(c))\\n\\n # Validation results\\n val_results = evaluate(j, e, solver, scores1, scores2, val_loader, logdir, \\n reference_point=settings['reference_point'],\\n split='val',\\n result_dict=val_results)\\n\\n # Test results\\n test_results = evaluate(j, e, solver, scores1, scores2, test_loader, logdir, \\n reference_point=settings['reference_point'],\\n split='test',\\n result_dict=test_results)\\n\\n # Train results\\n # train_results = evaluate(j, e, solver, scores1, scores2, train_loader, logdir, \\n # reference_point=settings['reference_point'],\\n # split='train',\\n # result_dict=train_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2f93e630e801ee382db37120ce049a\",\n \"score\": \"0.5909987\",\n \"text\": \"def compute(self, inputs, outputs):\\n # Add new variable value to the list in the dictionnay\\n for key, (name, listval, minval, maxval, setcommand, getcommand) in design_var_dict.items():\\n listval.append(inputs[key][0])\\n\\n # Evaluate the function to optimize\\n outputs[Rt.objective] = one_iteration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c2966319c32696f061c80666c712e5\",\n \"score\": \"0.59034103\",\n \"text\": \"def optimize_worker(circuit, options, q, x0):\\n _, xopt = options.inner_solver.solve_for_unitary(circuit, options, x0)\\n q.put((distance_for_x(xopt, options, circuit), xopt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1a43149e0714cbd36c1ddce69914d3\",\n \"score\": \"0.58992606\",\n \"text\": \"def run(self):\\n\\n self.best_score = 1\\n\\n # runs the program x times depending on the runamount\\n for z in range(self.runamount):\\n\\n done = False\\n\\n # restarts the program if an error occurs\\n while done == False:\\n\\n # sets first fold and adds to final\\n protein = Protein(self.user_input)\\n\\n # splits protein sequence into chunks of three amino acids\\n user_input_split = split_protein(self.user_input)\\n\\n # updates x,y coordinates based on most recently determined fold\\n coordinate_update = CoordinateUpdate()\\n coordinate_update.update_coordinates(protein.final_placement)\\n\\n for i in range(len(user_input_split)):\\n\\n current_x, current_y = coordinate_update.update_coordinates(protein.final_placement)\\n current_fold = protein.final_placement[-1][1]\\n current_amino = self.user_input[len(protein.final_placement)]\\n best_option = three_fold(protein.final_placement, user_input_split, current_fold, current_x, current_y, current_amino, i)\\n\\n # checks if an error has occured\\n if best_option == False:\\n break\\n\\n # adds the amino's the final placement\\n for i in range(len(best_option) - 1):\\n protein.add_amino_info(best_option[i])\\n\\n # checks if the end of protein hasn't been reached\\n if current_fold != 0:\\n current_x, current_y = coordinate_update.update_coordinates(protein.final_placement)\\n\\n # checks if last amino of sequence is reached\\n if len(protein.final_placement) == (len(self.user_input) - 1):\\n protein.add_last_amino_of_chunk_without_score(current_x, current_y, self.user_input)\\n\\n # end of protein has been reached\\n if protein.final_placement[-1][1] == 0:\\n done = True\\n self.stability = Stability()\\n\\n stability_score, stability_connections = self.stability.get_stability_score(protein.final_placement)\\n self.stability_coordinates = stability_connections\\n self.stability.stability_score_coordinates(self.stability_coordinates)\\n amino_stability_x = self.stability.amino_stability_x\\n amino_stability_y = self.stability.amino_stability_y\\n\\n # checks if current score is lower than the current lowest score\\n if stability_score < self.best_score:\\n self.best_score = stability_score\\n self.best_protein = protein.final_placement\\n self.best_stability = stability_score\\n self.amino_stability_x = amino_stability_x\\n self.amino_stability_y = amino_stability_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cacebf0e49a67d9d4db339e78cb52ee\",\n \"score\": \"0.5894279\",\n \"text\": \"def run(self):\\n logger.info('Running model to determine optimal solution.')\\n super(ModelBase, self).run()\\n logger.debug(f'Model summary outputs:\\\\n\\\\n{self.get_summary_outputs()}\\\\n')\\n logger.debug(f'Model time series outputs:\\\\n\\\\n{self.get_timeseries_outputs()}\\\\n')\\n if not psm.utils.has_consistent_outputs(model=self): # pragma: no cover\\n logger.critical('Model has inconsistent outputs. Check log files for details.')\\n logger.info('Done running model.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6033c8d21755839f103afdf4945d143\",\n \"score\": \"0.5881525\",\n \"text\": \"def bayesian_optimization(self):\\n # Set the parameters for the bayesian optimization\\n function = ho.objective_function\\n lower = np.array([self._learning_rate[0], self._batch_size[0], \\n self._number_filters[0], self._number_filters[0],\\n self._number_filters[0]])\\n upper = np.array([self._learning_rate[1], self._batch_size[1], \\n self._number_filters[1], self._number_filters[1],\\n self._number_filters[1]])\\n for i in range(RUNS):\\n results = bayesian_optimization(function, lower, upper,\\n num_iterations=ITERATIONS)\\n self.bo_performance.append(results['incumbent_values'])\\n self.bo_runtime.append(results['runtime'])\\n # Mean over all the runs\\n self.bo_performance = np.mean(self.bo_performance, axis=0)\\n # Cumulative sum of the mean for all the runs\\n self.bo_runtime = np.cumsum(np.mean(self.bo_runtime, axis=0), axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45a67ac8c145f3a587a2649aaa624666\",\n \"score\": \"0.5868775\",\n \"text\": \"def main():\\n\\n parameters = {\\n \\\"batch_size\\\": [16, 32, 64, 128, 256],\\n \\\"learner_lr\\\": [0.001, 0.01, 0.1, 1],\\n \\\"adversary_lr\\\": [0.001, 0.01, 0.1, 1],\\n }\\n\\n for dataset in [\\\"law_school\\\"]:\\n start = time.time()\\n print(\\\"\\\\n\\\" + 40 * \\\"-\\\")\\n print(f\\\"tuning hyperparameters for {dataset}\\\")\\n print(40 * \\\"-\\\" + \\\"\\\\n\\\")\\n best_param, best_auc, step = optimize_parameters(\\n parameters, dataset, train_steps=1000, pretrain_steps=250\\n )\\n stop = time.time()\\n print(f\\\"\\\\ntuning took {stop - start:.0f} seconds for {dataset}\\\")\\n print(\\n f\\\"the best parameters for {dataset} are:\\\\n{best_param}\\\\n\\\"\\n f\\\"found on step {step} with an AUC of {best_auc:.3f}\\\\n\\\",\\n )\\n\\n best_param[\\\"train_steps\\\"] = step\\n\\n filename = check_filename(f\\\"hyperparameters/{dataset}.txt\\\")\\n\\n try:\\n with open(filename, \\\"w\\\") as f:\\n f.write(str(best_param))\\n except:\\n print(\\\"results file could not be created\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97cdfdf753295a833474b09490809da5\",\n \"score\": \"0.5867343\",\n \"text\": \"def optimizeGeometry(self):\\n self.calcEnergyForce()\\n self.prepareFirstStep()\\n while self.state not in [OPT_STATE.CONVERGED, OPT_STATE.FAILED]:\\n self.step()\\n if self.state == OPT_STATE.NEEDS_EVALUATION:\\n self.calcEnergyForce()\\n self.evaluateStep()\\n if self.state == OPT_STATE.FAILED:\\n raise GeomOptNotConvergedError(\\\"Optimizer.optimizeGeometry() failed to converge.\\\")\\n # If we want to save the Hessian used by the optimizer (in Cartesian coordinates)\\n if self.params.write_cart_hess:\\n # One last Hessian update before writing it out\\n self.UpdateHessian()\\n logger.info(\\\"Saving current approximate Hessian (Cartesian coordinates) to %s\\\" % self.params.write_cart_hess)\\n Hx = self.IC.calcHessCart(self.X, self.G, self.H)\\n np.savetxt(self.params.write_cart_hess, Hx, fmt='% 14.10f')\\n return self.progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f100ed55b644b19f8bd8b64d421a741b\",\n \"score\": \"0.58617616\",\n \"text\": \"def execute(args):\\n LOGGER.info(\\\"Starting Scenic Quality Model\\\")\\n dem_raster_info = pygeoprocessing.get_raster_info(args['dem_path'])\\n\\n try:\\n do_valuation = bool(args['do_valuation'])\\n except KeyError:\\n do_valuation = False\\n\\n if do_valuation:\\n valuation_coefficients = {\\n 'a': float(args['a_coef']),\\n 'b': float(args['b_coef']),\\n }\\n if args['valuation_function'].startswith('linear'):\\n valuation_method = 'linear'\\n elif args['valuation_function'].startswith('logarithmic'):\\n valuation_method = 'logarithmic'\\n elif args['valuation_function'].startswith('exponential'):\\n valuation_method = 'exponential'\\n else:\\n raise ValueError('Valuation function type %s not recognized' %\\n args['valuation_function'])\\n\\n max_valuation_radius = float(args['max_valuation_radius'])\\n\\n # Create output and intermediate directory\\n output_dir = os.path.join(args['workspace_dir'], 'output')\\n intermediate_dir = os.path.join(args['workspace_dir'], 'intermediate')\\n utils.make_directories([output_dir, intermediate_dir])\\n\\n file_suffix = utils.make_suffix_string(\\n args, 'results_suffix')\\n\\n LOGGER.info('Building file registry')\\n file_registry = utils.build_file_registry(\\n [(_OUTPUT_BASE_FILES, output_dir),\\n (_INTERMEDIATE_BASE_FILES, intermediate_dir)],\\n file_suffix)\\n\\n work_token_dir = os.path.join(intermediate_dir, '_tmp_work_tokens')\\n try:\\n n_workers = int(args['n_workers'])\\n except (KeyError, ValueError, TypeError):\\n # KeyError when n_workers is not present in args\\n # ValueError when n_workers is an empty string.\\n # TypeError when n_workers is None.\\n n_workers = -1 # Synchronous execution\\n graph = taskgraph.TaskGraph(work_token_dir, n_workers)\\n\\n reprojected_aoi_task = graph.add_task(\\n pygeoprocessing.reproject_vector,\\n args=(args['aoi_path'],\\n dem_raster_info['projection'],\\n file_registry['aoi_reprojected']),\\n target_path_list=[file_registry['aoi_reprojected']],\\n task_name='reproject_aoi_to_dem')\\n\\n reprojected_viewpoints_task = graph.add_task(\\n pygeoprocessing.reproject_vector,\\n args=(args['structure_path'],\\n dem_raster_info['projection'],\\n file_registry['structures_reprojected']),\\n target_path_list=[file_registry['structures_reprojected']],\\n task_name='reproject_structures_to_dem')\\n\\n clipped_viewpoints_task = graph.add_task(\\n _clip_vector,\\n args=(file_registry['structures_reprojected'],\\n file_registry['aoi_reprojected'],\\n file_registry['structures_clipped']),\\n target_path_list=[file_registry['structures_clipped']],\\n dependent_task_list=[reprojected_aoi_task,\\n reprojected_viewpoints_task],\\n task_name='clip_reprojected_structures_to_aoi')\\n\\n clipped_dem_task = graph.add_task(\\n _clip_and_mask_dem,\\n args=(args['dem_path'],\\n file_registry['aoi_reprojected'],\\n file_registry['clipped_dem'],\\n intermediate_dir),\\n target_path_list=[file_registry['clipped_dem']],\\n dependent_task_list=[reprojected_aoi_task],\\n task_name='clip_dem_to_aoi')\\n\\n # viewshed calculation requires that the DEM and structures are all\\n # finished.\\n LOGGER.info('Waiting for clipping to finish')\\n clipped_dem_task.join()\\n clipped_viewpoints_task.join()\\n\\n # phase 2: calculate viewsheds.\\n LOGGER.info('Setting up viewshed tasks')\\n viewpoint_tuples = []\\n structures_vector = gdal.OpenEx(file_registry['structures_reprojected'],\\n gdal.OF_VECTOR)\\n for structures_layer_index in range(structures_vector.GetLayerCount()):\\n structures_layer = structures_vector.GetLayer(structures_layer_index)\\n layer_name = structures_layer.GetName()\\n LOGGER.info('Layer %s has %s features', layer_name,\\n structures_layer.GetFeatureCount())\\n\\n for point in structures_layer:\\n # Coordinates in map units to pass to viewshed algorithm\\n geometry = point.GetGeometryRef()\\n viewpoint = (geometry.GetX(), geometry.GetY())\\n\\n if not _viewpoint_within_raster(viewpoint, file_registry['clipped_dem']):\\n LOGGER.info(\\n ('Feature %s in layer %s is outside of the DEM bounding '\\n 'box. Skipping.'), layer_name, point.GetFID())\\n continue\\n\\n if _viewpoint_over_nodata(viewpoint, file_registry['clipped_dem']):\\n LOGGER.info(\\n 'Feature %s in layer %s is over nodata; skipping.',\\n point.GetFID(), layer_name)\\n continue\\n\\n # RADIUS is the suggested value for InVEST Scenic Quality\\n # RADIUS2 is for users coming from ArcGIS's viewshed.\\n # Assume positive infinity if neither field is provided.\\n # Positive infinity is represented in our viewshed by None.\\n max_radius = None\\n for fieldname in ('RADIUS', 'RADIUS2'):\\n try:\\n max_radius = math.fabs(point.GetField(fieldname))\\n break\\n except ValueError:\\n # When this field is not present.\\n pass\\n\\n try:\\n viewpoint_height = math.fabs(point.GetField('HEIGHT'))\\n except ValueError:\\n # When height field is not present, assume height of 0.0\\n viewpoint_height = 0.0\\n\\n try:\\n weight = float(point.GetField('WEIGHT'))\\n except ValueError:\\n # When no weight provided, set scale to 1\\n weight = 1.0\\n\\n viewpoint_tuples.append((viewpoint, max_radius, weight,\\n viewpoint_height))\\n structures_vector = None\\n\\n if not viewpoint_tuples:\\n raise ValueError('No valid viewpoints found. This may happen if '\\n 'viewpoints are beyond the edge of the DEM or are '\\n 'over nodata pixels.')\\n\\n # These are sorted outside the vector to ensure consistent ordering. This\\n # helps avoid unnecesary recomputation in taskgraph for when an ESRI\\n # Shapefile, for example, returns a different order of points because\\n # someone decided to repack it.\\n viewshed_files = []\\n viewshed_tasks = []\\n valuation_tasks = []\\n valuation_filepaths = []\\n weights = []\\n feature_index = 0\\n for viewpoint, max_radius, weight, viewpoint_height in sorted(\\n viewpoint_tuples, key=lambda x: x[0]):\\n weights.append(weight)\\n visibility_filepath = file_registry['visibility_pattern'].format(\\n id=feature_index)\\n viewshed_files.append(visibility_filepath)\\n viewshed_task = graph.add_task(\\n viewshed,\\n args=((file_registry['clipped_dem'], 1), # DEM\\n viewpoint,\\n visibility_filepath),\\n kwargs={'curved_earth': True, # SQ model always assumes this.\\n 'refraction_coeff': float(args['refraction']),\\n 'max_distance': max_radius,\\n 'viewpoint_height': viewpoint_height,\\n 'aux_filepath': None}, # Remove aux filepath after run\\n target_path_list=[visibility_filepath],\\n dependent_task_list=[clipped_dem_task,\\n clipped_viewpoints_task],\\n task_name='calculate_visibility_%s' % feature_index)\\n viewshed_tasks.append(viewshed_task)\\n\\n if do_valuation:\\n # calculate valuation\\n viewshed_valuation_path = file_registry['value_pattern'].format(\\n id=feature_index)\\n valuation_task = graph.add_task(\\n _calculate_valuation,\\n args=(visibility_filepath,\\n viewpoint,\\n weight, # user defined, from WEIGHT field in vector\\n valuation_method,\\n valuation_coefficients, # a, b from args, a dict.\\n max_valuation_radius,\\n viewshed_valuation_path),\\n target_path_list=[viewshed_valuation_path],\\n dependent_task_list=[viewshed_task],\\n task_name='calculate_valuation_for_viewshed_%s' % feature_index)\\n valuation_tasks.append(valuation_task)\\n valuation_filepaths.append(viewshed_valuation_path)\\n\\n feature_index += 1\\n\\n # The weighted visible structures raster is a leaf node\\n weighted_visible_structures_task = graph.add_task(\\n _count_and_weight_visible_structures,\\n args=(viewshed_files,\\n weights,\\n file_registry['clipped_dem'],\\n file_registry['n_visible_structures']),\\n target_path_list=[file_registry['n_visible_structures']],\\n dependent_task_list=sorted(viewshed_tasks),\\n task_name='sum_visibility_for_all_structures')\\n\\n # If we're not doing valuation, we can still compute visual quality,\\n # we'll just use the weighted visible structures raster instead of the\\n # sum of the valuation rasters.\\n if not do_valuation:\\n parent_visual_quality_task = weighted_visible_structures_task\\n parent_visual_quality_raster_path = (\\n file_registry['n_visible_structures'])\\n else:\\n parent_visual_quality_task = graph.add_task(\\n _sum_valuation_rasters,\\n args=(file_registry['clipped_dem'],\\n valuation_filepaths,\\n file_registry['viewshed_value']),\\n target_path_list=[file_registry['viewshed_value']],\\n dependent_task_list=sorted(valuation_tasks),\\n task_name='add_up_valuation_rasters')\\n parent_visual_quality_raster_path = file_registry['viewshed_value']\\n\\n # visual quality is one of the leaf nodes on the task graph.\\n graph.add_task(\\n _calculate_visual_quality,\\n args=(parent_visual_quality_raster_path,\\n intermediate_dir,\\n file_registry['viewshed_quality']),\\n dependent_task_list=[parent_visual_quality_task],\\n target_path_list=[file_registry['viewshed_quality']],\\n task_name='calculate_visual_quality'\\n )\\n\\n LOGGER.info('Waiting for Scenic Quality tasks to complete.')\\n graph.join()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0bdc81087000ae4b75a404ced2d8ae\",\n \"score\": \"0.58589476\",\n \"text\": \"def optimize(self):\\n # get the initial guess, flatten arrays and scale inputs\\n xinit = self.graph.input.getFlat(self.prob.v, scaled=True)\\n # Display a little information to check that things are working\\n self.msgQueue.put(\\n \\\"Starting NLopt Optimization at {0}\\\".format(\\n time.strftime(\\\"%a, %d %b %Y %H:%M:%S\\\", time.localtime())\\n )\\n )\\n self.msgQueue.put(\\\"\\\\nDecision Variables\\\\n---------------------\\\")\\n vals = self.graph.input.getFlat(self.prob.v, scaled=False)\\n for i, xn in enumerate(self.prob.v):\\n self.msgQueue.put(\\\"{0}: {1} scaled: {2}\\\".format(xn, vals[i], xinit[i]))\\n self.msgQueue.put(\\\"----------------------\\\")\\n n = len(xinit) # Get the number of inputs\\n # self.msgQueue.put(\\\"n = {0}\\\".format(n))\\n #\\n # Read solver options and handle any special cases of options\\n #\\n upper = self.options[\\\"upper\\\"].value # These are for scaled vars\\n lower = self.options[\\\"lower\\\"].value # usually 0 to 10 not usually\\n # changed but could be\\n # If upper and/or lower are scalar conver to array with the\\n # number of variables.\\n if type(upper) == float or type(upper) == int:\\n upper = upper * numpy.ones(n)\\n else:\\n upper = numpy.array(upper)\\n if type(lower) == float or type(lower) == int:\\n lower = lower * numpy.ones(n)\\n else:\\n lower = numpy.array(lower)\\n\\n # Get solver string and convert to NLopt enum\\n method = self.optMethod(self.options[\\\"Solver\\\"].value)\\n\\n # Get rest of options\\n absFunTol = self.options[\\\"tolfunabs\\\"].value\\n relFunTol = self.options[\\\"tolfunrel\\\"].value\\n absXtol = self.options[\\\"tolxabs\\\"].value\\n relXtol = self.options[\\\"tolxrel\\\"].value\\n maxeval = self.options[\\\"maxeval\\\"].value\\n maxtime = self.options[\\\"maxtime\\\"].value\\n saveRes = self.options[\\\"Save results\\\"].value\\n initStep = self.options[\\\"init step\\\"].value\\n pop = self.options[\\\"pop\\\"].value\\n setName = self.options[\\\"Set Name\\\"].value\\n self.bkp_int = self.options[\\\"Backup Interval\\\"].value\\n\\n # The set name to use when saving evaluations in flowsheet results\\n if saveRes:\\n setName = self.dat.flowsheet.results.incrimentSetName(setName)\\n #\\n # Create optimization object and set options\\n opt = nlopt.opt(method, n)\\n opt.set_lower_bounds(lower)\\n opt.set_upper_bounds(upper)\\n opt.set_ftol_rel(relFunTol)\\n opt.set_ftol_abs(absFunTol)\\n opt.set_xtol_rel(relXtol)\\n opt.set_xtol_abs(absXtol)\\n opt.set_maxeval(maxeval)\\n if initStep > 1e-11:\\n opt.set_initial_step(initStep)\\n if pop > 0:\\n opt.set_population(pop)\\n needLocalOpt = False # so far I have not added any method that\\n # would need this\\n if needLocalOpt:\\n opt2 = nlopt.opt(nlopt.LN_BOBYQA, n)\\n opt.set_local_optimizer(opt2)\\n # The solver is all setup and ready to go\\n start = time.time() # get start time\\n self.userInterupt = False #\\n self.bestSoFar = float(\\\"inf\\\") # set inital best values\\n self.bestSoFarList = [] # List of all previous best so far values\\n # self.bestSoFarList.append(self.bestSoFar)\\n\\n # self.prob is the optimzation problem. get it ready\\n self.prob.iterationNumber = 0\\n self.prob.initSolverParameters() #\\n self.prob.solverStart = start\\n self.prob.maxSolverTime = maxtime\\n if saveRes:\\n self.prob.storeResults = setName\\n else:\\n self.prob.storeResults = None\\n #\\n opt.set_min_objective(self.f) # set the solver objective function\\n self.prob.prep(self) # get problem ready for solving\\n self.bkp_timer = time.time() # timer for flowseet backup\\n # Run the optimzation\\n x = opt.optimize(xinit)\\n # Print some final words now that optimzation is done.\\n eltime = time.time() - start\\n self.msgQueue.put(\\n \\\"{0}, Total Elasped Time {1}s, Obj: {2}\\\".format(\\n self.prob.iterationNumber, math.floor(eltime), self.bestSoFar\\n )\\n )\\n self.resQueue.put([\\\"IT\\\", self.prob.iterationNumber, self.bestSoFar])\\n self.resQueue.put([\\\"BEST\\\", [self.bestSoFar], x])\\n # print(self.bestSoFarList)\\n min_obj_value = min(self.bestSoFarList, key=lambda t: t[1])\\n self.msgQueue.put(\\\"Best result found stored in graph\\\")\\n self.msgQueue.put(\\n \\\"The best value for objective function was obtained at iteration number {}\\\".format(\\n min_obj_value[0]\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fc057b79c320e3dbcd9f0192fb52f99\",\n \"score\": \"0.58526593\",\n \"text\": \"def optimize(self):\\r\\n try:\\r\\n model = Model(self.model_name)\\r\\n\\r\\n for rv_index in range(len(self.__regularVariablesLong)):\\r\\n\\r\\n if self.__regularVariablesLong[rv_index].variable_type == \\\"discrete\\\":\\r\\n for domain_index in range(len(self.__regularVariablesLong[rv_index].discrete_domain)):\\r\\n self.__w[rv_index, domain_index] = model.addVar(vtype=GRB.BINARY,\\r\\n name=self.__regularVariablesLong[\\r\\n rv_index].name + \\\"_w[[\\\" + str(\\r\\n domain_index) + \\\"]]\\\")\\r\\n self.__x[rv_index] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, ub=GRB.INFINITY,\\r\\n name=self.__regularVariablesLong[rv_index].name)\\r\\n else:\\r\\n self.__x[rv_index] = model.addVar(vtype=GRB.CONTINUOUS,\\r\\n lb=self.__regularVariablesLong[rv_index].lower_bound,\\r\\n ub=self.__regularVariablesLong[rv_index].upper_bound,\\r\\n name=self.__regularVariablesLong[rv_index].name)\\r\\n\\r\\n for pv_index in range(len(self.__predictedVariablesLong)):\\r\\n self.__y[pv_index] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, ub=GRB.INFINITY,\\r\\n name=self.__predictedVariablesLong[pv_index].name)\\r\\n\\r\\n model.update()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Associate binary w with continuous x\\r\\n only needed for discrete variables\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n for rv_index in range(len(self.__regularVariablesLong)):\\r\\n if self.__regularVariablesLong[rv_index].variable_type == \\\"discrete\\\":\\r\\n projection_expr = LinExpr(0.0)\\r\\n\\r\\n one_w_expr = LinExpr(0.0)\\r\\n for domain_index in range(len(self.__regularVariablesLong[rv_index].discrete_domain)):\\r\\n projection_expr += self.__regularVariablesLong[rv_index].discrete_domain[domain_index] * \\\\\\r\\n self.__w[rv_index, domain_index]\\r\\n one_w_expr += self.__w[rv_index, domain_index]\\r\\n model.addConstr(self.__x[rv_index] == projection_expr)\\r\\n model.addConstr(one_w_expr == 1)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Put constraints\\r\\n \\\"\\\"\\\"\\r\\n for con_index in range(len(self.__constraints)):\\r\\n\\r\\n con_expr = LinExpr(0.0)\\r\\n\\r\\n for term_index in range(len(self.__constraints[con_index].con_expr.expression_terms)):\\r\\n var_vector_index = self.__regularVariablesLong.index(\\r\\n self.__constraints[con_index].con_expr.expression_terms[term_index].regular_var)\\r\\n value_in_coeff = self.__constraints[con_index].con_expr.expression_terms[term_index].value\\r\\n con_expr += value_in_coeff * self.__x[var_vector_index]\\r\\n\\r\\n if self.__constraints[con_index].sense == 'less_equal':\\r\\n model.addConstr(con_expr <= self.__constraints[con_index].rhs)\\r\\n elif self.__constraints[con_index].sense == 'equal':\\r\\n model.addConstr(con_expr == self.__constraints[con_index].rhs)\\r\\n else:\\r\\n model.addConstr(con_expr >= self.__constraints[con_index].rhs)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Connect regular variables with predictive variables\\r\\n \\\"\\\"\\\"\\r\\n for pv_index in range(len(self.__predictedVariablesLong)):\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Neural Network\\r\\n \\\"\\\"\\\"\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, MLPRegressor):\\r\\n this_pm = self.__predictedVariablesLong[pv_index].opm\\r\\n for node in range(this_pm.n_nodes):\\r\\n self.__pre_relu_value[pv_index, node] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY,\\r\\n ub=GRB.INFINITY,\\r\\n name=self.__predictedVariablesLong[\\r\\n pv_index].name + \\\"_pre[[\\\" + str(\\r\\n node) + \\\"]]\\\")\\r\\n self.__post_relu_value[pv_index, node] = model.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY,\\r\\n ub=GRB.INFINITY,\\r\\n name=self.__predictedVariablesLong[\\r\\n pv_index].name + \\\"_post[[\\\" + str(\\r\\n node) + \\\"]]\\\")\\r\\n self.__need_activation_func[pv_index, node] = model.addVar(vtype=GRB.BINARY,\\r\\n name=self.__predictedVariablesLong[\\r\\n pv_index].name + \\\"_actFun[[\\\" + str(\\r\\n node) + \\\"]]\\\")\\r\\n model.update()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n obtain the detail of the predictive model\\r\\n \\\"\\\"\\\"\\r\\n predicted_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\\r\\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\\r\\n nn_coefs = predicted_model_for_var.coefs_\\r\\n nn_intercepts = predicted_model_for_var.intercepts_\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n generate matrices W and B and A\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n initialize the matrices W and A\\r\\n \\\"\\\"\\\"\\r\\n W = np.zeros((this_pm.n_nodes, this_pm.n_nodes), dtype=float)\\r\\n A = np.zeros((this_pm.n_nodes, this_pm.n_nodes), dtype=int)\\r\\n\\r\\n for layer in range(this_pm.n_layers - 1):\\r\\n for i in range(this_pm.layers_sizes[layer]):\\r\\n for j in range(this_pm.layers_sizes[layer + 1]):\\r\\n A[i + this_pm.offset[layer], j + this_pm.offset[layer + 1]] = 1\\r\\n W[i + this_pm.offset[layer], j + this_pm.offset[layer + 1]] = nn_coefs[layer][i][j]\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n initialize vector B\\r\\n \\\"\\\"\\\"\\r\\n B = np.zeros(this_pm.n_inputs, dtype=float).tolist()\\r\\n for layer in range(this_pm.n_layers - 1):\\r\\n B += nn_intercepts[layer].tolist()\\r\\n\\r\\n W = np.around(W, decimals=6)\\r\\n B = np.around(B, decimals=6)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Constructing matrices W, B and A\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n for node in range(this_pm.n_nodes):\\r\\n\\r\\n if this_pm.nodes_locations[node] == 0:\\r\\n \\\"\\\"\\\"\\r\\n input layer\\r\\n \\\"\\\"\\\"\\r\\n model.addConstr(\\r\\n self.__pre_relu_value[pv_index, node] == self.__post_relu_value[pv_index, node])\\r\\n\\r\\n feature_index = node\\r\\n\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\\r\\n rv_index = self.__regularVariablesLong.index(\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]])\\r\\n model.addConstr(self.__pre_relu_value[pv_index, node] == self.__x[rv_index])\\r\\n else:\\r\\n model.addConstr(self.__pre_relu_value[pv_index, node] ==\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]])\\r\\n\\r\\n elif this_pm.nodes_locations[node] == this_pm.n_layers - 1:\\r\\n \\\"\\\"\\\"\\r\\n output layer\\r\\n \\\"\\\"\\\"\\r\\n model.addConstr(\\r\\n self.__pre_relu_value[pv_index, node] == self.__post_relu_value[pv_index, node])\\r\\n\\r\\n incoming_arcs = LinExpr(0.0)\\r\\n for j in range(node):\\r\\n if A[j][node] == 1:\\r\\n incoming_arcs += W[j][node] * self.__post_relu_value[pv_index, j]\\r\\n model.addConstr(self.__pre_relu_value[pv_index, node] == incoming_arcs + B[node])\\r\\n\\r\\n else:\\r\\n \\\"\\\"\\\"\\r\\n hidden layers\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n incoming_arcs = LinExpr(0.0)\\r\\n for j in range(node):\\r\\n if A[j][node] == 1:\\r\\n incoming_arcs += W[j][node] * self.__post_relu_value[pv_index, j]\\r\\n model.addConstr(self.__pre_relu_value[pv_index, node] == incoming_arcs + B[node])\\r\\n\\r\\n model.addConstr(\\r\\n self.__post_relu_value[pv_index, node] <= JANOS.BIG_M * self.__need_activation_func[\\r\\n pv_index, node] + JANOS.EPSILON)\\r\\n model.addConstr(self.__post_relu_value[pv_index, node] >= 0.0)\\r\\n\\r\\n model.addConstr(\\r\\n self.__pre_relu_value[pv_index, node] <= JANOS.BIG_M * self.__need_activation_func[\\r\\n pv_index, node])\\r\\n model.addConstr(self.__pre_relu_value[pv_index, node] >= -1.0 * JANOS.BIG_M * (\\r\\n 1 - self.__need_activation_func[pv_index, node]) + JANOS.EPSILON)\\r\\n\\r\\n model.addConstr(self.__post_relu_value[pv_index, node] <= self.__pre_relu_value[\\r\\n pv_index, node] + JANOS.BIG_M * (1 - self.__need_activation_func[\\r\\n pv_index, node]) + JANOS.EPSILON)\\r\\n model.addConstr(self.__post_relu_value[pv_index, node] >= self.__pre_relu_value[\\r\\n pv_index, node] - JANOS.BIG_M * (1 - self.__need_activation_func[\\r\\n pv_index, node]) - JANOS.EPSILON)\\r\\n\\r\\n model.addConstr(self.__y[pv_index] == self.__post_relu_value[pv_index, this_pm.n_nodes - 1])\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Linear regression\\r\\n \\\"\\\"\\\"\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, LinearRegression):\\r\\n\\r\\n predictive_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\\r\\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\\r\\n\\r\\n lin_regr_expr = LinExpr(0.0)\\r\\n for feature_index in range(len(opt_pm_for_var.feature_names)):\\r\\n\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\\r\\n rv_index = self.__regularVariablesLong.index(\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]])\\r\\n lin_regr_expr += predictive_model_for_var.coef_[0][feature_index] * self.__x[rv_index]\\r\\n else:\\r\\n\\r\\n lin_regr_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]]\\r\\n\\r\\n lin_regr_expr += predictive_model_for_var.intercept_\\r\\n model.addConstr(lin_regr_expr == self.__y[pv_index])\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Logistic regression\\r\\n \\\"\\\"\\\"\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].opm.optimization_pm, LogisticRegression):\\r\\n\\r\\n n_intervals_discretization = self.__predictedVariablesLong[pv_index].opm.n_breakpoints\\r\\n\\r\\n predictive_model_for_var = self.__predictedVariablesLong[pv_index].opm.optimization_pm\\r\\n opt_pm_for_var = self.__predictedVariablesLong[pv_index].opm\\r\\n\\r\\n max_value_lin_expr = 0.0\\r\\n min_value_lin_expr = 0.0\\r\\n\\r\\n for feature_index in range(len(opt_pm_for_var.feature_names)):\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\\r\\n rv_index = self.__regularVariablesLong.index(\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]])\\r\\n if predictive_model_for_var.coef_[0][feature_index] > 0:\\r\\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__regularVariablesLong[rv_index].upper_bound\\r\\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__regularVariablesLong[rv_index].lower_bound\\r\\n else:\\r\\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__regularVariablesLong[rv_index].lower_bound\\r\\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__regularVariablesLong[rv_index].upper_bound\\r\\n else:\\r\\n max_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]]\\r\\n min_value_lin_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]]\\r\\n\\r\\n b = max_value_lin_expr + predictive_model_for_var.intercept_\\r\\n a = min_value_lin_expr + predictive_model_for_var.intercept_\\r\\n\\r\\n breakpoints_for_probs = np.linspace(calc_sigmoid_value(a), calc_sigmoid_value(b),\\r\\n n_intervals_discretization)\\r\\n breakpoints_for_probs = np.minimum(breakpoints_for_probs, 1 - JANOS.EPSILON)\\r\\n breakpoints_for_probs = np.maximum(breakpoints_for_probs, JANOS.EPSILON)\\r\\n breakpoints_for_lin_expr = np.apply_along_axis(calc_inverse_sigmoid, 0, breakpoints_for_probs)\\r\\n\\r\\n self.__z[pv_index] = model.addVars(n_intervals_discretization - 1, vtype=GRB.BINARY,\\r\\n name=\\\"z[\\\" + str(pv_index) + \\\"]\\\")\\r\\n\\r\\n model.update()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Pick at most one z\\r\\n \\\"\\\"\\\"\\r\\n one_z_expr = LinExpr(0.0)\\r\\n for interval_index in range(n_intervals_discretization - 1):\\r\\n one_z_expr += self.__z[pv_index][interval_index]\\r\\n model.addConstr(one_z_expr == 1)\\r\\n\\r\\n exp_argument_expr = LinExpr(0.0)\\r\\n for feature_index in range(len(opt_pm_for_var.feature_names)):\\r\\n if isinstance(self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]], RegularVariable):\\r\\n rv_index = self.__regularVariablesLong.index(\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]])\\r\\n exp_argument_expr += predictive_model_for_var.coef_[0][feature_index] * self.__x[rv_index]\\r\\n else:\\r\\n exp_argument_expr += predictive_model_for_var.coef_[0][feature_index] * \\\\\\r\\n self.__predictedVariablesLong[pv_index].variable_mapping[\\r\\n opt_pm_for_var.feature_names[feature_index]]\\r\\n\\r\\n exp_argument_expr += predictive_model_for_var.intercept_\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n link breakpoints with probs\\r\\n \\\"\\\"\\\"\\r\\n model.addConstr(0 <= self.__y[pv_index])\\r\\n model.addConstr(1 >= self.__y[pv_index])\\r\\n\\r\\n min_prob = min(breakpoints_for_probs)\\r\\n max_prob = max(breakpoints_for_probs)\\r\\n min_prob = float(min_prob)\\r\\n max_prob = float(max_prob)\\r\\n model.addConstr(min_prob <= self.__y[pv_index])\\r\\n model.addConstr(max_prob >= self.__y[pv_index])\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n link linear expression with binaries\\r\\n \\\"\\\"\\\"\\r\\n for interval_index in range(n_intervals_discretization - 1):\\r\\n lb_prob = breakpoints_for_probs[interval_index]\\r\\n ub_prob = breakpoints_for_probs[interval_index + 1]\\r\\n\\r\\n first_constant = 0.0\\r\\n second_constant = 0.0\\r\\n prob_val = 0.0\\r\\n\\r\\n if lb_prob > JANOS.EPSILON and ub_prob < 1 - JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 1\\r\\n \\\"\\\"\\\"\\r\\n first_constant = breakpoints_for_lin_expr[interval_index]\\r\\n second_constant = breakpoints_for_lin_expr[interval_index + 1]\\r\\n prob_val = calc_average_sigmoid_value(breakpoints_for_lin_expr[interval_index],\\r\\n breakpoints_for_lin_expr[interval_index + 1])\\r\\n elif lb_prob == 1 - JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 2\\r\\n \\\"\\\"\\\"\\r\\n first_constant = calc_inverse_sigmoid(1 - JANOS.EPSILON)\\r\\n second_constant = b[0]\\r\\n prob_val = 1 - JANOS.EPSILON\\r\\n elif lb_prob == JANOS.EPSILON and ub_prob == JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 3\\r\\n \\\"\\\"\\\"\\r\\n first_constant = a[0]\\r\\n second_constant = calc_inverse_sigmoid(JANOS.EPSILON)\\r\\n prob_val = JANOS.EPSILON\\r\\n elif lb_prob > JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 4\\r\\n \\\"\\\"\\\"\\r\\n first_constant = breakpoints_for_lin_expr[interval_index]\\r\\n second_constant = b[0]\\r\\n prob_val = 1 - JANOS.EPSILON\\r\\n elif lb_prob == JANOS.EPSILON and ub_prob < 1 - JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 5\\r\\n \\\"\\\"\\\"\\r\\n first_constant = a[0]\\r\\n second_constant = breakpoints_for_lin_expr[interval_index + 1]\\r\\n prob_val = JANOS.EPSILON\\r\\n elif lb_prob == JANOS.EPSILON and ub_prob == 1 - JANOS.EPSILON:\\r\\n \\\"\\\"\\\"\\r\\n case 6\\r\\n \\\"\\\"\\\"\\r\\n first_constant = a[0]\\r\\n second_constant = b[0]\\r\\n prob_val = 0.5\\r\\n else:\\r\\n print(\\\"JANOS Error: didn't find matched case for discretization.\\\")\\r\\n sys.exit(1)\\r\\n\\r\\n model.addConstr(\\r\\n (first_constant - a[0]) * self.__z[pv_index][interval_index] + a[0] <= exp_argument_expr)\\r\\n model.addConstr(\\r\\n (second_constant - b[0]) * self.__z[pv_index][interval_index] + b[0] >= exp_argument_expr)\\r\\n\\r\\n model.addConstr(\\r\\n min_prob + (prob_val - min_prob - JANOS.EPSILON) * self.__z[pv_index][interval_index] <=\\r\\n self.__y[pv_index])\\r\\n model.addConstr(\\r\\n max_prob + (prob_val - max_prob + JANOS.EPSILON) * self.__z[pv_index][interval_index] >=\\r\\n self.__y[pv_index])\\r\\n\\r\\n model.update()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Setting objective\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n obj_expr = LinExpr(0.0)\\r\\n\\r\\n for rv_index in range(len(self.__regularVariablesLong)):\\r\\n obj_expr += self.__regularVariablesLong[rv_index].objective_coefficient * self.__x[rv_index]\\r\\n for pv_index in range(len(self.__predictedVariablesLong)):\\r\\n obj_expr += self.__predictedVariablesLong[pv_index].objective_coefficient * self.__y[pv_index]\\r\\n\\r\\n model.setObjective(obj_expr, GRB.MAXIMIZE)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Associate\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n if self.write_lp_model:\\r\\n if self.get_lp_model_filename() != \\\"\\\":\\r\\n model.write(self.get_lp_model_filename())\\r\\n else:\\r\\n model.write(\\\"ipmodel.lp\\\")\\r\\n model.setParam(\\\"TimeLimit\\\", self.get_time_limit()) # OR: model.setParam.TimeLimit = self.get_time_limit()\\r\\n model.setParam(\\\"OutputFlag\\\", self.get_output_flag())\\r\\n for key, value in self.gurobi_param_settings.items():\\r\\n model.setParam(key, value)\\r\\n\\r\\n model.optimize()\\r\\n self.gurobi_model = model\\r\\n\\r\\n except GurobiError as e:\\r\\n print('Gurobi error ' + str(e.errno) + \\\": \\\" + str(e.message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c89f09177afb4b9634679dd13edb65\",\n \"score\": \"0.58515215\",\n \"text\": \"def run(self) -> Tuple[List[float], List[float]]:\\n self.opt_steps = 0\\n ln: int = self.num_steps\\n remainder = ln % self.window_size\\n if remainder != 0:\\n if self.verbose:\\n print(f'window_size must be a factor of len(energy_cost), trimming arrays from {ln} to {ln-remainder}')\\n self.energy_cost = self.energy_cost[:-remainder]\\n self.outdoor_temps = self.outdoor_temps[:-remainder]\\n ln -= remainder\\n self.num_steps -= remainder\\n\\n step_size = int(self.window_size / 2)\\n for i in range(step_size, ln + 1, step_size):\\n self.opt_steps += 1\\n h = HVAC(\\n self.energy_cost[i - step_size: i + step_size],\\n self.outdoor_temps[i - step_size: i + step_size],\\n low_temp_threshold=self.low_thresh,\\n high_temp_threshold=self.high_thresh,\\n initial_building_temperature=self.temperature,\\n verbose=self.verbose,\\n integer_opt_only=self.int_opt_only\\n )\\n states, temps = h.optimized_values()\\n these_states = states[:step_size]\\n self.control_regime.extend(these_states)\\n self.house_temps.extend(temps[:step_size])\\n self.temperature = temps[step_size - 1]\\n\\n self.compute_regime_cost()\\n\\n if self.plot:\\n self.plot_controls('Optimized Control by Energy Cost :')\\n\\n return self.control_regime, self.house_temps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad2bb3e31b7934abff201c4fd90a13d5\",\n \"score\": \"0.5849908\",\n \"text\": \"def main():\\n # PARAMETROS QUE NECESITAMOS\\n # los hiperparametros que queramos añadir al programa se tiene que pasar por linea de comandos. \\n # Cualquier parámetro que queramos añadir para que el optimizador de hiperparámetros los pueda modificar\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--batch-size', type=int, help='Batch size for the training')\\n parser.add_argument('--epochs', type=int, help='Number of epochs for the training')\\n # Para informar si vamos a hacer o no el hyperTuning\\n parser.add_argument('--hypertune', action='store_true', help='This is a hypertunning job')\\n parser.add_argument('--model-type', default='Dense', help='This is model type job')\\n \\n # Estos parámetros son opciones de para AI platform\\n # '--job-dir' obligatorio\\n # '--model-output-path' path donde escribiremos el binario del modelo después de entrenarlo y que utilizaremos en producción.\\n parser.add_argument('--job-dir', default=None, required=False, help='Option for AI Platform')\\n parser.add_argument('--model-output-path', help='Path to write the SaveModel format')\\n\\n args = parser.parse_args()\\n batch_size = args.batch_size\\n epochs = args.epochs\\n is_hypertune = args.hypertune\\n model_type = args.model_type\\n \\n job_dir = args.job_dir\\n model_output_path = args.model_output_path\\n\\n train_and_evaluate(batch_size, epochs, is_hypertune, model_type, job_dir, model_output_path)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0d9bd97d35f7c642cdaa3cb29bf5e9\",\n \"score\": \"0.5849762\",\n \"text\": \"def main():\\n subj_list = [s for s in range(1, 20)]\\n subj_list.remove(3)\\n subj_list.remove(11)\\n decondir = os.path.join(os.environ['avp'], 'nii',\\n 'deconvolve_outs_concat_dec')\\n f_suffx = 'Powered.cleanEPI_REML_fnirted_MNI2mm.nii.gz'\\n for vvar in ['coef', 'tstat']:\\n cond_dict = make_subbrck_dict(vvar)\\n\\n for subj in subj_list:\\n inpref = 'decon_out.mion.{}_concat'.format(subj)\\n infile_a = os.path.join(decondir, '{}.{}[{}]'.format(\\n inpref, f_suffx, cond_dict['AHighVLow']))\\n infile_b = os.path.join(decondir, '{}.{}[{}]'.format(\\n inpref, f_suffx, cond_dict['ALowVHigh']))\\n fx_dir = os.path.join(os.environ['avp'], 'nii',\\n 'ss{}_effects_dec'.format(subj))\\n mean_outname = os.path.join(fx_dir,\\n 'crossd_avg_ss{}_{}'.format(\\n subj, vvar))\\n logfile = setup_log(os.path.join(os.environ['avp'], 'logs',\\n 'calc_mean'))\\n logfile.info('Doing calc_mean.')\\n calc_mean(logfile, infile_a, infile_b, mean_outname)\\n\\n logfile = setup_log(os.path.join(os.environ['avp'], 'logs',\\n 'calc_subt'))\\n logfile.info('Doing calc_subt.')\\n hh_file = os.path.join(decondir, '{}.{}[{}]'.format(\\n inpref, f_suffx, cond_dict['AHighVHigh']))\\n hhout_contr = os.path.join(fx_dir, 'high_grad_ss{}_{}'.format(\\n subj, vvar))\\n calc_subt(logfile, hh_file, '{}+tlrc'.format(mean_outname),\\n hhout_contr)\\n\\n ll_file = os.path.join(decondir, '{}.{}[{}]'.format(\\n inpref, f_suffx, cond_dict['ALowVLow']))\\n llout_contr = os.path.join(fx_dir, 'low_grad_ss{}_{}'.format(\\n subj, vvar))\\n calc_subt(logfile, '{}+tlrc'.format(mean_outname), ll_file,\\n llout_contr)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1489,"cells":{"query_id":{"kind":"string","value":"dfc568703883c3f5c67fc15a44cb52d9"},"query":{"kind":"string","value":"Implement me! Returns the probability of word given label (i.e., P(word|label)) according to this NB model."},"positive_passages":{"kind":"list like","value":[{"docid":"313e08f48361dc9c32b17ec7c150e237","score":"0.84249485","text":"def p_word_given_label(self, word, label):\r\n count_word=self.class_word_counts[label][word]\r\n total_class_wordcount = self.class_total_word_counts[label]\r\n prob_wgl= count_word/total_class_wordcount\r\n return prob_wgl","title":""}],"string":"[\n {\n \"docid\": \"313e08f48361dc9c32b17ec7c150e237\",\n \"score\": \"0.84249485\",\n \"text\": \"def p_word_given_label(self, word, label):\\r\\n count_word=self.class_word_counts[label][word]\\r\\n total_class_wordcount = self.class_total_word_counts[label]\\r\\n prob_wgl= count_word/total_class_wordcount\\r\\n return prob_wgl\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d187e09e9d2bae1d2e9304a857550a75","score":"0.81052285","text":"def p_word_given_label(vocab, training_data, label):\n \n ''' the formula for computing prob of a word with given label is found on piazza '''\n smooth = 1 # smoothing factor\n word_prob = {}\n vocab_with_label = {}\n # initialize all counts to zero\n for word in vocab:\n vocab_with_label[word] = 0 \n vocab_with_label[None] = 0\n # count num of words with given label\n n = 0\n for data in training_data:\n if data['label'] == label:\n vocab_with_label, n = p_label_helper(data['bow'], vocab_with_label, n)\n for word, n_w in vocab_with_label.items():\n p = (n_w + smooth)/(n + smooth*(len(vocab)+1))\n word_prob[word] = log(p)\n return word_prob","title":""},{"docid":"8ba1d6dcb04805d9f4d5a26bb09c36b8","score":"0.8096108","text":"def get_word_probability(self, label, term):\r\n ###TODO\r\n pass\r\n t=0\r\n if label=='ham':\r\n \r\n return self.hamcnd[term]\r\n else:\r\n \r\n return self.spamcnd[term]","title":""},{"docid":"5121b3112979cfa8e4eb9c10164c6473","score":"0.78066164","text":"def p_word_given_label_and_psuedocount(self, word, label, alpha):\r\n V=len(self.vocab)\r\n total_class_wordcount = self.class_total_word_counts[label]\r\n count_word=self.class_word_counts[label][word]\r\n prob_wgl= (count_word+alpha)/(total_class_wordcount+(V*alpha))\r\n return prob_wgl","title":""},{"docid":"632a96c8e061d4ebd6553a239185f3e5","score":"0.7688731","text":"def prob(self, lang, word):\n return self.models[lang].word_prob(word.lower())","title":""},{"docid":"b0068ed5afed3463f0ed856e8bc230f8","score":"0.74491787","text":"def predict_prob(self, tweet: str, label: str) -> float:\n # Set initial probability to the P(label)\n probability = self.tweets[label] / (self.tweets[\"neutral\"] + self.tweets[\"discrim\"])\n\n other_label = \"discrim\" if label == \"neutral\" else \"neutral\"\n\n # Multiply by P(word|label)\n for word in tweet.split():\n if word in self.probs[label]:\n probability *= self.probs[label][word] / (\n self.probs[label][word] + self.probs[other_label][word]\n )\n\n return probability","title":""},{"docid":"fa2fd4edfefef62a2460c1990c6441be","score":"0.7431324","text":"def P(self, word):\n return self.WORDS[word] / float(self.N)","title":""},{"docid":"7d1ebf405569de7f26417d87cd0e850f","score":"0.7299071","text":"def probabilities(self, word):\n raise NotImplementedError","title":""},{"docid":"ab2b99c2e097578568d545bea28baf0a","score":"0.7263307","text":"def prob(self, word, context, verbose=False):\n context = tuple(context)\n if self._n == 1:\n if not(self._model.SUM_TO_ONE):\n # Smoothing models should do the right thing for unigrams\n # even if they're 'absent'\n return self._model.prob(word)\n else:\n try:\n return self._model.prob(word)\n except Exception:\n raise RuntimeError(\"No probability mass assigned\"\n \"to unigram %s\" % (word))\n if context + (word,) in self._ngrams:\n return self[context].prob(word)\n else:\n alpha = self._alpha(context)\n if alpha > 0:\n if verbose:\n print(\"backing off for %s\" % (context + (word,),))\n return alpha * self._backoff.prob(word, context[1:], verbose)\n else:\n if verbose:\n print(\"no backoff for %s as model doesn't do any smoothing\" % word)\n return alpha","title":""},{"docid":"fbfe989d51f3c49ac6d2f4e4bcd252e5","score":"0.7228096","text":"def calculate_conditional_probability(word, label, y_train, x_train_word_set, mat_contents, word_dict, label_count):\n if word not in word_dict or word_dict[word] < 1:\n probability = 1/(len(word_dict.keys()) + label_count)\n else:\n probability = word_dict[word] / label_count\n\n return probability","title":""},{"docid":"00779c8e0d219c736ac41f5d8b6fab4e","score":"0.7080215","text":"def prob_word_given_class_no(self, classifier_total, dataset_size, smoothing=SMOOTHING):\n self.p_no = (self.label_no+smoothing)/(classifier_total+dataset_size*smoothing)","title":""},{"docid":"c8afd51df636ed180a670ecc507952f9","score":"0.70092267","text":"def calculate_label_prob(self, tokens, sent_index):\n pClass = [0] * len(self.SENTIMENT_LABELS)\n\n # Get total times the sentiment was observed\n cSum = self.sum_vector(self.sentCount)\n\n # Number of words in each sentiment and then normalize. So we get P(s)\n for i in range(len(self.sentCount)):\n pClass[i] = self.sentCount[i] * 1.0 / cSum\n\n p = 1.0\n found_one = False\n for token in tokens:\n if token in self.sentOccurs:\n found_one = True\n probs = self.sentOccurs[token]\n # Calculate P(d/s) where d is word and s is sentiment\n p_word_given_class = probs[sent_index] / (self.sum_vector(probs))\n # Calculate (P(d/s)*P(s))/P(d) for each and keep on multiplying.\n # We are ignoring P(d).\n p = p *(p_word_given_class * pClass[sent_index])\n if found_one:\n return p\n else:\n return 0.0","title":""},{"docid":"1b03bc3f664bfcd0b084f06767ab8d0e","score":"0.69594145","text":"def P(word, N=sum(WORDS.values())):\n return WORDS[word] / N","title":""},{"docid":"d393ccf1768761f230cd90b074ea9c7b","score":"0.6953067","text":"def sampling_prob(word, probs):\n\n return probs[word]","title":""},{"docid":"9185e000dd7b036e6120ff131a6157e0","score":"0.691251","text":"def word_probability(self, word, cat):\n if word not in self.words_cats:\n return 0\n return self.words_cats[word][cat] / sum(self.words_cats[word].values())","title":""},{"docid":"d5bfa9ee1e66ec5ab8e301aa9c0b1eed","score":"0.6859041","text":"def GetLikelihoodProbability(self, tag, word):\n prob = 0.0\n ctagword = self.likelihood[tag][word]\n ctag = sum(self.likelihood[tag].values())\n prob = (ctagword + self.k) / (ctag + (self.k * self.V))\n return float(prob)","title":""},{"docid":"5c6b2847fb4b3a672cee087674a520f6","score":"0.67768896","text":"def predict(self, x):\n words = x.split()\n probability_label_word = [[],[]]\n counter = 0\n for label in self.labels:\n probability_label_word[0].append(label)\n probability_label_word[1].append(math.log(len(self.words_of_type[label])/self.number_of_words))\n for word in words:\n try:\n probability_label_word[1][counter] += self.model[word][label][1]\n except KeyError:\n pass\n counter += 1\n log = probability_label_word[1][0]\n label = probability_label_word[0][0]\n for counter in range(0, len(probability_label_word[1])):\n if probability_label_word[1][counter] > log:\n log = probability_label_word[1][counter]\n label = probability_label_word[0][counter]\n return label","title":""},{"docid":"b19618887ebc416f48568e4927f365d9","score":"0.67686546","text":"def convert_frequency_to_probability(label: str, a: int) -> None:\n words_with_a = self.words[label] + a*self.unique_words\n\n for word, count in self.probs[label].items():\n self.probs[label][word] = (count+a) / words_with_a","title":""},{"docid":"c2ef1f574ebdc7c67fc16aeaf1ad623d","score":"0.6746187","text":"def prob_word_given_class_yes(self, classifier_total, dataset_size, smoothing=SMOOTHING):\n self.p_yes = (self.label_yes+smoothing)/(classifier_total+dataset_size*smoothing)","title":""},{"docid":"4aad9f7311ebe5d537bff74906fd44e2","score":"0.67297155","text":"def bayesWordProb(wordList, wordFreq,totalFeatures, totalCntInClass):\n bayesWordProb = []\n for word in wordList:\n if word in wordFreq.keys():\n count = wordFreq[word]\n else:\n count = 0\n bayesWordProb.append((count+1)/(totalCntInClass + totalFeatures))\n return(bayesWordProb)","title":""},{"docid":"04bc4f1067656c0cb34c4741d61ff2f4","score":"0.67225623","text":"def label_likelihood(label, features, model):\n\n # get unique label values\n u_labs = np.unique(label)\n u_labs = u_labs[u_labs > 0]\n\n parcels = {}\n for lab in u_labs:\n parcels[lab] = np.where(label == lab)[0]\n\n fp = fullProbability(parcels, features, model)\n\n return fp","title":""},{"docid":"f454bab98bf1f83a877f6284ec64899f","score":"0.6689978","text":"def probability(self, sent):\n sent = [word.lower() for word in sent.split()]\n p = 1.0\n for word in sent:\n if self.fdist[word]==0: self.fdist[word] = 1\n p *= self.fdist[word]*1.0 / self.words_num\n return p","title":""},{"docid":"d78347d17c745853bfe689eb49a0f918","score":"0.66369116","text":"def prediction(word):\n # preprocess the text\n sentence = tokenize(word)\n X = bag_of_words(sentence, all_words)\n X = X.reshape(1, X.shape[0])\n X = torch.from_numpy(X).to(device)\n\n # pass it to the model and get the prediction\n output = model(X)\n _, predicted = torch.max(output, dim=1)\n \n # get the tag and probability\n tag = tags[predicted.item()]\n probs = torch.softmax(output, dim=1)\n prob = probs[0][predicted.item()]\n \n # return the tag and the probablity\n return prob, tag","title":""},{"docid":"35f9142919affeb1d8fda9ae8d552522","score":"0.65842927","text":"def __calculate_prob(self, ngram):\n first, last = ngram.split()\n\n last_prob = (self.unigram_counts[last] / self.words_set_size)\n first_word_prob_in_end_of_bigram = self.p_continuation[first] * last_prob\n last_word_prob = self.p_continuation[last] * last_prob\n\n if self.unigram_counts[first] > 0:\n prob = (max(self.bigram_counts[ngram] - self.d, 0) / self.unigram_counts[first]) + self.d * last_word_prob\n else:\n prob = last_word_prob + first_word_prob_in_end_of_bigram\n\n return np.log2(self.coeff + prob)","title":""},{"docid":"5129b303d6eac4519fcb943ff3038f04","score":"0.6561691","text":"def perplexity(lang=\"eng\"):\n pool = ProcessingPool(4)\n unigram_counter, mgram_counter, ngram_counter= pool.map(get_ngram_counter,\n [1,2,3],\n [lang] * 3)\n pool.close()\n pool.join()\n\n total_words = np.sum(np.array(list(unigram_counter.values())))\n print(\"total_words = \", total_words)\n\n ngram_conditionals = get_ngram_conditionals(ngram_counter,\n mgram_counter)\n\n probs = np.power(np.array(list(ngram_conditionals.values()),\n dtype=np.float64),\n -np.array(list(ngram_counter.values()), dtype=np.float64) \\\n / total_words)\n\n print(\"probs shape = \", probs.shape)\n\n PP = (np.prod(probs, dtype=np.float64))\n\n return PP","title":""},{"docid":"3ce49153048196ac3ca171c909c45bf0","score":"0.655377","text":"def out_prob(self, word, tag):\n return self._out.get(tag, {}).get(word, 0.0)","title":""},{"docid":"bcf4f6c6c099778550356e9109679593","score":"0.65441364","text":"def predict(self, X, prob=False):\n results = []\n\n for ind, text in enumerate(X):\n probabilities = dict.fromkeys(self.labels, 0)\n\n for label in probabilities:\n prob = self.labels_count[label] / sum(self.labels_count.values()) if sum(self.labels_count.values()) != 0 else 0\n probabilities[label] = math.log(prob) if prob != 0 else -100000\n\n for word in text.split():\n norm_form = self.morph.parse(word)[0].normal_form\n\n for label in self.labels:\n if norm_form in self.word_probability:\n probabilities[label] += math.log(self.word_probability[norm_form][label])\n\n max_prob = max(probabilities.values())\n\n for label in probabilities:\n if probabilities[label] == max_prob:\n results.append(label)\n break\n\n if prob:\n return list(probabilities.values())\n else:\n return results","title":""},{"docid":"cdc590ee373967c413c3f98a9180c665","score":"0.6528423","text":"def predict(self, words):\n pred = self.model.predict(self.vectorizer.transform([words]))\n conf = self.model.predict_proba(self.vectorizer.transform([words])).max()*100\n confidence = round(conf,2)\n return self.labels[pred[0]], confidence","title":""},{"docid":"7c5e50c5bc2aad59a6dc5a0f5e0b989a","score":"0.65226746","text":"def estimate_prob(target_word, context_words):\n\n target_vector = self.target_matrix[target_word]\n context_prob = 1\n for word in context_words:\n context_vector = self.context_matrix[:, word]\n # Computes dot product between the vectors of target and\n # context word\n dot_product = target_vector.dot(context_vector)\n # Turns the dot product into a probability with the sigmoid\n # function.\n word_prob = 1 / (1 + exp(-dot_product))\n # Keeps a tally of the total probability of the set of context\n # words.\n context_prob = context_prob * word_prob\n\n return context_prob","title":""},{"docid":"eae625487b6d4f491989037422acbe0a","score":"0.65044034","text":"def prob(ngram):\n return ngram_counts[key(ngram)] / corpus_size_f","title":""},{"docid":"dbeb3f4c0122c1b1051edeed043a4aca","score":"0.6467992","text":"def doc_probability(self, doc, cat):\n words = self.get_words(doc)\n weighted_probabilites = [self.weighted_word_probability(word, cat)\n for word in words]\n return reduce(mul, weighted_probabilites, 1)","title":""},{"docid":"17b8358ad971ac3c6093caccb516b5c3","score":"0.64561886","text":"def get_log_word_probability(self, word):\n # take the first letter of the word to get the unigram probability\n first_letter_index = self.letter_to_index(word[0])\n log_unigram_prob = (\n self.log_pi[first_letter_index] if first_letter_index in np.arange(26) else 0\n )\n\n # get all the bigram probabilties for the rest of the characters\n log_bigram_prob = 0\n\n for i in range(len(word) - 1):\n starting_letter_index = self.letter_to_index(word[i])\n ending_letter_index = self.letter_to_index(word[i + 1])\n\n log_bigram_prob += (\n self.log_M[starting_letter_index][ending_letter_index]\n if (starting_letter_index in np.arange(26))\n and (ending_letter_index in np.arange(26))\n else 0\n )\n\n return log_unigram_prob + log_bigram_prob","title":""},{"docid":"576d913a37c1c4a88db10feedcd51316","score":"0.6444651","text":"def get_word_with_probability(text):\n freq_each_words = Counter(text.split()).most_common()\n sum_of_total_char_occ = sum([frequency for (key, frequency) in freq_each_words])\n prob_each_word = {}\n for (key, frequency) in freq_each_words:\n prob_each_word[key] = frequency / sum_of_total_char_occ\n return prob_each_word","title":""},{"docid":"7f8da74242715ea84589937ba0c882f7","score":"0.63837475","text":"def word_probabilities(bag, *words):\n # First create a list of all the probability arrays for each word\n word_arrays = []\n for word in words:\n word_arrays.append(monogram_probabilites(bag, bag.clean_digits(word.upper())))\n\n if len(words) <= 1:\n # No possible bigrams\n return word_arrays\n\n # Now we apply the bigram probability algorithm\n bigram_array = []\n for first, second in zip(word_arrays, word_arrays[1:]):\n # First and second are the np.arrays with classification probabilities\n bigram_array.append(bigram_probabilities(bag, first, second))\n word_probs = bigram_sequence2(bigram_array)\n return word_probs","title":""},{"docid":"a2ec825d65de66cc0b836633f3cf2cfe","score":"0.6362686","text":"def get_label(self, word):\n if word not in self.labels.keys():\n self.labels[word] = len(self.labels) * 0.000001\n return self.labels[word]","title":""},{"docid":"9f0527844dde5fee13c0ecbf768b5cb7","score":"0.6347047","text":"def predict_proba(self, context):\n counts = self.get_counts(context)\n return counts / counts.sum()","title":""},{"docid":"50ed9948e14df7f8e19802006528db18","score":"0.6344372","text":"def extract_probs(label, x):\n\tpys = np.sum(label, axis=0) / float(label.shape[0])\n\tb = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))\n\tunique_array, unique_indices, unique_inverse_x, unique_counts = \\\n\t\tnp.unique(b, return_index=True, return_inverse=True, return_counts=True)\n\tunique_a = x[unique_indices]\n\tb1 = np.ascontiguousarray(unique_a).view(np.dtype((np.void, unique_a.dtype.itemsize * unique_a.shape[1])))\n\tpxs = unique_counts / float(np.sum(unique_counts))\n\tp_y_given_x = []\n\tfor i in range(0, len(unique_array)):\n\t\tindexs = unique_inverse_x == i\n\t\tpy_x_current = np.mean(label[indexs, :], axis=0)\n\t\tp_y_given_x.append(py_x_current)\n\tp_y_given_x = np.array(p_y_given_x).T\n\tb_y = np.ascontiguousarray(label).view(np.dtype((np.void, label.dtype.itemsize * label.shape[1])))\n\tunique_array_y, unique_indices_y, unique_inverse_y, unique_counts_y = \\\n\t\tnp.unique(b_y, return_index=True, return_inverse=True, return_counts=True)\n\tpys1 = unique_counts_y / float(np.sum(unique_counts_y))\n\treturn pys, pys1, p_y_given_x, b1, b, unique_a, unique_inverse_x, unique_inverse_y, pxs","title":""},{"docid":"f8852e3d79844eda09c499889112bb02","score":"0.63442904","text":"def monogram_probabilites(bag, word):\n word_prob_iter = map(lambda x: x[1], bag.raw_label_probabilities(word))\n return np.fromiter(word_prob_iter, np.float64)","title":""},{"docid":"e0c655efb5f561a5ecdf186efdeb8879","score":"0.6339912","text":"def _score_as(self, data, label):\n data_labeled = (data[0], data[1], label)\n prior = (self.prior_prob_pos if label == POSITIVE \n else self.prior_prob_neg)\n counts = self.pos if label == POSITIVE else self.neg\n smoothed_count_of_all_word_occurences = \\\n sum(counts.values()) + len(self.vocab)\n\n prob = math.log(prior)\n for f in self.featurize(data_labeled):\n # NOTE: Ignore words if they are completely unknown\n token = f[0]\n if token not in self.vocab:\n continue\n else: \n try:\n counts[token] # Check if the word is present\n # in this class context\n except KeyError:\n counts[token] = 0 # If not, set its count to 0\n # Perform laplace smoothing if word is unknown in \n # up to one context, with a vocab size = \n # unique tokens for all classes\n f_prob = ((counts[token] + 1) / \n smoothed_count_of_all_word_occurences)\n prob += math.log(f_prob)\n return prob","title":""},{"docid":"29187ed1eb5623b22d963e9bc6f7e42c","score":"0.6323051","text":"def get_probability(self, input_data):\n # Format input and get word-wise probability predictions from the langauge model\n inputs = self.ready_input(input_data)\n probabilities = self.calculate_probabilities(inputs[0])\n prob_array = np.concatenate(probabilities, 1)\n # Mask predictions at <PAD> and <EOS> positions\n if len(inputs) > 1:\n masked_array = prob_array * inputs[1]\n # Mask replaces predicted values with 1.0, thus negating their contribution\n # when computing probability products or sums of log-probability scores\n masked_array[masked_array == 0.0] = 1.0\n else:\n masked_array = np.copy(prob_array)\n total_prob = np.prod(masked_array, axis=1, keepdims=True)\n return total_prob, prob_array, masked_array","title":""},{"docid":"9e30367e4665a1cb0aab29c9e59b2ea9","score":"0.62966555","text":"def p_word_given_topic(W, topic_word_counts, topic_counts, word, topic, beta=0.1):\r\n return (topic_word_counts[topic][word] + beta) / (topic_counts[topic] + W * beta)","title":""},{"docid":"4d451f2f80b16d49dc0bd4f583ce8f88","score":"0.629447","text":"def calc_total_cond_probability(self):\n for known_word in self.unique_words:\n known_word.prob_word_given_class_yes(self.yes_label_words, len(self.unique_words))\n known_word.prob_word_given_class_no(self.no_label_words, len(self.unique_words))","title":""},{"docid":"942b3a8f5f37feb2df290ed916f0e4c7","score":"0.6275826","text":"def bern_predict(doc):\n doc = [*set(preprocess(doc)), ]\n pos_prob = pos_prior\n neg_prob = neg_prior\n for w in vocab:\n if w in doc:\n pos_prob += math.log(bern_pos_likelihood[w])\n neg_prob += math.log(bern_neg_likelihood[w])\n else:\n pos_prob += math.log(1 - bern_pos_likelihood[w])\n neg_prob += math.log(1 - bern_neg_likelihood[w])\n if pos_prob >= neg_prob:\n return 1\n else:\n return 0","title":""},{"docid":"d8a811dda902d55b779c090458854c70","score":"0.626946","text":"def bin_predict(doc):\n doc = [*set(preprocess(doc)), ]\n pos_prob = pos_prior\n neg_prob = neg_prior\n for w in doc:\n if w in vocab:\n pos_prob += bin_pos_likelihood[w]\n neg_prob += bin_neg_likelihood[w]\n\n if pos_prob >= neg_prob:\n return 1\n else:\n return 0","title":""},{"docid":"842c77aebd97055fc7c0388c7ecb1aab","score":"0.62572813","text":"def p_words(self, words):\n return np.float64(Corrector.product(self.p(w) for w in words))","title":""},{"docid":"0b45a47255d25a3ee3861ce59e8a6594","score":"0.6256548","text":"def score(self, word, context):\n trigram = tuple((context[0], context[1], word))\n return self.model.prob(trigram)","title":""},{"docid":"d7a1a5547d6cdc09203c6faddfb2a0c8","score":"0.6252507","text":"def get_probs(corpus: str):\n word_counts = Counter(re.findall(r'\\w+', corpus.lower()))\n total = sum(word_counts.values())\n probs = {w: (count / total) for w, count in word_counts.items()}\n return probs","title":""},{"docid":"122c40b395aa1dcea7aeb84aff726feb","score":"0.62225693","text":"def predict_proba(self, context, last_letter):\n if self.order:\n local = context[-self.order:]\n else:\n local = ''\n missed_freq = self.missed_counter_[local][last_letter] + self.smoothing_missed\n total_freq = self.total_counter_[local][last_letter] + self.smoothing_total\n return missed_freq / total_freq","title":""},{"docid":"4137f6208c676e2744a2b9c68e9ffa86","score":"0.6214035","text":"def prob(self, x, y):\n # check length of sentece and tagging\n assert len(x) == len(y)\n\n q_prob = self.tag_prob(y)\n e_prob = 1 \n for word, tag in zip(x, y):\n e_prob *= self.out_prob(word, tag)\n\n return q_prob * e_prob","title":""},{"docid":"075a174038811ab23f8fe76bf2228c2b","score":"0.62063336","text":"def compute_prob_not_sentence(sentence):\n doc = nlp_stanford.value(sentence)\n verb_count = 0\n word_count = 0\n for sent in doc.sentences:\n for word in sent.words:\n word_count += 1\n if word.upos in [\"VERB\", \"AUX\"]: # Can be customized\n if word.xpos in ['VBG']:\n verb_count += 0.5\n else:\n verb_count += 1\n return 1 - verb_count / word_count","title":""},{"docid":"ba1d070528ee2b08298c5de67ba823f2","score":"0.6200574","text":"def predict(doc):\n doc = preprocess(doc)\n pos_prob = pos_prior\n neg_prob = neg_prior\n for w in doc:\n if w in vocab:\n pos_prob += pos_likelihood[w]\n neg_prob += neg_likelihood[w]\n\n if pos_prob >= neg_prob:\n return 1\n else:\n return 0","title":""},{"docid":"5b5e0c14f54ed2129a94d114c7d9ed0b","score":"0.6184493","text":"def docprob(self, doc, cat):\n features = self.getFeatures(doc)\n return reduce(lambda f: self.wfprob(f, cat, self.fprob), features, 1.0)","title":""},{"docid":"e7e750095f981851bc82e5433064cce0","score":"0.61381","text":"def posprobloss(labelposprob):\n # print \"probmatrix\", labelposprob ### test\n Nclass = labelposprob.shape[0]\n labelposloss = [[-1 for pos in range(Nclass)] for label in range(Nclass)]\n for label in range(Nclass):\n for pos in range(Nclass):\n loss = 0.0\n for prob_pos in range(Nclass):\n loss += (abs(prob_pos - pos)*labelposprob[label,prob_pos])\n labelposloss[label][pos] = loss\n # print \"lossmatrix\",labelposloss ### test\n return np.array(labelposloss, dtype=np.float32)","title":""},{"docid":"ec396296ead13266b331b7eb23149224","score":"0.6111507","text":"def predict_next_word(self, features, params, precomputed):\n inputs = features[\"inputs\"]\n probs_num = precomputed\n probs_denom = tf.reduce_sum(probs_num, axis=-1)\n inputs_weights = common_utils.weights_nonzero(inputs) \n factors = tf.expand_dims(common_utils.safe_div(inputs_weights, probs_denom), -1)\n log_probs_sum = common_utils.safe_log(tf.reduce_sum(factors * probs_num, axis=-2))\n # log_probs_sum has shape [batch_size, target_vocab_size]\n return log_probs_sum","title":""},{"docid":"01141c3243d553d6f0edf18ff7c8da44","score":"0.61063594","text":"def prob(self, word1, word2):\n\t\tw1 = self.split(word1, \"pos\")\n\t\tw2 = self.split(word2, \"pos\")\n\t\tcount_word2 = len(self.search(self.corpus, self.split(word2, \"word\")))\n\t\tfilter = self.search(self.corpus, w1)\n\t\tcount_w1 = self.get_subsentences(filter, w1)\n\t\tcount_w1_w2 = self.search(count_w1, w2)\n\n\n\t\tprob = len(count_w1_w2) / float(len(count_w1))\n\t\tif w1 == w2: prob /= 2\n\n\t\treturn prob, count_word2","title":""},{"docid":"1dd85b07e6005e6aec24d2ce8f390a3e","score":"0.61016536","text":"def logprob(self, word, context, verbose=False):\n\n return -log(self.prob(word, context, verbose), 2)","title":""},{"docid":"af544f167a9e6998ceed3bf2ef2bf5e7","score":"0.609712","text":"def get_probability(self, class_label, symbolic_x, symbolic_y, l_size, u_size):\n query_string = '{}({},{})'.format(class_label, symbolic_x, symbolic_y)\n l_term = math.pow(self[symbolic_x], l_size) / math.factorial(l_size)\n u_term = math.pow(self[symbolic_y], u_size)\n return (l_term * u_term) / self.get(query_string)","title":""},{"docid":"9c03952bc4ada557843cbf378bcb4219","score":"0.6096188","text":"def _predict_proba(self, X):\n bag = self._transform_words(X)\n return self.clf.predict_proba(bag)","title":""},{"docid":"e075d6a3d5e87ab9d3b4ae151617eb9d","score":"0.60909724","text":"def compute_image_label_prediction(has_bbox, nn_output_class, y_true_class, P, class_nr):\n prob = tf.where(has_bbox, compute_image_label_from_localization_NORM(nn_output_class, y_true_class, P, class_nr),\n compute_image_label_in_classification_NORM(nn_output_class, P, class_nr))\n return prob","title":""},{"docid":"a0bc9fcb5b44c68e926801f543a24994","score":"0.60621995","text":"def prob(self, doc, cat):\n catprob = self.catcount(cat) / self.totalcount()\n docprob = self.docprob(doc, cat)\n return docprob * catprob","title":""},{"docid":"2b894a21ebb10aff2e86f323208d27cc","score":"0.60577583","text":"def log_likelihood(self, bow, label, alpha):\r\n likelihood_log=0\r\n for k in bow.keys():\r\n p_ofwordgivenclass = self.p_word_given_label_and_psuedocount(k,label,alpha)\r\n likelihood_log+= math.log(p_ofwordgivenclass)\r\n \r\n return likelihood_log","title":""},{"docid":"68390bba66ad5b888350ac0afb1782c9","score":"0.6057332","text":"def doc_prob(self, features, model):\n prob_0 = 1.0\n prob_1 = 1.0\n for feature in features:\n if feature not in model:\n continue\n prob_0 *= model[feature][0]\n prob_1 *= model[feature][1]\n return prob_0, prob_1","title":""},{"docid":"0864b11217c6b762f665504db485a56b","score":"0.6043003","text":"def score(self, doc, label):\n\t\tscore = 0\n\t\tfor element in doc:\n\t\t\tscore += doc[element] * self.weights[label][element]\n\t\tscore += self.weights[label]['g_bias']\n\t\treturn score","title":""},{"docid":"4350bfc06b26e0db8f21c6856d8349b2","score":"0.6033662","text":"def __init__(self, label):\n self.label = label\n self.prob = 1.0\n self.sequenceProb = 1.0","title":""},{"docid":"08d70aba3c543b3da055bee509c2bd03","score":"0.6029287","text":"def tag_prob(self, y):\n\n n = self._n\n y = addmarks(y, n)\n tag_length = len(y)\n\n tag_prob = 1\n for i in range(n - 1, tag_length):\n tag = y[i]\n prev_tags = y[i - n + 1: i]\n tag_prob *= self.trans_prob(tag, tuple(prev_tags))\n\n return tag_prob","title":""},{"docid":"b5d7e4defa4fec99500da3d02258a0c1","score":"0.60191256","text":"def prob(self, counts, name=\"prob\"):\n return super(DirichletMultinomial, self).prob(counts, name=name)","title":""},{"docid":"8d4e58c82fb9119cd6660c21ecd2a9be","score":"0.6001409","text":"def joint_prob(single_p, label, alphabet):\n\n p = [np.log(marginal[alphabet.index(c)]) for (c, marginal) in zip(label, single_p)]\n\n return np.sum(p)","title":""},{"docid":"49b876f4a66abe3a98c97cd357e7aafc","score":"0.5977567","text":"def score(self, sentence):\n # TODO your code here\n score = 0\n probability = 0\n for token in sentence: # iterate over words in the sentence\n if token in self.word_uni:\n\n score+= m.log(self.word_uni[token]+1)\n score-=m.log(self.sum_word)\n else:\n #self.word_uni['unk'] = self.word_uni['unk'] + 1\n score+=m.log(1);\n score-=m.log(self.sum_word)\n\n return score","title":""},{"docid":"f9efa75900c4788f6a5c4e70002c7b6c","score":"0.597637","text":"def get_target_prob(self,leaf_label) :\n ret = np.zeros((len(self.synset_list)*2,),np.float32)\n ret[0:len(self.synset_list)] = 1\n \n parents = Set()\n self._get_parents(self.nodes_dict[self.synset_list[leaf_label]],parents)\n \n for node in parents :\n ret[ self.label_to_synset[node.wnid] ] = 0\n ret[ len(self.synset_list)+self.label_to_synset[node.wnid] ] = 1\n return ret","title":""},{"docid":"026fbbfcbe71290249b35e6f379ce4ca","score":"0.5939614","text":"def pos_stat(self):\n result = 0\n for feature in self.training.features:\n if feature.classlabel == 1:\n result += 1\n self.prob_pos = result / len(self.training.features)","title":""},{"docid":"0a892fe7be7fe3199c1e407c080085a8","score":"0.5928033","text":"def prob(self, w):\n self.c_uw = {}\n for i in range(1,7):\n if self.prev_word[i] in self.counts[i]:\n self.c_uw[i] = self.counts[i][self.prev_word[i]][w]\n else:\n self.c_uw[i] = 0\n\n prob = {}\n prob[0] = max(0, self.words[w])/len(self.words)\n for i in range(1,7):\n if self.c_u[i] > 0:\n prob[i] = max(0, self.c_uw[i]-self.d[i])/self.c_u[i] + self.n_u[i]*self.d[i]/self.c_u[i] * prob[i-1]\n else:\n prob[i] = 0\n return prob[6]","title":""},{"docid":"fabd7ce398aad20fb43070fb9f9e5ec2","score":"0.58983773","text":"def calculate_perplexity(sentence, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\n n = len(list(n_gram_counts.keys())[0])\n sentence = [\"<s>\"] * n + sentence + [\"<e>\"]\n sentence = tuple(sentence)\n N = len(sentence)\n product_pi = 1.0\n\n for t in range(n, N):\n n_gram = sentence[t-n:t]\n word = sentence[t]\n probability = ung.estimate_probability(word, n_gram, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=k)\n product_pi *= 1/probability\n\n perplexity = product_pi**(1/N)\n\n return perplexity","title":""},{"docid":"4a9df43609919eb5928ec4b4cecfefa4","score":"0.5893399","text":"def prob_y(self, c):\n count = 0\n for e in self.data:\n if e.label == c:\n count += 1\n return count / self.n","title":""},{"docid":"cf2ba9c59dc0a45be9feef0f3cf2ab24","score":"0.58848995","text":"def compute_p_word_given_class(data_paths, vocab_size):\n \n ###########################################################\n # Implement your solution here\n ###########################################################\n\n count_words = {}\n total_num_words = 0\n \n for path in data_paths:\n \twords = get_words(open_file(path))\n \ttotal_num_words += len(words)\n \tfor word in words:\n \t\tif word in count_words:\n \t\t\tcount_words[word] += 1\n \t\telse:\n \t\t\tcount_words[word] = 1\n\n p_word_given_class = {}\n\n for word in count_words:\n \tprob = (count_words[word] + 1) / (total_num_words + vocab_size + 1)\n \tp_word_given_class[word] = prob\n \t#print(word + \" \", end=\"\")\n \t#print(p_word_given_class[word])\n\n p_word_given_class[\"UNKNOWN_WORD\"] = 1 / (total_num_words + vocab_size + 1)\n #print(p_word_given_class[\"UNKNOWN_WORD\"])\n\n return p_word_given_class","title":""},{"docid":"93c7a7a9545f9f09827f5c81ee9b95d8","score":"0.5867969","text":"def calculateFreq(self, words):\n nb = len(words)\n prob = {w: (words.count(w) / nb) for w in words}\n return prob","title":""},{"docid":"656aeed0c82e346d89ec5d0ceb643967","score":"0.5862735","text":"def modeling(wordcount_dict, one_doc_length, background_model, query_terms, word_instance):\n wordprob_dict = smoothing_unigram(wordcount_dict, one_doc_length, query_terms, 0.00001)\n #word_instance = len(list(wordcount_dict.keys()))\n score = 1\n for term in query_terms:\n if term in wordprob_dict:\n interpolate_prob = interpolation(term, wordprob_dict[term], background_model[term], 0.5)\n else:\n prob = lidston_smoothing(0, one_doc_length, word_instance, 0.00001)\n interpolate_prob = interpolation(term, prob, background_model[term], 0.5)\n score = score * interpolate_prob\n return score","title":""},{"docid":"1f02895638addd8a36337d08805ac1d6","score":"0.58622664","text":"def __get_word_prob(self, word_prob_map, word):\n if word in word_prob_map:\n return word_prob_map[word]\n return self.correction","title":""},{"docid":"bbcbe6dcb07bf03b1435fd418ec430f1","score":"0.5844216","text":"def score(self, sentence):\n # TODO your code here\n # <s> and </s> are already part of the corpus\n result = 0.0\n #self.V = len(self.unigram) #vocabulary of v\n #self.N = sum(self.unigram.values()) #total number of unigrams\n #print 'here ',self.N, self.V\n #print sentence\n for word in sentence:\n #word = data.word\n prob = float((self.unigram.get(word,0)+1))/(self.N+self.V) #convert to float!!\n #print word, prob\n result += math.log(prob)\n #print word , prob\n #print sentence, result\n return result","title":""},{"docid":"8a9c9ce27ca9584297dcd1d2c8f8aaad","score":"0.58417386","text":"def get_predict_label(pos_prob):\n # threshold should be (1, 0.5)\n neu_threshold = 0.5\n if pos_prob >= neu_threshold:\n label, key = 1, \"porn\"\n else:\n label, key = 0, \"not_porn\"\n return label, key","title":""},{"docid":"0a868a1fe3b47500b69b4f0c8f5343f1","score":"0.58333695","text":"def display_word_tag_probabilities(word_to_display):\n\n\tprint(\"p(\" + word_to_display + \" | t) for all tags t:\")\n\tfor prev_tag in p_word_given_t:\n\t\tfor word in p_word_given_t[prev_tag]:\n\t\t\tif word == word_to_display:\n\t\t\t\tprint(\" p(\" + word + \" | \" + prev_tag + \" ) = \" + str(p_word_given_t[prev_tag][word]))","title":""},{"docid":"f92b6b17e3726353c4ad5b2f5de5a252","score":"0.5830894","text":"def likelihood_ratio(self, word, alpha):\r\n numerator = 0\r\n denominator = 0\r\n likelihood_ratio = 0\r\n numerator = self.p_word_given_label_and_psuedocount(word, POS_LABEL, alpha)\r\n denominator = self.p_word_given_label_and_psuedocount(word, NEG_LABEL, alpha)\r\n likelihood_ratio = numerator/denominator\r\n return likelihood_ratio","title":""},{"docid":"202a2a32fde2b0ac0713edce085b8f2a","score":"0.582097","text":"def nb_classifier(self, doc):\n sum_c = {} # Initialise sum dictionary to hold sentence predicted values.\n\n # loop through priors and words in sentences to calculate the probability sums.\n for cls, lp in self.log_priors.items():\n # print('{} -> {}'.format(cls, lp))\n sum_c[cls] = lp\n for word in doc:\n if word in self.vocabulary:\n sum_c[cls] += self.vocabulary[word]['probability'][cls]\n\n return max(zip(sum_c.values(), sum_c.keys()))","title":""},{"docid":"183a858653f5b8827b2dbe8c6a6555bb","score":"0.5816077","text":"def prob(self, predictions, tags, words): \n #softmax = self.rnn.soft_max(numpy.asarray(contextwinbackwards(words, s['win'])).astype('int32')) #single array n_words * n_classes\n \n \n #try the restart incremental setting, where we access the whole sequence each time?\n #=======================================================================\n # prob_tagsequence = self.cpd_tags[\"START\"].prob(\"<f/>\") * rnn.softmax(words[0:1]).prob(\"<f/>\") * \\\n # self.cpd_tags[\"<f/>\"].prob(\"<rm-1/><rpMid/>\") * rnn.softmax(words[0:2]).prob(\"<rm-1/><rpMid/>\") * \\\n # self.cpd_tags[\"<rm-1/><rpMid/>\"].prob(\"<f/>\") * rnn.argmax(words[0:3]).prob(\"<f/>\")* \\\n # self.cpd_tags[\"<f/>\"].prob(\"<f/>\") * rnn.argmax(words[0:4]).prob(\"<f/>\") * \\\n # self.cpd_tags[\"<f/>\"].prob(\"END\")\n #=======================================================================\n \n \n #total_probs = 0.0 #always start with the first word\n #for i in range(0,len(words)+1):\n # if i == 0:\n # total_probs += log(self.cpd_tags[\"s\"].prob(tags[0]))\n # elif i == len(words):\n # total_probs += log(self.cpd_tags[i-1].prob(tags[\"se\"]))\n # break\n # else:\n # total_probs += log(self.cpd_tags[i-1].prob(tags[i]))\n # total_probs += log(softmax[i][self.tagToIndexDict[words[i]]])\n #print \"Test: the log probability of the tag sequence 'START f rm-1rpMid f f END' for 'I I like john' is:\", total_probs","title":""},{"docid":"c96289eed82abaae55cbc6f9d28a1939","score":"0.581517","text":"def probability(html):\n global default_classifier\n if default_classifier is None:\n default_classifier = Soft404Classifier()\n return default_classifier.predict(html)","title":""},{"docid":"377887987fad5a7fc3674e2c6cd8b00d","score":"0.5810774","text":"def calculate_final_probability(input_string, y_train, x_train_word_set, mat_contents, positive_word_dict, negative_word_dict, positive_count, negative_count):\n word_list = input_string.split(\" \")\n\n file_count = len(y_train)\n counter = collections.Counter(y_train)\n prior_pos = counter[1] / file_count\n prior_neg = counter[0] / file_count\n\n p1 = prior_pos\n p2 = prior_neg\n for word in word_list:\n p1 = p1 * calculate_conditional_probability(word, 0, y_train, x_train_word_set, mat_contents, negative_word_dict, negative_count)\n p2 = p2 * calculate_conditional_probability(word, 1, y_train, x_train_word_set, mat_contents, positive_word_dict, positive_count)\n\n return p1, p2","title":""},{"docid":"58112f8a25c3e069c17d46675665a863","score":"0.5802732","text":"def predict_word_dataset(word, dataset):\n # Get weight vector for word\n w = u.get_weight_vector(word, False)\n # bias is first value in weight vector\n b = w[0]\n # then update w to include everything but the bias\n w = w[1:]\n \n # Predict presence of word in each sentence, saving results\n results = []\n for i, x in enumerate(dataset):\n # predict result\n y = u.predict(x, w, b)\n # add to results either 0 or 1 depending on y value\n results.append(1 if y > 0.5 else 0)\n \n return results","title":""},{"docid":"910a1fe6854380b6dfc96032beb2788d","score":"0.5786453","text":"def rejected_given_word(self, word):\n\n if self.rprobs.has_key(word):\n return self.rprobs[word]\n else:\n # only calculate odds if we have samples of both good and rejected\n # text in the corpus.\n if self.good and self.rejected:\n p = (self.bad_count(word) / self.nrejected) * self.prejected\n pw = p + (self.good_count(word) / self.ngood)\n if p == 0 and pw == 0:\n #most data is from rejected emails, so a new word is slightly more\n #likely to be relevant.\n prob = .4 \n else:\n prob = max(min(p/pw, .99), .01)\n self.rprobs[word] = prob\n return prob\n else:\n return 0","title":""},{"docid":"215f4292c560c82979863d9fdb5cbceb","score":"0.57812506","text":"def sentence_probability(self, sentence: str):\n return get_sentence_probability(sentence, self.session, self.m, self.fasttext_model, self.max_word_length,\n self.char_vocab,\n self.rnn_state, self.word_vocab)","title":""},{"docid":"303e1d9618ca81be97da49ca53d80785","score":"0.5766169","text":"def class_prob(labels):\n\tresult = dict(zip(set(labels), [0] * len(set(labels))))\n\tfor label in labels:\n\t\tresult[label] = result[label] + 1\n\tfor label, val in result.items():\n\t\tresult[label] = val / float(len(labels))\n\treturn result","title":""},{"docid":"36705d8e76dcf7c8361126a736943ec9","score":"0.57533973","text":"def prob(self, given, group_ndims=0, name=None):\n with tf.name_scope(\n name, default_name=get_default_scope_name('prob', self)):\n return tf.exp(self.log_prob(given, group_ndims=group_ndims))","title":""},{"docid":"b4417966ec072994616b9c6f165a5285","score":"0.574045","text":"def get_replace_prob(self, all_words):\n cur_tf_idf = collections.defaultdict(int)\n for word in all_words:\n cur_tf_idf[word] += 1. / len(all_words) * self.idf[word]\n replace_prob = []\n for word in all_words:\n replace_prob += [cur_tf_idf[word]]\n replace_prob = np.array(replace_prob)\n replace_prob = np.max(replace_prob) - replace_prob\n replace_prob = (replace_prob / replace_prob.sum() *\n self.token_prob * len(all_words))\n return replace_prob","title":""},{"docid":"748e2e8ab9ee86c2e685a73f08369c33","score":"0.57289773","text":"def score(self, word, context):\n context = self.check_context(context)\n\n return self.ngrams[context].freq(word)","title":""},{"docid":"e5dedcbcd15442b47c230b94f2646cd0","score":"0.57187676","text":"def get_prior(label_column):\n\t\n\tcounts = label_column.value_counts()\n\tprobabilities = []\n\n\tprobabilities.append(counts['4top']/sum(counts))\n\tprobabilities.append(counts['ttbar']/sum(counts))\n\tprobabilities.append(counts['ttbarHiggs']/sum(counts))\n\tprobabilities.append(counts['ttbarW']/sum(counts))\n\tprobabilities.append(counts['ttbarZ']/sum(counts))\n\n\treturn np.array(probabilities)","title":""},{"docid":"137b0f0e8e71d5f9d458e370f8e865e2","score":"0.57169247","text":"def joint_prob(sentence, A, B):\n p = 1 # joint log prob. of words and tags\n\n #TODO complete the code\n global START,END, UNK, allTagCounts, perWordTagCounts, transitionCounts, emissionCounts, num_of_sentences\n V = len(perWordTagCounts)\n for i in range(len(sentence)):\n if i == 0:\n word, tag = sentence[i]\n try:\n A_prob = pow(10, A[(START, tag)])\n except KeyError:\n A_prob = 1/(num_of_sentences + V)\n try:\n B_prob = pow(10, B[(tag, word)])\n except KeyError:\n tag_counts = 0 if tag not in allTagCounts else allTagCounts[tag]\n B_prob = 1/(tag_counts + V)\n p *= A_prob * B_prob\n else:\n pre_word, pre_tag = sentence[i - 1]\n curr_word, curr_tag = sentence[i]\n try:\n A_prob = pow(10, A[(pre_tag, curr_tag)])\n except KeyError:\n pre_tag_counts = 0 if pre_tag not in allTagCounts else allTagCounts[pre_tag]\n A_prob = 1/(pre_tag_counts + V)\n try:\n B_prob = pow(10, B[(curr_tag, curr_word)])\n except KeyError:\n curr_tag_counts = 0 if curr_tag not in allTagCounts else allTagCounts[curr_tag]\n B_prob = 1/(curr_tag_counts + V)\n p *= A_prob * B_prob\n try:\n last_word, last_tag = sentence[-1]\n p *= pow(10, A[(last_tag, END)])\n except KeyError:\n p *= 1 / (num_of_sentences + V)\n p = log(p, 10)\n\n assert isfinite(p) and p<0 # Should be negative. Think why!\n return p","title":""},{"docid":"542dfe70150f3b983d868bb242363a01","score":"0.5712137","text":"def calculate_probability(self, probability_yes, probability_no):\n # priors\n combined_word_probability_yes = math.log(probability_yes, 10)\n combined_word_probability_no = math.log(probability_no, 10)\n\n for word in self.word_array:\n # this check is needed for filtered vocabulary;\n # which sets the prob of filtered words to \"filtered\"\n if isinstance(word.p_yes, float) and isinstance(word.p_no, float):\n combined_word_probability_yes += math.log(word.p_yes, 10)\n combined_word_probability_no += math.log(word.p_no, 10)\n\n self.p_yes = combined_word_probability_yes\n self.p_no = combined_word_probability_no","title":""},{"docid":"0045c1d20903e902af3c5d5abb75ba06","score":"0.57105845","text":"def perplexity(self, text):\n return pow(2.0, self.entropy(text))","title":""},{"docid":"1513e8b47451cc2e9eccc3e835d03e8b","score":"0.57052964","text":"def get_sentence_log_probability(self, sentence):\n # split the sentence into tokens\n tokens = sentence.split()\n # get the probability of a sentence\n log_sentence_prob = sum(\n [self.get_log_word_probability(token) for token in tokens]\n )\n return log_sentence_prob","title":""},{"docid":"516282294b220640d884ff0ffb5ea64d","score":"0.57037413","text":"def classify(self, bag):\n prob_pos = self.prob_pos\n prob_neg = self.prob_neg\n sumpos = self.sumpos\n sumneg = self.sumneg\n prob_x_neg, prob_x_pos = 1, 1\n for word in bag:\n prob_cur_pos = 0.0\n prob_cur_neg = 0.0\n if word in self.train_pos:\n prob_cur_pos = math.log10(float(self.train_pos[word]+self.lambda_val) / (sumpos + len(self.big_bag)))\n elif word in self.big_bag:\n prob_cur_pos = math.log10(float(self.lambda_val) / (sumpos + len(self.big_bag)))\n if word in self.train_neg:\n prob_cur_neg = math.log10(float(self.train_neg[word]+self.lambda_val) / (sumneg + len(self.big_bag)))\n elif word in self.big_bag:\n prob_cur_neg = math.log10(float(self.lambda_val) / (sumneg + len(self.big_bag)))\n #if math.fabs(self.listP[word]) < math.fabs(prob_cur_pos): self.listP[word] = prob_cur_pos\n #if math.fabs(self.listN[word]) < math.fabs(prob_cur_neg): self.listN[word] = prob_cur_neg\n prob_x_pos += prob_cur_pos\n prob_x_neg += prob_cur_neg\n pos = math.log10(prob_pos) + prob_x_pos\n neg = math.log10(prob_neg) + prob_x_neg\n self.temp1 = prob_pos\n self.temp2 = prob_neg\n self.temp3 += pos\n self.temp4 += neg\n if pos > neg:\n #print \"positive\\t\"+str(pos)\n return \"positive\"\n else: \n #print \"negative\\t\"+str(neg)\n return \"negative\"","title":""},{"docid":"0f7dacd27fdb7082fc98d4831c0d0f29","score":"0.57006943","text":"def predict_proba(self, X):\n check_is_fitted(self)\n X = check_array(X)\n votes_per_example = self.knn_.predict_proba(X)\n\n if len(self.missing_label_indices_) > 0:\n for i in self.missing_label_indices_:\n new_col = np.zeros(votes_per_example.shape[0])\n votes_per_example = np.insert(votes_per_example, i, new_col, axis=1)\n\n return votes_per_example","title":""},{"docid":"11c27e9ec76f68082f3b019ebe3f180d","score":"0.5699927","text":"def predict_all(self, w, b):\n # create a tensor of probabilities - dimensions (nof_subjects, nof_predicates, nof_objects)\n probs = np.zeros((len(self.relations_embed), b.shape[0], len(self.relations_embed)))\n\n # for each subject and object - calc probability per predicate\n for i in range(len(self.relations_embed)):\n probs[i, :, :] = np.dot(w, self.relations_embed[i].T) + b\n\n return probs","title":""},{"docid":"4ec43497555c0023a8d98615618f739e","score":"0.5698885","text":"def pred(self, model, phrase, librarian):\n phrase_ = self.vectorise(nltk.tokenize.word_tokenize(phrase), librarian)\n x = model.predict([phrase_])\n\n return x[0]","title":""}],"string":"[\n {\n \"docid\": \"d187e09e9d2bae1d2e9304a857550a75\",\n \"score\": \"0.81052285\",\n \"text\": \"def p_word_given_label(vocab, training_data, label):\\n \\n ''' the formula for computing prob of a word with given label is found on piazza '''\\n smooth = 1 # smoothing factor\\n word_prob = {}\\n vocab_with_label = {}\\n # initialize all counts to zero\\n for word in vocab:\\n vocab_with_label[word] = 0 \\n vocab_with_label[None] = 0\\n # count num of words with given label\\n n = 0\\n for data in training_data:\\n if data['label'] == label:\\n vocab_with_label, n = p_label_helper(data['bow'], vocab_with_label, n)\\n for word, n_w in vocab_with_label.items():\\n p = (n_w + smooth)/(n + smooth*(len(vocab)+1))\\n word_prob[word] = log(p)\\n return word_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba1d6dcb04805d9f4d5a26bb09c36b8\",\n \"score\": \"0.8096108\",\n \"text\": \"def get_word_probability(self, label, term):\\r\\n ###TODO\\r\\n pass\\r\\n t=0\\r\\n if label=='ham':\\r\\n \\r\\n return self.hamcnd[term]\\r\\n else:\\r\\n \\r\\n return self.spamcnd[term]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5121b3112979cfa8e4eb9c10164c6473\",\n \"score\": \"0.78066164\",\n \"text\": \"def p_word_given_label_and_psuedocount(self, word, label, alpha):\\r\\n V=len(self.vocab)\\r\\n total_class_wordcount = self.class_total_word_counts[label]\\r\\n count_word=self.class_word_counts[label][word]\\r\\n prob_wgl= (count_word+alpha)/(total_class_wordcount+(V*alpha))\\r\\n return prob_wgl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632a96c8e061d4ebd6553a239185f3e5\",\n \"score\": \"0.7688731\",\n \"text\": \"def prob(self, lang, word):\\n return self.models[lang].word_prob(word.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0068ed5afed3463f0ed856e8bc230f8\",\n \"score\": \"0.74491787\",\n \"text\": \"def predict_prob(self, tweet: str, label: str) -> float:\\n # Set initial probability to the P(label)\\n probability = self.tweets[label] / (self.tweets[\\\"neutral\\\"] + self.tweets[\\\"discrim\\\"])\\n\\n other_label = \\\"discrim\\\" if label == \\\"neutral\\\" else \\\"neutral\\\"\\n\\n # Multiply by P(word|label)\\n for word in tweet.split():\\n if word in self.probs[label]:\\n probability *= self.probs[label][word] / (\\n self.probs[label][word] + self.probs[other_label][word]\\n )\\n\\n return probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2fd4edfefef62a2460c1990c6441be\",\n \"score\": \"0.7431324\",\n \"text\": \"def P(self, word):\\n return self.WORDS[word] / float(self.N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d1ebf405569de7f26417d87cd0e850f\",\n \"score\": \"0.7299071\",\n \"text\": \"def probabilities(self, word):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2b99c2e097578568d545bea28baf0a\",\n \"score\": \"0.7263307\",\n \"text\": \"def prob(self, word, context, verbose=False):\\n context = tuple(context)\\n if self._n == 1:\\n if not(self._model.SUM_TO_ONE):\\n # Smoothing models should do the right thing for unigrams\\n # even if they're 'absent'\\n return self._model.prob(word)\\n else:\\n try:\\n return self._model.prob(word)\\n except Exception:\\n raise RuntimeError(\\\"No probability mass assigned\\\"\\n \\\"to unigram %s\\\" % (word))\\n if context + (word,) in self._ngrams:\\n return self[context].prob(word)\\n else:\\n alpha = self._alpha(context)\\n if alpha > 0:\\n if verbose:\\n print(\\\"backing off for %s\\\" % (context + (word,),))\\n return alpha * self._backoff.prob(word, context[1:], verbose)\\n else:\\n if verbose:\\n print(\\\"no backoff for %s as model doesn't do any smoothing\\\" % word)\\n return alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfe989d51f3c49ac6d2f4e4bcd252e5\",\n \"score\": \"0.7228096\",\n \"text\": \"def calculate_conditional_probability(word, label, y_train, x_train_word_set, mat_contents, word_dict, label_count):\\n if word not in word_dict or word_dict[word] < 1:\\n probability = 1/(len(word_dict.keys()) + label_count)\\n else:\\n probability = word_dict[word] / label_count\\n\\n return probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00779c8e0d219c736ac41f5d8b6fab4e\",\n \"score\": \"0.7080215\",\n \"text\": \"def prob_word_given_class_no(self, classifier_total, dataset_size, smoothing=SMOOTHING):\\n self.p_no = (self.label_no+smoothing)/(classifier_total+dataset_size*smoothing)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8afd51df636ed180a670ecc507952f9\",\n \"score\": \"0.70092267\",\n \"text\": \"def calculate_label_prob(self, tokens, sent_index):\\n pClass = [0] * len(self.SENTIMENT_LABELS)\\n\\n # Get total times the sentiment was observed\\n cSum = self.sum_vector(self.sentCount)\\n\\n # Number of words in each sentiment and then normalize. So we get P(s)\\n for i in range(len(self.sentCount)):\\n pClass[i] = self.sentCount[i] * 1.0 / cSum\\n\\n p = 1.0\\n found_one = False\\n for token in tokens:\\n if token in self.sentOccurs:\\n found_one = True\\n probs = self.sentOccurs[token]\\n # Calculate P(d/s) where d is word and s is sentiment\\n p_word_given_class = probs[sent_index] / (self.sum_vector(probs))\\n # Calculate (P(d/s)*P(s))/P(d) for each and keep on multiplying.\\n # We are ignoring P(d).\\n p = p *(p_word_given_class * pClass[sent_index])\\n if found_one:\\n return p\\n else:\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b03bc3f664bfcd0b084f06767ab8d0e\",\n \"score\": \"0.69594145\",\n \"text\": \"def P(word, N=sum(WORDS.values())):\\n return WORDS[word] / N\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d393ccf1768761f230cd90b074ea9c7b\",\n \"score\": \"0.6953067\",\n \"text\": \"def sampling_prob(word, probs):\\n\\n return probs[word]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9185e000dd7b036e6120ff131a6157e0\",\n \"score\": \"0.691251\",\n \"text\": \"def word_probability(self, word, cat):\\n if word not in self.words_cats:\\n return 0\\n return self.words_cats[word][cat] / sum(self.words_cats[word].values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5bfa9ee1e66ec5ab8e301aa9c0b1eed\",\n \"score\": \"0.6859041\",\n \"text\": \"def GetLikelihoodProbability(self, tag, word):\\n prob = 0.0\\n ctagword = self.likelihood[tag][word]\\n ctag = sum(self.likelihood[tag].values())\\n prob = (ctagword + self.k) / (ctag + (self.k * self.V))\\n return float(prob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6b2847fb4b3a672cee087674a520f6\",\n \"score\": \"0.67768896\",\n \"text\": \"def predict(self, x):\\n words = x.split()\\n probability_label_word = [[],[]]\\n counter = 0\\n for label in self.labels:\\n probability_label_word[0].append(label)\\n probability_label_word[1].append(math.log(len(self.words_of_type[label])/self.number_of_words))\\n for word in words:\\n try:\\n probability_label_word[1][counter] += self.model[word][label][1]\\n except KeyError:\\n pass\\n counter += 1\\n log = probability_label_word[1][0]\\n label = probability_label_word[0][0]\\n for counter in range(0, len(probability_label_word[1])):\\n if probability_label_word[1][counter] > log:\\n log = probability_label_word[1][counter]\\n label = probability_label_word[0][counter]\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19618887ebc416f48568e4927f365d9\",\n \"score\": \"0.67686546\",\n \"text\": \"def convert_frequency_to_probability(label: str, a: int) -> None:\\n words_with_a = self.words[label] + a*self.unique_words\\n\\n for word, count in self.probs[label].items():\\n self.probs[label][word] = (count+a) / words_with_a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ef1f574ebdc7c67fc16aeaf1ad623d\",\n \"score\": \"0.6746187\",\n \"text\": \"def prob_word_given_class_yes(self, classifier_total, dataset_size, smoothing=SMOOTHING):\\n self.p_yes = (self.label_yes+smoothing)/(classifier_total+dataset_size*smoothing)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aad9f7311ebe5d537bff74906fd44e2\",\n \"score\": \"0.67297155\",\n \"text\": \"def bayesWordProb(wordList, wordFreq,totalFeatures, totalCntInClass):\\n bayesWordProb = []\\n for word in wordList:\\n if word in wordFreq.keys():\\n count = wordFreq[word]\\n else:\\n count = 0\\n bayesWordProb.append((count+1)/(totalCntInClass + totalFeatures))\\n return(bayesWordProb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04bc4f1067656c0cb34c4741d61ff2f4\",\n \"score\": \"0.67225623\",\n \"text\": \"def label_likelihood(label, features, model):\\n\\n # get unique label values\\n u_labs = np.unique(label)\\n u_labs = u_labs[u_labs > 0]\\n\\n parcels = {}\\n for lab in u_labs:\\n parcels[lab] = np.where(label == lab)[0]\\n\\n fp = fullProbability(parcels, features, model)\\n\\n return fp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f454bab98bf1f83a877f6284ec64899f\",\n \"score\": \"0.6689978\",\n \"text\": \"def probability(self, sent):\\n sent = [word.lower() for word in sent.split()]\\n p = 1.0\\n for word in sent:\\n if self.fdist[word]==0: self.fdist[word] = 1\\n p *= self.fdist[word]*1.0 / self.words_num\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d78347d17c745853bfe689eb49a0f918\",\n \"score\": \"0.66369116\",\n \"text\": \"def prediction(word):\\n # preprocess the text\\n sentence = tokenize(word)\\n X = bag_of_words(sentence, all_words)\\n X = X.reshape(1, X.shape[0])\\n X = torch.from_numpy(X).to(device)\\n\\n # pass it to the model and get the prediction\\n output = model(X)\\n _, predicted = torch.max(output, dim=1)\\n \\n # get the tag and probability\\n tag = tags[predicted.item()]\\n probs = torch.softmax(output, dim=1)\\n prob = probs[0][predicted.item()]\\n \\n # return the tag and the probablity\\n return prob, tag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f9142919affeb1d8fda9ae8d552522\",\n \"score\": \"0.65842927\",\n \"text\": \"def __calculate_prob(self, ngram):\\n first, last = ngram.split()\\n\\n last_prob = (self.unigram_counts[last] / self.words_set_size)\\n first_word_prob_in_end_of_bigram = self.p_continuation[first] * last_prob\\n last_word_prob = self.p_continuation[last] * last_prob\\n\\n if self.unigram_counts[first] > 0:\\n prob = (max(self.bigram_counts[ngram] - self.d, 0) / self.unigram_counts[first]) + self.d * last_word_prob\\n else:\\n prob = last_word_prob + first_word_prob_in_end_of_bigram\\n\\n return np.log2(self.coeff + prob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5129b303d6eac4519fcb943ff3038f04\",\n \"score\": \"0.6561691\",\n \"text\": \"def perplexity(lang=\\\"eng\\\"):\\n pool = ProcessingPool(4)\\n unigram_counter, mgram_counter, ngram_counter= pool.map(get_ngram_counter,\\n [1,2,3],\\n [lang] * 3)\\n pool.close()\\n pool.join()\\n\\n total_words = np.sum(np.array(list(unigram_counter.values())))\\n print(\\\"total_words = \\\", total_words)\\n\\n ngram_conditionals = get_ngram_conditionals(ngram_counter,\\n mgram_counter)\\n\\n probs = np.power(np.array(list(ngram_conditionals.values()),\\n dtype=np.float64),\\n -np.array(list(ngram_counter.values()), dtype=np.float64) \\\\\\n / total_words)\\n\\n print(\\\"probs shape = \\\", probs.shape)\\n\\n PP = (np.prod(probs, dtype=np.float64))\\n\\n return PP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce49153048196ac3ca171c909c45bf0\",\n \"score\": \"0.655377\",\n \"text\": \"def out_prob(self, word, tag):\\n return self._out.get(tag, {}).get(word, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf4f6c6c099778550356e9109679593\",\n \"score\": \"0.65441364\",\n \"text\": \"def predict(self, X, prob=False):\\n results = []\\n\\n for ind, text in enumerate(X):\\n probabilities = dict.fromkeys(self.labels, 0)\\n\\n for label in probabilities:\\n prob = self.labels_count[label] / sum(self.labels_count.values()) if sum(self.labels_count.values()) != 0 else 0\\n probabilities[label] = math.log(prob) if prob != 0 else -100000\\n\\n for word in text.split():\\n norm_form = self.morph.parse(word)[0].normal_form\\n\\n for label in self.labels:\\n if norm_form in self.word_probability:\\n probabilities[label] += math.log(self.word_probability[norm_form][label])\\n\\n max_prob = max(probabilities.values())\\n\\n for label in probabilities:\\n if probabilities[label] == max_prob:\\n results.append(label)\\n break\\n\\n if prob:\\n return list(probabilities.values())\\n else:\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc590ee373967c413c3f98a9180c665\",\n \"score\": \"0.6528423\",\n \"text\": \"def predict(self, words):\\n pred = self.model.predict(self.vectorizer.transform([words]))\\n conf = self.model.predict_proba(self.vectorizer.transform([words])).max()*100\\n confidence = round(conf,2)\\n return self.labels[pred[0]], confidence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5e50c5bc2aad59a6dc5a0f5e0b989a\",\n \"score\": \"0.65226746\",\n \"text\": \"def estimate_prob(target_word, context_words):\\n\\n target_vector = self.target_matrix[target_word]\\n context_prob = 1\\n for word in context_words:\\n context_vector = self.context_matrix[:, word]\\n # Computes dot product between the vectors of target and\\n # context word\\n dot_product = target_vector.dot(context_vector)\\n # Turns the dot product into a probability with the sigmoid\\n # function.\\n word_prob = 1 / (1 + exp(-dot_product))\\n # Keeps a tally of the total probability of the set of context\\n # words.\\n context_prob = context_prob * word_prob\\n\\n return context_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eae625487b6d4f491989037422acbe0a\",\n \"score\": \"0.65044034\",\n \"text\": \"def prob(ngram):\\n return ngram_counts[key(ngram)] / corpus_size_f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbeb3f4c0122c1b1051edeed043a4aca\",\n \"score\": \"0.6467992\",\n \"text\": \"def doc_probability(self, doc, cat):\\n words = self.get_words(doc)\\n weighted_probabilites = [self.weighted_word_probability(word, cat)\\n for word in words]\\n return reduce(mul, weighted_probabilites, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b8358ad971ac3c6093caccb516b5c3\",\n \"score\": \"0.64561886\",\n \"text\": \"def get_log_word_probability(self, word):\\n # take the first letter of the word to get the unigram probability\\n first_letter_index = self.letter_to_index(word[0])\\n log_unigram_prob = (\\n self.log_pi[first_letter_index] if first_letter_index in np.arange(26) else 0\\n )\\n\\n # get all the bigram probabilties for the rest of the characters\\n log_bigram_prob = 0\\n\\n for i in range(len(word) - 1):\\n starting_letter_index = self.letter_to_index(word[i])\\n ending_letter_index = self.letter_to_index(word[i + 1])\\n\\n log_bigram_prob += (\\n self.log_M[starting_letter_index][ending_letter_index]\\n if (starting_letter_index in np.arange(26))\\n and (ending_letter_index in np.arange(26))\\n else 0\\n )\\n\\n return log_unigram_prob + log_bigram_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576d913a37c1c4a88db10feedcd51316\",\n \"score\": \"0.6444651\",\n \"text\": \"def get_word_with_probability(text):\\n freq_each_words = Counter(text.split()).most_common()\\n sum_of_total_char_occ = sum([frequency for (key, frequency) in freq_each_words])\\n prob_each_word = {}\\n for (key, frequency) in freq_each_words:\\n prob_each_word[key] = frequency / sum_of_total_char_occ\\n return prob_each_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8da74242715ea84589937ba0c882f7\",\n \"score\": \"0.63837475\",\n \"text\": \"def word_probabilities(bag, *words):\\n # First create a list of all the probability arrays for each word\\n word_arrays = []\\n for word in words:\\n word_arrays.append(monogram_probabilites(bag, bag.clean_digits(word.upper())))\\n\\n if len(words) <= 1:\\n # No possible bigrams\\n return word_arrays\\n\\n # Now we apply the bigram probability algorithm\\n bigram_array = []\\n for first, second in zip(word_arrays, word_arrays[1:]):\\n # First and second are the np.arrays with classification probabilities\\n bigram_array.append(bigram_probabilities(bag, first, second))\\n word_probs = bigram_sequence2(bigram_array)\\n return word_probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ec825d65de66cc0b836633f3cf2cfe\",\n \"score\": \"0.6362686\",\n \"text\": \"def get_label(self, word):\\n if word not in self.labels.keys():\\n self.labels[word] = len(self.labels) * 0.000001\\n return self.labels[word]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f0527844dde5fee13c0ecbf768b5cb7\",\n \"score\": \"0.6347047\",\n \"text\": \"def predict_proba(self, context):\\n counts = self.get_counts(context)\\n return counts / counts.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ed9948e14df7f8e19802006528db18\",\n \"score\": \"0.6344372\",\n \"text\": \"def extract_probs(label, x):\\n\\tpys = np.sum(label, axis=0) / float(label.shape[0])\\n\\tb = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))\\n\\tunique_array, unique_indices, unique_inverse_x, unique_counts = \\\\\\n\\t\\tnp.unique(b, return_index=True, return_inverse=True, return_counts=True)\\n\\tunique_a = x[unique_indices]\\n\\tb1 = np.ascontiguousarray(unique_a).view(np.dtype((np.void, unique_a.dtype.itemsize * unique_a.shape[1])))\\n\\tpxs = unique_counts / float(np.sum(unique_counts))\\n\\tp_y_given_x = []\\n\\tfor i in range(0, len(unique_array)):\\n\\t\\tindexs = unique_inverse_x == i\\n\\t\\tpy_x_current = np.mean(label[indexs, :], axis=0)\\n\\t\\tp_y_given_x.append(py_x_current)\\n\\tp_y_given_x = np.array(p_y_given_x).T\\n\\tb_y = np.ascontiguousarray(label).view(np.dtype((np.void, label.dtype.itemsize * label.shape[1])))\\n\\tunique_array_y, unique_indices_y, unique_inverse_y, unique_counts_y = \\\\\\n\\t\\tnp.unique(b_y, return_index=True, return_inverse=True, return_counts=True)\\n\\tpys1 = unique_counts_y / float(np.sum(unique_counts_y))\\n\\treturn pys, pys1, p_y_given_x, b1, b, unique_a, unique_inverse_x, unique_inverse_y, pxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8852e3d79844eda09c499889112bb02\",\n \"score\": \"0.63442904\",\n \"text\": \"def monogram_probabilites(bag, word):\\n word_prob_iter = map(lambda x: x[1], bag.raw_label_probabilities(word))\\n return np.fromiter(word_prob_iter, np.float64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c655efb5f561a5ecdf186efdeb8879\",\n \"score\": \"0.6339912\",\n \"text\": \"def _score_as(self, data, label):\\n data_labeled = (data[0], data[1], label)\\n prior = (self.prior_prob_pos if label == POSITIVE \\n else self.prior_prob_neg)\\n counts = self.pos if label == POSITIVE else self.neg\\n smoothed_count_of_all_word_occurences = \\\\\\n sum(counts.values()) + len(self.vocab)\\n\\n prob = math.log(prior)\\n for f in self.featurize(data_labeled):\\n # NOTE: Ignore words if they are completely unknown\\n token = f[0]\\n if token not in self.vocab:\\n continue\\n else: \\n try:\\n counts[token] # Check if the word is present\\n # in this class context\\n except KeyError:\\n counts[token] = 0 # If not, set its count to 0\\n # Perform laplace smoothing if word is unknown in \\n # up to one context, with a vocab size = \\n # unique tokens for all classes\\n f_prob = ((counts[token] + 1) / \\n smoothed_count_of_all_word_occurences)\\n prob += math.log(f_prob)\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29187ed1eb5623b22d963e9bc6f7e42c\",\n \"score\": \"0.6323051\",\n \"text\": \"def get_probability(self, input_data):\\n # Format input and get word-wise probability predictions from the langauge model\\n inputs = self.ready_input(input_data)\\n probabilities = self.calculate_probabilities(inputs[0])\\n prob_array = np.concatenate(probabilities, 1)\\n # Mask predictions at <PAD> and <EOS> positions\\n if len(inputs) > 1:\\n masked_array = prob_array * inputs[1]\\n # Mask replaces predicted values with 1.0, thus negating their contribution\\n # when computing probability products or sums of log-probability scores\\n masked_array[masked_array == 0.0] = 1.0\\n else:\\n masked_array = np.copy(prob_array)\\n total_prob = np.prod(masked_array, axis=1, keepdims=True)\\n return total_prob, prob_array, masked_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e30367e4665a1cb0aab29c9e59b2ea9\",\n \"score\": \"0.62966555\",\n \"text\": \"def p_word_given_topic(W, topic_word_counts, topic_counts, word, topic, beta=0.1):\\r\\n return (topic_word_counts[topic][word] + beta) / (topic_counts[topic] + W * beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d451f2f80b16d49dc0bd4f583ce8f88\",\n \"score\": \"0.629447\",\n \"text\": \"def calc_total_cond_probability(self):\\n for known_word in self.unique_words:\\n known_word.prob_word_given_class_yes(self.yes_label_words, len(self.unique_words))\\n known_word.prob_word_given_class_no(self.no_label_words, len(self.unique_words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"942b3a8f5f37feb2df290ed916f0e4c7\",\n \"score\": \"0.6275826\",\n \"text\": \"def bern_predict(doc):\\n doc = [*set(preprocess(doc)), ]\\n pos_prob = pos_prior\\n neg_prob = neg_prior\\n for w in vocab:\\n if w in doc:\\n pos_prob += math.log(bern_pos_likelihood[w])\\n neg_prob += math.log(bern_neg_likelihood[w])\\n else:\\n pos_prob += math.log(1 - bern_pos_likelihood[w])\\n neg_prob += math.log(1 - bern_neg_likelihood[w])\\n if pos_prob >= neg_prob:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a811dda902d55b779c090458854c70\",\n \"score\": \"0.626946\",\n \"text\": \"def bin_predict(doc):\\n doc = [*set(preprocess(doc)), ]\\n pos_prob = pos_prior\\n neg_prob = neg_prior\\n for w in doc:\\n if w in vocab:\\n pos_prob += bin_pos_likelihood[w]\\n neg_prob += bin_neg_likelihood[w]\\n\\n if pos_prob >= neg_prob:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842c77aebd97055fc7c0388c7ecb1aab\",\n \"score\": \"0.62572813\",\n \"text\": \"def p_words(self, words):\\n return np.float64(Corrector.product(self.p(w) for w in words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b45a47255d25a3ee3861ce59e8a6594\",\n \"score\": \"0.6256548\",\n \"text\": \"def score(self, word, context):\\n trigram = tuple((context[0], context[1], word))\\n return self.model.prob(trigram)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a1a5547d6cdc09203c6faddfb2a0c8\",\n \"score\": \"0.6252507\",\n \"text\": \"def get_probs(corpus: str):\\n word_counts = Counter(re.findall(r'\\\\w+', corpus.lower()))\\n total = sum(word_counts.values())\\n probs = {w: (count / total) for w, count in word_counts.items()}\\n return probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122c40b395aa1dcea7aeb84aff726feb\",\n \"score\": \"0.62225693\",\n \"text\": \"def predict_proba(self, context, last_letter):\\n if self.order:\\n local = context[-self.order:]\\n else:\\n local = ''\\n missed_freq = self.missed_counter_[local][last_letter] + self.smoothing_missed\\n total_freq = self.total_counter_[local][last_letter] + self.smoothing_total\\n return missed_freq / total_freq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4137f6208c676e2744a2b9c68e9ffa86\",\n \"score\": \"0.6214035\",\n \"text\": \"def prob(self, x, y):\\n # check length of sentece and tagging\\n assert len(x) == len(y)\\n\\n q_prob = self.tag_prob(y)\\n e_prob = 1 \\n for word, tag in zip(x, y):\\n e_prob *= self.out_prob(word, tag)\\n\\n return q_prob * e_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075a174038811ab23f8fe76bf2228c2b\",\n \"score\": \"0.62063336\",\n \"text\": \"def compute_prob_not_sentence(sentence):\\n doc = nlp_stanford.value(sentence)\\n verb_count = 0\\n word_count = 0\\n for sent in doc.sentences:\\n for word in sent.words:\\n word_count += 1\\n if word.upos in [\\\"VERB\\\", \\\"AUX\\\"]: # Can be customized\\n if word.xpos in ['VBG']:\\n verb_count += 0.5\\n else:\\n verb_count += 1\\n return 1 - verb_count / word_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1d070528ee2b08298c5de67ba823f2\",\n \"score\": \"0.6200574\",\n \"text\": \"def predict(doc):\\n doc = preprocess(doc)\\n pos_prob = pos_prior\\n neg_prob = neg_prior\\n for w in doc:\\n if w in vocab:\\n pos_prob += pos_likelihood[w]\\n neg_prob += neg_likelihood[w]\\n\\n if pos_prob >= neg_prob:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b5e0c14f54ed2129a94d114c7d9ed0b\",\n \"score\": \"0.6184493\",\n \"text\": \"def docprob(self, doc, cat):\\n features = self.getFeatures(doc)\\n return reduce(lambda f: self.wfprob(f, cat, self.fprob), features, 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e750095f981851bc82e5433064cce0\",\n \"score\": \"0.61381\",\n \"text\": \"def posprobloss(labelposprob):\\n # print \\\"probmatrix\\\", labelposprob ### test\\n Nclass = labelposprob.shape[0]\\n labelposloss = [[-1 for pos in range(Nclass)] for label in range(Nclass)]\\n for label in range(Nclass):\\n for pos in range(Nclass):\\n loss = 0.0\\n for prob_pos in range(Nclass):\\n loss += (abs(prob_pos - pos)*labelposprob[label,prob_pos])\\n labelposloss[label][pos] = loss\\n # print \\\"lossmatrix\\\",labelposloss ### test\\n return np.array(labelposloss, dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec396296ead13266b331b7eb23149224\",\n \"score\": \"0.6111507\",\n \"text\": \"def predict_next_word(self, features, params, precomputed):\\n inputs = features[\\\"inputs\\\"]\\n probs_num = precomputed\\n probs_denom = tf.reduce_sum(probs_num, axis=-1)\\n inputs_weights = common_utils.weights_nonzero(inputs) \\n factors = tf.expand_dims(common_utils.safe_div(inputs_weights, probs_denom), -1)\\n log_probs_sum = common_utils.safe_log(tf.reduce_sum(factors * probs_num, axis=-2))\\n # log_probs_sum has shape [batch_size, target_vocab_size]\\n return log_probs_sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01141c3243d553d6f0edf18ff7c8da44\",\n \"score\": \"0.61063594\",\n \"text\": \"def prob(self, word1, word2):\\n\\t\\tw1 = self.split(word1, \\\"pos\\\")\\n\\t\\tw2 = self.split(word2, \\\"pos\\\")\\n\\t\\tcount_word2 = len(self.search(self.corpus, self.split(word2, \\\"word\\\")))\\n\\t\\tfilter = self.search(self.corpus, w1)\\n\\t\\tcount_w1 = self.get_subsentences(filter, w1)\\n\\t\\tcount_w1_w2 = self.search(count_w1, w2)\\n\\n\\n\\t\\tprob = len(count_w1_w2) / float(len(count_w1))\\n\\t\\tif w1 == w2: prob /= 2\\n\\n\\t\\treturn prob, count_word2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd85b07e6005e6aec24d2ce8f390a3e\",\n \"score\": \"0.61016536\",\n \"text\": \"def logprob(self, word, context, verbose=False):\\n\\n return -log(self.prob(word, context, verbose), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af544f167a9e6998ceed3bf2ef2bf5e7\",\n \"score\": \"0.609712\",\n \"text\": \"def get_probability(self, class_label, symbolic_x, symbolic_y, l_size, u_size):\\n query_string = '{}({},{})'.format(class_label, symbolic_x, symbolic_y)\\n l_term = math.pow(self[symbolic_x], l_size) / math.factorial(l_size)\\n u_term = math.pow(self[symbolic_y], u_size)\\n return (l_term * u_term) / self.get(query_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c03952bc4ada557843cbf378bcb4219\",\n \"score\": \"0.6096188\",\n \"text\": \"def _predict_proba(self, X):\\n bag = self._transform_words(X)\\n return self.clf.predict_proba(bag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e075d6a3d5e87ab9d3b4ae151617eb9d\",\n \"score\": \"0.60909724\",\n \"text\": \"def compute_image_label_prediction(has_bbox, nn_output_class, y_true_class, P, class_nr):\\n prob = tf.where(has_bbox, compute_image_label_from_localization_NORM(nn_output_class, y_true_class, P, class_nr),\\n compute_image_label_in_classification_NORM(nn_output_class, P, class_nr))\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bc9fcb5b44c68e926801f543a24994\",\n \"score\": \"0.60621995\",\n \"text\": \"def prob(self, doc, cat):\\n catprob = self.catcount(cat) / self.totalcount()\\n docprob = self.docprob(doc, cat)\\n return docprob * catprob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b894a21ebb10aff2e86f323208d27cc\",\n \"score\": \"0.60577583\",\n \"text\": \"def log_likelihood(self, bow, label, alpha):\\r\\n likelihood_log=0\\r\\n for k in bow.keys():\\r\\n p_ofwordgivenclass = self.p_word_given_label_and_psuedocount(k,label,alpha)\\r\\n likelihood_log+= math.log(p_ofwordgivenclass)\\r\\n \\r\\n return likelihood_log\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68390bba66ad5b888350ac0afb1782c9\",\n \"score\": \"0.6057332\",\n \"text\": \"def doc_prob(self, features, model):\\n prob_0 = 1.0\\n prob_1 = 1.0\\n for feature in features:\\n if feature not in model:\\n continue\\n prob_0 *= model[feature][0]\\n prob_1 *= model[feature][1]\\n return prob_0, prob_1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0864b11217c6b762f665504db485a56b\",\n \"score\": \"0.6043003\",\n \"text\": \"def score(self, doc, label):\\n\\t\\tscore = 0\\n\\t\\tfor element in doc:\\n\\t\\t\\tscore += doc[element] * self.weights[label][element]\\n\\t\\tscore += self.weights[label]['g_bias']\\n\\t\\treturn score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4350bfc06b26e0db8f21c6856d8349b2\",\n \"score\": \"0.6033662\",\n \"text\": \"def __init__(self, label):\\n self.label = label\\n self.prob = 1.0\\n self.sequenceProb = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d70aba3c543b3da055bee509c2bd03\",\n \"score\": \"0.6029287\",\n \"text\": \"def tag_prob(self, y):\\n\\n n = self._n\\n y = addmarks(y, n)\\n tag_length = len(y)\\n\\n tag_prob = 1\\n for i in range(n - 1, tag_length):\\n tag = y[i]\\n prev_tags = y[i - n + 1: i]\\n tag_prob *= self.trans_prob(tag, tuple(prev_tags))\\n\\n return tag_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d7e4defa4fec99500da3d02258a0c1\",\n \"score\": \"0.60191256\",\n \"text\": \"def prob(self, counts, name=\\\"prob\\\"):\\n return super(DirichletMultinomial, self).prob(counts, name=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4e58c82fb9119cd6660c21ecd2a9be\",\n \"score\": \"0.6001409\",\n \"text\": \"def joint_prob(single_p, label, alphabet):\\n\\n p = [np.log(marginal[alphabet.index(c)]) for (c, marginal) in zip(label, single_p)]\\n\\n return np.sum(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b876f4a66abe3a98c97cd357e7aafc\",\n \"score\": \"0.5977567\",\n \"text\": \"def score(self, sentence):\\n # TODO your code here\\n score = 0\\n probability = 0\\n for token in sentence: # iterate over words in the sentence\\n if token in self.word_uni:\\n\\n score+= m.log(self.word_uni[token]+1)\\n score-=m.log(self.sum_word)\\n else:\\n #self.word_uni['unk'] = self.word_uni['unk'] + 1\\n score+=m.log(1);\\n score-=m.log(self.sum_word)\\n\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9efa75900c4788f6a5c4e70002c7b6c\",\n \"score\": \"0.597637\",\n \"text\": \"def get_target_prob(self,leaf_label) :\\n ret = np.zeros((len(self.synset_list)*2,),np.float32)\\n ret[0:len(self.synset_list)] = 1\\n \\n parents = Set()\\n self._get_parents(self.nodes_dict[self.synset_list[leaf_label]],parents)\\n \\n for node in parents :\\n ret[ self.label_to_synset[node.wnid] ] = 0\\n ret[ len(self.synset_list)+self.label_to_synset[node.wnid] ] = 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026fbbfcbe71290249b35e6f379ce4ca\",\n \"score\": \"0.5939614\",\n \"text\": \"def pos_stat(self):\\n result = 0\\n for feature in self.training.features:\\n if feature.classlabel == 1:\\n result += 1\\n self.prob_pos = result / len(self.training.features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a892fe7be7fe3199c1e407c080085a8\",\n \"score\": \"0.5928033\",\n \"text\": \"def prob(self, w):\\n self.c_uw = {}\\n for i in range(1,7):\\n if self.prev_word[i] in self.counts[i]:\\n self.c_uw[i] = self.counts[i][self.prev_word[i]][w]\\n else:\\n self.c_uw[i] = 0\\n\\n prob = {}\\n prob[0] = max(0, self.words[w])/len(self.words)\\n for i in range(1,7):\\n if self.c_u[i] > 0:\\n prob[i] = max(0, self.c_uw[i]-self.d[i])/self.c_u[i] + self.n_u[i]*self.d[i]/self.c_u[i] * prob[i-1]\\n else:\\n prob[i] = 0\\n return prob[6]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabd7ce398aad20fb43070fb9f9e5ec2\",\n \"score\": \"0.58983773\",\n \"text\": \"def calculate_perplexity(sentence, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\\n n = len(list(n_gram_counts.keys())[0])\\n sentence = [\\\"<s>\\\"] * n + sentence + [\\\"<e>\\\"]\\n sentence = tuple(sentence)\\n N = len(sentence)\\n product_pi = 1.0\\n\\n for t in range(n, N):\\n n_gram = sentence[t-n:t]\\n word = sentence[t]\\n probability = ung.estimate_probability(word, n_gram, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=k)\\n product_pi *= 1/probability\\n\\n perplexity = product_pi**(1/N)\\n\\n return perplexity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9df43609919eb5928ec4b4cecfefa4\",\n \"score\": \"0.5893399\",\n \"text\": \"def prob_y(self, c):\\n count = 0\\n for e in self.data:\\n if e.label == c:\\n count += 1\\n return count / self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2ba9c59dc0a45be9feef0f3cf2ab24\",\n \"score\": \"0.58848995\",\n \"text\": \"def compute_p_word_given_class(data_paths, vocab_size):\\n \\n ###########################################################\\n # Implement your solution here\\n ###########################################################\\n\\n count_words = {}\\n total_num_words = 0\\n \\n for path in data_paths:\\n \\twords = get_words(open_file(path))\\n \\ttotal_num_words += len(words)\\n \\tfor word in words:\\n \\t\\tif word in count_words:\\n \\t\\t\\tcount_words[word] += 1\\n \\t\\telse:\\n \\t\\t\\tcount_words[word] = 1\\n\\n p_word_given_class = {}\\n\\n for word in count_words:\\n \\tprob = (count_words[word] + 1) / (total_num_words + vocab_size + 1)\\n \\tp_word_given_class[word] = prob\\n \\t#print(word + \\\" \\\", end=\\\"\\\")\\n \\t#print(p_word_given_class[word])\\n\\n p_word_given_class[\\\"UNKNOWN_WORD\\\"] = 1 / (total_num_words + vocab_size + 1)\\n #print(p_word_given_class[\\\"UNKNOWN_WORD\\\"])\\n\\n return p_word_given_class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c7a7a9545f9f09827f5c81ee9b95d8\",\n \"score\": \"0.5867969\",\n \"text\": \"def calculateFreq(self, words):\\n nb = len(words)\\n prob = {w: (words.count(w) / nb) for w in words}\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"656aeed0c82e346d89ec5d0ceb643967\",\n \"score\": \"0.5862735\",\n \"text\": \"def modeling(wordcount_dict, one_doc_length, background_model, query_terms, word_instance):\\n wordprob_dict = smoothing_unigram(wordcount_dict, one_doc_length, query_terms, 0.00001)\\n #word_instance = len(list(wordcount_dict.keys()))\\n score = 1\\n for term in query_terms:\\n if term in wordprob_dict:\\n interpolate_prob = interpolation(term, wordprob_dict[term], background_model[term], 0.5)\\n else:\\n prob = lidston_smoothing(0, one_doc_length, word_instance, 0.00001)\\n interpolate_prob = interpolation(term, prob, background_model[term], 0.5)\\n score = score * interpolate_prob\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f02895638addd8a36337d08805ac1d6\",\n \"score\": \"0.58622664\",\n \"text\": \"def __get_word_prob(self, word_prob_map, word):\\n if word in word_prob_map:\\n return word_prob_map[word]\\n return self.correction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbcbe6dcb07bf03b1435fd418ec430f1\",\n \"score\": \"0.5844216\",\n \"text\": \"def score(self, sentence):\\n # TODO your code here\\n # <s> and </s> are already part of the corpus\\n result = 0.0\\n #self.V = len(self.unigram) #vocabulary of v\\n #self.N = sum(self.unigram.values()) #total number of unigrams\\n #print 'here ',self.N, self.V\\n #print sentence\\n for word in sentence:\\n #word = data.word\\n prob = float((self.unigram.get(word,0)+1))/(self.N+self.V) #convert to float!!\\n #print word, prob\\n result += math.log(prob)\\n #print word , prob\\n #print sentence, result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a9c9ce27ca9584297dcd1d2c8f8aaad\",\n \"score\": \"0.58417386\",\n \"text\": \"def get_predict_label(pos_prob):\\n # threshold should be (1, 0.5)\\n neu_threshold = 0.5\\n if pos_prob >= neu_threshold:\\n label, key = 1, \\\"porn\\\"\\n else:\\n label, key = 0, \\\"not_porn\\\"\\n return label, key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a868a1fe3b47500b69b4f0c8f5343f1\",\n \"score\": \"0.58333695\",\n \"text\": \"def display_word_tag_probabilities(word_to_display):\\n\\n\\tprint(\\\"p(\\\" + word_to_display + \\\" | t) for all tags t:\\\")\\n\\tfor prev_tag in p_word_given_t:\\n\\t\\tfor word in p_word_given_t[prev_tag]:\\n\\t\\t\\tif word == word_to_display:\\n\\t\\t\\t\\tprint(\\\" p(\\\" + word + \\\" | \\\" + prev_tag + \\\" ) = \\\" + str(p_word_given_t[prev_tag][word]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92b6b17e3726353c4ad5b2f5de5a252\",\n \"score\": \"0.5830894\",\n \"text\": \"def likelihood_ratio(self, word, alpha):\\r\\n numerator = 0\\r\\n denominator = 0\\r\\n likelihood_ratio = 0\\r\\n numerator = self.p_word_given_label_and_psuedocount(word, POS_LABEL, alpha)\\r\\n denominator = self.p_word_given_label_and_psuedocount(word, NEG_LABEL, alpha)\\r\\n likelihood_ratio = numerator/denominator\\r\\n return likelihood_ratio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"202a2a32fde2b0ac0713edce085b8f2a\",\n \"score\": \"0.582097\",\n \"text\": \"def nb_classifier(self, doc):\\n sum_c = {} # Initialise sum dictionary to hold sentence predicted values.\\n\\n # loop through priors and words in sentences to calculate the probability sums.\\n for cls, lp in self.log_priors.items():\\n # print('{} -> {}'.format(cls, lp))\\n sum_c[cls] = lp\\n for word in doc:\\n if word in self.vocabulary:\\n sum_c[cls] += self.vocabulary[word]['probability'][cls]\\n\\n return max(zip(sum_c.values(), sum_c.keys()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"183a858653f5b8827b2dbe8c6a6555bb\",\n \"score\": \"0.5816077\",\n \"text\": \"def prob(self, predictions, tags, words): \\n #softmax = self.rnn.soft_max(numpy.asarray(contextwinbackwards(words, s['win'])).astype('int32')) #single array n_words * n_classes\\n \\n \\n #try the restart incremental setting, where we access the whole sequence each time?\\n #=======================================================================\\n # prob_tagsequence = self.cpd_tags[\\\"START\\\"].prob(\\\"<f/>\\\") * rnn.softmax(words[0:1]).prob(\\\"<f/>\\\") * \\\\\\n # self.cpd_tags[\\\"<f/>\\\"].prob(\\\"<rm-1/><rpMid/>\\\") * rnn.softmax(words[0:2]).prob(\\\"<rm-1/><rpMid/>\\\") * \\\\\\n # self.cpd_tags[\\\"<rm-1/><rpMid/>\\\"].prob(\\\"<f/>\\\") * rnn.argmax(words[0:3]).prob(\\\"<f/>\\\")* \\\\\\n # self.cpd_tags[\\\"<f/>\\\"].prob(\\\"<f/>\\\") * rnn.argmax(words[0:4]).prob(\\\"<f/>\\\") * \\\\\\n # self.cpd_tags[\\\"<f/>\\\"].prob(\\\"END\\\")\\n #=======================================================================\\n \\n \\n #total_probs = 0.0 #always start with the first word\\n #for i in range(0,len(words)+1):\\n # if i == 0:\\n # total_probs += log(self.cpd_tags[\\\"s\\\"].prob(tags[0]))\\n # elif i == len(words):\\n # total_probs += log(self.cpd_tags[i-1].prob(tags[\\\"se\\\"]))\\n # break\\n # else:\\n # total_probs += log(self.cpd_tags[i-1].prob(tags[i]))\\n # total_probs += log(softmax[i][self.tagToIndexDict[words[i]]])\\n #print \\\"Test: the log probability of the tag sequence 'START f rm-1rpMid f f END' for 'I I like john' is:\\\", total_probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96289eed82abaae55cbc6f9d28a1939\",\n \"score\": \"0.581517\",\n \"text\": \"def probability(html):\\n global default_classifier\\n if default_classifier is None:\\n default_classifier = Soft404Classifier()\\n return default_classifier.predict(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"377887987fad5a7fc3674e2c6cd8b00d\",\n \"score\": \"0.5810774\",\n \"text\": \"def calculate_final_probability(input_string, y_train, x_train_word_set, mat_contents, positive_word_dict, negative_word_dict, positive_count, negative_count):\\n word_list = input_string.split(\\\" \\\")\\n\\n file_count = len(y_train)\\n counter = collections.Counter(y_train)\\n prior_pos = counter[1] / file_count\\n prior_neg = counter[0] / file_count\\n\\n p1 = prior_pos\\n p2 = prior_neg\\n for word in word_list:\\n p1 = p1 * calculate_conditional_probability(word, 0, y_train, x_train_word_set, mat_contents, negative_word_dict, negative_count)\\n p2 = p2 * calculate_conditional_probability(word, 1, y_train, x_train_word_set, mat_contents, positive_word_dict, positive_count)\\n\\n return p1, p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58112f8a25c3e069c17d46675665a863\",\n \"score\": \"0.5802732\",\n \"text\": \"def predict_word_dataset(word, dataset):\\n # Get weight vector for word\\n w = u.get_weight_vector(word, False)\\n # bias is first value in weight vector\\n b = w[0]\\n # then update w to include everything but the bias\\n w = w[1:]\\n \\n # Predict presence of word in each sentence, saving results\\n results = []\\n for i, x in enumerate(dataset):\\n # predict result\\n y = u.predict(x, w, b)\\n # add to results either 0 or 1 depending on y value\\n results.append(1 if y > 0.5 else 0)\\n \\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"910a1fe6854380b6dfc96032beb2788d\",\n \"score\": \"0.5786453\",\n \"text\": \"def rejected_given_word(self, word):\\n\\n if self.rprobs.has_key(word):\\n return self.rprobs[word]\\n else:\\n # only calculate odds if we have samples of both good and rejected\\n # text in the corpus.\\n if self.good and self.rejected:\\n p = (self.bad_count(word) / self.nrejected) * self.prejected\\n pw = p + (self.good_count(word) / self.ngood)\\n if p == 0 and pw == 0:\\n #most data is from rejected emails, so a new word is slightly more\\n #likely to be relevant.\\n prob = .4 \\n else:\\n prob = max(min(p/pw, .99), .01)\\n self.rprobs[word] = prob\\n return prob\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"215f4292c560c82979863d9fdb5cbceb\",\n \"score\": \"0.57812506\",\n \"text\": \"def sentence_probability(self, sentence: str):\\n return get_sentence_probability(sentence, self.session, self.m, self.fasttext_model, self.max_word_length,\\n self.char_vocab,\\n self.rnn_state, self.word_vocab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303e1d9618ca81be97da49ca53d80785\",\n \"score\": \"0.5766169\",\n \"text\": \"def class_prob(labels):\\n\\tresult = dict(zip(set(labels), [0] * len(set(labels))))\\n\\tfor label in labels:\\n\\t\\tresult[label] = result[label] + 1\\n\\tfor label, val in result.items():\\n\\t\\tresult[label] = val / float(len(labels))\\n\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36705d8e76dcf7c8361126a736943ec9\",\n \"score\": \"0.57533973\",\n \"text\": \"def prob(self, given, group_ndims=0, name=None):\\n with tf.name_scope(\\n name, default_name=get_default_scope_name('prob', self)):\\n return tf.exp(self.log_prob(given, group_ndims=group_ndims))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4417966ec072994616b9c6f165a5285\",\n \"score\": \"0.574045\",\n \"text\": \"def get_replace_prob(self, all_words):\\n cur_tf_idf = collections.defaultdict(int)\\n for word in all_words:\\n cur_tf_idf[word] += 1. / len(all_words) * self.idf[word]\\n replace_prob = []\\n for word in all_words:\\n replace_prob += [cur_tf_idf[word]]\\n replace_prob = np.array(replace_prob)\\n replace_prob = np.max(replace_prob) - replace_prob\\n replace_prob = (replace_prob / replace_prob.sum() *\\n self.token_prob * len(all_words))\\n return replace_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748e2e8ab9ee86c2e685a73f08369c33\",\n \"score\": \"0.57289773\",\n \"text\": \"def score(self, word, context):\\n context = self.check_context(context)\\n\\n return self.ngrams[context].freq(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5dedcbcd15442b47c230b94f2646cd0\",\n \"score\": \"0.57187676\",\n \"text\": \"def get_prior(label_column):\\n\\t\\n\\tcounts = label_column.value_counts()\\n\\tprobabilities = []\\n\\n\\tprobabilities.append(counts['4top']/sum(counts))\\n\\tprobabilities.append(counts['ttbar']/sum(counts))\\n\\tprobabilities.append(counts['ttbarHiggs']/sum(counts))\\n\\tprobabilities.append(counts['ttbarW']/sum(counts))\\n\\tprobabilities.append(counts['ttbarZ']/sum(counts))\\n\\n\\treturn np.array(probabilities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137b0f0e8e71d5f9d458e370f8e865e2\",\n \"score\": \"0.57169247\",\n \"text\": \"def joint_prob(sentence, A, B):\\n p = 1 # joint log prob. of words and tags\\n\\n #TODO complete the code\\n global START,END, UNK, allTagCounts, perWordTagCounts, transitionCounts, emissionCounts, num_of_sentences\\n V = len(perWordTagCounts)\\n for i in range(len(sentence)):\\n if i == 0:\\n word, tag = sentence[i]\\n try:\\n A_prob = pow(10, A[(START, tag)])\\n except KeyError:\\n A_prob = 1/(num_of_sentences + V)\\n try:\\n B_prob = pow(10, B[(tag, word)])\\n except KeyError:\\n tag_counts = 0 if tag not in allTagCounts else allTagCounts[tag]\\n B_prob = 1/(tag_counts + V)\\n p *= A_prob * B_prob\\n else:\\n pre_word, pre_tag = sentence[i - 1]\\n curr_word, curr_tag = sentence[i]\\n try:\\n A_prob = pow(10, A[(pre_tag, curr_tag)])\\n except KeyError:\\n pre_tag_counts = 0 if pre_tag not in allTagCounts else allTagCounts[pre_tag]\\n A_prob = 1/(pre_tag_counts + V)\\n try:\\n B_prob = pow(10, B[(curr_tag, curr_word)])\\n except KeyError:\\n curr_tag_counts = 0 if curr_tag not in allTagCounts else allTagCounts[curr_tag]\\n B_prob = 1/(curr_tag_counts + V)\\n p *= A_prob * B_prob\\n try:\\n last_word, last_tag = sentence[-1]\\n p *= pow(10, A[(last_tag, END)])\\n except KeyError:\\n p *= 1 / (num_of_sentences + V)\\n p = log(p, 10)\\n\\n assert isfinite(p) and p<0 # Should be negative. Think why!\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542dfe70150f3b983d868bb242363a01\",\n \"score\": \"0.5712137\",\n \"text\": \"def calculate_probability(self, probability_yes, probability_no):\\n # priors\\n combined_word_probability_yes = math.log(probability_yes, 10)\\n combined_word_probability_no = math.log(probability_no, 10)\\n\\n for word in self.word_array:\\n # this check is needed for filtered vocabulary;\\n # which sets the prob of filtered words to \\\"filtered\\\"\\n if isinstance(word.p_yes, float) and isinstance(word.p_no, float):\\n combined_word_probability_yes += math.log(word.p_yes, 10)\\n combined_word_probability_no += math.log(word.p_no, 10)\\n\\n self.p_yes = combined_word_probability_yes\\n self.p_no = combined_word_probability_no\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0045c1d20903e902af3c5d5abb75ba06\",\n \"score\": \"0.57105845\",\n \"text\": \"def perplexity(self, text):\\n return pow(2.0, self.entropy(text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1513e8b47451cc2e9eccc3e835d03e8b\",\n \"score\": \"0.57052964\",\n \"text\": \"def get_sentence_log_probability(self, sentence):\\n # split the sentence into tokens\\n tokens = sentence.split()\\n # get the probability of a sentence\\n log_sentence_prob = sum(\\n [self.get_log_word_probability(token) for token in tokens]\\n )\\n return log_sentence_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"516282294b220640d884ff0ffb5ea64d\",\n \"score\": \"0.57037413\",\n \"text\": \"def classify(self, bag):\\n prob_pos = self.prob_pos\\n prob_neg = self.prob_neg\\n sumpos = self.sumpos\\n sumneg = self.sumneg\\n prob_x_neg, prob_x_pos = 1, 1\\n for word in bag:\\n prob_cur_pos = 0.0\\n prob_cur_neg = 0.0\\n if word in self.train_pos:\\n prob_cur_pos = math.log10(float(self.train_pos[word]+self.lambda_val) / (sumpos + len(self.big_bag)))\\n elif word in self.big_bag:\\n prob_cur_pos = math.log10(float(self.lambda_val) / (sumpos + len(self.big_bag)))\\n if word in self.train_neg:\\n prob_cur_neg = math.log10(float(self.train_neg[word]+self.lambda_val) / (sumneg + len(self.big_bag)))\\n elif word in self.big_bag:\\n prob_cur_neg = math.log10(float(self.lambda_val) / (sumneg + len(self.big_bag)))\\n #if math.fabs(self.listP[word]) < math.fabs(prob_cur_pos): self.listP[word] = prob_cur_pos\\n #if math.fabs(self.listN[word]) < math.fabs(prob_cur_neg): self.listN[word] = prob_cur_neg\\n prob_x_pos += prob_cur_pos\\n prob_x_neg += prob_cur_neg\\n pos = math.log10(prob_pos) + prob_x_pos\\n neg = math.log10(prob_neg) + prob_x_neg\\n self.temp1 = prob_pos\\n self.temp2 = prob_neg\\n self.temp3 += pos\\n self.temp4 += neg\\n if pos > neg:\\n #print \\\"positive\\\\t\\\"+str(pos)\\n return \\\"positive\\\"\\n else: \\n #print \\\"negative\\\\t\\\"+str(neg)\\n return \\\"negative\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7dacd27fdb7082fc98d4831c0d0f29\",\n \"score\": \"0.57006943\",\n \"text\": \"def predict_proba(self, X):\\n check_is_fitted(self)\\n X = check_array(X)\\n votes_per_example = self.knn_.predict_proba(X)\\n\\n if len(self.missing_label_indices_) > 0:\\n for i in self.missing_label_indices_:\\n new_col = np.zeros(votes_per_example.shape[0])\\n votes_per_example = np.insert(votes_per_example, i, new_col, axis=1)\\n\\n return votes_per_example\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c27e9ec76f68082f3b019ebe3f180d\",\n \"score\": \"0.5699927\",\n \"text\": \"def predict_all(self, w, b):\\n # create a tensor of probabilities - dimensions (nof_subjects, nof_predicates, nof_objects)\\n probs = np.zeros((len(self.relations_embed), b.shape[0], len(self.relations_embed)))\\n\\n # for each subject and object - calc probability per predicate\\n for i in range(len(self.relations_embed)):\\n probs[i, :, :] = np.dot(w, self.relations_embed[i].T) + b\\n\\n return probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec43497555c0023a8d98615618f739e\",\n \"score\": \"0.5698885\",\n \"text\": \"def pred(self, model, phrase, librarian):\\n phrase_ = self.vectorise(nltk.tokenize.word_tokenize(phrase), librarian)\\n x = model.predict([phrase_])\\n\\n return x[0]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1490,"cells":{"query_id":{"kind":"string","value":"d7792d0e3f08f27f49da89a5874a6e0d"},"query":{"kind":"string","value":"Predicts from a model and writes out predictions to a csv."},"positive_passages":{"kind":"list like","value":[{"docid":"a75b79bfbc8af2d55b570c2c6d4c4896","score":"0.0","text":"def predict_model(\n predict_config: PredictConfig,\n video_loader_config: VideoLoaderConfig = None,\n):\n # get default VLC for model if not specified\n if video_loader_config is None:\n video_loader_config = ModelConfig(\n predict_config=predict_config, video_loader_config=video_loader_config\n ).video_loader_config\n\n # set up model\n model = instantiate_model(\n checkpoint=predict_config.checkpoint,\n )\n\n data_module = ZambaDataModule(\n video_loader_config=video_loader_config,\n transform=MODEL_MAPPING[model.__class__.__name__][\"transform\"],\n predict_metadata=predict_config.filepaths,\n batch_size=predict_config.batch_size,\n num_workers=predict_config.num_workers,\n )\n\n validate_species(model, data_module)\n\n if video_loader_config.cache_dir is None:\n logger.info(\"No cache dir is specified. Videos will not be cached.\")\n else:\n logger.info(f\"Videos will be cached to {video_loader_config.cache_dir}.\")\n\n accelerator, devices = configure_accelerator_and_devices_from_gpus(predict_config.gpus)\n\n trainer = pl.Trainer(\n accelerator=accelerator,\n devices=devices,\n logger=False,\n fast_dev_run=predict_config.dry_run,\n )\n\n configuration = {\n \"model_class\": model.model_class,\n \"species\": model.species,\n \"predict_config\": json.loads(predict_config.json(exclude={\"filepaths\"})),\n \"inference_start_time\": datetime.utcnow().isoformat(),\n \"video_loader_config\": json.loads(video_loader_config.json()),\n }\n\n if predict_config.save is not False:\n config_path = predict_config.save_dir / \"predict_configuration.yaml\"\n logger.info(f\"Writing out full configuration to {config_path}.\")\n with config_path.open(\"w\") as fp:\n yaml.dump(configuration, fp)\n\n dataloader = data_module.predict_dataloader()\n logger.info(\"Starting prediction...\")\n probas = trainer.predict(model=model, dataloaders=dataloader)\n\n df = pd.DataFrame(\n np.vstack(probas), columns=model.species, index=dataloader.dataset.original_indices\n )\n\n # change output format if specified\n if predict_config.proba_threshold is not None:\n df = (df > predict_config.proba_threshold).astype(int)\n\n elif predict_config.output_class_names:\n df = df.idxmax(axis=1)\n\n else: # round to a useful number of places\n df = df.round(5)\n\n if predict_config.save is not False:\n preds_path = predict_config.save_dir / \"zamba_predictions.csv\"\n logger.info(f\"Saving out predictions to {preds_path}.\")\n with preds_path.open(\"w\") as fp:\n df.to_csv(fp, index=True)\n\n return df","title":""}],"string":"[\n {\n \"docid\": \"a75b79bfbc8af2d55b570c2c6d4c4896\",\n \"score\": \"0.0\",\n \"text\": \"def predict_model(\\n predict_config: PredictConfig,\\n video_loader_config: VideoLoaderConfig = None,\\n):\\n # get default VLC for model if not specified\\n if video_loader_config is None:\\n video_loader_config = ModelConfig(\\n predict_config=predict_config, video_loader_config=video_loader_config\\n ).video_loader_config\\n\\n # set up model\\n model = instantiate_model(\\n checkpoint=predict_config.checkpoint,\\n )\\n\\n data_module = ZambaDataModule(\\n video_loader_config=video_loader_config,\\n transform=MODEL_MAPPING[model.__class__.__name__][\\\"transform\\\"],\\n predict_metadata=predict_config.filepaths,\\n batch_size=predict_config.batch_size,\\n num_workers=predict_config.num_workers,\\n )\\n\\n validate_species(model, data_module)\\n\\n if video_loader_config.cache_dir is None:\\n logger.info(\\\"No cache dir is specified. Videos will not be cached.\\\")\\n else:\\n logger.info(f\\\"Videos will be cached to {video_loader_config.cache_dir}.\\\")\\n\\n accelerator, devices = configure_accelerator_and_devices_from_gpus(predict_config.gpus)\\n\\n trainer = pl.Trainer(\\n accelerator=accelerator,\\n devices=devices,\\n logger=False,\\n fast_dev_run=predict_config.dry_run,\\n )\\n\\n configuration = {\\n \\\"model_class\\\": model.model_class,\\n \\\"species\\\": model.species,\\n \\\"predict_config\\\": json.loads(predict_config.json(exclude={\\\"filepaths\\\"})),\\n \\\"inference_start_time\\\": datetime.utcnow().isoformat(),\\n \\\"video_loader_config\\\": json.loads(video_loader_config.json()),\\n }\\n\\n if predict_config.save is not False:\\n config_path = predict_config.save_dir / \\\"predict_configuration.yaml\\\"\\n logger.info(f\\\"Writing out full configuration to {config_path}.\\\")\\n with config_path.open(\\\"w\\\") as fp:\\n yaml.dump(configuration, fp)\\n\\n dataloader = data_module.predict_dataloader()\\n logger.info(\\\"Starting prediction...\\\")\\n probas = trainer.predict(model=model, dataloaders=dataloader)\\n\\n df = pd.DataFrame(\\n np.vstack(probas), columns=model.species, index=dataloader.dataset.original_indices\\n )\\n\\n # change output format if specified\\n if predict_config.proba_threshold is not None:\\n df = (df > predict_config.proba_threshold).astype(int)\\n\\n elif predict_config.output_class_names:\\n df = df.idxmax(axis=1)\\n\\n else: # round to a useful number of places\\n df = df.round(5)\\n\\n if predict_config.save is not False:\\n preds_path = predict_config.save_dir / \\\"zamba_predictions.csv\\\"\\n logger.info(f\\\"Saving out predictions to {preds_path}.\\\")\\n with preds_path.open(\\\"w\\\") as fp:\\n df.to_csv(fp, index=True)\\n\\n return df\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"498bc8b339d0c559f2a53fd8dc4fa77e","score":"0.7373088","text":"def model_prediction(file_path, model_file, save_file) :\n model = load(os.path.join(cf.OUTPUTS_MODELS_DIR, model_file)) \n\n def probability_predictions(model, data, raw_data) :\n new_X = raw_data.copy()\n new_X[cf.PRED_PROBA_COL_NAME] = model.predict_proba(data)[:,1]\n new_X[cf.PRED_COL_NAME] = model.predict(data)\n return new_X\n\n cd = CleanDataTransformer(path=file_path)\n data = cd.load_cleaned_data()\n raw_data = cd.load_raw_data()\n\n preds = probability_predictions(model, data, raw_data)\n preds.to_csv(os.path.join(cf.OUTPUTS_DIR, save_file))","title":""},{"docid":"f258010802b4dc2b18d3c4772f0f41c6","score":"0.6951521","text":"def write_predictions(writer: base.Callable, model: base.ModelType, model_hdr: list, data_name: str, main_name: str,\n hyper_info: list, data: base.DataType, dataset: GeneralDataset, train_field: str,\n label_field: str, **kwargs) -> None:\n base = [_get_datestr(), main_name, data_name] + hyper_info\n info = [model.info.get(field, '-') for field in model_hdr]\n\n for doc in data:\n parsed = \" \".join(getattr(doc, train_field)).replace('\\n', ' ').replace('\\r', ' ')\n label = dataset.label_ix_lookup(getattr(doc, label_field))\n pred = dataset.label_ix_lookup(doc.pred)\n\n pred_info = [label, pred, doc.original, parsed]\n\n out = base + info + pred_info\n writer.writerow(out)\n return True","title":""},{"docid":"90b1ea2f8af2211b7bf2e1f71fc25c24","score":"0.6601534","text":"def generate_test_predictions(probabilities, model_dir):\n _batch_limit = 100000\n if batch_size > _batch_limit:\n print(\"WARNING: outputting test predictions of batchsize=%d to csv file\" % batch_size)\n predictions_loc = os.path.join(model_dir, \"predictions.csv\")\n with open(predictions_loc, 'w') as outfile:\n np.savetxt(outfile, probabilities, header=\"test_probabilities\", comments='')\n return","title":""},{"docid":"a4bc3f19e3191ef0ea979047f55744ee","score":"0.6589996","text":"def output_predictions(self, y_preds, csv_out):\n mol_ids = self.dataset.ids\n n_tasks = len(self.task_names)\n y_preds = np.reshape(y_preds, (len(y_preds), n_tasks))\n assert len(y_preds) == len(mol_ids)\n with open(csv_out, \"w\") as csvfile:\n csvwriter = csv.writer(csvfile)\n csvwriter.writerow([\"Compound\"] + self.dataset.get_task_names())\n for mol_id, y_pred in zip(mol_ids, y_preds):\n csvwriter.writerow([mol_id] + list(y_pred))","title":""},{"docid":"e75909d2c5f243de47230dc7626e5cc8","score":"0.65740174","text":"def model_predict(self, predictions_path=None, predictions_validation_path=None):\n self._model_service.predict()\n self.wait_for_state(self._model_service, 'predict', 'ready')\n \n if predictions_path:\n self._model_service.save_predictions(predictions_path)\n self.wait_for_state(self._model_service, 'save_predictions', 'ready')\n if predictions_validation_path:\n if not validate_predictions_csv(predictions_validation_path):\n raise Exception(\"Failed to validate predictions csv file at %s\" % predictions_validation_path)","title":""},{"docid":"a18ffbbdb708de2a9132ec3ce2550ac8","score":"0.65246016","text":"def iris_predict_bulk():\r\n # test_file is the name of variable in url\r\n df = pd.read_csv(request.files.get('test_file'))\r\n prediction = list(model_iris.predict(df))\r\n #generates prediction text for each prediction values\r\n prediction_text = [get_description(i) for i in prediction]\r\n return \"The prediction for CSV is: \" + str(prediction_text)","title":""},{"docid":"2cae3cf4eba3c6723b4136ace53be8cf","score":"0.65156144","text":"def predict_model(model, model_name):\n\n x_train, x_val, x_test = get_input_data('all')\n y_train, y_val, y_test = get_labels('all')\n\n train_loss, train_acc = model.evaluate(x_train, y_train)\n val_loss, val_acc = model.evaluate(x_val, y_val)\n test_loss, test_acc = model.evaluate(x_test, y_test)\n\n print('Training set Accuracy: ', train_acc)\n print('Validation set Accuracy: ', val_acc)\n print('Test set Accuracy: ', test_acc)\n\n train_predictions = model.predict(x_train)\n val_predictions = model.predict(x_val)\n test_predictions = model.predict(x_test)\n\n train_pred_classes = train_predictions.argmax(axis=-1)\n val_pred_classes = val_predictions.argmax(axis=-1)\n test_pred_classes = test_predictions.argmax(axis=-1)\n\n train_f1 = f1_score(y_train, train_pred_classes, average = 'macro')\n val_f1 = f1_score(y_val, val_pred_classes, average='macro')\n test_f1 = f1_score(y_test, test_pred_classes, average='macro')\n\n f1_scores = pd.Series({'Training': train_f1, 'Validation': val_f1, 'Testing': test_f1})\n f1_scores.name = model_name\n\n train_predictions_df = pd.DataFrame(train_predictions, columns=[l + '_prob' for l in enc.classes_],\n index=track_info.loc[track_info.split == 'training'].index)\n val_predictions_df = pd.DataFrame(val_predictions, columns=[l + '_prob' for l in enc.classes_],\n index=track_info.loc[track_info.split == 'validation'].index)\n test_predictions_df = pd.DataFrame(test_predictions, columns=[l + '_prob' for l in enc.classes_],\n index=track_info.loc[track_info.split == 'test'].index)\n\n return train_predictions_df, val_predictions_df, test_predictions_df, f1_scores","title":""},{"docid":"acb57231fc4b1ec50d7f1c08c65d02f6","score":"0.6504246","text":"def write_predictions(dir_names, y_predict, mode):\n test_set = pd.read_csv(dir_names['test_set'], delim_whitespace=True, header=0)\n if not os.path.exists(os.path.join(dir_names['results'])):\n os.makedirs(os.path.join(dir_names['results']))\n\n if mode == 'fine tune':\n score_level = .5\n elif mode == 'normal':\n score_level = .7\n else:\n sys.exit('mode does not support')\n predicted_labels = [\"binding\" if score >= score_level else \"non-binding\" for score in y_predict]\n\n # write prediction to new file\n with open(os.path.join(dir_names['results'], \"predictions.csv\"), 'w') as f:\n f.write('Peptide_seq' + ',' + 'Predicted_Scores' + ',' + 'Predicted_Labels' + '\\n')\n for i in range(len(y_predict)):\n f.write(str(test_set['Peptide_seq'].iloc[i]) + ',' + str(y_predict[i]) + ',' + predicted_labels[i] + '\\n')\n f.close()","title":""},{"docid":"d4a1c10fbb9d7c5d504d9df4d1b5c245","score":"0.64727813","text":"def predict_dataset(self, save_path, dataset_filepath, model, batch_size):\r\n pass","title":""},{"docid":"4b758ac9823b40a3738020def7ec090b","score":"0.64595246","text":"def output(pred, data_test, path):\n df = pd.DataFrame()\n df[\"id\"] = data_test[\"id\"]\n #pred = [int(round(num)) for num in pred]\n df[\"prediction\"] = pred\n df.to_csv(path, index = False)","title":""},{"docid":"cbbd72c9d89ba07563b49dd30f1a6dd2","score":"0.63975626","text":"def predict_results(self, prediction_batch_size):\n start_time = time.time()\n processed_rows = 0\n\n open(self._TEMP_PREDICTIONS_PATH, 'w').close()\n while processed_rows < self._pred_df.shape[0]:\n next_processed_rows = min(processed_rows + prediction_batch_size, self._pred_df.shape[0])\n X_pred = np.array(self._pred_df.iloc[processed_rows:next_processed_rows, :])\n predictions = [round(preds[0], 3) for preds in self._model.predict(X_pred)]\n\n ids = list(self._pred_df.index[processed_rows:next_processed_rows])\n\n with open(self._TEMP_PREDICTIONS_PATH, 'a') as f:\n writer = csv.writer(f)\n for i in range(len(predictions)):\n writer.writerow([ids[i], predictions[i]])\n\n processed_rows += prediction_batch_size\n\n print(\"Predicting the values took {0} seconds\".format(round(time.time() - start_time, 2)))","title":""},{"docid":"cb5d70019c2c5cf96d38f3b7138f4676","score":"0.63785636","text":"def predict_outcomes(self):\n model = SVC(gamma='auto')\n trained_models = self.trained_model\n for trained_model in trained_models:\n print(trained_model['z'])\n model.fit(trained_model['x_train'], trained_model['y_train'])\n predictions = model.predict(trained_model['x_valid'])\n print(accuracy_score(trained_model['y_valid'], predictions))\n print(confusion_matrix(trained_model['y_valid'], predictions))\n print(classification_report(trained_model['y_valid'], predictions))","title":""},{"docid":"8ab991ce427c8208c10acb8b9eb08d28","score":"0.6355206","text":"def predict_model(prediction_data, model):\n predictions = model.predict(input_fn=lambda: input_fn(prediction_data))\n return predictions","title":""},{"docid":"58ce34809a1fd7cb0d921e51971f5540","score":"0.63392764","text":"def predict(self, sess):\n # load model's checkpoint if exists\n self._load_from_checkpoint(sess)\n\n # write predictions\n if self.arg.dump:\n self.inference(sess, \"\", self.arg.remain_diff, self.arg.dump)","title":""},{"docid":"3b3906d5ca0967e90b797345c11104b4","score":"0.6336304","text":"def transformation():\n data = None\n\n # Convert from CSV to pandas\n if flask.request.content_type == 'text/csv':\n data = flask.request.data.decode('utf-8')\n s = StringIO.StringIO(data)\n data = pd.read_csv(s, header=None)\n else:\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\n\n print('Invoked with {} records'.format(data.shape[0]))\n\n # Do the prediction\n predictions = ScoringService.predict(data.values[:,0])\n\n # Convert from numpy back to CSV\n out = StringIO.StringIO()\n predictions.to_csv(out, header=True, index=False)\n result = out.getvalue()\n\n return flask.Response(response=result, status=200, mimetype='text/csv')","title":""},{"docid":"e3f4120d2a051c20ab80007f1f21dac8","score":"0.63343567","text":"def prediction(test_df, model_columns, model):\n test_df['prediction'] = model.predict(test_df[model_columns])\n\n return test_df","title":""},{"docid":"f90a2d585929f39f5f447d6fb759fd17","score":"0.63189596","text":"def make_predictions(self, model, X_train, y_train, X_test, submission_file=SUBMISSION_FILE):\n\n if self.n_test != len(X_test):\n raise ValueError(\"Somewhere along the way you lost test samples! Please fix.\")\n\n print \"Training %s using %d samples (%d%% of original training set)\" % \\\n (model.__class__.__name__, len(X_train), 100. * len(X_train) / self.n_train)\n model.fit(X_train, y_train)\n\n print \"Making predictions on the test set..\"\n # We want the probabilities, not just 0 or 1. Just take 2nd column\n y_pred = model.predict_proba(X_test)[:, 1]\n\n print \"Cross-validating model...\"\n scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"roc_auc\")\n print \"Cross-validated AUC score: %.4f (%s)\" % (scores.mean(), scores.round(4))\n\n print \"Saving predictions in %s...\" % submission_file\n submission = pd.DataFrame(dict(id=self.test.index, OpenStatus=y_pred)).set_index('id')\n submission.to_csv(submission_file)","title":""},{"docid":"62520719b47b1a4ae5fcc41de07b5450","score":"0.62694895","text":"def predict(toml_path):\n toml_path = Path(toml_path)\n cfg = config.parse.from_toml_path(toml_path)\n\n if cfg.predict is None:\n raise ValueError(\n f\"predict called with a config.toml file that does not have a PREDICT section: {toml_path}\"\n )\n\n # ---- set up logging ----------------------------------------------------------------------------------------------\n timenow = datetime.now().strftime(\"%y%m%d_%H%M%S\")\n logger = logging.get_logger(\n log_dst=cfg.predict.output_dir,\n caller=\"predict\",\n timestamp=timenow,\n logger_name=__name__,\n )\n logger.info(\"Logging results to {}\".format(cfg.prep.output_dir))\n\n model_config_map = config.models.map_from_path(toml_path, cfg.predict.models)\n\n core.predict(\n csv_path=cfg.predict.csv_path,\n checkpoint_path=cfg.predict.checkpoint_path,\n labelmap_path=cfg.predict.labelmap_path,\n model_config_map=model_config_map,\n window_size=cfg.dataloader.window_size,\n num_workers=cfg.predict.num_workers,\n spect_key=cfg.spect_params.spect_key,\n timebins_key=cfg.spect_params.timebins_key,\n spect_scaler_path=cfg.predict.spect_scaler_path,\n device=cfg.predict.device,\n annot_csv_filename=cfg.predict.annot_csv_filename,\n output_dir=cfg.predict.output_dir,\n min_segment_dur=cfg.predict.min_segment_dur,\n majority_vote=cfg.predict.majority_vote,\n save_net_outputs=cfg.predict.save_net_outputs,\n logger=logger,\n )","title":""},{"docid":"30f6560bbb3e217a9087c3e466259313","score":"0.6262948","text":"def transformation():\n data = None\n\n # Convert from CSV to pandas\n if flask.request.content_type == 'text/csv':\n data = flask.request.data.decode('utf-8')\n s = StringIO.StringIO(data)\n # Makoto.Sano@Mack-the-Psych.com\n # data = pd.read_csv(s, header=None)\n data = pd.read_csv(s)\n else:\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\n\n # Makoto.Sano@Mack-the-Psych.com\n # print('Invoked with {} records'.format(data.shape[0]))\n print('Invoked with {} records'.format(len(data)))\n \n # Do the prediction\n predictions = ScoringService.predict(data)\n\n # Convert from numpy back to CSV\n out = StringIO.StringIO()\n\n # Makoto.Sano@Mack-the-Psych.com\n # pd.DataFrame({'results':predictions}).to_csv(out, header=False, index=False)\n predictions.to_csv(out, index=False)\n result = out.getvalue()\n\n return flask.Response(response=result, status=200, mimetype='text/csv')","title":""},{"docid":"1dc0c592001b41d4dfeae85687364095","score":"0.6247642","text":"def transformation():\n data = None\n\n # Convert from CSV to pandas\n if flask.request.content_type == 'text/csv':\n data = flask.request.data.decode('utf-8')\n s = StringIO(data)\n data = pd.read_csv(s, header=None)\n\n else:\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\n\n print('Invoked with {} records'.format(data.shape[0]))\n\n # Do the prediction\n predictions = ScoringService.predict(data)\n print(predictions)\n print(type(predictions))\n predictions=predictions.reshape(len(predictions))\n # Convert from numpy back to CSV\n out = StringIO()\n pd.DataFrame({'results':predictions}).to_csv(out, header=False, index=False)\n result = out.getvalue()\n #result=predictions\n return flask.Response(response=result, status=200, mimetype='text/csv')","title":""},{"docid":"5bed1c37ab148f950310c5a287b76c16","score":"0.6241566","text":"def predict(self):\n\n # update file_list in ctlearn config\n with open(self.ctlearn_config_path, 'r') as config:\n myconfig = yaml.load(config)\n\n myconfig['Data']['file_list'] = os.path.join(\n self.working_directory, self.basic_config['prediction_file_list'])\n\n # modify ctlearn config to make sure that a prediction file will be created\n myconfig['Prediction']['export_as_file'] = True\n myconfig['Prediction']['true_labels_given'] = True\n\n # dump ctlearn configuration\n with open(self.ctlearn_config_path, 'w') as config:\n yaml.dump(myconfig, config)\n\n # run prediction\n run_model(myconfig, mode='predict', debug=False, log_to_file=True)","title":""},{"docid":"ef2cf0370f66deaacf5f6ae57cf1be3d","score":"0.62171954","text":"def save_predicted_results(self,predicted_results):\n\n\t\tself.predicted_results = predicted_results\n\n\t\tid_value = self.test_data[self.testdata_headers[0]]\n\t\tf = open(\"prediction.csv\",\"w\")\n\t\tf.write(\"Id,Cover_Type\\n\")\n\t\tfor i in xrange(len(self.predicted_results)):\n\t\t\tf.write(\"{},{}\\n\".format(id_value[i], self.predicted_results[i]))\n\t\tf.close()","title":""},{"docid":"b52a196aa9d2b42617310f2c8b1104ae","score":"0.621695","text":"def assess_model(self, predictions):\n\n print(classification_report(self.y_test, predictions))\n print(confusion_matrix(self.y_test, predictions))","title":""},{"docid":"29766c4181f0362243cf460e4ac7b527","score":"0.6211899","text":"def get_predictions(test_path, models, model_names, params=None):\n if params is None:\n params = TRAINING_DEFAULTS\n lr = params[\"lr\"]\n ep = params[\"epoch\"]\n dim = params[\"dim\"]\n ngrams = params[\"wordNgrams\"]\n\n samples = pd.read_csv(test_path, sep='\\n', header=None, names=[\"s\"], dtype=object)[\"s\"]\n true_labels = samples.str.split().str.get(0)\n samples = samples.str.replace(r'__label__[A-Za-z-]* ', \"\").str.strip()\n\n predictions = []\n probabilities = []\n\n for m in models:\n labels, probs = flatten_ft_predictions(m.predict(list(samples), k=1))\n predictions.append(pd.Series(labels, dtype=object))\n probabilities.append(pd.Series(probs))\n\n cols = [\"sample\", \"true_label\"]\n for n in model_names:\n cols.append(f\"{n}_pred\")\n cols.append(f\"{n}_prob\")\n\n df = pd.DataFrame([], columns=cols)\n df.loc[:, \"sample\"] = samples\n df.loc[:, \"true_label\"] = true_labels\n\n pred_cols = []\n for i in range(len(model_names)):\n df.loc[:, cols[i*2 + 2]] = predictions[i]\n df.loc[:, cols[i*2 + 3]] = probabilities[i]\n pred_cols.append(cols[i*2 + 2])\n\n fname = f\"{os.getcwd()}/results/preds_lr{lr}_ep{ep}_d{dim}_ng{ngrams}.csv\"\n df.to_csv(fname)\n\n _, is_diff = find_prediction_disagreement(samples, predictions)\n\n diffs = df[is_diff][[\"sample\"] + pred_cols]\n diff_fname = f\"{os.getcwd()}/results/diffs_lr{lr}_ep{ep}_d{dim}_ng{ngrams}.csv\"\n diffs.to_csv(diff_fname)","title":""},{"docid":"5c625c3a91cc964a2e7ca1a5adb4fb8e","score":"0.62084454","text":"def predict_file():\n inputdata=pd.read_csv(request.files.get(\"data\"),header=None)\n prediction=model.predict(inputdata)\n return str(list(prediction))","title":""},{"docid":"28aa654c455c6474677613d4c53b3560","score":"0.6196311","text":"def predict(\n self,\n test_dataset: data.TFRecordDataset,\n inference_signature: str = \"serving_default\",\n additional_features: dict = {},\n logs_dir: Optional[str] = None,\n logging_frequency: int = 25,\n ):\n if logs_dir:\n outfile = os.path.join(logs_dir, RelevanceModelConstants.MODEL_PREDICTIONS_CSV_FILE)\n # Delete file if it exists\n self.file_io.rm_file(outfile)\n\n _predict_fn = get_predict_fn(\n model=self.model,\n tfrecord_type=self.tfrecord_type,\n feature_config=self.feature_config,\n inference_signature=inference_signature,\n is_compiled=self.is_compiled,\n output_name=self.output_name,\n features_to_return=self.feature_config.get_features_to_log(),\n additional_features=additional_features,\n max_sequence_size=self.max_sequence_size,\n )\n\n predictions_df_list = list()\n batch_count = 0\n for predictions_dict in test_dataset.map(_predict_fn).take(-1):\n predictions_df = pd.DataFrame(predictions_dict)\n if logs_dir:\n if os.path.isfile(outfile):\n predictions_df.to_csv(outfile, mode=\"a\", header=False, index=False)\n else:\n # If writing first time, write headers to CSV file\n predictions_df.to_csv(outfile, mode=\"w\", header=True, index=False)\n else:\n predictions_df_list.append(predictions_df)\n\n batch_count += 1\n if batch_count % logging_frequency == 0:\n self.logger.info(\"Finished predicting scores for {} batches\".format(batch_count))\n\n predictions_df = None\n if logs_dir:\n self.logger.info(\"Model predictions written to -> {}\".format(outfile))\n else:\n predictions_df = pd.concat(predictions_df_list)\n\n return predictions_df","title":""},{"docid":"d3c851c1c1611bebabcbf7c0ba6f5a57","score":"0.61924016","text":"def prediction(self):\n final_df = pd.DataFrame(columns=['PARTICIPANT_ID','Y1_LOGISTIC','Y1_NAIVEBAYES','Y2_REGRESSION'])\n X,y = self.aggregateColumns(stype='predict')\n print 'Predicting by Logistic Regression'\n y_log = self.logistic.predict(X)\n print 'Predicted Rows : ', len(y_log)\n print 'Predicting by Naive Bayes'\n y_naive = self.naive.predict(X)\n print 'Predicted Rows :', len(y_naive)\n X,y = self.getRegData(stype='predict')\n print 'Predicting Linear Regression'\n y_lin = self.linear.predict(X)\n y_lin = y_lin * 42\n times = []\n for seconds in y_lin:\n m, s = divmod(seconds, 60)\n h, m = divmod(m, 60)\n time = \"%d:%02d:%02d\" % (h, m, s)\n times.append(time)\n print 'Predicted Rows :',len(times)\n final_df.PARTICIPANT_ID = range(UNIQUE_IDS)\n final_df.Y1_LOGISTIC = y_log\n final_df.Y1_NAIVEBAYES = y_naive\n final_df.Y2_REGRESSION = times\n final_df.to_csv(self.finalSub)","title":""},{"docid":"a0ac3e2151b12a1ca14f27657131e36e","score":"0.6179789","text":"def gen_pred_csv():\n # schemas\n SCHEMA = Config().read_schema() # dict id -> col_name, e.g. SCHEMA[1]='clk'\n del SCHEMA[1]\n\n # load data\n df = pd.read_table(FLAGS.pred_data + \"/pred1\")\n\n # save to csv\n df.to_csv(\"../data/pred/pred1.csv\", header=[v for k, v in SCHEMA.iteritems()], index=False)\n print(\"Csv generation finished.\")","title":""},{"docid":"0d551751b11df2b5d6d999c08bc0b9f4","score":"0.6166116","text":"def save_output(model, name, model_dict, men = True):\n d = pd.read_csv(\"AUS_SubmissionFormat.csv\")\n\n if men:\n ds_test = pd.read_csv(\"mens_test_file.csv\")\n ds_test_id = ds_test['id'].astype(str)+\"_mens\"\n else:\n ds_test = pd.read_csv(\"womens_test_file.csv\")\n ds_test_id = ds_test['id'].astype(str)+\"_womens\"\n \n ds_test_id.name = \"submission_id\" \n ds_test = pre_processing(ds_test, True)\n ds_test = feature_engineering(ds_test)\n ds_test = ds_test.drop(['id','train', 'outcome'], axis = 1)\n pred_test = model.predict_proba(ds_test.values)\n pred_test = pd.DataFrame(data = pred_test, columns = ['UE', 'FE','W'])\n d1 = d.iloc[:2000,:]\n test_id = pd.DataFrame(ds_test_id)\n pred_test = pd.concat((test_id, pred_test), axis = 1)\n pred_test = pd.merge(d1.iloc[:,:2], pred_test, on = \"submission_id\")\n \n if men:\n pred_test.to_csv(\"Xtest_{}_mens.csv\".format(name))\n joblib.dump(model_dict, \"./{}_mens\".format(name))\n else:\n pred_test.to_csv(\"Xtest_{}_womens.csv\".format(name))\n joblib.dump(model_dict, \"./{}_womens\".format(name))","title":""},{"docid":"b6d93f1f6d5b89f491ff8ba1dc587e95","score":"0.6166084","text":"def predict_and_dump(inputdir, outputdir, number_of_months=3):\n # Get absolute paths, to avoid problems\n abs_input_dir = os.path.abspath(inputdir)\n abs_output_dir = os.path.abspath(outputdir)\n\n # For every CSV file in input dir\n for lang_csv in os.listdir(abs_input_dir):\n if lang_csv.endswith(\".csv\"):\n # Read CSV file and make a DataFrame with it's data\n lang_df = pandas.read_csv(os.path.join(abs_input_dir, lang_csv))\n\n x_axis = lang_df.index # Index numbers\n y_axis = lang_df['coefficient'].tolist() # Coefficient numbers\n lang_poly = polyfit(x_axis, y_axis, 4) # 4rd-grade polynomial regression\n\n poly_line = poly1d(lang_poly) # Polynomial line\n future_months = arange(number_of_months) + len(lang_df.index) # Ten more months\n future_coefficient = poly_line(future_months) # Get the coefficient prediction for the next X months\n\n last_timelapse = lang_df['timelapse'][len(lang_df.index)-1] # Get last timelapse from timeseries\n timelapse_range = months_to_predict(number_of_months, last_timelapse) # List of timelapses to predict\n timelapse_iterator = year_month_iter(\n timelapse_range[0], timelapse_range[1], timelapse_range[2], timelapse_range[3])\n\n # Add predicted data to the timeseries (bottom of CSV file)\n coefficient_index = 0\n for timelapse in timelapse_iterator:\n lang_df.loc[len(lang_df.index)] = \\\n [os.path.splitext(lang_csv)[0], timelapse, int(future_coefficient[coefficient_index])]\n coefficient_index += 1\n\n # Save the real data along with predicted data in a CSV file, overwriting it if exists\n lang_df.to_csv(os.path.join(abs_output_dir, lang_csv), index=False, float_format='%.0f')","title":""},{"docid":"ca3714f0631ca2d3f0e600965cb15af1","score":"0.6156261","text":"def prediction(df, model_dict, model = 'MinsePIE_v3.sav'):\n features = ['length', 'VF_RTT_z', 'mmr', 'percC', 'pairedbases', 'Arun_maxlen', 'percA', 'percT','pos1compl', 'loop1_intact']\n # choose model\n pred_model = model_dict[model]\n # predict\n df['zscore']= pred_model.predict(df[features])\n df['percIns_predicted'] = df['zscore'] * df['std'] + df['mean']\n\n return df","title":""},{"docid":"e1933def6bbd702265245facd8dcd437","score":"0.6153989","text":"def save_predict(self, test_x, test_y):\n folder = \"runs\"\n op = os.path.join(os.getcwd(), folder)\n\n if not os.path.exists(op):\n os.makedirs(folder)\n\n # Predicted\n with open(os.path.join(op, \"predict.txt\"), \"a\") as pred:\n pred.write(\"Date and time: {}, {}\\n\".format(self.date, self.time))\n pred.write(\"Optimizer: {}\\n\".format(type(self).__name__))\n pred.write(\"NN structure: {}\\n\".format(self.layers))\n pred.write(\"lrate={}, \".format(self.lrate))\n pred.write(\"momentum={}, \".format(self.momentum))\n rmse = self.compute_error(test_x, test_y)\n pred.write(\"error: {}\\n\\n\".format(rmse))","title":""},{"docid":"d10962e7a0bf18ab8656a53158cdd750","score":"0.614656","text":"def predict(self) -> None:\n\n tot_predicted = []\n tot_labels = []\n\n with open(Config.PREDICTION, mode=\"w\") as out_file:\n\n for tk_sentence, or_label in zip(self.test_sentences, self.test_labels):\n tk_sentence = self.tokenizer.encode(tk_sentence)\n tokens, pr_labels, tmp_pred = self._predict(tk_sentence)\n\n tmp_label = [self.tag2idx[lb] for lb in or_label]\n\n tot_predicted.extend(tmp_pred)\n tot_labels.extend(tmp_label)\n\n for token, label in zip(tokens, pr_labels):\n out_file.write(\"{}:{} \".format(token, label))\n out_file.write(\"\\n\")\n\n f1 = f1_score(tot_predicted, tot_labels, average=\"micro\")\n pr = precision_score(tot_predicted, tot_labels, average=\"micro\")\n rc = recall_score(tot_predicted, tot_labels, average=\"micro\")\n\n print(\"F1 score: {}\\tPrecision: {}\\tRecall: {}\".format(f1, pr, rc))","title":""},{"docid":"fd2f5b786d83500a4d592f9fd22eb31d","score":"0.6143828","text":"def save_predictions(model, loader, output_dir):\n # Note that all eval runs on main process\n assert comm.is_main_process()\n if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n model = model.module\n\n device = torch.device(\"cuda:0\")\n os.makedirs(output_dir, exist_ok=True)\n\n for batch in tqdm.tqdm(loader):\n batch = loader.postprocess(batch, device)\n model_kwargs = {}\n module = model.module if hasattr(model, \"module\") else model\n if isinstance(module, VoxMeshMultiViewHead):\n model_kwargs[\"intrinsics\"] = batch[\"intrinsics\"]\n model_kwargs[\"extrinsics\"] = batch[\"extrinsics\"]\n if isinstance(module, VoxDepthHead):\n model_kwargs[\"masks\"] = batch[\"masks\"]\n if module.mvsnet is None:\n model_kwargs[\"depths\"] = batch[\"depths\"]\n model_outputs = model(batch[\"imgs\"], **model_kwargs)\n\n # TODO: debug only\n # save_debug_predictions(batch, model_outputs)\n # continue\n\n pred_mesh = model_outputs[\"meshes_pred\"][-1]\n gt_mesh = batch[\"meshes\"]\n pred_mesh = pred_mesh.scale_verts(P2M_SCALE)\n gt_mesh = gt_mesh.scale_verts(P2M_SCALE)\n\n pred_points = sample_points_from_meshes(\n pred_mesh, NUM_PRED_SURFACE_SAMPLES, return_normals=False\n )\n gt_points = sample_points_from_meshes(\n gt_mesh, NUM_GT_SURFACE_SAMPLES, return_normals=False\n )\n\n pred_points = pred_points.cpu().detach().numpy()\n gt_points = gt_points.cpu().detach().numpy()\n\n batch_size = pred_points.shape[0]\n for batch_idx in range(batch_size):\n label, label_appendix = batch[\"id_strs\"][batch_idx].split(\"-\")[:2]\n pred_filename = os.path.join(\n output_dir, \"{}_{}_predict.xyz\".format(label, label_appendix)\n )\n gt_filename = os.path.join(\n output_dir, \"{}_{}_ground.xyz\".format(label, label_appendix)\n )\n\n np.savetxt(pred_filename, pred_points[batch_idx])\n np.savetxt(gt_filename, gt_points[batch_idx])\n\n # pred_filename = pred_filename.replace(\".xyz\", \".obj\")\n # gt_filename = gt_filename.replace(\".xyz\", \".obj\")\n\n # pred_verts, pred_faces = pred_mesh[batch_idx] \\\n # .get_mesh_verts_faces(0)\n # gt_verts, gt_faces = gt_mesh[batch_idx] \\\n # .get_mesh_verts_faces(0)\n # save_obj(pred_filename, pred_verts, pred_faces)\n # save_obj(gt_filename, gt_verts, gt_faces)\n\n # metrics = compare_meshes(\n # pred_mesh[batch_idx], gt_mesh[batch_idx],\n # num_samples=NUM_GT_SURFACE_SAMPLES, scale=1.0,\n # thresholds=[0.01, 0.014142], reduce=True\n # )\n # print(\"%s_%s: %r\" % (label, label_appendix, metrics))","title":""},{"docid":"c2fd1fff51abc8b2cf2bef540b645eae","score":"0.6138033","text":"def test_predictions(dataloader, model, softmax=False):\n\n model.eval()\n test_file = r\"/home/andrewg/PycharmProjects/assignments/ProstateX-TestLesionInformation/ProstateX-Findings-Test.csv\"\n predictions = pd.read_csv(test_file)\n predictions.insert(4, \"ClinSig\", 0)\n predictions = predictions.drop([\"pos\", \"zone\"], axis=1)\n end_batch = 0\n\n for idx, batch in enumerate(dataloader):\n outputs = model(batch[\"image\"])\n if softmax:\n outputs = torch.tensor([tup[1] for tup in outputs])\n start_batch = end_batch\n end_batch = start_batch + len(outputs)\n predictions[\"ClinSig\"].iloc[start_batch: end_batch] = outputs.flatten().tolist()\n return predictions","title":""},{"docid":"87ee6d253706073744cb6f483bd833ec","score":"0.6133501","text":"def get(self):\n\n\n X = np.array([[0, 0], [0, 0], [0, 0], [1, 1], [1,1], [1,1]])\n y = np.array([0, 0, 0, 1, 1, 1])\n res = analyse(X, y)\n\n prediction = executeModel(res['modelo'], X[4:5])\n # self.render(\"index_path\")\n\n # print(prediction)\n # df = pandas.DataFrame(data=prediction, columns=['a'])\n # df.to_csv('eggs.csv')\n\n\n\n df = pandas.read_csv('eggs.csv')\n xx = df.ix[:, 1:(len(df.columns)-1)].as_matrix()\n print(xx)\n\n # print(str(type(prediction)))\n\n # with open('eggs.csv', 'w', newline='') as csvfile:\n # spamwriter = csv.writer(csvfile, delimiter=',',\n # quoting=csv.QUOTE_MINIMAL)\n # spamwriter.writerow(['Spam'] * 5 + ['Baked Beans'])\n # spamwriter.writerow(['Spam', 'Lovely Spam', 'Wonderful Spam'])\n\n # self.write(str(res['crossvalidation']) + str(prediction))\n\n ifile = open(\"eggs.csv\", \"r\")\n self.set_header ('Content-Type', 'text/csv')\n self.set_header ('Content-Disposition', 'attachment; filename='+\"eggs.csv\"+'')\n self.write (ifile.read())","title":""},{"docid":"0af4448d51421ed53fde72e055037051","score":"0.6104318","text":"def save_predictions(pred, file):\n\n with open(file, 'w') as csvfile:\n fieldnames = ['Stances']\n writer = DictWriter(csvfile, fieldnames=fieldnames)\n\n writer.writeheader()\n for instance in pred:\n writer.writerow({'Stances': label_ref_rev[instance]})","title":""},{"docid":"c313a7a731ec52239282f8e4f2b0c2d8","score":"0.6087462","text":"def hydra_predict_and_write_to_csv(all_eval_data: tf.data.Dataset,\n hydra_model: tf.keras.Model,\n missing_outcomes: bool,\n prediction_file: str,\n num_shards=100):\n # running evaluation on very large data can take an obscenely long time, so we shard the data as checkpointing\n for idx in range(num_shards):\n eval_data = all_eval_data.shard(num_shards=num_shards, index=idx)\n\n outputs = hydra_model.predict(x=eval_data)\n\n out_dict = {}\n if missing_outcomes:\n\n for t, g0 in enumerate(tf.unstack(outputs[0], axis=-1)):\n out_dict['g0_' + str(t)] = g0.numpy()\n\n for t, g1 in enumerate(tf.unstack(outputs[1], axis=-1)):\n out_dict['g1_' + str(t)] = g1.numpy()\n\n out_dict['prob_y_obs'] = np.squeeze(outputs[2])\n\n for out, q in enumerate(outputs[3:]):\n out_dict['q' + str(out)] = np.squeeze(q)\n\n else:\n\n for t, g in enumerate(tf.unstack(outputs[0], axis=-1)):\n out_dict['g_' + str(t)] = g.numpy()\n\n for out, q in enumerate(outputs[1:]):\n out_dict['q' + str(out)] = np.squeeze(q)\n\n predictions = pd.DataFrame(out_dict)\n\n label_dataset = eval_data.map(lambda f, l: l, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n data_df = dataset_to_pandas_df(label_dataset)\n\n outs = data_df.join(predictions)\n with tf.io.gfile.GFile(prediction_file + f'_index{idx}', \"w\") as writer:\n writer.write(outs.to_csv(sep=\"\\t\"))\n\n # merge shards and write final predictions csv\n dfs = []\n for idx in range(num_shards):\n dfs.append(pd.read_csv(prediction_file + f'_index{idx}', sep='\\t'))\n\n full_df = pd.concat(dfs)\n with tf.io.gfile.GFile(prediction_file, \"w\") as writer:\n writer.write(full_df.to_csv(sep=\"\\t\"))","title":""},{"docid":"8477b21b5850536b97f7d177ef499ffa","score":"0.6086274","text":"def save_predictions(test_filenames, predictions, out_filename='submission.csv', location='./'):\n\tsubmission = pd.DataFrame(predictions, columns=ALL_FISH_CATEGORIES)\n\tsubmission.insert(0, 'image', test_filenames)\n\tsubmission.to_csv(location+out_filename, index=False)\n\tprint 'Predictions saved to '+location+out_filename","title":""},{"docid":"65bdb1c80ae6fbaa8b98fedb569225ac","score":"0.60813093","text":"def write_predictions(ids, y_score, uniqname) :\r\n \r\n df_labels = pd.DataFrame(y_score)\r\n df_labels.columns = ['30-day_mortality']\r\n df_labels.insert(0, 'RecordID', ids)\r\n df_labels.to_csv(f'{uniqname}_icu.csv', index=False)","title":""},{"docid":"a2af78c2f9c7038d55a1e536c528ed81","score":"0.60705453","text":"def write_predictions(predictions, ids, outfile):\n with open(outfile,\"w+\") as f:\n # write header\n f.write(\"Id,Prediction\\n\")\n for i, history_id in enumerate(ids):\n f.write(\"%s,%d\\n\" % (history_id, predictions[i]))","title":""},{"docid":"51625e387666ce4fb9915200debd3cf6","score":"0.60703653","text":"def predict(\n output_directory,\n model_checkpoint_directory,\n predict_dataset_directory,\n predict_parse_fns,\n model_fn,\n model_hparams,\n):\n tf_config = os.environ.get('TF_CONFIG')\n logging.info(\"TF_CONFIG {}\".format(tf_config))\n\n # Load `RunConfig`.\n run_config = tf.estimator.RunConfig()\n run_config = run_config.replace(\n model_dir=output_directory,\n )\n\n predict_dataset = input.make_input_fn(\n dataset_directory=predict_dataset_directory,\n parse_fns=predict_parse_fns,\n )\n iterator = predict_dataset.make_one_shot_iterator()\n observations, distribution = iterator.get_next()\n\n # Rebuild the model\n predictions = model_fn(\n observations,\n distribution,\n tf.estimator.ModeKeys.EVAL,\n model_hparams).predictions\n\n # Manually load the latest checkpoint.\n saver = tf.train.Saver()\n with tf.Session() as sess:\n ckpt = tf.train.get_checkpoint_state(model_checkpoint_directory)\n saver.restore(sess, ckpt.model_checkpoint_path)\n\n # Loop through the batches and store predictions and labels\n output = []\n for i in range(1):\n try:\n preds_eval, dist_eval = sess.run([predictions, distribution])\n # Add distributions to `predictions`.\n preds_eval.update({'distribution': dist_eval})\n output.append(preds_eval)\n except tf.errors.OutOfRangeError:\n break\n\n return output","title":""},{"docid":"4c8bd15626f24a608e0d3a91143a5129","score":"0.6064927","text":"def predict(csv_file):\n\n #your code goes here...\n\n pass","title":""},{"docid":"ae9090233f6c69c32639aecd21ac5afe","score":"0.6058799","text":"def write_pred(pred_df, test_df, filename, id_col, data_col):\n # Append id column from test dataset to predictions for submission\n pred_df[id_col] = test_df[id_col]\n\n # Specify order of columns/format\n csv = pred_df.to_csv(\n path_or_buf=filename,\n index=False,\n columns=[id_col, data_col],\n )","title":""},{"docid":"9f7f102868f9bd62b36dbfafc3b3e85e","score":"0.6054072","text":"def predict_and_save(test_dir, model_dir, save_dir, filename, config):\n\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n tmp_dir = tempfile.mkdtemp()\n\n ds = input_fn(test_dir, repeat=False, shuffle=False)\n\n dataframes_to_concat = []\n\n it = 0\n\n df_files = []\n delays = np.array([])\n for predictors, target in ds:\n\n delays = np.append(delays, target)\n\n if it % 1000 == 0:\n\n aux_df = pd.DataFrame({\n \"Delay\": delays\n })\n\n dataframes_to_concat.append(aux_df)\n delays = np.array([])\n\n if it % 3000 == 0:\n df = pd.concat(dataframes_to_concat)\n file = os.path.join(tmp_dir, \"tmp_df_\" + str(it) + \".parquet\")\n df.to_parquet(file)\n df_files.append(file)\n dataframes_to_concat = []\n\n it += 1\n\n if it % 3000 != 0:\n if it % 1000 != 0:\n\n aux_df = pd.DataFrame({\n \"Delay\": delays\n })\n\n dataframes_to_concat.append(aux_df)\n\n df = pd.concat(dataframes_to_concat)\n file = os.path.join(tmp_dir, \"tmp_df_\" + str(it) + \".parquet\")\n df.to_parquet(file)\n df_files.append(file)\n\n df_list = []\n\n for file in df_files:\n df_list.append(pd.read_parquet(os.path.join(file)))\n\n df = pd.concat(df_list)\n\n file = os.path.join(save_dir, filename)\n df.to_csv(file)\n\n predictions = predict(test_dir, model_dir, config)\n\n df[\"Predicted_Delay\"] = predictions\n df['Absolute_Error'] = np.abs(df[\"Delay\"] - df[\"Predicted_Delay\"])\n df['Absolute_Percentage_Error'] = (df['Absolute_Error'] / np.abs(df[\"Delay\"]))*100\n\n return df['Absolute_Percentage_Error'].mean()","title":""},{"docid":"3f5f589c1ad6e702143d6c69115f3622","score":"0.60529906","text":"def test_03_predict(self):\r\n\r\n log_file = os.path.join(\"logs\", \"prediction-test.log\")\r\n if os.path.exists(log_file):\r\n os.remove(log_file)\r\n \r\n\r\n y_pred = [0]\r\n y_proba = [0.6, 0.4]\r\n runtime = \"00:00:06\"\r\n model_version = 0.1\r\n query = ['France', '2020', '01', '05']\r\n\r\n update_predict_log(self,y_pred, y_proba, query, runtime,\r\n model_version, test=True)\r\n\r\n self.assertTrue(os.path.exists(log_file))","title":""},{"docid":"c0ecac633e35e07ee4f5dfc1e143d450","score":"0.6044228","text":"def predict_model(self, data: pd.DataFrame, export_metrics: bool = False):\n\n self.predict = pcr.predict_model(estimator=self.model, data=data)\n evaluation_metrics(self.predict, export_metrics)\n\n return self.predict","title":""},{"docid":"8fdfe4ee30564cf1e87decfecf959979","score":"0.60374653","text":"def write_csv_proper(filename, predictions):\n with open(filename, 'w') as f:\n f.write('Id,Prediction\\n')\n for index, prediction in enumerate(predictions):\n f.write(str(index + 1))\n f.write(',')\n f.write(str(prediction))\n f.write('\\n')","title":""},{"docid":"e76fbc471b8ef7268000f725facace07","score":"0.6031369","text":"def write_predictions(predictions, ids, outfile):\n with open(outfile,\"w+\") as f:\n # write header\n f.write(\"Id,Prediction\\n\")\n for i, movie_id in enumerate(ids):\n f.write(\"%s,%f\\n\" % (movie_id, predictions[i]))","title":""},{"docid":"ce75deafae8ce1d4b46083a7fb0a6740","score":"0.60234386","text":"def write_hyperpartisan_predictions(predictions, eval_dataset, output_dir, cutoff=0.5):\n article_ids = [art.get_id() for art in eval_dataset.articles]\n\n ## Write to file\n with open(os.path.join(output_dir, 'prediction.txt'), 'w', encoding='utf-8') as out:\n for art_id, pred in zip(article_ids, predictions):\n print('{}\\t{}\\t{}'.format(\n art_id,\n 'true' if pred > cutoff else 'false',\n ((pred - 0.5) * 2) if pred > 0.5 else ((0.5 - pred) * 2)),\n file=out\n )","title":""},{"docid":"96a2fb687509f069e3c176bd1a861e0a","score":"0.5993838","text":"def sklearn_train_model(model, training_data, training_labels, test_data, test_labels, model_savename):\n starttime = datetime.now()\n model.fit(training_data, training_labels)\n finishtime = datetime.now()\n runtime = finishtime - starttime\n\n #predict the run\n test_predictions = model.predict(test_data)\n test_predictions_probabilities = model.predict_proba(test_data)[:, 1]\n # proba[:,1] returns just 1 of 2 columns. As they always add up, this is enough!\n\n sklearn_persist_model(model, model_savename)\n # append all this to a dataframe / JSON / whatever and\n return pe.PredictionEntry(test_labels, test_predictions, test_predictions_probabilities, runtime)","title":""},{"docid":"f4ed06579273e727997e87b389a5bff1","score":"0.59807587","text":"def predict_note_file():\n df_test = pd.read_csv(request.files.get(\"file\"))\n prediction = model.predict(df_test)\n return \"The predicted values for the csv is \" + str(list(prediction))","title":""},{"docid":"9c63b84484596ef55c49bf781afb4e29","score":"0.5975053","text":"def predict(model_path: str = None, input_data=None, input_path: str = None, output_path: str = None, **kwargs):\n\n try:\n # TODO add predict batch support?\n # TODO real logging\n model_handler.init(default_model_key=model_path)\n log.info(model_handler.predict(input_data, **kwargs))\n except:\n log.exception(\"Failed predict with model.\")","title":""},{"docid":"58e64f62e9fb9ab636e221c1ff933667","score":"0.59743667","text":"def get_prediction_df(input_df, model):\r\n predictions_df = predict_model(estimator = model, data = input_df)\r\n return predictions_df","title":""},{"docid":"8a6a4dba224691dfa6ee8e0726019e9e","score":"0.59676725","text":"def _predict(model_id, model_gcp_project, model_location,\n model_api_endpoint, gcp_project, bq_input_uri, bq_output_uri):\n credentials, project = google.auth.default(scopes=[GCP_SCOPE])\n\n authed_session = AuthorizedSession(credentials)\n\n request_url = f'https://{model_api_endpoint}/v1/projects/{model_gcp_project}/locations/{model_location}/batchPredictionJobs'\n request_data = json.dumps({\n 'displayName': f'{DEPLOYMENT_NAME}_{SOLUTION_PREFIX}_batch_predict - {datetime.datetime.now()}',\n 'inputConfig': {\n 'instancesFormat': 'bigquery',\n 'bigquerySource': {\n 'inputUri': bq_input_uri\n }\n },\n 'outputConfig': {\n 'predictionsFormat': 'bigquery',\n 'bigqueryDestination': {\n 'outputUri': bq_output_uri\n }\n },\n 'model': f'projects/{model_gcp_project}/locations/{model_location}/models/{model_id}'\n })\n logger.debug('Creating batch prediction. URL: %r, Data: %r',\n request_url, request_data)\n response = authed_session.post(request_url, data=request_data)\n response_dict = json.loads(response.text)\n logger.debug('Batch prediction created. Response: %r', response_dict)\n if 'error' in response_dict:\n raise Exception(f'Error while creating batch prediction job: {response_dict!r}')\n return response_dict['name']","title":""},{"docid":"c43d3e4c5ec95ba21e8e11135702c9d2","score":"0.59660226","text":"def predict_model(model, test_df, features):\n return model.predict(test_df[features])","title":""},{"docid":"b32f595827bc9822ea8a5984e172cd33","score":"0.5962195","text":"def predict(self, dataset, model=None, as_pandas=False):\n dataset = self.__get_dataset(dataset)\n return self._learner.predict(X=dataset, model=model, as_pandas=as_pandas)","title":""},{"docid":"6e93e574370832bc1f4bf03348a2127c","score":"0.5946272","text":"def predict(handle, test_file, model_file, output_file):\n\n return handle.predict(test_file, model_file, output_file)","title":""},{"docid":"297df5883ee5b4268ad0a00035666211","score":"0.59450823","text":"def predict(self, X, y):\n for self.model in self.models:\n self.model.fit(X, y)\n self.predictions_proba = self.model.predict_proba(self.points_1)\n \n self.blend_predictions()\n self.plot_predictions()","title":""},{"docid":"fad30e6161c7e38413331abdd493b5bd","score":"0.5941714","text":"def add_model_predictions_to_df(df, prediction_file, model_col_name,\n prediction_name, example_key, class_names):\n\n prediction_file = os.path.join(prediction_file,\n 'prediction.results-00000-of-00001')\n if not tf.gfile.Exists(prediction_file):\n raise ValueError(\n 'Prediction file does not exist.'\n ' You need to call prediction job and wait for completion.')\n\n def _load_predictions(pred_file):\n with file_io.FileIO(pred_file, 'r') as f:\n # prediction file needs to fit in memory.\n try:\n predictions = [json.loads(line) for line in f]\n except:\n predictions = []\n return predictions\n\n predictions = _load_predictions(prediction_file)\n\n if not predictions:\n raise ValueError(\n 'The prediction file returned by CMLE is empty.'\n ' It might be due to a badly formatted tfrecord input file that can not be'\n ' parsed by CMLE (wrong input signature given by a `Model` instance).'\n ' Check the logs of your CMLE job for further details.')\n if example_key not in predictions[0]:\n raise ValueError(\n \"Predictions do not contain the 'example_key' field.\"\n \" Verify that your 'example_key' parameter (set to {})\"\n \" matches the CMLE model signature.\".format(example_key))\n if prediction_name not in predictions[0]:\n raise ValueError(\n \"Predictions do not contain the 'prediction_name' field.\"\n \" Verify that your 'prediction_name' parameter (set to {})\"\n \" matches the CMLE model signature.\".format(prediction_name))\n if len(predictions) != len(df):\n raise ValueError('The dataframe and the prediction file do not contain'\n ' the same number of lines.')\n\n predictions = sorted(predictions, key=lambda x: x[example_key])\n if class_names is None:\n prediction_proba = [x[prediction_name][0] for x in predictions]\n df[model_col_name] = prediction_proba\n else:\n for i, class_name in enumerate(class_names):\n df['{}_{}'.format(model_col_name,class_name)] = [x[prediction_name][i] for x in predictions]\n\n return df","title":""},{"docid":"c53b51468acfc807cd8868a3fdf60316","score":"0.5940207","text":"def generate_pred_csv(testset, pred_labels_df, column_name, csv_name): \n testset[column_name] = pred_labels_df\n return testset.to_csv(csv_name, index = False)","title":""},{"docid":"3d88d5c020fa39ed2c5ad0972e55f7b1","score":"0.5932011","text":"def get_prediction(user_data, model):\n\n # Feature orders needed for model.predict's input data\n # The columns are required to be in the same order as the data input\n feature_order = [\n \"property_type\",\n \"room_type\",\n \"bathrooms\",\n \"cancellation_policy\",\n \"city\",\n \"host_since\",\n \"review_scores_rating\",\n \"bedrooms\",\n \"beds\",\n \"instant_bookable\",\n \"host_has_profile_pic\",\n \"host_identity_verified\",\n \"cleaning_fee\",\n \"Wireless Internet\",\n \"Air conditioning\",\n \"Kitchen\",\n \"Heating\",\n \"Family/kid friendly\",\n \"Hair dryer\",\n \"Iron\",\n \"Shampoo\",\n \"Fire extinguisher\",\n \"Laptop friendly workspace\",\n \"Indoor fireplace\",\n \"TV\",\n \"Cable TV\",\n ]\n\n # dictionary for forcing data types accepted by sklearn\n data_types = {\n \"property_type\": object,\n \"room_type\": object,\n \"bathrooms\": np.float64,\n \"cancellation_policy\": object,\n \"city\": object,\n \"host_since\": np.int64,\n \"review_scores_rating\": object,\n \"bedrooms\": np.float64,\n \"beds\": np.float64,\n \"instant_bookable\": bool,\n \"host_has_profile_pic\": bool,\n \"host_identity_verified\": bool,\n \"cleaning_fee\": bool,\n \"Wireless Internet\": bool,\n \"Air conditioning\": bool,\n \"Kitchen\": bool,\n \"Heating\": bool,\n \"Family/kid friendly\": bool,\n \"Hair dryer\": bool,\n \"Iron\": bool,\n \"Shampoo\": bool,\n \"Fire extinguisher\": bool,\n \"Laptop friendly workspace\": bool,\n \"Indoor fireplace\": bool,\n \"TV\": bool,\n \"Cable TV\": bool,\n }\n\n # Make sure the data is coming in correctly\n # This also serves as a simple backend data validation so the route returns\n # a value even if someone tries to spoof a data point\n try:\n\n # Check user data is valid length\n assert len(user_data) == len(\n feature_order), print(f'Expected user_data to have {len(feature_order)} items')\n\n # Convert host_since into number of days\n user_data[5] = (datetime.today() -\n datetime.strptime(user_data[5], '%Y-%m-%d')\n ).days\n\n # dictionary to construct dataframe\n data = {feature: [datum]\n for feature, datum in zip(feature_order, user_data)}\n assert len(data) == len(feature_order), print(\n 'dictionary size incorrect')\n\n # transform input into dataframe for model prediction\n transformed_input = pd.DataFrame(\n data).astype(data_types)[feature_order]\n\n # make prediction using model\n prediction = model.predict(transformed_input)\n\n except Exception as e:\n print('-----ERROR IN TRANSFORMING INPUT-----')\n print(\"User Data: \", user_data)\n print(e)\n # Return 0 so the endpoint still works\n prediction = [0]\n\n # return prediction in USD rounded to the penny\n return np.exp(prediction[0]).round(2)","title":""},{"docid":"45bc1a0e563b68a01985e9d2ce01d348","score":"0.5931972","text":"def save_predictions(\n self,\n strategy_name,\n dataset_name,\n y_true,\n y_pred,\n y_proba,\n index,\n cv_fold,\n train_or_test,\n fit_estimator_start_time=None,\n fit_estimator_end_time=None,\n predict_estimator_start_time=None,\n predict_estimator_end_time=None,\n ):\n # TODO y_proba is currently ignored\n key = (\n self._generate_key(strategy_name, dataset_name, cv_fold, train_or_test)\n + \".csv\"\n )\n # TODO find a more clever way to save the timestamps\n results = pd.DataFrame(\n {\n \"index\": index,\n \"y_true\": y_true,\n \"y_pred\": y_pred,\n \"fit_estimator_start_time\": fit_estimator_start_time,\n \"fit_estimator_end_time\": fit_estimator_end_time,\n \"predict_estimator_start_time\": predict_estimator_start_time,\n \"predict_estimator_end_time\": predict_estimator_end_time,\n }\n )\n results.to_csv(key, index=False, header=True)\n self._append_key(strategy_name, dataset_name)","title":""},{"docid":"1eca467972662ecf75fbd14bcca3f25d","score":"0.5931729","text":"def transformation():\n data = None\n model_name = flask.request.headers.get('CustomAttributes')\n if model_name == None:\n model_name = flask.request.headers.get('X-Amzn-Sagemaker-Custom-Attributes')\n print(model_name)\n # Convert from CSV to pandas\n if flask.request.content_type == 'application/python-pickle':\n data = flask.request.data\n df = pickle.loads(data)\n print('Invoked with {} records'.format(df.shape[0]))\n preds = h2o.H2OFrame(df)\n \n else:\n return flask.Response(response='This predictor only supports pickle files', status=415, mimetype='text/plain')\n \n # Do the prediction\n predictions = ScoringService.predict(preds, model_name)\n h2o.h2o.export_file(predictions,'/tmp/results.csv')\n predictions2 = pd.read_csv('/tmp/results.csv', header=None)\n os.remove(\"/tmp/results.csv\")\n # Convert from numpy back to CSV\n out = StringIO.StringIO()\n #pd.DataFrame({'results':predictions2}).to_csv(out, header=False, index=False)\n #result = out.getvalue()\n\n return flask.Response(response=predictions2.to_json(orient='columns'), status=200, mimetype='text/csv')","title":""},{"docid":"922fa31cf66c3b99aa5db0a3427465a9","score":"0.5931536","text":"def apply(input_dataframe):\n\n # import model\n \n filename = project_path+\"/model/housing_model.sav\"\n\n with open(filename,'rb') as model_file:\n pickle_model = pickle.load(model_file)\n\n return pickle_model.predict(input_dataframe)","title":""},{"docid":"e559f03e9885601c24b365dfec32ae5d","score":"0.59200865","text":"def write_predictions(self, user_data):\n def format_row(va):\n # We filled age with a default value of zero but do not want to\n # report this value in output files and graphs\n if float(va.age) == 0 and self.AGE_GROUP in ['adult', 'child']:\n va.age = ''\n return [va.sid, va.cause34, va.cause34_name, va.age, va.sex]\n\n filename = '{:s}-predictions.csv'.format(self.AGE_GROUP)\n with open(os.path.join(self.output_dir_path, filename), 'wb') as f:\n writer = csv.writer(f)\n writer.writerow([SID_KEY, 'cause', 'cause34', 'age', 'sex'])\n writer.writerows([format_row(va) for va in user_data])","title":""},{"docid":"7008700518d429866d082b8c47cee04a","score":"0.59159017","text":"def load_model_predict(model_name, X, labels, embedding_size, filename):\n rankModel, representation = tripletModel(X.shape[1], embedding_size)\n rankModel.load_weights(model_name)\n representation = Model(inputs=rankModel.input[0],\n outputs=rankModel.get_layer('hidden_layer').get_output_at(0))\n\n new_X = representation.predict(X)\n writeRepresentation(new_X, labels, embedding_size, filename + str(embedding_size) + \"D_RankOD\")\n scores = lesinn(new_X)\n rauc = aucPerformance(scores, labels, filename)\n writeResults(filename, embedding_size, rauc)\n writeOutlierScores(scores, labels, str(embedding_size) + \"D_\"+filename)\n return rauc","title":""},{"docid":"d618f2ccf52344b46d9c7b63d6065981","score":"0.5908038","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_pred = model.predict(X_test)\n \n for i in range(len(Y_test.columns)):\n print(\"=== {}. {} ===\".format(i+1, Y_test.columns[i]))\n print(classification_report(Y_test.iloc[:,i], Y_pred[:,i]))","title":""},{"docid":"b9d3274874effaff29bf0df5684a7ed7","score":"0.59024817","text":"def predictor(test_day, train_day):\n X_test, y_test = data_format(test_day=test_day)\n clf = pickle.load(open( os.path.join(train_day,\"Models/SVM.p\"), \"rb\"))\n # We load in our fitted model\n y_pred = clf.predict(X_test)\n # The predicted results\n recall = metrics.recall_score(y_test, y_pred)\n n = len(y_test)\n filtered = sum(np.count_nonzero(y_pred-np.ones(n))) / float(n)\n # The filter rate is the proportion of the data we predict to be -1\n result = [train_day, test_day, recall, filtered, netsav(recall,filtered)]\n # We write this as a list so that it will be on one row of the csv\n return result","title":""},{"docid":"00da40a01a2a82b635f244f071e0f78e","score":"0.58951724","text":"def serve_predict_model(model_name):\n # # get feature times\n # feature_times, features = load_features('motion_energy')\n\n X = np.random.randn(20, 10).astype('float32')\n model = r.get(f'model:{model_name}')\n model = pickle.loads(model)\n y_hat = model.predict(X)\n\n return f'Predicting {model_name} {len(y_hat)}'","title":""},{"docid":"65aef89b4c03ad8ab906408669028562","score":"0.5889908","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_pred = model.predict(X_test)\n for i in range(36):\n print(Y_test.columns[i], ':')\n print(classification_report(Y_test.iloc[:,i], Y_pred[:,i], target_names=category_names))","title":""},{"docid":"861ab6520d30aa0a50cc1711cc19ac95","score":"0.5887997","text":"def run_prediction(model: str):\n data = FingerprintsData(PATH_TEST / \"smiles.csv\", sanitize=False)\n modeller = SKModeller(model, data)\n modeller.load_model(\"swan_skmodeller.pkl\")\n modeller.data.load_scale()\n predicted = modeller.predict(data.fingerprints)\n assert not np.isnan(predicted).all()","title":""},{"docid":"53efa09e43483a2c504aba9d2bd3dbd9","score":"0.58835727","text":"def save_results(predictions, filename):\n with open(filename, 'w') as f:\n f.write(\"id,ACTION\\n\")\n for i, pred in enumerate(predictions):\n f.write(\"%d,%f\\n\" % (i + 1, pred))","title":""},{"docid":"63a27aea5e31f19bdcd95c98ae30bb80","score":"0.5875886","text":"def predict(self, *, model_id: str, data: list) -> list:","title":""},{"docid":"c5cbb3929bbce4740e6b730004320d49","score":"0.5871915","text":"def evaluate_model(model, X_test, y_test, labels):\n print('Evaluating model...')\n\n y_pred = model.predict(X_test) \n\n report = classification_report(y_test.values, y_pred, target_names=labels, output_dict = True)\n print(report)\n\n print('Saving training report...')\n metrics = list(map(lambda label: extract_category(report, label), labels))\n df_metrics = pd.DataFrame(metrics)\n df_metrics.to_csv(metrics_filename, index = False)\n\n print(f'Best score: {model.best_score_}')\n print(f'Best parameters: {model.best_params_}')","title":""},{"docid":"47aba0edc2b313a2d78dbea4504dd453","score":"0.58533657","text":"def eval_model_predictions(model, X_train, y_train, X_test):\n model.fit(X_train, y_train)\n pred = model.predict(X_test)\n print\n \"accuracy is \", metrics.accuracy_score(y_test, pred)\n return pred","title":""},{"docid":"70d79610109835be92a9125a1e1631e4","score":"0.5851333","text":"def main():\n x_train, x_test, y_train, y_test = fetch_processed('data/processed/transfusion_data.csv')\n \n # Train the model \n model = fit_model(x_train, y_train)\n \n # Store model and test set for prediction\n with open(ROOT / 'models/transfusion.model', 'wb') as fout:\n pickle.dump(model, fout, PROTOCOL)\n x_test.to_csv(ROOT / 'data/processed/transfusion_x_test.csv',\n index=False)\n y_test.to_csv(ROOT / 'data/processed/transfusion_y_test.csv',\n index=False)","title":""},{"docid":"132a5d7a57666276b51bda6ac297f527","score":"0.58437943","text":"def show_results(model, modelname, data, label, title):\n preds = model.predict(data, batch_size=1, verbose=1)\n y_pred = preds.argmax(axis=1)\n if preds.ndim == 1:\n y_actual = np.squeeze(label)\n else:\n y_actual = label.argmax(axis=1)\n\n report = pd.DataFrame(\n classification_report(y_actual, y_pred, output_dict=True)).T\n report.to_csv(\n 'model/' + modelname + '_' + title + '_classification_report.csv')\n print('\\n' + title + 'Stats\\n', classification_report(y_actual, y_pred))\n print_confusion_matrix(confusion_matrix(y_actual, y_pred),\n unique_labels(y_actual, y_pred), title, modelname)\n return","title":""},{"docid":"5aee10adc79312d30fa12a9b66ce6c2f","score":"0.5842588","text":"def evaluate_model(model, X_test, y_test, category_names):\n y_pred = model.predict(X_test)\n y_pred = pd.DataFrame(y_pred, columns=y_test.columns, index=y_test.index)\n print(classification_report(y_test, y_pred, target_names=category_names))","title":""},{"docid":"0014e611c5ff5c3368f282db339ad3a0","score":"0.58349687","text":"def predictobs(self):\n\n\t\tlogger.info(\"{}: starting predictions...\".format(self))\n\t\t\n\t\t# We read the catalog\n\t\tcat = megalut.tools.io.readpickle(self.meascatfilepath)\n\t\t#print cat.colnames\n\t\t#print len(cat)\n\t\t#exit()\n\t\t\n\t\t# We read the mlparams:\n\t\tmlparams = {}\n\t\texecfile(os.path.join(self.configdir, \"mlparams.py\"), mlparams)\n\t\n\t\t# Run MegaLUT\n\t\tcat = megalut.learn.run.predict(cat, self.configdir, mlparams[\"trainparamslist\"])\n\t\t\t\n\t\t# And write the output\n\t\tmegalut.tools.io.writepickle(cat, self.predcatfilepath)","title":""},{"docid":"676b5f327d993c49955f2b8d2053dffb","score":"0.58335316","text":"def _predict_by_model(self, model_names, data_type):\n\t\tn_models = len(model_names)\n\t\tif n_models == 0:\n\t\t\traise RuntimeError('There should be at least one model to predict')\n\t\t\"\"\"\n\t\tdv = self.client[:]\n\t\tis_probabilistic = dv.map(partial(read_model_meta, \n\t\t\t\t\t\t\t\t\tself.ensemble_path, \n\t\t\t\t\t\t\t\t\tkeys=['is_probabilistic'], default=False), \n\t\t\t\t\t\t\t\tmodel_names)\n\t\t#is_probabilistic = map(partial(read_model_meta, \n\t\t#\t\t\t\t\t\t\tself.ensemble_path, \n\t\t#\t\t\t\t\t\t\tkeys=['is_probabilistic'], default=False), \n\t\t#\t\t\t\t\t\tmodel_names)\n\t\tprint 'THIS STEP DONE'\n\t\tis_probabilistic = [d['is_probabilistic'] for d in is_probabilistic]\n\t\t\"\"\"\n\t\t## decided by the 'is_probabilistic' field in the model config\n\t\tis_probabilistic = [None for _ in xrange(n_models)]\n\t\tdata_types = [data_type for _ in xrange(n_models)]\n\t\tparams = zip(model_names, data_types, is_probabilistic)\n\t\tresults = parallel_predict_model(self.ensemble_path, params, self.client, verbose=True)\n\t\ttarget = results[0][1][0]\n\t\treturn (target, {mdl_name : yhat for (mdl_name, (y, yhat)) in results})","title":""},{"docid":"54d3f69c6e1fc985e8bf43a23cab93d5","score":"0.5826853","text":"def predict_fn(input_data, model):\n prediction = model.predict(input_data)\n pred_prob = model.predict_proba(input_data)\n \n return prediction","title":""},{"docid":"48221732be5888da4c0f7a50f35ec6db","score":"0.5825037","text":"def predict(model,\n X,\n Z,\n y,\n model_file,\n output,\n amount=None,\n return_mf1=False,\n use_softmax=False):\n\n print(\"Predicting with saved model: {0} ... \".format(model_file), end='')\n start_time = time()\n y_pred = model.predict([X[:amount], Z[:amount]])\n print(\"Done (took {:3.3f}s)\".format(time() - start_time))\n\n # print(y_pred)\n if use_softmax:\n y_pred = (y_pred == y_pred.max(axis=1)[:, None]).astype(int)\n else:\n y_pred[y_pred >= 0.5] = 1.\n y_pred[y_pred < 0.5] = 0.\n\n # print(y_pred)\n # print(y_pred.sum(axis=1))\n y_pred = sparse.csr_matrix(y_pred)\n\n F1 = 0.\n file_writer = open(output, \"a\")\n file_writer.write(\"\\n{0}\\n\".format(datetime.now()))\n file_writer.write(\"{:s}\\n\".format(model_file))\n\n # Begin evaluation\n print(\"Calculating Precision/Recall/F-1 scores ...\")\n eval_types = ['micro', 'macro', 'weighted']\n for eval_type in eval_types:\n p, r, f, _ = precision_recall_fscore_support(\n y, y_pred, average=eval_type)\n print(\"[{}]\\t{:3.3f}\\t{:3.3f}\\t{:3.3f}\".format(eval_type, p, r, f))\n file_writer.write(\"[{}]\\t{:3.3f}\\t{:3.3f}\\t{:3.3f}\\n\".format(\n eval_type, p, r, f))\n if eval_type == 'micro':\n F1 = f\n\n # Close file pointer\n file_writer.close()\n\n if return_mf1:\n return F1","title":""},{"docid":"4190f2f16918a947fdd83560be763f27","score":"0.58138597","text":"def write_classification_outputs(self, tasks, trial, split):\r\n log(\"Writing out predictions for\", tasks, split)\r\n predict_input_fn, _ = self._preprocessor.prepare_predict(tasks, split)\r\n results = self._estimator.predict(input_fn=predict_input_fn,\r\n yield_single_examples=True)\r\n # task name -> eid -> model-logits\r\n logits = collections.defaultdict(dict)\r\n for r in results:\r\n if r[\"task_id\"] != len(self._tasks):\r\n r = nest_dict(r, self._config.task_names)\r\n task_name = self._config.task_names[r[\"task_id\"]]\r\n logits[task_name][r[task_name][\"eid\"]] = (\r\n r[task_name][\"logits\"] if \"logits\" in r[task_name]\r\n else r[task_name][\"predictions\"])\r\n for task_name in logits:\r\n log(\"Getting predictions for {:} {:} examples ({:})\".format(\r\n len(logits[task_name]), task_name, split))\r\n logits = logits[task_name].values()\r\n return logits","title":""},{"docid":"8fbdafdcc12734115968bfa065f15852","score":"0.58072114","text":"def do_predict(args):\n\n # Load data\n Xt = XLinearModel.load_feature_matrix(args.inst_path)\n\n if args.select_output is not None:\n # Select Output\n select_outputs_csr = XLinearModel.load_feature_matrix(args.select_output)\n xlinear_model = XLinearModel.load(\n args.model_folder, is_predict_only=True, weight_matrix_type=\"CSC\"\n )\n else:\n # TopK\n select_outputs_csr = None\n xlinear_model = XLinearModel.load(args.model_folder, is_predict_only=True)\n\n # Model Predicting\n if args.batch_size is not None:\n Yts = []\n for i in range(0, Xt.shape[0], args.batch_size):\n Yte = xlinear_model.predict(\n Xt[i : i + args.batch_size, :],\n select_outputs_csr=select_outputs_csr[i : i + args.batch_size, :]\n if select_outputs_csr is not None\n else None,\n only_topk=args.only_topk,\n beam_size=args.beam_size,\n post_processor=args.post_processor,\n threads=args.threads,\n )\n Yts.append(Yte)\n # vstack_csr will retain indices order\n Yt_pred = smat_util.vstack_csr(Yts)\n else:\n Yt_pred = xlinear_model.predict(\n Xt,\n select_outputs_csr=select_outputs_csr,\n only_topk=args.only_topk,\n beam_size=args.beam_size,\n post_processor=args.post_processor,\n threads=args.threads,\n )\n\n # Save prediction\n if args.save_pred_path:\n smat_util.save_matrix(args.save_pred_path, Yt_pred)\n\n # Evaluate\n if args.label_path:\n Yt = XLinearModel.load_label_matrix(args.label_path)\n metric = smat_util.Metrics.generate(Yt, Yt_pred, topk=10)\n print(\"==== evaluation results ====\")\n print(metric)","title":""},{"docid":"bdf5e8ccd9f62a5406ca973f9afd8e41","score":"0.58066356","text":"def predict(test_data, results, model_name):\n model_params = DataFrame(results[model_name][0].params)\n formula = results[model_name][1]\n\n # Create regression friendly test DataFrame\n yt, xt = dmatrices(formula, data=test_data, return_type='dataframe')\n xt, model_params = get_dataframes_intersections(xt, xt.columns,\n model_params, model_params.index)\n # Convert to NumPy arrays for performance\n model_params = np.asarray(model_params)\n yt = np.asarray(yt)\n yt = yt.ravel()\n xt = np.asarray(xt)\n\n # Use our models to create predictions\n row, col = xt.shape\n model_parameters = model_params.ravel()\n model_array = list((model_parameters for parameter in xrange(row)))\n model_array = np.asarray(model_array)\n\n # Multiply matrix together\n predictions = np.multiply(xt, model_array)\n predictions = np.sum(predictions, axis=1)\n return predictions","title":""},{"docid":"9115b346fd5461f316e9a8fef584e07b","score":"0.58047765","text":"def transformation():\n data = None\n\n # Convert from CSV to pandas\n if flask.request.content_type == 'text/csv':\n data = flask.request.data.decode('utf-8')\n s = StringIO(data)\n data = pd.read_csv(s, header=None)\n else:\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\n\n nClasses, nFeatures, fptype = ScoringService.get_num_classes_and_features()\n\n if data.shape[1] != nFeatures:\n return flask.Response(response='Number of features is incorrect. %d is required\\n' % nFeatures, status=415, mimetype='text/plain')\n resultsToCompute = \"computeClassesLabels|computeClassesProbabilities\"\n print('Invoked with {} records'.format(data.shape[0]))\n if fptype == \"float\":\n fptype = \"float\"\n else:\n fptype = \"double\"\n dtype = (np.float64 if fptype == \"double\" else np.float32)\n # Do the prediction\n predictions = ScoringService.predict(input=np.ascontiguousarray(data.values, dtype=dtype), nClasses=nClasses,\n dtype=fptype, resultsToCompute=resultsToCompute)\n prediction = \"\".join(str(x[0])+ ' ' for x in predictions.prediction)\n probabilities = \"\"\n if nClasses > 2:\n i = 0\n while i < nClasses:\n probabilities += \"\".join(str(x[i])+ ' ' for x in predictions.probabilities) + \"\\n\"\n i = i + 1\n else:\n probabilities = \"\".join(str(x[0])+ ' ' for x in predictions.probabilities) + \"\\n\"\n\n result = prediction + \"\\n\" + probabilities\n\n return flask.Response(response=result, status=200, mimetype='text/csv')","title":""},{"docid":"e7612cc6e80a2c2d9f056d2c52adf219","score":"0.5799704","text":"def writePredictionsToCsv(predictions, out_path, label_strings):\n with open(out_path, 'w') as outfile:\n csvwriter = csv.writer(\n outfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL\n )\n # Write the header\n row_to_write = ['Id'] + [label for label in label_strings]\n csvwriter.writerow(row_to_write)\n # Write the rows using 18 digit precision\n for idx, prediction in enumerate(predictions):\n assert len(prediction) == len(label_strings)\n csvwriter.writerow(\n [str(idx+1)] +\n [\"%.18f\" % p for p in prediction]\n )","title":""},{"docid":"2897871b99887a03ded2fee27d5aaed3","score":"0.5798098","text":"def predict(self, dataset, prediction_directory=None, groundtruth_directory=None):\n\n if not isinstance(dataset, (Dataset, str)):\n raise TypeError(\"Must pass in an instance of Dataset containing your examples to be used for prediction\")\n if self.model is None:\n raise ValueError(\"Must fit or load a pickled model before predicting\")\n\n model = self.model\n medacy_pipeline = self.pipeline\n\n if isinstance(dataset, Dataset):\n # create directory to write predictions to\n prediction_directory = self.create_annotation_directory(directory=prediction_directory, training_dataset=dataset, option=\"predictions\")\n\n # create directory to write groundtruth to\n groundtruth_directory = self.create_annotation_directory(directory=groundtruth_directory, training_dataset=dataset, option=\"groundtruth\")\n\n for data_file in dataset.get_data_files():\n logging.info(\"Predicting file: %s\", data_file.file_name)\n with open(data_file.raw_path, 'r') as raw_text:\n doc = medacy_pipeline.spacy_pipeline.make_doc(raw_text.read())\n doc.set_extension('file_name', default=data_file.file_name, force=True)\n if data_file.metamapped_path is not None:\n doc.set_extension('metamapped_file', default=data_file.metamapped_path, force=True)\n\n # run through the pipeline\n doc = medacy_pipeline(doc, predict=True)\n\n annotations = predict_document(model, doc, medacy_pipeline)\n logging.debug(\"Writing to: %s\", os.path.join(prediction_directory, data_file.file_name + \".ann\"))\n annotations.to_ann(write_location=os.path.join(prediction_directory, data_file.file_name + \".ann\"))\n\n if isinstance(dataset, str):\n assert 'metamap_annotator' not in self.pipeline.get_components(), \\\n \"Cannot currently predict on the fly when metamap_component is in pipeline.\"\n\n doc = medacy_pipeline.spacy_pipeline.make_doc(dataset)\n doc.set_extension('file_name', default=\"STRING_INPUT\", force=True)\n doc = medacy_pipeline(doc, predict=True)\n annotations = predict_document(model, doc, medacy_pipeline)\n \n #display entity jason\n for entity_name, start, stop, end in annotations.get_entity_annotations():\n #span = Span(doc, start, end, label=medacy_pipeline.vocab.strings[entity_name])\n span = Span(doc, start, end, label=int(33333333))\n for token in span:\n token._.feature_is_frequency_indicator = True\n if len(span) > 1:\n retokenizer.merge(span)\n doc.ents = list(doc.ents) + [span]\n ########\n \n displacy.render(doc, style=\"ent\")\n \n return annotations","title":""},{"docid":"805683c26dce6a4020b5782345170234","score":"0.5792101","text":"def predict(self, model, Xval, Xtest):\n\n pred = model.predict(\n Xval, batch_size=self.batch_size_pred, verbose=0).squeeze()\n pred_test = model.predict(\n Xtest, batch_size=self.batch_size_pred, verbose=0).squeeze()\n\n return pred, pred_test","title":""},{"docid":"7f27f98375f5d38828d794f3739ff725","score":"0.579149","text":"def evaluate(model_path, test_path, model):\n\n if SAVE_MODELS:\n model = load_model(model_path, custom_objects={'TimestepDropout': TimestepDropout,\n 'Camouflage': Camouflage,\n 'SymmetricMasking': SymmetricMasking,\n 'Attention': Attention})\n\n test_data = dict(np.load(test_path, allow_pickle=True).item())\n\n test_inputs = test_data['input_ids']\n\n prediction = model.predict(test_inputs, batch_size=1)\n\n return prediction","title":""},{"docid":"c39275d7f0a70f1fbe13b97667eb22f5","score":"0.577839","text":"def save_results(predictions, filename):\n #this is borrowed from one of the starting codes\n with open(filename, 'w') as f:\n f.write(\"id,ACTION\\n\")\n for i, pred in enumerate(predictions):\n f.write(\"%d,%f\\n\" % (i + 1, pred))","title":""},{"docid":"4d4adf19003ac35ce6ba8a7edb6857eb","score":"0.5776986","text":"def evaluate(models, ypred, yreal, output_file_name=None, cutoff=5.0):\n cutoff = math.tanh(cutoff * 0.1)\n\n if type(models) is not list:\n models = [models]\n ypred = [ypred]\n yreal = [yreal]\n\n for model, yp, yr in zip(models, ypred, yreal):\n mat = get_accuracy_matrix(yp, yr, cutoff)\n score1 = mat[0][0] + mat[2][2] - mat[2][0] - mat[0][2]\n score1 = float(score1) / float(sum(mat[0]) + sum(mat[2]))\n score2 = sum(mat[i][i] for i in range(3)) - mat[2][0] - mat[0][2]\n score2 = float(score2) - 0.2 * (mat[0][1] + mat[1][0] + mat[1][2] + mat[2][1])\n score2 = score2 / float(sum(sum(mat[i]) for i in range(3)))\n if output_file_name is not None:\n with open(output_file_name + \".txt\", \"a\") as fp:\n fp.write(\"Model name: %s \\n\\n\" % model.cname)\n fp.write(str(mat))\n fp.write(\"\\n\\nScore1 = %f \\n\" % score1)\n fp.write(\"Score2 = %f \\n\\n\\n\\n\" % score2)\n else:\n print(mat)\n print(score1)\n print(score2)","title":""},{"docid":"e3ba69122cbb9cc2eb6b79a08264b796","score":"0.5770127","text":"def to_csv(prediction):\n return '\\n'.join('{},{}'.format(key, value)\n for key, value in prediction.items())","title":""},{"docid":"9e16a4f0db584492ec21241dceec4e71","score":"0.57673556","text":"def predict(model, dataset):\n dataset.reset()\n predictions = None\n nprocessed = 0\n for x, t in dataset:\n pred = model.fprop(x, inference=True).asnumpyarray()\n bsz = min(dataset.ndata - nprocessed, model.be.bsz)\n nprocessed += bsz\n if predictions is None:\n predictions = pred[:, :bsz]\n else:\n predictions = np.hstack((predictions, pred[:, :bsz]))\n return predictions","title":""},{"docid":"f33a13ac008415cc10015e96ff9cc319","score":"0.57618016","text":"def predict(main_project_id,\n input_path,\n demo_dataset,\n demo_table,\n model_id,\n service_acct,\n compute_region,\n config):\n logger.info('Starting entity extraction.')\n\n input_bucket_name = input_path.replace('gs://', '').split('/')[0]\n input_txt_folder = f\"gs://{input_bucket_name}/{demo_dataset}/txt\"\n\n list_fields = [x['field_name'] for x in config[\"model_ner\"][\"fields_to_extract\"]]\n list_fields.remove('gcs_path')\n\n storage_client = storage.Client.from_service_account_json(service_acct)\n bucket_name, path = utils.get_bucket_blob(input_txt_folder)\n bucket = storage_client.get_bucket(bucket_name)\n\n list_results = []\n for file in bucket.list_blobs(prefix=path):\n full_filename = os.path.join(input_txt_folder, os.path.basename(file.name))\n logger.info(full_filename)\n result = run_automl_single(ocr_path=full_filename,\n list_fields=list_fields,\n service_acct=service_acct,\n model_id=model_id,\n main_project_id=main_project_id,\n compute_region=compute_region)\n list_results.append(result)\n\n schema = [bigquery.SchemaField('file', 'STRING', mode='NULLABLE')]\n for field in list_fields:\n schema.append(bigquery.SchemaField(field, 'STRING', mode='NULLABLE'))\n \n utils.save_to_bq(\n demo_dataset,\n demo_table,\n list_results,\n service_acct,\n _create_table=True,\n schema=schema)\n\n logger.info('Entity extraction finished.\\n')","title":""},{"docid":"6525bf18de357f3038780dc0de0e1a9e","score":"0.57579637","text":"def predict(self):\n\t\tscore = self.model.evaluate(self.x_test, self.y_test, verbose=0)\n\t\tprint score","title":""},{"docid":"63b8561ba046fc7be7c9aa9e2b338120","score":"0.5754032","text":"def predict(self, model_path, x_test):\n\n init = tf.global_variables_initializer()\n prediction = []\n with tf.Session(config=config) as sess:\n # Initialize variables\n sess.run(init)\n\n # Restore model weights from previously saved model\n self.restore(sess, model_path, var_list=self.variables_to_restore)\n\n for test_x in x_test:\n y_dummy = np.empty((test_x.shape[0], test_x.shape[1], test_x.shape[2], self.n_class))\n prediction.append(sess.run(self.predictor, feed_dict={self.x: test_x, self.y: y_dummy,\n self.p: np.empty((self.batch_size, )),\n self.dropout_rate: 0.0,\n self.train_phase: False,\n self.need_pos: False}))\n\n return prediction","title":""},{"docid":"0757a5860142ba9879953525cc445c6f","score":"0.57459646","text":"def evaluate_model(model, X_test, Y_test, category_names):\n Y_pred = model.predict(X_test)\n\n for i in list(range(Y_pred.shape[1])):\n print(\"Report for column: \\\"\" + category_names[i] + \"\\\"\")\n print(classification_report(Y_test.values[:,i], Y_pred[:,i]))\n print(\"\\n\\n\")","title":""},{"docid":"646b0d14dd73dab40fe7e0e957bff30b","score":"0.5732246","text":"def predict(args):\n\n model = SentencePieceModel.load(args.model_folder)\n to_ids = True if args.output_type == \"ids\" else False\n model.predict_file(args.input_path, args.output_path, to_ids)","title":""},{"docid":"e087f8a6fd041fc152640ca27b1a867f","score":"0.57258976","text":"async def predict(req: request.Request, model_name: str) -> response.HTTPResponse: # pylint: disable=unused-variable\n model = app.predictors.get(model_name.lower())\n if model is None:\n raise ServerError(\"unknown model: {}\".format(model_name), status_code=400)\n\n data = req.json\n log_blob = {\"model\": model_name, \"inputs\": data, \"cached\": False, \"outputs\": {}}\n\n # See if we hit or not. In theory this could result in false positives.\n pre_hits = _caching_prediction.cache_info().hits # pylint: disable=no-value-for-parameter\n\n try:\n if cache_size > 0:\n # lru_cache insists that all function arguments be hashable,\n # so unfortunately we have to stringify the data.\n prediction = _caching_prediction(model, json.dumps(data))\n else:\n # if cache_size is 0, skip caching altogether\n prediction = model.predict_json(data)\n except KeyError as err:\n raise ServerError(\"Required JSON field not found: \" + err.args[0], status_code=400)\n\n post_hits = _caching_prediction.cache_info().hits # pylint: disable=no-value-for-parameter\n\n if post_hits > pre_hits:\n # Cache hit, so insert an artifical pause\n log_blob[\"cached\"] = True\n await asyncio.sleep(0.25)\n\n # The model predictions are extremely verbose, so we only log the most human-readable\n # parts of them.\n if model_name == \"machine-comprehension\":\n log_blob[\"outputs\"][\"best_span_str\"] = prediction[\"best_span_str\"]\n elif model_name == \"textual-entailment\":\n log_blob[\"outputs\"][\"label_probs\"] = prediction[\"label_probs\"]\n elif model_name == \"semantic-role-labeling\":\n verbs = []\n\n for verb in prediction[\"verbs\"]:\n # Don't want to log boring verbs with no semantic parses.\n good_tags = [tag for tag in verb[\"tags\"] if tag != \"0\"]\n if len(good_tags) > 1:\n verbs.append({\"verb\": verb[\"verb\"], \"description\": verb[\"description\"]})\n\n log_blob[\"outputs\"][\"verbs\"] = verbs\n\n logger.info(\"prediction: %s\", json.dumps(log_blob))\n\n return response.json(prediction)","title":""}],"string":"[\n {\n \"docid\": \"498bc8b339d0c559f2a53fd8dc4fa77e\",\n \"score\": \"0.7373088\",\n \"text\": \"def model_prediction(file_path, model_file, save_file) :\\n model = load(os.path.join(cf.OUTPUTS_MODELS_DIR, model_file)) \\n\\n def probability_predictions(model, data, raw_data) :\\n new_X = raw_data.copy()\\n new_X[cf.PRED_PROBA_COL_NAME] = model.predict_proba(data)[:,1]\\n new_X[cf.PRED_COL_NAME] = model.predict(data)\\n return new_X\\n\\n cd = CleanDataTransformer(path=file_path)\\n data = cd.load_cleaned_data()\\n raw_data = cd.load_raw_data()\\n\\n preds = probability_predictions(model, data, raw_data)\\n preds.to_csv(os.path.join(cf.OUTPUTS_DIR, save_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f258010802b4dc2b18d3c4772f0f41c6\",\n \"score\": \"0.6951521\",\n \"text\": \"def write_predictions(writer: base.Callable, model: base.ModelType, model_hdr: list, data_name: str, main_name: str,\\n hyper_info: list, data: base.DataType, dataset: GeneralDataset, train_field: str,\\n label_field: str, **kwargs) -> None:\\n base = [_get_datestr(), main_name, data_name] + hyper_info\\n info = [model.info.get(field, '-') for field in model_hdr]\\n\\n for doc in data:\\n parsed = \\\" \\\".join(getattr(doc, train_field)).replace('\\\\n', ' ').replace('\\\\r', ' ')\\n label = dataset.label_ix_lookup(getattr(doc, label_field))\\n pred = dataset.label_ix_lookup(doc.pred)\\n\\n pred_info = [label, pred, doc.original, parsed]\\n\\n out = base + info + pred_info\\n writer.writerow(out)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b1ea2f8af2211b7bf2e1f71fc25c24\",\n \"score\": \"0.6601534\",\n \"text\": \"def generate_test_predictions(probabilities, model_dir):\\n _batch_limit = 100000\\n if batch_size > _batch_limit:\\n print(\\\"WARNING: outputting test predictions of batchsize=%d to csv file\\\" % batch_size)\\n predictions_loc = os.path.join(model_dir, \\\"predictions.csv\\\")\\n with open(predictions_loc, 'w') as outfile:\\n np.savetxt(outfile, probabilities, header=\\\"test_probabilities\\\", comments='')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bc3f19e3191ef0ea979047f55744ee\",\n \"score\": \"0.6589996\",\n \"text\": \"def output_predictions(self, y_preds, csv_out):\\n mol_ids = self.dataset.ids\\n n_tasks = len(self.task_names)\\n y_preds = np.reshape(y_preds, (len(y_preds), n_tasks))\\n assert len(y_preds) == len(mol_ids)\\n with open(csv_out, \\\"w\\\") as csvfile:\\n csvwriter = csv.writer(csvfile)\\n csvwriter.writerow([\\\"Compound\\\"] + self.dataset.get_task_names())\\n for mol_id, y_pred in zip(mol_ids, y_preds):\\n csvwriter.writerow([mol_id] + list(y_pred))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75909d2c5f243de47230dc7626e5cc8\",\n \"score\": \"0.65740174\",\n \"text\": \"def model_predict(self, predictions_path=None, predictions_validation_path=None):\\n self._model_service.predict()\\n self.wait_for_state(self._model_service, 'predict', 'ready')\\n \\n if predictions_path:\\n self._model_service.save_predictions(predictions_path)\\n self.wait_for_state(self._model_service, 'save_predictions', 'ready')\\n if predictions_validation_path:\\n if not validate_predictions_csv(predictions_validation_path):\\n raise Exception(\\\"Failed to validate predictions csv file at %s\\\" % predictions_validation_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18ffbbdb708de2a9132ec3ce2550ac8\",\n \"score\": \"0.65246016\",\n \"text\": \"def iris_predict_bulk():\\r\\n # test_file is the name of variable in url\\r\\n df = pd.read_csv(request.files.get('test_file'))\\r\\n prediction = list(model_iris.predict(df))\\r\\n #generates prediction text for each prediction values\\r\\n prediction_text = [get_description(i) for i in prediction]\\r\\n return \\\"The prediction for CSV is: \\\" + str(prediction_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cae3cf4eba3c6723b4136ace53be8cf\",\n \"score\": \"0.65156144\",\n \"text\": \"def predict_model(model, model_name):\\n\\n x_train, x_val, x_test = get_input_data('all')\\n y_train, y_val, y_test = get_labels('all')\\n\\n train_loss, train_acc = model.evaluate(x_train, y_train)\\n val_loss, val_acc = model.evaluate(x_val, y_val)\\n test_loss, test_acc = model.evaluate(x_test, y_test)\\n\\n print('Training set Accuracy: ', train_acc)\\n print('Validation set Accuracy: ', val_acc)\\n print('Test set Accuracy: ', test_acc)\\n\\n train_predictions = model.predict(x_train)\\n val_predictions = model.predict(x_val)\\n test_predictions = model.predict(x_test)\\n\\n train_pred_classes = train_predictions.argmax(axis=-1)\\n val_pred_classes = val_predictions.argmax(axis=-1)\\n test_pred_classes = test_predictions.argmax(axis=-1)\\n\\n train_f1 = f1_score(y_train, train_pred_classes, average = 'macro')\\n val_f1 = f1_score(y_val, val_pred_classes, average='macro')\\n test_f1 = f1_score(y_test, test_pred_classes, average='macro')\\n\\n f1_scores = pd.Series({'Training': train_f1, 'Validation': val_f1, 'Testing': test_f1})\\n f1_scores.name = model_name\\n\\n train_predictions_df = pd.DataFrame(train_predictions, columns=[l + '_prob' for l in enc.classes_],\\n index=track_info.loc[track_info.split == 'training'].index)\\n val_predictions_df = pd.DataFrame(val_predictions, columns=[l + '_prob' for l in enc.classes_],\\n index=track_info.loc[track_info.split == 'validation'].index)\\n test_predictions_df = pd.DataFrame(test_predictions, columns=[l + '_prob' for l in enc.classes_],\\n index=track_info.loc[track_info.split == 'test'].index)\\n\\n return train_predictions_df, val_predictions_df, test_predictions_df, f1_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb57231fc4b1ec50d7f1c08c65d02f6\",\n \"score\": \"0.6504246\",\n \"text\": \"def write_predictions(dir_names, y_predict, mode):\\n test_set = pd.read_csv(dir_names['test_set'], delim_whitespace=True, header=0)\\n if not os.path.exists(os.path.join(dir_names['results'])):\\n os.makedirs(os.path.join(dir_names['results']))\\n\\n if mode == 'fine tune':\\n score_level = .5\\n elif mode == 'normal':\\n score_level = .7\\n else:\\n sys.exit('mode does not support')\\n predicted_labels = [\\\"binding\\\" if score >= score_level else \\\"non-binding\\\" for score in y_predict]\\n\\n # write prediction to new file\\n with open(os.path.join(dir_names['results'], \\\"predictions.csv\\\"), 'w') as f:\\n f.write('Peptide_seq' + ',' + 'Predicted_Scores' + ',' + 'Predicted_Labels' + '\\\\n')\\n for i in range(len(y_predict)):\\n f.write(str(test_set['Peptide_seq'].iloc[i]) + ',' + str(y_predict[i]) + ',' + predicted_labels[i] + '\\\\n')\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a1c10fbb9d7c5d504d9df4d1b5c245\",\n \"score\": \"0.64727813\",\n \"text\": \"def predict_dataset(self, save_path, dataset_filepath, model, batch_size):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b758ac9823b40a3738020def7ec090b\",\n \"score\": \"0.64595246\",\n \"text\": \"def output(pred, data_test, path):\\n df = pd.DataFrame()\\n df[\\\"id\\\"] = data_test[\\\"id\\\"]\\n #pred = [int(round(num)) for num in pred]\\n df[\\\"prediction\\\"] = pred\\n df.to_csv(path, index = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbbd72c9d89ba07563b49dd30f1a6dd2\",\n \"score\": \"0.63975626\",\n \"text\": \"def predict_results(self, prediction_batch_size):\\n start_time = time.time()\\n processed_rows = 0\\n\\n open(self._TEMP_PREDICTIONS_PATH, 'w').close()\\n while processed_rows < self._pred_df.shape[0]:\\n next_processed_rows = min(processed_rows + prediction_batch_size, self._pred_df.shape[0])\\n X_pred = np.array(self._pred_df.iloc[processed_rows:next_processed_rows, :])\\n predictions = [round(preds[0], 3) for preds in self._model.predict(X_pred)]\\n\\n ids = list(self._pred_df.index[processed_rows:next_processed_rows])\\n\\n with open(self._TEMP_PREDICTIONS_PATH, 'a') as f:\\n writer = csv.writer(f)\\n for i in range(len(predictions)):\\n writer.writerow([ids[i], predictions[i]])\\n\\n processed_rows += prediction_batch_size\\n\\n print(\\\"Predicting the values took {0} seconds\\\".format(round(time.time() - start_time, 2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5d70019c2c5cf96d38f3b7138f4676\",\n \"score\": \"0.63785636\",\n \"text\": \"def predict_outcomes(self):\\n model = SVC(gamma='auto')\\n trained_models = self.trained_model\\n for trained_model in trained_models:\\n print(trained_model['z'])\\n model.fit(trained_model['x_train'], trained_model['y_train'])\\n predictions = model.predict(trained_model['x_valid'])\\n print(accuracy_score(trained_model['y_valid'], predictions))\\n print(confusion_matrix(trained_model['y_valid'], predictions))\\n print(classification_report(trained_model['y_valid'], predictions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab991ce427c8208c10acb8b9eb08d28\",\n \"score\": \"0.6355206\",\n \"text\": \"def predict_model(prediction_data, model):\\n predictions = model.predict(input_fn=lambda: input_fn(prediction_data))\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ce34809a1fd7cb0d921e51971f5540\",\n \"score\": \"0.63392764\",\n \"text\": \"def predict(self, sess):\\n # load model's checkpoint if exists\\n self._load_from_checkpoint(sess)\\n\\n # write predictions\\n if self.arg.dump:\\n self.inference(sess, \\\"\\\", self.arg.remain_diff, self.arg.dump)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3906d5ca0967e90b797345c11104b4\",\n \"score\": \"0.6336304\",\n \"text\": \"def transformation():\\n data = None\\n\\n # Convert from CSV to pandas\\n if flask.request.content_type == 'text/csv':\\n data = flask.request.data.decode('utf-8')\\n s = StringIO.StringIO(data)\\n data = pd.read_csv(s, header=None)\\n else:\\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\\n\\n print('Invoked with {} records'.format(data.shape[0]))\\n\\n # Do the prediction\\n predictions = ScoringService.predict(data.values[:,0])\\n\\n # Convert from numpy back to CSV\\n out = StringIO.StringIO()\\n predictions.to_csv(out, header=True, index=False)\\n result = out.getvalue()\\n\\n return flask.Response(response=result, status=200, mimetype='text/csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f4120d2a051c20ab80007f1f21dac8\",\n \"score\": \"0.63343567\",\n \"text\": \"def prediction(test_df, model_columns, model):\\n test_df['prediction'] = model.predict(test_df[model_columns])\\n\\n return test_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90a2d585929f39f5f447d6fb759fd17\",\n \"score\": \"0.63189596\",\n \"text\": \"def make_predictions(self, model, X_train, y_train, X_test, submission_file=SUBMISSION_FILE):\\n\\n if self.n_test != len(X_test):\\n raise ValueError(\\\"Somewhere along the way you lost test samples! Please fix.\\\")\\n\\n print \\\"Training %s using %d samples (%d%% of original training set)\\\" % \\\\\\n (model.__class__.__name__, len(X_train), 100. * len(X_train) / self.n_train)\\n model.fit(X_train, y_train)\\n\\n print \\\"Making predictions on the test set..\\\"\\n # We want the probabilities, not just 0 or 1. Just take 2nd column\\n y_pred = model.predict_proba(X_test)[:, 1]\\n\\n print \\\"Cross-validating model...\\\"\\n scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\\\"roc_auc\\\")\\n print \\\"Cross-validated AUC score: %.4f (%s)\\\" % (scores.mean(), scores.round(4))\\n\\n print \\\"Saving predictions in %s...\\\" % submission_file\\n submission = pd.DataFrame(dict(id=self.test.index, OpenStatus=y_pred)).set_index('id')\\n submission.to_csv(submission_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62520719b47b1a4ae5fcc41de07b5450\",\n \"score\": \"0.62694895\",\n \"text\": \"def predict(toml_path):\\n toml_path = Path(toml_path)\\n cfg = config.parse.from_toml_path(toml_path)\\n\\n if cfg.predict is None:\\n raise ValueError(\\n f\\\"predict called with a config.toml file that does not have a PREDICT section: {toml_path}\\\"\\n )\\n\\n # ---- set up logging ----------------------------------------------------------------------------------------------\\n timenow = datetime.now().strftime(\\\"%y%m%d_%H%M%S\\\")\\n logger = logging.get_logger(\\n log_dst=cfg.predict.output_dir,\\n caller=\\\"predict\\\",\\n timestamp=timenow,\\n logger_name=__name__,\\n )\\n logger.info(\\\"Logging results to {}\\\".format(cfg.prep.output_dir))\\n\\n model_config_map = config.models.map_from_path(toml_path, cfg.predict.models)\\n\\n core.predict(\\n csv_path=cfg.predict.csv_path,\\n checkpoint_path=cfg.predict.checkpoint_path,\\n labelmap_path=cfg.predict.labelmap_path,\\n model_config_map=model_config_map,\\n window_size=cfg.dataloader.window_size,\\n num_workers=cfg.predict.num_workers,\\n spect_key=cfg.spect_params.spect_key,\\n timebins_key=cfg.spect_params.timebins_key,\\n spect_scaler_path=cfg.predict.spect_scaler_path,\\n device=cfg.predict.device,\\n annot_csv_filename=cfg.predict.annot_csv_filename,\\n output_dir=cfg.predict.output_dir,\\n min_segment_dur=cfg.predict.min_segment_dur,\\n majority_vote=cfg.predict.majority_vote,\\n save_net_outputs=cfg.predict.save_net_outputs,\\n logger=logger,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f6560bbb3e217a9087c3e466259313\",\n \"score\": \"0.6262948\",\n \"text\": \"def transformation():\\n data = None\\n\\n # Convert from CSV to pandas\\n if flask.request.content_type == 'text/csv':\\n data = flask.request.data.decode('utf-8')\\n s = StringIO.StringIO(data)\\n # Makoto.Sano@Mack-the-Psych.com\\n # data = pd.read_csv(s, header=None)\\n data = pd.read_csv(s)\\n else:\\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\\n\\n # Makoto.Sano@Mack-the-Psych.com\\n # print('Invoked with {} records'.format(data.shape[0]))\\n print('Invoked with {} records'.format(len(data)))\\n \\n # Do the prediction\\n predictions = ScoringService.predict(data)\\n\\n # Convert from numpy back to CSV\\n out = StringIO.StringIO()\\n\\n # Makoto.Sano@Mack-the-Psych.com\\n # pd.DataFrame({'results':predictions}).to_csv(out, header=False, index=False)\\n predictions.to_csv(out, index=False)\\n result = out.getvalue()\\n\\n return flask.Response(response=result, status=200, mimetype='text/csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc0c592001b41d4dfeae85687364095\",\n \"score\": \"0.6247642\",\n \"text\": \"def transformation():\\n data = None\\n\\n # Convert from CSV to pandas\\n if flask.request.content_type == 'text/csv':\\n data = flask.request.data.decode('utf-8')\\n s = StringIO(data)\\n data = pd.read_csv(s, header=None)\\n\\n else:\\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\\n\\n print('Invoked with {} records'.format(data.shape[0]))\\n\\n # Do the prediction\\n predictions = ScoringService.predict(data)\\n print(predictions)\\n print(type(predictions))\\n predictions=predictions.reshape(len(predictions))\\n # Convert from numpy back to CSV\\n out = StringIO()\\n pd.DataFrame({'results':predictions}).to_csv(out, header=False, index=False)\\n result = out.getvalue()\\n #result=predictions\\n return flask.Response(response=result, status=200, mimetype='text/csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bed1c37ab148f950310c5a287b76c16\",\n \"score\": \"0.6241566\",\n \"text\": \"def predict(self):\\n\\n # update file_list in ctlearn config\\n with open(self.ctlearn_config_path, 'r') as config:\\n myconfig = yaml.load(config)\\n\\n myconfig['Data']['file_list'] = os.path.join(\\n self.working_directory, self.basic_config['prediction_file_list'])\\n\\n # modify ctlearn config to make sure that a prediction file will be created\\n myconfig['Prediction']['export_as_file'] = True\\n myconfig['Prediction']['true_labels_given'] = True\\n\\n # dump ctlearn configuration\\n with open(self.ctlearn_config_path, 'w') as config:\\n yaml.dump(myconfig, config)\\n\\n # run prediction\\n run_model(myconfig, mode='predict', debug=False, log_to_file=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2cf0370f66deaacf5f6ae57cf1be3d\",\n \"score\": \"0.62171954\",\n \"text\": \"def save_predicted_results(self,predicted_results):\\n\\n\\t\\tself.predicted_results = predicted_results\\n\\n\\t\\tid_value = self.test_data[self.testdata_headers[0]]\\n\\t\\tf = open(\\\"prediction.csv\\\",\\\"w\\\")\\n\\t\\tf.write(\\\"Id,Cover_Type\\\\n\\\")\\n\\t\\tfor i in xrange(len(self.predicted_results)):\\n\\t\\t\\tf.write(\\\"{},{}\\\\n\\\".format(id_value[i], self.predicted_results[i]))\\n\\t\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52a196aa9d2b42617310f2c8b1104ae\",\n \"score\": \"0.621695\",\n \"text\": \"def assess_model(self, predictions):\\n\\n print(classification_report(self.y_test, predictions))\\n print(confusion_matrix(self.y_test, predictions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29766c4181f0362243cf460e4ac7b527\",\n \"score\": \"0.6211899\",\n \"text\": \"def get_predictions(test_path, models, model_names, params=None):\\n if params is None:\\n params = TRAINING_DEFAULTS\\n lr = params[\\\"lr\\\"]\\n ep = params[\\\"epoch\\\"]\\n dim = params[\\\"dim\\\"]\\n ngrams = params[\\\"wordNgrams\\\"]\\n\\n samples = pd.read_csv(test_path, sep='\\\\n', header=None, names=[\\\"s\\\"], dtype=object)[\\\"s\\\"]\\n true_labels = samples.str.split().str.get(0)\\n samples = samples.str.replace(r'__label__[A-Za-z-]* ', \\\"\\\").str.strip()\\n\\n predictions = []\\n probabilities = []\\n\\n for m in models:\\n labels, probs = flatten_ft_predictions(m.predict(list(samples), k=1))\\n predictions.append(pd.Series(labels, dtype=object))\\n probabilities.append(pd.Series(probs))\\n\\n cols = [\\\"sample\\\", \\\"true_label\\\"]\\n for n in model_names:\\n cols.append(f\\\"{n}_pred\\\")\\n cols.append(f\\\"{n}_prob\\\")\\n\\n df = pd.DataFrame([], columns=cols)\\n df.loc[:, \\\"sample\\\"] = samples\\n df.loc[:, \\\"true_label\\\"] = true_labels\\n\\n pred_cols = []\\n for i in range(len(model_names)):\\n df.loc[:, cols[i*2 + 2]] = predictions[i]\\n df.loc[:, cols[i*2 + 3]] = probabilities[i]\\n pred_cols.append(cols[i*2 + 2])\\n\\n fname = f\\\"{os.getcwd()}/results/preds_lr{lr}_ep{ep}_d{dim}_ng{ngrams}.csv\\\"\\n df.to_csv(fname)\\n\\n _, is_diff = find_prediction_disagreement(samples, predictions)\\n\\n diffs = df[is_diff][[\\\"sample\\\"] + pred_cols]\\n diff_fname = f\\\"{os.getcwd()}/results/diffs_lr{lr}_ep{ep}_d{dim}_ng{ngrams}.csv\\\"\\n diffs.to_csv(diff_fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c625c3a91cc964a2e7ca1a5adb4fb8e\",\n \"score\": \"0.62084454\",\n \"text\": \"def predict_file():\\n inputdata=pd.read_csv(request.files.get(\\\"data\\\"),header=None)\\n prediction=model.predict(inputdata)\\n return str(list(prediction))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28aa654c455c6474677613d4c53b3560\",\n \"score\": \"0.6196311\",\n \"text\": \"def predict(\\n self,\\n test_dataset: data.TFRecordDataset,\\n inference_signature: str = \\\"serving_default\\\",\\n additional_features: dict = {},\\n logs_dir: Optional[str] = None,\\n logging_frequency: int = 25,\\n ):\\n if logs_dir:\\n outfile = os.path.join(logs_dir, RelevanceModelConstants.MODEL_PREDICTIONS_CSV_FILE)\\n # Delete file if it exists\\n self.file_io.rm_file(outfile)\\n\\n _predict_fn = get_predict_fn(\\n model=self.model,\\n tfrecord_type=self.tfrecord_type,\\n feature_config=self.feature_config,\\n inference_signature=inference_signature,\\n is_compiled=self.is_compiled,\\n output_name=self.output_name,\\n features_to_return=self.feature_config.get_features_to_log(),\\n additional_features=additional_features,\\n max_sequence_size=self.max_sequence_size,\\n )\\n\\n predictions_df_list = list()\\n batch_count = 0\\n for predictions_dict in test_dataset.map(_predict_fn).take(-1):\\n predictions_df = pd.DataFrame(predictions_dict)\\n if logs_dir:\\n if os.path.isfile(outfile):\\n predictions_df.to_csv(outfile, mode=\\\"a\\\", header=False, index=False)\\n else:\\n # If writing first time, write headers to CSV file\\n predictions_df.to_csv(outfile, mode=\\\"w\\\", header=True, index=False)\\n else:\\n predictions_df_list.append(predictions_df)\\n\\n batch_count += 1\\n if batch_count % logging_frequency == 0:\\n self.logger.info(\\\"Finished predicting scores for {} batches\\\".format(batch_count))\\n\\n predictions_df = None\\n if logs_dir:\\n self.logger.info(\\\"Model predictions written to -> {}\\\".format(outfile))\\n else:\\n predictions_df = pd.concat(predictions_df_list)\\n\\n return predictions_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c851c1c1611bebabcbf7c0ba6f5a57\",\n \"score\": \"0.61924016\",\n \"text\": \"def prediction(self):\\n final_df = pd.DataFrame(columns=['PARTICIPANT_ID','Y1_LOGISTIC','Y1_NAIVEBAYES','Y2_REGRESSION'])\\n X,y = self.aggregateColumns(stype='predict')\\n print 'Predicting by Logistic Regression'\\n y_log = self.logistic.predict(X)\\n print 'Predicted Rows : ', len(y_log)\\n print 'Predicting by Naive Bayes'\\n y_naive = self.naive.predict(X)\\n print 'Predicted Rows :', len(y_naive)\\n X,y = self.getRegData(stype='predict')\\n print 'Predicting Linear Regression'\\n y_lin = self.linear.predict(X)\\n y_lin = y_lin * 42\\n times = []\\n for seconds in y_lin:\\n m, s = divmod(seconds, 60)\\n h, m = divmod(m, 60)\\n time = \\\"%d:%02d:%02d\\\" % (h, m, s)\\n times.append(time)\\n print 'Predicted Rows :',len(times)\\n final_df.PARTICIPANT_ID = range(UNIQUE_IDS)\\n final_df.Y1_LOGISTIC = y_log\\n final_df.Y1_NAIVEBAYES = y_naive\\n final_df.Y2_REGRESSION = times\\n final_df.to_csv(self.finalSub)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ac3e2151b12a1ca14f27657131e36e\",\n \"score\": \"0.6179789\",\n \"text\": \"def gen_pred_csv():\\n # schemas\\n SCHEMA = Config().read_schema() # dict id -> col_name, e.g. SCHEMA[1]='clk'\\n del SCHEMA[1]\\n\\n # load data\\n df = pd.read_table(FLAGS.pred_data + \\\"/pred1\\\")\\n\\n # save to csv\\n df.to_csv(\\\"../data/pred/pred1.csv\\\", header=[v for k, v in SCHEMA.iteritems()], index=False)\\n print(\\\"Csv generation finished.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d551751b11df2b5d6d999c08bc0b9f4\",\n \"score\": \"0.6166116\",\n \"text\": \"def save_output(model, name, model_dict, men = True):\\n d = pd.read_csv(\\\"AUS_SubmissionFormat.csv\\\")\\n\\n if men:\\n ds_test = pd.read_csv(\\\"mens_test_file.csv\\\")\\n ds_test_id = ds_test['id'].astype(str)+\\\"_mens\\\"\\n else:\\n ds_test = pd.read_csv(\\\"womens_test_file.csv\\\")\\n ds_test_id = ds_test['id'].astype(str)+\\\"_womens\\\"\\n \\n ds_test_id.name = \\\"submission_id\\\" \\n ds_test = pre_processing(ds_test, True)\\n ds_test = feature_engineering(ds_test)\\n ds_test = ds_test.drop(['id','train', 'outcome'], axis = 1)\\n pred_test = model.predict_proba(ds_test.values)\\n pred_test = pd.DataFrame(data = pred_test, columns = ['UE', 'FE','W'])\\n d1 = d.iloc[:2000,:]\\n test_id = pd.DataFrame(ds_test_id)\\n pred_test = pd.concat((test_id, pred_test), axis = 1)\\n pred_test = pd.merge(d1.iloc[:,:2], pred_test, on = \\\"submission_id\\\")\\n \\n if men:\\n pred_test.to_csv(\\\"Xtest_{}_mens.csv\\\".format(name))\\n joblib.dump(model_dict, \\\"./{}_mens\\\".format(name))\\n else:\\n pred_test.to_csv(\\\"Xtest_{}_womens.csv\\\".format(name))\\n joblib.dump(model_dict, \\\"./{}_womens\\\".format(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d93f1f6d5b89f491ff8ba1dc587e95\",\n \"score\": \"0.6166084\",\n \"text\": \"def predict_and_dump(inputdir, outputdir, number_of_months=3):\\n # Get absolute paths, to avoid problems\\n abs_input_dir = os.path.abspath(inputdir)\\n abs_output_dir = os.path.abspath(outputdir)\\n\\n # For every CSV file in input dir\\n for lang_csv in os.listdir(abs_input_dir):\\n if lang_csv.endswith(\\\".csv\\\"):\\n # Read CSV file and make a DataFrame with it's data\\n lang_df = pandas.read_csv(os.path.join(abs_input_dir, lang_csv))\\n\\n x_axis = lang_df.index # Index numbers\\n y_axis = lang_df['coefficient'].tolist() # Coefficient numbers\\n lang_poly = polyfit(x_axis, y_axis, 4) # 4rd-grade polynomial regression\\n\\n poly_line = poly1d(lang_poly) # Polynomial line\\n future_months = arange(number_of_months) + len(lang_df.index) # Ten more months\\n future_coefficient = poly_line(future_months) # Get the coefficient prediction for the next X months\\n\\n last_timelapse = lang_df['timelapse'][len(lang_df.index)-1] # Get last timelapse from timeseries\\n timelapse_range = months_to_predict(number_of_months, last_timelapse) # List of timelapses to predict\\n timelapse_iterator = year_month_iter(\\n timelapse_range[0], timelapse_range[1], timelapse_range[2], timelapse_range[3])\\n\\n # Add predicted data to the timeseries (bottom of CSV file)\\n coefficient_index = 0\\n for timelapse in timelapse_iterator:\\n lang_df.loc[len(lang_df.index)] = \\\\\\n [os.path.splitext(lang_csv)[0], timelapse, int(future_coefficient[coefficient_index])]\\n coefficient_index += 1\\n\\n # Save the real data along with predicted data in a CSV file, overwriting it if exists\\n lang_df.to_csv(os.path.join(abs_output_dir, lang_csv), index=False, float_format='%.0f')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca3714f0631ca2d3f0e600965cb15af1\",\n \"score\": \"0.6156261\",\n \"text\": \"def prediction(df, model_dict, model = 'MinsePIE_v3.sav'):\\n features = ['length', 'VF_RTT_z', 'mmr', 'percC', 'pairedbases', 'Arun_maxlen', 'percA', 'percT','pos1compl', 'loop1_intact']\\n # choose model\\n pred_model = model_dict[model]\\n # predict\\n df['zscore']= pred_model.predict(df[features])\\n df['percIns_predicted'] = df['zscore'] * df['std'] + df['mean']\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1933def6bbd702265245facd8dcd437\",\n \"score\": \"0.6153989\",\n \"text\": \"def save_predict(self, test_x, test_y):\\n folder = \\\"runs\\\"\\n op = os.path.join(os.getcwd(), folder)\\n\\n if not os.path.exists(op):\\n os.makedirs(folder)\\n\\n # Predicted\\n with open(os.path.join(op, \\\"predict.txt\\\"), \\\"a\\\") as pred:\\n pred.write(\\\"Date and time: {}, {}\\\\n\\\".format(self.date, self.time))\\n pred.write(\\\"Optimizer: {}\\\\n\\\".format(type(self).__name__))\\n pred.write(\\\"NN structure: {}\\\\n\\\".format(self.layers))\\n pred.write(\\\"lrate={}, \\\".format(self.lrate))\\n pred.write(\\\"momentum={}, \\\".format(self.momentum))\\n rmse = self.compute_error(test_x, test_y)\\n pred.write(\\\"error: {}\\\\n\\\\n\\\".format(rmse))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10962e7a0bf18ab8656a53158cdd750\",\n \"score\": \"0.614656\",\n \"text\": \"def predict(self) -> None:\\n\\n tot_predicted = []\\n tot_labels = []\\n\\n with open(Config.PREDICTION, mode=\\\"w\\\") as out_file:\\n\\n for tk_sentence, or_label in zip(self.test_sentences, self.test_labels):\\n tk_sentence = self.tokenizer.encode(tk_sentence)\\n tokens, pr_labels, tmp_pred = self._predict(tk_sentence)\\n\\n tmp_label = [self.tag2idx[lb] for lb in or_label]\\n\\n tot_predicted.extend(tmp_pred)\\n tot_labels.extend(tmp_label)\\n\\n for token, label in zip(tokens, pr_labels):\\n out_file.write(\\\"{}:{} \\\".format(token, label))\\n out_file.write(\\\"\\\\n\\\")\\n\\n f1 = f1_score(tot_predicted, tot_labels, average=\\\"micro\\\")\\n pr = precision_score(tot_predicted, tot_labels, average=\\\"micro\\\")\\n rc = recall_score(tot_predicted, tot_labels, average=\\\"micro\\\")\\n\\n print(\\\"F1 score: {}\\\\tPrecision: {}\\\\tRecall: {}\\\".format(f1, pr, rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2f5b786d83500a4d592f9fd22eb31d\",\n \"score\": \"0.6143828\",\n \"text\": \"def save_predictions(model, loader, output_dir):\\n # Note that all eval runs on main process\\n assert comm.is_main_process()\\n if isinstance(model, torch.nn.parallel.DistributedDataParallel):\\n model = model.module\\n\\n device = torch.device(\\\"cuda:0\\\")\\n os.makedirs(output_dir, exist_ok=True)\\n\\n for batch in tqdm.tqdm(loader):\\n batch = loader.postprocess(batch, device)\\n model_kwargs = {}\\n module = model.module if hasattr(model, \\\"module\\\") else model\\n if isinstance(module, VoxMeshMultiViewHead):\\n model_kwargs[\\\"intrinsics\\\"] = batch[\\\"intrinsics\\\"]\\n model_kwargs[\\\"extrinsics\\\"] = batch[\\\"extrinsics\\\"]\\n if isinstance(module, VoxDepthHead):\\n model_kwargs[\\\"masks\\\"] = batch[\\\"masks\\\"]\\n if module.mvsnet is None:\\n model_kwargs[\\\"depths\\\"] = batch[\\\"depths\\\"]\\n model_outputs = model(batch[\\\"imgs\\\"], **model_kwargs)\\n\\n # TODO: debug only\\n # save_debug_predictions(batch, model_outputs)\\n # continue\\n\\n pred_mesh = model_outputs[\\\"meshes_pred\\\"][-1]\\n gt_mesh = batch[\\\"meshes\\\"]\\n pred_mesh = pred_mesh.scale_verts(P2M_SCALE)\\n gt_mesh = gt_mesh.scale_verts(P2M_SCALE)\\n\\n pred_points = sample_points_from_meshes(\\n pred_mesh, NUM_PRED_SURFACE_SAMPLES, return_normals=False\\n )\\n gt_points = sample_points_from_meshes(\\n gt_mesh, NUM_GT_SURFACE_SAMPLES, return_normals=False\\n )\\n\\n pred_points = pred_points.cpu().detach().numpy()\\n gt_points = gt_points.cpu().detach().numpy()\\n\\n batch_size = pred_points.shape[0]\\n for batch_idx in range(batch_size):\\n label, label_appendix = batch[\\\"id_strs\\\"][batch_idx].split(\\\"-\\\")[:2]\\n pred_filename = os.path.join(\\n output_dir, \\\"{}_{}_predict.xyz\\\".format(label, label_appendix)\\n )\\n gt_filename = os.path.join(\\n output_dir, \\\"{}_{}_ground.xyz\\\".format(label, label_appendix)\\n )\\n\\n np.savetxt(pred_filename, pred_points[batch_idx])\\n np.savetxt(gt_filename, gt_points[batch_idx])\\n\\n # pred_filename = pred_filename.replace(\\\".xyz\\\", \\\".obj\\\")\\n # gt_filename = gt_filename.replace(\\\".xyz\\\", \\\".obj\\\")\\n\\n # pred_verts, pred_faces = pred_mesh[batch_idx] \\\\\\n # .get_mesh_verts_faces(0)\\n # gt_verts, gt_faces = gt_mesh[batch_idx] \\\\\\n # .get_mesh_verts_faces(0)\\n # save_obj(pred_filename, pred_verts, pred_faces)\\n # save_obj(gt_filename, gt_verts, gt_faces)\\n\\n # metrics = compare_meshes(\\n # pred_mesh[batch_idx], gt_mesh[batch_idx],\\n # num_samples=NUM_GT_SURFACE_SAMPLES, scale=1.0,\\n # thresholds=[0.01, 0.014142], reduce=True\\n # )\\n # print(\\\"%s_%s: %r\\\" % (label, label_appendix, metrics))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fd1fff51abc8b2cf2bef540b645eae\",\n \"score\": \"0.6138033\",\n \"text\": \"def test_predictions(dataloader, model, softmax=False):\\n\\n model.eval()\\n test_file = r\\\"/home/andrewg/PycharmProjects/assignments/ProstateX-TestLesionInformation/ProstateX-Findings-Test.csv\\\"\\n predictions = pd.read_csv(test_file)\\n predictions.insert(4, \\\"ClinSig\\\", 0)\\n predictions = predictions.drop([\\\"pos\\\", \\\"zone\\\"], axis=1)\\n end_batch = 0\\n\\n for idx, batch in enumerate(dataloader):\\n outputs = model(batch[\\\"image\\\"])\\n if softmax:\\n outputs = torch.tensor([tup[1] for tup in outputs])\\n start_batch = end_batch\\n end_batch = start_batch + len(outputs)\\n predictions[\\\"ClinSig\\\"].iloc[start_batch: end_batch] = outputs.flatten().tolist()\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ee6d253706073744cb6f483bd833ec\",\n \"score\": \"0.6133501\",\n \"text\": \"def get(self):\\n\\n\\n X = np.array([[0, 0], [0, 0], [0, 0], [1, 1], [1,1], [1,1]])\\n y = np.array([0, 0, 0, 1, 1, 1])\\n res = analyse(X, y)\\n\\n prediction = executeModel(res['modelo'], X[4:5])\\n # self.render(\\\"index_path\\\")\\n\\n # print(prediction)\\n # df = pandas.DataFrame(data=prediction, columns=['a'])\\n # df.to_csv('eggs.csv')\\n\\n\\n\\n df = pandas.read_csv('eggs.csv')\\n xx = df.ix[:, 1:(len(df.columns)-1)].as_matrix()\\n print(xx)\\n\\n # print(str(type(prediction)))\\n\\n # with open('eggs.csv', 'w', newline='') as csvfile:\\n # spamwriter = csv.writer(csvfile, delimiter=',',\\n # quoting=csv.QUOTE_MINIMAL)\\n # spamwriter.writerow(['Spam'] * 5 + ['Baked Beans'])\\n # spamwriter.writerow(['Spam', 'Lovely Spam', 'Wonderful Spam'])\\n\\n # self.write(str(res['crossvalidation']) + str(prediction))\\n\\n ifile = open(\\\"eggs.csv\\\", \\\"r\\\")\\n self.set_header ('Content-Type', 'text/csv')\\n self.set_header ('Content-Disposition', 'attachment; filename='+\\\"eggs.csv\\\"+'')\\n self.write (ifile.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af4448d51421ed53fde72e055037051\",\n \"score\": \"0.6104318\",\n \"text\": \"def save_predictions(pred, file):\\n\\n with open(file, 'w') as csvfile:\\n fieldnames = ['Stances']\\n writer = DictWriter(csvfile, fieldnames=fieldnames)\\n\\n writer.writeheader()\\n for instance in pred:\\n writer.writerow({'Stances': label_ref_rev[instance]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c313a7a731ec52239282f8e4f2b0c2d8\",\n \"score\": \"0.6087462\",\n \"text\": \"def hydra_predict_and_write_to_csv(all_eval_data: tf.data.Dataset,\\n hydra_model: tf.keras.Model,\\n missing_outcomes: bool,\\n prediction_file: str,\\n num_shards=100):\\n # running evaluation on very large data can take an obscenely long time, so we shard the data as checkpointing\\n for idx in range(num_shards):\\n eval_data = all_eval_data.shard(num_shards=num_shards, index=idx)\\n\\n outputs = hydra_model.predict(x=eval_data)\\n\\n out_dict = {}\\n if missing_outcomes:\\n\\n for t, g0 in enumerate(tf.unstack(outputs[0], axis=-1)):\\n out_dict['g0_' + str(t)] = g0.numpy()\\n\\n for t, g1 in enumerate(tf.unstack(outputs[1], axis=-1)):\\n out_dict['g1_' + str(t)] = g1.numpy()\\n\\n out_dict['prob_y_obs'] = np.squeeze(outputs[2])\\n\\n for out, q in enumerate(outputs[3:]):\\n out_dict['q' + str(out)] = np.squeeze(q)\\n\\n else:\\n\\n for t, g in enumerate(tf.unstack(outputs[0], axis=-1)):\\n out_dict['g_' + str(t)] = g.numpy()\\n\\n for out, q in enumerate(outputs[1:]):\\n out_dict['q' + str(out)] = np.squeeze(q)\\n\\n predictions = pd.DataFrame(out_dict)\\n\\n label_dataset = eval_data.map(lambda f, l: l, num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n data_df = dataset_to_pandas_df(label_dataset)\\n\\n outs = data_df.join(predictions)\\n with tf.io.gfile.GFile(prediction_file + f'_index{idx}', \\\"w\\\") as writer:\\n writer.write(outs.to_csv(sep=\\\"\\\\t\\\"))\\n\\n # merge shards and write final predictions csv\\n dfs = []\\n for idx in range(num_shards):\\n dfs.append(pd.read_csv(prediction_file + f'_index{idx}', sep='\\\\t'))\\n\\n full_df = pd.concat(dfs)\\n with tf.io.gfile.GFile(prediction_file, \\\"w\\\") as writer:\\n writer.write(full_df.to_csv(sep=\\\"\\\\t\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8477b21b5850536b97f7d177ef499ffa\",\n \"score\": \"0.6086274\",\n \"text\": \"def save_predictions(test_filenames, predictions, out_filename='submission.csv', location='./'):\\n\\tsubmission = pd.DataFrame(predictions, columns=ALL_FISH_CATEGORIES)\\n\\tsubmission.insert(0, 'image', test_filenames)\\n\\tsubmission.to_csv(location+out_filename, index=False)\\n\\tprint 'Predictions saved to '+location+out_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65bdb1c80ae6fbaa8b98fedb569225ac\",\n \"score\": \"0.60813093\",\n \"text\": \"def write_predictions(ids, y_score, uniqname) :\\r\\n \\r\\n df_labels = pd.DataFrame(y_score)\\r\\n df_labels.columns = ['30-day_mortality']\\r\\n df_labels.insert(0, 'RecordID', ids)\\r\\n df_labels.to_csv(f'{uniqname}_icu.csv', index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2af78c2f9c7038d55a1e536c528ed81\",\n \"score\": \"0.60705453\",\n \"text\": \"def write_predictions(predictions, ids, outfile):\\n with open(outfile,\\\"w+\\\") as f:\\n # write header\\n f.write(\\\"Id,Prediction\\\\n\\\")\\n for i, history_id in enumerate(ids):\\n f.write(\\\"%s,%d\\\\n\\\" % (history_id, predictions[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51625e387666ce4fb9915200debd3cf6\",\n \"score\": \"0.60703653\",\n \"text\": \"def predict(\\n output_directory,\\n model_checkpoint_directory,\\n predict_dataset_directory,\\n predict_parse_fns,\\n model_fn,\\n model_hparams,\\n):\\n tf_config = os.environ.get('TF_CONFIG')\\n logging.info(\\\"TF_CONFIG {}\\\".format(tf_config))\\n\\n # Load `RunConfig`.\\n run_config = tf.estimator.RunConfig()\\n run_config = run_config.replace(\\n model_dir=output_directory,\\n )\\n\\n predict_dataset = input.make_input_fn(\\n dataset_directory=predict_dataset_directory,\\n parse_fns=predict_parse_fns,\\n )\\n iterator = predict_dataset.make_one_shot_iterator()\\n observations, distribution = iterator.get_next()\\n\\n # Rebuild the model\\n predictions = model_fn(\\n observations,\\n distribution,\\n tf.estimator.ModeKeys.EVAL,\\n model_hparams).predictions\\n\\n # Manually load the latest checkpoint.\\n saver = tf.train.Saver()\\n with tf.Session() as sess:\\n ckpt = tf.train.get_checkpoint_state(model_checkpoint_directory)\\n saver.restore(sess, ckpt.model_checkpoint_path)\\n\\n # Loop through the batches and store predictions and labels\\n output = []\\n for i in range(1):\\n try:\\n preds_eval, dist_eval = sess.run([predictions, distribution])\\n # Add distributions to `predictions`.\\n preds_eval.update({'distribution': dist_eval})\\n output.append(preds_eval)\\n except tf.errors.OutOfRangeError:\\n break\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c8bd15626f24a608e0d3a91143a5129\",\n \"score\": \"0.6064927\",\n \"text\": \"def predict(csv_file):\\n\\n #your code goes here...\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9090233f6c69c32639aecd21ac5afe\",\n \"score\": \"0.6058799\",\n \"text\": \"def write_pred(pred_df, test_df, filename, id_col, data_col):\\n # Append id column from test dataset to predictions for submission\\n pred_df[id_col] = test_df[id_col]\\n\\n # Specify order of columns/format\\n csv = pred_df.to_csv(\\n path_or_buf=filename,\\n index=False,\\n columns=[id_col, data_col],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7f102868f9bd62b36dbfafc3b3e85e\",\n \"score\": \"0.6054072\",\n \"text\": \"def predict_and_save(test_dir, model_dir, save_dir, filename, config):\\n\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n\\n tmp_dir = tempfile.mkdtemp()\\n\\n ds = input_fn(test_dir, repeat=False, shuffle=False)\\n\\n dataframes_to_concat = []\\n\\n it = 0\\n\\n df_files = []\\n delays = np.array([])\\n for predictors, target in ds:\\n\\n delays = np.append(delays, target)\\n\\n if it % 1000 == 0:\\n\\n aux_df = pd.DataFrame({\\n \\\"Delay\\\": delays\\n })\\n\\n dataframes_to_concat.append(aux_df)\\n delays = np.array([])\\n\\n if it % 3000 == 0:\\n df = pd.concat(dataframes_to_concat)\\n file = os.path.join(tmp_dir, \\\"tmp_df_\\\" + str(it) + \\\".parquet\\\")\\n df.to_parquet(file)\\n df_files.append(file)\\n dataframes_to_concat = []\\n\\n it += 1\\n\\n if it % 3000 != 0:\\n if it % 1000 != 0:\\n\\n aux_df = pd.DataFrame({\\n \\\"Delay\\\": delays\\n })\\n\\n dataframes_to_concat.append(aux_df)\\n\\n df = pd.concat(dataframes_to_concat)\\n file = os.path.join(tmp_dir, \\\"tmp_df_\\\" + str(it) + \\\".parquet\\\")\\n df.to_parquet(file)\\n df_files.append(file)\\n\\n df_list = []\\n\\n for file in df_files:\\n df_list.append(pd.read_parquet(os.path.join(file)))\\n\\n df = pd.concat(df_list)\\n\\n file = os.path.join(save_dir, filename)\\n df.to_csv(file)\\n\\n predictions = predict(test_dir, model_dir, config)\\n\\n df[\\\"Predicted_Delay\\\"] = predictions\\n df['Absolute_Error'] = np.abs(df[\\\"Delay\\\"] - df[\\\"Predicted_Delay\\\"])\\n df['Absolute_Percentage_Error'] = (df['Absolute_Error'] / np.abs(df[\\\"Delay\\\"]))*100\\n\\n return df['Absolute_Percentage_Error'].mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5f589c1ad6e702143d6c69115f3622\",\n \"score\": \"0.60529906\",\n \"text\": \"def test_03_predict(self):\\r\\n\\r\\n log_file = os.path.join(\\\"logs\\\", \\\"prediction-test.log\\\")\\r\\n if os.path.exists(log_file):\\r\\n os.remove(log_file)\\r\\n \\r\\n\\r\\n y_pred = [0]\\r\\n y_proba = [0.6, 0.4]\\r\\n runtime = \\\"00:00:06\\\"\\r\\n model_version = 0.1\\r\\n query = ['France', '2020', '01', '05']\\r\\n\\r\\n update_predict_log(self,y_pred, y_proba, query, runtime,\\r\\n model_version, test=True)\\r\\n\\r\\n self.assertTrue(os.path.exists(log_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ecac633e35e07ee4f5dfc1e143d450\",\n \"score\": \"0.6044228\",\n \"text\": \"def predict_model(self, data: pd.DataFrame, export_metrics: bool = False):\\n\\n self.predict = pcr.predict_model(estimator=self.model, data=data)\\n evaluation_metrics(self.predict, export_metrics)\\n\\n return self.predict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdfe4ee30564cf1e87decfecf959979\",\n \"score\": \"0.60374653\",\n \"text\": \"def write_csv_proper(filename, predictions):\\n with open(filename, 'w') as f:\\n f.write('Id,Prediction\\\\n')\\n for index, prediction in enumerate(predictions):\\n f.write(str(index + 1))\\n f.write(',')\\n f.write(str(prediction))\\n f.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76fbc471b8ef7268000f725facace07\",\n \"score\": \"0.6031369\",\n \"text\": \"def write_predictions(predictions, ids, outfile):\\n with open(outfile,\\\"w+\\\") as f:\\n # write header\\n f.write(\\\"Id,Prediction\\\\n\\\")\\n for i, movie_id in enumerate(ids):\\n f.write(\\\"%s,%f\\\\n\\\" % (movie_id, predictions[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce75deafae8ce1d4b46083a7fb0a6740\",\n \"score\": \"0.60234386\",\n \"text\": \"def write_hyperpartisan_predictions(predictions, eval_dataset, output_dir, cutoff=0.5):\\n article_ids = [art.get_id() for art in eval_dataset.articles]\\n\\n ## Write to file\\n with open(os.path.join(output_dir, 'prediction.txt'), 'w', encoding='utf-8') as out:\\n for art_id, pred in zip(article_ids, predictions):\\n print('{}\\\\t{}\\\\t{}'.format(\\n art_id,\\n 'true' if pred > cutoff else 'false',\\n ((pred - 0.5) * 2) if pred > 0.5 else ((0.5 - pred) * 2)),\\n file=out\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96a2fb687509f069e3c176bd1a861e0a\",\n \"score\": \"0.5993838\",\n \"text\": \"def sklearn_train_model(model, training_data, training_labels, test_data, test_labels, model_savename):\\n starttime = datetime.now()\\n model.fit(training_data, training_labels)\\n finishtime = datetime.now()\\n runtime = finishtime - starttime\\n\\n #predict the run\\n test_predictions = model.predict(test_data)\\n test_predictions_probabilities = model.predict_proba(test_data)[:, 1]\\n # proba[:,1] returns just 1 of 2 columns. As they always add up, this is enough!\\n\\n sklearn_persist_model(model, model_savename)\\n # append all this to a dataframe / JSON / whatever and\\n return pe.PredictionEntry(test_labels, test_predictions, test_predictions_probabilities, runtime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ed06579273e727997e87b389a5bff1\",\n \"score\": \"0.59807587\",\n \"text\": \"def predict_note_file():\\n df_test = pd.read_csv(request.files.get(\\\"file\\\"))\\n prediction = model.predict(df_test)\\n return \\\"The predicted values for the csv is \\\" + str(list(prediction))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c63b84484596ef55c49bf781afb4e29\",\n \"score\": \"0.5975053\",\n \"text\": \"def predict(model_path: str = None, input_data=None, input_path: str = None, output_path: str = None, **kwargs):\\n\\n try:\\n # TODO add predict batch support?\\n # TODO real logging\\n model_handler.init(default_model_key=model_path)\\n log.info(model_handler.predict(input_data, **kwargs))\\n except:\\n log.exception(\\\"Failed predict with model.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e64f62e9fb9ab636e221c1ff933667\",\n \"score\": \"0.59743667\",\n \"text\": \"def get_prediction_df(input_df, model):\\r\\n predictions_df = predict_model(estimator = model, data = input_df)\\r\\n return predictions_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6a4dba224691dfa6ee8e0726019e9e\",\n \"score\": \"0.59676725\",\n \"text\": \"def _predict(model_id, model_gcp_project, model_location,\\n model_api_endpoint, gcp_project, bq_input_uri, bq_output_uri):\\n credentials, project = google.auth.default(scopes=[GCP_SCOPE])\\n\\n authed_session = AuthorizedSession(credentials)\\n\\n request_url = f'https://{model_api_endpoint}/v1/projects/{model_gcp_project}/locations/{model_location}/batchPredictionJobs'\\n request_data = json.dumps({\\n 'displayName': f'{DEPLOYMENT_NAME}_{SOLUTION_PREFIX}_batch_predict - {datetime.datetime.now()}',\\n 'inputConfig': {\\n 'instancesFormat': 'bigquery',\\n 'bigquerySource': {\\n 'inputUri': bq_input_uri\\n }\\n },\\n 'outputConfig': {\\n 'predictionsFormat': 'bigquery',\\n 'bigqueryDestination': {\\n 'outputUri': bq_output_uri\\n }\\n },\\n 'model': f'projects/{model_gcp_project}/locations/{model_location}/models/{model_id}'\\n })\\n logger.debug('Creating batch prediction. URL: %r, Data: %r',\\n request_url, request_data)\\n response = authed_session.post(request_url, data=request_data)\\n response_dict = json.loads(response.text)\\n logger.debug('Batch prediction created. Response: %r', response_dict)\\n if 'error' in response_dict:\\n raise Exception(f'Error while creating batch prediction job: {response_dict!r}')\\n return response_dict['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c43d3e4c5ec95ba21e8e11135702c9d2\",\n \"score\": \"0.59660226\",\n \"text\": \"def predict_model(model, test_df, features):\\n return model.predict(test_df[features])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b32f595827bc9822ea8a5984e172cd33\",\n \"score\": \"0.5962195\",\n \"text\": \"def predict(self, dataset, model=None, as_pandas=False):\\n dataset = self.__get_dataset(dataset)\\n return self._learner.predict(X=dataset, model=model, as_pandas=as_pandas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e93e574370832bc1f4bf03348a2127c\",\n \"score\": \"0.5946272\",\n \"text\": \"def predict(handle, test_file, model_file, output_file):\\n\\n return handle.predict(test_file, model_file, output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297df5883ee5b4268ad0a00035666211\",\n \"score\": \"0.59450823\",\n \"text\": \"def predict(self, X, y):\\n for self.model in self.models:\\n self.model.fit(X, y)\\n self.predictions_proba = self.model.predict_proba(self.points_1)\\n \\n self.blend_predictions()\\n self.plot_predictions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad30e6161c7e38413331abdd493b5bd\",\n \"score\": \"0.5941714\",\n \"text\": \"def add_model_predictions_to_df(df, prediction_file, model_col_name,\\n prediction_name, example_key, class_names):\\n\\n prediction_file = os.path.join(prediction_file,\\n 'prediction.results-00000-of-00001')\\n if not tf.gfile.Exists(prediction_file):\\n raise ValueError(\\n 'Prediction file does not exist.'\\n ' You need to call prediction job and wait for completion.')\\n\\n def _load_predictions(pred_file):\\n with file_io.FileIO(pred_file, 'r') as f:\\n # prediction file needs to fit in memory.\\n try:\\n predictions = [json.loads(line) for line in f]\\n except:\\n predictions = []\\n return predictions\\n\\n predictions = _load_predictions(prediction_file)\\n\\n if not predictions:\\n raise ValueError(\\n 'The prediction file returned by CMLE is empty.'\\n ' It might be due to a badly formatted tfrecord input file that can not be'\\n ' parsed by CMLE (wrong input signature given by a `Model` instance).'\\n ' Check the logs of your CMLE job for further details.')\\n if example_key not in predictions[0]:\\n raise ValueError(\\n \\\"Predictions do not contain the 'example_key' field.\\\"\\n \\\" Verify that your 'example_key' parameter (set to {})\\\"\\n \\\" matches the CMLE model signature.\\\".format(example_key))\\n if prediction_name not in predictions[0]:\\n raise ValueError(\\n \\\"Predictions do not contain the 'prediction_name' field.\\\"\\n \\\" Verify that your 'prediction_name' parameter (set to {})\\\"\\n \\\" matches the CMLE model signature.\\\".format(prediction_name))\\n if len(predictions) != len(df):\\n raise ValueError('The dataframe and the prediction file do not contain'\\n ' the same number of lines.')\\n\\n predictions = sorted(predictions, key=lambda x: x[example_key])\\n if class_names is None:\\n prediction_proba = [x[prediction_name][0] for x in predictions]\\n df[model_col_name] = prediction_proba\\n else:\\n for i, class_name in enumerate(class_names):\\n df['{}_{}'.format(model_col_name,class_name)] = [x[prediction_name][i] for x in predictions]\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c53b51468acfc807cd8868a3fdf60316\",\n \"score\": \"0.5940207\",\n \"text\": \"def generate_pred_csv(testset, pred_labels_df, column_name, csv_name): \\n testset[column_name] = pred_labels_df\\n return testset.to_csv(csv_name, index = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d88d5c020fa39ed2c5ad0972e55f7b1\",\n \"score\": \"0.5932011\",\n \"text\": \"def get_prediction(user_data, model):\\n\\n # Feature orders needed for model.predict's input data\\n # The columns are required to be in the same order as the data input\\n feature_order = [\\n \\\"property_type\\\",\\n \\\"room_type\\\",\\n \\\"bathrooms\\\",\\n \\\"cancellation_policy\\\",\\n \\\"city\\\",\\n \\\"host_since\\\",\\n \\\"review_scores_rating\\\",\\n \\\"bedrooms\\\",\\n \\\"beds\\\",\\n \\\"instant_bookable\\\",\\n \\\"host_has_profile_pic\\\",\\n \\\"host_identity_verified\\\",\\n \\\"cleaning_fee\\\",\\n \\\"Wireless Internet\\\",\\n \\\"Air conditioning\\\",\\n \\\"Kitchen\\\",\\n \\\"Heating\\\",\\n \\\"Family/kid friendly\\\",\\n \\\"Hair dryer\\\",\\n \\\"Iron\\\",\\n \\\"Shampoo\\\",\\n \\\"Fire extinguisher\\\",\\n \\\"Laptop friendly workspace\\\",\\n \\\"Indoor fireplace\\\",\\n \\\"TV\\\",\\n \\\"Cable TV\\\",\\n ]\\n\\n # dictionary for forcing data types accepted by sklearn\\n data_types = {\\n \\\"property_type\\\": object,\\n \\\"room_type\\\": object,\\n \\\"bathrooms\\\": np.float64,\\n \\\"cancellation_policy\\\": object,\\n \\\"city\\\": object,\\n \\\"host_since\\\": np.int64,\\n \\\"review_scores_rating\\\": object,\\n \\\"bedrooms\\\": np.float64,\\n \\\"beds\\\": np.float64,\\n \\\"instant_bookable\\\": bool,\\n \\\"host_has_profile_pic\\\": bool,\\n \\\"host_identity_verified\\\": bool,\\n \\\"cleaning_fee\\\": bool,\\n \\\"Wireless Internet\\\": bool,\\n \\\"Air conditioning\\\": bool,\\n \\\"Kitchen\\\": bool,\\n \\\"Heating\\\": bool,\\n \\\"Family/kid friendly\\\": bool,\\n \\\"Hair dryer\\\": bool,\\n \\\"Iron\\\": bool,\\n \\\"Shampoo\\\": bool,\\n \\\"Fire extinguisher\\\": bool,\\n \\\"Laptop friendly workspace\\\": bool,\\n \\\"Indoor fireplace\\\": bool,\\n \\\"TV\\\": bool,\\n \\\"Cable TV\\\": bool,\\n }\\n\\n # Make sure the data is coming in correctly\\n # This also serves as a simple backend data validation so the route returns\\n # a value even if someone tries to spoof a data point\\n try:\\n\\n # Check user data is valid length\\n assert len(user_data) == len(\\n feature_order), print(f'Expected user_data to have {len(feature_order)} items')\\n\\n # Convert host_since into number of days\\n user_data[5] = (datetime.today() -\\n datetime.strptime(user_data[5], '%Y-%m-%d')\\n ).days\\n\\n # dictionary to construct dataframe\\n data = {feature: [datum]\\n for feature, datum in zip(feature_order, user_data)}\\n assert len(data) == len(feature_order), print(\\n 'dictionary size incorrect')\\n\\n # transform input into dataframe for model prediction\\n transformed_input = pd.DataFrame(\\n data).astype(data_types)[feature_order]\\n\\n # make prediction using model\\n prediction = model.predict(transformed_input)\\n\\n except Exception as e:\\n print('-----ERROR IN TRANSFORMING INPUT-----')\\n print(\\\"User Data: \\\", user_data)\\n print(e)\\n # Return 0 so the endpoint still works\\n prediction = [0]\\n\\n # return prediction in USD rounded to the penny\\n return np.exp(prediction[0]).round(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45bc1a0e563b68a01985e9d2ce01d348\",\n \"score\": \"0.5931972\",\n \"text\": \"def save_predictions(\\n self,\\n strategy_name,\\n dataset_name,\\n y_true,\\n y_pred,\\n y_proba,\\n index,\\n cv_fold,\\n train_or_test,\\n fit_estimator_start_time=None,\\n fit_estimator_end_time=None,\\n predict_estimator_start_time=None,\\n predict_estimator_end_time=None,\\n ):\\n # TODO y_proba is currently ignored\\n key = (\\n self._generate_key(strategy_name, dataset_name, cv_fold, train_or_test)\\n + \\\".csv\\\"\\n )\\n # TODO find a more clever way to save the timestamps\\n results = pd.DataFrame(\\n {\\n \\\"index\\\": index,\\n \\\"y_true\\\": y_true,\\n \\\"y_pred\\\": y_pred,\\n \\\"fit_estimator_start_time\\\": fit_estimator_start_time,\\n \\\"fit_estimator_end_time\\\": fit_estimator_end_time,\\n \\\"predict_estimator_start_time\\\": predict_estimator_start_time,\\n \\\"predict_estimator_end_time\\\": predict_estimator_end_time,\\n }\\n )\\n results.to_csv(key, index=False, header=True)\\n self._append_key(strategy_name, dataset_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eca467972662ecf75fbd14bcca3f25d\",\n \"score\": \"0.5931729\",\n \"text\": \"def transformation():\\n data = None\\n model_name = flask.request.headers.get('CustomAttributes')\\n if model_name == None:\\n model_name = flask.request.headers.get('X-Amzn-Sagemaker-Custom-Attributes')\\n print(model_name)\\n # Convert from CSV to pandas\\n if flask.request.content_type == 'application/python-pickle':\\n data = flask.request.data\\n df = pickle.loads(data)\\n print('Invoked with {} records'.format(df.shape[0]))\\n preds = h2o.H2OFrame(df)\\n \\n else:\\n return flask.Response(response='This predictor only supports pickle files', status=415, mimetype='text/plain')\\n \\n # Do the prediction\\n predictions = ScoringService.predict(preds, model_name)\\n h2o.h2o.export_file(predictions,'/tmp/results.csv')\\n predictions2 = pd.read_csv('/tmp/results.csv', header=None)\\n os.remove(\\\"/tmp/results.csv\\\")\\n # Convert from numpy back to CSV\\n out = StringIO.StringIO()\\n #pd.DataFrame({'results':predictions2}).to_csv(out, header=False, index=False)\\n #result = out.getvalue()\\n\\n return flask.Response(response=predictions2.to_json(orient='columns'), status=200, mimetype='text/csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922fa31cf66c3b99aa5db0a3427465a9\",\n \"score\": \"0.5931536\",\n \"text\": \"def apply(input_dataframe):\\n\\n # import model\\n \\n filename = project_path+\\\"/model/housing_model.sav\\\"\\n\\n with open(filename,'rb') as model_file:\\n pickle_model = pickle.load(model_file)\\n\\n return pickle_model.predict(input_dataframe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e559f03e9885601c24b365dfec32ae5d\",\n \"score\": \"0.59200865\",\n \"text\": \"def write_predictions(self, user_data):\\n def format_row(va):\\n # We filled age with a default value of zero but do not want to\\n # report this value in output files and graphs\\n if float(va.age) == 0 and self.AGE_GROUP in ['adult', 'child']:\\n va.age = ''\\n return [va.sid, va.cause34, va.cause34_name, va.age, va.sex]\\n\\n filename = '{:s}-predictions.csv'.format(self.AGE_GROUP)\\n with open(os.path.join(self.output_dir_path, filename), 'wb') as f:\\n writer = csv.writer(f)\\n writer.writerow([SID_KEY, 'cause', 'cause34', 'age', 'sex'])\\n writer.writerows([format_row(va) for va in user_data])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7008700518d429866d082b8c47cee04a\",\n \"score\": \"0.59159017\",\n \"text\": \"def load_model_predict(model_name, X, labels, embedding_size, filename):\\n rankModel, representation = tripletModel(X.shape[1], embedding_size)\\n rankModel.load_weights(model_name)\\n representation = Model(inputs=rankModel.input[0],\\n outputs=rankModel.get_layer('hidden_layer').get_output_at(0))\\n\\n new_X = representation.predict(X)\\n writeRepresentation(new_X, labels, embedding_size, filename + str(embedding_size) + \\\"D_RankOD\\\")\\n scores = lesinn(new_X)\\n rauc = aucPerformance(scores, labels, filename)\\n writeResults(filename, embedding_size, rauc)\\n writeOutlierScores(scores, labels, str(embedding_size) + \\\"D_\\\"+filename)\\n return rauc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d618f2ccf52344b46d9c7b63d6065981\",\n \"score\": \"0.5908038\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_pred = model.predict(X_test)\\n \\n for i in range(len(Y_test.columns)):\\n print(\\\"=== {}. {} ===\\\".format(i+1, Y_test.columns[i]))\\n print(classification_report(Y_test.iloc[:,i], Y_pred[:,i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9d3274874effaff29bf0df5684a7ed7\",\n \"score\": \"0.59024817\",\n \"text\": \"def predictor(test_day, train_day):\\n X_test, y_test = data_format(test_day=test_day)\\n clf = pickle.load(open( os.path.join(train_day,\\\"Models/SVM.p\\\"), \\\"rb\\\"))\\n # We load in our fitted model\\n y_pred = clf.predict(X_test)\\n # The predicted results\\n recall = metrics.recall_score(y_test, y_pred)\\n n = len(y_test)\\n filtered = sum(np.count_nonzero(y_pred-np.ones(n))) / float(n)\\n # The filter rate is the proportion of the data we predict to be -1\\n result = [train_day, test_day, recall, filtered, netsav(recall,filtered)]\\n # We write this as a list so that it will be on one row of the csv\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00da40a01a2a82b635f244f071e0f78e\",\n \"score\": \"0.58951724\",\n \"text\": \"def serve_predict_model(model_name):\\n # # get feature times\\n # feature_times, features = load_features('motion_energy')\\n\\n X = np.random.randn(20, 10).astype('float32')\\n model = r.get(f'model:{model_name}')\\n model = pickle.loads(model)\\n y_hat = model.predict(X)\\n\\n return f'Predicting {model_name} {len(y_hat)}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65aef89b4c03ad8ab906408669028562\",\n \"score\": \"0.5889908\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_pred = model.predict(X_test)\\n for i in range(36):\\n print(Y_test.columns[i], ':')\\n print(classification_report(Y_test.iloc[:,i], Y_pred[:,i], target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab6520d30aa0a50cc1711cc19ac95\",\n \"score\": \"0.5887997\",\n \"text\": \"def run_prediction(model: str):\\n data = FingerprintsData(PATH_TEST / \\\"smiles.csv\\\", sanitize=False)\\n modeller = SKModeller(model, data)\\n modeller.load_model(\\\"swan_skmodeller.pkl\\\")\\n modeller.data.load_scale()\\n predicted = modeller.predict(data.fingerprints)\\n assert not np.isnan(predicted).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53efa09e43483a2c504aba9d2bd3dbd9\",\n \"score\": \"0.58835727\",\n \"text\": \"def save_results(predictions, filename):\\n with open(filename, 'w') as f:\\n f.write(\\\"id,ACTION\\\\n\\\")\\n for i, pred in enumerate(predictions):\\n f.write(\\\"%d,%f\\\\n\\\" % (i + 1, pred))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a27aea5e31f19bdcd95c98ae30bb80\",\n \"score\": \"0.5875886\",\n \"text\": \"def predict(self, *, model_id: str, data: list) -> list:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5cbb3929bbce4740e6b730004320d49\",\n \"score\": \"0.5871915\",\n \"text\": \"def evaluate_model(model, X_test, y_test, labels):\\n print('Evaluating model...')\\n\\n y_pred = model.predict(X_test) \\n\\n report = classification_report(y_test.values, y_pred, target_names=labels, output_dict = True)\\n print(report)\\n\\n print('Saving training report...')\\n metrics = list(map(lambda label: extract_category(report, label), labels))\\n df_metrics = pd.DataFrame(metrics)\\n df_metrics.to_csv(metrics_filename, index = False)\\n\\n print(f'Best score: {model.best_score_}')\\n print(f'Best parameters: {model.best_params_}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47aba0edc2b313a2d78dbea4504dd453\",\n \"score\": \"0.58533657\",\n \"text\": \"def eval_model_predictions(model, X_train, y_train, X_test):\\n model.fit(X_train, y_train)\\n pred = model.predict(X_test)\\n print\\n \\\"accuracy is \\\", metrics.accuracy_score(y_test, pred)\\n return pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d79610109835be92a9125a1e1631e4\",\n \"score\": \"0.5851333\",\n \"text\": \"def main():\\n x_train, x_test, y_train, y_test = fetch_processed('data/processed/transfusion_data.csv')\\n \\n # Train the model \\n model = fit_model(x_train, y_train)\\n \\n # Store model and test set for prediction\\n with open(ROOT / 'models/transfusion.model', 'wb') as fout:\\n pickle.dump(model, fout, PROTOCOL)\\n x_test.to_csv(ROOT / 'data/processed/transfusion_x_test.csv',\\n index=False)\\n y_test.to_csv(ROOT / 'data/processed/transfusion_y_test.csv',\\n index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132a5d7a57666276b51bda6ac297f527\",\n \"score\": \"0.58437943\",\n \"text\": \"def show_results(model, modelname, data, label, title):\\n preds = model.predict(data, batch_size=1, verbose=1)\\n y_pred = preds.argmax(axis=1)\\n if preds.ndim == 1:\\n y_actual = np.squeeze(label)\\n else:\\n y_actual = label.argmax(axis=1)\\n\\n report = pd.DataFrame(\\n classification_report(y_actual, y_pred, output_dict=True)).T\\n report.to_csv(\\n 'model/' + modelname + '_' + title + '_classification_report.csv')\\n print('\\\\n' + title + 'Stats\\\\n', classification_report(y_actual, y_pred))\\n print_confusion_matrix(confusion_matrix(y_actual, y_pred),\\n unique_labels(y_actual, y_pred), title, modelname)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aee10adc79312d30fa12a9b66ce6c2f\",\n \"score\": \"0.5842588\",\n \"text\": \"def evaluate_model(model, X_test, y_test, category_names):\\n y_pred = model.predict(X_test)\\n y_pred = pd.DataFrame(y_pred, columns=y_test.columns, index=y_test.index)\\n print(classification_report(y_test, y_pred, target_names=category_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0014e611c5ff5c3368f282db339ad3a0\",\n \"score\": \"0.58349687\",\n \"text\": \"def predictobs(self):\\n\\n\\t\\tlogger.info(\\\"{}: starting predictions...\\\".format(self))\\n\\t\\t\\n\\t\\t# We read the catalog\\n\\t\\tcat = megalut.tools.io.readpickle(self.meascatfilepath)\\n\\t\\t#print cat.colnames\\n\\t\\t#print len(cat)\\n\\t\\t#exit()\\n\\t\\t\\n\\t\\t# We read the mlparams:\\n\\t\\tmlparams = {}\\n\\t\\texecfile(os.path.join(self.configdir, \\\"mlparams.py\\\"), mlparams)\\n\\t\\n\\t\\t# Run MegaLUT\\n\\t\\tcat = megalut.learn.run.predict(cat, self.configdir, mlparams[\\\"trainparamslist\\\"])\\n\\t\\t\\t\\n\\t\\t# And write the output\\n\\t\\tmegalut.tools.io.writepickle(cat, self.predcatfilepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"676b5f327d993c49955f2b8d2053dffb\",\n \"score\": \"0.58335316\",\n \"text\": \"def _predict_by_model(self, model_names, data_type):\\n\\t\\tn_models = len(model_names)\\n\\t\\tif n_models == 0:\\n\\t\\t\\traise RuntimeError('There should be at least one model to predict')\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tdv = self.client[:]\\n\\t\\tis_probabilistic = dv.map(partial(read_model_meta, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.ensemble_path, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\tkeys=['is_probabilistic'], default=False), \\n\\t\\t\\t\\t\\t\\t\\t\\tmodel_names)\\n\\t\\t#is_probabilistic = map(partial(read_model_meta, \\n\\t\\t#\\t\\t\\t\\t\\t\\t\\tself.ensemble_path, \\n\\t\\t#\\t\\t\\t\\t\\t\\t\\tkeys=['is_probabilistic'], default=False), \\n\\t\\t#\\t\\t\\t\\t\\t\\tmodel_names)\\n\\t\\tprint 'THIS STEP DONE'\\n\\t\\tis_probabilistic = [d['is_probabilistic'] for d in is_probabilistic]\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t## decided by the 'is_probabilistic' field in the model config\\n\\t\\tis_probabilistic = [None for _ in xrange(n_models)]\\n\\t\\tdata_types = [data_type for _ in xrange(n_models)]\\n\\t\\tparams = zip(model_names, data_types, is_probabilistic)\\n\\t\\tresults = parallel_predict_model(self.ensemble_path, params, self.client, verbose=True)\\n\\t\\ttarget = results[0][1][0]\\n\\t\\treturn (target, {mdl_name : yhat for (mdl_name, (y, yhat)) in results})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d3f69c6e1fc985e8bf43a23cab93d5\",\n \"score\": \"0.5826853\",\n \"text\": \"def predict_fn(input_data, model):\\n prediction = model.predict(input_data)\\n pred_prob = model.predict_proba(input_data)\\n \\n return prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48221732be5888da4c0f7a50f35ec6db\",\n \"score\": \"0.5825037\",\n \"text\": \"def predict(model,\\n X,\\n Z,\\n y,\\n model_file,\\n output,\\n amount=None,\\n return_mf1=False,\\n use_softmax=False):\\n\\n print(\\\"Predicting with saved model: {0} ... \\\".format(model_file), end='')\\n start_time = time()\\n y_pred = model.predict([X[:amount], Z[:amount]])\\n print(\\\"Done (took {:3.3f}s)\\\".format(time() - start_time))\\n\\n # print(y_pred)\\n if use_softmax:\\n y_pred = (y_pred == y_pred.max(axis=1)[:, None]).astype(int)\\n else:\\n y_pred[y_pred >= 0.5] = 1.\\n y_pred[y_pred < 0.5] = 0.\\n\\n # print(y_pred)\\n # print(y_pred.sum(axis=1))\\n y_pred = sparse.csr_matrix(y_pred)\\n\\n F1 = 0.\\n file_writer = open(output, \\\"a\\\")\\n file_writer.write(\\\"\\\\n{0}\\\\n\\\".format(datetime.now()))\\n file_writer.write(\\\"{:s}\\\\n\\\".format(model_file))\\n\\n # Begin evaluation\\n print(\\\"Calculating Precision/Recall/F-1 scores ...\\\")\\n eval_types = ['micro', 'macro', 'weighted']\\n for eval_type in eval_types:\\n p, r, f, _ = precision_recall_fscore_support(\\n y, y_pred, average=eval_type)\\n print(\\\"[{}]\\\\t{:3.3f}\\\\t{:3.3f}\\\\t{:3.3f}\\\".format(eval_type, p, r, f))\\n file_writer.write(\\\"[{}]\\\\t{:3.3f}\\\\t{:3.3f}\\\\t{:3.3f}\\\\n\\\".format(\\n eval_type, p, r, f))\\n if eval_type == 'micro':\\n F1 = f\\n\\n # Close file pointer\\n file_writer.close()\\n\\n if return_mf1:\\n return F1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4190f2f16918a947fdd83560be763f27\",\n \"score\": \"0.58138597\",\n \"text\": \"def write_classification_outputs(self, tasks, trial, split):\\r\\n log(\\\"Writing out predictions for\\\", tasks, split)\\r\\n predict_input_fn, _ = self._preprocessor.prepare_predict(tasks, split)\\r\\n results = self._estimator.predict(input_fn=predict_input_fn,\\r\\n yield_single_examples=True)\\r\\n # task name -> eid -> model-logits\\r\\n logits = collections.defaultdict(dict)\\r\\n for r in results:\\r\\n if r[\\\"task_id\\\"] != len(self._tasks):\\r\\n r = nest_dict(r, self._config.task_names)\\r\\n task_name = self._config.task_names[r[\\\"task_id\\\"]]\\r\\n logits[task_name][r[task_name][\\\"eid\\\"]] = (\\r\\n r[task_name][\\\"logits\\\"] if \\\"logits\\\" in r[task_name]\\r\\n else r[task_name][\\\"predictions\\\"])\\r\\n for task_name in logits:\\r\\n log(\\\"Getting predictions for {:} {:} examples ({:})\\\".format(\\r\\n len(logits[task_name]), task_name, split))\\r\\n logits = logits[task_name].values()\\r\\n return logits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbdafdcc12734115968bfa065f15852\",\n \"score\": \"0.58072114\",\n \"text\": \"def do_predict(args):\\n\\n # Load data\\n Xt = XLinearModel.load_feature_matrix(args.inst_path)\\n\\n if args.select_output is not None:\\n # Select Output\\n select_outputs_csr = XLinearModel.load_feature_matrix(args.select_output)\\n xlinear_model = XLinearModel.load(\\n args.model_folder, is_predict_only=True, weight_matrix_type=\\\"CSC\\\"\\n )\\n else:\\n # TopK\\n select_outputs_csr = None\\n xlinear_model = XLinearModel.load(args.model_folder, is_predict_only=True)\\n\\n # Model Predicting\\n if args.batch_size is not None:\\n Yts = []\\n for i in range(0, Xt.shape[0], args.batch_size):\\n Yte = xlinear_model.predict(\\n Xt[i : i + args.batch_size, :],\\n select_outputs_csr=select_outputs_csr[i : i + args.batch_size, :]\\n if select_outputs_csr is not None\\n else None,\\n only_topk=args.only_topk,\\n beam_size=args.beam_size,\\n post_processor=args.post_processor,\\n threads=args.threads,\\n )\\n Yts.append(Yte)\\n # vstack_csr will retain indices order\\n Yt_pred = smat_util.vstack_csr(Yts)\\n else:\\n Yt_pred = xlinear_model.predict(\\n Xt,\\n select_outputs_csr=select_outputs_csr,\\n only_topk=args.only_topk,\\n beam_size=args.beam_size,\\n post_processor=args.post_processor,\\n threads=args.threads,\\n )\\n\\n # Save prediction\\n if args.save_pred_path:\\n smat_util.save_matrix(args.save_pred_path, Yt_pred)\\n\\n # Evaluate\\n if args.label_path:\\n Yt = XLinearModel.load_label_matrix(args.label_path)\\n metric = smat_util.Metrics.generate(Yt, Yt_pred, topk=10)\\n print(\\\"==== evaluation results ====\\\")\\n print(metric)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf5e8ccd9f62a5406ca973f9afd8e41\",\n \"score\": \"0.58066356\",\n \"text\": \"def predict(test_data, results, model_name):\\n model_params = DataFrame(results[model_name][0].params)\\n formula = results[model_name][1]\\n\\n # Create regression friendly test DataFrame\\n yt, xt = dmatrices(formula, data=test_data, return_type='dataframe')\\n xt, model_params = get_dataframes_intersections(xt, xt.columns,\\n model_params, model_params.index)\\n # Convert to NumPy arrays for performance\\n model_params = np.asarray(model_params)\\n yt = np.asarray(yt)\\n yt = yt.ravel()\\n xt = np.asarray(xt)\\n\\n # Use our models to create predictions\\n row, col = xt.shape\\n model_parameters = model_params.ravel()\\n model_array = list((model_parameters for parameter in xrange(row)))\\n model_array = np.asarray(model_array)\\n\\n # Multiply matrix together\\n predictions = np.multiply(xt, model_array)\\n predictions = np.sum(predictions, axis=1)\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9115b346fd5461f316e9a8fef584e07b\",\n \"score\": \"0.58047765\",\n \"text\": \"def transformation():\\n data = None\\n\\n # Convert from CSV to pandas\\n if flask.request.content_type == 'text/csv':\\n data = flask.request.data.decode('utf-8')\\n s = StringIO(data)\\n data = pd.read_csv(s, header=None)\\n else:\\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\\n\\n nClasses, nFeatures, fptype = ScoringService.get_num_classes_and_features()\\n\\n if data.shape[1] != nFeatures:\\n return flask.Response(response='Number of features is incorrect. %d is required\\\\n' % nFeatures, status=415, mimetype='text/plain')\\n resultsToCompute = \\\"computeClassesLabels|computeClassesProbabilities\\\"\\n print('Invoked with {} records'.format(data.shape[0]))\\n if fptype == \\\"float\\\":\\n fptype = \\\"float\\\"\\n else:\\n fptype = \\\"double\\\"\\n dtype = (np.float64 if fptype == \\\"double\\\" else np.float32)\\n # Do the prediction\\n predictions = ScoringService.predict(input=np.ascontiguousarray(data.values, dtype=dtype), nClasses=nClasses,\\n dtype=fptype, resultsToCompute=resultsToCompute)\\n prediction = \\\"\\\".join(str(x[0])+ ' ' for x in predictions.prediction)\\n probabilities = \\\"\\\"\\n if nClasses > 2:\\n i = 0\\n while i < nClasses:\\n probabilities += \\\"\\\".join(str(x[i])+ ' ' for x in predictions.probabilities) + \\\"\\\\n\\\"\\n i = i + 1\\n else:\\n probabilities = \\\"\\\".join(str(x[0])+ ' ' for x in predictions.probabilities) + \\\"\\\\n\\\"\\n\\n result = prediction + \\\"\\\\n\\\" + probabilities\\n\\n return flask.Response(response=result, status=200, mimetype='text/csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7612cc6e80a2c2d9f056d2c52adf219\",\n \"score\": \"0.5799704\",\n \"text\": \"def writePredictionsToCsv(predictions, out_path, label_strings):\\n with open(out_path, 'w') as outfile:\\n csvwriter = csv.writer(\\n outfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL\\n )\\n # Write the header\\n row_to_write = ['Id'] + [label for label in label_strings]\\n csvwriter.writerow(row_to_write)\\n # Write the rows using 18 digit precision\\n for idx, prediction in enumerate(predictions):\\n assert len(prediction) == len(label_strings)\\n csvwriter.writerow(\\n [str(idx+1)] +\\n [\\\"%.18f\\\" % p for p in prediction]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2897871b99887a03ded2fee27d5aaed3\",\n \"score\": \"0.5798098\",\n \"text\": \"def predict(self, dataset, prediction_directory=None, groundtruth_directory=None):\\n\\n if not isinstance(dataset, (Dataset, str)):\\n raise TypeError(\\\"Must pass in an instance of Dataset containing your examples to be used for prediction\\\")\\n if self.model is None:\\n raise ValueError(\\\"Must fit or load a pickled model before predicting\\\")\\n\\n model = self.model\\n medacy_pipeline = self.pipeline\\n\\n if isinstance(dataset, Dataset):\\n # create directory to write predictions to\\n prediction_directory = self.create_annotation_directory(directory=prediction_directory, training_dataset=dataset, option=\\\"predictions\\\")\\n\\n # create directory to write groundtruth to\\n groundtruth_directory = self.create_annotation_directory(directory=groundtruth_directory, training_dataset=dataset, option=\\\"groundtruth\\\")\\n\\n for data_file in dataset.get_data_files():\\n logging.info(\\\"Predicting file: %s\\\", data_file.file_name)\\n with open(data_file.raw_path, 'r') as raw_text:\\n doc = medacy_pipeline.spacy_pipeline.make_doc(raw_text.read())\\n doc.set_extension('file_name', default=data_file.file_name, force=True)\\n if data_file.metamapped_path is not None:\\n doc.set_extension('metamapped_file', default=data_file.metamapped_path, force=True)\\n\\n # run through the pipeline\\n doc = medacy_pipeline(doc, predict=True)\\n\\n annotations = predict_document(model, doc, medacy_pipeline)\\n logging.debug(\\\"Writing to: %s\\\", os.path.join(prediction_directory, data_file.file_name + \\\".ann\\\"))\\n annotations.to_ann(write_location=os.path.join(prediction_directory, data_file.file_name + \\\".ann\\\"))\\n\\n if isinstance(dataset, str):\\n assert 'metamap_annotator' not in self.pipeline.get_components(), \\\\\\n \\\"Cannot currently predict on the fly when metamap_component is in pipeline.\\\"\\n\\n doc = medacy_pipeline.spacy_pipeline.make_doc(dataset)\\n doc.set_extension('file_name', default=\\\"STRING_INPUT\\\", force=True)\\n doc = medacy_pipeline(doc, predict=True)\\n annotations = predict_document(model, doc, medacy_pipeline)\\n \\n #display entity jason\\n for entity_name, start, stop, end in annotations.get_entity_annotations():\\n #span = Span(doc, start, end, label=medacy_pipeline.vocab.strings[entity_name])\\n span = Span(doc, start, end, label=int(33333333))\\n for token in span:\\n token._.feature_is_frequency_indicator = True\\n if len(span) > 1:\\n retokenizer.merge(span)\\n doc.ents = list(doc.ents) + [span]\\n ########\\n \\n displacy.render(doc, style=\\\"ent\\\")\\n \\n return annotations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805683c26dce6a4020b5782345170234\",\n \"score\": \"0.5792101\",\n \"text\": \"def predict(self, model, Xval, Xtest):\\n\\n pred = model.predict(\\n Xval, batch_size=self.batch_size_pred, verbose=0).squeeze()\\n pred_test = model.predict(\\n Xtest, batch_size=self.batch_size_pred, verbose=0).squeeze()\\n\\n return pred, pred_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f27f98375f5d38828d794f3739ff725\",\n \"score\": \"0.579149\",\n \"text\": \"def evaluate(model_path, test_path, model):\\n\\n if SAVE_MODELS:\\n model = load_model(model_path, custom_objects={'TimestepDropout': TimestepDropout,\\n 'Camouflage': Camouflage,\\n 'SymmetricMasking': SymmetricMasking,\\n 'Attention': Attention})\\n\\n test_data = dict(np.load(test_path, allow_pickle=True).item())\\n\\n test_inputs = test_data['input_ids']\\n\\n prediction = model.predict(test_inputs, batch_size=1)\\n\\n return prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39275d7f0a70f1fbe13b97667eb22f5\",\n \"score\": \"0.577839\",\n \"text\": \"def save_results(predictions, filename):\\n #this is borrowed from one of the starting codes\\n with open(filename, 'w') as f:\\n f.write(\\\"id,ACTION\\\\n\\\")\\n for i, pred in enumerate(predictions):\\n f.write(\\\"%d,%f\\\\n\\\" % (i + 1, pred))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4adf19003ac35ce6ba8a7edb6857eb\",\n \"score\": \"0.5776986\",\n \"text\": \"def evaluate(models, ypred, yreal, output_file_name=None, cutoff=5.0):\\n cutoff = math.tanh(cutoff * 0.1)\\n\\n if type(models) is not list:\\n models = [models]\\n ypred = [ypred]\\n yreal = [yreal]\\n\\n for model, yp, yr in zip(models, ypred, yreal):\\n mat = get_accuracy_matrix(yp, yr, cutoff)\\n score1 = mat[0][0] + mat[2][2] - mat[2][0] - mat[0][2]\\n score1 = float(score1) / float(sum(mat[0]) + sum(mat[2]))\\n score2 = sum(mat[i][i] for i in range(3)) - mat[2][0] - mat[0][2]\\n score2 = float(score2) - 0.2 * (mat[0][1] + mat[1][0] + mat[1][2] + mat[2][1])\\n score2 = score2 / float(sum(sum(mat[i]) for i in range(3)))\\n if output_file_name is not None:\\n with open(output_file_name + \\\".txt\\\", \\\"a\\\") as fp:\\n fp.write(\\\"Model name: %s \\\\n\\\\n\\\" % model.cname)\\n fp.write(str(mat))\\n fp.write(\\\"\\\\n\\\\nScore1 = %f \\\\n\\\" % score1)\\n fp.write(\\\"Score2 = %f \\\\n\\\\n\\\\n\\\\n\\\" % score2)\\n else:\\n print(mat)\\n print(score1)\\n print(score2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ba69122cbb9cc2eb6b79a08264b796\",\n \"score\": \"0.5770127\",\n \"text\": \"def to_csv(prediction):\\n return '\\\\n'.join('{},{}'.format(key, value)\\n for key, value in prediction.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e16a4f0db584492ec21241dceec4e71\",\n \"score\": \"0.57673556\",\n \"text\": \"def predict(model, dataset):\\n dataset.reset()\\n predictions = None\\n nprocessed = 0\\n for x, t in dataset:\\n pred = model.fprop(x, inference=True).asnumpyarray()\\n bsz = min(dataset.ndata - nprocessed, model.be.bsz)\\n nprocessed += bsz\\n if predictions is None:\\n predictions = pred[:, :bsz]\\n else:\\n predictions = np.hstack((predictions, pred[:, :bsz]))\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f33a13ac008415cc10015e96ff9cc319\",\n \"score\": \"0.57618016\",\n \"text\": \"def predict(main_project_id,\\n input_path,\\n demo_dataset,\\n demo_table,\\n model_id,\\n service_acct,\\n compute_region,\\n config):\\n logger.info('Starting entity extraction.')\\n\\n input_bucket_name = input_path.replace('gs://', '').split('/')[0]\\n input_txt_folder = f\\\"gs://{input_bucket_name}/{demo_dataset}/txt\\\"\\n\\n list_fields = [x['field_name'] for x in config[\\\"model_ner\\\"][\\\"fields_to_extract\\\"]]\\n list_fields.remove('gcs_path')\\n\\n storage_client = storage.Client.from_service_account_json(service_acct)\\n bucket_name, path = utils.get_bucket_blob(input_txt_folder)\\n bucket = storage_client.get_bucket(bucket_name)\\n\\n list_results = []\\n for file in bucket.list_blobs(prefix=path):\\n full_filename = os.path.join(input_txt_folder, os.path.basename(file.name))\\n logger.info(full_filename)\\n result = run_automl_single(ocr_path=full_filename,\\n list_fields=list_fields,\\n service_acct=service_acct,\\n model_id=model_id,\\n main_project_id=main_project_id,\\n compute_region=compute_region)\\n list_results.append(result)\\n\\n schema = [bigquery.SchemaField('file', 'STRING', mode='NULLABLE')]\\n for field in list_fields:\\n schema.append(bigquery.SchemaField(field, 'STRING', mode='NULLABLE'))\\n \\n utils.save_to_bq(\\n demo_dataset,\\n demo_table,\\n list_results,\\n service_acct,\\n _create_table=True,\\n schema=schema)\\n\\n logger.info('Entity extraction finished.\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6525bf18de357f3038780dc0de0e1a9e\",\n \"score\": \"0.57579637\",\n \"text\": \"def predict(self):\\n\\t\\tscore = self.model.evaluate(self.x_test, self.y_test, verbose=0)\\n\\t\\tprint score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63b8561ba046fc7be7c9aa9e2b338120\",\n \"score\": \"0.5754032\",\n \"text\": \"def predict(self, model_path, x_test):\\n\\n init = tf.global_variables_initializer()\\n prediction = []\\n with tf.Session(config=config) as sess:\\n # Initialize variables\\n sess.run(init)\\n\\n # Restore model weights from previously saved model\\n self.restore(sess, model_path, var_list=self.variables_to_restore)\\n\\n for test_x in x_test:\\n y_dummy = np.empty((test_x.shape[0], test_x.shape[1], test_x.shape[2], self.n_class))\\n prediction.append(sess.run(self.predictor, feed_dict={self.x: test_x, self.y: y_dummy,\\n self.p: np.empty((self.batch_size, )),\\n self.dropout_rate: 0.0,\\n self.train_phase: False,\\n self.need_pos: False}))\\n\\n return prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0757a5860142ba9879953525cc445c6f\",\n \"score\": \"0.57459646\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\\n Y_pred = model.predict(X_test)\\n\\n for i in list(range(Y_pred.shape[1])):\\n print(\\\"Report for column: \\\\\\\"\\\" + category_names[i] + \\\"\\\\\\\"\\\")\\n print(classification_report(Y_test.values[:,i], Y_pred[:,i]))\\n print(\\\"\\\\n\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"646b0d14dd73dab40fe7e0e957bff30b\",\n \"score\": \"0.5732246\",\n \"text\": \"def predict(args):\\n\\n model = SentencePieceModel.load(args.model_folder)\\n to_ids = True if args.output_type == \\\"ids\\\" else False\\n model.predict_file(args.input_path, args.output_path, to_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e087f8a6fd041fc152640ca27b1a867f\",\n \"score\": \"0.57258976\",\n \"text\": \"async def predict(req: request.Request, model_name: str) -> response.HTTPResponse: # pylint: disable=unused-variable\\n model = app.predictors.get(model_name.lower())\\n if model is None:\\n raise ServerError(\\\"unknown model: {}\\\".format(model_name), status_code=400)\\n\\n data = req.json\\n log_blob = {\\\"model\\\": model_name, \\\"inputs\\\": data, \\\"cached\\\": False, \\\"outputs\\\": {}}\\n\\n # See if we hit or not. In theory this could result in false positives.\\n pre_hits = _caching_prediction.cache_info().hits # pylint: disable=no-value-for-parameter\\n\\n try:\\n if cache_size > 0:\\n # lru_cache insists that all function arguments be hashable,\\n # so unfortunately we have to stringify the data.\\n prediction = _caching_prediction(model, json.dumps(data))\\n else:\\n # if cache_size is 0, skip caching altogether\\n prediction = model.predict_json(data)\\n except KeyError as err:\\n raise ServerError(\\\"Required JSON field not found: \\\" + err.args[0], status_code=400)\\n\\n post_hits = _caching_prediction.cache_info().hits # pylint: disable=no-value-for-parameter\\n\\n if post_hits > pre_hits:\\n # Cache hit, so insert an artifical pause\\n log_blob[\\\"cached\\\"] = True\\n await asyncio.sleep(0.25)\\n\\n # The model predictions are extremely verbose, so we only log the most human-readable\\n # parts of them.\\n if model_name == \\\"machine-comprehension\\\":\\n log_blob[\\\"outputs\\\"][\\\"best_span_str\\\"] = prediction[\\\"best_span_str\\\"]\\n elif model_name == \\\"textual-entailment\\\":\\n log_blob[\\\"outputs\\\"][\\\"label_probs\\\"] = prediction[\\\"label_probs\\\"]\\n elif model_name == \\\"semantic-role-labeling\\\":\\n verbs = []\\n\\n for verb in prediction[\\\"verbs\\\"]:\\n # Don't want to log boring verbs with no semantic parses.\\n good_tags = [tag for tag in verb[\\\"tags\\\"] if tag != \\\"0\\\"]\\n if len(good_tags) > 1:\\n verbs.append({\\\"verb\\\": verb[\\\"verb\\\"], \\\"description\\\": verb[\\\"description\\\"]})\\n\\n log_blob[\\\"outputs\\\"][\\\"verbs\\\"] = verbs\\n\\n logger.info(\\\"prediction: %s\\\", json.dumps(log_blob))\\n\\n return response.json(prediction)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1491,"cells":{"query_id":{"kind":"string","value":"3610c59c09342df8d90687334c5c0136"},"query":{"kind":"string","value":"addSpeciesType(Model self, SpeciesType st) > int Adds a copy of the given SpeciesType object to this Model. Parameter 'st' is the SpeciesType object to add. Returns integer value indicating success/failure of the function."},"positive_passages":{"kind":"list like","value":[{"docid":"050ab371dc3ef3d31b4a907fadbd7500","score":"0.7829603","text":"def addSpeciesType(self, *args):\n return _libsbml.Model_addSpeciesType(self, *args)","title":""}],"string":"[\n {\n \"docid\": \"050ab371dc3ef3d31b4a907fadbd7500\",\n \"score\": \"0.7829603\",\n \"text\": \"def addSpeciesType(self, *args):\\n return _libsbml.Model_addSpeciesType(self, *args)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7d42d78767cc72ea43d1d585ff3ea5cd","score":"0.67754364","text":"def addSpecies(self, *args):\n return _libsbml.Model_addSpecies(self, *args)","title":""},{"docid":"7f2e0c31b7a71caa3c025d8f64b13c9d","score":"0.6621497","text":"def addSpeciesTypeInstance(self, *args):\n return _libsbml.MultiSpeciesType_addSpeciesTypeInstance(self, *args)","title":""},{"docid":"6bc7be1f504fe598a856c237d28acb61","score":"0.6480067","text":"def addSpeciesFeatureType(self, *args):\n return _libsbml.MultiSpeciesType_addSpeciesFeatureType(self, *args)","title":""},{"docid":"2ef7865cb6d9ee8350ced6f28595ce21","score":"0.59559953","text":"def createSpeciesType(self):\n return _libsbml.Model_createSpeciesType(self)","title":""},{"docid":"392746111eb4b9110754840a359ad57e","score":"0.5947202","text":"def addInSpeciesTypeBond(self, *args):\n return _libsbml.MultiSpeciesType_addInSpeciesTypeBond(self, *args)","title":""},{"docid":"17db98d0b6e5e2126952696a1035dd33","score":"0.59095746","text":"def add_species(self, side='R'): \n self.unpack_ecosystem()\n if side == 'L':\n self.ecosystem.insert(0,Species(self))\n elif side == \"R\":\n self.ecosystem.append(Species(self))\n self.num_species+=1\n self.pack_ecosystem()\n self.request_payment(\"discard\")","title":""},{"docid":"5ed244eac5d2762474ae49d11f042b91","score":"0.5886803","text":"def setSpeciesType(self, *args):\n return _libsbml.Species_setSpeciesType(self, *args)","title":""},{"docid":"b0dfe886e09d5fd78bfe57cc4b8d7b08","score":"0.58644074","text":"def setSpeciesType(self, *args):\n return _libsbml.SpeciesTypeInstance_setSpeciesType(self, *args)","title":""},{"docid":"7e706418def2206c79ba1118b7752eac","score":"0.5831992","text":"def addMultiSpeciesType(self, *args):\n return _libsbml.MultiModelPlugin_addMultiSpeciesType(self, *args)","title":""},{"docid":"84bdc03fc17af80dc910f327acd892dc","score":"0.5785126","text":"def addSpecies(self, species_id, amt, comp=\"c1\"):\n\n s1 = self.model.createSpecies()\n self.check(s1, \"create species s1\")\n self.check(s1.setCompartment(comp), \"set species s1 compartment\")\n if species_id[0] == \"[\" and species_id[len(species_id) - 1] == \"]\":\n self.check(\n s1.setInitialConcentration(amt), \"set initial concentration for s1\"\n )\n species_id = species_id[1 : (len(species_id) - 1)]\n else:\n self.check(s1.setInitialAmount(amt), \"set initial amount for s1\")\n self.check(\n s1.setSubstanceUnits(self.model.getSubstanceUnits()),\n \"set substance units for s1\",\n )\n if species_id[0] == \"$\":\n self.check(s1.setBoundaryCondition(True), 'set \"boundaryCondition\" on s1')\n self.check(s1.setConstant(False), 'set \"constant\" attribute on s1')\n self.check(s1.setId(species_id[1 : len(species_id)]), \"set species s1 id\")\n else:\n self.check(s1.setBoundaryCondition(False), 'set \"boundaryCondition\" on s1')\n self.check(s1.setConstant(False), 'set \"constant\" attribute on s1')\n self.check(s1.setId(species_id), \"set species s1 id\")\n self.check(\n s1.setHasOnlySubstanceUnits(False), 'set \"hasOnlySubstanceUnits\" on s1'\n )\n return s1","title":""},{"docid":"a24b070882f72069a6dd21244ce9bec1","score":"0.5714966","text":"def EquipmentStatExcelAddStatType(builder, StatType):\n return AddStatType(builder, StatType)","title":""},{"docid":"4d403197497f4f62ea27d2520840bf9b","score":"0.56937885","text":"def addSpeciesTypeComponentIndex(self, *args):\n return _libsbml.MultiSpeciesType_addSpeciesTypeComponentIndex(self, *args)","title":""},{"docid":"cd0525cd2fe75c4cd650e74fccdb9b3c","score":"0.566524","text":"def _AddType(self, entity_type):\n if not entity_type.IsValid():\n self.AddFindings(entity_type.GetFindings())\n return False\n return self.local_namespace.InsertType(entity_type)","title":""},{"docid":"422b6352a09fff2f4eedb3eb032b0b7f","score":"0.56438947","text":"def addSpeciesFeatureValue(self, *args):\n return _libsbml.SpeciesFeature_addSpeciesFeatureValue(self, *args)","title":""},{"docid":"c2a841fbb083f358472457f3ce405932","score":"0.55039126","text":"def AddSchedulee(self, s, schedulee_type):\n\n # error checking?\n\n try:\n \n schedulee = Schedulee(s, schedulee_type, self.verbose)\n \n except Exception, e:\n\n sys.stderr.write(\"Can't schedule simulation object: %s\" % e)\n \n return False\n \n self._schedulees.append(schedulee)\n\n return True","title":""},{"docid":"c5e58d0df000369b5305df1c48d3c210","score":"0.542081","text":"def add(self, workout, database):\n if not database.session:\n logger.error(\"no database session\")\n return False\n\n self.cleanup_sportstype(workout)\n self.associate_sport(database)\n id = database.session.query(SportsType.id).filter(\n SportsType.name == self.name).first()\n if id:\n self.id = id[0]\n return False\n else:\n try:\n database.session.add(self)\n database.session.flush()\n except exc.SQLAlchemyError as e:\n logger.error(\"Database error: {}\".format(e.args))\n return False\n logger.info(\"Adding new sportstype '{}' id {} of sport {}\".format(\n self.name, self.id, self.sport_id))\n return True","title":""},{"docid":"726e313e9da3341e456cc8a89c97b0a2","score":"0.53725445","text":"def addSpeciesFeature(self, *args):\n return _libsbml.MultiSpeciesPlugin_addSpeciesFeature(self, *args)","title":""},{"docid":"13772512bab2dd84801fb7e9d709baf8","score":"0.5350278","text":"def setSpeciesFeatureType(self, *args):\n return _libsbml.SpeciesFeature_setSpeciesFeatureType(self, *args)","title":""},{"docid":"c60da3e1773b85058877dd6c0d5a20db","score":"0.5339594","text":"def setSpeciesType(self, *args):\n return _libsbml.MultiSpeciesPlugin_setSpeciesType(self, *args)","title":""},{"docid":"26819fecd61ec380099f153157d26ed3","score":"0.5300599","text":"def test_ticket_type_add_ok(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('ticket_type add new_type')\n rv, output = self._execute('ticket_type list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","title":""},{"docid":"e64b1232a06828d6576d067622aec146","score":"0.5268285","text":"def addQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_addQualitativeSpecies(self, *args)","title":""},{"docid":"ba223653c6190a2e3699e73e7e5508cc","score":"0.52538675","text":"def addPossibleSpeciesFeatureValue(self, *args):\n return _libsbml.SpeciesFeatureType_addPossibleSpeciesFeatureValue(self, *args)","title":""},{"docid":"2ef9f466bc1be0a10b81e616e352157e","score":"0.5227218","text":"def removeSpeciesType(self, *args):\n return _libsbml.Model_removeSpeciesType(self, *args)","title":""},{"docid":"1933b6003ce43dc3669b5c58439f01cf","score":"0.5225152","text":"def getSpeciesType(self, *args):\n return _libsbml.Model_getSpeciesType(self, *args)","title":""},{"docid":"be62d42f71510803007e14d9fb2006e8","score":"0.52149636","text":"def add(self, filetype):\n\n if (filetype.add() == 0):\n self.__logger.debug(\"Adding file type %s to list\" % filetype)\n self.__filetypeList[filetype.file_format_id] = filetype\n return None\n else:\n return 1","title":""},{"docid":"15ff3876c593885ea78f321eb4db5c64","score":"0.5212585","text":"def getSpeciesType(self):\n return _libsbml.Species_getSpeciesType(self)","title":""},{"docid":"5cddaaeabf06ce606442985ce33f0a62","score":"0.5212477","text":"def usertype_add(request, simulation):\n # Create new distributions with good defaults.\n alphaTI = Distribution(type='NONE', mean=10)\n alphaTP = Distribution(type='NONE', mean=15)\n beta = Distribution(type='NONE', mean=5)\n delta = Distribution(type='NONE', mean=10)\n departureMu = Distribution(type='NONE', mean=2)\n gamma = Distribution(type='NONE', mean=20)\n modeMu = Distribution(type='NONE', mean=5)\n penaltyTP = Distribution(type='NONE', mean=2)\n routeMu = Distribution(type='NONE', mean=10)\n # Default value for t star is average arrival at middle of period and\n # uniform distribution over half of the period.\n mid_time = (simulation.startTime + simulation.lastRecord) / 2\n length = simulation.lastRecord - simulation.startTime\n tstar = Distribution(type='UNIFORM', mean=mid_time, std=length / (4 * sqrt(3)))\n # Save the distributions to generate ids.\n alphaTI.save()\n alphaTP.save()\n beta.save()\n delta.save()\n departureMu.save()\n gamma.save()\n modeMu.save()\n penaltyTP.save()\n routeMu.save()\n tstar.save()\n # Create the new user type.\n usertype = UserType()\n usertype.alphaTI = alphaTI\n usertype.alphaTP = alphaTP\n usertype.beta = beta\n usertype.delta = delta\n usertype.departureMu = departureMu\n usertype.gamma = gamma\n usertype.modeMu = modeMu\n usertype.penaltyTP = penaltyTP\n usertype.routeMu = routeMu\n usertype.tstar = tstar\n # Set user_id to user_id of previous usertype + 1.\n usertypes = get_query('usertype', simulation)\n if usertypes.exists():\n usertype.user_id = usertypes.last().user_id + 1\n else:\n usertype.user_id = 1\n usertype.save()\n # Create a demand segment and a matrix for the user type.\n matrix = Matrices()\n matrix.save()\n demandsegment = DemandSegment()\n demandsegment.usertype = usertype\n demandsegment.matrix = matrix\n demandsegment.save()\n demandsegment.demand.add(simulation.scenario.demand)\n # Return the view to edit the new user type.\n return HttpResponseRedirect(\n reverse('metro:usertype_edit', args=(simulation.id, demandsegment.id,))\n )","title":""},{"docid":"10407fa977b8a3cd9183373a4779bc16","score":"0.51866627","text":"def getSpeciesType(self):\n return _libsbml.SpeciesTypeInstance_getSpeciesType(self)","title":""},{"docid":"4f2f202493fc7ff76cdd3d6768227bec","score":"0.5183553","text":"def add_fish(self, fish_species):\n if isinstance(fish_species, str):\n self.fish[fish_species] += 1\n else:\n print(\"That doesn't look like a fish!\")","title":""},{"docid":"ac8c977caaee7dcb56d116c1a5331b37","score":"0.5162882","text":"def add_type(self, qid, type, type_system):\n if type_system not in self._type_systems:\n raise ValueError(\n f\"The type system {type_system} is not one of {self._type_systems.keys()}\"\n )\n self._type_systems[type_system].add_type(qid, type)","title":""},{"docid":"f28d4e21f7a86d94e92fd0037ea3ba0a","score":"0.51314074","text":"def add(self, model_dict, datatype):\n self.push_datatype(datatype)\n\n model_dict['institution'] = self._institution\n db.session.add(self.cur_datatype_model()(model_dict))\n\n self.pop_datatype()","title":""},{"docid":"dc156239eb2b4b8a0662c623f80997e0","score":"0.51309603","text":"def _add_type(self, production, index, m_type):\n fully_qualified_name = None\n current_namespace = self._get_current_namespace()\n if current_namespace is not None:\n fully_qualified_name = current_namespace.fully_qualified_name()\n namespace_types = self._get_type_or_namespace_from_fully_qualified_name(fully_qualified_name)\n if m_type.name in namespace_types:\n raise ParseError(self.production_to_coord(production, index),\n \"Name '{0}' already exists\".format(m_type.fully_qualified_name()))\n namespace_types[m_type.name] = m_type","title":""},{"docid":"a209a8fad1decb091ee9488e69c2d494","score":"0.5085266","text":"def AddFileType(self, type, name):\n return _gmat_py.FileManager_AddFileType(self, type, name)","title":""},{"docid":"50ebcf9f3f9d6f2347c5aa55053ff5c7","score":"0.5081777","text":"def createSpeciesFeatureType(self):\n return _libsbml.MultiSpeciesType_createSpeciesFeatureType(self)","title":""},{"docid":"848a39a8ac13ad727429c7cee52d09ed","score":"0.5030382","text":"def add_student(self, student: 'Student') -> None:\n # Add HOUSEHOLD attributes to the schools' composition\n self.total += 1\n self.composition += student.household.attributes\n self.students[student.idx] = student\n self.has_space = (self.total < self.capacity)","title":""},{"docid":"75f0257de0cfa66fecef4a1b10b9d29e","score":"0.5028135","text":"def createSpeciesTypeInstance(self):\n return _libsbml.MultiSpeciesType_createSpeciesTypeInstance(self)","title":""},{"docid":"af66129857350b2a5c027ba390a59a1b","score":"0.50236076","text":"def addType(self, name):\n setattr(self, name, name)\n self._type_names[name] = name\n if name in self._pending_type_names:\n del self._pending_type_names[name]","title":""},{"docid":"a7f36e6f219295597fd43f357821d89f","score":"0.5020349","text":"def getNumSpeciesTypes(self):\n return _libsbml.Model_getNumSpeciesTypes(self)","title":""},{"docid":"54319a6050fc3dd5df9e1ddbeb85cc2c","score":"0.49994937","text":"def createSpecies(self):\n return _libsbml.Model_createSpecies(self)","title":""},{"docid":"01dc84d201e5d046c42fa30aea94610c","score":"0.4984292","text":"def addStockType(self, item):\n # TODO\n # hint: Add an item to this.stocklist\n # No. 6\n self.stocklist.append(item)","title":""},{"docid":"9a319e1066c145b24391138adac49961","score":"0.49751258","text":"def __init__(self, *args):\n this = _libsbml.new_SpeciesType(*args)\n try: self.this.append(this)\n except: self.this = this","title":""},{"docid":"66fc2c9fb63fc269531203ffd1a1eb0e","score":"0.4974042","text":"def add(self, name, fieldtype):\r\n \r\n if name.startswith(\"_\"):\r\n raise FieldConfigurationError(\"Field names cannot start with an underscore\")\r\n elif name in self._by_name:\r\n raise FieldConfigurationError(\"Schema already has a field named %s\" % name)\r\n \r\n if callable(fieldtype):\r\n fieldtype = fieldtype()\r\n if not isinstance(fieldtype, FieldType):\r\n raise FieldConfigurationError(\"%r is not a FieldType object\" % fieldtype)\r\n \r\n fnum = len(self._by_number)\r\n self._numbers[name] = fnum\r\n self._by_number.append(fieldtype)\r\n self._names.append(name)\r\n self._by_name[name] = fieldtype","title":""},{"docid":"b56986da94bd99e6bd2fbce9a0d4f083","score":"0.49523765","text":"def add_type(self, typename, db):\n self._dbs[typename] = db\n return None","title":""},{"docid":"dffd7c9152ad1299bae1259661dab982","score":"0.49445945","text":"def isSetSpeciesType(self):\n return _libsbml.Species_isSetSpeciesType(self)","title":""},{"docid":"49a2ae188b23c4d15fe03b93b3543e8e","score":"0.4939201","text":"def add_type(self, name, validation_function):\n self.types[name] = validation_function","title":""},{"docid":"4c2abd6fd681b53fd7f2c612c774e2c8","score":"0.49324566","text":"def getTypeCode(self):\n return _libsbml.SpeciesType_getTypeCode(self)","title":""},{"docid":"d94a213c7baf9af33b28642450c10644","score":"0.49320993","text":"def EquipmentStatExcelAddStatLevelUpType_(builder, StatLevelUpType_):\n return AddStatLevelUpType_(builder, StatLevelUpType_)","title":""},{"docid":"4a5bc7869baef67a5d9b1ffc98bafea0","score":"0.4927225","text":"def add(self, type_name, source_node: ast.AST):\n for mv in self:\n mv.add(type_name, source_node)","title":""},{"docid":"e09bc4e3c1e758f85216416ba5413742","score":"0.48959425","text":"def testMakeNewSpecies(self):\n\n # adding 3 unique species:\n cerm = CoreEdgeReactionModel()\n\n spcs = [Species().fromSMILES('[OH]'), \n Species().fromSMILES('CC'),\n Species().fromSMILES('[CH3]')]\n\n for spc in spcs:\n cerm.makeNewSpecies(spc)\n\n self.assertEquals(len(cerm.speciesDict), len(spcs)) \n self.assertEquals(len(cerm.indexSpeciesDict), len(spcs))\n\n # adding 3 unique, and 1 already existing species:\n cerm = CoreEdgeReactionModel()\n\n spcs = [Species().fromSMILES('[OH]'), \n Species().fromSMILES('CC'),\n Species().fromSMILES('[CH3]'),\n Species().fromSMILES('CC')]#duplicate species\n\n for spc in spcs:\n cerm.makeNewSpecies(spc)\n\n self.assertEquals(len(cerm.speciesDict), len(spcs) - 1) \n self.assertEquals(len(cerm.indexSpeciesDict), len(spcs) - 1)","title":""},{"docid":"40ac544d4c8dd5adda61c7c600578652","score":"0.48915997","text":"def isSetSpeciesType(self):\n return _libsbml.SpeciesTypeInstance_isSetSpeciesType(self)","title":""},{"docid":"9783eb716b1398fb3ae6e16edad2266c","score":"0.48706302","text":"def addSpeciesGlyph(self, *args):\n return _libsbml.Layout_addSpeciesGlyph(self, *args)","title":""},{"docid":"f8c8aa02e2fcba83ecca87c12ed42ebe","score":"0.48699006","text":"def add_species(self, side='R'): \n #self.disable_all_buttons() \n Player.add_species(self, side=side)","title":""},{"docid":"11c727f21bfe6acf408f17bc9f162f51","score":"0.4856663","text":"def test_make_new_species(self):\n\n # adding 3 unique species:\n cerm = CoreEdgeReactionModel()\n\n spcs = [Species().from_smiles('[OH]'),\n Species().from_smiles('CC'),\n Species().from_smiles('[CH3]')]\n\n for spc in spcs:\n cerm.make_new_species(spc)\n\n self.assertEquals(len(cerm.species_dict), len(spcs))\n self.assertEquals(len(cerm.index_species_dict), len(spcs))\n\n # adding 3 unique, and 1 already existing species:\n cerm = CoreEdgeReactionModel()\n\n spcs = [Species().from_smiles('[OH]'),\n Species().from_smiles('CC'),\n Species().from_smiles('[CH3]'),\n Species().from_smiles('CC')] # duplicate species\n\n for spc in spcs:\n cerm.make_new_species(spc)\n\n self.assertEquals(len(cerm.species_dict), len(spcs) - 1)\n self.assertEquals(len(cerm.index_species_dict), len(spcs) - 1)","title":""},{"docid":"84e7b10cd087242e2b8472251f3122d6","score":"0.48541874","text":"def getSpeciesFeatureType(self):\n return _libsbml.SpeciesFeature_getSpeciesFeatureType(self)","title":""},{"docid":"4b7f576977b3e86ddbc9dc15db7ba7e7","score":"0.48370606","text":"def clone(self):\n return _libsbml.SpeciesType_clone(self)","title":""},{"docid":"88e2133d6117b3fc727d8d00b0f936a6","score":"0.48337075","text":"def getTypeCode(self):\n return _libsbml.SpeciesFeatureType_getTypeCode(self)","title":""},{"docid":"873a7d433d8e9ce86811c4523d4aa0c6","score":"0.48201782","text":"def getSpeciesType(self):\n return _libsbml.MultiSpeciesPlugin_getSpeciesType(self)","title":""},{"docid":"e73fb93701aaea7b1b2c3279b3e90058","score":"0.48116407","text":"def add_stat(self, stat_type, stat):\n assert stat_type in self.log_book, \"Wrong stat type\"\n assert isinstance(stat, int) or isinstance(stat, float), \"Wrong stat value\"\n \n self.log_book[stat_type].append(stat)","title":""},{"docid":"76dc27c6bf63cdc434d6738af737aa35","score":"0.48035103","text":"def _AppendType(type_name, register):\n if register.register_type is not 'v':\n raise ArgumentError('Only vector registers can have type appended.')\n\n if type_name in set([8, '8', 'i8', 's8', 'u8']):\n subtype = 'b'\n subtype_bits = 8\n elif type_name in set([16, '16', 'i16', 's16', 'u16']):\n subtype = 'h'\n subtype_bits = 16\n elif type_name in set([32, '32', 'i32', 's32', 'u32', 'f32']):\n subtype = 's'\n subtype_bits = 32\n elif type_name in set([64, '64', 'i64', 's64', 'u64', 'f64']):\n subtype = 'd'\n subtype_bits = 64\n else:\n raise ArgumentError('Unknown type: %s' % type_name)\n\n new_register = register.Copy()\n new_register.register_subtype = subtype\n new_register.register_subtype_count = register.register_bits / subtype_bits\n return new_register","title":""},{"docid":"34ec509dbd8fa104764630a9cf9039f4","score":"0.48026356","text":"def getTypeCode(self):\n return _libsbml.Species_getTypeCode(self)","title":""},{"docid":"6453c65ff32d72396d6bded692f347c6","score":"0.47930247","text":"def getTypeCode(self):\n return _libsbml.SpeciesFeature_getTypeCode(self)","title":""},{"docid":"cfdf26a4566cf173173e8b2774d759d4","score":"0.47913948","text":"def add(self, cheese, index):\n self._stools[index].append(cheese)","title":""},{"docid":"03917e16dcfd3ed1a5b36172eea31b13","score":"0.478958","text":"def add(cls, name):\n cls.validate_name(name)\n new_status = ServerType(name)\n DB.session.add(new_status)\n DB.session.commit()\n return new_status","title":""},{"docid":"a08e3d9692eaa2f67f9054ba4049f415","score":"0.47544125","text":"def createMultiSpeciesType(self):\n return _libsbml.MultiModelPlugin_createMultiSpeciesType(self)","title":""},{"docid":"93c07467f3a5d256bd334b68a1221fdd","score":"0.4730164","text":"def make_mutable_type(type_):\n _mutable_types.append(type_)","title":""},{"docid":"2b2122eecb54f18a112b76d81281df21","score":"0.4719878","text":"def addsos(m, sosdict, sostype):\n soslist = []\n # Sort by name to get deterministic ordering. Note that\n # names may be plain integers, that is why we use str(name)\n # to pass them to the SOS constructor.\n for name in sorted(sosdict):\n indices = []\n weights = []\n for v, val in sosdict[name].items():\n indices.append(v._xprs[0])\n weights.append(val)\n soslist.append(xpress.sos(indices, weights, sostype, str(name)))\n if len(soslist):\n m.addSOS(soslist)","title":""},{"docid":"5ad815cc8e366ce143b4c37792f41a56","score":"0.47150287","text":"def add_recipe(self, recipe): \n\t\tfor key, val in self.recipes_list.items():\n\t\t\tif key == recipe.recipe_type:\n\t\t\t\tself.recipes_list[key][recipe.name] = recipe","title":""},{"docid":"ee46b8044f05b3fa24aa5480b1df9c7e","score":"0.47009113","text":"def addTest(self, test):\r\n self.tests.append(test)\r\n return","title":""},{"docid":"06ed67bd5f0fa260887afd2e72f925c0","score":"0.46655354","text":"def getTypeCode(self):\n return _libsbml.SpeciesTypeInstance_getTypeCode(self)","title":""},{"docid":"a2a971ce40fae86c23f9c8a1f4884885","score":"0.46585146","text":"def isSetSpeciesType(self):\n return _libsbml.MultiSpeciesPlugin_isSetSpeciesType(self)","title":""},{"docid":"98262e9f25ab8d25d4b65ec6d1aafa5a","score":"0.4653468","text":"def getTypeCode(self):\n return _libsbml.SubListOfSpeciesFeatures_getTypeCode(self)","title":""},{"docid":"ae6060a46b9893b13a15bcd696ac00f5","score":"0.46433422","text":"def getTypeCode(self):\n return _libsbml.ListOfSpeciesFeatureTypes_getTypeCode(self)","title":""},{"docid":"39a66cad42b832a9f2dd5654b6bda9c0","score":"0.46432438","text":"def update_species(self, mixture, concentration):\n # Create component specific species\n # self.product = add_species(model, \"Type\", name)\n \n # Create any other species needed by component mechanisms\n mechanisms = get_mechanisms(mixture, self)\n for name in mechanisms:\n mechanism = mechanisms[name]\n mechanism.update_species(mixture, self, concentration)\n \n # If the default member function gets used, issue a warning\n warn(\"component: default __init__ called for \" + name)","title":""},{"docid":"df6e7cb05974aff0ddd6e974c359c7a8","score":"0.4642216","text":"def add_car(sefl):\n make = \"Test_Toyota\"\n body_type = \"Seden\"\n colour = \"Black\"\n seats = 5\n location = \"-37.814, 144.96332\"\n cost_per_hour = 10.5\n\n newCar = Car( make = make,\n body_type = body_type,\n colour = colour,\n seats = seats,\n location = location,\n cost_per_hour = cost_per_hour,\n booked = True\n\n )\n db.session.add(newCar)\n db.session.commit()\n self.assertTrue(self.carExists(make))","title":""},{"docid":"1e8e4291b120c7bba85cec5e96f37273","score":"0.46387902","text":"def _insert_new_type(self, newType):\n existingType = self.knownTypes.getKey(newType)\n if existingType is None:\n self.knownTypes[newType] = []\n return\n\n if isinstance(existingType, ast.Builtin):\n raise RedefBuiltinTypeError(newType)\n else:\n raise RedefUserTypeError(newType, existingType)","title":""},{"docid":"fc946741a3e9c67164b9203f811706a5","score":"0.46375737","text":"def getTypeCode(self):\n return _libsbml.QualitativeSpecies_getTypeCode(self)","title":""},{"docid":"05c496120c4d73e1017148a410c92a4a","score":"0.46317688","text":"def add_test(self, sol_src, run_test_type=False):\n\n if sol_src in self.sol_srcs: # already added this solution\n return\n if run_test_type:\n env = dict(List=List, Dict=Dict, Set=Set)\n time0 = time.perf_counter()\n my_exec(sol_src + \"\\n\" + \"answer = sol()\", env, description=self.name)\n\n sol_val = env[\"answer\"]\n\n assert sol_val is not None, \"sol returned None\"\n\n assert type_check(run_test_type, sol_val)\n\n answer = decode(encode(sol_val))\n assert answer == sol_val, \"encode/decode round trip failed\"\n\n env2 = dict(answer=answer, List=List, Dict=Dict, Set=Set) # in case they mucked with env\n my_exec(self.src + \"\\n\" + \"assert sat(answer)\", env2, description=self.name)\n dur = time.perf_counter() - time0\n if dur > (DEFAULT_TIMEOUT if self.timeout is None else self.timeout * DEFAULT_TIMEOUT):\n utils.warn(f\"Took {dur}s to test {self.name} (self.timeout={self.timeout})\")\n self.sol_srcs.append(sol_src)","title":""},{"docid":"216913032bad46a792120bcda0e9e94b","score":"0.46216056","text":"def add_population(self, net_id, component_id, type_, **args):\n nmlnetwork = NeuroMLSimpleNetwork(net_id)\n nmlnetwork.add_component(component_id, type_, **args)\n self._population = nmlnetwork.build()","title":""},{"docid":"c415542d28163fade5759c1e64e80fd5","score":"0.46182367","text":"def add_piece(self, color, piece_type, location):\n\n coordinates = self.translate_to_grid(location)\n piece_to_add = piece_type(color, coordinates)\n self._board[coordinates[0]][coordinates[1]] = piece_to_add\n self._active_pieces[color].append(piece_to_add)\n return","title":""},{"docid":"f99ce59411dbbb8662b7d1a4f61daf33","score":"0.46114206","text":"def getTypeCode(self):\n return _libsbml.SpeciesReference_getTypeCode(self)","title":""},{"docid":"9b399ef89de98cfafc56d3149c639d53","score":"0.46075428","text":"def getId(self):\n return _libsbml.SpeciesType_getId(self)","title":""},{"docid":"11468a0b63d4c419a896060a301c1566","score":"0.46048138","text":"def Add(self, type, number, lit = -1, msg = None):\n self.__errors[number] = (lit, type, msg)","title":""},{"docid":"6f9cbd5843f79690f7c6f1d0a0ad6534","score":"0.4599954","text":"def add(self, specification):\n self.specifications.append(specification)","title":""},{"docid":"b51572608fffb5f8a9e28c9287db5c62","score":"0.45989323","text":"def getListOfSpeciesTypes(self, *args):\n return _libsbml.Model_getListOfSpeciesTypes(self, *args)","title":""},{"docid":"ec2323f64479d7a061a1ba04c1972235","score":"0.4597089","text":"def getTypeCode(self):\n return _libsbml.BindingSiteSpeciesType_getTypeCode(self)","title":""},{"docid":"836053c39fa6e188edb6da1ec0110604","score":"0.459297","text":"def getTypeCode(self):\n return _libsbml.ListOfSpeciesTypeInstances_getTypeCode(self)","title":""},{"docid":"b253efb03cc04ba36e8fd2dbd47a4844","score":"0.4591635","text":"def removeSpeciesTypes(self):\n return _libsbml.Model_removeSpeciesTypes(self)","title":""},{"docid":"546cdbfffc75e4cff6c1d0d7d562c14b","score":"0.4591102","text":"def add(self, attr):\n self.validate_type(attr)\n self.categories.add(attr.value)","title":""},{"docid":"6847083ff0847819c47ce47d5f1e8157","score":"0.458189","text":"def add_xsd_type(self, name, xsd_type):\n if self.xsd_types is None:\n self.xsd_types = {name: xsd_type}\n else:\n obj = self.xsd_types.get(name)\n if obj is None:\n self.xsd_types[name] = xsd_type\n elif not isinstance(obj, list):\n if obj is not xsd_type:\n self.xsd_types[name] = [obj, xsd_type]\n elif xsd_type not in obj:\n obj.append(xsd_type)\n\n return xsd_type","title":""},{"docid":"c578e4ecdbc8b8bb1190c9ca34dad387","score":"0.45817897","text":"def setName(self, *args):\n return _libsbml.SpeciesType_setName(self, *args)","title":""},{"docid":"01cc61f3ff77265a465f9e377f00e1ff","score":"0.45757905","text":"def _add(self, type_or_name: str | type, value: Callable | type | tuple[bool, Callable]):\n key = type_or_name if isinstance(type_or_name, str) else type_or_name.__name__\n self._values[key] = value","title":""},{"docid":"8f9c979c49c13b7cbde28d948f44a1e4","score":"0.45730388","text":"def getTypeCode(self):\n return _libsbml.MultiSpeciesType_getTypeCode(self)","title":""},{"docid":"123a1afb50fd15ea0e3ed44959c97186","score":"0.45728263","text":"def addCompartmentType(self, *args):\n return _libsbml.Model_addCompartmentType(self, *args)","title":""},{"docid":"60ba5a2f19798a20f12bb7594084c6df","score":"0.45703354","text":"def addStudent():\n name = input(\"Name: \")\n number = input(\"Number: \")\n gpa = input(\"GPA: \")\n field = input(\"Field: \")\n student = Student(name, number, gpa, field)\n if t.insert(number, student):\n ht.insert(student)\n print(name, \"added successfully.\")\n else:\n print(\"student number is not valid.\")","title":""},{"docid":"0624389e01b69c8915b60408f72d1b1d","score":"0.45610204","text":"def set_species(self, species):\n self.species = species","title":""},{"docid":"d47fe57be4a1180323675e838b39ebf0","score":"0.45418972","text":"def getTypeCode(self):\n return _libsbml.SpeciesFeatureValue_getTypeCode(self)","title":""},{"docid":"7032d3b1278b139a60d67f383d798019","score":"0.45381978","text":"def process_simple_type(self, s, name=None):\n retval = None\n\n if name is None:\n name = s.name\n\n if s.list is not None:\n retval = self.process_simple_type_list(s, name)\n\n elif s.union is not None:\n retval = self.process_simple_type_union(s, name)\n\n elif s.restriction is not None:\n retval = self.process_simple_type_restriction(s, name)\n\n if retval is None:\n self.debug1(\"skipping simple type: %s\", name)\n return\n\n self.retval[self.tns].types[s.name] = retval\n\n key = self.get_name(name)\n dependents = self.pending_simple_types[key]\n for s, name in set(dependents):\n st = self.process_simple_type(s, name)\n if st is not None:\n self.retval[self.tns].types[s.name] = st\n\n self.debug2(\"added back simple type: %s\", s.name)\n dependents.remove((s, name))\n\n if len(dependents) == 0:\n del self.pending_simple_types[key]\n\n return retval","title":""},{"docid":"e8058c9bfc9b5dd5204f03d01f9fbe4e","score":"0.45367673","text":"def add_test(self, testsuite, test):\n self.tests[testsuite].append(TestCase(test, self))\n self.num_tests += 1","title":""},{"docid":"6ce71aec840c3b145a6d46e357e7284e","score":"0.4534546","text":"def add(self, rank, birth_year, enlisting_year, shirt_color, name):\n # Your implementation here","title":""},{"docid":"bbe2cec73c1cc57e01b2f53b94663f5c","score":"0.45296344","text":"def add(self, filetype, **kwargs):\n\n location = self.location(filetype, **kwargs)\n sas_module, location = location.split(sep, 1) if location else (None, location)\n\n # set proper sasdir based on access method\n sasdir = 'sas' if self.access_mode == 'curl' else ''\n source = self.url(filetype, sasdir=sasdir, **kwargs)\n\n # raise error if attempting to add a software product path\n if 'svn.sdss.org' in source:\n raise AccessError('Rsync/Curl Access not allowed for svn paths. Please use HttpAccess.')\n\n if 'full' not in kwargs:\n destination = self.full(filetype, **kwargs)\n else:\n destination = kwargs.get('full')\n\n if sas_module and location and source and destination:\n self.initial_stream.append_task(\n sas_module=sas_module, location=location, source=source, destination=destination)\n else:\n print(\"There is no file with filetype=%r to access in the tree module loaded\" % filetype)","title":""}],"string":"[\n {\n \"docid\": \"7d42d78767cc72ea43d1d585ff3ea5cd\",\n \"score\": \"0.67754364\",\n \"text\": \"def addSpecies(self, *args):\\n return _libsbml.Model_addSpecies(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f2e0c31b7a71caa3c025d8f64b13c9d\",\n \"score\": \"0.6621497\",\n \"text\": \"def addSpeciesTypeInstance(self, *args):\\n return _libsbml.MultiSpeciesType_addSpeciesTypeInstance(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc7be1f504fe598a856c237d28acb61\",\n \"score\": \"0.6480067\",\n \"text\": \"def addSpeciesFeatureType(self, *args):\\n return _libsbml.MultiSpeciesType_addSpeciesFeatureType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef7865cb6d9ee8350ced6f28595ce21\",\n \"score\": \"0.59559953\",\n \"text\": \"def createSpeciesType(self):\\n return _libsbml.Model_createSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392746111eb4b9110754840a359ad57e\",\n \"score\": \"0.5947202\",\n \"text\": \"def addInSpeciesTypeBond(self, *args):\\n return _libsbml.MultiSpeciesType_addInSpeciesTypeBond(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17db98d0b6e5e2126952696a1035dd33\",\n \"score\": \"0.59095746\",\n \"text\": \"def add_species(self, side='R'): \\n self.unpack_ecosystem()\\n if side == 'L':\\n self.ecosystem.insert(0,Species(self))\\n elif side == \\\"R\\\":\\n self.ecosystem.append(Species(self))\\n self.num_species+=1\\n self.pack_ecosystem()\\n self.request_payment(\\\"discard\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed244eac5d2762474ae49d11f042b91\",\n \"score\": \"0.5886803\",\n \"text\": \"def setSpeciesType(self, *args):\\n return _libsbml.Species_setSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dfe886e09d5fd78bfe57cc4b8d7b08\",\n \"score\": \"0.58644074\",\n \"text\": \"def setSpeciesType(self, *args):\\n return _libsbml.SpeciesTypeInstance_setSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e706418def2206c79ba1118b7752eac\",\n \"score\": \"0.5831992\",\n \"text\": \"def addMultiSpeciesType(self, *args):\\n return _libsbml.MultiModelPlugin_addMultiSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bdc03fc17af80dc910f327acd892dc\",\n \"score\": \"0.5785126\",\n \"text\": \"def addSpecies(self, species_id, amt, comp=\\\"c1\\\"):\\n\\n s1 = self.model.createSpecies()\\n self.check(s1, \\\"create species s1\\\")\\n self.check(s1.setCompartment(comp), \\\"set species s1 compartment\\\")\\n if species_id[0] == \\\"[\\\" and species_id[len(species_id) - 1] == \\\"]\\\":\\n self.check(\\n s1.setInitialConcentration(amt), \\\"set initial concentration for s1\\\"\\n )\\n species_id = species_id[1 : (len(species_id) - 1)]\\n else:\\n self.check(s1.setInitialAmount(amt), \\\"set initial amount for s1\\\")\\n self.check(\\n s1.setSubstanceUnits(self.model.getSubstanceUnits()),\\n \\\"set substance units for s1\\\",\\n )\\n if species_id[0] == \\\"$\\\":\\n self.check(s1.setBoundaryCondition(True), 'set \\\"boundaryCondition\\\" on s1')\\n self.check(s1.setConstant(False), 'set \\\"constant\\\" attribute on s1')\\n self.check(s1.setId(species_id[1 : len(species_id)]), \\\"set species s1 id\\\")\\n else:\\n self.check(s1.setBoundaryCondition(False), 'set \\\"boundaryCondition\\\" on s1')\\n self.check(s1.setConstant(False), 'set \\\"constant\\\" attribute on s1')\\n self.check(s1.setId(species_id), \\\"set species s1 id\\\")\\n self.check(\\n s1.setHasOnlySubstanceUnits(False), 'set \\\"hasOnlySubstanceUnits\\\" on s1'\\n )\\n return s1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24b070882f72069a6dd21244ce9bec1\",\n \"score\": \"0.5714966\",\n \"text\": \"def EquipmentStatExcelAddStatType(builder, StatType):\\n return AddStatType(builder, StatType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d403197497f4f62ea27d2520840bf9b\",\n \"score\": \"0.56937885\",\n \"text\": \"def addSpeciesTypeComponentIndex(self, *args):\\n return _libsbml.MultiSpeciesType_addSpeciesTypeComponentIndex(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0525cd2fe75c4cd650e74fccdb9b3c\",\n \"score\": \"0.566524\",\n \"text\": \"def _AddType(self, entity_type):\\n if not entity_type.IsValid():\\n self.AddFindings(entity_type.GetFindings())\\n return False\\n return self.local_namespace.InsertType(entity_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422b6352a09fff2f4eedb3eb032b0b7f\",\n \"score\": \"0.56438947\",\n \"text\": \"def addSpeciesFeatureValue(self, *args):\\n return _libsbml.SpeciesFeature_addSpeciesFeatureValue(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a841fbb083f358472457f3ce405932\",\n \"score\": \"0.55039126\",\n \"text\": \"def AddSchedulee(self, s, schedulee_type):\\n\\n # error checking?\\n\\n try:\\n \\n schedulee = Schedulee(s, schedulee_type, self.verbose)\\n \\n except Exception, e:\\n\\n sys.stderr.write(\\\"Can't schedule simulation object: %s\\\" % e)\\n \\n return False\\n \\n self._schedulees.append(schedulee)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e58d0df000369b5305df1c48d3c210\",\n \"score\": \"0.542081\",\n \"text\": \"def add(self, workout, database):\\n if not database.session:\\n logger.error(\\\"no database session\\\")\\n return False\\n\\n self.cleanup_sportstype(workout)\\n self.associate_sport(database)\\n id = database.session.query(SportsType.id).filter(\\n SportsType.name == self.name).first()\\n if id:\\n self.id = id[0]\\n return False\\n else:\\n try:\\n database.session.add(self)\\n database.session.flush()\\n except exc.SQLAlchemyError as e:\\n logger.error(\\\"Database error: {}\\\".format(e.args))\\n return False\\n logger.info(\\\"Adding new sportstype '{}' id {} of sport {}\\\".format(\\n self.name, self.id, self.sport_id))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726e313e9da3341e456cc8a89c97b0a2\",\n \"score\": \"0.53725445\",\n \"text\": \"def addSpeciesFeature(self, *args):\\n return _libsbml.MultiSpeciesPlugin_addSpeciesFeature(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13772512bab2dd84801fb7e9d709baf8\",\n \"score\": \"0.5350278\",\n \"text\": \"def setSpeciesFeatureType(self, *args):\\n return _libsbml.SpeciesFeature_setSpeciesFeatureType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60da3e1773b85058877dd6c0d5a20db\",\n \"score\": \"0.5339594\",\n \"text\": \"def setSpeciesType(self, *args):\\n return _libsbml.MultiSpeciesPlugin_setSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26819fecd61ec380099f153157d26ed3\",\n \"score\": \"0.5300599\",\n \"text\": \"def test_ticket_type_add_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('ticket_type add new_type')\\n rv, output = self._execute('ticket_type list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64b1232a06828d6576d067622aec146\",\n \"score\": \"0.5268285\",\n \"text\": \"def addQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_addQualitativeSpecies(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba223653c6190a2e3699e73e7e5508cc\",\n \"score\": \"0.52538675\",\n \"text\": \"def addPossibleSpeciesFeatureValue(self, *args):\\n return _libsbml.SpeciesFeatureType_addPossibleSpeciesFeatureValue(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef9f466bc1be0a10b81e616e352157e\",\n \"score\": \"0.5227218\",\n \"text\": \"def removeSpeciesType(self, *args):\\n return _libsbml.Model_removeSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1933b6003ce43dc3669b5c58439f01cf\",\n \"score\": \"0.5225152\",\n \"text\": \"def getSpeciesType(self, *args):\\n return _libsbml.Model_getSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be62d42f71510803007e14d9fb2006e8\",\n \"score\": \"0.52149636\",\n \"text\": \"def add(self, filetype):\\n\\n if (filetype.add() == 0):\\n self.__logger.debug(\\\"Adding file type %s to list\\\" % filetype)\\n self.__filetypeList[filetype.file_format_id] = filetype\\n return None\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ff3876c593885ea78f321eb4db5c64\",\n \"score\": \"0.5212585\",\n \"text\": \"def getSpeciesType(self):\\n return _libsbml.Species_getSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cddaaeabf06ce606442985ce33f0a62\",\n \"score\": \"0.5212477\",\n \"text\": \"def usertype_add(request, simulation):\\n # Create new distributions with good defaults.\\n alphaTI = Distribution(type='NONE', mean=10)\\n alphaTP = Distribution(type='NONE', mean=15)\\n beta = Distribution(type='NONE', mean=5)\\n delta = Distribution(type='NONE', mean=10)\\n departureMu = Distribution(type='NONE', mean=2)\\n gamma = Distribution(type='NONE', mean=20)\\n modeMu = Distribution(type='NONE', mean=5)\\n penaltyTP = Distribution(type='NONE', mean=2)\\n routeMu = Distribution(type='NONE', mean=10)\\n # Default value for t star is average arrival at middle of period and\\n # uniform distribution over half of the period.\\n mid_time = (simulation.startTime + simulation.lastRecord) / 2\\n length = simulation.lastRecord - simulation.startTime\\n tstar = Distribution(type='UNIFORM', mean=mid_time, std=length / (4 * sqrt(3)))\\n # Save the distributions to generate ids.\\n alphaTI.save()\\n alphaTP.save()\\n beta.save()\\n delta.save()\\n departureMu.save()\\n gamma.save()\\n modeMu.save()\\n penaltyTP.save()\\n routeMu.save()\\n tstar.save()\\n # Create the new user type.\\n usertype = UserType()\\n usertype.alphaTI = alphaTI\\n usertype.alphaTP = alphaTP\\n usertype.beta = beta\\n usertype.delta = delta\\n usertype.departureMu = departureMu\\n usertype.gamma = gamma\\n usertype.modeMu = modeMu\\n usertype.penaltyTP = penaltyTP\\n usertype.routeMu = routeMu\\n usertype.tstar = tstar\\n # Set user_id to user_id of previous usertype + 1.\\n usertypes = get_query('usertype', simulation)\\n if usertypes.exists():\\n usertype.user_id = usertypes.last().user_id + 1\\n else:\\n usertype.user_id = 1\\n usertype.save()\\n # Create a demand segment and a matrix for the user type.\\n matrix = Matrices()\\n matrix.save()\\n demandsegment = DemandSegment()\\n demandsegment.usertype = usertype\\n demandsegment.matrix = matrix\\n demandsegment.save()\\n demandsegment.demand.add(simulation.scenario.demand)\\n # Return the view to edit the new user type.\\n return HttpResponseRedirect(\\n reverse('metro:usertype_edit', args=(simulation.id, demandsegment.id,))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10407fa977b8a3cd9183373a4779bc16\",\n \"score\": \"0.51866627\",\n \"text\": \"def getSpeciesType(self):\\n return _libsbml.SpeciesTypeInstance_getSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2f202493fc7ff76cdd3d6768227bec\",\n \"score\": \"0.5183553\",\n \"text\": \"def add_fish(self, fish_species):\\n if isinstance(fish_species, str):\\n self.fish[fish_species] += 1\\n else:\\n print(\\\"That doesn't look like a fish!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8c977caaee7dcb56d116c1a5331b37\",\n \"score\": \"0.5162882\",\n \"text\": \"def add_type(self, qid, type, type_system):\\n if type_system not in self._type_systems:\\n raise ValueError(\\n f\\\"The type system {type_system} is not one of {self._type_systems.keys()}\\\"\\n )\\n self._type_systems[type_system].add_type(qid, type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28d4e21f7a86d94e92fd0037ea3ba0a\",\n \"score\": \"0.51314074\",\n \"text\": \"def add(self, model_dict, datatype):\\n self.push_datatype(datatype)\\n\\n model_dict['institution'] = self._institution\\n db.session.add(self.cur_datatype_model()(model_dict))\\n\\n self.pop_datatype()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc156239eb2b4b8a0662c623f80997e0\",\n \"score\": \"0.51309603\",\n \"text\": \"def _add_type(self, production, index, m_type):\\n fully_qualified_name = None\\n current_namespace = self._get_current_namespace()\\n if current_namespace is not None:\\n fully_qualified_name = current_namespace.fully_qualified_name()\\n namespace_types = self._get_type_or_namespace_from_fully_qualified_name(fully_qualified_name)\\n if m_type.name in namespace_types:\\n raise ParseError(self.production_to_coord(production, index),\\n \\\"Name '{0}' already exists\\\".format(m_type.fully_qualified_name()))\\n namespace_types[m_type.name] = m_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a209a8fad1decb091ee9488e69c2d494\",\n \"score\": \"0.5085266\",\n \"text\": \"def AddFileType(self, type, name):\\n return _gmat_py.FileManager_AddFileType(self, type, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ebcf9f3f9d6f2347c5aa55053ff5c7\",\n \"score\": \"0.5081777\",\n \"text\": \"def createSpeciesFeatureType(self):\\n return _libsbml.MultiSpeciesType_createSpeciesFeatureType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848a39a8ac13ad727429c7cee52d09ed\",\n \"score\": \"0.5030382\",\n \"text\": \"def add_student(self, student: 'Student') -> None:\\n # Add HOUSEHOLD attributes to the schools' composition\\n self.total += 1\\n self.composition += student.household.attributes\\n self.students[student.idx] = student\\n self.has_space = (self.total < self.capacity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f0257de0cfa66fecef4a1b10b9d29e\",\n \"score\": \"0.5028135\",\n \"text\": \"def createSpeciesTypeInstance(self):\\n return _libsbml.MultiSpeciesType_createSpeciesTypeInstance(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af66129857350b2a5c027ba390a59a1b\",\n \"score\": \"0.50236076\",\n \"text\": \"def addType(self, name):\\n setattr(self, name, name)\\n self._type_names[name] = name\\n if name in self._pending_type_names:\\n del self._pending_type_names[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f36e6f219295597fd43f357821d89f\",\n \"score\": \"0.5020349\",\n \"text\": \"def getNumSpeciesTypes(self):\\n return _libsbml.Model_getNumSpeciesTypes(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54319a6050fc3dd5df9e1ddbeb85cc2c\",\n \"score\": \"0.49994937\",\n \"text\": \"def createSpecies(self):\\n return _libsbml.Model_createSpecies(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01dc84d201e5d046c42fa30aea94610c\",\n \"score\": \"0.4984292\",\n \"text\": \"def addStockType(self, item):\\n # TODO\\n # hint: Add an item to this.stocklist\\n # No. 6\\n self.stocklist.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a319e1066c145b24391138adac49961\",\n \"score\": \"0.49751258\",\n \"text\": \"def __init__(self, *args):\\n this = _libsbml.new_SpeciesType(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fc2c9fb63fc269531203ffd1a1eb0e\",\n \"score\": \"0.4974042\",\n \"text\": \"def add(self, name, fieldtype):\\r\\n \\r\\n if name.startswith(\\\"_\\\"):\\r\\n raise FieldConfigurationError(\\\"Field names cannot start with an underscore\\\")\\r\\n elif name in self._by_name:\\r\\n raise FieldConfigurationError(\\\"Schema already has a field named %s\\\" % name)\\r\\n \\r\\n if callable(fieldtype):\\r\\n fieldtype = fieldtype()\\r\\n if not isinstance(fieldtype, FieldType):\\r\\n raise FieldConfigurationError(\\\"%r is not a FieldType object\\\" % fieldtype)\\r\\n \\r\\n fnum = len(self._by_number)\\r\\n self._numbers[name] = fnum\\r\\n self._by_number.append(fieldtype)\\r\\n self._names.append(name)\\r\\n self._by_name[name] = fieldtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56986da94bd99e6bd2fbce9a0d4f083\",\n \"score\": \"0.49523765\",\n \"text\": \"def add_type(self, typename, db):\\n self._dbs[typename] = db\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffd7c9152ad1299bae1259661dab982\",\n \"score\": \"0.49445945\",\n \"text\": \"def isSetSpeciesType(self):\\n return _libsbml.Species_isSetSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a2ae188b23c4d15fe03b93b3543e8e\",\n \"score\": \"0.4939201\",\n \"text\": \"def add_type(self, name, validation_function):\\n self.types[name] = validation_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c2abd6fd681b53fd7f2c612c774e2c8\",\n \"score\": \"0.49324566\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesType_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94a213c7baf9af33b28642450c10644\",\n \"score\": \"0.49320993\",\n \"text\": \"def EquipmentStatExcelAddStatLevelUpType_(builder, StatLevelUpType_):\\n return AddStatLevelUpType_(builder, StatLevelUpType_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5bc7869baef67a5d9b1ffc98bafea0\",\n \"score\": \"0.4927225\",\n \"text\": \"def add(self, type_name, source_node: ast.AST):\\n for mv in self:\\n mv.add(type_name, source_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e09bc4e3c1e758f85216416ba5413742\",\n \"score\": \"0.48959425\",\n \"text\": \"def testMakeNewSpecies(self):\\n\\n # adding 3 unique species:\\n cerm = CoreEdgeReactionModel()\\n\\n spcs = [Species().fromSMILES('[OH]'), \\n Species().fromSMILES('CC'),\\n Species().fromSMILES('[CH3]')]\\n\\n for spc in spcs:\\n cerm.makeNewSpecies(spc)\\n\\n self.assertEquals(len(cerm.speciesDict), len(spcs)) \\n self.assertEquals(len(cerm.indexSpeciesDict), len(spcs))\\n\\n # adding 3 unique, and 1 already existing species:\\n cerm = CoreEdgeReactionModel()\\n\\n spcs = [Species().fromSMILES('[OH]'), \\n Species().fromSMILES('CC'),\\n Species().fromSMILES('[CH3]'),\\n Species().fromSMILES('CC')]#duplicate species\\n\\n for spc in spcs:\\n cerm.makeNewSpecies(spc)\\n\\n self.assertEquals(len(cerm.speciesDict), len(spcs) - 1) \\n self.assertEquals(len(cerm.indexSpeciesDict), len(spcs) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ac544d4c8dd5adda61c7c600578652\",\n \"score\": \"0.48915997\",\n \"text\": \"def isSetSpeciesType(self):\\n return _libsbml.SpeciesTypeInstance_isSetSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9783eb716b1398fb3ae6e16edad2266c\",\n \"score\": \"0.48706302\",\n \"text\": \"def addSpeciesGlyph(self, *args):\\n return _libsbml.Layout_addSpeciesGlyph(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c8aa02e2fcba83ecca87c12ed42ebe\",\n \"score\": \"0.48699006\",\n \"text\": \"def add_species(self, side='R'): \\n #self.disable_all_buttons() \\n Player.add_species(self, side=side)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c727f21bfe6acf408f17bc9f162f51\",\n \"score\": \"0.4856663\",\n \"text\": \"def test_make_new_species(self):\\n\\n # adding 3 unique species:\\n cerm = CoreEdgeReactionModel()\\n\\n spcs = [Species().from_smiles('[OH]'),\\n Species().from_smiles('CC'),\\n Species().from_smiles('[CH3]')]\\n\\n for spc in spcs:\\n cerm.make_new_species(spc)\\n\\n self.assertEquals(len(cerm.species_dict), len(spcs))\\n self.assertEquals(len(cerm.index_species_dict), len(spcs))\\n\\n # adding 3 unique, and 1 already existing species:\\n cerm = CoreEdgeReactionModel()\\n\\n spcs = [Species().from_smiles('[OH]'),\\n Species().from_smiles('CC'),\\n Species().from_smiles('[CH3]'),\\n Species().from_smiles('CC')] # duplicate species\\n\\n for spc in spcs:\\n cerm.make_new_species(spc)\\n\\n self.assertEquals(len(cerm.species_dict), len(spcs) - 1)\\n self.assertEquals(len(cerm.index_species_dict), len(spcs) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e7b10cd087242e2b8472251f3122d6\",\n \"score\": \"0.48541874\",\n \"text\": \"def getSpeciesFeatureType(self):\\n return _libsbml.SpeciesFeature_getSpeciesFeatureType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7f576977b3e86ddbc9dc15db7ba7e7\",\n \"score\": \"0.48370606\",\n \"text\": \"def clone(self):\\n return _libsbml.SpeciesType_clone(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e2133d6117b3fc727d8d00b0f936a6\",\n \"score\": \"0.48337075\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesFeatureType_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"873a7d433d8e9ce86811c4523d4aa0c6\",\n \"score\": \"0.48201782\",\n \"text\": \"def getSpeciesType(self):\\n return _libsbml.MultiSpeciesPlugin_getSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73fb93701aaea7b1b2c3279b3e90058\",\n \"score\": \"0.48116407\",\n \"text\": \"def add_stat(self, stat_type, stat):\\n assert stat_type in self.log_book, \\\"Wrong stat type\\\"\\n assert isinstance(stat, int) or isinstance(stat, float), \\\"Wrong stat value\\\"\\n \\n self.log_book[stat_type].append(stat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76dc27c6bf63cdc434d6738af737aa35\",\n \"score\": \"0.48035103\",\n \"text\": \"def _AppendType(type_name, register):\\n if register.register_type is not 'v':\\n raise ArgumentError('Only vector registers can have type appended.')\\n\\n if type_name in set([8, '8', 'i8', 's8', 'u8']):\\n subtype = 'b'\\n subtype_bits = 8\\n elif type_name in set([16, '16', 'i16', 's16', 'u16']):\\n subtype = 'h'\\n subtype_bits = 16\\n elif type_name in set([32, '32', 'i32', 's32', 'u32', 'f32']):\\n subtype = 's'\\n subtype_bits = 32\\n elif type_name in set([64, '64', 'i64', 's64', 'u64', 'f64']):\\n subtype = 'd'\\n subtype_bits = 64\\n else:\\n raise ArgumentError('Unknown type: %s' % type_name)\\n\\n new_register = register.Copy()\\n new_register.register_subtype = subtype\\n new_register.register_subtype_count = register.register_bits / subtype_bits\\n return new_register\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ec509dbd8fa104764630a9cf9039f4\",\n \"score\": \"0.48026356\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.Species_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6453c65ff32d72396d6bded692f347c6\",\n \"score\": \"0.47930247\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesFeature_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfdf26a4566cf173173e8b2774d759d4\",\n \"score\": \"0.47913948\",\n \"text\": \"def add(self, cheese, index):\\n self._stools[index].append(cheese)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03917e16dcfd3ed1a5b36172eea31b13\",\n \"score\": \"0.478958\",\n \"text\": \"def add(cls, name):\\n cls.validate_name(name)\\n new_status = ServerType(name)\\n DB.session.add(new_status)\\n DB.session.commit()\\n return new_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08e3d9692eaa2f67f9054ba4049f415\",\n \"score\": \"0.47544125\",\n \"text\": \"def createMultiSpeciesType(self):\\n return _libsbml.MultiModelPlugin_createMultiSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c07467f3a5d256bd334b68a1221fdd\",\n \"score\": \"0.4730164\",\n \"text\": \"def make_mutable_type(type_):\\n _mutable_types.append(type_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b2122eecb54f18a112b76d81281df21\",\n \"score\": \"0.4719878\",\n \"text\": \"def addsos(m, sosdict, sostype):\\n soslist = []\\n # Sort by name to get deterministic ordering. Note that\\n # names may be plain integers, that is why we use str(name)\\n # to pass them to the SOS constructor.\\n for name in sorted(sosdict):\\n indices = []\\n weights = []\\n for v, val in sosdict[name].items():\\n indices.append(v._xprs[0])\\n weights.append(val)\\n soslist.append(xpress.sos(indices, weights, sostype, str(name)))\\n if len(soslist):\\n m.addSOS(soslist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad815cc8e366ce143b4c37792f41a56\",\n \"score\": \"0.47150287\",\n \"text\": \"def add_recipe(self, recipe): \\n\\t\\tfor key, val in self.recipes_list.items():\\n\\t\\t\\tif key == recipe.recipe_type:\\n\\t\\t\\t\\tself.recipes_list[key][recipe.name] = recipe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee46b8044f05b3fa24aa5480b1df9c7e\",\n \"score\": \"0.47009113\",\n \"text\": \"def addTest(self, test):\\r\\n self.tests.append(test)\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ed67bd5f0fa260887afd2e72f925c0\",\n \"score\": \"0.46655354\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesTypeInstance_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a971ce40fae86c23f9c8a1f4884885\",\n \"score\": \"0.46585146\",\n \"text\": \"def isSetSpeciesType(self):\\n return _libsbml.MultiSpeciesPlugin_isSetSpeciesType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98262e9f25ab8d25d4b65ec6d1aafa5a\",\n \"score\": \"0.4653468\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SubListOfSpeciesFeatures_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6060a46b9893b13a15bcd696ac00f5\",\n \"score\": \"0.46433422\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.ListOfSpeciesFeatureTypes_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39a66cad42b832a9f2dd5654b6bda9c0\",\n \"score\": \"0.46432438\",\n \"text\": \"def update_species(self, mixture, concentration):\\n # Create component specific species\\n # self.product = add_species(model, \\\"Type\\\", name)\\n \\n # Create any other species needed by component mechanisms\\n mechanisms = get_mechanisms(mixture, self)\\n for name in mechanisms:\\n mechanism = mechanisms[name]\\n mechanism.update_species(mixture, self, concentration)\\n \\n # If the default member function gets used, issue a warning\\n warn(\\\"component: default __init__ called for \\\" + name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6e7cb05974aff0ddd6e974c359c7a8\",\n \"score\": \"0.4642216\",\n \"text\": \"def add_car(sefl):\\n make = \\\"Test_Toyota\\\"\\n body_type = \\\"Seden\\\"\\n colour = \\\"Black\\\"\\n seats = 5\\n location = \\\"-37.814, 144.96332\\\"\\n cost_per_hour = 10.5\\n\\n newCar = Car( make = make,\\n body_type = body_type,\\n colour = colour,\\n seats = seats,\\n location = location,\\n cost_per_hour = cost_per_hour,\\n booked = True\\n\\n )\\n db.session.add(newCar)\\n db.session.commit()\\n self.assertTrue(self.carExists(make))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8e4291b120c7bba85cec5e96f37273\",\n \"score\": \"0.46387902\",\n \"text\": \"def _insert_new_type(self, newType):\\n existingType = self.knownTypes.getKey(newType)\\n if existingType is None:\\n self.knownTypes[newType] = []\\n return\\n\\n if isinstance(existingType, ast.Builtin):\\n raise RedefBuiltinTypeError(newType)\\n else:\\n raise RedefUserTypeError(newType, existingType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc946741a3e9c67164b9203f811706a5\",\n \"score\": \"0.46375737\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.QualitativeSpecies_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c496120c4d73e1017148a410c92a4a\",\n \"score\": \"0.46317688\",\n \"text\": \"def add_test(self, sol_src, run_test_type=False):\\n\\n if sol_src in self.sol_srcs: # already added this solution\\n return\\n if run_test_type:\\n env = dict(List=List, Dict=Dict, Set=Set)\\n time0 = time.perf_counter()\\n my_exec(sol_src + \\\"\\\\n\\\" + \\\"answer = sol()\\\", env, description=self.name)\\n\\n sol_val = env[\\\"answer\\\"]\\n\\n assert sol_val is not None, \\\"sol returned None\\\"\\n\\n assert type_check(run_test_type, sol_val)\\n\\n answer = decode(encode(sol_val))\\n assert answer == sol_val, \\\"encode/decode round trip failed\\\"\\n\\n env2 = dict(answer=answer, List=List, Dict=Dict, Set=Set) # in case they mucked with env\\n my_exec(self.src + \\\"\\\\n\\\" + \\\"assert sat(answer)\\\", env2, description=self.name)\\n dur = time.perf_counter() - time0\\n if dur > (DEFAULT_TIMEOUT if self.timeout is None else self.timeout * DEFAULT_TIMEOUT):\\n utils.warn(f\\\"Took {dur}s to test {self.name} (self.timeout={self.timeout})\\\")\\n self.sol_srcs.append(sol_src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216913032bad46a792120bcda0e9e94b\",\n \"score\": \"0.46216056\",\n \"text\": \"def add_population(self, net_id, component_id, type_, **args):\\n nmlnetwork = NeuroMLSimpleNetwork(net_id)\\n nmlnetwork.add_component(component_id, type_, **args)\\n self._population = nmlnetwork.build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c415542d28163fade5759c1e64e80fd5\",\n \"score\": \"0.46182367\",\n \"text\": \"def add_piece(self, color, piece_type, location):\\n\\n coordinates = self.translate_to_grid(location)\\n piece_to_add = piece_type(color, coordinates)\\n self._board[coordinates[0]][coordinates[1]] = piece_to_add\\n self._active_pieces[color].append(piece_to_add)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99ce59411dbbb8662b7d1a4f61daf33\",\n \"score\": \"0.46114206\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesReference_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b399ef89de98cfafc56d3149c639d53\",\n \"score\": \"0.46075428\",\n \"text\": \"def getId(self):\\n return _libsbml.SpeciesType_getId(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11468a0b63d4c419a896060a301c1566\",\n \"score\": \"0.46048138\",\n \"text\": \"def Add(self, type, number, lit = -1, msg = None):\\n self.__errors[number] = (lit, type, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9cbd5843f79690f7c6f1d0a0ad6534\",\n \"score\": \"0.4599954\",\n \"text\": \"def add(self, specification):\\n self.specifications.append(specification)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51572608fffb5f8a9e28c9287db5c62\",\n \"score\": \"0.45989323\",\n \"text\": \"def getListOfSpeciesTypes(self, *args):\\n return _libsbml.Model_getListOfSpeciesTypes(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec2323f64479d7a061a1ba04c1972235\",\n \"score\": \"0.4597089\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.BindingSiteSpeciesType_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"836053c39fa6e188edb6da1ec0110604\",\n \"score\": \"0.459297\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.ListOfSpeciesTypeInstances_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b253efb03cc04ba36e8fd2dbd47a4844\",\n \"score\": \"0.4591635\",\n \"text\": \"def removeSpeciesTypes(self):\\n return _libsbml.Model_removeSpeciesTypes(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546cdbfffc75e4cff6c1d0d7d562c14b\",\n \"score\": \"0.4591102\",\n \"text\": \"def add(self, attr):\\n self.validate_type(attr)\\n self.categories.add(attr.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6847083ff0847819c47ce47d5f1e8157\",\n \"score\": \"0.458189\",\n \"text\": \"def add_xsd_type(self, name, xsd_type):\\n if self.xsd_types is None:\\n self.xsd_types = {name: xsd_type}\\n else:\\n obj = self.xsd_types.get(name)\\n if obj is None:\\n self.xsd_types[name] = xsd_type\\n elif not isinstance(obj, list):\\n if obj is not xsd_type:\\n self.xsd_types[name] = [obj, xsd_type]\\n elif xsd_type not in obj:\\n obj.append(xsd_type)\\n\\n return xsd_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c578e4ecdbc8b8bb1190c9ca34dad387\",\n \"score\": \"0.45817897\",\n \"text\": \"def setName(self, *args):\\n return _libsbml.SpeciesType_setName(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01cc61f3ff77265a465f9e377f00e1ff\",\n \"score\": \"0.45757905\",\n \"text\": \"def _add(self, type_or_name: str | type, value: Callable | type | tuple[bool, Callable]):\\n key = type_or_name if isinstance(type_or_name, str) else type_or_name.__name__\\n self._values[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9c979c49c13b7cbde28d948f44a1e4\",\n \"score\": \"0.45730388\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.MultiSpeciesType_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123a1afb50fd15ea0e3ed44959c97186\",\n \"score\": \"0.45728263\",\n \"text\": \"def addCompartmentType(self, *args):\\n return _libsbml.Model_addCompartmentType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ba5a2f19798a20f12bb7594084c6df\",\n \"score\": \"0.45703354\",\n \"text\": \"def addStudent():\\n name = input(\\\"Name: \\\")\\n number = input(\\\"Number: \\\")\\n gpa = input(\\\"GPA: \\\")\\n field = input(\\\"Field: \\\")\\n student = Student(name, number, gpa, field)\\n if t.insert(number, student):\\n ht.insert(student)\\n print(name, \\\"added successfully.\\\")\\n else:\\n print(\\\"student number is not valid.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0624389e01b69c8915b60408f72d1b1d\",\n \"score\": \"0.45610204\",\n \"text\": \"def set_species(self, species):\\n self.species = species\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47fe57be4a1180323675e838b39ebf0\",\n \"score\": \"0.45418972\",\n \"text\": \"def getTypeCode(self):\\n return _libsbml.SpeciesFeatureValue_getTypeCode(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7032d3b1278b139a60d67f383d798019\",\n \"score\": \"0.45381978\",\n \"text\": \"def process_simple_type(self, s, name=None):\\n retval = None\\n\\n if name is None:\\n name = s.name\\n\\n if s.list is not None:\\n retval = self.process_simple_type_list(s, name)\\n\\n elif s.union is not None:\\n retval = self.process_simple_type_union(s, name)\\n\\n elif s.restriction is not None:\\n retval = self.process_simple_type_restriction(s, name)\\n\\n if retval is None:\\n self.debug1(\\\"skipping simple type: %s\\\", name)\\n return\\n\\n self.retval[self.tns].types[s.name] = retval\\n\\n key = self.get_name(name)\\n dependents = self.pending_simple_types[key]\\n for s, name in set(dependents):\\n st = self.process_simple_type(s, name)\\n if st is not None:\\n self.retval[self.tns].types[s.name] = st\\n\\n self.debug2(\\\"added back simple type: %s\\\", s.name)\\n dependents.remove((s, name))\\n\\n if len(dependents) == 0:\\n del self.pending_simple_types[key]\\n\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8058c9bfc9b5dd5204f03d01f9fbe4e\",\n \"score\": \"0.45367673\",\n \"text\": \"def add_test(self, testsuite, test):\\n self.tests[testsuite].append(TestCase(test, self))\\n self.num_tests += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce71aec840c3b145a6d46e357e7284e\",\n \"score\": \"0.4534546\",\n \"text\": \"def add(self, rank, birth_year, enlisting_year, shirt_color, name):\\n # Your implementation here\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe2cec73c1cc57e01b2f53b94663f5c\",\n \"score\": \"0.45296344\",\n \"text\": \"def add(self, filetype, **kwargs):\\n\\n location = self.location(filetype, **kwargs)\\n sas_module, location = location.split(sep, 1) if location else (None, location)\\n\\n # set proper sasdir based on access method\\n sasdir = 'sas' if self.access_mode == 'curl' else ''\\n source = self.url(filetype, sasdir=sasdir, **kwargs)\\n\\n # raise error if attempting to add a software product path\\n if 'svn.sdss.org' in source:\\n raise AccessError('Rsync/Curl Access not allowed for svn paths. Please use HttpAccess.')\\n\\n if 'full' not in kwargs:\\n destination = self.full(filetype, **kwargs)\\n else:\\n destination = kwargs.get('full')\\n\\n if sas_module and location and source and destination:\\n self.initial_stream.append_task(\\n sas_module=sas_module, location=location, source=source, destination=destination)\\n else:\\n print(\\\"There is no file with filetype=%r to access in the tree module loaded\\\" % filetype)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1492,"cells":{"query_id":{"kind":"string","value":"c1fb9aa873b120f39122ceb96f8db1b6"},"query":{"kind":"string","value":"Create instance object with output path."},"positive_passages":{"kind":"list like","value":[{"docid":"45e9ab49e0ca8ee32110fd94438539a7","score":"0.7741643","text":"def __init__(self, out_path):\n self.outPath = out_path","title":""}],"string":"[\n {\n \"docid\": \"45e9ab49e0ca8ee32110fd94438539a7\",\n \"score\": \"0.7741643\",\n \"text\": \"def __init__(self, out_path):\\n self.outPath = out_path\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"617798d336acf639edf2f5e43857c80e","score":"0.7930995","text":"def __init__(self, output_path):\n self.output_path = output_path","title":""},{"docid":"862151cf8491febec26fe8d244e68d0f","score":"0.7642023","text":"def __init__(self, output_path):\r\n Output.__init__(self)\r\n self._output_path = output_path\r\n self._output_file = None","title":""},{"docid":"875c313fdb68d287d0ef92f8fd33693c","score":"0.74678016","text":"def __init__(self, path):\n if not os.path.isdir(path):\n os.makedirs(path)\n self.output_path = path","title":""},{"docid":"b651d93c95d294a803320babfa5a77bf","score":"0.70197433","text":"def __init__(self, output_path):\n\t\tself.out_file = output_path\n\t\tself.VM_file = self.open_file()","title":""},{"docid":"34242f0616ce87b18dde8a5e8061a50d","score":"0.7013511","text":"def __init__(self, output_file):\n self.output_file = output_file","title":""},{"docid":"f7d43e42a2f7f3ce56565e2908966498","score":"0.69653815","text":"def __init__(self, input_path, output_path):\n assert isinstance(input_path, Path)\n assert isinstance(output_path, Path)\n self.input_path = input_path\n self.output_path = output_path","title":""},{"docid":"d5080dd64c9f40073a28f3c0a000efd0","score":"0.6903254","text":"def __init__(self, output_directory: str):\r\n self.output_directory = output_directory","title":""},{"docid":"6918bcc57e6aec3e025f6daab1a05a65","score":"0.68974674","text":"def __init__(self, output_file):\n self.__file = output_file","title":""},{"docid":"65c52ef0b403edebcd5b00a13728e1bd","score":"0.68647796","text":"def __init__(self, out_file):\n self.out_file = open(out_file, 'w')","title":""},{"docid":"5ce31e7c322a358541746f236e9001d1","score":"0.682767","text":"def create(self):\n self.write(self.data())\n for key in [\"StandardOutPath\", \"StandardErrorPath\"]:\n path = getattr(self, key, None)\n if path and not os.path.exists(os.path.dirname(path)):\n os.makedirs(os.path.dirname(path))\n return self","title":""},{"docid":"8e93599e89764f6fa2d2725837747348","score":"0.68144375","text":"def __init__(self, exec_path: Union[str, Path], output_name: str):\n self.exec_path = Path(exec_path)\n self.working_dir = self.exec_path.parent\n self.output_name = output_name","title":""},{"docid":"61e594b5b34f9c881462e159a1224a90","score":"0.6727177","text":"def __init__(self, output=None, path=None, verify=True):\n self.path = path or tempfile.mkdtemp()\n self.output = output\n self.verify = verify\n self.check_dependencies()\n if isinstance(output, Output):\n self.url = output.reference\n self._file = None\n elif isinstance(output, (str, Path)):\n self.url = output\n self._file = Path(output)\n else:\n raise NotImplementedError","title":""},{"docid":"44341fee07e09dcac33bebeb44e28b50","score":"0.66921866","text":"def create_output(self):\n raise NotImplementedError()","title":""},{"docid":"94a5c0a57dbbc85b825e8e7136082df7","score":"0.6684631","text":"def __init__(self, outdir):\n self.outdir = outdir\n if not os.path.isdir(outdir):\n os.mkdir(outdir)","title":""},{"docid":"8004c07f15e481e047d94fb2b90780b7","score":"0.66142386","text":"def set_output(self, o_path):\n\t\tif not os.path.isdir(o_path):\n\t\t\tos.makedirs(o_path)\n\t\tself.output_path = o_path","title":""},{"docid":"235b3d768dfc7a7e75d6f4c35cf18190","score":"0.6598007","text":"def __init__(self,output_file):\n self.f = open(output_file, 'w')","title":""},{"docid":"63e6f4e587b2ed5022c08bdd002a45b5","score":"0.6593374","text":"def __init__(self, output_filename=\"vasprun.xml\"):\n self.output_filename = output_filename","title":""},{"docid":"ab8a1757acd90bf4c661f9c5f9c763d8","score":"0.6584197","text":"def _create_output_path(self):\n self.output_dir = os.path.abspath(os.path.expanduser(self.output_dir))\n\n try:\n os.makedirs(self.output_dir)\n except FileExistsError:\n pass","title":""},{"docid":"0f361c6e50b83e1146056ec253029574","score":"0.65395606","text":"def __init__(self, output):\n self.output = output","title":""},{"docid":"7a0d051cd40b5f15b873f231fd7d5ff9","score":"0.6457707","text":"def __init__(self,outdir):\n self.outdir = outdir","title":""},{"docid":"b116135b4623ffe92aaf578d292dccc0","score":"0.6377173","text":"def __init__(self, output_directory):\n super(FileWriter, self).__init__()\n self._output_directory = output_directory","title":""},{"docid":"be3f221d4886f1abf04deb9900371b85","score":"0.63611645","text":"def init_output_file(base_path: Path, file_name: str) -> Path:\n base_path.mkdir(parents=True, exist_ok=True)\n\n file = base_path / file_name\n open(file, \"w\", encoding=\"utf-8\").close()\n return file","title":""},{"docid":"208abb0ad25d2fece976e2439b57a955","score":"0.6355812","text":"def as_file(self, **kwargs):\n kwargs[\"path_class\"] = self.file_class()\n return self.as_class(**kwargs)\n #return self.create_file(self.path, **kwargs)","title":""},{"docid":"04ddbedbcb8767a498a8fd38a8593bcd","score":"0.6318313","text":"def instantiate(self, root_directory: Path) -> Path:\n output_filepath = root_directory / str(self.relative_path) / str(self.filename)\n output_filepath.parent.mkdir(exist_ok=True, parents=True) # create relative directories\n with output_filepath.open('wb') as output:\n output.write(self.file_content.read())\n return output_filepath","title":""},{"docid":"e58726d147c7f34a3fb3b5989938aa1f","score":"0.6273526","text":"def _output_dir(work_dir: P, instance: str, path):\n return work_dir / \"compiled\" / instance / path","title":""},{"docid":"a3bcb9d96ac335565aebf9573491fefd","score":"0.6231194","text":"def get_output_path(self) -> str:","title":""},{"docid":"ce802d6f330f148e9ddb90f0d0a89db9","score":"0.61948186","text":"def __init__(self, output_folder: Path, run: int) -> None:\n self.run = run\n self.output_folder = Path(\n output_folder, \"run_\"+str(run)).absolute()\n self.weights_folder = Path(self.output_folder, \"weights\").absolute()\n self.img_folder = Path(self.output_folder, \"obj\").absolute()\n self.train_txt = Path(self.output_folder, \"train.txt\").absolute()\n self.test_txt = Path(self.output_folder, \"test.txt\").absolute()\n self.classes_txt = Path(self.img_folder, \"classes.txt\").absolute()\n self.yolo_cfg = Path(self.output_folder, \"yolo.cfg\").absolute()\n self.darknet_data = Path(self.output_folder, \"darknet.data\").absolute()","title":""},{"docid":"a6bbf47045cb4a28f187f624e1247c01","score":"0.6185619","text":"def CreatePath(self):","title":""},{"docid":"a6bbf47045cb4a28f187f624e1247c01","score":"0.6185292","text":"def CreatePath(self):","title":""},{"docid":"ef38f33c6f7d86df8c1cee5862589715","score":"0.6181896","text":"def __init__(self, output_dir, temp_dir, output_dict, db_params, prefix='resAnnotator'):\n self.output_dir = os.path.abspath(output_dir)\n self.temp_dir = os.path.abspath(temp_dir)\n self.prefix = prefix\n self.output_dict = output_dict\n self.db_params = db_params\n self.db_name = str(os.path.splitext(db_params['dbname'])[0])\n self.file_prefix = '{}_{}'.format(str(self.prefix), self.db_name)\n #self.output_filename = os.path.join(self.output_dir,\"{}_{}_results_tab.txt\".format(str(self.prefix),db_params['dbname']))\n #self.output_filepath = os.path.join(self.output_dir,self.file_prefix)\n #logger.debug('Created Output object')\n #logger.info(repr(self))","title":""},{"docid":"65a0131c05379d4f0879b647209258dd","score":"0.6160868","text":"def initialize_output_file():\n if not os.path.exists(OUTPUT_FILE):\n open(OUTPUT_FILE, \"w+\")","title":""},{"docid":"6022420351ed033bfb445da71fabad3f","score":"0.61544937","text":"def __init__(self, outStr): \n\n self.readOutFile(outStr)","title":""},{"docid":"087c13c591abd19f1dd1f0b4a1024b46","score":"0.6153439","text":"def output_path(self):\n return '%s/%s' % (os.path.abspath(os.path.dirname(__file__) + '/../../outputs'),\n self.identifier)","title":""},{"docid":"dcf3abb60d953789a2432f9577aad670","score":"0.6104884","text":"def __init__(self, outputfile, env, fn = None):\n self._env = weakref.proxy(env)\n self._fn = fn\n if isinstance(outputfile, type(\"\")):\n self._file = open(outputfile, \"w\")\n else:\n self._file = outputfile\n if self._file is not None:\n if not hasattr(self._file, \"write\"):\n raise CplexError(\"Output object must have write method\")\n elif not callable(self._file.write):\n raise CplexError(\"Output object must have write method\")\n if not hasattr(self._file, \"flush\"):\n raise CplexError(\"Output object must have flush method\")\n elif not callable(self._file.flush):\n raise CplexError(\"Output object must have flush method\")","title":""},{"docid":"e5468b950470ab1895d05f43d4beaa1a","score":"0.60868907","text":"def __init__(self, output_stream: typing.TextIO) -> None:\n self.output_stream = output_stream","title":""},{"docid":"0ac3163bef1e94dbf949515f5d6c2b65","score":"0.6083279","text":"def __init__(self, input_path=getcwd()):\n self._input_path = input_path + \"/\"","title":""},{"docid":"6ed74ab07e61d5e951883d26028ad1bf","score":"0.6079307","text":"def __init__(self, out_dir, product):\n if not os.path.isdir(out_dir):\n raise ValueError(\"not a directory: %s\" % out_dir)\n # Check that we really point to the out directory.\n # Chomp any trailing slashes so that os.path.split() returns 'out'\n # instead of the empty string\n out_dir = out_dir.rstrip(\"/\")\n if not os.path.split(out_dir)[1] == 'out':\n raise ValueError(\"not pointing to 'out' directory: %s\" % out_dir)\n if not os.path.isdir(\n os.path.join(out_dir, 'target', 'product', product)):\n raise ValueError(\"not a build tree for product: %s\" % product)\n self._out_dir = out_dir\n self._product = product","title":""},{"docid":"5bf76e69d7ce25f11ae860d5466c2178","score":"0.6079076","text":"def _setup_output(filename):\n output = path.join(InterfaceProvider.getPathConstructor().getVisitDirectory(), filename)\n logger.debug('Creating user log to write to %s', output)\n if path.exists(output) and not path.isfile(output):\n raise ValueError('filename exists and is not a file')\n if not path.exists(path.dirname(output)):\n print 'Creating parent directory for user log'\n import os\n os.makedirs(path.dirname(output))\n return output","title":""},{"docid":"c9d8ad790b3d84d11c5352a6253e313c","score":"0.6039111","text":"def output_path(self) -> Path:\n return self._output_path","title":""},{"docid":"c9d8ad790b3d84d11c5352a6253e313c","score":"0.6039111","text":"def output_path(self) -> Path:\n return self._output_path","title":""},{"docid":"eb4df360081a5c37aae2c82c1e8270b9","score":"0.60366386","text":"def outputPath(self):\n return os.path.join(self.pathName, self.outputName)","title":""},{"docid":"068ae64fd6ea0eda57a7c015d047b9e8","score":"0.60307425","text":"def script_output(self):\n\n raise NotImplementedError(\"subclass should define the script output path\")","title":""},{"docid":"65fa0d6aa9f54404dfdb852f79a2d5c5","score":"0.6015264","text":"def __init__(self, f_path):\n self.results_path = f_path","title":""},{"docid":"1a4ebde6922a7c2b5fb3f1dcfac27f30","score":"0.60122234","text":"def get_output(output: Optional[str] = None) -> Path:\n now = datetime.now().strftime(\"%Y%m%d%H%M\")\n name = f\"openmetadata_{now}_backup.sql\"\n\n if output:\n # Create the output directory if it does not exist\n if not Path(output).is_dir():\n Path(output).mkdir(parents=True, exist_ok=True)\n\n return Path(output) / name\n\n return Path(name)","title":""},{"docid":"5b6cefe32626c087317dbec81e86d780","score":"0.60108656","text":"def _create_output_directory(self):\n\n try:\n self.paths['temp'] = tempfile.mkdtemp()\n except OSError as err:\n self.exit(err)","title":""},{"docid":"501fec174bd386efa9f123152fd023c9","score":"0.59976786","text":"def __init__(__self__, *,\n output_config: pulumi.Input['OutputConfigArgs'],\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"output_config\", output_config)\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if project is not None:\n pulumi.set(__self__, \"project\", project)","title":""},{"docid":"a483b96e2add9cc53949baf9e7f1e863","score":"0.5996942","text":"def _output_path(self) -> Path:\n return self.parent.output_path / self.analysis_name","title":""},{"docid":"748de0933d149239c3ceedaeb4f5b281","score":"0.59801215","text":"def __init__(self, output_file, root=\"expression\"):\n self.output_file = output_file\n self.root = root","title":""},{"docid":"afb3eb8b026084ab0d1ba36a1ea836dd","score":"0.59669256","text":"def outputPath(self, outputPath):\n self.__outputPath = outputPath","title":""},{"docid":"1d3db6b241c058b679e7f0bb45007a4d","score":"0.5946757","text":"def _create_output_path(self, output_path):\n if not path.exists(path.dirname(output_path)):\n makedirs(path.dirname(output_path))","title":""},{"docid":"5486bba813b664a0a49d421c393664ea","score":"0.59370756","text":"def __init__(self, output_stream: typing.TextIO) -> None:\n self.filename: str = ''\n self.output_stream = output_stream\n self.segments_pointers = {'local': \"LCL\", 'argument': \"ARG\", 'this': \"THIS\", 'that': \"THAT\"}\n\n self.temp_addr = 5\n self.lines_counter = 0\n self.files_counter = 0\n self.calls_count = 0","title":""},{"docid":"1bae77d9f053a8d412b22a9d53937aca","score":"0.5918695","text":"def _setup_output(self):\n if not self._output_dir:\n self._output_dir = self._s3_tmp_uri + 'output/'\n log.info('Job will output -> %s' % self._output_dir)","title":""},{"docid":"0dcbb8ce0ce34fe0de9cb10ce0117568","score":"0.5906739","text":"def __init__(self, **kwargs):\n super(MergeProcess, self).__init__(**kwargs)\n if not self.output:\n self.output = RasterFileIO(name='result', uri=self.get_outpath())","title":""},{"docid":"6737e9b411ec0be0769c258fbd754a7b","score":"0.5906335","text":"def Generate(self, project_configuration, output_writer):","title":""},{"docid":"4540620339c3f35c0a7926242686bbaa","score":"0.58832103","text":"def _init(args):\n if args.verbose:\n LOGGER.setLevel('DEBUG')\n else:\n LOGGER.setLevel('WARNING')\n LOGGER.debug(\"args: %s\", args)\n\n if not os.path.exists(args.output_dir):\n os.mkdir(args.output_dir)\n LOGGER.debug(\"{} is created\".format(args.output_dir))\n if not os.path.exists(os.path.join(args.output_dir, \"intermediate\")):\n LOGGER.debug(\"{} is created\".format(os.path.join(args.output_dir, \"intermediate\")))\n os.mkdir(os.path.join(args.output_dir, \"intermediate\"))\n if not os.path.exists(os.path.join(args.output_dir, \"final\")):\n LOGGER.debug(\"{} is created\".format(os.path.join(args.output_dir, \"final\")))\n os.mkdir(os.path.join(args.output_dir, \"final\"))","title":""},{"docid":"bde238bb9d2e9c46e6849af1b7daa82f","score":"0.5881929","text":"def _build_output(self):\n pass","title":""},{"docid":"fcec81b416e809340a7aa35c920d4d6a","score":"0.58746475","text":"def createOutputFile():\n outputFile = open(\"output/\" + outputName + \".nom\", \"w\")\n outputFile.write(\"#### CONVERTED FROME A SIF FILE ####\\n\\n\")\n return outputFile","title":""},{"docid":"69e7460d5cf077e653bfd2f90cfe30f4","score":"0.58724064","text":"def _set_out_path(self):\n outpath = dict()\n nf = self.nf\n tiler = self.tiler\n outpath['info'] = '.' if nf else os.path.join(self.path_outputs, 'info') \n outpath['visu'] = '.' if nf else os.path.join(self.path_outputs, 'visu')\n if tiler == 'simple':\n outpath['tiles'] = '.' if nf else os.path.join(self.path_outputs, self.name_wsi)\n else:\n outpath['tiles'] = '.' if nf else os.path.join(self.path_outputs, 'mat')\n #Creates the dirs.\n [os.makedirs(v, exist_ok=True) for k,v in outpath.items()]\n return outpath","title":""},{"docid":"4eab1f0d13b8e6c2c370d79e66d4a4e2","score":"0.58581877","text":"def create_file(data, output='output_file'):\n output_file = os.path.normpath(output)\n with open(output_file, 'w') as testfile:\n testfile.write(data)\n testfile.close()\n return output_file","title":""},{"docid":"762ca5dd7cbbe2bc985ecb973cb302dd","score":"0.5856423","text":"def outputPath(self):\n return self.__outputPath","title":""},{"docid":"e72293e2c4357dbfaa9a257da0fd5797","score":"0.5855325","text":"def _create_output_directory(self):\n try:\n os.makedirs(self._output_dir)\n except FileExistsError:\n pass","title":""},{"docid":"12992429bd5d7f1901a0c5754a8271f7","score":"0.58538026","text":"def _create_implementation_output_dir(self):\n if not self._write_flag:\n return\n # Get run names\n if self._gcp_implementation:\n all_files = [x.name for x in self._gcp_bucket.list_blobs() if\n x.name.find(\n self._strategy_implementation_model_dir) > -1]\n strip_str = self._strategy_implementation_model_dir+'/'\n all_files = [x.replace(strip_str, '') for x in all_files]\n all_files = [x.split('/')[0] for x in all_files]\n if all_files:\n max_model = max(all_files)\n new_ind = int(max_model.replace('models_', '')) + 1\n else:\n new_ind = 1\n path = os.path.join(self._strategy_implementation_model_dir,\n 'models_{0:04d}'.format(new_ind))\n else:\n # Check if directory structure exists\n if not os.path.isdir(self._ram_implementation_dir):\n os.mkdir(self._ram_implementation_dir)\n path = os.path.join(self._ram_implementation_dir,\n self.__class__.__name__)\n if not os.path.isdir(path):\n os.mkdir(path)\n path = os.path.join(path, 'trained_models')\n if not os.path.isdir(path):\n os.mkdir(path)\n # Search for new ind\n all_dirs = [x for x in os.listdir(\n self._strategy_implementation_model_dir) if x[:7] == 'models_']\n new_ind = int(max(all_dirs).split('_')[1]) + 1 if all_dirs else 1\n path = os.path.join(self._strategy_implementation_model_dir,\n 'models_{0:04d}'.format(new_ind))\n os.mkdir(path)\n print('\\nCreated Implementation output at:')\n print(path)\n print('\\n')\n self.implementation_output_dir = path","title":""},{"docid":"ded942ccdf6d7a2eb1f0b6f5831515f5","score":"0.58484095","text":"def __init__(self, format=None):\n if format is not None and format in IO_REGISTRY:\n io_format = IO_REGISTRY[format.upper()]\n io_module = importlib.import_module(\"hashpy.io.\" + io_format[\"module\"]) # TODO catch keyerror, importerror\n output_fxn_name = io_format.get(\"out\", \"output\")\n self._output = getattr(io_module, output_fxn_name)\n else:\n self._output = outputSTRING","title":""},{"docid":"7027bba2414c23c80d314e562c998365","score":"0.5841829","text":"def with_output_dir(self, dirname): \n self.props['out_dir'] = dirname\n \n return self","title":""},{"docid":"3cd7a02ed78da3a4666a1b8478d09c5b","score":"0.58400136","text":"def __save_object(self):\r\n # Create directory for output results\r\n path = os.path.join('results', self.name)\r\n try: \r\n os.makedirs(path)\r\n except OSError:\r\n if not os.path.isdir(path):\r\n raise\r\n dat_f = os.path.join(path, '{}_init_analysis.dat'.format(self.name))\r\n \r\n with open(dat_f, 'w') as f:\r\n dill.dump(self, f)","title":""},{"docid":"b0f1f0e5bcd4081fce989d456d216420","score":"0.5838033","text":"def _get_output_file_path(cls) -> pathlib.Path:\n if cls.NAME is None:\n raise NotImplementedError(\n f\"Child ETL class needs to specify `cls.NAME` (currently \"\n f\"{cls.NAME}).\"\n )\n\n output_file_path = cls.DATA_PATH / \"dataset\" / f\"{cls.NAME}\" / \"usa.csv\"\n return output_file_path","title":""},{"docid":"97d1be297db35d52db0093e568bdc7e7","score":"0.5829184","text":"def create_output_folder(output_path: str) -> Path:\n path = Path(output_path)\n if not path.exists():\n path.mkdir(parents=True, exist_ok=True)\n return path","title":""},{"docid":"4287b59c4031a1edd197110e66b9d3b4","score":"0.582118","text":"def makeModelFile(self, out_path):\n modelstr = self.makeModelStr()\n with open(out_path, \"w\") as fd:\n fd.write(modelstr)","title":""},{"docid":"785f08412bf00ebcd70c7108e0ff2de6","score":"0.5818117","text":"def create(standard_out_path, nested_out_path, binary_out_path):\n\n __create_standard(standard_out_path)\n __create_nested(nested_out_path)\n __create_binary(binary_out_path)","title":""},{"docid":"8d7ba12c022b3507203a092e8a884c48","score":"0.5813484","text":"def create_new_output_from_config(self, output_config=None):\n pi = PortInstance(self, 'output', configuration=output_config)\n self.outputs.append(pi)","title":""},{"docid":"596a1cb55f8f21b5426edeb5f8567a79","score":"0.5809847","text":"def handle_outpath(self):\n out_path = self.kwargs[\"outpath\"]\n\n # should make this a pathlib.Path object - but not sure if str is assumed later on in the code\n if os.path.isdir(out_path):\n logging.debug(f\"out path set to {out_path}\")\n return out_path\n elif not os.path.isdir(out_path):\n logging.debug(f\"outpath does not exits - creating\")\n try:\n os.makedirs(out_path)\n logging.debug(f\"out path set to {out_path}\")\n return out_path\n except OSError as e:\n logging.error(\n f\"Cannot create output directory {out_path}. Please make sure you \"\n f\"have write permission. Error message: {e}\"\n )","title":""},{"docid":"f1f57880db2c61614199c62551f520b6","score":"0.58002114","text":"def __init__(self, facade, output_file_prefix=''):\n self.facade = facade\n self.output_file_prefix = output_file_prefix","title":""},{"docid":"c6fcbef3d8a68d898c655aa1812b8d95","score":"0.57888085","text":"def __init__(self, path, args):\n super(TextOutputHandler, self).__init__()\n self.path = path","title":""},{"docid":"af02932ea178e9bc47e908d2f6b4e78a","score":"0.578035","text":"def outfile_path(ctx: Context, target: Target, instance: Instance, *args: str) -> str:\n rundir = ctx.starttime.strftime(\"run.%Y-%m-%d.%H-%M-%S\")\n path = os.path.join(\n ctx.paths.pool_results, rundir, target.name, instance.name, *args\n )\n os.makedirs(os.path.dirname(path), exist_ok=True)\n\n # Maintain a 'last' symlink to latest results\n symname = os.path.join(ctx.paths.pool_results, \"last\")\n if os.path.exists(symname):\n os.unlink(symname)\n os.symlink(\n os.path.join(ctx.paths.pool_results, rundir), symname, target_is_directory=True\n )\n\n return path","title":""},{"docid":"7c2dad8c316daa9f3c6e8114e21eb7e8","score":"0.5774025","text":"def __init__(self, MRIObj, outputdir = None, tasks = ['pRF', 'FA']):\n\n # need to initialize parent class (Model), indicating output infos\n super().__init__(MRIObj = MRIObj, outputdir = outputdir, tasks = tasks)\n\n # if output dir not defined, then make it in derivatives\n if outputdir is None:\n self.outputdir = op.join(self.MRIObj.derivatives_pth, self.MRIObj.params['mri']['fitting']['FA']['fit_folder']['full_stim'])\n else:\n self.outputdir = outputdir","title":""},{"docid":"37c2fb5214f02dc5f5f8210c9cd73445","score":"0.5771118","text":"def create_object_file():\n folder = get_hash(get_full_path(file))[0:2]\n file_name = get_hash(get_full_path(file))[2:]\n file_content = read_file(get_full_path(file))\n create_file(folder, file_name, file_content)","title":""},{"docid":"c6146be068006ffe98a060753701ec9d","score":"0.5770426","text":"def get_output_path(self):\n print self.output_path\n return self.output_path","title":""},{"docid":"3bbb3cfe6219d8e8bfe175155cb49713","score":"0.57586825","text":"def set_output_to(self, output_to: PathOrString) -> None:\n if isinstance(output_to, Path):\n output_to = str(output_to)\n self.output_to = output_to\n self.create_filesystem()","title":""},{"docid":"4162302b90bcb7a7dc389318fd666148","score":"0.5755165","text":"def output_file_path(self):\r\n return self.__output_file_path","title":""},{"docid":"84d4d53a58910e493c28a3a82fcd5468","score":"0.5750242","text":"def create_as(cls, instance, path_class, **kwargs):\n if path_class or (cls is not cls.path_class()):\n return instance\n\n if instance.is_file():\n instance = instance.as_file()\n\n elif instance.is_dir():\n instance = instance.as_dir()\n\n else:\n # let's assume a file if it has an extension\n if instance.ext:\n instance = instance.as_file()\n\n return instance","title":""},{"docid":"0eb4e1fa6634597ac42c66af16f9705e","score":"0.5749699","text":"def create_temporary_file_object(self):\n try:\n self.f_rot = h5py.File(os.path.join(self.output_folder, 'intermediate_obj_rot.h5'), 'w', driver='mpio', comm=comm)\n except:\n self.f_rot = h5py.File(os.path.join(self.output_folder, 'intermediate_obj_rot.h5'), 'w')\n self.dset_rot = self.f_rot.create_dataset('obj', shape=self.full_size, dtype='float64')","title":""},{"docid":"9cc7bc855348b9a29e7e30909b0f40a0","score":"0.5747224","text":"def setup_output_dir(self):\n # output dir\n if self.args.output:\n output_dir = Path(self.args.output).expanduser()\n output_dir.mkdir(parents=True, exist_ok=True)\n else:\n output_dir = Path(\n self.args.checkpoint_path).expanduser().parent.parent\n output_dir.mkdir(parents=True, exist_ok=True)\n self.output_dir = output_dir","title":""},{"docid":"fea2ab29edcd5657e1568e0f9bfb0722","score":"0.574417","text":"def _InstanceFile(cls, instance_name):\n return utils.PathJoin(cls._ROOT_DIR, instance_name)","title":""},{"docid":"10b7d84e0c3b27d55af14a1a017a7ace","score":"0.5734924","text":"def file(self):\n if self._file is None:\n self.output.writeToDisk(path=self.path, verify=self.verify)\n self._file = Path(self.output.filePath)\n return self._file","title":""},{"docid":"6ffe9defd631eb2012bb48ef52afa206","score":"0.57344615","text":"def __init__(self, loglevel = 'Info'):\n import sys\n import os\n from datetime import datetime\n now_time = datetime.now()\n nowN = now_time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n filename = \"ipamml-dump-%s.txt\" % nowN\n abs_path = os.path.abspath(filename)\n sys.__stderr__.write(\"\\nipamml Dump:%s\\n\" % abs_path)\n self.output = open(filename,'w')","title":""},{"docid":"f847f5612cd56eeb31b5c43298cb8bb6","score":"0.57322174","text":"def output(self):\n return {\n \"target_path\" : self.config[\"target_dir\"]\n }","title":""},{"docid":"9d6b1e5a5578868b0576c30c2293f9b9","score":"0.57256955","text":"def _produceOutputFiles(self):\n pass","title":""},{"docid":"2ecc8e1b3def0d67b8a502ed5ca7c78c","score":"0.57223654","text":"def init(self, args): \n for attr in args:\n utils.updateObjAttr(self,attr,args[attr]) \n # new output directory\n if not(self.dir):\n self.dir = 'output/%s (xdim=%d, ydim=%d, discrete=%d, stochastic=%s) - %s/' \\\n % (self.problemPRS.description, self.problemPRS.dim, \n len(self.problemPRS.objectives), self.problemPRS.discreteLevel,\n str(self.problemPRS.stochastic), datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n if self.output and not(os.path.exists(self.dir)):\n os.makedirs(self.dir) \n return","title":""},{"docid":"4234b55e540b804f803f5793600a00e6","score":"0.57191","text":"def __init__(self, MRIObj, outputdir = None, tasks = ['pRF', 'FA']):\n\n # need to initialize parent class (Model), indicating output infos\n super().__init__(MRIObj = MRIObj, outputdir = outputdir, tasks = tasks)\n\n # if output dir not defined, then make it in derivatives\n if outputdir is None:\n self.outputdir = op.join(self.MRIObj.derivatives_pth, self.MRIObj.params['mri']['fitting']['FA']['fit_folder']['gain'])\n else:\n self.outputdir = outputdir","title":""},{"docid":"ff898c6900d4b7742ff706574ad35bcd","score":"0.5717662","text":"def __init__(self, input_path: Path, output_path=None, in_format=\"infer\", out_format=\"infer\", compressed=\"infer\"):\n # input path\n self.input = input_path\n\n # we assume input is not a directorys\n self._isdir = False\n\n # check if input is a directory:\n if self.input.is_dir():\n self._isdir = True\n self.all_files_in_dir = self.get_file_type_dict(self.input)\n\n # check if input is compressed - gzip or bzip2\n self.input_compressed = self.guess_input_compression(self.input)\n\n # check if input format was specified\n self.infer_input = False\n if in_format == \"infer\":\n self.infer_input = True\n\n # infer input\n if self.infer_input:\n self.input_file_type = self.guess_sequence_file_type(self.input)\n # input type was set by user\n else:\n self.input_file_type = in_format\n\n # output format must set before output\n self._accepted_output_format = [\"infer\", \"json\", \"csv\", \"tsv\", \"feather\", \"stdout\"]\n self._accepted_output_format_suffix = [\"json\", \"csv\", \"tsv\", \"feather\"]\n self._accepted_output_compression = [\"gz\", \"bz2\", \"infer\"]\n self._accepted_output_compression_suffix = [\"gz\", \"bz2\"]\n self.output_format = out_format\n self.infered_output_format = None\n self.output_compressed = compressed\n self.infered_output_compression = None\n self.output = output_path","title":""},{"docid":"bc36d7cbaf370533d36a86d243306009","score":"0.5717648","text":"def __init__(self, path_to_file):\n if not exists(path_to_file):\n with open(path_to_file, 'w') as f:\n pass\n self.file_path = path_to_file","title":""},{"docid":"b64d3b772535bc1808859876336916db","score":"0.5708106","text":"def __init__(self, path, alwaysCreate=False):\n self.path = abspath(path)\n self.alwaysCreate = alwaysCreate","title":""},{"docid":"f05b775fd3ba5bd1e405d23970a74399","score":"0.5707817","text":"def __init__(self, image, outdir, **kwargs):\n self.image = image\n self.outdir = outdir\n\n if not os.path.exists(self.outdir):\n logging.info(\"Creating output directory %s\", self.outdir)\n os.mkdir(self.outdir)\n\n self._parse(**kwargs)\n self.run()","title":""},{"docid":"d79015d50969186b6744b4afe3406c70","score":"0.5703144","text":"def make_output_structure(output_path):\n\n Path(f\"{output_path}/mosaics\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/chips/pre\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/chips/post\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/loc\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/dmg\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/over\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/shapes\").mkdir(parents=True, exist_ok=True)\n\n return True","title":""},{"docid":"27efabe675d6fe113439ad6c3a28b420","score":"0.5700462","text":"def _set_outputdir(self):\n self.OUTPUTDIR = os.path.join(os.getcwd(), \"output\")\n os.makedirs(self.OUTPUTDIR, exist_ok=True)","title":""},{"docid":"67ce357022fed3aeecca20de2ec98311","score":"0.5697593","text":"def export(self, output_dir):\n pass","title":""},{"docid":"82bbb03ac8ebbc26454e3b5ff4d79d9e","score":"0.569726","text":"def output(self):\n output_path\n return luigi.LocalTarget(output_path)","title":""},{"docid":"829701aba575918aabdbdd55bae2a227","score":"0.5680432","text":"def set_output_file(self, path):\n self.outputdir = os.path.dirname(path)\n self.sol200filepath = path","title":""},{"docid":"87bf2d0d9155b9622d7455a499931fea","score":"0.56763244","text":"def build(self, output_path):\n output_file = output_path / self.filename\n if output_file.exists():\n raise FileExistsError(f\"File already exists: {output_file}\")\n\n self._make_dist_info()\n with zipfile.ZipFile(output_file, mode=\"w\", compression=zipfile.ZIP_DEFLATED) as zip_file:\n for cur_file in self._temp_dir.glob(\"**/*\"):\n if cur_file.is_dir():\n continue\n rel_path = cur_file.relative_to(self._temp_dir)\n zinfo = zipfile.ZipInfo(str(rel_path))\n with cur_file.open(\"rb\") as src:\n zip_file.writestr(zinfo, src.read(), compress_type=zipfile.ZIP_DEFLATED)\n return output_file","title":""},{"docid":"3c627b1bc391c578146e09afa8a969c1","score":"0.5673309","text":"def _construct_arff_file(self, output_path):\n arff_file_location = output_path + self._ARFF_FILE_NAME\n with open(self._arff_file_with_path, 'w+') as arff_file:\n arff_metadata = self._generate_arff_metadata()\n arff_file.write(arff_metadata)","title":""}],"string":"[\n {\n \"docid\": \"617798d336acf639edf2f5e43857c80e\",\n \"score\": \"0.7930995\",\n \"text\": \"def __init__(self, output_path):\\n self.output_path = output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862151cf8491febec26fe8d244e68d0f\",\n \"score\": \"0.7642023\",\n \"text\": \"def __init__(self, output_path):\\r\\n Output.__init__(self)\\r\\n self._output_path = output_path\\r\\n self._output_file = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875c313fdb68d287d0ef92f8fd33693c\",\n \"score\": \"0.74678016\",\n \"text\": \"def __init__(self, path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\\n self.output_path = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b651d93c95d294a803320babfa5a77bf\",\n \"score\": \"0.70197433\",\n \"text\": \"def __init__(self, output_path):\\n\\t\\tself.out_file = output_path\\n\\t\\tself.VM_file = self.open_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34242f0616ce87b18dde8a5e8061a50d\",\n \"score\": \"0.7013511\",\n \"text\": \"def __init__(self, output_file):\\n self.output_file = output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7d43e42a2f7f3ce56565e2908966498\",\n \"score\": \"0.69653815\",\n \"text\": \"def __init__(self, input_path, output_path):\\n assert isinstance(input_path, Path)\\n assert isinstance(output_path, Path)\\n self.input_path = input_path\\n self.output_path = output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5080dd64c9f40073a28f3c0a000efd0\",\n \"score\": \"0.6903254\",\n \"text\": \"def __init__(self, output_directory: str):\\r\\n self.output_directory = output_directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6918bcc57e6aec3e025f6daab1a05a65\",\n \"score\": \"0.68974674\",\n \"text\": \"def __init__(self, output_file):\\n self.__file = output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c52ef0b403edebcd5b00a13728e1bd\",\n \"score\": \"0.68647796\",\n \"text\": \"def __init__(self, out_file):\\n self.out_file = open(out_file, 'w')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce31e7c322a358541746f236e9001d1\",\n \"score\": \"0.682767\",\n \"text\": \"def create(self):\\n self.write(self.data())\\n for key in [\\\"StandardOutPath\\\", \\\"StandardErrorPath\\\"]:\\n path = getattr(self, key, None)\\n if path and not os.path.exists(os.path.dirname(path)):\\n os.makedirs(os.path.dirname(path))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e93599e89764f6fa2d2725837747348\",\n \"score\": \"0.68144375\",\n \"text\": \"def __init__(self, exec_path: Union[str, Path], output_name: str):\\n self.exec_path = Path(exec_path)\\n self.working_dir = self.exec_path.parent\\n self.output_name = output_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e594b5b34f9c881462e159a1224a90\",\n \"score\": \"0.6727177\",\n \"text\": \"def __init__(self, output=None, path=None, verify=True):\\n self.path = path or tempfile.mkdtemp()\\n self.output = output\\n self.verify = verify\\n self.check_dependencies()\\n if isinstance(output, Output):\\n self.url = output.reference\\n self._file = None\\n elif isinstance(output, (str, Path)):\\n self.url = output\\n self._file = Path(output)\\n else:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44341fee07e09dcac33bebeb44e28b50\",\n \"score\": \"0.66921866\",\n \"text\": \"def create_output(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a5c0a57dbbc85b825e8e7136082df7\",\n \"score\": \"0.6684631\",\n \"text\": \"def __init__(self, outdir):\\n self.outdir = outdir\\n if not os.path.isdir(outdir):\\n os.mkdir(outdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8004c07f15e481e047d94fb2b90780b7\",\n \"score\": \"0.66142386\",\n \"text\": \"def set_output(self, o_path):\\n\\t\\tif not os.path.isdir(o_path):\\n\\t\\t\\tos.makedirs(o_path)\\n\\t\\tself.output_path = o_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235b3d768dfc7a7e75d6f4c35cf18190\",\n \"score\": \"0.6598007\",\n \"text\": \"def __init__(self,output_file):\\n self.f = open(output_file, 'w')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e6f4e587b2ed5022c08bdd002a45b5\",\n \"score\": \"0.6593374\",\n \"text\": \"def __init__(self, output_filename=\\\"vasprun.xml\\\"):\\n self.output_filename = output_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8a1757acd90bf4c661f9c5f9c763d8\",\n \"score\": \"0.6584197\",\n \"text\": \"def _create_output_path(self):\\n self.output_dir = os.path.abspath(os.path.expanduser(self.output_dir))\\n\\n try:\\n os.makedirs(self.output_dir)\\n except FileExistsError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f361c6e50b83e1146056ec253029574\",\n \"score\": \"0.65395606\",\n \"text\": \"def __init__(self, output):\\n self.output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0d051cd40b5f15b873f231fd7d5ff9\",\n \"score\": \"0.6457707\",\n \"text\": \"def __init__(self,outdir):\\n self.outdir = outdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b116135b4623ffe92aaf578d292dccc0\",\n \"score\": \"0.6377173\",\n \"text\": \"def __init__(self, output_directory):\\n super(FileWriter, self).__init__()\\n self._output_directory = output_directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3f221d4886f1abf04deb9900371b85\",\n \"score\": \"0.63611645\",\n \"text\": \"def init_output_file(base_path: Path, file_name: str) -> Path:\\n base_path.mkdir(parents=True, exist_ok=True)\\n\\n file = base_path / file_name\\n open(file, \\\"w\\\", encoding=\\\"utf-8\\\").close()\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208abb0ad25d2fece976e2439b57a955\",\n \"score\": \"0.6355812\",\n \"text\": \"def as_file(self, **kwargs):\\n kwargs[\\\"path_class\\\"] = self.file_class()\\n return self.as_class(**kwargs)\\n #return self.create_file(self.path, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ddbedbcb8767a498a8fd38a8593bcd\",\n \"score\": \"0.6318313\",\n \"text\": \"def instantiate(self, root_directory: Path) -> Path:\\n output_filepath = root_directory / str(self.relative_path) / str(self.filename)\\n output_filepath.parent.mkdir(exist_ok=True, parents=True) # create relative directories\\n with output_filepath.open('wb') as output:\\n output.write(self.file_content.read())\\n return output_filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58726d147c7f34a3fb3b5989938aa1f\",\n \"score\": \"0.6273526\",\n \"text\": \"def _output_dir(work_dir: P, instance: str, path):\\n return work_dir / \\\"compiled\\\" / instance / path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3bcb9d96ac335565aebf9573491fefd\",\n \"score\": \"0.6231194\",\n \"text\": \"def get_output_path(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce802d6f330f148e9ddb90f0d0a89db9\",\n \"score\": \"0.61948186\",\n \"text\": \"def __init__(self, output_folder: Path, run: int) -> None:\\n self.run = run\\n self.output_folder = Path(\\n output_folder, \\\"run_\\\"+str(run)).absolute()\\n self.weights_folder = Path(self.output_folder, \\\"weights\\\").absolute()\\n self.img_folder = Path(self.output_folder, \\\"obj\\\").absolute()\\n self.train_txt = Path(self.output_folder, \\\"train.txt\\\").absolute()\\n self.test_txt = Path(self.output_folder, \\\"test.txt\\\").absolute()\\n self.classes_txt = Path(self.img_folder, \\\"classes.txt\\\").absolute()\\n self.yolo_cfg = Path(self.output_folder, \\\"yolo.cfg\\\").absolute()\\n self.darknet_data = Path(self.output_folder, \\\"darknet.data\\\").absolute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6bbf47045cb4a28f187f624e1247c01\",\n \"score\": \"0.6185619\",\n \"text\": \"def CreatePath(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6bbf47045cb4a28f187f624e1247c01\",\n \"score\": \"0.6185292\",\n \"text\": \"def CreatePath(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef38f33c6f7d86df8c1cee5862589715\",\n \"score\": \"0.6181896\",\n \"text\": \"def __init__(self, output_dir, temp_dir, output_dict, db_params, prefix='resAnnotator'):\\n self.output_dir = os.path.abspath(output_dir)\\n self.temp_dir = os.path.abspath(temp_dir)\\n self.prefix = prefix\\n self.output_dict = output_dict\\n self.db_params = db_params\\n self.db_name = str(os.path.splitext(db_params['dbname'])[0])\\n self.file_prefix = '{}_{}'.format(str(self.prefix), self.db_name)\\n #self.output_filename = os.path.join(self.output_dir,\\\"{}_{}_results_tab.txt\\\".format(str(self.prefix),db_params['dbname']))\\n #self.output_filepath = os.path.join(self.output_dir,self.file_prefix)\\n #logger.debug('Created Output object')\\n #logger.info(repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a0131c05379d4f0879b647209258dd\",\n \"score\": \"0.6160868\",\n \"text\": \"def initialize_output_file():\\n if not os.path.exists(OUTPUT_FILE):\\n open(OUTPUT_FILE, \\\"w+\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6022420351ed033bfb445da71fabad3f\",\n \"score\": \"0.61544937\",\n \"text\": \"def __init__(self, outStr): \\n\\n self.readOutFile(outStr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087c13c591abd19f1dd1f0b4a1024b46\",\n \"score\": \"0.6153439\",\n \"text\": \"def output_path(self):\\n return '%s/%s' % (os.path.abspath(os.path.dirname(__file__) + '/../../outputs'),\\n self.identifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf3abb60d953789a2432f9577aad670\",\n \"score\": \"0.6104884\",\n \"text\": \"def __init__(self, outputfile, env, fn = None):\\n self._env = weakref.proxy(env)\\n self._fn = fn\\n if isinstance(outputfile, type(\\\"\\\")):\\n self._file = open(outputfile, \\\"w\\\")\\n else:\\n self._file = outputfile\\n if self._file is not None:\\n if not hasattr(self._file, \\\"write\\\"):\\n raise CplexError(\\\"Output object must have write method\\\")\\n elif not callable(self._file.write):\\n raise CplexError(\\\"Output object must have write method\\\")\\n if not hasattr(self._file, \\\"flush\\\"):\\n raise CplexError(\\\"Output object must have flush method\\\")\\n elif not callable(self._file.flush):\\n raise CplexError(\\\"Output object must have flush method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5468b950470ab1895d05f43d4beaa1a\",\n \"score\": \"0.60868907\",\n \"text\": \"def __init__(self, output_stream: typing.TextIO) -> None:\\n self.output_stream = output_stream\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac3163bef1e94dbf949515f5d6c2b65\",\n \"score\": \"0.6083279\",\n \"text\": \"def __init__(self, input_path=getcwd()):\\n self._input_path = input_path + \\\"/\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ed74ab07e61d5e951883d26028ad1bf\",\n \"score\": \"0.6079307\",\n \"text\": \"def __init__(self, out_dir, product):\\n if not os.path.isdir(out_dir):\\n raise ValueError(\\\"not a directory: %s\\\" % out_dir)\\n # Check that we really point to the out directory.\\n # Chomp any trailing slashes so that os.path.split() returns 'out'\\n # instead of the empty string\\n out_dir = out_dir.rstrip(\\\"/\\\")\\n if not os.path.split(out_dir)[1] == 'out':\\n raise ValueError(\\\"not pointing to 'out' directory: %s\\\" % out_dir)\\n if not os.path.isdir(\\n os.path.join(out_dir, 'target', 'product', product)):\\n raise ValueError(\\\"not a build tree for product: %s\\\" % product)\\n self._out_dir = out_dir\\n self._product = product\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf76e69d7ce25f11ae860d5466c2178\",\n \"score\": \"0.6079076\",\n \"text\": \"def _setup_output(filename):\\n output = path.join(InterfaceProvider.getPathConstructor().getVisitDirectory(), filename)\\n logger.debug('Creating user log to write to %s', output)\\n if path.exists(output) and not path.isfile(output):\\n raise ValueError('filename exists and is not a file')\\n if not path.exists(path.dirname(output)):\\n print 'Creating parent directory for user log'\\n import os\\n os.makedirs(path.dirname(output))\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d8ad790b3d84d11c5352a6253e313c\",\n \"score\": \"0.6039111\",\n \"text\": \"def output_path(self) -> Path:\\n return self._output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d8ad790b3d84d11c5352a6253e313c\",\n \"score\": \"0.6039111\",\n \"text\": \"def output_path(self) -> Path:\\n return self._output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4df360081a5c37aae2c82c1e8270b9\",\n \"score\": \"0.60366386\",\n \"text\": \"def outputPath(self):\\n return os.path.join(self.pathName, self.outputName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"068ae64fd6ea0eda57a7c015d047b9e8\",\n \"score\": \"0.60307425\",\n \"text\": \"def script_output(self):\\n\\n raise NotImplementedError(\\\"subclass should define the script output path\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65fa0d6aa9f54404dfdb852f79a2d5c5\",\n \"score\": \"0.6015264\",\n \"text\": \"def __init__(self, f_path):\\n self.results_path = f_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4ebde6922a7c2b5fb3f1dcfac27f30\",\n \"score\": \"0.60122234\",\n \"text\": \"def get_output(output: Optional[str] = None) -> Path:\\n now = datetime.now().strftime(\\\"%Y%m%d%H%M\\\")\\n name = f\\\"openmetadata_{now}_backup.sql\\\"\\n\\n if output:\\n # Create the output directory if it does not exist\\n if not Path(output).is_dir():\\n Path(output).mkdir(parents=True, exist_ok=True)\\n\\n return Path(output) / name\\n\\n return Path(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6cefe32626c087317dbec81e86d780\",\n \"score\": \"0.60108656\",\n \"text\": \"def _create_output_directory(self):\\n\\n try:\\n self.paths['temp'] = tempfile.mkdtemp()\\n except OSError as err:\\n self.exit(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501fec174bd386efa9f123152fd023c9\",\n \"score\": \"0.59976786\",\n \"text\": \"def __init__(__self__, *,\\n output_config: pulumi.Input['OutputConfigArgs'],\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"output_config\\\", output_config)\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if project is not None:\\n pulumi.set(__self__, \\\"project\\\", project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a483b96e2add9cc53949baf9e7f1e863\",\n \"score\": \"0.5996942\",\n \"text\": \"def _output_path(self) -> Path:\\n return self.parent.output_path / self.analysis_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748de0933d149239c3ceedaeb4f5b281\",\n \"score\": \"0.59801215\",\n \"text\": \"def __init__(self, output_file, root=\\\"expression\\\"):\\n self.output_file = output_file\\n self.root = root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb3eb8b026084ab0d1ba36a1ea836dd\",\n \"score\": \"0.59669256\",\n \"text\": \"def outputPath(self, outputPath):\\n self.__outputPath = outputPath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3db6b241c058b679e7f0bb45007a4d\",\n \"score\": \"0.5946757\",\n \"text\": \"def _create_output_path(self, output_path):\\n if not path.exists(path.dirname(output_path)):\\n makedirs(path.dirname(output_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5486bba813b664a0a49d421c393664ea\",\n \"score\": \"0.59370756\",\n \"text\": \"def __init__(self, output_stream: typing.TextIO) -> None:\\n self.filename: str = ''\\n self.output_stream = output_stream\\n self.segments_pointers = {'local': \\\"LCL\\\", 'argument': \\\"ARG\\\", 'this': \\\"THIS\\\", 'that': \\\"THAT\\\"}\\n\\n self.temp_addr = 5\\n self.lines_counter = 0\\n self.files_counter = 0\\n self.calls_count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bae77d9f053a8d412b22a9d53937aca\",\n \"score\": \"0.5918695\",\n \"text\": \"def _setup_output(self):\\n if not self._output_dir:\\n self._output_dir = self._s3_tmp_uri + 'output/'\\n log.info('Job will output -> %s' % self._output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcbb8ce0ce34fe0de9cb10ce0117568\",\n \"score\": \"0.5906739\",\n \"text\": \"def __init__(self, **kwargs):\\n super(MergeProcess, self).__init__(**kwargs)\\n if not self.output:\\n self.output = RasterFileIO(name='result', uri=self.get_outpath())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6737e9b411ec0be0769c258fbd754a7b\",\n \"score\": \"0.5906335\",\n \"text\": \"def Generate(self, project_configuration, output_writer):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4540620339c3f35c0a7926242686bbaa\",\n \"score\": \"0.58832103\",\n \"text\": \"def _init(args):\\n if args.verbose:\\n LOGGER.setLevel('DEBUG')\\n else:\\n LOGGER.setLevel('WARNING')\\n LOGGER.debug(\\\"args: %s\\\", args)\\n\\n if not os.path.exists(args.output_dir):\\n os.mkdir(args.output_dir)\\n LOGGER.debug(\\\"{} is created\\\".format(args.output_dir))\\n if not os.path.exists(os.path.join(args.output_dir, \\\"intermediate\\\")):\\n LOGGER.debug(\\\"{} is created\\\".format(os.path.join(args.output_dir, \\\"intermediate\\\")))\\n os.mkdir(os.path.join(args.output_dir, \\\"intermediate\\\"))\\n if not os.path.exists(os.path.join(args.output_dir, \\\"final\\\")):\\n LOGGER.debug(\\\"{} is created\\\".format(os.path.join(args.output_dir, \\\"final\\\")))\\n os.mkdir(os.path.join(args.output_dir, \\\"final\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde238bb9d2e9c46e6849af1b7daa82f\",\n \"score\": \"0.5881929\",\n \"text\": \"def _build_output(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcec81b416e809340a7aa35c920d4d6a\",\n \"score\": \"0.58746475\",\n \"text\": \"def createOutputFile():\\n outputFile = open(\\\"output/\\\" + outputName + \\\".nom\\\", \\\"w\\\")\\n outputFile.write(\\\"#### CONVERTED FROME A SIF FILE ####\\\\n\\\\n\\\")\\n return outputFile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e7460d5cf077e653bfd2f90cfe30f4\",\n \"score\": \"0.58724064\",\n \"text\": \"def _set_out_path(self):\\n outpath = dict()\\n nf = self.nf\\n tiler = self.tiler\\n outpath['info'] = '.' if nf else os.path.join(self.path_outputs, 'info') \\n outpath['visu'] = '.' if nf else os.path.join(self.path_outputs, 'visu')\\n if tiler == 'simple':\\n outpath['tiles'] = '.' if nf else os.path.join(self.path_outputs, self.name_wsi)\\n else:\\n outpath['tiles'] = '.' if nf else os.path.join(self.path_outputs, 'mat')\\n #Creates the dirs.\\n [os.makedirs(v, exist_ok=True) for k,v in outpath.items()]\\n return outpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eab1f0d13b8e6c2c370d79e66d4a4e2\",\n \"score\": \"0.58581877\",\n \"text\": \"def create_file(data, output='output_file'):\\n output_file = os.path.normpath(output)\\n with open(output_file, 'w') as testfile:\\n testfile.write(data)\\n testfile.close()\\n return output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762ca5dd7cbbe2bc985ecb973cb302dd\",\n \"score\": \"0.5856423\",\n \"text\": \"def outputPath(self):\\n return self.__outputPath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72293e2c4357dbfaa9a257da0fd5797\",\n \"score\": \"0.5855325\",\n \"text\": \"def _create_output_directory(self):\\n try:\\n os.makedirs(self._output_dir)\\n except FileExistsError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12992429bd5d7f1901a0c5754a8271f7\",\n \"score\": \"0.58538026\",\n \"text\": \"def _create_implementation_output_dir(self):\\n if not self._write_flag:\\n return\\n # Get run names\\n if self._gcp_implementation:\\n all_files = [x.name for x in self._gcp_bucket.list_blobs() if\\n x.name.find(\\n self._strategy_implementation_model_dir) > -1]\\n strip_str = self._strategy_implementation_model_dir+'/'\\n all_files = [x.replace(strip_str, '') for x in all_files]\\n all_files = [x.split('/')[0] for x in all_files]\\n if all_files:\\n max_model = max(all_files)\\n new_ind = int(max_model.replace('models_', '')) + 1\\n else:\\n new_ind = 1\\n path = os.path.join(self._strategy_implementation_model_dir,\\n 'models_{0:04d}'.format(new_ind))\\n else:\\n # Check if directory structure exists\\n if not os.path.isdir(self._ram_implementation_dir):\\n os.mkdir(self._ram_implementation_dir)\\n path = os.path.join(self._ram_implementation_dir,\\n self.__class__.__name__)\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n path = os.path.join(path, 'trained_models')\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n # Search for new ind\\n all_dirs = [x for x in os.listdir(\\n self._strategy_implementation_model_dir) if x[:7] == 'models_']\\n new_ind = int(max(all_dirs).split('_')[1]) + 1 if all_dirs else 1\\n path = os.path.join(self._strategy_implementation_model_dir,\\n 'models_{0:04d}'.format(new_ind))\\n os.mkdir(path)\\n print('\\\\nCreated Implementation output at:')\\n print(path)\\n print('\\\\n')\\n self.implementation_output_dir = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded942ccdf6d7a2eb1f0b6f5831515f5\",\n \"score\": \"0.58484095\",\n \"text\": \"def __init__(self, format=None):\\n if format is not None and format in IO_REGISTRY:\\n io_format = IO_REGISTRY[format.upper()]\\n io_module = importlib.import_module(\\\"hashpy.io.\\\" + io_format[\\\"module\\\"]) # TODO catch keyerror, importerror\\n output_fxn_name = io_format.get(\\\"out\\\", \\\"output\\\")\\n self._output = getattr(io_module, output_fxn_name)\\n else:\\n self._output = outputSTRING\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7027bba2414c23c80d314e562c998365\",\n \"score\": \"0.5841829\",\n \"text\": \"def with_output_dir(self, dirname): \\n self.props['out_dir'] = dirname\\n \\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd7a02ed78da3a4666a1b8478d09c5b\",\n \"score\": \"0.58400136\",\n \"text\": \"def __save_object(self):\\r\\n # Create directory for output results\\r\\n path = os.path.join('results', self.name)\\r\\n try: \\r\\n os.makedirs(path)\\r\\n except OSError:\\r\\n if not os.path.isdir(path):\\r\\n raise\\r\\n dat_f = os.path.join(path, '{}_init_analysis.dat'.format(self.name))\\r\\n \\r\\n with open(dat_f, 'w') as f:\\r\\n dill.dump(self, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f1f0e5bcd4081fce989d456d216420\",\n \"score\": \"0.5838033\",\n \"text\": \"def _get_output_file_path(cls) -> pathlib.Path:\\n if cls.NAME is None:\\n raise NotImplementedError(\\n f\\\"Child ETL class needs to specify `cls.NAME` (currently \\\"\\n f\\\"{cls.NAME}).\\\"\\n )\\n\\n output_file_path = cls.DATA_PATH / \\\"dataset\\\" / f\\\"{cls.NAME}\\\" / \\\"usa.csv\\\"\\n return output_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d1be297db35d52db0093e568bdc7e7\",\n \"score\": \"0.5829184\",\n \"text\": \"def create_output_folder(output_path: str) -> Path:\\n path = Path(output_path)\\n if not path.exists():\\n path.mkdir(parents=True, exist_ok=True)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4287b59c4031a1edd197110e66b9d3b4\",\n \"score\": \"0.582118\",\n \"text\": \"def makeModelFile(self, out_path):\\n modelstr = self.makeModelStr()\\n with open(out_path, \\\"w\\\") as fd:\\n fd.write(modelstr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785f08412bf00ebcd70c7108e0ff2de6\",\n \"score\": \"0.5818117\",\n \"text\": \"def create(standard_out_path, nested_out_path, binary_out_path):\\n\\n __create_standard(standard_out_path)\\n __create_nested(nested_out_path)\\n __create_binary(binary_out_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7ba12c022b3507203a092e8a884c48\",\n \"score\": \"0.5813484\",\n \"text\": \"def create_new_output_from_config(self, output_config=None):\\n pi = PortInstance(self, 'output', configuration=output_config)\\n self.outputs.append(pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"596a1cb55f8f21b5426edeb5f8567a79\",\n \"score\": \"0.5809847\",\n \"text\": \"def handle_outpath(self):\\n out_path = self.kwargs[\\\"outpath\\\"]\\n\\n # should make this a pathlib.Path object - but not sure if str is assumed later on in the code\\n if os.path.isdir(out_path):\\n logging.debug(f\\\"out path set to {out_path}\\\")\\n return out_path\\n elif not os.path.isdir(out_path):\\n logging.debug(f\\\"outpath does not exits - creating\\\")\\n try:\\n os.makedirs(out_path)\\n logging.debug(f\\\"out path set to {out_path}\\\")\\n return out_path\\n except OSError as e:\\n logging.error(\\n f\\\"Cannot create output directory {out_path}. Please make sure you \\\"\\n f\\\"have write permission. Error message: {e}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f57880db2c61614199c62551f520b6\",\n \"score\": \"0.58002114\",\n \"text\": \"def __init__(self, facade, output_file_prefix=''):\\n self.facade = facade\\n self.output_file_prefix = output_file_prefix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6fcbef3d8a68d898c655aa1812b8d95\",\n \"score\": \"0.57888085\",\n \"text\": \"def __init__(self, path, args):\\n super(TextOutputHandler, self).__init__()\\n self.path = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af02932ea178e9bc47e908d2f6b4e78a\",\n \"score\": \"0.578035\",\n \"text\": \"def outfile_path(ctx: Context, target: Target, instance: Instance, *args: str) -> str:\\n rundir = ctx.starttime.strftime(\\\"run.%Y-%m-%d.%H-%M-%S\\\")\\n path = os.path.join(\\n ctx.paths.pool_results, rundir, target.name, instance.name, *args\\n )\\n os.makedirs(os.path.dirname(path), exist_ok=True)\\n\\n # Maintain a 'last' symlink to latest results\\n symname = os.path.join(ctx.paths.pool_results, \\\"last\\\")\\n if os.path.exists(symname):\\n os.unlink(symname)\\n os.symlink(\\n os.path.join(ctx.paths.pool_results, rundir), symname, target_is_directory=True\\n )\\n\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2dad8c316daa9f3c6e8114e21eb7e8\",\n \"score\": \"0.5774025\",\n \"text\": \"def __init__(self, MRIObj, outputdir = None, tasks = ['pRF', 'FA']):\\n\\n # need to initialize parent class (Model), indicating output infos\\n super().__init__(MRIObj = MRIObj, outputdir = outputdir, tasks = tasks)\\n\\n # if output dir not defined, then make it in derivatives\\n if outputdir is None:\\n self.outputdir = op.join(self.MRIObj.derivatives_pth, self.MRIObj.params['mri']['fitting']['FA']['fit_folder']['full_stim'])\\n else:\\n self.outputdir = outputdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c2fb5214f02dc5f5f8210c9cd73445\",\n \"score\": \"0.5771118\",\n \"text\": \"def create_object_file():\\n folder = get_hash(get_full_path(file))[0:2]\\n file_name = get_hash(get_full_path(file))[2:]\\n file_content = read_file(get_full_path(file))\\n create_file(folder, file_name, file_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6146be068006ffe98a060753701ec9d\",\n \"score\": \"0.5770426\",\n \"text\": \"def get_output_path(self):\\n print self.output_path\\n return self.output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bbb3cfe6219d8e8bfe175155cb49713\",\n \"score\": \"0.57586825\",\n \"text\": \"def set_output_to(self, output_to: PathOrString) -> None:\\n if isinstance(output_to, Path):\\n output_to = str(output_to)\\n self.output_to = output_to\\n self.create_filesystem()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4162302b90bcb7a7dc389318fd666148\",\n \"score\": \"0.5755165\",\n \"text\": \"def output_file_path(self):\\r\\n return self.__output_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84d4d53a58910e493c28a3a82fcd5468\",\n \"score\": \"0.5750242\",\n \"text\": \"def create_as(cls, instance, path_class, **kwargs):\\n if path_class or (cls is not cls.path_class()):\\n return instance\\n\\n if instance.is_file():\\n instance = instance.as_file()\\n\\n elif instance.is_dir():\\n instance = instance.as_dir()\\n\\n else:\\n # let's assume a file if it has an extension\\n if instance.ext:\\n instance = instance.as_file()\\n\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eb4e1fa6634597ac42c66af16f9705e\",\n \"score\": \"0.5749699\",\n \"text\": \"def create_temporary_file_object(self):\\n try:\\n self.f_rot = h5py.File(os.path.join(self.output_folder, 'intermediate_obj_rot.h5'), 'w', driver='mpio', comm=comm)\\n except:\\n self.f_rot = h5py.File(os.path.join(self.output_folder, 'intermediate_obj_rot.h5'), 'w')\\n self.dset_rot = self.f_rot.create_dataset('obj', shape=self.full_size, dtype='float64')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc7bc855348b9a29e7e30909b0f40a0\",\n \"score\": \"0.5747224\",\n \"text\": \"def setup_output_dir(self):\\n # output dir\\n if self.args.output:\\n output_dir = Path(self.args.output).expanduser()\\n output_dir.mkdir(parents=True, exist_ok=True)\\n else:\\n output_dir = Path(\\n self.args.checkpoint_path).expanduser().parent.parent\\n output_dir.mkdir(parents=True, exist_ok=True)\\n self.output_dir = output_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea2ab29edcd5657e1568e0f9bfb0722\",\n \"score\": \"0.574417\",\n \"text\": \"def _InstanceFile(cls, instance_name):\\n return utils.PathJoin(cls._ROOT_DIR, instance_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b7d84e0c3b27d55af14a1a017a7ace\",\n \"score\": \"0.5734924\",\n \"text\": \"def file(self):\\n if self._file is None:\\n self.output.writeToDisk(path=self.path, verify=self.verify)\\n self._file = Path(self.output.filePath)\\n return self._file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffe9defd631eb2012bb48ef52afa206\",\n \"score\": \"0.57344615\",\n \"text\": \"def __init__(self, loglevel = 'Info'):\\n import sys\\n import os\\n from datetime import datetime\\n now_time = datetime.now()\\n nowN = now_time.strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n filename = \\\"ipamml-dump-%s.txt\\\" % nowN\\n abs_path = os.path.abspath(filename)\\n sys.__stderr__.write(\\\"\\\\nipamml Dump:%s\\\\n\\\" % abs_path)\\n self.output = open(filename,'w')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f847f5612cd56eeb31b5c43298cb8bb6\",\n \"score\": \"0.57322174\",\n \"text\": \"def output(self):\\n return {\\n \\\"target_path\\\" : self.config[\\\"target_dir\\\"]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6b1e5a5578868b0576c30c2293f9b9\",\n \"score\": \"0.57256955\",\n \"text\": \"def _produceOutputFiles(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ecc8e1b3def0d67b8a502ed5ca7c78c\",\n \"score\": \"0.57223654\",\n \"text\": \"def init(self, args): \\n for attr in args:\\n utils.updateObjAttr(self,attr,args[attr]) \\n # new output directory\\n if not(self.dir):\\n self.dir = 'output/%s (xdim=%d, ydim=%d, discrete=%d, stochastic=%s) - %s/' \\\\\\n % (self.problemPRS.description, self.problemPRS.dim, \\n len(self.problemPRS.objectives), self.problemPRS.discreteLevel,\\n str(self.problemPRS.stochastic), datetime.datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n if self.output and not(os.path.exists(self.dir)):\\n os.makedirs(self.dir) \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4234b55e540b804f803f5793600a00e6\",\n \"score\": \"0.57191\",\n \"text\": \"def __init__(self, MRIObj, outputdir = None, tasks = ['pRF', 'FA']):\\n\\n # need to initialize parent class (Model), indicating output infos\\n super().__init__(MRIObj = MRIObj, outputdir = outputdir, tasks = tasks)\\n\\n # if output dir not defined, then make it in derivatives\\n if outputdir is None:\\n self.outputdir = op.join(self.MRIObj.derivatives_pth, self.MRIObj.params['mri']['fitting']['FA']['fit_folder']['gain'])\\n else:\\n self.outputdir = outputdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff898c6900d4b7742ff706574ad35bcd\",\n \"score\": \"0.5717662\",\n \"text\": \"def __init__(self, input_path: Path, output_path=None, in_format=\\\"infer\\\", out_format=\\\"infer\\\", compressed=\\\"infer\\\"):\\n # input path\\n self.input = input_path\\n\\n # we assume input is not a directorys\\n self._isdir = False\\n\\n # check if input is a directory:\\n if self.input.is_dir():\\n self._isdir = True\\n self.all_files_in_dir = self.get_file_type_dict(self.input)\\n\\n # check if input is compressed - gzip or bzip2\\n self.input_compressed = self.guess_input_compression(self.input)\\n\\n # check if input format was specified\\n self.infer_input = False\\n if in_format == \\\"infer\\\":\\n self.infer_input = True\\n\\n # infer input\\n if self.infer_input:\\n self.input_file_type = self.guess_sequence_file_type(self.input)\\n # input type was set by user\\n else:\\n self.input_file_type = in_format\\n\\n # output format must set before output\\n self._accepted_output_format = [\\\"infer\\\", \\\"json\\\", \\\"csv\\\", \\\"tsv\\\", \\\"feather\\\", \\\"stdout\\\"]\\n self._accepted_output_format_suffix = [\\\"json\\\", \\\"csv\\\", \\\"tsv\\\", \\\"feather\\\"]\\n self._accepted_output_compression = [\\\"gz\\\", \\\"bz2\\\", \\\"infer\\\"]\\n self._accepted_output_compression_suffix = [\\\"gz\\\", \\\"bz2\\\"]\\n self.output_format = out_format\\n self.infered_output_format = None\\n self.output_compressed = compressed\\n self.infered_output_compression = None\\n self.output = output_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc36d7cbaf370533d36a86d243306009\",\n \"score\": \"0.5717648\",\n \"text\": \"def __init__(self, path_to_file):\\n if not exists(path_to_file):\\n with open(path_to_file, 'w') as f:\\n pass\\n self.file_path = path_to_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64d3b772535bc1808859876336916db\",\n \"score\": \"0.5708106\",\n \"text\": \"def __init__(self, path, alwaysCreate=False):\\n self.path = abspath(path)\\n self.alwaysCreate = alwaysCreate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05b775fd3ba5bd1e405d23970a74399\",\n \"score\": \"0.5707817\",\n \"text\": \"def __init__(self, image, outdir, **kwargs):\\n self.image = image\\n self.outdir = outdir\\n\\n if not os.path.exists(self.outdir):\\n logging.info(\\\"Creating output directory %s\\\", self.outdir)\\n os.mkdir(self.outdir)\\n\\n self._parse(**kwargs)\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d79015d50969186b6744b4afe3406c70\",\n \"score\": \"0.5703144\",\n \"text\": \"def make_output_structure(output_path):\\n\\n Path(f\\\"{output_path}/mosaics\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/chips/pre\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/chips/post\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/loc\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/dmg\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/over\\\").mkdir(parents=True, exist_ok=True)\\n Path(f\\\"{output_path}/shapes\\\").mkdir(parents=True, exist_ok=True)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27efabe675d6fe113439ad6c3a28b420\",\n \"score\": \"0.5700462\",\n \"text\": \"def _set_outputdir(self):\\n self.OUTPUTDIR = os.path.join(os.getcwd(), \\\"output\\\")\\n os.makedirs(self.OUTPUTDIR, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ce357022fed3aeecca20de2ec98311\",\n \"score\": \"0.5697593\",\n \"text\": \"def export(self, output_dir):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82bbb03ac8ebbc26454e3b5ff4d79d9e\",\n \"score\": \"0.569726\",\n \"text\": \"def output(self):\\n output_path\\n return luigi.LocalTarget(output_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829701aba575918aabdbdd55bae2a227\",\n \"score\": \"0.5680432\",\n \"text\": \"def set_output_file(self, path):\\n self.outputdir = os.path.dirname(path)\\n self.sol200filepath = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87bf2d0d9155b9622d7455a499931fea\",\n \"score\": \"0.56763244\",\n \"text\": \"def build(self, output_path):\\n output_file = output_path / self.filename\\n if output_file.exists():\\n raise FileExistsError(f\\\"File already exists: {output_file}\\\")\\n\\n self._make_dist_info()\\n with zipfile.ZipFile(output_file, mode=\\\"w\\\", compression=zipfile.ZIP_DEFLATED) as zip_file:\\n for cur_file in self._temp_dir.glob(\\\"**/*\\\"):\\n if cur_file.is_dir():\\n continue\\n rel_path = cur_file.relative_to(self._temp_dir)\\n zinfo = zipfile.ZipInfo(str(rel_path))\\n with cur_file.open(\\\"rb\\\") as src:\\n zip_file.writestr(zinfo, src.read(), compress_type=zipfile.ZIP_DEFLATED)\\n return output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c627b1bc391c578146e09afa8a969c1\",\n \"score\": \"0.5673309\",\n \"text\": \"def _construct_arff_file(self, output_path):\\n arff_file_location = output_path + self._ARFF_FILE_NAME\\n with open(self._arff_file_with_path, 'w+') as arff_file:\\n arff_metadata = self._generate_arff_metadata()\\n arff_file.write(arff_metadata)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1493,"cells":{"query_id":{"kind":"string","value":"47c5c3b342c8110430cd9c4b54133708"},"query":{"kind":"string","value":"Creates a point C in (ab) direction so that \\\\|aC\\\\| = L"},"positive_passages":{"kind":"list like","value":[{"docid":"5eed3aac87a36900c48ad1ea7b85b842","score":"0.5909008","text":"def extend(point_a: Tuple[float, float], point_b: Tuple[float, float],\n L: float) -> Tuple[float, float]:\n\n xa, ya = point_a\n xb, yb = point_b\n u_vec = [xb - xa, yb - ya]\n u_vec /= np.linalg.norm(u_vec)\n\n xc = xa + L * u_vec[0]\n yc = ya + L * u_vec[1]\n return xc, yc","title":""}],"string":"[\n {\n \"docid\": \"5eed3aac87a36900c48ad1ea7b85b842\",\n \"score\": \"0.5909008\",\n \"text\": \"def extend(point_a: Tuple[float, float], point_b: Tuple[float, float],\\n L: float) -> Tuple[float, float]:\\n\\n xa, ya = point_a\\n xb, yb = point_b\\n u_vec = [xb - xa, yb - ya]\\n u_vec /= np.linalg.norm(u_vec)\\n\\n xc = xa + L * u_vec[0]\\n yc = ya + L * u_vec[1]\\n return xc, yc\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"89d197710005889193a344a3915b0ba2","score":"0.5912336","text":"def tangent(A,B,C):\n AB=pointplan.len_deux_coord(A,B)\n AC=pointplan.len_deux_coord(A,C)\n BC=pointplan.len_deux_coord(B,C)\n longeur=-AB+AC+BC\n cercleC=cercle.create(C,longeur/2)\n cercle.draw_circle(cercleC)\n longeur1=AB+AC-BC\n cercleA=cercle.create(A,longeur1/2)\n cercle.draw_circle(cercleA)\n longeur3=AB-AC+BC\n cercleB=cercle.create(B,longeur3/2)\n cercle.draw_circle(cercleB)","title":""},{"docid":"408ea1b17009e9805a8b1abd548a5327","score":"0.58020055","text":"def __mul__(self, c: float):\n x = self.x * c\n y = self.y * c\n z = self.z * c\n return(Point(x, y, z))","title":""},{"docid":"a73d1005bdf309bf9bf5c2af23a4fce3","score":"0.57875377","text":"def abc_to_center_abc(a, b, c, w, h):\n x0 = w/2.\n y0 = h/2.\n return (a, b, a*x0 + b*y0 + c)","title":""},{"docid":"1a5726fe0f2e3ed98539cbb6221ff852","score":"0.5769577","text":"def make_point (x, y):\r\n return complex(x, y)","title":""},{"docid":"f28053ae5206d7f37286c33f7e0302b0","score":"0.5725666","text":"def pos(s, c):\n return s.point + c*s.vel","title":""},{"docid":"4df092c8d079dac5d6987c1eb48c1a2a","score":"0.5722327","text":"def angle_CAB(point_c, point_a, point_b):\n edge_a = Geometry.get_distance(point_c, point_b)\n edge_b = Geometry.get_distance(point_c, point_a)\n edge_c = Geometry.get_distance(point_a, point_b)\n\n acos_input = ((edge_b ** 2) + (edge_c **2) - (edge_a ** 2)) / (2 * edge_b * edge_c)\n\n if acos_input < -1:\n acos_input = -1\n if acos_input > 1:\n acos_input = 1\n return math.acos(acos_input)","title":""},{"docid":"e2638171f6b8411bd8a1720ef5609611","score":"0.56611675","text":"def make_caustic():\n\n\t\t\tfrom Caustics import Caustics as caus\n\t\t\tcaustic = caus(lens=self)\n\t\t\tcaustic.calculate(points=100)\n\t\t\treturn (caustic.x, caustic.y)","title":""},{"docid":"7b76eaed92f4ed236a0eeece4744a238","score":"0.56472087","text":"def calcBarycentricCoordinate(self, p, a, b, c): \n fabXY = float((p[1]-a[1]) * (b[0]-a[0]) - (b[1]-a[1]) * (p[0]-a[0]))\n fabXcYc = float((c[1]-a[1]) * (b[0]-a[0]) - (b[1]-a[1]) * (c[0]-a[0]))\n return fabXY / fabXcYc","title":""},{"docid":"c437f3b216ef182cc99bb2ef66d687e0","score":"0.5531534","text":"def A(x, y, z, ocx, ocy, ocz, ocpx, ocpy, ocpz, snx, sny, snz):\n return -(1.0/24.0)*x*y*z*(\n + 12. * (-2.0 + ocx**2 + ocy**2 + ocz**2) *\n (ocpx*snx + ocpy*sny + ocpz*snz)\n - 6.0 * ((-2.0 + 2.0 * ocpx * ocx + ocx**2 + ocy**2 + ocz**2)\n * snx + 2.0 * ocx * (ocpy * sny + ocpz * snz)) * x\n + 4.0 * (ocpx*snx + 2.0 * ocx*snx + ocpy *\n sny + ocpz*snz) * x**2 - 3.0 * snx * x**3\n - 6.0 * (2.0 * ocpx * ocy * snx + (-2.0 + ocx**2 + 2.0 * ocpy *\n ocy + ocy**2 + ocz**2) * sny\n + 2.0 * ocpz * ocy * snz) * y\n + 6.0 * (ocy*snx + ocx*sny)*x*y\n - 2.0 * sny*x**2*y + 4.0*(\n ocpx*snx + ocpy*sny + 2.0 * ocy * sny + ocpz*snz) * y**2\n - 2.0 * snx*x*y**2 - 3.0*sny*y**3\n - 6.0 * (2.0*ocpx*ocz*snx + 2.0*ocpy*ocz*sny +\n (-2.0 + ocx**2 + ocy**2 + 2*ocpz*ocz + ocz**2)*snz)*z\n + 6.0 * (ocz*snx + ocx*snz)*x*z - 2.0*snz*x**2*z +\n 6.0*(ocz*sny + ocy*snz)*y*z - 2.0*snz*y**2*z\n + 4.0 * (ocpx*snx + ocpy*sny + (ocpz + 2.0 * ocz)*snz) *\n z**2 - 2.0*snx*x*z**2 - 2.0*sny*y*z**2 - 3.0*snz*z**3\n )","title":""},{"docid":"5e732beef5082e3c9166409c941c54b5","score":"0.55264735","text":"def startingPoint(G, c, A, b, guess):\n m,n = A.shape\n x0, y0, l0 = guess\n\n # initialize linear system\n N = np.zeros((n+m+m, n+m+m))\n N[:n,:n] = G\n N[:n, n+m:] = -A.T\n N[n:n+m, :n] = A\n N[n:n+m, n:n+m] = -np.eye(m)\n N[n+m:, n:n+m] = np.diag(l0)\n N[n+m:, n+m:] = np.diag(y0)\n rhs = np.empty(n+m+m)\n rhs[:n] = -(G.dot(x0) - A.T.dot(l0)+c)\n rhs[n:n+m] = -(A.dot(x0) - y0 - b)\n rhs[n+m:] = -(y0*l0)\n\n sol = la.solve(N, rhs)\n dx = sol[:n]\n dy = sol[n:n+m]\n dl = sol[n+m:]\n\n y0 = np.maximum(1, np.abs(y0 + dy))\n l0 = np.maximum(1, np.abs(l0+dl))\n\n return x0, y0, l0","title":""},{"docid":"fca89e8d6adedf9a74a141de22c8da0e","score":"0.55216974","text":"def B(x, y, z, ocpx, ocpy, ocpz, ow):\n return 1.0/(6.0 * ow**2) * (x*y*z) * \\\n (\n + x**2 + y**2 + z**2\n - 3.0 * (\n + ocpx * x\n + ocpy * y\n + ocpz * z\n )\n + 3.0 * (ocpx**2 + ocpy**2 + ocpz**2)\n - 6.0 * ow**2\n )","title":""},{"docid":"3921dd14847aaadebed3e410c217425a","score":"0.54290444","text":"def startingPoint(G, c, A, b, guess):\n m,n = A.shape\n x0, y0, l0 = guess\n\n N = np.zeros((n+m+m, n+m+m))\n N[:n,:n] = G\n N[:n, n+m:] = -A.T\n N[n:n+m, :n] = A\n N[n:n+m, n:n+m] = -np.eye(m)\n N[n+m:, n:n+m] = np.diag(l0)\n N[n+m:, n+m:] = np.diag(y0)\n rhs = np.empty(n+m+m)\n rhs[:n] = -(G.dot(x0) - A.T.dot(l0)+c)\n rhs[n:n+m] = -(A.dot(x0) - y0 - b)\n rhs[n+m:] = -(y0*l0)\n\n sol = la.solve(N, rhs)\n dx = sol[:n]\n dy = sol[n:n+m]\n dl = sol[n+m:]\n\n y0 = np.maximum(1, np.abs(y0 + dy))\n l0 = np.maximum(1, np.abs(l0+dl))\n\n return x0, y0, l0","title":""},{"docid":"05d7398f40d7fef99d7d5be348a59d01","score":"0.5386732","text":"def ApexConversionCoefficient(v_a, l_a, b_a):\n l_a = l_a * deg2rad\n b_a = b_a * deg2rad\n X = v_a * np.cos(l_a) * np.cos(b_a)\n Y = v_a * np.sin(l_a) * np.cos(b_a)\n Z = v_a * np.sin(b_a)\n return [X, Y, Z]","title":""},{"docid":"8528465d92ffca935fc4e52b77088475","score":"0.53191376","text":"def build_C(self):\n F = self.F\n ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\n ctrl_pts_y_top = -1 * np.ones(int(F / 2))\n ctrl_pts_y_bottom = np.ones(int(F / 2))\n ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\n ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\n C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\n return C # F x 2","title":""},{"docid":"784015a7cddc6c148791ce8f6a58d4ea","score":"0.5316926","text":"def sCA(ax, ay, cx, cy):\n return sqrt(((int(ax) - int(cx)) ** 2) + ((int(ay) - int(cy)) ** 2))","title":""},{"docid":"71d86d502689f1e6352432f595a762fc","score":"0.52961034","text":"def cria_coordenada(r, c):\r\n if not (isinstance(r, int) and isinstance(c, int) and 0 <= r <= 2 and 0 <= c <= 2):\r\n raise ValueError('cria_coordenada: argumentos invalidos.')\r\n return r, c","title":""},{"docid":"29550c9aaa09f222e0efd3ddb10fa5a1","score":"0.5288331","text":"def _coordinate_conversion(self):\n self.gamma_x = (self.east_B - self.east_A) / \\\n (self.crline_B - self.crline_A)\n self.beta_x = (self.east_C - self.east_B) / \\\n (self.inline_C - self.inline_B)\n self.alpha_x = self.east_A - \\\n self.beta_x * self.inline_A - \\\n self.gamma_x * self.crline_A\n self.gamma_y = (self.north_B - self.north_A) / \\\n (self.crline_B - self.crline_A)\n self.beta_y = (self.north_C - self.north_B) / \\\n (self.inline_C - self.inline_B)\n self.alpha_y = self.north_A - \\\n self.beta_y * self.inline_A - \\\n self.gamma_y * self.crline_A","title":""},{"docid":"03e94d242e1a71e258912502f9a6f729","score":"0.5278674","text":"def expand_C(C_voigt):\n \n ## Voight notation: xx, yy, zz, yz, xz, xy\n mapping = [[0,0],[1,1],[2,2],[1,2],[0,2],[0,1]] \n \n C = np.zeros((3,3,3,3)) \n for i in range(3):\n for j in range(3):\n for k in range(3):\n for l in range(3):\n C[i,j,k,l] = C_voigt[mapping.index([min(i,j),max(i,j)]),\n mapping.index([min(k,l),max(k,l)])] \n \n return C","title":""},{"docid":"dab073feea75aeb672df6675a3cc3273","score":"0.5273901","text":"def proj(self, x, c):\n return x","title":""},{"docid":"2f10a2b2824c9d271190c1eb1861a64b","score":"0.5208139","text":"def conv_coord(c: Coord) -> Point:\n if isinstance(c, Point):\n return c\n return Point(c[0], c[1])","title":""},{"docid":"8c397c1536fb3f61324700367ff2fac5","score":"0.51899934","text":"def make_point(coordinates):\n # Components in floats\n x = float(coordinates[0])\n y = float(coordinates[1])\n z = float(coordinates[2])\n return gp_Pnt(x, y, z)","title":""},{"docid":"dbb3d9cc0c9159d780c1c83e28961a50","score":"0.5180762","text":"def _build_C(self, F):\n ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\n ctrl_pts_y_top = -1 * np.ones(int(F / 2))\n ctrl_pts_y_bottom = np.ones(int(F / 2))\n ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\n ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\n C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\n return C # F x 2","title":""},{"docid":"9e053c7bd285924ece8ad46be25564f4","score":"0.5176157","text":"def center_small_entoure(cercleA,cercleB,cercleC):\n k1=1/(cercle.get_radius(cercleA))\n k2=1/(cercle.get_radius(cercleB))\n k3=-(1/(cercle.get_radius(cercleC)))\n z1=pointplan.coord_to_complex(cercle.get_center(cercleA))\n z2=pointplan.coord_to_complex(cercle.get_center(cercleB))\n z3=pointplan.coord_to_complex(cercle.get_center(cercleC))\n base=(k1*z1)+(k2*z2)+(z3*k3)\n k4=rayons_cercle_entoure(cercleA,cercleB,cercleC)\n baseinterieur=(k1*k2*z1*z2)+(k2*k3*z2*z3)+(k1*k3*z1*z3)\n z4_1=k4*(base+2*cmath.sqrt(baseinterieur))\n z4_2=k4*(base-2*cmath.sqrt(baseinterieur))\n return (pointplan.complex_to_coord(z4_1),pointplan.complex_to_coord(z4_2))","title":""},{"docid":"06fb14377892ce497085a939a8cd9368","score":"0.51698905","text":"def get_point(self, c):\n c = self.comparator.expand_coordinates(c)\n shift = c / self.n @ self.comparator.atoms.cell\n positions = -self.comparator.positions + shift\n return self.comparator.calculate_rmsd(positions)","title":""},{"docid":"bbf38f6c1cb582a8a2c233f89bb01b9b","score":"0.5161089","text":"def push_from_center(P,C,d):\n a = np.arctan2(P[0]-C[0],P[1]-C[1])\n dx = np.sin(a)*d\n dy = np.cos(a)*d\n \n \n return [P[0]+dx,P[1]+dy]","title":""},{"docid":"fe4b136b9bdafe2ced11ffc623288259","score":"0.51540864","text":"def _get_3rd_point(a, b):\n direction = a - b\n third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)\n\n return third_pt","title":""},{"docid":"dbb4e989dff8feaf821601aa0d83b351","score":"0.51429945","text":"def center_small_inscrit(cercleA,cercleB,cercleC):\n k1=1/(cercle.get_radius(cercleA))\n k2=1/(cercle.get_radius(cercleB))\n k3=(1/(cercle.get_radius(cercleC)))\n z1=pointplan.coord_to_complex(cercle.get_center(cercleA))\n z2=pointplan.coord_to_complex(cercle.get_center(cercleB))\n z3=pointplan.coord_to_complex(cercle.get_center(cercleC))\n base=(k1*z1)+(k2*z2)+(z3*k3)\n k4=rayons_cercle(cercleA,cercleB,cercleC)\n baseinterieur=(k1*k2*z1*z2)+(k2*k3*z2*z3)+(k1*k3*z1*z3)\n z4_1=k4*(base+2*cmath.sqrt(baseinterieur))\n return pointplan.complex_to_coord(z4_1)","title":""},{"docid":"35a794a71bbb3b4e4d817b5ab57ed864","score":"0.5128175","text":"def calculate_position(c, t):\n#divido il perscorso in tanti piccoli punti\n\n return c[0] * t**5 + c[1] * t**4 + c[2] * t**3 + c[3] * t**2 + c[4] * t + c[5]","title":""},{"docid":"4d789508c6cabd43f0e475407a325d89","score":"0.5127938","text":"def point(self, a, b=None, type='focalxy'):\n\n # allow us to input either a coordinate object, or a pair of coordinates\n # logger.info('Cartographer pointing a new coordinate:', 1)\n try:\n assert (a.__class__.__base__.__name__ == 'position')\n type = a.__class__.__name__\n temp = a\n a, b = temp.a, temp.b\n del temp\n except:\n pass\n # logger.info('using [{a}, {b}, {type}] as input'.format(a=a,b=b, type=type), 2)\n\n\n # make sure the input coordinates can be understood\n # assert(type in self.possibleinputs)\n # logger.info('input coordinate is parsable', 2)\n\n # remove any previous coordinate definitions\n # logger.info('clearing previous coordinates', 2)\n for coord in self.possibleinputs:\n try:\n del self.__dict__[coord]\n except:\n pass\n\n # assign the coordinates to Cartographer's memory\n self.input = type\n self.coordinate = eval('{type}(a,b,cartographer=self)'.format(a=a, b=b, type=type))\n self.__dict__[self.input] = self.coordinate\n logger.debug('pointing at {0}'.format(self.coordinate))\n return self.coordinate","title":""},{"docid":"0adac0c024d66fff0ff55120045b1220","score":"0.5127829","text":"def l2pa(a):\n\tr=PointArray(len(a))\n\tfor i,j in enumerate(a):\n\t\tr.set_vector_at(i,Vec3f(j[0],j[1],0),j[2])\n\t\n\treturn r","title":""},{"docid":"7b5110be8077ccfef98dc02b71097fdf","score":"0.5111919","text":"def _get_3rd_point(a, b):\n assert len(a) == 2\n assert len(b) == 2\n direction = a - b\n third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)\n\n return third_pt","title":""},{"docid":"83c8a146ce139b499f17e8dc7729a360","score":"0.51105744","text":"def eval2seq(points, c, x):\n p1, p2 = points\n if p1 > p2:\n p1, p2 = p2, p1\n x1, y1 = p1\n x2, y2 = p2\n d = x - x2\n\n return (d + 1) * y2 - d * y1 + d * (d + 1) * c","title":""},{"docid":"7afd6ef093f2b45b11fba506be447bb7","score":"0.5097706","text":"def makePointGeom(*args):\n return _collide.makePointGeom(*args)","title":""},{"docid":"36503bb813b49957f4b27bcd075bd953","score":"0.50870234","text":"def get_xcp(lcap,lcyl,ltot,xcg0,Acap,Acyl,delta_cg):\n\txcp0=(Acap*1/4*lcap+Acyl*(lcap+0.5*lcyl))/(Acap+Acyl)\n\txcp=ltot-xcg0-xcp0+delta_cg\n\treturn xcp","title":""},{"docid":"f773876d3d4d9b883f83600e53f298e2","score":"0.50827265","text":"def get_c(box_plus, lx,ly,lz,charge,w):\n lx += box_plus * 2.0 * 1.8897\n ly += box_plus * 2.0 * 1.8897\n lz += box_plus * 2.0 * 1.8897\n v_sample = lx*ly*lz / (1.8897 * 1.8897 * 1.8897) / (1e8 * 1e8 * 1e8)\n c = charge * charge / (2.0 * w) * (1.6 * 1e-19 * 1.6 * 1e-19) / v_sample\n return c","title":""},{"docid":"3ce1846ad44d5b53d1b6de478216d714","score":"0.5082113","text":"def conv_ang(self):\n x = self.head_loc[0] - self.conv_loc[0]\n y = self.head_loc[1] - self.conv_loc[1]\n return arctan2(y, x) + pi - self.thic_ang()","title":""},{"docid":"637fbb9bebbc71b08258233ca2b682c5","score":"0.5081217","text":"def cauchy_point(gk, Bk, deltak):\r\n # calculate ti_k\r\n ti_k = 0\r\n temp = np.dot(np.transpose(gk), np.dot(Bk, gk))\r\n if temp <= 0:\r\n ti_k = 1\r\n else:\r\n ti_k = min(1, (np.linalg.norm(x=gk, ord=2)**3)/(deltak*temp))\r\n\r\n # p cauchy\r\n pc_k = -(ti_k)*(deltak/np.linalg.norm(x=gk, ord=2)) * gk\r\n\r\n return pc_k","title":""},{"docid":"697ed7f957439fe8d8b23c6cd1c71341","score":"0.50804514","text":"def c_to_ab(a, b, sum):\n return - 1.0 / 2 * (2 * a * b - sum ** 2) / float(sum)","title":""},{"docid":"e64158c60b23627308bfd2b70f13c8b9","score":"0.5068203","text":"def pointPosition(local, world):","title":""},{"docid":"4e63abe9e14c6f6b234a808dacd59121","score":"0.5067843","text":"def aabb_to_floor_point(X):\n return general_transform(X, lambda x,y,w,h: (x + w/2, y + h))","title":""},{"docid":"230d849ca648c07e9bdac2a78b82dc12","score":"0.5061441","text":"def create_laplacian_2d(nx, ny, lx, ly, pbc=True):\n if type(nx) != int or type(ny) != int:\n raise TypeError('We need an integer')\n if type(lx) != int and type(lx) != float:\n raise TypeError('We need a number')\n if type(ly) != int and type(ly) != float:\n raise TypeError('We need a number')\n if nx < 2 or ny < 2:\n raise ValueError('We need at least two grid points in each direction')\n if nx / lx < 0 or ny / ly < 0:\n raise ValueError('We need positive lengths')\n if pbc not in (True, False):\n raise TypeError('We need a boolean as pbc')\n \n n = nx * ny # total number of grid points\n laplacian = np.zeros((n, n))\n hx = (lx / nx) ** 2\n hy = (ly / ny) ** 2\n \n for i in range(n):\n laplacian[i, i] -= (2 / hx + 2 / hy)\n for j in range(ny - 1):\n for i in range(nx):\n laplacian[j * nx + i, (j + 1) * nx + i] += 1 / hy\n laplacian[(j + 1) * nx + i, j * nx + i] += 1 / hy\n for j in range(ny):\n for i in range(nx - 1):\n laplacian[j * nx + i, j * nx + i + 1] += 1 / hx\n laplacian[j * nx + i + 1, j * nx + i] += 1 / hx\n\n if pbc:\n for i in range(ny):\n laplacian[i, ny * nx - ny + i - 1] += 1 / hy\n laplacian[ny * nx - ny + i - 1, i] += 1 / hy\n for i in range(ny - 1):\n laplacian[i * nx, (i + 1) * nx - 1] += 1 / hx\n laplacian[(i + 1) * nx - 1, i * nx] += 1 / hx\n\n return laplacian","title":""},{"docid":"420f07439ff11eb1cacf549c0b2c2388","score":"0.5061317","text":"def deformation(p,c,r):\n def distance(p,q):\n \"\"\"distance entre les 2 points p et q\"\"\"\n return ((q[0]-p[0])**2 + (q[1]-p[1])**2 + (q[2]-p[2])**2)**0.5\n d = distance(p,c) \n if d >= r:\n res = p\n else:\n if d > EPS: \n facteur = r/d\n else:\n facteur = 1\n x2 = c[0] + (p[0]-c[0]) * facteur\n y2 = c[1] + (p[1]-c[1]) * facteur\n z2 = c[2] + (p[2]-c[2]) * facteur\n res = (x2,y2,z2)\n return res","title":""},{"docid":"8647917b50afe4162b00303dcf2912e4","score":"0.50439364","text":"def point_aleatoire(largeur=800, hauteur=600):\n return Point(uniform(0, largeur), uniform(0, hauteur))","title":""},{"docid":"f9ad6a45ea2405081b42e7628b5a9817","score":"0.5041951","text":"def calculatePosition(T_B_Cabinet, x, y, z):\n (rotX, rotY, rotZ) = T_B_Cabinet.M.GetRPY()\n\n posX = T_B_Cabinet.p.x() + math.cos(rotZ)*x - math.sin(rotZ)*y\n posY = T_B_Cabinet.p.y() + math.sin(rotZ)*x + math.cos(rotZ)*y\n posZ = T_B_Cabinet.p.z() + z/2.0 - 0.23\n alpha = - math.pi + rotZ\n\n if alpha < -math.pi:\n alpha = 2*math.pi + alpha\n\n return [posX, posY, posZ, alpha]","title":""},{"docid":"5bb4399658678d4570fe148e1926a0bd","score":"0.5041624","text":"def create_point(self):\n # see if line intersects plane x==1\n planex1 = Plane(1,0,0,-1)\n # xxx need to make sure plane is not parallel to line\n return PointFromIntersectingPlaneAndLine( planex1, self)","title":""},{"docid":"6db8de8f7acca706abedd160660b268c","score":"0.50278395","text":"def calc_landmark_position(particle, z):\n lm = np.zeros((1, 2))\n lm[0, 0] = particle.x + z[0] * cos(z[1] + particle.theta)\n lm[0, 1] = particle.y + z[0] * sin(z[1] + particle.theta)\n return lm","title":""},{"docid":"929355939a303004e89f504dec388ab9","score":"0.502005","text":"def _make_cnots(self, anc_l):\n\n (north_dict, east_dict, south_dict, west_dict) = ({}, {}, {},\n {})\n for (m, n) in anc_l:\n if m > 0 and n < self.dist:\n north_dict[(m, n)] = (m - 1, n)\n if m < self.dist and n < self.dist:\n east_dict[(m, n)] = (m, n)\n if m < self.dist and n > 0:\n south_dict[(m, n)] = (m, n - 1)\n if m > 0 and n > 0:\n west_dict[(m, n)] = (m - 1, n - 1)\n return (north_dict, east_dict, south_dict, west_dict)","title":""},{"docid":"dd97a90ff8f7b095f0468dcbbe666bd6","score":"0.50170493","text":"def aabb_to_center_point(X):\n return general_transform(X, lambda x,y,w,h: (x + w/2, y + h/2))","title":""},{"docid":"0e4fb8f2bef6cfe0ece1af7fedc20e99","score":"0.5015521","text":"def absoluteBCPIn(anchor, BCPIn):\n\treturn (BCPIn[0] + anchor[0], BCPIn[1] + anchor[1])","title":""},{"docid":"03538f47f41563a05d3c659ea8d89f9d","score":"0.5010013","text":"def f2c(ctemp):\r\n ftemp = (5 * (ctemp + 40) / 9) - 40\r\n\r\n return ftemp","title":""},{"docid":"f9b1748b4330cddffe1daef712e16c7e","score":"0.50080925","text":"def get_3rd_point(a, b):\n direct = a - b\n return b + np.array([-direct[1], direct[0]], dtype=np.float32)","title":""},{"docid":"fb973c56b324b514b768cb0cbefb3b22","score":"0.5006531","text":"def _comp_point_coordinate(self):\n Rbo = self.get_Rbo()\n\n H1 = self.get_H1()\n\n # alpha is the angle to rotate Z0 so ||Z1,Z8|| = W0\n alpha = float(arcsin(self.W0 / (2 * Rbo)))\n\n # comp point coordinate (in complex)\n Z0 = Rbo * exp(1j * 0)\n Z1 = Z0 * exp(1j * alpha)\n\n if self.is_outwards():\n Z2 = Z1 + self.H0\n Z3 = Z2 + H1 + (self.W1 - self.W0) * 1j / 2.0\n Z4 = Z3 + (self.H2 - self.R1) + (self.W2 - self.W1) / 2.0 * 1j\n Z5 = Z4 + self.R1 - self.R1 * 1j\n rot_sign = 1 # Rotation direction for Arc1\n else: # inward slot\n Z2 = Z1 - self.H0\n Z3 = Z2 - H1 + (self.W1 - self.W0) * 1j / 2.0\n Z4 = Z3 - (self.H2 - self.R1) + (self.W2 - self.W1) / 2.0 * 1j\n Z5 = Z4 - self.R1 - self.R1 * 1j\n rot_sign = -1 # Rotation direction for Arc1\n\n # symetry\n Z6 = Z5.conjugate()\n Z7 = Z4.conjugate()\n Z8 = Z3.conjugate()\n Z9 = Z2.conjugate()\n Z10 = Z1.conjugate()\n return [Z10, Z9, Z8, Z7, Z6, Z5, Z4, Z3, Z2, Z1, rot_sign]","title":""},{"docid":"a88be767d5733f88870556e73c2cae54","score":"0.50050044","text":"def point(self, t):\n # algebraically equivalent to\n # P0*(1-t)**3 + 3*P1*t*(1-t)**2 + 3*P2*(1-t)*t**2 + P3*t**3\n # for (P0, P1, P2, P3) = self.bpoints()\n return self.start + t*(\n 3*(self.control1 - self.start) + t*(\n 3*(self.start + self.control2) - 6*self.control1 + t*(\n -self.start + 3*(self.control1 - self.control2) + self.end\n )))","title":""},{"docid":"295760f651417fefe75a980c6124c3be","score":"0.50049335","text":"def f(Ui, Cai):\r\n\treturn Ui + delta_z * ((-Kv * Cai)/B)","title":""},{"docid":"d4b1886b4351156bb727e560a4d8fe85","score":"0.49930733","text":"def prueba_clase():\n \n \n pos_ini = (0, 1, 0, 1, 0,\n 0, 0, 1, 1, 0,\n 0, 0, 0, 1, 1,\n 0, 0, 1, 1, 1,\n 0, 0, 0, 1, 1)\n\n pos_a0 = (1, 0, 0, 1, 0,\n 1, 0, 1, 1, 0,\n 0, 0, 0, 1, 1,\n 0, 0, 1, 1, 1,\n 0, 0, 0, 1, 1)\n\n pos_a4 = (1, 0, 0, 0, 1,\n 1, 0, 1, 1, 1,\n 0, 0, 0, 1, 1,\n 0, 0, 1, 1, 1,\n 0, 0, 0, 1, 1)\n\n pos_a24 = (1, 0, 0, 0, 1,\n 1, 0, 1, 1, 1,\n 0, 0, 0, 1, 1,\n 0, 0, 1, 1, 0,\n 0, 0, 0, 0, 0)\n\n pos_a15 = (1, 0, 0, 0, 1,\n 1, 0, 1, 1, 1,\n 1, 0, 0, 1, 1,\n 1, 1, 1, 1, 0,\n 1, 0, 0, 0, 0)\n\n pos_a12 = (1, 0, 0, 0, 1,\n 1, 0, 0, 1, 1,\n 1, 1, 1, 0, 1,\n 1, 1, 0, 1, 0,\n 1, 0, 0, 0, 0)\n\n\n entorno = Lights_out(pos_ini)","title":""},{"docid":"1b020833114e08976d74e0ed547b0af6","score":"0.49900976","text":"def costcone(x0,x1,y0,y1,a,b):\n\n\n if (a/(2.0*b)*abs(x0-y0))< np.pi:\n return 4*b**2*(y1+x1-2*np.sqrt(y1*x1)*np.cos(a/(2.0*b)*(x0-y0)))\n else:\n return 4*b**2*(y1+x1+2*np.sqrt(y1*x1))","title":""},{"docid":"7eb1c9590c537c46d12085627312a9d6","score":"0.49885118","text":"def createFromPoint(point,color=mycolors.WHITE,width=1,arrow=[0.1,0.5]):\n return Vector(point.x,point.y,color=color,width=width,arrow=arrow)","title":""},{"docid":"320df5b2ffaf4b6e7fcb5bcdcfbeb939","score":"0.49841446","text":"def find_vertex(a, b, c):\n\n delta = find_delta(a, b, c)\n\n vertex = ( -b/(2*a), -(delta/(4*a)) )\n return vertex","title":""},{"docid":"965c73e33f4982c4056311584bfed475","score":"0.49791396","text":"def overlap_ss(Ax, Ay, Az, Cx, Cy, Cz, alpha_bra, alpha_ket, c1, c2):\n A = np.array([Ax, Ay, Az])\n C = np.array([Cx, Cy, Cz])\n ss = ((np.pi / (alpha_bra + alpha_ket))**(3/2) * np.exp((-alpha_bra * alpha_ket * np.dot(A-C, A-C)) / (alpha_bra + alpha_ket)))\n return ss * c1 * c2","title":""},{"docid":"1b4ea4b7d2690b491ad67f448d293bba","score":"0.49661538","text":"def V_corner_long(A, B, C):\n return m.pi/2*B*C**2 + m.pi**2/8*(A-B)*C**2","title":""},{"docid":"60e723fb6504829215bb63194f170159","score":"0.49635208","text":"def lattice_to_coords(c):\n new_c = []\n for p in c:\n x_coord = p[1] * .5 + p[0]\n y_coord = p[1] * ( math.sqrt(3) / 2)\n new_c.append( ( round(x_coord,3), round(y_coord,3) ) )\n return new_c","title":""},{"docid":"1c88837e31a2f23ea2267e257703def1","score":"0.4963375","text":"def gen_center():\n v = [random.choice(C),random.choice(C),random.choice(C)]\n return v","title":""},{"docid":"d57c58a4743c3ab888fe883c69d89bb7","score":"0.49522927","text":"def from_centers(self, name, c):\n a = self.get_var(name)\n a.v()[:,:] = a.v() + a.grid.dx**2*a.lap()/24.0","title":""},{"docid":"73d504083c391e3523468c05f697604a","score":"0.49476403","text":"def translate_cord(self, cord):\n x = cord[0]\n y = cord[1]\n\n x = ord(x) - ord(\"a\")\n y = abs(int(y) - 8)\n\n return x, y","title":""},{"docid":"6fa0b63a02ca972d3577b3c00a77b54e","score":"0.4930278","text":"def arbitrary_Crotation(x, y, z):\n c = np.cos(y / 2)\n s = np.sin(y / 2)\n return np.array(\n [\n [1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],\n [0, 0, np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c],\n ]\n )","title":""},{"docid":"7d59953a219a0e550a239c5b289f2634","score":"0.49295154","text":"def beale(position):\n x, y = position\n return (1.5 - x + x * y) ** 2 + (2.25 - x + x * y ** 2) ** 2 + (2.625 - x + x * y ** 3) ** 2","title":""},{"docid":"2337def17ddc49f2fd275460cfede022","score":"0.49275446","text":"def newCTCenter(self, otherC, a, b):\n if self.is_cistrans and DEBUG:\n ## That would make this carbon the center of an allene!\n ## Keeping track of stereochem for allenes is Hard.\n ## TODO: Make it so that we can in fact handle allenes?\n raise AlleneError(\"Attempt to add extra CTcenter to %s\" % str(self))\n if not (self.neighbors[otherC] == 2 and otherC.neighbors[self] == 2):\n if DEBUG:\n raise StandardError(\"cis-trans center without double bond\")\n else:\n return\n self.CTotherC = otherC\n self.CTa = a\n self.CTb = b","title":""},{"docid":"15fbbbf1366c565314dd04ac8b82422b","score":"0.49266827","text":"def coor(n,a,b):\n coordinates = []\n length = (b-a)/float(n+1)\n for i in range(n+1):\n coordinates.append(a + length * (i + 1))\n return coordinates","title":""},{"docid":"a9a687fa8b43a5162f19a7d454282229","score":"0.4923","text":"def fn(point: Point, i: int, list_: List[Point]) -> str:\n # Start control point\n cp = control_point_calc(list_[i - 1], list_[i - 2], point)\n\n # End control point\n try:\n next_ = list_[i + 1]\n except IndexError:\n next_ = None\n\n cpe = control_point_calc(\n current=point, previous=list_[i - 1], next_=next_, reverse=True\n )\n\n return f\"C {cp.x},{cp.y} {cpe.x},{cpe.y} {point.x},{point.y}\"","title":""},{"docid":"a3a90bc95e3a1a4ed7d9237bb6b35877","score":"0.4918844","text":"def crossProdOrientation(a: Point, b: Point, c: Point):\n return (b[1] - a[1]) * (c[0] - a[0]) - (b[0] - a[0]) * (c[1] - a[1])","title":""},{"docid":"81e51b534420098023676411ad2ead3c","score":"0.4915555","text":"def create_point_around_centre_of_polyline_with_pts_list(poly_pts, start_Pt, left_length):\n global aim_pt\n length = measure_langth_of_bezier(poly_pts)\n aim = (length-left_length)/2\n\n ## select the starting point ##\n dis = start_Pt.distance_to_point(poly_pts[0])\n dis_ = start_Pt.distance_to_point(poly_pts[-1])\n if dis <= dis_:\n poly_pts = poly_pts\n elif dis > dis_:\n poly_pts.reverse()\n\n pts_string = zip(poly_pts[:-1], poly_pts[1:])\n measure = 0\n for pre_pt, next_pt in pts_string:\n distance = pre_pt.distance_to_point(next_pt)\n measure += distance\n if measure > aim:\n extra = measure - aim\n aim_pt = pre_pt * (extra/distance) + next_pt * (1-(extra/distance))\n break\n return aim_pt","title":""},{"docid":"ac686ca3bbb226af49b31a214a144e8f","score":"0.49102253","text":"def L(x, y, z, Nx1, Nx0, Ny1, Ny0, Nz1, Nz0):\n return C([x, y, z]) * P(x, y, z, Nx1, Nx0, Ny1, Ny0, Nz1, Nz0)","title":""},{"docid":"1e4f19e1f5b0e4bd1a83dbf5a7057eff","score":"0.49047747","text":"def xyzFoci(self):\n peri = self.xyzPeriastron()\n apas = self.xyzApastron()\n center = (peri + apas) / 2.0\n direc = (peri - apas) / numpy.sqrt(((peri - apas)**2).sum())\n ae = self.a * self.e\n return (center + ae * direc, center - ae * direc)","title":""},{"docid":"fff940f5dcedd01e6b4ca219db7b2a46","score":"0.4900721","text":"def coor_2(n,a,b):\n length = (b-a)/float(n+1)\n coordinates = [a + length * (i + 1) for i in range(n+1)]\n return coordinates","title":""},{"docid":"e3a7a1d540f19ef9e03afcdc2f9e0065","score":"0.48950747","text":"def createFromTwoPoints(point1,point2,color=mycolors.WHITE,width=1,arrow=[0.1,0.5]):\n x=point2.x-point1.x\n y=point2.y-point1.y\n return Vector(x,y,color=color,width=width,arrow=arrow)","title":""},{"docid":"963013cc946629cdd7c6c08aeb668b8f","score":"0.48936197","text":"def centripetal(pnts, a=0., b=1.):\n pnts = array(pnts, dtype=float64)\n n = len(pnts)\n dtotal = sum(norm(diff(pnts, axis=0), axis=1))\n u = zeros(n, dtype=float64)\n u[0] = a\n u[-1] = b\n if dtotal <= 0.:\n return u\n for i in range(1, n - 1):\n di = (norm(pnts[i] - pnts[i - 1]) / dtotal) ** 0.5\n u[i] = u[i - 1] + di\n return u","title":""},{"docid":"f04922e658dbe78aec2e45cd47285714","score":"0.48836562","text":"def inclination(tanip, p):\n return np.arctan(np.sqrt(tanip)*p)","title":""},{"docid":"d39b4a3d05a2db557bed4313b1065291","score":"0.48832005","text":"def _make_alpha(self, x):\n if self.alph_unit == 'codon':\n if self.alph_constraint == 'arbitrary':\n return torch.einsum('cjd,ijd->icj', self.params['uln'], x)\n elif self.alph_constraint == 'finite':\n return torch.einsum('cja,ad,ijd->icj', self.params['xt'],\n self.params['vln'], x)\n if self.alph_unit == 'nuc':\n if self.alph_constraint == 'finite':\n vtilde = self.params['vtilde']\n vln = vtilde - torch.logsumexp(vtilde, axis=1, keepdim=True)\n elif self.alph_constraint == 'enzymatic':\n vln = self.Stauln[self.params['tau']-1]\n nuc_lp = torch.einsum('cjla,ab->cjlb', self.params['xt'], vln)\n aa_lp = bu.codon_to_aa_lp(nuc_lp, self.transfer, self.mask)\n return torch.einsum('cjd,ijd->icj', aa_lp, x)","title":""},{"docid":"1fa386d9d411d31e18c9b044416f585c","score":"0.4878083","text":"def linear(x, m, c):\n return m*x + c","title":""},{"docid":"0d16b060554701bcc07c743c0d98839d","score":"0.48745748","text":"def cLon(lon):\r\n\tif lon<0:\r\n\t\tlon=360+lon\r\n\treturn lon","title":""},{"docid":"e7ffa4d3839610011d1244e82a09ee47","score":"0.4874358","text":"def cart2cyl(x, y, z=0):\n theta = np.arctan2(y, x)\n r = np.hypot(x, y)\n if isinstance(z, (int, float)):\n z = np.ones_like(r)*z\n return r, theta, z","title":""},{"docid":"adfaec75cfc34b3c82349da631b05086","score":"0.4869807","text":"def __init__(self, a, b):\n self.line = [a.y - b.y, b.x - a.x, 0]\n self.line[2] = -self.line[0] * a.x - self.line[1] * a.y","title":""},{"docid":"7c204ec569e2d11faa7b7692405a2013","score":"0.48661202","text":"def xyzTolonlat(pt):\n pt = pt/np.linalg.norm(pt) #normalize\n lat = np.arcsin(pt[2])\n lon = np.arctan2(pt[1],pt[0])\n return lon,lat","title":""},{"docid":"b46a651150ca0d944033e41e309bad0e","score":"0.48635912","text":"def __ccw(a,b,c):\n return a[0]*b[1] + b[0]*c[1] + c[0]*a[1] - a[0]*c[1] - a[1]*b[0] - b[1]*c[0]","title":""},{"docid":"2bb7262ec4ab6dd3e201b7f9825c2f1b","score":"0.48634666","text":"def point3(x=0, y=0, z=0):\n return sp.Matrix([x, y, z])","title":""},{"docid":"9ce9fe0e5dc808f3b367fd6cb2d70737","score":"0.48617086","text":"def lambertian():\n p1, p2 = np.random.uniform(0, 1, 2)\n theta = np.arcsin(np.sqrt(p1))\n phi = 2 * np.pi * p2\n coords = spherical_to_cart(theta, phi)\n return coords","title":""},{"docid":"a18e9957a0bd19dd607649cddcac3723","score":"0.48595825","text":"def absoluteBCPOut(anchor, BCPOut):\n\treturn (BCPOut[0] + anchor[0], BCPOut[1] + anchor[1])","title":""},{"docid":"3a3f5339df2d0b4fb6676f98667c011c","score":"0.4859303","text":"def point_projection(p, a, b):\n l = np.dot(p - a, b - a)\n ab = np.linalg.norm(b - a)\n ab2 = np.square(ab)\n m = a + l * (b - a) / ab2\n return m","title":""},{"docid":"d1dbb2d1bbc06eac3fb5bb30248add0c","score":"0.48561144","text":"def angle(a, b, c):\n # add code to return the correct angle relationship","title":""},{"docid":"908f91e60b056a09be5d58ef8f5908a2","score":"0.4853992","text":"def calcCoordinate(a,b,c,bond_len,di_angle,theta):\n a,b,c = np.array(a),np.array(b),np.array(c)\n di_angle = di_angle*pi/180.0\n\n u = c - b\n x = a - b\n v = ((x) - (np.dot((x), u)/np.dot(u,u))*u)\n\n w = np.cross(u, x)\n\n q = (v/np.linalg.norm(v))*np.cos(di_angle)\n e = (w/np.linalg.norm(w))*np.sin(di_angle)\n\n d = (b + (q+e))\n\n u1 = b - c\n y1 = d - c\n mag_y1 = sqrt((y1[0]**2)+(y1[1]**2)+(y1[2]**2))\n mag_u1 = sqrt((u1[0]**2)+(u1[1]**2)+(u1[2]**2))\n\n theta_bcd = acos(np.dot(u1,y1)/(mag_u1*mag_y1))\n rotate = theta - degrees(theta_bcd)\n\n z = np.cross(u1, y1)\n n = z/sqrt((z[0]**2)+(z[1]**2)+(z[2]**2))\n\n pos_ini = c + y1*np.cos(rotate*pi/180) \\\n +(np.cross(n,y1)*np.sin(rotate*pi/180))\\\n + n*(np.dot(n,y1))*(1-np.cos(rotate*pi/180))\n\n pos_BL = ((pos_ini-c)*(bond_len/np.linalg.norm(pos_ini-c)))+c\n\n return pos_BL.tolist()","title":""},{"docid":"7b62bb83c40542e7b7551228946c86e5","score":"0.4848173","text":"def cartesien_vers_polaire(z):\n x = z.real\n y = z.imag\n print(x)\n print(y) \n module = sqrt(x**2 + y**2)\n argument = atan2(y,x)\n return (module,argument)","title":""},{"docid":"367dc2500b3d757ab283f9e17ab8eb6d","score":"0.48444164","text":"def zPt(coordTuple):\n c = coordTuple\n x = c[0]\n y = c[1]\n if len(c) == 3:\n z = c[2]\n else:\n z = 0.0\n point = (x,y,z)\n return (x,y,z)","title":""},{"docid":"3a531454377dffeb9073bd58a41b9418","score":"0.48437122","text":"def Cao(yAo, Po, To, R=8.314):\r\n return yAo*Po / (R*To)","title":""},{"docid":"04480d1ffd6cc713b5eb008ca159ab04","score":"0.48408157","text":"def relativeBCPIn(anchor, BCPIn):\n\treturn (BCPIn[0] - anchor[0], BCPIn[1] - anchor[1])","title":""},{"docid":"93c7af31ebf608e16d3753a023e51428","score":"0.48379734","text":"def eval2(points, c, x):\n p1, pn = points\n if p1 > pn:\n p1, pn = pn, p1\n x1, y1 = p1\n xn, yn = pn\n d = xn - x1 - 1\n\n x2 = x1 + 1\n y2 = (d * y1 - d * (d + 1) * c + yn) / (d + 1)\n points = [(x1, y1), (x2, y2)]\n return eval2seq(points, c, x)","title":""},{"docid":"d6a787b300cf4fc56fa0861a8de1dac2","score":"0.48284885","text":"def compute_Cl(self, alpha):\n alpha_abscissa=np.array([-45., -35., -25., -15., -10., -5., 0, 5, 10, 15, 25, 35, 45, 55, 70, 90, 110, 125, 135]) * np.pi / 180\n Clmax=1.2\n Cl=[-np.cos(alpha_abscissa[6+6]), \\\n -np.cos(alpha_abscissa[5+6]), \\\n -np.cos(alpha_abscissa[4+6]), \\\n -Clmax, \\\n -Clmax*10/15, \\\n -Clmax*5/15, \\\n 0., \\\n Clmax*5/15, \\\n Clmax*10/15, \\\n Clmax, \\\n np.cos(alpha_abscissa[4+6]), \\\n np.cos(alpha_abscissa[5+6]), \\\n np.cos(alpha_abscissa[6+6]), \\\n np.cos(alpha_abscissa[7+6]), \\\n np.cos(alpha_abscissa[8+6]), \\\n np.cos(alpha_abscissa[9+6]), \\\n -np.cos(alpha_abscissa[8+6]), \\\n -np.cos(alpha_abscissa[7+6]), \\\n -np.cos(alpha_abscissa[6+6])]\n\n ##print('alpha', alpha)\n \n polynomial_coef = np.polyfit(alpha_abscissa, Cl, 7)\n return np.poly1d(polynomial_coef)(alpha)","title":""},{"docid":"4d9ba4f8de16651f265b17ec77027824","score":"0.48242936","text":"def lawOfCosines(a, b, c):\n ang = np.arccos((a**2 - b**2 - c**2)/(-2.0*b*c))\n return ang","title":""},{"docid":"b22421efa9868be7ee6fee4930cf1ad6","score":"0.48238954","text":"def get_segment_from_coeffs(A, B, C):\n line = ogr.Geometry(ogr.wkbLineString())\n if A == 0:\n # line is parallell to x-axis\n line.AddPoint(0, C / B, 0)\n line.AddPoint(10, C / B, 0)\n\n elif B == 0:\n # line is parallell to y-axis\n line.AddPoint(C / A, 0, 0)\n line.AddPoint(C / A, 10, 0)\n else:\n line.AddPoint(C / A, 0, 0)\n line.AddPoint(0, C / B, 0)\n return line","title":""},{"docid":"efa28da711a1ba71a947233dfcc51ba6","score":"0.48238045","text":"def __init__(self, x, y, z, a, b, c, color):\n\n vertices = np.array([[x, y, z]]*8, dtype=DTYPE)\n for i in range(4):\n vertices[1 + i + 2 * int(i/2)][0] += a\n vertices[2 + 2 * int(i/2) + i][1] += b\n vertices[4 + i ][2] += c\n\n self.vertices = vertices\n self.color = color","title":""},{"docid":"233838f2561fdc7490c47edf34980f0b","score":"0.48231193","text":"def _normal_3_points(a, b, c):\n if type(a) is list or type(a) is tuple:\n a = Vector3(*a)\n b = Vector3(*b)\n c = Vector3(*c)\n elif isinstance(a, Point3):\n a = Vector3(*a.export_to_list())\n b = Vector3(*b.export_to_list())\n c = Vector3(*c.export_to_list())\n cross_result = (a - c).cross(b - c).get_normalized()\n if cross_result.get_x() == -0.0:\n cross_result.set_x(0.0)\n if cross_result.get_y() == -0.0:\n cross_result.set_y(0.0)\n if cross_result.get_z() == -0.0:\n cross_result.set_z(0.0)\n return cross_result","title":""}],"string":"[\n {\n \"docid\": \"89d197710005889193a344a3915b0ba2\",\n \"score\": \"0.5912336\",\n \"text\": \"def tangent(A,B,C):\\n AB=pointplan.len_deux_coord(A,B)\\n AC=pointplan.len_deux_coord(A,C)\\n BC=pointplan.len_deux_coord(B,C)\\n longeur=-AB+AC+BC\\n cercleC=cercle.create(C,longeur/2)\\n cercle.draw_circle(cercleC)\\n longeur1=AB+AC-BC\\n cercleA=cercle.create(A,longeur1/2)\\n cercle.draw_circle(cercleA)\\n longeur3=AB-AC+BC\\n cercleB=cercle.create(B,longeur3/2)\\n cercle.draw_circle(cercleB)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408ea1b17009e9805a8b1abd548a5327\",\n \"score\": \"0.58020055\",\n \"text\": \"def __mul__(self, c: float):\\n x = self.x * c\\n y = self.y * c\\n z = self.z * c\\n return(Point(x, y, z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73d1005bdf309bf9bf5c2af23a4fce3\",\n \"score\": \"0.57875377\",\n \"text\": \"def abc_to_center_abc(a, b, c, w, h):\\n x0 = w/2.\\n y0 = h/2.\\n return (a, b, a*x0 + b*y0 + c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5726fe0f2e3ed98539cbb6221ff852\",\n \"score\": \"0.5769577\",\n \"text\": \"def make_point (x, y):\\r\\n return complex(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28053ae5206d7f37286c33f7e0302b0\",\n \"score\": \"0.5725666\",\n \"text\": \"def pos(s, c):\\n return s.point + c*s.vel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df092c8d079dac5d6987c1eb48c1a2a\",\n \"score\": \"0.5722327\",\n \"text\": \"def angle_CAB(point_c, point_a, point_b):\\n edge_a = Geometry.get_distance(point_c, point_b)\\n edge_b = Geometry.get_distance(point_c, point_a)\\n edge_c = Geometry.get_distance(point_a, point_b)\\n\\n acos_input = ((edge_b ** 2) + (edge_c **2) - (edge_a ** 2)) / (2 * edge_b * edge_c)\\n\\n if acos_input < -1:\\n acos_input = -1\\n if acos_input > 1:\\n acos_input = 1\\n return math.acos(acos_input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2638171f6b8411bd8a1720ef5609611\",\n \"score\": \"0.56611675\",\n \"text\": \"def make_caustic():\\n\\n\\t\\t\\tfrom Caustics import Caustics as caus\\n\\t\\t\\tcaustic = caus(lens=self)\\n\\t\\t\\tcaustic.calculate(points=100)\\n\\t\\t\\treturn (caustic.x, caustic.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b76eaed92f4ed236a0eeece4744a238\",\n \"score\": \"0.56472087\",\n \"text\": \"def calcBarycentricCoordinate(self, p, a, b, c): \\n fabXY = float((p[1]-a[1]) * (b[0]-a[0]) - (b[1]-a[1]) * (p[0]-a[0]))\\n fabXcYc = float((c[1]-a[1]) * (b[0]-a[0]) - (b[1]-a[1]) * (c[0]-a[0]))\\n return fabXY / fabXcYc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c437f3b216ef182cc99bb2ef66d687e0\",\n \"score\": \"0.5531534\",\n \"text\": \"def A(x, y, z, ocx, ocy, ocz, ocpx, ocpy, ocpz, snx, sny, snz):\\n return -(1.0/24.0)*x*y*z*(\\n + 12. * (-2.0 + ocx**2 + ocy**2 + ocz**2) *\\n (ocpx*snx + ocpy*sny + ocpz*snz)\\n - 6.0 * ((-2.0 + 2.0 * ocpx * ocx + ocx**2 + ocy**2 + ocz**2)\\n * snx + 2.0 * ocx * (ocpy * sny + ocpz * snz)) * x\\n + 4.0 * (ocpx*snx + 2.0 * ocx*snx + ocpy *\\n sny + ocpz*snz) * x**2 - 3.0 * snx * x**3\\n - 6.0 * (2.0 * ocpx * ocy * snx + (-2.0 + ocx**2 + 2.0 * ocpy *\\n ocy + ocy**2 + ocz**2) * sny\\n + 2.0 * ocpz * ocy * snz) * y\\n + 6.0 * (ocy*snx + ocx*sny)*x*y\\n - 2.0 * sny*x**2*y + 4.0*(\\n ocpx*snx + ocpy*sny + 2.0 * ocy * sny + ocpz*snz) * y**2\\n - 2.0 * snx*x*y**2 - 3.0*sny*y**3\\n - 6.0 * (2.0*ocpx*ocz*snx + 2.0*ocpy*ocz*sny +\\n (-2.0 + ocx**2 + ocy**2 + 2*ocpz*ocz + ocz**2)*snz)*z\\n + 6.0 * (ocz*snx + ocx*snz)*x*z - 2.0*snz*x**2*z +\\n 6.0*(ocz*sny + ocy*snz)*y*z - 2.0*snz*y**2*z\\n + 4.0 * (ocpx*snx + ocpy*sny + (ocpz + 2.0 * ocz)*snz) *\\n z**2 - 2.0*snx*x*z**2 - 2.0*sny*y*z**2 - 3.0*snz*z**3\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e732beef5082e3c9166409c941c54b5\",\n \"score\": \"0.55264735\",\n \"text\": \"def startingPoint(G, c, A, b, guess):\\n m,n = A.shape\\n x0, y0, l0 = guess\\n\\n # initialize linear system\\n N = np.zeros((n+m+m, n+m+m))\\n N[:n,:n] = G\\n N[:n, n+m:] = -A.T\\n N[n:n+m, :n] = A\\n N[n:n+m, n:n+m] = -np.eye(m)\\n N[n+m:, n:n+m] = np.diag(l0)\\n N[n+m:, n+m:] = np.diag(y0)\\n rhs = np.empty(n+m+m)\\n rhs[:n] = -(G.dot(x0) - A.T.dot(l0)+c)\\n rhs[n:n+m] = -(A.dot(x0) - y0 - b)\\n rhs[n+m:] = -(y0*l0)\\n\\n sol = la.solve(N, rhs)\\n dx = sol[:n]\\n dy = sol[n:n+m]\\n dl = sol[n+m:]\\n\\n y0 = np.maximum(1, np.abs(y0 + dy))\\n l0 = np.maximum(1, np.abs(l0+dl))\\n\\n return x0, y0, l0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca89e8d6adedf9a74a141de22c8da0e\",\n \"score\": \"0.55216974\",\n \"text\": \"def B(x, y, z, ocpx, ocpy, ocpz, ow):\\n return 1.0/(6.0 * ow**2) * (x*y*z) * \\\\\\n (\\n + x**2 + y**2 + z**2\\n - 3.0 * (\\n + ocpx * x\\n + ocpy * y\\n + ocpz * z\\n )\\n + 3.0 * (ocpx**2 + ocpy**2 + ocpz**2)\\n - 6.0 * ow**2\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3921dd14847aaadebed3e410c217425a\",\n \"score\": \"0.54290444\",\n \"text\": \"def startingPoint(G, c, A, b, guess):\\n m,n = A.shape\\n x0, y0, l0 = guess\\n\\n N = np.zeros((n+m+m, n+m+m))\\n N[:n,:n] = G\\n N[:n, n+m:] = -A.T\\n N[n:n+m, :n] = A\\n N[n:n+m, n:n+m] = -np.eye(m)\\n N[n+m:, n:n+m] = np.diag(l0)\\n N[n+m:, n+m:] = np.diag(y0)\\n rhs = np.empty(n+m+m)\\n rhs[:n] = -(G.dot(x0) - A.T.dot(l0)+c)\\n rhs[n:n+m] = -(A.dot(x0) - y0 - b)\\n rhs[n+m:] = -(y0*l0)\\n\\n sol = la.solve(N, rhs)\\n dx = sol[:n]\\n dy = sol[n:n+m]\\n dl = sol[n+m:]\\n\\n y0 = np.maximum(1, np.abs(y0 + dy))\\n l0 = np.maximum(1, np.abs(l0+dl))\\n\\n return x0, y0, l0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d7398f40d7fef99d7d5be348a59d01\",\n \"score\": \"0.5386732\",\n \"text\": \"def ApexConversionCoefficient(v_a, l_a, b_a):\\n l_a = l_a * deg2rad\\n b_a = b_a * deg2rad\\n X = v_a * np.cos(l_a) * np.cos(b_a)\\n Y = v_a * np.sin(l_a) * np.cos(b_a)\\n Z = v_a * np.sin(b_a)\\n return [X, Y, Z]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8528465d92ffca935fc4e52b77088475\",\n \"score\": \"0.53191376\",\n \"text\": \"def build_C(self):\\n F = self.F\\n ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\\n ctrl_pts_y_top = -1 * np.ones(int(F / 2))\\n ctrl_pts_y_bottom = np.ones(int(F / 2))\\n ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\\n ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\\n C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\\n return C # F x 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784015a7cddc6c148791ce8f6a58d4ea\",\n \"score\": \"0.5316926\",\n \"text\": \"def sCA(ax, ay, cx, cy):\\n return sqrt(((int(ax) - int(cx)) ** 2) + ((int(ay) - int(cy)) ** 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d86d502689f1e6352432f595a762fc\",\n \"score\": \"0.52961034\",\n \"text\": \"def cria_coordenada(r, c):\\r\\n if not (isinstance(r, int) and isinstance(c, int) and 0 <= r <= 2 and 0 <= c <= 2):\\r\\n raise ValueError('cria_coordenada: argumentos invalidos.')\\r\\n return r, c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29550c9aaa09f222e0efd3ddb10fa5a1\",\n \"score\": \"0.5288331\",\n \"text\": \"def _coordinate_conversion(self):\\n self.gamma_x = (self.east_B - self.east_A) / \\\\\\n (self.crline_B - self.crline_A)\\n self.beta_x = (self.east_C - self.east_B) / \\\\\\n (self.inline_C - self.inline_B)\\n self.alpha_x = self.east_A - \\\\\\n self.beta_x * self.inline_A - \\\\\\n self.gamma_x * self.crline_A\\n self.gamma_y = (self.north_B - self.north_A) / \\\\\\n (self.crline_B - self.crline_A)\\n self.beta_y = (self.north_C - self.north_B) / \\\\\\n (self.inline_C - self.inline_B)\\n self.alpha_y = self.north_A - \\\\\\n self.beta_y * self.inline_A - \\\\\\n self.gamma_y * self.crline_A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e94d242e1a71e258912502f9a6f729\",\n \"score\": \"0.5278674\",\n \"text\": \"def expand_C(C_voigt):\\n \\n ## Voight notation: xx, yy, zz, yz, xz, xy\\n mapping = [[0,0],[1,1],[2,2],[1,2],[0,2],[0,1]] \\n \\n C = np.zeros((3,3,3,3)) \\n for i in range(3):\\n for j in range(3):\\n for k in range(3):\\n for l in range(3):\\n C[i,j,k,l] = C_voigt[mapping.index([min(i,j),max(i,j)]),\\n mapping.index([min(k,l),max(k,l)])] \\n \\n return C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab073feea75aeb672df6675a3cc3273\",\n \"score\": \"0.5273901\",\n \"text\": \"def proj(self, x, c):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f10a2b2824c9d271190c1eb1861a64b\",\n \"score\": \"0.5208139\",\n \"text\": \"def conv_coord(c: Coord) -> Point:\\n if isinstance(c, Point):\\n return c\\n return Point(c[0], c[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c397c1536fb3f61324700367ff2fac5\",\n \"score\": \"0.51899934\",\n \"text\": \"def make_point(coordinates):\\n # Components in floats\\n x = float(coordinates[0])\\n y = float(coordinates[1])\\n z = float(coordinates[2])\\n return gp_Pnt(x, y, z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb3d9cc0c9159d780c1c83e28961a50\",\n \"score\": \"0.5180762\",\n \"text\": \"def _build_C(self, F):\\n ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\\n ctrl_pts_y_top = -1 * np.ones(int(F / 2))\\n ctrl_pts_y_bottom = np.ones(int(F / 2))\\n ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\\n ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\\n C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\\n return C # F x 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e053c7bd285924ece8ad46be25564f4\",\n \"score\": \"0.5176157\",\n \"text\": \"def center_small_entoure(cercleA,cercleB,cercleC):\\n k1=1/(cercle.get_radius(cercleA))\\n k2=1/(cercle.get_radius(cercleB))\\n k3=-(1/(cercle.get_radius(cercleC)))\\n z1=pointplan.coord_to_complex(cercle.get_center(cercleA))\\n z2=pointplan.coord_to_complex(cercle.get_center(cercleB))\\n z3=pointplan.coord_to_complex(cercle.get_center(cercleC))\\n base=(k1*z1)+(k2*z2)+(z3*k3)\\n k4=rayons_cercle_entoure(cercleA,cercleB,cercleC)\\n baseinterieur=(k1*k2*z1*z2)+(k2*k3*z2*z3)+(k1*k3*z1*z3)\\n z4_1=k4*(base+2*cmath.sqrt(baseinterieur))\\n z4_2=k4*(base-2*cmath.sqrt(baseinterieur))\\n return (pointplan.complex_to_coord(z4_1),pointplan.complex_to_coord(z4_2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06fb14377892ce497085a939a8cd9368\",\n \"score\": \"0.51698905\",\n \"text\": \"def get_point(self, c):\\n c = self.comparator.expand_coordinates(c)\\n shift = c / self.n @ self.comparator.atoms.cell\\n positions = -self.comparator.positions + shift\\n return self.comparator.calculate_rmsd(positions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf38f6c1cb582a8a2c233f89bb01b9b\",\n \"score\": \"0.5161089\",\n \"text\": \"def push_from_center(P,C,d):\\n a = np.arctan2(P[0]-C[0],P[1]-C[1])\\n dx = np.sin(a)*d\\n dy = np.cos(a)*d\\n \\n \\n return [P[0]+dx,P[1]+dy]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4b136b9bdafe2ced11ffc623288259\",\n \"score\": \"0.51540864\",\n \"text\": \"def _get_3rd_point(a, b):\\n direction = a - b\\n third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)\\n\\n return third_pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb4e989dff8feaf821601aa0d83b351\",\n \"score\": \"0.51429945\",\n \"text\": \"def center_small_inscrit(cercleA,cercleB,cercleC):\\n k1=1/(cercle.get_radius(cercleA))\\n k2=1/(cercle.get_radius(cercleB))\\n k3=(1/(cercle.get_radius(cercleC)))\\n z1=pointplan.coord_to_complex(cercle.get_center(cercleA))\\n z2=pointplan.coord_to_complex(cercle.get_center(cercleB))\\n z3=pointplan.coord_to_complex(cercle.get_center(cercleC))\\n base=(k1*z1)+(k2*z2)+(z3*k3)\\n k4=rayons_cercle(cercleA,cercleB,cercleC)\\n baseinterieur=(k1*k2*z1*z2)+(k2*k3*z2*z3)+(k1*k3*z1*z3)\\n z4_1=k4*(base+2*cmath.sqrt(baseinterieur))\\n return pointplan.complex_to_coord(z4_1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a794a71bbb3b4e4d817b5ab57ed864\",\n \"score\": \"0.5128175\",\n \"text\": \"def calculate_position(c, t):\\n#divido il perscorso in tanti piccoli punti\\n\\n return c[0] * t**5 + c[1] * t**4 + c[2] * t**3 + c[3] * t**2 + c[4] * t + c[5]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d789508c6cabd43f0e475407a325d89\",\n \"score\": \"0.5127938\",\n \"text\": \"def point(self, a, b=None, type='focalxy'):\\n\\n # allow us to input either a coordinate object, or a pair of coordinates\\n # logger.info('Cartographer pointing a new coordinate:', 1)\\n try:\\n assert (a.__class__.__base__.__name__ == 'position')\\n type = a.__class__.__name__\\n temp = a\\n a, b = temp.a, temp.b\\n del temp\\n except:\\n pass\\n # logger.info('using [{a}, {b}, {type}] as input'.format(a=a,b=b, type=type), 2)\\n\\n\\n # make sure the input coordinates can be understood\\n # assert(type in self.possibleinputs)\\n # logger.info('input coordinate is parsable', 2)\\n\\n # remove any previous coordinate definitions\\n # logger.info('clearing previous coordinates', 2)\\n for coord in self.possibleinputs:\\n try:\\n del self.__dict__[coord]\\n except:\\n pass\\n\\n # assign the coordinates to Cartographer's memory\\n self.input = type\\n self.coordinate = eval('{type}(a,b,cartographer=self)'.format(a=a, b=b, type=type))\\n self.__dict__[self.input] = self.coordinate\\n logger.debug('pointing at {0}'.format(self.coordinate))\\n return self.coordinate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0adac0c024d66fff0ff55120045b1220\",\n \"score\": \"0.5127829\",\n \"text\": \"def l2pa(a):\\n\\tr=PointArray(len(a))\\n\\tfor i,j in enumerate(a):\\n\\t\\tr.set_vector_at(i,Vec3f(j[0],j[1],0),j[2])\\n\\t\\n\\treturn r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b5110be8077ccfef98dc02b71097fdf\",\n \"score\": \"0.5111919\",\n \"text\": \"def _get_3rd_point(a, b):\\n assert len(a) == 2\\n assert len(b) == 2\\n direction = a - b\\n third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)\\n\\n return third_pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c8a146ce139b499f17e8dc7729a360\",\n \"score\": \"0.51105744\",\n \"text\": \"def eval2seq(points, c, x):\\n p1, p2 = points\\n if p1 > p2:\\n p1, p2 = p2, p1\\n x1, y1 = p1\\n x2, y2 = p2\\n d = x - x2\\n\\n return (d + 1) * y2 - d * y1 + d * (d + 1) * c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7afd6ef093f2b45b11fba506be447bb7\",\n \"score\": \"0.5097706\",\n \"text\": \"def makePointGeom(*args):\\n return _collide.makePointGeom(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36503bb813b49957f4b27bcd075bd953\",\n \"score\": \"0.50870234\",\n \"text\": \"def get_xcp(lcap,lcyl,ltot,xcg0,Acap,Acyl,delta_cg):\\n\\txcp0=(Acap*1/4*lcap+Acyl*(lcap+0.5*lcyl))/(Acap+Acyl)\\n\\txcp=ltot-xcg0-xcp0+delta_cg\\n\\treturn xcp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f773876d3d4d9b883f83600e53f298e2\",\n \"score\": \"0.50827265\",\n \"text\": \"def get_c(box_plus, lx,ly,lz,charge,w):\\n lx += box_plus * 2.0 * 1.8897\\n ly += box_plus * 2.0 * 1.8897\\n lz += box_plus * 2.0 * 1.8897\\n v_sample = lx*ly*lz / (1.8897 * 1.8897 * 1.8897) / (1e8 * 1e8 * 1e8)\\n c = charge * charge / (2.0 * w) * (1.6 * 1e-19 * 1.6 * 1e-19) / v_sample\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce1846ad44d5b53d1b6de478216d714\",\n \"score\": \"0.5082113\",\n \"text\": \"def conv_ang(self):\\n x = self.head_loc[0] - self.conv_loc[0]\\n y = self.head_loc[1] - self.conv_loc[1]\\n return arctan2(y, x) + pi - self.thic_ang()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637fbb9bebbc71b08258233ca2b682c5\",\n \"score\": \"0.5081217\",\n \"text\": \"def cauchy_point(gk, Bk, deltak):\\r\\n # calculate ti_k\\r\\n ti_k = 0\\r\\n temp = np.dot(np.transpose(gk), np.dot(Bk, gk))\\r\\n if temp <= 0:\\r\\n ti_k = 1\\r\\n else:\\r\\n ti_k = min(1, (np.linalg.norm(x=gk, ord=2)**3)/(deltak*temp))\\r\\n\\r\\n # p cauchy\\r\\n pc_k = -(ti_k)*(deltak/np.linalg.norm(x=gk, ord=2)) * gk\\r\\n\\r\\n return pc_k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"697ed7f957439fe8d8b23c6cd1c71341\",\n \"score\": \"0.50804514\",\n \"text\": \"def c_to_ab(a, b, sum):\\n return - 1.0 / 2 * (2 * a * b - sum ** 2) / float(sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64158c60b23627308bfd2b70f13c8b9\",\n \"score\": \"0.5068203\",\n \"text\": \"def pointPosition(local, world):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e63abe9e14c6f6b234a808dacd59121\",\n \"score\": \"0.5067843\",\n \"text\": \"def aabb_to_floor_point(X):\\n return general_transform(X, lambda x,y,w,h: (x + w/2, y + h))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230d849ca648c07e9bdac2a78b82dc12\",\n \"score\": \"0.5061441\",\n \"text\": \"def create_laplacian_2d(nx, ny, lx, ly, pbc=True):\\n if type(nx) != int or type(ny) != int:\\n raise TypeError('We need an integer')\\n if type(lx) != int and type(lx) != float:\\n raise TypeError('We need a number')\\n if type(ly) != int and type(ly) != float:\\n raise TypeError('We need a number')\\n if nx < 2 or ny < 2:\\n raise ValueError('We need at least two grid points in each direction')\\n if nx / lx < 0 or ny / ly < 0:\\n raise ValueError('We need positive lengths')\\n if pbc not in (True, False):\\n raise TypeError('We need a boolean as pbc')\\n \\n n = nx * ny # total number of grid points\\n laplacian = np.zeros((n, n))\\n hx = (lx / nx) ** 2\\n hy = (ly / ny) ** 2\\n \\n for i in range(n):\\n laplacian[i, i] -= (2 / hx + 2 / hy)\\n for j in range(ny - 1):\\n for i in range(nx):\\n laplacian[j * nx + i, (j + 1) * nx + i] += 1 / hy\\n laplacian[(j + 1) * nx + i, j * nx + i] += 1 / hy\\n for j in range(ny):\\n for i in range(nx - 1):\\n laplacian[j * nx + i, j * nx + i + 1] += 1 / hx\\n laplacian[j * nx + i + 1, j * nx + i] += 1 / hx\\n\\n if pbc:\\n for i in range(ny):\\n laplacian[i, ny * nx - ny + i - 1] += 1 / hy\\n laplacian[ny * nx - ny + i - 1, i] += 1 / hy\\n for i in range(ny - 1):\\n laplacian[i * nx, (i + 1) * nx - 1] += 1 / hx\\n laplacian[(i + 1) * nx - 1, i * nx] += 1 / hx\\n\\n return laplacian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420f07439ff11eb1cacf549c0b2c2388\",\n \"score\": \"0.5061317\",\n \"text\": \"def deformation(p,c,r):\\n def distance(p,q):\\n \\\"\\\"\\\"distance entre les 2 points p et q\\\"\\\"\\\"\\n return ((q[0]-p[0])**2 + (q[1]-p[1])**2 + (q[2]-p[2])**2)**0.5\\n d = distance(p,c) \\n if d >= r:\\n res = p\\n else:\\n if d > EPS: \\n facteur = r/d\\n else:\\n facteur = 1\\n x2 = c[0] + (p[0]-c[0]) * facteur\\n y2 = c[1] + (p[1]-c[1]) * facteur\\n z2 = c[2] + (p[2]-c[2]) * facteur\\n res = (x2,y2,z2)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8647917b50afe4162b00303dcf2912e4\",\n \"score\": \"0.50439364\",\n \"text\": \"def point_aleatoire(largeur=800, hauteur=600):\\n return Point(uniform(0, largeur), uniform(0, hauteur))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ad6a45ea2405081b42e7628b5a9817\",\n \"score\": \"0.5041951\",\n \"text\": \"def calculatePosition(T_B_Cabinet, x, y, z):\\n (rotX, rotY, rotZ) = T_B_Cabinet.M.GetRPY()\\n\\n posX = T_B_Cabinet.p.x() + math.cos(rotZ)*x - math.sin(rotZ)*y\\n posY = T_B_Cabinet.p.y() + math.sin(rotZ)*x + math.cos(rotZ)*y\\n posZ = T_B_Cabinet.p.z() + z/2.0 - 0.23\\n alpha = - math.pi + rotZ\\n\\n if alpha < -math.pi:\\n alpha = 2*math.pi + alpha\\n\\n return [posX, posY, posZ, alpha]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb4399658678d4570fe148e1926a0bd\",\n \"score\": \"0.5041624\",\n \"text\": \"def create_point(self):\\n # see if line intersects plane x==1\\n planex1 = Plane(1,0,0,-1)\\n # xxx need to make sure plane is not parallel to line\\n return PointFromIntersectingPlaneAndLine( planex1, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db8de8f7acca706abedd160660b268c\",\n \"score\": \"0.50278395\",\n \"text\": \"def calc_landmark_position(particle, z):\\n lm = np.zeros((1, 2))\\n lm[0, 0] = particle.x + z[0] * cos(z[1] + particle.theta)\\n lm[0, 1] = particle.y + z[0] * sin(z[1] + particle.theta)\\n return lm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929355939a303004e89f504dec388ab9\",\n \"score\": \"0.502005\",\n \"text\": \"def _make_cnots(self, anc_l):\\n\\n (north_dict, east_dict, south_dict, west_dict) = ({}, {}, {},\\n {})\\n for (m, n) in anc_l:\\n if m > 0 and n < self.dist:\\n north_dict[(m, n)] = (m - 1, n)\\n if m < self.dist and n < self.dist:\\n east_dict[(m, n)] = (m, n)\\n if m < self.dist and n > 0:\\n south_dict[(m, n)] = (m, n - 1)\\n if m > 0 and n > 0:\\n west_dict[(m, n)] = (m - 1, n - 1)\\n return (north_dict, east_dict, south_dict, west_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd97a90ff8f7b095f0468dcbbe666bd6\",\n \"score\": \"0.50170493\",\n \"text\": \"def aabb_to_center_point(X):\\n return general_transform(X, lambda x,y,w,h: (x + w/2, y + h/2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4fb8f2bef6cfe0ece1af7fedc20e99\",\n \"score\": \"0.5015521\",\n \"text\": \"def absoluteBCPIn(anchor, BCPIn):\\n\\treturn (BCPIn[0] + anchor[0], BCPIn[1] + anchor[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03538f47f41563a05d3c659ea8d89f9d\",\n \"score\": \"0.5010013\",\n \"text\": \"def f2c(ctemp):\\r\\n ftemp = (5 * (ctemp + 40) / 9) - 40\\r\\n\\r\\n return ftemp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b1748b4330cddffe1daef712e16c7e\",\n \"score\": \"0.50080925\",\n \"text\": \"def get_3rd_point(a, b):\\n direct = a - b\\n return b + np.array([-direct[1], direct[0]], dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb973c56b324b514b768cb0cbefb3b22\",\n \"score\": \"0.5006531\",\n \"text\": \"def _comp_point_coordinate(self):\\n Rbo = self.get_Rbo()\\n\\n H1 = self.get_H1()\\n\\n # alpha is the angle to rotate Z0 so ||Z1,Z8|| = W0\\n alpha = float(arcsin(self.W0 / (2 * Rbo)))\\n\\n # comp point coordinate (in complex)\\n Z0 = Rbo * exp(1j * 0)\\n Z1 = Z0 * exp(1j * alpha)\\n\\n if self.is_outwards():\\n Z2 = Z1 + self.H0\\n Z3 = Z2 + H1 + (self.W1 - self.W0) * 1j / 2.0\\n Z4 = Z3 + (self.H2 - self.R1) + (self.W2 - self.W1) / 2.0 * 1j\\n Z5 = Z4 + self.R1 - self.R1 * 1j\\n rot_sign = 1 # Rotation direction for Arc1\\n else: # inward slot\\n Z2 = Z1 - self.H0\\n Z3 = Z2 - H1 + (self.W1 - self.W0) * 1j / 2.0\\n Z4 = Z3 - (self.H2 - self.R1) + (self.W2 - self.W1) / 2.0 * 1j\\n Z5 = Z4 - self.R1 - self.R1 * 1j\\n rot_sign = -1 # Rotation direction for Arc1\\n\\n # symetry\\n Z6 = Z5.conjugate()\\n Z7 = Z4.conjugate()\\n Z8 = Z3.conjugate()\\n Z9 = Z2.conjugate()\\n Z10 = Z1.conjugate()\\n return [Z10, Z9, Z8, Z7, Z6, Z5, Z4, Z3, Z2, Z1, rot_sign]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a88be767d5733f88870556e73c2cae54\",\n \"score\": \"0.50050044\",\n \"text\": \"def point(self, t):\\n # algebraically equivalent to\\n # P0*(1-t)**3 + 3*P1*t*(1-t)**2 + 3*P2*(1-t)*t**2 + P3*t**3\\n # for (P0, P1, P2, P3) = self.bpoints()\\n return self.start + t*(\\n 3*(self.control1 - self.start) + t*(\\n 3*(self.start + self.control2) - 6*self.control1 + t*(\\n -self.start + 3*(self.control1 - self.control2) + self.end\\n )))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295760f651417fefe75a980c6124c3be\",\n \"score\": \"0.50049335\",\n \"text\": \"def f(Ui, Cai):\\r\\n\\treturn Ui + delta_z * ((-Kv * Cai)/B)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b1886b4351156bb727e560a4d8fe85\",\n \"score\": \"0.49930733\",\n \"text\": \"def prueba_clase():\\n \\n \\n pos_ini = (0, 1, 0, 1, 0,\\n 0, 0, 1, 1, 0,\\n 0, 0, 0, 1, 1,\\n 0, 0, 1, 1, 1,\\n 0, 0, 0, 1, 1)\\n\\n pos_a0 = (1, 0, 0, 1, 0,\\n 1, 0, 1, 1, 0,\\n 0, 0, 0, 1, 1,\\n 0, 0, 1, 1, 1,\\n 0, 0, 0, 1, 1)\\n\\n pos_a4 = (1, 0, 0, 0, 1,\\n 1, 0, 1, 1, 1,\\n 0, 0, 0, 1, 1,\\n 0, 0, 1, 1, 1,\\n 0, 0, 0, 1, 1)\\n\\n pos_a24 = (1, 0, 0, 0, 1,\\n 1, 0, 1, 1, 1,\\n 0, 0, 0, 1, 1,\\n 0, 0, 1, 1, 0,\\n 0, 0, 0, 0, 0)\\n\\n pos_a15 = (1, 0, 0, 0, 1,\\n 1, 0, 1, 1, 1,\\n 1, 0, 0, 1, 1,\\n 1, 1, 1, 1, 0,\\n 1, 0, 0, 0, 0)\\n\\n pos_a12 = (1, 0, 0, 0, 1,\\n 1, 0, 0, 1, 1,\\n 1, 1, 1, 0, 1,\\n 1, 1, 0, 1, 0,\\n 1, 0, 0, 0, 0)\\n\\n\\n entorno = Lights_out(pos_ini)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b020833114e08976d74e0ed547b0af6\",\n \"score\": \"0.49900976\",\n \"text\": \"def costcone(x0,x1,y0,y1,a,b):\\n\\n\\n if (a/(2.0*b)*abs(x0-y0))< np.pi:\\n return 4*b**2*(y1+x1-2*np.sqrt(y1*x1)*np.cos(a/(2.0*b)*(x0-y0)))\\n else:\\n return 4*b**2*(y1+x1+2*np.sqrt(y1*x1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb1c9590c537c46d12085627312a9d6\",\n \"score\": \"0.49885118\",\n \"text\": \"def createFromPoint(point,color=mycolors.WHITE,width=1,arrow=[0.1,0.5]):\\n return Vector(point.x,point.y,color=color,width=width,arrow=arrow)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320df5b2ffaf4b6e7fcb5bcdcfbeb939\",\n \"score\": \"0.49841446\",\n \"text\": \"def find_vertex(a, b, c):\\n\\n delta = find_delta(a, b, c)\\n\\n vertex = ( -b/(2*a), -(delta/(4*a)) )\\n return vertex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965c73e33f4982c4056311584bfed475\",\n \"score\": \"0.49791396\",\n \"text\": \"def overlap_ss(Ax, Ay, Az, Cx, Cy, Cz, alpha_bra, alpha_ket, c1, c2):\\n A = np.array([Ax, Ay, Az])\\n C = np.array([Cx, Cy, Cz])\\n ss = ((np.pi / (alpha_bra + alpha_ket))**(3/2) * np.exp((-alpha_bra * alpha_ket * np.dot(A-C, A-C)) / (alpha_bra + alpha_ket)))\\n return ss * c1 * c2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4ea4b7d2690b491ad67f448d293bba\",\n \"score\": \"0.49661538\",\n \"text\": \"def V_corner_long(A, B, C):\\n return m.pi/2*B*C**2 + m.pi**2/8*(A-B)*C**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e723fb6504829215bb63194f170159\",\n \"score\": \"0.49635208\",\n \"text\": \"def lattice_to_coords(c):\\n new_c = []\\n for p in c:\\n x_coord = p[1] * .5 + p[0]\\n y_coord = p[1] * ( math.sqrt(3) / 2)\\n new_c.append( ( round(x_coord,3), round(y_coord,3) ) )\\n return new_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c88837e31a2f23ea2267e257703def1\",\n \"score\": \"0.4963375\",\n \"text\": \"def gen_center():\\n v = [random.choice(C),random.choice(C),random.choice(C)]\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57c58a4743c3ab888fe883c69d89bb7\",\n \"score\": \"0.49522927\",\n \"text\": \"def from_centers(self, name, c):\\n a = self.get_var(name)\\n a.v()[:,:] = a.v() + a.grid.dx**2*a.lap()/24.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d504083c391e3523468c05f697604a\",\n \"score\": \"0.49476403\",\n \"text\": \"def translate_cord(self, cord):\\n x = cord[0]\\n y = cord[1]\\n\\n x = ord(x) - ord(\\\"a\\\")\\n y = abs(int(y) - 8)\\n\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa0b63a02ca972d3577b3c00a77b54e\",\n \"score\": \"0.4930278\",\n \"text\": \"def arbitrary_Crotation(x, y, z):\\n c = np.cos(y / 2)\\n s = np.sin(y / 2)\\n return np.array(\\n [\\n [1, 0, 0, 0],\\n [0, 1, 0, 0],\\n [0, 0, np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],\\n [0, 0, np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c],\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d59953a219a0e550a239c5b289f2634\",\n \"score\": \"0.49295154\",\n \"text\": \"def beale(position):\\n x, y = position\\n return (1.5 - x + x * y) ** 2 + (2.25 - x + x * y ** 2) ** 2 + (2.625 - x + x * y ** 3) ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2337def17ddc49f2fd275460cfede022\",\n \"score\": \"0.49275446\",\n \"text\": \"def newCTCenter(self, otherC, a, b):\\n if self.is_cistrans and DEBUG:\\n ## That would make this carbon the center of an allene!\\n ## Keeping track of stereochem for allenes is Hard.\\n ## TODO: Make it so that we can in fact handle allenes?\\n raise AlleneError(\\\"Attempt to add extra CTcenter to %s\\\" % str(self))\\n if not (self.neighbors[otherC] == 2 and otherC.neighbors[self] == 2):\\n if DEBUG:\\n raise StandardError(\\\"cis-trans center without double bond\\\")\\n else:\\n return\\n self.CTotherC = otherC\\n self.CTa = a\\n self.CTb = b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15fbbbf1366c565314dd04ac8b82422b\",\n \"score\": \"0.49266827\",\n \"text\": \"def coor(n,a,b):\\n coordinates = []\\n length = (b-a)/float(n+1)\\n for i in range(n+1):\\n coordinates.append(a + length * (i + 1))\\n return coordinates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a687fa8b43a5162f19a7d454282229\",\n \"score\": \"0.4923\",\n \"text\": \"def fn(point: Point, i: int, list_: List[Point]) -> str:\\n # Start control point\\n cp = control_point_calc(list_[i - 1], list_[i - 2], point)\\n\\n # End control point\\n try:\\n next_ = list_[i + 1]\\n except IndexError:\\n next_ = None\\n\\n cpe = control_point_calc(\\n current=point, previous=list_[i - 1], next_=next_, reverse=True\\n )\\n\\n return f\\\"C {cp.x},{cp.y} {cpe.x},{cpe.y} {point.x},{point.y}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a90bc95e3a1a4ed7d9237bb6b35877\",\n \"score\": \"0.4918844\",\n \"text\": \"def crossProdOrientation(a: Point, b: Point, c: Point):\\n return (b[1] - a[1]) * (c[0] - a[0]) - (b[0] - a[0]) * (c[1] - a[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e51b534420098023676411ad2ead3c\",\n \"score\": \"0.4915555\",\n \"text\": \"def create_point_around_centre_of_polyline_with_pts_list(poly_pts, start_Pt, left_length):\\n global aim_pt\\n length = measure_langth_of_bezier(poly_pts)\\n aim = (length-left_length)/2\\n\\n ## select the starting point ##\\n dis = start_Pt.distance_to_point(poly_pts[0])\\n dis_ = start_Pt.distance_to_point(poly_pts[-1])\\n if dis <= dis_:\\n poly_pts = poly_pts\\n elif dis > dis_:\\n poly_pts.reverse()\\n\\n pts_string = zip(poly_pts[:-1], poly_pts[1:])\\n measure = 0\\n for pre_pt, next_pt in pts_string:\\n distance = pre_pt.distance_to_point(next_pt)\\n measure += distance\\n if measure > aim:\\n extra = measure - aim\\n aim_pt = pre_pt * (extra/distance) + next_pt * (1-(extra/distance))\\n break\\n return aim_pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac686ca3bbb226af49b31a214a144e8f\",\n \"score\": \"0.49102253\",\n \"text\": \"def L(x, y, z, Nx1, Nx0, Ny1, Ny0, Nz1, Nz0):\\n return C([x, y, z]) * P(x, y, z, Nx1, Nx0, Ny1, Ny0, Nz1, Nz0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f19e1f5b0e4bd1a83dbf5a7057eff\",\n \"score\": \"0.49047747\",\n \"text\": \"def xyzFoci(self):\\n peri = self.xyzPeriastron()\\n apas = self.xyzApastron()\\n center = (peri + apas) / 2.0\\n direc = (peri - apas) / numpy.sqrt(((peri - apas)**2).sum())\\n ae = self.a * self.e\\n return (center + ae * direc, center - ae * direc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff940f5dcedd01e6b4ca219db7b2a46\",\n \"score\": \"0.4900721\",\n \"text\": \"def coor_2(n,a,b):\\n length = (b-a)/float(n+1)\\n coordinates = [a + length * (i + 1) for i in range(n+1)]\\n return coordinates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a7a1d540f19ef9e03afcdc2f9e0065\",\n \"score\": \"0.48950747\",\n \"text\": \"def createFromTwoPoints(point1,point2,color=mycolors.WHITE,width=1,arrow=[0.1,0.5]):\\n x=point2.x-point1.x\\n y=point2.y-point1.y\\n return Vector(x,y,color=color,width=width,arrow=arrow)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963013cc946629cdd7c6c08aeb668b8f\",\n \"score\": \"0.48936197\",\n \"text\": \"def centripetal(pnts, a=0., b=1.):\\n pnts = array(pnts, dtype=float64)\\n n = len(pnts)\\n dtotal = sum(norm(diff(pnts, axis=0), axis=1))\\n u = zeros(n, dtype=float64)\\n u[0] = a\\n u[-1] = b\\n if dtotal <= 0.:\\n return u\\n for i in range(1, n - 1):\\n di = (norm(pnts[i] - pnts[i - 1]) / dtotal) ** 0.5\\n u[i] = u[i - 1] + di\\n return u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f04922e658dbe78aec2e45cd47285714\",\n \"score\": \"0.48836562\",\n \"text\": \"def inclination(tanip, p):\\n return np.arctan(np.sqrt(tanip)*p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39b4a3d05a2db557bed4313b1065291\",\n \"score\": \"0.48832005\",\n \"text\": \"def _make_alpha(self, x):\\n if self.alph_unit == 'codon':\\n if self.alph_constraint == 'arbitrary':\\n return torch.einsum('cjd,ijd->icj', self.params['uln'], x)\\n elif self.alph_constraint == 'finite':\\n return torch.einsum('cja,ad,ijd->icj', self.params['xt'],\\n self.params['vln'], x)\\n if self.alph_unit == 'nuc':\\n if self.alph_constraint == 'finite':\\n vtilde = self.params['vtilde']\\n vln = vtilde - torch.logsumexp(vtilde, axis=1, keepdim=True)\\n elif self.alph_constraint == 'enzymatic':\\n vln = self.Stauln[self.params['tau']-1]\\n nuc_lp = torch.einsum('cjla,ab->cjlb', self.params['xt'], vln)\\n aa_lp = bu.codon_to_aa_lp(nuc_lp, self.transfer, self.mask)\\n return torch.einsum('cjd,ijd->icj', aa_lp, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa386d9d411d31e18c9b044416f585c\",\n \"score\": \"0.4878083\",\n \"text\": \"def linear(x, m, c):\\n return m*x + c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d16b060554701bcc07c743c0d98839d\",\n \"score\": \"0.48745748\",\n \"text\": \"def cLon(lon):\\r\\n\\tif lon<0:\\r\\n\\t\\tlon=360+lon\\r\\n\\treturn lon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ffa4d3839610011d1244e82a09ee47\",\n \"score\": \"0.4874358\",\n \"text\": \"def cart2cyl(x, y, z=0):\\n theta = np.arctan2(y, x)\\n r = np.hypot(x, y)\\n if isinstance(z, (int, float)):\\n z = np.ones_like(r)*z\\n return r, theta, z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adfaec75cfc34b3c82349da631b05086\",\n \"score\": \"0.4869807\",\n \"text\": \"def __init__(self, a, b):\\n self.line = [a.y - b.y, b.x - a.x, 0]\\n self.line[2] = -self.line[0] * a.x - self.line[1] * a.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c204ec569e2d11faa7b7692405a2013\",\n \"score\": \"0.48661202\",\n \"text\": \"def xyzTolonlat(pt):\\n pt = pt/np.linalg.norm(pt) #normalize\\n lat = np.arcsin(pt[2])\\n lon = np.arctan2(pt[1],pt[0])\\n return lon,lat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46a651150ca0d944033e41e309bad0e\",\n \"score\": \"0.48635912\",\n \"text\": \"def __ccw(a,b,c):\\n return a[0]*b[1] + b[0]*c[1] + c[0]*a[1] - a[0]*c[1] - a[1]*b[0] - b[1]*c[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb7262ec4ab6dd3e201b7f9825c2f1b\",\n \"score\": \"0.48634666\",\n \"text\": \"def point3(x=0, y=0, z=0):\\n return sp.Matrix([x, y, z])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce9fe0e5dc808f3b367fd6cb2d70737\",\n \"score\": \"0.48617086\",\n \"text\": \"def lambertian():\\n p1, p2 = np.random.uniform(0, 1, 2)\\n theta = np.arcsin(np.sqrt(p1))\\n phi = 2 * np.pi * p2\\n coords = spherical_to_cart(theta, phi)\\n return coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18e9957a0bd19dd607649cddcac3723\",\n \"score\": \"0.48595825\",\n \"text\": \"def absoluteBCPOut(anchor, BCPOut):\\n\\treturn (BCPOut[0] + anchor[0], BCPOut[1] + anchor[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3f5339df2d0b4fb6676f98667c011c\",\n \"score\": \"0.4859303\",\n \"text\": \"def point_projection(p, a, b):\\n l = np.dot(p - a, b - a)\\n ab = np.linalg.norm(b - a)\\n ab2 = np.square(ab)\\n m = a + l * (b - a) / ab2\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1dbb2d1bbc06eac3fb5bb30248add0c\",\n \"score\": \"0.48561144\",\n \"text\": \"def angle(a, b, c):\\n # add code to return the correct angle relationship\",\n \"title\": \"\"\n },\n {\n \"docid\": \"908f91e60b056a09be5d58ef8f5908a2\",\n \"score\": \"0.4853992\",\n \"text\": \"def calcCoordinate(a,b,c,bond_len,di_angle,theta):\\n a,b,c = np.array(a),np.array(b),np.array(c)\\n di_angle = di_angle*pi/180.0\\n\\n u = c - b\\n x = a - b\\n v = ((x) - (np.dot((x), u)/np.dot(u,u))*u)\\n\\n w = np.cross(u, x)\\n\\n q = (v/np.linalg.norm(v))*np.cos(di_angle)\\n e = (w/np.linalg.norm(w))*np.sin(di_angle)\\n\\n d = (b + (q+e))\\n\\n u1 = b - c\\n y1 = d - c\\n mag_y1 = sqrt((y1[0]**2)+(y1[1]**2)+(y1[2]**2))\\n mag_u1 = sqrt((u1[0]**2)+(u1[1]**2)+(u1[2]**2))\\n\\n theta_bcd = acos(np.dot(u1,y1)/(mag_u1*mag_y1))\\n rotate = theta - degrees(theta_bcd)\\n\\n z = np.cross(u1, y1)\\n n = z/sqrt((z[0]**2)+(z[1]**2)+(z[2]**2))\\n\\n pos_ini = c + y1*np.cos(rotate*pi/180) \\\\\\n +(np.cross(n,y1)*np.sin(rotate*pi/180))\\\\\\n + n*(np.dot(n,y1))*(1-np.cos(rotate*pi/180))\\n\\n pos_BL = ((pos_ini-c)*(bond_len/np.linalg.norm(pos_ini-c)))+c\\n\\n return pos_BL.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b62bb83c40542e7b7551228946c86e5\",\n \"score\": \"0.4848173\",\n \"text\": \"def cartesien_vers_polaire(z):\\n x = z.real\\n y = z.imag\\n print(x)\\n print(y) \\n module = sqrt(x**2 + y**2)\\n argument = atan2(y,x)\\n return (module,argument)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"367dc2500b3d757ab283f9e17ab8eb6d\",\n \"score\": \"0.48444164\",\n \"text\": \"def zPt(coordTuple):\\n c = coordTuple\\n x = c[0]\\n y = c[1]\\n if len(c) == 3:\\n z = c[2]\\n else:\\n z = 0.0\\n point = (x,y,z)\\n return (x,y,z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a531454377dffeb9073bd58a41b9418\",\n \"score\": \"0.48437122\",\n \"text\": \"def Cao(yAo, Po, To, R=8.314):\\r\\n return yAo*Po / (R*To)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04480d1ffd6cc713b5eb008ca159ab04\",\n \"score\": \"0.48408157\",\n \"text\": \"def relativeBCPIn(anchor, BCPIn):\\n\\treturn (BCPIn[0] - anchor[0], BCPIn[1] - anchor[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c7af31ebf608e16d3753a023e51428\",\n \"score\": \"0.48379734\",\n \"text\": \"def eval2(points, c, x):\\n p1, pn = points\\n if p1 > pn:\\n p1, pn = pn, p1\\n x1, y1 = p1\\n xn, yn = pn\\n d = xn - x1 - 1\\n\\n x2 = x1 + 1\\n y2 = (d * y1 - d * (d + 1) * c + yn) / (d + 1)\\n points = [(x1, y1), (x2, y2)]\\n return eval2seq(points, c, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a787b300cf4fc56fa0861a8de1dac2\",\n \"score\": \"0.48284885\",\n \"text\": \"def compute_Cl(self, alpha):\\n alpha_abscissa=np.array([-45., -35., -25., -15., -10., -5., 0, 5, 10, 15, 25, 35, 45, 55, 70, 90, 110, 125, 135]) * np.pi / 180\\n Clmax=1.2\\n Cl=[-np.cos(alpha_abscissa[6+6]), \\\\\\n -np.cos(alpha_abscissa[5+6]), \\\\\\n -np.cos(alpha_abscissa[4+6]), \\\\\\n -Clmax, \\\\\\n -Clmax*10/15, \\\\\\n -Clmax*5/15, \\\\\\n 0., \\\\\\n Clmax*5/15, \\\\\\n Clmax*10/15, \\\\\\n Clmax, \\\\\\n np.cos(alpha_abscissa[4+6]), \\\\\\n np.cos(alpha_abscissa[5+6]), \\\\\\n np.cos(alpha_abscissa[6+6]), \\\\\\n np.cos(alpha_abscissa[7+6]), \\\\\\n np.cos(alpha_abscissa[8+6]), \\\\\\n np.cos(alpha_abscissa[9+6]), \\\\\\n -np.cos(alpha_abscissa[8+6]), \\\\\\n -np.cos(alpha_abscissa[7+6]), \\\\\\n -np.cos(alpha_abscissa[6+6])]\\n\\n ##print('alpha', alpha)\\n \\n polynomial_coef = np.polyfit(alpha_abscissa, Cl, 7)\\n return np.poly1d(polynomial_coef)(alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9ba4f8de16651f265b17ec77027824\",\n \"score\": \"0.48242936\",\n \"text\": \"def lawOfCosines(a, b, c):\\n ang = np.arccos((a**2 - b**2 - c**2)/(-2.0*b*c))\\n return ang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b22421efa9868be7ee6fee4930cf1ad6\",\n \"score\": \"0.48238954\",\n \"text\": \"def get_segment_from_coeffs(A, B, C):\\n line = ogr.Geometry(ogr.wkbLineString())\\n if A == 0:\\n # line is parallell to x-axis\\n line.AddPoint(0, C / B, 0)\\n line.AddPoint(10, C / B, 0)\\n\\n elif B == 0:\\n # line is parallell to y-axis\\n line.AddPoint(C / A, 0, 0)\\n line.AddPoint(C / A, 10, 0)\\n else:\\n line.AddPoint(C / A, 0, 0)\\n line.AddPoint(0, C / B, 0)\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa28da711a1ba71a947233dfcc51ba6\",\n \"score\": \"0.48238045\",\n \"text\": \"def __init__(self, x, y, z, a, b, c, color):\\n\\n vertices = np.array([[x, y, z]]*8, dtype=DTYPE)\\n for i in range(4):\\n vertices[1 + i + 2 * int(i/2)][0] += a\\n vertices[2 + 2 * int(i/2) + i][1] += b\\n vertices[4 + i ][2] += c\\n\\n self.vertices = vertices\\n self.color = color\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233838f2561fdc7490c47edf34980f0b\",\n \"score\": \"0.48231193\",\n \"text\": \"def _normal_3_points(a, b, c):\\n if type(a) is list or type(a) is tuple:\\n a = Vector3(*a)\\n b = Vector3(*b)\\n c = Vector3(*c)\\n elif isinstance(a, Point3):\\n a = Vector3(*a.export_to_list())\\n b = Vector3(*b.export_to_list())\\n c = Vector3(*c.export_to_list())\\n cross_result = (a - c).cross(b - c).get_normalized()\\n if cross_result.get_x() == -0.0:\\n cross_result.set_x(0.0)\\n if cross_result.get_y() == -0.0:\\n cross_result.set_y(0.0)\\n if cross_result.get_z() == -0.0:\\n cross_result.set_z(0.0)\\n return cross_result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1494,"cells":{"query_id":{"kind":"string","value":"45cdf2c4e620f0dc0d1d7b188af964b8"},"query":{"kind":"string","value":"max_output_buffer(multiply_const_vff_sptr self, int i) > long"},"positive_passages":{"kind":"list like","value":[{"docid":"4abd98a7eb6a5530fbc26f8e30a4a042","score":"0.8243991","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_vff_sptr_max_output_buffer(self, *args, **kwargs)","title":""}],"string":"[\n {\n \"docid\": \"4abd98a7eb6a5530fbc26f8e30a4a042\",\n \"score\": \"0.8243991\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_vff_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d1807baede23d2ee26625f9e60539d6a","score":"0.81873864","text":"def max_output_buffer(self, i):\n return _howto_swig.suma_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"33ca18b8c3a6f11fa3d85dadf64bf841","score":"0.81466925","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_ff_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"bdcbae69e25ca3b638892b0d3e63140c","score":"0.8113833","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.Geometric_Loss_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"2281d66767f539e6604a238f5c683ad4","score":"0.8080759","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_ff_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"4a5342156d164435f0bc74210b300e51","score":"0.80377907","text":"def max_output_buffer(self, i):\n return _howto_swig.square_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"cb9b08a6e681a81e5e4a7dbbc968ffba","score":"0.8011743","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_vii_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"afca08796c61282d422b782f1a5314bc","score":"0.79874444","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.Optical_Receiver_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"d1e071d1bd57e4ed3a20682b2a25732d","score":"0.79646957","text":"def max_output_buffer(self, i):\n return _myinterleaver_swig.interleaver_sptr_max_output_buffer(self, i)","title":""},{"docid":"e7fb21edf8e269d15abb0ec5ed475e68","score":"0.7959704","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.Laser_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"ace189147ad7993f53da34287a8cc479","score":"0.793647","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.Turbulence_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"cb00c26ad8995c30e8ef266de58a94f2","score":"0.7929729","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.Channel_Analyser_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"e07803d6cdb3ede062ddb53ddfb13343","score":"0.7914703","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_ii_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"e419acbc6b162dfa7c42e68e24d74069","score":"0.7883383","text":"def max_output_buffer(self, i):\n return _myinterleaver_swig.deinterleaver_sptr_max_output_buffer(self, i)","title":""},{"docid":"cf0fef8eeac62c19ee17f15380eb7cf0","score":"0.78676635","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_ii_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"54cb06bb6d73b18cba21c45ad02b0d69","score":"0.7814868","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_vcc_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"c8c8f2c9f5d5f4a63bba7c8d5ffed8dd","score":"0.7805858","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.max_ff_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"4d240e22e4dfa324552abd60981b6b43","score":"0.77629334","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.integrate_ff_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"6eb54163ee9d1278bafe8677148e023c","score":"0.7738633","text":"def set_max_output_buffer(self, *args):\n return _blocks_swig3.multiply_const_vff_sptr_set_max_output_buffer(self, *args)","title":""},{"docid":"86afebbcbcbb2eb9337175df21157375","score":"0.77251494","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.float_to_int_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"63ac299b3d2df3cf39490985f59f2065","score":"0.7723559","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_vss_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"7ae12155ac7f462a95f66135f341b682","score":"0.77086633","text":"def max_output_buffer(self, i):\n return _cac_cpp_swig.cac_bb_sptr_max_output_buffer(self, i)","title":""},{"docid":"dd72c21ebc671db8f7dab929025049b9","score":"0.7676586","text":"def max_output_buffer(self, i):\n return _FSO_Comm_swig.FogSmoke_Loss_ff_sptr_max_output_buffer(self, i)","title":""},{"docid":"409e03ee8bba892e29f0610c6fa5363d","score":"0.76517993","text":"def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.int_to_float_sptr_max_output_buffer(self, *args, **kwargs)","title":""},{"docid":"52338c7d7c0523c7f01d3687dc74e8e3","score":"0.76412153","text":"def set_max_output_buffer(self, *args):\n return _blocks_swig3.multiply_const_ff_sptr_set_max_output_buffer(self, *args)","title":""},{"docid":"0deb16bc71d5b910a8cfb3393be4c7b3","score":"0.76347876","text":"def max_output_buffer(self, *args, 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_FSO_Comm_swig.Optical_Receiver_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e071d1bd57e4ed3a20682b2a25732d\",\n \"score\": \"0.79646957\",\n \"text\": \"def max_output_buffer(self, i):\\n return _myinterleaver_swig.interleaver_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7fb21edf8e269d15abb0ec5ed475e68\",\n \"score\": \"0.7959704\",\n \"text\": \"def max_output_buffer(self, i):\\n return _FSO_Comm_swig.Laser_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace189147ad7993f53da34287a8cc479\",\n \"score\": \"0.793647\",\n \"text\": \"def max_output_buffer(self, i):\\n return _FSO_Comm_swig.Turbulence_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb00c26ad8995c30e8ef266de58a94f2\",\n \"score\": \"0.7929729\",\n \"text\": \"def max_output_buffer(self, i):\\n return _FSO_Comm_swig.Channel_Analyser_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07803d6cdb3ede062ddb53ddfb13343\",\n \"score\": \"0.7914703\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_ii_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e419acbc6b162dfa7c42e68e24d74069\",\n \"score\": \"0.7883383\",\n \"text\": \"def max_output_buffer(self, i):\\n return _myinterleaver_swig.deinterleaver_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf0fef8eeac62c19ee17f15380eb7cf0\",\n \"score\": \"0.78676635\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_ii_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cb06bb6d73b18cba21c45ad02b0d69\",\n \"score\": \"0.7814868\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_vcc_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c8f2c9f5d5f4a63bba7c8d5ffed8dd\",\n \"score\": \"0.7805858\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.max_ff_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d240e22e4dfa324552abd60981b6b43\",\n \"score\": \"0.77629334\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.integrate_ff_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eb54163ee9d1278bafe8677148e023c\",\n \"score\": \"0.7738633\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_vff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86afebbcbcbb2eb9337175df21157375\",\n \"score\": \"0.77251494\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.float_to_int_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ac299b3d2df3cf39490985f59f2065\",\n \"score\": \"0.7723559\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_vss_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae12155ac7f462a95f66135f341b682\",\n \"score\": \"0.77086633\",\n \"text\": \"def max_output_buffer(self, i):\\n return _cac_cpp_swig.cac_bb_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd72c21ebc671db8f7dab929025049b9\",\n \"score\": \"0.7676586\",\n \"text\": \"def max_output_buffer(self, i):\\n return _FSO_Comm_swig.FogSmoke_Loss_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409e03ee8bba892e29f0610c6fa5363d\",\n \"score\": \"0.76517993\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.int_to_float_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": 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{\n \"docid\": \"9f03b9120cb6586e03dbabb305f52aaf\",\n \"score\": \"0.7597945\",\n \"text\": \"def max_output_buffer(self, i):\\n return _squitter_mux_swig.squitter_output_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82668845763f4a6f703dd6cb557fbf24\",\n \"score\": \"0.75952274\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.max_ii_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c386e2b53c7a87588328cac6a58485ec\",\n \"score\": \"0.75778997\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6605671bd2afa718d15e655fa9b07e\",\n \"score\": \"0.757269\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.moving_average_ff_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad02970e416c4d92ffe85809dcfedd2\",\n \"score\": \"0.7554172\",\n \"text\": \"def max_output_buffer(self, i):\\n return _FSO_Comm_swig.Pointing_Errors_ff_sptr_max_output_buffer(self, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c253d68dc126d9960d4a12c93ac9e5\",\n \"score\": \"0.7547474\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _ruiy_swig.nonlinear_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"617f2eb15a306a66c3c8438e7469bb2e\",\n \"score\": \"0.7533168\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_ss_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7b96c7291d598a7b18b15c0dc5a949\",\n \"score\": \"0.7521605\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.multiply_cc_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": 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\"title\": \"\"\n },\n {\n \"docid\": \"61b84cedad26148b2793b1eff9010302\",\n \"score\": \"0.7361418\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.max_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb1aee10732c1af1a97c160935b1696\",\n \"score\": \"0.7331097\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_ii_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcca66dbc265c8c04a17378bbdfd9b02\",\n \"score\": \"0.73221064\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.interleave_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e54166d4a89c040fdeeed9d7f8b8482\",\n \"score\": \"0.7298744\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_ii_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b7dd048346af5072c193653812090b\",\n \"score\": \"0.7254943\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _pager_swig.slicer_fb_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f8dabd098b00e529ed896348db1eb22\",\n \"score\": \"0.72527516\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.interleaved_short_to_complex_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34111960dd2e59acb59be12b5447065c\",\n \"score\": \"0.72499263\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.mute_ii_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b18c8f186eec80a42f11fdd1f3136d\",\n \"score\": \"0.72493356\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.float_to_short_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5adc4c3ad05b2f05851e72d430585d\",\n \"score\": \"0.7247384\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.float_to_complex_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4c552044905cf7d8952243e0bae1594\",\n \"score\": \"0.7230838\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.float_to_int_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27edeb8a5e69236080d630fe7676361e\",\n \"score\": \"0.72298723\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.integrate_cc_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f69c752110de2ba012969f4dfb03b0\",\n \"score\": \"0.7201463\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.integrate_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96eeeb9c2271ed5dcdb87f486586f62\",\n \"score\": \"0.7197497\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_const_vff_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19f83f78297878ec4beb7bcab5778845\",\n \"score\": \"0.7196799\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_vcc_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b91ce6dc91a55e11437782d2069b16\",\n \"score\": \"0.71865004\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.int_to_float_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f4e7ea186faefb2695c88bcd4e93fa\",\n \"score\": \"0.7178923\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _ruiy_swig.carrier_offset_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": 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\"daa71f75bc8d11e430353809c18f42b4\",\n \"score\": \"0.71268773\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _ra_blocks_swig.synch_detect_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992f8d33d9764498851767a47dd71f43\",\n \"score\": \"0.7119626\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_const_ff_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c805e3eafb913df6af5d806ea5651a\",\n \"score\": \"0.71138823\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.moving_average_ss_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473a577875ba87fadeb9c20ece3769e0\",\n \"score\": \"0.71125287\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.interleaved_char_to_complex_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f5a62c160ec5d6a9f82590e6341ff3\",\n \"score\": \"0.70909554\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _ra_blocks_swig.vector_power_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8cb299c21a9263ad22d097349a6bbc0\",\n \"score\": \"0.7081289\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.lfsr_32k_source_s_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"515e03fca697bcb78e58eb6a87957d51\",\n \"score\": \"0.7077548\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _channelcoding_swig.binary_symmetric_channel_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d57c277cfd1e6ec562e83804f2d765e\",\n \"score\": \"0.70608264\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_ff_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970e507a12b91c0bb35eba3bdf517b66\",\n \"score\": \"0.7055981\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_vss_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d2c5defbd6844e173bd1654148a025\",\n \"score\": \"0.6997653\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_cc_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c939892a85ae03b256d2cb066e837e0d\",\n \"score\": \"0.6992574\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.moving_average_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a44b3d6a9fb6f6f7d81c8fb2aa3d18c2\",\n \"score\": \"0.6979804\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _howto_swig.square_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b5e1b0793c0a68346bb1582341eaa9\",\n \"score\": \"0.69583887\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_vff_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3536ce7734e1215661aedc578af435d2\",\n \"score\": \"0.69581884\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _pager_swig.flex_deinterleave_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a7bd6343606f44fba23bc1025ae561b\",\n \"score\": \"0.6944916\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_const_vii_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d855d6058da30e54083deaf0194fe403\",\n \"score\": \"0.69349897\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_const_ss_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3960a8d0154a1b88dabf49a8453e412\",\n \"score\": \"0.69332397\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.keep_one_in_n_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da91debcbbfd090e76c311734c3b71b4\",\n \"score\": \"0.69319254\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.keep_m_in_n_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc14852b4a0a0f514baa4a9b4ffc6f1f\",\n \"score\": \"0.69286454\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_cc_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc0aae437a2fa42f3403ea83f2a73aa\",\n \"score\": \"0.69277346\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _ra_blocks_swig.synch_clock_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071e15dbec55c661af294d3f4238168d\",\n \"score\": \"0.69210947\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.mute_cc_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfcdd09fd8c667182442f219bb38272\",\n \"score\": \"0.69146466\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.mute_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f2a4152cd2f74a5512500e5fa4b4af1\",\n \"score\": \"0.69009185\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _ruiy_swig.nonlinear_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7c93180af78245af25d90c6d352cf7\",\n \"score\": \"0.6885423\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.float_to_uchar_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a674ac10dc9087b2e9d50c5e0e89ddbc\",\n \"score\": \"0.6881533\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _blocks_swig3.multiply_const_ff_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4fcc0c2dfd344eefe692ac05e37358\",\n \"score\": \"0.6865493\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.integrate_ii_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aad288295c3e9671617624ddfee20cc\",\n \"score\": \"0.6863072\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.multiply_ss_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f68ac0c95f2d7ecf0c7cd912bc97680\",\n \"score\": \"0.6859125\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_const_ii_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc26bfb8b44856bd7c8574a1d668560b\",\n \"score\": \"0.6853904\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig3.multiply_const_vcc_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32ec60f8d2d22ce92c44f5794aa1980\",\n \"score\": \"0.68485934\",\n \"text\": \"def max_output_buffer(self, *args, **kwargs):\\n return _blocks_swig3.mute_ss_sptr_max_output_buffer(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430bba531a6c0e5cc68a811d973e945f\",\n \"score\": \"0.6846602\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.float_to_complex_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2486c0d2f355805f124b2c337a38ff3\",\n \"score\": \"0.6836156\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _FSO_Comm_swig.Geometric_Loss_ff_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e47cfa1904a37da1d96a616543765c3\",\n \"score\": \"0.6828148\",\n \"text\": \"def set_max_output_buffer(self, *args):\\n return _blocks_swig3.interleaved_short_to_complex_sptr_set_max_output_buffer(self, *args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1495,"cells":{"query_id":{"kind":"string","value":"9624bf2ef681ef113225db87904e272c"},"query":{"kind":"string","value":"r\"\"\"svn_client_conflict_option_set_merged_propval(svn_client_conflict_option_t option, svn_string_t const merged_propval)"},"positive_passages":{"kind":"list like","value":[{"docid":"916a9dd4ce41cb29b672dfb56689247e","score":"0.9387135","text":"def svn_client_conflict_option_set_merged_propval(option: \"svn_client_conflict_option_t\", merged_propval: \"svn_string_t const *\") -> \"void\":\n return _client.svn_client_conflict_option_set_merged_propval(option, merged_propval)","title":""}],"string":"[\n {\n \"docid\": \"916a9dd4ce41cb29b672dfb56689247e\",\n \"score\": \"0.9387135\",\n \"text\": \"def svn_client_conflict_option_set_merged_propval(option: \\\"svn_client_conflict_option_t\\\", merged_propval: \\\"svn_string_t const *\\\") -> \\\"void\\\":\\n return _client.svn_client_conflict_option_set_merged_propval(option, merged_propval)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7a9333759aa8c8c2519ef7f00daab0e9","score":"0.5422297","text":"def svn_client_conflict_option_set_moved_to_repos_relpath(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_option_set_moved_to_repos_relpath(*args)","title":""},{"docid":"f30d3319c2e878ae53581a1a6cc9b4a1","score":"0.52568007","text":"def svn_client_conflict_option_get_id(option: \"svn_client_conflict_option_t\") -> \"svn_client_conflict_option_id_t\":\n return _client.svn_client_conflict_option_get_id(option)","title":""},{"docid":"deeebb3cf365ffd797cfc481e429cc09","score":"0.5241247","text":"def svn_client_conflict_prop_get_resolution(conflict: \"svn_client_conflict_t\", propname: \"char const *\") -> \"svn_client_conflict_option_id_t\":\n return _client.svn_client_conflict_prop_get_resolution(conflict, propname)","title":""},{"docid":"52fe757adbcb1e6d81da0ab53d8a82b2","score":"0.5239707","text":"def svn_client_conflict_prop_resolve(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_prop_resolve(*args)","title":""},{"docid":"10c2f22a8a69766482cdcbdab67b61d9","score":"0.5229968","text":"def svn_client_conflict_option_set_moved_to_repos_relpath2(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_option_set_moved_to_repos_relpath2(*args)","title":""},{"docid":"e03a903ea0e8135b52d77decd5e38621","score":"0.51518136","text":"def svn_client_merge(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge(*args)","title":""},{"docid":"563535787d087f8693bc75326f55e818","score":"0.5141013","text":"def svn_client_conflict_get_recommended_option_id(conflict: \"svn_client_conflict_t\") -> \"svn_client_conflict_option_id_t\":\n return _client.svn_client_conflict_get_recommended_option_id(conflict)","title":""},{"docid":"2c13f90f6075e23ef3f1c2c387ca8cbf","score":"0.5080195","text":"def set_opt(self, opt, value=None):\n if value and sys.platform==\"win32\":\n value = utils.quote_win32(value)\n if opt.startswith(\"--\"):\n # long option\n f = u\"%s=%s\"%(opt,value) if value else opt\n else:\n f = u\"%s %s\"%(opt,value) if value else opt\n self._opts.append(f)","title":""},{"docid":"a793070ffe52deddd6023f54188578f1","score":"0.4953071","text":"def svn_client_merge_peg(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_peg(*args)","title":""},{"docid":"05916a235d45d97e350c05f3bf0dea26","score":"0.49263033","text":"def svn_client_conflict_prop_get_propvals(*args) -> \"svn_string_t **, svn_string_t **, svn_string_t **, svn_string_t **\":\n return _client.svn_client_conflict_prop_get_propvals(*args)","title":""},{"docid":"8c671eade4978bcba969b78e23b6334b","score":"0.48673284","text":"def svn_client_propset_remote(*args) -> \"svn_error_t *\":\n return _client.svn_client_propset_remote(*args)","title":""},{"docid":"ed57249d7bd34f25eaf264042ee1c7d5","score":"0.48635712","text":"def svn_client_conflict_option_set_moved_to_abspath(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_option_set_moved_to_abspath(*args)","title":""},{"docid":"6d05d0342d417007839a92aec358d295","score":"0.48157758","text":"def overwriteMergeResolve(map1, map2, key):\n rv = defaultMergeResolve(map1, map2, key)\n if rv == \"mismatch\":\n rv = \"overwrite\"\n return rv","title":""},{"docid":"23070fb0ddbb6d68d7796265d1b40b93","score":"0.48093876","text":"def svn_client_conflict_get(*args) -> \"svn_client_conflict_t **\":\n return _client.svn_client_conflict_get(*args)","title":""},{"docid":"ab1fe5fef16046de5d56e30c628443b4","score":"0.4807368","text":"def svn_client_mergeinfo_get_merged(*args) -> \"apr_hash_t **\":\n return _client.svn_client_mergeinfo_get_merged(*args)","title":""},{"docid":"4e54e0f711af49e3d835670b87426fd2","score":"0.4785328","text":"def override_options(self, options, overrides, sep=','):\r\n\r\n if isinstance(options, str):\r\n options = options.split(sep) if options else ()\r\n return_str = True\r\n else:\r\n return_str = False\r\n\r\n modified_options = set(options)\r\n\r\n if isinstance(overrides, str):\r\n overrides = overrides.split(sep)\r\n\r\n for override in overrides:\r\n if not override:\r\n continue\r\n elif override[0] == '+':\r\n modified_options.add(override[1:])\r\n elif override[0] == '-':\r\n modified_options.discard(override[1:])\r\n else:\r\n modified_options.add(override)\r\n\r\n if return_str:\r\n return sep.join(modified_options)\r\n else:\r\n return list(modified_options)","title":""},{"docid":"452dc6f5980bad602180bef248df5b9e","score":"0.47783032","text":"def svn_client_conflict_option_get_description(*args) -> \"char const *\":\n return _client.svn_client_conflict_option_get_description(*args)","title":""},{"docid":"7742200eef0b19d8962dbcc744c7aa52","score":"0.47623602","text":"def svn_client_propset(*args) -> \"svn_error_t *\":\n return _client.svn_client_propset(*args)","title":""},{"docid":"b6bff2bd847ecc7141dade5b2d2b5fc4","score":"0.47621503","text":"def svn_client_conflict_prop_resolve_by_id(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_prop_resolve_by_id(*args)","title":""},{"docid":"6bf57482aa3ec547ed2b48191f38c856","score":"0.47218755","text":"def svn_client_merge3(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge3(*args)","title":""},{"docid":"c7c13818122dc373e78188689e6d6ee3","score":"0.46811393","text":"def svn_client_merge2(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge2(*args)","title":""},{"docid":"0fdb2e482c9b33d23609d038aedfd269","score":"0.46802527","text":"def svn_client_conflict_option_set_moved_to_abspath2(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_option_set_moved_to_abspath2(*args)","title":""},{"docid":"d27a7c0fede0912c3d553ebcd6eae855","score":"0.46597937","text":"def svn_client_propset_local(*args) -> \"svn_error_t *\":\n return _client.svn_client_propset_local(*args)","title":""},{"docid":"5313cc94d3f976e8f9c10db65faf77e8","score":"0.46316466","text":"def svn_client_merge_reintegrate(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_reintegrate(*args)","title":""},{"docid":"b7950054ecd68ad9388ccb334fbf6bb2","score":"0.46316433","text":"def onChangeMerged(self, change, source):","title":""},{"docid":"77a0386136ae73a4285d026df6eadc72","score":"0.4619795","text":"def svn_client_mergeinfo_log_merged(*args) -> \"svn_error_t *\":\n return _client.svn_client_mergeinfo_log_merged(*args)","title":""},{"docid":"ca14daca2ad27994d2b43fca056f5dbb","score":"0.46165133","text":"def svn_client_conflict_prop_get_resolution_options(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_prop_get_resolution_options(*args)","title":""},{"docid":"a1e95fa9f39ca1dd2d845f95f6ea2490","score":"0.45974246","text":"def svn_client_suggest_merge_sources(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_suggest_merge_sources(*args)","title":""},{"docid":"11eca4ead631c7b5be4a28e6e4520a51","score":"0.45859647","text":"def svn_client_conflict_get_conflicted(*args) -> \"svn_boolean_t *, apr_array_header_t **, svn_boolean_t *\":\n return _client.svn_client_conflict_get_conflicted(*args)","title":""},{"docid":"db51210b0cff96733b2fa1982b5ec6bd","score":"0.45813322","text":"def svn_client_merge_peg4(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_peg4(*args)","title":""},{"docid":"748b65f430bac4390f0e825e673aaa14","score":"0.4579535","text":"def svn_client_conflict_option_get_label(*args) -> \"char const *\":\n return _client.svn_client_conflict_option_get_label(*args)","title":""},{"docid":"01c9e6e17b14f2f1e7890ed3f287d77f","score":"0.4565108","text":"def set_option(option,invalue):\n _ldap_function_call(_ldap.set_option,option,invalue)","title":""},{"docid":"6fc8f548e561be9d6509674a5939bdac","score":"0.45549023","text":"def svn_client_conflict_option_find_by_id(options: \"apr_array_header_t\", option_id: \"svn_client_conflict_option_id_t\") -> \"svn_client_conflict_option_t *\":\n return _client.svn_client_conflict_option_find_by_id(options, option_id)","title":""},{"docid":"4aae0e865601dc5fe180586e6199364e","score":"0.45506808","text":"def svn_client_merge_peg3(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_peg3(*args)","title":""},{"docid":"1f3e8f523fe656541ee6a422d7544722","score":"0.45503554","text":"def svn_client_merge_peg2(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_peg2(*args)","title":""},{"docid":"7dc46239cacaea0ee0f87bb523d17103","score":"0.45422912","text":"def merged_tag_id(self, merged_tag_id):\n\n self._merged_tag_id = merged_tag_id","title":""},{"docid":"e906a1d9d051b2de793012b6a1dfcf84","score":"0.4526777","text":"def svn_client_merge4(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge4(*args)","title":""},{"docid":"722b84eab48b3ef3f6fa8883eea0cb49","score":"0.4511051","text":"def override_option(self, key, value):\n self.document[key] = value","title":""},{"docid":"46aa2922f272d3dc268482d66587e5b6","score":"0.4501483","text":"def svn_client_conflict_option_get_moved_to_repos_relpath_candidates(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_option_get_moved_to_repos_relpath_candidates(*args)","title":""},{"docid":"dc080a996c107c72a3a1fe4589620bd9","score":"0.4493974","text":"def svn_client_conflict_option_get_moved_to_repos_relpath_candidates2(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_option_get_moved_to_repos_relpath_candidates2(*args)","title":""},{"docid":"e1af3f04c960abd45d97c3b97845632a","score":"0.44900593","text":"def svn_client_conflict_prop_get_description(*args) -> \"char const **\":\n return _client.svn_client_conflict_prop_get_description(*args)","title":""},{"docid":"43275b2fc88633f49931e87f21e6e04e","score":"0.44598076","text":"def set_component_overrides(cfg, versions):\n\n click.secho(\"Setting component overrides...\", bold=True)\n for component_name, overrides in versions.items():\n if component_name not in cfg.get_components():\n continue\n component = cfg.get_components()[component_name]\n needs_checkout = False\n if \"url\" in overrides:\n url = overrides[\"url\"]\n if cfg.debug:\n click.echo(f\" > Set URL for {component.name}: {url}\")\n needs_checkout = git.update_remote(component.repo, url)\n component = cfg.set_repo_url(component_name, url)\n if \"version\" in overrides:\n version = overrides[\"version\"]\n if cfg.debug:\n click.echo(f\" > Set version for {component.name}: {version}\")\n needs_checkout = True\n component = cfg.set_component_version(component_name, version)\n if needs_checkout:\n try:\n git.checkout_version(component.repo, component.version)\n except git.RefError as e:\n raise click.ClickException(\n f\"While setting component override: {e}\"\n ) from e\n # Create symlinks again with correctly checked out components\n create_component_symlinks(cfg, component)","title":""},{"docid":"613c0addfc0736b6d821bfee4405d42d","score":"0.44544944","text":"def orgsetting(self, orgsetting):\n\n self._orgsetting = orgsetting","title":""},{"docid":"80fda71418c466fd8543665a8b058e6a","score":"0.44456077","text":"def svn_client_propset2(*args) -> \"svn_error_t *\":\n return _client.svn_client_propset2(*args)","title":""},{"docid":"3b9d2269d90c7860cf7b477e1ca87f92","score":"0.441075","text":"def options(opt):\n if not hasattr(opt, 'auto_options_style'):\n opt.auto_options_style = 'plain'\n if not hasattr(opt, 'auto_options_define'):\n opt.auto_options_define = 'WITH_%s'","title":""},{"docid":"79e529bfe10c7e25e009e3ec11597dd1","score":"0.43996334","text":"def set_option(option, value): # pragma: no cover\n options[option] = value","title":""},{"docid":"cc97f2d6f762df5d4bf49d5a1659d3b6","score":"0.43877867","text":"def svn_client_conflict_text_get_resolution(conflict: \"svn_client_conflict_t\") -> \"svn_client_conflict_option_id_t\":\n return _client.svn_client_conflict_text_get_resolution(conflict)","title":""},{"docid":"0e96300c6e3894a0f10f88e9e064c0a9","score":"0.4361785","text":"def get_conflict_options():","title":""},{"docid":"4dc67f8c552acef94f234348c67b6a3e","score":"0.43591973","text":"def _set_config(self, target, option):\n opt_val = getattr(self.options, option, None)\n if opt_val is not None:\n target[option] = opt_val","title":""},{"docid":"ede3904dfeaa5f94c3aaefc35525ae40","score":"0.4352757","text":"def aggr_set_option(self, aggregate, option_value, option_name):\n return self.request( \"aggr-set-option\", {\n 'aggregate': [ aggregate, 'aggregate', [ basestring, 'None' ], False ],\n 'option_value': [ option_value, 'option-value', [ basestring, 'None' ], False ],\n 'option_name': [ option_name, 'option-name', [ basestring, 'None' ], False ],\n }, {\n } )","title":""},{"docid":"1929203b3ad2d37fc9bb42069e93ff5e","score":"0.42929354","text":"def svn_client_propset3(*args) -> \"svn_commit_info_t **\":\n return _client.svn_client_propset3(*args)","title":""},{"docid":"bd7fe1de7f9bc4f5025d7da8e955ff3b","score":"0.4273531","text":"def svn_client_conflict_text_resolve(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_text_resolve(*args)","title":""},{"docid":"865736fd5c5513f014c0d9dea05c81b3","score":"0.42552164","text":"def write(self, rcfile, option, value):\n rcparser = ConfigParser.ConfigParser()\n rcparser.read(rcfile)\n\n if option in rcparser._defaults.keys():\n rcparser.set('DEFAULT',option,value)\n\n for section in rcparser.sections():\n if rcparser._sections[sections].has_key(option):\n rcparser.set(section, option, value)\n rcparser.write(rcfile,'w')\n return True\n\n print \"No option\"\n sys.exit(1)","title":""},{"docid":"a5e7057fccca8b642a1e52f93fd3206a","score":"0.42318904","text":"def svn_client_revprop_set(*args) -> \"svn_revnum_t *\":\n return _client.svn_client_revprop_set(*args)","title":""},{"docid":"81be3d509112078b2334133579f2f3f3","score":"0.42266944","text":"def defaultRev(self):\n nHeads = len(self.heads)\n if nHeads == 2:\n return self.heads[1] #merge with the second head implied\n \n # Anything else is an error, but let's help figure out the problem\n if nHeads > 2: #need explicit merge if more than 2 heads\n raise util.Abort(_(\"branch '%s' has %d heads - \"\n \"please merge with an explicit rev\") %\n (self.branch, nHeads))\n #Single head only, clarify our feedback\n if len(self.repo.heads()) > 1: #...there's a named branch merge, requiring explicit --rev\n raise util.Abort(_(\"branch '%s' has one head - \"\n \"please merge with an explicit rev\") %\n self.branch)\n #...or merging isn't an option\n msg = _('there is nothing to merge')\n if parent != repo.lookup(repo[None].branch()):\n msg = _('%s - use \"hg update\" instead') % msg\n raise util.Abort(msg)","title":""},{"docid":"0b90eb96ccf00cbd11f68661f1f202f0","score":"0.42236117","text":"def svn_client_merge_peg5(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge_peg5(*args)","title":""},{"docid":"be456105a51d54a54c9ddbf757720026","score":"0.422268","text":"def test_geoip_merge_options(self):\n for option in self.global_options:\n cmd = 'simeon-geoip merge {o} --help'.format(o=option)\n msg = 'Checking that option {o} for merge exists'.format(\n o=option\n )\n with self.subTest(msg):\n self.proc = _run_command(cmd)\n self.assertEqual(self.proc.returncode, 0)\n self.tearDown()","title":""},{"docid":"1cb5335d2f7342d2c63d18ba2ee456b5","score":"0.4214757","text":"def set_option(self, key, value=''):\n self.options[key] = value","title":""},{"docid":"91ae5bd3eca09ff8e3b07aa880fa50ce","score":"0.41967955","text":"def svn_client_conflict_text_get_resolution_options(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_text_get_resolution_options(*args)","title":""},{"docid":"632e562c2e8f665ee64837a7e76ddc76","score":"0.41935688","text":"def SoOverrideElement_setPickStyleOverride(*args):\n return _coin.SoOverrideElement_setPickStyleOverride(*args)","title":""},{"docid":"7277f5df37a4a263f918234cb60ae1ad","score":"0.4173259","text":"def svn_client_conflict_prop_get_reject_abspath(conflict: \"svn_client_conflict_t\") -> \"char const *\":\n return _client.svn_client_conflict_prop_get_reject_abspath(conflict)","title":""},{"docid":"8396f34e8e99eb11227d111c32a6a686","score":"0.4168025","text":"def merge(self, merged, mergee):\n self.mergeMapping(merged, mergee)","title":""},{"docid":"7dbb0159be00ca47f4fee2fe13d46443","score":"0.4165934","text":"def merge_type(self) -> Optional['SourceApiAssociationConfigMergeType']:\n return pulumi.get(self, \"merge_type\")","title":""},{"docid":"b12399347ce334d7427c4c12367513f4","score":"0.41622418","text":"def _merge_opts(opts, new_opts):\n if isinstance(new_opts, str):\n new_opts = new_opts.split()\n if new_opts:\n opt_set = set(opts)\n new_opts = [opt for opt in new_opts if opt not in opt_set]\n return opts + new_opts\n return opts","title":""},{"docid":"836cb3f127d90f6de27d83951e23f07d","score":"0.4160536","text":"def set_option(self, key, value, project=None, user=None):\r\n from .helpers import set_option\r\n return set_option(self._get_option_key(key), value, project, user)","title":""},{"docid":"6b8d96bf00ea25475e6d24c4386f6c77","score":"0.41595054","text":"def pref_set(opt, val):\n\n # Load prefs data.\n if os.path.isfile(CONFIGFILE):\n try:\n with open(CONFIGFILE, 'r') as fread:\n allprefs = fread.readlines()\n except (IOError, OSError) as ex:\n print_error('Unable to open config file: {}'.format(CONFIGFILE),\n exc=ex,\n boldtext=CONFIGFILE)\n return False\n else:\n # No config file yet.\n allprefs = []\n\n # new options line for xchat.conf.\n optline = '{} = {}'.format(opt, str(val))\n\n # Search preferences for this option.\n existingopt = None\n for line in allprefs:\n if line.startswith(opt):\n # Found the line, retrieve its whole content.\n existingopt = line\n break\n\n # Existing pref.\n if existingopt:\n if existingopt == optline:\n # Pref already set.\n return True\n\n allprefs[allprefs.index(existingopt)] = optline\n # New pref.\n else:\n allprefs.append(optline)\n\n # Remove blank lines..\n while '' in allprefs:\n allprefs.remove('')\n while '\\n' in allprefs:\n allprefs.remove('\\n')\n\n # Write preferences.\n try:\n with open(CONFIGFILE, 'w') as fwrite:\n fwrite.writelines(allprefs)\n fwrite.write('\\n')\n return True\n except (IOError, OSError) as ex:\n print_error(\n 'Unable to write to config file: {}'.format(CONFIGFILE),\n exc=ex,\n boldtext=CONFIGFILE\n )\n return False","title":""},{"docid":"a1ea44ec6328ceff25f0af28a2e8dac8","score":"0.41570336","text":"def merge_kwargs(kwargs, key, value):\n if key is not None:\n if key in kwargs:\n value = ' '.join([kwargs[key], value])\n kwargs[key] = value","title":""},{"docid":"0e3b90d3fef7c9f01c3b2dcf1798f94c","score":"0.41432467","text":"def svn_client_conflict_walk(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_walk(*args)","title":""},{"docid":"67a4bdc436e31a6443578983c916de65","score":"0.41329443","text":"def _validate_merge_cfg(\n lhs,\n rhs,\n left_on,\n right_on,\n on,\n how,\n left_index=False,\n right_index=False,\n lsuffix=None,\n rsuffix=None,\n ):\n len_left_on = len(left_on) if left_on is not None else 0\n len_right_on = len(right_on) if right_on is not None else 0\n\n # must actually support the requested merge type\n if how not in [\"left\", \"inner\", \"outer\", \"leftanti\", \"leftsemi\"]:\n raise NotImplementedError(f\"{how} merge not supported yet\")\n\n # Passing 'on' with 'left_on' or 'right_on' is potentially ambiguous\n if on:\n if left_on or right_on:\n raise ValueError(\n 'Can only pass argument \"on\" OR \"left_on\" '\n 'and \"right_on\", not a combination of both.'\n )\n\n # Require same total number of columns to join on in both operands\n if not (len_left_on + left_index * len(lhs.index.names)) == (\n len_right_on + right_index * len(rhs.index.names)\n ):\n raise ValueError(\n \"Merge operands must have same number of join key columns\"\n )\n\n # If nothing specified, must have common cols to use implicitly\n same_named_columns = set(lhs._data.keys()) & set(rhs._data.keys())\n if not (left_index or right_index):\n if not (left_on or right_on):\n if len(same_named_columns) == 0:\n raise ValueError(\"No common columns to perform merge on\")\n\n for name in same_named_columns:\n if not (\n name in left_on\n and name in right_on\n and (left_on.index(name) == right_on.index(name))\n ):\n if not (lsuffix or rsuffix):\n raise ValueError(\n \"there are overlapping columns but \"\n \"lsuffix and rsuffix are not defined\"\n )\n\n if on:\n on_keys = [on] if not isinstance(on, list) else on\n for key in on_keys:\n if not (key in lhs._data.keys() and key in rhs._data.keys()):\n raise KeyError(f\"Key {on} not in both operands\")\n else:\n for key in left_on:\n if key not in lhs._data.keys():\n raise KeyError(f'Key \"{key}\" not in left operand')\n for key in right_on:\n if key not in rhs._data.keys():\n raise KeyError(f'Key \"{key}\" not in right operand')","title":""},{"docid":"2201b11ac04044c12a55b9354af5e546","score":"0.4119452","text":"def set_optional_property(destination, source, prop_name):\n\n if prop_name in source:\n destination[prop_name] = source[prop_name]","title":""},{"docid":"b69f56befc013b3fb180d240b055d968","score":"0.41069052","text":"def set(self, section, option, value):\n option_obj = self._get_option(section, option)\n # make sure the value is of the right type for the option\n if not option_obj.validate(value):\n raise TypeError(\"{0} is not a valid {1} value.\".format(\n value, type(option_obj).__name__))\n # cast value to a string because SafeConfigParser only allows\n # strings to be set\n str_value = option_obj.to_string(value)\n if not self.has_section(section):\n # Don't call .add_section here because 2.6 complains\n # about sections called '__main__'\n self._sections[section] = {}\n super(SchemaConfigParser, self).set(section, option, str_value)\n filename = self.locate(option)\n self._dirty[filename][section][option] = str_value","title":""},{"docid":"b47459ee3430e12983ae365580e1ee1d","score":"0.4104096","text":"def test_merge3(self):\n self.al.add_argument('arg')\n self.al.add_default(\"'This is my block'\")\n expect = [('arg', \"'This is my block'\")]\n\n self.assertEqual(expect, self.al.merge())","title":""},{"docid":"557878659393f390bbc423213b20d0f6","score":"0.41007844","text":"def optionsStringWasSet(option, text, default=None):\n if default is None:\n return _to_tuple(PISM.cpp.OptionString(option, text, \"\"), False)\n else:\n return _to_tuple(PISM.cpp.OptionString(option, text, default), True)","title":""},{"docid":"295bb7f9cb88be596884a438a2b8bce3","score":"0.41004133","text":"def set_option(self, option_name, option):\n pass","title":""},{"docid":"776015cacb0530ee649def69c47d93d5","score":"0.40966","text":"def override(self, section, option, value):\n e_message = 'section and/or option do not exist in the' \\\n 'base and addition of new keys is disabled: '\n exists = (section in self.sections()) and \\\n (option in self.options(section))\n #stop if the keys don't exist and new ones are not allowed\n if not exists and not self._allow_new_keys:\n raise KeyError(e_message, section, option)\n #otherwise add/overwrite override\n try: self._override[section][option] = value\n except KeyError: self._override[section] = {option:value}","title":""},{"docid":"7fa495e2c005822f2d9bc2b0dae6fea5","score":"0.4094626","text":"def _safe_set(self, attribute, cfg, section, option, validation=None):\n try:\n if validation is None:\n value = cfg.get(section, option)\n else:\n value = validation(cfg, section, option)\n setattr(self, attribute, value)\n except (NoSectionError, NoOptionError):\n pass","title":""},{"docid":"3b59bc974b8833ce14831df272700351","score":"0.40944052","text":"def svn_client_merge5(*args) -> \"svn_error_t *\":\n return _client.svn_client_merge5(*args)","title":""},{"docid":"a6c97e3cf77b09be2d88076f13257880","score":"0.40909466","text":"def add_merge_parser( subparsers):\n parser = subparsers.add_parser(\"merge\", help = \"nucleoatac function: Merge occ and nuc calls\")\n group1 = parser.add_argument_group('Required', 'Necessary arguments')\n group1.add_argument('--occpeaks', metavar = 'occpeaks_file', required = True,\n help = \"Output from occ utility\")\n group1.add_argument('--nucpos', metavar = 'nucpos_file', required = True,\n help = \"Output from nuc utility\")\n group2 = parser.add_argument_group(\"Options\",\"optional\")\n group2.add_argument('--out', metavar = 'out_basename', help = \"output file basename\")\n group2.add_argument('--sep', metavar = 'min_separation', default = 120, \n help = \"minimum separation between call\")\n group2.add_argument('--min_occ', metavar = 'min_occ', default = 0.1,\n help = \"minimum lower bound occupancy of nucleosomes to be considered for excluding NFR. default is 0.1\")","title":""},{"docid":"372f21a5c2f41c5a83583c154d9d18d7","score":"0.40843523","text":"def test_merge1(self):\n self.al.add_argument('arg')\n expect = [('arg', None)]\n\n self.assertEqual(expect, self.al.merge())","title":""},{"docid":"157994a6800578c18d24edfe31dd7c0f","score":"0.4081932","text":"def optval(self, option, default=None):\n return getattr(self._options_, option, default)","title":""},{"docid":"d72c22012db297f21d50bf5907b121df","score":"0.40810117","text":"def svn_client_get_merging_summary(*args) -> \"svn_boolean_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **\":\n return _client.svn_client_get_merging_summary(*args)","title":""},{"docid":"c98770a8292e211a1dc3817c93af88b3","score":"0.40803456","text":"def hgupdate(repo, node, branchmerge, force, partial): \n\n wlock = repo.wlock()\n \n try:\n wc = repo[None] ###working copy change context\n if node is None: ###see nothing passing it node\n # tip of current branch\n try:\n node = repo.branchtags()[wc.branch()]\n except KeyError:\n if wc.branch() == \"default\": # no default branch!\n node = repo.lookup(\"tip\") # update to tip\n else:\n raise util.Abort(_(\"branch %s not found\") % wc.branch())\n overwrite = force and not branchmerge\n pl = wc.parents() ###ParentList of working copy\n p1, p2 = pl[0], repo[node] ### MyChangeContext, MergeChangeContet\n pa = p1.ancestor(p2) ### CommonAncestorChangeContext\n fp1, fp2, xp1, xp2 = p1.node(), p2.node(), str(p1), str(p2) ##changeset hashes & short hashes\n fastforward = False\n\n ### check phase\n if not overwrite and len(pl) > 1: ### wc has multiple parents\n raise util.Abort(_(\"outstanding uncommitted merges\"))\n if branchmerge:\n if pa == p2: ###she is the common ancestor\n raise util.Abort(_(\"can't merge with ancestor\"))\n elif pa == p1: ###me is the common ancestor\n if p1.branch() != p2.branch(): ###...but we are different named branches (usually 'default')\n fastforward = True ### Merging a branch back into common ancestor\n else:\n raise util.Abort(_(\"nothing to merge (use 'hg update'\"\n \" or check 'hg heads')\"))\n if not force and (wc.files() or wc.deleted()): ### edits, adds, or mods in wc\n raise util.Abort(_(\"outstanding uncommitted changes\"))\n elif not overwrite:\n if pa == p1 or pa == p2: # linear\n pass # all good\n elif p1.branch() == p2.branch():\n if wc.files() or wc.deleted():\n raise util.Abort(_(\"crosses branches (use 'hg merge' or \"\n \"'hg update -C' to discard changes)\"))\n raise util.Abort(_(\"crosses branches (use 'hg merge' \"\n \"or 'hg update -C')\"))\n elif wc.files() or wc.deleted():\n raise util.Abort(_(\"crosses named branches (use \"\n \"'hg update -C' to discard changes)\"))\n else:\n # Allow jumping branches if there are no changes\n overwrite = True\n\n ### calculate phase\n action = []\n if not force:\n merge._checkunknown(wc, p2)\n #~ if not util.checkcase(repo.path):\n #~ _checkcollision(p2)\n action += merge._forgetremoved(wc, p2, branchmerge)\n action += merge.manifestmerge(repo, wc, p2, pa, overwrite, partial)\n\n ### apply phase\n if not branchmerge: # just jump to the new rev\n fp1, fp2, xp1, xp2 = fp2, mercurial.node.nullid, xp2, ''\n if not partial:\n repo.hook('preupdate', throw=True, parent1=xp1, parent2=xp2)\n\n stats = merge.applyupdates(repo, action, wc, p2, pa)\n \n #added: I want to record updates even if partial merge\n #merge.recordupdates(repo, action, branchmerge)\n \n if not partial:\n repo.dirstate.setparents(fp1, fp2)\n merge.recordupdates(repo, action, branchmerge)\n if not branchmerge and not fastforward:\n repo.dirstate.setbranch(p2.branch()) \n \n finally:\n wlock.release()\n \n if not partial:\n repo.hook('update', parent1=xp1, parent2=xp2, error=stats[3])\n return stats","title":""},{"docid":"958e09a9c7c983b88f58e40b26fa09df","score":"0.40657353","text":"def test_merge_standard_from_existing():\n\n\tos.chdir('/usr/share/licorn/extensions/squid/data')\n\n\t# start with an empty file.\n\tm1 = LSCF(filename='squid.minimal.test.conf')\n\n\tm1.merge(LSCF(filename='squid.replacements.conf', snipplet=True), partial_match=True, on_conflict='replace')\n\t#print '>> +replacements', m1.to_string()\n\tassert m1.changed\n\n\tm1.merge(LSCF(filename='squid.licorn_lan_1.conf', snipplet=True), partial_match=True, on_conflict='replace')\n\t#print '>> +lan_1', m1.to_string()\n\tassert m1.changed\n\n\tm1.merge(LSCF(filename='squid.licorn_lan_2.conf', snipplet=True))\n\t#print '>> +lan_2\\n', m1.to_string()\n\tassert m1.changed\n\n\tmcheck = LSCF(filename='squid.merged.for_check.conf')\n\t#print '>> +check\\n', mcheck.to_string()\n\tassert m1 == mcheck","title":""},{"docid":"33f7d3903688d3d685a5cdf9cd7a0349","score":"0.4063083","text":"def set_option(self, option):\n options = self.options_as_set()\n options.add(option)\n self.attrib[ATTRIB_OPTIONS] = ','.join(options)\n return self","title":""},{"docid":"9176293ee87448ec087c01bf61b2f3ba","score":"0.4060783","text":"def add_unique_option(self, option):\n if option.name in self.options:\n raise BlobException(\"Option name '%s' is already defined\" % option.name)\n option.repository = self.repository\n option.module = self\n self.options[option.name] = option","title":""},{"docid":"35e5a2493aedc265eee89fb03a8c9394","score":"0.4060452","text":"def merge(base, other):\r\n if isinstance(other, basestring):\r\n cherrypy.engine.autoreload.files.add(other)\r\n\r\n # Load other into base\r\n for section, value_map in reprconf.as_dict(other).items():\r\n if not isinstance(value_map, dict):\r\n raise ValueError(\r\n \"Application config must include section headers, but the \"\r\n \"config you tried to merge doesn't have any sections. \"\r\n \"Wrap your config in another dict with paths as section \"\r\n \"headers, for example: {'/': config}.\")\r\n base.setdefault(section, {}).update(value_map)","title":""},{"docid":"554f32cef7033b790895482a5c2cf89e","score":"0.40583217","text":"def svn_client_conflict_tree_get_resolution(conflict: \"svn_client_conflict_t\") -> \"svn_client_conflict_option_id_t\":\n return _client.svn_client_conflict_tree_get_resolution(conflict)","title":""},{"docid":"f2c45139ba7a20f254cd0c05a7fea43b","score":"0.4045485","text":"def make_full_id(repo: Repo, change_id_override: str = \"\") -> str:\n retstr = \"\"\n if ((repo.id in change_id_override or urllib.parse.quote_plus(repo.id) in change_id_override)\n and (repo.branch in change_id_override or urllib.parse.quote_plus(repo.branch) in change_id_override)):\n # Already formatted!\n retstr = change_id_override\n elif change_id_override:\n retstr = f\"{repo.id}~{repo.branch}~{change_id_override}\"\n elif repo.proposal:\n retstr = f\"{repo.id}~{repo.branch}~{repo.proposal.change_id}\"\n return retstr","title":""},{"docid":"cc506d024d405d1eb9e6dba845f4b43d","score":"0.40431756","text":"def set_option(self, name, value):\n if name not in self.settable:\n raise SCons.Errors.UserError(\"This option is not settable from a SConscript file: %s\"%name)\n\n if name == 'num_jobs':\n try:\n value = int(value)\n if value < 1:\n raise ValueError\n except ValueError:\n raise SCons.Errors.UserError(\"A positive integer is required: %s\"%repr(value))\n elif name == 'max_drift':\n try:\n value = int(value)\n except ValueError:\n raise SCons.Errors.UserError(\"An integer is required: %s\"%repr(value))\n elif name == 'duplicate':\n try:\n value = str(value)\n except ValueError:\n raise SCons.Errors.UserError(\"A string is required: %s\"%repr(value))\n if value not in SCons.Node.FS.Valid_Duplicates:\n raise SCons.Errors.UserError(\"Not a valid duplication style: %s\" % value)\n # Set the duplicate style right away so it can affect linking\n # of SConscript files.\n SCons.Node.FS.set_duplicate(value)\n elif name == 'diskcheck':\n try:\n value = diskcheck_convert(value)\n except ValueError as v:\n raise SCons.Errors.UserError(\"Not a valid diskcheck value: %s\"%v)\n if 'diskcheck' not in self.__dict__:\n # No --diskcheck= option was specified on the command line.\n # Set this right away so it can affect the rest of the\n # file/Node lookups while processing the SConscript files.\n SCons.Node.FS.set_diskcheck(value)\n elif name == 'stack_size':\n try:\n value = int(value)\n except ValueError:\n raise SCons.Errors.UserError(\"An integer is required: %s\"%repr(value))\n elif name == 'md5_chunksize':\n try:\n value = int(value)\n except ValueError:\n raise SCons.Errors.UserError(\"An integer is required: %s\"%repr(value))\n elif name == 'warn':\n if SCons.Util.is_String(value):\n value = [value]\n value = self.__SConscript_settings__.get(name, []) + value\n SCons.Warnings.process_warn_strings(value)\n\n self.__SConscript_settings__[name] = value","title":""},{"docid":"89c9165f7b2e2697cb6886fb50ce274f","score":"0.40381363","text":"def add_option_to_file(self,key,value):#or update\n \n option_dict=self.read_file(for_update=True)\n if option_dict is not None:\n if key in self.default_values:\n option_dict[key]=value\n try:\n codecs.open(self.option_file_path,'w',\"utf-8\").write(\\\n json.dumps(option_dict,sort_keys=True, indent=4, separators=(',',':')))\n except IOError:#permission error\n pass#updating a file that is not accessible is impossible","title":""},{"docid":"e3219b6c78154ef74c64bc9a39aadbb4","score":"0.40285555","text":"def svn_client_conflict_tree_get_resolution_options(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_tree_get_resolution_options(*args)","title":""},{"docid":"b88ef0d1a3a3c7670b53b97bedf3c8c0","score":"0.4028405","text":"def svn_client_conflict_option_get_moved_to_abspath_candidates2(*args) -> \"apr_array_header_t **\":\n return _client.svn_client_conflict_option_get_moved_to_abspath_candidates2(*args)","title":""},{"docid":"864c0e95e828e3da1b8c741bc7621369","score":"0.40259787","text":"def merge(self, branch, message=None, author=None):","title":""},{"docid":"95011528a8040e265dab195b08e69362","score":"0.40231258","text":"def test_merge_str(self):\n list_to_merge = '1.1.1.1,2.2.2.2'\n result = models.ServerList(self.VALID_SERVERS)\n self.assertEqual(set(self.VALID_SERVERS), set(result.servers))\n expected_result = [*self.VALID_SERVERS, *(list_to_merge.split(','))]\n result.merge(list_to_merge)\n self.assertEqual(set(expected_result), set(result.servers))","title":""},{"docid":"c6abea2d6990588b587000c424894d41","score":"0.40174183","text":"def set(self, section,option,value):\n\t\tpass","title":""},{"docid":"c6abea2d6990588b587000c424894d41","score":"0.40174183","text":"def set(self, section,option,value):\n\t\tpass","title":""},{"docid":"7037da05123d009f8341e0bff81a9e60","score":"0.40153354","text":"def setPickStyleOverride(*args):\n return _coin.SoOverrideElement_setPickStyleOverride(*args)","title":""},{"docid":"4c3575abb5f774c675be79ae222ef46a","score":"0.40126106","text":"def svn_client_mergeinfo_log(*args) -> \"svn_error_t *\":\n return _client.svn_client_mergeinfo_log(*args)","title":""},{"docid":"075f9791a636986d53a3ba6e91ef0605","score":"0.39992234","text":"def conflicting_config(self):\n return self.conflicts.get()[0].new_config","title":""},{"docid":"fd33e71c700086e6c825a8e460af20af","score":"0.39967567","text":"def test_set_merge_upstream(self) -> None:\n\n given = True\n expected = True\n\n self.config_loader.merge_upstream = given\n\n actual = self.config_loader.merge_upstream\n\n self.assertEqual(expected, actual)","title":""}],"string":"[\n {\n \"docid\": \"7a9333759aa8c8c2519ef7f00daab0e9\",\n \"score\": \"0.5422297\",\n \"text\": \"def svn_client_conflict_option_set_moved_to_repos_relpath(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_option_set_moved_to_repos_relpath(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30d3319c2e878ae53581a1a6cc9b4a1\",\n \"score\": \"0.52568007\",\n \"text\": \"def svn_client_conflict_option_get_id(option: \\\"svn_client_conflict_option_t\\\") -> \\\"svn_client_conflict_option_id_t\\\":\\n return _client.svn_client_conflict_option_get_id(option)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deeebb3cf365ffd797cfc481e429cc09\",\n \"score\": \"0.5241247\",\n \"text\": \"def svn_client_conflict_prop_get_resolution(conflict: \\\"svn_client_conflict_t\\\", propname: \\\"char const *\\\") -> \\\"svn_client_conflict_option_id_t\\\":\\n return _client.svn_client_conflict_prop_get_resolution(conflict, propname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52fe757adbcb1e6d81da0ab53d8a82b2\",\n \"score\": \"0.5239707\",\n \"text\": \"def svn_client_conflict_prop_resolve(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_prop_resolve(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c2f22a8a69766482cdcbdab67b61d9\",\n \"score\": \"0.5229968\",\n \"text\": \"def svn_client_conflict_option_set_moved_to_repos_relpath2(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_option_set_moved_to_repos_relpath2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e03a903ea0e8135b52d77decd5e38621\",\n \"score\": \"0.51518136\",\n \"text\": \"def svn_client_merge(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563535787d087f8693bc75326f55e818\",\n \"score\": \"0.5141013\",\n \"text\": \"def svn_client_conflict_get_recommended_option_id(conflict: \\\"svn_client_conflict_t\\\") -> \\\"svn_client_conflict_option_id_t\\\":\\n return _client.svn_client_conflict_get_recommended_option_id(conflict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c13f90f6075e23ef3f1c2c387ca8cbf\",\n \"score\": \"0.5080195\",\n \"text\": \"def set_opt(self, opt, value=None):\\n if value and sys.platform==\\\"win32\\\":\\n value = utils.quote_win32(value)\\n if opt.startswith(\\\"--\\\"):\\n # long option\\n f = u\\\"%s=%s\\\"%(opt,value) if value else opt\\n else:\\n f = u\\\"%s %s\\\"%(opt,value) if value else opt\\n self._opts.append(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a793070ffe52deddd6023f54188578f1\",\n \"score\": \"0.4953071\",\n \"text\": \"def svn_client_merge_peg(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_peg(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05916a235d45d97e350c05f3bf0dea26\",\n \"score\": \"0.49263033\",\n \"text\": \"def svn_client_conflict_prop_get_propvals(*args) -> \\\"svn_string_t **, svn_string_t **, svn_string_t **, svn_string_t **\\\":\\n return _client.svn_client_conflict_prop_get_propvals(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c671eade4978bcba969b78e23b6334b\",\n \"score\": \"0.48673284\",\n \"text\": \"def svn_client_propset_remote(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_propset_remote(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed57249d7bd34f25eaf264042ee1c7d5\",\n \"score\": \"0.48635712\",\n \"text\": \"def svn_client_conflict_option_set_moved_to_abspath(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_option_set_moved_to_abspath(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d05d0342d417007839a92aec358d295\",\n \"score\": \"0.48157758\",\n \"text\": \"def overwriteMergeResolve(map1, map2, key):\\n rv = defaultMergeResolve(map1, map2, key)\\n if rv == \\\"mismatch\\\":\\n rv = \\\"overwrite\\\"\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23070fb0ddbb6d68d7796265d1b40b93\",\n \"score\": \"0.48093876\",\n \"text\": \"def svn_client_conflict_get(*args) -> \\\"svn_client_conflict_t **\\\":\\n return _client.svn_client_conflict_get(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab1fe5fef16046de5d56e30c628443b4\",\n \"score\": \"0.4807368\",\n \"text\": \"def svn_client_mergeinfo_get_merged(*args) -> \\\"apr_hash_t **\\\":\\n return _client.svn_client_mergeinfo_get_merged(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e54e0f711af49e3d835670b87426fd2\",\n \"score\": \"0.4785328\",\n \"text\": \"def override_options(self, options, overrides, sep=','):\\r\\n\\r\\n if isinstance(options, str):\\r\\n options = options.split(sep) if options else ()\\r\\n return_str = True\\r\\n else:\\r\\n return_str = False\\r\\n\\r\\n modified_options = set(options)\\r\\n\\r\\n if isinstance(overrides, str):\\r\\n overrides = overrides.split(sep)\\r\\n\\r\\n for override in overrides:\\r\\n if not override:\\r\\n continue\\r\\n elif override[0] == '+':\\r\\n modified_options.add(override[1:])\\r\\n elif override[0] == '-':\\r\\n modified_options.discard(override[1:])\\r\\n else:\\r\\n modified_options.add(override)\\r\\n\\r\\n if return_str:\\r\\n return sep.join(modified_options)\\r\\n else:\\r\\n return list(modified_options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"452dc6f5980bad602180bef248df5b9e\",\n \"score\": \"0.47783032\",\n \"text\": \"def svn_client_conflict_option_get_description(*args) -> \\\"char const *\\\":\\n return _client.svn_client_conflict_option_get_description(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7742200eef0b19d8962dbcc744c7aa52\",\n \"score\": \"0.47623602\",\n \"text\": \"def svn_client_propset(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_propset(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bff2bd847ecc7141dade5b2d2b5fc4\",\n \"score\": \"0.47621503\",\n \"text\": \"def svn_client_conflict_prop_resolve_by_id(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_prop_resolve_by_id(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf57482aa3ec547ed2b48191f38c856\",\n \"score\": \"0.47218755\",\n \"text\": \"def svn_client_merge3(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge3(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c13818122dc373e78188689e6d6ee3\",\n \"score\": \"0.46811393\",\n \"text\": \"def svn_client_merge2(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdb2e482c9b33d23609d038aedfd269\",\n \"score\": \"0.46802527\",\n \"text\": \"def svn_client_conflict_option_set_moved_to_abspath2(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_option_set_moved_to_abspath2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27a7c0fede0912c3d553ebcd6eae855\",\n \"score\": \"0.46597937\",\n \"text\": \"def svn_client_propset_local(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_propset_local(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5313cc94d3f976e8f9c10db65faf77e8\",\n \"score\": \"0.46316466\",\n \"text\": \"def svn_client_merge_reintegrate(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_reintegrate(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7950054ecd68ad9388ccb334fbf6bb2\",\n \"score\": \"0.46316433\",\n \"text\": \"def onChangeMerged(self, change, source):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a0386136ae73a4285d026df6eadc72\",\n \"score\": \"0.4619795\",\n \"text\": \"def svn_client_mergeinfo_log_merged(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_mergeinfo_log_merged(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca14daca2ad27994d2b43fca056f5dbb\",\n \"score\": \"0.46165133\",\n \"text\": \"def svn_client_conflict_prop_get_resolution_options(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_prop_get_resolution_options(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e95fa9f39ca1dd2d845f95f6ea2490\",\n \"score\": \"0.45974246\",\n \"text\": \"def svn_client_suggest_merge_sources(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_suggest_merge_sources(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11eca4ead631c7b5be4a28e6e4520a51\",\n \"score\": \"0.45859647\",\n \"text\": \"def svn_client_conflict_get_conflicted(*args) -> \\\"svn_boolean_t *, apr_array_header_t **, svn_boolean_t *\\\":\\n return _client.svn_client_conflict_get_conflicted(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db51210b0cff96733b2fa1982b5ec6bd\",\n \"score\": \"0.45813322\",\n \"text\": \"def svn_client_merge_peg4(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_peg4(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748b65f430bac4390f0e825e673aaa14\",\n \"score\": \"0.4579535\",\n \"text\": \"def svn_client_conflict_option_get_label(*args) -> \\\"char const *\\\":\\n return _client.svn_client_conflict_option_get_label(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c9e6e17b14f2f1e7890ed3f287d77f\",\n \"score\": \"0.4565108\",\n \"text\": \"def set_option(option,invalue):\\n _ldap_function_call(_ldap.set_option,option,invalue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc8f548e561be9d6509674a5939bdac\",\n \"score\": \"0.45549023\",\n \"text\": \"def svn_client_conflict_option_find_by_id(options: \\\"apr_array_header_t\\\", option_id: \\\"svn_client_conflict_option_id_t\\\") -> \\\"svn_client_conflict_option_t *\\\":\\n return _client.svn_client_conflict_option_find_by_id(options, option_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aae0e865601dc5fe180586e6199364e\",\n \"score\": \"0.45506808\",\n \"text\": \"def svn_client_merge_peg3(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_peg3(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3e8f523fe656541ee6a422d7544722\",\n \"score\": \"0.45503554\",\n \"text\": \"def svn_client_merge_peg2(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_peg2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc46239cacaea0ee0f87bb523d17103\",\n \"score\": \"0.45422912\",\n \"text\": \"def merged_tag_id(self, merged_tag_id):\\n\\n self._merged_tag_id = merged_tag_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e906a1d9d051b2de793012b6a1dfcf84\",\n \"score\": \"0.4526777\",\n \"text\": \"def svn_client_merge4(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge4(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722b84eab48b3ef3f6fa8883eea0cb49\",\n \"score\": \"0.4511051\",\n \"text\": \"def override_option(self, key, value):\\n self.document[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46aa2922f272d3dc268482d66587e5b6\",\n \"score\": \"0.4501483\",\n \"text\": \"def svn_client_conflict_option_get_moved_to_repos_relpath_candidates(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_option_get_moved_to_repos_relpath_candidates(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc080a996c107c72a3a1fe4589620bd9\",\n \"score\": \"0.4493974\",\n \"text\": \"def svn_client_conflict_option_get_moved_to_repos_relpath_candidates2(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_option_get_moved_to_repos_relpath_candidates2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1af3f04c960abd45d97c3b97845632a\",\n \"score\": \"0.44900593\",\n \"text\": \"def svn_client_conflict_prop_get_description(*args) -> \\\"char const **\\\":\\n return _client.svn_client_conflict_prop_get_description(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43275b2fc88633f49931e87f21e6e04e\",\n \"score\": \"0.44598076\",\n \"text\": \"def set_component_overrides(cfg, versions):\\n\\n click.secho(\\\"Setting component overrides...\\\", bold=True)\\n for component_name, overrides in versions.items():\\n if component_name not in cfg.get_components():\\n continue\\n component = cfg.get_components()[component_name]\\n needs_checkout = False\\n if \\\"url\\\" in overrides:\\n url = overrides[\\\"url\\\"]\\n if cfg.debug:\\n click.echo(f\\\" > Set URL for {component.name}: {url}\\\")\\n needs_checkout = git.update_remote(component.repo, url)\\n component = cfg.set_repo_url(component_name, url)\\n if \\\"version\\\" in overrides:\\n version = overrides[\\\"version\\\"]\\n if cfg.debug:\\n click.echo(f\\\" > Set version for {component.name}: {version}\\\")\\n needs_checkout = True\\n component = cfg.set_component_version(component_name, version)\\n if needs_checkout:\\n try:\\n git.checkout_version(component.repo, component.version)\\n except git.RefError as e:\\n raise click.ClickException(\\n f\\\"While setting component override: {e}\\\"\\n ) from e\\n # Create symlinks again with correctly checked out components\\n create_component_symlinks(cfg, component)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"613c0addfc0736b6d821bfee4405d42d\",\n \"score\": \"0.44544944\",\n \"text\": \"def orgsetting(self, orgsetting):\\n\\n self._orgsetting = orgsetting\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fda71418c466fd8543665a8b058e6a\",\n \"score\": \"0.44456077\",\n \"text\": \"def svn_client_propset2(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_propset2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9d2269d90c7860cf7b477e1ca87f92\",\n \"score\": \"0.441075\",\n \"text\": \"def options(opt):\\n if not hasattr(opt, 'auto_options_style'):\\n opt.auto_options_style = 'plain'\\n if not hasattr(opt, 'auto_options_define'):\\n opt.auto_options_define = 'WITH_%s'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79e529bfe10c7e25e009e3ec11597dd1\",\n \"score\": \"0.43996334\",\n \"text\": \"def set_option(option, value): # pragma: no cover\\n options[option] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc97f2d6f762df5d4bf49d5a1659d3b6\",\n \"score\": \"0.43877867\",\n \"text\": \"def svn_client_conflict_text_get_resolution(conflict: \\\"svn_client_conflict_t\\\") -> \\\"svn_client_conflict_option_id_t\\\":\\n return _client.svn_client_conflict_text_get_resolution(conflict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e96300c6e3894a0f10f88e9e064c0a9\",\n \"score\": \"0.4361785\",\n \"text\": \"def get_conflict_options():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc67f8c552acef94f234348c67b6a3e\",\n \"score\": \"0.43591973\",\n \"text\": \"def _set_config(self, target, option):\\n opt_val = getattr(self.options, option, None)\\n if opt_val is not None:\\n target[option] = opt_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede3904dfeaa5f94c3aaefc35525ae40\",\n \"score\": \"0.4352757\",\n \"text\": \"def aggr_set_option(self, aggregate, option_value, option_name):\\n return self.request( \\\"aggr-set-option\\\", {\\n 'aggregate': [ aggregate, 'aggregate', [ basestring, 'None' ], False ],\\n 'option_value': [ option_value, 'option-value', [ basestring, 'None' ], False ],\\n 'option_name': [ option_name, 'option-name', [ basestring, 'None' ], False ],\\n }, {\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1929203b3ad2d37fc9bb42069e93ff5e\",\n \"score\": \"0.42929354\",\n \"text\": \"def svn_client_propset3(*args) -> \\\"svn_commit_info_t **\\\":\\n return _client.svn_client_propset3(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7fe1de7f9bc4f5025d7da8e955ff3b\",\n \"score\": \"0.4273531\",\n \"text\": \"def svn_client_conflict_text_resolve(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_text_resolve(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865736fd5c5513f014c0d9dea05c81b3\",\n \"score\": \"0.42552164\",\n \"text\": \"def write(self, rcfile, option, value):\\n rcparser = ConfigParser.ConfigParser()\\n rcparser.read(rcfile)\\n\\n if option in rcparser._defaults.keys():\\n rcparser.set('DEFAULT',option,value)\\n\\n for section in rcparser.sections():\\n if rcparser._sections[sections].has_key(option):\\n rcparser.set(section, option, value)\\n rcparser.write(rcfile,'w')\\n return True\\n\\n print \\\"No option\\\"\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e7057fccca8b642a1e52f93fd3206a\",\n \"score\": \"0.42318904\",\n \"text\": \"def svn_client_revprop_set(*args) -> \\\"svn_revnum_t *\\\":\\n return _client.svn_client_revprop_set(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81be3d509112078b2334133579f2f3f3\",\n \"score\": \"0.42266944\",\n \"text\": \"def defaultRev(self):\\n nHeads = len(self.heads)\\n if nHeads == 2:\\n return self.heads[1] #merge with the second head implied\\n \\n # Anything else is an error, but let's help figure out the problem\\n if nHeads > 2: #need explicit merge if more than 2 heads\\n raise util.Abort(_(\\\"branch '%s' has %d heads - \\\"\\n \\\"please merge with an explicit rev\\\") %\\n (self.branch, nHeads))\\n #Single head only, clarify our feedback\\n if len(self.repo.heads()) > 1: #...there's a named branch merge, requiring explicit --rev\\n raise util.Abort(_(\\\"branch '%s' has one head - \\\"\\n \\\"please merge with an explicit rev\\\") %\\n self.branch)\\n #...or merging isn't an option\\n msg = _('there is nothing to merge')\\n if parent != repo.lookup(repo[None].branch()):\\n msg = _('%s - use \\\"hg update\\\" instead') % msg\\n raise util.Abort(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b90eb96ccf00cbd11f68661f1f202f0\",\n \"score\": \"0.42236117\",\n \"text\": \"def svn_client_merge_peg5(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge_peg5(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be456105a51d54a54c9ddbf757720026\",\n \"score\": \"0.422268\",\n \"text\": \"def test_geoip_merge_options(self):\\n for option in self.global_options:\\n cmd = 'simeon-geoip merge {o} --help'.format(o=option)\\n msg = 'Checking that option {o} for merge exists'.format(\\n o=option\\n )\\n with self.subTest(msg):\\n self.proc = _run_command(cmd)\\n self.assertEqual(self.proc.returncode, 0)\\n self.tearDown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb5335d2f7342d2c63d18ba2ee456b5\",\n \"score\": \"0.4214757\",\n \"text\": \"def set_option(self, key, value=''):\\n self.options[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ae5bd3eca09ff8e3b07aa880fa50ce\",\n \"score\": \"0.41967955\",\n \"text\": \"def svn_client_conflict_text_get_resolution_options(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_text_get_resolution_options(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632e562c2e8f665ee64837a7e76ddc76\",\n \"score\": \"0.41935688\",\n \"text\": \"def SoOverrideElement_setPickStyleOverride(*args):\\n return _coin.SoOverrideElement_setPickStyleOverride(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7277f5df37a4a263f918234cb60ae1ad\",\n \"score\": \"0.4173259\",\n \"text\": \"def svn_client_conflict_prop_get_reject_abspath(conflict: \\\"svn_client_conflict_t\\\") -> \\\"char const *\\\":\\n return _client.svn_client_conflict_prop_get_reject_abspath(conflict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8396f34e8e99eb11227d111c32a6a686\",\n \"score\": \"0.4168025\",\n \"text\": \"def merge(self, merged, mergee):\\n self.mergeMapping(merged, mergee)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dbb0159be00ca47f4fee2fe13d46443\",\n \"score\": \"0.4165934\",\n \"text\": \"def merge_type(self) -> Optional['SourceApiAssociationConfigMergeType']:\\n return pulumi.get(self, \\\"merge_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b12399347ce334d7427c4c12367513f4\",\n \"score\": \"0.41622418\",\n \"text\": \"def _merge_opts(opts, new_opts):\\n if isinstance(new_opts, str):\\n new_opts = new_opts.split()\\n if new_opts:\\n opt_set = set(opts)\\n new_opts = [opt for opt in new_opts if opt not in opt_set]\\n return opts + new_opts\\n return opts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"836cb3f127d90f6de27d83951e23f07d\",\n \"score\": \"0.4160536\",\n \"text\": \"def set_option(self, key, value, project=None, user=None):\\r\\n from .helpers import set_option\\r\\n return set_option(self._get_option_key(key), value, project, user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8d96bf00ea25475e6d24c4386f6c77\",\n \"score\": \"0.41595054\",\n \"text\": \"def pref_set(opt, val):\\n\\n # Load prefs data.\\n if os.path.isfile(CONFIGFILE):\\n try:\\n with open(CONFIGFILE, 'r') as fread:\\n allprefs = fread.readlines()\\n except (IOError, OSError) as ex:\\n print_error('Unable to open config file: {}'.format(CONFIGFILE),\\n exc=ex,\\n boldtext=CONFIGFILE)\\n return False\\n else:\\n # No config file yet.\\n allprefs = []\\n\\n # new options line for xchat.conf.\\n optline = '{} = {}'.format(opt, str(val))\\n\\n # Search preferences for this option.\\n existingopt = None\\n for line in allprefs:\\n if line.startswith(opt):\\n # Found the line, retrieve its whole content.\\n existingopt = line\\n break\\n\\n # Existing pref.\\n if existingopt:\\n if existingopt == optline:\\n # Pref already set.\\n return True\\n\\n allprefs[allprefs.index(existingopt)] = optline\\n # New pref.\\n else:\\n allprefs.append(optline)\\n\\n # Remove blank lines..\\n while '' in allprefs:\\n allprefs.remove('')\\n while '\\\\n' in allprefs:\\n allprefs.remove('\\\\n')\\n\\n # Write preferences.\\n try:\\n with open(CONFIGFILE, 'w') as fwrite:\\n fwrite.writelines(allprefs)\\n fwrite.write('\\\\n')\\n return True\\n except (IOError, OSError) as ex:\\n print_error(\\n 'Unable to write to config file: {}'.format(CONFIGFILE),\\n exc=ex,\\n boldtext=CONFIGFILE\\n )\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ea44ec6328ceff25f0af28a2e8dac8\",\n \"score\": \"0.41570336\",\n \"text\": \"def merge_kwargs(kwargs, key, value):\\n if key is not None:\\n if key in kwargs:\\n value = ' '.join([kwargs[key], value])\\n kwargs[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3b90d3fef7c9f01c3b2dcf1798f94c\",\n \"score\": \"0.41432467\",\n \"text\": \"def svn_client_conflict_walk(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_conflict_walk(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67a4bdc436e31a6443578983c916de65\",\n \"score\": \"0.41329443\",\n \"text\": \"def _validate_merge_cfg(\\n lhs,\\n rhs,\\n left_on,\\n right_on,\\n on,\\n how,\\n left_index=False,\\n right_index=False,\\n lsuffix=None,\\n rsuffix=None,\\n ):\\n len_left_on = len(left_on) if left_on is not None else 0\\n len_right_on = len(right_on) if right_on is not None else 0\\n\\n # must actually support the requested merge type\\n if how not in [\\\"left\\\", \\\"inner\\\", \\\"outer\\\", \\\"leftanti\\\", \\\"leftsemi\\\"]:\\n raise NotImplementedError(f\\\"{how} merge not supported yet\\\")\\n\\n # Passing 'on' with 'left_on' or 'right_on' is potentially ambiguous\\n if on:\\n if left_on or right_on:\\n raise ValueError(\\n 'Can only pass argument \\\"on\\\" OR \\\"left_on\\\" '\\n 'and \\\"right_on\\\", not a combination of both.'\\n )\\n\\n # Require same total number of columns to join on in both operands\\n if not (len_left_on + left_index * len(lhs.index.names)) == (\\n len_right_on + right_index * len(rhs.index.names)\\n ):\\n raise ValueError(\\n \\\"Merge operands must have same number of join key columns\\\"\\n )\\n\\n # If nothing specified, must have common cols to use implicitly\\n same_named_columns = set(lhs._data.keys()) & set(rhs._data.keys())\\n if not (left_index or right_index):\\n if not (left_on or right_on):\\n if len(same_named_columns) == 0:\\n raise ValueError(\\\"No common columns to perform merge on\\\")\\n\\n for name in same_named_columns:\\n if not (\\n name in left_on\\n and name in right_on\\n and (left_on.index(name) == right_on.index(name))\\n ):\\n if not (lsuffix or rsuffix):\\n raise ValueError(\\n \\\"there are overlapping columns but \\\"\\n \\\"lsuffix and rsuffix are not defined\\\"\\n )\\n\\n if on:\\n on_keys = [on] if not isinstance(on, list) else on\\n for key in on_keys:\\n if not (key in lhs._data.keys() and key in rhs._data.keys()):\\n raise KeyError(f\\\"Key {on} not in both operands\\\")\\n else:\\n for key in left_on:\\n if key not in lhs._data.keys():\\n raise KeyError(f'Key \\\"{key}\\\" not in left operand')\\n for key in right_on:\\n if key not in rhs._data.keys():\\n raise KeyError(f'Key \\\"{key}\\\" not in right operand')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2201b11ac04044c12a55b9354af5e546\",\n \"score\": \"0.4119452\",\n \"text\": \"def set_optional_property(destination, source, prop_name):\\n\\n if prop_name in source:\\n destination[prop_name] = source[prop_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69f56befc013b3fb180d240b055d968\",\n \"score\": \"0.41069052\",\n \"text\": \"def set(self, section, option, value):\\n option_obj = self._get_option(section, option)\\n # make sure the value is of the right type for the option\\n if not option_obj.validate(value):\\n raise TypeError(\\\"{0} is not a valid {1} value.\\\".format(\\n value, type(option_obj).__name__))\\n # cast value to a string because SafeConfigParser only allows\\n # strings to be set\\n str_value = option_obj.to_string(value)\\n if not self.has_section(section):\\n # Don't call .add_section here because 2.6 complains\\n # about sections called '__main__'\\n self._sections[section] = {}\\n super(SchemaConfigParser, self).set(section, option, str_value)\\n filename = self.locate(option)\\n self._dirty[filename][section][option] = str_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47459ee3430e12983ae365580e1ee1d\",\n \"score\": \"0.4104096\",\n \"text\": \"def test_merge3(self):\\n self.al.add_argument('arg')\\n self.al.add_default(\\\"'This is my block'\\\")\\n expect = [('arg', \\\"'This is my block'\\\")]\\n\\n self.assertEqual(expect, self.al.merge())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557878659393f390bbc423213b20d0f6\",\n \"score\": \"0.41007844\",\n \"text\": \"def optionsStringWasSet(option, text, default=None):\\n if default is None:\\n return _to_tuple(PISM.cpp.OptionString(option, text, \\\"\\\"), False)\\n else:\\n return _to_tuple(PISM.cpp.OptionString(option, text, default), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295bb7f9cb88be596884a438a2b8bce3\",\n \"score\": \"0.41004133\",\n \"text\": \"def set_option(self, option_name, option):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776015cacb0530ee649def69c47d93d5\",\n \"score\": \"0.40966\",\n \"text\": \"def override(self, section, option, value):\\n e_message = 'section and/or option do not exist in the' \\\\\\n 'base and addition of new keys is disabled: '\\n exists = (section in self.sections()) and \\\\\\n (option in self.options(section))\\n #stop if the keys don't exist and new ones are not allowed\\n if not exists and not self._allow_new_keys:\\n raise KeyError(e_message, section, option)\\n #otherwise add/overwrite override\\n try: self._override[section][option] = value\\n except KeyError: self._override[section] = {option:value}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa495e2c005822f2d9bc2b0dae6fea5\",\n \"score\": \"0.4094626\",\n \"text\": \"def _safe_set(self, attribute, cfg, section, option, validation=None):\\n try:\\n if validation is None:\\n value = cfg.get(section, option)\\n else:\\n value = validation(cfg, section, option)\\n setattr(self, attribute, value)\\n except (NoSectionError, NoOptionError):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b59bc974b8833ce14831df272700351\",\n \"score\": \"0.40944052\",\n \"text\": \"def svn_client_merge5(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_merge5(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c97e3cf77b09be2d88076f13257880\",\n \"score\": \"0.40909466\",\n \"text\": \"def add_merge_parser( subparsers):\\n parser = subparsers.add_parser(\\\"merge\\\", help = \\\"nucleoatac function: Merge occ and nuc calls\\\")\\n group1 = parser.add_argument_group('Required', 'Necessary arguments')\\n group1.add_argument('--occpeaks', metavar = 'occpeaks_file', required = True,\\n help = \\\"Output from occ utility\\\")\\n group1.add_argument('--nucpos', metavar = 'nucpos_file', required = True,\\n help = \\\"Output from nuc utility\\\")\\n group2 = parser.add_argument_group(\\\"Options\\\",\\\"optional\\\")\\n group2.add_argument('--out', metavar = 'out_basename', help = \\\"output file basename\\\")\\n group2.add_argument('--sep', metavar = 'min_separation', default = 120, \\n help = \\\"minimum separation between call\\\")\\n group2.add_argument('--min_occ', metavar = 'min_occ', default = 0.1,\\n help = \\\"minimum lower bound occupancy of nucleosomes to be considered for excluding NFR. default is 0.1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372f21a5c2f41c5a83583c154d9d18d7\",\n \"score\": \"0.40843523\",\n \"text\": \"def test_merge1(self):\\n self.al.add_argument('arg')\\n expect = [('arg', None)]\\n\\n self.assertEqual(expect, self.al.merge())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157994a6800578c18d24edfe31dd7c0f\",\n \"score\": \"0.4081932\",\n \"text\": \"def optval(self, option, default=None):\\n return getattr(self._options_, option, default)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72c22012db297f21d50bf5907b121df\",\n \"score\": \"0.40810117\",\n \"text\": \"def svn_client_get_merging_summary(*args) -> \\\"svn_boolean_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **, svn_revnum_t *, char const **\\\":\\n return _client.svn_client_get_merging_summary(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98770a8292e211a1dc3817c93af88b3\",\n \"score\": \"0.40803456\",\n \"text\": \"def hgupdate(repo, node, branchmerge, force, partial): \\n\\n wlock = repo.wlock()\\n \\n try:\\n wc = repo[None] ###working copy change context\\n if node is None: ###see nothing passing it node\\n # tip of current branch\\n try:\\n node = repo.branchtags()[wc.branch()]\\n except KeyError:\\n if wc.branch() == \\\"default\\\": # no default branch!\\n node = repo.lookup(\\\"tip\\\") # update to tip\\n else:\\n raise util.Abort(_(\\\"branch %s not found\\\") % wc.branch())\\n overwrite = force and not branchmerge\\n pl = wc.parents() ###ParentList of working copy\\n p1, p2 = pl[0], repo[node] ### MyChangeContext, MergeChangeContet\\n pa = p1.ancestor(p2) ### CommonAncestorChangeContext\\n fp1, fp2, xp1, xp2 = p1.node(), p2.node(), str(p1), str(p2) ##changeset hashes & short hashes\\n fastforward = False\\n\\n ### check phase\\n if not overwrite and len(pl) > 1: ### wc has multiple parents\\n raise util.Abort(_(\\\"outstanding uncommitted merges\\\"))\\n if branchmerge:\\n if pa == p2: ###she is the common ancestor\\n raise util.Abort(_(\\\"can't merge with ancestor\\\"))\\n elif pa == p1: ###me is the common ancestor\\n if p1.branch() != p2.branch(): ###...but we are different named branches (usually 'default')\\n fastforward = True ### Merging a branch back into common ancestor\\n else:\\n raise util.Abort(_(\\\"nothing to merge (use 'hg update'\\\"\\n \\\" or check 'hg heads')\\\"))\\n if not force and (wc.files() or wc.deleted()): ### edits, adds, or mods in wc\\n raise util.Abort(_(\\\"outstanding uncommitted changes\\\"))\\n elif not overwrite:\\n if pa == p1 or pa == p2: # linear\\n pass # all good\\n elif p1.branch() == p2.branch():\\n if wc.files() or wc.deleted():\\n raise util.Abort(_(\\\"crosses branches (use 'hg merge' or \\\"\\n \\\"'hg update -C' to discard changes)\\\"))\\n raise util.Abort(_(\\\"crosses branches (use 'hg merge' \\\"\\n \\\"or 'hg update -C')\\\"))\\n elif wc.files() or wc.deleted():\\n raise util.Abort(_(\\\"crosses named branches (use \\\"\\n \\\"'hg update -C' to discard changes)\\\"))\\n else:\\n # Allow jumping branches if there are no changes\\n overwrite = True\\n\\n ### calculate phase\\n action = []\\n if not force:\\n merge._checkunknown(wc, p2)\\n #~ if not util.checkcase(repo.path):\\n #~ _checkcollision(p2)\\n action += merge._forgetremoved(wc, p2, branchmerge)\\n action += merge.manifestmerge(repo, wc, p2, pa, overwrite, partial)\\n\\n ### apply phase\\n if not branchmerge: # just jump to the new rev\\n fp1, fp2, xp1, xp2 = fp2, mercurial.node.nullid, xp2, ''\\n if not partial:\\n repo.hook('preupdate', throw=True, parent1=xp1, parent2=xp2)\\n\\n stats = merge.applyupdates(repo, action, wc, p2, pa)\\n \\n #added: I want to record updates even if partial merge\\n #merge.recordupdates(repo, action, branchmerge)\\n \\n if not partial:\\n repo.dirstate.setparents(fp1, fp2)\\n merge.recordupdates(repo, action, branchmerge)\\n if not branchmerge and not fastforward:\\n repo.dirstate.setbranch(p2.branch()) \\n \\n finally:\\n wlock.release()\\n \\n if not partial:\\n repo.hook('update', parent1=xp1, parent2=xp2, error=stats[3])\\n return stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958e09a9c7c983b88f58e40b26fa09df\",\n \"score\": \"0.40657353\",\n \"text\": \"def test_merge_standard_from_existing():\\n\\n\\tos.chdir('/usr/share/licorn/extensions/squid/data')\\n\\n\\t# start with an empty file.\\n\\tm1 = LSCF(filename='squid.minimal.test.conf')\\n\\n\\tm1.merge(LSCF(filename='squid.replacements.conf', snipplet=True), partial_match=True, on_conflict='replace')\\n\\t#print '>> +replacements', m1.to_string()\\n\\tassert m1.changed\\n\\n\\tm1.merge(LSCF(filename='squid.licorn_lan_1.conf', snipplet=True), partial_match=True, on_conflict='replace')\\n\\t#print '>> +lan_1', m1.to_string()\\n\\tassert m1.changed\\n\\n\\tm1.merge(LSCF(filename='squid.licorn_lan_2.conf', snipplet=True))\\n\\t#print '>> +lan_2\\\\n', m1.to_string()\\n\\tassert m1.changed\\n\\n\\tmcheck = LSCF(filename='squid.merged.for_check.conf')\\n\\t#print '>> +check\\\\n', mcheck.to_string()\\n\\tassert m1 == mcheck\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f7d3903688d3d685a5cdf9cd7a0349\",\n \"score\": \"0.4063083\",\n \"text\": \"def set_option(self, option):\\n options = self.options_as_set()\\n options.add(option)\\n self.attrib[ATTRIB_OPTIONS] = ','.join(options)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9176293ee87448ec087c01bf61b2f3ba\",\n \"score\": \"0.4060783\",\n \"text\": \"def add_unique_option(self, option):\\n if option.name in self.options:\\n raise BlobException(\\\"Option name '%s' is already defined\\\" % option.name)\\n option.repository = self.repository\\n option.module = self\\n self.options[option.name] = option\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e5a2493aedc265eee89fb03a8c9394\",\n \"score\": \"0.4060452\",\n \"text\": \"def merge(base, other):\\r\\n if isinstance(other, basestring):\\r\\n cherrypy.engine.autoreload.files.add(other)\\r\\n\\r\\n # Load other into base\\r\\n for section, value_map in reprconf.as_dict(other).items():\\r\\n if not isinstance(value_map, dict):\\r\\n raise ValueError(\\r\\n \\\"Application config must include section headers, but the \\\"\\r\\n \\\"config you tried to merge doesn't have any sections. \\\"\\r\\n \\\"Wrap your config in another dict with paths as section \\\"\\r\\n \\\"headers, for example: {'/': config}.\\\")\\r\\n base.setdefault(section, {}).update(value_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"554f32cef7033b790895482a5c2cf89e\",\n \"score\": \"0.40583217\",\n \"text\": \"def svn_client_conflict_tree_get_resolution(conflict: \\\"svn_client_conflict_t\\\") -> \\\"svn_client_conflict_option_id_t\\\":\\n return _client.svn_client_conflict_tree_get_resolution(conflict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c45139ba7a20f254cd0c05a7fea43b\",\n \"score\": \"0.4045485\",\n \"text\": \"def make_full_id(repo: Repo, change_id_override: str = \\\"\\\") -> str:\\n retstr = \\\"\\\"\\n if ((repo.id in change_id_override or urllib.parse.quote_plus(repo.id) in change_id_override)\\n and (repo.branch in change_id_override or urllib.parse.quote_plus(repo.branch) in change_id_override)):\\n # Already formatted!\\n retstr = change_id_override\\n elif change_id_override:\\n retstr = f\\\"{repo.id}~{repo.branch}~{change_id_override}\\\"\\n elif repo.proposal:\\n retstr = f\\\"{repo.id}~{repo.branch}~{repo.proposal.change_id}\\\"\\n return retstr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc506d024d405d1eb9e6dba845f4b43d\",\n \"score\": \"0.40431756\",\n \"text\": \"def set_option(self, name, value):\\n if name not in self.settable:\\n raise SCons.Errors.UserError(\\\"This option is not settable from a SConscript file: %s\\\"%name)\\n\\n if name == 'num_jobs':\\n try:\\n value = int(value)\\n if value < 1:\\n raise ValueError\\n except ValueError:\\n raise SCons.Errors.UserError(\\\"A positive integer is required: %s\\\"%repr(value))\\n elif name == 'max_drift':\\n try:\\n value = int(value)\\n except ValueError:\\n raise SCons.Errors.UserError(\\\"An integer is required: %s\\\"%repr(value))\\n elif name == 'duplicate':\\n try:\\n value = str(value)\\n except ValueError:\\n raise SCons.Errors.UserError(\\\"A string is required: %s\\\"%repr(value))\\n if value not in SCons.Node.FS.Valid_Duplicates:\\n raise SCons.Errors.UserError(\\\"Not a valid duplication style: %s\\\" % value)\\n # Set the duplicate style right away so it can affect linking\\n # of SConscript files.\\n SCons.Node.FS.set_duplicate(value)\\n elif name == 'diskcheck':\\n try:\\n value = diskcheck_convert(value)\\n except ValueError as v:\\n raise SCons.Errors.UserError(\\\"Not a valid diskcheck value: %s\\\"%v)\\n if 'diskcheck' not in self.__dict__:\\n # No --diskcheck= option was specified on the command line.\\n # Set this right away so it can affect the rest of the\\n # file/Node lookups while processing the SConscript files.\\n SCons.Node.FS.set_diskcheck(value)\\n elif name == 'stack_size':\\n try:\\n value = int(value)\\n except ValueError:\\n raise SCons.Errors.UserError(\\\"An integer is required: %s\\\"%repr(value))\\n elif name == 'md5_chunksize':\\n try:\\n value = int(value)\\n except ValueError:\\n raise SCons.Errors.UserError(\\\"An integer is required: %s\\\"%repr(value))\\n elif name == 'warn':\\n if SCons.Util.is_String(value):\\n value = [value]\\n value = self.__SConscript_settings__.get(name, []) + value\\n SCons.Warnings.process_warn_strings(value)\\n\\n self.__SConscript_settings__[name] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c9165f7b2e2697cb6886fb50ce274f\",\n \"score\": \"0.40381363\",\n \"text\": \"def add_option_to_file(self,key,value):#or update\\n \\n option_dict=self.read_file(for_update=True)\\n if option_dict is not None:\\n if key in self.default_values:\\n option_dict[key]=value\\n try:\\n codecs.open(self.option_file_path,'w',\\\"utf-8\\\").write(\\\\\\n json.dumps(option_dict,sort_keys=True, indent=4, separators=(',',':')))\\n except IOError:#permission error\\n pass#updating a file that is not accessible is impossible\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3219b6c78154ef74c64bc9a39aadbb4\",\n \"score\": \"0.40285555\",\n \"text\": \"def svn_client_conflict_tree_get_resolution_options(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_tree_get_resolution_options(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b88ef0d1a3a3c7670b53b97bedf3c8c0\",\n \"score\": \"0.4028405\",\n \"text\": \"def svn_client_conflict_option_get_moved_to_abspath_candidates2(*args) -> \\\"apr_array_header_t **\\\":\\n return _client.svn_client_conflict_option_get_moved_to_abspath_candidates2(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"864c0e95e828e3da1b8c741bc7621369\",\n \"score\": \"0.40259787\",\n \"text\": \"def merge(self, branch, message=None, author=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95011528a8040e265dab195b08e69362\",\n \"score\": \"0.40231258\",\n \"text\": \"def test_merge_str(self):\\n list_to_merge = '1.1.1.1,2.2.2.2'\\n result = models.ServerList(self.VALID_SERVERS)\\n self.assertEqual(set(self.VALID_SERVERS), set(result.servers))\\n expected_result = [*self.VALID_SERVERS, *(list_to_merge.split(','))]\\n result.merge(list_to_merge)\\n self.assertEqual(set(expected_result), set(result.servers))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6abea2d6990588b587000c424894d41\",\n \"score\": \"0.40174183\",\n \"text\": \"def set(self, section,option,value):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6abea2d6990588b587000c424894d41\",\n \"score\": \"0.40174183\",\n \"text\": \"def set(self, section,option,value):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7037da05123d009f8341e0bff81a9e60\",\n \"score\": \"0.40153354\",\n \"text\": \"def setPickStyleOverride(*args):\\n return _coin.SoOverrideElement_setPickStyleOverride(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3575abb5f774c675be79ae222ef46a\",\n \"score\": \"0.40126106\",\n \"text\": \"def svn_client_mergeinfo_log(*args) -> \\\"svn_error_t *\\\":\\n return _client.svn_client_mergeinfo_log(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075f9791a636986d53a3ba6e91ef0605\",\n \"score\": \"0.39992234\",\n \"text\": \"def conflicting_config(self):\\n return self.conflicts.get()[0].new_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd33e71c700086e6c825a8e460af20af\",\n \"score\": \"0.39967567\",\n \"text\": \"def test_set_merge_upstream(self) -> None:\\n\\n given = True\\n expected = True\\n\\n self.config_loader.merge_upstream = given\\n\\n actual = self.config_loader.merge_upstream\\n\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1496,"cells":{"query_id":{"kind":"string","value":"80d65ada762dd0277d9b7c795d0b6a29"},"query":{"kind":"string","value":"It should apply strict validation to the metadata and requirements dicts. It should reject protocols with questionable metadata and requirements dicts, even though these protocols may be accepted by other parts of the system."},"positive_passages":{"kind":"list like","value":[{"docid":"89db704406804ea5572fd250cd8532ff","score":"0.691029","text":"def test_strict_metatada_requirements_validation(tmp_path: Path) -> None:\n protocol_source = textwrap.dedent(\n \"\"\"\n # apiLevel in both metadata and requirements\n metadata = {\"apiLevel\": \"2.15\"}\n requirements = {\"apiLevel\": \"2.15\"}\n\n def run(protocol):\n pass\n \"\"\"\n )\n\n protocol_source_file = tmp_path / \"protocol.py\"\n protocol_source_file.write_text(protocol_source, encoding=\"utf-8\")\n\n result = _get_analysis_result([protocol_source_file])\n\n assert result.exit_code != 0\n\n expected_message = (\n \"You may only put apiLevel in the metadata dict or the requirements dict\"\n )\n assert expected_message in result.stdout_stderr","title":""}],"string":"[\n {\n \"docid\": \"89db704406804ea5572fd250cd8532ff\",\n \"score\": \"0.691029\",\n \"text\": \"def test_strict_metatada_requirements_validation(tmp_path: Path) -> None:\\n protocol_source = textwrap.dedent(\\n \\\"\\\"\\\"\\n # apiLevel in both metadata and requirements\\n metadata = {\\\"apiLevel\\\": \\\"2.15\\\"}\\n requirements = {\\\"apiLevel\\\": \\\"2.15\\\"}\\n\\n def run(protocol):\\n pass\\n \\\"\\\"\\\"\\n )\\n\\n protocol_source_file = tmp_path / \\\"protocol.py\\\"\\n protocol_source_file.write_text(protocol_source, encoding=\\\"utf-8\\\")\\n\\n result = _get_analysis_result([protocol_source_file])\\n\\n assert result.exit_code != 0\\n\\n expected_message = (\\n \\\"You may only put apiLevel in the metadata dict or the requirements dict\\\"\\n )\\n assert expected_message in result.stdout_stderr\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"820a3eeeabd346ddabb8917309ff21c3","score":"0.64846337","text":"def _check_requirements(self):","title":""},{"docid":"7eb573492516fe12f09ad7ec6dae1277","score":"0.64280707","text":"def verify_model_typing(self):\n for k, v in self.requirements.items():\n if not isinstance(v, bool):\n raise ValueError(f\"Model Requirement \\'{k}\\' must be a boolean value\")\n if v == True:\n if self.types[k] is None:\n raise ValueError(f\"\\'{k}\\' Cannot be None in typing delarations\")\n if self.dictionary[k] is None:\n raise ValueError(f\"\\'{k}\\' Cannot be None inside of the main model dictionary\")","title":""},{"docid":"aaab101e5528f611d32270936b73f998","score":"0.62626535","text":"def enforce_specification (self):\n\n \"\"\" Must include a version. For now, there is only one. \"\"\"\n assert self.spec.get ('ros') == 0.1\n\n \"\"\" Validate workflow object. \"\"\"\n workflow = self.spec.get ('workflow', {})\n assert len(workflow) > 0, \"Workflow element exists but contains no steps.\"\n\n \"\"\" Validate information section. \"\"\"\n info = self.spec.get (\"info\", {})\n assert info, \"Missing 'info' section.\"\n version = info.get (\"version\")\n assert version, \"Missing info version \"\n assert re.match(\"^\\d+?\\.\\d+?.\\d+?$\", version), \"Version is not a float.\"\n\n title = info.get (\"title\")\n assert isinstance(title, str), \"Title is not valid.\"\n\n description = info.get (\"description\")\n assert isinstance(description, str), \"Description is not valid.\"","title":""},{"docid":"cb096a097134a918ca51a269eb1067d8","score":"0.6163803","text":"def requirements(self):\n pass","title":""},{"docid":"b0455422add1997b0a0e6e462bcade66","score":"0.6162088","text":"def requirements(self):","title":""},{"docid":"eabcb74d2026d25a276533c52dcd05c1","score":"0.613765","text":"def validate(self):\n\n super(self.__class__, self).validate()\n\n if self.properties['Protocol'] == 1:\n if not self.properties.get('Icmp'):\n error(self, 'Icmp must be specified when protocol is 1')\n elif self.properties['Protocol'] in [6, 17]: # TCP, UDP\n if not self.properties.get('PortRange'):\n error(self, 'PortRange must be specified when protocol '\n 'protocol is 6 or 17')","title":""},{"docid":"3b600312071e03bb920e36f6915501bb","score":"0.6028801","text":"def test_sender_constraints_against_schema(self):\n r = requests.get(URI + \"/v1.0/single/senders/\"+self.senders[0]+\"/constraints/\")\n schema = self.load_schema(\"schemas/v1.0-constraints-schema.json\")\n jschema = json.loads(schema)\n validate(r.json(), jschema)","title":""},{"docid":"d76ca1c5be4c362c38706b2a927d889f","score":"0.5947436","text":"def validate_constraints(self):\n struct_value = self.get_struct_value()\n self.validate_struct_value(struct_value)","title":""},{"docid":"5dc3a7cf28bc6b25ddf3dd76caac5555","score":"0.5850915","text":"def validate_metadata(self, std_meta, meta):","title":""},{"docid":"1ff7df5af71f817983591e6eb649df58","score":"0.58160347","text":"def test_receiver_constraints_against_schema(self):\n r = requests.get(URI + \"/v1.0/single/receivers/\"+self.receivers[0]+\"/constraints/\")\n schema = self.load_schema(\"schemas/v1.0-constraints-schema.json\")\n jschema = json.loads(schema)\n validate(r.json(), jschema)","title":""},{"docid":"10f86afd57d10299acceb17142169eca","score":"0.575473","text":"def _validate(self):\n\n self._check_dict()\n self._check_comment()\n self._check_unitcell()\n self._check_sites()","title":""},{"docid":"a80e85efd4c09a7495f2ebdce9c48e61","score":"0.57252026","text":"def validate(self): # pragma: no cover\n pass","title":""},{"docid":"effa04d0bb92195a52d0d6fb2b83138f","score":"0.57195306","text":"def _validate(self):\n if self._definition.get('required'):\n required_missing = set(self._definition.get('required')) - set(self._fast_attribute_access.keys())\n if required_missing:\n raise InvalidMISPObject('{} are required.'.format(required_missing))\n if self._definition.get('requiredOneOf'):\n if not set(self._definition['requiredOneOf']) & set(self._fast_attribute_access.keys()):\n # We ecpect at least one of the object_relation in requiredOneOf, and it isn't the case\n raise InvalidMISPObject('At least one of the following attributes is required: {}'.format(', '.join(self._definition['requiredOneOf'])))\n for rel, attrs in self._fast_attribute_access.items():\n if len(attrs) == 1:\n # object_relation's here only once, everything's cool, moving on\n continue\n if not self._definition['attributes'][rel].get('multiple'):\n # object_relation's here more than once, but it isn't allowed in the template.\n raise InvalidMISPObject('Multiple occurrences of {} is not allowed'.format(rel))\n return True","title":""},{"docid":"1c93d5721f610fe9178d6da95dcc9174","score":"0.56559634","text":"def validate(self, format):\n required, optionals = bibpy.requirements.check(self, format)\n\n if required or optionals:\n raise bibpy.error.RequiredFieldError(self, required, optionals)","title":""},{"docid":"1827193231f5c677620676d09427fa2a","score":"0.56420887","text":"def _check_params(self):\n if self.required_params is not None:\n if self.required_params != len(self.params):\n raise SpecError('The \"{} rule expects {} parameters.\"'.format(self.name(), self.required_params))\n try:\n self._sanitize_params()\n except SpecError as e:\n raise e\n except Exception:\n raise SpecError('The params {} passed to the rule {} are invalid.'.format(self.params, self.name()))","title":""},{"docid":"ef970b5b99eba9135ff220f23c8bb1ed","score":"0.56247985","text":"def _validate(self) -> None:\n pass","title":""},{"docid":"6e81bc60aad1f367e4047d4be3bab89f","score":"0.56228924","text":"def validate(self):\n raise NotImplementedError","title":""},{"docid":"67a52c5b8f055f192a766fffa9b5f77a","score":"0.55986077","text":"def checkRequirements(self):\n return True","title":""},{"docid":"258578f86fc04915dadefa5344ed1761","score":"0.5594791","text":"def validate(self, *specs) -> None:\n super().validate(*specs)\n\n # Validate constituent data\n self.diff_data.validate(*specs)\n self.spec_dict[\"secr_data\"].validate(*specs)\n self.bcs.validate(*specs)","title":""},{"docid":"07ab15271630f0b8d6a8f6a97c13eefd","score":"0.55673105","text":"def _validate(self):\n\n config_reqs = {\n 'ip_addr': str,\n 'dir_ptrn': str,\n 'file_ptrn': str,\n 'file_ptrn_abbr': str,\n 'columns': (list, tuple),\n 'schema': dict,\n 's3_bucket': str\n }\n\n logger.info('Validating config parameters')\n for req_param, req_type in config_reqs.items():\n self.key_check(req_param, self.__dict__, 'config')\n self.type_check(getattr(self, req_param), req_type, f\"{req_param}\")\n\n logger.info('Validating schema parameters')\n self._validate_schema()\n self.iter_compare(\n 'val', self.schema, self.columns, 'schema', 'columns')\n\n if 'row_skip' in self.__dict__:\n logger.info('Validating row_skip parameters')\n self._validate_row_skip()\n\n if 'partition' in self.__dict__:\n logger.info('Validating partition parameters')\n self._validate_partition()\n\n if self.mem_cap < 5 * 10 ** 8:\n logger.warning(\n 'mem_cap is %i; a minimum size of 5 * 10 ** 8 is recommended',\n self.mem_cap)","title":""},{"docid":"b68edf09cdeb823ac20b77020dad0998","score":"0.5565948","text":"def test_4_required_check(self):\n class FussyRecord(Record):\n id = Property(required=True, isa=int)\n natural = SafeProperty(check=lambda i: i > 0)\n must = SafeProperty(required=True)\n rbn = SafeProperty(required=True, isa=(str, type(None)))\n\n with self.assertRaises(ValueError):\n fr = FussyRecord()\n\n fr = FussyRecord(id=123, must=\"sugary\", rbn=\"Hello\")\n self.assertIn(\"Hello\", str(fr))\n self.assertEqual(fr, eval(repr(fr)))\n with self.assertRaises(ValueError):\n del fr.must\n with self.assertRaises(ValueError):\n fr.must = None\n fr.must = \"barmy\"\n with self.assertRaises(ValueError):\n del fr.rbn\n fr.rbn = None\n\n fr.natural = 7\n with self.assertRaises(ValueError):\n fr.natural = 0","title":""},{"docid":"334f144f46c63fd03f2848c7dc75c5d0","score":"0.5548117","text":"def test_validate_metadata_correct(self, metadata):\n\t\tdatatype.validate_metadata(metadata) #Should not give an exception.","title":""},{"docid":"db222bc963c8ada20cf81764ba037278","score":"0.5483188","text":"def set_attributes(self, attributes, valid_attributes=None, partial=False):\n required = self.type.get_required_attributes()\n if valid_attributes is None:\n valid_attributes = Attribute.all_by_name(\n 'Protocol', site=self.site\n )\n\n # Temporarily mark required attributes as ``required`` at run-time for\n # injecting required_attributes into validation.\n for r in required:\n if r in valid_attributes:\n valid_attributes[r].required = True\n\n return super(Protocol, self).set_attributes(\n attributes, valid_attributes=valid_attributes,\n partial=partial\n )","title":""},{"docid":"36fc0d8d273c71e348b6de8abd091c04","score":"0.546973","text":"def _validate(self):\n return","title":""},{"docid":"2e261861d385bf9a599465720c0c2aec","score":"0.5434504","text":"def test_model_validator(self):\n privacy_engine = PrivacyEngine(\n models.resnet18(),\n batch_size=self.BATCH_SIZE,\n sample_size=self.DATA_SIZE,\n alphas=self.ALPHAS,\n noise_multiplier=1.3,\n max_grad_norm=1,\n )\n with self.assertRaises(IncompatibleModuleException):\n privacy_engine.attach(self.private_optimizer)","title":""},{"docid":"90294209ab56f9de49a67dfc414a901d","score":"0.5433354","text":"def required(self):\n raise NotImplementedError","title":""},{"docid":"8ec026a262ff640a4b36d6071ad7a441","score":"0.54138446","text":"def validate(self):\n\n super(self.__class__, self).validate()\n\n if self.properties.get('NetworkInterfaces'):\n if self.properties.get('SecurityGroupIds'):\n error(self, 'Can not specify both NetworkInterfaces and '\n 'SecurityGroupIds')\n if self.properties.get('SubnetId'):\n error(self, 'Can not specify both NetworkInterfaces and '\n 'SubnetId')\n\n inst_ten = self.properties.get('Tenancy')\n if inst_ten:\n if inst_ten not in ['default', 'dedicated']:\n error(self, 'Tenancy must be one of \"default\", \"dedicated\"')","title":""},{"docid":"27ac79c28f25da34ec43f4fc85010c5a","score":"0.5409859","text":"def validate_module(self, pipeline):\n if (\n self.assignment_method == ASSIGN_RULES\n and self.matching_choice == MATCH_BY_METADATA\n and len(self.assignments) > 1\n ):\n joins = self.join.parse()\n for name in self.get_column_names():\n for join in joins:\n if join.get(name) is None:\n break\n else:\n return\n raise ValidationError(\n \"At least one channel must have all metadata keys specified. \"\n \"All channels have at least one metadata key of (None).\",\n self.join,\n )","title":""},{"docid":"3840356306bbaebcdaf5cbc4f20c5702","score":"0.5409455","text":"def test_receiver_staged_schema_valid(self):\n r = requests.get(URI + \"/v1.0/single/receivers/\"+self.receivers[0]+\"/staged/\")\n schema = self.load_schema(\"schemas/v1.0-receiver-response-schema.json\")\n checker = FormatChecker([\"ipv4\", \"ipv6\"])\n validate(r.json(), json.loads(schema), format_checker = checker)","title":""},{"docid":"bfcac560f8f8b402bd84d16219b715f8","score":"0.53979933","text":"def validate(self):\n\n jsonschema.validate(self.request_json, EVENT_SCHEMA)\n jsonschema.validate(self.data, self.topic.schema)","title":""},{"docid":"ee16e61be4e6161164417fc7373b3c28","score":"0.5394138","text":"def test_wrapper_ssh_cmd_send_case6(self):\n\n with self.assertRaises(ValidationError):\n self.schema.load(\n {\n \"MISSING\": \"REQUIRED_FIELDS\",\n\n }\n )","title":""},{"docid":"dea95f9f4c2712faaf9b05606e3dfa2f","score":"0.5391442","text":"def _validate(self):\n return True","title":""},{"docid":"b97890ca90e18db80ec8befe516405cd","score":"0.5383073","text":"def _validate_init(self, param, requirements):\n\n _validate(param, 'param', {'type': dict})\n _validate(requirements, 'requirements',\n [{'type': dict}, {'type_in': [list, tuple, dict]}])\n\n if not sorted(param.keys()) == sorted(requirements.keys()):\n raise KeyError('param and requirements must have the same keys.')","title":""},{"docid":"4a4cecb8917e250bce22e2fcc5381d7b","score":"0.53768003","text":"def setup_validation(self, client):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"4a4cecb8917e250bce22e2fcc5381d7b","score":"0.53768003","text":"def setup_validation(self, client):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"5dc3cd4371a8297bb3b2f72de3331b54","score":"0.53757226","text":"def _checkSpecs(self, specs):\n self._checkSpec(specs, 'name', [isinstance, (str, unicode)])\n self._checkSpec(specs, 'metric', [isinstance, bool])\n self._checkSpec(specs, 'oal', [isinstance, (float, int)], [gt, 0.0])","title":""},{"docid":"545cc7fbf99879c6e291e36f99bc246b","score":"0.5365856","text":"def test_eschema_syntax_constraint_07(self):","title":""},{"docid":"dd9b09ab145973064f55723e432e55d9","score":"0.53627896","text":"def check_requirements(\n self,\n pipeline_proto: beam_runner_api_pb2.Pipeline,\n supported_requirements: Iterable[str]):\n\n # Imported here to avoid circular dependencies.\n # pylint: disable=wrong-import-order, wrong-import-position\n from apache_beam.runners.portability.fn_api_runner import translations\n supported_requirements = set(supported_requirements)\n for requirement in pipeline_proto.requirements:\n if requirement not in supported_requirements:\n raise ValueError(\n 'Unable to run pipeline with requirement: %s' % requirement)\n for transform in pipeline_proto.components.transforms.values():\n if transform.spec.urn == common_urns.primitives.TEST_STREAM.urn:\n if common_urns.primitives.TEST_STREAM.urn not in supported_requirements:\n raise NotImplementedError(transform.spec.urn)\n elif transform.spec.urn in translations.PAR_DO_URNS:\n payload = beam_runner_api_pb2.ParDoPayload.FromString(\n transform.spec.payload)\n for timer in payload.timer_family_specs.values():\n if timer.time_domain not in (\n beam_runner_api_pb2.TimeDomain.EVENT_TIME,\n beam_runner_api_pb2.TimeDomain.PROCESSING_TIME):\n raise NotImplementedError(timer.time_domain)","title":""},{"docid":"73548df03a522d40ac9ac0c6e10299be","score":"0.5362035","text":"def setup_validation(self, client, *args, **keyword_args):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"729941147a3f539a7925d4b8f67f53a7","score":"0.5360922","text":"def test_eschema_syntax_constraint_06(self):","title":""},{"docid":"75b4405d1e8288ae35f83e4d3755514e","score":"0.5352467","text":"def test_eschema_syntax_constraint_05(self):","title":""},{"docid":"35a43e496ac6e818c5a718fc1f1230c2","score":"0.5351768","text":"def test_required(self):\n obj_def = {'type': 'object', 'properties': self.properties}\n builder = ObjectBuilder(self.provider)\n for required in (True, False):\n self.assertEqual(required, builder.build_schema(obj_def, required=required).required)","title":""},{"docid":"1b5e7604ea9f5f9345a597198675907a","score":"0.53488547","text":"def test_eschema_syntax_constraint_09(self):","title":""},{"docid":"e699b1255da7ab8d91c2b52417c2e957","score":"0.53485715","text":"def test_required_fields(self):\n fields = self.fields.copy()\n\n def run_on_empty_args() -> DocMetadata:\n return DocMetadata(**fields) # type: ignore\n\n with self.assertRaises(TypeError) as ctx:\n run_on_empty_args()\n\n # Do not indent us or we will not run and be tested!:\n self.assertTrue('missing 14 required positional arguments' in str(ctx.exception))\n #\n self.assertTrue('raw_safe' in str(ctx.exception))\n self.assertTrue('arxiv_id' in str(ctx.exception))\n self.assertTrue('arxiv_id_v' in str(ctx.exception))\n self.assertTrue('arxiv_identifier' in str(ctx.exception))\n self.assertTrue('modified' in str(ctx.exception))\n self.assertTrue('title' in str(ctx.exception))\n self.assertTrue('abstract' in str(ctx.exception))\n self.assertTrue('authors' in str(ctx.exception))\n self.assertTrue('submitter' in str(ctx.exception))\n self.assertTrue('categories' in str(ctx.exception))\n self.assertTrue('primary_category' in str(ctx.exception))\n self.assertTrue('primary_archive' in str(ctx.exception))\n self.assertTrue('primary_group' in str(ctx.exception))\n self.assertTrue('secondary_categories' in str(ctx.exception))\n #\n self.assertTrue('journal_ref' not in str(ctx.exception))\n self.assertTrue('report_num' not in str(ctx.exception))\n self.assertTrue('doi' not in str(ctx.exception))\n self.assertTrue('acm_class' not in str(ctx.exception))\n self.assertTrue('msc_class' not in str(ctx.exception))\n self.assertTrue('proxy' not in str(ctx.exception))\n self.assertTrue('comments' not in str(ctx.exception))\n self.assertTrue('version' not in str(ctx.exception))\n self.assertTrue('license' not in str(ctx.exception))\n self.assertTrue('version_history' not in str(ctx.exception))\n self.assertTrue('private' not in str(ctx.exception))","title":""},{"docid":"932a44657b4b5fa94ffefe9e9a3d0fe9","score":"0.53445613","text":"def validate(self, params: Dict = None) -> None:\n pass","title":""},{"docid":"f85bb9408bc7a1713341c20f531d28ca","score":"0.533717","text":"def validate(self):\n raise NotImplemented","title":""},{"docid":"36406907fdea63433b503ef79c3f439b","score":"0.53353614","text":"def test_required_fields():\n payload = boards.structure_payload(khoros, 'test-forum', 'Test Forum', 'forum')\n data_fields_to_check = {\n 'conversation_style': 'forum',\n 'id': 'test-forum',\n 'title': 'Test Forum'\n }\n assert verify_data_fields(payload, data_fields_to_check) is True # nosec","title":""},{"docid":"7c721aaa714d6e3c68dc9472f0daa066","score":"0.53321564","text":"def validate_main(self):\n super(DnsBruteCombine, self).validate_main()","title":""},{"docid":"4146024b65340df31826e25b372b8dbe","score":"0.5330618","text":"def validate_rule(self):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"689ad35061a266df1fe42a9289c2e5a2","score":"0.5320653","text":"def test_eschema_syntax_constraint_02(self):","title":""},{"docid":"c8145cddce3d2f5f97e9f09bb0fec4f0","score":"0.53195155","text":"def validate(self):\n for p in self._required_params:\n if self._params.get(p, None) is None:\n raise ValueError(f\"Required configuration parameter {p} missing.\")","title":""},{"docid":"d8064f7d8f7975c8ada61712b3a0d442","score":"0.5319296","text":"def test_sender_staged_schema_valid(self):\n r = requests.get(URI + \"/v1.0/single/senders/\"+self.senders[0]+\"/staged/\")\n schema = self.load_schema(\"schemas/v1.0-sender-response-schema.json\")\n checker = FormatChecker([\"ipv4\", \"ipv6\"])\n validate(r.json(), json.loads(schema), format_checker = checker)","title":""},{"docid":"b019e4e25294fecfa72fa556bf8d2918","score":"0.531186","text":"def requirements(self):\n return []","title":""},{"docid":"e833cc4553ecd1b4f6daa8c5e9db6623","score":"0.5307088","text":"def check_compatible(global_reqs, constraints):\n def satisfied(reqs, name, version, failures):\n if name not in reqs:\n return True\n tested = []\n for constraint, _ in reqs[name]:\n spec = specifiers.SpecifierSet(constraint.specifiers)\n # pre-releases are allowed by policy but discouraged\n if spec.contains(version, prereleases=True):\n return True\n tested.append(constraint.specifiers)\n failures.append('Constraint %s for %s does not match requirement %s' %\n (version, name, tested))\n return False\n failures = []\n for pkg_constraints in constraints.values():\n for constraint, _ in pkg_constraints:\n name = constraint.package\n version = constraint.specifiers[3:]\n satisfied(global_reqs, name, version, failures)\n return failures","title":""},{"docid":"894ef96503f4bb8955e0808354a7f566","score":"0.5306498","text":"def test_eschema_syntax_constraint_10(self):","title":""},{"docid":"e3ec92c01c429d3dbe38524abbb995f1","score":"0.53028697","text":"def _validate_required(self):\n required_attrs = [\n 'id', 'emfetch_id', 'table', 'func',\n 'time_field', 'time_type', \n ]\n for a in required_attrs:\n val = getattr(self, a)\n if (val is None) or (val == ''):\n raise ValueError(\"Missing {k} in #{id}\"\n .format(k=a, id=self.id))","title":""},{"docid":"fdc94f75b40ef3f57e8b836551c72633","score":"0.53007454","text":"def ensure_protocol_is_given(func): # pylint: disable=no-self-argument\n\n @functools.wraps(func)\n def wrapper(self, *args, **kwargs):\n if not self.protocol:\n raise ValueError(\"<self.protocol> is not given.\")\n\n return func(self, *args, **kwargs) # pylint: disable=not-callable\n\n return wrapper","title":""},{"docid":"bb28ca1d328d960496029ad48e057867","score":"0.5300677","text":"def validate_spec(self, value):\n if self.spec:\n spec = dict(self.spec)\n spec['type'] = 'object'\n json_schema_validator(value, spec)","title":""},{"docid":"e55c1dbdbcce6cd1c72b41a99e3b99af","score":"0.5292425","text":"def ensure_protocol_is_given(func): # pylint: disable=no-self-argument\n\n @functools.wraps(func)\n def wrapper(self, *args, **kwargs):\n if self.protocol is None:\n raise RuntimeError(\"<self.protocol> is not given.\")\n\n return func(self, *args, **kwargs) # pylint: disable=not-callable\n\n return wrapper","title":""},{"docid":"05733d667293d7e7617e0653d393a1ec","score":"0.52899516","text":"def validate_rule(self, client):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"59d38e3818eb9c33183bc0814e7ac37c","score":"0.528854","text":"def _validate_structure(self):\n\n self._validate_structure_payload_directory()\n self._validate_structure_tag_files()","title":""},{"docid":"e70ee1be3e195bee9971dfd7b2c12150","score":"0.5287867","text":"def validate(self):\n pass # pylint: disable=unnecessary-pass","title":""},{"docid":"7b63d3b6fa01aaf4ca94076ec537c2ae","score":"0.5275397","text":"def check_compatible(self):\r\n return True","title":""},{"docid":"6bcb2e7e95c9d7412205c8a02b971771","score":"0.5271401","text":"def validate(self) -> None:\n try:\n mo = self._schema_re.search(self.schema)\n if mo is None:\n raise ValueError\n schema_groups = mo.groups()\n self._type = MessageType(schema_groups[0])\n self._data_schema_version = StrictVersion(schema_groups[1])\n except (AttributeError, ValueError):\n raise ValidationError(f'Invalid schema found: {self.schema}')\n\n _get_validator().validate(self)","title":""},{"docid":"ad63269de2374035c9cb932d51e8abf3","score":"0.52711904","text":"def validate(self) -> None:\n self.require_valid_title(self.title)\n self.require_valid_description(self.description)\n assert self.url_fragment is not None\n utils.require_valid_url_fragment(\n self.url_fragment, 'Story Url Fragment',\n constants.MAX_CHARS_IN_STORY_URL_FRAGMENT)\n utils.require_valid_meta_tag_content(self.meta_tag_content)\n if self.thumbnail_filename is not None:\n self.require_valid_thumbnail_filename(self.thumbnail_filename)\n if self.thumbnail_bg_color is not None and not (\n self.require_valid_thumbnail_bg_color(self.thumbnail_bg_color)):\n raise utils.ValidationError(\n 'Story thumbnail background color %s is not supported.' % (\n self.thumbnail_bg_color))\n if self.thumbnail_bg_color and self.thumbnail_filename is None:\n raise utils.ValidationError(\n 'Story thumbnail image is not provided.')\n if self.thumbnail_filename and self.thumbnail_bg_color is None:\n raise utils.ValidationError(\n 'Story thumbnail background color is not specified.')\n if not isinstance(self.notes, str):\n raise utils.ValidationError(\n 'Expected notes to be a string, received %s' % self.notes)\n\n if not isinstance(self.story_contents_schema_version, int):\n raise utils.ValidationError(\n 'Expected story contents schema version to be an integer, '\n 'received %s' % self.story_contents_schema_version)\n\n if (self.story_contents_schema_version !=\n feconf.CURRENT_STORY_CONTENTS_SCHEMA_VERSION):\n raise utils.ValidationError(\n 'Expected story contents schema version to be %s, '\n 'received %s' % (\n feconf.CURRENT_STORY_CONTENTS_SCHEMA_VERSION,\n self.story_contents_schema_version))\n\n if not isinstance(self.language_code, str):\n raise utils.ValidationError(\n 'Expected language code to be a string, received %s' %\n self.language_code)\n\n if not utils.is_valid_language_code(self.language_code):\n raise utils.ValidationError(\n 'Invalid language code: %s' % self.language_code)\n\n if not isinstance(self.corresponding_topic_id, str):\n raise utils.ValidationError(\n 'Expected corresponding_topic_id should be a string, received: '\n '%s' % self.corresponding_topic_id)\n\n self.story_contents.validate()","title":""},{"docid":"2f810a20b42a607b9d21808614bc0abd","score":"0.526885","text":"def c_validate(self):\n pass","title":""},{"docid":"297980f28f5f04f92e1289528b41364b","score":"0.52603394","text":"def _safety_checks(self):\n\n pass","title":""},{"docid":"9d37bcb7f761cf73ee94ae1df2a391c3","score":"0.52601695","text":"def pre_validate_struct(self, struct):\n pass","title":""},{"docid":"d9a62024c17c10a2296732b1132ec584","score":"0.52600735","text":"def _check_fields(self):\n raise NotImplementedError()","title":""},{"docid":"aca0a255c1118b4e95a725457712e25b","score":"0.5256323","text":"def requirementsMet(self):\n\n try:\n input_length = len(self.value())\n except TypeError:\n # A TypeError is returned if self.value() returns None, which may\n # happen when the json-defined blank value is set to 'isEmpty':\n # 'pass'.\n input_length = 0\n\n if input_length > 0:\n return True\n return False","title":""},{"docid":"fb361a2dfa2eabf5dbbc76e982858993","score":"0.52526546","text":"def check_rules(self) -> YieldFlake8Error:","title":""},{"docid":"0387601257489363888084b8ebc5085d","score":"0.52481604","text":"def pre_validate(self, schema, data, sp):\n raise NotImplementedError","title":""},{"docid":"c0782e2f88167f1e95558e29e2fdd266","score":"0.52443594","text":"def MakeValid(self):","title":""},{"docid":"e84011cd66009c26049849d42b17ae92","score":"0.52420676","text":"def validate_part_a(self):\n for req_field in self.required_fields:\n if not getattr(self, req_field):\n return False\n return True","title":""},{"docid":"84d755a1da13d0c710d51964cb8d6a69","score":"0.5236112","text":"def validate(self, context, runtime, config):\n raise NotImplementedError(\n \"Concrete implementations should override validate\")","title":""},{"docid":"213187bb282d1b725fc5ac864133ac44","score":"0.5229793","text":"def test_valid_modeling_rule_schema(self, pack: Pack):\n modeling_rule_schema = pack.create_modeling_rule(\"modeling_rule\").schema\n validator = StructureValidator(modeling_rule_schema.path)\n assert validator.is_valid_scheme()","title":""},{"docid":"e3bad40845b3513759d8580f3c4e0c98","score":"0.5229034","text":"def validate(self):\n for name, option in self._options.items():\n if option.required:\n if not name in self._values:\n raise OptionRequired(name, option)\n\n constraints = getattr(self, '__constraints__', [])\n for constraint in constraints:\n constraint.validate(self)","title":""},{"docid":"75283345e5cfa4cb44414808baf8d479","score":"0.5214733","text":"def validate_metadata_json(metadata_json: Dict[str, Any]) -> None:\n jsonschema_validate(instance=metadata_json, schema=metadata_schema)","title":""},{"docid":"57ec1fcedc921c0205c3ca97849d0cd8","score":"0.5208268","text":"def test_raise_when_no_protocol_specified():\n\n with pytest.raises(AttributeError, match=r\"requires backend protocol\"):\n\n class UseBackend(HasBackends):\n pass","title":""},{"docid":"e3c87fca217dd875a683e0cc49e2e4bf","score":"0.5204537","text":"def sanity_check(self):","title":""},{"docid":"5b2adbb87b7674f0bc903f8f9ecc8dba","score":"0.52039057","text":"def validate_rule(self, client, *args, **keyword_args):\n raise NotImplementedError(\"Please fix me.\")","title":""},{"docid":"ec6f733ebcb07b300a66b24b8969950c","score":"0.5203211","text":"def validate_config(data: typing.Dict): # pylint: disable=unused-argument","title":""},{"docid":"9e28cc109200342fe5d5eaf42987cb8d","score":"0.51978666","text":"def valid(self, format):\n required, optionals = bibpy.requirements.check(self, format)\n\n return not required and not optionals","title":""},{"docid":"24644a14186c3199144f6183adfef258","score":"0.5196336","text":"def test_example_valid():\n def set_additional_properties_false(data):\n if isinstance(data, list):\n for item in data:\n set_additional_properties_false(item)\n elif isinstance(data, dict):\n if 'properties' in data:\n data['additionalProperties'] = False\n for value in data.values():\n set_additional_properties_false(value)\n\n for basename in ('release-schema.json', 'release-package-schema.json', 'record-package-schema.json'):\n if os.path.isfile(os.path.join(cwd, basename)):\n patched = patch_schema(basename)\n break\n else:\n return\n\n set_additional_properties_false(patched)\n\n validator = Draft4Validator(patched, format_checker=FormatChecker())\n\n for i, text, data in examples():\n # Skip packages (only occurs once in ocds_ppp_extension).\n if 'releases' in data:\n continue\n\n release = deepcopy(minimal_release)\n json_merge_patch.merge(release, data)\n if 'tender' in release and 'id' not in release['tender']:\n release['tender']['id'] = '1'\n\n errors = validate_schema('README.md', release, validator)\n\n assert not errors, f'README.md: JSON block {i} is invalid. See warnings below.'","title":""},{"docid":"83f5245701229fa181e9ea9c7a034727","score":"0.5193923","text":"def check_requirement(cls):","title":""},{"docid":"1756d8fb5f64bcd57909e753247e5cd8","score":"0.519358","text":"def validate_required_fields_are_provided(model: object, data: dict):\n if not hasattr(model, 'required_fields'):\n return\n\n\n missing_required_fields = set(model.required_fields) - set(data.keys())\n\n if missing_required_fields:\n error_msg = f\"Missing required field(s): {', '.join(missing_required_fields)}\"\n return validation_error(error_msg)\n\n return","title":""},{"docid":"439fe253bfffab9e37af686ac93a2a78","score":"0.51917464","text":"def __proto_match_validated(self) -> bool:\n if ip_network(self.dest_prefix).version == ip_address(self.next_hop_ip).version:\n return True\n else:\n return False","title":""},{"docid":"2dc83412a4bab5efaf62c7cfea34c49b","score":"0.51762605","text":"def test_validate_required_error(self):\n required = ['key1', 'key2']\n data = {'key1': 'val1'}\n\n with pytest.raises(MercuryClientException) as exc_info:\n self.req_service.validate_required(required, data)\n if hasattr(exc_info.value, 'message'):\n assert exc_info.value.message == (\n \"Message is missing required data: ['key2']\")\n else:\n assert exc_info.value.msg == \\\n \"Message is missing required data: ['key2']\"","title":""},{"docid":"edd52ef3607c027023b8acfdb2f7a9f3","score":"0.51733655","text":"def validate(self):\n raise NotImplementedError(\n \"%s: subclasses must define their own validate() method\"\n % self.__class__.__name__)","title":""},{"docid":"a4b01bfbc03a8391fdbd5b49cfb144e9","score":"0.51721007","text":"def _validate_metadata(cls):\n required_attributes = ['title', 'description']\n for attr in required_attributes:\n if not attr in cls.__dict__:\n raise errors.MissingMetadata(check_name=cls.__name__, attr=attr)\n\n if not cls.result_fields:\n raise errors.NoResultField(check_name=cls.__name__)","title":""},{"docid":"4eb5d18c7dbcbd5e88d8f701a5fa558a","score":"0.5171257","text":"def VerifyHasRequiredProperties(self):\n\n # TODO(mark): A stronger verification mechanism is needed. Some\n # subclasses need to perform validation beyond what the schema can enforce.\n for property, attributes in self._schema.items():\n (is_list, property_type, is_strong, is_required) = attributes[0:4]\n if is_required and not property in self._properties:\n raise KeyError(self.__class__.__name__ + ' requires ' + property)","title":""},{"docid":"4573dfc999b03d49fd46f71ac2a1c28e","score":"0.51680565","text":"def test_required(self):\n\n with pytest.raises(ValidationError):\n name = DummyStringField(required=True)\n name._load(None)","title":""},{"docid":"5fb98df04261e80a5129ede39f100cb9","score":"0.51677203","text":"def test_disable_compat_check(self, graph_with_incompatible_arg_numbers):\n # ensure global options can disable compat check\n graph: _WrapDiGraph = graph_with_incompatible_arg_numbers\n with spype.options(check_compatibility=False):\n try:\n graph.validate()\n except InvalidPype:\n pytest.fail(\"should not fail when type checking is turned off\")\n # ensure the validate call can disable compat check\n try:\n graph.validate(check_task_compatibility=False)\n except InvalidPype:\n pytest.fail(\"should not raise\")","title":""},{"docid":"7aa870a3935cd5c450545c978fba2d14","score":"0.51650184","text":"def requires(self):","title":""},{"docid":"1a632392f45a61d746bb0e650c3f896d","score":"0.516243","text":"def __validate_document_has_allowlisted_keys(doc, filepath, required_keys=[], optional_keys=[], required_values=[]):\n required_keys_not_found = []\n invalid_types = []\n\n for key in required_keys:\n if key not in doc:\n required_keys_not_found.append(key)\n elif not isinstance(doc[key], required_keys[key]):\n __fail('Error parsing file: %s. Required key %s has invalid type %s which should be %s' % (\n filepath, key, type(doc[key]), required_keys[key]))\n if key in required_values and doc[key] != required_values[key]:\n __fail('Error parsing file: %s. Required key %s with required value `%s` equals `%s`' % (\n filepath, key, required_values[key], doc[key]))\n\n if required_keys_not_found:\n prefixed_required_keys_not_found = [\"🔑 ~> \" + key for key in required_keys_not_found] \n __fail('Error parsing file: %s. Required keys not found:\\n%s' % (\n filepath, \"\\n\".join(prefixed_required_keys_not_found)))\n\n for key in optional_keys:\n if key not in doc:\n continue\n elif not isinstance(doc[key], optional_keys[key]):\n __fail('Error parsing file: %s. Optional key %s has invalid type %s which should be %s' % (\n filepath, key, type(doc[key]), optional_keys[key])) \n if key in required_values and doc[key] != required_values[key]:\n __fail('Error parsing file: %s. Required key %s with required value `%s` equals `%s`' % (\n filepath, key, required_values[key], doc[key]))","title":""},{"docid":"03c557760aa2ee83ba6df848f8af03d7","score":"0.51599604","text":"def prevalidate_put(self):\n self.kind = self.get_query_argument('kind', default='file')\n\n if self.kind not in ('file', 'folder'):\n raise exceptions.InvalidParameters('Kind must be file, folder or unspecified (interpreted as file), not {}'.format(self.kind))\n\n length = self.request.headers.get('Content-Length')\n\n if length is None and self.kind == 'file':\n # Length Required\n raise exceptions.InvalidParameters('Content-Length is required for file uploads', code=411)\n\n try:\n if length is not None and int(length) > 0 and self.kind == 'folder':\n # Payload Too Large\n raise exceptions.InvalidParameters('Folder creation requests may not have a body', code=413)\n except ValueError:\n raise exceptions.InvalidParameters('Invalid Content-Length')","title":""},{"docid":"4119a130d6f3509c0e8bca20b278f3b1","score":"0.5159814","text":"def embedded_check_compatibility(self):\n if self.nvsram:\n self.embedded_check_nvsram_compatibility()\n if self.firmware:\n self.embedded_check_bundle_compatibility()","title":""},{"docid":"349b283afc855b3ba0f124ef34622d68","score":"0.5158597","text":"def validate(cls, full_config: ,) -> ,:\n ...","title":""},{"docid":"349b283afc855b3ba0f124ef34622d68","score":"0.5158597","text":"def validate(cls, full_config: ,) -> ,:\n ...","title":""},{"docid":"3ee9661927785b545825043fbf3ad68a","score":"0.5157086","text":"def validate_platform(self):","title":""}],"string":"[\n {\n \"docid\": \"820a3eeeabd346ddabb8917309ff21c3\",\n \"score\": \"0.64846337\",\n \"text\": \"def _check_requirements(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb573492516fe12f09ad7ec6dae1277\",\n \"score\": \"0.64280707\",\n \"text\": \"def verify_model_typing(self):\\n for k, v in self.requirements.items():\\n if not isinstance(v, bool):\\n raise ValueError(f\\\"Model Requirement \\\\'{k}\\\\' must be a boolean value\\\")\\n if v == True:\\n if self.types[k] is None:\\n raise ValueError(f\\\"\\\\'{k}\\\\' Cannot be None in typing delarations\\\")\\n if self.dictionary[k] is None:\\n raise ValueError(f\\\"\\\\'{k}\\\\' Cannot be None inside of the main model dictionary\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaab101e5528f611d32270936b73f998\",\n \"score\": \"0.62626535\",\n \"text\": \"def enforce_specification (self):\\n\\n \\\"\\\"\\\" Must include a version. For now, there is only one. \\\"\\\"\\\"\\n assert self.spec.get ('ros') == 0.1\\n\\n \\\"\\\"\\\" Validate workflow object. \\\"\\\"\\\"\\n workflow = self.spec.get ('workflow', {})\\n assert len(workflow) > 0, \\\"Workflow element exists but contains no steps.\\\"\\n\\n \\\"\\\"\\\" Validate information section. \\\"\\\"\\\"\\n info = self.spec.get (\\\"info\\\", {})\\n assert info, \\\"Missing 'info' section.\\\"\\n version = info.get (\\\"version\\\")\\n assert version, \\\"Missing info version \\\"\\n assert re.match(\\\"^\\\\d+?\\\\.\\\\d+?.\\\\d+?$\\\", version), \\\"Version is not a float.\\\"\\n\\n title = info.get (\\\"title\\\")\\n assert isinstance(title, str), \\\"Title is not valid.\\\"\\n\\n description = info.get (\\\"description\\\")\\n assert isinstance(description, str), \\\"Description is not valid.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb096a097134a918ca51a269eb1067d8\",\n \"score\": \"0.6163803\",\n \"text\": \"def requirements(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0455422add1997b0a0e6e462bcade66\",\n \"score\": \"0.6162088\",\n \"text\": \"def requirements(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabcb74d2026d25a276533c52dcd05c1\",\n \"score\": \"0.613765\",\n \"text\": \"def validate(self):\\n\\n super(self.__class__, self).validate()\\n\\n if self.properties['Protocol'] == 1:\\n if not self.properties.get('Icmp'):\\n error(self, 'Icmp must be specified when protocol is 1')\\n elif self.properties['Protocol'] in [6, 17]: # TCP, UDP\\n if not self.properties.get('PortRange'):\\n error(self, 'PortRange must be specified when protocol '\\n 'protocol is 6 or 17')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b600312071e03bb920e36f6915501bb\",\n \"score\": \"0.6028801\",\n \"text\": \"def test_sender_constraints_against_schema(self):\\n r = requests.get(URI + \\\"/v1.0/single/senders/\\\"+self.senders[0]+\\\"/constraints/\\\")\\n schema = self.load_schema(\\\"schemas/v1.0-constraints-schema.json\\\")\\n jschema = json.loads(schema)\\n validate(r.json(), jschema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76ca1c5be4c362c38706b2a927d889f\",\n \"score\": \"0.5947436\",\n \"text\": \"def validate_constraints(self):\\n struct_value = self.get_struct_value()\\n self.validate_struct_value(struct_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc3a7cf28bc6b25ddf3dd76caac5555\",\n \"score\": \"0.5850915\",\n \"text\": \"def validate_metadata(self, std_meta, meta):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff7df5af71f817983591e6eb649df58\",\n \"score\": \"0.58160347\",\n \"text\": \"def test_receiver_constraints_against_schema(self):\\n r = requests.get(URI + \\\"/v1.0/single/receivers/\\\"+self.receivers[0]+\\\"/constraints/\\\")\\n schema = self.load_schema(\\\"schemas/v1.0-constraints-schema.json\\\")\\n jschema = json.loads(schema)\\n validate(r.json(), jschema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10f86afd57d10299acceb17142169eca\",\n \"score\": \"0.575473\",\n \"text\": \"def _validate(self):\\n\\n self._check_dict()\\n self._check_comment()\\n self._check_unitcell()\\n self._check_sites()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80e85efd4c09a7495f2ebdce9c48e61\",\n \"score\": \"0.57252026\",\n \"text\": \"def validate(self): # pragma: no cover\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effa04d0bb92195a52d0d6fb2b83138f\",\n \"score\": \"0.57195306\",\n \"text\": \"def _validate(self):\\n if self._definition.get('required'):\\n required_missing = set(self._definition.get('required')) - set(self._fast_attribute_access.keys())\\n if required_missing:\\n raise InvalidMISPObject('{} are required.'.format(required_missing))\\n if self._definition.get('requiredOneOf'):\\n if not set(self._definition['requiredOneOf']) & set(self._fast_attribute_access.keys()):\\n # We ecpect at least one of the object_relation in requiredOneOf, and it isn't the case\\n raise InvalidMISPObject('At least one of the following attributes is required: {}'.format(', '.join(self._definition['requiredOneOf'])))\\n for rel, attrs in self._fast_attribute_access.items():\\n if len(attrs) == 1:\\n # object_relation's here only once, everything's cool, moving on\\n continue\\n if not self._definition['attributes'][rel].get('multiple'):\\n # object_relation's here more than once, but it isn't allowed in the template.\\n raise InvalidMISPObject('Multiple occurrences of {} is not allowed'.format(rel))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c93d5721f610fe9178d6da95dcc9174\",\n \"score\": \"0.56559634\",\n \"text\": \"def validate(self, format):\\n required, optionals = bibpy.requirements.check(self, format)\\n\\n if required or optionals:\\n raise bibpy.error.RequiredFieldError(self, required, optionals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1827193231f5c677620676d09427fa2a\",\n \"score\": \"0.56420887\",\n \"text\": \"def _check_params(self):\\n if self.required_params is not None:\\n if self.required_params != len(self.params):\\n raise SpecError('The \\\"{} rule expects {} parameters.\\\"'.format(self.name(), self.required_params))\\n try:\\n self._sanitize_params()\\n except SpecError as e:\\n raise e\\n except Exception:\\n raise SpecError('The params {} passed to the rule {} are invalid.'.format(self.params, self.name()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef970b5b99eba9135ff220f23c8bb1ed\",\n \"score\": \"0.56247985\",\n \"text\": \"def _validate(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e81bc60aad1f367e4047d4be3bab89f\",\n \"score\": \"0.56228924\",\n \"text\": \"def validate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67a52c5b8f055f192a766fffa9b5f77a\",\n \"score\": \"0.55986077\",\n \"text\": \"def checkRequirements(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258578f86fc04915dadefa5344ed1761\",\n \"score\": \"0.5594791\",\n \"text\": \"def validate(self, *specs) -> None:\\n super().validate(*specs)\\n\\n # Validate constituent data\\n self.diff_data.validate(*specs)\\n self.spec_dict[\\\"secr_data\\\"].validate(*specs)\\n self.bcs.validate(*specs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ab15271630f0b8d6a8f6a97c13eefd\",\n \"score\": \"0.55673105\",\n \"text\": \"def _validate(self):\\n\\n config_reqs = {\\n 'ip_addr': str,\\n 'dir_ptrn': str,\\n 'file_ptrn': str,\\n 'file_ptrn_abbr': str,\\n 'columns': (list, tuple),\\n 'schema': dict,\\n 's3_bucket': str\\n }\\n\\n logger.info('Validating config parameters')\\n for req_param, req_type in config_reqs.items():\\n self.key_check(req_param, self.__dict__, 'config')\\n self.type_check(getattr(self, req_param), req_type, f\\\"{req_param}\\\")\\n\\n logger.info('Validating schema parameters')\\n self._validate_schema()\\n self.iter_compare(\\n 'val', self.schema, self.columns, 'schema', 'columns')\\n\\n if 'row_skip' in self.__dict__:\\n logger.info('Validating row_skip parameters')\\n self._validate_row_skip()\\n\\n if 'partition' in self.__dict__:\\n logger.info('Validating partition parameters')\\n self._validate_partition()\\n\\n if self.mem_cap < 5 * 10 ** 8:\\n logger.warning(\\n 'mem_cap is %i; a minimum size of 5 * 10 ** 8 is recommended',\\n self.mem_cap)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b68edf09cdeb823ac20b77020dad0998\",\n \"score\": \"0.5565948\",\n \"text\": \"def test_4_required_check(self):\\n class FussyRecord(Record):\\n id = Property(required=True, isa=int)\\n natural = SafeProperty(check=lambda i: i > 0)\\n must = SafeProperty(required=True)\\n rbn = SafeProperty(required=True, isa=(str, type(None)))\\n\\n with self.assertRaises(ValueError):\\n fr = FussyRecord()\\n\\n fr = FussyRecord(id=123, must=\\\"sugary\\\", rbn=\\\"Hello\\\")\\n self.assertIn(\\\"Hello\\\", str(fr))\\n self.assertEqual(fr, eval(repr(fr)))\\n with self.assertRaises(ValueError):\\n del fr.must\\n with self.assertRaises(ValueError):\\n fr.must = None\\n fr.must = \\\"barmy\\\"\\n with self.assertRaises(ValueError):\\n del fr.rbn\\n fr.rbn = None\\n\\n fr.natural = 7\\n with self.assertRaises(ValueError):\\n fr.natural = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334f144f46c63fd03f2848c7dc75c5d0\",\n \"score\": \"0.5548117\",\n \"text\": \"def test_validate_metadata_correct(self, metadata):\\n\\t\\tdatatype.validate_metadata(metadata) #Should not give an exception.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db222bc963c8ada20cf81764ba037278\",\n \"score\": \"0.5483188\",\n \"text\": \"def set_attributes(self, attributes, valid_attributes=None, partial=False):\\n required = self.type.get_required_attributes()\\n if valid_attributes is None:\\n valid_attributes = Attribute.all_by_name(\\n 'Protocol', site=self.site\\n )\\n\\n # Temporarily mark required attributes as ``required`` at run-time for\\n # injecting required_attributes into validation.\\n for r in required:\\n if r in valid_attributes:\\n valid_attributes[r].required = True\\n\\n return super(Protocol, self).set_attributes(\\n attributes, valid_attributes=valid_attributes,\\n partial=partial\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36fc0d8d273c71e348b6de8abd091c04\",\n \"score\": \"0.546973\",\n \"text\": \"def _validate(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e261861d385bf9a599465720c0c2aec\",\n \"score\": \"0.5434504\",\n \"text\": \"def test_model_validator(self):\\n privacy_engine = PrivacyEngine(\\n models.resnet18(),\\n batch_size=self.BATCH_SIZE,\\n sample_size=self.DATA_SIZE,\\n alphas=self.ALPHAS,\\n noise_multiplier=1.3,\\n max_grad_norm=1,\\n )\\n with self.assertRaises(IncompatibleModuleException):\\n privacy_engine.attach(self.private_optimizer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90294209ab56f9de49a67dfc414a901d\",\n \"score\": \"0.5433354\",\n \"text\": \"def required(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec026a262ff640a4b36d6071ad7a441\",\n \"score\": \"0.54138446\",\n \"text\": \"def validate(self):\\n\\n super(self.__class__, self).validate()\\n\\n if self.properties.get('NetworkInterfaces'):\\n if self.properties.get('SecurityGroupIds'):\\n error(self, 'Can not specify both NetworkInterfaces and '\\n 'SecurityGroupIds')\\n if self.properties.get('SubnetId'):\\n error(self, 'Can not specify both NetworkInterfaces and '\\n 'SubnetId')\\n\\n inst_ten = self.properties.get('Tenancy')\\n if inst_ten:\\n if inst_ten not in ['default', 'dedicated']:\\n error(self, 'Tenancy must be one of \\\"default\\\", \\\"dedicated\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ac79c28f25da34ec43f4fc85010c5a\",\n \"score\": \"0.5409859\",\n \"text\": \"def validate_module(self, pipeline):\\n if (\\n self.assignment_method == ASSIGN_RULES\\n and self.matching_choice == MATCH_BY_METADATA\\n and len(self.assignments) > 1\\n ):\\n joins = self.join.parse()\\n for name in self.get_column_names():\\n for join in joins:\\n if join.get(name) is None:\\n break\\n else:\\n return\\n raise ValidationError(\\n \\\"At least one channel must have all metadata keys specified. \\\"\\n \\\"All channels have at least one metadata key of (None).\\\",\\n self.join,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3840356306bbaebcdaf5cbc4f20c5702\",\n \"score\": \"0.5409455\",\n \"text\": \"def test_receiver_staged_schema_valid(self):\\n r = requests.get(URI + \\\"/v1.0/single/receivers/\\\"+self.receivers[0]+\\\"/staged/\\\")\\n schema = self.load_schema(\\\"schemas/v1.0-receiver-response-schema.json\\\")\\n checker = FormatChecker([\\\"ipv4\\\", \\\"ipv6\\\"])\\n validate(r.json(), json.loads(schema), format_checker = checker)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcac560f8f8b402bd84d16219b715f8\",\n \"score\": \"0.53979933\",\n \"text\": \"def validate(self):\\n\\n jsonschema.validate(self.request_json, EVENT_SCHEMA)\\n jsonschema.validate(self.data, self.topic.schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee16e61be4e6161164417fc7373b3c28\",\n \"score\": \"0.5394138\",\n \"text\": \"def test_wrapper_ssh_cmd_send_case6(self):\\n\\n with self.assertRaises(ValidationError):\\n self.schema.load(\\n {\\n \\\"MISSING\\\": \\\"REQUIRED_FIELDS\\\",\\n\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea95f9f4c2712faaf9b05606e3dfa2f\",\n \"score\": \"0.5391442\",\n \"text\": \"def _validate(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97890ca90e18db80ec8befe516405cd\",\n \"score\": \"0.5383073\",\n \"text\": \"def _validate_init(self, param, requirements):\\n\\n _validate(param, 'param', {'type': dict})\\n _validate(requirements, 'requirements',\\n [{'type': dict}, {'type_in': [list, tuple, dict]}])\\n\\n if not sorted(param.keys()) == sorted(requirements.keys()):\\n raise KeyError('param and requirements must have the same keys.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4cecb8917e250bce22e2fcc5381d7b\",\n \"score\": \"0.53768003\",\n \"text\": \"def setup_validation(self, client):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4cecb8917e250bce22e2fcc5381d7b\",\n \"score\": \"0.53768003\",\n \"text\": \"def setup_validation(self, client):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc3cd4371a8297bb3b2f72de3331b54\",\n \"score\": \"0.53757226\",\n \"text\": \"def _checkSpecs(self, specs):\\n self._checkSpec(specs, 'name', [isinstance, (str, unicode)])\\n self._checkSpec(specs, 'metric', [isinstance, bool])\\n self._checkSpec(specs, 'oal', [isinstance, (float, int)], [gt, 0.0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545cc7fbf99879c6e291e36f99bc246b\",\n \"score\": \"0.5365856\",\n \"text\": \"def test_eschema_syntax_constraint_07(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9b09ab145973064f55723e432e55d9\",\n \"score\": \"0.53627896\",\n \"text\": \"def check_requirements(\\n self,\\n pipeline_proto: beam_runner_api_pb2.Pipeline,\\n supported_requirements: Iterable[str]):\\n\\n # Imported here to avoid circular dependencies.\\n # pylint: disable=wrong-import-order, wrong-import-position\\n from apache_beam.runners.portability.fn_api_runner import translations\\n supported_requirements = set(supported_requirements)\\n for requirement in pipeline_proto.requirements:\\n if requirement not in supported_requirements:\\n raise ValueError(\\n 'Unable to run pipeline with requirement: %s' % requirement)\\n for transform in pipeline_proto.components.transforms.values():\\n if transform.spec.urn == common_urns.primitives.TEST_STREAM.urn:\\n if common_urns.primitives.TEST_STREAM.urn not in supported_requirements:\\n raise NotImplementedError(transform.spec.urn)\\n elif transform.spec.urn in translations.PAR_DO_URNS:\\n payload = beam_runner_api_pb2.ParDoPayload.FromString(\\n transform.spec.payload)\\n for timer in payload.timer_family_specs.values():\\n if timer.time_domain not in (\\n beam_runner_api_pb2.TimeDomain.EVENT_TIME,\\n beam_runner_api_pb2.TimeDomain.PROCESSING_TIME):\\n raise NotImplementedError(timer.time_domain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73548df03a522d40ac9ac0c6e10299be\",\n \"score\": \"0.5362035\",\n \"text\": \"def setup_validation(self, client, *args, **keyword_args):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729941147a3f539a7925d4b8f67f53a7\",\n \"score\": \"0.5360922\",\n \"text\": \"def test_eschema_syntax_constraint_06(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b4405d1e8288ae35f83e4d3755514e\",\n \"score\": \"0.5352467\",\n \"text\": \"def test_eschema_syntax_constraint_05(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a43e496ac6e818c5a718fc1f1230c2\",\n \"score\": \"0.5351768\",\n \"text\": \"def test_required(self):\\n obj_def = {'type': 'object', 'properties': self.properties}\\n builder = ObjectBuilder(self.provider)\\n for required in (True, False):\\n self.assertEqual(required, builder.build_schema(obj_def, required=required).required)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5e7604ea9f5f9345a597198675907a\",\n \"score\": \"0.53488547\",\n \"text\": \"def test_eschema_syntax_constraint_09(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e699b1255da7ab8d91c2b52417c2e957\",\n \"score\": \"0.53485715\",\n \"text\": \"def test_required_fields(self):\\n fields = self.fields.copy()\\n\\n def run_on_empty_args() -> DocMetadata:\\n return DocMetadata(**fields) # type: ignore\\n\\n with self.assertRaises(TypeError) as ctx:\\n run_on_empty_args()\\n\\n # Do not indent us or we will not run and be tested!:\\n self.assertTrue('missing 14 required positional arguments' in str(ctx.exception))\\n #\\n self.assertTrue('raw_safe' in str(ctx.exception))\\n self.assertTrue('arxiv_id' in str(ctx.exception))\\n self.assertTrue('arxiv_id_v' in str(ctx.exception))\\n self.assertTrue('arxiv_identifier' in str(ctx.exception))\\n self.assertTrue('modified' in str(ctx.exception))\\n self.assertTrue('title' in str(ctx.exception))\\n self.assertTrue('abstract' in str(ctx.exception))\\n self.assertTrue('authors' in str(ctx.exception))\\n self.assertTrue('submitter' in str(ctx.exception))\\n self.assertTrue('categories' in str(ctx.exception))\\n self.assertTrue('primary_category' in str(ctx.exception))\\n self.assertTrue('primary_archive' in str(ctx.exception))\\n self.assertTrue('primary_group' in str(ctx.exception))\\n self.assertTrue('secondary_categories' in str(ctx.exception))\\n #\\n self.assertTrue('journal_ref' not in str(ctx.exception))\\n self.assertTrue('report_num' not in str(ctx.exception))\\n self.assertTrue('doi' not in str(ctx.exception))\\n self.assertTrue('acm_class' not in str(ctx.exception))\\n self.assertTrue('msc_class' not in str(ctx.exception))\\n self.assertTrue('proxy' not in str(ctx.exception))\\n self.assertTrue('comments' not in str(ctx.exception))\\n self.assertTrue('version' not in str(ctx.exception))\\n self.assertTrue('license' not in str(ctx.exception))\\n self.assertTrue('version_history' not in str(ctx.exception))\\n self.assertTrue('private' not in str(ctx.exception))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932a44657b4b5fa94ffefe9e9a3d0fe9\",\n \"score\": \"0.53445613\",\n \"text\": \"def validate(self, params: Dict = None) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85bb9408bc7a1713341c20f531d28ca\",\n \"score\": \"0.533717\",\n \"text\": \"def validate(self):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36406907fdea63433b503ef79c3f439b\",\n \"score\": \"0.53353614\",\n \"text\": \"def test_required_fields():\\n payload = boards.structure_payload(khoros, 'test-forum', 'Test Forum', 'forum')\\n data_fields_to_check = {\\n 'conversation_style': 'forum',\\n 'id': 'test-forum',\\n 'title': 'Test Forum'\\n }\\n assert verify_data_fields(payload, data_fields_to_check) is True # nosec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c721aaa714d6e3c68dc9472f0daa066\",\n \"score\": \"0.53321564\",\n \"text\": \"def validate_main(self):\\n super(DnsBruteCombine, self).validate_main()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4146024b65340df31826e25b372b8dbe\",\n \"score\": \"0.5330618\",\n \"text\": \"def validate_rule(self):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689ad35061a266df1fe42a9289c2e5a2\",\n \"score\": \"0.5320653\",\n \"text\": \"def test_eschema_syntax_constraint_02(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8145cddce3d2f5f97e9f09bb0fec4f0\",\n \"score\": \"0.53195155\",\n \"text\": \"def validate(self):\\n for p in self._required_params:\\n if self._params.get(p, None) is None:\\n raise ValueError(f\\\"Required configuration parameter {p} missing.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8064f7d8f7975c8ada61712b3a0d442\",\n \"score\": \"0.5319296\",\n \"text\": \"def test_sender_staged_schema_valid(self):\\n r = requests.get(URI + \\\"/v1.0/single/senders/\\\"+self.senders[0]+\\\"/staged/\\\")\\n schema = self.load_schema(\\\"schemas/v1.0-sender-response-schema.json\\\")\\n checker = FormatChecker([\\\"ipv4\\\", \\\"ipv6\\\"])\\n validate(r.json(), json.loads(schema), format_checker = checker)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b019e4e25294fecfa72fa556bf8d2918\",\n \"score\": \"0.531186\",\n \"text\": \"def requirements(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e833cc4553ecd1b4f6daa8c5e9db6623\",\n \"score\": \"0.5307088\",\n \"text\": \"def check_compatible(global_reqs, constraints):\\n def satisfied(reqs, name, version, failures):\\n if name not in reqs:\\n return True\\n tested = []\\n for constraint, _ in reqs[name]:\\n spec = specifiers.SpecifierSet(constraint.specifiers)\\n # pre-releases are allowed by policy but discouraged\\n if spec.contains(version, prereleases=True):\\n return True\\n tested.append(constraint.specifiers)\\n failures.append('Constraint %s for %s does not match requirement %s' %\\n (version, name, tested))\\n return False\\n failures = []\\n for pkg_constraints in constraints.values():\\n for constraint, _ in pkg_constraints:\\n name = constraint.package\\n version = constraint.specifiers[3:]\\n satisfied(global_reqs, name, version, failures)\\n return failures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894ef96503f4bb8955e0808354a7f566\",\n \"score\": \"0.5306498\",\n \"text\": \"def test_eschema_syntax_constraint_10(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ec92c01c429d3dbe38524abbb995f1\",\n \"score\": \"0.53028697\",\n \"text\": \"def _validate_required(self):\\n required_attrs = [\\n 'id', 'emfetch_id', 'table', 'func',\\n 'time_field', 'time_type', \\n ]\\n for a in required_attrs:\\n val = getattr(self, a)\\n if (val is None) or (val == ''):\\n raise ValueError(\\\"Missing {k} in #{id}\\\"\\n .format(k=a, id=self.id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc94f75b40ef3f57e8b836551c72633\",\n \"score\": \"0.53007454\",\n \"text\": \"def ensure_protocol_is_given(func): # pylint: disable=no-self-argument\\n\\n @functools.wraps(func)\\n def wrapper(self, *args, **kwargs):\\n if not self.protocol:\\n raise ValueError(\\\"<self.protocol> is not given.\\\")\\n\\n return func(self, *args, **kwargs) # pylint: disable=not-callable\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb28ca1d328d960496029ad48e057867\",\n \"score\": \"0.5300677\",\n \"text\": \"def validate_spec(self, value):\\n if self.spec:\\n spec = dict(self.spec)\\n spec['type'] = 'object'\\n json_schema_validator(value, spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55c1dbdbcce6cd1c72b41a99e3b99af\",\n \"score\": \"0.5292425\",\n \"text\": \"def ensure_protocol_is_given(func): # pylint: disable=no-self-argument\\n\\n @functools.wraps(func)\\n def wrapper(self, *args, **kwargs):\\n if self.protocol is None:\\n raise RuntimeError(\\\"<self.protocol> is not given.\\\")\\n\\n return func(self, *args, **kwargs) # pylint: disable=not-callable\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05733d667293d7e7617e0653d393a1ec\",\n \"score\": \"0.52899516\",\n \"text\": \"def validate_rule(self, client):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d38e3818eb9c33183bc0814e7ac37c\",\n \"score\": \"0.528854\",\n \"text\": \"def _validate_structure(self):\\n\\n self._validate_structure_payload_directory()\\n self._validate_structure_tag_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70ee1be3e195bee9971dfd7b2c12150\",\n \"score\": \"0.5287867\",\n \"text\": \"def validate(self):\\n pass # pylint: disable=unnecessary-pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b63d3b6fa01aaf4ca94076ec537c2ae\",\n \"score\": \"0.5275397\",\n \"text\": \"def check_compatible(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcb2e7e95c9d7412205c8a02b971771\",\n \"score\": \"0.5271401\",\n \"text\": \"def validate(self) -> None:\\n try:\\n mo = self._schema_re.search(self.schema)\\n if mo is None:\\n raise ValueError\\n schema_groups = mo.groups()\\n self._type = MessageType(schema_groups[0])\\n self._data_schema_version = StrictVersion(schema_groups[1])\\n except (AttributeError, ValueError):\\n raise ValidationError(f'Invalid schema found: {self.schema}')\\n\\n _get_validator().validate(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad63269de2374035c9cb932d51e8abf3\",\n \"score\": \"0.52711904\",\n \"text\": \"def validate(self) -> None:\\n self.require_valid_title(self.title)\\n self.require_valid_description(self.description)\\n assert self.url_fragment is not None\\n utils.require_valid_url_fragment(\\n self.url_fragment, 'Story Url Fragment',\\n constants.MAX_CHARS_IN_STORY_URL_FRAGMENT)\\n utils.require_valid_meta_tag_content(self.meta_tag_content)\\n if self.thumbnail_filename is not None:\\n self.require_valid_thumbnail_filename(self.thumbnail_filename)\\n if self.thumbnail_bg_color is not None and not (\\n self.require_valid_thumbnail_bg_color(self.thumbnail_bg_color)):\\n raise utils.ValidationError(\\n 'Story thumbnail background color %s is not supported.' % (\\n self.thumbnail_bg_color))\\n if self.thumbnail_bg_color and self.thumbnail_filename is None:\\n raise utils.ValidationError(\\n 'Story thumbnail image is not provided.')\\n if self.thumbnail_filename and self.thumbnail_bg_color is None:\\n raise utils.ValidationError(\\n 'Story thumbnail background color is not specified.')\\n if not isinstance(self.notes, str):\\n raise utils.ValidationError(\\n 'Expected notes to be a string, received %s' % self.notes)\\n\\n if not isinstance(self.story_contents_schema_version, int):\\n raise utils.ValidationError(\\n 'Expected story contents schema version to be an integer, '\\n 'received %s' % self.story_contents_schema_version)\\n\\n if (self.story_contents_schema_version !=\\n feconf.CURRENT_STORY_CONTENTS_SCHEMA_VERSION):\\n raise utils.ValidationError(\\n 'Expected story contents schema version to be %s, '\\n 'received %s' % (\\n feconf.CURRENT_STORY_CONTENTS_SCHEMA_VERSION,\\n self.story_contents_schema_version))\\n\\n if not isinstance(self.language_code, str):\\n raise utils.ValidationError(\\n 'Expected language code to be a string, received %s' %\\n self.language_code)\\n\\n if not utils.is_valid_language_code(self.language_code):\\n raise utils.ValidationError(\\n 'Invalid language code: %s' % self.language_code)\\n\\n if not isinstance(self.corresponding_topic_id, str):\\n raise utils.ValidationError(\\n 'Expected corresponding_topic_id should be a string, received: '\\n '%s' % self.corresponding_topic_id)\\n\\n self.story_contents.validate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f810a20b42a607b9d21808614bc0abd\",\n \"score\": \"0.526885\",\n \"text\": \"def c_validate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297980f28f5f04f92e1289528b41364b\",\n \"score\": \"0.52603394\",\n \"text\": \"def _safety_checks(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d37bcb7f761cf73ee94ae1df2a391c3\",\n \"score\": \"0.52601695\",\n \"text\": \"def pre_validate_struct(self, struct):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a62024c17c10a2296732b1132ec584\",\n \"score\": \"0.52600735\",\n \"text\": \"def _check_fields(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca0a255c1118b4e95a725457712e25b\",\n \"score\": \"0.5256323\",\n \"text\": \"def requirementsMet(self):\\n\\n try:\\n input_length = len(self.value())\\n except TypeError:\\n # A TypeError is returned if self.value() returns None, which may\\n # happen when the json-defined blank value is set to 'isEmpty':\\n # 'pass'.\\n input_length = 0\\n\\n if input_length > 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb361a2dfa2eabf5dbbc76e982858993\",\n \"score\": \"0.52526546\",\n \"text\": \"def check_rules(self) -> YieldFlake8Error:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0387601257489363888084b8ebc5085d\",\n \"score\": \"0.52481604\",\n \"text\": \"def pre_validate(self, schema, data, sp):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0782e2f88167f1e95558e29e2fdd266\",\n \"score\": \"0.52443594\",\n \"text\": \"def MakeValid(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e84011cd66009c26049849d42b17ae92\",\n \"score\": \"0.52420676\",\n \"text\": \"def validate_part_a(self):\\n for req_field in self.required_fields:\\n if not getattr(self, req_field):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84d755a1da13d0c710d51964cb8d6a69\",\n \"score\": \"0.5236112\",\n \"text\": \"def validate(self, context, runtime, config):\\n raise NotImplementedError(\\n \\\"Concrete implementations should override validate\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213187bb282d1b725fc5ac864133ac44\",\n \"score\": \"0.5229793\",\n \"text\": \"def test_valid_modeling_rule_schema(self, pack: Pack):\\n modeling_rule_schema = pack.create_modeling_rule(\\\"modeling_rule\\\").schema\\n validator = StructureValidator(modeling_rule_schema.path)\\n assert validator.is_valid_scheme()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3bad40845b3513759d8580f3c4e0c98\",\n \"score\": \"0.5229034\",\n \"text\": \"def validate(self):\\n for name, option in self._options.items():\\n if option.required:\\n if not name in self._values:\\n raise OptionRequired(name, option)\\n\\n constraints = getattr(self, '__constraints__', [])\\n for constraint in constraints:\\n constraint.validate(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75283345e5cfa4cb44414808baf8d479\",\n \"score\": \"0.5214733\",\n \"text\": \"def validate_metadata_json(metadata_json: Dict[str, Any]) -> None:\\n jsonschema_validate(instance=metadata_json, schema=metadata_schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ec1fcedc921c0205c3ca97849d0cd8\",\n \"score\": \"0.5208268\",\n \"text\": \"def test_raise_when_no_protocol_specified():\\n\\n with pytest.raises(AttributeError, match=r\\\"requires backend protocol\\\"):\\n\\n class UseBackend(HasBackends):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c87fca217dd875a683e0cc49e2e4bf\",\n \"score\": \"0.5204537\",\n \"text\": \"def sanity_check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2adbb87b7674f0bc903f8f9ecc8dba\",\n \"score\": \"0.52039057\",\n \"text\": \"def validate_rule(self, client, *args, **keyword_args):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec6f733ebcb07b300a66b24b8969950c\",\n \"score\": \"0.5203211\",\n \"text\": \"def validate_config(data: typing.Dict): # pylint: disable=unused-argument\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e28cc109200342fe5d5eaf42987cb8d\",\n \"score\": \"0.51978666\",\n \"text\": \"def valid(self, format):\\n required, optionals = bibpy.requirements.check(self, format)\\n\\n return not required and not optionals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24644a14186c3199144f6183adfef258\",\n \"score\": \"0.5196336\",\n \"text\": \"def test_example_valid():\\n def set_additional_properties_false(data):\\n if isinstance(data, list):\\n for item in data:\\n set_additional_properties_false(item)\\n elif isinstance(data, dict):\\n if 'properties' in data:\\n data['additionalProperties'] = False\\n for value in data.values():\\n set_additional_properties_false(value)\\n\\n for basename in ('release-schema.json', 'release-package-schema.json', 'record-package-schema.json'):\\n if os.path.isfile(os.path.join(cwd, basename)):\\n patched = patch_schema(basename)\\n break\\n else:\\n return\\n\\n set_additional_properties_false(patched)\\n\\n validator = Draft4Validator(patched, format_checker=FormatChecker())\\n\\n for i, text, data in examples():\\n # Skip packages (only occurs once in ocds_ppp_extension).\\n if 'releases' in data:\\n continue\\n\\n release = deepcopy(minimal_release)\\n json_merge_patch.merge(release, data)\\n if 'tender' in release and 'id' not in release['tender']:\\n release['tender']['id'] = '1'\\n\\n errors = validate_schema('README.md', release, validator)\\n\\n assert not errors, f'README.md: JSON block {i} is invalid. See warnings below.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f5245701229fa181e9ea9c7a034727\",\n \"score\": \"0.5193923\",\n \"text\": \"def check_requirement(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1756d8fb5f64bcd57909e753247e5cd8\",\n \"score\": \"0.519358\",\n \"text\": \"def validate_required_fields_are_provided(model: object, data: dict):\\n if not hasattr(model, 'required_fields'):\\n return\\n\\n\\n missing_required_fields = set(model.required_fields) - set(data.keys())\\n\\n if missing_required_fields:\\n error_msg = f\\\"Missing required field(s): {', '.join(missing_required_fields)}\\\"\\n return validation_error(error_msg)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439fe253bfffab9e37af686ac93a2a78\",\n \"score\": \"0.51917464\",\n \"text\": \"def __proto_match_validated(self) -> bool:\\n if ip_network(self.dest_prefix).version == ip_address(self.next_hop_ip).version:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc83412a4bab5efaf62c7cfea34c49b\",\n \"score\": \"0.51762605\",\n \"text\": \"def test_validate_required_error(self):\\n required = ['key1', 'key2']\\n data = {'key1': 'val1'}\\n\\n with pytest.raises(MercuryClientException) as exc_info:\\n self.req_service.validate_required(required, data)\\n if hasattr(exc_info.value, 'message'):\\n assert exc_info.value.message == (\\n \\\"Message is missing required data: ['key2']\\\")\\n else:\\n assert exc_info.value.msg == \\\\\\n \\\"Message is missing required data: ['key2']\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd52ef3607c027023b8acfdb2f7a9f3\",\n \"score\": \"0.51733655\",\n \"text\": \"def validate(self):\\n raise NotImplementedError(\\n \\\"%s: subclasses must define their own validate() method\\\"\\n % self.__class__.__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4b01bfbc03a8391fdbd5b49cfb144e9\",\n \"score\": \"0.51721007\",\n \"text\": \"def _validate_metadata(cls):\\n required_attributes = ['title', 'description']\\n for attr in required_attributes:\\n if not attr in cls.__dict__:\\n raise errors.MissingMetadata(check_name=cls.__name__, attr=attr)\\n\\n if not cls.result_fields:\\n raise errors.NoResultField(check_name=cls.__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb5d18c7dbcbd5e88d8f701a5fa558a\",\n \"score\": \"0.5171257\",\n \"text\": \"def VerifyHasRequiredProperties(self):\\n\\n # TODO(mark): A stronger verification mechanism is needed. Some\\n # subclasses need to perform validation beyond what the schema can enforce.\\n for property, attributes in self._schema.items():\\n (is_list, property_type, is_strong, is_required) = attributes[0:4]\\n if is_required and not property in self._properties:\\n raise KeyError(self.__class__.__name__ + ' requires ' + property)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4573dfc999b03d49fd46f71ac2a1c28e\",\n \"score\": \"0.51680565\",\n \"text\": \"def test_required(self):\\n\\n with pytest.raises(ValidationError):\\n name = DummyStringField(required=True)\\n name._load(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb98df04261e80a5129ede39f100cb9\",\n \"score\": \"0.51677203\",\n \"text\": \"def test_disable_compat_check(self, graph_with_incompatible_arg_numbers):\\n # ensure global options can disable compat check\\n graph: _WrapDiGraph = graph_with_incompatible_arg_numbers\\n with spype.options(check_compatibility=False):\\n try:\\n graph.validate()\\n except InvalidPype:\\n pytest.fail(\\\"should not fail when type checking is turned off\\\")\\n # ensure the validate call can disable compat check\\n try:\\n graph.validate(check_task_compatibility=False)\\n except InvalidPype:\\n pytest.fail(\\\"should not raise\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa870a3935cd5c450545c978fba2d14\",\n \"score\": \"0.51650184\",\n \"text\": \"def requires(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a632392f45a61d746bb0e650c3f896d\",\n \"score\": \"0.516243\",\n \"text\": \"def __validate_document_has_allowlisted_keys(doc, filepath, required_keys=[], optional_keys=[], required_values=[]):\\n required_keys_not_found = []\\n invalid_types = []\\n\\n for key in required_keys:\\n if key not in doc:\\n required_keys_not_found.append(key)\\n elif not isinstance(doc[key], required_keys[key]):\\n __fail('Error parsing file: %s. Required key %s has invalid type %s which should be %s' % (\\n filepath, key, type(doc[key]), required_keys[key]))\\n if key in required_values and doc[key] != required_values[key]:\\n __fail('Error parsing file: %s. Required key %s with required value `%s` equals `%s`' % (\\n filepath, key, required_values[key], doc[key]))\\n\\n if required_keys_not_found:\\n prefixed_required_keys_not_found = [\\\"🔑 ~> \\\" + key for key in required_keys_not_found] \\n __fail('Error parsing file: %s. Required keys not found:\\\\n%s' % (\\n filepath, \\\"\\\\n\\\".join(prefixed_required_keys_not_found)))\\n\\n for key in optional_keys:\\n if key not in doc:\\n continue\\n elif not isinstance(doc[key], optional_keys[key]):\\n __fail('Error parsing file: %s. Optional key %s has invalid type %s which should be %s' % (\\n filepath, key, type(doc[key]), optional_keys[key])) \\n if key in required_values and doc[key] != required_values[key]:\\n __fail('Error parsing file: %s. Required key %s with required value `%s` equals `%s`' % (\\n filepath, key, required_values[key], doc[key]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c557760aa2ee83ba6df848f8af03d7\",\n \"score\": \"0.51599604\",\n \"text\": \"def prevalidate_put(self):\\n self.kind = self.get_query_argument('kind', default='file')\\n\\n if self.kind not in ('file', 'folder'):\\n raise exceptions.InvalidParameters('Kind must be file, folder or unspecified (interpreted as file), not {}'.format(self.kind))\\n\\n length = self.request.headers.get('Content-Length')\\n\\n if length is None and self.kind == 'file':\\n # Length Required\\n raise exceptions.InvalidParameters('Content-Length is required for file uploads', code=411)\\n\\n try:\\n if length is not None and int(length) > 0 and self.kind == 'folder':\\n # Payload Too Large\\n raise exceptions.InvalidParameters('Folder creation requests may not have a body', code=413)\\n except ValueError:\\n raise exceptions.InvalidParameters('Invalid Content-Length')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4119a130d6f3509c0e8bca20b278f3b1\",\n \"score\": \"0.5159814\",\n \"text\": \"def embedded_check_compatibility(self):\\n if self.nvsram:\\n self.embedded_check_nvsram_compatibility()\\n if self.firmware:\\n self.embedded_check_bundle_compatibility()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349b283afc855b3ba0f124ef34622d68\",\n \"score\": \"0.5158597\",\n \"text\": \"def validate(cls, full_config: ,) -> ,:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349b283afc855b3ba0f124ef34622d68\",\n \"score\": \"0.5158597\",\n \"text\": \"def validate(cls, full_config: ,) -> ,:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee9661927785b545825043fbf3ad68a\",\n \"score\": \"0.5157086\",\n \"text\": \"def validate_platform(self):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1497,"cells":{"query_id":{"kind":"string","value":"de2fbfe2e08ded1a0e9d9495536a4140"},"query":{"kind":"string","value":"Convert sysctl configuration dict to text with each property value pair separated on new line"},"positive_passages":{"kind":"list like","value":[{"docid":"fec04bc0ed396013d09614a8396f6414","score":"0.75843936","text":"def __convert_sysctl_dict_to_text():\n import params\n sysctl_file_content = \"### HAWQ System Parameters ###########\\n\"\n for key, value in params.hawq_sysctl.iteritems():\n if not __valid_input(value):\n raise Exception(\"Value {0} for parameter {1} contains non-numeric characters which are not allowed (except whitespace), please fix the value and retry\".format(value, key))\n sysctl_file_content += \"{0} = {1}\\n\".format(key, value)\n return sysctl_file_content","title":""}],"string":"[\n {\n \"docid\": \"fec04bc0ed396013d09614a8396f6414\",\n \"score\": \"0.75843936\",\n \"text\": \"def __convert_sysctl_dict_to_text():\\n import params\\n sysctl_file_content = \\\"### HAWQ System Parameters ###########\\\\n\\\"\\n for key, value in params.hawq_sysctl.iteritems():\\n if not __valid_input(value):\\n raise Exception(\\\"Value {0} for parameter {1} contains non-numeric characters which are not allowed (except whitespace), please fix the value and retry\\\".format(value, key))\\n sysctl_file_content += \\\"{0} = {1}\\\\n\\\".format(key, value)\\n return sysctl_file_content\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6cce26b37701ab14f49d551619616a99","score":"0.6548037","text":"def dict_to_conf_str(obj):\n config_list = []\n for key in sorted(obj.keys()):\n value = obj[key]\n config_list.append(\"%s=%s\" % (key, obj[key]))\n return '\\n'.join(config_list)","title":""},{"docid":"aeac65c8eb2f21b877d87ffb2b97247f","score":"0.6303967","text":"def test_show_config_file(tmp_path):\n config = str(tmp_path / \"sysctl.conf\")\n with fopen(config, \"w\", encoding=\"utf-8\") as config_file:\n config_file.write(\n \"\"\"\\\n# Use dump-core from kdump-tools Debian package.\nkernel.core_pattern = |/usr/share/kdump-tools/dump-core %p %s %t %e\n # Stop low-level messages on console = less logging\n kernel.printk = 3 4 1 3\n\nnet.ipv4.ip_forward=1\nnet.ipv4.tcp_rmem\t=\t4096\t131072\t6291456\n\"\"\"\n )\n assert linux_sysctl.show(config) == {\n \"kernel.core_pattern\": \"|/usr/share/kdump-tools/dump-core %p %s %t %e\",\n \"kernel.printk\": \"3 4 1 3\",\n \"net.ipv4.ip_forward\": \"1\",\n \"net.ipv4.tcp_rmem\": \"4096\\t131072\\t6291456\",\n }","title":""},{"docid":"81275518b16f3e94da5a86a00c8b26ac","score":"0.61630404","text":"def format_config(self):\n l = []\n for uid in self.config:\n d = self.config[uid]\n l.append([d[\"ref_screen_name\"], d[\"shortcuts\"],\n d[\"timezone\"], str(d[\"interval\"]), str(d[\"f_interval\"])])\n s = tabulate(l, headers=[\"Name\", \"Abbr.\", \"Timezone\", \"Intv.\", \"FIntv.\"])\n return '```\\n{}\\n```'.format(self.E('`')(s))","title":""},{"docid":"f1589a4fb2f0eac90004afacf615b221","score":"0.5929702","text":"def show(config_file=False):\n ret = {}\n if config_file:\n # If the file doesn't exist, return an empty list\n if not os.path.exists(config_file):\n return []\n\n try:\n with salt.utils.files.fopen(config_file) as fp_:\n for line in fp_:\n line = salt.utils.stringutils.to_str(line).strip()\n if not line.startswith(\"#\") and \"=\" in line:\n key, value = line.split(\"=\", 1)\n ret[key.rstrip()] = value.lstrip()\n except OSError:\n log.error(\"Could not open sysctl file\")\n return None\n else:\n _sysctl = \"{}\".format(_which(\"sysctl\"))\n cmd = [_sysctl, \"-a\"]\n out = __salt__[\"cmd.run_stdout\"](cmd, output_loglevel=\"trace\")\n for line in out.splitlines():\n if not line or \" = \" not in line:\n continue\n comps = line.split(\" = \", 1)\n ret[comps[0]] = comps[1]\n return ret","title":""},{"docid":"6172aa27def38e236539ef575bea05f0","score":"0.59032184","text":"def write_prox_config(prox_config):\n out = []\n for i, (section_name, section) in enumerate(prox_config):\n out.append(\"[{}]\".format(section_name))\n for index, item in enumerate(section):\n key, value = item\n if key == \"__name__\":\n continue\n if value is not None and value != '@':\n key = \"=\".join((key, str(value).replace('\\n', '\\n\\t')))\n out.append(key)\n else:\n key = str(key).replace('\\n', '\\n\\t')\n out.append(key)\n return os.linesep.join(out)","title":""},{"docid":"6f7d7d8a974a71394f5e761acc0f7938","score":"0.58417326","text":"def _dict_to_string(self, data):\n temp = \"\"\n for key, value in data.items():\n temp += \" {0} : {1}\\n\".format(key, value)\n return temp","title":""},{"docid":"142e64ee195d6ff92861ce8effa2ca88","score":"0.58183986","text":"def dStr(dict) :\n\ts = SFMT % \"VM\"\n\tvmname = dict.keys()[0] #basically a random key from outer dict\n\tfor testShortName in dict[vmname].keys() :\n\t\ts += SFMT % testShortName\n\ts += \"\\n\"\n\t\n\tfor k1 in dict.keys() :\n\t\ts += SFMT % k1\n\t\tfor k2 in dict[k1].keys() :\n\t\t\ts += FFMT % dict[k1][k2]\n\t\ts += \"\\n\"\n\treturn s","title":""},{"docid":"2db69c0e738ca76ccceb9d1e640df264","score":"0.57095635","text":"def _print_config_property(console, config_property, output_format):\n\n if output_format == 'JSON':\n console.printout(sap.cli.core.json_dumps(config_property))\n else:\n console.printout(f'Key: {config_property[\"key\"]}')\n console.printout(f'Value: {config_property[\"value\"]}')","title":""},{"docid":"9ac2521271a6faaec58cc36a552ee009","score":"0.5702244","text":"def __repr__(self):\n L = [\"[%s]\"% self.section ]\n for k in self.ini.get(self.section):\n v = self.ini.get(self.section, k)\n L.append(\"%s=%s\"% (k, v))\n return '\\n'.join(L)","title":""},{"docid":"1b8f89d0b927e7ded74713de64eacb23","score":"0.5683285","text":"def print_as_string(self):\n for section in self.config:\n print(\"[\" + section + \"]\\n\")\n for el in self.config[section]:\n print(el + \"=\" + self.config[section][el] + \"\\n\")","title":""},{"docid":"3a8d5765d9e6bb692e1b5f156494e3f5","score":"0.56807","text":"def __config_as_string(self):\n config_as_string = \"\"\n for section in self.config:\n config_as_string += \"[\" + section + \"]\\n\"\n for el in self.config[section]:\n config_as_string += el + \"=\" + self.config[section][el] + \"\\n\"\n return config_as_string","title":""},{"docid":"d46e6536898238a36ed9f07a4ae88ddc","score":"0.5662441","text":"def __getStr__(self):\n prodDict = self.__getDict__()\n txt = []\n for k, v in prodDict.iteritems():\n if isinstance(v, str):\n txt.append(\"%s = %r\" % (k, v))\n else:\n txt.append(\"%s = %s\" % (k, v))\n return '\\n'.join(txt)","title":""},{"docid":"83c90cf15a8a9d650edb48c8b4daa8fd","score":"0.5650667","text":"def pretty_print_config_to_json(self, services, hostname=None):\n descriptor = self.get_config_dict(services, hostname)\n return json.dumps(descriptor, sort_keys=True, indent=2,\n separators=(',', ': '))","title":""},{"docid":"0a25099065b62315662805d2fa986383","score":"0.56340194","text":"def __str__(self):\n\n overview = []\n for k in sorted(self._config.keys()):\n overview.append('{0}: {1}\\n'.format(k, self._config[k]))\n\n return ''.join(overview)","title":""},{"docid":"e8d44cc08d06a6d1b7e0df634286ab4e","score":"0.5614748","text":"def print_config(config, logger):\n for k, v in config.items():\n logger.info(\"{}:\\t{}\".format(k.ljust(15), v))","title":""},{"docid":"940fb4bdb319779634de075eb18d9f98","score":"0.56119144","text":"def create(sysctl_dict, sysctl_file):\n try:\n sysctl_dict_parsed = yaml.safe_load(sysctl_dict)\n except yaml.YAMLError:\n log(\"Error parsing YAML sysctl_dict: {}\".format(sysctl_dict),\n level=ERROR)\n return\n\n with open(sysctl_file, \"w\") as fd:\n for key, value in sysctl_dict_parsed.items():\n fd.write(\"{}={}\\n\".format(key, value))\n\n log(\"Updating sysctl_file: %s values: %s\" % (sysctl_file, sysctl_dict_parsed),\n level=DEBUG)\n\n check_call([\"sysctl\", \"-p\", sysctl_file])","title":""},{"docid":"7f839c1f25c082fc60d7f6b6ffe3f647","score":"0.559881","text":"def dict2str(result_dict):\r\n\r\n result_str = ''\r\n for metric, value in result_dict.items():\r\n result_str += str(metric) + ' : ' + str(value) + ' '\r\n return result_str","title":""},{"docid":"e7d84e7df3108cabee877ee16c1b4017","score":"0.5593959","text":"def _stringify_dict(d):\n\t\treturn \"\\n\".join(list(\"\\t{key} - {value}\".format(key=key, value=value) for key, value in d.items()))","title":""},{"docid":"d7f4174e531f1dbb5637e6a19b03c11d","score":"0.55822194","text":"def get_sysctl_data(item):\n command = (\"sysctl {}\").format(item)\n result = os.popen(command).read().rstrip().replace(item, '').replace(': ','')\n return result","title":""},{"docid":"0e719ccfd41e39cfd13204e183a95df1","score":"0.5579042","text":"def render_configs(self, configs):\n keys = [k for k in configs.keys()]\n keys.sort()\n\n s = \"\"\n for k in keys:\n s += \"%s=%s\\n\" % (k, str(configs[k]))\n return s","title":""},{"docid":"78d72be4cfb472de50e7c34416738ac9","score":"0.5558033","text":"def _str_fmt_dct(dct):\n return \" | \".join(\"%s: %.4g\" % (k, v) for k, v in dct.items())","title":""},{"docid":"431da784c69a196ee5d7ae65aa9c565c","score":"0.55388117","text":"def format_dict(D):\n keys = keys_to_str(D.keys())\n n = max([len(k) for k in keys])+1\n return '\\n'.join([' %s:%s%s' % (k, ' '*(n-len(k)), D[k]) for k in keys])","title":""},{"docid":"8cb0bf4a12216360f916cd324e85f09d","score":"0.5527431","text":"def _print_config(config):\n for key, value in config.items():\n print(key, \"-\", value)","title":""},{"docid":"fefc1cfd1391b9381c0656bb09008a2f","score":"0.5512504","text":"def print_config(self):\n print_str = f''\n for hw_type, arch in self.architecture.items():\n if arch:\n for cpu_type, num in self.architecture[hw_type].items():\n print_str+=f'{str(cpu_type)}: {num} CPUs\\n'\n return print_str","title":""},{"docid":"1c585a8c9c5dd2bb64283bae9ba3011d","score":"0.54950297","text":"def dump(self):\n data = []\n for cl in self.config_data:\n data.append(cl.raw)\n return '\\n'.join(data)","title":""},{"docid":"600bfc8368b276b4a79a2298a9e6014c","score":"0.5474739","text":"def _sanitize_sysctl_value(value):\n return re.sub(r\"\\s+\", \"\\t\", str(value))","title":""},{"docid":"13a0b0534aa63fa87a2c12b8cf4d7b7c","score":"0.5465268","text":"def _makeConfigString(config):\n result = []\n for i in config:\n # ignoring 'internal' todo-configuration and the format (should be used as extension)\n if i == 'todo' or i == 'format' or ('height' in config and 'width' in config):\n continue\n elif i == 'height':\n result.append(str(config[i]) + 'p')\n elif i == 'width':\n result.append(str(config[i]) + 'w')\n elif i == 'muted' and config[i]:\n result.append('m')\n if 'height' in config and 'width' in config:\n result.append(str(config['width']) + 'x' + str(config['height']))\n # seperate each configuration attribute by a dot - makes it easier to parse it later\n return '.'.join(result)","title":""},{"docid":"379c3b564b1327a6b732c5ed93daee8d","score":"0.54405177","text":"def __repr__(self):\n string = ''\n for key, val in self.setting().items():\n string += '{}({})\\n'.format(key, val)\n return string","title":""},{"docid":"ec9675736196298ce7f291fba35deec9","score":"0.5418939","text":"def render_config(self, spec, conf):\n config = deepcopy(spec)\n config['severity'] = conf.get('severity')\n\n syslog_value = conf.get('syslog')\n if syslog_value != None:\n syslog = []\n for each in syslog_value['host']:\n syslog.append(each['address'])\n config['syslog'] = syslog\n\n return utils.remove_empties(config)","title":""},{"docid":"4048067a1ff5ea6a4efa267cbebca002","score":"0.54139096","text":"def to_vtund_config(self):\n indent_unit = '\\t'\n cr_lf = '\\n';\n config = ''\n config += 'options {' + cr_lf\n config += indent_unit + 'port ' + str(self.vtun_server_tcp_port) + ';' + cr_lf\n if self.restricted_iface:\n config += indent_unit + 'bindaddr { iface ' + self.restricted_iface + '; };' + cr_lf\n config += indent_unit + 'syslog daemon;' + cr_lf\n \n config += indent_unit + 'ppp /usr/sbin/pppd;' + cr_lf\n config += indent_unit + 'ifconfig /sbin/ifconfig;' + cr_lf\n config += indent_unit + 'route /sbin/route;' + cr_lf\n config += indent_unit + 'ip /sbin/ip;' + cr_lf\n config += '}' + cr_lf\n config += cr_lf\n config += self.vtun_tunnel_name + ' {' + cr_lf\n if not self.interface_name is None:\n config += indent_unit + 'device ' + str(self.interface_name) + ';' + cr_lf\n config += indent_unit + 'passwd ' + str(self.vtun_shared_secret) + ';' + cr_lf\n config += indent_unit + 'type ' + self.tunnel_mode.get_equivalent_vtun_type() + ';' + cr_lf\n config += indent_unit + 'proto ' + self.vtun_protocol + ';' + cr_lf\n config += indent_unit + 'compress ' + self.vtun_compression + ';' + cr_lf\n config += indent_unit + 'encrypt '\n if self.vtun_encryption:\n config += 'yes'\n else:\n config += 'no'\n config += ';' + cr_lf\n config += indent_unit + 'keepalive '\n if self.vtun_keepalive:\n config += 'yes'\n else:\n config += 'no'\n config += ';' + cr_lf\n \n config += ' ' + cr_lf\n config += indent_unit + 'up {' + cr_lf\n config += indent_unit*2 + 'ifconfig \"%% ' + str(self.tunnel_near_end_ip) + ' pointopoint ' + str(self.tunnel_far_end_ip) + ' mtu 1450\";' + cr_lf\n if not self.up_additional_commands is None:\n for command in self.up_additional_commands:\n if command[0] == '/':\n config += indent_unit*2 + 'program ' + str(command) + ';' + cr_lf\n else:\n raise Exception('NotAFullPathCommand')\n config += indent_unit + '};' + cr_lf\n config += indent_unit + 'down {' + cr_lf\n if not self.down_additional_commands is None:\n for command in self.down_additional_commands:\n if command[0] == '/':\n config += indent_unit*2 + 'program ' + str(command) + ';' + cr_lf\n else:\n raise Exception('NotAFullPathCommand')\n config += indent_unit + '};' + cr_lf\n config += '}' + cr_lf\n return config","title":""},{"docid":"5eb5a659d1e46f9de3250aa8e407609c","score":"0.54089016","text":"def __str__(self):\n import os\n output = \"\"\n for key, value in self.__dict__.items():\n output += str(key) + \": \" + str(value) + os.linesep\n return output","title":""},{"docid":"a6b68f7e6c90dad6d70d07314b9c0180","score":"0.53945345","text":"def __str__(self):\r\n\r\n ret = \"\"\r\n # Sort our map by VM Address, this should already be how it is but cant hurt\r\n sortedmap = {k: v for k, v in sorted(self.map.items(), key=lambda item: item[1].vmaddr)}\r\n\r\n for (key, obj) in sortedmap.items():\r\n # 'string'.ljust(16) adds space padding\r\n # 'string'[2:].zfill(9) removes the first 2 chars and pads the string with 9 zeros\r\n # then we just re-add the 0x manually.\r\n\r\n # this gives us a nice list with visually clear columns and rows\r\n ret += f'{key.ljust(16)} || Start: 0x{hex(obj.vmaddr)[2:].zfill(9)} | ' \\\r\n f'End: 0x{hex(obj.vmend)[2:].zfill(9)} | ' \\\r\n f'Size: 0x{hex(obj.size)[2:].zfill(9)} | Slice ' \\\r\n f'Offset: 0x{hex(obj.fileaddr)[2:].zfill(9)} ||' \\\r\n f' File Offset: 0x{hex(obj.fileaddr + self.slice.offset)[2:].zfill(9)}\\n '\r\n return ret","title":""},{"docid":"c7fccf95e699e63e0468b10677fa0bf0","score":"0.53740907","text":"def display_cluster_config(cluster_config):\n for name, value in cluster_config._asdict().items():\n str_value = str(value.__dict__).replace('\\n', '') if hasattr(value, '__dict__') else str(value)\n LOGGER.info('Config %s: %s', name, str_value)\n LOGGER.info('-' * 40)","title":""},{"docid":"5d275350df2c4b3aa13ed514e87de7df","score":"0.53692997","text":"def config():\n for k, v in current_app.config.items():\n print(\"%s: %s\" % (k, v))","title":""},{"docid":"d4bf261f369b729c4cce9180d418435a","score":"0.5355675","text":"def map_to_properties(map):\n ret = \"\"\n for key, val in list(map.items()):\n key = re.sub(\"[^a-zA-Z0-9_]\", \"\", key)\n if isinstance(val, str):\n ret += key + \"=\" + val + os.linesep\n else:\n ret += key + \"=\" + json_save_small(val) + os.linesep\n return ret","title":""},{"docid":"8ec3b04def848afe263382ff7e1e141e","score":"0.5347017","text":"def print_conf_table(conf=conf_dict):\n print(\"{:<25}{:<30}{:<30}\".format('group', 'key', 'value'))\n print('=' * 75)\n for group, val in sorted(conf.items()):\n for g_key, g_val in sorted(val.items()):\n print(\"{:<25}{:<30}{:<15} \\n\".format(group, g_key, str(g_val)))\n print('-' * 75 + '\\n')","title":""},{"docid":"66f24c90efb2c9d1ed92b17784302e2f","score":"0.5340365","text":"def dump(self):\n data = []\n for cl in self.config_data:\n data.append(cl.raw)\n return self.newline.join(data)","title":""},{"docid":"49b610e43297fc8bea1d83e45f365ecb","score":"0.53303504","text":"def __str__(self):\n cfg_str = pprint.pformat(self.config, indent=2, width=120)\n # cfg_str = re.sub(r\".*password.*\",\n # \"# this line was removed since it included a password\",\n # cfg_str)\n # cfg_str = re.sub(r\".*user_id.*\",\n # \"# this line was removed since it included a password\",\n # cfg_str)\n # cfg_str = re.sub(r\".*api_key.*\",\n # \"# this line was removed since it included a password\",\n # cfg_str)\n return cfg_str","title":""},{"docid":"f54ab9c5a0c5394bfd8d5dd3a27f08be","score":"0.5324538","text":"def showConfig():\n for key in dir(Config):\n if key[:2]!='__':\n val=getattr(Config, key)\n if val==None or val==\"None\":\n line=str(key) + \"=\\n\"\n else:\n line=str(key) + \"=\" + str(val) + \"\\n\"\n print line,","title":""},{"docid":"2d5fa2e50171ade49310645db67eee19","score":"0.52737963","text":"def _mvalue_to_str(self, m, multiline=1):\n if type(m) is not type({}):\n return ''\n str = ''\n keys = m.keys()\n keys.sort()\n for k in keys:\n str = str + k + ' | ' + m[k]\n if multiline:\n str += '\\n'\n else:\n str += '\\\\n'\n return str","title":""},{"docid":"4be92a2410808175f97768e294f3a1f2","score":"0.52723","text":"def to_string(self):\n return (\"{\\n\\t\" +\n ',\\n\\t'.join(['\"{}\":{}'.format(k, to_string(v))\n for k, v in self.items()]) +\n \"\\n}\")","title":""},{"docid":"6d0461ba4d40fe794615c5ac364da3c5","score":"0.52718997","text":"def generate_vtysh_conf(cls, node: CoreNode) -> str:\n return \"service integrated-vtysh-config\\n\"","title":""},{"docid":"55ba4563f2614a651c1ea442b225419a","score":"0.5241447","text":"def print_dict(dic):\r\n print(\"\\n\".join(\"{:10s}: {}\".format(key, values) for key, values in dic.items()))","title":""},{"docid":"129df923219001ad7e6f215120660adb","score":"0.52343446","text":"def format_props(props, prop_template=\"{{k}} = { {{v}} }\", delim=\"\\n\"):\n vars_ = []\n props_ = []\n for k, v in list(props.items()):\n vars_.append(Template(\"var {{k}} = {{v}};\").render(k=k,v=json.dumps(v)))\n props_.append(Template(prop_template).render(k=k, v=k))\n return \"\\n\".join(vars_), delim.join(props_)","title":""},{"docid":"0a5e908b2b28675c92735a8116a3b8f9","score":"0.52321815","text":"def config_status_to_str(self, status):\n\n\t\tpchar = self._libinput.libinput_config_status_to_str(status)\n\t\treturn string_at(pchar)","title":""},{"docid":"ad3314e54cfd09b75bc860524371ecad","score":"0.5219203","text":"def pretty_print_config_to_json(self, services, hostname=None):\n descriptor = self.get_openapi_dict(services, hostname)\n return json.dumps(descriptor, sort_keys=True, indent=2,\n separators=(',', ': '))","title":""},{"docid":"f99e7725af51d2d1ad029533809c2e29","score":"0.5212987","text":"def test_persist_value_with_spaces_update_config(tmp_path):\n config = str(tmp_path / \"existing_sysctl_with_spaces.conf\")\n value = \"|/usr/share/kdump-tools/dump-core %p %s %t %e\"\n with fopen(config, \"w\", encoding=\"utf-8\") as config_file:\n config_file.write(\"kernel.core_pattern =\\n\")\n mock_run = MagicMock()\n mock_run_all = MagicMock(\n return_value={\n \"pid\": 1337,\n \"retcode\": 0,\n \"stderr\": \"\",\n \"stdout\": \"kernel.core_pattern = \" + value,\n }\n )\n which_mock = MagicMock(return_value=\"/usr/sbin/sysctl\")\n with patch(\"salt.utils.path.which\", which_mock):\n with patch(\"os.path.exists\", MagicMock(return_value=True)), patch.dict(\n linux_sysctl.__salt__, {\"cmd.run\": mock_run, \"cmd.run_all\": mock_run_all}\n ):\n assert (\n linux_sysctl.persist(\"kernel.core_pattern\", value, config=config)\n == \"Updated\"\n )\n mock_run.assert_not_called()\n mock_run_all.assert_called_once_with(\n [\"/usr/sbin/sysctl\", \"-w\", \"kernel.core_pattern=\" + value],\n python_shell=False,\n )\n assert os.path.isfile(config)\n with fopen(config, encoding=\"utf-8\") as config_file:\n written = config_file.read()\n assert written == f\"kernel.core_pattern = {value}\\n\"","title":""},{"docid":"b764e3549c99528527b490999a47658e","score":"0.521115","text":"def config_str(self, compact = False):\n string = ''\n for attr in ('survival_atp','repro_atp','initial_atp',\n 'max_amount_per_step','penalize_edges',\n 'survive_num_timesteps', 'survive_reset_nodes', 'survive_reset_food'):\n string += f'{attr}: {self.__dict__[attr]}'\n string += ', ' if compact else '\\n'\n return string","title":""},{"docid":"22600c0616b7cb3eb57b459f370104cb","score":"0.5200858","text":"def print_conf(name, conf):\n print(\"\\n\" + \"-\" * 60)\n print(\" {} :\".format(name))\n print(\"\" + \"-\" * 60)\n # for key, val in conf.items():\n for key, val in sorted(conf.items()):\n print(\"{:40} : {}\".format(key, val))\n print(\"-\" * 60 + \"\\n\")","title":""},{"docid":"83ad6ebe7ed97f6cab7aa79ec6d3ea5b","score":"0.51957583","text":"def __str__(self):\n items = ['({!r})'.format(item) for item in self.data.keys()]\n return '[{}]'.format(' -> '.join(items))","title":""},{"docid":"d35788bd9b277f97ece984086301da9c","score":"0.5193704","text":"def python_dict_to_hcl_map(dict):\n dict_string = str(dict).replace(\"\\'\", \"\\\"\").replace(\n \":\", \" =\").replace(\",\", \"\\n\")\n dict_string = dict_string.replace(\"{\", \"{\\n \").replace(\"}\", \"\\n}\")\n return dict_string","title":""},{"docid":"dd0a9a4d0688c4f6fe24773c8a2429ac","score":"0.51848996","text":"def write_config_console(self, config):\n pass","title":""},{"docid":"3af0a80eadccfeae95055d05b6aaa40b","score":"0.5179516","text":"def printConfig(config):\n for section in config.sections():\n for key in config[section]: \n print(section+'.'+key+'='+config.get(section, key))","title":""},{"docid":"a096a5ace7c710394c3082b2de688d7b","score":"0.5174283","text":"def __str__(self):\n config_str = 'Configurations\\n'\n config_str += pprint.pformat(self.__dict__)\n return config_str","title":""},{"docid":"2ab7ecd1d9de6992e0fad32bee8aa861","score":"0.5169066","text":"def pretty_print(self):\n print ET.tostring(self.config) # Well, not exactly pretty, but better than nothing","title":""},{"docid":"c8450c8b2c7d1318449b04a78af90ccd","score":"0.5164321","text":"def __str__(self):\n strs = []\n for key, value in self.to_wsgi_list():\n strs.append('%s: %s' % (key, value))\n strs.append('\\r\\n')\n return '\\r\\n'.join(strs)","title":""},{"docid":"59e41b9eea89ede642096bf258ad1d24","score":"0.5150572","text":"def pprint_info(label, mapping):\n print('', label, '=' * len(label), sep='\\n', file=file)\n for k, v in mapping.items():\n if not isinstance(v, str):\n v = str(v)\n if '\\n' in v:\n # Make each new line start at the appropriate indentation level\n v = v.replace('\\n', '\\n' + (' ' * (padding + len(separator))))\n print(k.rjust(padding), separator, v, sep='', file=file)","title":""},{"docid":"9ba736fee930aeed1cf150b23242b749","score":"0.5147476","text":"def dict_to_str( a_dict ):\n info_str = \"\"\n for key, value in a_dict.items():\n if info_str == \"\":\n info_str += f\"{key}:{value}\"\n else:\n info_str += f\"\\n{key}:{value}\"\n\n return info_str","title":""},{"docid":"8d8e1ba29e6d9d770499f429aab47f0f","score":"0.5144735","text":"def _vartxt(env,return_values=True,comment=None):\n subenv = dict((k,v) for k,v in env.items() if k not in INIT_VARS)\n \n if return_values:\n txt = pprint.pformat(subenv,indent=1)\n else:\n txt = pprint.pformat(list(subenv.keys()),indent=1)\n\n if comment is None:\n return txt\n \n if not any(comment.endswith(c) for c in \" \\t\"): \n comment += ' ' # make sure ends with space\n \n return '\\n'.join(comment + t for t in txt.split('\\n'))","title":""},{"docid":"14be1dfc311b69e52128dd968532d36e","score":"0.5142728","text":"def print_full_conf(conf=CONF):\n for group, val in sorted(conf.items()):\n print('=' * 75)\n print('{}'.format(group))\n print('=' * 75)\n for g_key, g_val in sorted(val.items()):\n print('{}'.format(g_key))\n for gg_key, gg_val in g_val.items():\n print('{}{}'.format(' ' * 4, gg_key))\n body = '\\n'.join(['\\n'.join(textwrap.wrap(line, width=110, break_long_words=False,\n replace_whitespace=False,\n initial_indent=' ' * 8, subsequent_indent=' ' * 8))\n for line in str(gg_val).splitlines() if line.strip() != ''])\n print(body)\n print('\\n')","title":""},{"docid":"20d3bb288c0cf3b70272c237c8318ed5","score":"0.5129692","text":"def format(self):\n return{\n 'hostname': self.hostname,\n 'ip_address_v4': self.ip_address_v4,\n 'ip_address_v6': self.ip_address_v6,\n 'personality': self.personality,\n 'status': self.status,\n 'system_info': self.system_info.format()\n }","title":""},{"docid":"46b5bec78c19f1439554aa24d0895e5f","score":"0.5125728","text":"def __str__(self):\n string = []\n for key, value in self.to_wsgi_list():\n string.append('%s: %s' % (key, value))\n string.append('\\r\\n')\n return '\\r\\n'.join(string)","title":""},{"docid":"526dfcf1e79feacd8f05065914b1eb17","score":"0.5112593","text":"def dump_config(config, modified_color=36, quote_str=False):\n strs = []\n tab = ' '\n\n def quote(v):\n if quote_str and isinstance(v, str) and v:\n return \"'{}'\".format(v)\n return v\n\n def repr_dict(k, v):\n if isinstance(v, (dict, list)):\n v = ''\n return \"{}: {}\\n\".format(k, quote(v))\n\n def repr_list(_k, v):\n if isinstance(v, (dict, list)):\n return \"- \"\n return \"- {}\\n\".format(quote(v))\n\n def dump(data, indent, last, firstline_nopref=False):\n modified = config._sconf_modified.get(id(data), set())\n\n N = len(data)\n for i, (k, v) in enumerate(kv_iter(data)):\n prefix = indent\n if firstline_nopref:\n prefix = ''\n firstline_nopref = False\n\n # indentation is determined by parent data type\n if isinstance(data, dict):\n s = repr_dict(k, v)\n skip_first_indent = False\n elif isinstance(data, list):\n s = repr_list(k, v)\n skip_first_indent = True\n else:\n raise ValueError(type(data))\n\n is_last = last and i == N-1\n if is_last and not isinstance(v, (dict, list)):\n s = s.rstrip('\\n')\n\n if k in modified:\n s = colorize(s, modified_color)\n strs.append(prefix + s)\n if isinstance(v, (dict, list)):\n dump(v, indent + tab, is_last, skip_first_indent)\n\n dump(config.get_data(), '', True)\n return ''.join(strs)","title":""},{"docid":"45d82e8e802c078aef5f66e05274c663","score":"0.51124203","text":"def __str__(self):\n lines = []\n for k, v in self.__options.items():\n if v is None:\n continue\n lines.append(\"%s %s\" % (k, v))\n return \"\\n\".join(lines)","title":""},{"docid":"a499e781ff1608595c1a654401c54103","score":"0.5108561","text":"def __str__(self):\n ana = str( self.cfg_ana )\n count = ''\n ave = ''\n if hasattr(self, 'counters') and len( self.counters.counters ) > 0:\n count = '\\n'.join(map(str, self.counters.counters))\n if hasattr(self, 'averages') and len( self.averages ) > 0:\n ave = '\\n'.join(map(str, self.averages))\n return '\\n'.join( [ana, count, ave] )","title":""},{"docid":"2a7bc7c227e7e7cf1c04dc77a6d3d6bb","score":"0.5097259","text":"def sysctl_set(key, value):\n p = Popen(\"sysctl -w %s=%s\" % (key, value), shell=True, stdout=PIPE,\n stderr=PIPE)\n # Output should be empty; otherwise, we have an issue. \n stdout, stderr = p.communicate()\n stdout_expected = \"%s = %s\\n\" % (key, value)\n if stdout != stdout_expected:\n raise Exception(\"Popen returned unexpected stdout: %s != %s\" %\n (stdout, stdout_expected))\n if stderr:\n raise Exception(\"Popen returned unexpected stderr: %s\" % stderr)","title":""},{"docid":"2a7bc7c227e7e7cf1c04dc77a6d3d6bb","score":"0.5097259","text":"def sysctl_set(key, value):\n p = Popen(\"sysctl -w %s=%s\" % (key, value), shell=True, stdout=PIPE,\n stderr=PIPE)\n # Output should be empty; otherwise, we have an issue. \n stdout, stderr = p.communicate()\n stdout_expected = \"%s = %s\\n\" % (key, value)\n if stdout != stdout_expected:\n raise Exception(\"Popen returned unexpected stdout: %s != %s\" %\n (stdout, stdout_expected))\n if stderr:\n raise Exception(\"Popen returned unexpected stderr: %s\" % stderr)","title":""},{"docid":"04c09e13747e18ae29ebfe6ad16f9bd7","score":"0.50951976","text":"def convert_cfg_markdown(cfg):\n r = \"\"\n s = []\n\n def helper(cfg):\n s_indent = []\n for k, v in sorted(cfg.items()):\n seperator = \" \"\n attr_str = \" \\n{}:{}{} \\n\".format(str(k), seperator, str(v))\n s_indent.append(attr_str)\n return s_indent\n\n for k, v in sorted(cfg.items()):\n seperator = \" \" if isinstance(v, str) else \" \\n\"\n val = helper(v)\n val_str = \"\"\n for line in val:\n val_str += line\n attr_str = \"##{}:{}{} \\n\".format(str(k), seperator, val_str)\n s.append(attr_str)\n for line in s:\n r += \" \\n\" + line + \" \\n\"\n return r","title":""},{"docid":"c5b91c47c7c39745726834f9e1a8264e","score":"0.5094097","text":"def pretty(dic):\n print package(dic, sort_keys=True, indent=4)","title":""},{"docid":"4463fa3eb4768721e6d6c072a0e6b075","score":"0.50915563","text":"def __str__(self):\n return (\n \"Device Manager\\n\"\n + \"=\" * 14\n + \"\\n\"\n + \"\\n\".join(str(device) for device in self.__device_dict.values())\n )","title":""},{"docid":"129ad459e4939f5b60b2f29600aaf7b8","score":"0.5084082","text":"def _get_config(self) -> str:\n try:\n vips = self.authentication().tm.ltm.virtuals.get_collection(\n partition=self.partition\n )\n for vip in vips:\n print(f\"{vip.name}\")\n print()\n self.logging().info(f\"{vip.name}\")\n for key, value in vip.raw.items():\n print(key, value)\n print(\"#\" * 79)\n\n except Exception as e:\n self.logging().warning(e)\n print(e)","title":""},{"docid":"9a96fdc3e164dead10d8dc8604f1c535","score":"0.5082336","text":"def __str__(self):\n return json.dumps(self._config, indent=2)","title":""},{"docid":"7db0e90e0857c3d4f8dfce77be9cf68d","score":"0.50823224","text":"def dict_to_nice_string(control_dict: Dict) -> str:\n script_text = \"control_dict = {\\n\"\n for key, value in control_dict.items():\n script_text += \"\\t\\\"\" + key + \"\\\": \"\n first = False\n if (type(value) == dict):\n if (\"do\" in value): # do should always be first in this list\n script_text += \"{\\\"do\\\":\" + str(value[\"do\"]) + \",\"\n if (len(value) > 1):\n script_text += \"\\n\"\n first = True\n for key2, value2 in value.items(): # alternative keys\n\n # prefix\n if (first):\n prefix = \" \"\n first = False\n else:\n prefix = \"\\t\\t\"\n\n # key_val\n if (key2 == \"do\"):\n continue\n elif (type(value2) == dict):\n script_text += prefix + \"\\\"\" + str(key2) + \"\\\": \" + _inline_dict(value2, \"\\t\\t\\t\") + \",\\n\"\n else:\n script_text += prefix + \"\\\"\" + str(key2) + \"\\\": \" + str(value2) + \",\"\n script_text += prefix + \" },\\n\"\n else:\n script_text += str(value) + \",\\n\"\n script_text += \"}\\n\"\n return script_text","title":""},{"docid":"c8ac01d88ba2128bc85e21c60b0f2a07","score":"0.5077761","text":"def to_string(self, source=COMMANDLINE|CONFIGFILE):\n if source & BUILTIN: func = repr\n else: func = str\n s = ''\n keys = [x for x in list(self.keys()) if x != DEFAULTSECT]\n keys.sort()\n if self[DEFAULTSECT]:\n keys.insert(0, DEFAULTSECT)\n for section in keys:\n content = func(self[section]).strip()\n if content:\n s += \"[%s]\\n%s\\n\\n\" % (section, content)\n return s","title":""},{"docid":"66de61c356a43a29cfe09a015bae8365","score":"0.5075009","text":"def print_dict(d, wrap=0):\r\n pt = prettytable.PrettyTable(['Property', 'Value'],\r\n caching=False, print_empty=False)\r\n pt.aligns = ['l', 'l']\r\n for (prop, value) in six.iteritems(d):\r\n if value is None:\r\n value = ''\r\n value = _word_wrap(value, max_length=wrap)\r\n pt.add_row([prop, value])\r\n encoded = strutils.safe_encode(pt.get_string(sortby='Property'))\r\n if six.PY3:\r\n encoded = encoded.decode()\r\n print(encoded)","title":""},{"docid":"ecab168c7b6308215c94723ec3386c3d","score":"0.5074404","text":"def GetConfigAsString(self, config, enum_values, index):\n string = textwrap.dedent(\"\"\"\\\n [{index}] = {{\n .input = {input_name},\n .voltage = {voltage_name},\n .type = {type_str},\n .channel = {channel},\n .volts_per_count = {volts_per_count}f,\n .offset = {offset}f,\n .nominal = {nominal}f,\n .min = {min}f,\n .max = {max}f}},\n \"\"\").format(\n index=index,\n input_name=enum_values['input'],\n voltage_name=enum_values['voltage'],\n type_str=type_helper.Name(config['type']),\n **config)\n return string","title":""},{"docid":"f2fc8ca59d50a1db093f92fa6de58b9b","score":"0.50692046","text":"def dump_string(self):\n result = []\n for key, value in self.fields.items():\n pattern = '%s: %s'\n if str(value).startswith('\\n'):\n pattern = '%s:%s'\n result.append(pattern % (key, self.fields[key]))\n\n return \"\\n\".join(result)","title":""},{"docid":"8e284c0538808f3c30890f8feca02c9f","score":"0.506259","text":"def __repr__(self):\n rep = \"SshConfig(\"\n entries = []\n for k, v in self.__entries.items():\n if v is None:\n continue\n if k == \"default\":\n entries.append(repr(v))\n else:\n entries.append(\"%s = %s\" % (k.partition('ssh_')[2], repr(v)))\n rep += \", \".join(entries)\n rep += \")\"\n return rep","title":""},{"docid":"576dd19ada320e3307f62350bf22df07","score":"0.5057918","text":"def dump_items(self):\n for key, value in self.config.items():\n print (\"%s = %s\") % (key, value)","title":""},{"docid":"64eccabc92ddfb3cb2819137af183b49","score":"0.50572014","text":"def format_metric_output(results):\n metric_string = \"\"\n for name, value in results.items():\n metric_string = \"{0} {1}: {2:.3f} \".format(metric_string, name, value)\n\n return metric_string","title":""},{"docid":"49b8eddc6c4f63efcc5b990b0bd0ff79","score":"0.5054041","text":"def do_show_network_config(vnic_utils):\n\n api_show_network_config()\n\n _logger.info(\"Operating System level network configuration\")\n\n ret = vnic_utils.get_network_config()\n\n _fmt = \"{:6} {:15} {:15} {:5} {:15} {:10} {:3} {:15} {:5} {:11} {:5} {:17} {}\"\n print(_fmt.format('CONFIG', 'ADDR', 'SPREFIX', 'SBITS', 'VIRTRT',\n 'NS', 'IND', 'IFACE', 'VLTAG', 'VLAN', 'STATE', 'MAC', 'VNIC'))\n for item in ret:\n print(_fmt.format(item['CONFSTATE'],\n item['ADDR'], item['SPREFIX'],\n item['SBITS'], item['VIRTRT'],\n item['NS'], item['IND'],\n item['IFACE'], item['VLTAG'],\n item['VLAN'], item['STATE'],\n item['MAC'], item['VNIC']))","title":""},{"docid":"f8768abd30f9f44810847f1b386644a0","score":"0.5034202","text":"def __str__(self):\n output = 'CAPABILITIES:\\n============\\n'\n for cap_name in sorted(self.capabilities.keys()):\n output += str(self.capabilities[cap_name]) + '\\n'\n output += '\\nACTORS:\\n======\\n'\n for actor_name in sorted(self.actors.keys()):\n output += str(self.actors[actor_name]) + '\\n'\n output += '\\nLANGUAGES:\\n==========\\n'\n for lang_name in sorted(self.languages.keys()):\n output += str(self.languages[lang_name]) + '\\n'\n output += '\\nCOMPONENTS:\\n==========\\n'\n for cpt_name in sorted(self.components.keys()):\n output += str(self.components[cpt_name]) + '\\n'\n return output","title":""},{"docid":"5394a76de5617ee7c2c28deba822fe0a","score":"0.50322247","text":"def construct_output_line(odict):\n outline = ''\n for vnum in range(1, len(odict) + 1):\n outline += OVAR_FMT[vnum].format(odict[vnum])\n outline += '\\n'\n return outline","title":""},{"docid":"69aa8a944e46a897932b04ddb3311ab7","score":"0.5031642","text":"def __str__(self):\n string = ''\n for key in self._dict.keys():\n string += key + '\\n'\n return string[:-2]","title":""},{"docid":"6db0a70c87bbbaec9764c1ae5aad389b","score":"0.5024539","text":"def bar_head_format(config):\n return ''.join([c if isinstance(c, str) else ' '*c for c in config])","title":""},{"docid":"0956730fc5897a2dd99c20187c444447","score":"0.50194275","text":"def print_dict(d, wrap=0):\n pt = prettytable.PrettyTable(['Property', 'Value'],\n caching=False, print_empty=False)\n pt.aligns = ['l', 'l']\n for (prop, value) in six.iteritems(d):\n if value is None:\n value = ''\n value = _word_wrap(value, max_length=wrap)\n pt.add_row([prop, value])\n encoded = encodeutils.safe_encode(pt.get_string(sortby='Property'))\n if six.PY3:\n encoded = encoded.decode()\n print(encoded)","title":""},{"docid":"04433b3e28ddacd46979d4e58d0c4df2","score":"0.5013424","text":"def __str__(self):\n s = 'Available object configurations:'\n for k in self.options.keys():\n s += f'\\n{k}'\n return s","title":""},{"docid":"35b5920597bb1de1cba4612ca3967b1a","score":"0.50130534","text":"def __str__(self):\n\t\tret = \"\"\n\t\tfor section in self.keys():\n\t\t\tret += \"%s:\\n\" % section\n\t\t\tfor option, value in self[section].items():\n\t\t\t\tif option != '__name__':\n\t\t\t\t\tret += \" %s: %s\\n\" % (option, value)\n\t\treturn ret","title":""},{"docid":"227affe6ada4c68e2292fd0a20d5a99e","score":"0.50006443","text":"def inspect(self):\n\n print \n \n keys = self._cfg.keys()\n keys.sort()\n for key in keys:\n\t print ' {:<25} = {:<25}'.format(key, self._cfg[key])\n \n print","title":""},{"docid":"227affe6ada4c68e2292fd0a20d5a99e","score":"0.50006443","text":"def inspect(self):\n\n print \n \n keys = self._cfg.keys()\n keys.sort()\n for key in keys:\n\t print ' {:<25} = {:<25}'.format(key, self._cfg[key])\n \n print","title":""},{"docid":"d380f95686dfb99b09facf971db2b042","score":"0.49970165","text":"def test_show():\n mock_cmd = MagicMock(\n return_value=\"\"\"\\\nkernel.core_pattern = |/usr/share/kdump-tools/dump-core %p %s %t %e\nkernel.printk = 3 4 1 3\nnet.ipv4.ip_forward = 1\nnet.ipv4.tcp_rmem = 4096\t131072\t6291456\n\"\"\"\n )\n which_mock = MagicMock(return_value=\"/usr/sbin/sysctl\")\n with patch(\"salt.utils.path.which\", which_mock):\n with patch.dict(linux_sysctl.__salt__, {\"cmd.run_stdout\": mock_cmd}):\n assert linux_sysctl.show() == {\n \"kernel.core_pattern\": \"|/usr/share/kdump-tools/dump-core %p %s %t %e\",\n \"kernel.printk\": \"3 4 1 3\",\n \"net.ipv4.ip_forward\": \"1\",\n \"net.ipv4.tcp_rmem\": \"4096\\t131072\\t6291456\",\n }\n mock_cmd.assert_called_once_with(\n [\"/usr/sbin/sysctl\", \"-a\"], output_loglevel=\"trace\"\n )","title":""},{"docid":"50f4c3cb2f873919b919f730b55fc331","score":"0.49955997","text":"def _build_legacy_output(config: Config) -> str:\n parameter_list = \"\"\n for option in _config_options_sorted_by_key(config):\n if option.value is not None:\n parameter_list += f'{option.key} = {option.value} (macro name: \"{option.macro_name}\")\\n'\n else:\n parameter_list += f\"{option.key} has no value\\n\"\n\n macro_list = \"\"\n for macro in _config_macros_sorted_by_name(config):\n if macro.value is not None:\n macro_list += f\"{macro.name}={macro.value}\\n\"\n else:\n macro_list += f\"{macro.name}\\n\"\n\n output_template = (\n \"Configuration parameters\\n\"\n \"------------------------\\n\"\n f\"{parameter_list}\\n\"\n \"\\n\"\n \"Macros\\n\"\n \"------\\n\"\n f\"{macro_list}\"\n )\n return output_template","title":""},{"docid":"2a467eb4507c13014cb3b239036036fc","score":"0.4989747","text":"def __repr__(self):\n return '{%s}' % ', '.join(['%r: %r' % (k, v) for k, v in self.items()])","title":""},{"docid":"27235a48c41581afb0f2ed4001105733","score":"0.4988618","text":"def format_entries(self, entries):\n longest_hotkey = max(set(len(entry[0]) for entry in entries))\n dots_length = longest_hotkey + ADDITIONAL_DOTS\n output = list()\n for hotkey, info in entries:\n output.append(\"{hotkey} {dots} {info}\".format(\n hotkey=hotkey,\n dots=\".\" * (dots_length - len(hotkey)),\n info=info\n ))\n\n return \"\\n\".join(output)","title":""},{"docid":"f155c421fea9b1d020ed91c81a846b2b","score":"0.49859962","text":"def format_properties(\n properties: PropertyDict,\n *,\n prop_formatter: Callable[[str, PropertyValue], str] = format_property,\n) -> str:\n return \" \".join((prop_formatter(name, value) for name, value in properties.items()))","title":""},{"docid":"bef7277c32b946864c748c9079666f20","score":"0.49854976","text":"def visit_dict(self, value):\n items = ['%s:%s' % (self.encode(k), self.encode(v))\n for k, v in six.iteritems(value)]\n return '{%s}' % ','.join(items)","title":""},{"docid":"a47344c9e3bb86fad258e789a98d2c8e","score":"0.4969965","text":"def get_display_string(self):\n s = super(Data73, self).get_display_string()\n keys = sorted(list(self.settings.keys()))\n for key in keys:\n s += key + ' : ' + str(self.settings[key]) + \"\\n\"\n return s","title":""},{"docid":"0dca2f4719e9b95048f32f63e2e9f60c","score":"0.49686962","text":"def get_info(self):\n\n info_str = 'Information on Agent:\\n'\n for key, value in vars(self).items():\n # if value is dict, numpy array or list print its size\n if isinstance(value, (list, np.ndarray, dict)):\n key += ' size'\n value = len(value)\n # variable tab space for clean printing\n tab_space = ':' + '\\t' * (5 - int(len(key) / 8)) if 5 - int(len(key) / 8) > 0 else ':\\t'\n info_str += key + tab_space + str(value) + '\\n'\n return info_str","title":""},{"docid":"ecfbe76fb59c3fc1e7c15197de1a69b8","score":"0.4966254","text":"def __str__(self):\n lines = []\n sortfunc = lambda t: t[1].priority()\n for h, e in sorted(self.__entries.items(), key=sortfunc):\n opts = str(e)\n if not h == \"default\":\n lines.append(\"Host %s\" % h.partition(\"ssh_\")[2])\n opts = \"\\n\".join([\" %s\" % s for s in opts.split(\"\\n\")])\n lines.append(opts)\n return \"\\n\".join(lines)","title":""},{"docid":"352996bd8c406f008bd904593cc83cbb","score":"0.49561518","text":"def test_persist_value_with_spaces_already_configured(tmp_path):\n config = str(tmp_path / \"existing_sysctl_with_spaces.conf\")\n value = \"|/usr/share/kdump-tools/dump-core %p %s %t %e\"\n config_file_content = f\"kernel.core_pattern = {value}\\n\"\n with fopen(config, \"w\", encoding=\"utf-8\") as config_file:\n config_file.write(config_file_content)\n mock_run = MagicMock(return_value=\"\")\n mock_run_all = MagicMock(\n return_value={\n \"pid\": 1337,\n \"retcode\": 0,\n \"stderr\": \"\",\n \"stdout\": \"kernel.core_pattern = \" + value,\n }\n )\n which_mock = MagicMock(return_value=\"/usr/sbin/sysctl\")\n with patch(\"salt.utils.path.which\", which_mock):\n with patch(\"os.path.exists\", MagicMock(return_value=True)), patch.dict(\n linux_sysctl.__salt__, {\"cmd.run\": mock_run, \"cmd.run_all\": mock_run_all}\n ):\n assert (\n linux_sysctl.persist(\"kernel.core_pattern\", value, config=config)\n == \"Updated\"\n )\n mock_run.assert_called_once_with(\n [\"/usr/sbin/sysctl\", \"-n\", \"kernel.core_pattern\"], python_shell=False\n )\n mock_run_all.assert_called_once_with(\n [\"/usr/sbin/sysctl\", \"-w\", \"kernel.core_pattern=\" + value],\n python_shell=False,\n )\n assert os.path.isfile(config)\n with fopen(config, encoding=\"utf-8\") as config_file:\n written = config_file.read()\n assert written == config_file_content","title":""}],"string":"[\n {\n \"docid\": \"6cce26b37701ab14f49d551619616a99\",\n \"score\": \"0.6548037\",\n \"text\": \"def dict_to_conf_str(obj):\\n config_list = []\\n for key in sorted(obj.keys()):\\n value = obj[key]\\n config_list.append(\\\"%s=%s\\\" % (key, obj[key]))\\n return '\\\\n'.join(config_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeac65c8eb2f21b877d87ffb2b97247f\",\n \"score\": \"0.6303967\",\n \"text\": \"def test_show_config_file(tmp_path):\\n config = str(tmp_path / \\\"sysctl.conf\\\")\\n with fopen(config, \\\"w\\\", encoding=\\\"utf-8\\\") as config_file:\\n config_file.write(\\n \\\"\\\"\\\"\\\\\\n# Use dump-core from kdump-tools Debian package.\\nkernel.core_pattern = |/usr/share/kdump-tools/dump-core %p %s %t %e\\n # Stop low-level messages on console = less logging\\n kernel.printk = 3 4 1 3\\n\\nnet.ipv4.ip_forward=1\\nnet.ipv4.tcp_rmem\\t=\\t4096\\t131072\\t6291456\\n\\\"\\\"\\\"\\n )\\n assert linux_sysctl.show(config) == {\\n \\\"kernel.core_pattern\\\": \\\"|/usr/share/kdump-tools/dump-core %p %s %t %e\\\",\\n \\\"kernel.printk\\\": \\\"3 4 1 3\\\",\\n \\\"net.ipv4.ip_forward\\\": \\\"1\\\",\\n \\\"net.ipv4.tcp_rmem\\\": \\\"4096\\\\t131072\\\\t6291456\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81275518b16f3e94da5a86a00c8b26ac\",\n \"score\": \"0.61630404\",\n \"text\": \"def format_config(self):\\n l = []\\n for uid in self.config:\\n d = self.config[uid]\\n l.append([d[\\\"ref_screen_name\\\"], d[\\\"shortcuts\\\"],\\n d[\\\"timezone\\\"], str(d[\\\"interval\\\"]), str(d[\\\"f_interval\\\"])])\\n s = tabulate(l, headers=[\\\"Name\\\", \\\"Abbr.\\\", \\\"Timezone\\\", \\\"Intv.\\\", \\\"FIntv.\\\"])\\n return '```\\\\n{}\\\\n```'.format(self.E('`')(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1589a4fb2f0eac90004afacf615b221\",\n \"score\": \"0.5929702\",\n \"text\": \"def show(config_file=False):\\n ret = {}\\n if config_file:\\n # If the file doesn't exist, return an empty list\\n if not os.path.exists(config_file):\\n return []\\n\\n try:\\n with salt.utils.files.fopen(config_file) as fp_:\\n for line in fp_:\\n line = salt.utils.stringutils.to_str(line).strip()\\n if not line.startswith(\\\"#\\\") and \\\"=\\\" in line:\\n key, value = line.split(\\\"=\\\", 1)\\n ret[key.rstrip()] = value.lstrip()\\n except OSError:\\n log.error(\\\"Could not open sysctl file\\\")\\n return None\\n else:\\n _sysctl = \\\"{}\\\".format(_which(\\\"sysctl\\\"))\\n cmd = [_sysctl, \\\"-a\\\"]\\n out = __salt__[\\\"cmd.run_stdout\\\"](cmd, output_loglevel=\\\"trace\\\")\\n for line in out.splitlines():\\n if not line or \\\" = \\\" not in line:\\n continue\\n comps = line.split(\\\" = \\\", 1)\\n ret[comps[0]] = comps[1]\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6172aa27def38e236539ef575bea05f0\",\n \"score\": \"0.59032184\",\n \"text\": \"def write_prox_config(prox_config):\\n out = []\\n for i, (section_name, section) in enumerate(prox_config):\\n out.append(\\\"[{}]\\\".format(section_name))\\n for index, item in enumerate(section):\\n key, value = item\\n if key == \\\"__name__\\\":\\n continue\\n if value is not None and value != '@':\\n key = \\\"=\\\".join((key, str(value).replace('\\\\n', '\\\\n\\\\t')))\\n out.append(key)\\n else:\\n key = str(key).replace('\\\\n', '\\\\n\\\\t')\\n out.append(key)\\n return os.linesep.join(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7d7d8a974a71394f5e761acc0f7938\",\n \"score\": \"0.58417326\",\n \"text\": \"def _dict_to_string(self, data):\\n temp = \\\"\\\"\\n for key, value in data.items():\\n temp += \\\" {0} : {1}\\\\n\\\".format(key, value)\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142e64ee195d6ff92861ce8effa2ca88\",\n \"score\": \"0.58183986\",\n \"text\": \"def dStr(dict) :\\n\\ts = SFMT % \\\"VM\\\"\\n\\tvmname = dict.keys()[0] #basically a random key from outer dict\\n\\tfor testShortName in dict[vmname].keys() :\\n\\t\\ts += SFMT % testShortName\\n\\ts += \\\"\\\\n\\\"\\n\\t\\n\\tfor k1 in dict.keys() :\\n\\t\\ts += SFMT % k1\\n\\t\\tfor k2 in dict[k1].keys() :\\n\\t\\t\\ts += FFMT % dict[k1][k2]\\n\\t\\ts += \\\"\\\\n\\\"\\n\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db69c0e738ca76ccceb9d1e640df264\",\n \"score\": \"0.57095635\",\n \"text\": \"def _print_config_property(console, config_property, output_format):\\n\\n if output_format == 'JSON':\\n console.printout(sap.cli.core.json_dumps(config_property))\\n else:\\n console.printout(f'Key: {config_property[\\\"key\\\"]}')\\n console.printout(f'Value: {config_property[\\\"value\\\"]}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac2521271a6faaec58cc36a552ee009\",\n \"score\": \"0.5702244\",\n \"text\": \"def __repr__(self):\\n L = [\\\"[%s]\\\"% self.section ]\\n for k in self.ini.get(self.section):\\n v = self.ini.get(self.section, k)\\n L.append(\\\"%s=%s\\\"% (k, v))\\n return '\\\\n'.join(L)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8f89d0b927e7ded74713de64eacb23\",\n \"score\": \"0.5683285\",\n \"text\": \"def print_as_string(self):\\n for section in self.config:\\n print(\\\"[\\\" + section + \\\"]\\\\n\\\")\\n for el in self.config[section]:\\n print(el + \\\"=\\\" + self.config[section][el] + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8d5765d9e6bb692e1b5f156494e3f5\",\n \"score\": \"0.56807\",\n \"text\": \"def __config_as_string(self):\\n config_as_string = \\\"\\\"\\n for section in self.config:\\n config_as_string += \\\"[\\\" + section + \\\"]\\\\n\\\"\\n for el in self.config[section]:\\n config_as_string += el + \\\"=\\\" + self.config[section][el] + \\\"\\\\n\\\"\\n return config_as_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46e6536898238a36ed9f07a4ae88ddc\",\n \"score\": \"0.5662441\",\n \"text\": \"def __getStr__(self):\\n prodDict = self.__getDict__()\\n txt = []\\n for k, v in prodDict.iteritems():\\n if isinstance(v, str):\\n txt.append(\\\"%s = %r\\\" % (k, v))\\n else:\\n txt.append(\\\"%s = %s\\\" % (k, v))\\n return '\\\\n'.join(txt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c90cf15a8a9d650edb48c8b4daa8fd\",\n \"score\": \"0.5650667\",\n \"text\": \"def pretty_print_config_to_json(self, services, hostname=None):\\n descriptor = self.get_config_dict(services, hostname)\\n return json.dumps(descriptor, sort_keys=True, indent=2,\\n separators=(',', ': '))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a25099065b62315662805d2fa986383\",\n \"score\": \"0.56340194\",\n \"text\": \"def __str__(self):\\n\\n overview = []\\n for k in sorted(self._config.keys()):\\n overview.append('{0}: {1}\\\\n'.format(k, self._config[k]))\\n\\n return ''.join(overview)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d44cc08d06a6d1b7e0df634286ab4e\",\n \"score\": \"0.5614748\",\n \"text\": \"def print_config(config, logger):\\n for k, v in config.items():\\n logger.info(\\\"{}:\\\\t{}\\\".format(k.ljust(15), v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940fb4bdb319779634de075eb18d9f98\",\n \"score\": \"0.56119144\",\n \"text\": \"def create(sysctl_dict, sysctl_file):\\n try:\\n sysctl_dict_parsed = yaml.safe_load(sysctl_dict)\\n except yaml.YAMLError:\\n log(\\\"Error parsing YAML sysctl_dict: {}\\\".format(sysctl_dict),\\n level=ERROR)\\n return\\n\\n with open(sysctl_file, \\\"w\\\") as fd:\\n for key, value in sysctl_dict_parsed.items():\\n fd.write(\\\"{}={}\\\\n\\\".format(key, value))\\n\\n log(\\\"Updating sysctl_file: %s values: %s\\\" % (sysctl_file, sysctl_dict_parsed),\\n level=DEBUG)\\n\\n check_call([\\\"sysctl\\\", \\\"-p\\\", sysctl_file])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f839c1f25c082fc60d7f6b6ffe3f647\",\n \"score\": \"0.559881\",\n \"text\": \"def dict2str(result_dict):\\r\\n\\r\\n result_str = ''\\r\\n for metric, value in result_dict.items():\\r\\n result_str += str(metric) + ' : ' + str(value) + ' '\\r\\n return result_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d84e7df3108cabee877ee16c1b4017\",\n \"score\": \"0.5593959\",\n \"text\": \"def _stringify_dict(d):\\n\\t\\treturn \\\"\\\\n\\\".join(list(\\\"\\\\t{key} - {value}\\\".format(key=key, value=value) for key, value in d.items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f4174e531f1dbb5637e6a19b03c11d\",\n \"score\": \"0.55822194\",\n \"text\": \"def get_sysctl_data(item):\\n command = (\\\"sysctl {}\\\").format(item)\\n result = os.popen(command).read().rstrip().replace(item, '').replace(': ','')\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e719ccfd41e39cfd13204e183a95df1\",\n \"score\": \"0.5579042\",\n \"text\": \"def render_configs(self, configs):\\n keys = [k for k in configs.keys()]\\n keys.sort()\\n\\n s = \\\"\\\"\\n for k in keys:\\n s += \\\"%s=%s\\\\n\\\" % (k, str(configs[k]))\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d72be4cfb472de50e7c34416738ac9\",\n \"score\": \"0.5558033\",\n \"text\": \"def _str_fmt_dct(dct):\\n return \\\" | \\\".join(\\\"%s: %.4g\\\" % (k, v) for k, v in dct.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"431da784c69a196ee5d7ae65aa9c565c\",\n \"score\": \"0.55388117\",\n \"text\": \"def format_dict(D):\\n keys = keys_to_str(D.keys())\\n n = max([len(k) for k in keys])+1\\n return '\\\\n'.join([' %s:%s%s' % (k, ' '*(n-len(k)), D[k]) for k in keys])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb0bf4a12216360f916cd324e85f09d\",\n \"score\": \"0.5527431\",\n \"text\": \"def _print_config(config):\\n for key, value in config.items():\\n print(key, \\\"-\\\", value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fefc1cfd1391b9381c0656bb09008a2f\",\n \"score\": \"0.5512504\",\n \"text\": \"def print_config(self):\\n print_str = f''\\n for hw_type, arch in self.architecture.items():\\n if arch:\\n for cpu_type, num in self.architecture[hw_type].items():\\n print_str+=f'{str(cpu_type)}: {num} CPUs\\\\n'\\n return print_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c585a8c9c5dd2bb64283bae9ba3011d\",\n \"score\": \"0.54950297\",\n \"text\": \"def dump(self):\\n data = []\\n for cl in self.config_data:\\n data.append(cl.raw)\\n return '\\\\n'.join(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600bfc8368b276b4a79a2298a9e6014c\",\n \"score\": \"0.5474739\",\n \"text\": \"def _sanitize_sysctl_value(value):\\n return re.sub(r\\\"\\\\s+\\\", \\\"\\\\t\\\", str(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a0b0534aa63fa87a2c12b8cf4d7b7c\",\n \"score\": \"0.5465268\",\n \"text\": \"def _makeConfigString(config):\\n result = []\\n for i in config:\\n # ignoring 'internal' todo-configuration and the format (should be used as extension)\\n if i == 'todo' or i == 'format' or ('height' in config and 'width' in config):\\n continue\\n elif i == 'height':\\n result.append(str(config[i]) + 'p')\\n elif i == 'width':\\n result.append(str(config[i]) + 'w')\\n elif i == 'muted' and config[i]:\\n result.append('m')\\n if 'height' in config and 'width' in config:\\n result.append(str(config['width']) + 'x' + str(config['height']))\\n # seperate each configuration attribute by a dot - makes it easier to parse it later\\n return '.'.join(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379c3b564b1327a6b732c5ed93daee8d\",\n \"score\": \"0.54405177\",\n \"text\": \"def __repr__(self):\\n string = ''\\n for key, val in self.setting().items():\\n string += '{}({})\\\\n'.format(key, val)\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec9675736196298ce7f291fba35deec9\",\n \"score\": \"0.5418939\",\n \"text\": \"def render_config(self, spec, conf):\\n config = deepcopy(spec)\\n config['severity'] = conf.get('severity')\\n\\n syslog_value = conf.get('syslog')\\n if syslog_value != None:\\n syslog = []\\n for each in syslog_value['host']:\\n syslog.append(each['address'])\\n config['syslog'] = syslog\\n\\n return utils.remove_empties(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4048067a1ff5ea6a4efa267cbebca002\",\n \"score\": \"0.54139096\",\n \"text\": \"def to_vtund_config(self):\\n indent_unit = '\\\\t'\\n cr_lf = '\\\\n';\\n config = ''\\n config += 'options {' + cr_lf\\n config += indent_unit + 'port ' + str(self.vtun_server_tcp_port) + ';' + cr_lf\\n if self.restricted_iface:\\n config += indent_unit + 'bindaddr { iface ' + self.restricted_iface + '; };' + cr_lf\\n config += indent_unit + 'syslog daemon;' + cr_lf\\n \\n config += indent_unit + 'ppp /usr/sbin/pppd;' + cr_lf\\n config += indent_unit + 'ifconfig /sbin/ifconfig;' + cr_lf\\n config += indent_unit + 'route /sbin/route;' + cr_lf\\n config += indent_unit + 'ip /sbin/ip;' + cr_lf\\n config += '}' + cr_lf\\n config += cr_lf\\n config += self.vtun_tunnel_name + ' {' + cr_lf\\n if not self.interface_name is None:\\n config += indent_unit + 'device ' + str(self.interface_name) + ';' + cr_lf\\n config += indent_unit + 'passwd ' + str(self.vtun_shared_secret) + ';' + cr_lf\\n config += indent_unit + 'type ' + self.tunnel_mode.get_equivalent_vtun_type() + ';' + cr_lf\\n config += indent_unit + 'proto ' + self.vtun_protocol + ';' + cr_lf\\n config += indent_unit + 'compress ' + self.vtun_compression + ';' + cr_lf\\n config += indent_unit + 'encrypt '\\n if self.vtun_encryption:\\n config += 'yes'\\n else:\\n config += 'no'\\n config += ';' + cr_lf\\n config += indent_unit + 'keepalive '\\n if self.vtun_keepalive:\\n config += 'yes'\\n else:\\n config += 'no'\\n config += ';' + cr_lf\\n \\n config += ' ' + cr_lf\\n config += indent_unit + 'up {' + cr_lf\\n config += indent_unit*2 + 'ifconfig \\\"%% ' + str(self.tunnel_near_end_ip) + ' pointopoint ' + str(self.tunnel_far_end_ip) + ' mtu 1450\\\";' + cr_lf\\n if not self.up_additional_commands is None:\\n for command in self.up_additional_commands:\\n if command[0] == '/':\\n config += indent_unit*2 + 'program ' + str(command) + ';' + cr_lf\\n else:\\n raise Exception('NotAFullPathCommand')\\n config += indent_unit + '};' + cr_lf\\n config += indent_unit + 'down {' + cr_lf\\n if not self.down_additional_commands is None:\\n for command in self.down_additional_commands:\\n if command[0] == '/':\\n config += indent_unit*2 + 'program ' + str(command) + ';' + cr_lf\\n else:\\n raise Exception('NotAFullPathCommand')\\n config += indent_unit + '};' + cr_lf\\n config += '}' + cr_lf\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb5a659d1e46f9de3250aa8e407609c\",\n \"score\": \"0.54089016\",\n \"text\": \"def __str__(self):\\n import os\\n output = \\\"\\\"\\n for key, value in self.__dict__.items():\\n output += str(key) + \\\": \\\" + str(value) + os.linesep\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b68f7e6c90dad6d70d07314b9c0180\",\n \"score\": \"0.53945345\",\n \"text\": \"def __str__(self):\\r\\n\\r\\n ret = \\\"\\\"\\r\\n # Sort our map by VM Address, this should already be how it is but cant hurt\\r\\n sortedmap = {k: v for k, v in sorted(self.map.items(), key=lambda item: item[1].vmaddr)}\\r\\n\\r\\n for (key, obj) in sortedmap.items():\\r\\n # 'string'.ljust(16) adds space padding\\r\\n # 'string'[2:].zfill(9) removes the first 2 chars and pads the string with 9 zeros\\r\\n # then we just re-add the 0x manually.\\r\\n\\r\\n # this gives us a nice list with visually clear columns and rows\\r\\n ret += f'{key.ljust(16)} || Start: 0x{hex(obj.vmaddr)[2:].zfill(9)} | ' \\\\\\r\\n f'End: 0x{hex(obj.vmend)[2:].zfill(9)} | ' \\\\\\r\\n f'Size: 0x{hex(obj.size)[2:].zfill(9)} | Slice ' \\\\\\r\\n f'Offset: 0x{hex(obj.fileaddr)[2:].zfill(9)} ||' \\\\\\r\\n f' File Offset: 0x{hex(obj.fileaddr + self.slice.offset)[2:].zfill(9)}\\\\n '\\r\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fccf95e699e63e0468b10677fa0bf0\",\n \"score\": \"0.53740907\",\n \"text\": \"def display_cluster_config(cluster_config):\\n for name, value in cluster_config._asdict().items():\\n str_value = str(value.__dict__).replace('\\\\n', '') if hasattr(value, '__dict__') else str(value)\\n LOGGER.info('Config %s: %s', name, str_value)\\n LOGGER.info('-' * 40)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d275350df2c4b3aa13ed514e87de7df\",\n \"score\": \"0.53692997\",\n \"text\": \"def config():\\n for k, v in current_app.config.items():\\n print(\\\"%s: %s\\\" % (k, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4bf261f369b729c4cce9180d418435a\",\n \"score\": \"0.5355675\",\n \"text\": \"def map_to_properties(map):\\n ret = \\\"\\\"\\n for key, val in list(map.items()):\\n key = re.sub(\\\"[^a-zA-Z0-9_]\\\", \\\"\\\", key)\\n if isinstance(val, str):\\n ret += key + \\\"=\\\" + val + os.linesep\\n else:\\n ret += key + \\\"=\\\" + json_save_small(val) + os.linesep\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec3b04def848afe263382ff7e1e141e\",\n \"score\": \"0.5347017\",\n \"text\": \"def print_conf_table(conf=conf_dict):\\n print(\\\"{:<25}{:<30}{:<30}\\\".format('group', 'key', 'value'))\\n print('=' * 75)\\n for group, val in sorted(conf.items()):\\n for g_key, g_val in sorted(val.items()):\\n print(\\\"{:<25}{:<30}{:<15} \\\\n\\\".format(group, g_key, str(g_val)))\\n print('-' * 75 + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f24c90efb2c9d1ed92b17784302e2f\",\n \"score\": \"0.5340365\",\n \"text\": \"def dump(self):\\n data = []\\n for cl in self.config_data:\\n data.append(cl.raw)\\n return self.newline.join(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b610e43297fc8bea1d83e45f365ecb\",\n \"score\": \"0.53303504\",\n \"text\": \"def __str__(self):\\n cfg_str = pprint.pformat(self.config, indent=2, width=120)\\n # cfg_str = re.sub(r\\\".*password.*\\\",\\n # \\\"# this line was removed since it included a password\\\",\\n # cfg_str)\\n # cfg_str = re.sub(r\\\".*user_id.*\\\",\\n # \\\"# this line was removed since it included a password\\\",\\n # cfg_str)\\n # cfg_str = re.sub(r\\\".*api_key.*\\\",\\n # \\\"# this line was removed since it included a password\\\",\\n # cfg_str)\\n return cfg_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54ab9c5a0c5394bfd8d5dd3a27f08be\",\n \"score\": \"0.5324538\",\n \"text\": \"def showConfig():\\n for key in dir(Config):\\n if key[:2]!='__':\\n val=getattr(Config, key)\\n if val==None or val==\\\"None\\\":\\n line=str(key) + \\\"=\\\\n\\\"\\n else:\\n line=str(key) + \\\"=\\\" + str(val) + \\\"\\\\n\\\"\\n print line,\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d5fa2e50171ade49310645db67eee19\",\n \"score\": \"0.52737963\",\n \"text\": \"def _mvalue_to_str(self, m, multiline=1):\\n if type(m) is not type({}):\\n return ''\\n str = ''\\n keys = m.keys()\\n keys.sort()\\n for k in keys:\\n str = str + k + ' | ' + m[k]\\n if multiline:\\n str += '\\\\n'\\n else:\\n str += '\\\\\\\\n'\\n return str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be92a2410808175f97768e294f3a1f2\",\n \"score\": \"0.52723\",\n \"text\": \"def to_string(self):\\n return (\\\"{\\\\n\\\\t\\\" +\\n ',\\\\n\\\\t'.join(['\\\"{}\\\":{}'.format(k, to_string(v))\\n for k, v in self.items()]) +\\n \\\"\\\\n}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0461ba4d40fe794615c5ac364da3c5\",\n \"score\": \"0.52718997\",\n \"text\": \"def generate_vtysh_conf(cls, node: CoreNode) -> str:\\n return \\\"service integrated-vtysh-config\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ba4563f2614a651c1ea442b225419a\",\n \"score\": \"0.5241447\",\n \"text\": \"def print_dict(dic):\\r\\n print(\\\"\\\\n\\\".join(\\\"{:10s}: {}\\\".format(key, values) for key, values in dic.items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129df923219001ad7e6f215120660adb\",\n \"score\": \"0.52343446\",\n \"text\": \"def format_props(props, prop_template=\\\"{{k}} = { {{v}} }\\\", delim=\\\"\\\\n\\\"):\\n vars_ = []\\n props_ = []\\n for k, v in list(props.items()):\\n vars_.append(Template(\\\"var {{k}} = {{v}};\\\").render(k=k,v=json.dumps(v)))\\n props_.append(Template(prop_template).render(k=k, v=k))\\n return \\\"\\\\n\\\".join(vars_), delim.join(props_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5e908b2b28675c92735a8116a3b8f9\",\n \"score\": \"0.52321815\",\n \"text\": \"def config_status_to_str(self, status):\\n\\n\\t\\tpchar = self._libinput.libinput_config_status_to_str(status)\\n\\t\\treturn string_at(pchar)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad3314e54cfd09b75bc860524371ecad\",\n \"score\": \"0.5219203\",\n \"text\": \"def pretty_print_config_to_json(self, services, hostname=None):\\n descriptor = self.get_openapi_dict(services, hostname)\\n return json.dumps(descriptor, sort_keys=True, indent=2,\\n separators=(',', ': '))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99e7725af51d2d1ad029533809c2e29\",\n \"score\": \"0.5212987\",\n \"text\": \"def test_persist_value_with_spaces_update_config(tmp_path):\\n config = str(tmp_path / \\\"existing_sysctl_with_spaces.conf\\\")\\n value = \\\"|/usr/share/kdump-tools/dump-core %p %s %t %e\\\"\\n with fopen(config, \\\"w\\\", encoding=\\\"utf-8\\\") as config_file:\\n config_file.write(\\\"kernel.core_pattern =\\\\n\\\")\\n mock_run = MagicMock()\\n mock_run_all = MagicMock(\\n return_value={\\n \\\"pid\\\": 1337,\\n \\\"retcode\\\": 0,\\n \\\"stderr\\\": \\\"\\\",\\n \\\"stdout\\\": \\\"kernel.core_pattern = \\\" + value,\\n }\\n )\\n which_mock = MagicMock(return_value=\\\"/usr/sbin/sysctl\\\")\\n with patch(\\\"salt.utils.path.which\\\", which_mock):\\n with patch(\\\"os.path.exists\\\", MagicMock(return_value=True)), patch.dict(\\n linux_sysctl.__salt__, {\\\"cmd.run\\\": mock_run, \\\"cmd.run_all\\\": mock_run_all}\\n ):\\n assert (\\n linux_sysctl.persist(\\\"kernel.core_pattern\\\", value, config=config)\\n == \\\"Updated\\\"\\n )\\n mock_run.assert_not_called()\\n mock_run_all.assert_called_once_with(\\n [\\\"/usr/sbin/sysctl\\\", \\\"-w\\\", \\\"kernel.core_pattern=\\\" + value],\\n python_shell=False,\\n )\\n assert os.path.isfile(config)\\n with fopen(config, encoding=\\\"utf-8\\\") as config_file:\\n written = config_file.read()\\n assert written == f\\\"kernel.core_pattern = {value}\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b764e3549c99528527b490999a47658e\",\n \"score\": \"0.521115\",\n \"text\": \"def config_str(self, compact = False):\\n string = ''\\n for attr in ('survival_atp','repro_atp','initial_atp',\\n 'max_amount_per_step','penalize_edges',\\n 'survive_num_timesteps', 'survive_reset_nodes', 'survive_reset_food'):\\n string += f'{attr}: {self.__dict__[attr]}'\\n string += ', ' if compact else '\\\\n'\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22600c0616b7cb3eb57b459f370104cb\",\n \"score\": \"0.5200858\",\n \"text\": \"def print_conf(name, conf):\\n print(\\\"\\\\n\\\" + \\\"-\\\" * 60)\\n print(\\\" {} :\\\".format(name))\\n print(\\\"\\\" + \\\"-\\\" * 60)\\n # for key, val in conf.items():\\n for key, val in sorted(conf.items()):\\n print(\\\"{:40} : {}\\\".format(key, val))\\n print(\\\"-\\\" * 60 + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ad6ebe7ed97f6cab7aa79ec6d3ea5b\",\n \"score\": \"0.51957583\",\n \"text\": \"def __str__(self):\\n items = ['({!r})'.format(item) for item in self.data.keys()]\\n return '[{}]'.format(' -> '.join(items))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35788bd9b277f97ece984086301da9c\",\n \"score\": \"0.5193704\",\n \"text\": \"def python_dict_to_hcl_map(dict):\\n dict_string = str(dict).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\").replace(\\n \\\":\\\", \\\" =\\\").replace(\\\",\\\", \\\"\\\\n\\\")\\n dict_string = dict_string.replace(\\\"{\\\", \\\"{\\\\n \\\").replace(\\\"}\\\", \\\"\\\\n}\\\")\\n return dict_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0a9a4d0688c4f6fe24773c8a2429ac\",\n \"score\": \"0.51848996\",\n \"text\": \"def write_config_console(self, config):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af0a80eadccfeae95055d05b6aaa40b\",\n \"score\": \"0.5179516\",\n \"text\": \"def printConfig(config):\\n for section in config.sections():\\n for key in config[section]: \\n print(section+'.'+key+'='+config.get(section, key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a096a5ace7c710394c3082b2de688d7b\",\n \"score\": \"0.5174283\",\n \"text\": \"def __str__(self):\\n config_str = 'Configurations\\\\n'\\n config_str += pprint.pformat(self.__dict__)\\n return config_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab7ecd1d9de6992e0fad32bee8aa861\",\n \"score\": \"0.5169066\",\n \"text\": \"def pretty_print(self):\\n print ET.tostring(self.config) # Well, not exactly pretty, but better than nothing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8450c8b2c7d1318449b04a78af90ccd\",\n \"score\": \"0.5164321\",\n \"text\": \"def __str__(self):\\n strs = []\\n for key, value in self.to_wsgi_list():\\n strs.append('%s: %s' % (key, value))\\n strs.append('\\\\r\\\\n')\\n return '\\\\r\\\\n'.join(strs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e41b9eea89ede642096bf258ad1d24\",\n \"score\": \"0.5150572\",\n \"text\": \"def pprint_info(label, mapping):\\n print('', label, '=' * len(label), sep='\\\\n', file=file)\\n for k, v in mapping.items():\\n if not isinstance(v, str):\\n v = str(v)\\n if '\\\\n' in v:\\n # Make each new line start at the appropriate indentation level\\n v = v.replace('\\\\n', '\\\\n' + (' ' * (padding + len(separator))))\\n print(k.rjust(padding), separator, v, sep='', file=file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba736fee930aeed1cf150b23242b749\",\n \"score\": \"0.5147476\",\n \"text\": \"def dict_to_str( a_dict ):\\n info_str = \\\"\\\"\\n for key, value in a_dict.items():\\n if info_str == \\\"\\\":\\n info_str += f\\\"{key}:{value}\\\"\\n else:\\n info_str += f\\\"\\\\n{key}:{value}\\\"\\n\\n return info_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8e1ba29e6d9d770499f429aab47f0f\",\n \"score\": \"0.5144735\",\n \"text\": \"def _vartxt(env,return_values=True,comment=None):\\n subenv = dict((k,v) for k,v in env.items() if k not in INIT_VARS)\\n \\n if return_values:\\n txt = pprint.pformat(subenv,indent=1)\\n else:\\n txt = pprint.pformat(list(subenv.keys()),indent=1)\\n\\n if comment is None:\\n return txt\\n \\n if not any(comment.endswith(c) for c in \\\" \\\\t\\\"): \\n comment += ' ' # make sure ends with space\\n \\n return '\\\\n'.join(comment + t for t in txt.split('\\\\n'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14be1dfc311b69e52128dd968532d36e\",\n \"score\": \"0.5142728\",\n \"text\": \"def print_full_conf(conf=CONF):\\n for group, val in sorted(conf.items()):\\n print('=' * 75)\\n print('{}'.format(group))\\n print('=' * 75)\\n for g_key, g_val in sorted(val.items()):\\n print('{}'.format(g_key))\\n for gg_key, gg_val in g_val.items():\\n print('{}{}'.format(' ' * 4, gg_key))\\n body = '\\\\n'.join(['\\\\n'.join(textwrap.wrap(line, width=110, break_long_words=False,\\n replace_whitespace=False,\\n initial_indent=' ' * 8, subsequent_indent=' ' * 8))\\n for line in str(gg_val).splitlines() if line.strip() != ''])\\n print(body)\\n print('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d3bb288c0cf3b70272c237c8318ed5\",\n \"score\": \"0.5129692\",\n \"text\": \"def format(self):\\n return{\\n 'hostname': self.hostname,\\n 'ip_address_v4': self.ip_address_v4,\\n 'ip_address_v6': self.ip_address_v6,\\n 'personality': self.personality,\\n 'status': self.status,\\n 'system_info': self.system_info.format()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b5bec78c19f1439554aa24d0895e5f\",\n \"score\": \"0.5125728\",\n \"text\": \"def __str__(self):\\n string = []\\n for key, value in self.to_wsgi_list():\\n string.append('%s: %s' % (key, value))\\n string.append('\\\\r\\\\n')\\n return '\\\\r\\\\n'.join(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526dfcf1e79feacd8f05065914b1eb17\",\n \"score\": \"0.5112593\",\n \"text\": \"def dump_config(config, modified_color=36, quote_str=False):\\n strs = []\\n tab = ' '\\n\\n def quote(v):\\n if quote_str and isinstance(v, str) and v:\\n return \\\"'{}'\\\".format(v)\\n return v\\n\\n def repr_dict(k, v):\\n if isinstance(v, (dict, list)):\\n v = ''\\n return \\\"{}: {}\\\\n\\\".format(k, quote(v))\\n\\n def repr_list(_k, v):\\n if isinstance(v, (dict, list)):\\n return \\\"- \\\"\\n return \\\"- {}\\\\n\\\".format(quote(v))\\n\\n def dump(data, indent, last, firstline_nopref=False):\\n modified = config._sconf_modified.get(id(data), set())\\n\\n N = len(data)\\n for i, (k, v) in enumerate(kv_iter(data)):\\n prefix = indent\\n if firstline_nopref:\\n prefix = ''\\n firstline_nopref = False\\n\\n # indentation is determined by parent data type\\n if isinstance(data, dict):\\n s = repr_dict(k, v)\\n skip_first_indent = False\\n elif isinstance(data, list):\\n s = repr_list(k, v)\\n skip_first_indent = True\\n else:\\n raise ValueError(type(data))\\n\\n is_last = last and i == N-1\\n if is_last and not isinstance(v, (dict, list)):\\n s = s.rstrip('\\\\n')\\n\\n if k in modified:\\n s = colorize(s, modified_color)\\n strs.append(prefix + s)\\n if isinstance(v, (dict, list)):\\n dump(v, indent + tab, is_last, skip_first_indent)\\n\\n dump(config.get_data(), '', True)\\n return ''.join(strs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d82e8e802c078aef5f66e05274c663\",\n \"score\": \"0.51124203\",\n \"text\": \"def __str__(self):\\n lines = []\\n for k, v in self.__options.items():\\n if v is None:\\n continue\\n lines.append(\\\"%s %s\\\" % (k, v))\\n return \\\"\\\\n\\\".join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a499e781ff1608595c1a654401c54103\",\n \"score\": \"0.5108561\",\n \"text\": \"def __str__(self):\\n ana = str( self.cfg_ana )\\n count = ''\\n ave = ''\\n if hasattr(self, 'counters') and len( self.counters.counters ) > 0:\\n count = '\\\\n'.join(map(str, self.counters.counters))\\n if hasattr(self, 'averages') and len( self.averages ) > 0:\\n ave = '\\\\n'.join(map(str, self.averages))\\n return '\\\\n'.join( [ana, count, ave] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7bc7c227e7e7cf1c04dc77a6d3d6bb\",\n \"score\": \"0.5097259\",\n \"text\": \"def sysctl_set(key, value):\\n p = Popen(\\\"sysctl -w %s=%s\\\" % (key, value), shell=True, stdout=PIPE,\\n stderr=PIPE)\\n # Output should be empty; otherwise, we have an issue. \\n stdout, stderr = p.communicate()\\n stdout_expected = \\\"%s = %s\\\\n\\\" % (key, value)\\n if stdout != stdout_expected:\\n raise Exception(\\\"Popen returned unexpected stdout: %s != %s\\\" %\\n (stdout, stdout_expected))\\n if stderr:\\n raise Exception(\\\"Popen returned unexpected stderr: %s\\\" % stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7bc7c227e7e7cf1c04dc77a6d3d6bb\",\n \"score\": \"0.5097259\",\n \"text\": \"def sysctl_set(key, value):\\n p = Popen(\\\"sysctl -w %s=%s\\\" % (key, value), shell=True, stdout=PIPE,\\n stderr=PIPE)\\n # Output should be empty; otherwise, we have an issue. \\n stdout, stderr = p.communicate()\\n stdout_expected = \\\"%s = %s\\\\n\\\" % (key, value)\\n if stdout != stdout_expected:\\n raise Exception(\\\"Popen returned unexpected stdout: %s != %s\\\" %\\n (stdout, stdout_expected))\\n if stderr:\\n raise Exception(\\\"Popen returned unexpected stderr: %s\\\" % stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c09e13747e18ae29ebfe6ad16f9bd7\",\n \"score\": \"0.50951976\",\n \"text\": \"def convert_cfg_markdown(cfg):\\n r = \\\"\\\"\\n s = []\\n\\n def helper(cfg):\\n s_indent = []\\n for k, v in sorted(cfg.items()):\\n seperator = \\\" \\\"\\n attr_str = \\\" \\\\n{}:{}{} \\\\n\\\".format(str(k), seperator, str(v))\\n s_indent.append(attr_str)\\n return s_indent\\n\\n for k, v in sorted(cfg.items()):\\n seperator = \\\" \\\" if isinstance(v, str) else \\\" \\\\n\\\"\\n val = helper(v)\\n val_str = \\\"\\\"\\n for line in val:\\n val_str += line\\n attr_str = \\\"##{}:{}{} \\\\n\\\".format(str(k), seperator, val_str)\\n s.append(attr_str)\\n for line in s:\\n r += \\\" \\\\n\\\" + line + \\\" \\\\n\\\"\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b91c47c7c39745726834f9e1a8264e\",\n \"score\": \"0.5094097\",\n \"text\": \"def pretty(dic):\\n print package(dic, sort_keys=True, indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4463fa3eb4768721e6d6c072a0e6b075\",\n \"score\": \"0.50915563\",\n \"text\": \"def __str__(self):\\n return (\\n \\\"Device Manager\\\\n\\\"\\n + \\\"=\\\" * 14\\n + \\\"\\\\n\\\"\\n + \\\"\\\\n\\\".join(str(device) for device in self.__device_dict.values())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129ad459e4939f5b60b2f29600aaf7b8\",\n \"score\": \"0.5084082\",\n \"text\": \"def _get_config(self) -> str:\\n try:\\n vips = self.authentication().tm.ltm.virtuals.get_collection(\\n partition=self.partition\\n )\\n for vip in vips:\\n print(f\\\"{vip.name}\\\")\\n print()\\n self.logging().info(f\\\"{vip.name}\\\")\\n for key, value in vip.raw.items():\\n print(key, value)\\n print(\\\"#\\\" * 79)\\n\\n except Exception as e:\\n self.logging().warning(e)\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a96fdc3e164dead10d8dc8604f1c535\",\n \"score\": \"0.5082336\",\n \"text\": \"def __str__(self):\\n return json.dumps(self._config, indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db0e90e0857c3d4f8dfce77be9cf68d\",\n \"score\": \"0.50823224\",\n \"text\": \"def dict_to_nice_string(control_dict: Dict) -> str:\\n script_text = \\\"control_dict = {\\\\n\\\"\\n for key, value in control_dict.items():\\n script_text += \\\"\\\\t\\\\\\\"\\\" + key + \\\"\\\\\\\": \\\"\\n first = False\\n if (type(value) == dict):\\n if (\\\"do\\\" in value): # do should always be first in this list\\n script_text += \\\"{\\\\\\\"do\\\\\\\":\\\" + str(value[\\\"do\\\"]) + \\\",\\\"\\n if (len(value) > 1):\\n script_text += \\\"\\\\n\\\"\\n first = True\\n for key2, value2 in value.items(): # alternative keys\\n\\n # prefix\\n if (first):\\n prefix = \\\" \\\"\\n first = False\\n else:\\n prefix = \\\"\\\\t\\\\t\\\"\\n\\n # key_val\\n if (key2 == \\\"do\\\"):\\n continue\\n elif (type(value2) == dict):\\n script_text += prefix + \\\"\\\\\\\"\\\" + str(key2) + \\\"\\\\\\\": \\\" + _inline_dict(value2, \\\"\\\\t\\\\t\\\\t\\\") + \\\",\\\\n\\\"\\n else:\\n script_text += prefix + \\\"\\\\\\\"\\\" + str(key2) + \\\"\\\\\\\": \\\" + str(value2) + \\\",\\\"\\n script_text += prefix + \\\" },\\\\n\\\"\\n else:\\n script_text += str(value) + \\\",\\\\n\\\"\\n script_text += \\\"}\\\\n\\\"\\n return script_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ac01d88ba2128bc85e21c60b0f2a07\",\n \"score\": \"0.5077761\",\n \"text\": \"def to_string(self, source=COMMANDLINE|CONFIGFILE):\\n if source & BUILTIN: func = repr\\n else: func = str\\n s = ''\\n keys = [x for x in list(self.keys()) if x != DEFAULTSECT]\\n keys.sort()\\n if self[DEFAULTSECT]:\\n keys.insert(0, DEFAULTSECT)\\n for section in keys:\\n content = func(self[section]).strip()\\n if content:\\n s += \\\"[%s]\\\\n%s\\\\n\\\\n\\\" % (section, content)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66de61c356a43a29cfe09a015bae8365\",\n \"score\": \"0.5075009\",\n \"text\": \"def print_dict(d, wrap=0):\\r\\n pt = prettytable.PrettyTable(['Property', 'Value'],\\r\\n caching=False, print_empty=False)\\r\\n pt.aligns = ['l', 'l']\\r\\n for (prop, value) in six.iteritems(d):\\r\\n if value is None:\\r\\n value = ''\\r\\n value = _word_wrap(value, max_length=wrap)\\r\\n pt.add_row([prop, value])\\r\\n encoded = strutils.safe_encode(pt.get_string(sortby='Property'))\\r\\n if six.PY3:\\r\\n encoded = encoded.decode()\\r\\n print(encoded)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecab168c7b6308215c94723ec3386c3d\",\n \"score\": \"0.5074404\",\n \"text\": \"def GetConfigAsString(self, config, enum_values, index):\\n string = textwrap.dedent(\\\"\\\"\\\"\\\\\\n [{index}] = {{\\n .input = {input_name},\\n .voltage = {voltage_name},\\n .type = {type_str},\\n .channel = {channel},\\n .volts_per_count = {volts_per_count}f,\\n .offset = {offset}f,\\n .nominal = {nominal}f,\\n .min = {min}f,\\n .max = {max}f}},\\n \\\"\\\"\\\").format(\\n index=index,\\n input_name=enum_values['input'],\\n voltage_name=enum_values['voltage'],\\n type_str=type_helper.Name(config['type']),\\n **config)\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2fc8ca59d50a1db093f92fa6de58b9b\",\n \"score\": \"0.50692046\",\n \"text\": \"def dump_string(self):\\n result = []\\n for key, value in self.fields.items():\\n pattern = '%s: %s'\\n if str(value).startswith('\\\\n'):\\n pattern = '%s:%s'\\n result.append(pattern % (key, self.fields[key]))\\n\\n return \\\"\\\\n\\\".join(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e284c0538808f3c30890f8feca02c9f\",\n \"score\": \"0.506259\",\n \"text\": \"def __repr__(self):\\n rep = \\\"SshConfig(\\\"\\n entries = []\\n for k, v in self.__entries.items():\\n if v is None:\\n continue\\n if k == \\\"default\\\":\\n entries.append(repr(v))\\n else:\\n entries.append(\\\"%s = %s\\\" % (k.partition('ssh_')[2], repr(v)))\\n rep += \\\", \\\".join(entries)\\n rep += \\\")\\\"\\n return rep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576dd19ada320e3307f62350bf22df07\",\n \"score\": \"0.5057918\",\n \"text\": \"def dump_items(self):\\n for key, value in self.config.items():\\n print (\\\"%s = %s\\\") % (key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64eccabc92ddfb3cb2819137af183b49\",\n \"score\": \"0.50572014\",\n \"text\": \"def format_metric_output(results):\\n metric_string = \\\"\\\"\\n for name, value in results.items():\\n metric_string = \\\"{0} {1}: {2:.3f} \\\".format(metric_string, name, value)\\n\\n return metric_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b8eddc6c4f63efcc5b990b0bd0ff79\",\n \"score\": \"0.5054041\",\n \"text\": \"def do_show_network_config(vnic_utils):\\n\\n api_show_network_config()\\n\\n _logger.info(\\\"Operating System level network configuration\\\")\\n\\n ret = vnic_utils.get_network_config()\\n\\n _fmt = \\\"{:6} {:15} {:15} {:5} {:15} {:10} {:3} {:15} {:5} {:11} {:5} {:17} {}\\\"\\n print(_fmt.format('CONFIG', 'ADDR', 'SPREFIX', 'SBITS', 'VIRTRT',\\n 'NS', 'IND', 'IFACE', 'VLTAG', 'VLAN', 'STATE', 'MAC', 'VNIC'))\\n for item in ret:\\n print(_fmt.format(item['CONFSTATE'],\\n item['ADDR'], item['SPREFIX'],\\n item['SBITS'], item['VIRTRT'],\\n item['NS'], item['IND'],\\n item['IFACE'], item['VLTAG'],\\n item['VLAN'], item['STATE'],\\n item['MAC'], item['VNIC']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8768abd30f9f44810847f1b386644a0\",\n \"score\": \"0.5034202\",\n \"text\": \"def __str__(self):\\n output = 'CAPABILITIES:\\\\n============\\\\n'\\n for cap_name in sorted(self.capabilities.keys()):\\n output += str(self.capabilities[cap_name]) + '\\\\n'\\n output += '\\\\nACTORS:\\\\n======\\\\n'\\n for actor_name in sorted(self.actors.keys()):\\n output += str(self.actors[actor_name]) + '\\\\n'\\n output += '\\\\nLANGUAGES:\\\\n==========\\\\n'\\n for lang_name in sorted(self.languages.keys()):\\n output += str(self.languages[lang_name]) + '\\\\n'\\n output += '\\\\nCOMPONENTS:\\\\n==========\\\\n'\\n for cpt_name in sorted(self.components.keys()):\\n output += str(self.components[cpt_name]) + '\\\\n'\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5394a76de5617ee7c2c28deba822fe0a\",\n \"score\": \"0.50322247\",\n \"text\": \"def construct_output_line(odict):\\n outline = ''\\n for vnum in range(1, len(odict) + 1):\\n outline += OVAR_FMT[vnum].format(odict[vnum])\\n outline += '\\\\n'\\n return outline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69aa8a944e46a897932b04ddb3311ab7\",\n \"score\": \"0.5031642\",\n \"text\": \"def __str__(self):\\n string = ''\\n for key in self._dict.keys():\\n string += key + '\\\\n'\\n return string[:-2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db0a70c87bbbaec9764c1ae5aad389b\",\n \"score\": \"0.5024539\",\n \"text\": \"def bar_head_format(config):\\n return ''.join([c if isinstance(c, str) else ' '*c for c in config])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0956730fc5897a2dd99c20187c444447\",\n \"score\": \"0.50194275\",\n \"text\": \"def print_dict(d, wrap=0):\\n pt = prettytable.PrettyTable(['Property', 'Value'],\\n caching=False, print_empty=False)\\n pt.aligns = ['l', 'l']\\n for (prop, value) in six.iteritems(d):\\n if value is None:\\n value = ''\\n value = _word_wrap(value, max_length=wrap)\\n pt.add_row([prop, value])\\n encoded = encodeutils.safe_encode(pt.get_string(sortby='Property'))\\n if six.PY3:\\n encoded = encoded.decode()\\n print(encoded)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04433b3e28ddacd46979d4e58d0c4df2\",\n \"score\": \"0.5013424\",\n \"text\": \"def __str__(self):\\n s = 'Available object configurations:'\\n for k in self.options.keys():\\n s += f'\\\\n{k}'\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b5920597bb1de1cba4612ca3967b1a\",\n \"score\": \"0.50130534\",\n \"text\": \"def __str__(self):\\n\\t\\tret = \\\"\\\"\\n\\t\\tfor section in self.keys():\\n\\t\\t\\tret += \\\"%s:\\\\n\\\" % section\\n\\t\\t\\tfor option, value in self[section].items():\\n\\t\\t\\t\\tif option != '__name__':\\n\\t\\t\\t\\t\\tret += \\\" %s: %s\\\\n\\\" % (option, value)\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227affe6ada4c68e2292fd0a20d5a99e\",\n \"score\": \"0.50006443\",\n \"text\": \"def inspect(self):\\n\\n print \\n \\n keys = self._cfg.keys()\\n keys.sort()\\n for key in keys:\\n\\t print ' {:<25} = {:<25}'.format(key, self._cfg[key])\\n \\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227affe6ada4c68e2292fd0a20d5a99e\",\n \"score\": \"0.50006443\",\n \"text\": \"def inspect(self):\\n\\n print \\n \\n keys = self._cfg.keys()\\n keys.sort()\\n for key in keys:\\n\\t print ' {:<25} = {:<25}'.format(key, self._cfg[key])\\n \\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d380f95686dfb99b09facf971db2b042\",\n \"score\": \"0.49970165\",\n \"text\": \"def test_show():\\n mock_cmd = MagicMock(\\n return_value=\\\"\\\"\\\"\\\\\\nkernel.core_pattern = |/usr/share/kdump-tools/dump-core %p %s %t %e\\nkernel.printk = 3 4 1 3\\nnet.ipv4.ip_forward = 1\\nnet.ipv4.tcp_rmem = 4096\\t131072\\t6291456\\n\\\"\\\"\\\"\\n )\\n which_mock = MagicMock(return_value=\\\"/usr/sbin/sysctl\\\")\\n with patch(\\\"salt.utils.path.which\\\", which_mock):\\n with patch.dict(linux_sysctl.__salt__, {\\\"cmd.run_stdout\\\": mock_cmd}):\\n assert linux_sysctl.show() == {\\n \\\"kernel.core_pattern\\\": \\\"|/usr/share/kdump-tools/dump-core %p %s %t %e\\\",\\n \\\"kernel.printk\\\": \\\"3 4 1 3\\\",\\n \\\"net.ipv4.ip_forward\\\": \\\"1\\\",\\n \\\"net.ipv4.tcp_rmem\\\": \\\"4096\\\\t131072\\\\t6291456\\\",\\n }\\n mock_cmd.assert_called_once_with(\\n [\\\"/usr/sbin/sysctl\\\", \\\"-a\\\"], output_loglevel=\\\"trace\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f4c3cb2f873919b919f730b55fc331\",\n \"score\": \"0.49955997\",\n \"text\": \"def _build_legacy_output(config: Config) -> str:\\n parameter_list = \\\"\\\"\\n for option in _config_options_sorted_by_key(config):\\n if option.value is not None:\\n parameter_list += f'{option.key} = {option.value} (macro name: \\\"{option.macro_name}\\\")\\\\n'\\n else:\\n parameter_list += f\\\"{option.key} has no value\\\\n\\\"\\n\\n macro_list = \\\"\\\"\\n for macro in _config_macros_sorted_by_name(config):\\n if macro.value is not None:\\n macro_list += f\\\"{macro.name}={macro.value}\\\\n\\\"\\n else:\\n macro_list += f\\\"{macro.name}\\\\n\\\"\\n\\n output_template = (\\n \\\"Configuration parameters\\\\n\\\"\\n \\\"------------------------\\\\n\\\"\\n f\\\"{parameter_list}\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"Macros\\\\n\\\"\\n \\\"------\\\\n\\\"\\n f\\\"{macro_list}\\\"\\n )\\n return output_template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a467eb4507c13014cb3b239036036fc\",\n \"score\": \"0.4989747\",\n \"text\": \"def __repr__(self):\\n return '{%s}' % ', '.join(['%r: %r' % (k, v) for k, v in self.items()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27235a48c41581afb0f2ed4001105733\",\n \"score\": \"0.4988618\",\n \"text\": \"def format_entries(self, entries):\\n longest_hotkey = max(set(len(entry[0]) for entry in entries))\\n dots_length = longest_hotkey + ADDITIONAL_DOTS\\n output = list()\\n for hotkey, info in entries:\\n output.append(\\\"{hotkey} {dots} {info}\\\".format(\\n hotkey=hotkey,\\n dots=\\\".\\\" * (dots_length - len(hotkey)),\\n info=info\\n ))\\n\\n return \\\"\\\\n\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f155c421fea9b1d020ed91c81a846b2b\",\n \"score\": \"0.49859962\",\n \"text\": \"def format_properties(\\n properties: PropertyDict,\\n *,\\n prop_formatter: Callable[[str, PropertyValue], str] = format_property,\\n) -> str:\\n return \\\" \\\".join((prop_formatter(name, value) for name, value in properties.items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef7277c32b946864c748c9079666f20\",\n \"score\": \"0.49854976\",\n \"text\": \"def visit_dict(self, value):\\n items = ['%s:%s' % (self.encode(k), self.encode(v))\\n for k, v in six.iteritems(value)]\\n return '{%s}' % ','.join(items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47344c9e3bb86fad258e789a98d2c8e\",\n \"score\": \"0.4969965\",\n \"text\": \"def get_display_string(self):\\n s = super(Data73, self).get_display_string()\\n keys = sorted(list(self.settings.keys()))\\n for key in keys:\\n s += key + ' : ' + str(self.settings[key]) + \\\"\\\\n\\\"\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dca2f4719e9b95048f32f63e2e9f60c\",\n \"score\": \"0.49686962\",\n \"text\": \"def get_info(self):\\n\\n info_str = 'Information on Agent:\\\\n'\\n for key, value in vars(self).items():\\n # if value is dict, numpy array or list print its size\\n if isinstance(value, (list, np.ndarray, dict)):\\n key += ' size'\\n value = len(value)\\n # variable tab space for clean printing\\n tab_space = ':' + '\\\\t' * (5 - int(len(key) / 8)) if 5 - int(len(key) / 8) > 0 else ':\\\\t'\\n info_str += key + tab_space + str(value) + '\\\\n'\\n return info_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecfbe76fb59c3fc1e7c15197de1a69b8\",\n \"score\": \"0.4966254\",\n \"text\": \"def __str__(self):\\n lines = []\\n sortfunc = lambda t: t[1].priority()\\n for h, e in sorted(self.__entries.items(), key=sortfunc):\\n opts = str(e)\\n if not h == \\\"default\\\":\\n lines.append(\\\"Host %s\\\" % h.partition(\\\"ssh_\\\")[2])\\n opts = \\\"\\\\n\\\".join([\\\" %s\\\" % s for s in opts.split(\\\"\\\\n\\\")])\\n lines.append(opts)\\n return \\\"\\\\n\\\".join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352996bd8c406f008bd904593cc83cbb\",\n \"score\": \"0.49561518\",\n \"text\": \"def test_persist_value_with_spaces_already_configured(tmp_path):\\n config = str(tmp_path / \\\"existing_sysctl_with_spaces.conf\\\")\\n value = \\\"|/usr/share/kdump-tools/dump-core %p %s %t %e\\\"\\n config_file_content = f\\\"kernel.core_pattern = {value}\\\\n\\\"\\n with fopen(config, \\\"w\\\", encoding=\\\"utf-8\\\") as config_file:\\n config_file.write(config_file_content)\\n mock_run = MagicMock(return_value=\\\"\\\")\\n mock_run_all = MagicMock(\\n return_value={\\n \\\"pid\\\": 1337,\\n \\\"retcode\\\": 0,\\n \\\"stderr\\\": \\\"\\\",\\n \\\"stdout\\\": \\\"kernel.core_pattern = \\\" + value,\\n }\\n )\\n which_mock = MagicMock(return_value=\\\"/usr/sbin/sysctl\\\")\\n with patch(\\\"salt.utils.path.which\\\", which_mock):\\n with patch(\\\"os.path.exists\\\", MagicMock(return_value=True)), patch.dict(\\n linux_sysctl.__salt__, {\\\"cmd.run\\\": mock_run, \\\"cmd.run_all\\\": mock_run_all}\\n ):\\n assert (\\n linux_sysctl.persist(\\\"kernel.core_pattern\\\", value, config=config)\\n == \\\"Updated\\\"\\n )\\n mock_run.assert_called_once_with(\\n [\\\"/usr/sbin/sysctl\\\", \\\"-n\\\", \\\"kernel.core_pattern\\\"], python_shell=False\\n )\\n mock_run_all.assert_called_once_with(\\n [\\\"/usr/sbin/sysctl\\\", \\\"-w\\\", \\\"kernel.core_pattern=\\\" + value],\\n python_shell=False,\\n )\\n assert os.path.isfile(config)\\n with fopen(config, encoding=\\\"utf-8\\\") as config_file:\\n written = config_file.read()\\n assert written == config_file_content\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1498,"cells":{"query_id":{"kind":"string","value":"5a170df1f6f4046749fb8246b5d863cb"},"query":{"kind":"string","value":"Run all tasks with the given action."},"positive_passages":{"kind":"list like","value":[{"docid":"2768e253d9a9feb8ff2c752474a9baf9","score":"0.0","text":"def run(self, path):\r\n self._has_run = True\r\n result = None\r\n errors = []\r\n for task in self._tasks:\r\n if not errors or not task.may_run_user_code():\r\n try:\r\n task_result = task.run(path)\r\n except AssertionError, e:\r\n error = str(e)\r\n if not error:\r\n raise RuntimeError(\"Empty error message from %r\" % task)\r\n errors.append(error)\r\n else:\r\n # XXX That's actually a bit weird. What if a call() really\r\n # returned None? This would improperly change the semantic\r\n # of this process without any good reason. Test that with two\r\n # call()s in sequence.\r\n if task_result is not None:\r\n result = task_result\r\n if errors:\r\n message = [str(self.path)]\r\n if str(path) != message[0]:\r\n message.append(\"- Run: %s\" % path)\r\n for error in errors:\r\n lines = error.splitlines()\r\n message.append(\"- \" + lines.pop(0))\r\n message.extend([\" \" + line for line in lines])\r\n raise AssertionError(os.linesep.join(message))\r\n return result","title":""}],"string":"[\n {\n \"docid\": \"2768e253d9a9feb8ff2c752474a9baf9\",\n \"score\": \"0.0\",\n \"text\": \"def run(self, path):\\r\\n self._has_run = True\\r\\n result = None\\r\\n errors = []\\r\\n for task in self._tasks:\\r\\n if not errors or not task.may_run_user_code():\\r\\n try:\\r\\n task_result = task.run(path)\\r\\n except AssertionError, e:\\r\\n error = str(e)\\r\\n if not error:\\r\\n raise RuntimeError(\\\"Empty error message from %r\\\" % task)\\r\\n errors.append(error)\\r\\n else:\\r\\n # XXX That's actually a bit weird. What if a call() really\\r\\n # returned None? This would improperly change the semantic\\r\\n # of this process without any good reason. Test that with two\\r\\n # call()s in sequence.\\r\\n if task_result is not None:\\r\\n result = task_result\\r\\n if errors:\\r\\n message = [str(self.path)]\\r\\n if str(path) != message[0]:\\r\\n message.append(\\\"- Run: %s\\\" % path)\\r\\n for error in errors:\\r\\n lines = error.splitlines()\\r\\n message.append(\\\"- \\\" + lines.pop(0))\\r\\n message.extend([\\\" \\\" + line for line in lines])\\r\\n raise AssertionError(os.linesep.join(message))\\r\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"741c76fa42f3f650741d4694317cd0bb","score":"0.70779276","text":"def do_action(self, action: Action):\n for manager in self.managers:\n manager.on_action(self, action)","title":""},{"docid":"3ab12bb7530d4844d428ed9a7da185d5","score":"0.69594294","text":"def run_hooks(self, action: str, **kwargs) -> None:\n for func in self._hooks[action]:\n func(self, **kwargs)","title":""},{"docid":"a300e9733de8783de1358da06d566b88","score":"0.6761709","text":"def execute_actions(self):\n # execute all actions\n items_to_execute_action = self.sorted_items(self.pokebot.config.mode)\n for item in items_to_execute_action:\n # check if we can execute action\n if item.action.check_action():\n item.try_action()\n # delete item if action is successeded\n del item.data","title":""},{"docid":"cbfd53031fb903373820acf75f6d9646","score":"0.65779597","text":"def do_nodes_tasks_by_action(self, arg):\n if arg:\n argv = arg.split()\n if len(argv) == 1:\n try:\n print()\n pretty_print(\n es.tasks.list(detailed=True, group_by=\"nodes\", actions=argv[0]),\n indent=2,\n )\n print()\n except Exception:\n error()\n else:\n print()\n print(\"Please enter action name.\")\n print()\n else:\n try:\n print()\n pretty_print(es.tasks.list(detailed=True, group_by=\"nodes\"), indent=2)\n print()\n except Exception:\n error()","title":""},{"docid":"3a5cf76a4f916c2b877d401ce94396b9","score":"0.6569431","text":"def run_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):\n policy = self.get_policy()\n results = []\n runner = self.get_runner(policy, kwargs)\n for action_list in self.get_actions(action_name, config_name, instances, map_name, **kwargs):\n try:\n for res in runner.run_actions(action_list):\n results.append(res)\n except ActionException as ae:\n raise ActionRunnerException.from_action_exception(ae, results)\n except:\n exc_info = sys.exc_info()\n raise PartialResultsError(exc_info, results)\n return results","title":""},{"docid":"f2c39ba31b578dc47fb9833bc1452873","score":"0.64825743","text":"def runall(self, changes_dict):\n for action in changes_dict.keys():\n fun = getattr(self, action)\n ok_users, fail_users = fun(changes_dict[action])\n if len(fail_users):\n self._log.error(\"Got error during runall, aborted on action: %s\" % (action,))\n break","title":""},{"docid":"b3dd9744a94434333337e87fc6e32c98","score":"0.64585215","text":"def run(self, action: str, args: list):\n\n worker = ProjectActionWorker(getattr(self.project, action), args, self.logger)\n worker.actionDone.connect(self.stateChanged)\n worker.actionDone.connect(self.stageChanged)\n worker.actionDone.connect(self.actionDone)\n\n self.workers_pool.start(worker) # will automatically place to the queue","title":""},{"docid":"38cc09291115b9dc4316a4d4c721ae15","score":"0.6387707","text":"def run(self, action):\n WSModule.run(self, action)\n self.done = True","title":""},{"docid":"fbd3736190a86066373b627aec2f9516","score":"0.6364364","text":"def run(tasks: List[Task]):\n for task in tasks:\n _run_task(task, tasks)","title":""},{"docid":"7ac50137a3421803d69046a539a5b891","score":"0.63419443","text":"async def run_all(self) -> None:\n await gather(*[task.run() for task in self._schedule])","title":""},{"docid":"2049241c9e542a697c73c03a4b955d3c","score":"0.63146615","text":"def perform_action(self, action):\n Operator.perform_action(action)","title":""},{"docid":"6e1fa7b473f24b15d4309473ccdcf208","score":"0.6278072","text":"def _action_loop(self):\n while not self.stop_action:\n for action in self.get_actions():\n if not self.stop_action:\n try:\n action()\n except Exception:\n self.logger.exception('Error in ProcessController action_loop: ')\n if not self.stop_action:\n time.sleep(self.action_loop_interval)","title":""},{"docid":"5185466a60ab1480a20ddb3c67ca9792","score":"0.62277395","text":"def doAction(self, action):\n pass","title":""},{"docid":"d05b75bb46d9fe82ff1f09fed52dd62d","score":"0.62233424","text":"def loop(action: any):\n # Execute the action, every action has to return an answer\n answer = action()\n\n # Map the next action\n action_next = map_action(answer[\"action\"])\n\n # Recursive call\n loop(action_next)","title":""},{"docid":"0b4ea538c07586d81091dd71fe84df9e","score":"0.61468273","text":"def step(self, action):\n # exec action's function\n self._act(*action)","title":""},{"docid":"76737006d67e6e11a9d66a36f64d5a89","score":"0.6119997","text":"def performAction(self, action):\n self._lastAction = []\n\n # Markups chosen for each generator.\n actions = self._allActions[action]\n\n n = self.numOffbids * len(self.generators)\n markups = actions[:n]\n withholds = actions[n:]\n\n# print \"ALL ACTIONS:\", self._allActions\n# print \"ACTIONS:\", markups, withholds\n\n self._offbid(markups, withholds)","title":""},{"docid":"6571538722924a1d68fb1dc9d0852a45","score":"0.60859454","text":"def _process_list(self, action: List[tuple]):\n for sub_action in action:\n self._process_instruction(sub_action)","title":""},{"docid":"bf7d362186cf4d8a5374d41c1f46b8e3","score":"0.60499614","text":"def run(self,action):\n script = str(action.text())\n if script in self.files:\n self.runScript(script)","title":""},{"docid":"1a98c2af944bbb459c617e3d55c9c70b","score":"0.5992066","text":"def process_action(self, action):\n #TODO defer\n pass","title":""},{"docid":"d1a813068509617b6e22a812146a943e","score":"0.59797025","text":"def tasks_action(request, action):\n empty_response = HttpResponse('')\n\n if request.method != 'POST' or not (action in supported_actions):\n return empty_response\n\n # TODO: return comprehensible response anytime\n # TODO: rewrite with django auth system\n #if not request.user.groups.filter(name='vomsrole:/atlas/Role=production'):\n # return empty_response\n\n owner = request.user.username\n if not owner:\n return empty_response\n\n data_json = request.body\n if not data_json:\n return empty_response\n data = json.loads(data_json)\n\n tasks = data.get(\"tasks\")\n if not tasks:\n return empty_response\n\n params = data.get(\"parameters\", [])\n response = do_tasks_action(owner, tasks, action, *params)\n return HttpResponse(json.dumps(response))","title":""},{"docid":"a9796e63f27732498be57a548aa6a6bb","score":"0.5973363","text":"def process_action():\n for q in QUEUE:\n actions = {\n \"list\": q.list,\n \"update\": q.update,\n \"dirty\": q.dirty,\n \"verbose\": q.verbose,\n \"remotes\": q.remotes,\n \"fetch\": q.fetch,\n \"move2opt\": q.move2opt,\n \"move2start\": q.move2start,\n \"git\": q.git,\n \"exe\": q.exe,\n \"log\": q.git,\n \"graph\": q.git,\n \"web\": q.web,\n \"helptags\": q.helptags,\n \"backup\": q.backup,\n \"restore\": q.restore,\n \"terminal\": q.terminal,\n \"logs\": q.logs,\n \"errors\": q.logs,\n \"lastdiff\": q.lastdiff,\n }\n log(actions[ACTION]())\n\n # packages may have been queued for async processing\n process_asynch()\n\n # if updating packages, write a log file\n if LOGFILE:\n write_logfiles()","title":""},{"docid":"33639ddf4bbf91897648bfb0b8c4abf2","score":"0.5962084","text":"def perform_input_actions(self):\n while not self.action_queue.empty():\n callback = self.action_queue.get()\n callback()","title":""},{"docid":"927bb8a882eeac19778287f83d52a5c5","score":"0.5954346","text":"def run_all_tasks(data_directory):\n print(\"Training and testing for all tasks ...\")\n for t in range(20):\n run_task(data_directory, task_id=t + 1)","title":""},{"docid":"5d004f49484fdb0d60ce6e0ca1f5a86f","score":"0.5908969","text":"async def _run_action(self):\n if not self._url_map:\n logger.warning(\"No URLs to check!\")\n return\n logger.info(\"Starting %s\", self.__class__.__name__)\n await self._start_kafka_producer()\n async with aiohttp.ClientSession(headers=self._headers) as session:\n self._session = session\n await self._process_urls()\n logger.debug(\"Waiting until all worker tasks are cancelled\")\n logger.info(\"Produced %d results\", self._counter)\n await asyncio.gather(*self._tasks, return_exceptions=True)\n logger.debug(\"Stopping Kafka producer\")\n await self._kafka_producer.stop()","title":""},{"docid":"fde4db385561da8cd8d330fabce4b706","score":"0.5874224","text":"def do(self, action):\n raise NotImplementedError()","title":""},{"docid":"058f9030bb2b497f9728319ae54c8dc3","score":"0.5866326","text":"def _call_action(self, key: str):\n cache = set()\n if self._action[key]:\n for index, (action, id_) in enumerate(self._action[key]):\n if id_ not in cache:\n try:\n action(self)\n cache.add(id_)\n # uses set to make it faster (hopefully)\n except TimeoutError:\n # print(f\"action {action} timeout, removed\"\n self._action[key].pop(index)","title":""},{"docid":"416ea77a9470069e729563ff71e7a70d","score":"0.5797252","text":"def _execute_action(self):\n if self._current_action_item != None:\n self.do_trigger_action(self._current_action_item)\n self._last_action_item = self._current_action_item\n self._current_action_item = None\n self.update()\n return","title":""},{"docid":"c0fb33cb1084e487e0065cf525180e1d","score":"0.57941335","text":"def sync_process(self, action):\n actions = self.pure(action)\n for middleware in self.middlewares:\n actions = self.bind(middleware, self, actions)\n for _action in actions:\n self.state = self.reducer(self.state, _action)\n self.in_progress = True\n self.notify(self.state)\n self.in_progress = False","title":""},{"docid":"eba9c5087c5585e78328357db38cc99c","score":"0.5779899","text":"def performAction(self, action):\n raise NotImplementedError('subclasses must override performAction()!')","title":""},{"docid":"4ef9fc456104380fa8e39e14d5612123","score":"0.57568955","text":"def _register_all_actions(self) -> None:\n import inspect\n\n actions = utils.all_subclasses(Action)\n\n for action in actions:\n if (\n not action.__module__.startswith(\"rasa_core.\")\n and not action.__module__.startswith(\"rasa.\")\n and not action.__module__.startswith(\"rasa_sdk.\")\n and not action.__module__.startswith(\"rasa_core_sdk.\")\n and not inspect.isabstract(action)\n ):\n self.register_action(action)","title":""},{"docid":"40dae63b15b629cb6db2264b7f300473","score":"0.575349","text":"def RunTasks(self):\n starttime = time.time()\n self.LogGameState()\n logging.info('Running all tasks...')\n map(lambda x: x(), [self.OfferTask, self.AcceptTask, self.ReofferTask,\n self.ProvideTask, self.SolveTask])\n logging.info('Ending Balance: $%0.4f' % self.context.endbalance)\n endtime = time.time()\n logging.info('Gameplay took %s' % (endtime - starttime))","title":""},{"docid":"629194bf3d22b95dc038247691e3b810","score":"0.5747402","text":"def step_async(self, actions):\n pass","title":""},{"docid":"629194bf3d22b95dc038247691e3b810","score":"0.5747402","text":"def step_async(self, actions):\n pass","title":""},{"docid":"629194bf3d22b95dc038247691e3b810","score":"0.5747402","text":"def step_async(self, actions):\n pass","title":""},{"docid":"4ff0c16e67cb67a264a75803799eae3a","score":"0.57445127","text":"def process_action_list(actions, clock_step_delay):\n\n def get_delay(a):\n if not hasattr(a, 'delay'):\n return getattr(a, 'time', 0)\n elif a.delay is not None:\n if type(a.delay) == dict:\n return 0\n else:\n return a.delay\n else:\n return clock_step_delay\n\n background_pool = ThreadPool(0)\n new_action_list = []\n for a in actions:\n delay = get_delay(a)\n new_action_list += background_pool.process(a, delay)\n return new_action_list","title":""},{"docid":"144a1b0c21cd8a10558891cc8d34c670","score":"0.5724031","text":"def run_action(self, action_name, *args, **kwargs):\n try:\n action = getattr(self, 'action_{}'.format(action_name))\n\n return action(*args, **kwargs)\n except KeyError:\n raise Exception('Unknown action: {}'.format(action_name))","title":""},{"docid":"144a1b0c21cd8a10558891cc8d34c670","score":"0.5724031","text":"def run_action(self, action_name, *args, **kwargs):\n try:\n action = getattr(self, 'action_{}'.format(action_name))\n\n return action(*args, **kwargs)\n except KeyError:\n raise Exception('Unknown action: {}'.format(action_name))","title":""},{"docid":"e40e61f049e950d697bb4326ba3796f3","score":"0.5722171","text":"def performAction(self, action):\n # scaling\n self.incStep()\n action = (action + 1.0) / 2.0 * self.dif + self.env.fraktMin * self.env.dists[0]\n #Clipping the maximal change in actions (max force clipping)\n action = clip(action, self.action - self.maxSpeed, self.action + self.maxSpeed)\n EpisodicTask.performAction(self, action)\n self.action = action.copy()","title":""},{"docid":"4332969cf7dc30907cf9c067cbe565aa","score":"0.5711305","text":"def do_action(name, *args):\n for _, hook in _HOOKS_ACTIONS[name]:\n hook(*args)","title":""},{"docid":"5ef2ee3c43f09cb978c7142979d16f7a","score":"0.57022667","text":"def _execute_action(self, action):\n action_type = action[\"action\"]\n #log.debug(\"Preload action %s id=%s\", action_type, action.get(\"id\", \"\"))\n scope, func_type = action_type.split(\":\", 1)\n default_funcname = \"_load_%s_%s\" % (scope, func_type)\n action_func = getattr(self, default_funcname, None)\n if not action_func:\n action_funcname = self.preload_cfg[\"action_plugins\"].get(action_type, {})\n if not action_funcname:\n log.warn(\"Unknown action: %s\", action_type)\n return\n action_func = getattr(self, action_funcname, None)\n if not action_func:\n log.warn(\"Action function %s not found for action %s\", action_funcname, action_type)\n return\n action_func(action)","title":""},{"docid":"d902416627fa3e0e566b29d4131050b9","score":"0.57017106","text":"def call(self, action_name, container, instances=None, map_name=None, **kwargs):\n return self.run_actions(action_name, container, instances=instances, map_name=map_name, **kwargs)","title":""},{"docid":"aceea536c61e311b276f37603477706c","score":"0.56802726","text":"async def _chan_action(self, action: str,\n guild_id: int, user_id: int):\n chan_ids = await self.app.storage.get_channel_ids(guild_id)\n\n for chan_id in chan_ids:\n\n # only do an action for users that can\n # actually read the channel to start with.\n chan_perms = await get_permissions(\n user_id, chan_id,\n storage=self.main_dispatcher.app.storage)\n\n if not chan_perms.bits.read_messages:\n log.debug('skipping cid={}, no read messages',\n chan_id)\n continue\n\n log.debug('sending raw action {!r} to chan={}',\n action, chan_id)\n\n await self.main_dispatcher.action(\n 'channel', action, chan_id, user_id\n )","title":""},{"docid":"9d27c6d0f66ddeb1df3ddba723601f8a","score":"0.5675175","text":"def trigger_all(self):\n for func in self.schedules:\n self.trigger(func)","title":""},{"docid":"0e0f824fd3d31883b1313e263db2fe47","score":"0.5661001","text":"def executeActions(self):\n\t\tnumbersactions = len(self.list_action)\n\t\tcounter = 1\n\t\tfor listalbum in self.list_action:\n\t\t\tself.signalrun.emit((counter / numbersactions) * 100, listalbum[2])\n\t\t\tmessage = \"\\n- {act} ({num}/{tot}) : {nam}\".format(act = listalbum[2],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnum = counter,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttot = numbersactions, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnam = listalbum[5])\n\t\t\tself.signaltxt.emit(message, 1)\n\t\t\tif listalbum[2] == 'DELETE':\n\t\t\t\tself.deleteAlbum(listalbum[3])\n\t\t\telif listalbum[2] == 'UPDATE':\n\t\t\t\t#print('UPDATE', listalbum[0], listalbum[1], listalbum[5], listalbum[3])\n\t\t\t\tself.updateAlbum(listalbum[0], listalbum[1], listalbum[5], listalbum[3])\n\t\t\telif listalbum[2] == 'ADD':\n\t\t\t\t#print('ADD', listalbum[0], listalbum[1], listalbum[5], listalbum[5])\n\t\t\t\tself.addAlbum(listalbum[0], listalbum[1], listalbum[5])\n\t\t\telse:\n\t\t\t\tself.signaltxt.emit('ERROR : Operation \"' + listalbum[2] + '\" error', 3)\n\t\t\tcounter += 1 \n\t\t#QApplication.processEvents()\n\t\tself.signalrun.emit(100, 'Completed')\n\t\tself.signalend.emit()","title":""},{"docid":"4cbd7a6834af85ab7a209ff1074f5a5b","score":"0.5660399","text":"def PerformUIActionStrategy(self, addr, action):\n return self.__uiActionStrategy.Perform(action)","title":""},{"docid":"13bfa86cbfc703b3681c928bc7c5c8dc","score":"0.5660297","text":"def dispatch(self, action):\n if self.in_progress:\n # Add asynchronous action to backlog\n self.queue.put(action)\n return\n\n # Synchronous processing\n self.sync_process(action)\n\n # Process backlog\n while not self.queue.empty():\n next_action = self.queue.get()\n self.sync_process(next_action)\n self.queue.task_done()","title":""},{"docid":"e04555d341261e91d4a1411e28a593c2","score":"0.56542945","text":"async def call_action(self, action: str, **params) -> Optional[Dict[str, Any]]:\n pass","title":""},{"docid":"3c8e3748b7bf3bf1727a2d54aa7c2268","score":"0.5642351","text":"def all_actions(cls, logger=None, data=None):\n packages_info = cls.load_all_data(data=data, logger=logger)\n installers = cls.create_installers(packages_info=packages_info, logger=logger)\n cls.run_installers(installers=installers, logger=logger)","title":""},{"docid":"0576027e7acc2d305bc27990c31199be","score":"0.5630087","text":"async def async_reaper_run_action(self, action_id: str) -> None:\n await self.coordinator.reaperdaw.sendCommand(action_id)\n await self.coordinator.async_refresh()","title":""},{"docid":"4a19042124be0e5500cee7063106c07a","score":"0.5626104","text":"def fire(self, action, args=None):\n if action not in self.actions:\n if self.parent is not None:\n self.parent.fire(action, args)\n else:\n raise ActionNotBoundError(action + ' not bound to any '\n 'callbacks.')\n\n for callback in self.actions[action]:\n callback(args)","title":""},{"docid":"4fe8025e1c51e6c2c8e9eaa8b21fb7ad","score":"0.561324","text":"def _do(self, action):\n next_state = self.state.do(action)\n if next_state:\n self._change_state(next_state)","title":""},{"docid":"c71f3039df0448732f91cd6a5a570e3a","score":"0.5610245","text":"def perform_all_tasks(self):\n\n # Reset self.msg as it is starting a new process here\n self.msg = '*** {0} is performing all required tasks, please standby. ***\\n'.format(self)\n\n # Utlize a while loop to safely pop from list while iterating\n while self.tasks_to_perform:\n task = self.tasks_to_perform.pop(0)\n self.perform_task(task)\n\n print(\"\\n*** {0} has completed all inital tasks! ***\\n\".format(self))","title":""},{"docid":"26ae44292b561b9e9af9a2758db181e9","score":"0.5600253","text":"def execute(self, *args, **kwargs):\n for action in self.actions:\n result = action.execute(*args, **kwargs)\n if result:\n self.state.update(result)\n\n return self.transition()","title":""},{"docid":"7198da5a449f16fc6abbd8caf070d021","score":"0.5589689","text":"def PerformAIActionStrategy(self, addr, action):\n return self.__agentActionStrategy.Perform(action)","title":""},{"docid":"3a1ad289143fbeb7d4466e98b4269efd","score":"0.55783844","text":"def get_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):\n policy = self.get_policy()\n action_generator = self.get_action_generator(action_name, policy, kwargs)\n for state in self.get_states(action_name, config_name, instances=instances, map_name=map_name, **kwargs):\n log.debug(\"Evaluating state: %s.\", state)\n actions = action_generator.get_state_actions(state, **kwargs)\n if actions:\n log.debug(\"Running actions: %s\", actions)\n yield actions\n else:\n log.debug(\"No actions returned.\")","title":""},{"docid":"f53c3b520840d14cee51db2bf262ed2c","score":"0.55736154","text":"def ExecuteTestRunActions(self, request):\n test_run = ndb_models.TestRun.get_by_id(request.test_run_id)\n if not test_run:\n raise endpoints.NotFoundException('no test run found for ID %s' %\n request.test_run_id)\n action_refs = mtt_messages.ConvertList(request.refs,\n ndb_models.TestRunActionRef)\n actions = [ref.ToAction() for ref in action_refs]\n test_run_hook.ExecuteHooks(\n request.test_run_id,\n ndb_models.TestRunPhase.MANUAL,\n test_run_actions=actions)\n return message_types.VoidMessage()","title":""},{"docid":"a114cbec749b26f425bd1f74853d8b8c","score":"0.5573016","text":"def action(self, action):\n #self._act = lambda x: action(*x)\n super().action(lambda x: action(*x))","title":""},{"docid":"31d89f26eac2d57cbfb85776d216fa78","score":"0.554585","text":"def do_action(self, state, action):\r\n pass","title":""},{"docid":"a41a323cf3a77f6b194c61840ca24576","score":"0.55441785","text":"def actions(self, actions):\n\n self._actions = actions","title":""},{"docid":"a41a323cf3a77f6b194c61840ca24576","score":"0.55441785","text":"def actions(self, actions):\n\n self._actions = actions","title":""},{"docid":"92de7eb64d272e5064c7d3675b2c56b3","score":"0.5544089","text":"def do(self, action):\n try:\n # Do the action\n return self.actions[action]()\n except KeyError:\n # Action not supported\n return None\n except TypeError:\n # State change not allowed\n return None","title":""},{"docid":"e7c497f5aa72f7612eeee39214f2d0ea","score":"0.554174","text":"def _take_action(self, action):\n pass","title":""},{"docid":"00f7d63bbbbedeb80d7d060626e410ed","score":"0.55393153","text":"def perform_task(self, session):\n cl = self.read_all_categories(session)\n tm = categorymgr.TallyMan()\n if self._redis_conn is not None:\n tm._redis_host = self._redis_host\n tm._redis_port = self._redis_port\n tm._redis_conn = self._redis_conn\n\n for c in cl:\n self.all_photos_by_category(session, tm, c)","title":""},{"docid":"8d35b3940b6fa2b1e2b43c2fc3d3b3e6","score":"0.5532784","text":"def step(self, action):\n if self.int_actions:\n # If actions are taken as int, convert them to the correct format\n action = self._reversed_actions_mapping[action] \n self._act(*action)","title":""},{"docid":"440b288e7a9a7fc5dd1b80c7e02083e5","score":"0.5518246","text":"def step(self):\n # Pick up earliest action\n action, timestamp = min(self.actions.items(), key=operator.itemgetter(1))\n del self.actions[action]\n logging.info(\"Next action at %s: %s(%s).\", self.strftime(timestamp), action.action_type.name, action.argument)\n # Sleep.\n sleep_length = (timestamp - datetime.now()).total_seconds()\n if sleep_length > 0.0:\n time.sleep(sleep_length)\n # Perform the action.\n if action.action_type == ActionType.sync:\n self.sync()\n elif action.action_type == ActionType.collect_resource:\n self.collect_resource(action.argument)\n elif action.action_type == ActionType.check_alliance_help:\n self.check_alliance_help()\n elif action.action_type == ActionType.send_gifts:\n self.send_gifts()\n elif action.action_type == ActionType.farm_gifts:\n self.farm_gifts()","title":""},{"docid":"0f5eb5402150394539f26cd4e281fa50","score":"0.5503614","text":"def choose_action(action_id):\n if action_id == 1:\n today_list()\n elif action_id == 2:\n week_list()\n elif action_id == 3:\n all_tasks_list()\n elif action_id == 4:\n missed_list()\n elif action_id == 5:\n add_task()\n elif action_id == 6:\n delete_task()","title":""},{"docid":"d038edcaf7bce4310f5fbd32b69bfda2","score":"0.54804343","text":"def take_action(self, action):\n return self.model.take_action(action)","title":""},{"docid":"e3887799ebf9f098b4461dbf33edec41","score":"0.54617685","text":"def run_actions(\n installation_id,\n installation_token,\n event_type,\n data,\n pull,\n match,\n checks,\n previous_conclusions,\n):\n\n actions_ran = set()\n conclusions = {}\n # Run actions\n for rule, missing_conditions in match.matching_rules:\n for action in rule[\"actions\"]:\n check_name = \"Rule: %s (%s)\" % (rule[\"name\"], action)\n\n previous_conclusion = get_previous_conclusion(\n previous_conclusions, check_name, checks\n )\n\n done_by_another_action = (\n rule[\"actions\"][action].only_once and action in actions_ran\n )\n\n if missing_conditions:\n method_name = \"cancel\"\n expected_conclusions = [\"neutral\", \"cancelled\"]\n else:\n method_name = \"run\"\n expected_conclusions = [\"success\", \"failure\"]\n actions_ran.add(action)\n\n # TODO(sileht): Backward compatibility, drop this in 2 months (February 2020)\n if (\n previous_conclusions.get(\"deprecated_summary\", False)\n and action == \"comment\"\n ):\n deprecated_done_in_the_past = rule[\"actions\"][\n action\n ].deprecated_already_done_protection(pull)\n else:\n deprecated_done_in_the_past = None\n\n done_in_the_past = not rule[\"actions\"][action].always_run and (\n previous_conclusion in expected_conclusions\n or deprecated_done_in_the_past\n )\n\n if done_in_the_past:\n report = None\n message = \"ignored, already in expected state: %s/%s\" % (\n method_name,\n previous_conclusion,\n )\n\n elif done_by_another_action:\n # NOTE(sileht) We can't run two action merge for example,\n # This assumes the action produce a report\n report = (\"success\", \"Another %s action already ran\" % action, \"\")\n message = \"ignored, another action `%s` has already been run\" % action\n\n else:\n # NOTE(sileht): check state change so we have to run \"run\" or \"cancel\"\n report = exec_action(\n method_name,\n rule,\n action,\n installation_id,\n installation_token,\n event_type,\n data,\n pull,\n missing_conditions,\n )\n message = \"`%s` executed\" % method_name\n\n if report and report[0] is not None and method_name == \"run\":\n statsd.increment(\"engine.actions.count\", tags=[\"name:%s\" % action])\n\n if report:\n conclusion, title, summary = report\n status = \"completed\" if conclusion else \"in_progress\"\n try:\n check_api.set_check_run(\n pull.g_pull,\n check_name,\n status,\n conclusion,\n output={\"title\": title, \"summary\": summary},\n )\n except Exception:\n pull.log.error(\"Fail to post check `%s`\", check_name, exc_info=True)\n conclusions[check_name] = conclusion\n else:\n # NOTE(sileht): action doesn't have report (eg:\n # comment/request_reviews/..) So just assume it succeed\n conclusions[check_name] = expected_conclusions[0]\n\n pull.log.info(\n \"action evaluation: %s\",\n message,\n report=report,\n previous_conclusion=previous_conclusion,\n conclusion=conclusions[check_name],\n check_name=check_name,\n missing_conditions=missing_conditions,\n event_type=event_type,\n )\n\n return conclusions","title":""},{"docid":"33c2b4311851e27ba72080176a0aab55","score":"0.54610187","text":"def run_action(cmd):\n # set the url include action information\n url = BASE_URL + 'run/?action=' + cmd\n print(f'url: {url}')\n # post request with url\n __request__(url)","title":""},{"docid":"368ab4fc33b0051776380cdacf66bc80","score":"0.5460237","text":"def step(self, actions):\n self.step_async(actions)\n return self.step_wait()","title":""},{"docid":"b969ce4fed34b83fe4de767551959109","score":"0.543787","text":"def perform_action(self):\n pass","title":""},{"docid":"b912c2eb44740517a00bd69dbefd8bf7","score":"0.5429898","text":"def run(self):\n\t\tfor time_step in range(self.num_time_steps):\n\t\t\t# flow during single time step\n\t\t\tactions = self.population.draw_actions()\n\n\t\t\tutilities = self.environment.get_utilities(actions)\n\n\t\t\tself.population.select(utilities)\n\n\t\t\tself.population.reproduce(utilities)","title":""},{"docid":"8bc3f18c2c3191a2aa4b07d2d8ce73f5","score":"0.54282385","text":"def start_action(self, evidence, helper, request, action_id):\n index = helper.get_request_value(request, 'index', False)\n size = int(helper.get_request_value(request, 'size', self._max_size))\n sort = helper.get_request_value(request, 'sort', False)\n order = helper.get_request_value(request, 'order', 'asc')\n all = bool(helper.get_request_value(request, 'all', False))\n check = bool(helper.get_request_value(request, 'check', True))\n action_lock = threading.Lock()\n queue = []\n\n if not index:\n self._actions[action_id]['status'] = \"Action plugin requires an index, but none found\"\n\n # Get Plugin Object\n plugin = helper.plugin_manager.get_plugin_by_name(str(self._actions[action_id]['plugin']).lower())\n if not plugin:\n self._actions[action_id]['status'] = \"Failed: Could not find plugin \" \\\n + str(self._actions[action_id]['plugin']).lower()\n\n # Get Events\n query_body = helper.get_filters(request, [], [])\n if sort and not all:\n query_body['sort'] = {sort: order}\n\n if size > self._max_size or all:\n query_body['size'] = 0\n temp_events = [helper.db_util.elasticsearch.search(index=index, doc_type='plaso_event', body=query_body)]\n self._actions[action_id]['total'] = temp_events[0]['hits']['total']\n del query_body['size']\n events = elasticsearch.helpers.scan(helper.db_util.elasticsearch, query_body,\n scroll=u'240m', size=self._max_size)\n for item in events:\n with action_lock:\n self._actions[action_id]['status'] = 'active'\n\n queue.append(item)\n if len(queue) >= self._max_queue_size:\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id,\n plugin, index, check,\n self._actions, action_lock))\n queue = []\n else:\n query_body['size'] = size\n events = [helper.db_util.elasticsearch.search(index=index, doc_type='plaso_event', body=query_body)]\n self._actions[action_id]['total'] = min(events[0]['hits']['total'], int(size))\n\n for event in events:\n with action_lock:\n self._actions[action_id]['status'] = 'active'\n\n # Run Events\n for item in event['hits']['hits']:\n queue.append(item)\n if len(queue) == self._max_queue_size:\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id,\n plugin, index, check,\n self._actions, action_lock))\n queue = []\n\n if len(queue) > 0:\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id, plugin,\n index, check, self._actions, action_lock))","title":""},{"docid":"8431090c3f928071efb867deef0bd379","score":"0.54272443","text":"def execute_all_operations(self):\n for operation in self._operation_queue:\n operation.execute()\n\n self._operation_queue = []","title":""},{"docid":"a61653895bea3e70de3448685925ba54","score":"0.5417552","text":"def notify_all(self, action, args=''):\n for client_id in self.clients:\n client = self.clients[client_id]\n self.send(client, client_id, action, args)","title":""},{"docid":"81152d6ea37f7724dc04009fa3b4facf","score":"0.54091567","text":"def execute_instructions():\n if is_stitch:\n self._process_stitch(action)\n elif is_cable:\n self._process_cable(action)\n elif is_list:\n self._process_list(action)\n else:\n self._process_instruction(action)","title":""},{"docid":"bd84a58fe25d6b5218344650732e19ce","score":"0.5402685","text":"def dispatch(self, action: Action) -> None:\n return self._action_dispatcher.dispatch(action)","title":""},{"docid":"94405e57b9337e5cfb7e3475b403fccb","score":"0.53948057","text":"def performAction(self, action):\n gs = [g for g in self.case.online_generators if g.bus.type !=REFERENCE]\n\n assert len(action) == len(gs)\n\n logger.info(\"Action: %s\" % list(action))\n\n # Set the output of each (non-reference) generator.\n for i, g in enumerate(gs):\n g.p = action[i]\n\n # Compute power flows and slack generator set-point.\n NewtonPF(self.case, verbose=False).solve()\n #FastDecoupledPF(self.case, verbose=False).solve()\n\n # Store all generator set-points (only used for plotting).\n self._Pg[:, self._step] = [g.p for g in self.case.online_generators]\n\n # Apply the next load profile value to the original demand at each bus.\n if self._step != len(self.profile) - 1:\n pq_buses = [b for b in self.case.buses if b.type == PQ]\n for i, b in enumerate(pq_buses):\n b.p_demand = self._Pd0[i] * self.profile[self._step + 1]\n\n self._step += 1\n\n logger.info(\"Entering step %d.\" % self._step)","title":""},{"docid":"36aabf8d70083881361e7f18c062a28f","score":"0.53830296","text":"def _apply_rl_actions(self, rl_actions):\n # flag to activate sumo actuate baselines or not to\n if self.benchmark_params.sumo_actuated_baseline:\n\n # check file to track number of simulations completed during training.\n if not os.path.isfile(self.benchmark_params.full_path):\n return\n else:\n # read csv file if it exists\n df = pd.read_csv(self.benchmark_params.full_path, index_col=False)\n n_iter = df.training_iteration.iat[-1]\n\n if n_iter < self.benchmark_params.sumo_actuated_simulations:\n return\n\n rl_ids = np.arange(self.num_traffic_lights)\n actions = self.action_dict[rl_actions]\n\n for i, rl_action in zip(rl_ids, actions):\n execute_action(self, i, rl_action)","title":""},{"docid":"f0a78872763b4e64c1924ef9ef6dcabf","score":"0.53788704","text":"def run_action(socketio, msg):\n if session.state != session.STATE_IDLE:\n send_error_message(socketio, 'CANNOT RUN ACTION, REQUEST ALREADY IN PLACE')\n return\n\n sync_system_state(socketio, session.STATE_RUN_PROCESS)\n\n try:\n ## Standardized calibration sequences. Functions are defined in action.py\n if msg['id'] == 'run-std-calibration':\n run_standard_calibration(socketio, msg['data'])\n elif msg['id'] == 'run-system-calibration':\n run_system_calibration(socketio, msg['data'])\n elif msg['id'].endswith('calibration-signoff'):\n run_calibration_signoff(socketio, msg['data'],\n msg['id'].startswith('system'))\n elif msg['id'] == 'rerun-single':\n run_process_extend(socketio, msg['data'])\n elif msg['id'] == 'image-settings':\n run_image_settings(socketio, msg['data'])\n elif msg['id'] == 'zscan-settings':\n run_zscan_settings(socketio, msg['data'])\n elif msg['id'] == 'lowlight-settings':\n run_lowlight_settings(socketio, msg['data'])\n elif msg['id'] == 'lumialign-settings':\n run_lumialign_settings(socketio, msg['data'])\n elif msg['id'] == 'picoscope-settings':\n run_picoscope_settings(socketio, msg['data'])\n elif msg['id'] == 'drs-settings':\n run_drs_settings(socketio, msg['data'])\n elif msg['id'] == 'drs-calib':\n run_drs_calib(socketio) ## Data less command.\n else:\n send_error_message(socketio, f'Unsupported action item {msg[\"id\"]}')\n print(msg)\n time.sleep(5)\n except Exception as e:\n send_error_message(socketio, str(e))\n\n sync_system_state(socketio, session.STATE_IDLE)","title":""},{"docid":"b1e2da4d3726c1f8f72efc02ea34674a","score":"0.5368779","text":"def _action_process(items, helper, request, action_id, plugin, index, check, _actions, action_lock):\n for item in items:\n try:\n source = item['_source']\n if 'pathspec' not in source:\n with action_lock:\n _actions[action_id]['fail'] += 1\n _actions[action_id]['errors'] += source['pathspec'] + '\\nNot found in Elasticsearch'\n else:\n efetch_dictionary = helper.pathspec_helper.\\\n get_evidence_item(source['pathspec'], index, plugin.cache,\n hasattr(plugin, 'fast') and plugin.fast)\n efetch_dictionary['_id'] = item['_id']\n efetch_dictionary['doc_type'] = item['_type']\n if not check or plugin.check(efetch_dictionary, efetch_dictionary['file_cache_path']):\n plugin.get(efetch_dictionary, helper, efetch_dictionary['file_cache_path'], request)\n with action_lock:\n _actions[action_id]['success'] += 1\n except Exception as e:\n with action_lock:\n _actions[action_id]['fail'] += 1\n _actions[action_id]['error'] += source['pathspec'] + '\\n' +\\\n e.message + '\\n' + traceback.format_exc() + '\\n\\n'\n finally:\n with action_lock:\n _actions[action_id]['current'] += 1\n _actions[action_id]['percent'] = \"{0:.0f}%\".format(_actions[action_id]['current'] /\n _actions[action_id]['total'] * 100)\n if _actions[action_id]['current'] == _actions[action_id]['total']:\n _actions[action_id]['status'] = 'done'","title":""},{"docid":"f78195394a32380ca8401773245dcc5c","score":"0.5367644","text":"def __call__(self):\n for test in self.tests:\n self.run_test(test)","title":""},{"docid":"c874ddc9cc35915aa813abe911e71cf4","score":"0.5364056","text":"def perform_action(action_id):\n RPR.Main_OnCommand(action_id, 0)","title":""},{"docid":"e775327975dda7610506ee71e84c59bf","score":"0.53569466","text":"def __call__(self):\n for job in self._jobs:\n job()","title":""},{"docid":"e2d76cd653a230118410abc31365c478","score":"0.5339642","text":"def run(self,*p,**k):\n for url in self.urls:\n self._run_one(url,*p,**k)","title":""},{"docid":"acd0d3533f9359527b9ea28d3a540572","score":"0.53329337","text":"def act_python_shell(self, action):\n self.call('execute_shell')","title":""},{"docid":"5e3b1e175b9db9350b16c75b1bbf73d9","score":"0.53304577","text":"def execute(self, nabuAction):\n return","title":""},{"docid":"c88cd342a4d23859f8cfbd6fb85317db","score":"0.53267777","text":"def step_sim(self, action):\n\n return self.loop.run_until_complete(self._step_sim(action))","title":""},{"docid":"eb164af3746c58342cd46c1b25586644","score":"0.53171074","text":"def run(self) -> None:\n if self.actions.switch.required:\n self.actions.switch()\n\n self.actions.update()\n\n if self.actions.commit.required:\n self.actions.commit()\n\n if self.actions.rollback.required:\n self.actions.rollback()\n return\n\n if self.actions.push.required:\n self.actions.push()\n\n if self.actions.pull_request.required:\n self.actions.pull_request()","title":""},{"docid":"c2b3065fb675549f220bdc8b23541675","score":"0.53150105","text":"def run_action(self, delta_time):\r\n #print(88, self._waiting_action, self._action)\r\n action_result = [0]*5\r\n if self._waiting_action is not None:\r\n # If a new action is waiting to be executed, interrupt any current actions and start execution of\r\n # the new one\r\n if self._action is not None:\r\n self._action.end(CommandStatus.INTERRUPTED)\r\n\r\n self._action = self._waiting_action\r\n self._waiting_action = None\r\n self._action.start()\r\n\r\n if self._action is not None:\r\n # Update the current action if valid\r\n status = self._action.get_status()\r\n\r\n if status == CommandStatus.RUNNING:\r\n action_result = self._action.run(delta_time)\r\n else:\r\n self._action.end(status)\r\n self._action = None\r\n\r\n if self._action is None and self._waiting_action is None and self._default_action is not None:\r\n # If there is not action running and no actions are waiting, run the default action\r\n self._action = self._default_action\r\n self._action.start()\r\n \r\n return action_result","title":""},{"docid":"d4866ee9deb6ac2f721ea248a5670d96","score":"0.5306498","text":"def setAction(self, action):\n self.actions_queue.put(action)","title":""},{"docid":"d4866ee9deb6ac2f721ea248a5670d96","score":"0.5306498","text":"def setAction(self, action):\n self.actions_queue.put(action)","title":""},{"docid":"5b6fea86be9356a98b8004f4d16af0c3","score":"0.5300462","text":"def doAction(self):\r\n pass","title":""},{"docid":"b7f7726025ee5673786c9b598392e79c","score":"0.5295762","text":"def pre_action(self, action):\n if not hasattr(self, action):\n return\n\n resources = [r for r in self.rset.resources if \\\n not r.skip and not r.is_disabled() and \\\n r.type == self.type]\n\n for resource in sorted(resources):\n for dataset in resource.dataset:\n resource._sync_update(dataset)","title":""},{"docid":"1b4c993fe7cb367518e78690b10853a0","score":"0.52923036","text":"def _execute(self, method, **kwargs):\n assert(self.group_name in self.hydra.app_group)\n tasks_list = self.tasks_list\n for task_id in tasks_list:\n info = self.hydra.apps[self.app_name]['ip_port_map'][task_id]\n port = info[0]\n ip = info[1]\n ha = self.analyser(ip, port, task_id)\n l.debug(\"ip:port %s:%s\", ip, str(port))\n assert(method in dir(ha))\n func = getattr(ha, method)\n func(**kwargs)\n ha.stop()","title":""},{"docid":"e8997f7ce7f3ad83e8fa5f260fd8c89c","score":"0.5287773","text":"def run_rules(self):\n for rule in self.rule_list:\n if self.apply_rule(rule):\n self.fire_rule(rule)\n return","title":""},{"docid":"9022f00d91c2f757316df53cd06ee5b5","score":"0.52611005","text":"def actions(self, state):\n # try all possible moves for the tile puzzle\n for action in ACTIONS:\n # try to make a new board from the current action\n new_state = apply_action(state, action)\n\n # an invalid solution is one that returns the original board\n if np.array_equal(new_state, state):\n continue\n\n # if the action is valid, yield it as a result\n\n yield action\n # yield np.array([[1, 2, 3], [4, 5, 6], [7, 0, 8]])","title":""},{"docid":"03a37bf4fca2436e9d43881a53af14ff","score":"0.5260837","text":"def step(self, action: Tuple[PlayerID, Any]):\n\n raise NotImplemented","title":""},{"docid":"bba03b865194bc10245613dc232773b3","score":"0.5252358","text":"def tasks(self):\n pass","title":""},{"docid":"9ad737192c3cbb4b8bc303f0f2a5b2fb","score":"0.52382404","text":"def batch(self):\n self.pre_run()\n self.run()","title":""},{"docid":"93571320ef0f5156cc3806e630be3247","score":"0.5230582","text":"def _take_action(self,action):\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"741c76fa42f3f650741d4694317cd0bb\",\n \"score\": \"0.70779276\",\n \"text\": \"def do_action(self, action: Action):\\n for manager in self.managers:\\n manager.on_action(self, action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab12bb7530d4844d428ed9a7da185d5\",\n \"score\": \"0.69594294\",\n \"text\": \"def run_hooks(self, action: str, **kwargs) -> None:\\n for func in self._hooks[action]:\\n func(self, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a300e9733de8783de1358da06d566b88\",\n \"score\": \"0.6761709\",\n \"text\": \"def execute_actions(self):\\n # execute all actions\\n items_to_execute_action = self.sorted_items(self.pokebot.config.mode)\\n for item in items_to_execute_action:\\n # check if we can execute action\\n if item.action.check_action():\\n item.try_action()\\n # delete item if action is successeded\\n del item.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbfd53031fb903373820acf75f6d9646\",\n \"score\": \"0.65779597\",\n \"text\": \"def do_nodes_tasks_by_action(self, arg):\\n if arg:\\n argv = arg.split()\\n if len(argv) == 1:\\n try:\\n print()\\n pretty_print(\\n es.tasks.list(detailed=True, group_by=\\\"nodes\\\", actions=argv[0]),\\n indent=2,\\n )\\n print()\\n except Exception:\\n error()\\n else:\\n print()\\n print(\\\"Please enter action name.\\\")\\n print()\\n else:\\n try:\\n print()\\n pretty_print(es.tasks.list(detailed=True, group_by=\\\"nodes\\\"), indent=2)\\n print()\\n except Exception:\\n error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5cf76a4f916c2b877d401ce94396b9\",\n \"score\": \"0.6569431\",\n \"text\": \"def run_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):\\n policy = self.get_policy()\\n results = []\\n runner = self.get_runner(policy, kwargs)\\n for action_list in self.get_actions(action_name, config_name, instances, map_name, **kwargs):\\n try:\\n for res in runner.run_actions(action_list):\\n results.append(res)\\n except ActionException as ae:\\n raise ActionRunnerException.from_action_exception(ae, results)\\n except:\\n exc_info = sys.exc_info()\\n raise PartialResultsError(exc_info, results)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c39ba31b578dc47fb9833bc1452873\",\n \"score\": \"0.64825743\",\n \"text\": \"def runall(self, changes_dict):\\n for action in changes_dict.keys():\\n fun = getattr(self, action)\\n ok_users, fail_users = fun(changes_dict[action])\\n if len(fail_users):\\n self._log.error(\\\"Got error during runall, aborted on action: %s\\\" % (action,))\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3dd9744a94434333337e87fc6e32c98\",\n \"score\": \"0.64585215\",\n \"text\": \"def run(self, action: str, args: list):\\n\\n worker = ProjectActionWorker(getattr(self.project, action), args, self.logger)\\n worker.actionDone.connect(self.stateChanged)\\n worker.actionDone.connect(self.stageChanged)\\n worker.actionDone.connect(self.actionDone)\\n\\n self.workers_pool.start(worker) # will automatically place to the queue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cc09291115b9dc4316a4d4c721ae15\",\n \"score\": \"0.6387707\",\n \"text\": \"def run(self, action):\\n WSModule.run(self, action)\\n self.done = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd3736190a86066373b627aec2f9516\",\n \"score\": \"0.6364364\",\n \"text\": \"def run(tasks: List[Task]):\\n for task in tasks:\\n _run_task(task, tasks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac50137a3421803d69046a539a5b891\",\n \"score\": \"0.63419443\",\n \"text\": \"async def run_all(self) -> None:\\n await gather(*[task.run() for task in self._schedule])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2049241c9e542a697c73c03a4b955d3c\",\n \"score\": \"0.63146615\",\n \"text\": \"def perform_action(self, action):\\n Operator.perform_action(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1fa7b473f24b15d4309473ccdcf208\",\n \"score\": \"0.6278072\",\n \"text\": \"def _action_loop(self):\\n while not self.stop_action:\\n for action in self.get_actions():\\n if not self.stop_action:\\n try:\\n action()\\n except Exception:\\n self.logger.exception('Error in ProcessController action_loop: ')\\n if not self.stop_action:\\n time.sleep(self.action_loop_interval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5185466a60ab1480a20ddb3c67ca9792\",\n \"score\": \"0.62277395\",\n \"text\": \"def doAction(self, action):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05b75bb46d9fe82ff1f09fed52dd62d\",\n \"score\": \"0.62233424\",\n \"text\": \"def loop(action: any):\\n # Execute the action, every action has to return an answer\\n answer = action()\\n\\n # Map the next action\\n action_next = map_action(answer[\\\"action\\\"])\\n\\n # Recursive call\\n loop(action_next)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b4ea538c07586d81091dd71fe84df9e\",\n \"score\": \"0.61468273\",\n \"text\": \"def step(self, action):\\n # exec action's function\\n self._act(*action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76737006d67e6e11a9d66a36f64d5a89\",\n \"score\": \"0.6119997\",\n \"text\": \"def performAction(self, action):\\n self._lastAction = []\\n\\n # Markups chosen for each generator.\\n actions = self._allActions[action]\\n\\n n = self.numOffbids * len(self.generators)\\n markups = actions[:n]\\n withholds = actions[n:]\\n\\n# print \\\"ALL ACTIONS:\\\", self._allActions\\n# print \\\"ACTIONS:\\\", markups, withholds\\n\\n self._offbid(markups, withholds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6571538722924a1d68fb1dc9d0852a45\",\n \"score\": \"0.60859454\",\n \"text\": \"def _process_list(self, action: List[tuple]):\\n for sub_action in action:\\n self._process_instruction(sub_action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf7d362186cf4d8a5374d41c1f46b8e3\",\n \"score\": \"0.60499614\",\n \"text\": \"def run(self,action):\\n script = str(action.text())\\n if script in self.files:\\n self.runScript(script)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a98c2af944bbb459c617e3d55c9c70b\",\n \"score\": \"0.5992066\",\n \"text\": \"def process_action(self, action):\\n #TODO defer\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a813068509617b6e22a812146a943e\",\n \"score\": \"0.59797025\",\n \"text\": \"def tasks_action(request, action):\\n empty_response = HttpResponse('')\\n\\n if request.method != 'POST' or not (action in supported_actions):\\n return empty_response\\n\\n # TODO: return comprehensible response anytime\\n # TODO: rewrite with django auth system\\n #if not request.user.groups.filter(name='vomsrole:/atlas/Role=production'):\\n # return empty_response\\n\\n owner = request.user.username\\n if not owner:\\n return empty_response\\n\\n data_json = request.body\\n if not data_json:\\n return empty_response\\n data = json.loads(data_json)\\n\\n tasks = data.get(\\\"tasks\\\")\\n if not tasks:\\n return empty_response\\n\\n params = data.get(\\\"parameters\\\", [])\\n response = do_tasks_action(owner, tasks, action, *params)\\n return HttpResponse(json.dumps(response))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9796e63f27732498be57a548aa6a6bb\",\n \"score\": \"0.5973363\",\n \"text\": \"def process_action():\\n for q in QUEUE:\\n actions = {\\n \\\"list\\\": q.list,\\n \\\"update\\\": q.update,\\n \\\"dirty\\\": q.dirty,\\n \\\"verbose\\\": q.verbose,\\n \\\"remotes\\\": q.remotes,\\n \\\"fetch\\\": q.fetch,\\n \\\"move2opt\\\": q.move2opt,\\n \\\"move2start\\\": q.move2start,\\n \\\"git\\\": q.git,\\n \\\"exe\\\": q.exe,\\n \\\"log\\\": q.git,\\n \\\"graph\\\": q.git,\\n \\\"web\\\": q.web,\\n \\\"helptags\\\": q.helptags,\\n \\\"backup\\\": q.backup,\\n \\\"restore\\\": q.restore,\\n \\\"terminal\\\": q.terminal,\\n \\\"logs\\\": q.logs,\\n \\\"errors\\\": q.logs,\\n \\\"lastdiff\\\": q.lastdiff,\\n }\\n log(actions[ACTION]())\\n\\n # packages may have been queued for async processing\\n process_asynch()\\n\\n # if updating packages, write a log file\\n if LOGFILE:\\n write_logfiles()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33639ddf4bbf91897648bfb0b8c4abf2\",\n \"score\": \"0.5962084\",\n \"text\": \"def perform_input_actions(self):\\n while not self.action_queue.empty():\\n callback = self.action_queue.get()\\n callback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927bb8a882eeac19778287f83d52a5c5\",\n \"score\": \"0.5954346\",\n \"text\": \"def run_all_tasks(data_directory):\\n print(\\\"Training and testing for all tasks ...\\\")\\n for t in range(20):\\n run_task(data_directory, task_id=t + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d004f49484fdb0d60ce6e0ca1f5a86f\",\n \"score\": \"0.5908969\",\n \"text\": \"async def _run_action(self):\\n if not self._url_map:\\n logger.warning(\\\"No URLs to check!\\\")\\n return\\n logger.info(\\\"Starting %s\\\", self.__class__.__name__)\\n await self._start_kafka_producer()\\n async with aiohttp.ClientSession(headers=self._headers) as session:\\n self._session = session\\n await self._process_urls()\\n logger.debug(\\\"Waiting until all worker tasks are cancelled\\\")\\n logger.info(\\\"Produced %d results\\\", self._counter)\\n await asyncio.gather(*self._tasks, return_exceptions=True)\\n logger.debug(\\\"Stopping Kafka producer\\\")\\n await self._kafka_producer.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde4db385561da8cd8d330fabce4b706\",\n \"score\": \"0.5874224\",\n \"text\": \"def do(self, action):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058f9030bb2b497f9728319ae54c8dc3\",\n \"score\": \"0.5866326\",\n \"text\": \"def _call_action(self, key: str):\\n cache = set()\\n if self._action[key]:\\n for index, (action, id_) in enumerate(self._action[key]):\\n if id_ not in cache:\\n try:\\n action(self)\\n cache.add(id_)\\n # uses set to make it faster (hopefully)\\n except TimeoutError:\\n # print(f\\\"action {action} timeout, removed\\\"\\n self._action[key].pop(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416ea77a9470069e729563ff71e7a70d\",\n \"score\": \"0.5797252\",\n \"text\": \"def _execute_action(self):\\n if self._current_action_item != None:\\n self.do_trigger_action(self._current_action_item)\\n self._last_action_item = self._current_action_item\\n self._current_action_item = None\\n self.update()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fb33cb1084e487e0065cf525180e1d\",\n \"score\": \"0.57941335\",\n \"text\": \"def sync_process(self, action):\\n actions = self.pure(action)\\n for middleware in self.middlewares:\\n actions = self.bind(middleware, self, actions)\\n for _action in actions:\\n self.state = self.reducer(self.state, _action)\\n self.in_progress = True\\n self.notify(self.state)\\n self.in_progress = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba9c5087c5585e78328357db38cc99c\",\n \"score\": \"0.5779899\",\n \"text\": \"def performAction(self, action):\\n raise NotImplementedError('subclasses must override performAction()!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef9fc456104380fa8e39e14d5612123\",\n \"score\": \"0.57568955\",\n \"text\": \"def _register_all_actions(self) -> None:\\n import inspect\\n\\n actions = utils.all_subclasses(Action)\\n\\n for action in actions:\\n if (\\n not action.__module__.startswith(\\\"rasa_core.\\\")\\n and not action.__module__.startswith(\\\"rasa.\\\")\\n and not action.__module__.startswith(\\\"rasa_sdk.\\\")\\n and not action.__module__.startswith(\\\"rasa_core_sdk.\\\")\\n and not inspect.isabstract(action)\\n ):\\n self.register_action(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40dae63b15b629cb6db2264b7f300473\",\n \"score\": \"0.575349\",\n \"text\": \"def RunTasks(self):\\n starttime = time.time()\\n self.LogGameState()\\n logging.info('Running all tasks...')\\n map(lambda x: x(), [self.OfferTask, self.AcceptTask, self.ReofferTask,\\n self.ProvideTask, self.SolveTask])\\n logging.info('Ending Balance: $%0.4f' % self.context.endbalance)\\n endtime = time.time()\\n logging.info('Gameplay took %s' % (endtime - starttime))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629194bf3d22b95dc038247691e3b810\",\n \"score\": \"0.5747402\",\n \"text\": \"def step_async(self, actions):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629194bf3d22b95dc038247691e3b810\",\n \"score\": \"0.5747402\",\n \"text\": \"def step_async(self, actions):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629194bf3d22b95dc038247691e3b810\",\n \"score\": \"0.5747402\",\n \"text\": \"def step_async(self, actions):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff0c16e67cb67a264a75803799eae3a\",\n \"score\": \"0.57445127\",\n \"text\": \"def process_action_list(actions, clock_step_delay):\\n\\n def get_delay(a):\\n if not hasattr(a, 'delay'):\\n return getattr(a, 'time', 0)\\n elif a.delay is not None:\\n if type(a.delay) == dict:\\n return 0\\n else:\\n return a.delay\\n else:\\n return clock_step_delay\\n\\n background_pool = ThreadPool(0)\\n new_action_list = []\\n for a in actions:\\n delay = get_delay(a)\\n new_action_list += background_pool.process(a, delay)\\n return new_action_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144a1b0c21cd8a10558891cc8d34c670\",\n \"score\": \"0.5724031\",\n \"text\": \"def run_action(self, action_name, *args, **kwargs):\\n try:\\n action = getattr(self, 'action_{}'.format(action_name))\\n\\n return action(*args, **kwargs)\\n except KeyError:\\n raise Exception('Unknown action: {}'.format(action_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144a1b0c21cd8a10558891cc8d34c670\",\n \"score\": \"0.5724031\",\n \"text\": \"def run_action(self, action_name, *args, **kwargs):\\n try:\\n action = getattr(self, 'action_{}'.format(action_name))\\n\\n return action(*args, **kwargs)\\n except KeyError:\\n raise Exception('Unknown action: {}'.format(action_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40e61f049e950d697bb4326ba3796f3\",\n \"score\": \"0.5722171\",\n \"text\": \"def performAction(self, action):\\n # scaling\\n self.incStep()\\n action = (action + 1.0) / 2.0 * self.dif + self.env.fraktMin * self.env.dists[0]\\n #Clipping the maximal change in actions (max force clipping)\\n action = clip(action, self.action - self.maxSpeed, self.action + self.maxSpeed)\\n EpisodicTask.performAction(self, action)\\n self.action = action.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4332969cf7dc30907cf9c067cbe565aa\",\n \"score\": \"0.5711305\",\n \"text\": \"def do_action(name, *args):\\n for _, hook in _HOOKS_ACTIONS[name]:\\n hook(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef2ee3c43f09cb978c7142979d16f7a\",\n \"score\": \"0.57022667\",\n \"text\": \"def _execute_action(self, action):\\n action_type = action[\\\"action\\\"]\\n #log.debug(\\\"Preload action %s id=%s\\\", action_type, action.get(\\\"id\\\", \\\"\\\"))\\n scope, func_type = action_type.split(\\\":\\\", 1)\\n default_funcname = \\\"_load_%s_%s\\\" % (scope, func_type)\\n action_func = getattr(self, default_funcname, None)\\n if not action_func:\\n action_funcname = self.preload_cfg[\\\"action_plugins\\\"].get(action_type, {})\\n if not action_funcname:\\n log.warn(\\\"Unknown action: %s\\\", action_type)\\n return\\n action_func = getattr(self, action_funcname, None)\\n if not action_func:\\n log.warn(\\\"Action function %s not found for action %s\\\", action_funcname, action_type)\\n return\\n action_func(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d902416627fa3e0e566b29d4131050b9\",\n \"score\": \"0.57017106\",\n \"text\": \"def call(self, action_name, container, instances=None, map_name=None, **kwargs):\\n return self.run_actions(action_name, container, instances=instances, map_name=map_name, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aceea536c61e311b276f37603477706c\",\n \"score\": \"0.56802726\",\n \"text\": \"async def _chan_action(self, action: str,\\n guild_id: int, user_id: int):\\n chan_ids = await self.app.storage.get_channel_ids(guild_id)\\n\\n for chan_id in chan_ids:\\n\\n # only do an action for users that can\\n # actually read the channel to start with.\\n chan_perms = await get_permissions(\\n user_id, chan_id,\\n storage=self.main_dispatcher.app.storage)\\n\\n if not chan_perms.bits.read_messages:\\n log.debug('skipping cid={}, no read messages',\\n chan_id)\\n continue\\n\\n log.debug('sending raw action {!r} to chan={}',\\n action, chan_id)\\n\\n await self.main_dispatcher.action(\\n 'channel', action, chan_id, user_id\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d27c6d0f66ddeb1df3ddba723601f8a\",\n \"score\": \"0.5675175\",\n \"text\": \"def trigger_all(self):\\n for func in self.schedules:\\n self.trigger(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e0f824fd3d31883b1313e263db2fe47\",\n \"score\": \"0.5661001\",\n \"text\": \"def executeActions(self):\\n\\t\\tnumbersactions = len(self.list_action)\\n\\t\\tcounter = 1\\n\\t\\tfor listalbum in self.list_action:\\n\\t\\t\\tself.signalrun.emit((counter / numbersactions) * 100, listalbum[2])\\n\\t\\t\\tmessage = \\\"\\\\n- {act} ({num}/{tot}) : {nam}\\\".format(act = listalbum[2],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnum = counter,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttot = numbersactions, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnam = listalbum[5])\\n\\t\\t\\tself.signaltxt.emit(message, 1)\\n\\t\\t\\tif listalbum[2] == 'DELETE':\\n\\t\\t\\t\\tself.deleteAlbum(listalbum[3])\\n\\t\\t\\telif listalbum[2] == 'UPDATE':\\n\\t\\t\\t\\t#print('UPDATE', listalbum[0], listalbum[1], listalbum[5], listalbum[3])\\n\\t\\t\\t\\tself.updateAlbum(listalbum[0], listalbum[1], listalbum[5], listalbum[3])\\n\\t\\t\\telif listalbum[2] == 'ADD':\\n\\t\\t\\t\\t#print('ADD', listalbum[0], listalbum[1], listalbum[5], listalbum[5])\\n\\t\\t\\t\\tself.addAlbum(listalbum[0], listalbum[1], listalbum[5])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.signaltxt.emit('ERROR : Operation \\\"' + listalbum[2] + '\\\" error', 3)\\n\\t\\t\\tcounter += 1 \\n\\t\\t#QApplication.processEvents()\\n\\t\\tself.signalrun.emit(100, 'Completed')\\n\\t\\tself.signalend.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cbd7a6834af85ab7a209ff1074f5a5b\",\n \"score\": \"0.5660399\",\n \"text\": \"def PerformUIActionStrategy(self, addr, action):\\n return self.__uiActionStrategy.Perform(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bfa86cbfc703b3681c928bc7c5c8dc\",\n \"score\": \"0.5660297\",\n \"text\": \"def dispatch(self, action):\\n if self.in_progress:\\n # Add asynchronous action to backlog\\n self.queue.put(action)\\n return\\n\\n # Synchronous processing\\n self.sync_process(action)\\n\\n # Process backlog\\n while not self.queue.empty():\\n next_action = self.queue.get()\\n self.sync_process(next_action)\\n self.queue.task_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04555d341261e91d4a1411e28a593c2\",\n \"score\": \"0.56542945\",\n \"text\": \"async def call_action(self, action: str, **params) -> Optional[Dict[str, Any]]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8e3748b7bf3bf1727a2d54aa7c2268\",\n \"score\": \"0.5642351\",\n \"text\": \"def all_actions(cls, logger=None, data=None):\\n packages_info = cls.load_all_data(data=data, logger=logger)\\n installers = cls.create_installers(packages_info=packages_info, logger=logger)\\n cls.run_installers(installers=installers, logger=logger)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0576027e7acc2d305bc27990c31199be\",\n \"score\": \"0.5630087\",\n \"text\": \"async def async_reaper_run_action(self, action_id: str) -> None:\\n await self.coordinator.reaperdaw.sendCommand(action_id)\\n await self.coordinator.async_refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a19042124be0e5500cee7063106c07a\",\n \"score\": \"0.5626104\",\n \"text\": \"def fire(self, action, args=None):\\n if action not in self.actions:\\n if self.parent is not None:\\n self.parent.fire(action, args)\\n else:\\n raise ActionNotBoundError(action + ' not bound to any '\\n 'callbacks.')\\n\\n for callback in self.actions[action]:\\n callback(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe8025e1c51e6c2c8e9eaa8b21fb7ad\",\n \"score\": \"0.561324\",\n \"text\": \"def _do(self, action):\\n next_state = self.state.do(action)\\n if next_state:\\n self._change_state(next_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71f3039df0448732f91cd6a5a570e3a\",\n \"score\": \"0.5610245\",\n \"text\": \"def perform_all_tasks(self):\\n\\n # Reset self.msg as it is starting a new process here\\n self.msg = '*** {0} is performing all required tasks, please standby. ***\\\\n'.format(self)\\n\\n # Utlize a while loop to safely pop from list while iterating\\n while self.tasks_to_perform:\\n task = self.tasks_to_perform.pop(0)\\n self.perform_task(task)\\n\\n print(\\\"\\\\n*** {0} has completed all inital tasks! ***\\\\n\\\".format(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ae44292b561b9e9af9a2758db181e9\",\n \"score\": \"0.5600253\",\n \"text\": \"def execute(self, *args, **kwargs):\\n for action in self.actions:\\n result = action.execute(*args, **kwargs)\\n if result:\\n self.state.update(result)\\n\\n return self.transition()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7198da5a449f16fc6abbd8caf070d021\",\n \"score\": \"0.5589689\",\n \"text\": \"def PerformAIActionStrategy(self, addr, action):\\n return self.__agentActionStrategy.Perform(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1ad289143fbeb7d4466e98b4269efd\",\n \"score\": \"0.55783844\",\n \"text\": \"def get_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):\\n policy = self.get_policy()\\n action_generator = self.get_action_generator(action_name, policy, kwargs)\\n for state in self.get_states(action_name, config_name, instances=instances, map_name=map_name, **kwargs):\\n log.debug(\\\"Evaluating state: %s.\\\", state)\\n actions = action_generator.get_state_actions(state, **kwargs)\\n if actions:\\n log.debug(\\\"Running actions: %s\\\", actions)\\n yield actions\\n else:\\n log.debug(\\\"No actions returned.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53c3b520840d14cee51db2bf262ed2c\",\n \"score\": \"0.55736154\",\n \"text\": \"def ExecuteTestRunActions(self, request):\\n test_run = ndb_models.TestRun.get_by_id(request.test_run_id)\\n if not test_run:\\n raise endpoints.NotFoundException('no test run found for ID %s' %\\n request.test_run_id)\\n action_refs = mtt_messages.ConvertList(request.refs,\\n ndb_models.TestRunActionRef)\\n actions = [ref.ToAction() for ref in action_refs]\\n test_run_hook.ExecuteHooks(\\n request.test_run_id,\\n ndb_models.TestRunPhase.MANUAL,\\n test_run_actions=actions)\\n return message_types.VoidMessage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a114cbec749b26f425bd1f74853d8b8c\",\n \"score\": \"0.5573016\",\n \"text\": \"def action(self, action):\\n #self._act = lambda x: action(*x)\\n super().action(lambda x: action(*x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d89f26eac2d57cbfb85776d216fa78\",\n \"score\": \"0.554585\",\n \"text\": \"def do_action(self, state, action):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41a323cf3a77f6b194c61840ca24576\",\n \"score\": \"0.55441785\",\n \"text\": \"def actions(self, actions):\\n\\n self._actions = actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41a323cf3a77f6b194c61840ca24576\",\n \"score\": \"0.55441785\",\n \"text\": \"def actions(self, actions):\\n\\n self._actions = actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92de7eb64d272e5064c7d3675b2c56b3\",\n \"score\": \"0.5544089\",\n \"text\": \"def do(self, action):\\n try:\\n # Do the action\\n return self.actions[action]()\\n except KeyError:\\n # Action not supported\\n return None\\n except TypeError:\\n # State change not allowed\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c497f5aa72f7612eeee39214f2d0ea\",\n \"score\": \"0.554174\",\n \"text\": \"def _take_action(self, action):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f7d63bbbbedeb80d7d060626e410ed\",\n \"score\": \"0.55393153\",\n \"text\": \"def perform_task(self, session):\\n cl = self.read_all_categories(session)\\n tm = categorymgr.TallyMan()\\n if self._redis_conn is not None:\\n tm._redis_host = self._redis_host\\n tm._redis_port = self._redis_port\\n tm._redis_conn = self._redis_conn\\n\\n for c in cl:\\n self.all_photos_by_category(session, tm, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d35b3940b6fa2b1e2b43c2fc3d3b3e6\",\n \"score\": \"0.5532784\",\n \"text\": \"def step(self, action):\\n if self.int_actions:\\n # If actions are taken as int, convert them to the correct format\\n action = self._reversed_actions_mapping[action] \\n self._act(*action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440b288e7a9a7fc5dd1b80c7e02083e5\",\n \"score\": \"0.5518246\",\n \"text\": \"def step(self):\\n # Pick up earliest action\\n action, timestamp = min(self.actions.items(), key=operator.itemgetter(1))\\n del self.actions[action]\\n logging.info(\\\"Next action at %s: %s(%s).\\\", self.strftime(timestamp), action.action_type.name, action.argument)\\n # Sleep.\\n sleep_length = (timestamp - datetime.now()).total_seconds()\\n if sleep_length > 0.0:\\n time.sleep(sleep_length)\\n # Perform the action.\\n if action.action_type == ActionType.sync:\\n self.sync()\\n elif action.action_type == ActionType.collect_resource:\\n self.collect_resource(action.argument)\\n elif action.action_type == ActionType.check_alliance_help:\\n self.check_alliance_help()\\n elif action.action_type == ActionType.send_gifts:\\n self.send_gifts()\\n elif action.action_type == ActionType.farm_gifts:\\n self.farm_gifts()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f5eb5402150394539f26cd4e281fa50\",\n \"score\": \"0.5503614\",\n \"text\": \"def choose_action(action_id):\\n if action_id == 1:\\n today_list()\\n elif action_id == 2:\\n week_list()\\n elif action_id == 3:\\n all_tasks_list()\\n elif action_id == 4:\\n missed_list()\\n elif action_id == 5:\\n add_task()\\n elif action_id == 6:\\n delete_task()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d038edcaf7bce4310f5fbd32b69bfda2\",\n \"score\": \"0.54804343\",\n \"text\": \"def take_action(self, action):\\n return self.model.take_action(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3887799ebf9f098b4461dbf33edec41\",\n \"score\": \"0.54617685\",\n \"text\": \"def run_actions(\\n installation_id,\\n installation_token,\\n event_type,\\n data,\\n pull,\\n match,\\n checks,\\n previous_conclusions,\\n):\\n\\n actions_ran = set()\\n conclusions = {}\\n # Run actions\\n for rule, missing_conditions in match.matching_rules:\\n for action in rule[\\\"actions\\\"]:\\n check_name = \\\"Rule: %s (%s)\\\" % (rule[\\\"name\\\"], action)\\n\\n previous_conclusion = get_previous_conclusion(\\n previous_conclusions, check_name, checks\\n )\\n\\n done_by_another_action = (\\n rule[\\\"actions\\\"][action].only_once and action in actions_ran\\n )\\n\\n if missing_conditions:\\n method_name = \\\"cancel\\\"\\n expected_conclusions = [\\\"neutral\\\", \\\"cancelled\\\"]\\n else:\\n method_name = \\\"run\\\"\\n expected_conclusions = [\\\"success\\\", \\\"failure\\\"]\\n actions_ran.add(action)\\n\\n # TODO(sileht): Backward compatibility, drop this in 2 months (February 2020)\\n if (\\n previous_conclusions.get(\\\"deprecated_summary\\\", False)\\n and action == \\\"comment\\\"\\n ):\\n deprecated_done_in_the_past = rule[\\\"actions\\\"][\\n action\\n ].deprecated_already_done_protection(pull)\\n else:\\n deprecated_done_in_the_past = None\\n\\n done_in_the_past = not rule[\\\"actions\\\"][action].always_run and (\\n previous_conclusion in expected_conclusions\\n or deprecated_done_in_the_past\\n )\\n\\n if done_in_the_past:\\n report = None\\n message = \\\"ignored, already in expected state: %s/%s\\\" % (\\n method_name,\\n previous_conclusion,\\n )\\n\\n elif done_by_another_action:\\n # NOTE(sileht) We can't run two action merge for example,\\n # This assumes the action produce a report\\n report = (\\\"success\\\", \\\"Another %s action already ran\\\" % action, \\\"\\\")\\n message = \\\"ignored, another action `%s` has already been run\\\" % action\\n\\n else:\\n # NOTE(sileht): check state change so we have to run \\\"run\\\" or \\\"cancel\\\"\\n report = exec_action(\\n method_name,\\n rule,\\n action,\\n installation_id,\\n installation_token,\\n event_type,\\n data,\\n pull,\\n missing_conditions,\\n )\\n message = \\\"`%s` executed\\\" % method_name\\n\\n if report and report[0] is not None and method_name == \\\"run\\\":\\n statsd.increment(\\\"engine.actions.count\\\", tags=[\\\"name:%s\\\" % action])\\n\\n if report:\\n conclusion, title, summary = report\\n status = \\\"completed\\\" if conclusion else \\\"in_progress\\\"\\n try:\\n check_api.set_check_run(\\n pull.g_pull,\\n check_name,\\n status,\\n conclusion,\\n output={\\\"title\\\": title, \\\"summary\\\": summary},\\n )\\n except Exception:\\n pull.log.error(\\\"Fail to post check `%s`\\\", check_name, exc_info=True)\\n conclusions[check_name] = conclusion\\n else:\\n # NOTE(sileht): action doesn't have report (eg:\\n # comment/request_reviews/..) So just assume it succeed\\n conclusions[check_name] = expected_conclusions[0]\\n\\n pull.log.info(\\n \\\"action evaluation: %s\\\",\\n message,\\n report=report,\\n previous_conclusion=previous_conclusion,\\n conclusion=conclusions[check_name],\\n check_name=check_name,\\n missing_conditions=missing_conditions,\\n event_type=event_type,\\n )\\n\\n return conclusions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c2b4311851e27ba72080176a0aab55\",\n \"score\": \"0.54610187\",\n \"text\": \"def run_action(cmd):\\n # set the url include action information\\n url = BASE_URL + 'run/?action=' + cmd\\n print(f'url: {url}')\\n # post request with url\\n __request__(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368ab4fc33b0051776380cdacf66bc80\",\n \"score\": \"0.5460237\",\n \"text\": \"def step(self, actions):\\n self.step_async(actions)\\n return self.step_wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b969ce4fed34b83fe4de767551959109\",\n \"score\": \"0.543787\",\n \"text\": \"def perform_action(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b912c2eb44740517a00bd69dbefd8bf7\",\n \"score\": \"0.5429898\",\n \"text\": \"def run(self):\\n\\t\\tfor time_step in range(self.num_time_steps):\\n\\t\\t\\t# flow during single time step\\n\\t\\t\\tactions = self.population.draw_actions()\\n\\n\\t\\t\\tutilities = self.environment.get_utilities(actions)\\n\\n\\t\\t\\tself.population.select(utilities)\\n\\n\\t\\t\\tself.population.reproduce(utilities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc3f18c2c3191a2aa4b07d2d8ce73f5\",\n \"score\": \"0.54282385\",\n \"text\": \"def start_action(self, evidence, helper, request, action_id):\\n index = helper.get_request_value(request, 'index', False)\\n size = int(helper.get_request_value(request, 'size', self._max_size))\\n sort = helper.get_request_value(request, 'sort', False)\\n order = helper.get_request_value(request, 'order', 'asc')\\n all = bool(helper.get_request_value(request, 'all', False))\\n check = bool(helper.get_request_value(request, 'check', True))\\n action_lock = threading.Lock()\\n queue = []\\n\\n if not index:\\n self._actions[action_id]['status'] = \\\"Action plugin requires an index, but none found\\\"\\n\\n # Get Plugin Object\\n plugin = helper.plugin_manager.get_plugin_by_name(str(self._actions[action_id]['plugin']).lower())\\n if not plugin:\\n self._actions[action_id]['status'] = \\\"Failed: Could not find plugin \\\" \\\\\\n + str(self._actions[action_id]['plugin']).lower()\\n\\n # Get Events\\n query_body = helper.get_filters(request, [], [])\\n if sort and not all:\\n query_body['sort'] = {sort: order}\\n\\n if size > self._max_size or all:\\n query_body['size'] = 0\\n temp_events = [helper.db_util.elasticsearch.search(index=index, doc_type='plaso_event', body=query_body)]\\n self._actions[action_id]['total'] = temp_events[0]['hits']['total']\\n del query_body['size']\\n events = elasticsearch.helpers.scan(helper.db_util.elasticsearch, query_body,\\n scroll=u'240m', size=self._max_size)\\n for item in events:\\n with action_lock:\\n self._actions[action_id]['status'] = 'active'\\n\\n queue.append(item)\\n if len(queue) >= self._max_queue_size:\\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id,\\n plugin, index, check,\\n self._actions, action_lock))\\n queue = []\\n else:\\n query_body['size'] = size\\n events = [helper.db_util.elasticsearch.search(index=index, doc_type='plaso_event', body=query_body)]\\n self._actions[action_id]['total'] = min(events[0]['hits']['total'], int(size))\\n\\n for event in events:\\n with action_lock:\\n self._actions[action_id]['status'] = 'active'\\n\\n # Run Events\\n for item in event['hits']['hits']:\\n queue.append(item)\\n if len(queue) == self._max_queue_size:\\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id,\\n plugin, index, check,\\n self._actions, action_lock))\\n queue = []\\n\\n if len(queue) > 0:\\n self._thread_pool.apply_async(_action_process, args=(queue, helper, request, action_id, plugin,\\n index, check, self._actions, action_lock))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8431090c3f928071efb867deef0bd379\",\n \"score\": \"0.54272443\",\n \"text\": \"def execute_all_operations(self):\\n for operation in self._operation_queue:\\n operation.execute()\\n\\n self._operation_queue = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61653895bea3e70de3448685925ba54\",\n \"score\": \"0.5417552\",\n \"text\": \"def notify_all(self, action, args=''):\\n for client_id in self.clients:\\n client = self.clients[client_id]\\n self.send(client, client_id, action, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81152d6ea37f7724dc04009fa3b4facf\",\n \"score\": \"0.54091567\",\n \"text\": \"def execute_instructions():\\n if is_stitch:\\n self._process_stitch(action)\\n elif is_cable:\\n self._process_cable(action)\\n elif is_list:\\n self._process_list(action)\\n else:\\n self._process_instruction(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd84a58fe25d6b5218344650732e19ce\",\n \"score\": \"0.5402685\",\n \"text\": \"def dispatch(self, action: Action) -> None:\\n return self._action_dispatcher.dispatch(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94405e57b9337e5cfb7e3475b403fccb\",\n \"score\": \"0.53948057\",\n \"text\": \"def performAction(self, action):\\n gs = [g for g in self.case.online_generators if g.bus.type !=REFERENCE]\\n\\n assert len(action) == len(gs)\\n\\n logger.info(\\\"Action: %s\\\" % list(action))\\n\\n # Set the output of each (non-reference) generator.\\n for i, g in enumerate(gs):\\n g.p = action[i]\\n\\n # Compute power flows and slack generator set-point.\\n NewtonPF(self.case, verbose=False).solve()\\n #FastDecoupledPF(self.case, verbose=False).solve()\\n\\n # Store all generator set-points (only used for plotting).\\n self._Pg[:, self._step] = [g.p for g in self.case.online_generators]\\n\\n # Apply the next load profile value to the original demand at each bus.\\n if self._step != len(self.profile) - 1:\\n pq_buses = [b for b in self.case.buses if b.type == PQ]\\n for i, b in enumerate(pq_buses):\\n b.p_demand = self._Pd0[i] * self.profile[self._step + 1]\\n\\n self._step += 1\\n\\n logger.info(\\\"Entering step %d.\\\" % self._step)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36aabf8d70083881361e7f18c062a28f\",\n \"score\": \"0.53830296\",\n \"text\": \"def _apply_rl_actions(self, rl_actions):\\n # flag to activate sumo actuate baselines or not to\\n if self.benchmark_params.sumo_actuated_baseline:\\n\\n # check file to track number of simulations completed during training.\\n if not os.path.isfile(self.benchmark_params.full_path):\\n return\\n else:\\n # read csv file if it exists\\n df = pd.read_csv(self.benchmark_params.full_path, index_col=False)\\n n_iter = df.training_iteration.iat[-1]\\n\\n if n_iter < self.benchmark_params.sumo_actuated_simulations:\\n return\\n\\n rl_ids = np.arange(self.num_traffic_lights)\\n actions = self.action_dict[rl_actions]\\n\\n for i, rl_action in zip(rl_ids, actions):\\n execute_action(self, i, rl_action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a78872763b4e64c1924ef9ef6dcabf\",\n \"score\": \"0.53788704\",\n \"text\": \"def run_action(socketio, msg):\\n if session.state != session.STATE_IDLE:\\n send_error_message(socketio, 'CANNOT RUN ACTION, REQUEST ALREADY IN PLACE')\\n return\\n\\n sync_system_state(socketio, session.STATE_RUN_PROCESS)\\n\\n try:\\n ## Standardized calibration sequences. Functions are defined in action.py\\n if msg['id'] == 'run-std-calibration':\\n run_standard_calibration(socketio, msg['data'])\\n elif msg['id'] == 'run-system-calibration':\\n run_system_calibration(socketio, msg['data'])\\n elif msg['id'].endswith('calibration-signoff'):\\n run_calibration_signoff(socketio, msg['data'],\\n msg['id'].startswith('system'))\\n elif msg['id'] == 'rerun-single':\\n run_process_extend(socketio, msg['data'])\\n elif msg['id'] == 'image-settings':\\n run_image_settings(socketio, msg['data'])\\n elif msg['id'] == 'zscan-settings':\\n run_zscan_settings(socketio, msg['data'])\\n elif msg['id'] == 'lowlight-settings':\\n run_lowlight_settings(socketio, msg['data'])\\n elif msg['id'] == 'lumialign-settings':\\n run_lumialign_settings(socketio, msg['data'])\\n elif msg['id'] == 'picoscope-settings':\\n run_picoscope_settings(socketio, msg['data'])\\n elif msg['id'] == 'drs-settings':\\n run_drs_settings(socketio, msg['data'])\\n elif msg['id'] == 'drs-calib':\\n run_drs_calib(socketio) ## Data less command.\\n else:\\n send_error_message(socketio, f'Unsupported action item {msg[\\\"id\\\"]}')\\n print(msg)\\n time.sleep(5)\\n except Exception as e:\\n send_error_message(socketio, str(e))\\n\\n sync_system_state(socketio, session.STATE_IDLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e2da4d3726c1f8f72efc02ea34674a\",\n \"score\": \"0.5368779\",\n \"text\": \"def _action_process(items, helper, request, action_id, plugin, index, check, _actions, action_lock):\\n for item in items:\\n try:\\n source = item['_source']\\n if 'pathspec' not in source:\\n with action_lock:\\n _actions[action_id]['fail'] += 1\\n _actions[action_id]['errors'] += source['pathspec'] + '\\\\nNot found in Elasticsearch'\\n else:\\n efetch_dictionary = helper.pathspec_helper.\\\\\\n get_evidence_item(source['pathspec'], index, plugin.cache,\\n hasattr(plugin, 'fast') and plugin.fast)\\n efetch_dictionary['_id'] = item['_id']\\n efetch_dictionary['doc_type'] = item['_type']\\n if not check or plugin.check(efetch_dictionary, efetch_dictionary['file_cache_path']):\\n plugin.get(efetch_dictionary, helper, efetch_dictionary['file_cache_path'], request)\\n with action_lock:\\n _actions[action_id]['success'] += 1\\n except Exception as e:\\n with action_lock:\\n _actions[action_id]['fail'] += 1\\n _actions[action_id]['error'] += source['pathspec'] + '\\\\n' +\\\\\\n e.message + '\\\\n' + traceback.format_exc() + '\\\\n\\\\n'\\n finally:\\n with action_lock:\\n _actions[action_id]['current'] += 1\\n _actions[action_id]['percent'] = \\\"{0:.0f}%\\\".format(_actions[action_id]['current'] /\\n _actions[action_id]['total'] * 100)\\n if _actions[action_id]['current'] == _actions[action_id]['total']:\\n _actions[action_id]['status'] = 'done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78195394a32380ca8401773245dcc5c\",\n \"score\": \"0.5367644\",\n \"text\": \"def __call__(self):\\n for test in self.tests:\\n self.run_test(test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c874ddc9cc35915aa813abe911e71cf4\",\n \"score\": \"0.5364056\",\n \"text\": \"def perform_action(action_id):\\n RPR.Main_OnCommand(action_id, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e775327975dda7610506ee71e84c59bf\",\n \"score\": \"0.53569466\",\n \"text\": \"def __call__(self):\\n for job in self._jobs:\\n job()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d76cd653a230118410abc31365c478\",\n \"score\": \"0.5339642\",\n \"text\": \"def run(self,*p,**k):\\n for url in self.urls:\\n self._run_one(url,*p,**k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd0d3533f9359527b9ea28d3a540572\",\n \"score\": \"0.53329337\",\n \"text\": \"def act_python_shell(self, action):\\n self.call('execute_shell')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3b1e175b9db9350b16c75b1bbf73d9\",\n \"score\": \"0.53304577\",\n \"text\": \"def execute(self, nabuAction):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c88cd342a4d23859f8cfbd6fb85317db\",\n \"score\": \"0.53267777\",\n \"text\": \"def step_sim(self, action):\\n\\n return self.loop.run_until_complete(self._step_sim(action))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb164af3746c58342cd46c1b25586644\",\n \"score\": \"0.53171074\",\n \"text\": \"def run(self) -> None:\\n if self.actions.switch.required:\\n self.actions.switch()\\n\\n self.actions.update()\\n\\n if self.actions.commit.required:\\n self.actions.commit()\\n\\n if self.actions.rollback.required:\\n self.actions.rollback()\\n return\\n\\n if self.actions.push.required:\\n self.actions.push()\\n\\n if self.actions.pull_request.required:\\n self.actions.pull_request()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2b3065fb675549f220bdc8b23541675\",\n \"score\": \"0.53150105\",\n \"text\": \"def run_action(self, delta_time):\\r\\n #print(88, self._waiting_action, self._action)\\r\\n action_result = [0]*5\\r\\n if self._waiting_action is not None:\\r\\n # If a new action is waiting to be executed, interrupt any current actions and start execution of\\r\\n # the new one\\r\\n if self._action is not None:\\r\\n self._action.end(CommandStatus.INTERRUPTED)\\r\\n\\r\\n self._action = self._waiting_action\\r\\n self._waiting_action = None\\r\\n self._action.start()\\r\\n\\r\\n if self._action is not None:\\r\\n # Update the current action if valid\\r\\n status = self._action.get_status()\\r\\n\\r\\n if status == CommandStatus.RUNNING:\\r\\n action_result = self._action.run(delta_time)\\r\\n else:\\r\\n self._action.end(status)\\r\\n self._action = None\\r\\n\\r\\n if self._action is None and self._waiting_action is None and self._default_action is not None:\\r\\n # If there is not action running and no actions are waiting, run the default action\\r\\n self._action = self._default_action\\r\\n self._action.start()\\r\\n \\r\\n return action_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4866ee9deb6ac2f721ea248a5670d96\",\n \"score\": \"0.5306498\",\n \"text\": \"def setAction(self, action):\\n self.actions_queue.put(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4866ee9deb6ac2f721ea248a5670d96\",\n \"score\": \"0.5306498\",\n \"text\": \"def setAction(self, action):\\n self.actions_queue.put(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6fea86be9356a98b8004f4d16af0c3\",\n \"score\": \"0.5300462\",\n \"text\": \"def doAction(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f7726025ee5673786c9b598392e79c\",\n \"score\": \"0.5295762\",\n \"text\": \"def pre_action(self, action):\\n if not hasattr(self, action):\\n return\\n\\n resources = [r for r in self.rset.resources if \\\\\\n not r.skip and not r.is_disabled() and \\\\\\n r.type == self.type]\\n\\n for resource in sorted(resources):\\n for dataset in resource.dataset:\\n resource._sync_update(dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4c993fe7cb367518e78690b10853a0\",\n \"score\": \"0.52923036\",\n \"text\": \"def _execute(self, method, **kwargs):\\n assert(self.group_name in self.hydra.app_group)\\n tasks_list = self.tasks_list\\n for task_id in tasks_list:\\n info = self.hydra.apps[self.app_name]['ip_port_map'][task_id]\\n port = info[0]\\n ip = info[1]\\n ha = self.analyser(ip, port, task_id)\\n l.debug(\\\"ip:port %s:%s\\\", ip, str(port))\\n assert(method in dir(ha))\\n func = getattr(ha, method)\\n func(**kwargs)\\n ha.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8997f7ce7f3ad83e8fa5f260fd8c89c\",\n \"score\": \"0.5287773\",\n \"text\": \"def run_rules(self):\\n for rule in self.rule_list:\\n if self.apply_rule(rule):\\n self.fire_rule(rule)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9022f00d91c2f757316df53cd06ee5b5\",\n \"score\": \"0.52611005\",\n \"text\": \"def actions(self, state):\\n # try all possible moves for the tile puzzle\\n for action in ACTIONS:\\n # try to make a new board from the current action\\n new_state = apply_action(state, action)\\n\\n # an invalid solution is one that returns the original board\\n if np.array_equal(new_state, state):\\n continue\\n\\n # if the action is valid, yield it as a result\\n\\n yield action\\n # yield np.array([[1, 2, 3], [4, 5, 6], [7, 0, 8]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a37bf4fca2436e9d43881a53af14ff\",\n \"score\": \"0.5260837\",\n \"text\": \"def step(self, action: Tuple[PlayerID, Any]):\\n\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba03b865194bc10245613dc232773b3\",\n \"score\": \"0.5252358\",\n \"text\": \"def tasks(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad737192c3cbb4b8bc303f0f2a5b2fb\",\n \"score\": \"0.52382404\",\n \"text\": \"def batch(self):\\n self.pre_run()\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93571320ef0f5156cc3806e630be3247\",\n \"score\": \"0.5230582\",\n \"text\": \"def _take_action(self,action):\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1499,"cells":{"query_id":{"kind":"string","value":"66b0d1302ed03c32c185e08cd405c3a2"},"query":{"kind":"string","value":"A function to generate package version desc"},"positive_passages":{"kind":"list like","value":[{"docid":"b96b6bd4310f475a4d836afbb3e9dac3","score":"0.6709719","text":"def gen_package_desc(pkgs, host):\n desc = \"<table>\"\n for pkg in pkgs:\n try:\n pkg_str = str(package.Package.from_name(pkg, host=host))\n except package.PackageNotExistsError:\n pkg_str = 'not installed'\n desc += \"<tr><td>%s</td><td>%s</td></tr>\" % (pkg, pkg_str)\n desc += \"</table><br/>\"\n return desc","title":""}],"string":"[\n {\n \"docid\": \"b96b6bd4310f475a4d836afbb3e9dac3\",\n \"score\": \"0.6709719\",\n \"text\": \"def gen_package_desc(pkgs, host):\\n desc = \\\"<table>\\\"\\n for pkg in pkgs:\\n try:\\n pkg_str = str(package.Package.from_name(pkg, host=host))\\n except package.PackageNotExistsError:\\n pkg_str = 'not installed'\\n desc += \\\"<tr><td>%s</td><td>%s</td></tr>\\\" % (pkg, pkg_str)\\n desc += \\\"</table><br/>\\\"\\n return desc\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c794c2a1887c60a516bf706705a90cd9","score":"0.7154892","text":"def version(self) -> str:","title":""},{"docid":"7b9bce8f2f1ee8c453738d801bdc065e","score":"0.7082373","text":"def version_desc(self):\n return self._version_desc","title":""},{"docid":"80aae4a26484a30b5b92ca00ad992ed3","score":"0.692764","text":"def Version():\n return ''","title":""},{"docid":"ce3ec83e553abfdc4d44f0757e49b8be","score":"0.6898433","text":"def get_version():","title":""},{"docid":"e193b28387286a082be4f8506426cd57","score":"0.6889548","text":"def format_version(self) -> Optional[str]:","title":""},{"docid":"8c7be4152b2bda21f208d09e73228ac5","score":"0.6834845","text":"def printable_version(self):\n if int(self.pkg_version) > 1:\n return '{0.version}~{0.pkg_version}'.format(self)\n else:\n return self.version","title":""},{"docid":"b8f8d0465ec1922295e6c4a9ba2c35df","score":"0.6738958","text":"def make_ver_str(_tuple):\n return '{}.{}'.format(_tuple[0], _tuple[1])","title":""},{"docid":"3da781fe657752b43efb2dbae257c47f","score":"0.6735101","text":"def __str__(self) -> str:\n package_str = self.name\n if self.version:\n package_str += f':\"{self.version}\"'\n return package_str","title":""},{"docid":"e3b8dc3969b06c64abd6bb4a44726194","score":"0.6725117","text":"def get_model_desc(nlp, model_name):\n lang_cls = spacy.util.get_lang_class(nlp.lang)\n lang_name = lang_cls.__name__\n model_version = nlp.meta['version']\n return '{} - {} (v{})'.format(lang_name, model_name, model_version)","title":""},{"docid":"4b90e92eece8057a63fc1d0ff72310d0","score":"0.67146206","text":"def gen_package_info(self):\n module = get_module(self.stmt).arg\n return ''.join([package_info.format(' ' + module, ''), self.pkg, ';'])","title":""},{"docid":"a7db6c20ae5de8eb0708a66079c2442b","score":"0.6631941","text":"def generate_version_str(increment_if_repo_chnaged: bool = True) -> str:\n import warnings\n warnings.warn('function generate_version_str() is deprecated, use predict() instead',\n DeprecationWarning, stacklevel=2)\n from .query import predict_caller\n return predict_caller(2).to_str()","title":""},{"docid":"85ed8a4a061efbadd822b4301bb45d2e","score":"0.6611523","text":"def getVersionString(version):\n result = '%s %s' % (version.package, version.short())\n return result","title":""},{"docid":"00f7c35899ee5ac37959ac9ceb208fff","score":"0.660769","text":"def display_version(self):\n\t\tif self.version.tree == 0:\n\t\t\treturn 'devel'\n\t\telse:\n\t\t\treturn str(self.version.tree)","title":""},{"docid":"d7a9c6964745eea3b7e8ed3e706873b7","score":"0.65625685","text":"def get_version_string():\n vl = Pogrep.get_version_number()\n\n return '''pogrep.py {0}.{1}.{2}\nNew BSD License.\nCopyright (C) 2017-2018 Hitoshi Yamauchi\n'''.format(vl[0], vl[1], vl[2])","title":""},{"docid":"493efbf09c870f58ca715c7867bd5f2d","score":"0.64421064","text":"def get_version_name():\n\n raise NotImplementedError()","title":""},{"docid":"bac196f1c3061665babab243d77d58a6","score":"0.6431327","text":"def version():\n print('version = 1.1')","title":""},{"docid":"22cf580f8abf7dee2c93a048269a7f9e","score":"0.64219713","text":"def get_long_desc() -> str:\n get_version()\n release = re.compile(r'(\\d+\\.){0,2}\\d+$')\n with open(os.path.join(wd, 'README.md')) as fd:\n if _version == '0.0.0' or not release.match(_version):\n _long_description = fd.read()\n else:\n lines = fd.readlines()\n for i, line in enumerate(lines):\n if not line.startswith('['):\n break\n if 'travis' in line:\n lines[i] = line.replace('master', _version)\n elif 'codecov' in line:\n commit = get_commit()\n if commit != '':\n lines[i] = line.replace('branch/master',\n 'commit/' + commit)\n _long_description = ''.join(lines)\n return _long_description","title":""},{"docid":"3cbf4cf3220f22a9ea4ff733f863a5a5","score":"0.64010197","text":"def get_version():\n pass","title":""},{"docid":"24bff2abe4de93b7f7001ca411453c43","score":"0.6400759","text":"def GetVersionInformation(self):\n return f'plaso - {self.NAME:s} version {plaso.__version__:s}'","title":""},{"docid":"b7d8e05470c7187221833aa4c897be35","score":"0.6399095","text":"def description():","title":""},{"docid":"b7d8e05470c7187221833aa4c897be35","score":"0.6399095","text":"def description():","title":""},{"docid":"228e448790e7d16129a8d3937dde3c57","score":"0.6393017","text":"def name(self):\n return f'Open Shortest Path First version {self._info.version}' # pylint: disable=E1101","title":""},{"docid":"82617d505661d9c4cbc0b5b994311b32","score":"0.63759077","text":"def get_version():\n return \"%s.%s.%s\" % (VERSION[0], VERSION[1], VERSION[2])","title":""},{"docid":"352dfd80489e1ecfb48a7f7249d7f2c1","score":"0.6344219","text":"def get_metadata_version() -> str:\n\n metadata_pkg_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\")\n metadata_pkg_dir = os.path.abspath(metadata_pkg_dir)\n\n return f\"metadata {get_client_version()} from {metadata_pkg_dir} (python {get_major_minor_version()})\"","title":""},{"docid":"f1dd7c813565ddacc1519562cb397adb","score":"0.63385373","text":"def __str__(self) -> str:\n return '.'.join(str(v) for v in self.ver)","title":""},{"docid":"2842f898263f48abdee67728d3cf8ee3","score":"0.63306844","text":"def _version():\n\n print \"%s version %s %s\\n\" % (__projectname__, __version__, __copyright__)","title":""},{"docid":"d0929c9d09b7d90db206a913cecbe955","score":"0.63264227","text":"def nameAndVersion(self):\n\t\treturn ''","title":""},{"docid":"0a34cad11759b3721363130f24115456","score":"0.63199204","text":"def get_version() -> str:\n return VERSION","title":""},{"docid":"f623b944e0c248f4f3326ede8333fb02","score":"0.6310899","text":"def version_string():\n\n if not _PACKAGED_VERSION.startswith('@@'):\n return _PACKAGED_VERSION\n\n if not _PACKED_VERSION.startswith('@@'):\n return _PACKED_VERSION\n\n revno = None\n version = old_version\n bzrdir = os.path.abspath(os.path.join(__file__, '..', '..', '.bzr'))\n if os.path.isdir(bzrdir):\n try:\n out = subprocess.check_output(['bzr', 'revno'], cwd=bzrdir)\n revno = out.decode('utf-8').strip()\n if revno:\n version += \"~bzr%s\" % revno\n except subprocess.CalledProcessError:\n pass\n\n return version","title":""},{"docid":"a70179a66a1633b2e689ca36991c6290","score":"0.6304212","text":"def printVersion():\n print(__appname__ + \" version \" + __version__)","title":""},{"docid":"340150a0b7ba2920fe72aef4b346f52a","score":"0.6294145","text":"def main():\n # don't use version.major, version.minor, this is pys >= 2.7\n print(\"%u.%u\" % (v[0], v[1]))","title":""},{"docid":"586976ecc7243855edab583aef1d0ede","score":"0.6278637","text":"def get_version():\n return '.'.join([str(i) for i in _VERSION])","title":""},{"docid":"8331b3ab18c3aa1b3e3b85096c488976","score":"0.62295693","text":"def getversion(txt=\"\"):\n if txt: return \"JSONBOT %s %s\" % (version, txt)\n else: return \"JSONBOT %s\" % version","title":""},{"docid":"9f2e9670a116dbdbfdfbac74ca877e16","score":"0.6218018","text":"def get_version():\n return \".\".join(map(str,VERSION))","title":""},{"docid":"01c02ed03237e500c333143652b683a5","score":"0.6209032","text":"def version(ctx, rev):\n vs = \"%s.%i.%i\" % (rev, build_version, patch_version)\n with open(\"bower.json\") as f:\n info = json.load(f)\n info['version'] = vs\n with open(\"bower.json\", 'w') as f:\n json.dump(info, f, sort_keys=True, indent=2)\n return vs","title":""},{"docid":"746417045d1b3e2b54c0330726f6ce7c","score":"0.61891204","text":"def get_version():\n return \"0.0.1\"","title":""},{"docid":"5eb10450865b764cdb5bdd43192ebaf1","score":"0.6178098","text":"def new_version():","title":""},{"docid":"3e908a490e5694ecf359e93e92d160ed","score":"0.6176602","text":"def version(self) -> str:\n raise NotImplementedError","title":""},{"docid":"0c281dbf4ab37e9bb30a170017c1f7dd","score":"0.61612463","text":"def version(short=False, tag=TAG):\n\n if short:\n _version = \"{:d}.{:d}\".format(API, FEATURE)\n\n else:\n _tag = '_{:s}'.format(tag) if tag else ''\n _version = \"{:d}.{:d}.{:d}\".format(API, FEATURE, BUG) + _tag\n\n return _version","title":""},{"docid":"c284331351d7dabd5e6047a4e633fecc","score":"0.6153683","text":"def dep(self) -> str:","title":""},{"docid":"08d09608943413c4215db9f45f6ef916","score":"0.6147769","text":"def version_long():\n\n def package_versions():\n \"\"\"Return the package versions.\"\"\"\n from .version import package_version\n\n required_packages = self.version_long_packages()\n found_packages = []\n\n for package_name in sorted(required_packages):\n package_version(\n package_name,\n found_packages,\n )\n\n return [\n {key: value} for key, value in found_packages\n ]\n\n from sys import version as py_version\n\n return {\n \"version\": self._version,\n \"python\": {\n \"version\": py_version.replace(\"\\n\", \"\"),\n \"packages\": package_versions(),\n },\n }","title":""},{"docid":"6cfc4935ddb6b6b4a186d09b8f1b4a8d","score":"0.6147157","text":"def get_string(self):\n if not self:\n raise ValueError\n return \"%s.%s\" % (self._ver_major, self._ver_minor)","title":""},{"docid":"fe8a9ae409284ef12541090b8b33a1d5","score":"0.61444443","text":"def format_help(self):\n return \"version: {}\\n\".format(__version__) + \\\n super(_VersionInHelpParser, self).format_help()","title":""},{"docid":"19868e07802737b36c57cd7819ae07ae","score":"0.6136238","text":"def getDescription():","title":""},{"docid":"2835c4ebef3b66a3ef408356a80161a5","score":"0.6133208","text":"def version(c):\n for k,v in get_versions().items():\n print('{label}: {version}'.format(label=k, version=v))","title":""},{"docid":"acb63fef331aea7b0018be66a376bbec","score":"0.6131961","text":"def get_version():\n return \".\".join([str(i) for i in VERSION])","title":""},{"docid":"5aab4fbc2b6dcaf6494eb0f9239e3be7","score":"0.6101695","text":"def version():\n print('netplan-parser ' + VERSION_STRING)","title":""},{"docid":"07fb4d6d8c2443a05d2f064b0f131371","score":"0.6091122","text":"def base(self):\n if self.prerelease is None:\n pre = \"\"\n else:\n pre = \"pre%s\" % (self.prerelease,)\n return '%d.%d.%d%s' % (self.major,\n self.minor,\n self.micro,\n pre)","title":""},{"docid":"d66fc816509e1aedc9de6add7ed17aeb","score":"0.60842174","text":"def version(self):\n return self._issue_command('*VER\\n')","title":""},{"docid":"b28e9a20dc4707d8844100c50959c87d","score":"0.60837543","text":"def version_string(self):\r\n return self.server_version + ' ' + self.sys_version","title":""},{"docid":"39f3973e73e25317a25a73d56fa2c525","score":"0.6077671","text":"def generate_version(max_major: int = 1, max_minor: int = 7,\n max_patch: int = 15) -> str:\n major = randint(0, max_major)\n minor = randint(0, max_minor)\n patch = randint(0, max_patch)\n return '{:d}.{:d}.{:d}'.format(major, minor, patch)","title":""},{"docid":"cbbb4582cfc118dabaaca9b2898b85fb","score":"0.6077226","text":"def print_version(verbose=True):\n import pkg_resources # part of setuptools\n version = pkg_resources.require(\"mbed-greentea\")[0].version\n if verbose:\n print version\n return version","title":""},{"docid":"fa5388e9b8dedb0a0347d74c10a7ccff","score":"0.6067569","text":"def name(self):\n return \"Loxone Software Version\"","title":""},{"docid":"94152657b24b7e648f91b6b491c1b093","score":"0.60607475","text":"def showVersion():\n from armi import getApp\n\n prog = context.APP_NAME\n app = getApp()\n if app is None or prog == \"armi\":\n print(\"{0} {1}\".format(prog, meta.__version__))\n else:\n print(\"{0} {1}\".format(prog, app.version))","title":""},{"docid":"353a03b6049d31b162d00357772ccda8","score":"0.6054039","text":"def showversion(self, d, **keywords):\n html = ''\n if self.cfg.show_version and not keywords.get('print_mode', 0):\n html = (u'<div id=\"version\">MoinMoin Release %s [Revision %s], '\n 'Copyright by Juergen Hermann et al.</div>') % (version.release, version.revision, )\n return html","title":""},{"docid":"54a6b6bc8e653aa5efc6086daf84824c","score":"0.60536385","text":"def get_version(short=False):\n if __version_info__['releaselevel'] not in ('alpha', 'beta', 'final'):\n raise ValueError(\n \"unknown release level '{}', select alpha, beta, or final.\".format(\n __version_info__['releaselevel']\n )\n )\n\n vers = [\"{major}.{minor}\".format(**__version_info__)]\n\n if __version_info__['micro']:\n vers.append(\".{micro}\".format(**__version_info__))\n\n if __version_info__['releaselevel'] != 'final' and not short:\n vers.append('{}{}'.format(__version_info__['releaselevel'][0],\n __version_info__['serial']))\n\n return ''.join(vers)","title":""},{"docid":"577bba9657a2aa35eba1c676ee0ac2f9","score":"0.60518956","text":"def version(self, major=True):\n\t\treturn 0 if major else '0.0.0'","title":""},{"docid":"bc22681de8abce01ad7b121243b093bd","score":"0.60481995","text":"def version():\n return VERSION","title":""},{"docid":"bc22681de8abce01ad7b121243b093bd","score":"0.60481995","text":"def version():\n return VERSION","title":""},{"docid":"9a865d887a1765b95ce5de8c4b616eb0","score":"0.6046585","text":"def systemVersionString(self):\n \n return \"%d.%d\" % tuple(_VERSION)","title":""},{"docid":"03821344e856e7a07a653d4283b5f910","score":"0.60444766","text":"def get_describe(self):\n\n long_name =str(self.year) +' '+ self.model +' '+ self.make\n return long_name.title()","title":""},{"docid":"69da5716a8a0ac5434774c0bacb2686c","score":"0.60444695","text":"def __str__(self):\n return '%s - v%s' % (self.name, self.version)","title":""},{"docid":"92897d5121d2a3c865c30d6293a4245a","score":"0.60223573","text":"def getVersion():\n return VERSION","title":""},{"docid":"94a84c2f32abd146b3030c7717a9cd54","score":"0.60207516","text":"def get_version(self):\n return self.write('gv')","title":""},{"docid":"37c674bd3cbddcab0a694ac9d9b50b6f","score":"0.6011863","text":"def version_string():\n return SERVER_VERSION + ' ' + SYS_VERSION","title":""},{"docid":"67d0653a440257376bc44bdde82e6bc4","score":"0.59993166","text":"def _format_version_info(vi):\n # type: (sys._version_info) -> str\n return \"%d.%d.%d\" % (vi.major, vi.minor, vi.micro)","title":""},{"docid":"8dcc12dca0b438476570e2e769a0eb90","score":"0.5981438","text":"def get_version():\r\n return '.'.join((str(each) for each in VERSION[:4]))","title":""},{"docid":"a33214377e07917e1e987865d7315880","score":"0.598109","text":"def description(self, package):\n # pylint: disable=arguments-differ\n\n for package_info in self.package_list:\n if package_info[0] == package:\n return package_info[0] + \" (\" + package_info[2].split(\";\", 1)[0] + \")\"\n return package","title":""},{"docid":"2679f1959a234b132020d73bdb6efbb8","score":"0.5978326","text":"def info(self, zolo, module, args):\n print(f\"[Math] Version {module.version}\")","title":""},{"docid":"f69531a089d9cf895021a31bdb835a7d","score":"0.59779435","text":"def version_display_name(self) -> str:\n return pulumi.get(self, \"version_display_name\")","title":""},{"docid":"3d1b741967486bfea617984a3f86c656","score":"0.59741473","text":"def show_version() -> None:\n from . import __version__\n print('debinsight V' + __version__)","title":""},{"docid":"6b75b86e08056f2dee4eb127182afd97","score":"0.59544647","text":"def version():\n github_version = majam_repo.get_commits().totalCount\n return f\"v {str(github_version)[:0] or '0'}.{str(github_version)[:1] or '0'}.{str(github_version)[1:] or '0'}\"","title":""},{"docid":"9e23d5685ff7c656a43e7a75b7aeebd4","score":"0.5937596","text":"def packageInfo():\n # first the easy ones\n info = {\n \"version\": version(),\n \"prefix\": prefix,\n \"path\": prefix / \"bin\",\n \"ldpath\": prefix / \"lib\",\n \"pythonpath\": home.parent,\n \"includes\": f\"-I{prefix}/include\",\n }\n\n # the libraries\n libs = [\"pyre\", \"journal\"]\n # get the host\n host = executive.host\n # if the host is a linux box\n if isinstance(host, platforms.linux()):\n # we have to link against the real time clock library\n libs.append(\"rt\")\n # assemble the libraries\n libs = \" \".join(f\"-l{lib}\" for lib in libs)\n # attach\n info[\"libs\"] = f\"-L{prefix}/lib {libs}\"\n\n # all done\n return info","title":""},{"docid":"f1e427eb06388bdcdab576b93b7e2bac","score":"0.593472","text":"def version():\n\n return _version_info","title":""},{"docid":"2bcec24396940391b755b492bf94ecec","score":"0.5931155","text":"def version():\n print('compile_all version {__version__} by {__author__}.'.format(\n **globals()))","title":""},{"docid":"54581efbc2f94b9c10250580df0615a6","score":"0.59254414","text":"def write_version_py(filename=\"version.py\"):\n\tfile_content = \"\"\"\n# FILE CONTENT GENERATED FROM SETUP.PY\nshort_version = '%(version)s'\nversion = '%(version)s'\nfull_version = '%(full_version)s'\nrelease = %(isrelease)s\n\nif not release:\n version = full_version\n\t\"\"\"\n\n\tfilename = os.path.join(os.path.dirname(__file__), 'seaborn_qqplot/%s' % filename)\n\n\tVERSION, ISRELEASED = get_version_info()\n\n\t_file = open(filename,'w+')\n\n\ttry:\n\t\t_file.write(file_content % {'version': VERSION,\n 'full_version' : VERSION,\n 'isrelease': str(ISRELEASED)})\n\tfinally:\n\t\t_file.close()","title":""},{"docid":"6fbaf96609974d0b8124823697d4f685","score":"0.5920995","text":"def version():\n return meta.version","title":""},{"docid":"36d3bfb384c508aac931ddc2d712dfcf","score":"0.5916318","text":"def make_package_filename(self):\r\n if not (self.package and self.version and self.architecture):\r\n raise Exception('Not enough information to build the package filename')\r\n return self.package + '_' + self.version + '_' + self.architecture + '.wapt'","title":""},{"docid":"75229d1e271a6d4788874449de4d3e12","score":"0.5913115","text":"def get_version_string():\n return \"kubesplit v\" + __version__","title":""},{"docid":"d145295ca4f01ad397255e4a10c02f3e","score":"0.58944476","text":"def pkg_version(i):\n return versions.convert(info(i).get('pkg-version', None))","title":""},{"docid":"06846e2940f6a53eacc3940c8fae8509","score":"0.5891879","text":"def get_version(self) -> str:\n s = self.get_help()\n version = s[s.find(\"ensembl-vep\") : s.find(\"Help\")].split(\":\")[1].strip()\n return version","title":""},{"docid":"0d0768ccd7ea9a9f365204571ef46cf8","score":"0.5886079","text":"def _init_str(self) -> str:\n init_str = _copyright_header_str(self.protocol_specification.author)\n init_str += \"\\n\"\n init_str += '\"\"\"This module contains the support resources for the {} protocol.\"\"\"\\n'.format(\n self.protocol_specification.name\n )\n\n return init_str","title":""},{"docid":"84252deed9726ba172b01dea7360c225","score":"0.5876867","text":"def test_format_ver(self):\n self.assertEqual(\n \"1.2.3\", self.wrapper.Requirements._format_ver((1, 2, 3))\n )\n self.assertEqual(\"1\", self.wrapper.Requirements._format_ver([1]))","title":""},{"docid":"e9aa3d99155a775c445825fa629c64e7","score":"0.587374","text":"def template_version(self):","title":""},{"docid":"effe6b84c87180540fe7d772bce3fe14","score":"0.5871742","text":"def get_version():\n # TODO: use build infos\n return 'PyS2OPC v' + VERSION + ' on ' + ffi.string(libsub.SOPC_LibSub_GetVersion()).decode()","title":""},{"docid":"9ccc9abba9218c6a679850e7122f6fb6","score":"0.5869735","text":"def write_version():\n template = \"\"\"\\\n__version__ = '{0}'\n\"\"\"\n filename = os.path.join(\n os.path.dirname(__file__), 'khal', 'version.py')\n with open(filename, 'w') as versionfile:\n versionfile.write(template.format(VERSION))\n print(\"wrote khal/version.py with version={0}\".format(VERSION))","title":""},{"docid":"b4e6c3010e3af7c27d351fbf9e6ab800","score":"0.58690697","text":"def test_pkgname_complex_description_works(self):\n package = {\"pid\":{\"fork\":\"antoinealb\"}}\n self.assertEqual(\"pid\", package_name_from_desc(package))","title":""},{"docid":"f8dda056eb62d512e3cfdc170d071333","score":"0.5854294","text":"async def get_version(self, **kwargs: Any) -> str:\n ...","title":""},{"docid":"6f41de318c8930dff7193fd066b20877","score":"0.58533245","text":"def description(self) -> str:","title":""},{"docid":"f0a8adcfcb3478c572e94d34248cbb84","score":"0.5852459","text":"def show_version():\n print(\"{} {}\".format(__title__, __version__))\n sys.exit(0)","title":""},{"docid":"9f5d92fa27f84b86fa4674b9f65b97cc","score":"0.5849972","text":"def version(self):\n return VERSION_STRING","title":""},{"docid":"3edf53217499f0495d32dcb406eabefa","score":"0.5849417","text":"def ver_str(version):\n return '.'.join(map(str, version))","title":""},{"docid":"a7abc3c2d0224c801e14823de096b07f","score":"0.5848965","text":"def plugin_version_str():\n cfg = ConfigParser.ConfigParser()\n cfg.read(os.path.join(os.path.dirname(__file__), 'metadata.txt'))\n return cfg.get('general', 'version')","title":""},{"docid":"7b8d4baadeb38a1c1849be0c76f5b272","score":"0.58461946","text":"def get_distribution_version():\n return distro.codename()","title":""},{"docid":"1bc41e71161731769d14ac6c80be826c","score":"0.5845808","text":"def version(self) -> str:\n return get_version()","title":""},{"docid":"c75534a3daf737b77e5d0d7356d73fa4","score":"0.5844963","text":"def __str__(self) -> str:\n\t\treturn f\"{self.major}.{self.minor}\"","title":""},{"docid":"9272566490b127fbe30c2fbed7871b39","score":"0.5844912","text":"async def ver(self):\n\n message = \"Current cog version: **\" + self.version + \"** (\" + self.update_type + \")\\n\"\n message += \"For patchnotes use `\" + self.bot.command_prefix[0] + \"drawing info`\"\n await self.bot.say(message)","title":""},{"docid":"132082e40f291320f39c3511aed3618e","score":"0.58417165","text":"def get_default_version_string() -> str:\n\n return '{}.{}.{}'.format(*_SICD_DEFAULT_TUPLE)","title":""},{"docid":"fb67f40633f32841455421dd607f0f18","score":"0.5839716","text":"def descibe(self):\r\n long_name = f'{self.make} {self.model} {self.macuftaor}'\r\n return long_name.title()","title":""},{"docid":"ecc015090dd13e2a9d8ab0c471ffc5e0","score":"0.5838861","text":"def test_calculate_spec_info_version():\n spec = {}\n schemas = {}\n\n spec_info = build.calculate_spec_info(spec=spec, schemas=schemas)\n\n assert spec_info.version == \"63581fa2bdc5cef14183\"","title":""}],"string":"[\n {\n \"docid\": \"c794c2a1887c60a516bf706705a90cd9\",\n \"score\": \"0.7154892\",\n \"text\": \"def version(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b9bce8f2f1ee8c453738d801bdc065e\",\n \"score\": \"0.7082373\",\n \"text\": \"def version_desc(self):\\n return self._version_desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80aae4a26484a30b5b92ca00ad992ed3\",\n \"score\": \"0.692764\",\n \"text\": \"def Version():\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce3ec83e553abfdc4d44f0757e49b8be\",\n \"score\": \"0.6898433\",\n \"text\": \"def get_version():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e193b28387286a082be4f8506426cd57\",\n \"score\": \"0.6889548\",\n \"text\": \"def format_version(self) -> Optional[str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7be4152b2bda21f208d09e73228ac5\",\n \"score\": \"0.6834845\",\n \"text\": \"def printable_version(self):\\n if int(self.pkg_version) > 1:\\n return '{0.version}~{0.pkg_version}'.format(self)\\n else:\\n return self.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f8d0465ec1922295e6c4a9ba2c35df\",\n \"score\": \"0.6738958\",\n \"text\": \"def make_ver_str(_tuple):\\n return '{}.{}'.format(_tuple[0], _tuple[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da781fe657752b43efb2dbae257c47f\",\n \"score\": \"0.6735101\",\n \"text\": \"def __str__(self) -> str:\\n package_str = self.name\\n if self.version:\\n package_str += f':\\\"{self.version}\\\"'\\n return package_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b8dc3969b06c64abd6bb4a44726194\",\n \"score\": \"0.6725117\",\n \"text\": \"def get_model_desc(nlp, model_name):\\n lang_cls = spacy.util.get_lang_class(nlp.lang)\\n lang_name = lang_cls.__name__\\n model_version = nlp.meta['version']\\n return '{} - {} (v{})'.format(lang_name, model_name, model_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b90e92eece8057a63fc1d0ff72310d0\",\n \"score\": \"0.67146206\",\n \"text\": \"def gen_package_info(self):\\n module = get_module(self.stmt).arg\\n return ''.join([package_info.format(' ' + module, ''), self.pkg, ';'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7db6c20ae5de8eb0708a66079c2442b\",\n \"score\": \"0.6631941\",\n \"text\": \"def generate_version_str(increment_if_repo_chnaged: bool = True) -> str:\\n import warnings\\n warnings.warn('function generate_version_str() is deprecated, use predict() instead',\\n DeprecationWarning, stacklevel=2)\\n from .query import predict_caller\\n return predict_caller(2).to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ed8a4a061efbadd822b4301bb45d2e\",\n \"score\": \"0.6611523\",\n \"text\": \"def getVersionString(version):\\n result = '%s %s' % (version.package, version.short())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f7c35899ee5ac37959ac9ceb208fff\",\n \"score\": \"0.660769\",\n \"text\": \"def display_version(self):\\n\\t\\tif self.version.tree == 0:\\n\\t\\t\\treturn 'devel'\\n\\t\\telse:\\n\\t\\t\\treturn str(self.version.tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a9c6964745eea3b7e8ed3e706873b7\",\n \"score\": \"0.65625685\",\n \"text\": \"def get_version_string():\\n vl = Pogrep.get_version_number()\\n\\n return '''pogrep.py {0}.{1}.{2}\\nNew BSD License.\\nCopyright (C) 2017-2018 Hitoshi Yamauchi\\n'''.format(vl[0], vl[1], vl[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493efbf09c870f58ca715c7867bd5f2d\",\n \"score\": \"0.64421064\",\n \"text\": \"def get_version_name():\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac196f1c3061665babab243d77d58a6\",\n \"score\": \"0.6431327\",\n \"text\": \"def version():\\n print('version = 1.1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22cf580f8abf7dee2c93a048269a7f9e\",\n \"score\": \"0.64219713\",\n \"text\": \"def get_long_desc() -> str:\\n get_version()\\n release = re.compile(r'(\\\\d+\\\\.){0,2}\\\\d+$')\\n with open(os.path.join(wd, 'README.md')) as fd:\\n if _version == '0.0.0' or not release.match(_version):\\n _long_description = fd.read()\\n else:\\n lines = fd.readlines()\\n for i, line in enumerate(lines):\\n if not line.startswith('['):\\n break\\n if 'travis' in line:\\n lines[i] = line.replace('master', _version)\\n elif 'codecov' in line:\\n commit = get_commit()\\n if commit != '':\\n lines[i] = line.replace('branch/master',\\n 'commit/' + commit)\\n _long_description = ''.join(lines)\\n return _long_description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbf4cf3220f22a9ea4ff733f863a5a5\",\n \"score\": \"0.64010197\",\n \"text\": \"def get_version():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bff2abe4de93b7f7001ca411453c43\",\n \"score\": \"0.6400759\",\n \"text\": \"def GetVersionInformation(self):\\n return f'plaso - {self.NAME:s} version {plaso.__version__:s}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d8e05470c7187221833aa4c897be35\",\n \"score\": \"0.6399095\",\n \"text\": \"def description():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d8e05470c7187221833aa4c897be35\",\n \"score\": \"0.6399095\",\n \"text\": \"def description():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228e448790e7d16129a8d3937dde3c57\",\n \"score\": \"0.6393017\",\n \"text\": \"def name(self):\\n return f'Open Shortest Path First version {self._info.version}' # pylint: disable=E1101\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82617d505661d9c4cbc0b5b994311b32\",\n \"score\": \"0.63759077\",\n \"text\": \"def get_version():\\n return \\\"%s.%s.%s\\\" % (VERSION[0], VERSION[1], VERSION[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352dfd80489e1ecfb48a7f7249d7f2c1\",\n \"score\": \"0.6344219\",\n \"text\": \"def get_metadata_version() -> str:\\n\\n metadata_pkg_dir = os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"..\\\")\\n metadata_pkg_dir = os.path.abspath(metadata_pkg_dir)\\n\\n return f\\\"metadata {get_client_version()} from {metadata_pkg_dir} (python {get_major_minor_version()})\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1dd7c813565ddacc1519562cb397adb\",\n \"score\": \"0.63385373\",\n \"text\": \"def __str__(self) -> str:\\n return '.'.join(str(v) for v in self.ver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2842f898263f48abdee67728d3cf8ee3\",\n \"score\": \"0.63306844\",\n \"text\": \"def _version():\\n\\n print \\\"%s version %s %s\\\\n\\\" % (__projectname__, __version__, __copyright__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0929c9d09b7d90db206a913cecbe955\",\n \"score\": \"0.63264227\",\n \"text\": \"def nameAndVersion(self):\\n\\t\\treturn ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a34cad11759b3721363130f24115456\",\n \"score\": \"0.63199204\",\n \"text\": \"def get_version() -> str:\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f623b944e0c248f4f3326ede8333fb02\",\n \"score\": \"0.6310899\",\n \"text\": \"def version_string():\\n\\n if not _PACKAGED_VERSION.startswith('@@'):\\n return _PACKAGED_VERSION\\n\\n if not _PACKED_VERSION.startswith('@@'):\\n return _PACKED_VERSION\\n\\n revno = None\\n version = old_version\\n bzrdir = os.path.abspath(os.path.join(__file__, '..', '..', '.bzr'))\\n if os.path.isdir(bzrdir):\\n try:\\n out = subprocess.check_output(['bzr', 'revno'], cwd=bzrdir)\\n revno = out.decode('utf-8').strip()\\n if revno:\\n version += \\\"~bzr%s\\\" % revno\\n except subprocess.CalledProcessError:\\n pass\\n\\n return version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70179a66a1633b2e689ca36991c6290\",\n \"score\": \"0.6304212\",\n \"text\": \"def printVersion():\\n print(__appname__ + \\\" version \\\" + __version__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340150a0b7ba2920fe72aef4b346f52a\",\n \"score\": \"0.6294145\",\n \"text\": \"def main():\\n # don't use version.major, version.minor, this is pys >= 2.7\\n print(\\\"%u.%u\\\" % (v[0], v[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586976ecc7243855edab583aef1d0ede\",\n \"score\": \"0.6278637\",\n \"text\": \"def get_version():\\n return '.'.join([str(i) for i in _VERSION])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8331b3ab18c3aa1b3e3b85096c488976\",\n \"score\": \"0.62295693\",\n \"text\": \"def getversion(txt=\\\"\\\"):\\n if txt: return \\\"JSONBOT %s %s\\\" % (version, txt)\\n else: return \\\"JSONBOT %s\\\" % version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2e9670a116dbdbfdfbac74ca877e16\",\n \"score\": \"0.6218018\",\n \"text\": \"def get_version():\\n return \\\".\\\".join(map(str,VERSION))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c02ed03237e500c333143652b683a5\",\n \"score\": \"0.6209032\",\n \"text\": \"def version(ctx, rev):\\n vs = \\\"%s.%i.%i\\\" % (rev, build_version, patch_version)\\n with open(\\\"bower.json\\\") as f:\\n info = json.load(f)\\n info['version'] = vs\\n with open(\\\"bower.json\\\", 'w') as f:\\n json.dump(info, f, sort_keys=True, indent=2)\\n return vs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746417045d1b3e2b54c0330726f6ce7c\",\n \"score\": \"0.61891204\",\n \"text\": \"def get_version():\\n return \\\"0.0.1\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb10450865b764cdb5bdd43192ebaf1\",\n \"score\": \"0.6178098\",\n \"text\": \"def new_version():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e908a490e5694ecf359e93e92d160ed\",\n \"score\": \"0.6176602\",\n \"text\": \"def version(self) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c281dbf4ab37e9bb30a170017c1f7dd\",\n \"score\": \"0.61612463\",\n \"text\": \"def version(short=False, tag=TAG):\\n\\n if short:\\n _version = \\\"{:d}.{:d}\\\".format(API, FEATURE)\\n\\n else:\\n _tag = '_{:s}'.format(tag) if tag else ''\\n _version = \\\"{:d}.{:d}.{:d}\\\".format(API, FEATURE, BUG) + _tag\\n\\n return _version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c284331351d7dabd5e6047a4e633fecc\",\n \"score\": \"0.6153683\",\n \"text\": \"def dep(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d09608943413c4215db9f45f6ef916\",\n \"score\": \"0.6147769\",\n \"text\": \"def version_long():\\n\\n def package_versions():\\n \\\"\\\"\\\"Return the package versions.\\\"\\\"\\\"\\n from .version import package_version\\n\\n required_packages = self.version_long_packages()\\n found_packages = []\\n\\n for package_name in sorted(required_packages):\\n package_version(\\n package_name,\\n found_packages,\\n )\\n\\n return [\\n {key: value} for key, value in found_packages\\n ]\\n\\n from sys import version as py_version\\n\\n return {\\n \\\"version\\\": self._version,\\n \\\"python\\\": {\\n \\\"version\\\": py_version.replace(\\\"\\\\n\\\", \\\"\\\"),\\n \\\"packages\\\": package_versions(),\\n },\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfc4935ddb6b6b4a186d09b8f1b4a8d\",\n \"score\": \"0.6147157\",\n \"text\": \"def get_string(self):\\n if not self:\\n raise ValueError\\n return \\\"%s.%s\\\" % (self._ver_major, self._ver_minor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8a9ae409284ef12541090b8b33a1d5\",\n \"score\": \"0.61444443\",\n \"text\": \"def format_help(self):\\n return \\\"version: {}\\\\n\\\".format(__version__) + \\\\\\n super(_VersionInHelpParser, self).format_help()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19868e07802737b36c57cd7819ae07ae\",\n \"score\": \"0.6136238\",\n \"text\": \"def getDescription():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2835c4ebef3b66a3ef408356a80161a5\",\n \"score\": \"0.6133208\",\n \"text\": \"def version(c):\\n for k,v in get_versions().items():\\n print('{label}: {version}'.format(label=k, version=v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb63fef331aea7b0018be66a376bbec\",\n \"score\": \"0.6131961\",\n \"text\": \"def get_version():\\n return \\\".\\\".join([str(i) for i in VERSION])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aab4fbc2b6dcaf6494eb0f9239e3be7\",\n \"score\": \"0.6101695\",\n \"text\": \"def version():\\n print('netplan-parser ' + VERSION_STRING)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fb4d6d8c2443a05d2f064b0f131371\",\n \"score\": \"0.6091122\",\n \"text\": \"def base(self):\\n if self.prerelease is None:\\n pre = \\\"\\\"\\n else:\\n pre = \\\"pre%s\\\" % (self.prerelease,)\\n return '%d.%d.%d%s' % (self.major,\\n self.minor,\\n self.micro,\\n pre)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66fc816509e1aedc9de6add7ed17aeb\",\n \"score\": \"0.60842174\",\n \"text\": \"def version(self):\\n return self._issue_command('*VER\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28e9a20dc4707d8844100c50959c87d\",\n \"score\": \"0.60837543\",\n \"text\": \"def version_string(self):\\r\\n return self.server_version + ' ' + self.sys_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f3973e73e25317a25a73d56fa2c525\",\n \"score\": \"0.6077671\",\n \"text\": \"def generate_version(max_major: int = 1, max_minor: int = 7,\\n max_patch: int = 15) -> str:\\n major = randint(0, max_major)\\n minor = randint(0, max_minor)\\n patch = randint(0, max_patch)\\n return '{:d}.{:d}.{:d}'.format(major, minor, patch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbbb4582cfc118dabaaca9b2898b85fb\",\n \"score\": \"0.6077226\",\n \"text\": \"def print_version(verbose=True):\\n import pkg_resources # part of setuptools\\n version = pkg_resources.require(\\\"mbed-greentea\\\")[0].version\\n if verbose:\\n print version\\n return version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5388e9b8dedb0a0347d74c10a7ccff\",\n \"score\": \"0.6067569\",\n \"text\": \"def name(self):\\n return \\\"Loxone Software Version\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94152657b24b7e648f91b6b491c1b093\",\n \"score\": \"0.60607475\",\n \"text\": \"def showVersion():\\n from armi import getApp\\n\\n prog = context.APP_NAME\\n app = getApp()\\n if app is None or prog == \\\"armi\\\":\\n print(\\\"{0} {1}\\\".format(prog, meta.__version__))\\n else:\\n print(\\\"{0} {1}\\\".format(prog, app.version))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"353a03b6049d31b162d00357772ccda8\",\n \"score\": \"0.6054039\",\n \"text\": \"def showversion(self, d, **keywords):\\n html = ''\\n if self.cfg.show_version and not keywords.get('print_mode', 0):\\n html = (u'<div id=\\\"version\\\">MoinMoin Release %s [Revision %s], '\\n 'Copyright by Juergen Hermann et al.</div>') % (version.release, version.revision, )\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a6b6bc8e653aa5efc6086daf84824c\",\n \"score\": \"0.60536385\",\n \"text\": \"def get_version(short=False):\\n if __version_info__['releaselevel'] not in ('alpha', 'beta', 'final'):\\n raise ValueError(\\n \\\"unknown release level '{}', select alpha, beta, or final.\\\".format(\\n __version_info__['releaselevel']\\n )\\n )\\n\\n vers = [\\\"{major}.{minor}\\\".format(**__version_info__)]\\n\\n if __version_info__['micro']:\\n vers.append(\\\".{micro}\\\".format(**__version_info__))\\n\\n if __version_info__['releaselevel'] != 'final' and not short:\\n vers.append('{}{}'.format(__version_info__['releaselevel'][0],\\n __version_info__['serial']))\\n\\n return ''.join(vers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577bba9657a2aa35eba1c676ee0ac2f9\",\n \"score\": \"0.60518956\",\n \"text\": \"def version(self, major=True):\\n\\t\\treturn 0 if major else '0.0.0'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc22681de8abce01ad7b121243b093bd\",\n \"score\": \"0.60481995\",\n \"text\": \"def version():\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc22681de8abce01ad7b121243b093bd\",\n \"score\": \"0.60481995\",\n \"text\": \"def version():\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a865d887a1765b95ce5de8c4b616eb0\",\n \"score\": \"0.6046585\",\n \"text\": \"def systemVersionString(self):\\n \\n return \\\"%d.%d\\\" % tuple(_VERSION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03821344e856e7a07a653d4283b5f910\",\n \"score\": \"0.60444766\",\n \"text\": \"def get_describe(self):\\n\\n long_name =str(self.year) +' '+ self.model +' '+ self.make\\n return long_name.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69da5716a8a0ac5434774c0bacb2686c\",\n \"score\": \"0.60444695\",\n \"text\": \"def __str__(self):\\n return '%s - v%s' % (self.name, self.version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92897d5121d2a3c865c30d6293a4245a\",\n \"score\": \"0.60223573\",\n \"text\": \"def getVersion():\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a84c2f32abd146b3030c7717a9cd54\",\n \"score\": \"0.60207516\",\n \"text\": \"def get_version(self):\\n return self.write('gv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c674bd3cbddcab0a694ac9d9b50b6f\",\n \"score\": \"0.6011863\",\n \"text\": \"def version_string():\\n return SERVER_VERSION + ' ' + SYS_VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d0653a440257376bc44bdde82e6bc4\",\n \"score\": \"0.59993166\",\n \"text\": \"def _format_version_info(vi):\\n # type: (sys._version_info) -> str\\n return \\\"%d.%d.%d\\\" % (vi.major, vi.minor, vi.micro)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcc12dca0b438476570e2e769a0eb90\",\n \"score\": \"0.5981438\",\n \"text\": \"def get_version():\\r\\n return '.'.join((str(each) for each in VERSION[:4]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33214377e07917e1e987865d7315880\",\n \"score\": \"0.598109\",\n \"text\": \"def description(self, package):\\n # pylint: disable=arguments-differ\\n\\n for package_info in self.package_list:\\n if package_info[0] == package:\\n return package_info[0] + \\\" (\\\" + package_info[2].split(\\\";\\\", 1)[0] + \\\")\\\"\\n return package\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2679f1959a234b132020d73bdb6efbb8\",\n \"score\": \"0.5978326\",\n \"text\": \"def info(self, zolo, module, args):\\n print(f\\\"[Math] Version {module.version}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69531a089d9cf895021a31bdb835a7d\",\n \"score\": \"0.59779435\",\n \"text\": \"def version_display_name(self) -> str:\\n return pulumi.get(self, \\\"version_display_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d1b741967486bfea617984a3f86c656\",\n \"score\": \"0.59741473\",\n \"text\": \"def show_version() -> None:\\n from . import __version__\\n print('debinsight V' + __version__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b75b86e08056f2dee4eb127182afd97\",\n \"score\": \"0.59544647\",\n \"text\": \"def version():\\n github_version = majam_repo.get_commits().totalCount\\n return f\\\"v {str(github_version)[:0] or '0'}.{str(github_version)[:1] or '0'}.{str(github_version)[1:] or '0'}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e23d5685ff7c656a43e7a75b7aeebd4\",\n \"score\": \"0.5937596\",\n \"text\": \"def packageInfo():\\n # first the easy ones\\n info = {\\n \\\"version\\\": version(),\\n \\\"prefix\\\": prefix,\\n \\\"path\\\": prefix / \\\"bin\\\",\\n \\\"ldpath\\\": prefix / \\\"lib\\\",\\n \\\"pythonpath\\\": home.parent,\\n \\\"includes\\\": f\\\"-I{prefix}/include\\\",\\n }\\n\\n # the libraries\\n libs = [\\\"pyre\\\", \\\"journal\\\"]\\n # get the host\\n host = executive.host\\n # if the host is a linux box\\n if isinstance(host, platforms.linux()):\\n # we have to link against the real time clock library\\n libs.append(\\\"rt\\\")\\n # assemble the libraries\\n libs = \\\" \\\".join(f\\\"-l{lib}\\\" for lib in libs)\\n # attach\\n info[\\\"libs\\\"] = f\\\"-L{prefix}/lib {libs}\\\"\\n\\n # all done\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e427eb06388bdcdab576b93b7e2bac\",\n \"score\": \"0.593472\",\n \"text\": \"def version():\\n\\n return _version_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bcec24396940391b755b492bf94ecec\",\n \"score\": \"0.5931155\",\n \"text\": \"def version():\\n print('compile_all version {__version__} by {__author__}.'.format(\\n **globals()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54581efbc2f94b9c10250580df0615a6\",\n \"score\": \"0.59254414\",\n \"text\": \"def write_version_py(filename=\\\"version.py\\\"):\\n\\tfile_content = \\\"\\\"\\\"\\n# FILE CONTENT GENERATED FROM SETUP.PY\\nshort_version = '%(version)s'\\nversion = '%(version)s'\\nfull_version = '%(full_version)s'\\nrelease = %(isrelease)s\\n\\nif not release:\\n version = full_version\\n\\t\\\"\\\"\\\"\\n\\n\\tfilename = os.path.join(os.path.dirname(__file__), 'seaborn_qqplot/%s' % filename)\\n\\n\\tVERSION, ISRELEASED = get_version_info()\\n\\n\\t_file = open(filename,'w+')\\n\\n\\ttry:\\n\\t\\t_file.write(file_content % {'version': VERSION,\\n 'full_version' : VERSION,\\n 'isrelease': str(ISRELEASED)})\\n\\tfinally:\\n\\t\\t_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbaf96609974d0b8124823697d4f685\",\n \"score\": \"0.5920995\",\n \"text\": \"def version():\\n return meta.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d3bfb384c508aac931ddc2d712dfcf\",\n \"score\": \"0.5916318\",\n \"text\": \"def make_package_filename(self):\\r\\n if not (self.package and self.version and self.architecture):\\r\\n raise Exception('Not enough information to build the package filename')\\r\\n return self.package + '_' + self.version + '_' + self.architecture + '.wapt'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75229d1e271a6d4788874449de4d3e12\",\n \"score\": \"0.5913115\",\n \"text\": \"def get_version_string():\\n return \\\"kubesplit v\\\" + __version__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d145295ca4f01ad397255e4a10c02f3e\",\n \"score\": \"0.58944476\",\n \"text\": \"def pkg_version(i):\\n return versions.convert(info(i).get('pkg-version', None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06846e2940f6a53eacc3940c8fae8509\",\n \"score\": \"0.5891879\",\n \"text\": \"def get_version(self) -> str:\\n s = self.get_help()\\n version = s[s.find(\\\"ensembl-vep\\\") : s.find(\\\"Help\\\")].split(\\\":\\\")[1].strip()\\n return version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d0768ccd7ea9a9f365204571ef46cf8\",\n \"score\": \"0.5886079\",\n \"text\": \"def _init_str(self) -> str:\\n init_str = _copyright_header_str(self.protocol_specification.author)\\n init_str += \\\"\\\\n\\\"\\n init_str += '\\\"\\\"\\\"This module contains the support resources for the {} protocol.\\\"\\\"\\\"\\\\n'.format(\\n self.protocol_specification.name\\n )\\n\\n return init_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84252deed9726ba172b01dea7360c225\",\n \"score\": \"0.5876867\",\n \"text\": \"def test_format_ver(self):\\n self.assertEqual(\\n \\\"1.2.3\\\", self.wrapper.Requirements._format_ver((1, 2, 3))\\n )\\n self.assertEqual(\\\"1\\\", self.wrapper.Requirements._format_ver([1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9aa3d99155a775c445825fa629c64e7\",\n \"score\": \"0.587374\",\n \"text\": \"def template_version(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effe6b84c87180540fe7d772bce3fe14\",\n \"score\": \"0.5871742\",\n \"text\": \"def get_version():\\n # TODO: use build infos\\n return 'PyS2OPC v' + VERSION + ' on ' + ffi.string(libsub.SOPC_LibSub_GetVersion()).decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ccc9abba9218c6a679850e7122f6fb6\",\n \"score\": \"0.5869735\",\n \"text\": \"def write_version():\\n template = \\\"\\\"\\\"\\\\\\n__version__ = '{0}'\\n\\\"\\\"\\\"\\n filename = os.path.join(\\n os.path.dirname(__file__), 'khal', 'version.py')\\n with open(filename, 'w') as versionfile:\\n versionfile.write(template.format(VERSION))\\n print(\\\"wrote khal/version.py with version={0}\\\".format(VERSION))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4e6c3010e3af7c27d351fbf9e6ab800\",\n \"score\": \"0.58690697\",\n \"text\": \"def test_pkgname_complex_description_works(self):\\n package = {\\\"pid\\\":{\\\"fork\\\":\\\"antoinealb\\\"}}\\n self.assertEqual(\\\"pid\\\", package_name_from_desc(package))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dda056eb62d512e3cfdc170d071333\",\n \"score\": \"0.5854294\",\n \"text\": \"async def get_version(self, **kwargs: Any) -> str:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f41de318c8930dff7193fd066b20877\",\n \"score\": \"0.58533245\",\n \"text\": \"def description(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a8adcfcb3478c572e94d34248cbb84\",\n \"score\": \"0.5852459\",\n \"text\": \"def show_version():\\n print(\\\"{} {}\\\".format(__title__, __version__))\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5d92fa27f84b86fa4674b9f65b97cc\",\n \"score\": \"0.5849972\",\n \"text\": \"def version(self):\\n return VERSION_STRING\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edf53217499f0495d32dcb406eabefa\",\n \"score\": \"0.5849417\",\n \"text\": \"def ver_str(version):\\n return '.'.join(map(str, version))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7abc3c2d0224c801e14823de096b07f\",\n \"score\": \"0.5848965\",\n \"text\": \"def plugin_version_str():\\n cfg = ConfigParser.ConfigParser()\\n cfg.read(os.path.join(os.path.dirname(__file__), 'metadata.txt'))\\n return cfg.get('general', 'version')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8d4baadeb38a1c1849be0c76f5b272\",\n \"score\": \"0.58461946\",\n \"text\": \"def get_distribution_version():\\n return distro.codename()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc41e71161731769d14ac6c80be826c\",\n \"score\": \"0.5845808\",\n \"text\": \"def version(self) -> str:\\n return get_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75534a3daf737b77e5d0d7356d73fa4\",\n \"score\": \"0.5844963\",\n \"text\": \"def __str__(self) -> str:\\n\\t\\treturn f\\\"{self.major}.{self.minor}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9272566490b127fbe30c2fbed7871b39\",\n \"score\": \"0.5844912\",\n \"text\": \"async def ver(self):\\n\\n message = \\\"Current cog version: **\\\" + self.version + \\\"** (\\\" + self.update_type + \\\")\\\\n\\\"\\n message += \\\"For patchnotes use `\\\" + self.bot.command_prefix[0] + \\\"drawing info`\\\"\\n await self.bot.say(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132082e40f291320f39c3511aed3618e\",\n \"score\": \"0.58417165\",\n \"text\": \"def get_default_version_string() -> str:\\n\\n return '{}.{}.{}'.format(*_SICD_DEFAULT_TUPLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb67f40633f32841455421dd607f0f18\",\n \"score\": \"0.5839716\",\n \"text\": \"def descibe(self):\\r\\n long_name = f'{self.make} {self.model} {self.macuftaor}'\\r\\n return long_name.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc015090dd13e2a9d8ab0c471ffc5e0\",\n \"score\": \"0.5838861\",\n \"text\": \"def test_calculate_spec_info_version():\\n spec = {}\\n schemas = {}\\n\\n spec_info = build.calculate_spec_info(spec=spec, schemas=schemas)\\n\\n assert spec_info.version == \\\"63581fa2bdc5cef14183\\\"\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
f1afdeee4b2f9d6f24abcb35c1b901dc | Load a vacation collection for a given vacation. | [
{
"docid": "85660bba0ac6441d7e79bb25855aa99c",
"score": "0.63729554",
"text": "async def load_by_parent(self, parent_ref_id: EntityId) -> VacationCollection:\n query_stmt = select(self._vacation_collection_table).where(\n self._vacation_collection_table.c.workspace_ref_id\n ... | [
{
"docid": "2dcc34d62d7e088ee7f7d98983583dd4",
"score": "0.5612025",
"text": "def fetch_data(self) -> Collection[Vacancy]:\n try:\n self._data_source.connect()\n except Exception as err:\n logging.error(str(err))\n sys.exit()\n\n fetched_data: Collec... |
78b5383d6095e742657dbe3eb54e9c4d | Create subdirectory inside the top directory. | [
{
"docid": "78efd7c6aeb57dabd0d69e2a2d5e2163",
"score": "0.7553697",
"text": "def create_subdirectory(self, path: str, reset_if_exist: bool = False):\n if not self.top_directory_ready:\n # create subdirectory first\n self.create_top_directory()\n\n # Create subdirecto... | [
{
"docid": "c2decc66b4b8d56fd53d553fc7f7010b",
"score": "0.7763871",
"text": "def create_sub_folder(self):\n self.sub_dir = self.dir + 'subpage/'\n if not(os.path.isdir(self.sub_dir)):\n os.makedirs(os.path.join(self.sub_dir))",
"title": ""
},
{
"docid": "574f0b27338b163cf2818df... |
9fbdaee9817b0c7325aa89a7bd3b0e9f | Clones a bunch of openstack repos. | [
{
"docid": "bb202350474cfb8881b37437d3274d42",
"score": "0.58093774",
"text": "def clone_repos(save_dir, projects):\n repos = {}\n for project, short_project in tqdm(\n projects, unit='repo', desc='Cloning %s repos' % len(projects)):\n gitutils.clone_repo(save_dir, project)\n ... | [
{
"docid": "5e6275aec9d2b50b8b07475ea2bec5c0",
"score": "0.76600814",
"text": "def git_clones():\n print('Cloning git repos')\n git_repos = [ # (from url, to folder),\n ('gmarik/Vundle.vim.git', '~/.vim/bundle/Vundle.vim'),\n ]\n\n for repo, to_folder in git_repos:\n if to_fol... |
927be685086fc91ec64d021a4dcc49da | build a report of donors and their donations | [
{
"docid": "8f78a8dc99783894f3021e2b091eb737",
"score": "0.6960833",
"text": "def build_report(client, database):\n\n print(\"\\n\")\n print(\"Donor Name | Total Given | Num Gifts | Average Gift\")\n\n all_donors = database.distinct('donor')\n\n for donor in all_donors:\n t... | [
{
"docid": "420f1a4991bc744761545236f692c377",
"score": "0.78948355",
"text": "def generate_report():\n print(donors.generate_report())",
"title": ""
},
{
"docid": "d0adbb10c6b494889604d590e32db293",
"score": "0.7708783",
"text": "def create_a_report():\n donors_donation_list =... |
cb0ddeca3dc7638e06e9b469917ee008 | Returns the string representation of the model | [
{
"docid": "73b4fec9cb4bae4ece4b5ca8a3a6d67c",
"score": "0.0",
"text": "def to_str(self):\n return pformat(self.to_dict())",
"title": ""
}
] | [
{
"docid": "ade3a9362342e8a6cfb9797910bc27b2",
"score": "0.7877549",
"text": "def __str__(self):\n return str(self.save())",
"title": ""
},
{
"docid": "e0d6e19cf000b176b8a683aeabfa4bc6",
"score": "0.7860356",
"text": "def __str__(self) -> str:\n return f\"{self.model} {... |
d6997f1f0563d134172dee4a49d8596e | Return PageRank values for each page by iteratively updating PageRank values until convergence. Return a dictionary where keys are page names, and values are their estimated PageRank value (a value between 0 and 1). All PageRank values should sum to 1. | [
{
"docid": "d5c1f5601f0faa0f285833afd9e1ae5a",
"score": "0.67862046",
"text": "def iterate_pagerank(corpus, damping_factor):\n # Initialize page ranks as equal\n pageRanks = {}\n initialPageRank = 1 / len(corpus)\n for page in corpus:\n pageRanks[page] = initialPageRank\n # Initial... | [
{
"docid": "3048db3d40c19b77dfac17b6256cf0d0",
"score": "0.714354",
"text": "def iter_rank(corpus, damping_factor):\n # createempty dict to fill later\n iterate_PR = {}\n # safe the number of pages in variable\n num_pages = len(corpus)\n # iterate over all corpus pages and initially assig... |
8b3af98327fbe27b5c9a4fdd0c21a9e3 | Move all selected `Card`s. | [
{
"docid": "d7fcd12cc4c7bf6edc400fc2ab01dda2",
"score": "0.758956",
"text": "def MoveSelected(self, dx, dy):\n for c in self.GetSelection():\n self.GetParent().MoveCard(c, dx, dy)",
"title": ""
}
] | [
{
"docid": "4514bd20bcdeb882e2fe16e8b46f3956",
"score": "0.70516664",
"text": "def move_all_cards(self, destination_list):\n\n self.client.fetch_json(\n '/lists/' + self.id + '/moveAllCards',\n http_method='POST',\n post_args = {\n \"idBoard\": dest... |
644710bdb60e77cb43fc0da10f6caa4e | The maturity, termination, or end date of the reportable SB swap transaction. | [
{
"docid": "841df26a2b32646bd8c5e37e2a45a443",
"score": "0.5082271",
"text": "def end_date(self) -> datetime.date:\n return self.__end_date",
"title": ""
}
] | [
{
"docid": "9fead2249493cff7f801f24ee2578851",
"score": "0.6179479",
"text": "def settlement_date(self) -> datetime.date:\n return self.__settlement_date",
"title": ""
},
{
"docid": "664ab6507f766b82f69941e9169e6bb0",
"score": "0.6054682",
"text": "def sell_settle_date(self) -... |
942f51a34946decb8ec3299f2e99ae07 | Convert unit type/unit name dictionaries to dataframes. | [
{
"docid": "a14718a37e5bef727a39017b00423150",
"score": "0.5903174",
"text": "def equip_dict_to_df(eq_dict):\n eq_df = pd.DataFrame.from_dict(\n eq_dict, orient='index'\n ).reset_index()\n\n eq_df = pd.melt(\n eq_df, id_vars=... | [
{
"docid": "e927cb57e8243474a257b46261088027",
"score": "0.60803866",
"text": "def to_dataframe(self, filename, unit_in_name=False):\n data, t, items = self.__read(filename=filename)\n\n if unit_in_name:\n names = [f\"{item.name} ({item.unit.name})\" for item in items]\n ... |
48c474ddd0ddd9b7692df1c5de671eeb | Produces the correct results when all pairs given. | [
{
"docid": "a20ac071a6db1af125736dc812dbee3f",
"score": "0.56504786",
"text": "def test_all_pairs_lowest_common_ancestor2(self):\n all_pairs = list(product(self.DG.nodes(), self.DG.nodes()))\n ans = all_pairs_lca(self.DG, pairs=all_pairs)\n self.assert_lca_dicts_same(dict(ans), self... | [
{
"docid": "a9f13bdf52c9ee5451771617b15d6d93",
"score": "0.68829733",
"text": "def swissPairings():",
"title": ""
},
{
"docid": "11649affe0005a65af7a487a76cf9e3f",
"score": "0.6846344",
"text": "def test_get_pairs(europarl):\n expected = [\n (\n \"Madam President... |
a910746d592fcbddf45883ed699e6f7e | This starts the proxy and creates the dump file. | [
{
"docid": "fa78f2588c524ea80ce8d6325fee4118",
"score": "0.7243998",
"text": "def start_proxy_dump(self, filters=None):\n filters = filters or ''\n date = datetime.now().strftime('%y-%m-%d_%H-%M-%S')\n dirname = os.path.dirname(self.path)\n basename = os.path.basename(self.pa... | [
{
"docid": "122028d80abf75134450ed43f8d67951",
"score": "0.62544495",
"text": "def setup_reverse_proxy():\n from deployments.reverse_proxy.backup import BackupReverseProxy\n bring_up_service_at(reverse_proxy_path)\n run_tests_at(reverse_proxy_path)\n try:\n run(\n \"docke... |
fe61d0c539cb04a1f7676efb838fe4d0 | Dump SQL Server Table to dat file(csv format). | [
{
"docid": "dab1ca17e1bedadafcbfd27a52894c91",
"score": "0.0",
"text": "def usage():",
"title": ""
}
] | [
{
"docid": "ba7e8a551a836ac335d1b49fb8511fc6",
"score": "0.68503034",
"text": "def save_table(table, filename):\n\n LOGGER.info(\"Saving output table to file %s\", filename)\n \n table.to_csv(filename, sep=\";\")\n LOGGER.info(\"Saved output table.\")",
"title": ""
},
{
"docid": ... |
faa168dc342a422408b106105912764e | Just make sure it contains no dodgy characters | [
{
"docid": "d69e1627af922e0513ee4dd1ecb0f421",
"score": "0.0",
"text": "def safeId(id, nospaces=0):\r\n lowercase = 'abcdefghijklmnopqrstuvwxyz'\r\n digits = '0123456789'\r\n specials = '_-.'\r\n allowed = lowercase + lowercase.upper() + digits + specials\r\n if not nospaces:\r\n a... | [
{
"docid": "326394741baadb708199f0b15c9f11ba",
"score": "0.70018435",
"text": "def test_remove_invalid_chars():\n raise NotImplementedError()",
"title": ""
},
{
"docid": "8ed81153b1dddca4aa75da28f61daddf",
"score": "0.68707514",
"text": "def test_ignorable(self):\n self.ass... |
a93a8ef699749e6b2fbe25bb0c49f74f | Load a program into memory. | [
{
"docid": "f9548bb6e8a528802b5f5293b739d253",
"score": "0.63188136",
"text": "def load(self, filename=None):\n\n address = 0\n\n # For now, we've just hardcoded a program:\n cur_dir = dirname(realpath(__file__))\n files = [\n join(cur_dir, 'examples/stackoverflow.... | [
{
"docid": "dcc4d9b85d451400c4577561ba4cac93",
"score": "0.75949234",
"text": "def load(self):\n\t\tprogram_filename = sys.argv[1]\n\t\t\n\t\t# Making sure the file is the right type\n\t\tif program_filename[-4:] == '.ls8':\n\t\t\t# Just a counter to keep track of where we're writing the program\n\t\t\t... |
c393f76985e8d182fd8dc8e84c7e08cf | Return the filtered velocity fields with Gaussian filter. Use a series of 1D Gaussian filtering filters for the | [
{
"docid": "c5c6d17113ba0eca8ad93a2ee7251208",
"score": "0.68930113",
"text": "def gaussian_filter(self, width):\n\n gamma = 6.0\n sigma = width / np.sqrt(2 * gamma)\n Ufh = [\n spn.fourier_gaussian(self.Uf[0], sigma, n=self.N[0]),\n spn.fourier_gaussian(self.U... | [
{
"docid": "ded8d65993da7ee8904d0ad73b8d5fd2",
"score": "0.68064284",
"text": "def gaussian_filt(self):\n ndimage.gaussian_filter(self.im, sigma=np.random.rand(), output=self.im)",
"title": ""
},
{
"docid": "bd9ecfb02c9df225b5c035a4143ed5a2",
"score": "0.6735361",
"text": "def... |
d9fdb9eb82dae7ded48d2e6a106abf81 | Print a todo section out | [
{
"docid": "44ab5bb39d531f4591cd9ec679b6bddc",
"score": "0.5477549",
"text": "def get_next_todo(todo_items):\n\n return '\\n'.join([\n str(item) for item in todo_items\n if isinstance(item, TodoItem) and not item.completed\n ])",
"title": ""
}
] | [
{
"docid": "80527e3445f17ecd69f252cc036a11e0",
"score": "0.749889",
"text": "def showTodo():\n\tif len(todo):\n\t\tprint(\"Here are your todos:\")\n\t\tfor item in todo:\n\t\t print(item)\n\telse:\n\t\tprint(\"You have nothing in your todo list!\")",
"title": ""
},
{
"docid": "4f1d8917521... |
9c87842ac85e4526743e3f842bc422e5 | Return whether or not `user` can make changes to the test_case. | [
{
"docid": "a1eb7ffe1198777232e3a041fff9654d",
"score": "0.76742005",
"text": "def can_edit(self, user):\n return self.testable.project.can_edit(user)",
"title": ""
}
] | [
{
"docid": "0066bbf933f56ee1de39bee789c10729",
"score": "0.731527",
"text": "def can_be_modified(self, user):\n return self == user or user.is_admin",
"title": ""
},
{
"docid": "1f6cdafa2d5eadcccf84f6e00675ca7e",
"score": "0.727605",
"text": "def can_user_modify(self, user):\n... |
0e68618b3578b6018b49781dc9048702 | TMscore superposition between densest clusters for reference and variant addTmsupdd(id='') This is a reference to the field c.vars[var]['conreg'][i][j]['tmsup'] where 'i' is ID of densest cluster for variant and 'j' is ID of densest cluster for reference. | [
{
"docid": "fca03411d4a5cb41dabab81594f45d80",
"score": "0.49816653",
"text": "def addTmsupcc(id=''):\n \n if id:\n c=cl.loadCAN(id)\n if c.addTmsupcc()!=False: c.pickleDump()\n else:\n cl.cycleOverList('addTmsupcc')",
"title": ""
}
] | [
{
"docid": "0db64ad0a8bcf688b2759dac76a2f6f9",
"score": "0.79602724",
"text": "def addTmsupdd(self,cc=False):\n\n\n if not self.vars['00']['spk']['outfile']: return\n spkref=self.vars['00']['spk']['spkobj']\n dref=''\n if spkref:\n if cc: dref=spkref.rankIDsByCscor... |
5b326c7e8130a0b45a6a8520dd709589 | ListFiles returns a list of all files in a SourceContext. The. information about each file includes its path and its hash. The result is ordered by path. Pagination is supported. | [
{
"docid": "36159b456f3fca52ddc60d64df27ce66",
"score": "0.65616167",
"text": "def ListFiles(self, request, global_params=None):\n config = self.GetMethodConfig('ListFiles')\n return self._RunMethod(\n config, request, global_params=global_params)",
"title": ""
}
] | [
{
"docid": "759b058c723a1f7b7f4c2a446ab032e3",
"score": "0.6895244",
"text": "def list_files(source_directory, fnmatch_list=None):\n if not fnmatch_list:\n cfignore_filename = os.path.join(source_directory, '.cfignore')\n fnmatch_list = parse_ignore_file(cfignore_filename, include_star=... |
c541be8386fc0e02f711ffd4e3220819 | Create a Keras Sequential model with layers. | [
{
"docid": "85dc3f8d4375fbbea2fac3435330474f",
"score": "0.0",
"text": "def model_fn(learning_rate, lam, dropout):\n input = Input(shape=(60,60,3))\n\n # 1 block\n model = Dropout(0.2, input_shape=(60, 60, 3))(input)\n model = Conv2D(20, (5, 5), strides=(1, 1), padding='valid',\n ... | [
{
"docid": "ebfdbe83725ef7866f439a166505388d",
"score": "0.7731051",
"text": "def createModel():\r\n\tmodel = Sequential()\r\n\t\r\n\t# Normalization layer\r\n\tmodel.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160,320,3)))\r\n\t\r\n\t# Additional cropping layer to speed up training and testing... |
3d7894374246ac18f98737dfccaa69f7 | Message sent on DISCARD action, e.g. | [
{
"docid": "c415b5706dbdef0cc8897751937646f5",
"score": "0.0",
"text": "def format_discard(index, suit, rank, order):\n return '{{\"type\":\"discard\",\"failed\":false,\"which\":{{\"index\":{},\"suit\":{},\"rank\":{},\"order\":{}}}}}'.format(\n index,\n suit,\n rank,\n ord... | [
{
"docid": "e3704e98d4f4215bd3ef675ee0c77b04",
"score": "0.6684795",
"text": "def on_disconnect(self, raw_msg, server, port, **kwargs):",
"title": ""
},
{
"docid": "d9ae247043c9ad63b5b37dcc64651072",
"score": "0.65165585",
"text": "def discard_cmd(self, var, wrapper, message):\n\n ... |
4697db0d385c7d65fd4825f4173c7040 | Asks user to specify a city, month, and day to analyze. | [
{
"docid": "5a208cf47b642c844236effef09698f1",
"score": "0.0",
"text": "def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city = ''\n while(True):\n ... | [
{
"docid": "e4f3b0f25f0820149c931407b8598148",
"score": "0.6613673",
"text": "def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n# TO DO: get user input for city (chicago, new york city, washington).\n# HINT: Use a while loop to handle invalid inputs\n \n input_city =... |
fa52ef09e41034b571df8a663fd1a71c | Given a list of partitions and its two partitions, combine these two partitions into a new one appending to the partitions and remove the previous two partitions from the list of partitions | [
{
"docid": "db0cfc36ba57fae707db20e7ebcb0123",
"score": "0.7283396",
"text": "def combine_two_partitions(\n partition_0: Partition,\n partition_1: Partition,\n partitions: List[Partition]\n) -> None:\n partition = Partition(len(partitions))\n partition.nodes = partition_0.nodes.union(part... | [
{
"docid": "3092602c6698cfea60c099575ddd9d72",
"score": "0.6489745",
"text": "def _concat_partitions(partition_index_list, partition_index):\n if not partition_index_list:\n partition_index_list.append(partition_index)\n else:\n i = 0\n has_concat = False\n while i < le... |
766a8ed5893561946ee8338f5e9ec661 | Trigger spray area spray effectiveness notification. | [
{
"docid": "37e35c83b5a5830963758b9903cb41ec",
"score": "0.6021749",
"text": "def daily_spray_effectiveness(request):\n flow_uuid = getattr(settings, \"RAPIDPRO_DAILY_SPRAY_SUCCESS_FLOW_ID\")\n spray_date = request._request.GET.get(\"spray_date\", now().date())\n daily_spray_effectiveness_task.... | [
{
"docid": "d3a2ec55252627fde8057e7f18ba109f",
"score": "0.56746113",
"text": "def _notification(self, context, method, payload):\n # By now, we have no scheduling feature, so we fanout\n # to all of the DHCP agents\n self._notification_fanout(context, method, payload)",
"title"... |
5d596ca5aff2bc658db87e37ec9a5831 | Update OSQP problem arguments | [
{
"docid": "85fd0ca03a6211f36d7c64479c40479e",
"score": "0.49582827",
"text": "def update(self, q=None, l=None, u=None,\n Px=None, Px_idx=np.array([]), Ax=None, Ax_idx=np.array([])):\n\n # get problem dimensions\n (n, m) = self._model.dimensions()\n\n # check consisten... | [
{
"docid": "67ab6d33bb00e354b1eead3d2bb42f3d",
"score": "0.6087764",
"text": "def update(self, arguments):\n puts_err(colored.red(\"Not implemented!\"))",
"title": ""
},
{
"docid": "93198cf31cb832ee9023f2884620d955",
"score": "0.58223057",
"text": "def update(self, args):\n ... |
0bfbfa9a8de563a66e2b485023da259f | Can the vehicle disarm when requested | [
{
"docid": "47444b30c731aeba5cbf6b05bcd85b83",
"score": "0.63557",
"text": "def SetDisarmable(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')",
"tit... | [
{
"docid": "c26b3438d02bbfaa13c01e9f26999d36",
"score": "0.6797184",
"text": "def disarming_transition(self):\n self.flight_state = States.DISARMING\n print(\"disarm transition\")\n self.disarm()\n self.release_control()",
"title": ""
},
{
"docid": "025caa5111898b... |
65f6545c811aa7d7bbf897f34d994365 | Amount of times outcome was classified incorrectly | [
{
"docid": "824226f15e76141065a3c840a11b04e0",
"score": "0.5682221",
"text": "def incorrect(self):\n return self.false_negative + self.false_positive",
"title": ""
}
] | [
{
"docid": "b2898d842bea3619a255c764bbe01d3b",
"score": "0.6302869",
"text": "def correctly_processed(classified,actual,percentage):\n correct=0\n for i in range(0,classified.shape[0]):\n if (classified[i]==actual[i]):\n correct+=1\n return int(correct/(0.01*classified.shape[0... |
6a039a186cc7f8731d7a4aef82bf9b0e | Add rule to inference engine. This function adds a rule to the inference engine's rule | [
{
"docid": "fbf3f5d55f7654ece06cf5362b6e8685",
"score": "0.7260785",
"text": "def add_rule(self, name, rule, weight=0):\n self.rules.append((name, rule, weight))",
"title": ""
}
] | [
{
"docid": "91d0fffa1b9c2aed4bd25dbbf11734a1",
"score": "0.7664341",
"text": "def addRule(self, rule):\n self.rules.append(rule)",
"title": ""
},
{
"docid": "f62592a92b48eadd92eb4660cb02ab40",
"score": "0.7472987",
"text": "def add_rule(self, rule):\n self.append(rule)"... |
d0ca24271bdb6995caef5341535fc1c7 | Runs the map/reduce pipeline of the AnswerMigrationJob locally, with optional retrying capabilities. If fail_predicate is provided, it will be called everytime insert_submitted_answers is called in the migration job. The predicate function is passed the exploration ID, state_name, and submitted answer dict list being s... | [
{
"docid": "ee6cb46b4126c33760a7cf0dd2590e73",
"score": "0.72241515",
"text": "def _run_migration_job_internal(self, fail_predicate=None, retry_count=0):\n orig_insert_submitted_answers = (\n stats_models.StateAnswersModel.insert_submitted_answers)\n def proxy_insert_submitted_a... | [
{
"docid": "5add337bcc8849dd884986b1c112dc4a",
"score": "0.5465001",
"text": "def test_migration_job_should_migrate_100_answers(self):\n state_name = self.text_input_state_name\n\n rule_spec_str = 'Contains(ate)'\n html_answer = 'Plate'\n self._record_old_answer(\n ... |
cd790f26c6d80542d5884ed580b671dd | Test case for get_user_data_for_search | [
{
"docid": "84c765ec801a9ce9392971fa2b8d3a99",
"score": "0.93368226",
"text": "def test_get_user_data_for_search(self):\n pass",
"title": ""
}
] | [
{
"docid": "a42516521631d8436291877f200a11c6",
"score": "0.7881401",
"text": "def test_search_user_by_userid():",
"title": ""
},
{
"docid": "0e6558664382dfc46b13da586e74b181",
"score": "0.7840525",
"text": "def test_user_data_get(self):\n pass",
"title": ""
},
{
"d... |
844971ebe7e4e1ff04562b7717e192d7 | Plots cumulative conversation volume. Inputs | [
{
"docid": "0cf4e290322051ea49fb697263f4fa57",
"score": "0.5487842",
"text": "def plot_cumulative_volume(date_lists, count_lists,\n ax=None, labels=None, styles=ALL_STYLES):\n if ax is None:\n ax = plt.figure().gca()\n\n if labels is None:\n labels = [None] ... | [
{
"docid": "a703f001d21e5cfb5df58def37f60f05",
"score": "0.61506516",
"text": "def show_compositions( self ):\n from matplotlib import pyplot as plt\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n for key in self.comps.keys():\n cumulative = np.cumsum(self.com... |
8e0ca203a0401157052fafc596401782 | Setter method for output_power_c_band, mapped from YANG variable /optical_amplifier/amplifiers/amplifier/state/output_power_c_band (container) | [
{
"docid": "f7b8231afb127ad25e552c2dbd5f4574",
"score": "0.9030513",
"text": "def _set_output_power_c_band(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_output_power_c_band_openconfig_optical_amplifier__optical_amplifier_amplifie... | [
{
"docid": "6fc5538fe9c7f2e03e0a0f2674d4fad2",
"score": "0.79875803",
"text": "def _set_input_power_c_band(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_input_power_c_band_openconfig_optical_amplifier__optical_amplifier_amplifier... |
d0f1acc5760468aa2d7d14eeab6b8077 | Translate a package/path specification into a real file system pathname. | [
{
"docid": "dcc68419c7223e5a0222285f0c51cf91",
"score": "0.57839155",
"text": "def package_to_directory(package_path: str) -> str:\n pkgname, subpath = package_path.split('/', 1) if '/' in package_path else (package_path, '')\n module = import_module(pkgname)\n path = Path(module.__spec__.origi... | [
{
"docid": "0cf0a32315ec4f5c8931f5f75f03d0c9",
"score": "0.6578487",
"text": "def abspath(self, spec, current_package, current_directory):\n return spec",
"title": ""
},
{
"docid": "03b9f49071db0b631155ca087358bdc1",
"score": "0.65406543",
"text": "def resolve_path(somepath):\... |
d8be451c29765bdb311de831c5688955 | Function to remove the item. | [
{
"docid": "0859e250316204cdc8befe292896312b",
"score": "0.0",
"text": "def remove_item(item_id):\n try:\n # Delete the item\n delete_item = collection.delete_one({\"id\": int(item_id)})\n\n if delete_item.deleted_count > 0 :\n # Prepare the response\n retur... | [
{
"docid": "7d69d8ee32b7d32fecfe39b8263daef1",
"score": "0.87598383",
"text": "def remove(self, item):\n pass",
"title": ""
},
{
"docid": "9e8babe5e0473f3e866e22e16da97497",
"score": "0.8442763",
"text": "def remove_item(self, item):\n self.items.remove(item)",
"title":... |
c3701aa3a35cc17482957bafa88bd3ed | Retorna una lista (sin ningun orden en especifico) de todos los correos con notas validas. Con el proposito no enviar notificacion a los alumnos que no hicieron examen. | [
{
"docid": "8e5f6f89e5fbf338849ed6d1214bc8ee",
"score": "0.7157397",
"text": "def correos_validos(self,examen,**kwargs):\n #SI EXAMEN ES NONE RETORNE LA LISTA COMPLETA DE TODOS LOS \n #CORRREOS SIN NINGUNA VALIDACION\n\n campos = kwargs.get('campos', None)\n salida = []\n ... | [
{
"docid": "ad80dc83c79d5f8857f153bf37a56f0b",
"score": "0.6151421",
"text": "def Mujicanos(self):\n\n #Obtener lista de usuarios\n query = \"\"\"SELECT usuario FROM transacciones WHERE nota='Bono Inicial'\"\"\"\n\n self.cursor.row_factory = lambda cursor, row: row[0]\n\n res... |
d4af32e4ade14e75f4df812e056c89b4 | Empty the block queue, appending valid blocks | [
{
"docid": "6fbdd6ccb9b599c1a5f41d79a3ad4d7f",
"score": "0.67230886",
"text": "def handle_blocks(self):\n while self.block_queue:\n chain = self.block_queue.pop()\n valid = verify_blockchain(chain)\n if valid:\n if valid > len(self.blockchain):\n ... | [
{
"docid": "a18544eade4f97602067a54d682b7a76",
"score": "0.7343603",
"text": "def clear_queue(self):\n self.queue = []",
"title": ""
},
{
"docid": "8d6d69644c1c81fc54adfce51a3d53c9",
"score": "0.72773266",
"text": "def clear(self):\n\n self.queue = []",
"title": ""
... |
f1a969c5811c19c6d83737e5a49392ff | Returns the input sequences ready to feed to the model. | [
{
"docid": "ce8f9f3ea31d3c63fb699fc2d55948b5",
"score": "0.0",
"text": "def get_input_sequences(queue, interaction_group, win_size = win_size):\n seq = []\n for row in interaction_group.itertuples():\n str_uid = str(row.user_id)\n if str_uid in queue:\n user_queue = queue[... | [
{
"docid": "1ebeaad2705ccbc5053f5118e3e2bbfa",
"score": "0.71023315",
"text": "def get_all_sequences(self):\n return self._data",
"title": ""
},
{
"docid": "1ebeaad2705ccbc5053f5118e3e2bbfa",
"score": "0.71023315",
"text": "def get_all_sequences(self):\n return self._da... |
f81939cd80877f12bbdc863d961d356f | Subclasses should override this to return a tuple (attribute_model, keyword_args), where attribute_model is a model class and keyword_args is a dict of args to pass to attribute_model.objects.get() to get an instance of the given attribute on this object. | [
{
"docid": "7ac88cb163adbd5854d85d11134298a7",
"score": "0.7411711",
"text": "def _get_attribute_model_and_args(self, attribute):\r\n raise NotImplementedError",
"title": ""
}
] | [
{
"docid": "9265517073b45928e1459bb4bf1079bc",
"score": "0.67041254",
"text": "def get_attribute(\n self, key=None, category=None, value=None, strvalue=None, obj=None, attrtype=None, **kwargs\n ):\n dbmodel = self.model.__dbclass__.__name__.lower()\n query = [(\"attribute__db_att... |
215688abfb9e533a2e3c162c5e0037ff | Discretise the evaluations of the field over the reciprocal space in the cube [pi/LN, pi/LN]^d = [pi/delta_L, pi/delta_L]^d where delta_L = L/N and N is the number of voxels along each axis This transforming G linearly such that [pi/delta_L, pi/delta_L]^d is mapped to [0, N]^d. Then it the resulting vectors are rounded... | [
{
"docid": "12a63d6493bd68bf516f4d21d52c8122",
"score": "0.54301816",
"text": "def make_voxel_grid(G, SG, L, N, rot=None):\n \n d, n = G.shape\n absSG = np.abs(SG)\n voxel_width = 2*np.pi / L\n grid_width = voxel_width * N # = 2*pi/delta_L\n \n if rot is None:\n rot = np.eye(... | [
{
"docid": "27a886d3e40bb8d40d0a4fa5674a7df9",
"score": "0.62240213",
"text": "def shell_Green_grid_Nmn_vec_mp(n,k, rsqrgrid,rdiffgrid, RgBgrid,RgPgrid, ImBgrid,ImPgrid, vecBgrid,vecPgrid):\n #rsqrgrid = rgrid**2\n #rdiffgrid = np.diff(rgrid)\n \n RgNvecNrsqr_grid = (RgBgrid*vecBgrid+RgPgrid... |
a42e24db4d41be9d57a2da0e98801c4c | Multiply this fractional operand to another fractional operand. a_b/c multiply d_e/f = ((c a + b) (d f + e)) / (c f) | [
{
"docid": "b760acb6b5453b73ab2176946c36ef31",
"score": "0.55668175",
"text": "def multiply(self, operand2):\n self.makeIrregularFraction()\n operand2.makeIrregularFraction()\n isResultNegative = self.isNegative != operand2.isNegative\n self.numerator *= operand2.numerator\n ... | [
{
"docid": "f5fbb124c279ae869835889d24601e6f",
"score": "0.69394886",
"text": "def __mul__(self, frac):\n new_numerator = self.numerator * frac.numerator\n new_denominator = self.denominator * frac.denominator\n return Fraction(new_numerator, new_denominator)",
"title": ""
},
... |
f448a2968c600d37dce93bf997441498 | This method links a Category to a list of Group | [
{
"docid": "eede6ce81939bfbe1959381b93ac746a",
"score": "0.7888721",
"text": "def _link_cat_group(self, cat, cat_obj, group_list):\n cat_obj_list = list()\n try:\n # Category.groups.add\n cat_obj.groups.add(*group_list)\n # save\n cat_obj.save()\... | [
{
"docid": "dc07fb5791871e78f48da84cdfa1a3df",
"score": "0.64206755",
"text": "def _api_category_to_group(self, entropy, category):\n spm_class = entropy.Spm_class()\n groups = spm_class.get_package_groups()\n for group, g_data in groups.items():\n for cat in g_data['cate... |
f2d83f40b6357c0ca0aac647c671560a | Should get the start and end of each offset. | [
{
"docid": "90b9e75305135641634abe74bceeb5b8",
"score": "0.0",
"text": "def test_next_subpart_offsets(text, expected):\n assert reg_text.next_subpart_offsets(text) == expected",
"title": ""
}
] | [
{
"docid": "7401ab83408dcd1b7fb745a88aaff7e5",
"score": "0.7382333",
"text": "def get_offset(self):",
"title": ""
},
{
"docid": "437dd14916c2fa17b6ec1235a2555499",
"score": "0.7259288",
"text": "def get_doc_offsets(self, doc):\n\n start = self.source.get_offset(doc.location)\n ... |
57be657a4d09eecd5e0fed332fe96af9 | Return the entity reference that corresponds to code | [
{
"docid": "8cb7f31221bab232aa84a5805aa6852b",
"score": "0.8832552",
"text": "def entity_reference_for(self, code: str) -> Optional[EntityReference]:",
"title": ""
}
] | [
{
"docid": "807c490e8a8d52d2472e371fec59ac73",
"score": "0.6120491",
"text": "def get_reference(self):\t\t\n\t\treturn self._reference",
"title": ""
},
{
"docid": "7a5d6d8ad8993b466605566d66559b08",
"score": "0.60175407",
"text": "def ReturnCodeObject(self,code):\n\n if code i... |
e4b168585c64da48116edd182e4474db | The same logic as for font_refine. | [
{
"docid": "3cc440947cb24b5d69238fa0709d5508",
"score": "0.0",
"text": "def height_refine(self, texts):\n height_freq = nltk.FreqDist(text.get('height') for text in texts)\n height_target = int(height_freq.most_common(1)[0][0])\n height_refined_list = [pos for pos, text in enumerate... | [
{
"docid": "e5aafa17e3cd27f10d29e4a465aec290",
"score": "0.71339905",
"text": "def font_refine(self, texts):\n font_freq = nltk.FreqDist(text.get('font') for text in texts)\n font_target = font_freq.most_common(1)[0][0]\n font_refined_list = [pos for pos, text in enumerate(texts) if... |
fa15aafbafc9c886d9e75e75f4558262 | testGetAllAlbums Ensures all albums are returned | [
{
"docid": "2773cfcfdd3cca7300991f7987b267f2",
"score": "0.90769607",
"text": "def testGetAllAlbums(self):\n result = self.musiclibrary.get_all_albums()\n for i in result:\n self.assertTrue(isinstance(i, Album))\n self.assertEqual(len(result), 2)",
"title": ""
}
] | [
{
"docid": "febe3ac780c2a1b610564fafaa7267aa",
"score": "0.794768",
"text": "def test_list_albums(self):\n response = self.client.get('/api/albums/')\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.data[0]['name'], 'A Love Supreme')\n self.assertEq... |
803e18208a2933bfdcf3c062afc2fd57 | Fail the current test case unless stdout contains these strings. | [
{
"docid": "78e699e59f04d2a473a0a5749a4d38b2",
"score": "0.75157183",
"text": "def failUnlessStdOutContains(self, *strings):\n self.failUnlessFileContains(self._get_std_out_file_path(), *strings)",
"title": ""
}
] | [
{
"docid": "3f5950c753466f7067e29ceaf7b32730",
"score": "0.7515792",
"text": "def failIfStdOutContains(self, *strings):\n self.failIfFileContains(self._get_std_out_file_path(), *strings)",
"title": ""
},
{
"docid": "5d0310fe70521192fdc21e9b5b829600",
"score": "0.71225023",
"te... |
85ec884276afd7f9873afbd2d7d50e7c | Verify Unpacker component behavior when run as a script. Test to make sure running the Unpacker as a script does the async setup work that we expect and then launches the unpacker service. | [
{
"docid": "d84feecbb8f3b12f18a1534c64684729",
"score": "0.68615866",
"text": "async def test_script_main(config: TestConfig,\n mocker: MockerFixture,\n monkeypatch: MonkeyPatch,\n path_map_mock: MagicMock) -> None:\n logge... | [
{
"docid": "91d5e1ffe2fea93dfbe4295d2bfbd760",
"score": "0.67049116",
"text": "async def test_unpacker_run(config: TestConfig, mocker: MockerFixture, path_map_mock: MagicMock) -> None:\n logger_mock = mocker.MagicMock()\n p = Unpacker(config, logger_mock)\n p._do_work = AsyncMock() # type: ign... |
0ba32729d4e607f28a3b97350e739912 | Abstract classes can override this to customize issubclass(). This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached). | [
{
"docid": "af451e6cbe8b5cad490094662b26436f",
"score": "0.0",
"text": "def __subclasshook__(self, *args, **kwargs): # real signature unknown\n pass",
"title": ""
}
] | [
{
"docid": "72d481b44f2f757d89c6d3ae6d69e1d4",
"score": "0.68862",
"text": "def __sublcasscheck__(self, subclass):\n return True",
"title": ""
},
{
"docid": "fa76d0451cde61e8f4b0638f59e42be2",
"score": "0.6714018",
"text": "def IsSubclass(self):",
"title": ""
},
{
... |
ed37ca3ea6a892f90d435affc14d6375 | Normalize inputs and reshape into 1d vector | [
{
"docid": "58c664560df240c01f46ed27135cf05b",
"score": "0.0",
"text": "def preprocess_mnist_data_test(X, y, max_X, min_X):\n new_X = ((X - min_X) / (max_X - min_X)).reshape(X.shape[0], X.shape[1] * X.shape[2])\n new_y = y\n return new_X, new_y",
"title": ""
}
] | [
{
"docid": "42da61e6a09edc364e97d04c5f5a3421",
"score": "0.7477076",
"text": "def normalize(x: np.ndarray):\n return x/np.linalg.norm(x, ord=2, axis=0, keepdims=True)",
"title": ""
},
{
"docid": "8e9096b1b7c5ecb04066feacafa4353a",
"score": "0.73763716",
"text": "def normalize_inpu... |
8a3e2d7d8ac79b58a23548c4d9a3f56c | Do not include CtrlNet in link messages describing this session. | [
{
"docid": "38d6897cfe95cbb9b5a71d7bff33ab7e",
"score": "0.0",
"text": "def all_link_data(self, flags):\n return []",
"title": ""
}
] | [
{
"docid": "9a85a0587df89835fc8d53e39a092f2e",
"score": "0.52527857",
"text": "def on_privmsg(self, bot, source, target, message, network, **kwargs):\n\t\tpass",
"title": ""
},
{
"docid": "d86095bd563251b57030e764a2e1298b",
"score": "0.51981294",
"text": "def DisableConnectionStatusM... |
a67f8dfd9f00af424c4b5eb9c3b70773 | This function takes an S3 bucket name and prefix (flat directory path) and returns a list of GeoTiffs. This function utilizes boto3's continuation token to iterate over an unlimited number of records. BUCKETNAME A bucket on S3 containing GeoTiffs of interest PREFIXNAME A S3 prefix. NAMESELECTOR A string used for select... | [
{
"docid": "71f74ac9049ac615ff22dabbc62b266c",
"score": "0.71820045",
"text": "def s3List(bucketName, prefixName, nameSelector, fileformat):\r\n # Set the Boto3 client\r\n s3_client = boto3.client('s3')\r\n # Get a list of objects (keys) within a specific bucket and prefix on S3\r\n... | [
{
"docid": "b048d500640d188e494a2c7bc7f77874",
"score": "0.6359494",
"text": "def list_of_bucket_files(inp:str, prefix='/', delimiter='/',\n include_prefix=False):\n if inp[0:2] == 's3':\n parsed_s3 = urlparse(inp)\n bucket_name = parsed_s3.netloc\n prefix... |
737d257fb52c537ba1d558bdecfe5d06 | Check if the archive at the given path, if present, is suitable for providing the simulation data necessary to perform the analysis specified by this data point. Simulation duration and number of stimulus patterns need to be greater in the archive than in the analysis settings, while the number of trials that can be ex... | [
{
"docid": "5ab1a005ca5dedd83dfe5741230b9c03",
"score": "0.71677554",
"text": "def is_spike_archive_compatible(self, path):\n path_sdur = float(path.rstrip('.hdf5').partition('sdur')[2])\n path_n_trials = float(path.rpartition('_t')[2].partition('_sdur')[0]) * (1 + max(0, (path_sdur - self... | [
{
"docid": "4a519fd0648d595e4f083b3d64be836a",
"score": "0.5734603",
"text": "def zipfile_ok( path_to_archive ):\n basename = os.path.realpath( os.path.dirname( path_to_archive ) )\n zip_archive = zipfile.ZipFile( path_to_archive )\n for member in zip_archive.namelist():\n member_path = ... |
f72da818333b04e8ed1cb8df88d6a471 | This tests that the output for angles is in degrees, and angles must be between (inclusively) 0 and 90 degrees. | [
{
"docid": "5120335bf2c6a9dcb637cc7f2a25e75b",
"score": "0.64520794",
"text": "def test_solve_atmospheric_entry_angle_check(planet, input_data):\r\n frame = planet.solve_atmospheric_entry(**input_data)\r\n assert frame['angle'].min()>= 0 and frame['angle'].max()<= 90",
"title": ""
}
] | [
{
"docid": "62064195b3460b005c4129e23e67aef3",
"score": "0.7224768",
"text": "def check_angle_units(self):\n\t\tif self.degrees_var.get():\n\t\t\tself.degrees = True\n\t\t\tself.angle_unit = 'degrees'\n\t\telse:\n\t\t\tself.degrees = False\n\t\t\tself.angle_unit = 'radians'",
"title": ""
},
{
... |
3f582cc9fc2111a6593a61a3f9554967 | Sets bidask flag to ask | [
{
"docid": "1a529871bd0eeb77af2acd81cd5d796e",
"score": "0.8801817",
"text": "def settoask(self):\n self.bidask = 'ask'\n return",
"title": ""
}
] | [
{
"docid": "8401524260e2e8a345c92c117cd435c3",
"score": "0.8016198",
"text": "def settobid(self):\n self.bidask = 'bid'\n return",
"title": ""
},
{
"docid": "8401524260e2e8a345c92c117cd435c3",
"score": "0.8016198",
"text": "def settobid(self):\n self.bidask = 'bi... |
dc399628be6866fc4fec335cb68facd6 | Returns a string, suitable to be printed on the command line, that contains a record of the repos and commits that are about to be modified. If no ref info is passed in, return None. | [
{
"docid": "5e20dd6cb9fa5c9d8237673a629aedcb",
"score": "0.6277848",
"text": "def todo_list(ref_info):\n if not ref_info:\n return None\n\n entries = []\n for repo, commit_info in ref_info.items():\n when = datetime.datetime.strptime(commit_info['committer']['date'], \"%Y-%m-%dT%H... | [
{
"docid": "d6ec6c1c1f20e3b56b72dc2b29202232",
"score": "0.68548274",
"text": "def _get_git_devstr():\n from os import path\n from subprocess import Popen, PIPE\n\n currdir = path.abspath(path.split(__file__)[0])\n\n p = Popen(['git', 'rev-list', 'HEAD'], cwd=currdir,\n stdout=P... |
4a917e74a71847fe3ac5875c20a236a2 | Return the typecode as Numeric would | [
{
"docid": "c7dcf52b76450dd3554d2aad8cd6395a",
"score": "0.858498",
"text": "def typecode (self) :\n return self.numeric_typecode",
"title": ""
}
] | [
{
"docid": "002d821b322dc801f58b021a7ad5f06f",
"score": "0.7552601",
"text": "def numericType(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "002d821b322dc801f58b021a7ad5f06f",
"score": "0.7552601",
"text": "def numericType(*args, **kwargs):\n \n pass",
... |
69d24f3777f5162334d860172da1770f | Performs WhitePatch to the image | [
{
"docid": "08fdb7af102bc64c6545aefd89e8179b",
"score": "0.71088153",
"text": "def preprocess_whitePatch(im):\n\tbmax, gmax, rmax = np.amax(np.amax(im,axis=0),axis=0)\n\n\talpha = gmax/rmax\n\tbeta = gmax/bmax\n\n\tred = im[:,:,2]\n\tred = alpha * red\n\tred[red > 255] = 255\n\tim[:,:,2] = red\n\n\tblue... | [
{
"docid": "d8ef3bddadc3a4389f28ae8b7fe424ce",
"score": "0.70620036",
"text": "def make_white(self):\n pixel_values = self.get_pixel_list()\n for i in range(len(pixel_values)):\n pixel_values[i] = (255,255,255)\n self.img.putdata(pixel_values)\n return None",
"... |
b0f814314e3cc76797644ae25ab03afe | Stop the timer and return the time since the timer was started. | [
{
"docid": "11cb95140a051401fa310bafae79d06c",
"score": "0.766232",
"text": "def stop(self):\n timeNow = time.time()\n return (timeNow - self.__startTime)",
"title": ""
}
] | [
{
"docid": "abb5e2efe8b4fc6df91392ed90285c2c",
"score": "0.8665982",
"text": "def stop_timer(self):\n\n end_time = time.time()\n return end_time - self.start_time",
"title": ""
},
{
"docid": "88f0dc969dfc0aa2fde845afc5d122b1",
"score": "0.7649472",
"text": "def stop(sel... |
119882ab97b19f78c60d13fce2c82f65 | r"""Returns the BPM (beats per minutes) of a list of beats, assuming the list is complete and accurate. | [
{
"docid": "592f458b77b2d0adc510f0f41daee559",
"score": "0.69709456",
"text": "def ground_truth_bpm(beats):\n\n bpm = 60 / (beats[1:] - beats[:-1]).mean()\n\n return bpm",
"title": ""
}
] | [
{
"docid": "d54d03f51d099aa21181ae32d5e769ca",
"score": "0.6421484",
"text": "def milliseconds(BPM, duration):\t\n\tif duration == None: \n\t\tduration = 1\n\t#Divide BPM by 60s to get beats per second\n\t#Divide by 1000ms to get milliseconds per beat\n\t#Multiply by fractional duration to get total mil... |
8bc0e5129267125afb0c3e5f0947ebaf | Delete the given IAM ARN from the KMS key policy. | [
{
"docid": "9da9d7c99c157c44a23c702a00e9eddf",
"score": "0.6872568",
"text": "def cloudformation_delete(event, context):\n if DEBUG_MODE is True:\n print(\"Delete Option: Attempting to remove IAM ARN from KMS key policy\")\n original_policy_document = get_kms_key_policy(event, context)\n ... | [
{
"docid": "a12c55925ad0ccb847242cecccbbc109",
"score": "0.6743698",
"text": "def delete_old_access_key(key_to_delete):\n iam_client = boto3.client('iam')\n iam_client.delete_access_key(AccessKeyId=key_to_delete)\n print('Old access key deleted from IAM')\n return True",
"title": ""
},... |
3a862f5cfff57b8de066b6f30c9a98b2 | 1. Cleanup A sample dataset of request logs is given in data/DataSample.csv. We consider records that have identical geoinfo and timest as suspicious. Please clean up the sample dataset by filtering out those suspicious request records. | [
{
"docid": "6335b04ed831ca4cb7949ca1fd767921",
"score": "0.0",
"text": "def main(data_file, poi_list_file, output_file):\n # _ID, TimeSt,Country,Province,City,Latitude,Longitude\n df = get_clean_df(data_file, ['TimeSt', 'Latitude', 'Longitude'])\n\n \"\"\"\n 2. Label\n Assign each request... | [
{
"docid": "75a4f3cedefe50cd0d8aa5ff969fb08e",
"score": "0.63649476",
"text": "def clean():\n try:\n data_mining.clean_data(adult_data_test)\n except Exception as e:\n raise e",
"title": ""
},
{
"docid": "2255c8d3ff02585a1ef548fa2a757b33",
"score": "0.62893784",
"... |
0c21c3aa64ea60a0322f8d5c32cfb4ae | Method used to destroy a database | [
{
"docid": "39da3f35b7a57f0914b8b981896ebe3b",
"score": "0.73417217",
"text": "def teardown_db():\n DBSession.rollback()\n DBSession.remove()\n engine = config['pylons.app_globals'].sa_engine\n model.metadata.drop_all(engine)\n print \"TEARDOWN DB\"\n\n #transaction.doom()",
"title... | [
{
"docid": "485663d4dd82036f50bfe78448227ad2",
"score": "0.8324287",
"text": "def destroy():\n\tif db:\n\t\tdb.close()\n\n\trelease_local(local)",
"title": ""
},
{
"docid": "b6cde607122211416d61546f50e65669",
"score": "0.81100446",
"text": "def destroy():\n\tdb.drop_tables()",
"t... |
4f29c4e79331b1b7e2a2d5ad66f74405 | Test scale virtual machine under useraccount | [
{
"docid": "cc8dbe8949a2e0f61bf17786ac092c7e",
"score": "0.79915226",
"text": "def test_04_scale_vm_with_user_account(self):\n # Validate the following\n # Create a user Account and create a VM in it.\n # Scale up the vm and see if it scales to the new svc offering\n\n # Crea... | [
{
"docid": "a1b60e7b5bdf8ff8b895fd3e192e9be7",
"score": "0.70089865",
"text": "def test_01_scale_vm(self):\n # Validate the following\n # Scale up the vm and see if it scales to the new svc offering and is\n # finally in running state\n\n # VirtualMachine should be upd... |
601ad08ca2b8fc896799d6e5961d8229 | turn 90 degree counterclockwise | [
{
"docid": "2748b958352a2102af5bcd2b3f2bd4fd",
"score": "0.0",
"text": "def turn_left(self):\r\n ###### add the connection to EV3 here!!\r\n self.current_direction = (self.current_direction + 1) % 4\r\n self.turn_count += 1\r\n if self.algo == \"dfs\":\r\n self.dif... | [
{
"docid": "ef247b04cc2412111a1afce9d44cb88c",
"score": "0.7279652",
"text": "def counterclockwise(self):\n\n self.current_frame -= 1\n if self.current_frame < 0: \n self.current_frame = len(self.frames) - 1",
"title": ""
},
{
"docid": "4df79113cd85186c2b9a4a2cfcf300... |
39a308cdc700abad40d5c0b3ae2e734e | Split password output by newline, extract user and name (1st and 5th columns), strip trailing commas from name, replace multiple commas in name with a single space return dict of keys = user, values = name. | [
{
"docid": "b2f724e328de9a078cfa3a1d2390356e",
"score": "0.7304084",
"text": "def get_users(passwd: str) -> dict:\n output = {}\n for row in passwd.strip().splitlines():\n fields = row.split(':')\n username = fields[0]\n name = re.sub(r',+', r' ', fields[4].strip(',')) or 'unk... | [
{
"docid": "a6573ab85894ae51cc1a1c7f22042722",
"score": "0.6514482",
"text": "def parse_passport_fields(passport: str) -> Dict[str, str]:\n fields = [f.split(\":\") for f in passport.replace(\"\\n\", \" \").split(\" \")]\n return {k: v for k, v in fields}",
"title": ""
},
{
"docid": "c... |
400e82e3319afc16e891b92f2f5025f8 | This program loads an image and applies the narok filter to it by setting "bright" pixels to grayscale values. | [
{
"docid": "553d12c6a59be2318aa810742e2de8e7",
"score": "0.7051816",
"text": "def main():\n image = SimpleImage('images/simba-sq.jpg')\n\n # visit every pixel (for loop)\n for pixel in image:\n # find the average\n average = (pixel.red + pixel.green + pixel.blue) // 3\n # i... | [
{
"docid": "aaff04cfdc2cf4e68577283169eb7f6d",
"score": "0.64812326",
"text": "def main():\n image = SimpleImage('images/girl.jpeg')\n\n for pixel in image:\n if should_be_black(pixel):\n pixel.red = 255\n pixel.green = 255\n pixel.blue = 255\n else:\... |
3025508deddeea3b3da3e3544ebf574f | Function that plots the predictions's distribution of the generated SMILES strings, obtained by the unbiased and biased generators. | [
{
"docid": "310efd8be6cc05366413082477410959",
"score": "0.6883328",
"text": "def plot_hist_both(prediction_unb,prediction_b, n_to_generate,valid_unb,valid_b,property_identifier):\r\n prediction_unb = np.array(prediction_unb)\r\n prediction_b = np.array(prediction_b)\r\n \r\n legend_unb = ''... | [
{
"docid": "a0e8f36e4c8ac676c433979230d45dde",
"score": "0.72062135",
"text": "def plot_hist(prediction, n_to_generate,valid,property_identifier):\r\n prediction = np.array(prediction)\r\n x_label = ''\r\n plot_title = '' \r\n \r\n print(\"Proportion of valid SMILES:\", valid/n_to_generat... |
c95ad2851b4b88758091bdb9b28f0a00 | Refine classified proposals and filter overlaps and return final detections. | [
{
"docid": "1e09d1cd9b53b578bde145b240202520",
"score": "0.549406",
"text": "def detections(rois, probs, deltas, image_meta, config):\n window = image_utils.unmold_meta(image_meta)[\"window\"]\n\n # Class IDs per ROI\n _, class_ids = torch.max(probs, dim=1)\n\n # Class probability of the top... | [
{
"docid": "f4ac702ac1652ea8b9c97ef26a274eef",
"score": "0.67093545",
"text": "def filter_region_proposals(self):\n for i, datum in enumerate(self.data):\n H, W = datum['h'], datum['w']\n scale = self.target_image_size / max(H, W)\n\n scale /= 8\n okay ... |
997b40ed375c4758b625122b5db3683f | This method will be called after `connect` is called. After this method finishes, the writer will be drained. Subclasses should make use of this if they need to send data to Telegram to indicate which connection mode will be used. | [
{
"docid": "7601b8873ffa5e67c8ece4089717af6f",
"score": "0.54554355",
"text": "def _init_conn(self):\n if self._codec.tag:\n self._writer.write(self._codec.tag)",
"title": ""
}
] | [
{
"docid": "38bb38c729d480f310e2383f029b560b",
"score": "0.6488908",
"text": "def writer(self):\n while self.alive:\n try:\n data = self.socket.recv(1024)\n if not data:\n break\n self.log.info(\"<-- %s\", list(self.rfc221... |
b8bd007d7f453e89f2351907b7a4429b | Implements the predicate by determining if value == operand. | [
{
"docid": "deb7e0aa559f80739b46afe69143c166",
"score": "0.5602225",
"text": "def __call__(self, context, value):\n if isinstance(value, basestring):\n return self.__check_operand_and_call(context, basestring, value, STR_EQ)\n if isinstance(value, dict):\n return self.__check_operand_and... | [
{
"docid": "11a75fcbb4e743ecc5c8b98a89b568f8",
"score": "0.6947697",
"text": "def __eq__(self, value):\n\t\treturn self.value == value",
"title": ""
},
{
"docid": "931edb90b66dae2fb0daf56159d740ba",
"score": "0.6722959",
"text": "def __contains__(self, value):\n\n return self.... |
d5a70aa3d0079f848917f115d0b46540 | Get the currently applied filters with their readable values. Looks up the humanfriendly value for fields with choices and calculates the url to remove each filter. | [
{
"docid": "7fa5890a2804d20117836bbd1bcbfb4a",
"score": "0.6289799",
"text": "def get_readable_filters(self, with_remove_links=False):\n filters = {}\n\n for name, value in self.get_raw_filters().items():\n value = copy.copy(value)\n key = self.get_filter_key(name)\n ... | [
{
"docid": "303fa0458ac682ca8979e0fa2166fdc4",
"score": "0.65556693",
"text": "def get_filters(self):\r\n return self.__filters",
"title": ""
},
{
"docid": "8e795a969cb39f4fbbad82ed8a31d14d",
"score": "0.6443963",
"text": "def get_filter(self):\n return self.__filters[s... |
9c8cd9ef14c8648c41dbce6c4d7aaf41 | Will be deprecated in favor of Simulation.initialize(). Replace in code, will be removed at or before v1.0.0 | [
{
"docid": "501d6281c92d3fe97c4124f65fc3dce0",
"score": "0.8208679",
"text": "def initialize_simulation(self):\n\n warnings.warn(\n \"Simulation.initialize_simulation() is replaced by Simulation.initialize() and will be deprecated at or before defSim v1.0.0\",\n category=Fut... | [
{
"docid": "469c26f0edd3c0dded13ebf120d8c8b2",
"score": "0.68212414",
"text": "def _setup_sim(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ddcb531260db5f74dbf20d2a6c653f02",
"score": "0.6753155",
"text": "def init_sim():\n\n print(\"Creating population\")... |
e832510024261fa3c983fe9e7e7854e3 | Delete a new BridgeDomain. | [
{
"docid": "4b6866a437046c6754ad6e6ff7cc6f3a",
"score": "0.0",
"text": "def delete(self, node_type, node_id):\n url = self._url('node', node_type, node_id)\n self._delete(url)",
"title": ""
}
] | [
{
"docid": "216d0d7b993759eb24ef3fbae02bc061",
"score": "0.7052366",
"text": "def delete_domain(self, domain_spec, delete_dependencies=False):\n pass",
"title": ""
},
{
"docid": "f324136af1685db477ea2804a46409e7",
"score": "0.69567776",
"text": "def delete_domain(self, domain_... |
61d3b6d8df4e0289ea1bd7a1758748b7 | Counts the total number of bags contained in the bag | [
{
"docid": "f2ba0b2855248ddcd7616a456eabd28b",
"score": "0.7824124",
"text": "def inside_bag_count(self):\n if not self.containees:\n return 0\n\n count = 0\n for number, name in self.containees:\n count += number * (1 + Bag.collection[name].inside_bag_count)\n... | [
{
"docid": "3b7d03054db7468d5c2144763718f1ec",
"score": "0.8159138",
"text": "def num_bags(bag_type: str) -> int:\n\n result = 1\n\n for key, value in rules[bag_type].items():\n # We add 1 to num_bags(key) so that we count the actual bag with type key.\n result += value * (num_bags(k... |
dbfd34aebb1c0ddd81638ddcf1da768c | Get Passive total data | [
{
"docid": "4cf6299277031da97255163e759e2785",
"score": "0.0",
"text": "def getPT(self):\n if not self.ptapi:\n return False\n command = \"curl -m 25 --insecure -s -u \" + self.ptuser + \":\" + self.ptkey + \" 'https://api.riskiq.net/pt/v2/dns/passive?query=\" + self.ip + \"'\"\... | [
{
"docid": "231da07473aef7149cd6581d0b1881bc",
"score": "0.7550525",
"text": "def patrimony_total(self):\n pass",
"title": ""
},
{
"docid": "f15ad4868023119d2906fb1a4dddec28",
"score": "0.71757656",
"text": "def total(self):\n\t\treturn self._total",
"title": ""
},
{
... |
37e7d438ef02abdf6317338e042de9cd | Test the `config.commands.search.context` setting. | [
{
"docid": "5df43a952d6d15b82c678129969031a8",
"score": "0.73624146",
"text": "def test_context_configuration(self, setup: Any) -> None:\n config.commands.search.context = 2\n\n cmd = SearchCommand(\n \"einstein\",\n \"-i\",\n \"-c\",\n \"2\",\n ... | [
{
"docid": "e10689036335fbbb48a0cd39ef5563c5",
"score": "0.62758994",
"text": "def test_contexts_search(self):\n from .mockers import create_context, create_contextA, create_contextB\n\n self.create_context(create_context)\n self.create_context(create_contextA)\n self.create_... |
5156c7a834bb7a3488e6720c34c7a1d2 | Pops next token value | [
{
"docid": "716e5a5cb8b66a74e098e1bb9a782765",
"score": "0.7025629",
"text": "def next(tokens):\n return tokens.next_pair()[1]",
"title": ""
}
] | [
{
"docid": "1c44fe9e59a741515ad58d732a600e80",
"score": "0.77126354",
"text": "def _pop_token(self):\n return self._tokens.pop(0)",
"title": ""
},
{
"docid": "e5f3e681cbe93e988efa84ffc58ddd7a",
"score": "0.7448798",
"text": "def pop_next():",
"title": ""
},
{
"doci... |
13e3bf1628847f08c6bbeb374b7ecc03 | r"""__init__(GreaterImageFilter self) > GreaterImageFilter | [
{
"docid": "3a6e3a1b8ec12a640b671ba41e2e245f",
"score": "0.812999",
"text": "def __init__(self):\n _SimpleITK.GreaterImageFilter_swiginit(self, _SimpleITK.new_GreaterImageFilter())",
"title": ""
}
] | [
{
"docid": "3516fce8e9fb3bf328b4cc48fdf578fd",
"score": "0.7975903",
"text": "def __init__(self):\n _SimpleITK.GreaterEqualImageFilter_swiginit(self, _SimpleITK.new_GreaterEqualImageFilter())",
"title": ""
},
{
"docid": "f164024e19dfe04953f63af16a9ffdb8",
"score": "0.71127456",
... |
847ca392c243018c190d6237ec0e2352 | list all the G(r) plotted on the figure now | [
{
"docid": "242e919f2ea08f56bc288b869e8ee391",
"score": "0.6194304",
"text": "def get_current_grs(self):\n return list(self._grDict.keys())",
"title": ""
}
] | [
{
"docid": "065bd8f9420f9d7fe78a71dd623e2dcf",
"score": "0.69316924",
"text": "def plot(self):\n return []",
"title": ""
},
{
"docid": "62de181701f8f7af09e91c98fc09444f",
"score": "0.63560665",
"text": "def show_plots():",
"title": ""
},
{
"docid": "62de181701f8f7a... |
31f6c1ca506d156c73e219cee470ccee | Generate a point in a circle, or can think of it as a vector pointing a random direction with a random magnitude <= radius | [
{
"docid": "d4835c19c0b6821c9b1897a49c1ba71c",
"score": "0.81278366",
"text": "def rand_in_circle(center: Vec2d, radius: float) -> Vec2d:\n # random angle\n angle = 2 * math.pi * random.random()\n # random radius\n r = radius * random.random()\n # calculating coordinates\n return Vec2d... | [
{
"docid": "5d91a770b378eed66a786ea00913c8e5",
"score": "0.7678962",
"text": "def generate_random_circle():\n\n exp_range = numpy.random.exponential(scale=0.1, size=100)\n d = cumsum(exp_range[exp_range > 0.05])\n t = d[d < 2*pi]\n\n x = sin(t)\n y = cos(t)\n\n return t, x, y",
"ti... |
06680ac87d9b16ae182ac51377a8d556 | Test the accuracy of a model on some labelled test dataset. | [
{
"docid": "074b10b233659b2c8690b8d1868318ee",
"score": "0.0",
"text": "def test(model, dataloader):\n # Switch the model to eval mode.\n model.eval()\n device = model.device\n\n time_start = time.time()\n batch_time = 0.0\n accuracy = 0.0\n\n # Deactivate autograd for evaluation.\n... | [
{
"docid": "948ec61063e23c45cf71c53a41bbcd25",
"score": "0.7570441",
"text": "def test(self, dataset):\n self.model.eval() \n with torch.no_grad():\n total_samples, corrects = 0, 0\n pred_class, label_class = [], []\n for images, labels in dataset:\n ... |
7486922858b721cc978be76d5a1a6712 | Verify that the filepath argument is a valid file. | [
{
"docid": "6f18d6f3c6e04367161933d692e6fbb5",
"score": "0.7751014",
"text": "def verify_filepath(filepath: str) -> str:\n filepath = os.path.abspath(os.path.expandvars(os.path.expanduser(filepath)))\n if not os.path.isfile(filepath):\n raise ValueError(f\"'{filepath}' is not a valid filepa... | [
{
"docid": "7b3f148a410921b24e902a0a56697f98",
"score": "0.81745076",
"text": "def valid_file(filepath):\n return os.path.isfile(filepath)",
"title": ""
},
{
"docid": "8c15f7c1c876f4b72d4f5a952f1150be",
"score": "0.7708852",
"text": "def chkfile(path):\n if not os.path.isfile(p... |
e4689a2a4bb3d8dde2e8fd0626a3ea9f | Execute a serverside transaction | [
{
"docid": "4a1b857f9e5292ec65e493069ace3cef",
"score": "0.0",
"text": "def transaction(self, collections, action, wait_for_sync = False, lock_timeout = None, params = None):\n payload = {\n \"collections\": collections,\n \"action\": action,\n \"wait_... | [
{
"docid": "b2f4adf30873647179657713cd0f4368",
"score": "0.7078037",
"text": "def transaction(self, *args, **kwargs):\n return self._operate(Connection.transaction, args, kwargs)",
"title": ""
},
{
"docid": "ba488214cac0d5aab92031b2d45241dc",
"score": "0.65641505",
"text": "de... |
13466d15f5f4824688018c31f9057ed5 | Fetch messages up to the maximum, return email.Message objects. | [
{
"docid": "5942301071fd7181362ee182b6626593",
"score": "0.7981605",
"text": "def fetch_messages(self, max_messages=15):\n # Connect to the IMAP4 mailbox\n mailbox = imaplib.IMAP4(self.host, int(self.port))\n mailbox.login(self.user, self.passwd)\n mailbox.select()\n \... | [
{
"docid": "3388dfd7de27679aac6ba6880a661420",
"score": "0.79869056",
"text": "def fetch_messages(self, max_messages=15):\n # Connect to the POP3 mailbox\n mailbox = poplib.POP3(self.host, self.port)\n mailbox.user(self.user)\n mailbox.pass_(self.passwd)\n\n # Look up ... |
09b5d53d36a23680222292030a612cd8 | This function goes through all the points in the data set and returns the indices of the samples to put into the training set. X is the data set to split while k is the number of points that should be put into the training set. | [
{
"docid": "58b0f5ab32ed2344cfb887e83fa446e4",
"score": "0.6394534",
"text": "def fft_idx(X, k):\n\n # Creating the matrix of the distances\n dist_mat_glob = np.zeros(shape=(X.shape[0], X.shape[0]))\n\n for i in range(X.shape[0] - 1):\n for j in range(i + 1, X.shape[0]):\n dis... | [
{
"docid": "88ab12ff6a2547a51bc03c813ace4c65",
"score": "0.7543154",
"text": "def k_split(dataset, k):\n\n\n index = np.random.permutation(dataset[0].shape[0])\n fold_index= np.split(index, k)\n\n return fold_index",
"title": ""
},
{
"docid": "cdd15b0fabbd27eecfdfd8f7ba95f9cd",
... |
4dbccca105258ecee5465e2cf45445a6 | EfficientNetB1 AdvProp. Tensorflow compatible variant | [
{
"docid": "2683435426b061f5b465f905f5964349",
"score": "0.6500853",
"text": "def tf_efficientnet_b1_ap(pretrained=False, **kwargs):\n kwargs[\"bn_eps\"] = BN_EPS_TF_DEFAULT\n kwargs[\"pad_type\"] = \"same\"\n model = _gen_efficientnet(\"tf_efficientnet_b1_ap\",\n c... | [
{
"docid": "61f985ccd4b9affc2bdd78c1c09e8bfd",
"score": "0.5862529",
"text": "def task_metalearn(inp, reuse=True):\n inputa, inputb, labela, labelb = inp # input = (NK,latent_dim) label = (NK, num of au, N), N = num of class\n #inputa = tf.reshape(inputa, [int(inputa.shap... |
66654227fe48409f5632140195afb885 | Simulate the given `move' on the current game to see if it is worth making. This does not modify the game passed in, it returns a modified copy. | [
{
"docid": "a544d23f0e32584a8c635fe1f84a69c4",
"score": "0.75738066",
"text": "def simulate(game, move):\n\n def play_one_move(game, current_player, memory, user_args):\n return move\n\n # Make a copy\n new_game = deepcopy(game)\n new_game = play_one_turn(new_game,\n ... | [
{
"docid": "5e4c62077b64c1a05e5257a263e86b6b",
"score": "0.71304905",
"text": "def make_move(self, game):\n cell_map = game.field.get_cell_mapping()\n\n # if random.random() < 0.5:\n return self.make_random_birth_move(game, cell_map)\n\n # return self.make_random_kill_move(ga... |
9b4661bdc794e3f86f83595908714d28 | Get MySQL config via config file or command line. | [
{
"docid": "1828b595138a6ad40c43e966d3bdd3d4",
"score": "0.75491196",
"text": "def get_config(args):\n config = {\n 'mysql_host': '127.0.0.1',\n 'mysql_port': 3306,\n 'mysql_user': 'root',\n 'mysql_password': '',\n 'tcpproxy_mgmt_port': args.tcpproxy_mgmt_port,\n ... | [
{
"docid": "d874b506a34823a8e1687f474869fe88",
"score": "0.68761575",
"text": "def load_mysql_config(mysql_config_path, config):\n\n parser = SafeConfigParser()\n try:\n parser.read(mysql_config_path)\n except Exception as e:\n logger.warning(\"cannot read config from %s: %s\", my... |
e86cabd8a25cb1c663355bfcf3083d53 | Run the optimization algorithm. | [
{
"docid": "498e1f0984a2bba66c03fc845c00e36d",
"score": "0.5889775",
"text": "def optimize(self, fw_spec, manager_id):\n x = list(fw_spec[\"_x\"])\n y = fw_spec[\"_y\"]\n if isinstance(y, (list, tuple)):\n if len(y) == 1:\n y = y[0]\n self.n_objs... | [
{
"docid": "2e4784ec13198e30e578db97a463bfa4",
"score": "0.7996382",
"text": "def run(self):\n # set up logging, write params\n self.setup_optimization()\n\n # set up pool of workers\n self.setup_pool()\n\n with self.open_data('w'):\n # run optimization\n ... |
dfc568703883c3f5c67fc15a44cb52d9 | Implement me! Returns the probability of word given label (i.e., P(word|label)) according to this NB model. | [
{
"docid": "313e08f48361dc9c32b17ec7c150e237",
"score": "0.84249485",
"text": "def p_word_given_label(self, word, label):\r\n count_word=self.class_word_counts[label][word]\r\n total_class_wordcount = self.class_total_word_counts[label]\r\n prob_wgl= count_word/total_class_wordcount... | [
{
"docid": "d187e09e9d2bae1d2e9304a857550a75",
"score": "0.81052285",
"text": "def p_word_given_label(vocab, training_data, label):\n \n ''' the formula for computing prob of a word with given label is found on piazza '''\n smooth = 1 # smoothing factor\n word_prob = {}\n vocab_with_label... |
d7792d0e3f08f27f49da89a5874a6e0d | Predicts from a model and writes out predictions to a csv. | [
{
"docid": "a75b79bfbc8af2d55b570c2c6d4c4896",
"score": "0.0",
"text": "def predict_model(\n predict_config: PredictConfig,\n video_loader_config: VideoLoaderConfig = None,\n):\n # get default VLC for model if not specified\n if video_loader_config is None:\n video_loader_config = Mod... | [
{
"docid": "498bc8b339d0c559f2a53fd8dc4fa77e",
"score": "0.7373088",
"text": "def model_prediction(file_path, model_file, save_file) :\n model = load(os.path.join(cf.OUTPUTS_MODELS_DIR, model_file)) \n\n def probability_predictions(model, data, raw_data) :\n new_X = raw_data.copy()\n ... |
3610c59c09342df8d90687334c5c0136 | addSpeciesType(Model self, SpeciesType st) > int Adds a copy of the given SpeciesType object to this Model. Parameter 'st' is the SpeciesType object to add. Returns integer value indicating success/failure of the function. | [
{
"docid": "050ab371dc3ef3d31b4a907fadbd7500",
"score": "0.7829603",
"text": "def addSpeciesType(self, *args):\n return _libsbml.Model_addSpeciesType(self, *args)",
"title": ""
}
] | [
{
"docid": "7d42d78767cc72ea43d1d585ff3ea5cd",
"score": "0.67754364",
"text": "def addSpecies(self, *args):\n return _libsbml.Model_addSpecies(self, *args)",
"title": ""
},
{
"docid": "7f2e0c31b7a71caa3c025d8f64b13c9d",
"score": "0.6621497",
"text": "def addSpeciesTypeInstance... |
c1fb9aa873b120f39122ceb96f8db1b6 | Create instance object with output path. | [
{
"docid": "45e9ab49e0ca8ee32110fd94438539a7",
"score": "0.7741643",
"text": "def __init__(self, out_path):\n self.outPath = out_path",
"title": ""
}
] | [
{
"docid": "617798d336acf639edf2f5e43857c80e",
"score": "0.7930995",
"text": "def __init__(self, output_path):\n self.output_path = output_path",
"title": ""
},
{
"docid": "862151cf8491febec26fe8d244e68d0f",
"score": "0.7642023",
"text": "def __init__(self, output_path):\r\n ... |
47c5c3b342c8110430cd9c4b54133708 | Creates a point C in (ab) direction so that \\|aC\\| = L | [
{
"docid": "5eed3aac87a36900c48ad1ea7b85b842",
"score": "0.5909008",
"text": "def extend(point_a: Tuple[float, float], point_b: Tuple[float, float],\n L: float) -> Tuple[float, float]:\n\n xa, ya = point_a\n xb, yb = point_b\n u_vec = [xb - xa, yb - ya]\n u_vec /= np.linalg.norm(u_... | [
{
"docid": "89d197710005889193a344a3915b0ba2",
"score": "0.5912336",
"text": "def tangent(A,B,C):\n AB=pointplan.len_deux_coord(A,B)\n AC=pointplan.len_deux_coord(A,C)\n BC=pointplan.len_deux_coord(B,C)\n longeur=-AB+AC+BC\n cercleC=cercle.create(C,longeur/2)\n cercle.draw_circle(cercl... |
45cdf2c4e620f0dc0d1d7b188af964b8 | max_output_buffer(multiply_const_vff_sptr self, int i) > long | [
{
"docid": "4abd98a7eb6a5530fbc26f8e30a4a042",
"score": "0.8243991",
"text": "def max_output_buffer(self, *args, **kwargs):\n return _blocks_swig3.multiply_const_vff_sptr_max_output_buffer(self, *args, **kwargs)",
"title": ""
}
] | [
{
"docid": "d1807baede23d2ee26625f9e60539d6a",
"score": "0.81873864",
"text": "def max_output_buffer(self, i):\n return _howto_swig.suma_ff_sptr_max_output_buffer(self, i)",
"title": ""
},
{
"docid": "33ca18b8c3a6f11fa3d85dadf64bf841",
"score": "0.81466925",
"text": "def max_o... |
9624bf2ef681ef113225db87904e272c | r"""svn_client_conflict_option_set_merged_propval(svn_client_conflict_option_t option, svn_string_t const merged_propval) | [
{
"docid": "916a9dd4ce41cb29b672dfb56689247e",
"score": "0.9387135",
"text": "def svn_client_conflict_option_set_merged_propval(option: \"svn_client_conflict_option_t\", merged_propval: \"svn_string_t const *\") -> \"void\":\n return _client.svn_client_conflict_option_set_merged_propval(option, merge... | [
{
"docid": "7a9333759aa8c8c2519ef7f00daab0e9",
"score": "0.5422297",
"text": "def svn_client_conflict_option_set_moved_to_repos_relpath(*args) -> \"svn_error_t *\":\n return _client.svn_client_conflict_option_set_moved_to_repos_relpath(*args)",
"title": ""
},
{
"docid": "f30d3319c2e878ae5... |
80d65ada762dd0277d9b7c795d0b6a29 | It should apply strict validation to the metadata and requirements dicts. It should reject protocols with questionable metadata and requirements dicts, even though these protocols may be accepted by other parts of the system. | [
{
"docid": "89db704406804ea5572fd250cd8532ff",
"score": "0.691029",
"text": "def test_strict_metatada_requirements_validation(tmp_path: Path) -> None:\n protocol_source = textwrap.dedent(\n \"\"\"\n # apiLevel in both metadata and requirements\n metadata = {\"apiLevel\": \"2.15\"... | [
{
"docid": "820a3eeeabd346ddabb8917309ff21c3",
"score": "0.64846337",
"text": "def _check_requirements(self):",
"title": ""
},
{
"docid": "7eb573492516fe12f09ad7ec6dae1277",
"score": "0.64280707",
"text": "def verify_model_typing(self):\n for k, v in self.requirements.items():... |
de2fbfe2e08ded1a0e9d9495536a4140 | Convert sysctl configuration dict to text with each property value pair separated on new line | [
{
"docid": "fec04bc0ed396013d09614a8396f6414",
"score": "0.75843936",
"text": "def __convert_sysctl_dict_to_text():\n import params\n sysctl_file_content = \"### HAWQ System Parameters ###########\\n\"\n for key, value in params.hawq_sysctl.iteritems():\n if not __valid_input(value):\n raise ... | [
{
"docid": "6cce26b37701ab14f49d551619616a99",
"score": "0.6548037",
"text": "def dict_to_conf_str(obj):\n config_list = []\n for key in sorted(obj.keys()):\n value = obj[key]\n config_list.append(\"%s=%s\" % (key, obj[key]))\n return '\\n'.join(config_list)",
"title": ""
},... |
5a170df1f6f4046749fb8246b5d863cb | Run all tasks with the given action. | [
{
"docid": "2768e253d9a9feb8ff2c752474a9baf9",
"score": "0.0",
"text": "def run(self, path):\r\n self._has_run = True\r\n result = None\r\n errors = []\r\n for task in self._tasks:\r\n if not errors or not task.may_run_user_code():\r\n try:\r\n ... | [
{
"docid": "741c76fa42f3f650741d4694317cd0bb",
"score": "0.70779276",
"text": "def do_action(self, action: Action):\n for manager in self.managers:\n manager.on_action(self, action)",
"title": ""
},
{
"docid": "3ab12bb7530d4844d428ed9a7da185d5",
"score": "0.69594294",
... |
66b0d1302ed03c32c185e08cd405c3a2 | A function to generate package version desc | [
{
"docid": "b96b6bd4310f475a4d836afbb3e9dac3",
"score": "0.6709719",
"text": "def gen_package_desc(pkgs, host):\n desc = \"<table>\"\n for pkg in pkgs:\n try:\n pkg_str = str(package.Package.from_name(pkg, host=host))\n except package.PackageNotExistsError:\n pk... | [
{
"docid": "c794c2a1887c60a516bf706705a90cd9",
"score": "0.7154892",
"text": "def version(self) -> str:",
"title": ""
},
{
"docid": "7b9bce8f2f1ee8c453738d801bdc065e",
"score": "0.7082373",
"text": "def version_desc(self):\n return self._version_desc",
"title": ""
},
{... |