BoghdadyJR/codesearch_python · Data Studio

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def train(model, X_train=None, Y_train=None, save=False, predictions_adv=None, evaluate=None, args=None, rng=None, var_list=None, attack=None, attack_args=None): """ Train a TF Eager model :param model: cleverhans.model.Model :param X_train: numpy array with training inputs :para...
def model_eval(model, X_test=None, Y_test=None, args=None, attack=None, attack_args=None): """ Compute the accuracy of a TF Eager model on some data :param model: instance of cleverhans.model.Model_Eager with pretrained weights for evaluation. :param X_test: numpy array with tra...
def model_argmax(model, samples): """ Helper function that computes the current class prediction :param samples: numpy array with input samples (dims must match x) :return: the argmax output of predictions, i.e. the current predicted class """ tfe = tf.contrib.eager tf_samples = tfe.Variable(samples) pr...
def generate(self, x, kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(kwargs) if self.ord != np.inf: rai...
def initialize_dual(neural_net_params_object, init_dual_file=None, random_init_variance=0.01, init_nu=200.0): """Function to initialize the dual variables of the class. Args: neural_net_params_object: Object with the neural net weights, biases and types init_dual_file: Path to fil...
def eig_one_step(current_vector, learning_rate, vector_prod_fn): """Function that performs one step of gd (variant) for min eigen value. Args: current_vector: current estimate of the eigen vector with minimum eigen value. learning_rate: learning rate. vector_prod_fn: function which returns produc...
def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn): """Computes eigenvector which corresponds to minimum eigenvalue. Args: x: initial value of eigenvector. num_steps: number of optimization steps. learning_rate: learning rate. vector_prod_fn: function which takes x and returns pr...
def tf_lanczos_smallest_eigval(vector_prod_fn, matrix_dim, initial_vector, num_iter=1000, max_iter=1000, collapse_tol=1e-9, dtype=tf.f...
def get_vars(self): """ Provides access to the model's Variables. This may include Variables that are not parameters, such as batch norm running moments. :return: A list of all Variables defining the model. """ # Catch eager execution and assert function overload. try: if tf.execu...
def fprop(self, x, dropout=False, dropout_dict=None, **kwargs): """ Forward propagation as either no-op or dropping random units. :param x: The input to the layer :param dropout: bool specifying whether to drop units :param dropout_dict: dict This dictionary is usually not needed. In...
def generate(self, x, **kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: A tensor with the inputs. :param kwargs: See `parse_params` """ assert self.sess is not None, \ 'Cannot...
def parse_params(self, y=None, y_target=None, batch_size=1, confidence=0, learning_rate=5e-3, binary_search_steps=5, max_iterations=1000, abort_early=True, ...
def attack(self, imgs, targets): """ Perform the L_2 attack on the given instance for the given targets. If self.targeted is true, then the targets represents the target labels If self.targeted is false, then targets are the original class labels """ r = [] for i in range(0, len(imgs), sel...
def attack_batch(self, imgs, labs): """ Run the attack on a batch of instance and labels. """ def compare(x, y): if not isinstance(x, (float, int, np.int64)): x = np.copy(x) if self.TARGETED: x[y] -= self.CONFIDENCE else: x[y] += self.CONFIDENCE ...
def maybe_load_model(savedir, container): """Load model if present at the specified path.""" if savedir is None: return state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip')) if container is not None: logger.log("Attempting to download model from Azure") found_model = container....
def check_installation(cur_file): """Warn user if running cleverhans from a different directory than tutorial.""" cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0] ch_dir = os.path.split(cleverhans.__path__[0])[0] if cur_dir != ch_dir: warnings.warn("It appears that you have at least tw...
def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser( description='Tool to download dataset images.') parser.add_argument('--input_file', required=True, help='Location of dataset.csv') parser.add_argument('--output_dir', required=True, ...
def get_image(row, output_dir): """Downloads the image that corresponds to the given row. Prints a notification if the download fails.""" if not download_image(image_id=row[0], url=row[1], x1=float(row[2]), y1=float(row[3]), ...
def download_image(image_id, url, x1, y1, x2, y2, output_dir): """Downloads one image, crops it, resizes it and saves it locally.""" output_filename = os.path.join(output_dir, image_id + '.png') if os.path.exists(output_filename): # Don't download image if it's already there return True try: # Downl...
def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None): """Custom py_func with gradient support """ # Need to generate a unique name to avoid duplicates: rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8)) tf.RegisterGradient(rnd_name)(grad) g = tf.get_default_graph() with g.gradie...
def fprop(self, x): """ Forward propagation throught the network :return: dictionary with layer names mapping to activation values. """ # Feed forward through the network layers for layer_name in self.layer_names: if layer_name == 'input': prev_layer_act = x continue ...
def get_layer_params(self, layer_name): """ Provides access to the parameters of the given layer. Works arounds the non-availability of graph collections in eager mode. :layer_name: name of the layer for which parameters are required, must be one of the string in the ...
def get_params(self): """ Provides access to the model's parameters. Works arounds the non-availability of graph collections in eager mode. :return: A list of all Variables defining the model parameters. """ assert tf.executing_eagerly() out = [] # Collecting params ...
def pair_visual(original, adversarial, figure=None): """ This function displays two images: the original and the adversarial sample :param original: the original input :param adversarial: the input after perturbations have been applied :param figure: if we've already displayed images, use the same plot :ret...
def grid_visual(data): """ This function displays a grid of images to show full misclassification :param data: grid data of the form; [nb_classes : nb_classes : img_rows : img_cols : nb_channels] :return: if necessary, the matplot figure to reuse """ import matplotlib.pyplot as plt # Ensure interac...
def get_logits_over_interval(sess, model, x_data, fgsm_params, min_epsilon=-10., max_epsilon=10., num_points=21): """Get logits when the input is perturbed in an interval in adv direction. Args: sess: Tf session model: Model for which we wish to...
def linear_extrapolation_plot(log_prob_adv_array, y, file_name, min_epsilon=-10, max_epsilon=10, num_points=21): """Generate linear extrapolation plot. Args: log_prob_adv_array: Numpy array containing log probabilities y: Tf placeholder for th...
def _send_cmd(self, cmd: str): """Encode IQFeed API messages.""" self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict'))
def iq_query(self, message: str): """Send data query to IQFeed API.""" end_msg = '!ENDMSG!' recv_buffer = 4096 # Send the historical data request message and buffer the data self._send_cmd(message) chunk = "" data = "" while True: chunk = sel...
def get_historical_minute_data(self, ticker: str): """Request historical 5 minute data from DTN.""" start = self._start stop = self._stop if len(stop) > 4: stop = stop[:4] if len(start) > 4: start = start[:4] for year in range(int(start), int(st...
def add_data_to_df(self, data: np.array): """Build Pandas Dataframe in memory""" col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi'] data = np.array(data).reshape(-1, len(col_names) + 1) df = pd.DataFrame(data=data[:, 1:], index=data[:, 0], co...
def get_tickers_from_file(self, filename): """Load ticker list from txt file""" if not os.path.exists(filename): log.error("Ticker List file does not exist: %s", filename) tickers = [] with io.open(filename, 'r') as fd: for ticker in fd: tickers.a...
def write_dataframe_to_idb(self, ticker): """Write Pandas Dataframe to InfluxDB database""" cachepath = self._cache cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker)) if not os.path.exists(cachefile): log.warn('Import file does not exist: %s' % (cache...
def connect(self): """connect events""" self._cidmotion = self.canvas.mpl_connect('motion_notify_event', self.onmove) self._ciddraw = self.canvas.mpl_connect('draw_event', self.clear)
def disconnect(self): """disconnect events""" self.canvas.mpl_disconnect(self._cidmotion) self.canvas.mpl_disconnect(self._ciddraw)
def connect(self, kwargs): """ Connect to window and set it foreground Args: kwargs: optional arguments Returns: None """ self.app = self._app.connect(**kwargs) try: self._top_window = self.app.top_window().wrapper_object...
def get_rect(self): """ Get rectangle of app or desktop resolution Returns: RECT(left, top, right, bottom) """ if self.handle: left, top, right, bottom = win32gui.GetWindowRect(self.handle) return RECT(left, top, right, bottom) else: ...
def snapshot(self, filename="tmp.png"): """ Take a screenshot and save it to `tmp.png` filename by default Args: filename: name of file where to store the screenshot Returns: display the screenshot """ if not filename: filename = "tm...
def extract_data(self): """从数据中获取到绘图相关的有用信息.""" self.time_axis = [] self.cpu_axis = [] self.mem_axis = [] self.timestamp_list = [] plot_data = self.data.get("plot_data", []) # 按照时间分割线，划分成几段数据，取其中的最值 for i in plot_data: timestamp = i["timestamp"...
def get_each_method_maximun_cpu_mem(self): """获取每个方法中的cpu和内存耗费最值点.""" # 本函数用于丰富self.method_exec_info的信息:存入cpu、mem最值点 self.method_exec_info = deepcopy(self.data.get("method_exec_info", [])) method_exec_info = deepcopy(self.method_exec_info) # 用来辅助循环 method_index, cpu_max, cpu_max...
def _get_graph_title(self): """获取图像的title.""" start_time = datetime.fromtimestamp(int(self.timestamp_list[0])) end_time = datetime.fromtimestamp(int(self.timestamp_list[-1])) end_time = end_time.strftime('%H:%M:%S') title = "Timespan: %s —— %s" % (start_time, end_time) r...
def plot_cpu_mem_keypoints(self): """绘制CPU/Mem/特征点数量.""" plt.figure(1) # 开始绘制子图: plt.subplot(311) title = self._get_graph_title() plt.title(title, loc="center") # 设置绘图的标题 mem_ins = plt.plot(self.time_axis, self.mem_axis, "-", label="Mem(MB)", color='deepskyblue',...
def refresh_method_objects(self): """初始化方法对象.""" self.method_object_dict = {} for key, method in self.MATCHING_METHODS.items(): method_object = method(self.im_search, self.im_source, self.threshold, self.rgb) self.method_object_dict.update({key: method_object})
def _get_result(self, method_name="kaze"): """获取特征点.""" method_object = self.method_object_dict.get(method_name) # 提取结果和特征点: try: result = method_object.find_best_result() except Exception: import traceback traceback.print_exc() ret...
def get_and_plot_keypoints(self, method_name, plot=False): """获取并且绘制出特征点匹配结果.""" if method_name not in self.method_object_dict.keys(): print("'%s' is not in MATCHING_METHODS" % method_name) return None kp_sch, kp_src, good, result = self._get_result(method_name) ...
def run(self): """开始线程.""" while not self.stop_flag: timestamp = time.time() cpu_percent = self.process.cpu_percent() / self.cpu_num # mem_percent = mem = self.process.memory_percent() mem_info = dict(self.process.memory_info()._asdict()) mem_g...
def load_images(self, search_file, source_file): """加载待匹配图片.""" self.search_file, self.source_file = search_file, source_file self.im_search, self.im_source = imread(self.search_file), imread(self.source_file) # 初始化对象 self.check_macthing_object = CheckKeypointResult(self.im_searc...
def profile_methods(self, method_list): """帮助函数执行时记录数据.""" self.method_exec_info = [] # 开始数据记录进程 self.record_thread.stop_flag = False self.record_thread.start() for name in method_list: if name not in self.check_macthing_object.MATCHING_METHODS.keys(): ...
def wite_to_json(self, dir_path="", file_name=""): """将性能数据写入文件.""" # 提取数据 data = { "plot_data": self.record_thread.profile_data, "method_exec_info": self.method_exec_info, "search_file": self.search_file, "source_file": self.source_file} #...
def translate_poco_step(self, step): """ 处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤 Parameters ---------- step 一个完整的操作，如click prev_step 前一个步骤，应该是截图 Returns ------- """ ret = {} prev_step = self._steps[-1] if prev_step...
def func_desc_poco(self, step): """ 把对应的poco操作显示成中文""" desc = { "touch": u"点击UI组件 {name}".format(name=step.get("text", "")), } if step['type'] in desc: return desc.get(step['type']) else: return self._translate_desc(step)
def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name): """对指定的图片进行性能测试.""" profiler = ProfileRecorder(0.05) # 加载图片 profiler.load_images(search_file, screen_file) # 传入待测试的方法列表 profiler.profile_methods(method_list) # 将性能数据写入文件 profiler.wite_to_json(dir_p...
def plot_profiled_all_images_table(method_list): """绘制多个图片的结果.""" high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json" rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json" text_dir_path, text_file_name = "result", "text.json" image_list = ['high_dpi', 'rich_tex...
def get_color_list(method_list): """获取method对应的color列表.""" color_list = [] for method in method_list: color = tuple([random() for _ in range(3)]) # 随机颜色画线 color_list.append(color) return color_list
def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111): """绘制了对比表格.""" row_labels = image_list # 写入值： table_vals = [] for i in range(len(row_labels)): row_vals = [] for method in method_list: row_vals.append(compare_dict[method...
def plot_compare_curves(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111): """绘制对比曲线.""" plt.subplot(fig_num) plt.title(fig_name, loc="center") # 设置绘图的标题 mix_ins = [] for index, method in enumerate(method_list): mem_ins = plt.plot(image_list, compare_dict[method],...
def ReadTag(buffer, pos): """Read a tag from the buffer, and return a (tag_bytes, new_pos) tuple. We return the raw bytes of the tag rather than decoding them. The raw bytes can then be used to look up the proper decoder. This effectively allows us to trade some work that would be done in pure-python (decodi...
def _SimpleDecoder(wire_type, decode_value): """Return a constructor for a decoder for fields of a particular type. Args: wire_type: The field's wire type. decode_value: A function which decodes an individual value, e.g. _DecodeVarint() """ def SpecificDecoder(field_number, is_repeated, ...
def _ModifiedDecoder(wire_type, decode_value, modify_value): """Like SimpleDecoder but additionally invokes modify_value on every value before storing it. Usually modify_value is ZigZagDecode. """ # Reusing _SimpleDecoder is slightly slower than copying a bunch of code, but # not enough to make a significan...
def _StructPackDecoder(wire_type, format): """Return a constructor for a decoder for a fixed-width field. Args: wire_type: The field's wire type. format: The format string to pass to struct.unpack(). """ value_size = struct.calcsize(format) local_unpack = struct.unpack # Reusing _SimpleDeco...
def _FloatDecoder(): """Returns a decoder for a float field. This code works around a bug in struct.unpack for non-finite 32-bit floating-point values. """ local_unpack = struct.unpack def InnerDecode(buffer, pos): # We expect a 32-bit value in little-endian byte order. Bit 1 is the sign # bit, ...
def _DoubleDecoder(): """Returns a decoder for a double field. This code works around a bug in struct.unpack for not-a-number. """ local_unpack = struct.unpack def InnerDecode(buffer, pos): # We expect a 64-bit value in little-endian byte order. Bit 1 is the sign # bit, bits 2-12 represent the exp...
def StringDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a string field.""" local_DecodeVarint = _DecodeVarint local_unicode = six.text_type def _ConvertToUnicode(byte_str): try: return local_unicode(byte_str, 'utf-8') except UnicodeDecodeError as e: ...
def BytesDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a bytes field.""" local_DecodeVarint = _DecodeVarint assert not is_packed if is_repeated: tag_bytes = encoder.TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED) ...
def GroupDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a group field.""" end_tag_bytes = encoder.TagBytes(field_number, wire_format.WIRETYPE_END_GROUP) end_tag_len = len(end_tag_bytes) assert not is_packed if is_repeated: tag...
def MapDecoder(field_descriptor, new_default, is_message_map): """Returns a decoder for a map field.""" key = field_descriptor tag_bytes = encoder.TagBytes(field_descriptor.number, wire_format.WIRETYPE_LENGTH_DELIMITED) tag_len = len(tag_bytes) local_DecodeVarint = _DecodeVarin...
def _SkipVarint(buffer, pos, end): """Skip a varint value. Returns the new position.""" # Previously ord(buffer[pos]) raised IndexError when pos is out of range. # With this code, ord(b'') raises TypeError. Both are handled in # python_message.py to generate a 'Truncated message' error. while ord(buffer[pos...
def _SkipLengthDelimited(buffer, pos, end): """Skip a length-delimited value. Returns the new position.""" (size, pos) = _DecodeVarint(buffer, pos) pos += size if pos > end: raise _DecodeError('Truncated message.') return pos
def _SkipGroup(buffer, pos, end): """Skip sub-group. Returns the new position.""" while 1: (tag_bytes, pos) = ReadTag(buffer, pos) new_pos = SkipField(buffer, pos, end, tag_bytes) if new_pos == -1: return pos pos = new_pos
def _FieldSkipper(): """Constructs the SkipField function.""" WIRETYPE_TO_SKIPPER = [ _SkipVarint, _SkipFixed64, _SkipLengthDelimited, _SkipGroup, _EndGroup, _SkipFixed32, _RaiseInvalidWireType, _RaiseInvalidWireType, ] wiretype_mask = wire_format.TAG_TYPE_M...
def _parse_node(graph, text): """parse dumped node""" match = _NODEPAT.match(text) if match is not None: node = match.group(1) graph.node(node, label=match.group(2), shape='circle') return node match = _LEAFPAT.match(text) if match is not None: node = match.group(1) ...
def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs): """Plot specified tree. Parameters ---------- booster : Booster, XGBModel Booster or XGBModel instance num_trees : int, default 0 Specify the ordinal number of target tree rankdir : str, default "UT" P...
def construct (self, properties = [], targets = []): """ Constructs the dependency graph. properties: the build properties. targets: the targets to consider. If none is specified, uses all. """ if not targets: for name, project in self.projects ().project...
def evaluate(self, dataset, metric='auto', missing_value_action='auto'): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFrame Dataset of new observations. Must include colum...
def predict(self, dataset, output_type='class', missing_value_action='auto'): """ A flexible and advanced prediction API. The target column is provided during :func:`~turicreate.decision_tree.create`. If the target column is in the `dataset` it will be ignored. Paramete...
def predict_topk(self, dataset, output_type="probability", k=3, missing_value_action='auto'): """ Return top-k predictions for the ``dataset``, using the trained model. Predictions are returned as an SFrame with three columns: `id`, `class`, and `probability`, `margin`, or `rank`, depen...
def classify(self, dataset, missing_value_action='auto'): """ Return a classification, for each example in the ``dataset``, using the trained model. The output SFrame contains predictions as class labels (0 or 1) and probabilities associated with the the example. Parameters ...
def slave_envs(self): """ get enviroment variables for slaves can be passed in as args or envs """ if self.hostIP == 'dns': host = socket.gethostname() elif self.hostIP == 'ip': host = socket.gethostbyname(socket.getfqdn()) else: ...
def find_share_ring(self, tree_map, parent_map, r): """ get a ring structure that tends to share nodes with the tree return a list starting from r """ nset = set(tree_map[r]) cset = nset - set([parent_map[r]]) if len(cset) == 0: return [r] rlst...
def get_ring(self, tree_map, parent_map): """ get a ring connection used to recover local data """ assert parent_map[0] == -1 rlst = self.find_share_ring(tree_map, parent_map, 0) assert len(rlst) == len(tree_map) ring_map = {} nslave = len(tree_map) ...
def get_link_map(self, nslave): """ get the link map, this is a bit hacky, call for better algorithm to place similar nodes together """ tree_map, parent_map = self.get_tree(nslave) ring_map = self.get_ring(tree_map, parent_map) rmap = {0 : 0} k = 0 ...
def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'): """ Helper rule to generate a faster alternative to MSVC setup scripts. We used to call MSVC setup scripts directly in every action, however in newer MSVC versions (10.0+) they make long-lasting registry querie...
def create(dataset, target, features=None, validation_set = 'auto', verbose=True): """ Automatically create a suitable classifier model based on the provided training data. To use specific options of a desired model, use the ``create`` function of the corresponding model. Parameters ...
def add_column(self, data, column_name="", inplace=False): """ Adds the specified column to this SFrame. The number of elements in the data given must match every other column of the SFrame. If inplace == False (default) this operation does not modify the current SFrame, return...
def add_columns(self, data, column_names=None, inplace=False): """ Adds columns to the SFrame. The number of elements in all columns must match every other column of the SFrame. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFr...
def remove_column(self, column_name, inplace=False): """ Removes the column with the given name from the SFrame. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFrame. If inplace == True, this operation modifies the current ...
def swap_columns(self, column_name_1, column_name_2, inplace=False): """ Swaps the columns with the given names. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFrame. If inplace == True, this operation modifies the current ...
def rename(self, names, inplace=False): """ Rename the columns using the 'names' dict. This changes the names of the columns given as the keys and replaces them with the names given as the values. If inplace == False (default) this operation does not modify the current ...
def num_rows(self): """ Returns the number of rows. Returns ------- out : int Number of rows in the SFrame. """ if self._is_vertex_frame(): return self.__graph__.summary()['num_vertices'] elif self._is_edge_frame(): ret...
def column_names(self): """ Returns the column names. Returns ------- out : list[string] Column names of the SFrame. """ if self._is_vertex_frame(): return self.__graph__.__proxy__.get_vertex_fields() elif self._is_edge_frame(): ...
def column_types(self): """ Returns the column types. Returns ------- out : list[type] Column types of the SFrame. """ if self.__type__ == VERTEX_GFRAME: return self.__graph__.__proxy__.get_vertex_field_types() elif self.__type__ =...
def create(dataset, target, features=None, validation_set = 'auto', verbose=True): """ Automatically create a suitable regression model based on the provided training data. To use specific options of a desired model, use the ``create`` function of the corresponding model. Parameters ...
def removable(self, node): ''' node is removable only if all of its children are as well. ''' throw_away = [] for child in self.children(node): throw_away.append(self.visit(child)) if self.mode == 'exclusive': return all(throw_away) elif self.mode == 'inclusive': ret...
def reduce(self, body): ''' remove nodes from a list ''' i = 0 while i < len(body): stmnt = body[i] if self.visit(stmnt): body.pop(i) else: i += 1
def load_audio(path, with_path=True, recursive=True, ignore_failure=True, random_order=False): """ Loads WAV file(s) from a path. Parameters ---------- path : str Path to WAV files to be loaded. with_path : bool, optional Indicates whether a path column is added to the returned...
def RegisterMessage(self, message): """Registers the given message type in the local database. Calls to GetSymbol() and GetMessages() will return messages registered here. Args: message: a message.Message, to be registered. Returns: The provided message. """ desc = message.DESCRI...
def GetMessages(self, files): # TODO(amauryfa): Fix the differences with MessageFactory. """Gets all registered messages from a specified file. Only messages already created and registered will be returned; (this is the case for imported _pb2 modules) But unlike MessageFactory, this version also re...
def _string_hash(s): """String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`).""" h = 5381 for c in s: h = h * 33 + ord(c) return h
def draw_bounding_boxes(images, annotations, confidence_threshold=0): """ Visualizes bounding boxes (ground truth or predictions) by returning annotated copies of the images. Parameters ---------- images: SArray or Image An `SArray` of type `Image`. A single `Image` instance may also be...
def create(dataset, target, model_name, features=None, validation_set='auto', distributed='auto', verbose=True, seed=None, **kwargs): """ Create a :class:`~turicreate.toolkits.SupervisedLearningModel`, This is generic function that allows you to create any model that implements Su...

def train(model, X_train=None, Y_train=None, save=False, predictions_adv=None, evaluate=None, args=None, rng=None, var_list=None, attack=None, attack_args=None): """ Train a TF Eager model :param model: cleverhans.model.Model :param X_train: numpy array with training inputs :para...

def model_eval(model, X_test=None, Y_test=None, args=None, attack=None, attack_args=None): """ Compute the accuracy of a TF Eager model on some data :param model: instance of cleverhans.model.Model_Eager with pretrained weights for evaluation. :param X_test: numpy array with tra...

def model_argmax(model, samples): """ Helper function that computes the current class prediction :param samples: numpy array with input samples (dims must match x) :return: the argmax output of predictions, i.e. the current predicted class """ tfe = tf.contrib.eager tf_samples = tfe.Variable(samples) pr...

def generate(self, x, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(**kwargs) if self.ord != np.inf: rai...

def initialize_dual(neural_net_params_object, init_dual_file=None, random_init_variance=0.01, init_nu=200.0): """Function to initialize the dual variables of the class. Args: neural_net_params_object: Object with the neural net weights, biases and types init_dual_file: Path to fil...

def eig_one_step(current_vector, learning_rate, vector_prod_fn): """Function that performs one step of gd (variant) for min eigen value. Args: current_vector: current estimate of the eigen vector with minimum eigen value. learning_rate: learning rate. vector_prod_fn: function which returns produc...

def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn): """Computes eigenvector which corresponds to minimum eigenvalue. Args: x: initial value of eigenvector. num_steps: number of optimization steps. learning_rate: learning rate. vector_prod_fn: function which takes x and returns pr...

def tf_lanczos_smallest_eigval(vector_prod_fn, matrix_dim, initial_vector, num_iter=1000, max_iter=1000, collapse_tol=1e-9, dtype=tf.f...

def get_vars(self): """ Provides access to the model's Variables. This may include Variables that are not parameters, such as batch norm running moments. :return: A list of all Variables defining the model. """ # Catch eager execution and assert function overload. try: if tf.execu...

def fprop(self, x, dropout=False, dropout_dict=None, **kwargs): """ Forward propagation as either no-op or dropping random units. :param x: The input to the layer :param dropout: bool specifying whether to drop units :param dropout_dict: dict This dictionary is usually not needed. In...

def generate(self, x, **kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: A tensor with the inputs. :param kwargs: See `parse_params` """ assert self.sess is not None, \ 'Cannot...

def parse_params(self, y=None, y_target=None, batch_size=1, confidence=0, learning_rate=5e-3, binary_search_steps=5, max_iterations=1000, abort_early=True, ...

def attack(self, imgs, targets): """ Perform the L_2 attack on the given instance for the given targets. If self.targeted is true, then the targets represents the target labels If self.targeted is false, then targets are the original class labels """ r = [] for i in range(0, len(imgs), sel...

def attack_batch(self, imgs, labs): """ Run the attack on a batch of instance and labels. """ def compare(x, y): if not isinstance(x, (float, int, np.int64)): x = np.copy(x) if self.TARGETED: x[y] -= self.CONFIDENCE else: x[y] += self.CONFIDENCE ...

def maybe_load_model(savedir, container): """Load model if present at the specified path.""" if savedir is None: return state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip')) if container is not None: logger.log("Attempting to download model from Azure") found_model = container....

def check_installation(cur_file): """Warn user if running cleverhans from a different directory than tutorial.""" cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0] ch_dir = os.path.split(cleverhans.__path__[0])[0] if cur_dir != ch_dir: warnings.warn("It appears that you have at least tw...

def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser( description='Tool to download dataset images.') parser.add_argument('--input_file', required=True, help='Location of dataset.csv') parser.add_argument('--output_dir', required=True, ...

def get_image(row, output_dir): """Downloads the image that corresponds to the given row. Prints a notification if the download fails.""" if not download_image(image_id=row[0], url=row[1], x1=float(row[2]), y1=float(row[3]), ...

def download_image(image_id, url, x1, y1, x2, y2, output_dir): """Downloads one image, crops it, resizes it and saves it locally.""" output_filename = os.path.join(output_dir, image_id + '.png') if os.path.exists(output_filename): # Don't download image if it's already there return True try: # Downl...

def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None): """Custom py_func with gradient support """ # Need to generate a unique name to avoid duplicates: rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8)) tf.RegisterGradient(rnd_name)(grad) g = tf.get_default_graph() with g.gradie...

def fprop(self, x): """ Forward propagation throught the network :return: dictionary with layer names mapping to activation values. """ # Feed forward through the network layers for layer_name in self.layer_names: if layer_name == 'input': prev_layer_act = x continue ...

def get_layer_params(self, layer_name): """ Provides access to the parameters of the given layer. Works arounds the non-availability of graph collections in eager mode. :layer_name: name of the layer for which parameters are required, must be one of the string in the ...

def get_params(self): """ Provides access to the model's parameters. Works arounds the non-availability of graph collections in eager mode. :return: A list of all Variables defining the model parameters. """ assert tf.executing_eagerly() out = [] # Collecting params ...

def pair_visual(original, adversarial, figure=None): """ This function displays two images: the original and the adversarial sample :param original: the original input :param adversarial: the input after perturbations have been applied :param figure: if we've already displayed images, use the same plot :ret...

def grid_visual(data): """ This function displays a grid of images to show full misclassification :param data: grid data of the form; [nb_classes : nb_classes : img_rows : img_cols : nb_channels] :return: if necessary, the matplot figure to reuse """ import matplotlib.pyplot as plt # Ensure interac...

def get_logits_over_interval(sess, model, x_data, fgsm_params, min_epsilon=-10., max_epsilon=10., num_points=21): """Get logits when the input is perturbed in an interval in adv direction. Args: sess: Tf session model: Model for which we wish to...

def linear_extrapolation_plot(log_prob_adv_array, y, file_name, min_epsilon=-10, max_epsilon=10, num_points=21): """Generate linear extrapolation plot. Args: log_prob_adv_array: Numpy array containing log probabilities y: Tf placeholder for th...

def _send_cmd(self, cmd: str): """Encode IQFeed API messages.""" self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict'))

def iq_query(self, message: str): """Send data query to IQFeed API.""" end_msg = '!ENDMSG!' recv_buffer = 4096 # Send the historical data request message and buffer the data self._send_cmd(message) chunk = "" data = "" while True: chunk = sel...

def get_historical_minute_data(self, ticker: str): """Request historical 5 minute data from DTN.""" start = self._start stop = self._stop if len(stop) > 4: stop = stop[:4] if len(start) > 4: start = start[:4] for year in range(int(start), int(st...

def add_data_to_df(self, data: np.array): """Build Pandas Dataframe in memory""" col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi'] data = np.array(data).reshape(-1, len(col_names) + 1) df = pd.DataFrame(data=data[:, 1:], index=data[:, 0], co...

def get_tickers_from_file(self, filename): """Load ticker list from txt file""" if not os.path.exists(filename): log.error("Ticker List file does not exist: %s", filename) tickers = [] with io.open(filename, 'r') as fd: for ticker in fd: tickers.a...

def write_dataframe_to_idb(self, ticker): """Write Pandas Dataframe to InfluxDB database""" cachepath = self._cache cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker)) if not os.path.exists(cachefile): log.warn('Import file does not exist: %s' % (cache...

def connect(self): """connect events""" self._cidmotion = self.canvas.mpl_connect('motion_notify_event', self.onmove) self._ciddraw = self.canvas.mpl_connect('draw_event', self.clear)

def disconnect(self): """disconnect events""" self.canvas.mpl_disconnect(self._cidmotion) self.canvas.mpl_disconnect(self._ciddraw)

def connect(self, **kwargs): """ Connect to window and set it foreground Args: **kwargs: optional arguments Returns: None """ self.app = self._app.connect(**kwargs) try: self._top_window = self.app.top_window().wrapper_object...

def get_rect(self): """ Get rectangle of app or desktop resolution Returns: RECT(left, top, right, bottom) """ if self.handle: left, top, right, bottom = win32gui.GetWindowRect(self.handle) return RECT(left, top, right, bottom) else: ...

def snapshot(self, filename="tmp.png"): """ Take a screenshot and save it to `tmp.png` filename by default Args: filename: name of file where to store the screenshot Returns: display the screenshot """ if not filename: filename = "tm...

def extract_data(self): """从数据中获取到绘图相关的有用信息.""" self.time_axis = [] self.cpu_axis = [] self.mem_axis = [] self.timestamp_list = [] plot_data = self.data.get("plot_data", []) # 按照时间分割线，划分成几段数据，取其中的最值 for i in plot_data: timestamp = i["timestamp"...

def get_each_method_maximun_cpu_mem(self): """获取每个方法中的cpu和内存耗费最值点.""" # 本函数用于丰富self.method_exec_info的信息:存入cpu、mem最值点 self.method_exec_info = deepcopy(self.data.get("method_exec_info", [])) method_exec_info = deepcopy(self.method_exec_info) # 用来辅助循环 method_index, cpu_max, cpu_max...

def _get_graph_title(self): """获取图像的title.""" start_time = datetime.fromtimestamp(int(self.timestamp_list[0])) end_time = datetime.fromtimestamp(int(self.timestamp_list[-1])) end_time = end_time.strftime('%H:%M:%S') title = "Timespan: %s —— %s" % (start_time, end_time) r...

def plot_cpu_mem_keypoints(self): """绘制CPU/Mem/特征点数量.""" plt.figure(1) # 开始绘制子图: plt.subplot(311) title = self._get_graph_title() plt.title(title, loc="center") # 设置绘图的标题 mem_ins = plt.plot(self.time_axis, self.mem_axis, "-", label="Mem(MB)", color='deepskyblue',...

def refresh_method_objects(self): """初始化方法对象.""" self.method_object_dict = {} for key, method in self.MATCHING_METHODS.items(): method_object = method(self.im_search, self.im_source, self.threshold, self.rgb) self.method_object_dict.update({key: method_object})

def _get_result(self, method_name="kaze"): """获取特征点.""" method_object = self.method_object_dict.get(method_name) # 提取结果和特征点: try: result = method_object.find_best_result() except Exception: import traceback traceback.print_exc() ret...

def get_and_plot_keypoints(self, method_name, plot=False): """获取并且绘制出特征点匹配结果.""" if method_name not in self.method_object_dict.keys(): print("'%s' is not in MATCHING_METHODS" % method_name) return None kp_sch, kp_src, good, result = self._get_result(method_name) ...

def run(self): """开始线程.""" while not self.stop_flag: timestamp = time.time() cpu_percent = self.process.cpu_percent() / self.cpu_num # mem_percent = mem = self.process.memory_percent() mem_info = dict(self.process.memory_info()._asdict()) mem_g...

def load_images(self, search_file, source_file): """加载待匹配图片.""" self.search_file, self.source_file = search_file, source_file self.im_search, self.im_source = imread(self.search_file), imread(self.source_file) # 初始化对象 self.check_macthing_object = CheckKeypointResult(self.im_searc...

def profile_methods(self, method_list): """帮助函数执行时记录数据.""" self.method_exec_info = [] # 开始数据记录进程 self.record_thread.stop_flag = False self.record_thread.start() for name in method_list: if name not in self.check_macthing_object.MATCHING_METHODS.keys(): ...

def wite_to_json(self, dir_path="", file_name=""): """将性能数据写入文件.""" # 提取数据 data = { "plot_data": self.record_thread.profile_data, "method_exec_info": self.method_exec_info, "search_file": self.search_file, "source_file": self.source_file} #...

def translate_poco_step(self, step): """ 处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤 Parameters ---------- step 一个完整的操作，如click prev_step 前一个步骤，应该是截图 Returns ------- """ ret = {} prev_step = self._steps[-1] if prev_step...

def func_desc_poco(self, step): """ 把对应的poco操作显示成中文""" desc = { "touch": u"点击UI组件 {name}".format(name=step.get("text", "")), } if step['type'] in desc: return desc.get(step['type']) else: return self._translate_desc(step)

def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name): """对指定的图片进行性能测试.""" profiler = ProfileRecorder(0.05) # 加载图片 profiler.load_images(search_file, screen_file) # 传入待测试的方法列表 profiler.profile_methods(method_list) # 将性能数据写入文件 profiler.wite_to_json(dir_p...

def plot_profiled_all_images_table(method_list): """绘制多个图片的结果.""" high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json" rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json" text_dir_path, text_file_name = "result", "text.json" image_list = ['high_dpi', 'rich_tex...

def get_color_list(method_list): """获取method对应的color列表.""" color_list = [] for method in method_list: color = tuple([random() for _ in range(3)]) # 随机颜色画线 color_list.append(color) return color_list

def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111): """绘制了对比表格.""" row_labels = image_list # 写入值： table_vals = [] for i in range(len(row_labels)): row_vals = [] for method in method_list: row_vals.append(compare_dict[method...

def plot_compare_curves(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111): """绘制对比曲线.""" plt.subplot(fig_num) plt.title(fig_name, loc="center") # 设置绘图的标题 mix_ins = [] for index, method in enumerate(method_list): mem_ins = plt.plot(image_list, compare_dict[method],...

def ReadTag(buffer, pos): """Read a tag from the buffer, and return a (tag_bytes, new_pos) tuple. We return the raw bytes of the tag rather than decoding them. The raw bytes can then be used to look up the proper decoder. This effectively allows us to trade some work that would be done in pure-python (decodi...

def _SimpleDecoder(wire_type, decode_value): """Return a constructor for a decoder for fields of a particular type. Args: wire_type: The field's wire type. decode_value: A function which decodes an individual value, e.g. _DecodeVarint() """ def SpecificDecoder(field_number, is_repeated, ...

def _ModifiedDecoder(wire_type, decode_value, modify_value): """Like SimpleDecoder but additionally invokes modify_value on every value before storing it. Usually modify_value is ZigZagDecode. """ # Reusing _SimpleDecoder is slightly slower than copying a bunch of code, but # not enough to make a significan...

def _StructPackDecoder(wire_type, format): """Return a constructor for a decoder for a fixed-width field. Args: wire_type: The field's wire type. format: The format string to pass to struct.unpack(). """ value_size = struct.calcsize(format) local_unpack = struct.unpack # Reusing _SimpleDeco...

def _FloatDecoder(): """Returns a decoder for a float field. This code works around a bug in struct.unpack for non-finite 32-bit floating-point values. """ local_unpack = struct.unpack def InnerDecode(buffer, pos): # We expect a 32-bit value in little-endian byte order. Bit 1 is the sign # bit, ...

def _DoubleDecoder(): """Returns a decoder for a double field. This code works around a bug in struct.unpack for not-a-number. """ local_unpack = struct.unpack def InnerDecode(buffer, pos): # We expect a 64-bit value in little-endian byte order. Bit 1 is the sign # bit, bits 2-12 represent the exp...

def StringDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a string field.""" local_DecodeVarint = _DecodeVarint local_unicode = six.text_type def _ConvertToUnicode(byte_str): try: return local_unicode(byte_str, 'utf-8') except UnicodeDecodeError as e: ...

def BytesDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a bytes field.""" local_DecodeVarint = _DecodeVarint assert not is_packed if is_repeated: tag_bytes = encoder.TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED) ...

def GroupDecoder(field_number, is_repeated, is_packed, key, new_default): """Returns a decoder for a group field.""" end_tag_bytes = encoder.TagBytes(field_number, wire_format.WIRETYPE_END_GROUP) end_tag_len = len(end_tag_bytes) assert not is_packed if is_repeated: tag...

def MapDecoder(field_descriptor, new_default, is_message_map): """Returns a decoder for a map field.""" key = field_descriptor tag_bytes = encoder.TagBytes(field_descriptor.number, wire_format.WIRETYPE_LENGTH_DELIMITED) tag_len = len(tag_bytes) local_DecodeVarint = _DecodeVarin...

def _SkipVarint(buffer, pos, end): """Skip a varint value. Returns the new position.""" # Previously ord(buffer[pos]) raised IndexError when pos is out of range. # With this code, ord(b'') raises TypeError. Both are handled in # python_message.py to generate a 'Truncated message' error. while ord(buffer[pos...

def _SkipLengthDelimited(buffer, pos, end): """Skip a length-delimited value. Returns the new position.""" (size, pos) = _DecodeVarint(buffer, pos) pos += size if pos > end: raise _DecodeError('Truncated message.') return pos

def _SkipGroup(buffer, pos, end): """Skip sub-group. Returns the new position.""" while 1: (tag_bytes, pos) = ReadTag(buffer, pos) new_pos = SkipField(buffer, pos, end, tag_bytes) if new_pos == -1: return pos pos = new_pos

def _FieldSkipper(): """Constructs the SkipField function.""" WIRETYPE_TO_SKIPPER = [ _SkipVarint, _SkipFixed64, _SkipLengthDelimited, _SkipGroup, _EndGroup, _SkipFixed32, _RaiseInvalidWireType, _RaiseInvalidWireType, ] wiretype_mask = wire_format.TAG_TYPE_M...

def _parse_node(graph, text): """parse dumped node""" match = _NODEPAT.match(text) if match is not None: node = match.group(1) graph.node(node, label=match.group(2), shape='circle') return node match = _LEAFPAT.match(text) if match is not None: node = match.group(1) ...

def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs): """Plot specified tree. Parameters ---------- booster : Booster, XGBModel Booster or XGBModel instance num_trees : int, default 0 Specify the ordinal number of target tree rankdir : str, default "UT" P...

def construct (self, properties = [], targets = []): """ Constructs the dependency graph. properties: the build properties. targets: the targets to consider. If none is specified, uses all. """ if not targets: for name, project in self.projects ().project...

def evaluate(self, dataset, metric='auto', missing_value_action='auto'): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFrame Dataset of new observations. Must include colum...

def predict(self, dataset, output_type='class', missing_value_action='auto'): """ A flexible and advanced prediction API. The target column is provided during :func:`~turicreate.decision_tree.create`. If the target column is in the `dataset` it will be ignored. Paramete...

def predict_topk(self, dataset, output_type="probability", k=3, missing_value_action='auto'): """ Return top-k predictions for the ``dataset``, using the trained model. Predictions are returned as an SFrame with three columns: `id`, `class`, and `probability`, `margin`, or `rank`, depen...

def classify(self, dataset, missing_value_action='auto'): """ Return a classification, for each example in the ``dataset``, using the trained model. The output SFrame contains predictions as class labels (0 or 1) and probabilities associated with the the example. Parameters ...

def slave_envs(self): """ get enviroment variables for slaves can be passed in as args or envs """ if self.hostIP == 'dns': host = socket.gethostname() elif self.hostIP == 'ip': host = socket.gethostbyname(socket.getfqdn()) else: ...

def find_share_ring(self, tree_map, parent_map, r): """ get a ring structure that tends to share nodes with the tree return a list starting from r """ nset = set(tree_map[r]) cset = nset - set([parent_map[r]]) if len(cset) == 0: return [r] rlst...

def get_ring(self, tree_map, parent_map): """ get a ring connection used to recover local data """ assert parent_map[0] == -1 rlst = self.find_share_ring(tree_map, parent_map, 0) assert len(rlst) == len(tree_map) ring_map = {} nslave = len(tree_map) ...

def get_link_map(self, nslave): """ get the link map, this is a bit hacky, call for better algorithm to place similar nodes together """ tree_map, parent_map = self.get_tree(nslave) ring_map = self.get_ring(tree_map, parent_map) rmap = {0 : 0} k = 0 ...

def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'): """ Helper rule to generate a faster alternative to MSVC setup scripts. We used to call MSVC setup scripts directly in every action, however in newer MSVC versions (10.0+) they make long-lasting registry querie...

def create(dataset, target, features=None, validation_set = 'auto', verbose=True): """ Automatically create a suitable classifier model based on the provided training data. To use specific options of a desired model, use the ``create`` function of the corresponding model. Parameters ...

def add_column(self, data, column_name="", inplace=False): """ Adds the specified column to this SFrame. The number of elements in the data given must match every other column of the SFrame. If inplace == False (default) this operation does not modify the current SFrame, return...

def add_columns(self, data, column_names=None, inplace=False): """ Adds columns to the SFrame. The number of elements in all columns must match every other column of the SFrame. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFr...

def remove_column(self, column_name, inplace=False): """ Removes the column with the given name from the SFrame. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFrame. If inplace == True, this operation modifies the current ...

def swap_columns(self, column_name_1, column_name_2, inplace=False): """ Swaps the columns with the given names. If inplace == False (default) this operation does not modify the current SFrame, returning a new SFrame. If inplace == True, this operation modifies the current ...

def rename(self, names, inplace=False): """ Rename the columns using the 'names' dict. This changes the names of the columns given as the keys and replaces them with the names given as the values. If inplace == False (default) this operation does not modify the current ...

def num_rows(self): """ Returns the number of rows. Returns ------- out : int Number of rows in the SFrame. """ if self._is_vertex_frame(): return self.__graph__.summary()['num_vertices'] elif self._is_edge_frame(): ret...

def column_names(self): """ Returns the column names. Returns ------- out : list[string] Column names of the SFrame. """ if self._is_vertex_frame(): return self.__graph__.__proxy__.get_vertex_fields() elif self._is_edge_frame(): ...

def column_types(self): """ Returns the column types. Returns ------- out : list[type] Column types of the SFrame. """ if self.__type__ == VERTEX_GFRAME: return self.__graph__.__proxy__.get_vertex_field_types() elif self.__type__ =...

def create(dataset, target, features=None, validation_set = 'auto', verbose=True): """ Automatically create a suitable regression model based on the provided training data. To use specific options of a desired model, use the ``create`` function of the corresponding model. Parameters ...

def removable(self, node): ''' node is removable only if all of its children are as well. ''' throw_away = [] for child in self.children(node): throw_away.append(self.visit(child)) if self.mode == 'exclusive': return all(throw_away) elif self.mode == 'inclusive': ret...

def reduce(self, body): ''' remove nodes from a list ''' i = 0 while i < len(body): stmnt = body[i] if self.visit(stmnt): body.pop(i) else: i += 1

def load_audio(path, with_path=True, recursive=True, ignore_failure=True, random_order=False): """ Loads WAV file(s) from a path. Parameters ---------- path : str Path to WAV files to be loaded. with_path : bool, optional Indicates whether a path column is added to the returned...

def RegisterMessage(self, message): """Registers the given message type in the local database. Calls to GetSymbol() and GetMessages() will return messages registered here. Args: message: a message.Message, to be registered. Returns: The provided message. """ desc = message.DESCRI...

def GetMessages(self, files): # TODO(amauryfa): Fix the differences with MessageFactory. """Gets all registered messages from a specified file. Only messages already created and registered will be returned; (this is the case for imported _pb2 modules) But unlike MessageFactory, this version also re...

def _string_hash(s): """String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`).""" h = 5381 for c in s: h = h * 33 + ord(c) return h

def draw_bounding_boxes(images, annotations, confidence_threshold=0): """ Visualizes bounding boxes (ground truth or predictions) by returning annotated copies of the images. Parameters ---------- images: SArray or Image An `SArray` of type `Image`. A single `Image` instance may also be...

def create(dataset, target, model_name, features=None, validation_set='auto', distributed='auto', verbose=True, seed=None, **kwargs): """ Create a :class:`~turicreate.toolkits.SupervisedLearningModel`, This is generic function that allows you to create any model that implements Su...