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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/perspective.py | python | PerspectiveManager.RemovePerspective | (self, name) | Removes a named perspective from the managed set
@param name: name of perspective to remove/delete | Removes a named perspective from the managed set
@param name: name of perspective to remove/delete | [
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"""Removes a named perspective from the managed set
@param name: name of perspective to remove/delete
"""
if name in self._viewset:
del self._viewset[name]
rem_id = self._menu.RemoveItemByName(name)
if rem_id:
self._ids.remove(rem_id) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/fictitious_play.py | python | JointPolicy.__init__ | (self, game, policies) | Initializes a joint policy from a table of callables.
Args:
game: The game being played.
policies: A dictionary mapping player number to a function `state` ->
list of (action, prob). | Initializes a joint policy from a table of callables. | [
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"""Initializes a joint policy from a table of callables.
Args:
game: The game being played.
policies: A dictionary mapping player number to a function `state` ->
list of (action, prob).
"""
super().__init__(game, list(range(game.num_players())))
self.policies = policies | [
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etotheipi/BitcoinArmory | 2a6fc5355bb0c6fe26e387ccba30a5baafe8cd98 | urllib3/packages/ordered_dict.py | python | OrderedDict.pop | (self, key, default=__marker) | return default | od.pop(k[,d]) -> v, remove specified key and return the corresponding value.
If key is not found, d is returned if given, otherwise KeyError is raised. | od.pop(k[,d]) -> v, remove specified key and return the corresponding value.
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'''od.pop(k[,d]) -> v, remove specified key and return the corresponding value.
If key is not found, d is returned if given, otherwise KeyError is raised.
'''
if key in self:
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/array_ops.py | python | _ExpandDimsShape | (op) | return [tensor_shape.TensorShape(result_shape)] | Determine shape for expand op's output tensor.
Args:
op: Operation for which to determine shape.
op.inputs[0] is the input tensor.
op.inputs[1] is the dimension in which to expand.
Returns:
Shape of op's output tensor.
Raises:
ValueError: If dim is outside of [-rank - 1, rank], where rank is the number
of dimensions in the input tensor. | Determine shape for expand op's output tensor. | [
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] | def _ExpandDimsShape(op):
"""Determine shape for expand op's output tensor.
Args:
op: Operation for which to determine shape.
op.inputs[0] is the input tensor.
op.inputs[1] is the dimension in which to expand.
Returns:
Shape of op's output tensor.
Raises:
ValueError: If dim is outside of [-rank - 1, rank], where rank is the number
of dimensions in the input tensor.
"""
input_shape = op.inputs[0].get_shape()
if input_shape.dims is None:
return [tensor_shape.unknown_shape()]
dim = tensor_util.constant_value(op.inputs[1])
input_ndims = input_shape.ndims
if dim < -input_ndims - 1 or dim > input_ndims:
raise ValueError(
"dim %d not in [%d, %d]." % (dim, -input_ndims, input_ndims))
if dim < 0:
dim += (input_ndims + 1)
result_shape = list(input_shape.dims)
result_shape.insert(dim, 1)
return [tensor_shape.TensorShape(result_shape)] | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/utils/tf_utils.py | python | dataset_is_infinite | (dataset) | True if the passed dataset is infinite. | True if the passed dataset is infinite. | [
"True",
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"dataset",
"is",
"infinite",
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] | def dataset_is_infinite(dataset):
"""True if the passed dataset is infinite."""
if ops.executing_eagerly_outside_functions():
return math_ops.equal(
cardinality.cardinality(dataset), cardinality.INFINITE)
else:
dataset_size = K.get_session().run(cardinality.cardinality(dataset))
return dataset_size == cardinality.INFINITE | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/moosetree/search.py | python | find | (node, func=None, method=None, **kwargs) | return nodes[0] if nodes else None | Operates in the same fashion as "findall"; however, if a match is found the search is terminated
and the node is returned. | Operates in the same fashion as "findall"; however, if a match is found the search is terminated
and the node is returned. | [
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] | def find(node, func=None, method=None, **kwargs):
"""
Operates in the same fashion as "findall"; however, if a match is found the search is terminated
and the node is returned.
"""
if (func is None) and (kwargs):
func = lambda n: any(n.attributes.get(key, None)==value for key, value in kwargs.items())
nodes = list(iterate(node, func, True, method))
return nodes[0] if nodes else None | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/_distutils_hack/__init__.py | python | do_override | () | Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation. | Ensure that the local copy of distutils is preferred over stdlib. | [
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"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils() | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/blocktool/core/parseheader.py | python | BlockHeaderParser.get_header_info | (self) | return self.parsed_data | PyGCCXML header code parser
magic happens here!
: returns the parsed header data in python dict
: return dict keys: namespace, class, io_signature, make,
properties, methods
: Can be used as an CLI command or an external API | PyGCCXML header code parser
magic happens here!
: returns the parsed header data in python dict
: return dict keys: namespace, class, io_signature, make,
properties, methods
: Can be used as an CLI command or an external API | [
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"""
PyGCCXML header code parser
magic happens here!
: returns the parsed header data in python dict
: return dict keys: namespace, class, io_signature, make,
properties, methods
: Can be used as an CLI command or an external API
"""
gr = self.modname.split('-')[0]
module = self.modname.split('-')[-1]
self.parsed_data['module_name'] = module
generator_path, generator_name = utils.find_xml_generator()
xml_generator_config = parser.xml_generator_configuration_t(
xml_generator_path=generator_path,
xml_generator=generator_name,
include_paths=self.include_paths,
compiler='gcc',
define_symbols=['BOOST_ATOMIC_DETAIL_EXTRA_BACKEND_GENERIC'],
cflags='-std=c++11')
decls = parser.parse(
[self.target_file], xml_generator_config)
global_namespace = declarations.get_global_namespace(decls)
# namespace
try:
self.parsed_data['namespace'] = []
ns = global_namespace.namespace(gr)
if ns is None:
raise BlockToolException
main_namespace = ns.namespace(module)
if main_namespace is None:
raise BlockToolException('namespace cannot be none')
self.parsed_data['namespace'] = [gr, module]
if main_namespace.declarations:
for _namespace in main_namespace.declarations:
if isinstance(_namespace, declarations.namespace_t):
if Constants.KERNEL not in str(_namespace):
main_namespace = _namespace
self.parsed_data['namespace'].append(
str(_namespace).split('::')[-1].split(' ')[0])
except RuntimeError:
raise BlockToolException(
'Invalid namespace format in the block header file')
# class
try:
self.parsed_data['class'] = ''
for _class in main_namespace.declarations:
if isinstance(_class, declarations.class_t):
expected_class_name = self.filename.split('.')[0]
if expected_class_name in str(_class):
main_class = _class
self.parsed_data['class'] = str(_class).split('::')[
2].split(' ')[0]
# in more complicated blocks, there are many classes included in this declaration
# Break after the first class - safe to assume this is the "main class"?
if len(main_class.bases) > 0:
self.parsed_data['block_type'] = main_class.bases[0].declaration_path[-1]
break
except RuntimeError:
raise BlockToolException(
'Block header namespace {} must consist of a valid class instance'.format(module))
# io_signature, message_ports
self.parsed_data['io_signature'] = {}
self.parsed_data['message_port'] = {}
if os.path.isfile(self.impl_file) and exist_comments(self):
self.parsed_data['io_signature'] = io_signature(
self.impl_file)
self.parsed_data['message_port'] = message_port(
self.impl_file)
read_comments(self)
elif os.path.isfile(self.impl_file) and not exist_comments(self):
self.parsed_data['io_signature'] = io_signature(
self.impl_file)
self.parsed_data['message_port'] = message_port(
self.impl_file)
if self.addcomments:
add_comments(self)
elif not os.path.isfile(self.impl_file) and exist_comments(self):
read_comments(self)
else:
self.parsed_data['io_signature'] = {
"input": [],
"output": []
}
self.parsed_data['message_port'] = self.parsed_data['io_signature']
# make
try:
self.parsed_data['make'] = {}
self.parsed_data['make']['arguments'] = []
query_m = declarations.custom_matcher_t(
lambda mem_fun: mem_fun.name.startswith('make'))
query_make = query_m & declarations.access_type_matcher_t('public')
make_func = main_class.member_functions(function=query_make,
allow_empty=True,
header_file=self.target_file)
criteria = declarations.calldef_matcher(name='make')
_make_fun = declarations.matcher.get_single(criteria, main_class)
_make_fun = str(_make_fun).split(
'make')[-1].split(')')[0].split('(')[1].lstrip().rstrip().split(',')
if make_func:
for arg in make_func[0].arguments:
make_arguments = None
'''
for _arg in _make_fun:
if str(arg.name) in _arg:
make_arguments = {
"name": str(arg.name),
"dtype": str(arg.decl_type),
"default": ""
}
if re.findall(r'[-+]?\d*\.\d+|\d+', _arg):
make_arguments['default'] = re.findall(
r'[-+]?\d*\.\d+|\d+', _arg)[0]
elif re.findall(r'\"(.+?)\"', _arg):
make_arguments['default'] = re.findall(
r'\"(.+?)\"', _arg)[0]
elif "true" in _arg:
make_arguments['default'] = "True"
elif "false" in _arg:
make_arguments['default'] = "False"
'''
# In case the search did not find an argument in the inner loop
# This happens while parsing digital/symbol_sync_cc.h
if make_arguments:
self.parsed_data['make']['arguments'].append(
make_arguments.copy())
else:
self.parsed_data['make']['arguments'].append(
{
"name": str(arg.name),
"dtype": str(arg.decl_type),
"default": arg.default_value # can we get default argument directly from arg
})
except RuntimeError:
self.parsed_data['make'] = {}
self.parsed_data['make']['arguments'] = []
# setters
try:
self.parsed_data['methods'] = []
query_methods = declarations.access_type_matcher_t('public')
setters = main_class.member_functions(function=query_methods,
allow_empty=True,
header_file=self.target_file)
getter_arguments = []
if setters:
for setter in setters:
if str(setter.name).startswith('set_') and setter.arguments:
setter_args = {
"name": str(setter.name),
"arguments_type": []
}
for argument in setter.arguments:
args = {
"name": str(argument.name),
"dtype": str(argument.decl_type)
}
getter_arguments.append(args['name'])
setter_args['arguments_type'].append(args.copy())
self.parsed_data['methods'].append(setter_args.copy())
except RuntimeError:
self.parsed_data['methods'] = []
# getters
try:
self.parsed_data['properties'] = []
query_properties = declarations.access_type_matcher_t('public')
getters = main_class.member_functions(function=query_properties,
allow_empty=True,
header_file=self.target_file)
if getters:
for getter in getters:
if not getter.arguments or getter.has_const:
getter_args = {
"name": str(getter.name),
"dtype": str(getter.return_type),
"read_only": True
}
if getter_args['name'] in getter_arguments:
getter_args["read_only"] = False
self.parsed_data['properties'].append(
getter_args.copy())
except RuntimeError:
self.parsed_data['properties'] = []
# all member functions
# setters and getters do not return all member functions for a block
try:
self.parsed_data['member_functions'] = []
query_methods = declarations.access_type_matcher_t('public')
functions = main_class.member_functions(function=query_methods,
allow_empty=True,
header_file=self.target_file)
if functions:
for fcn in functions:
if str(fcn.name) not in [main_class.name, '~' + main_class.name, 'make']:
fcn_args = {
"name": str(fcn.name),
"arguments": []
}
for argument in fcn.arguments:
args = {
"name": str(argument.name),
"dtype": str(argument.decl_type),
"default": argument.default_value
}
fcn_args['arguments'].append(args.copy())
self.parsed_data['member_functions'].append(
fcn_args.copy())
except RuntimeError:
self.parsed_data['member_functions'] = []
# documentation
try:
_index = None
header_file = codecs.open(self.target_file, 'r', 'cp932')
self.parsed_data['docstring'] = re.compile(
r'//.*?$|/\*.*?\*/', re.DOTALL | re.MULTILINE).findall(
header_file.read())[2:]
header_file.close()
for doc in self.parsed_data['docstring']:
if Constants.BLOCKTOOL in doc:
_index = self.parsed_data['docstring'].index(doc)
if _index is not None:
self.parsed_data['docstring'] = self.parsed_data['docstring'][: _index]
except:
self.parsed_data['docstring'] = []
return self.parsed_data | [
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] | https://github.com/gnuradio/gnuradio/blob/09c3c4fa4bfb1a02caac74cb5334dfe065391e3b/gr-utils/blocktool/core/parseheader.py#L85-L317 | |
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/models/_infer_shapes_nn_mlmodel.py | python | infer_shapes | (nn_spec, input_spec, input_shape_dict=None) | return shape_dict | Input:
spec : mlmodel spec
input_shape_dict: dictionary of string --> tuple
string: input name
tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W)
If input_shape_dict is not provided, input shapes are inferred from the input description in the mlmodel.
Since the description in the specification only contains values of C,H,W; Seq and Batch dimensions are set to 1.
Output:
shape_dict: dictionary containing all the blobs in the neural network and their shapes, expressed as length 5 tuples,
to be interpreted in order (Seq, Batch, C, H, W). | Input: | [
"Input",
":"
] | def infer_shapes(nn_spec, input_spec, input_shape_dict=None):
"""
Input:
spec : mlmodel spec
input_shape_dict: dictionary of string --> tuple
string: input name
tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W)
If input_shape_dict is not provided, input shapes are inferred from the input description in the mlmodel.
Since the description in the specification only contains values of C,H,W; Seq and Batch dimensions are set to 1.
Output:
shape_dict: dictionary containing all the blobs in the neural network and their shapes, expressed as length 5 tuples,
to be interpreted in order (Seq, Batch, C, H, W).
"""
shape_dict = {}
if input_shape_dict:
for key, value in input_shape_dict.items():
assert len(value) == 5, "Shape of the input must be of length 5"
shape_dict[key] = value
# construct input_shape_dict from the model description
else:
for inp in input_spec:
input_name = inp.name
C = H = W = 1
if inp.type.WhichOneof("Type") == "imageType":
W = int(inp.type.imageType.width)
H = int(inp.type.imageType.height)
colorspace = _FeatureTypes_pb2.ImageFeatureType.ColorSpace.Name(
inp.type.imageType.colorSpace
)
if colorspace == "GRAYSCALE":
C = 1
elif colorspace == "RGB" or colorspace == "BGR":
C = 3
else:
raise ValueError("Input %s : Invalid Colorspace" % (input_name))
elif inp.type.WhichOneof("Type") == "multiArrayType":
array_shape = inp.type.multiArrayType.shape
if len(array_shape) == 1:
C = array_shape[0]
elif len(array_shape) == 3:
C, H, W = map(int, array_shape)
else:
raise ValueError(
"Input %s : Multi array must be of length 1 or 3" % (input_name)
)
else:
raise ValueError(
"Input %s : Input type must be image or multi-array" % (input_name)
)
shape_dict[input_name] = (1, 1, C, H, W)
layers = nn_spec.layers
for i, layer in enumerate(layers):
for inp in layer.input:
assert inp in shape_dict, "Input %s shape not cannot be determined" % (inp)
layer_type = layer.WhichOneof("layer")
if layer_type == "custom":
break
layer_translator = _get_translator_function(layer_type)
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mandiant/flare-wmi | b0a5a094ff9ca7d7a1c4fc711dc00c74dec4b6b1 | python-cim/samples/auto_carve_class_definitions.py | python | filetime2datetime | (ft) | return datetime.datetime.utcfromtimestamp(float(ft) * 1e-7 - 11644473600) | convert a FILETIME 64-bit integer to a timestamp.
Args:
ft (int): the FILETIME number.
Returns:
datetime.datetime: the python timestamp. | convert a FILETIME 64-bit integer to a timestamp.
Args:
ft (int): the FILETIME number. | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | Max._max | (self, a, b) | return res, (mask0, mask1) | Args:
a (CTensor): First operand
b (CTensor): Second operand
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deepmind/streetlearn | ccf1d60b9c45154894d45a897748aee85d7eb69b | streetlearn/python/environment/batched_streetlearn.py | python | BatchedStreetLearn.action_set | (self) | return self._envs[0].action_set() | Returns the set of actions, mapping integer actions to 1D arrays. | Returns the set of actions, mapping integer actions to 1D arrays. | [
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wesnoth/wesnoth | 6ccac5a5e8ff75303c9190c0da60580925cb32c0 | data/tools/wesnoth/wmltools3.py | python | pop_to_top | (whoami) | Pop upward to the top-level directory. | Pop upward to the top-level directory. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/resources/ico_tools.py | python | RebuildANDMask | (iconimage) | return iconimage[:40 + xor_palette_size + xor_size] + and_data | Rebuild the AND mask in an icon image.
GIMP (<=2.8.14) creates a bad AND mask on 32-bit icon images (pixels with <50%
opacity are marked as transparent, which end up looking black on Windows). So,
if this is a 32-bit image, throw the mask away and recompute it from the alpha
data. (See: https://bugzilla.gnome.org/show_bug.cgi?id=755200)
Args:
iconimage: Bytes of an icon image (the BMP data for an entry in an ICO
file). Must be in BMP format, not PNG. Does not need to be 32-bit (if it
is not 32-bit, this is a no-op).
Returns:
An updated |iconimage|, with the AND mask re-computed using
ComputeANDMaskFromAlpha. | Rebuild the AND mask in an icon image. | [
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] | def RebuildANDMask(iconimage):
"""Rebuild the AND mask in an icon image.
GIMP (<=2.8.14) creates a bad AND mask on 32-bit icon images (pixels with <50%
opacity are marked as transparent, which end up looking black on Windows). So,
if this is a 32-bit image, throw the mask away and recompute it from the alpha
data. (See: https://bugzilla.gnome.org/show_bug.cgi?id=755200)
Args:
iconimage: Bytes of an icon image (the BMP data for an entry in an ICO
file). Must be in BMP format, not PNG. Does not need to be 32-bit (if it
is not 32-bit, this is a no-op).
Returns:
An updated |iconimage|, with the AND mask re-computed using
ComputeANDMaskFromAlpha.
"""
# Parse BITMAPINFOHEADER.
(_, width, height, _, bpp, _, _, _, _, num_colors, _) = struct.unpack(
'<LLLHHLLLLLL', iconimage[:40])
if bpp != 32:
# No alpha channel, so the mask cannot be "wrong" (it is the only source of
# transparency information).
return iconimage
height /= 2
xor_size = int(math.ceil(width * bpp / 32.0)) * 4 * height
# num_colors can be 0, implying 2^bpp colors.
xor_palette_size = (num_colors or (1 << bpp if bpp < 24 else 0)) * 4
xor_data = iconimage[40 + xor_palette_size :
40 + xor_palette_size + xor_size]
and_data = ComputeANDMaskFromAlpha(xor_data, width, height)
# Replace the AND mask in the original icon data.
return iconimage[:40 + xor_palette_size + xor_size] + and_data | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/importlib_metadata/_compat.py | python | install | (cls) | return cls | Class decorator for installation on sys.meta_path.
Adds the backport DistributionFinder to sys.meta_path and
attempts to disable the finder functionality of the stdlib
DistributionFinder. | Class decorator for installation on sys.meta_path. | [
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"""
Class decorator for installation on sys.meta_path.
Adds the backport DistributionFinder to sys.meta_path and
attempts to disable the finder functionality of the stdlib
DistributionFinder.
"""
sys.meta_path.append(cls())
disable_stdlib_finder()
return cls | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | llvm/examples/Kaleidoscope/MCJIT/complete/genk-timing.py | python | KScriptGenerator.updateFunctionCallMap | (self, caller, callee) | Maintains a map of functions that are called from other functions | Maintains a map of functions that are called from other functions | [
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self.calledFunctionTable[caller] = []
if not callee in self.calledFunctionTable[caller]:
self.calledFunctionTable[caller].append(callee)
if not caller in self.comprehensiveCalledFunctionTable:
self.comprehensiveCalledFunctionTable[caller] = []
self.comprehensiveCalledFunctionTable[caller].append(callee) | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | clang/bindings/python/clang/cindex.py | python | Type.get_declaration | (self) | return conf.lib.clang_getTypeDeclaration(self) | Return the cursor for the declaration of the given type. | Return the cursor for the declaration of the given type. | [
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"""
Return the cursor for the declaration of the given type.
"""
return conf.lib.clang_getTypeDeclaration(self) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/factorization/python/ops/factorization_ops.py | python | WALSModel.row_weights | (self) | return self._row_weights | Returns a list of tensors corresponding to row weight shards. | Returns a list of tensors corresponding to row weight shards. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pyio.py | python | IOBase.flush | (self) | Flush write buffers, if applicable.
This is not implemented for read-only and non-blocking streams. | Flush write buffers, if applicable. | [
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"""Flush write buffers, if applicable.
This is not implemented for read-only and non-blocking streams.
"""
self._checkClosed() | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/lib2to3/fixer_base.py | python | BaseFix.start_tree | (self, tree, filename) | Some fixers need to maintain tree-wide state.
This method is called once, at the start of tree fix-up.
tree - the root node of the tree to be processed.
filename - the name of the file the tree came from. | Some fixers need to maintain tree-wide state.
This method is called once, at the start of tree fix-up. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/idlelib/format.py | python | FormatRegion.dedent_region_event | (self, event=None) | return "break" | Dedent region by indentwidth spaces. | Dedent region by indentwidth spaces. | [
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if line:
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effective = max(effective - self.editwin.indentwidth, 0)
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/telnetlib.py | python | Telnet.read_eager | (self) | return self.read_very_lazy() | Read readily available data.
Raise EOFError if connection closed and no cooked data
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Don't block unless in the midst of an IAC sequence. | Read readily available data. | [
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"""Read readily available data.
Raise EOFError if connection closed and no cooked data
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Don't block unless in the midst of an IAC sequence.
"""
self.process_rawq()
while not self.cookedq and not self.eof and self.sock_avail():
self.fill_rawq()
self.process_rawq()
return self.read_very_lazy() | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/common/utils.py | python | GetApacheSchemePortFromListen | () | return None | Gets scheme, port number that Apache is running on.
Gets scheme and port number from Listen directive of httpd config file:
Format of Listen directive:
Listen [IP-address:]portnumber [protocol]
Note: IPv6 addresses must be surrounded in square brackets:
Listen [2001:db8::a00:20ff:fea7:ccea]:80
Returns:
tuple (scheme, port number) that Apache is listening on or None. | Gets scheme, port number that Apache is running on. | [
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"""Gets scheme, port number that Apache is running on.
Gets scheme and port number from Listen directive of httpd config file:
Format of Listen directive:
Listen [IP-address:]portnumber [protocol]
Note: IPv6 addresses must be surrounded in square brackets:
Listen [2001:db8::a00:20ff:fea7:ccea]:80
Returns:
tuple (scheme, port number) that Apache is listening on or None.
"""
match = MatchPattern(
GEHTTPD_CONF_PATH,
r"^Listen\s+(?:\[?([a-fA-F\d\.\:]+)\]?:)?(\d+)(?:\s+(https?))?")
if match:
(scheme, port) = (match[2], match[1])
assert port
if not scheme:
scheme = "https" if port == "443" else "http"
return (scheme, port)
logging.error("Listen directive is not specified in gehttpd config.")
return None | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/requests/packages/urllib3/filepost.py | python | encode_multipart_formdata | (fields, boundary=None) | return body.getvalue(), content_type | Encode a dictionary of ``fields`` using the multipart/form-data MIME format.
:param fields:
Dictionary of fields or list of (key, :class:`~urllib3.fields.RequestField`).
:param boundary:
If not specified, then a random boundary will be generated using
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"""
Encode a dictionary of ``fields`` using the multipart/form-data MIME format.
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for field in iter_field_objects(fields):
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/learn/python/learn/utils/checkpoints.py | python | init_from_checkpoint | (checkpoint_dir, assignment_map) | See `tf.contrib.framework.init_from_checkpoint`. | See `tf.contrib.framework.init_from_checkpoint`. | [
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] | def init_from_checkpoint(checkpoint_dir, assignment_map):
"""See `tf.contrib.framework.init_from_checkpoint`."""
checkpoint_utils.init_from_checkpoint(checkpoint_dir, assignment_map) | [
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rrwick/Unicycler | 96ffea71e3a78d63ade19d6124946773e65cf129 | unicycler/assembly_graph.py | python | AssemblyGraph.get_copy_number | (self, segment) | return len(self.copy_depths[segment.number]) | Returns the segment's copy number (0 if copy number determination did not occur for this
segment). | Returns the segment's copy number (0 if copy number determination did not occur for this
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"""
Returns the segment's copy number (0 if copy number determination did not occur for this
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"""
if segment.number not in self.copy_depths:
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return len(self.copy_depths[segment.number]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tarfile.py | python | _Stream.__read | (self, size) | return t[:size] | Return size bytes from stream. If internal buffer is empty,
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c = len(self.buf)
t = [self.buf]
while c < size:
buf = self.fileobj.read(self.bufsize)
if not buf:
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t.append(buf)
c += len(buf)
t = b"".join(t)
self.buf = t[size:]
return t[:size] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/mailbox.py | python | _create_temporary | (path) | return _create_carefully('%s.%s.%s.%s' % (path, int(time.time()),
socket.gethostname(),
os.getpid())) | Create a temp file based on path and open for reading and writing. | Create a temp file based on path and open for reading and writing. | [
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return _create_carefully('%s.%s.%s.%s' % (path, int(time.time()),
socket.gethostname(),
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/control_flow_ops.py | python | GradLoopState.switch_map | (self) | return self._switch_map | The map that records all the Switch ops for the While loop. | The map that records all the Switch ops for the While loop. | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site.py | python | execsitecustomize | () | Run custom site specific code, if available. | Run custom site specific code, if available. | [
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"""Run custom site specific code, if available."""
try:
import sitecustomize
except ImportError:
pass
except Exception:
if sys.flags.verbose:
sys.excepthook(*sys.exc_info())
else:
print >>sys.stderr, \
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/fancy_getopt.py | python | FancyGetopt._grok_option_table | (self) | Populate the various data structures that keep tabs on the
option table. Called by 'getopt()' before it can do anything
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"""Populate the various data structures that keep tabs on the
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"""
self.long_opts = []
self.short_opts = []
self.short2long.clear()
self.repeat = {}
for option in self.option_table:
if len(option) == 3:
long, short, help = option
repeat = 0
elif len(option) == 4:
long, short, help, repeat = option
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# the option table is part of the code, so simply
# assert that it is correct
raise ValueError("invalid option tuple: %r" % (option,))
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if not isinstance(long, str) or len(long) < 2:
raise DistutilsGetoptError(("invalid long option '%s': "
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raise DistutilsGetoptError(
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self.attr_name[long] = self.get_attr_name(long)
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/polynomial/laguerre.py | python | laggrid3d | (x, y, z, c) | return c | Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z.
This function returns the values:
.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
`a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
a grid with `x` in the first dimension, `y` in the second, and `z` in
the third.
The parameters `x`, `y`, and `z` are converted to arrays only if they
are tuples or a lists, otherwise they are treated as a scalars. In
either case, either `x`, `y`, and `z` or their elements must support
multiplication and addition both with themselves and with the elements
of `c`.
If `c` has fewer than three dimensions, ones are implicitly appended to
its shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape + y.shape + z.shape.
Parameters
----------
x, y, z : array_like, compatible objects
The three dimensional series is evaluated at the points in the
Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a
list or tuple, it is first converted to an ndarray, otherwise it is
left unchanged and, if it isn't an ndarray, it is treated as a
scalar.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree i,j are contained in ``c[i,j]``. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
lagval, lagval2d, laggrid2d, lagval3d
Notes
-----
.. versionadded::1.7.0 | Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z. | [
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"""
Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z.
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.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
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x, y, z : array_like, compatible objects
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list or tuple, it is first converted to an ndarray, otherwise it is
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Array of coefficients ordered so that the coefficients for terms of
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Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
lagval, lagval2d, laggrid2d, lagval3d
Notes
-----
.. versionadded::1.7.0
"""
c = lagval(x, c)
c = lagval(y, c)
c = lagval(z, c)
return c | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/mux.py | python | _LogicalStream._send_pong | (self, body) | Overrides Stream._send_pong | Overrides Stream._send_pong | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/datastore_range_iterators.py | python | RangeIterator.from_json | (cls, json) | Reverse of to_json. | Reverse of to_json. | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/LargeScaleStructures/geometry_writer.py | python | MantidGeom.addDetectorIds | (self, idname, idlist) | Add the detector IDs. A list is provided that must be divisible by 3.
The list should be specified as [start1, end1, step1, start2, end2,
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"""
Add the detector IDs. A list is provided that must be divisible by 3.
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num_ids = len(idlist) / 3
id_element = self._append_child("idlist", self._root, idname=idname)
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | uCSIsThaana | (code) | return ret | Check whether the character is part of Thaana UCS Block | Check whether the character is part of Thaana UCS Block | [
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sslab-gatech/qsym | 78702ba8928519ffb9beb7859ec2f7ddce2b2fe4 | third_party/pin-2.14-71313-gcc.4.4.7-linux/source/tools/Utils/and-launch.py | python | RunCommand | (cmd) | return out | Execute a shell command and wait for it to complete. If the command fails, an error is printed
and E{ReturnCode} is set to non-zero.
@param cmd: The shell command to run.
@type cmd: string.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/tempfile.py | python | _mkstemp_inner | (dir, pre, suf, flags) | Code common to mkstemp, TemporaryFile, and NamedTemporaryFile. | Code common to mkstemp, TemporaryFile, and NamedTemporaryFile. | [
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"""Code common to mkstemp, TemporaryFile, and NamedTemporaryFile."""
names = _get_candidate_names()
for seq in xrange(TMP_MAX):
name = names.next()
file = _os.path.join(dir, pre + name + suf)
try:
fd = _os.open(file, flags, 0600)
_set_cloexec(fd)
return (fd, _os.path.abspath(file))
except OSError, e:
if e.errno == _errno.EEXIST:
continue # try again
raise
raise IOError, (_errno.EEXIST, "No usable temporary file name found") | [
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GoSSIP-SJTU/Armariris | ad5d868482956b2194a77b39c8d543c7c2318200 | tools/clang/bindings/python/clang/cindex.py | python | Type.kind | (self) | return TypeKind.from_id(self._kind_id) | Return the kind of this type. | Return the kind of this type. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PGTextCtrlEditor_OnTextCtrlEvent | (*args, **kwargs) | return _propgrid.PGTextCtrlEditor_OnTextCtrlEvent(*args, **kwargs) | PGTextCtrlEditor_OnTextCtrlEvent(PropertyGrid propgrid, PGProperty property, Window ctrl,
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"""
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return _propgrid.PGTextCtrlEditor_OnTextCtrlEvent(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/metrics/cluster/_supervised.py | python | homogeneity_completeness_v_measure | (labels_true, labels_pred, beta=1.0) | return homogeneity, completeness, v_measure_score | Compute the homogeneity and completeness and V-Measure scores at once.
Those metrics are based on normalized conditional entropy measures of
the clustering labeling to evaluate given the knowledge of a Ground
Truth class labels of the same samples.
A clustering result satisfies homogeneity if all of its clusters
contain only data points which are members of a single class.
A clustering result satisfies completeness if all the data points
that are members of a given class are elements of the same cluster.
Both scores have positive values between 0.0 and 1.0, larger values
being desirable.
Those 3 metrics are independent of the absolute values of the labels:
a permutation of the class or cluster label values won't change the
score values in any way.
V-Measure is furthermore symmetric: swapping ``labels_true`` and
``label_pred`` will give the same score. This does not hold for
homogeneity and completeness. V-Measure is identical to
:func:`normalized_mutual_info_score` with the arithmetic averaging
method.
Read more in the :ref:`User Guide <homogeneity_completeness>`.
Parameters
----------
labels_true : int array, shape = [n_samples]
ground truth class labels to be used as a reference
labels_pred : array-like of shape (n_samples,)
cluster labels to evaluate
beta : float
Ratio of weight attributed to ``homogeneity`` vs ``completeness``.
If ``beta`` is greater than 1, ``completeness`` is weighted more
strongly in the calculation. If ``beta`` is less than 1,
``homogeneity`` is weighted more strongly.
Returns
-------
homogeneity : float
score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling
completeness : float
score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
v_measure : float
harmonic mean of the first two
See also
--------
homogeneity_score
completeness_score
v_measure_score | Compute the homogeneity and completeness and V-Measure scores at once. | [
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"""Compute the homogeneity and completeness and V-Measure scores at once.
Those metrics are based on normalized conditional entropy measures of
the clustering labeling to evaluate given the knowledge of a Ground
Truth class labels of the same samples.
A clustering result satisfies homogeneity if all of its clusters
contain only data points which are members of a single class.
A clustering result satisfies completeness if all the data points
that are members of a given class are elements of the same cluster.
Both scores have positive values between 0.0 and 1.0, larger values
being desirable.
Those 3 metrics are independent of the absolute values of the labels:
a permutation of the class or cluster label values won't change the
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V-Measure is furthermore symmetric: swapping ``labels_true`` and
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method.
Read more in the :ref:`User Guide <homogeneity_completeness>`.
Parameters
----------
labels_true : int array, shape = [n_samples]
ground truth class labels to be used as a reference
labels_pred : array-like of shape (n_samples,)
cluster labels to evaluate
beta : float
Ratio of weight attributed to ``homogeneity`` vs ``completeness``.
If ``beta`` is greater than 1, ``completeness`` is weighted more
strongly in the calculation. If ``beta`` is less than 1,
``homogeneity`` is weighted more strongly.
Returns
-------
homogeneity : float
score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling
completeness : float
score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
v_measure : float
harmonic mean of the first two
See also
--------
homogeneity_score
completeness_score
v_measure_score
"""
labels_true, labels_pred = check_clusterings(labels_true, labels_pred)
if len(labels_true) == 0:
return 1.0, 1.0, 1.0
entropy_C = entropy(labels_true)
entropy_K = entropy(labels_pred)
contingency = contingency_matrix(labels_true, labels_pred, sparse=True)
MI = mutual_info_score(None, None, contingency=contingency)
homogeneity = MI / (entropy_C) if entropy_C else 1.0
completeness = MI / (entropy_K) if entropy_K else 1.0
if homogeneity + completeness == 0.0:
v_measure_score = 0.0
else:
v_measure_score = ((1 + beta) * homogeneity * completeness
/ (beta * homogeneity + completeness))
return homogeneity, completeness, v_measure_score | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/supertooltip.py | python | SuperToolTip.GetHeader | (self) | return self._header | Returns the header text. | Returns the header text. | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/examples/speech_commands/models.py | python | create_model | (fingerprint_input, model_settings, model_architecture,
is_training, runtime_settings=None) | Builds a model of the requested architecture compatible with the settings.
There are many possible ways of deriving predictions from a spectrogram
input, so this function provides an abstract interface for creating different
kinds of models in a black-box way. You need to pass in a TensorFlow node as
the 'fingerprint' input, and this should output a batch of 1D features that
describe the audio. Typically this will be derived from a spectrogram that's
been run through an MFCC, but in theory it can be any feature vector of the
size specified in model_settings['fingerprint_size'].
The function will build the graph it needs in the current TensorFlow graph,
and return the tensorflow output that will contain the 'logits' input to the
softmax prediction process. If training flag is on, it will also return a
placeholder node that can be used to control the dropout amount.
See the implementations below for the possible model architectures that can be
requested.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
model_architecture: String specifying which kind of model to create.
is_training: Whether the model is going to be used for training.
runtime_settings: Dictionary of information about the runtime.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
Raises:
Exception: If the architecture type isn't recognized. | Builds a model of the requested architecture compatible with the settings. | [
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is_training, runtime_settings=None):
"""Builds a model of the requested architecture compatible with the settings.
There are many possible ways of deriving predictions from a spectrogram
input, so this function provides an abstract interface for creating different
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The function will build the graph it needs in the current TensorFlow graph,
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See the implementations below for the possible model architectures that can be
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Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
model_architecture: String specifying which kind of model to create.
is_training: Whether the model is going to be used for training.
runtime_settings: Dictionary of information about the runtime.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
Raises:
Exception: If the architecture type isn't recognized.
"""
if model_architecture == 'single_fc':
return create_single_fc_model(fingerprint_input, model_settings,
is_training)
elif model_architecture == 'conv':
return create_conv_model(fingerprint_input, model_settings, is_training)
elif model_architecture == 'low_latency_conv':
return create_low_latency_conv_model(fingerprint_input, model_settings,
is_training)
elif model_architecture == 'low_latency_svdf':
return create_low_latency_svdf_model(fingerprint_input, model_settings,
is_training, runtime_settings)
else:
raise Exception('model_architecture argument "' + model_architecture +
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/base.py | python | _trim_front | (strings) | return trimmed | Trims zeros and decimal points. | Trims zeros and decimal points. | [
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"""
Trims zeros and decimal points.
"""
trimmed = strings
while len(strings) > 0 and all(x[0] == " " for x in trimmed):
trimmed = [x[1:] for x in trimmed]
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | scripts/cpp_lint.py | python | PrintUsage | (message) | Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message. | Prints a brief usage string and exits, optionally with an error message. | [
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"""Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message.
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sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | AuiManager.SavePerspective | (*args, **kwargs) | return _aui.AuiManager_SavePerspective(*args, **kwargs) | SavePerspective(self) -> String | SavePerspective(self) -> String | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/io/matlab/mio5.py | python | VarWriter5.write | (self, arr) | Write `arr` to stream at top and sub levels
Parameters
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arr : array_like
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''' Write `arr` to stream at top and sub levels
Parameters
----------
arr : array_like
array-like object to create writer for
'''
# store position, so we can update the matrix tag
mat_tag_pos = self.file_stream.tell()
# First check if these are sparse
if scipy.sparse.issparse(arr):
self.write_sparse(arr)
self.update_matrix_tag(mat_tag_pos)
return
# Try to convert things that aren't arrays
narr = to_writeable(arr)
if narr is None:
raise TypeError('Could not convert %s (type %s) to array'
% (arr, type(arr)))
if isinstance(narr, MatlabObject):
self.write_object(narr)
elif isinstance(narr, MatlabFunction):
raise MatWriteError('Cannot write matlab functions')
elif narr is EmptyStructMarker: # empty struct array
self.write_empty_struct()
elif narr.dtype.fields: # struct array
self.write_struct(narr)
elif narr.dtype.hasobject: # cell array
self.write_cells(narr)
elif narr.dtype.kind in ('U', 'S'):
if self.unicode_strings:
codec = 'UTF8'
else:
codec = 'ascii'
self.write_char(narr, codec)
else:
self.write_numeric(narr)
self.update_matrix_tag(mat_tag_pos) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/common/dtype.py | python | dtype_to_pytype | (type_) | return {
bool_: bool,
int_: int,
int8: int,
int16: int,
int32: int,
int64: int,
uint8: int,
uint16: int,
uint32: int,
uint64: int,
float_: float,
float16: float,
float32: float,
float64: float,
list_: list,
tuple_: tuple,
string: str,
complex64: complex,
complex128: complex,
type_none: type(None)
}[type_] | Convert MindSpore dtype to python data type.
Args:
type_ (:class:`mindspore.dtype`): MindSpore's dtype.
Returns:
Type of python. | Convert MindSpore dtype to python data type. | [
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"type",
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] | def dtype_to_pytype(type_):
"""
Convert MindSpore dtype to python data type.
Args:
type_ (:class:`mindspore.dtype`): MindSpore's dtype.
Returns:
Type of python.
"""
return {
bool_: bool,
int_: int,
int8: int,
int16: int,
int32: int,
int64: int,
uint8: int,
uint16: int,
uint32: int,
uint64: int,
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float32: float,
float64: float,
list_: list,
tuple_: tuple,
string: str,
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complex128: complex,
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/distribute/distributed_training_utils_v1.py | python | set_weights | (distribution_strategy, dist_model, weights) | Sets the weights of the replicated models.
The weights of the replicated models are set to the weights of the original
model. The weights of the replicated model are Mirrored variables and hence
we need to use the `update` call within a DistributionStrategy scope.
Args:
distribution_strategy: DistributionStrategy used to distribute training
and validation.
dist_model: The replicated models on the different devices.
weights: The weights of the original model. | Sets the weights of the replicated models. | [
"Sets",
"the",
"weights",
"of",
"the",
"replicated",
"models",
"."
] | def set_weights(distribution_strategy, dist_model, weights):
"""Sets the weights of the replicated models.
The weights of the replicated models are set to the weights of the original
model. The weights of the replicated model are Mirrored variables and hence
we need to use the `update` call within a DistributionStrategy scope.
Args:
distribution_strategy: DistributionStrategy used to distribute training
and validation.
dist_model: The replicated models on the different devices.
weights: The weights of the original model.
"""
assign_ops = []
for layer in dist_model.layers:
num_param = len(layer.weights)
layer_weights = weights[:num_param]
for sw, w in zip(layer.weights, layer_weights):
if ops.executing_eagerly_outside_functions():
sw.assign(w)
else:
assign_ops.append(distribution_strategy.unwrap(sw.assign(w)))
weights = weights[num_param:]
if not ops.executing_eagerly_outside_functions():
backend.get_session(assign_ops).run(assign_ops) | [
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facebook/fbthrift | fb9c8562aba04c4fd9b17716eb5d970cc88a75bb | build/fbcode_builder/getdeps/expr.py | python | parse_expr | (expr_text, valid_variables) | return p.parse() | parses the simple criteria expression syntax used in
dependency specifications.
Returns an ExprNode instance that can be evaluated like this:
```
expr = parse_expr("os=windows")
ok = expr.eval({
"os": "windows"
})
```
Whitespace is allowed between tokens. The following terms
are recognized:
KEY = VALUE # Evaluates to True if ctx[KEY] == VALUE
not(EXPR) # Evaluates to True if EXPR evaluates to False
# and vice versa
all(EXPR1, EXPR2, ...) # Evaluates True if all of the supplied
# EXPR's also evaluate True
any(EXPR1, EXPR2, ...) # Evaluates True if any of the supplied
# EXPR's also evaluate True, False if
# none of them evaluated true. | parses the simple criteria expression syntax used in
dependency specifications.
Returns an ExprNode instance that can be evaluated like this: | [
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"that",
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"like",
"this",
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] | def parse_expr(expr_text, valid_variables):
"""parses the simple criteria expression syntax used in
dependency specifications.
Returns an ExprNode instance that can be evaluated like this:
```
expr = parse_expr("os=windows")
ok = expr.eval({
"os": "windows"
})
```
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are recognized:
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all(EXPR1, EXPR2, ...) # Evaluates True if all of the supplied
# EXPR's also evaluate True
any(EXPR1, EXPR2, ...) # Evaluates True if any of the supplied
# EXPR's also evaluate True, False if
# none of them evaluated true.
"""
p = Parser(expr_text, valid_variables)
return p.parse() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/dateutil/tz/_common.py | python | tzname_in_python2 | (namefunc) | Change unicode output into bytestrings in Python 2
tzname() API changed in Python 3. It used to return bytes, but was changed
to unicode strings | Change unicode output into bytestrings in Python 2 | [
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"output",
"into",
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"in",
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"2"
] | def tzname_in_python2(namefunc):
"""Change unicode output into bytestrings in Python 2
tzname() API changed in Python 3. It used to return bytes, but was changed
to unicode strings
"""
if PY2:
@wraps(namefunc)
def adjust_encoding(*args, **kwargs):
name = namefunc(*args, **kwargs)
if name is not None:
name = name.encode()
return name
return adjust_encoding
else:
return namefunc | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | xpcom/idl-parser/xpidl.py | python | IDLParser.p_paramtype | (self, p) | paramtype : IN
| INOUT
| OUT | paramtype : IN
| INOUT
| OUT | [
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"""paramtype : IN
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p[0] = p[1] | [
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zhaoweicai/hwgq | ebc706bee3e2d145de1da4be446ce8de8740738f | scripts/cpp_lint.py | python | CheckVlogArguments | (filename, clean_lines, linenum, error) | Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks that VLOG() is only used for defining a logging level. | [
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] | def CheckVlogArguments(filename, clean_lines, linenum, error):
"""Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
error(filename, linenum, 'runtime/vlog', 5,
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/training/moving_averages.py | python | ExponentialMovingAverage.apply | (self, var_list=None) | Maintains moving averages of variables.
`var_list` must be a list of `Variable` or `Tensor` objects. This method
creates shadow variables for all elements of `var_list`. Shadow variables
for `Variable` objects are initialized to the variable's initial value.
They will be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
For `Tensor` objects, the shadow variables are initialized to 0.
shadow variables are created with `trainable=False` and added to the
`GraphKeys.ALL_VARIABLES` collection. They will be returned by calls to
`tf.all_variables()`.
Returns an op that updates all shadow variables as described above.
Note that `apply()` can be called multiple times with different lists of
variables.
Args:
var_list: A list of Variable or Tensor objects. The variables
and Tensors must be of types float16, float32, or float64.
Returns:
An Operation that updates the moving averages.
Raises:
TypeError: If the arguments are not all float16, float32, or float64.
ValueError: If the moving average of one of the variables is already
being computed. | Maintains moving averages of variables. | [
"Maintains",
"moving",
"averages",
"of",
"variables",
"."
] | def apply(self, var_list=None):
"""Maintains moving averages of variables.
`var_list` must be a list of `Variable` or `Tensor` objects. This method
creates shadow variables for all elements of `var_list`. Shadow variables
for `Variable` objects are initialized to the variable's initial value.
They will be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
For `Tensor` objects, the shadow variables are initialized to 0.
shadow variables are created with `trainable=False` and added to the
`GraphKeys.ALL_VARIABLES` collection. They will be returned by calls to
`tf.all_variables()`.
Returns an op that updates all shadow variables as described above.
Note that `apply()` can be called multiple times with different lists of
variables.
Args:
var_list: A list of Variable or Tensor objects. The variables
and Tensors must be of types float16, float32, or float64.
Returns:
An Operation that updates the moving averages.
Raises:
TypeError: If the arguments are not all float16, float32, or float64.
ValueError: If the moving average of one of the variables is already
being computed.
"""
# TODO(touts): op_scope
if var_list is None:
var_list = variables.trainable_variables()
for var in var_list:
if var.dtype.base_dtype not in [dtypes.float16, dtypes.float32,
dtypes.float64]:
raise TypeError("The variables must be half, float, or double: %s" %
var.name)
if var in self._averages:
raise ValueError("Moving average already computed for: %s" % var.name)
# For variables: to lower communication bandwidth across devices we keep
# the moving averages on the same device as the variables. For other
# tensors, we rely on the existing device allocation mechanism.
with ops.control_dependencies(None):
if isinstance(var, variables.Variable):
avg = slot_creator.create_slot(var,
var.initialized_value(),
self._name,
colocate_with_primary=True)
# NOTE(mrry): We only add `tf.Variable` objects to the
# `MOVING_AVERAGE_VARIABLES` collection.
ops.add_to_collection(ops.GraphKeys.MOVING_AVERAGE_VARIABLES, var)
else:
avg = slot_creator.create_zeros_slot(
var,
self._name,
colocate_with_primary=(var.op.type == "Variable"))
self._averages[var] = avg
with ops.name_scope(self._name) as scope:
decay = ops.convert_to_tensor(self._decay, name="decay")
if self._num_updates is not None:
num_updates = math_ops.cast(self._num_updates,
dtypes.float32,
name="num_updates")
decay = math_ops.minimum(decay,
(1.0 + num_updates) / (10.0 + num_updates))
updates = []
for var in var_list:
updates.append(assign_moving_average(self._averages[var], var, decay))
return control_flow_ops.group(*updates, name=scope) | [
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mickem/nscp | 79f89fdbb6da63f91bc9dedb7aea202fe938f237 | scripts/python/lib/google/protobuf/internal/python_message.py | python | _AddClearExtensionMethod | (cls) | Helper for _AddMessageMethods(). | Helper for _AddMessageMethods(). | [
"Helper",
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"_AddMessageMethods",
"()",
"."
] | def _AddClearExtensionMethod(cls):
"""Helper for _AddMessageMethods()."""
def ClearExtension(self, extension_handle):
_VerifyExtensionHandle(self, extension_handle)
# Similar to ClearField(), above.
if extension_handle in self._fields:
del self._fields[extension_handle]
self._Modified()
cls.ClearExtension = ClearExtension | [
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francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | api/ctpx/ctptd.py | python | CtpTd.onRtnCombAction | (self, CombActionField) | 申请组合通知 | 申请组合通知 | [
"申请组合通知"
] | def onRtnCombAction(self, CombActionField):
"""申请组合通知"""
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Builder.py | python | BuilderBase.add_src_builder | (self, builder) | Add a new Builder to the list of src_builders.
This requires wiping out cached values so that the computed
lists of source suffixes get re-calculated. | Add a new Builder to the list of src_builders. | [
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] | def add_src_builder(self, builder):
"""
Add a new Builder to the list of src_builders.
This requires wiping out cached values so that the computed
lists of source suffixes get re-calculated.
"""
self._memo = {}
self.src_builder.append(builder) | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | Sigmoid.forward | (self, x) | return out | Args:
x (CTensor): Input tensor
Returns:
CTensor, the output | Args:
x (CTensor): Input tensor
Returns:
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"""
Args:
x (CTensor): Input tensor
Returns:
CTensor, the output
"""
out = singa.Sigmoid(x)
if training:
self.cache = (out,)
return out | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/tools/gyp/pylib/gyp/common.py | python | GetFlavor | (params) | return 'linux' | Returns |params.flavor| if it's set, the system's default flavor else. | Returns |params.flavor| if it's set, the system's default flavor else. | [
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"""Returns |params.flavor| if it's set, the system's default flavor else."""
flavors = {
'cygwin': 'win',
'win32': 'win',
'darwin': 'mac',
}
if 'flavor' in params:
return params['flavor']
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return flavors[sys.platform]
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if sys.platform.startswith('aix'):
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if sys.platform.startswith(('os390', 'zos')):
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return 'linux' | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/javascripttokens.py | python | JavaScriptToken.IsAssignment | (self) | return (self.type == JavaScriptTokenType.OPERATOR and
self.string.endswith('=') and
self.string not in ('==', '!=', '>=', '<=', '===', '!==')) | Tests if this token is an assignment operator.
Returns:
True if this token is an assignment operator. | Tests if this token is an assignment operator. | [
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] | def IsAssignment(self):
"""Tests if this token is an assignment operator.
Returns:
True if this token is an assignment operator.
"""
return (self.type == JavaScriptTokenType.OPERATOR and
self.string.endswith('=') and
self.string not in ('==', '!=', '>=', '<=', '===', '!==')) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/importlib/util.py | python | resolve_name | (name, package) | return _resolve_name(name[level:], package, level) | Resolve a relative module name to an absolute one. | Resolve a relative module name to an absolute one. | [
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] | def resolve_name(name, package):
"""Resolve a relative module name to an absolute one."""
if not name.startswith('.'):
return name
elif not package:
raise ImportError(f'no package specified for {repr(name)} '
'(required for relative module names)')
level = 0
for character in name:
if character != '.':
break
level += 1
return _resolve_name(name[level:], package, level) | [
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facebookresearch/habitat-sim | 63b6c71d9ca8adaefb140b198196f5d0ca1f1e34 | src_python/habitat_sim/utils/viz_utils.py | python | observation_to_image | (
observation_image: np.ndarray,
observation_type: str,
depth_clip: Optional[float] = 10.0,
) | return rgb_image | Generate an rgb image from a sensor observation. Supported types are: "color", "depth", "semantic"
:param observation_image: Raw observation image from sensor output.
:param observation_type: Observation type ("color", "depth", "semantic" supported)
:param depth_clip: Defines default depth clip normalization for all depth images.
:return: PIL Image object or None if fails. | Generate an rgb image from a sensor observation. Supported types are: "color", "depth", "semantic" | [
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] | def observation_to_image(
observation_image: np.ndarray,
observation_type: str,
depth_clip: Optional[float] = 10.0,
):
"""Generate an rgb image from a sensor observation. Supported types are: "color", "depth", "semantic"
:param observation_image: Raw observation image from sensor output.
:param observation_type: Observation type ("color", "depth", "semantic" supported)
:param depth_clip: Defines default depth clip normalization for all depth images.
:return: PIL Image object or None if fails.
"""
rgb_image = None
if observation_type == "color":
rgb_image = Image.fromarray(np.uint8(observation_image))
elif observation_type == "depth":
rgb_image = Image.fromarray(
depth_to_rgb(observation_image, clip_max=depth_clip)
)
elif observation_type == "semantic":
rgb_image = semantic_to_rgb(observation_image)
else:
print(
"semantic_to_rgb : Failed, unsupported observation type: "
+ observation_type
)
return rgb_image | [
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brndnmtthws/conky | 8f5014b90f1bc9f999beff752a9b369e4885f0d6 | cmake/scripts/clang-format-check-changed.py | python | run_clang_format | (clang_format_bin, changed_files) | return 0 | Run clang format on a list of files
@return 0 if formatted correctly. | Run clang format on a list of files | [
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] | def run_clang_format(clang_format_bin, changed_files):
"""
Run clang format on a list of files
@return 0 if formatted correctly.
"""
if len(changed_files) == 0:
return 0
cmd = [clang_format_bin, "-style=file",
"-output-replacements-xml"] + changed_files
print("clang-format cmd = {}".format(cmd))
try:
cmd_output = subprocess.check_output(cmd)
if "replacement offset" in cmd_output:
print("ERROR: Changed files don't match format")
return 1
except subprocess.CalledProcessError, e:
print("Error calling clang-format [{}]".format(e))
return e.returncode
return 0 | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/examples/python/file_extract.py | python | FileExtract.get_c_string | (self) | return cstr | Extract a single NULL terminated C string from the binary file at the current file position, returns a single C string | Extract a single NULL terminated C string from the binary file at the current file position, returns a single C string | [
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] | def get_c_string(self):
'''Extract a single NULL terminated C string from the binary file at the current file position, returns a single C string'''
cstr = ''
byte = self.get_uint8()
while byte != 0:
cstr += "%c" % byte
byte = self.get_uint8()
return cstr | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/jinja2/environment.py | python | Template.generate | (self, *args, **kwargs) | For very large templates it can be useful to not render the whole
template at once but evaluate each statement after another and yield
piece for piece. This method basically does exactly that and returns
a generator that yields one item after another as unicode strings.
It accepts the same arguments as :meth:`render`. | For very large templates it can be useful to not render the whole
template at once but evaluate each statement after another and yield
piece for piece. This method basically does exactly that and returns
a generator that yields one item after another as unicode strings. | [
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"""For very large templates it can be useful to not render the whole
template at once but evaluate each statement after another and yield
piece for piece. This method basically does exactly that and returns
a generator that yields one item after another as unicode strings.
It accepts the same arguments as :meth:`render`.
"""
vars = dict(*args, **kwargs)
try:
for event in self.root_render_func(self.new_context(vars)):
yield event
except Exception:
exc_info = sys.exc_info()
else:
return
yield self.environment.handle_exception(exc_info, True) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/backends/chrome_inspector/inspector_runtime.py | python | InspectorRuntime.Evaluate | (self, expr, context_id, timeout) | return res['result']['result']['value'] | Evaluates a javascript expression and returns the result.
|context_id| can refer to an iframe. The main page has context_id=1, the
first iframe context_id=2, etc.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
websocket.WebSocketException
socket.error | Evaluates a javascript expression and returns the result. | [
"Evaluates",
"a",
"javascript",
"expression",
"and",
"returns",
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"result",
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] | def Evaluate(self, expr, context_id, timeout):
"""Evaluates a javascript expression and returns the result.
|context_id| can refer to an iframe. The main page has context_id=1, the
first iframe context_id=2, etc.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
websocket.WebSocketException
socket.error
"""
request = {
'method': 'Runtime.evaluate',
'params': {
'expression': expr,
'returnByValue': True
}
}
if context_id is not None:
self.EnableAllContexts()
request['params']['contextId'] = context_id
res = self._inspector_websocket.SyncRequest(request, timeout)
if 'error' in res:
raise exceptions.EvaluateException(res['error']['message'])
if 'wasThrown' in res['result'] and res['result']['wasThrown']:
# TODO(nduca): propagate stacks from javascript up to the python
# exception.
raise exceptions.EvaluateException(res['result']['result']['description'])
if res['result']['result']['type'] == 'undefined':
return None
return res['result']['result']['value'] | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/dataset.py | python | Dataset.reader | (self, init_net=None, cursor_name=None, batch_size=1,
enforce_batch_size=False) | return reader | Create a Reader object that is used to iterate through the dataset.
This will append operations to `init_net` that create a TreeCursor,
used to iterate through the data.
NOTE: Currently, it is not safe to append to a dataset while reading.
Args:
init_net: net that will be run once to create the cursor.
cursor_name: optional name for the blob containing a pointer
to the cursor.
batch_size: how many samples to read per iteration.
Returns:
A _DatasetReader that can be used to create operators that will
iterate through the dataset. | Create a Reader object that is used to iterate through the dataset. | [
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] | def reader(self, init_net=None, cursor_name=None, batch_size=1,
enforce_batch_size=False):
"""Create a Reader object that is used to iterate through the dataset.
This will append operations to `init_net` that create a TreeCursor,
used to iterate through the data.
NOTE: Currently, it is not safe to append to a dataset while reading.
Args:
init_net: net that will be run once to create the cursor.
cursor_name: optional name for the blob containing a pointer
to the cursor.
batch_size: how many samples to read per iteration.
Returns:
A _DatasetReader that can be used to create operators that will
iterate through the dataset.
"""
assert self.field_blobs, 'Dataset not initialized.'
reader = _DatasetReader(self, cursor_name, batch_size,
enforce_batch_size)
if init_net is not None:
reader.setup_ex(init_net, None)
return reader | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/internals/blocks.py | python | Block._slice | (self, slicer) | return self.values[slicer] | return a slice of my values | return a slice of my values | [
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""" return a slice of my values """
return self.values[slicer] | [
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/utilities/datasetFromImages/datasetFromImages.py | python | load_categories | (file_name) | return categories | Loads the category index from file. Each category label is
the name of a class specified on a separate line. The entry order
is the index of the class. | Loads the category index from file. Each category label is
the name of a class specified on a separate line. The entry order
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] | def load_categories(file_name):
"""
Loads the category index from file. Each category label is
the name of a class specified on a separate line. The entry order
is the index of the class.
"""
labels = []
with open(file_name) as f:
labels = f.read().splitlines()
categories = {}
for category in labels:
categories[category] = len(categories)
return categories | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Rect.Set | (*args, **kwargs) | return _core_.Rect_Set(*args, **kwargs) | Set(self, int x=0, int y=0, int width=0, int height=0)
Set all rectangle properties. | Set(self, int x=0, int y=0, int width=0, int height=0) | [
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"""
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return _core_.Rect_Set(*args, **kwargs) | [
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | lldb/utils/lui/lldbutil.py | python | run_break_set_by_source_regexp | (
test,
regexp,
extra_options=None,
num_expected_locations=-1) | return get_bpno_from_match(break_results) | Set a breakpoint by source regular expression. Common options are the same as run_break_set_by_file_and_line. | Set a breakpoint by source regular expression. Common options are the same as run_break_set_by_file_and_line. | [
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] | def run_break_set_by_source_regexp(
test,
regexp,
extra_options=None,
num_expected_locations=-1):
"""Set a breakpoint by source regular expression. Common options are the same as run_break_set_by_file_and_line."""
command = 'breakpoint set -p "%s"' % (regexp)
if extra_options:
command += " " + extra_options
break_results = run_break_set_command(test, command)
check_breakpoint_result(
test,
break_results,
num_locations=num_expected_locations)
return get_bpno_from_match(break_results) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/minimum-number-of-increments-on-subarrays-to-form-a-target-array.py | python | Solution2.minNumberOperations | (self, target) | return sum(max(b-a, 0) for b, a in itertools.izip(target, [0]+target)) | :type target: List[int]
:rtype: int | :type target: List[int]
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"""
:type target: List[int]
:rtype: int
"""
return sum(max(b-a, 0) for b, a in itertools.izip(target, [0]+target)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/internal/python_message.py | python | _AddEnumValues | (descriptor, cls) | Sets class-level attributes for all enum fields defined in this message.
Also exporting a class-level object that can name enum values.
Args:
descriptor: Descriptor object for this message type.
cls: Class we're constructing for this message type. | Sets class-level attributes for all enum fields defined in this message. | [
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"""Sets class-level attributes for all enum fields defined in this message.
Also exporting a class-level object that can name enum values.
Args:
descriptor: Descriptor object for this message type.
cls: Class we're constructing for this message type.
"""
for enum_type in descriptor.enum_types:
setattr(cls, enum_type.name, enum_type_wrapper.EnumTypeWrapper(enum_type))
for enum_value in enum_type.values:
setattr(cls, enum_value.name, enum_value.number) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_extends/graph_kernel/model/graph_split.py | python | GraphSplitByPattern.fuse | (self, selector) | return changed | Fuse areas | Fuse areas | [
"Fuse",
"areas"
] | def fuse(self, selector):
"""Fuse areas"""
def _fuse_area():
for dominant in self.areas:
result = selector(dominant)
if result is None or not result[0]:
continue
fuse_areas, is_forward = result
fuse_areas = self.limit_area_size(dominant, fuse_areas)
if not fuse_areas:
continue
if is_forward:
for area in fuse_areas:
dominant.fuse(area)
self.set_area_map(area.ops, dominant)
self.areas.remove(area)
else:
forward_area = dominant
for area in fuse_areas:
area.fuse(forward_area)
self.set_area_map(forward_area.ops, area)
self.areas.remove(forward_area)
forward_area = area
return True
return False
changed, do_again = False, True
while do_again:
do_again = _fuse_area()
changed = changed or do_again
return changed | [
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zachriggle/ida-splode | a4aee3be415b318a0e051a523ebd0a8d6d5e0026 | py/idasplode/analysis/reconstruct.py | python | EnsureHeapMetadataHomogeneity | (Metadata) | return (Sizes.pop(), Offsets.pop(), Frames) | Ensures that all of the metadata provded are homoenous on the
size, offset-from-base, and backtrace for all heap interactions.
Returns:
Tuple containing (size,offset,backtrace) for the common
allocation type. | Ensures that all of the metadata provded are homoenous on the
size, offset-from-base, and backtrace for all heap interactions. | [
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] | def EnsureHeapMetadataHomogeneity(Metadata):
"""Ensures that all of the metadata provded are homoenous on the
size, offset-from-base, and backtrace for all heap interactions.
Returns:
Tuple containing (size,offset,backtrace) for the common
allocation type.
"""
HeapMeta = tuple(M for M in Metadata if M.Heap)
Sizes = set(M.Heap.Size for M in HeapMeta)
Offsets = set(M.Heap.Offset for M in HeapMeta)
Frames = set(M.Heap.Frames for M in HeapMeta)
if not len(HeapMeta):
raise Exception("No heap data found")
if len(HeapMeta) != len(Metadata):
print "Not all interactions are heap metadata, only looking at heap data!"
if len(Sizes) != 1:
raise Exception("Multiple sizes %r, cannot analyze" % Sizes)
if len(Offsets) != 1:
raise Exception("Multiple offsets %r, cannot analyze" % Offsets)
#if len(Frames) != 1:
# raise Exception("Multiple allocation stacks, cannot analyze")
return (Sizes.pop(), Offsets.pop(), Frames) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/training/input.py | python | batch | (tensors, batch_size, num_threads=1, capacity=32,
enqueue_many=False, shapes=None, dynamic_pad=False,
allow_smaller_final_batch=False, shared_name=None, name=None) | return _batch(
tensors,
batch_size,
keep_input=True,
num_threads=num_threads,
capacity=capacity,
enqueue_many=enqueue_many,
shapes=shapes,
dynamic_pad=dynamic_pad,
allow_smaller_final_batch=allow_smaller_final_batch,
shared_name=shared_name,
name=name) | Creates batches of tensors in `tensors`.
The argument `tensors` can be a list or a dictionary of tensors.
The value returned by the function will be of the same type
as `tensors`.
This function is implemented using a queue. A `QueueRunner` for the
queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.
If `enqueue_many` is `False`, `tensors` is assumed to represent a single
example. An input tensor with shape `[x, y, z]` will be output as a tensor
with shape `[batch_size, x, y, z]`.
If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
examples, where the first dimension is indexed by example, and all members of
`tensors` should have the same size in the first dimension. If an input
tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
y, z]`. The `capacity` argument controls the how long the prefetching is
allowed to grow the queues.
The returned operation is a dequeue operation and will throw
`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
operation is feeding another input queue, its queue runner will catch
this exception, however, if this operation is used in your main thread
you are responsible for catching this yourself.
*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
(i) the `shapes` argument is passed, or (ii) all of the tensors in
`tensors` must have fully-defined shapes. `ValueError` will be
raised if neither of these conditions holds.
If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
tensors is known, but individual dimensions may have shape `None`.
In this case, for each enqueue the dimensions with value `None`
may have a variable length; upon dequeue, the output tensors will be padded
on the right to the maximum shape of the tensors in the current minibatch.
For numbers, this padding takes value 0. For strings, this padding is
the empty string. See `PaddingFIFOQueue` for more info.
If `allow_smaller_final_batch` is `True`, a smaller batch value than
`batch_size` is returned when the queue is closed and there are not enough
elements to fill the batch, otherwise the pending elements are discarded.
In addition, all output tensors' static shapes, as accessed via the
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operations that depend on fixed batch_size would fail.
Args:
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
num_threads: The number of threads enqueuing `tensors`. The batching will
be nondeterministic if `num_threads > 1`.
capacity: An integer. The maximum number of elements in the queue.
enqueue_many: Whether each tensor in `tensors` is a single example.
shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for `tensors`.
dynamic_pad: Boolean. Allow variable dimensions in input shapes.
The given dimensions are padded upon dequeue so that tensors within a
batch have the same shapes.
allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
batch to be smaller if there are insufficient items left in the queue.
shared_name: (Optional). If set, this queue will be shared under the given
name across multiple sessions.
name: (Optional) A name for the operations.
Returns:
A list or dictionary of tensors with the same types as `tensors` (except if
the input is a list of one element, then it returns a tensor, not a list).
Raises:
ValueError: If the `shapes` are not specified, and cannot be
inferred from the elements of `tensors`. | Creates batches of tensors in `tensors`. | [
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enqueue_many=False, shapes=None, dynamic_pad=False,
allow_smaller_final_batch=False, shared_name=None, name=None):
"""Creates batches of tensors in `tensors`.
The argument `tensors` can be a list or a dictionary of tensors.
The value returned by the function will be of the same type
as `tensors`.
This function is implemented using a queue. A `QueueRunner` for the
queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.
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*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
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may have a variable length; upon dequeue, the output tensors will be padded
on the right to the maximum shape of the tensors in the current minibatch.
For numbers, this padding takes value 0. For strings, this padding is
the empty string. See `PaddingFIFOQueue` for more info.
If `allow_smaller_final_batch` is `True`, a smaller batch value than
`batch_size` is returned when the queue is closed and there are not enough
elements to fill the batch, otherwise the pending elements are discarded.
In addition, all output tensors' static shapes, as accessed via the
`get_shape` method will have a first `Dimension` value of `None`, and
operations that depend on fixed batch_size would fail.
Args:
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
num_threads: The number of threads enqueuing `tensors`. The batching will
be nondeterministic if `num_threads > 1`.
capacity: An integer. The maximum number of elements in the queue.
enqueue_many: Whether each tensor in `tensors` is a single example.
shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for `tensors`.
dynamic_pad: Boolean. Allow variable dimensions in input shapes.
The given dimensions are padded upon dequeue so that tensors within a
batch have the same shapes.
allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
batch to be smaller if there are insufficient items left in the queue.
shared_name: (Optional). If set, this queue will be shared under the given
name across multiple sessions.
name: (Optional) A name for the operations.
Returns:
A list or dictionary of tensors with the same types as `tensors` (except if
the input is a list of one element, then it returns a tensor, not a list).
Raises:
ValueError: If the `shapes` are not specified, and cannot be
inferred from the elements of `tensors`.
"""
return _batch(
tensors,
batch_size,
keep_input=True,
num_threads=num_threads,
capacity=capacity,
enqueue_many=enqueue_many,
shapes=shapes,
dynamic_pad=dynamic_pad,
allow_smaller_final_batch=allow_smaller_final_batch,
shared_name=shared_name,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Wm.wm_geometry | (self, newGeometry=None) | return self.tk.call('wm', 'geometry', self._w, newGeometry) | Set geometry to NEWGEOMETRY of the form =widthxheight+x+y. Return
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mickem/nscp | 79f89fdbb6da63f91bc9dedb7aea202fe938f237 | scripts/python/lib/google/protobuf/service_reflection.py | python | GeneratedServiceStubType.__init__ | (cls, name, bases, dictionary) | Creates a message service stub class.
Args:
name: Name of the class (ignored, here).
bases: Base classes of the class being constructed.
dictionary: The class dictionary of the class being constructed.
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descriptor = dictionary[GeneratedServiceStubType._DESCRIPTOR_KEY]
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/__init__.py | python | FlagValues.UseGnuGetOpt | (self, use_gnu_getopt=True) | Use GNU-style scanning. Allows mixing of flag and non-flag arguments.
See http://docs.python.org/library/getopt.html#getopt.gnu_getopt
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"""Use GNU-style scanning. Allows mixing of flag and non-flag arguments.
See http://docs.python.org/library/getopt.html#getopt.gnu_getopt
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/nvmserialupdi.py | python | NvmAccessProviderSerial.stop | (self) | Stop the debugging session | Stop the debugging session | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/directtools/DirectSelection.py | python | SelectedNodePaths.forEachSelectedNodePathDo | (self, func) | Perform given func on selected node paths. No node path
connectivity verification performed | Perform given func on selected node paths. No node path
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selectedNodePaths = self.getSelectedAsList()
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathAreaOp.py | python | ObjectOp.areaOpOnDocumentRestored | (self, obj) | areaOpOnDocumentRestored(obj) ... overwrite to fully restore receiver | areaOpOnDocumentRestored(obj) ... overwrite to fully restore receiver | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythplugins/mytharchive/mythburn/scripts/mythburn.py | python | createDVDAuthorXMLNoMainMenu | (screensize, numberofitems) | Creates the xml file for dvdauthor to use the MythBurn menus. | Creates the xml file for dvdauthor to use the MythBurn menus. | [
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# creates a simple DVD with only a chapter menus shown before each video
# can contain an intro movie and each title can have a details page
# displayed before each title
write( "Creating DVD XML file for dvd author (No Main Menu)")
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/tseries/holiday.py | python | sunday_to_monday | (dt) | return dt | If holiday falls on Sunday, use day thereafter (Monday) instead. | If holiday falls on Sunday, use day thereafter (Monday) instead. | [
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"""
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if dt.weekday() == 6:
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microsoft/EdgeML | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | examples/pytorch/vision/Face_Detection/models/RPool_Face_M4.py | python | S3FD.__init__ | (self, phase, base, head, num_classes) | self.priorbox = PriorBox(size,cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True) | self.priorbox = PriorBox(size,cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True) | [
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'''
self.priorbox = PriorBox(size,cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True)
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# SSD network
self.conv = ConvBNReLU(1, 4, stride=2)
self.unfold = nn.Unfold(kernel_size=(8,8),stride=(4,4))
self.rnn_model = RNNPool(8, 8, 16, 16, 4,
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self.mob = nn.ModuleList(base)
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self.L2Norm3_3 = L2Norm(32, 10)
self.L2Norm4_3 = L2Norm(32, 8)
self.L2Norm5_3 = L2Norm(64, 5)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if self.phase == 'test':
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/saved_model/builder_impl.py | python | copy_assets_to_destination_dir | (asset_filename_map, destination_dir) | Copy all assets from source path to destination path. | Copy all assets from source path to destination path. | [
"Copy",
"all",
"assets",
"from",
"source",
"path",
"to",
"destination",
"path",
"."
] | def copy_assets_to_destination_dir(asset_filename_map, destination_dir):
"""Copy all assets from source path to destination path."""
assets_destination_dir = saved_model_utils.get_or_create_assets_dir(
destination_dir)
# Copy each asset from source path to destination path.
for asset_basename, asset_source_filepath in asset_filename_map.items():
asset_destination_filepath = file_io.join(
compat.as_bytes(assets_destination_dir),
compat.as_bytes(asset_basename))
# Only copy the asset file to the destination if it does not already
# exist. This is to ensure that an asset with the same name defined as
# part of multiple graphs is only copied the first time.
if not file_io.file_exists(asset_destination_filepath):
file_io.copy(asset_source_filepath, asset_destination_filepath)
tf_logging.info("Assets written to: %s",
compat.as_text(assets_destination_dir)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/base_handler.py | python | TaskQueueHandler.task_retry_count | (self) | return int(self.request.headers.get("X-AppEngine-TaskExecutionCount", 0)) | Number of times this task has been retried. | Number of times this task has been retried. | [
"Number",
"of",
"times",
"this",
"task",
"has",
"been",
"retried",
"."
] | def task_retry_count(self):
"""Number of times this task has been retried."""
return int(self.request.headers.get("X-AppEngine-TaskExecutionCount", 0)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/generic.py | python | NDFrame.describe | (
self: FrameOrSeries, percentiles=None, include=None, exclude=None
) | return d | Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding ``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
unique 2
top 2010-01-01 00:00:00
freq 2
first 2000-01-01 00:00:00
last 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[np.object])
object
count 3
unique 3
top c
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top f
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f c
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0 | Generate descriptive statistics. | [
"Generate",
"descriptive",
"statistics",
"."
] | def describe(
self: FrameOrSeries, percentiles=None, include=None, exclude=None
) -> FrameOrSeries:
"""
Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding ``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
unique 2
top 2010-01-01 00:00:00
freq 2
first 2000-01-01 00:00:00
last 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[np.object])
object
count 3
unique 3
top c
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top f
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f c
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
"""
if self.ndim == 2 and self.columns.size == 0:
raise ValueError("Cannot describe a DataFrame without columns")
if percentiles is not None:
# explicit conversion of `percentiles` to list
percentiles = list(percentiles)
# get them all to be in [0, 1]
validate_percentile(percentiles)
# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
percentiles = np.asarray(percentiles)
else:
percentiles = np.array([0.25, 0.5, 0.75])
# sort and check for duplicates
unique_pcts = np.unique(percentiles)
if len(unique_pcts) < len(percentiles):
raise ValueError("percentiles cannot contain duplicates")
percentiles = unique_pcts
formatted_percentiles = format_percentiles(percentiles)
def describe_numeric_1d(series):
stat_index = (
["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
)
d = (
[series.count(), series.mean(), series.std(), series.min()]
+ series.quantile(percentiles).tolist()
+ [series.max()]
)
return pd.Series(d, index=stat_index, name=series.name)
def describe_categorical_1d(data):
names = ["count", "unique"]
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
dtype = None
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_any_dtype(data):
tz = data.dt.tz
asint = data.dropna().values.view("i8")
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ["top", "freq", "first", "last"]
result += [
top,
freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz),
]
else:
names += ["top", "freq"]
result += [top, freq]
# If the DataFrame is empty, set 'top' and 'freq' to None
# to maintain output shape consistency
else:
names += ["top", "freq"]
result += [np.nan, np.nan]
dtype = "object"
return pd.Series(result, index=names, name=data.name, dtype=dtype)
def describe_1d(data):
if is_bool_dtype(data):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_timedelta64_dtype(data):
return describe_numeric_1d(data)
else:
return describe_categorical_1d(data)
if self.ndim == 1:
return describe_1d(self)
elif (include is None) and (exclude is None):
# when some numerics are found, keep only numerics
data = self.select_dtypes(include=[np.number])
if len(data.columns) == 0:
data = self
elif include == "all":
if exclude is not None:
msg = "exclude must be None when include is 'all'"
raise ValueError(msg)
data = self
else:
data = self.select_dtypes(include=include, exclude=exclude)
ldesc = [describe_1d(s) for _, s in data.items()]
# set a convenient order for rows
names: List[Optional[Hashable]] = []
ldesc_indexes = sorted((x.index for x in ldesc), key=len)
for idxnames in ldesc_indexes:
for name in idxnames:
if name not in names:
names.append(name)
d = pd.concat([x.reindex(names, copy=False) for x in ldesc], axis=1, sort=False)
d.columns = data.columns.copy()
return d | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/multiclass.py | python | OutputCodeClassifier.predict | (self, X) | return self.classes_[pred] | Predict multi-class targets using underlying estimators.
Parameters
----------
X : (sparse) array-like of shape (n_samples, n_features)
Data.
Returns
-------
y : numpy array of shape [n_samples]
Predicted multi-class targets. | Predict multi-class targets using underlying estimators. | [
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"""Predict multi-class targets using underlying estimators.
Parameters
----------
X : (sparse) array-like of shape (n_samples, n_features)
Data.
Returns
-------
y : numpy array of shape [n_samples]
Predicted multi-class targets.
"""
check_is_fitted(self)
X = check_array(X)
Y = np.array([_predict_binary(e, X) for e in self.estimators_]).T
pred = euclidean_distances(Y, self.code_book_).argmin(axis=1)
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py | python | add_range_estimator_configs | (fq_to_hw_confs, config) | return fq_to_hw_confs | Expand fake quantize configuration with range_estimator config
:param fq_to_hw_confs: dictionary with fake quantize names as keys and its configurations as values
:param config: tool config used to create range_estimator config
:return dictionary with fake quantize nodes names as keys and its configurations as values
extended with range_estimator config | Expand fake quantize configuration with range_estimator config
:param fq_to_hw_confs: dictionary with fake quantize names as keys and its configurations as values
:param config: tool config used to create range_estimator config
:return dictionary with fake quantize nodes names as keys and its configurations as values
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/data_flow_ops.py | python | MapStagingArea.clear | (self, name=None) | return self._clear_fn(
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Args:
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if name is None:
name = "%s_clear" % self._name
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omnisci/omniscidb | b9c95f1bd602b4ffc8b0edf18bfad61031e08d86 | QueryEngine/scripts/generate_TableFunctionsFactory_init.py | python | Parser.parse_template | (self) | return TemplateNode(key, tuple(types)) | fmt: off
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types = []
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/ExportExperimentLog.py | python | ExportExperimentLog._processInputs | (self) | return | Process input properties | Process input properties | [
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""" Process input properties
"""
self._wksp = self.getProperty("InputWorkspace").value
self._logfilename = self.getProperty("OutputFilename").value
# Field and keys
self._headerTitles = self.getProperty("SampleLogTitles").value
self._sampleLogNames = self.getProperty("SampleLogNames").value
ops = self.getProperty("SampleLogOperation").value
if len(self._sampleLogNames) != len(ops):
raise RuntimeError("Size of sample log names and sample operations are unequal!")
self._sampleLogOperations = []
for i in range(len(self._sampleLogNames)):
value = ops[i]
self._sampleLogOperations.append(value)
# ENDFOR
if len(self._headerTitles) > 0 and len(self._headerTitles) != len(self._sampleLogNames):
raise RuntimeError("Input header titles have a different length to sample log names")
# Output file format
self._fileformat = self.getProperty("FileFormat").value
if self._fileformat == "tab":
self._valuesep = "\t"
else:
self._valuesep = ","
# Output file's postfix
if self._fileformat == "comma (csv)":
fileName, fileExtension = os.path.splitext(self._logfilename)
if fileExtension != ".csv":
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self._logfilename = "%s.csv" % (fileName)
# Determine file mode
if os.path.exists(self._logfilename) is False:
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self.log().debug("Log file %s does not exist. So file mode is NEW." % (self._logfilename))
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self.log().debug("FileMode is from user specified value.")
# Examine the file mode
if self._filemode == "new" or self._filemode == "append":
if len(self._headerTitles) != len(self._sampleLogNames):
raise RuntimeError("In mode new or append, there must be same number of sample titles and names")
self.log().information("File mode is %s. " % (self._filemode))
# This is left for a feature that might be needed in future.
self._reorderOld = False
self._timezone = self.getProperty("TimeZone").value
# Determine whether output log-record file should be ordered by value of some log
self._orderRecord = False
self._titleToOrder = None
if self._filemode != "new":
ordertitle = self.getProperty("OrderByTitle").value
if ordertitle in self._headerTitles:
self._orderRecord = True
self._removeDupRecord = self.getProperty("RemoveDuplicateRecord").value
self.titleToOrder = ordertitle
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self.log().warning("Specified title to order by (%s) is not in given log titles." % (ordertitle))
if self._orderRecord is False:
self._removeDupRecord = False
# Override log values: it will not work in fastappend mode to override
overridelist = self.getProperty("OverrideLogValue").value
if len(self._headerTitles) > 0:
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raise RuntimeError("Number of items in OverrideLogValue must be even.")
self._ovrdTitleValueDict = {}
for i in range(int(len(overridelist)/2)):
title = overridelist[2*i]
if title in self._headerTitles:
self._ovrdTitleValueDict[title] = overridelist[2*i+1]
else:
self.log().warning("Override title %s is not recognized. " % (title))
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathOpGui.py | python | ViewProvider.getSelectionFactory | (self) | return PathSelection.select(self.OpName) | getSelectionFactory() ... return a factory function that can be used to create the selection observer. | getSelectionFactory() ... return a factory function that can be used to create the selection observer. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextBox.__init__ | (self, *args) | __init__(self, RichTextObject parent=None) -> RichTextBox
__init__(self, RichTextBox obj) -> RichTextBox | __init__(self, RichTextObject parent=None) -> RichTextBox
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/pyparsing.py | python | ungroup | (expr) | return TokenConverter(expr).addParseAction(lambda t: t[0]) | Helper to undo pyparsing's default grouping of And expressions,
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"""Helper to undo pyparsing's default grouping of And expressions,
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/git.py | python | Repository._run_cmd | (self, cmd, args) | return self._run_process(cmd, params, cwd=self.directory) | Run the git command and return a GitCommandResult instance. | Run the git command and return a GitCommandResult instance. | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/graph_editor/reroute.py | python | _check_ts_compatibility | (ts0, ts1) | Make sure the shape and dtype of the two tensor's lists are compatible.
Args:
ts0: an object convertible to a list of `tf.Tensor`.
ts1: an object convertible to a list of `tf.Tensor`.
Raises:
ValueError: if any pair of tensors (same index in ts0 and ts1) have
a dtype or a shape which is not compatible. | Make sure the shape and dtype of the two tensor's lists are compatible. | [
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] | def _check_ts_compatibility(ts0, ts1):
"""Make sure the shape and dtype of the two tensor's lists are compatible.
Args:
ts0: an object convertible to a list of `tf.Tensor`.
ts1: an object convertible to a list of `tf.Tensor`.
Raises:
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"""
ts0 = _util.make_list_of_t(ts0)
ts1 = _util.make_list_of_t(ts1)
if len(ts0) != len(ts1):
raise ValueError("ts0 and ts1 have different sizes: {} != {}".format(
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for t0, t1 in zip(ts0, ts1):
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dtype0, dtype1 = t0.dtype, t1.dtype
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shape0, shape1 = t0.get_shape(), t1.get_shape()
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/linalg/python/ops/linear_operator_util.py | python | assert_no_entries_with_modulus_zero | (
x, message=None, name="assert_no_entries_with_modulus_zero") | Returns `Op` that asserts Tensor `x` has no entries with modulus zero.
Args:
x: Numeric `Tensor`, real, integer, or complex.
message: A string message to prepend to failure message.
name: A name to give this `Op`.
Returns:
An `Op` that asserts `x` has no entries with modulus zero. | Returns `Op` that asserts Tensor `x` has no entries with modulus zero. | [
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] | def assert_no_entries_with_modulus_zero(
x, message=None, name="assert_no_entries_with_modulus_zero"):
"""Returns `Op` that asserts Tensor `x` has no entries with modulus zero.
Args:
x: Numeric `Tensor`, real, integer, or complex.
message: A string message to prepend to failure message.
name: A name to give this `Op`.
Returns:
An `Op` that asserts `x` has no entries with modulus zero.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
dtype = x.dtype.base_dtype
should_be_nonzero = math_ops.abs(x)
zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
return check_ops.assert_less(zero, should_be_nonzero, message=message) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/motionplanning.py | python | CSpaceInterface.setNeighborhoodSampler | (self, pySamp) | return _motionplanning.CSpaceInterface_setNeighborhoodSampler(self, pySamp) | setNeighborhoodSampler(CSpaceInterface self, PyObject * pySamp) | setNeighborhoodSampler(CSpaceInterface self, PyObject * pySamp) | [
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] | def setNeighborhoodSampler(self, pySamp):
"""
setNeighborhoodSampler(CSpaceInterface self, PyObject * pySamp)
"""
return _motionplanning.CSpaceInterface_setNeighborhoodSampler(self, pySamp) | [
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