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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/factorization/python/ops/gmm_ops.py | python | GmmAlgorithm._define_full_covariance_probs | (self, shard_id, shard) | Defines the full covariance probabilities per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions. | Defines the full covariance probabilities per example in a class. | [
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"""Defines the full covariance probabilities per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = linalg_ops.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * math_ops.reduce_sum(
math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
x_mu_cov = math_ops.square(
linalg_ops.matrix_triangular_solve(
cholesky, array_ops.transpose(
diff, perm=[0, 2, 1]), lower=True))
diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = (
-0.5 * (diag_m + math_ops.cast(self._dimensions, dtypes.float32) *
math_ops.log(2 * np.pi) + log_det_covs)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py | python | _MaskedPrintOption.__init__ | (self, display) | Create the masked_print_option object. | Create the masked_print_option object. | [
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"""
Create the masked_print_option object.
"""
self._display = display
self._enabled = True | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/polynomial/_polybase.py | python | ABCPolyBase._generate_string | (self, term_method) | return out | Generate the full string representation of the polynomial, using
``term_method`` to generate each polynomial term. | Generate the full string representation of the polynomial, using
``term_method`` to generate each polynomial term. | [
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] | def _generate_string(self, term_method):
"""
Generate the full string representation of the polynomial, using
``term_method`` to generate each polynomial term.
"""
# Get configuration for line breaks
linewidth = np.get_printoptions().get('linewidth', 75)
if linewidth < 1:
linewidth = 1
out = f"{self.coef[0]}"
for i, coef in enumerate(self.coef[1:]):
out += " "
power = str(i + 1)
# Polynomial coefficient
# The coefficient array can be an object array with elements that
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# complex). In this case, represent the coeficient as-is.
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line_len += 2
# Handle linebreaking
if line_len >= linewidth:
next_term = next_term.replace(" ", "\n", 1)
out += next_term
return out | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/codecs.py | python | IncrementalEncoder.reset | (self) | Resets the encoder to the initial state. | Resets the encoder to the initial state. | [
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"""
Resets the encoder to the initial state.
""" | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/hpmc/external/wall.py | python | wall.set_volume | (self, volume) | R"""Set the volume associated with the intersection of all walls in the system. # noqa
This number will subsequently change when the box is resized and walls are scaled appropriately.
Example::
mc = hpmc.integrate.sphere(seed = 415236);
ext_wall = hpmc.compute.wall(mc);
ext_wall.add_sphere_wall(radius = 1.0, origin = [0, 0, 0], inside = True);
ext_wall.set_volume(4./3.*np.pi); | R"""Set the volume associated with the intersection of all walls in the system. # noqa | [
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R"""Set the volume associated with the intersection of all walls in the system. # noqa
This number will subsequently change when the box is resized and walls are scaled appropriately.
Example::
mc = hpmc.integrate.sphere(seed = 415236);
ext_wall = hpmc.compute.wall(mc);
ext_wall.add_sphere_wall(radius = 1.0, origin = [0, 0, 0], inside = True);
ext_wall.set_volume(4./3.*np.pi);
"""
self.cpp_compute.setVolume(volume) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/nanops.py | python | nanmean | (values, axis=None, skipna=True, mask=None) | return _wrap_results(the_mean, dtype) | Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanmean(s)
1.5 | Compute the mean of the element along an axis ignoring NaNs | [
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"""
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : float
Unless input is a float array, in which case use the same
precision as the input array.
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanmean(s)
1.5
"""
values, mask, dtype, dtype_max, _ = _get_values(
values, skipna, fill_value=0, mask=mask
)
dtype_sum = dtype_max
dtype_count = np.float64
if (
is_integer_dtype(dtype)
or is_timedelta64_dtype(dtype)
or is_datetime64_dtype(dtype)
or is_datetime64tz_dtype(dtype)
):
dtype_sum = np.float64
elif is_float_dtype(dtype):
dtype_sum = dtype
dtype_count = dtype
count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
if axis is not None and getattr(the_sum, "ndim", False):
with np.errstate(all="ignore"):
# suppress division by zero warnings
the_mean = the_sum / count
ct_mask = count == 0
if ct_mask.any():
the_mean[ct_mask] = np.nan
else:
the_mean = the_sum / count if count > 0 else np.nan
return _wrap_results(the_mean, dtype) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/turtle.py | python | RawTurtle.begin_fill | (self) | Called just before drawing a shape to be filled.
No argument.
Example (for a Turtle instance named turtle):
>>> turtle.color("black", "red")
>>> turtle.begin_fill()
>>> turtle.circle(60)
>>> turtle.end_fill() | Called just before drawing a shape to be filled. | [
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] | def begin_fill(self):
"""Called just before drawing a shape to be filled.
No argument.
Example (for a Turtle instance named turtle):
>>> turtle.color("black", "red")
>>> turtle.begin_fill()
>>> turtle.circle(60)
>>> turtle.end_fill()
"""
if not self.filling():
self._fillitem = self.screen._createpoly()
self.items.append(self._fillitem)
self._fillpath = [self._position]
self._newLine()
if self.undobuffer:
self.undobuffer.push(("beginfill", self._fillitem))
self._update() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/smtplib.py | python | SMTP.auth_cram_md5 | (self, challenge=None) | return self.user + " " + hmac.HMAC(
self.password.encode('ascii'), challenge, 'md5').hexdigest() | Authobject to use with CRAM-MD5 authentication. Requires self.user
and self.password to be set. | Authobject to use with CRAM-MD5 authentication. Requires self.user
and self.password to be set. | [
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""" Authobject to use with CRAM-MD5 authentication. Requires self.user
and self.password to be set."""
# CRAM-MD5 does not support initial-response.
if challenge is None:
return None
return self.user + " " + hmac.HMAC(
self.password.encode('ascii'), challenge, 'md5').hexdigest() | [
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/rpc/requests.py | python | launch | (pipeline_id, logical_plan_message, resource_message, commit_args=None, context=None) | Send the rpc command to the other end to launch the logical plan
Args:
Raises:
error.BigflowRPCException: if any error happened | Send the rpc command to the other end to launch the logical plan | [
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"""
Send the rpc command to the other end to launch the logical plan
Args:
Raises:
error.BigflowRPCException: if any error happened
"""
request = service_pb2.LaunchRequest()
request.pipeline_id = pipeline_id
request.logical_plan.CopyFrom(logical_plan_message)
request.resource.CopyFrom(resource_message)
if commit_args is not None:
request.hadoop_commit_args.extend(commit_args)
if context is not None:
assert isinstance(context, str)
request.pipeline_context = context
response = _service.request(request, "launch")
import google.protobuf.json_format as json_format
res = json_format.Parse(response, service_pb2.VoidResponse())
#logger.info(res)
if not res.status.success:
backend_log_path = os.getenv("BIGFLOW_LOG_FILE_BACKEND", "")
error_message = "Job ran failed"
if len(_message) > 0:
error_message += ", possible_reason: \n" + "".join(_message)
if backend_log_path:
error_message += "Please check backend log['%s.log'] for details" % backend_log_path
raise error.BigflowRuntimeException(error_message) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/formats/format.py | python | DataFrameFormatter._is_in_terminal | (self) | return bool(self.max_cols == 0 or self.max_rows == 0) | Check if the output is to be shown in terminal. | Check if the output is to be shown in terminal. | [
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] | def _is_in_terminal(self) -> bool:
"""Check if the output is to be shown in terminal."""
return bool(self.max_cols == 0 or self.max_rows == 0) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/mantid/fitfunctions.py | python | FunctionWrapper.fix | (self, name) | Fix a parameter.
:param name: name of parameter to be fixed | Fix a parameter. | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/third_party/jinja2/utils.py | python | LRUCache.get | (self, key, default=None) | Return an item from the cache dict or `default` | Return an item from the cache dict or `default` | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | llvm/bindings/python/llvm/object.py | python | Section.size | (self) | return lib.LLVMGetSectionSize(self) | The size of the section, in long bytes. | The size of the section, in long bytes. | [
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"""The size of the section, in long bytes."""
if self.expired:
raise Exception('Section instance has expired.')
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/ma/extras.py | python | mask_cols | (a, axis=None) | return mask_rowcols(a, 1) | Mask columns of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.
See Also
--------
mask_rowcols : Mask rows and/or columns of a 2D array.
masked_where : Mask where a condition is met.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=np.int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
[[0 0 0]
[0 -- 0]
[0 0 0]],
mask =
[[False False False]
[False True False]
[False False False]],
fill_value=999999)
>>> ma.mask_cols(a)
masked_array(data =
[[0 -- 0]
[0 -- 0]
[0 -- 0]],
mask =
[[False True False]
[False True False]
[False True False]],
fill_value=999999) | Mask columns of a 2D array that contain masked values. | [
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] | def mask_cols(a, axis=None):
"""
Mask columns of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.
See Also
--------
mask_rowcols : Mask rows and/or columns of a 2D array.
masked_where : Mask where a condition is met.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=np.int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
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mask =
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fill_value=999999)
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Xilinx/XRT | dd071c90309df61d3ecdd92dca39f43804915c99 | .github/scripts/clang-tidy-review.py | python | get_line_ranges | (diff, files) | return json.dumps(line_filter_json, separators=(",", ":")) | Return the line ranges of added lines in diff, suitable for the
line-filter argument of clang-tidy | Return the line ranges of added lines in diff, suitable for the
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"""Return the line ranges of added lines in diff, suitable for the
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"""
lines_by_file = {}
for filename in diff:
if filename.target_file[2:] not in files:
continue
added_lines = []
for hunk in filename:
for line in hunk:
if line.is_added:
added_lines.append(line.target_line_no)
for _, group in itertools.groupby(
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lines_by_file.setdefault(filename.target_file[2:], []).append(
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line_filter_json = []
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line_filter_json.append(str({"name": name, "lines": lines}))
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/traceback.py | python | format_tb | (tb, limit = None) | return format_list(extract_tb(tb, limit)) | A shorthand for 'format_list(extract_stack(f, limit)). | A shorthand for 'format_list(extract_stack(f, limit)). | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/idl/idl/parser.py | python | _parse_chained_types | (ctxt, node) | return chained_items | Parse a chained types section in a struct in the IDL file. | Parse a chained types section in a struct in the IDL file. | [
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# type: (errors.ParserContext, yaml.nodes.MappingNode) -> List[syntax.ChainedType]
"""Parse a chained types section in a struct in the IDL file."""
chained_items = []
field_name_set = set() # type: Set[str]
for [first_node, second_node] in node.value:
first_name = first_node.value
if first_name in field_name_set:
ctxt.add_duplicate_error(first_node, first_name)
continue
# Simple Scalar
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chain = syntax.ChainedType(ctxt.file_name, node.start_mark.line, node.start_mark.column)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/backend.py | python | set_image_data_format | (data_format) | Sets the value of the image data format convention.
Arguments:
data_format: string. `'channels_first'` or `'channels_last'`.
Example:
```python
>>> from keras import backend as K
>>> K.image_data_format()
'channels_first'
>>> K.set_image_data_format('channels_last')
>>> K.image_data_format()
'channels_last'
```
Raises:
ValueError: In case of invalid `data_format` value. | Sets the value of the image data format convention. | [
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] | def set_image_data_format(data_format):
"""Sets the value of the image data format convention.
Arguments:
data_format: string. `'channels_first'` or `'channels_last'`.
Example:
```python
>>> from keras import backend as K
>>> K.image_data_format()
'channels_first'
>>> K.set_image_data_format('channels_last')
>>> K.image_data_format()
'channels_last'
```
Raises:
ValueError: In case of invalid `data_format` value.
"""
global _IMAGE_DATA_FORMAT
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('Unknown data_format:', data_format)
_IMAGE_DATA_FORMAT = str(data_format) | [
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sofa-framework/sofa | 70628e35a44fcc258cf8250109b5e4eba8c5abe9 | applications/plugins/PSL/python/pslparserhjson.py | python | treeToString | (node, space) | return res | Converts a Sofa node and its children & objects into an h-json representation | Converts a Sofa node and its children & objects into an h-json representation | [
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nspace=space+" "
res = ""
instanceof = node.getData("psl_instanceof")
if instanceof != None:
res += space+str(node.psl_instanceof)+" : {"+ "\n"
for k,v in eval(node.psl_properties):
res += space+" "+k+" : "+str(v)+ "\n"
res += space+"}"+ "\n"
return res
res += space+"Node : {"
ores = ""
for datafield in node.getListOfDataFields():
if datafield.isPersistant():
if datafield.hasParent():
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ores += "\n" + nspace + link.name + " : \"" + link.getValueString() + "\""
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ores += "\n"
dres = ""
for object in node.getObjects():
dres += objectToString(object, space+" ")
cres = ""
for child in node.getChildren():
cres += treeToString(child, space+" ")
ores = ores + dres + cres
res += ores
if ores == "":
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return res | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/basic_fitting/basic_fitting_view.py | python | BasicFittingView.set_workspace_combo_box_label | (self, text: str) | Sets the label text next to the workspace selector combobox. | Sets the label text next to the workspace selector combobox. | [
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"""Sets the label text next to the workspace selector combobox."""
self.workspace_selector.set_data_combo_box_label(text) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Source/bindings/scripts/v8_interface.py | python | effective_overload_set | (F) | return S | Returns the effective overload set of an overloaded function.
An effective overload set is the set of overloaded functions + signatures
(type list of arguments, with optional and variadic arguments included or
not), and is used in the overload resolution algorithm.
For example, given input [f1(optional long x), f2(DOMString s)], the output
is informally [f1(), f1(long), f2(DOMString)], and formally
[(f1, [], []), (f1, [long], [optional]), (f2, [DOMString], [required])].
Currently the optionality list is a list of |is_optional| booleans (True
means optional, False means required); to support variadics this needs to
be tri-valued as required, optional, or variadic.
Formally:
An effective overload set represents the allowable invocations for a
particular operation, constructor (specified with [Constructor] or
[NamedConstructor]), legacy caller or callback function.
An additional argument N (argument count) is needed when overloading
variadics, but we don't use that currently.
Spec: http://heycam.github.io/webidl/#dfn-effective-overload-set
Formally the input and output lists are sets, but methods are stored
internally as dicts, which can't be stored in a set because they are not
hashable, so we use lists instead.
Arguments:
F: list of overloads for a given callable name.
Returns:
S: list of tuples of the form (callable, type list, optionality list). | Returns the effective overload set of an overloaded function. | [
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"""Returns the effective overload set of an overloaded function.
An effective overload set is the set of overloaded functions + signatures
(type list of arguments, with optional and variadic arguments included or
not), and is used in the overload resolution algorithm.
For example, given input [f1(optional long x), f2(DOMString s)], the output
is informally [f1(), f1(long), f2(DOMString)], and formally
[(f1, [], []), (f1, [long], [optional]), (f2, [DOMString], [required])].
Currently the optionality list is a list of |is_optional| booleans (True
means optional, False means required); to support variadics this needs to
be tri-valued as required, optional, or variadic.
Formally:
An effective overload set represents the allowable invocations for a
particular operation, constructor (specified with [Constructor] or
[NamedConstructor]), legacy caller or callback function.
An additional argument N (argument count) is needed when overloading
variadics, but we don't use that currently.
Spec: http://heycam.github.io/webidl/#dfn-effective-overload-set
Formally the input and output lists are sets, but methods are stored
internally as dicts, which can't be stored in a set because they are not
hashable, so we use lists instead.
Arguments:
F: list of overloads for a given callable name.
Returns:
S: list of tuples of the form (callable, type list, optionality list).
"""
# Code closely follows the algorithm in the spec, for clarity and
# correctness, and hence is not very Pythonic.
# 1. Initialize S to ∅.
# (We use a list because we can't use a set, as noted above.)
S = []
# 2. Let F be a set with elements as follows, according to the kind of
# effective overload set:
# (Passed as argument, nothing to do.)
# 3. & 4. (maxarg, m) are only needed for variadics, not used.
# 5. For each operation, extended attribute or callback function X in F:
for X in F: # X is the "callable", F is the overloads.
arguments = X['arguments']
# 1. Let n be the number of arguments X is declared to take.
n = len(arguments)
# 2. Let t0..n−1 be a list of types, where ti is the type of X’s
# argument at index i.
# (“type list”)
t = tuple(argument['idl_type_object'] for argument in arguments)
# 3. Let o0..n−1 be a list of optionality values, where oi is “variadic”
# if X’s argument at index i is a final, variadic argument, “optional”
# if the argument is optional, and “required” otherwise.
# (“optionality list”)
# (We’re just using a boolean for optional/variadic vs. required.)
o = tuple(argument['is_optional'] or argument['is_variadic']
for argument in arguments)
# 4. Add to S the tuple <X, t0..n−1, o0..n−1>.
S.append((X, t, o))
# 5. If X is declared to be variadic, then:
# (Not used, so not implemented.)
# 6. Initialize i to n−1.
i = n - 1
# 7. While i ≥ 0:
# Spec bug (fencepost error); should be “While i > 0:”
# https://www.w3.org/Bugs/Public/show_bug.cgi?id=25590
while i > 0:
# 1. If argument i of X is not optional, then break this loop.
if not o[i]:
break
# 2. Otherwise, add to S the tuple <X, t0..i−1, o0..i−1>.
S.append((X, t[:i], o[:i]))
# 3. Set i to i−1.
i = i - 1
# 8. If n > 0 and all arguments of X are optional, then add to S the
# tuple <X, (), ()> (where “()” represents the empty list).
if n > 0 and all(oi for oi in o):
S.append((X, [], []))
# 6. The effective overload set is S.
return S | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/special/basic.py | python | jvp | (v, z, n=1) | Compute nth derivative of Bessel function Jv(z) with respect to `z`.
Parameters
----------
v : float
Order of Bessel function
z : complex
Argument at which to evaluate the derivative
n : int, default 1
Order of derivative
Notes
-----
The derivative is computed using the relation DLFM 10.6.7 [2]_.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996, chapter 5.
https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html
.. [2] NIST Digital Library of Mathematical Functions.
https://dlmf.nist.gov/10.6.E7 | Compute nth derivative of Bessel function Jv(z) with respect to `z`. | [
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] | def jvp(v, z, n=1):
"""Compute nth derivative of Bessel function Jv(z) with respect to `z`.
Parameters
----------
v : float
Order of Bessel function
z : complex
Argument at which to evaluate the derivative
n : int, default 1
Order of derivative
Notes
-----
The derivative is computed using the relation DLFM 10.6.7 [2]_.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996, chapter 5.
https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html
.. [2] NIST Digital Library of Mathematical Functions.
https://dlmf.nist.gov/10.6.E7
"""
n = _nonneg_int_or_fail(n, 'n')
if n == 0:
return jv(v, z)
else:
return _bessel_diff_formula(v, z, n, jv, -1) | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/internetcontent/nv_python_libs/xsltfunctions/cinemarv_api.py | python | xpathFunctions.cinemarvIsCustomHTML | (self, context, *args) | Check if the link is for a custom HTML
Example call: mnvXpath:cinemarvIsCustomHTML(('dummy'))
return True if the link does not starts with "http://"
return False if the link starts with "http://" | Check if the link is for a custom HTML
Example call: mnvXpath:cinemarvIsCustomHTML(('dummy'))
return True if the link does not starts with "http://"
return False if the link starts with "http://" | [
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'''Check if the link is for a custom HTML
Example call: mnvXpath:cinemarvIsCustomHTML(('dummy'))
return True if the link does not starts with "http://"
return False if the link starts with "http://"
'''
if self.persistence['cinemarvLinkGeneration'] is None:
return False
if self.persistence['cinemarvLinkGeneration'].startswith('http://'):
return False
else:
return True | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | CheckListBox.Create | (*args, **kwargs) | return _controls_.CheckListBox_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, wxArrayString choices=wxPyEmptyStringArray,
long style=0, Validator validator=DefaultValidator,
String name=ListBoxNameStr) -> bool | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, wxArrayString choices=wxPyEmptyStringArray,
long style=0, Validator validator=DefaultValidator,
String name=ListBoxNameStr) -> bool | [
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"""
Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
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long style=0, Validator validator=DefaultValidator,
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return _controls_.CheckListBox_Create(*args, **kwargs) | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/lib/polynomial.py | python | polyder | (p, m=1) | return val | Return the derivative of the specified order of a polynomial.
Parameters
----------
p : poly1d or sequence
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
m : int, optional
Order of differentiation (default: 1)
Returns
-------
der : poly1d
A new polynomial representing the derivative.
See Also
--------
polyint : Anti-derivative of a polynomial.
poly1d : Class for one-dimensional polynomials.
Examples
--------
The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is:
>>> p = np.poly1d([1,1,1,1])
>>> p2 = np.polyder(p)
>>> p2
poly1d([3, 2, 1])
which evaluates to:
>>> p2(2.)
17.0
We can verify this, approximating the derivative with
``(f(x + h) - f(x))/h``:
>>> (p(2. + 0.001) - p(2.)) / 0.001
17.007000999997857
The fourth-order derivative of a 3rd-order polynomial is zero:
>>> np.polyder(p, 2)
poly1d([6, 2])
>>> np.polyder(p, 3)
poly1d([6])
>>> np.polyder(p, 4)
poly1d([ 0.]) | Return the derivative of the specified order of a polynomial. | [
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] | def polyder(p, m=1):
"""
Return the derivative of the specified order of a polynomial.
Parameters
----------
p : poly1d or sequence
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
m : int, optional
Order of differentiation (default: 1)
Returns
-------
der : poly1d
A new polynomial representing the derivative.
See Also
--------
polyint : Anti-derivative of a polynomial.
poly1d : Class for one-dimensional polynomials.
Examples
--------
The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is:
>>> p = np.poly1d([1,1,1,1])
>>> p2 = np.polyder(p)
>>> p2
poly1d([3, 2, 1])
which evaluates to:
>>> p2(2.)
17.0
We can verify this, approximating the derivative with
``(f(x + h) - f(x))/h``:
>>> (p(2. + 0.001) - p(2.)) / 0.001
17.007000999997857
The fourth-order derivative of a 3rd-order polynomial is zero:
>>> np.polyder(p, 2)
poly1d([6, 2])
>>> np.polyder(p, 3)
poly1d([6])
>>> np.polyder(p, 4)
poly1d([ 0.])
"""
m = int(m)
if m < 0:
raise ValueError, "Order of derivative must be positive (see polyint)"
truepoly = isinstance(p, poly1d)
p = NX.asarray(p)
n = len(p) - 1
y = p[:-1] * NX.arange(n, 0, -1)
if m == 0:
val = p
else:
val = polyder(y, m - 1)
if truepoly:
val = poly1d(val)
return val | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_SelfTest_REQUEST.__init__ | (self, fullTest = 0) | This command causes the TPM to perform a test of its capabilities.
If the fullTest is YES, the TPM will test all functions. If fullTest =
NO, the TPM will only test those functions that have not previously been
tested.
Attributes:
fullTest (int): YES if full test to be performed
NO if only test of untested functions required | This command causes the TPM to perform a test of its capabilities.
If the fullTest is YES, the TPM will test all functions. If fullTest =
NO, the TPM will only test those functions that have not previously been
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""" This command causes the TPM to perform a test of its capabilities.
If the fullTest is YES, the TPM will test all functions. If fullTest =
NO, the TPM will only test those functions that have not previously been
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uber/neuropod | de304c40ec0634a868d7ef41ba7bf89ebc364f10 | source/python/neuropod/utils/dtype_utils.py | python | get_dtype | (arg) | return np.dtype(arg) | Get numpy dtypes from strings in a python 2 and 3 compatible way | Get numpy dtypes from strings in a python 2 and 3 compatible way | [
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Get numpy dtypes from strings in a python 2 and 3 compatible way
"""
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arg = "str"
return np.dtype(arg) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/pexpect/screen.py | python | screen.cursor_restore_attrs | (self) | Restores cursor position after a Save Cursor. | Restores cursor position after a Save Cursor. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_perl.py | python | KeywordString | (option=0) | Returns the specified Keyword String
@note: not used by most modules | Returns the specified Keyword String
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@note: not used by most modules
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return PERL_KW[1]
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_base/arrayman.py | python | ArrayObjman.init_postload_external | (self) | Called after set state.
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"""
Called after set state.
Link internal states.
"""
TableMixin.init_postload_external(self)
self._init_attributes()
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DayBreak-u/yolo-face-with-landmark | 29cc7454a578cc9a23d95a712af70d467f69fedd | utils/utils.py | python | box_iou | (box1, box2) | return inter / (area1[:, None] + area2 - inter) | Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
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iou (Tensor[N, M]): the NxM matrix containing the pairwise
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
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"""
def box_area(box):
# box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(box1.t())
area2 = box_area(box2.t())
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
return inter / (area1[:, None] + area2 - inter) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/jinja2/environment.py | python | Template.from_module_dict | (cls, environment, module_dict, globals) | return cls._from_namespace(environment, module_dict, globals) | Creates a template object from a module. This is used by the
module loader to create a template object.
.. versionadded:: 2.4 | Creates a template object from a module. This is used by the
module loader to create a template object. | [
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.. versionadded:: 2.4
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py | python | CudnnParamsFormatConverterLSTM._cudnn_to_tf_weights | (self, *cu_weights) | r"""Stitching cudnn canonical weights to generate tf canonical weights. | r"""Stitching cudnn canonical weights to generate tf canonical weights. | [
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r"""Stitching cudnn canonical weights to generate tf canonical weights."""
if self._num_proj:
w_i, w_f, w_c, w_o, r_i, r_f, r_c, r_o, pw = cu_weights
else:
w_i, w_f, w_c, w_o, r_i, r_f, r_c, r_o = cu_weights
# pylint: disable=invalid-name
W_i = array_ops.concat([w_i, r_i], axis=1)
W_f = array_ops.concat([w_f, r_f], axis=1)
W_c = array_ops.concat([w_c, r_c], axis=1)
W_o = array_ops.concat([w_o, r_o], axis=1)
# pylint: enable=invalid-name
# Cudnn LSTM weights are in ifco order, other tf LSTMs are in icfo order.
reordered = self._cudnn_to_tf_gate_params(*[W_i, W_f, W_c, W_o])
if self._num_proj:
return (array_ops.transpose(array_ops.concat(reordered, axis=0)),
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else:
return (array_ops.transpose(array_ops.concat(reordered, axis=0)),) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/contexts/fitting_contexts/basic_fitting_context.py | python | BasicFittingContext.chi_squared_for_undo | (self, chi_squared: list) | Sets the chi squared from previous fits used for single fitting. | Sets the chi squared from previous fits used for single fitting. | [
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"""Sets the chi squared from previous fits used for single fitting."""
self._chi_squared_for_undo = chi_squared | [
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google/tink | 59bb34495d1cb8f9d9dbc0f0a52c4f9e21491a14 | python/tink/jwt/_raw_jwt.py | python | RawJwt.json_payload | (self) | return _json_util.json_dumps(self._payload) | Returns the payload encoded as JSON string. | Returns the payload encoded as JSON string. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/latex2mathml.py | python | parse_latex_math | (string, inline=True) | return tree | parse_latex_math(string [,inline]) -> MathML-tree
Returns a MathML-tree parsed from string. inline=True is for
inline math and inline=False is for displayed math.
tree is the whole tree and node is the current element. | parse_latex_math(string [,inline]) -> MathML-tree | [
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"""parse_latex_math(string [,inline]) -> MathML-tree
Returns a MathML-tree parsed from string. inline=True is for
inline math and inline=False is for displayed math.
tree is the whole tree and node is the current element."""
# Normalize white-space:
string = ' '.join(string.split())
if inline:
node = mrow()
tree = math(node, inline=True)
else:
node = mtd()
tree = math(mtable(mtr(node)), inline=False)
while len(string) > 0:
n = len(string)
c = string[0]
skip = 1 # number of characters consumed
if n > 1:
c2 = string[1]
else:
c2 = ''
## print n, string, c, c2, node.__class__.__name__
if c == ' ':
pass
elif c == '\\':
if c2 in '{}':
node = node.append(mo(c2))
skip = 2
elif c2 == ' ':
node = node.append(mspace())
skip = 2
elif c2 == ',': # TODO: small space
node = node.append(mspace())
skip = 2
elif c2.isalpha():
# We have a LaTeX-name:
i = 2
while i < n and string[i].isalpha():
i += 1
name = string[1:i]
node, skip = handle_keyword(name, node, string[i:])
skip += i
elif c2 == '\\':
# End of a row:
entry = mtd()
row = mtr(entry)
node.close().close().append(row)
node = entry
skip = 2
else:
raise SyntaxError(ur'Syntax error: "%s%s"' % (c, c2))
elif c.isalpha():
node = node.append(mi(c))
elif c.isdigit():
node = node.append(mn(c))
elif c in "+-*/=()[]|<>,.!?':;@":
node = node.append(mo(c))
elif c == '_':
child = node.delete_child()
if isinstance(child, msup):
sub = msubsup(child.children, reversed=True)
elif isinstance(child, mo) and child.data in sumintprod:
sub = munder(child)
else:
sub = msub(child)
node.append(sub)
node = sub
elif c == '^':
child = node.delete_child()
if isinstance(child, msub):
sup = msubsup(child.children)
elif isinstance(child, mo) and child.data in sumintprod:
sup = mover(child)
elif (isinstance(child, munder) and
child.children[0].data in sumintprod):
sup = munderover(child.children)
else:
sup = msup(child)
node.append(sup)
node = sup
elif c == '{':
row = mrow()
node.append(row)
node = row
elif c == '}':
node = node.close()
elif c == '&':
entry = mtd()
node.close().append(entry)
node = entry
else:
raise SyntaxError(ur'Illegal character: "%s"' % c)
string = string[skip:]
return tree | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/graphviz/py2/graphviz/files.py | python | File.unflatten | (self, stagger=None, fanout=False, chain=None) | return Source(out,
filename=self.filename, directory=self.directory,
format=self._format, engine=self._engine,
encoding=self._encoding) | Return a new :class:`.Source` instance with the source piped through the Graphviz *unflatten* preprocessor.
Args:
stagger (int): Stagger the minimum length of leaf edges between 1 and this small integer.
fanout (bool): Fanout nodes with indegree = outdegree = 1 when staggering (requires ``stagger``).
chain (int): Form disconnected nodes into chains of up to this many nodes.
Returns:
Source: Prepocessed DOT source code (improved layout aspect ratio).
Raises:
graphviz.RequiredArgumentError: If ``fanout`` is given but ``stagger`` is None.
graphviz.ExecutableNotFound: If the Graphviz unflatten executable is not found.
subprocess.CalledProcessError: If the exit status is non-zero.
See also:
https://www.graphviz.org/pdf/unflatten.1.pdf | Return a new :class:`.Source` instance with the source piped through the Graphviz *unflatten* preprocessor. | [
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"""Return a new :class:`.Source` instance with the source piped through the Graphviz *unflatten* preprocessor.
Args:
stagger (int): Stagger the minimum length of leaf edges between 1 and this small integer.
fanout (bool): Fanout nodes with indegree = outdegree = 1 when staggering (requires ``stagger``).
chain (int): Form disconnected nodes into chains of up to this many nodes.
Returns:
Source: Prepocessed DOT source code (improved layout aspect ratio).
Raises:
graphviz.RequiredArgumentError: If ``fanout`` is given but ``stagger`` is None.
graphviz.ExecutableNotFound: If the Graphviz unflatten executable is not found.
subprocess.CalledProcessError: If the exit status is non-zero.
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https://www.graphviz.org/pdf/unflatten.1.pdf
"""
out = backend.unflatten(self.source,
stagger=stagger, fanout=fanout, chain=chain,
encoding=self._encoding)
return Source(out,
filename=self.filename, directory=self.directory,
format=self._format, engine=self._engine,
encoding=self._encoding) | [
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OAID/Caffe-HRT | aae71e498ab842c6f92bcc23fc668423615a4d65 | scripts/cpp_lint.py | python | IsCppString | (line) | return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
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"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
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"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/trade_bin.py | python | TradeBin.timestamp | (self, timestamp) | Sets the timestamp of this TradeBin.
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"""Sets the timestamp of this TradeBin.
:param timestamp: The timestamp of this TradeBin. # noqa: E501
:type: datetime
"""
if timestamp is None:
raise ValueError("Invalid value for `timestamp`, must not be `None`") # noqa: E501
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/xml/etree/ElementTree.py | python | ProcessingInstruction | (target, text=None) | return element | Processing Instruction element factory.
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This function creates a special element which the standard serializer
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*target* is a string containing the processing instruction, *text* is a
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element = Element(ProcessingInstruction)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/llvmlite/binding/transforms.py | python | PassManagerBuilder.loop_vectorize | (self) | return ffi.lib.LLVMPY_PassManagerBuilderGetLoopVectorize(self) | If true, allow vectorizing loops. | If true, allow vectorizing loops. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/asyncio/protocols.py | python | BaseProtocol.connection_made | (self, transport) | Called when a connection is made.
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/tune/attr_tuner.py | python | ScaleSolver.solve_one | (self, tunable) | return False | Solve one step. | Solve one step. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2_extras/auth.py | python | AuthStore.get_session | (self, request) | return store.get_session(self.config['cookie_name'],
backend=self.config['session_backend']) | Returns an auth session.
:param request:
A :class:`webapp2.Request` instance.
:returns:
A session dict. | Returns an auth session. | [
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"""Returns an auth session.
:param request:
A :class:`webapp2.Request` instance.
:returns:
A session dict.
"""
store = sessions.get_store(request=request)
return store.get_session(self.config['cookie_name'],
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KhronosGroup/SPIRV-LLVM | 1eb85593f3fe2c39379b9a9b088d51eda4f42b8b | examples/Kaleidoscope/MCJIT/lazy/genk-timing.py | python | KScriptGenerator.setCallWeighting | (self, weight) | Sets the probably of generating a function call | Sets the probably of generating a function call | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/dataview.py | python | DataViewChoiceRenderer.GetChoice | (*args, **kwargs) | return _dataview.DataViewChoiceRenderer_GetChoice(*args, **kwargs) | GetChoice(self, size_t index) -> String | GetChoice(self, size_t index) -> String | [
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return _dataview.DataViewChoiceRenderer_GetChoice(*args, **kwargs) | [
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yyzybb537/libgo | 4af17b7c67643c4d54aa354dcc77963ea07847d0 | third_party/boost.context/tools/build/src/build/type.py | python | reset | () | Clear the module state. This is mainly for testing purposes.
Note that this must be called _after_ resetting the module 'feature'. | Clear the module state. This is mainly for testing purposes.
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""" Clear the module state. This is mainly for testing purposes.
Note that this must be called _after_ resetting the module 'feature'.
"""
global __prefixes_suffixes, __suffixes_to_types, __types, __rule_names_to_types, __target_suffixes_cache
__register_features ()
# Stores suffixes for generated targets.
__prefixes_suffixes = [property.PropertyMap(), property.PropertyMap()]
# Maps suffixes to types
__suffixes_to_types = {}
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# 'derived': a list of names of type which derive from this one
# 'scanner': the scanner class registered for this type, if any
__types = {}
# Caches suffixes for targets with certain properties.
__target_suffixes_cache = {} | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/layers/utils.py | python | assert_same_structure | (nest1, nest2, check_types=True) | Confirm two nested structures with the same structure. | Confirm two nested structures with the same structure. | [
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] | def assert_same_structure(nest1, nest2, check_types=True):
"""
Confirm two nested structures with the same structure.
"""
len_nest1 = len(flatten(nest1)) if is_sequence(nest1) else 1
len_nest2 = len(flatten(nest2)) if is_sequence(nest2) else 1
if len_nest1 != len_nest2:
raise ValueError("The two structures don't have the same number of "
"elements.\n\nFirst structure (%i elements): %s\n\n"
"Second structure (%i elements): %s" %
(len_nest1, nest1, len_nest2, nest2))
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_PCR_Allocate_REQUEST.fromBytes | (buffer) | return TpmBuffer(buffer).createObj(TPM2_PCR_Allocate_REQUEST) | Returns new TPM2_PCR_Allocate_REQUEST object constructed from its
marshaled representation in the given byte buffer | Returns new TPM2_PCR_Allocate_REQUEST object constructed from its
marshaled representation in the given byte buffer | [
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] | def fromBytes(buffer):
""" Returns new TPM2_PCR_Allocate_REQUEST object constructed from its
marshaled representation in the given byte buffer
"""
return TpmBuffer(buffer).createObj(TPM2_PCR_Allocate_REQUEST) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/libmintsgshell.py | python | ShellInfo.ncartesian | (self) | return self.PYncartesian | Return the total number of functions if this shell was Cartesian | Return the total number of functions if this shell was Cartesian | [
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return self.PYncartesian | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/OpenSSL/crypto.py | python | X509.to_cryptography | (self) | return _Certificate(backend, self._x509) | Export as a ``cryptography`` certificate.
:rtype: ``cryptography.x509.Certificate``
.. versionadded:: 17.1.0 | Export as a ``cryptography`` certificate. | [
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] | def to_cryptography(self):
"""
Export as a ``cryptography`` certificate.
:rtype: ``cryptography.x509.Certificate``
.. versionadded:: 17.1.0
"""
from cryptography.hazmat.backends.openssl.x509 import _Certificate
backend = _get_backend()
return _Certificate(backend, self._x509) | [
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HKUST-Aerial-Robotics/Fast-Planner | 2ddd7793eecd573dbb5b47e2c985aa06606df3cf | uav_simulator/Utils/multi_map_server/quadrotor_msgs/build/catkin_generated/installspace/_setup_util.py | python | prepend_env_variables | (environ, env_var_subfolders, workspaces) | return lines | Generate shell code to prepend environment variables
for the all workspaces. | Generate shell code to prepend environment variables
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] | def prepend_env_variables(environ, env_var_subfolders, workspaces):
'''
Generate shell code to prepend environment variables
for the all workspaces.
'''
lines = []
lines.append(comment('prepend folders of workspaces to environment variables'))
paths = [path for path in workspaces.split(os.pathsep) if path]
prefix = _prefix_env_variable(environ, 'CMAKE_PREFIX_PATH', paths, '')
lines.append(prepend(environ, 'CMAKE_PREFIX_PATH', prefix))
for key in sorted([key for key in env_var_subfolders.keys() if key != 'CMAKE_PREFIX_PATH']):
subfolder = env_var_subfolders[key]
prefix = _prefix_env_variable(environ, key, paths, subfolder)
lines.append(prepend(environ, key, prefix))
return lines | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/ma/mrecords.py | python | _guessvartypes | (arr) | return vartypes | Tries to guess the dtypes of the str_ ndarray `arr`.
Guesses by testing element-wise conversion. Returns a list of dtypes.
The array is first converted to ndarray. If the array is 2D, the test
is performed on the first line. An exception is raised if the file is
3D or more. | Tries to guess the dtypes of the str_ ndarray `arr`. | [
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"""
Tries to guess the dtypes of the str_ ndarray `arr`.
Guesses by testing element-wise conversion. Returns a list of dtypes.
The array is first converted to ndarray. If the array is 2D, the test
is performed on the first line. An exception is raised if the file is
3D or more.
"""
vartypes = []
arr = np.asarray(arr)
if arr.ndim == 2:
arr = arr[0]
elif arr.ndim > 2:
raise ValueError("The array should be 2D at most!")
# Start the conversion loop.
for f in arr:
try:
int(f)
except (ValueError, TypeError):
try:
float(f)
except (ValueError, TypeError):
try:
complex(f)
except (ValueError, TypeError):
vartypes.append(arr.dtype)
else:
vartypes.append(np.dtype(complex))
else:
vartypes.append(np.dtype(float))
else:
vartypes.append(np.dtype(int))
return vartypes | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | xmlDoc.htmlSaveFile | (self, filename) | return ret | Dump an HTML document to a file. If @filename is "-" the
stdout file is used. | Dump an HTML document to a file. If | [
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"""Dump an HTML document to a file. If @filename is "-" the
stdout file is used. """
ret = libxml2mod.htmlSaveFile(filename, self._o)
return ret | [
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qbittorrent/qBittorrent | 78eaa49cd6b3b59c064cd461fe6d30eceaeac770 | src/searchengine/nova3/nova2.py | python | displayCapabilities | (supported_engines) | Display capabilities in XML format
<capabilities>
<engine_short_name>
<name>long name</name>
<url>http://example.com</url>
<categories>movies music games</categories>
</engine_short_name>
</capabilities> | Display capabilities in XML format
<capabilities>
<engine_short_name>
<name>long name</name>
<url>http://example.com</url>
<categories>movies music games</categories>
</engine_short_name>
</capabilities> | [
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"""
Display capabilities in XML format
<capabilities>
<engine_short_name>
<name>long name</name>
<url>http://example.com</url>
<categories>movies music games</categories>
</engine_short_name>
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"""
xml = "".join(("<capabilities>\n",
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print(xml) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/calendar.py | python | Calendar.yeardatescalendar | (self, year, width=3) | return [months[i:i+width] for i in range(0, len(months), width) ] | Return the data for the specified year ready for formatting. The return
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/ntupleDataFormat.py | python | BeamSpot.__init__ | (self, tree) | Constructor.
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | external_tools/ply_info/example/BASIC/basparse.py | python | p_program | (p) | program : program statement
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | getboolean | (s) | return _default_root.tk.getboolean(s) | Convert true and false to integer values 1 and 0. | Convert true and false to integer values 1 and 0. | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/avr8target.py | python | TinyXAvrTarget.sib_read | (self) | return self.protocol.memory_read(Avr8Protocol.AVR8_MEMTYPE_SIB, 0, 32) | Reads the System Information Block
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | HardSigmoid.__init__ | (self, alpha=0.2, gamma=0.5) | Args:
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/optimizer/optimizer.py | python | Optimizer._get_lr | (self, index) | return self._get_lrs([index])[0] | Gets the learning rate given the index of the weight.
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/model/trajectory.py | python | SO3Trajectory.getPointTrajectory | (self,localPt) | return Trajectory(self.times,[so3.apply(m,localPt) for m in self.milestones]) | Returns a Trajectory describing the movement of the point localPt
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/graph_editor/reroute.py | python | swap_inputs | (sgv0, sgv1) | return _reroute_sgv_inputs(sgv0, sgv1, _RerouteMode.swap) | Swap all the inputs of sgv0 and sgv1 (see reroute_inputs). | Swap all the inputs of sgv0 and sgv1 (see reroute_inputs). | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | gpu/command_buffer/build_gles2_cmd_buffer.py | python | Function.WriteCmdComputeSize | (self, file) | Writes the ComputeSize function for the command. | Writes the ComputeSize function for the command. | [
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twhui/LiteFlowNet | 00925aebf2db9ac50f4b1666f718688b10dd10d1 | scripts/cpp_lint.py | python | _FunctionState.End | (self) | Stop analyzing function body. | Stop analyzing function body. | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/sans/algorithm_detail/calculate_transmission_helper.py | python | apply_flat_background_correction_to_detectors | (workspace, flat_background_correction_start,
flat_background_correction_stop) | return workspace | Applies the flat background correction to all detectors which are not monitors
:param workspace: the workspace which contains detector spectra which will be corrected.
:param flat_background_correction_start: the start of the flat background region
:param flat_background_correction_stop: the end of the flat background region
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py | python | MinitaurBulletDuckEnv.__init__ | (
self,
urdf_root=pybullet_data.getDataPath(),
action_repeat=1,
distance_weight=1.0,
energy_weight=0.005,
shake_weight=0.0,
drift_weight=0.0,
distance_limit=float("inf"),
observation_noise_stdev=0.0,
self_collision_enabled=True,
motor_velocity_limit=np.inf,
pd_control_enabled=False, #not needed to be true if accurate motor model is enabled (has its own better PD)
leg_model_enabled=True,
accurate_motor_model_enabled=True,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=True,
hard_reset=True,
on_rack=False,
render=False,
kd_for_pd_controllers=0.3,
env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()) | Initialize the minitaur gym environment.
Args:
urdf_root: The path to the urdf data folder.
action_repeat: The number of simulation steps before actions are applied.
distance_weight: The weight of the distance term in the reward.
energy_weight: The weight of the energy term in the reward.
shake_weight: The weight of the vertical shakiness term in the reward.
drift_weight: The weight of the sideways drift term in the reward.
distance_limit: The maximum distance to terminate the episode.
observation_noise_stdev: The standard deviation of observation noise.
self_collision_enabled: Whether to enable self collision in the sim.
motor_velocity_limit: The velocity limit of each motor.
pd_control_enabled: Whether to use PD controller for each motor.
leg_model_enabled: Whether to use a leg motor to reparameterize the action
space.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model.
motor_kd: derivative gain for the accurate motor model.
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
hard_reset: Whether to wipe the simulation and load everything when reset
is called. If set to false, reset just place the minitaur back to start
position and set its pose to initial configuration.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
render: Whether to render the simulation.
kd_for_pd_controllers: kd value for the pd controllers of the motors
env_randomizer: An EnvRandomizer to randomize the physical properties
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shake_weight=0.0,
drift_weight=0.0,
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urdf_root: The path to the urdf data folder.
action_repeat: The number of simulation steps before actions are applied.
distance_weight: The weight of the distance term in the reward.
energy_weight: The weight of the energy term in the reward.
shake_weight: The weight of the vertical shakiness term in the reward.
drift_weight: The weight of the sideways drift term in the reward.
distance_limit: The maximum distance to terminate the episode.
observation_noise_stdev: The standard deviation of observation noise.
self_collision_enabled: Whether to enable self collision in the sim.
motor_velocity_limit: The velocity limit of each motor.
pd_control_enabled: Whether to use PD controller for each motor.
leg_model_enabled: Whether to use a leg motor to reparameterize the action
space.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model.
motor_kd: derivative gain for the accurate motor model.
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
hard_reset: Whether to wipe the simulation and load everything when reset
is called. If set to false, reset just place the minitaur back to start
position and set its pose to initial configuration.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
render: Whether to render the simulation.
kd_for_pd_controllers: kd value for the pd controllers of the motors
env_randomizer: An EnvRandomizer to randomize the physical properties
during reset().
"""
self._time_step = 0.01
self._action_repeat = action_repeat
self._num_bullet_solver_iterations = 300
self._urdf_root = urdf_root
self._self_collision_enabled = self_collision_enabled
self._motor_velocity_limit = motor_velocity_limit
self._observation = []
self._env_step_counter = 0
self._is_render = render
self._last_base_position = [0, 0, 0]
self._distance_weight = distance_weight
self._energy_weight = energy_weight
self._drift_weight = drift_weight
self._shake_weight = shake_weight
self._distance_limit = distance_limit
self._observation_noise_stdev = observation_noise_stdev
self._action_bound = 1
self._pd_control_enabled = pd_control_enabled
self._leg_model_enabled = leg_model_enabled
self._accurate_motor_model_enabled = accurate_motor_model_enabled
self._motor_kp = motor_kp
self._motor_kd = motor_kd
self._torque_control_enabled = torque_control_enabled
self._motor_overheat_protection = motor_overheat_protection
self._on_rack = on_rack
self._cam_dist = 1.0
self._cam_yaw = 0
self._duckId = -1
self._cam_pitch = -30
self._hard_reset = True
self._kd_for_pd_controllers = kd_for_pd_controllers
self._last_frame_time = 0.0
print("urdf_root=" + self._urdf_root)
self._env_randomizer = env_randomizer
# PD control needs smaller time step for stability.
if pd_control_enabled or accurate_motor_model_enabled:
self._time_step /= NUM_SUBSTEPS
self._num_bullet_solver_iterations /= NUM_SUBSTEPS
self._action_repeat *= NUM_SUBSTEPS
if self._is_render:
self._pybullet_client = bc.BulletClient(connection_mode=pybullet.GUI)
else:
self._pybullet_client = bc.BulletClient()
self.seed()
self.reset()
observation_high = (self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
observation_low = (self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
action_dim = 8
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
self.observation_space = spaces.Box(observation_low, observation_high, dtype=np.float32)
self.viewer = None
self._hard_reset = hard_reset | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/distutils/cmd.py | python | Command.set_undefined_options | (self, src_cmd, *option_pairs) | Set the values of any "undefined" options from corresponding
option values in some other command object. "Undefined" here means
"is None", which is the convention used to indicate that an option
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setattr(self, dst_option,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/logging/config.py | python | BaseConfigurator.ext_convert | (self, value) | return self.resolve(value) | Default converter for the ext:// protocol. | Default converter for the ext:// protocol. | [
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BestSonny/SSTD | 174d452189f6bf9cf4b6957719392008bd974069 | python/caffe/draw.py | python | get_layer_label | (layer, rankdir) | return node_label | Define node label based on layer type.
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----------
layer : ?
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Direction of graph layout.
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layer : ?
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Direction of graph layout.
Returns
-------
string :
A label for the current layer
"""
if rankdir in ('TB', 'BT'):
# If graph orientation is vertical, horizontal space is free and
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separator = ' '
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# If graph orientation is horizontal, vertical space is free and
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separator = '\\n'
if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
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node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
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else:
node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/utils/__init__.py | python | all_estimators | (include_meta_estimators=None,
include_other=None, type_filter=None,
include_dont_test=None) | return sorted(set(estimators), key=itemgetter(0)) | Get a list of all estimators from sklearn.
This function crawls the module and gets all classes that inherit
from BaseEstimator. Classes that are defined in test-modules are not
included.
By default meta_estimators such as GridSearchCV are also not included.
Parameters
----------
include_meta_estimators : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_meta_estimators`` has been deprecated and has no effect in
0.21 and will be removed in 0.23.
include_other : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_other`` has been deprecated and has not effect in 0.21 and
will be removed in 0.23.
type_filter : string, list of string, or None, default=None
Which kind of estimators should be returned. If None, no filter is
applied and all estimators are returned. Possible values are
'classifier', 'regressor', 'cluster' and 'transformer' to get
estimators only of these specific types, or a list of these to
get the estimators that fit at least one of the types.
include_dont_test : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_dont_test`` has been deprecated and has no effect in 0.21
and will be removed in 0.23.
Returns
-------
estimators : list of tuples
List of (name, class), where ``name`` is the class name as string
and ``class`` is the actuall type of the class. | Get a list of all estimators from sklearn. | [
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include_other=None, type_filter=None,
include_dont_test=None):
"""Get a list of all estimators from sklearn.
This function crawls the module and gets all classes that inherit
from BaseEstimator. Classes that are defined in test-modules are not
included.
By default meta_estimators such as GridSearchCV are also not included.
Parameters
----------
include_meta_estimators : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_meta_estimators`` has been deprecated and has no effect in
0.21 and will be removed in 0.23.
include_other : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_other`` has been deprecated and has not effect in 0.21 and
will be removed in 0.23.
type_filter : string, list of string, or None, default=None
Which kind of estimators should be returned. If None, no filter is
applied and all estimators are returned. Possible values are
'classifier', 'regressor', 'cluster' and 'transformer' to get
estimators only of these specific types, or a list of these to
get the estimators that fit at least one of the types.
include_dont_test : boolean, default=False
Deprecated, ignored.
.. deprecated:: 0.21
``include_dont_test`` has been deprecated and has no effect in 0.21
and will be removed in 0.23.
Returns
-------
estimators : list of tuples
List of (name, class), where ``name`` is the class name as string
and ``class`` is the actuall type of the class.
"""
# lazy import to avoid circular imports from sklearn.base
from ._testing import ignore_warnings
from ..base import (BaseEstimator, ClassifierMixin, RegressorMixin,
TransformerMixin, ClusterMixin)
def is_abstract(c):
if not(hasattr(c, '__abstractmethods__')):
return False
if not len(c.__abstractmethods__):
return False
return True
if include_other is not None:
warnings.warn("include_other was deprecated in version 0.21,"
" has no effect and will be removed in 0.23",
DeprecationWarning)
if include_dont_test is not None:
warnings.warn("include_dont_test was deprecated in version 0.21,"
" has no effect and will be removed in 0.23",
DeprecationWarning)
if include_meta_estimators is not None:
warnings.warn("include_meta_estimators was deprecated in version 0.21,"
" has no effect and will be removed in 0.23",
DeprecationWarning)
all_classes = []
modules_to_ignore = {"tests", "externals", "setup", "conftest"}
root = str(Path(__file__).parent.parent) # sklearn package
# Ignore deprecation warnings triggered at import time and from walking
# packages
with ignore_warnings(category=FutureWarning):
for importer, modname, ispkg in pkgutil.walk_packages(
path=[root], prefix='sklearn.'):
mod_parts = modname.split(".")
if (any(part in modules_to_ignore for part in mod_parts)
or '._' in modname):
continue
module = import_module(modname)
classes = inspect.getmembers(module, inspect.isclass)
classes = [(name, est_cls) for name, est_cls in classes
if not name.startswith("_")]
# TODO: Remove when FeatureHasher is implemented in PYPY
# Skips FeatureHasher for PYPY
if IS_PYPY and 'feature_extraction' in modname:
classes = [(name, est_cls) for name, est_cls in classes
if name == "FeatureHasher"]
all_classes.extend(classes)
all_classes = set(all_classes)
estimators = [c for c in all_classes
if (issubclass(c[1], BaseEstimator) and
c[0] != 'BaseEstimator')]
# get rid of abstract base classes
estimators = [c for c in estimators if not is_abstract(c[1])]
if type_filter is not None:
if not isinstance(type_filter, list):
type_filter = [type_filter]
else:
type_filter = list(type_filter) # copy
filtered_estimators = []
filters = {'classifier': ClassifierMixin,
'regressor': RegressorMixin,
'transformer': TransformerMixin,
'cluster': ClusterMixin}
for name, mixin in filters.items():
if name in type_filter:
type_filter.remove(name)
filtered_estimators.extend([est for est in estimators
if issubclass(est[1], mixin)])
estimators = filtered_estimators
if type_filter:
raise ValueError("Parameter type_filter must be 'classifier', "
"'regressor', 'transformer', 'cluster' or "
"None, got"
" %s." % repr(type_filter))
# drop duplicates, sort for reproducibility
# itemgetter is used to ensure the sort does not extend to the 2nd item of
# the tuple
return sorted(set(estimators), key=itemgetter(0)) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/bijector.py | python | _Bijector.inverse_log_det_jacobian | (self, x, name='inverse_log_det_jacobian') | Returns the (log o det o Jacobian o inverse)(x).
Mathematically, returns: log(det(dY/dX g^{-1}))(Y).
Args:
x: `Tensor`. The input to the "inverse" Jacobian evaluation.
name: The name to give this op.
Returns:
`Tensor`. | Returns the (log o det o Jacobian o inverse)(x). | [
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"""Returns the (log o det o Jacobian o inverse)(x).
Mathematically, returns: log(det(dY/dX g^{-1}))(Y).
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x: `Tensor`. The input to the "inverse" Jacobian evaluation.
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with ops.name_scope(self.name):
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/lib2to3/fixes/fix_metaclass.py | python | fixup_indent | (suite) | If an INDENT is followed by a thing with a prefix then nuke the prefix
Otherwise we get in trouble when removing __metaclass__ at suite start | If an INDENT is followed by a thing with a prefix then nuke the prefix
Otherwise we get in trouble when removing __metaclass__ at suite start | [
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""" If an INDENT is followed by a thing with a prefix then nuke the prefix
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"""
kids = suite.children[::-1]
# find the first indent
while kids:
node = kids.pop()
if node.type == token.INDENT:
break
# find the first Leaf
while kids:
node = kids.pop()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/html.py | python | HtmlPrintout.SetHtmlText | (*args, **kwargs) | return _html.HtmlPrintout_SetHtmlText(*args, **kwargs) | SetHtmlText(self, String html, String basepath=EmptyString, bool isdir=True) | SetHtmlText(self, String html, String basepath=EmptyString, bool isdir=True) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/httplib.py | python | HTTPResponse.getheaders | (self) | return self.msg.items() | Return list of (header, value) tuples. | Return list of (header, value) tuples. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Image.ResampleBilinear | (*args, **kwargs) | return _core_.Image_ResampleBilinear(*args, **kwargs) | ResampleBilinear(self, int width, int height) -> Image | ResampleBilinear(self, int width, int height) -> Image | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-time.py | python | SConsTimer.outdent | (self, s) | return '\n'.join([strip_initial_spaces(l) for l in lines]) + '\n' | Strip as many spaces from each line as are found at the beginning
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/storage_uri.py | python | FileStorageUri.names_directory | (self) | return os.path.isdir(self.object_name) | Returns True if this URI names a directory. | Returns True if this URI names a directory. | [
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"""Returns True if this URI names a directory."""
if self.stream:
return False
return os.path.isdir(self.object_name) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib2to3/fixer_util.py | python | in_special_context | (node) | return False | Returns true if node is in an environment where all that is required
of it is being iterable (ie, it doesn't matter if it returns a list
or an iterator).
See test_map_nochange in test_fixers.py for some examples and tests. | Returns true if node is in an environment where all that is required
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or an iterator).
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or an iterator).
See test_map_nochange in test_fixers.py for some examples and tests.
"""
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if not pats_built:
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pats_built = True
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for pattern, parent in zip(patterns, attr_chain(node, "parent")):
results = {}
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/framework/ops.py | python | register_tensor_conversion_function | (base_type, conversion_func,
priority=100) | Registers a function for converting objects of `base_type` to `Tensor`.
The conversion function must have the following signature:
```python
def conversion_func(value, dtype=None, name=None, as_ref=False):
# ...
```
It must return a `Tensor` with the given `dtype` if specified. If the
conversion function creates a new `Tensor`, it should use the given
`name` if specified. All exceptions will be propagated to the caller.
The conversion function may return `NotImplemented` for some
inputs. In this case, the conversion process will continue to try
subsequent conversion functions.
If `as_ref` is true, the function must return a `Tensor` reference,
such as a `Variable`.
NOTE: The conversion functions will execute in order of priority,
followed by order of registration. To ensure that a conversion function
`F` runs before another conversion function `G`, ensure that `F` is
registered with a smaller priority than `G`.
Args:
base_type: The base type or tuple of base types for all objects that
`conversion_func` accepts.
conversion_func: A function that converts instances of `base_type` to
`Tensor`.
priority: Optional integer that indicates the priority for applying this
conversion function. Conversion functions with smaller priority values
run earlier than conversion functions with larger priority values.
Defaults to 100.
Raises:
TypeError: If the arguments do not have the appropriate type. | Registers a function for converting objects of `base_type` to `Tensor`. | [
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] | def register_tensor_conversion_function(base_type, conversion_func,
priority=100):
"""Registers a function for converting objects of `base_type` to `Tensor`.
The conversion function must have the following signature:
```python
def conversion_func(value, dtype=None, name=None, as_ref=False):
# ...
```
It must return a `Tensor` with the given `dtype` if specified. If the
conversion function creates a new `Tensor`, it should use the given
`name` if specified. All exceptions will be propagated to the caller.
The conversion function may return `NotImplemented` for some
inputs. In this case, the conversion process will continue to try
subsequent conversion functions.
If `as_ref` is true, the function must return a `Tensor` reference,
such as a `Variable`.
NOTE: The conversion functions will execute in order of priority,
followed by order of registration. To ensure that a conversion function
`F` runs before another conversion function `G`, ensure that `F` is
registered with a smaller priority than `G`.
Args:
base_type: The base type or tuple of base types for all objects that
`conversion_func` accepts.
conversion_func: A function that converts instances of `base_type` to
`Tensor`.
priority: Optional integer that indicates the priority for applying this
conversion function. Conversion functions with smaller priority values
run earlier than conversion functions with larger priority values.
Defaults to 100.
Raises:
TypeError: If the arguments do not have the appropriate type.
"""
if not (isinstance(base_type, type) or
(isinstance(base_type, tuple)
and all(isinstance(x, type) for x in base_type))):
raise TypeError("base_type must be a type or a tuple of types.")
if not callable(conversion_func):
raise TypeError("conversion_func must be callable.")
try:
funcs_at_priority = _tensor_conversion_func_registry[priority]
except KeyError:
funcs_at_priority = []
_tensor_conversion_func_registry[priority] = funcs_at_priority
funcs_at_priority.append((base_type, conversion_func)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/actions/action_runner.py | python | ActionRunner.ScrollBouncePage | (self, left_start_ratio=0.5, top_start_ratio=0.5,
direction='down', distance=100,
overscroll=10, repeat_count=10,
speed_in_pixels_per_second=400) | Perform scroll bounce gesture on the page.
This gesture scrolls the page by the number of pixels specified in
distance, in the given direction, followed by a scroll by
(distance + overscroll) pixels in the opposite direction.
The above gesture is repeated repeat_count times.
Args:
left_start_ratio: The horizontal starting coordinate of the
gesture, as a ratio of the visible bounding rectangle for
document.body.
top_start_ratio: The vertical starting coordinate of the
gesture, as a ratio of the visible bounding rectangle for
document.body.
direction: The direction of scroll, either 'left', 'right',
'up', 'down', 'upleft', 'upright', 'downleft', or 'downright'
distance: The distance to scroll (in pixel).
overscroll: The number of additional pixels to scroll back, in
addition to the givendistance.
repeat_count: How often we want to repeat the full gesture.
speed_in_pixels_per_second: The speed of the gesture (in pixels/s). | Perform scroll bounce gesture on the page. | [
"Perform",
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"on",
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"page",
"."
] | def ScrollBouncePage(self, left_start_ratio=0.5, top_start_ratio=0.5,
direction='down', distance=100,
overscroll=10, repeat_count=10,
speed_in_pixels_per_second=400):
"""Perform scroll bounce gesture on the page.
This gesture scrolls the page by the number of pixels specified in
distance, in the given direction, followed by a scroll by
(distance + overscroll) pixels in the opposite direction.
The above gesture is repeated repeat_count times.
Args:
left_start_ratio: The horizontal starting coordinate of the
gesture, as a ratio of the visible bounding rectangle for
document.body.
top_start_ratio: The vertical starting coordinate of the
gesture, as a ratio of the visible bounding rectangle for
document.body.
direction: The direction of scroll, either 'left', 'right',
'up', 'down', 'upleft', 'upright', 'downleft', or 'downright'
distance: The distance to scroll (in pixel).
overscroll: The number of additional pixels to scroll back, in
addition to the givendistance.
repeat_count: How often we want to repeat the full gesture.
speed_in_pixels_per_second: The speed of the gesture (in pixels/s).
"""
self._RunAction(ScrollBounceAction(
left_start_ratio=left_start_ratio, top_start_ratio=top_start_ratio,
direction=direction, distance=distance,
overscroll=overscroll, repeat_count=repeat_count,
speed_in_pixels_per_second=speed_in_pixels_per_second)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | ColourRGB | (*args, **kwargs) | return val | ColourRGB(unsigned long colRGB) -> Colour
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"""
ColourRGB(unsigned long colRGB) -> Colour
Constructs a colour from a packed RGB value.
"""
val = _gdi_.new_ColourRGB(*args, **kwargs)
return val | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/telnetlib.py | python | Telnet.__init__ | (self, host=None, port=0,
timeout=socket._GLOBAL_DEFAULT_TIMEOUT) | Constructor.
When called without arguments, create an unconnected instance.
With a hostname argument, it connects the instance; port number
and timeout are optional. | Constructor. | [
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] | def __init__(self, host=None, port=0,
timeout=socket._GLOBAL_DEFAULT_TIMEOUT):
"""Constructor.
When called without arguments, create an unconnected instance.
With a hostname argument, it connects the instance; port number
and timeout are optional.
"""
self.debuglevel = DEBUGLEVEL
self.host = host
self.port = port
self.timeout = timeout
self.sock = None
self.rawq = ''
self.irawq = 0
self.cookedq = ''
self.eof = 0
self.iacseq = '' # Buffer for IAC sequence.
self.sb = 0 # flag for SB and SE sequence.
self.sbdataq = ''
self.option_callback = None
self._has_poll = hasattr(select, 'poll')
if host is not None:
self.open(host, port, timeout) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/nn.py | python | nce_loss | (weights,
biases,
inputs,
labels,
num_sampled,
num_classes,
num_true=1,
sampled_values=None,
remove_accidental_hits=False,
partition_strategy="mod",
name="nce_loss") | return _sum_rows(sampled_losses) | Computes and returns the noise-contrastive estimation training loss.
See [Noise-contrastive estimation: A new estimation principle for
unnormalized statistical models]
(http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
Also see our [Candidate Sampling Algorithms Reference]
(../../extras/candidate_sampling.pdf)
Note: In the case where `num_true` > 1, we assign to each target class
the target probability 1 / `num_true` so that the target probabilities
sum to 1 per-example.
Note: It would be useful to allow a variable number of target classes per
example. We hope to provide this functionality in a future release.
For now, if you have a variable number of target classes, you can pad them
out to a constant number by either repeating them or by padding
with an otherwise unused class.
Args:
weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
objects whose concatenation along dimension 0 has shape
[num_classes, dim]. The (possibly-partitioned) class embeddings.
biases: A `Tensor` of shape `[num_classes]`. The class biases.
inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
activations of the input network.
labels: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
num_sampled: An `int`. The number of classes to randomly sample per batch.
num_classes: An `int`. The number of possible classes.
num_true: An `int`. The number of target classes per training example.
sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
`sampled_expected_count`) returned by a `*_candidate_sampler` function.
(if None, we default to `log_uniform_candidate_sampler`)
remove_accidental_hits: A `bool`. Whether to remove "accidental hits"
where a sampled class equals one of the target classes. If set to
`True`, this is a "Sampled Logistic" loss instead of NCE, and we are
learning to generate log-odds instead of log probabilities. See
our [Candidate Sampling Algorithms Reference]
(../../extras/candidate_sampling.pdf).
Default is False.
partition_strategy: A string specifying the partitioning strategy, relevant
if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
name: A name for the operation (optional).
Returns:
A `batch_size` 1-D tensor of per-example NCE losses. | Computes and returns the noise-contrastive estimation training loss. | [
"Computes",
"and",
"returns",
"the",
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"-",
"contrastive",
"estimation",
"training",
"loss",
"."
] | def nce_loss(weights,
biases,
inputs,
labels,
num_sampled,
num_classes,
num_true=1,
sampled_values=None,
remove_accidental_hits=False,
partition_strategy="mod",
name="nce_loss"):
"""Computes and returns the noise-contrastive estimation training loss.
See [Noise-contrastive estimation: A new estimation principle for
unnormalized statistical models]
(http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
Also see our [Candidate Sampling Algorithms Reference]
(../../extras/candidate_sampling.pdf)
Note: In the case where `num_true` > 1, we assign to each target class
the target probability 1 / `num_true` so that the target probabilities
sum to 1 per-example.
Note: It would be useful to allow a variable number of target classes per
example. We hope to provide this functionality in a future release.
For now, if you have a variable number of target classes, you can pad them
out to a constant number by either repeating them or by padding
with an otherwise unused class.
Args:
weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
objects whose concatenation along dimension 0 has shape
[num_classes, dim]. The (possibly-partitioned) class embeddings.
biases: A `Tensor` of shape `[num_classes]`. The class biases.
inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
activations of the input network.
labels: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
num_sampled: An `int`. The number of classes to randomly sample per batch.
num_classes: An `int`. The number of possible classes.
num_true: An `int`. The number of target classes per training example.
sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
`sampled_expected_count`) returned by a `*_candidate_sampler` function.
(if None, we default to `log_uniform_candidate_sampler`)
remove_accidental_hits: A `bool`. Whether to remove "accidental hits"
where a sampled class equals one of the target classes. If set to
`True`, this is a "Sampled Logistic" loss instead of NCE, and we are
learning to generate log-odds instead of log probabilities. See
our [Candidate Sampling Algorithms Reference]
(../../extras/candidate_sampling.pdf).
Default is False.
partition_strategy: A string specifying the partitioning strategy, relevant
if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
name: A name for the operation (optional).
Returns:
A `batch_size` 1-D tensor of per-example NCE losses.
"""
logits, labels = _compute_sampled_logits(
weights,
biases,
inputs,
labels,
num_sampled,
num_classes,
num_true=num_true,
sampled_values=sampled_values,
subtract_log_q=True,
remove_accidental_hits=remove_accidental_hits,
partition_strategy=partition_strategy,
name=name)
sampled_losses = sigmoid_cross_entropy_with_logits(
logits, labels, name="sampled_losses")
# sampled_losses is batch_size x {true_loss, sampled_losses...}
# We sum out true and sampled losses.
return _sum_rows(sampled_losses) | [
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] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/ops/nn.py#L1122-L1198 | |
llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py | python | pooling_ndhwc_max | (
I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD, S.OH * S.SH + S.KH * S.DH,
S.OW * S.SW + S.KW * S.DW, S.C),
K=TensorDef(T2, S.KD, S.KH, S.KW, index_dims=[D.kd, D.kh, D.kw]),
O=TensorDef(U, S.N, S.OD, S.OH, S.OW, S.C, output=True),
strides=IndexAttrDef(S.SD, S.SH, S.SW),
dilations=IndexAttrDef(S.DD, S.DH, S.DW)) | Performs 3D max pooling.
Numeric casting is performed on the input operand, promoting it to the same
data type as the accumulator/output. | Performs 3D max pooling. | [
"Performs",
"3D",
"max",
"pooling",
"."
] | def pooling_ndhwc_max(
I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD, S.OH * S.SH + S.KH * S.DH,
S.OW * S.SW + S.KW * S.DW, S.C),
K=TensorDef(T2, S.KD, S.KH, S.KW, index_dims=[D.kd, D.kh, D.kw]),
O=TensorDef(U, S.N, S.OD, S.OH, S.OW, S.C, output=True),
strides=IndexAttrDef(S.SD, S.SH, S.SW),
dilations=IndexAttrDef(S.DD, S.DH, S.DW)):
"""Performs 3D max pooling.
Numeric casting is performed on the input operand, promoting it to the same
data type as the accumulator/output.
"""
implements(ConvolutionOpInterface)
domain(D.n, D.od, D.oh, D.ow, D.c, D.kd, D.kh, D.kw)
O[D.n, D.od, D.oh, D.ow, D.c] = ReduceFn.max[D.kd, D.kh, D.kw](
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U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/inspect.py | python | getmro | (cls) | return cls.__mro__ | Return tuple of base classes (including cls) in method resolution order. | Return tuple of base classes (including cls) in method resolution order. | [
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return cls.__mro__ | [
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facebook/mysql-5.6 | 65a650660ec7b4d627d1b738f397252ff4706207 | arcanist/lint/cpp_linter/cpplint.py | python | _FunctionState.Check | (self, error, filename, linenum) | Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check. | Report if too many lines in function body. | [
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"""Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check.
"""
if Match(r'T(EST|est)', self.current_function):
base_trigger = self._TEST_TRIGGER
else:
base_trigger = self._NORMAL_TRIGGER
trigger = base_trigger * 2**_VerboseLevel()
if self.lines_in_function > trigger:
error_level = int(math.log(self.lines_in_function / base_trigger, 2))
# 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
if error_level > 5:
error_level = 5
error(filename, linenum, 'readability/fn_size', error_level,
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' %s has %d non-comment lines'
' (error triggered by exceeding %d lines).' % (
self.current_function, self.lines_in_function, trigger)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/parser.py | python | PythonParser._raw_parse | (self) | Parse the source to find the interesting facts about its lines.
A handful of member fields are updated. | Parse the source to find the interesting facts about its lines. | [
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] | def _raw_parse(self):
"""Parse the source to find the interesting facts about its lines.
A handful of member fields are updated.
"""
# Find lines which match an exclusion pattern.
if self.exclude:
self.excluded = self.lines_matching(self.exclude)
# Tokenize, to find excluded suites, to find docstrings, and to find
# multi-line statements.
indent = 0
exclude_indent = 0
excluding = False
prev_toktype = token.INDENT
first_line = None
empty = True
tokgen = generate_tokens(self.text)
for toktype, ttext, (slineno, _), (elineno, _), ltext in tokgen:
if self.show_tokens: # pragma: not covered
print("%10s %5s %-20r %r" % (
tokenize.tok_name.get(toktype, toktype),
nice_pair((slineno, elineno)), ttext, ltext
))
if toktype == token.INDENT:
indent += 1
elif toktype == token.DEDENT:
indent -= 1
elif toktype == token.NAME and ttext == 'class':
# Class definitions look like branches in the byte code, so
# we need to exclude them. The simplest way is to note the
# lines with the 'class' keyword.
self.classdefs.add(slineno)
elif toktype == token.OP and ttext == ':':
if not excluding and elineno in self.excluded:
# Start excluding a suite. We trigger off of the colon
# token so that the #pragma comment will be recognized on
# the same line as the colon.
exclude_indent = indent
excluding = True
elif toktype == token.STRING and prev_toktype == token.INDENT:
# Strings that are first on an indented line are docstrings.
# (a trick from trace.py in the stdlib.) This works for
# 99.9999% of cases. For the rest (!) see:
# http://stackoverflow.com/questions/1769332/x/1769794#1769794
self.docstrings.update(range(slineno, elineno+1))
elif toktype == token.NEWLINE:
if first_line is not None and elineno != first_line:
# We're at the end of a line, and we've ended on a
# different line than the first line of the statement,
# so record a multi-line range.
for l in range(first_line, elineno+1):
self.multiline[l] = first_line
first_line = None
if ttext.strip() and toktype != tokenize.COMMENT:
# A non-whitespace token.
empty = False
if first_line is None:
# The token is not whitespace, and is the first in a
# statement.
first_line = slineno
# Check whether to end an excluded suite.
if excluding and indent <= exclude_indent:
excluding = False
if excluding:
self.excluded.add(elineno)
prev_toktype = toktype
# Find the starts of the executable statements.
if not empty:
self.statement_starts.update(self.byte_parser._find_statements()) | [
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xiaolonw/caffe-video_triplet | c39ea1ad6e937ccf7deba4510b7e555165abf05f | scripts/cpp_lint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum. | If input points to ) or } or ] or >, finds the position that opens it. | [
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] | def ReverseCloseExpression(clean_lines, linenum, pos):
"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
return (line, 0, -1)
if endchar == ')': startchar = '('
if endchar == ']': startchar = '['
if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
line, pos, 0, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
line, len(line) - 1, num_open, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find startchar before beginning of file, give up
return (line, 0, -1) | [
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borglab/gtsam | a5bee157efce6a0563704bce6a5d188c29817f39 | wrap/gtwrap/interface_parser/namespace.py | python | Namespace.top_level | (self) | Return the top level namespace. | Return the top level namespace. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PGCell.SetBitmap | (*args, **kwargs) | return _propgrid.PGCell_SetBitmap(*args, **kwargs) | SetBitmap(self, Bitmap bitmap) | SetBitmap(self, Bitmap bitmap) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/key_binding/bindings/vi.py | python | TextObject.cut | (self, buffer) | return new_document, clipboard_data | Turn text object into `ClipboardData` instance. | Turn text object into `ClipboardData` instance. | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/rospy/src/rospy/impl/tcpros_service.py | python | ServiceProxy.__call__ | (self, *args, **kwds) | return self.call(*args, **kwds) | Callable-style version of the service API. This accepts either a request message instance,
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/image_ops_impl.py | python | combined_non_max_suppression | (boxes,
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'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
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iou_threshold = ops.convert_to_tensor(
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score_threshold = ops.convert_to_tensor(
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/code.py | python | InteractiveInterpreter.__init__ | (self, locals=None) | Constructor.
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/distribution_util.py | python | get_broadcast_shape | (*tensors) | return d_shape | Get broadcast shape as a Python list of integers (preferred) or `Tensor`.
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NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | samples/python/yolov3_onnx/yolov3_to_onnx.py | python | GraphBuilderONNX.build_onnx_graph | (
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Keyword arguments:
layer_configs -- an OrderedDict object with all parsed layers' configurations
weights_file_path -- location of the weights file
verbose -- toggles if the graph is printed after creation (default: True) | Iterate over all layer configs (parsed from the DarkNet representation
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] | def build_onnx_graph(
self,
layer_configs,
weights_file_path,
verbose=True):
"""Iterate over all layer configs (parsed from the DarkNet representation
of YOLOv3-608), create an ONNX graph, populate it with weights from the weights
file and return the graph definition.
Keyword arguments:
layer_configs -- an OrderedDict object with all parsed layers' configurations
weights_file_path -- location of the weights file
verbose -- toggles if the graph is printed after creation (default: True)
"""
for layer_name in layer_configs.keys():
layer_dict = layer_configs[layer_name]
major_node_specs = self._make_onnx_node(layer_name, layer_dict)
if major_node_specs.name is not None:
self.major_node_specs.append(major_node_specs)
outputs = list()
for tensor_name in self.output_tensors.keys():
output_dims = [self.batch_size, ] + \
self.output_tensors[tensor_name]
output_tensor = helper.make_tensor_value_info(
tensor_name, TensorProto.FLOAT, output_dims)
outputs.append(output_tensor)
inputs = [self.input_tensor]
weight_loader = WeightLoader(weights_file_path)
initializer = list()
# If a layer has parameters, add them to the initializer and input lists.
for layer_name in self.param_dict.keys():
_, layer_type = layer_name.split('_', 1)
params = self.param_dict[layer_name]
if layer_type == 'convolutional':
initializer_layer, inputs_layer = weight_loader.load_conv_weights(
params)
initializer.extend(initializer_layer)
inputs.extend(inputs_layer)
elif layer_type == "upsample":
initializer_layer, inputs_layer = weight_loader.load_resize_scales(
params)
initializer.extend(initializer_layer)
inputs.extend(inputs_layer)
del weight_loader
self.graph_def = helper.make_graph(
nodes=self._nodes,
name='YOLOv3-608',
inputs=inputs,
outputs=outputs,
initializer=initializer
)
if verbose:
print(helper.printable_graph(self.graph_def))
model_def = helper.make_model(self.graph_def,
producer_name='NVIDIA TensorRT sample')
return model_def | [
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] | https://github.com/NVIDIA/TensorRT/blob/42805f078052daad1a98bc5965974fcffaad0960/samples/python/yolov3_onnx/yolov3_to_onnx.py#L385-L440 | |
ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | dom/bindings/Codegen.py | python | CGIncludeGuard.__init__ | (self, prefix, child) | |prefix| is the filename without the extension. | |prefix| is the filename without the extension. | [
"|prefix|",
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"the",
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"without",
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"extension",
"."
] | def __init__(self, prefix, child):
"""|prefix| is the filename without the extension."""
define = 'mozilla_dom_%s_h' % prefix
CGWrapper.__init__(self, child,
declarePre='#ifndef %s\n#define %s\n\n' % (define, define),
declarePost='\n#endif // %s\n' % define) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/paginate.py | python | TokenDecoder._decode | (self, token, encoded_keys) | return token | Find each encoded value and decode it. | Find each encoded value and decode it. | [
"Find",
"each",
"encoded",
"value",
"and",
"decode",
"it",
"."
] | def _decode(self, token, encoded_keys):
"""Find each encoded value and decode it."""
for key in encoded_keys:
encoded = self._path_get(token, key)
decoded = base64.b64decode(encoded.encode('utf-8'))
self._path_set(token, key, decoded)
return token | [
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