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dmlc/xgboost | python-package/xgboost/core.py | _maybe_dt_array | def _maybe_dt_array(array):
""" Extract numpy array from single column data table """
if not isinstance(array, DataTable) or array is None:
return array
if array.shape[1] > 1:
raise ValueError('DataTable for label or weight cannot have multiple columns')
# below requires new dt version... | python | def _maybe_dt_array(array):
""" Extract numpy array from single column data table """
if not isinstance(array, DataTable) or array is None:
return array
if array.shape[1] > 1:
raise ValueError('DataTable for label or weight cannot have multiple columns')
# below requires new dt version... | [
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix._init_from_csr | def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
"""
if len(csr.indices) != len(csr.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFro... | python | def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
"""
if len(csr.indices) != len(csr.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
handle = ctypes.c_void_p()
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix._init_from_csc | def _init_from_csc(self, csc):
"""
Initialize data from a CSC matrix.
"""
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"""
Initialize data from a CSC matrix.
"""
if len(csc.indices) != len(csc.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
handle = ctypes.c_void_p()
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix._init_from_npy2d | def _init_from_npy2d(self, mat, missing, nthread):
"""
Initialize data from a 2-D numpy matrix.
If ``mat`` does not have ``order='C'`` (aka row-major) or is not contiguous,
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If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will be... | python | def _init_from_npy2d(self, mat, missing, nthread):
"""
Initialize data from a 2-D numpy matrix.
If ``mat`` does not have ``order='C'`` (aka row-major) or is not contiguous,
a temporary copy will be made.
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix._init_from_dt | def _init_from_dt(self, data, nthread):
"""
Initialize data from a datatable Frame.
"""
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
# datatable>0.8.0
for icol in range(data.ncols):
... | python | def _init_from_dt(self, data, nthread):
"""
Initialize data from a datatable Frame.
"""
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
# datatable>0.8.0
for icol in range(data.ncols):
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.set_float_info | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
if getattr(data, 'base', None) is not... | python | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.set_float_info_npy2d | def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix
for numpy 2d array input
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
... | python | def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix
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Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.set_uint_info | def set_uint_info(self, field, data):
"""Set uint type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
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"""Set uint type property into the DMatrix.
Parameters
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field: str
The field name of the information
data: numpy array
The array of data to be set
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.save_binary | def save_binary(self, fname, silent=True):
"""Save DMatrix to an XGBoost buffer. Saved binary can be later loaded
by providing the path to :py:func:`xgboost.DMatrix` as input.
Parameters
----------
fname : string
Name of the output buffer file.
silent : bool... | python | def save_binary(self, fname, silent=True):
"""Save DMatrix to an XGBoost buffer. Saved binary can be later loaded
by providing the path to :py:func:`xgboost.DMatrix` as input.
Parameters
----------
fname : string
Name of the output buffer file.
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.set_group | def set_group(self, group):
"""Set group size of DMatrix (used for ranking).
Parameters
----------
group : array like
Group size of each group
"""
_check_call(_LIB.XGDMatrixSetGroup(self.handle,
c_array(ctypes.c_uint... | python | def set_group(self, group):
"""Set group size of DMatrix (used for ranking).
Parameters
----------
group : array like
Group size of each group
"""
_check_call(_LIB.XGDMatrixSetGroup(self.handle,
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.feature_names | def feature_names(self):
"""Get feature names (column labels).
Returns
-------
feature_names : list or None
"""
if self._feature_names is None:
self._feature_names = ['f{0}'.format(i) for i in range(self.num_col())]
return self._feature_names | python | def feature_names(self):
"""Get feature names (column labels).
Returns
-------
feature_names : list or None
"""
if self._feature_names is None:
self._feature_names = ['f{0}'.format(i) for i in range(self.num_col())]
return self._feature_names | [
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.feature_names | def feature_names(self, feature_names):
"""Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if feature_names is not None:
# validate feature name
... | python | def feature_names(self, feature_names):
"""Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
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if feature_names is not None:
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dmlc/xgboost | python-package/xgboost/core.py | DMatrix.feature_types | def feature_types(self, feature_types):
"""Set feature types (column types).
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Parameters
----------
feature_types : list or None
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dmlc/xgboost | python-package/xgboost/core.py | Booster.load_rabit_checkpoint | def load_rabit_checkpoint(self):
"""Initialize the model by load from rabit checkpoint.
Returns
-------
version: integer
The version number of the model.
"""
version = ctypes.c_int()
_check_call(_LIB.XGBoosterLoadRabitCheckpoint(
self.hand... | python | def load_rabit_checkpoint(self):
"""Initialize the model by load from rabit checkpoint.
Returns
-------
version: integer
The version number of the model.
"""
version = ctypes.c_int()
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dmlc/xgboost | python-package/xgboost/core.py | Booster.attr | def attr(self, key):
"""Get attribute string from the Booster.
Parameters
----------
key : str
The key to get attribute from.
Returns
-------
value : str
The attribute value of the key, returns None if attribute do not exist.
"""
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"""Get attribute string from the Booster.
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key : str
The key to get attribute from.
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value : str
The attribute value of the key, returns None if attribute do not exist.
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dmlc/xgboost | python-package/xgboost/core.py | Booster.attributes | def attributes(self):
"""Get attributes stored in the Booster as a dictionary.
Returns
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result : dictionary of attribute_name: attribute_value pairs of strings.
Returns an empty dict if there's no attributes.
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Returns
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Returns an empty dict if there's no attributes.
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dmlc/xgboost | python-package/xgboost/core.py | Booster.set_attr | def set_attr(self, **kwargs):
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The attributes to set. Setting a value to None deletes an attribute.
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list of key,value pairs, dict of key to value or simply str key
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dmlc/xgboost | python-package/xgboost/core.py | Booster.eval | def eval(self, data, name='eval', iteration=0):
"""Evaluate the model on mat.
Parameters
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data : DMatrix
The dmatrix storing the input.
name : str, optional
The name of the dataset.
iteration : int, optional
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dmlc/xgboost | python-package/xgboost/core.py | Booster.predict | def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False,
pred_contribs=False, approx_contribs=False, pred_interactions=False,
validate_features=True):
"""
Predict with data.
.. note:: This function is not thread safe.
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pred_contribs=False, approx_contribs=False, pred_interactions=False,
validate_features=True):
"""
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dmlc/xgboost | python-package/xgboost/core.py | Booster.save_model | def save_model(self, fname):
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Save the model to a file.
The model is saved in an XGBoost internal binary format which is
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To pre... | python | def save_model(self, fname):
"""
Save the model to a file.
The model is saved in an XGBoost internal binary format which is
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dmlc/xgboost | python-package/xgboost/core.py | Booster.load_model | def load_model(self, fname):
"""
Load the model from a file.
The model is loaded from an XGBoost internal binary format which is
universal among the various XGBoost interfaces. Auxiliary attributes of
the Python Booster object (such as feature_names) will not be loaded.
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Load the model from a file.
The model is loaded from an XGBoost internal binary format which is
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dmlc/xgboost | python-package/xgboost/core.py | Booster.dump_model | def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
"""
Dump model into a text or JSON file.
Parameters
----------
fout : string
Output file name.
fmap : string, optional
Name of the file containing feature map names.
... | python | def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
"""
Dump model into a text or JSON file.
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----------
fout : string
Output file name.
fmap : string, optional
Name of the file containing feature map names.
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dmlc/xgboost | python-package/xgboost/core.py | Booster.get_dump | def get_dump(self, fmap='', with_stats=False, dump_format="text"):
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Returns the model dump as a list of strings.
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dmlc/xgboost | python-package/xgboost/core.py | Booster.get_score | def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in... | python | def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
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dmlc/xgboost | python-package/xgboost/core.py | Booster.trees_to_dataframe | def trees_to_dataframe(self, fmap=''):
"""Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
learner (`booster in {gbtree, dart}`). It is not defined for other base learner
types, such as li... | python | def trees_to_dataframe(self, fmap=''):
"""Parse a boosted tree model text dump into a pandas DataFrame structure.
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dmlc/xgboost | python-package/xgboost/core.py | Booster._validate_features | def _validate_features(self, data):
"""
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix
"""
if self.feature_names is None:
self.feature_names = data.feature_names
self.feature_types = data.feature_t... | python | def _validate_features(self, data):
"""
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix
"""
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self.feature_names = data.feature_names
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dmlc/xgboost | python-package/xgboost/core.py | Booster.get_split_value_histogram | def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):
"""Get split value histogram of a feature
Parameters
----------
feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
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feature: str
The name of the feature.
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The name of feature map file.
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dmlc/xgboost | python-package/xgboost/plotting.py | plot_importance | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
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"""Plot im... | python | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
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dmlc/xgboost | python-package/xgboost/plotting.py | _parse_node | def _parse_node(graph, text, condition_node_params, leaf_node_params):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **condition_node_params)
return node
match = _LEAFPAT.match(text)
if ... | python | def _parse_node(graph, text, condition_node_params, leaf_node_params):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **condition_node_params)
return node
match = _LEAFPAT.match(text)
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dmlc/xgboost | python-package/xgboost/plotting.py | _parse_edge | def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge"""
try:
match = _EDGEPAT.match(text)
if match is not None:
yes, no, missing = match.groups()
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"""parse dumped edge"""
try:
match = _EDGEPAT.match(text)
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yes, no, missing = match.groups()
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dmlc/xgboost | python-package/xgboost/plotting.py | to_graphviz | def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
yes_color='#0000FF', no_color='#FF0000',
condition_node_params=None, leaf_node_params=None, **kwargs):
"""Convert specified tree to graphviz instance. IPython can automatically plot the
returned graphiz instance. Otherw... | python | def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
yes_color='#0000FF', no_color='#FF0000',
condition_node_params=None, leaf_node_params=None, **kwargs):
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tzutalin/labelImg | libs/utils.py | newAction | def newAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts, etc."""
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
if shortcut is not None:
if isi... | python | def newAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts, etc."""
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
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tzutalin/labelImg | libs/utils.py | natural_sort | def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_... | python | def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
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tzutalin/labelImg | libs/canvas.py | Canvas.mouseMoveEvent | def mouseMoveEvent(self, ev):
"""Update line with last point and current coordinates."""
pos = self.transformPos(ev.pos())
# Update coordinates in status bar if image is opened
window = self.parent().window()
if window.filePath is not None:
self.parent().window().lab... | python | def mouseMoveEvent(self, ev):
"""Update line with last point and current coordinates."""
pos = self.transformPos(ev.pos())
# Update coordinates in status bar if image is opened
window = self.parent().window()
if window.filePath is not None:
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tzutalin/labelImg | libs/canvas.py | Canvas.selectShapePoint | def selectShapePoint(self, point):
"""Select the first shape created which contains this point."""
self.deSelectShape()
if self.selectedVertex(): # A vertex is marked for selection.
index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
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"""Select the first shape created which contains this point."""
self.deSelectShape()
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index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
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tzutalin/labelImg | libs/canvas.py | Canvas.snapPointToCanvas | def snapPointToCanvas(self, x, y):
"""
Moves a point x,y to within the boundaries of the canvas.
:return: (x,y,snapped) where snapped is True if x or y were changed, False if not.
"""
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"""
Moves a point x,y to within the boundaries of the canvas.
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"""
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tzutalin/labelImg | libs/canvas.py | Canvas.intersectingEdges | def intersectingEdges(self, x1y1, x2y2, points):
"""For each edge formed by `points', yield the intersection
with the line segment `(x1,y1) - (x2,y2)`, if it exists.
Also return the distance of `(x2,y2)' to the middle of the
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"""For each edge formed by `points', yield the intersection
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tzutalin/labelImg | labelImg.py | get_main_app | def get_main_app(argv=[]):
"""
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread
"""
app = QApplication(argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("app"))
# Tzutalin 201705+: Accept extra ... | python | def get_main_app(argv=[]):
"""
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread
"""
app = QApplication(argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("app"))
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tzutalin/labelImg | labelImg.py | MainWindow.toggleActions | def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
for z in self.actions.zoomActions:
z.setEnabled(value)
for action in self.actions.onLoadActive:
action.setEnabled(value) | python | def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
for z in self.actions.zoomActions:
z.setEnabled(value)
for action in self.actions.onLoadActive:
action.setEnabled(value) | [
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tzutalin/labelImg | labelImg.py | MainWindow.toggleDrawingSensitive | def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled."""
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
print('Cancel creation.')
self.canva... | python | def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled."""
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
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tzutalin/labelImg | labelImg.py | MainWindow.btnstate | def btnstate(self, item= None):
""" Function to handle difficult examples
Update on each object """
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
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""" Function to handle difficult examples
Update on each object """
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
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tzutalin/labelImg | labelImg.py | MainWindow.newShape | def newShape(self):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
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... | python | def newShape(self):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
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tzutalin/labelImg | labelImg.py | MainWindow.loadFile | def loadFile(self, filePath=None):
"""Load the specified file, or the last opened file if None."""
self.resetState()
self.canvas.setEnabled(False)
if filePath is None:
filePath = self.settings.get(SETTING_FILENAME)
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"""Load the specified file, or the last opened file if None."""
self.resetState()
self.canvas.setEnabled(False)
if filePath is None:
filePath = self.settings.get(SETTING_FILENAME)
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tzutalin/labelImg | labelImg.py | MainWindow.scaleFitWindow | def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget."""
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
a1 = w1 / h1
# Calculate a new scale value based... | python | def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget."""
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
a1 = w1 / h1
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tzutalin/labelImg | libs/ustr.py | ustr | def ustr(x):
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if sys.version_info < (3, 0, 0):
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if type(x) == str:
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#https://blog.csdn.net/friendan/article/details/51088476
#https://b... | python | def ustr(x):
'''py2/py3 unicode helper'''
if sys.version_info < (3, 0, 0):
from PyQt4.QtCore import QString
if type(x) == str:
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Return a pretty-printed XML string for the Element.
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tzutalin/labelImg | libs/pascal_voc_io.py | PascalVocWriter.genXML | def genXML(self):
"""
Return XML root
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if self.filename is None or \
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return None
top = Element('annotation')
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"""
Return XML root
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# Check conditions
if self.filename is None or \
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ccxt/ccxt | python/ccxt/async_support/base/exchange.py | Exchange.fetch | async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
... | python | async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.fetch2 | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method,... | python | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method,... | [
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.request | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""Exchange.request is the entry point for all generated methods"""
return self.fetch2(path, api, method, params, headers, body) | python | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""Exchange.request is the entry point for all generated methods"""
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.find_broadly_matched_key | def find_broadly_matched_key(self, broad, string):
"""A helper method for matching error strings exactly vs broadly"""
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for i in range(0, len(keys)):
key = keys[i]
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return None | python | def find_broadly_matched_key(self, broad, string):
"""A helper method for matching error strings exactly vs broadly"""
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if string.find(key) >= 0:
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.fetch | def fetch(self, url, method='GET', headers=None, body=None):
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url = self.proxy + url
if self.verbose:
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"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.safe_either | def safe_either(method, dictionary, key1, key2, default_value=None):
"""A helper-wrapper for the safe_value_2() family."""
value = method(dictionary, key1)
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.truncate | def truncate(num, precision=0):
"""Deprecated, use decimal_to_precision instead"""
if precision > 0:
decimal_precision = math.pow(10, precision)
return math.trunc(num * decimal_precision) / decimal_precision
return int(Exchange.truncate_to_string(num, precision)) | python | def truncate(num, precision=0):
"""Deprecated, use decimal_to_precision instead"""
if precision > 0:
decimal_precision = math.pow(10, precision)
return math.trunc(num * decimal_precision) / decimal_precision
return int(Exchange.truncate_to_string(num, precision)) | [
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.truncate_to_string | def truncate_to_string(num, precision=0):
"""Deprecated, todo: remove references from subclasses"""
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decimal_digits = parts[1][:precision].rstrip('0')
decimal_digits = decimal_digits if ... | python | def truncate_to_string(num, precision=0):
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ccxt/ccxt | python/ccxt/base/exchange.py | Exchange.check_address | def check_address(self, address):
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if address is None:
self.raise_error(InvalidAddress, details='address is None')
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"""Checks an address is not the same character repeated or an empty sequence"""
if address is None:
self.raise_error(InvalidAddress, details='address is None')
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mozilla/DeepSpeech | bin/benchmark_plotter.py | reduce_filename | def reduce_filename(f):
r'''
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r'''
local helper to just keep digits
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local helper to just keep digits
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mozilla/DeepSpeech | util/stm.py | parse_stm_file | def parse_stm_file(stm_file):
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.
"""
stm_segments = []
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r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.
"""
stm_segments = []
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mozilla/DeepSpeech | examples/vad_transcriber/wavSplit.py | read_wave | def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
num_channels = wf.getnchannels()
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assert sample_width ... | python | def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
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mozilla/DeepSpeech | examples/vad_transcriber/wavSplit.py | frame_generator | def frame_generator(frame_duration_ms, audio, sample_rate):
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Takes the desired frame duration in milliseconds, the PCM data, and
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Yields Frames of the requested duration.
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mozilla/DeepSpeech | examples/vad_transcriber/audioTranscript_gui.py | Worker.run | def run(self):
'''
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'''
# Retrieve args/kwargs here; and fire up the processing using them
try:
transcript = self.fn(*self.args, **self.kwargs)
except:
traceback.print_exc()
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'''
Initialise the runner function with the passed args, kwargs
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# Retrieve args/kwargs here; and fire up the processing using them
try:
transcript = self.fn(*self.args, **self.kwargs)
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traceback.print_exc()
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mozilla/DeepSpeech | bin/benchmark_nc.py | exec_command | def exec_command(command, cwd=None):
r'''
Helper to exec locally (subprocess) or remotely (paramiko)
'''
rc = None
stdout = stderr = None
if ssh_conn is None:
ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')}
p = subprocess.Popen(command, stdo... | python | def exec_command(command, cwd=None):
r'''
Helper to exec locally (subprocess) or remotely (paramiko)
'''
rc = None
stdout = stderr = None
if ssh_conn is None:
ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')}
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mozilla/DeepSpeech | bin/benchmark_nc.py | get_arch_string | def get_arch_string():
r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
rc, stdout, stderr = exec_command('uname -sm')
if rc > 0:
raise AssertionError('Error checking OS')
stdout = stdout.lower().strip()
if not 'linux' in stdout:
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r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
rc, stdout, stderr = exec_command('uname -sm')
if rc > 0:
raise AssertionError('Error checking OS')
stdout = stdout.lower().strip()
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mozilla/DeepSpeech | bin/benchmark_nc.py | extract_native_client_tarball | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
assert_valid_dir(dir)
target_tarball = os.path.join(dir, 'native_client.tar.xz')
if os.path.isfile(target_tarball) and os.stat(target_tarball).st_size == 0:
retu... | python | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
assert_valid_dir(dir)
target_tarball = os.path.join(dir, 'native_client.tar.xz')
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mozilla/DeepSpeech | bin/benchmark_nc.py | is_zip_file | def is_zip_file(models):
r'''
Ensure that a path is a zip file by:
- checking length is 1
- checking extension is '.zip'
'''
ext = os.path.splitext(models[0])[1]
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r'''
Ensure that a path is a zip file by:
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- checking extension is '.zip'
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ext = os.path.splitext(models[0])[1]
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mozilla/DeepSpeech | bin/benchmark_nc.py | maybe_inspect_zip | def maybe_inspect_zip(models):
r'''
Detect if models is a list of protocolbuffer files or a ZIP file.
If the latter, then unzip it and return the list of protocolbuffer files
that were inside.
'''
if not(is_zip_file(models)):
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... | python | def maybe_inspect_zip(models):
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Detect if models is a list of protocolbuffer files or a ZIP file.
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mozilla/DeepSpeech | bin/benchmark_nc.py | all_files | def all_files(models=[]):
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... | python | def all_files(models=[]):
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mozilla/DeepSpeech | bin/benchmark_nc.py | setup_tempdir | def setup_tempdir(dir, models, wav, alphabet, lm_binary, trie, binaries):
r'''
Copy models, libs and binary to a directory (new one if dir is None)
'''
if dir is None:
dir = tempfile.mkdtemp(suffix='dsbench')
sorted_models = all_files(models=models)
if binaries is None:
maybe_do... | python | def setup_tempdir(dir, models, wav, alphabet, lm_binary, trie, binaries):
r'''
Copy models, libs and binary to a directory (new one if dir is None)
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if dir is None:
dir = tempfile.mkdtemp(suffix='dsbench')
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mozilla/DeepSpeech | bin/benchmark_nc.py | teardown_tempdir | def teardown_tempdir(dir):
r'''
Cleanup temporary directory.
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mozilla/DeepSpeech | bin/benchmark_nc.py | get_sshconfig | def get_sshconfig():
r'''
Read user's SSH configuration file
'''
with open(os.path.expanduser('~/.ssh/config')) as f:
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cfg.parse(f)
ret_dict = {}
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# Avoid buggy behavior with strange h... | python | def get_sshconfig():
r'''
Read user's SSH configuration file
'''
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cfg = paramiko.SSHConfig()
cfg.parse(f)
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mozilla/DeepSpeech | bin/benchmark_nc.py | establish_ssh | def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True):
r'''
Establish a SSH connection to a remote host. It should be able to use
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mozilla/DeepSpeech | bin/benchmark_nc.py | run_benchmarks | def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
r'''
Core of the running of the benchmarks. We will run on all of models, against
the WAV file provided as wav, and the provided alphabet.
'''
assert_valid_dir(dir)
inference_times = [ ]
for model in mode... | python | def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
r'''
Core of the running of the benchmarks. We will run on all of models, against
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assert_valid_dir(dir)
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mozilla/DeepSpeech | bin/benchmark_nc.py | produce_csv | def produce_csv(input, output):
r'''
Take an input dictionnary and write it to the object-file output.
'''
output.write('"model","mean","std"\n')
for model_data in input:
output.write('"%s",%f,%f\n' % (model_data['name'], model_data['mean'], model_data['stddev']))
output.flush()
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r'''
Take an input dictionnary and write it to the object-file output.
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mozilla/DeepSpeech | util/feeding.py | to_sparse_tuple | def to_sparse_tuple(sequence):
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Returns a tuple with (indices, values, shape)
"""
indices = np.asarray(list(zip([0]*len(sequence), range(len(sequence)))), dtype=np.int64)
shape = np.asarray([1, len(sequence)], dtype=np.int64)
return indice... | python | def to_sparse_tuple(sequence):
r"""Creates a sparse representention of ``sequence``.
Returns a tuple with (indices, values, shape)
"""
indices = np.asarray(list(zip([0]*len(sequence), range(len(sequence)))), dtype=np.int64)
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mozilla/DeepSpeech | bin/import_voxforge.py | _parallel_extracter | def _parallel_extracter(data_dir, number_of_test, number_of_dev, total, counter):
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This works by currying the above given arguments into a closure
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mozilla/DeepSpeech | bin/import_voxforge.py | AtomicCounter.increment | def increment(self, amount=1):
"""Increments the counter by the given amount
:param amount: the amount to increment by (default 1)
:return: the incremented value of the counter
"""
self.__lock.acquire()
self.__count += amount
v = self.value()
self.__... | python | def increment(self, amount=1):
"""Increments the counter by the given amount
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self.__lock.acquire()
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mozilla/DeepSpeech | util/evaluate_tools.py | calculate_report | def calculate_report(labels, decodings, distances, losses):
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'... | python | def calculate_report(labels, decodings, distances, losses):
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This routine will calculate a WER report.
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mozilla/DeepSpeech | evaluate.py | sparse_tensor_value_to_texts | def sparse_tensor_value_to_texts(value, alphabet):
r"""
Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
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"""
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r"""
Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
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mozilla/DeepSpeech | bin/import_gram_vaani.py | parse_args | def parse_args(args):
"""Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
:obj:`argparse.Namespace`: command line parameters namespace
"""
parser = argparse.ArgumentParser(
description="Imports GramVaani data for Deep Speech"
... | python | def parse_args(args):
"""Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
:obj:`argparse.Namespace`: command line parameters namespace
"""
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mozilla/DeepSpeech | bin/import_gram_vaani.py | setup_logging | def setup_logging(level):
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Args:
level (int): minimum log level for emitting messages
"""
format = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
logging.basicConfig(
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"""Setup basic logging
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level (int): minimum log level for emitting messages
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mozilla/DeepSpeech | bin/import_gram_vaani.py | main | def main(args):
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args ([str]): command line parameter list
"""
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args ([str]): command line parameter list
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mozilla/DeepSpeech | bin/import_gram_vaani.py | GramVaaniDownloader.download | def download(self):
"""Downloads the data associated with this instance
Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded
"""
mp3_directory = self._pre_download()
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Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded
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mp3_directory = self._pre_download()
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mozilla/DeepSpeech | bin/import_gram_vaani.py | GramVaaniConverter.convert | def convert(self):
"""Converts the mp3's associated with this instance to wav's
Return:
wav_directory (os.path): The directory into which the associated wav's were downloaded
"""
wav_directory = self._pre_convert()
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"""Converts the mp3's associated with this instance to wav's
Return:
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mozilla/DeepSpeech | util/text.py | text_to_char_array | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string.
"""
return np.asarray([alphabet.label_from_string(c) for c in original]) | python | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string.
"""
return np.asarray([alphabet.label_from_string(c) for c in original]) | [
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mozilla/DeepSpeech | util/text.py | wer_cer_batch | def wer_cer_batch(originals, results):
r"""
The WER is defined as the editing/Levenshtein distance on word level
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In case of the original having more words (N) than the result and both
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r"""
The WER is defined as the editing/Levenshtein distance on word level
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mozilla/DeepSpeech | DeepSpeech.py | variable_on_cpu | def variable_on_cpu(name, shape, initializer):
r"""
Next we concern ourselves with graph creation.
However, before we do so we must introduce a utility function ``variable_on_cpu()``
used to create a variable in CPU memory.
"""
# Use the /cpu:0 device for scoped operations
with tf.device(Con... | python | def variable_on_cpu(name, shape, initializer):
r"""
Next we concern ourselves with graph creation.
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"""
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mozilla/DeepSpeech | DeepSpeech.py | calculate_mean_edit_distance_and_loss | def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
the decoded result and the batch's original Y.
'''
# Obtain th... | python | def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
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mozilla/DeepSpeech | DeepSpeech.py | get_tower_results | def get_tower_results(iterator, optimizer, dropout_rates):
r'''
With this preliminary step out of the way, we can for each GPU introduce a
tower for which's batch we calculate and return the optimization gradients
and the average loss across towers.
'''
# To calculate the mean of the losses
... | python | def get_tower_results(iterator, optimizer, dropout_rates):
r'''
With this preliminary step out of the way, we can for each GPU introduce a
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mozilla/DeepSpeech | DeepSpeech.py | average_gradients | def average_gradients(tower_gradients):
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
GPUs to be finished with their mini-batch before it can run to completion.
'''
# List ... | python | def average_gradients(tower_gradients):
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
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mozilla/DeepSpeech | DeepSpeech.py | log_variable | def log_variable(variable, gradient=None):
r'''
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient.
'''
name = ... | python | def log_variable(variable, gradient=None):
r'''
We introduce a function for logging a tensor variable's current state.
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mozilla/DeepSpeech | DeepSpeech.py | export | def export():
r'''
Restores the trained variables into a simpler graph that will be exported for serving.
'''
log_info('Exporting the model...')
from tensorflow.python.framework.ops import Tensor, Operation
inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=... | python | def export():
r'''
Restores the trained variables into a simpler graph that will be exported for serving.
'''
log_info('Exporting the model...')
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inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=... | [
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mozilla/DeepSpeech | native_client/ctcdecode/__init__.py | ctc_beam_search_decoder | def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2... | python | def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the CTC Beam Search Decoder.
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mozilla/DeepSpeech | native_client/ctcdecode/__init__.py | ctc_beam_search_decoder_batch | def ctc_beam_search_decoder_batch(probs_seq,
seq_lengths,
alphabet,
beam_size,
num_processes,
cutoff_prob=1.0,
cutof... | python | def ctc_beam_search_decoder_batch(probs_seq,
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mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | Audio.resample | def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio r... | python | def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
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data (binary): Input audio stream
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mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | Audio.read_resampled | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
return self.resample(data=self.buffer_queue.get(),
input_rate=self.input_rate) | python | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
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input_rate=self.input_rate) | [
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mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | VADAudio.frame_generator | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled() | python | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled() | [
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