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limix/numpy-sugar | numpy_sugar/linalg/_kron.py | kron_dot | def kron_dot(A, B, C, out=None):
r""" Kronecker product followed by dot product.
Let :math:`\mathrm A`, :math:`\mathrm B`, and :math:`\mathrm C` be matrices of
dimensions :math:`p\times p`, :math:`n\times d`, and :math:`d\times p`.
It computes
.. math::
\text{unvec}((\mathrm A\otimes\mat... | python | def kron_dot(A, B, C, out=None):
r""" Kronecker product followed by dot product.
Let :math:`\mathrm A`, :math:`\mathrm B`, and :math:`\mathrm C` be matrices of
dimensions :math:`p\times p`, :math:`n\times d`, and :math:`d\times p`.
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.. math::
\text{unvec}((\mathrm A\otimes\mat... | [
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limix/numpy-sugar | numpy_sugar/linalg/property.py | check_semidefinite_positiveness | def check_semidefinite_positiveness(A):
"""Check if ``A`` is a semi-definite positive matrix.
Args:
A (array_like): Matrix.
Returns:
bool: ``True`` if ``A`` is definite positive; ``False`` otherwise.
"""
B = empty_like(A)
B[:] = A
B[diag_indices_from(B)] += sqrt(finfo(float... | python | def check_semidefinite_positiveness(A):
"""Check if ``A`` is a semi-definite positive matrix.
Args:
A (array_like): Matrix.
Returns:
bool: ``True`` if ``A`` is definite positive; ``False`` otherwise.
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limix/numpy-sugar | numpy_sugar/linalg/property.py | check_symmetry | def check_symmetry(A):
"""Check if ``A`` is a symmetric matrix.
Args:
A (array_like): Matrix.
Returns:
bool: ``True`` if ``A`` is symmetric; ``False`` otherwise.
"""
A = asanyarray(A)
if A.ndim != 2:
raise ValueError("Checks symmetry only for bi-dimensional arrays.")
... | python | def check_symmetry(A):
"""Check if ``A`` is a symmetric matrix.
Args:
A (array_like): Matrix.
Returns:
bool: ``True`` if ``A`` is symmetric; ``False`` otherwise.
"""
A = asanyarray(A)
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limix/numpy-sugar | numpy_sugar/linalg/cho.py | cho_solve | def cho_solve(L, b):
r"""Solve for Cholesky decomposition.
Solve the linear equations :math:`\mathrm A \mathbf x = \mathbf b`,
given the Cholesky factorization of :math:`\mathrm A`.
Args:
L (array_like): Lower triangular matrix.
b (array_like): Right-hand side.
Returns:
:c... | python | def cho_solve(L, b):
r"""Solve for Cholesky decomposition.
Solve the linear equations :math:`\mathrm A \mathbf x = \mathbf b`,
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Args:
L (array_like): Lower triangular matrix.
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opentargets/validator | opentargets_validator/helpers.py | file_or_resource | def file_or_resource(fname=None):
'''get filename and check if in getcwd then get from
the package resources folder
'''
if fname is not None:
filename = os.path.expanduser(fname)
resource_package = opentargets_validator.__name__
resource_path = os.path.sep.join(('resources',... | python | def file_or_resource(fname=None):
'''get filename and check if in getcwd then get from
the package resources folder
'''
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filename = os.path.expanduser(fname)
resource_package = opentargets_validator.__name__
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limix/numpy-sugar | numpy_sugar/linalg/tri.py | stl | def stl(A, b):
r"""Shortcut to ``solve_triangular(A, b, lower=True, check_finite=False)``.
Solve linear systems :math:`\mathrm A \mathbf x = \mathbf b` when
:math:`\mathrm A` is a lower-triangular matrix.
Args:
A (array_like): A lower-triangular matrix.
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r"""Shortcut to ``solve_triangular(A, b, lower=True, check_finite=False)``.
Solve linear systems :math:`\mathrm A \mathbf x = \mathbf b` when
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DiamondLightSource/python-procrunner | procrunner/__init__.py | _windows_resolve | def _windows_resolve(command):
"""
Try and find the full path and file extension of the executable to run.
This is so that e.g. calls to 'somescript' will point at 'somescript.cmd'
without the need to set shell=True in the subprocess.
If the executable contains periods it is a special case. Here the... | python | def _windows_resolve(command):
"""
Try and find the full path and file extension of the executable to run.
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DiamondLightSource/python-procrunner | procrunner/__init__.py | run | def run(
command,
timeout=None,
debug=False,
stdin=None,
print_stdout=True,
print_stderr=True,
callback_stdout=None,
callback_stderr=None,
environment=None,
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Run an external process.
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command,
timeout=None,
debug=False,
stdin=None,
print_stdout=True,
print_stderr=True,
callback_stdout=None,
callback_stderr=None,
environment=None,
environment_override=None,
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working_directory=None,
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DiamondLightSource/python-procrunner | procrunner/__init__.py | run_process_dummy | def run_process_dummy(command, **kwargs):
"""
A stand-in function that returns a valid result dictionary indicating a
successful execution. The external process is not run.
"""
warnings.warn(
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DiamondLightSource/python-procrunner | procrunner/__init__.py | run_process | def run_process(*args, **kwargs):
"""API used up to version 0.2.0."""
warnings.warn(
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warnings.warn(
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DiamondLightSource/python-procrunner | procrunner/__init__.py | _LineAggregator.add | def add(self, data):
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data = self._decoder.decode(data)
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DiamondLightSource/python-procrunner | procrunner/__init__.py | _NonBlockingStreamReader.get_output | def get_output(self):
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Retrieve the stored data in full.
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limix/numpy-sugar | numpy_sugar/linalg/diag.py | trace2 | def trace2(A, B):
r"""Trace of :math:`\mathrm A \mathrm B^\intercal`.
Args:
A (array_like): Left-hand side.
B (array_like): Right-hand side.
Returns:
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layout_error... | python | def trace2(A, B):
r"""Trace of :math:`\mathrm A \mathrm B^\intercal`.
Args:
A (array_like): Left-hand side.
B (array_like): Right-hand side.
Returns:
float: Trace of :math:`\mathrm A \mathrm B^\intercal`.
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limix/numpy-sugar | numpy_sugar/linalg/diag.py | sum2diag | def sum2diag(A, D, out=None):
r"""Add values ``D`` to the diagonal of matrix ``A``.
Args:
A (array_like): Left-hand side.
D (array_like or float): Values to add.
out (:class:`numpy.ndarray`, optional): copy result to.
Returns:
:class:`numpy.ndarray`: Resulting matrix.
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r"""Add values ``D`` to the diagonal of matrix ``A``.
Args:
A (array_like): Left-hand side.
D (array_like or float): Values to add.
out (:class:`numpy.ndarray`, optional): copy result to.
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ikegami-yukino/jaconv | jaconv/jaconv.py | hira2kata | def hira2kata(text, ignore=''):
"""Convert Hiragana to Full-width (Zenkaku) Katakana.
Parameters
----------
text : str
Hiragana string.
ignore : str
Characters to be ignored in converting.
Return
------
str
Katakana string.
Examples
--------
>>> pri... | python | def hira2kata(text, ignore=''):
"""Convert Hiragana to Full-width (Zenkaku) Katakana.
Parameters
----------
text : str
Hiragana string.
ignore : str
Characters to be ignored in converting.
Return
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str
Katakana string.
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--------
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>>> print(jaconv.hira2kata('ともえまみ'))
トモエマミ... | [
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ikegami-yukino/jaconv | jaconv/jaconv.py | hira2hkata | def hira2hkata(text, ignore=''):
"""Convert Hiragana to Half-width (Hankaku) Katakana
Parameters
----------
text : str
Hiragana string.
ignore : str
Characters to be ignored in converting.
Return
------
str
Half-width Katakana string.
Examples
--------
... | python | def hira2hkata(text, ignore=''):
"""Convert Hiragana to Half-width (Hankaku) Katakana
Parameters
----------
text : str
Hiragana string.
ignore : str
Characters to be ignored in converting.
Return
------
str
Half-width Katakana string.
Examples
--------
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ikegami-yukino/jaconv | jaconv/jaconv.py | kata2hira | def kata2hira(text, ignore=''):
"""Convert Full-width Katakana to Hiragana
Parameters
----------
text : str
Full-width Katakana string.
ignore : str
Characters to be ignored in converting.
Return
------
str
Hiragana string.
Examples
--------
>>> pri... | python | def kata2hira(text, ignore=''):
"""Convert Full-width Katakana to Hiragana
Parameters
----------
text : str
Full-width Katakana string.
ignore : str
Characters to be ignored in converting.
Return
------
str
Hiragana string.
Examples
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巴まみ
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ikegami-yukino/jaconv | jaconv/jaconv.py | h2z | def h2z(text, ignore='', kana=True, ascii=False, digit=False):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana
Parameters
----------
text : str
Half-width Katakana string.
ignore : str
Characters to be ignored in converting.
kana : bool
Either c... | python | def h2z(text, ignore='', kana=True, ascii=False, digit=False):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana
Parameters
----------
text : str
Half-width Katakana string.
ignore : str
Characters to be ignored in converting.
kana : bool
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ikegami-yukino/jaconv | jaconv/jaconv.py | z2h | def z2h(text, ignore='', kana=True, ascii=False, digit=False):
"""Convert Full-width (Zenkaku) Katakana to Half-width (Hankaku) Katakana
Parameters
----------
text : str
Full-width Katakana string.
ignore : str
Characters to be ignored in converting.
kana : bool
Either c... | python | def z2h(text, ignore='', kana=True, ascii=False, digit=False):
"""Convert Full-width (Zenkaku) Katakana to Half-width (Hankaku) Katakana
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text : str
Full-width Katakana string.
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Characters to be ignored in converting.
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ikegami-yukino/jaconv | jaconv/jaconv.py | normalize | def normalize(text, mode='NFKC', ignore=''):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
... | python | def normalize(text, mode='NFKC', ignore=''):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
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text : str
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ikegami-yukino/jaconv | jaconv/jaconv.py | kana2alphabet | def kana2alphabet(text):
"""Convert Hiragana to hepburn-style alphabets
Parameters
----------
text : str
Hiragana string.
Return
------
str
Hepburn-style alphabets string.
Examples
--------
>>> print(jaconv.kana2alphabet('まみさん'))
mamisan
"""
text = ... | python | def kana2alphabet(text):
"""Convert Hiragana to hepburn-style alphabets
Parameters
----------
text : str
Hiragana string.
Return
------
str
Hepburn-style alphabets string.
Examples
--------
>>> print(jaconv.kana2alphabet('まみさん'))
mamisan
"""
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mamisan | [
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ikegami-yukino/jaconv | jaconv/jaconv.py | alphabet2kana | def alphabet2kana(text):
"""Convert alphabets to Hiragana
Parameters
----------
text : str
Alphabets string.
Return
------
str
Hiragana string.
Examples
--------
>>> print(jaconv.alphabet2kana('mamisan'))
まみさん
"""
text = text.replace('kya', 'きゃ').re... | python | def alphabet2kana(text):
"""Convert alphabets to Hiragana
Parameters
----------
text : str
Alphabets string.
Return
------
str
Hiragana string.
Examples
--------
>>> print(jaconv.alphabet2kana('mamisan'))
まみさん
"""
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ibab/matplotlib-hep | matplotlib_hep/__init__.py | histpoints | def histpoints(x, bins=None, xerr=None, yerr='gamma', normed=False, **kwargs):
"""
Plot a histogram as a series of data points.
Compute and draw the histogram of *x* using individual (x,y) points
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"""
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kevinconway/daemons | daemons/message/eventlet.py | EventletMessageManager.pool | def pool(self):
"""Get an eventlet pool used to dispatch requests."""
self._pool = self._pool or eventlet.GreenPool(size=self.pool_size)
return self._pool | python | def pool(self):
"""Get an eventlet pool used to dispatch requests."""
self._pool = self._pool or eventlet.GreenPool(size=self.pool_size)
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kevinconway/daemons | daemons/startstop/simple.py | SimpleStartStopManager.start | def start(self):
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kevinconway/daemons | daemons/startstop/simple.py | SimpleStartStopManager.stop | def stop(self):
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"""Stop the daemonized process.
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kevinconway/daemons | daemons/signal/simple.py | SimpleSignalManager.handle | def handle(self, signum, handler):
"""Set a function to run when the given signal is recieved.
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"""Set a function to run when the given signal is recieved.
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DEIB-GECO/PyGMQL | gmql/dataset/loaders/Loader.py | preprocess_path | def preprocess_path(path):
""" Given a dataset path, the following structure is to be expected:
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... | python | def preprocess_path(path):
""" Given a dataset path, the following structure is to be expected:
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DEIB-GECO/PyGMQL | gmql/dataset/loaders/Loader.py | check_for_dataset | def check_for_dataset(files):
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DEIB-GECO/PyGMQL | gmql/dataset/loaders/Loader.py | load_from_path | def load_from_path(local_path=None, parser=None, all_load=False):
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DEIB-GECO/PyGMQL | gmql/dataset/loaders/Loader.py | load_from_remote | def load_from_remote(remote_name, owner=None):
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nathan-hoad/python-iwlib | iwlib/iwlist.py | scan | def scan(interface):
"""Perform a scan for access points in the area.
Arguments:
interface - device to use for scanning (e.g. eth1, wlan0).
"""
interface = _get_bytes(interface)
head = ffi.new('wireless_scan_head *')
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range = _get_range_info(interfa... | python | def scan(interface):
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.login | def login(self, username=None, password=None):
""" Before doing any remote operation, the user has to login to the GMQL serivice.
This can be done in the two following ways:
* Guest mode: the user has no credentials and uses the system only as a temporary guest
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.logout | def logout(self):
""" Logout from the remote account
:return: None
"""
url = self.address + "/logout"
header = self.__check_authentication()
response = requests.get(url, headers=header)
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""" Logout from the remote account
:return: None
"""
url = self.address + "/logout"
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.get_dataset_list | def get_dataset_list(self):
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:return: a pandas Dataframe
"""
url = self.address + "/datasets"
header = self.__check_authentication()
response = requests.get(url, headers=header)
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.get_dataset_samples | def get_dataset_samples(self, dataset_name, owner=None):
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:param dataset_name: the dataset name
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.get_dataset_schema | def get_dataset_schema(self, dataset_name, owner=None):
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.upload_dataset | def upload_dataset(self, dataset, dataset_name, schema_path=None):
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:param dataset: the local path of the dataset
:param dataset_name: the name you want to assign to the dataset remotely
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u... | python | def upload_dataset(self, dataset, dataset_name, schema_path=None):
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.delete_dataset | def delete_dataset(self, dataset_name):
""" Deletes the dataset having the specified name
:param dataset_name: the name that the dataset has on the repository
:return: None
"""
url = self.address + "/datasets/" + dataset_name
header = self.__check_authentication()
... | python | def delete_dataset(self, dataset_name):
""" Deletes the dataset having the specified name
:param dataset_name: the name that the dataset has on the repository
:return: None
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url = self.address + "/datasets/" + dataset_name
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.download_dataset | def download_dataset(self, dataset_name, local_path, how="stream"):
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:param local_path: where you want to save the dataset
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.query | def query(self, query, output_path=None, file_name="query", output="tab"):
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DEIB-GECO/PyGMQL | gmql/RemoteConnection/RemoteManager.py | RemoteManager.trace_job | def trace_job(self, jobId):
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:param jobId: the job identifier
:return: a dictionary with the information
"""
header = self.__check_authentication()
status_url = self.address + "/jobs/" + jobId + "/trace"
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DEIB-GECO/PyGMQL | gmql/settings.py | set_mode | def set_mode(how):
""" Sets the behavior of the API
:param how: if 'remote' all the execution is performed on the remote server; if 'local' all
it is executed locally. Default = 'local'
:return: None
"""
global __mode
if how == "local":
__mode = how
elif how == "remote":
... | python | def set_mode(how):
""" Sets the behavior of the API
:param how: if 'remote' all the execution is performed on the remote server; if 'local' all
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:return: None
"""
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DEIB-GECO/PyGMQL | gmql/settings.py | set_progress | def set_progress(how):
""" Enables or disables the progress bars for the loading, writing and downloading
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:param how: True if you want the progress bar, False otherwise
:return: None
Example::
import gmql as gl
gl.set_progress(True) # abilitates progress bars
... | python | def set_progress(how):
""" Enables or disables the progress bars for the loading, writing and downloading
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DEIB-GECO/PyGMQL | gmql/settings.py | set_meta_profiling | def set_meta_profiling(how):
""" Enables or disables the profiling of metadata at the loading of a GMQLDataset
:param how: True if you want to analyze the metadata when a GMQLDataset is created
by a load_from_*. False otherwise. (Default=True)
:return: None
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""" Enables or disables the profiling of metadata at the loading of a GMQLDataset
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.parse_regions | def parse_regions(self, path):
""" Given a file path, it loads it into memory as a Pandas dataframe
:param path: file path
:return: a Pandas Dataframe
"""
if self.schema_format.lower() == GTF.lower():
res = self._parse_gtf_regions(path)
else:
res ... | python | def parse_regions(self, path):
""" Given a file path, it loads it into memory as a Pandas dataframe
:param path: file path
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"""
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.get_attributes | def get_attributes(self):
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.get_ordered_attributes | def get_ordered_attributes(self):
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.get_types | def get_types(self):
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.get_name_type_dict | def get_name_type_dict(self):
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DEIB-GECO/PyGMQL | gmql/dataset/parsers/RegionParser.py | RegionParser.get_ordered_types | def get_ordered_types(self):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.xmeans | def xmeans(cls, initial_centers=None, kmax=20, tolerance=0.025, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore=False):
"""
Constructor of the x-means clustering.rst algorithm
:param initial_centers: Initial coordinates of centers of clusters that are represented by list: [center... | python | def xmeans(cls, initial_centers=None, kmax=20, tolerance=0.025, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore=False):
"""
Constructor of the x-means clustering.rst algorithm
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.clarans | def clarans(cls, number_clusters, num_local, max_neighbour):
"""
Constructor of the CLARANS clustering.rst algorithm
:param number_clusters: the number of clusters to be allocated
:param num_local: the number of local minima obtained (amount of iterations for solving the problem).
... | python | def clarans(cls, number_clusters, num_local, max_neighbour):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.rock | def rock(cls, data, eps, number_clusters, threshold=0.5, ccore=False):
"""
Constructor of the ROCK cluster analysis algorithm
:param eps: Connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius
:param number_clusters: De... | python | def rock(cls, data, eps, number_clusters, threshold=0.5, ccore=False):
"""
Constructor of the ROCK cluster analysis algorithm
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.optics | def optics(cls, data, eps, minpts, ccore=False):
"""
Constructor of OPTICS clustering.rst algorithm
:param data: Input data that is presented as a list of points (objects), where each point is represented by list or tuple
:param eps: Connectivity radius between points, points may be con... | python | def optics(cls, data, eps, minpts, ccore=False):
"""
Constructor of OPTICS clustering.rst algorithm
:param data: Input data that is presented as a list of points (objects), where each point is represented by list or tuple
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.is_pyclustering_instance | def is_pyclustering_instance(model):
"""
Checks if the clustering.rst algorithm belongs to pyclustering
:param model: the clustering.rst algorithm model
:return: the truth value (Boolean)
"""
return any(isinstance(model, i) for i in [xmeans, clarans, rock, optics]) | python | def is_pyclustering_instance(model):
"""
Checks if the clustering.rst algorithm belongs to pyclustering
:param model: the clustering.rst algorithm model
:return: the truth value (Boolean)
"""
return any(isinstance(model, i) for i in [xmeans, clarans, rock, optics]) | [
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.fit | def fit(self, data=None):
"""
Performs clustering.rst
:param data: Data to be fit
:return: the clustering.rst object
"""
if self.is_pyclustering_instance(self.model):
if isinstance(self.model, xmeans):
data = self.input_preprocess(data)
... | python | def fit(self, data=None):
"""
Performs clustering.rst
:param data: Data to be fit
:return: the clustering.rst object
"""
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if isinstance(self.model, xmeans):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering._labels_from_pyclusters | def _labels_from_pyclusters(self):
"""
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:return: The list of labels
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... | python | def _labels_from_pyclusters(self):
"""
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.retrieve_cluster | def retrieve_cluster(self, df, cluster_no):
"""
Extracts the cluster at the given index from the input dataframe
:param df: the dataframe that contains the clusters
:param cluster_no: the cluster number
:return: returns the extracted cluster
"""
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"""
Extracts the cluster at the given index from the input dataframe
:param df: the dataframe that contains the clusters
:param cluster_no: the cluster number
:return: returns the extracted cluster
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.get_labels | def get_labels(obj):
"""
Retrieve the labels of a clustering.rst object
:param obj: the clustering.rst object
:return: the resulting labels
"""
if Clustering.is_pyclustering_instance(obj.model):
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ret... | python | def get_labels(obj):
"""
Retrieve the labels of a clustering.rst object
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.silhouette_n_clusters | def silhouette_n_clusters(data, k_min, k_max, distance='euclidean'):
"""
Computes and plot the silhouette score vs number of clusters graph to help selecting the number of clusters visually
:param data: The data object
:param k_min: lowerbound of the cluster range
:param k_max: ... | python | def silhouette_n_clusters(data, k_min, k_max, distance='euclidean'):
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Computes and plot the silhouette score vs number of clusters graph to help selecting the number of clusters visually
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.elbow_method | def elbow_method(data, k_min, k_max, distance='euclidean'):
"""
Calculates and plots the plot of variance explained - number of clusters
Implementation reference: https://github.com/sarguido/k-means-clustering.rst
:param data: The dataset
:param k_min: lowerbound of the cluster ... | python | def elbow_method(data, k_min, k_max, distance='euclidean'):
"""
Calculates and plots the plot of variance explained - number of clusters
Implementation reference: https://github.com/sarguido/k-means-clustering.rst
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.adjusted_mutual_info | def adjusted_mutual_info(self, reference_clusters):
"""
Calculates the adjusted mutual information score w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: returns the value of the adjusted mutual information... | python | def adjusted_mutual_info(self, reference_clusters):
"""
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.adjusted_rand_score | def adjusted_rand_score(self, reference_clusters):
"""
Calculates the adjusted rand score w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: returns the value of the adjusted rand score
"""
re... | python | def adjusted_rand_score(self, reference_clusters):
"""
Calculates the adjusted rand score w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: returns the value of the adjusted rand score
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.completeness_score | def completeness_score(self, reference_clusters):
"""
Calculates the completeness score w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: the resulting completeness score
"""
return completen... | python | def completeness_score(self, reference_clusters):
"""
Calculates the completeness score w.r.t. the reference clusters (explicit evaluation)
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.fowlkes_mallows | def fowlkes_mallows(self, reference_clusters):
"""
Calculates the Fowlkes-Mallows index (FMI) w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: The resulting Fowlkes-Mallows score.
"""
return... | python | def fowlkes_mallows(self, reference_clusters):
"""
Calculates the Fowlkes-Mallows index (FMI) w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: The resulting Fowlkes-Mallows score.
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.homogeneity_score | def homogeneity_score(self, reference_clusters):
"""
Calculates the homogeneity score w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: The resulting homogeneity score.
"""
return homogeneity... | python | def homogeneity_score(self, reference_clusters):
"""
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.mutual_info_score | def mutual_info_score(self, reference_clusters):
"""
Calculates the MI (mutual information) w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: The resulting MI score.
"""
return mutual_info_sc... | python | def mutual_info_score(self, reference_clusters):
"""
Calculates the MI (mutual information) w.r.t. the reference clusters (explicit evaluation)
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.normalized_mutual_info_score | def normalized_mutual_info_score(self, reference_clusters):
"""
Calculates the normalized mutual information w.r.t. the reference clusters (explicit evaluation)
:param reference_clusters: Clusters that are to be used as reference
:return: The resulting normalized mutual information scor... | python | def normalized_mutual_info_score(self, reference_clusters):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/clustering.py | Clustering.silhouette_score | def silhouette_score(self, data, metric='euclidean', sample_size=None, random_state=None, **kwds):
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:param data: The data that the clusters are generated from
:param metric: the pairwise distance metric
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Computes the mean Silhouette Coefficient of all samples (implicit evaluation)
:param data: The data that the clusters are generated from
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DEIB-GECO/PyGMQL | gmql/dataset/loaders/Materializations.py | materialize | def materialize(datasets):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/biclustering.py | Biclustering.retrieve_bicluster | def retrieve_bicluster(self, df, row_no, column_no):
"""
Extracts the bicluster at the given row bicluster number and the column bicluster number from the input dataframe.
:param df: the input dataframe whose values were biclustered
:param row_no: the number of the row bicluster
... | python | def retrieve_bicluster(self, df, row_no, column_no):
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Extracts the bicluster at the given row bicluster number and the column bicluster number from the input dataframe.
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/biclustering.py | Biclustering.bicluster_similarity | def bicluster_similarity(self, reference_model):
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Calculates the similarity between the current model of biclusters and the reference model of biclusters
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"""
Calculates the similarity between the current model of biclusters and the reference model of biclusters
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DEIB-GECO/PyGMQL | gmql/ml/multi_ref_model.py | MultiRefModel.load | def load(self, path, genes_uuid, regs=['chr', 'left', 'right', 'strand'], meta=[], values=[], full_load=False):
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Loads the multi referenced mapped data from the file system
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DEIB-GECO/PyGMQL | gmql/ml/multi_ref_model.py | MultiRefModel.merge | def merge(self, samples_uuid):
"""
The method to merge the datamodels belonging to different references
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:return: Returns the merged dataframe
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DEIB-GECO/PyGMQL | gmql/ml/multi_ref_model.py | MultiRefModel.compact_view | def compact_view(self, merged_data, selected_meta, reference_no):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/preprocessing.py | Preprocessing.prune_by_missing_percent | def prune_by_missing_percent(df, percentage=0.4):
"""
The method to remove the attributes (genes) with more than a percentage of missing values
:param df: the dataframe containing the attributes to be pruned
:param percentage: the percentage threshold (0.4 by default)
:return: t... | python | def prune_by_missing_percent(df, percentage=0.4):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/preprocessing.py | Preprocessing.impute_using_statistics | def impute_using_statistics(df, method='min'):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/preprocessing.py | Preprocessing.impute_knn | def impute_knn(df, k=3):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/preprocessing.py | Preprocessing.impute_svd | def impute_svd(df, rank=10, convergence_threshold=0.00001, max_iters=200):
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DEIB-GECO/PyGMQL | gmql/ml/algorithms/preprocessing.py | Preprocessing.feature_selection | def feature_selection(df, labels, n_features, method='chi2'):
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chi2_fs(gs.data, labels, 50)
:param df: The inpu... | python | def feature_selection(df, labels, n_features, method='chi2'):
"""
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DEIB-GECO/PyGMQL | gmql/configuration.py | Configuration.set_spark_conf | def set_spark_conf(self, key=None, value=None, d=None):
""" Sets a spark property as a ('key', 'value') pair of using a dictionary
{'key': 'value', ...}
:param key: string
:param value: string
:param d: dictionary
:return: None
"""
if isinstance(d, dict):... | python | def set_spark_conf(self, key=None, value=None, d=None):
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DEIB-GECO/PyGMQL | gmql/configuration.py | Configuration.set_system_conf | def set_system_conf(self, key=None, value=None, d=None):
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DEIB-GECO/PyGMQL | gmql/dataset/DataStructures/MetaField.py | MetaField.isin | def isin(self, values):
""" Selects the samples having the metadata attribute between the values provided
as input
:param values: a list of elements
:return a new complex condition
"""
if not isinstance(values, list):
raise TypeError("Input should be a string... | python | def isin(self, values):
""" Selects the samples having the metadata attribute between the values provided
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PragmaticMates/django-flatpages-i18n | flatpages_i18n/templatetags/flatpages_i18n.py | get_flatpages_i18n | def get_flatpages_i18n(parser, token):
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Retrieves all flatpage objects available for the current site and
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kevinconway/daemons | daemons/daemonize/simple.py | SimpleDaemonizeManager.daemonize | def daemonize(self):
"""Double fork and set the pid."""
self._double_fork()
# Write pidfile.
self.pid = os.getpid()
LOG.info(
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) | python | def daemonize(self):
"""Double fork and set the pid."""
self._double_fork()
# Write pidfile.
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kevinconway/daemons | daemons/daemonize/simple.py | SimpleDaemonizeManager._double_fork | def _double_fork(self):
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http://www.erlenstar.demon.co.uk/unix/faq_2.html#SEC16
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.from_memory | def from_memory(cls, data, meta):
"""
Overloaded constructor to create the GenometricSpace object from memory data and meta variables.
The indexes of the data and meta dataframes should be the same.
:param data: The data model
:param meta: The metadata
:return: A Genome... | python | def from_memory(cls, data, meta):
"""
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.load | def load(self, _path, regs=['chr', 'left', 'right', 'strand'], meta=[], values=[], full_load=False, file_extension="gdm"):
"""Parses and loads the data into instance attributes.
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.set_meta | def set_meta(self, selected_meta):
"""Sets one axis of the 2D multi-indexed dataframe
index to the selected meta data.
:param selected_meta: The list of the metadata users want to index with.
"""
meta_names = list(selected_meta)
meta_names.append('sample')
m... | python | def set_meta(self, selected_meta):
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meta_names = list(selected_meta)
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.to_matrix | def to_matrix(self, values, selected_regions, default_value=0):
"""Creates a 2D multi-indexed matrix representation of the data.
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.get_values | def get_values(self, set, selected_meta):
"""
Retrieves the selected metadata values of the given set
:param set: cluster that contains the data
:param selected_meta: the values of the selected_meta
:return: the values of the selected meta of the cluster
"""
warn... | python | def get_values(self, set, selected_meta):
"""
Retrieves the selected metadata values of the given set
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.group_statistics | def group_statistics(self, group, selected_meta, stat_code='mean'):
"""
Provides statistics of a group based on the meta data selected.
:param group:The result of a classification or clustering.rst or biclustering algorithm
:param selected_meta: The metadata that we are interested in
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.to_bag_of_genomes | def to_bag_of_genomes(self, clustering_object):
"""
Creates a bag of genomes representation for data mining purposes
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.to_term_document_matrix | def to_term_document_matrix(path_to_bag_of_genomes, max_df=0.99, min_df=1, use_idf=False):
"""
Creates a term-document matrix which is a mathematical matrix that describes the frequency of terms
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Creates a term-document matrix which is a mathematical matrix that describes the frequency of terms
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DEIB-GECO/PyGMQL | gmql/ml/genometric_space.py | GenometricSpace.tf | def tf(cluster):
"""
Computes the term frequency and stores it as a dictionary
:param cluster: the cluster that contains the metadata
:return: tf dictionary
"""
counts = dict()
words = cluster.split(' ')
for word in words:
counts[word] = count... | python | def tf(cluster):
"""
Computes the term frequency and stores it as a dictionary
:param cluster: the cluster that contains the metadata
:return: tf dictionary
"""
counts = dict()
words = cluster.split(' ')
for word in words:
counts[word] = count... | [
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