id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
322,100 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.ArpackError | class ArpackError(RuntimeError):
"""
ARPACK error
"""
def __init__(self, info, infodict=None):
if infodict is None:
infodict = _NAUPD_ERRORS
msg = infodict.get(info, 'Unknown error')
super().__init__(f'ARPACK error {info}: {msg}') | class ArpackError(RuntimeError):
'''
ARPACK error
'''
def __init__(self, info, infodict=None):
pass | 2 | 1 | 6 | 1 | 5 | 0 | 2 | 0.5 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 12 | 11 | 2 | 6 | 3 | 4 | 3 | 6 | 3 | 4 | 2 | 4 | 1 | 2 |
322,101 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.ArpackNoConvergence | class ArpackNoConvergence(ArpackError):
"""
ARPACK iteration did not converge
Attributes
----------
eigenvalues : ndarray
Partial result. Converged eigenvalues.
eigenvectors : ndarray
Partial result. Converged eigenvectors.
"""
def __init__(self, msg, eigenvalues, eige... | class ArpackNoConvergence(ArpackError):
'''
ARPACK iteration did not converge
Attributes
----------
eigenvalues : ndarray
Partial result. Converged eigenvalues.
eigenvectors : ndarray
Partial result. Converged eigenvectors.
'''
def __init__(self, msg, eigenvalues, eigenv... | 2 | 1 | 4 | 0 | 4 | 0 | 1 | 1.8 | 1 | 0 | 0 | 0 | 1 | 2 | 1 | 13 | 17 | 3 | 5 | 4 | 3 | 9 | 5 | 4 | 3 | 1 | 5 | 0 | 1 |
322,102 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.IterInv | import numpy as np
from scipy.sparse.linalg._interface import aslinearoperator, LinearOperator
class IterInv(LinearOperator):
"""
IterInv:
helper class to repeatedly solve M*x=b
using an iterative method.
"""
def __init__(self, M, ifunc=gmres_loose, tol=0):
self.M = M
if ... |
class IterInv(LinearOperator):
'''
IterInv:
helper class to repeatedly solve M*x=b
using an iterative method.
'''
def __init__(self, M, ifunc=gmres_loose, tol=0):
pass
def _matvec(self, x):
pass | 3 | 1 | 12 | 1 | 10 | 1 | 3 | 0.33 | 1 | 2 | 0 | 0 | 2 | 5 | 2 | 30 | 31 | 3 | 21 | 10 | 18 | 7 | 17 | 10 | 14 | 3 | 1 | 1 | 5 |
322,103 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.IterOpInv | import numpy as np
from scipy.sparse.linalg._interface import aslinearoperator, LinearOperator
class IterOpInv(LinearOperator):
"""
IterOpInv:
helper class to repeatedly solve [A-sigma*M]*x = b
using an iterative method
"""
def __init__(self, A, M, sigma, ifunc=gmres_loose, tol=0):
... |
class IterOpInv(LinearOperator):
'''
IterOpInv:
helper class to repeatedly solve [A-sigma*M]*x = b
using an iterative method
'''
def __init__(self, A, M, sigma, ifunc=gmres_loose, tol=0):
pass
def mult_func(x):
pass
def mult_func_M_None(x):
... | 7 | 1 | 9 | 1 | 8 | 0 | 2 | 0.19 | 1 | 2 | 0 | 0 | 3 | 7 | 3 | 31 | 50 | 7 | 36 | 17 | 29 | 7 | 27 | 16 | 21 | 3 | 1 | 1 | 8 |
322,104 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.LuInv | from scipy.linalg import eig, eigh, lu_factor, lu_solve
from scipy.sparse.linalg._interface import aslinearoperator, LinearOperator
class LuInv(LinearOperator):
"""
LuInv:
helper class to repeatedly solve M*x=b
using an LU-decomposition of M
"""
def __init__(self, M):
self.M_lu =... |
class LuInv(LinearOperator):
'''
LuInv:
helper class to repeatedly solve M*x=b
using an LU-decomposition of M
'''
def __init__(self, M):
pass
def _matvec(self, x):
pass | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.71 | 1 | 0 | 0 | 0 | 2 | 3 | 2 | 30 | 14 | 2 | 7 | 6 | 4 | 5 | 7 | 6 | 4 | 1 | 1 | 0 | 2 |
322,105 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack.SpLuInv | from scipy.sparse.linalg import gmres, splu
from scipy.sparse.linalg._interface import aslinearoperator, LinearOperator
import numpy as np
class SpLuInv(LinearOperator):
"""
SpLuInv:
helper class to repeatedly solve M*x=b
using a sparse LU-decomposition of M
"""
def __init__(self, M):
... |
class SpLuInv(LinearOperator):
'''
SpLuInv:
helper class to repeatedly solve M*x=b
using a sparse LU-decomposition of M
'''
def __init__(self, M):
pass
def _matvec(self, x):
pass | 3 | 1 | 7 | 0 | 6 | 1 | 2 | 0.54 | 1 | 0 | 0 | 0 | 2 | 4 | 2 | 30 | 22 | 2 | 13 | 7 | 10 | 7 | 11 | 7 | 8 | 2 | 1 | 1 | 3 |
322,106 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack._ArpackParams | import numpy as np
class _ArpackParams:
def __init__(self, n, k, tp, mode=1, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
if k <= 0:
raise ValueError(f'k must be positive, k={k}')
if maxiter is None:
maxiter = n * 10
if maxiter <= 0:
... |
class _ArpackParams:
def __init__(self, n, k, tp, mode=1, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
pass
def _raise_no_convergence(self):
pass | 3 | 0 | 35 | 5 | 28 | 3 | 6 | 0.11 | 0 | 3 | 2 | 2 | 2 | 15 | 2 | 2 | 72 | 10 | 57 | 26 | 53 | 6 | 53 | 24 | 50 | 9 | 0 | 2 | 11 |
322,107 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack._SymmetricArpackParams | import numpy as np
from . import _arpack
from scipy._lib._util import _aligned_zeros
class _SymmetricArpackParams(_ArpackParams):
def __init__(self, n, k, tp, matvec, mode=1, M_matvec=None, Minv_matvec=None, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
if mode == 1:
if matv... |
class _SymmetricArpackParams(_ArpackParams):
def __init__(self, n, k, tp, matvec, mode=1, M_matvec=None, Minv_matvec=None, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
pass
def iterate(self):
pass
def extract(self, return_eigenvectors):
pass | 4 | 0 | 71 | 5 | 47 | 19 | 13 | 0.39 | 1 | 3 | 1 | 0 | 3 | 20 | 3 | 5 | 215 | 18 | 143 | 32 | 137 | 56 | 113 | 30 | 109 | 25 | 1 | 2 | 38 |
322,108 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py | scipy.sparse.linalg._eigen.arpack.arpack._UnsymmetricArpackParams | from . import _arpack
from scipy._lib._util import _aligned_zeros
import numpy as np
class _UnsymmetricArpackParams(_ArpackParams):
def __init__(self, n, k, tp, matvec, mode=1, M_matvec=None, Minv_matvec=None, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
if mode == 1:
if ma... |
class _UnsymmetricArpackParams(_ArpackParams):
def __init__(self, n, k, tp, matvec, mode=1, M_matvec=None, Minv_matvec=None, sigma=None, ncv=None, v0=None, maxiter=None, which='LM', tol=0):
pass
def iterate(self):
pass
def extract(self, return_eigenvectors):
pass | 4 | 0 | 99 | 10 | 67 | 24 | 16 | 0.36 | 1 | 3 | 1 | 0 | 3 | 24 | 3 | 5 | 301 | 32 | 202 | 48 | 196 | 72 | 146 | 42 | 142 | 20 | 1 | 5 | 48 |
322,109 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_expm_multiply.py | scipy.sparse.linalg._expm_multiply.LazyOperatorNormInfo | class LazyOperatorNormInfo:
"""
Information about an operator is lazily computed.
The information includes the exact 1-norm of the operator,
in addition to estimates of 1-norms of powers of the operator.
This uses the notation of Computing the Action (2011).
This class is specialized enough to ... | class LazyOperatorNormInfo:
'''
Information about an operator is lazily computed.
The information includes the exact 1-norm of the operator,
in addition to estimates of 1-norms of powers of the operator.
This uses the notation of Computing the Action (2011).
This class is specialized enough to p... | 6 | 6 | 9 | 0 | 4 | 5 | 1 | 1.7 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 63 | 9 | 20 | 12 | 14 | 34 | 20 | 12 | 14 | 2 | 0 | 1 | 7 |
322,110 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface.IdentityOperator | class IdentityOperator(LinearOperator):
def __init__(self, shape, dtype=None):
super().__init__(dtype, shape)
def _matvec(self, x):
return x
def _rmatvec(self, x):
return x
def _rmatmat(self, x):
return x
def _matmat(self, x):
return x
def _adjoint(s... | class IdentityOperator(LinearOperator):
def __init__(self, shape, dtype=None):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _rmatmat(self, x):
pass
def _matmat(self, x):
pass
def _adjoint(self):
pass | 7 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 | 0 | 6 | 34 | 18 | 5 | 13 | 7 | 6 | 0 | 13 | 7 | 6 | 1 | 1 | 0 | 6 |
322,111 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface.LinearOperator | import warnings
import numpy as np
from scipy.sparse import issparse
from scipy.sparse._sputils import isshape, isintlike, asmatrix, is_pydata_spmatrix
class LinearOperator:
"""Common interface for performing matrix vector products
Many iterative methods (e.g. `cg`, `gmres`) do not need to know the
indivi... |
class LinearOperator:
'''Common interface for performing matrix vector products
Many iterative methods (e.g. `cg`, `gmres`) do not need to know the
individual entries of a matrix to solve a linear system ``A@x = b``.
Such solvers only require the computation of matrix vector
products, ``A@v`` where... | 29 | 17 | 15 | 2 | 7 | 5 | 3 | 1.09 | 0 | 19 | 7 | 27 | 28 | 2 | 28 | 28 | 536 | 113 | 202 | 48 | 173 | 221 | 169 | 46 | 140 | 7 | 0 | 2 | 74 |
322,112 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface.MatrixLinearOperator | class MatrixLinearOperator(LinearOperator):
def __init__(self, A):
super().__init__(A.dtype, A.shape)
self.A = A
self.__adj = None
self.args = (A,)
def _matmat(self, X):
return self.A.dot(X)
def _adjoint(self):
if self.__adj is None:
self.__adj ... | class MatrixLinearOperator(LinearOperator):
def __init__(self, A):
pass
def _matmat(self, X):
pass
def _adjoint(self):
pass | 4 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 2 | 1 | 1 | 3 | 3 | 3 | 31 | 14 | 2 | 12 | 7 | 8 | 0 | 12 | 7 | 8 | 2 | 1 | 1 | 4 |
322,113 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._AdjointLinearOperator | class _AdjointLinearOperator(LinearOperator):
"""Adjoint of arbitrary Linear Operator"""
def __init__(self, A):
shape = (A.shape[1], A.shape[0])
super().__init__(dtype=A.dtype, shape=shape)
self.A = A
self.args = (A,)
def _matvec(self, x):
return self.A._rmatvec(x)
... | class _AdjointLinearOperator(LinearOperator):
'''Adjoint of arbitrary Linear Operator'''
def __init__(self, A):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _matmat(self, x):
pass
def _rmatmat(self, x):
pass | 6 | 1 | 3 | 0 | 3 | 0 | 1 | 0.07 | 1 | 1 | 0 | 0 | 5 | 2 | 5 | 33 | 20 | 5 | 14 | 9 | 8 | 1 | 14 | 9 | 8 | 1 | 1 | 0 | 5 |
322,114 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._AdjointMatrixOperator | class _AdjointMatrixOperator(MatrixLinearOperator):
def __init__(self, adjoint_array):
self.A = adjoint_array.T.conj()
self.args = (adjoint_array,)
self.shape = (adjoint_array.shape[1], adjoint_array.shape[0])
@property
def dtype(self):
return self.args[0].dtype
def _a... | class _AdjointMatrixOperator(MatrixLinearOperator):
def __init__(self, adjoint_array):
pass
@property
def dtype(self):
pass
def _adjoint(self):
pass | 5 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 3 | 3 | 34 | 12 | 2 | 10 | 8 | 5 | 0 | 9 | 7 | 5 | 1 | 2 | 0 | 3 |
322,115 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._CustomLinearOperator | class _CustomLinearOperator(LinearOperator):
"""Linear operator defined in terms of user-specified operations."""
def __init__(self, shape, matvec, rmatvec=None, matmat=None, dtype=None, rmatmat=None):
super().__init__(dtype, shape)
self.args = ()
self.__matvec_impl = matvec
sel... | class _CustomLinearOperator(LinearOperator):
'''Linear operator defined in terms of user-specified operations.'''
def __init__(self, shape, matvec, rmatvec=None, matmat=None, dtype=None, rmatmat=None):
pass
def _matmat(self, X):
pass
def _matvec(self, x):
pass
def _rmatve... | 7 | 1 | 6 | 1 | 6 | 0 | 2 | 0.03 | 1 | 2 | 0 | 0 | 6 | 5 | 6 | 34 | 44 | 9 | 34 | 14 | 26 | 1 | 26 | 13 | 19 | 2 | 1 | 1 | 9 |
322,116 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._PowerLinearOperator | from scipy.sparse._sputils import isshape, isintlike, asmatrix, is_pydata_spmatrix
import numpy as np
class _PowerLinearOperator(LinearOperator):
def __init__(self, A, p):
if not isinstance(A, LinearOperator):
raise ValueError('LinearOperator expected as A')
if A.shape[0] != A.shape[1]... |
class _PowerLinearOperator(LinearOperator):
def __init__(self, A, p):
pass
def _power(self, fun, x):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _rmatmat(self, x):
pass
def _matmat(self, x):
pass
def _adjoint(self):
... | 8 | 0 | 4 | 0 | 4 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 7 | 1 | 7 | 35 | 33 | 7 | 26 | 12 | 18 | 0 | 26 | 12 | 18 | 4 | 1 | 1 | 11 |
322,117 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._ProductLinearOperator | class _ProductLinearOperator(LinearOperator):
def __init__(self, A, B):
if not isinstance(A, LinearOperator) or not isinstance(B, LinearOperator):
raise ValueError('both operands have to be a LinearOperator')
if A.shape[1] != B.shape[0]:
raise ValueError(f'cannot multiply {A... | class _ProductLinearOperator(LinearOperator):
def __init__(self, A, B):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _rmatmat(self, x):
pass
def _matmat(self, x):
pass
def _adjoint(self):
pass | 7 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 6 | 1 | 6 | 34 | 26 | 5 | 21 | 9 | 14 | 0 | 19 | 9 | 12 | 3 | 1 | 1 | 8 |
322,118 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._ScaledLinearOperator | import numpy as np
class _ScaledLinearOperator(LinearOperator):
def __init__(self, A, alpha):
if not isinstance(A, LinearOperator):
raise ValueError('LinearOperator expected as A')
if not np.isscalar(alpha):
raise ValueError('scalar expected as alpha')
if isinstance... |
class _ScaledLinearOperator(LinearOperator):
def __init__(self, A, alpha):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _rmatmat(self, x):
pass
def _matmat(self, x):
pass
def _adjoint(self):
pass | 7 | 0 | 4 | 0 | 4 | 0 | 2 | 0.13 | 1 | 3 | 0 | 0 | 6 | 1 | 6 | 34 | 32 | 6 | 23 | 11 | 16 | 3 | 23 | 11 | 16 | 4 | 1 | 1 | 9 |
322,119 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._SumLinearOperator | class _SumLinearOperator(LinearOperator):
def __init__(self, A, B):
if not isinstance(A, LinearOperator) or not isinstance(B, LinearOperator):
raise ValueError('both operands have to be a LinearOperator')
if A.shape != B.shape:
raise ValueError(f'cannot add {A} and {B}: shap... | class _SumLinearOperator(LinearOperator):
def __init__(self, A, B):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _rmatmat(self, x):
pass
def _matmat(self, x):
pass
def _adjoint(self):
pass | 7 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 6 | 1 | 6 | 34 | 25 | 5 | 20 | 9 | 13 | 0 | 19 | 9 | 12 | 3 | 1 | 1 | 8 |
322,120 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_interface.py | scipy.sparse.linalg._interface._TransposedLinearOperator | import numpy as np
class _TransposedLinearOperator(LinearOperator):
"""Transposition of arbitrary Linear Operator"""
def __init__(self, A):
shape = (A.shape[1], A.shape[0])
super().__init__(dtype=A.dtype, shape=shape)
self.A = A
self.args = (A,)
def _matvec(self, x):
... |
class _TransposedLinearOperator(LinearOperator):
'''Transposition of arbitrary Linear Operator'''
def __init__(self, A):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _matmat(self, x):
pass
def _rmatmat(self, x):
pass | 6 | 1 | 3 | 0 | 3 | 0 | 1 | 0.21 | 1 | 1 | 0 | 0 | 5 | 2 | 5 | 33 | 22 | 5 | 14 | 9 | 8 | 3 | 14 | 9 | 8 | 1 | 1 | 0 | 5 |
322,121 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_matfuncs.py | scipy.sparse.linalg._matfuncs.MatrixPowerOperator | from scipy.sparse.linalg._interface import LinearOperator
class MatrixPowerOperator(LinearOperator):
def __init__(self, A, p, structure=None):
if A.ndim != 2 or A.shape[0] != A.shape[1]:
raise ValueError('expected A to be like a square matrix')
if p < 0:
raise ValueError('e... |
class MatrixPowerOperator(LinearOperator):
def __init__(self, A, p, structure=None):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _matmat(self, X):
pass
@property
def T(self):
pass | 7 | 0 | 5 | 0 | 5 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 5 | 6 | 5 | 33 | 34 | 5 | 29 | 17 | 22 | 0 | 28 | 16 | 22 | 3 | 1 | 1 | 10 |
322,122 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_matfuncs.py | scipy.sparse.linalg._matfuncs.ProductOperator | from scipy.sparse.linalg._interface import LinearOperator
import numpy as np
class ProductOperator(LinearOperator):
"""
For now, this is limited to products of multiple square matrices.
"""
def __init__(self, *args, **kwargs):
self._structure = kwargs.get('structure', None)
for A in ar... |
class ProductOperator(LinearOperator):
'''
For now, this is limited to products of multiple square matrices.
'''
def __init__(self, *args, **kwargs):
pass
def _matvec(self, x):
pass
def _rmatvec(self, x):
pass
def _matmat(self, X):
pass
@property
... | 7 | 1 | 7 | 0 | 7 | 0 | 3 | 0.08 | 1 | 2 | 0 | 0 | 5 | 5 | 5 | 33 | 45 | 5 | 37 | 19 | 30 | 3 | 32 | 18 | 26 | 7 | 1 | 4 | 14 |
322,123 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_matfuncs.py | scipy.sparse.linalg._matfuncs._ExpmPadeHelper | from ._expm_multiply import _ident_like, _exact_1_norm as _onenorm
class _ExpmPadeHelper:
"""
Help lazily evaluate a matrix exponential.
The idea is to not do more work than we need for high expm precision,
so we lazily compute matrix powers and store or precompute
other properties of the matrix.
... |
class _ExpmPadeHelper:
'''
Help lazily evaluate a matrix exponential.
The idea is to not do more work than we need for high expm precision,
so we lazily compute matrix powers and store or precompute
other properties of the matrix.
'''
def __init__(self, A, structure=None, use_exact_onenorm... | 33 | 2 | 9 | 0 | 8 | 1 | 2 | 0.12 | 0 | 0 | 0 | 0 | 19 | 17 | 19 | 19 | 207 | 22 | 165 | 71 | 132 | 20 | 118 | 58 | 98 | 4 | 0 | 2 | 40 |
322,124 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_special_sparse_arrays.py | scipy.sparse.linalg._special_sparse_arrays.LaplacianNd | import numpy as np
from scipy.sparse.linalg import LinearOperator
from scipy.sparse import kron, eye_array, dia_array
class LaplacianNd(LinearOperator):
"""
The grid Laplacian in ``N`` dimensions and its eigenvalues/eigenvectors.
Construct Laplacian on a uniform rectangular grid in `N` dimensions
and ... |
class LaplacianNd(LinearOperator):
'''
The grid Laplacian in ``N`` dimensions and its eigenvalues/eigenvectors.
Construct Laplacian on a uniform rectangular grid in `N` dimensions
and output its eigenvalues and eigenvectors.
The Laplacian ``L`` is square, negative definite, real symmetric array
... | 13 | 8 | 22 | 2 | 14 | 5 | 3 | 1.58 | 1 | 10 | 1 | 0 | 12 | 2 | 12 | 40 | 506 | 65 | 172 | 59 | 157 | 271 | 134 | 57 | 121 | 7 | 1 | 3 | 39 |
322,125 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_special_sparse_arrays.py | scipy.sparse.linalg._special_sparse_arrays.MikotaK | import numpy as np
from scipy.sparse.linalg import LinearOperator
class MikotaK(LinearOperator):
"""
Construct a stiffness matrix in various formats of Mikota pair.
The stiffness matrix `K` is square real tri-diagonal symmetric
positive definite with integer entries.
Parameters
----------
... |
class MikotaK(LinearOperator):
'''
Construct a stiffness matrix in various formats of Mikota pair.
The stiffness matrix `K` is square real tri-diagonal symmetric
positive definite with integer entries.
Parameters
----------
shape : tuple of int
The shape of the matrix.
dtype : d... | 9 | 3 | 6 | 0 | 4 | 2 | 1 | 0.97 | 1 | 1 | 0 | 0 | 8 | 4 | 8 | 36 | 77 | 10 | 34 | 19 | 24 | 33 | 31 | 19 | 21 | 1 | 1 | 0 | 8 |
322,126 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_special_sparse_arrays.py | scipy.sparse.linalg._special_sparse_arrays.MikotaM | import numpy as np
from scipy.sparse.linalg import LinearOperator
class MikotaM(LinearOperator):
"""
Construct a mass matrix in various formats of Mikota pair.
The mass matrix `M` is square real diagonal
positive definite with entries that are reciprocal to integers.
Parameters
----------
... |
class MikotaM(LinearOperator):
'''
Construct a mass matrix in various formats of Mikota pair.
The mass matrix `M` is square real diagonal
positive definite with entries that are reciprocal to integers.
Parameters
----------
shape : tuple of int
The shape of the matrix.
dtype : d... | 10 | 3 | 4 | 0 | 3 | 1 | 1 | 1.33 | 1 | 1 | 0 | 0 | 9 | 2 | 9 | 37 | 67 | 11 | 24 | 13 | 13 | 32 | 23 | 13 | 12 | 1 | 1 | 0 | 9 |
322,127 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_special_sparse_arrays.py | scipy.sparse.linalg._special_sparse_arrays.MikotaPair | import numpy as np
class MikotaPair:
"""
Construct the Mikota pair of matrices in various formats and
eigenvalues of the generalized eigenproblem with them.
The Mikota pair of matrices [1, 2]_ models a vibration problem
of a linear mass-spring system with the ends attached where
the stiffness ... |
class MikotaPair:
'''
Construct the Mikota pair of matrices in various formats and
eigenvalues of the generalized eigenproblem with them.
The Mikota pair of matrices [1, 2]_ models a vibration problem
of a linear mass-spring system with the ends attached where
the stiffness of the springs and t... | 3 | 2 | 12 | 1 | 6 | 6 | 2 | 7.5 | 0 | 2 | 2 | 0 | 2 | 5 | 2 | 2 | 114 | 12 | 12 | 9 | 9 | 90 | 12 | 9 | 9 | 2 | 0 | 1 | 3 |
322,128 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_special_sparse_arrays.py | scipy.sparse.linalg._special_sparse_arrays.Sakurai | from scipy.sparse.linalg import LinearOperator
import numpy as np
class Sakurai(LinearOperator):
"""
Construct a Sakurai matrix in various formats and its eigenvalues.
Constructs the "Sakurai" matrix motivated by reference [1]_:
square real symmetric positive definite and 5-diagonal
with the main ... |
class Sakurai(LinearOperator):
'''
Construct a Sakurai matrix in various formats and its eigenvalues.
Constructs the "Sakurai" matrix motivated by reference [1]_:
square real symmetric positive definite and 5-diagonal
with the main diagonal ``[5, 6, 6, ..., 6, 6, 5], the ``+1`` and ``-1``
diago... | 10 | 6 | 8 | 0 | 4 | 3 | 1 | 2.74 | 1 | 1 | 0 | 0 | 9 | 2 | 9 | 37 | 168 | 22 | 39 | 21 | 28 | 107 | 36 | 21 | 25 | 2 | 1 | 1 | 10 |
322,129 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/sparse/linalg/_svdp.py | scipy.sparse.linalg._svdp._AProd | from scipy.sparse.linalg import aslinearoperator
import numpy as np
class _AProd:
"""
Wrapper class for linear operator
The call signature of the __call__ method matches the callback of
the PROPACK routines.
"""
def __init__(self, A):
try:
self.A = aslinearoperator(A)
... |
class _AProd:
'''
Wrapper class for linear operator
The call signature of the __call__ method matches the callback of
the PROPACK routines.
'''
def __init__(self, A):
pass
def __call__(self, transa, m, n, x, y, sparm, iparm):
pass
@property
def shape(self):
... | 7 | 1 | 4 | 0 | 4 | 0 | 2 | 0.25 | 0 | 2 | 0 | 0 | 4 | 1 | 4 | 4 | 29 | 4 | 20 | 8 | 13 | 5 | 17 | 6 | 12 | 2 | 0 | 1 | 7 |
322,130 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/_kdtree.py | scipy.spatial._kdtree.KDTree | from ._ckdtree import cKDTree, cKDTreeNode
import numpy as np
class KDTree(cKDTree):
"""kd-tree for quick nearest-neighbor lookup.
This class provides an index into a set of k-dimensional points
which can be used to rapidly look up the nearest neighbors of any
point.
Parameters
----------
... |
class KDTree(cKDTree):
'''kd-tree for quick nearest-neighbor lookup.
This class provides an index into a set of k-dimensional points
which can be used to rapidly look up the nearest neighbors of any
point.
Parameters
----------
data : array_like, shape (n,m)
The n data points of dim... | 32 | 8 | 26 | 4 | 4 | 19 | 1 | 5.38 | 1 | 5 | 1 | 0 | 8 | 1 | 8 | 8 | 661 | 112 | 86 | 41 | 50 | 463 | 70 | 30 | 45 | 4 | 1 | 1 | 30 |
322,131 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/_kdtree.py | scipy.spatial._kdtree.Rectangle | import numpy as np
class Rectangle:
"""Hyperrectangle class.
Represents a Cartesian product of intervals.
"""
def __init__(self, maxes, mins):
"""Construct a hyperrectangle."""
self.maxes = np.maximum(maxes, mins).astype(float)
self.mins = np.minimum(maxes, mins).astype(float)... |
class Rectangle:
'''Hyperrectangle class.
Represents a Cartesian product of intervals.
'''
def __init__(self, maxes, mins):
'''Construct a hyperrectangle.'''
pass
def __repr__(self):
pass
def volume(self):
'''Total volume.'''
pass
def split(self, ... | 9 | 8 | 12 | 1 | 4 | 6 | 1 | 1.56 | 0 | 3 | 0 | 0 | 8 | 3 | 8 | 8 | 106 | 19 | 34 | 15 | 25 | 53 | 25 | 15 | 16 | 1 | 0 | 0 | 8 |
322,132 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/_spherical_voronoi.py | scipy.spatial._spherical_voronoi.SphericalVoronoi | import numpy as np
import scipy
from scipy.spatial import cKDTree
from . import _voronoi
class SphericalVoronoi:
""" Voronoi diagrams on the surface of a sphere.
.. versionadded:: 0.18.0
Parameters
----------
points : ndarray of floats, shape (npoints, ndim)
Coordinates of points from whi... |
class SphericalVoronoi:
''' Voronoi diagrams on the surface of a sphere.
.. versionadded:: 0.18.0
Parameters
----------
points : ndarray of floats, shape (npoints, ndim)
Coordinates of points from which to construct a spherical
Voronoi diagram.
radius : float, optional
R... | 7 | 4 | 28 | 4 | 13 | 11 | 2 | 2.25 | 0 | 6 | 0 | 0 | 6 | 8 | 6 | 6 | 306 | 46 | 80 | 44 | 73 | 180 | 68 | 43 | 61 | 6 | 0 | 1 | 14 |
322,133 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/distance.py | scipy.spatial.distance.CDistMetricWrapper | import numpy as np
import dataclasses
from . import _hausdorff, _distance_pybind, _distance_wrap
@dataclasses.dataclass(frozen=True)
class CDistMetricWrapper:
metric_name: str
def __call__(self, XA, XB, *, out=None, **kwargs):
XA = np.ascontiguousarray(XA)
XB = np.ascontiguousarray(XB)
... | @dataclasses.dataclass(frozen=True)
class CDistMetricWrapper:
def __call__(self, XA, XB, *, out=None, **kwargs):
pass | 3 | 0 | 21 | 2 | 18 | 1 | 2 | 0.05 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 24 | 3 | 20 | 11 | 18 | 1 | 18 | 11 | 16 | 2 | 0 | 1 | 2 |
322,134 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/distance.py | scipy.spatial.distance.MetricInfo | import dataclasses
from collections.abc import Callable
@dataclasses.dataclass(frozen=True)
class MetricInfo:
canonical_name: str
aka: set[str]
dist_func: Callable
cdist_func: Callable
pdist_func: Callable
validator: Callable | None = None
types: list[str] = dataclasses.field(default_factor... | @dataclasses.dataclass(frozen=True)
class MetricInfo:
pass | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1.22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 9 | 4 | 8 | 11 | 9 | 4 | 8 | 0 | 0 | 0 | 0 |
322,135 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/distance.py | scipy.spatial.distance.PDistMetricWrapper | import numpy as np
import dataclasses
from . import _hausdorff, _distance_pybind, _distance_wrap
@dataclasses.dataclass(frozen=True)
class PDistMetricWrapper:
metric_name: str
def __call__(self, X, *, out=None, **kwargs):
X = np.ascontiguousarray(X)
m, n = X.shape
metric_name = self.me... | @dataclasses.dataclass(frozen=True)
class PDistMetricWrapper:
def __call__(self, X, *, out=None, **kwargs):
pass | 3 | 0 | 19 | 1 | 17 | 1 | 2 | 0.05 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 22 | 2 | 19 | 11 | 17 | 1 | 17 | 11 | 15 | 2 | 0 | 1 | 2 |
322,136 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/spatial/transform/_rotation_spline.py | scipy.spatial.transform._rotation_spline.RotationSpline | from ._rotation import Rotation
import numpy as np
from scipy.linalg import solve_banded
class RotationSpline:
"""Interpolate rotations with continuous angular rate and acceleration.
The rotation vectors between each consecutive orientation are cubic
functions of time and it is guaranteed that angular rat... |
class RotationSpline:
'''Interpolate rotations with continuous angular rate and acceleration.
The rotation vectors between each consecutive orientation are cubic
functions of time and it is guaranteed that angular rate and acceleration
are continuous. Such interpolation are analogous to cubic spline
... | 4 | 2 | 43 | 8 | 30 | 5 | 6 | 0.77 | 0 | 4 | 1 | 0 | 3 | 3 | 3 | 3 | 210 | 44 | 94 | 33 | 89 | 72 | 77 | 33 | 72 | 7 | 0 | 2 | 17 |
322,137 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_mptestutils.py | scipy.special._mptestutils.Arg | import numpy as np
class Arg:
"""Generate a set of numbers on the real axis, concentrating on
'interesting' regions and covering all orders of magnitude.
"""
def __init__(self, a=-np.inf, b=np.inf, inclusive_a=True, inclusive_b=True):
if a > b:
raise ValueError('a should be less t... |
class Arg:
'''Generate a set of numbers on the real axis, concentrating on
'interesting' regions and covering all orders of magnitude.
'''
def __init__(self, a=-np.inf, b=np.inf, inclusive_a=True, inclusive_b=True):
pass
def _positive_values(self, a, b, n):
pass
def values(se... | 4 | 2 | 34 | 3 | 27 | 4 | 8 | 0.2 | 0 | 3 | 0 | 0 | 3 | 4 | 3 | 3 | 109 | 12 | 81 | 22 | 77 | 16 | 70 | 22 | 66 | 10 | 0 | 2 | 23 |
322,138 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_mptestutils.py | scipy.special._mptestutils.ComplexArg | import numpy as np
class ComplexArg:
def __init__(self, a=complex(-np.inf, -np.inf), b=complex(np.inf, np.inf)):
self.real = Arg(a.real, b.real)
self.imag = Arg(a.imag, b.imag)
def values(self, n):
m = int(np.floor(np.sqrt(n)))
x = self.real.values(m)
y = self.imag.val... |
class ComplexArg:
def __init__(self, a=complex(-np.inf, -np.inf), b=complex(np.inf, np.inf)):
pass
def values(self, n):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 0 | 3 | 1 | 0 | 2 | 2 | 2 | 2 | 10 | 1 | 9 | 8 | 6 | 0 | 9 | 8 | 6 | 1 | 0 | 0 | 2 |
322,139 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_mptestutils.py | scipy.special._mptestutils.FixedArg | import numpy as np
class FixedArg:
def __init__(self, values):
self._values = np.asarray(values)
def values(self, n):
return self._values |
class FixedArg:
def __init__(self, values):
pass
def values(self, n):
pass | 3 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 6 | 1 | 5 | 4 | 2 | 0 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
322,140 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_mptestutils.py | scipy.special._mptestutils.IntArg | import numpy as np
class IntArg:
def __init__(self, a=-1000, b=1000):
self.a = a
self.b = b
def values(self, n):
v1 = Arg(self.a, self.b).values(max(1 + n // 2, n - 5)).astype(int)
v2 = np.arange(-5, 5)
v = np.unique(np.r_[v1, v2])
v = v[(v >= self.a) & (v < se... |
class IntArg:
def __init__(self, a=-1000, b=1000):
pass
def values(self, n):
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 2 | 2 | 2 | 2 | 11 | 1 | 10 | 8 | 7 | 0 | 10 | 8 | 7 | 1 | 0 | 0 | 2 |
322,141 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_mptestutils.py | scipy.special._mptestutils.MpmathData | import sys
import numpy as np
from scipy.special._testutils import assert_func_equal
import os
class MpmathData:
def __init__(self, scipy_func, mpmath_func, arg_spec, name=None, dps=None, prec=None, n=None, rtol=1e-07, atol=1e-300, ignore_inf_sign=False, distinguish_nan_and_inf=True, nan_ok=True, param_filter=Non... |
class MpmathData:
def __init__(self, scipy_func, mpmath_func, arg_spec, name=None, dps=None, prec=None, n=None, rtol=1e-07, atol=1e-300, ignore_inf_sign=False, distinguish_nan_and_inf=True, nan_ok=True, param_filter=None):
pass
def check(self):
pass
def mptype(x):
... | 7 | 0 | 19 | 2 | 16 | 2 | 4 | 0.11 | 0 | 8 | 1 | 0 | 3 | 14 | 3 | 3 | 109 | 12 | 87 | 30 | 77 | 10 | 65 | 27 | 58 | 8 | 0 | 5 | 21 |
322,142 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_multiufuncs.py | scipy.special._multiufuncs.MultiUFunc | import numpy as np
import collections
class MultiUFunc:
def __init__(self, ufunc_or_ufuncs, doc=None, *, force_complex_output=False, **default_kwargs):
if not isinstance(ufunc_or_ufuncs, np.ufunc):
if isinstance(ufunc_or_ufuncs, collections.abc.Mapping):
ufuncs_iter = ufunc_or_... |
class MultiUFunc:
def __init__(self, ufunc_or_ufuncs, doc=None, *, force_complex_output=False, **default_kwargs):
pass
@property
def __doc__(self):
pass
def _override_key(self, func):
'''Set `key` method by decorating a function.
'''
pass
def _override_ufu... | 11 | 3 | 12 | 2 | 9 | 1 | 3 | 0.08 | 0 | 6 | 0 | 0 | 9 | 9 | 9 | 9 | 116 | 25 | 84 | 35 | 72 | 7 | 69 | 33 | 59 | 7 | 0 | 3 | 23 |
322,143 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_orthogonal.py | scipy.special._orthogonal.orthopoly1d | import numpy as np
from numpy import exp, inf, pi, sqrt, floor, sin, cos, around, hstack, arccos, arange
class orthopoly1d(np.poly1d):
def __init__(self, roots, weights=None, hn=1.0, kn=1.0, wfunc=None, limits=None, monic=False, eval_func=None):
equiv_weights = [weights[k] / wfunc(roots[k]) for k in range... |
class orthopoly1d(np.poly1d):
def __init__(self, roots, weights=None, hn=1.0, kn=1.0, wfunc=None, limits=None, monic=False, eval_func=None):
pass
def eval_func(x):
pass
def __call__(self, v):
pass
def _scale(self, p):
pass | 5 | 0 | 10 | 1 | 9 | 1 | 2 | 0.06 | 1 | 4 | 0 | 0 | 3 | 5 | 3 | 34 | 43 | 7 | 34 | 17 | 28 | 2 | 31 | 16 | 26 | 3 | 1 | 2 | 9 |
322,144 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_sf_error.py | scipy.special._sf_error.SpecialFunctionError | class SpecialFunctionError(Exception):
"""Exception that can be raised by special functions."""
pass | class SpecialFunctionError(Exception):
'''Exception that can be raised by special functions.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 3 | 0 | 0 |
322,145 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_sf_error.py | scipy.special._sf_error.SpecialFunctionWarning | class SpecialFunctionWarning(Warning):
"""Warning that can be emitted by special functions."""
pass | class SpecialFunctionWarning(Warning):
'''Warning that can be emitted by special functions.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 3 | 0 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,146 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_support_alternative_backends.py | scipy.special._support_alternative_backends._FuncInfo | from types import ModuleType
import numpy as np
from scipy._lib._array_api import array_namespace, scipy_namespace_for, is_numpy, is_dask, is_marray, xp_promote, xp_capabilities, SCIPY_ARRAY_API
from collections.abc import Callable
import functools
import scipy._lib.array_api_extra as xpx
import operator
from dataclass... | @dataclass
class _FuncInfo:
@property
def name(self):
pass
def __hash__(self):
pass
def __eq__(self, other):
pass
@property
def wrapper(self):
pass
@functools.wraps(self.func)
def wrapped(*args, **kwargs):
pass
... | 15 | 0 | 12 | 1 | 8 | 3 | 2 | 0.55 | 0 | 1 | 0 | 0 | 5 | 0 | 5 | 5 | 115 | 18 | 64 | 27 | 49 | 35 | 57 | 23 | 46 | 7 | 0 | 2 | 17 |
322,147 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_testutils.py | scipy.special._testutils.FuncData | import pytest
import operator
from numpy.testing import assert_
import os
import numpy as np
class FuncData:
"""
Data set for checking a special function.
Parameters
----------
func : function
Function to test
data : numpy array
columnar data to use for testing
param_column... |
class FuncData:
'''
Data set for checking a special function.
Parameters
----------
func : function
Function to test
data : numpy array
columnar data to use for testing
param_columns : int or tuple of ints
Columns indices in which the parameters to `func` lie.
... | 7 | 3 | 34 | 3 | 29 | 2 | 7 | 0.31 | 0 | 14 | 0 | 0 | 4 | 14 | 4 | 4 | 234 | 25 | 159 | 63 | 149 | 50 | 141 | 60 | 134 | 18 | 0 | 3 | 39 |
322,148 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/special/_testutils.py | scipy.special._testutils.MissingModule | class MissingModule:
def __init__(self, name):
self.name = name | class MissingModule:
def __init__(self, name):
pass | 2 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 3 | 0 | 3 | 3 | 1 | 0 | 3 | 3 | 1 | 1 | 0 | 0 | 1 |
322,149 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_axis_nan_policy.py | scipy.stats._axis_nan_policy.SmallSampleWarning | class SmallSampleWarning(RuntimeWarning):
pass | class SmallSampleWarning(RuntimeWarning):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 5 | 0 | 0 |
322,150 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_binomtest.py | scipy.stats._binomtest.BinomTestResult | from ._common import ConfidenceInterval
class BinomTestResult:
"""
Result of `scipy.stats.binomtest`.
Attributes
----------
k : int
The number of successes (copied from `binomtest` input).
n : int
The number of trials (copied from `binomtest` input).
alternative : str
... |
class BinomTestResult:
'''
Result of `scipy.stats.binomtest`.
Attributes
----------
k : int
The number of successes (copied from `binomtest` input).
n : int
The number of trials (copied from `binomtest` input).
alternative : str
Indicates the alternative hypothesis s... | 4 | 2 | 28 | 2 | 11 | 15 | 2 | 1.85 | 0 | 1 | 0 | 0 | 3 | 6 | 3 | 3 | 105 | 11 | 33 | 12 | 29 | 61 | 20 | 12 | 16 | 4 | 0 | 1 | 6 |
322,151 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_censored_data.py | scipy.stats._censored_data.CensoredData | import numpy as np
class CensoredData:
"""
Instances of this class represent censored data.
Instances may be passed to the ``fit`` method of continuous
univariate SciPy distributions for maximum likelihood estimation.
The *only* method of the univariate continuous distributions that
understand... |
class CensoredData:
'''
Instances of this class represent censored data.
Instances may be passed to the ``fit`` method of continuous
univariate SciPy distributions for maximum likelihood estimation.
The *only* method of the univariate continuous distributions that
understands `CensoredData` is ... | 16 | 8 | 18 | 2 | 7 | 9 | 2 | 2.83 | 0 | 1 | 0 | 0 | 9 | 4 | 12 | 12 | 400 | 63 | 88 | 39 | 71 | 249 | 73 | 35 | 60 | 5 | 0 | 1 | 20 |
322,152 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.FitDataError | class FitDataError(ValueError):
"""Raised when input data is inconsistent with fixed parameters."""
def __init__(self, distr, lower, upper):
self.args = (f'Invalid values in `data`. Maximum likelihood estimation with {distr!r} requires that {lower!r} < (x - loc)/scale < {upper!r} for each x in `data`... | class FitDataError(ValueError):
'''Raised when input data is inconsistent with fixed parameters.'''
def __init__(self, distr, lower, upper):
pass | 2 | 1 | 6 | 0 | 6 | 0 | 1 | 0.57 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 12 | 11 | 0 | 7 | 3 | 5 | 4 | 3 | 3 | 1 | 1 | 4 | 0 | 1 |
322,153 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.FitSolverError | from scipy.stats._warnings_errors import FitError
class FitSolverError(FitError):
"""
Raised when a solver fails to converge while fitting a distribution.
"""
def __init__(self, mesg):
emsg = 'Solver for the MLE equations failed to converge: '
emsg += mesg.replace('\n', '')
sel... |
class FitSolverError(FitError):
'''
Raised when a solver fails to converge while fitting a distribution.
'''
def __init__(self, mesg):
pass | 2 | 1 | 4 | 0 | 4 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 13 | 10 | 0 | 5 | 4 | 3 | 5 | 5 | 4 | 3 | 1 | 5 | 0 | 1 |
322,154 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.FitUniformFixedScaleDataError | class FitUniformFixedScaleDataError(FitDataError):
def __init__(self, ptp, fscale):
self.args = f'Invalid values in `data`. Maximum likelihood estimation with the uniform distribution and fixed scale requires that np.ptp(data) <= fscale, but np.ptp(data) = {ptp} and fscale = {fscale}.' | class FitUniformFixedScaleDataError(FitDataError):
def __init__(self, ptp, fscale):
pass | 2 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 13 | 8 | 0 | 8 | 3 | 6 | 0 | 3 | 3 | 1 | 1 | 5 | 0 | 1 |
322,155 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.alpha_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class alpha_gen(rv_continuous):
"""An alpha continuous random variable.
%(before_notes)s
Notes
-----
... |
class alpha_gen(rv_continuous):
'''An alpha continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `alpha` ([1]_, [2]_) is:
.. math::
f(x, a) = \frac{1}{x^2 \Phi(a) \sqrt{2\pi}} *
\exp(-\frac{1}{2} (a-1/x)^2)
where :math:`\Phi... | 7 | 1 | 2 | 0 | 2 | 0 | 1 | 1.57 | 1 | 1 | 1 | 0 | 6 | 0 | 6 | 83 | 52 | 16 | 14 | 8 | 7 | 22 | 14 | 8 | 7 | 1 | 2 | 0 | 6 |
322,156 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.anglit_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class anglit_gen(rv_continuous):
"""An anglit continuous random variable.
%(before_notes)s
Notes
-----... |
class anglit_gen(rv_continuous):
'''An anglit continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `anglit` is:
.. math::
f(x) = \sin(2x + \pi/2) = \cos(2x)
for :math:`-\pi/4 \le x \le \pi/4`.
%(after_notes)s
%(example)s
'''
... | 8 | 1 | 2 | 0 | 2 | 0 | 1 | 0.8 | 1 | 0 | 0 | 0 | 7 | 0 | 7 | 84 | 41 | 14 | 15 | 8 | 7 | 12 | 15 | 8 | 7 | 1 | 2 | 0 | 7 |
322,157 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.arcsine_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class arcsine_gen(rv_continuous):
"""An arcsine continuous random variable.
%(before_notes)s
Notes
---... |
class arcsine_gen(rv_continuous):
'''An arcsine continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `arcsine` is:
.. math::
f(x) = \frac{1}{\pi \sqrt{x (1-x)}}
for :math:`0 < x < 1`.
%(after_notes)s
%(example)s
'''
def _shap... | 7 | 1 | 3 | 0 | 3 | 0 | 1 | 0.67 | 1 | 1 | 0 | 0 | 6 | 0 | 6 | 83 | 43 | 13 | 18 | 11 | 11 | 12 | 18 | 11 | 11 | 1 | 2 | 1 | 6 |
322,158 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.argus_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class argus_gen(rv_continuous):
"""
Argus distribution
%(before_notes)s
Not... |
class argus_gen(rv_continuous):
'''
Argus distribution
%(before_notes)s
Notes
-----
The probability density function for `argus` is:
.. math::
f(x, \chi) = \frac{\chi^3}{\sqrt{2\pi} \Psi(\chi)} x \sqrt{1-x^2}
\exp(-\chi^2 (1 - x^2)/2)
for :math:`0 < x < 1` a... | 9 | 1 | 19 | 0 | 11 | 7 | 3 | 0.95 | 1 | 7 | 1 | 0 | 8 | 0 | 8 | 85 | 200 | 23 | 91 | 38 | 82 | 86 | 83 | 38 | 74 | 9 | 2 | 3 | 20 |
322,159 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.beta_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from scipy._lib._util import _lazyselect
from ._censored_data import CensoredData
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInf... |
class beta_gen(rv_continuous):
'''A beta continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `beta` is:
.. math::
f(x, a, b) = \frac{\Gamma(a+b) x^{a-1} (1-x)^{b-1}}
{\Gamma(a) \Gamma(b)}
for :math:`0 <= x <= 1`, :m... | 21 | 1 | 13 | 2 | 10 | 2 | 2 | 0.34 | 1 | 7 | 4 | 0 | 12 | 0 | 12 | 89 | 251 | 49 | 151 | 60 | 126 | 51 | 111 | 59 | 92 | 10 | 2 | 2 | 28 |
322,160 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.betaprime_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
import scipy.stats as stats
class betaprime_gen(rv_c... |
class betaprime_gen(rv_continuous):
'''A beta prime continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `betaprime` is:
.. math::
f(x, a, b) = \frac{x^{a-1} (1+x)^{-a-b}}{\beta(a, b)}
for :math:`x >= 0`, :math:`a > 0`, :math:`b > 0`, where
... | 9 | 1 | 7 | 0 | 5 | 2 | 1 | 1.15 | 1 | 4 | 1 | 0 | 8 | 0 | 8 | 85 | 109 | 21 | 41 | 17 | 32 | 47 | 31 | 17 | 22 | 3 | 2 | 2 | 10 |
322,161 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.bradford_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class bradford_gen(rv_continuous):
"""A Bradford continuous random variable.
%(befor... |
class bradford_gen(rv_continuous):
'''A Bradford continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `bradford` is:
.. math::
f(x, c) = \frac{c}{\log(1+c) (1+cx)}
for :math:`0 <= x <= 1` and :math:`c > 0`.
`bradford` takes ``c`` as a sha... | 7 | 1 | 4 | 0 | 4 | 0 | 1 | 0.5 | 1 | 1 | 1 | 0 | 6 | 0 | 6 | 83 | 53 | 14 | 26 | 13 | 19 | 13 | 25 | 13 | 18 | 3 | 2 | 1 | 8 |
322,162 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.burr12_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class burr12_gen(rv_continuous):
"""A Burr (Type... |
class burr12_gen(rv_continuous):
'''A Burr (Type XII) continuous random variable.
%(before_notes)s
See Also
--------
fisk : a special case of either `burr` or `burr12` with ``d=1``
burr : Burr Type III distribution
Notes
-----
The probability density function for `burr12` is:
..... | 12 | 1 | 3 | 0 | 3 | 0 | 1 | 1.22 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 85 | 25 | 27 | 14 | 15 | 33 | 26 | 14 | 14 | 1 | 2 | 0 | 11 |
322,163 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.burr_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class burr_gen(rv_continuous):
"""A Burr (Type I... |
class burr_gen(rv_continuous):
'''A Burr (Type III) continuous random variable.
%(before_notes)s
See Also
--------
fisk : a special case of either `burr` or `burr12` with ``d=1``
burr12 : Burr Type XII distribution
mielke : Mielke Beta-Kappa / Dagum distribution
Notes
-----
The ... | 13 | 1 | 5 | 0 | 5 | 0 | 1 | 0.62 | 1 | 1 | 1 | 1 | 11 | 0 | 11 | 88 | 120 | 23 | 60 | 25 | 47 | 37 | 41 | 25 | 28 | 2 | 2 | 1 | 15 |
322,164 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.cauchy_gen | import scipy._lib.array_api_extra as xpx
from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._censored_data import CensoredData
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isinteg... |
class cauchy_gen(rv_continuous):
'''A Cauchy continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `cauchy` is
.. math::
f(x) = \frac{1}{\pi (1 + x^2)}
for a real number :math:`x`.
This distribution uses routines from the Boost Math C++ li... | 11 | 1 | 4 | 0 | 3 | 1 | 1 | 0.9 | 1 | 2 | 1 | 0 | 10 | 0 | 10 | 87 | 74 | 19 | 29 | 13 | 18 | 26 | 26 | 13 | 15 | 2 | 2 | 1 | 11 |
322,165 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.chi2_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class chi2_gen(rv_continuous):
"""A chi-squared ... |
class chi2_gen(rv_continuous):
'''A chi-squared continuous random variable.
For the noncentral chi-square distribution, see `ncx2`.
%(before_notes)s
See Also
--------
ncx2
Notes
-----
The probability density function for `chi2` is:
.. math::
f(x, k) = \frac{1}{2^{k/2} \G... | 13 | 1 | 5 | 0 | 4 | 1 | 1 | 0.77 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 86 | 24 | 35 | 20 | 22 | 27 | 32 | 20 | 19 | 1 | 2 | 0 | 12 |
322,166 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.chi_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class chi_gen(rv_continuous):
"""A chi continuou... |
class chi_gen(rv_continuous):
'''A chi continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `chi` is:
.. math::
f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
x^{k-1} \exp \left( -x^2/2 \right)
for :math:`x >= 0` a... | 13 | 1 | 4 | 0 | 3 | 0 | 1 | 0.7 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 79 | 23 | 33 | 18 | 20 | 23 | 31 | 18 | 18 | 1 | 2 | 0 | 12 |
322,167 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.cosine_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special._ufuncs as scu
class cosine_gen(rv_continuous):
"""A c... |
class cosine_gen(rv_continuous):
'''A cosine continuous random variable.
%(before_notes)s
Notes
-----
The cosine distribution is an approximation to the normal distribution.
The probability density function for `cosine` is:
.. math::
f(x) = \frac{1}{2\pi} (1+\cos(x))
for :math:`... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 0.54 | 1 | 0 | 0 | 0 | 9 | 0 | 9 | 86 | 53 | 16 | 24 | 13 | 14 | 13 | 22 | 13 | 12 | 1 | 2 | 0 | 9 |
322,168 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.crystalball_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class crystalball_gen(rv_continuous):
"""
Cr... |
class crystalball_gen(rv_continuous):
'''
Crystalball distribution
%(before_notes)s
Notes
-----
The probability density function for `crystalball` is:
.. math::
f(x, \beta, m) = \begin{cases}
N \exp(-x^2 / 2), &\text{for } x > -\beta\\
... | 21 | 8 | 8 | 1 | 6 | 2 | 1 | 0.73 | 1 | 5 | 1 | 0 | 9 | 0 | 9 | 86 | 166 | 36 | 75 | 41 | 54 | 55 | 62 | 41 | 41 | 2 | 2 | 1 | 21 |
322,169 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.dgamma_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.stats as stats
import scipy.special as sc
class dgamma_gen(rv_continuous):
"""A double gamma continuous... |
class dgamma_gen(rv_continuous):
'''A double gamma continuous random variable.
The double gamma distribution is also known as the reflected gamma
distribution [1]_.
%(before_notes)s
Notes
-----
The probability density function for `dgamma` is:
.. math::
f(x, a) = \frac{1}{2\Gamm... | 11 | 1 | 3 | 0 | 3 | 0 | 1 | 0.62 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 75 | 20 | 34 | 16 | 23 | 21 | 26 | 16 | 15 | 1 | 2 | 0 | 10 |
322,170 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.dpareto_lognorm_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special._ufuncs as scu
import scipy.special as sc
class dpareto_lognorm_gen(rv_continuous):
"""A double... |
class dpareto_lognorm_gen(rv_continuous):
'''A double Pareto lognormal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `dpareto_lognorm` is:
.. math::
f(x, \mu, \sigma, \alpha, \beta) =
\frac{\alpha \beta}{(\alpha + \beta) x}
... | 13 | 1 | 5 | 0 | 5 | 1 | 1 | 0.75 | 1 | 3 | 1 | 0 | 12 | 0 | 12 | 89 | 133 | 24 | 64 | 41 | 51 | 48 | 61 | 41 | 48 | 1 | 2 | 1 | 12 |
322,171 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.dweibull_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.stats as stats
import scipy.special as sc
class dweibull_gen(rv_continuous):
"""A double Weibull contin... |
class dweibull_gen(rv_continuous):
'''A double Weibull continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `dweibull` is given by
.. math::
f(x, c) = c / 2 |x|^{c-1} \exp(-|x|^c)
for a real number :math:`x` and :math:`c > 0`.
`dweibull` ... | 12 | 1 | 3 | 0 | 3 | 0 | 1 | 0.46 | 1 | 1 | 1 | 0 | 11 | 0 | 11 | 88 | 70 | 19 | 35 | 23 | 23 | 16 | 35 | 23 | 23 | 1 | 2 | 0 | 11 |
322,172 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.erlang_gen | import numpy as np
import warnings
from ._censored_data import CensoredData
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
from scipy._lib.doccer import extend_notes_in_docstring, replace... |
class erlang_gen(gamma_gen):
'''An Erlang continuous random variable.
%(before_notes)s
See Also
--------
gamma
Notes
-----
The Erlang distribution is a special case of the Gamma distribution, with
the shape parameter `a` an integer. Note that this restriction is not
enforced by... | 6 | 1 | 5 | 0 | 4 | 1 | 2 | 0.83 | 1 | 5 | 2 | 0 | 4 | 0 | 4 | 94 | 53 | 9 | 24 | 15 | 12 | 20 | 16 | 8 | 11 | 2 | 3 | 1 | 6 |
322,173 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.expon_gen | from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstring, inherit_docstring_from
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.... |
class expon_gen(rv_continuous):
'''An exponential continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `expon` is:
.. math::
f(x) = \exp(-x)
for :math:`x \ge 0`.
%(after_notes)s
A common parameterization for `expon` is in terms of the... | 15 | 1 | 5 | 1 | 4 | 1 | 2 | 0.43 | 1 | 4 | 1 | 0 | 12 | 0 | 12 | 89 | 107 | 30 | 54 | 19 | 34 | 23 | 44 | 18 | 31 | 7 | 2 | 2 | 18 |
322,174 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.exponnorm_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class exponnorm_gen(rv_continuous):
"""An exponentially modified Normal continuous random variable.
Also known ... |
class exponnorm_gen(rv_continuous):
'''An exponentially modified Normal continuous random variable.
Also known as the exponentially modified Gaussian distribution [1]_.
%(before_notes)s
Notes
-----
The probability density function for `exponnorm` is:
.. math::
f(x, K) = \frac{1}{2K}... | 8 | 1 | 4 | 0 | 4 | 0 | 1 | 0.93 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 76 | 20 | 29 | 22 | 21 | 27 | 29 | 22 | 21 | 1 | 2 | 0 | 7 |
322,175 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.exponpow_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class exponpow_gen(rv_continuous):
"""An exponential power continuous random variable.
... |
class exponpow_gen(rv_continuous):
'''An exponential power continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `exponpow` is:
.. math::
f(x, b) = b x^{b-1} \exp(1 + x^b - \exp(x^b))
for :math:`x \ge 0`, :math:`b > 0`. Note that this is a di... | 8 | 1 | 2 | 0 | 2 | 0 | 1 | 1.06 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 51 | 16 | 17 | 10 | 9 | 18 | 17 | 10 | 9 | 1 | 2 | 0 | 7 |
322,176 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.exponweib_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class exponweib_gen(rv_continuous):
"""An exponentiated Weibull continuous random variabl... |
class exponweib_gen(rv_continuous):
'''An exponentiated Weibull continuous random variable.
%(before_notes)s
See Also
--------
weibull_min, numpy.random.Generator.weibull
Notes
-----
The probability density function for `exponweib` is:
.. math::
f(x, a, c) = a c [1-\exp(-x^c... | 8 | 1 | 3 | 0 | 3 | 0 | 1 | 1.23 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 70 | 21 | 22 | 14 | 14 | 27 | 21 | 14 | 13 | 1 | 2 | 0 | 7 |
322,177 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.f_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class f_gen(rv_continuous):
"""An F continuous r... |
class f_gen(rv_continuous):
'''An F continuous random variable.
For the noncentral F distribution, see `ncf`.
%(before_notes)s
See Also
--------
ncf
Notes
-----
The F distribution with :math:`df_1 > 0` and :math:`df_2 > 0` degrees of freedom is
the distribution of the ratio of t... | 10 | 1 | 7 | 1 | 6 | 1 | 1 | 0.58 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 86 | 108 | 26 | 52 | 24 | 42 | 30 | 35 | 24 | 25 | 1 | 2 | 0 | 9 |
322,178 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.fatiguelife_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class fatiguelife_gen(rv_continuous):
"""A fatigue-life (Birnbaum-Saunders) continuous random variable.
%(befor... |
class fatiguelife_gen(rv_continuous):
'''A fatigue-life (Birnbaum-Saunders) continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `fatiguelife` is:
.. math::
f(x, c) = \frac{x+1}{2c\sqrt{2\pi x^3}} \exp(-\frac{(x-1)^2}{2x c^2})
for :math:`x >=... | 10 | 1 | 4 | 0 | 3 | 1 | 1 | 0.7 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 86 | 75 | 19 | 33 | 23 | 23 | 23 | 32 | 23 | 22 | 1 | 2 | 0 | 9 |
322,179 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.fisk_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class fisk_gen(burr_gen):
"""A Fisk continuous random variable.
The Fisk distribution is also known as the log-... |
class fisk_gen(burr_gen):
'''A Fisk continuous random variable.
The Fisk distribution is also known as the log-logistic distribution.
%(before_notes)s
See Also
--------
burr
Notes
-----
The probability density function for `fisk` is:
.. math::
f(x, c) = \frac{c x^{c-1}}
... | 13 | 1 | 2 | 0 | 2 | 0 | 1 | 1.12 | 1 | 1 | 1 | 0 | 12 | 0 | 12 | 100 | 80 | 27 | 25 | 13 | 12 | 28 | 25 | 13 | 12 | 1 | 3 | 0 | 12 |
322,180 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.foldcauchy_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class foldcauchy_gen(rv_continuous):
"""A folded Cauchy continuous random variable.
%(before_notes)s
Notes... |
class foldcauchy_gen(rv_continuous):
'''A folded Cauchy continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `foldcauchy` is:
.. math::
f(x, c) = \frac{1}{\pi (1+(x-c)^2)} + \frac{1}{\pi (1+(x+c)^2)}
for :math:`x \ge 0` and :math:`c \ge 0`.
... | 8 | 1 | 3 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 46 | 14 | 16 | 8 | 8 | 16 | 15 | 8 | 7 | 1 | 2 | 0 | 7 |
322,181 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.foldnorm_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class foldnorm_gen(rv_continuous):
"""A folded normal continuous random variable.
%(... |
class foldnorm_gen(rv_continuous):
'''A folded normal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `foldnorm` is:
.. math::
f(x, c) = \sqrt{2/\pi} cosh(c x) \exp(-\frac{x^2+c^2}{2})
for :math:`x \ge 0` and :math:`c \ge 0`.
`foldno... | 8 | 1 | 5 | 1 | 3 | 1 | 1 | 0.64 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 60 | 19 | 25 | 15 | 17 | 16 | 25 | 15 | 17 | 1 | 2 | 0 | 7 |
322,182 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gamma_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._censored_data import CensoredData
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
from scipy._lib.doccer import extend_no... |
class gamma_gen(rv_continuous):
'''A gamma continuous random variable.
%(before_notes)s
See Also
--------
erlang, expon
Notes
-----
The probability density function for `gamma` is:
.. math::
f(x, a) = \frac{x^{a-1} e^{-x}}{\Gamma(a)}
for :math:`x \ge 0`, :math:`a > 0`. H... | 17 | 1 | 10 | 1 | 6 | 3 | 2 | 0.66 | 1 | 5 | 3 | 1 | 13 | 0 | 13 | 90 | 208 | 45 | 98 | 39 | 75 | 65 | 85 | 32 | 69 | 16 | 2 | 2 | 31 |
322,183 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gausshyper_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class gausshyper_gen(rv_continuous):
"""A Gauss hypergeometric continuous random variable... |
class gausshyper_gen(rv_continuous):
'''A Gauss hypergeometric continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `gausshyper` is:
.. math::
f(x, a, b, c, z) = C x^{a-1} (1-x)^{b-1} (1+zx)^{-c}
for :math:`0 \le x \le 1`, :math:`a,b > 0`, :m... | 5 | 1 | 5 | 0 | 4 | 0 | 1 | 1.22 | 1 | 1 | 1 | 0 | 4 | 0 | 4 | 81 | 54 | 14 | 18 | 13 | 13 | 22 | 17 | 13 | 12 | 1 | 2 | 0 | 4 |
322,184 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genexpon_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class genexpon_gen(rv_continuous):
"""A generalized exponential continuous random variabl... |
class genexpon_gen(rv_continuous):
'''A generalized exponential continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `genexpon` is:
.. math::
f(x, a, b, c) = (a + b (1 - \exp(-c x)))
\exp(-a x - b x + \frac{b}{c} (1-\exp(... | 8 | 1 | 3 | 0 | 3 | 0 | 1 | 0.96 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 62 | 17 | 23 | 15 | 15 | 22 | 22 | 15 | 14 | 1 | 2 | 0 | 7 |
322,185 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genextreme_gen | import scipy._lib.array_api_extra as xpx
from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._censored_data import CensoredData
import operator
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _ku... |
class genextreme_gen(rv_continuous):
'''A generalized extreme value continuous random variable.
%(before_notes)s
See Also
--------
gumbel_r
Notes
-----
For :math:`c=0`, `genextreme` is equal to `gumbel_r` with
probability density function
.. math::
f(x) = \exp(-\exp(-x))... | 23 | 1 | 6 | 0 | 5 | 1 | 1 | 0.34 | 1 | 3 | 2 | 0 | 15 | 0 | 15 | 92 | 169 | 36 | 99 | 50 | 76 | 34 | 79 | 50 | 56 | 3 | 2 | 1 | 24 |
322,186 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gengamma_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class gengamma_gen(rv_continuous):
"""A generali... |
class gengamma_gen(rv_continuous):
'''A generalized gamma continuous random variable.
%(before_notes)s
See Also
--------
gamma, invgamma, weibull_min
Notes
-----
The probability density function for `gengamma` is ([1]_):
.. math::
f(x, a, c) = \frac{|c| x^{c a-1} \exp(-x^c)}... | 14 | 1 | 5 | 0 | 4 | 0 | 1 | 0.45 | 1 | 1 | 1 | 0 | 11 | 0 | 11 | 88 | 94 | 23 | 49 | 31 | 35 | 22 | 44 | 31 | 30 | 1 | 2 | 0 | 13 |
322,187 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genhalflogistic_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class genhalflogistic_gen(rv_continuous):
"""A generalized half-logistic continuous random variable.
%(before_n... |
class genhalflogistic_gen(rv_continuous):
'''A generalized half-logistic continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `genhalflogistic` is:
.. math::
f(x, c) = \frac{2 (1 - c x)^{1/(c-1)}}{[1 + (1 - c x)^{1/c}]^2}
for :math:`0 \le x \... | 7 | 1 | 4 | 0 | 3 | 0 | 1 | 0.7 | 1 | 1 | 1 | 0 | 6 | 0 | 6 | 83 | 48 | 14 | 20 | 14 | 13 | 14 | 20 | 14 | 13 | 1 | 2 | 0 | 6 |
322,188 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genhyperbolic_gen | import numpy as np
import warnings
from scipy._lib._ccallback import LowLevelCallable
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import ctypes
import scipy.special as sc
from scipy im... |
class genhyperbolic_gen(rv_continuous):
'''A generalized hyperbolic continuous random variable.
%(before_notes)s
See Also
--------
t, norminvgauss, geninvgauss, laplace, cauchy
Notes
-----
The probability density function for `genhyperbolic` is:
.. math::
f(x, p, a, b) =
... | 17 | 2 | 11 | 0 | 8 | 2 | 1 | 1.01 | 1 | 7 | 2 | 0 | 9 | 0 | 10 | 87 | 227 | 36 | 95 | 43 | 78 | 96 | 59 | 40 | 46 | 3 | 2 | 1 | 14 |
322,189 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.geninvgauss_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
import warnings
from scipy._lib._ccallback import LowLevelCallable
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import ctypes... |
class geninvgauss_gen(rv_continuous):
'''A Generalized Inverse Gaussian continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `geninvgauss` is:
.. math::
f(x, p, b) = x^{p-1} \exp(-b (x + 1/x) / 2) / (2 K_p(b))
where ``x > 0``, `p` is a real n... | 15 | 1 | 20 | 2 | 14 | 5 | 3 | 0.47 | 1 | 12 | 2 | 0 | 10 | 0 | 10 | 87 | 314 | 45 | 184 | 78 | 169 | 87 | 161 | 78 | 146 | 18 | 2 | 3 | 39 |
322,190 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genlogistic_gen | import scipy._lib.array_api_extra as xpx
from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit... |
class genlogistic_gen(rv_continuous):
'''A generalized logistic continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `genlogistic` is:
.. math::
f(x, c) = c \frac{\exp(-x)}
{(1 + \exp(-x))^{c+1}}
for real :math:`x` an... | 11 | 1 | 4 | 0 | 3 | 1 | 1 | 0.94 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 83 | 19 | 33 | 18 | 22 | 31 | 30 | 18 | 19 | 1 | 2 | 0 | 10 |
322,191 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gennorm_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class gennorm_gen(rv_continuous):
"""A generalized normal continuous random variable.
... |
class gennorm_gen(rv_continuous):
'''A generalized normal continuous random variable.
%(before_notes)s
See Also
--------
laplace : Laplace distribution
norm : normal distribution
Notes
-----
The probability density function for `gennorm` is [1]_:
.. math::
f(x, \beta) = ... | 12 | 1 | 4 | 0 | 3 | 0 | 1 | 0.92 | 1 | 1 | 1 | 0 | 11 | 0 | 11 | 88 | 94 | 23 | 37 | 19 | 25 | 34 | 36 | 19 | 24 | 3 | 2 | 1 | 13 |
322,192 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.genpareto_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class genpareto_gen(rv_continuous):
"""A general... |
class genpareto_gen(rv_continuous):
'''A generalized Pareto continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `genpareto` is:
.. math::
f(x, c) = (1 + c x)^{-1 - 1/c}
defined for :math:`x \ge 0` if :math:`c \ge 0`, and for
:math:`0 \le... | 15 | 1 | 5 | 0 | 5 | 0 | 1 | 0.34 | 1 | 2 | 1 | 0 | 13 | 0 | 13 | 90 | 115 | 33 | 61 | 21 | 46 | 21 | 45 | 21 | 30 | 5 | 2 | 1 | 19 |
322,193 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gibrat_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class gibrat_gen(rv_continuous):
"""A Gibrat continuous random variable.
%(before_notes)s
Notes
-----
... |
class gibrat_gen(rv_continuous):
'''A Gibrat continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `gibrat` is:
.. math::
f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)
for :math:`x >= 0`.
`gibrat` is a special case of `logn... | 11 | 1 | 3 | 0 | 3 | 0 | 1 | 0.48 | 1 | 0 | 0 | 0 | 10 | 0 | 10 | 87 | 59 | 19 | 27 | 17 | 16 | 13 | 27 | 17 | 16 | 1 | 2 | 0 | 10 |
322,194 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gompertz_gen | import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import scipy.special as sc
class gompertz_gen(rv_continuous):
"""A Gompertz (or truncated Gumbel) continuous random ... |
class gompertz_gen(rv_continuous):
'''A Gompertz (or truncated Gumbel) continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `gompertz` is:
.. math::
f(x, c) = c \exp(x) \exp(-c (e^x-1))
for :math:`x \ge 0`, :math:`c > 0`.
`gompertz` takes... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 0.76 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 46 | 16 | 17 | 9 | 8 | 13 | 17 | 9 | 8 | 1 | 2 | 0 | 8 |
322,195 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gumbel_l_gen | from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
from sci... |
class gumbel_l_gen(rv_continuous):
'''A left-skewed Gumbel continuous random variable.
%(before_notes)s
See Also
--------
gumbel_r, gompertz, genextreme
Notes
-----
The probability density function for `gumbel_l` is:
.. math::
f(x) = \exp(x - e^x)
for real :math:`x`.
... | 14 | 1 | 3 | 0 | 2 | 1 | 1 | 0.86 | 1 | 0 | 0 | 0 | 11 | 0 | 11 | 88 | 76 | 22 | 29 | 14 | 15 | 25 | 26 | 13 | 14 | 2 | 2 | 1 | 12 |
322,196 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.gumbel_r_gen | from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
from sci... |
class gumbel_r_gen(rv_continuous):
'''A right-skewed Gumbel continuous random variable.
%(before_notes)s
See Also
--------
gumbel_l, gompertz, genextreme
Notes
-----
The probability density function for `gumbel_r` is:
.. math::
f(x) = \exp(-(x + e^{-x}))
for real :math:`... | 18 | 1 | 7 | 1 | 4 | 2 | 1 | 0.74 | 1 | 0 | 0 | 0 | 11 | 0 | 11 | 88 | 127 | 28 | 57 | 27 | 39 | 42 | 49 | 26 | 33 | 5 | 2 | 2 | 19 |
322,197 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.halfcauchy_gen | from scipy.optimize import root_scalar
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstrin... |
class halfcauchy_gen(rv_continuous):
'''A Half-Cauchy continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `halfcauchy` is:
.. math::
f(x) = \frac{2}{\pi (1 + x^2)}
for :math:`x \ge 0`.
%(after_notes)s
%(example)s
'''
def _sh... | 15 | 1 | 6 | 1 | 5 | 1 | 1 | 0.35 | 1 | 3 | 1 | 0 | 10 | 0 | 10 | 87 | 88 | 24 | 48 | 24 | 33 | 17 | 43 | 23 | 30 | 5 | 2 | 2 | 16 |
322,198 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.halfgennorm_gen | import scipy.special as sc
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class halfgennorm_gen(rv_continuous):
"""The upper half of a generalized normal continuou... |
class halfgennorm_gen(rv_continuous):
'''The upper half of a generalized normal continuous random variable.
%(before_notes)s
See Also
--------
gennorm : generalized normal distribution
expon : exponential distribution
halfnorm : half normal distribution
Notes
-----
The probabili... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 1.59 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 62 | 18 | 17 | 9 | 8 | 27 | 17 | 9 | 8 | 1 | 2 | 0 | 8 |
322,199 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/stats/_continuous_distns.py | scipy.stats._continuous_distns.halflogistic_gen | import scipy._lib.array_api_extra as xpx
from ._constants import _XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI, _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI
import numpy as np
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit... |
class halflogistic_gen(rv_continuous):
'''A half-logistic continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `halflogistic` is:
.. math::
f(x) = \frac{ 2 e^{-x} }{ (1+e^{-x})^2 }
= \frac{1}{2} \text{sech}(x/2)^2
for :math:`x \g... | 14 | 1 | 11 | 1 | 8 | 2 | 2 | 0.46 | 1 | 2 | 1 | 0 | 10 | 0 | 10 | 87 | 124 | 25 | 69 | 32 | 55 | 32 | 62 | 31 | 50 | 6 | 2 | 2 | 21 |
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