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322,200 | 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.halfnorm_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
from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstring, inherit_docstring_from
import scipy.... |
class halfnorm_gen(rv_continuous):
'''A half-normal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `halfnorm` is:
.. math::
f(x) = \sqrt{2/\pi} \exp(-x^2 / 2)
for :math:`x >= 0`.
`halfnorm` is a special case of `chi` with ``df=1``.
... | 14 | 1 | 4 | 0 | 4 | 0 | 1 | 0.33 | 1 | 2 | 1 | 0 | 11 | 0 | 11 | 88 | 81 | 24 | 43 | 17 | 29 | 14 | 35 | 16 | 23 | 5 | 2 | 2 | 15 |
322,201 | 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.hypsecant_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 hypsecant_gen(rv_continuous):
"""A hyperbolic secant continuous random variable.
%(before_notes)s
No... |
class hypsecant_gen(rv_continuous):
'''A hyperbolic secant continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `hypsecant` is:
.. math::
f(x) = \frac{1}{\pi} \text{sech}(x)
for a real number :math:`x`.
%(after_notes)s
%(example)s
... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 0.71 | 1 | 0 | 0 | 0 | 8 | 0 | 8 | 85 | 44 | 15 | 17 | 9 | 8 | 12 | 17 | 9 | 8 | 1 | 2 | 0 | 8 |
322,202 | 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.invgamma_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 invgamma_gen(rv_continuous):
"""An inverte... |
class invgamma_gen(rv_continuous):
'''An inverted gamma continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `invgamma` is:
.. math::
f(x, a) = \frac{x^{-a-1}}{\Gamma(a)} \exp(-\frac{1}{x})
for :math:`x >= 0`, :math:`a > 0`. :math:`\Gamma` is... | 12 | 1 | 5 | 0 | 4 | 0 | 1 | 0.51 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 86 | 88 | 23 | 43 | 19 | 31 | 22 | 34 | 19 | 22 | 3 | 2 | 1 | 13 |
322,203 | 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.invgauss_gen | 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
import scipy.special._ufuncs as scu
from scipy._lib.doccer import extend_notes_i... |
class invgauss_gen(rv_continuous):
'''An inverse Gaussian continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `invgauss` is:
.. math::
f(x; \mu) = \frac{1}{\sqrt{2 \pi x^3}}
\exp\left(-\frac{(x-\mu)^2}{2 \mu^2 x}\right)
f... | 15 | 2 | 7 | 0 | 5 | 2 | 1 | 0.75 | 1 | 6 | 4 | 1 | 13 | 0 | 13 | 90 | 149 | 32 | 68 | 34 | 53 | 51 | 63 | 33 | 49 | 5 | 2 | 2 | 17 |
322,204 | 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.invweibull_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
import s... |
class invweibull_gen(rv_continuous):
'''An inverted Weibull continuous random variable.
This distribution is also known as the Fréchet distribution or the
type II extreme value distribution.
%(before_notes)s
Notes
-----
The probability density function for `invweibull` is:
.. math::
... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 0.8 | 1 | 2 | 1 | 0 | 9 | 0 | 9 | 86 | 65 | 20 | 25 | 14 | 15 | 20 | 25 | 14 | 15 | 2 | 2 | 0 | 10 |
322,205 | 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.irwinhall_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
from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstring, inherit_docstring_from
import scipy.... |
class irwinhall_gen(rv_continuous):
'''An Irwin-Hall (Uniform Sum) continuous random variable.
An `Irwin-Hall <https://en.wikipedia.org/wiki/Irwin-Hall_distribution/>`_
continuous random variable is the sum of :math:`n` independent
standard uniform random variables [1]_ [2]_.
%(before_notes)s
N... | 20 | 1 | 4 | 0 | 3 | 1 | 1 | 1.23 | 1 | 5 | 2 | 0 | 10 | 0 | 11 | 88 | 126 | 21 | 47 | 26 | 24 | 58 | 38 | 20 | 21 | 2 | 2 | 0 | 17 |
322,206 | 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.jf_skew_t_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 jf_skew_t_gen(rv_continuous):
"""Jones and... |
class jf_skew_t_gen(rv_continuous):
'''Jones and Faddy skew-t distribution.
%(before_notes)s
Notes
-----
The probability density function for `jf_skew_t` is:
.. math::
f(x; a, b) = C_{a,b}^{-1}
\left(1+\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{a+1/2}
... | 9 | 3 | 9 | 1 | 6 | 2 | 1 | 0.81 | 1 | 2 | 1 | 0 | 7 | 0 | 7 | 84 | 98 | 22 | 42 | 29 | 33 | 34 | 37 | 29 | 28 | 1 | 2 | 0 | 8 |
322,207 | 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.johnsonsb_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 johnsonsb_gen(rv_continuous):
"""A Johnson SB continuous random variable.
%(be... |
class johnsonsb_gen(rv_continuous):
'''A Johnson SB continuous random variable.
%(before_notes)s
See Also
--------
johnsonsu
Notes
-----
The probability density function for `johnsonsb` is:
.. math::
f(x, a, b) = \frac{b}{x(1-x)} \phi(a + b \log \frac{x}{1-x} )
where :m... | 8 | 1 | 3 | 0 | 2 | 0 | 1 | 0.95 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 54 | 17 | 19 | 12 | 11 | 18 | 19 | 12 | 11 | 1 | 2 | 0 | 7 |
322,208 | 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.johnsonsu_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 johnsonsu_gen(rv_continuous):
"""A Johnson SU continuous random variable.
%(be... |
class johnsonsu_gen(rv_continuous):
'''A Johnson SU continuous random variable.
%(before_notes)s
See Also
--------
johnsonsb
Notes
-----
The probability density function for `johnsonsu` is:
.. math::
f(x, a, b) = \frac{b}{\sqrt{x^2 + 1}}
\phi(a + b \log(... | 9 | 1 | 6 | 0 | 5 | 1 | 2 | 0.67 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 91 | 21 | 42 | 22 | 33 | 28 | 42 | 22 | 33 | 5 | 2 | 1 | 12 |
322,209 | 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.kappa3_gen | from scipy import integrate
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 kappa3_gen(rv_continuous):
"""Kappa 3 parameter distrib... |
class kappa3_gen(rv_continuous):
'''Kappa 3 parameter distribution.
%(before_notes)s
Notes
-----
The probability density function for `kappa3` is:
.. math::
f(x, a) = a (a + x^a)^{-(a + 1)/a}
for :math:`x > 0` and :math:`a > 0`.
`kappa3` takes ``a`` as a shape parameter for :mat... | 9 | 1 | 4 | 0 | 4 | 1 | 1 | 0.9 | 1 | 3 | 1 | 0 | 8 | 1 | 8 | 85 | 75 | 20 | 30 | 18 | 21 | 27 | 30 | 17 | 21 | 2 | 2 | 1 | 10 |
322,210 | 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.kappa4_gen | import numpy as np
from scipy._lib._util import _lazyselect
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
from scipy import integrate
class kappa4_gen(rv_cont... |
class kappa4_gen(rv_continuous):
'''Kappa 4 parameter distribution.
%(before_notes)s
Notes
-----
The probability density function for kappa4 is:
.. math::
f(x, h, k) = (1 - k x)^{1/k - 1} (1 - h (1 - k x)^{1/k})^{1/h-1}
if :math:`h` and :math:`k` are not equal to 0.
If :math:`h`... | 32 | 10 | 8 | 1 | 5 | 2 | 1 | 0.83 | 1 | 3 | 1 | 0 | 11 | 1 | 11 | 88 | 269 | 59 | 115 | 48 | 83 | 95 | 82 | 47 | 50 | 2 | 2 | 1 | 33 |
322,211 | 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.ksone_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 ksone_gen(rv_continuous):
"""Kolmogorov-Smirnov... |
class ksone_gen(rv_continuous):
'''Kolmogorov-Smirnov one-sided test statistic distribution.
This is the distribution of the one-sided Kolmogorov-Smirnov (KS)
statistics :math:`D_n^+` and :math:`D_n^-`
for a finite sample size ``n >= 1`` (the shape parameter).
%(before_notes)s
See Also
----... | 8 | 1 | 2 | 0 | 2 | 0 | 1 | 3.27 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 88 | 24 | 15 | 8 | 7 | 49 | 15 | 8 | 7 | 1 | 2 | 0 | 7 |
322,212 | 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.kstwo_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
from ._ksstats import kolmogn, kolmognp, kolmogni
from collections.abc import Iterable
class kstwo_gen(rv_continuous):
... |
class kstwo_gen(rv_continuous):
'''Kolmogorov-Smirnov two-sided test statistic distribution.
This is the distribution of the two-sided Kolmogorov-Smirnov (KS)
statistic :math:`D_n` for a finite sample size ``n >= 1``
(the shape parameter).
%(before_notes)s
See Also
--------
kstwobign, k... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 2.67 | 1 | 2 | 1 | 0 | 8 | 0 | 8 | 85 | 91 | 25 | 18 | 9 | 9 | 48 | 17 | 9 | 8 | 2 | 2 | 0 | 9 |
322,213 | 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.kstwobign_gen | import scipy.special as sc
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 kstwobign_gen(rv_continuous):
"""Limiting distribution of scaled K... |
class kstwobign_gen(rv_continuous):
'''Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
This is the asymptotic distribution of the two-sided Kolmogorov-Smirnov
statistic :math:`\sqrt{n} D_n` that measures the maximum absolute
distance of the theoretical (continuous) CDF from... | 7 | 1 | 2 | 0 | 2 | 0 | 1 | 1.92 | 1 | 0 | 0 | 0 | 6 | 0 | 6 | 83 | 54 | 16 | 13 | 7 | 6 | 25 | 13 | 7 | 6 | 1 | 2 | 0 | 6 |
322,214 | 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.landau_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
from ._censored_data import CensoredData
class landau_gen(rv_continuous):
"""A L... |
class landau_gen(rv_continuous):
'''A Landau continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `landau` ([1]_, [2]_) is:
.. math::
f(x) = \frac{1}{\pi}\int_0^\infty \exp(-t \log t - xt)\sin(\pi t) dt
for a real number :math:`x`.
%(afte... | 12 | 1 | 3 | 0 | 3 | 0 | 1 | 1.13 | 1 | 1 | 1 | 0 | 11 | 0 | 11 | 88 | 87 | 21 | 31 | 17 | 19 | 35 | 30 | 17 | 18 | 2 | 2 | 1 | 13 |
322,215 | 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.laplace_asymmetric_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 laplace_asymmetric_gen(rv_continuous):
"""An asymmetric Laplace continuous random variable.
%(before_note... |
class laplace_asymmetric_gen(rv_continuous):
'''An asymmetric Laplace continuous random variable.
%(before_notes)s
See Also
--------
laplace : Laplace distribution
Notes
-----
The probability density function for `laplace_asymmetric` is
.. math::
f(x, \kappa) &= \frac{1}{\kap... | 10 | 1 | 5 | 0 | 5 | 0 | 1 | 0.7 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 86 | 94 | 21 | 43 | 25 | 33 | 30 | 35 | 25 | 25 | 1 | 2 | 0 | 9 |
322,216 | 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.laplace_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
class laplac... |
class laplace_gen(rv_continuous):
'''A Laplace continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `laplace` is
.. math::
f(x) = \frac{1}{2} \exp(-|x|)
for a real number :math:`x`.
%(after_notes)s
%(example)s
'''
def _shape_... | 13 | 1 | 4 | 0 | 3 | 1 | 1 | 0.48 | 1 | 1 | 0 | 0 | 10 | 0 | 10 | 87 | 70 | 21 | 33 | 13 | 17 | 16 | 27 | 12 | 16 | 3 | 2 | 1 | 12 |
322,217 | 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.levy_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 levy_gen(rv_continuous):
"""A Levy continuous random variable.
%(before_notes)... |
class levy_gen(rv_continuous):
'''A Levy continuous random variable.
%(before_notes)s
See Also
--------
levy_stable, levy_l
Notes
-----
The probability density function for `levy` is:
.. math::
f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp\left(-\frac{1}{2x}\right)
for :math:`x ... | 8 | 1 | 3 | 0 | 2 | 0 | 1 | 3.06 | 1 | 0 | 0 | 0 | 7 | 0 | 7 | 84 | 99 | 30 | 17 | 10 | 9 | 52 | 17 | 10 | 9 | 1 | 2 | 0 | 7 |
322,218 | 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.levy_l_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 levy_l_gen(rv_continuous):
"""A left-skewed Levy continuous random variable.
%(before_notes)s
See Al... |
class levy_l_gen(rv_continuous):
'''A left-skewed Levy continuous random variable.
%(before_notes)s
See Also
--------
levy, levy_stable
Notes
-----
The probability density function for `levy_l` is:
.. math::
f(x) = \frac{1}{|x| \sqrt{2\pi |x|}} \exp{ \left(-\frac{1}{2|x|} \r... | 8 | 1 | 3 | 0 | 3 | 0 | 1 | 2.5 | 1 | 0 | 0 | 0 | 7 | 0 | 7 | 84 | 99 | 29 | 20 | 13 | 12 | 50 | 20 | 13 | 12 | 1 | 2 | 0 | 7 |
322,219 | 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.loggamma_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 loggamma_gen(rv_continuous):
'''A log gamma continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `loggamma` is:
.. math::
f(x, c) = \frac{\exp(c x - \exp(x))}
{\Gamma(c)}
for all :math:`x, c > 0`. Here, :math:`\Ga... | 13 | 1 | 7 | 0 | 4 | 3 | 1 | 1 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 115 | 21 | 47 | 22 | 34 | 47 | 34 | 22 | 21 | 1 | 2 | 0 | 12 |
322,220 | 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.logistic_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
from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstring, inherit_docstring_from
import scipy.... |
class logistic_gen(rv_continuous):
'''A logistic (or Sech-squared) continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `logistic` is:
.. math::
f(x) = \frac{\exp(-x)}
{(1+\exp(-x))^2}
`logistic` is a special case of `genl... | 19 | 1 | 5 | 1 | 4 | 1 | 1 | 0.44 | 1 | 1 | 0 | 0 | 13 | 0 | 13 | 90 | 114 | 29 | 59 | 26 | 40 | 26 | 53 | 25 | 36 | 5 | 2 | 1 | 20 |
322,221 | 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.loglaplace_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
class loglap... |
class loglaplace_gen(rv_continuous):
'''A log-Laplace continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `loglaplace` is:
.. math::
f(x, c) = \begin{cases}\frac{c}{2} x^{ c-1} &\text{for } 0 < x < 1\\
\frac{c}{2}... | 12 | 1 | 6 | 0 | 4 | 1 | 2 | 0.8 | 1 | 5 | 2 | 0 | 9 | 0 | 9 | 86 | 95 | 23 | 40 | 18 | 28 | 32 | 34 | 17 | 24 | 8 | 2 | 1 | 16 |
322,222 | 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.lognorm_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
from scipy.op... |
class lognorm_gen(rv_continuous):
'''A lognormal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `lognorm` is:
.. math::
f(x, s) = \frac{1}{s x \sqrt{2\pi}}
\exp\left(-\frac{\log^2(x)}{2s^2}\right)
for :math:`x > 0`, :m... | 19 | 1 | 9 | 1 | 6 | 2 | 2 | 0.56 | 1 | 3 | 2 | 0 | 13 | 0 | 13 | 90 | 196 | 38 | 102 | 44 | 68 | 57 | 83 | 43 | 66 | 11 | 2 | 2 | 27 |
322,223 | 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.lomax_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 lomax_gen(rv_continuous):
"""A Lomax (Pareto of the second kind) continuous random ... |
class lomax_gen(rv_continuous):
'''A Lomax (Pareto of the second kind) continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `lomax` is:
.. math::
f(x, c) = \frac{c}{(1+x)^{c+1}}
for :math:`x \ge 0`, :math:`c > 0`.
`lomax` takes ``c`` as a... | 11 | 1 | 2 | 0 | 2 | 0 | 1 | 0.64 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 55 | 19 | 22 | 12 | 11 | 14 | 22 | 12 | 11 | 1 | 2 | 0 | 10 |
322,224 | 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.maxwell_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
import s... |
class maxwell_gen(rv_continuous):
'''A Maxwell continuous random variable.
%(before_notes)s
Notes
-----
A special case of a `chi` distribution, with ``df=3``, ``loc=0.0``,
and given ``scale = a``, where ``a`` is the parameter used in the
Mathworld description [1]_.
The probability dens... | 11 | 1 | 3 | 0 | 3 | 0 | 1 | 0.73 | 1 | 1 | 0 | 0 | 10 | 0 | 10 | 87 | 63 | 18 | 26 | 12 | 15 | 19 | 23 | 12 | 12 | 1 | 2 | 1 | 10 |
322,225 | 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.mielke_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 mielke_gen(rv_continuous):
"""A Mielke Bet... |
class mielke_gen(rv_continuous):
'''A Mielke Beta-Kappa / Dagum continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `mielke` is:
.. math::
f(x, k, s) = \frac{k x^{k-1}}{(1+x^s)^{1+k/s}}
for :math:`x > 0` and :math:`k, s > 0`. The distributio... | 8 | 1 | 3 | 0 | 3 | 0 | 1 | 1.32 | 1 | 2 | 1 | 0 | 6 | 0 | 6 | 83 | 60 | 16 | 19 | 11 | 11 | 25 | 19 | 11 | 11 | 1 | 2 | 1 | 7 |
322,226 | 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.moyal_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 moyal_gen(rv_continuous):
"""A Moyal continuous random variable.
%(before_note... |
class moyal_gen(rv_continuous):
'''A Moyal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `moyal` is:
.. math::
f(x) = \exp(-(x + \exp(-x))/2) / \sqrt{2\pi}
for a real number :math:`x`.
%(after_notes)s
This distribution has util... | 9 | 1 | 5 | 0 | 5 | 0 | 2 | 0.85 | 1 | 0 | 0 | 0 | 8 | 0 | 8 | 85 | 90 | 18 | 39 | 18 | 30 | 33 | 34 | 18 | 25 | 5 | 2 | 1 | 12 |
322,227 | 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.nakagami_gen | 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
import scipy.special as sc
import scipy.stats as stats
class nakagami_gen(rv_co... |
class nakagami_gen(rv_continuous):
'''A Nakagami continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `nakagami` is:
.. math::
f(x, \nu) = \frac{2 \nu^\nu}{\Gamma(\nu)} x^{2\nu-1} \exp(-\nu x^2)
for :math:`x >= 0`, :math:`\nu > 0`. The distri... | 13 | 1 | 5 | 0 | 4 | 1 | 1 | 0.7 | 1 | 2 | 2 | 0 | 12 | 0 | 12 | 89 | 98 | 21 | 46 | 26 | 33 | 32 | 45 | 26 | 32 | 3 | 2 | 1 | 14 |
322,228 | 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.ncf_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 ncf_gen(rv_continuous):
"""A non-central F dist... |
class ncf_gen(rv_continuous):
'''A non-central F distribution continuous random variable.
%(before_notes)s
See Also
--------
scipy.stats.f : Fisher distribution
Notes
-----
The probability density function for `ncf` is:
.. math::
f(x, n_1, n_2, \lambda) =
\exp\le... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 1.62 | 1 | 2 | 1 | 0 | 9 | 0 | 9 | 86 | 94 | 21 | 29 | 17 | 19 | 47 | 28 | 17 | 18 | 3 | 2 | 1 | 11 |
322,229 | 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.nct_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 nct_gen(rv_continuous):
"""A non-central Studen... |
class nct_gen(rv_continuous):
'''A non-central Student's t continuous random variable.
%(before_notes)s
Notes
-----
If :math:`Y` is a standard normal random variable and :math:`V` is
an independent chi-square random variable (`chi2`) with :math:`k` degrees
of freedom, then
.. math::
... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 0.83 | 1 | 2 | 1 | 0 | 9 | 0 | 9 | 86 | 69 | 18 | 29 | 18 | 19 | 24 | 29 | 18 | 19 | 3 | 2 | 1 | 11 |
322,230 | 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.ncx2_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 ncx2_gen(rv_continuous):
"""A non... |
class ncx2_gen(rv_continuous):
'''A non-central chi-squared continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `ncx2` is:
.. math::
f(x, k, \lambda) = \frac{1}{2} \exp(-(\lambda+x)/2)
(x/\lambda)^{(k-2)/4} I_{(k-2)/2}(\sqrt{\lambda... | 12 | 1 | 4 | 0 | 4 | 0 | 1 | 0.62 | 1 | 2 | 1 | 0 | 10 | 0 | 10 | 87 | 91 | 20 | 47 | 18 | 35 | 29 | 34 | 18 | 22 | 1 | 2 | 1 | 11 |
322,231 | 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.norm_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 norm_gen(rv_continuous):
'''A normal continuous random variable.
The location (``loc``) keyword specifies the mean.
The scale (``scale``) keyword specifies the standard deviation.
%(before_notes)s
Notes
-----
The probability density function for `norm` is:
.. math::
f(x) =... | 17 | 2 | 5 | 1 | 4 | 1 | 1 | 0.36 | 1 | 2 | 0 | 0 | 14 | 0 | 14 | 91 | 110 | 30 | 59 | 20 | 36 | 21 | 47 | 19 | 32 | 5 | 2 | 1 | 20 |
322,232 | 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.norminvgauss_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
from scipy import integrate
from scipy import optimize
class norminvgauss_gen(rv_continuous):... |
class norminvgauss_gen(rv_continuous):
'''A Normal Inverse Gaussian continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `norminvgauss` is:
.. math::
f(x, a, b) = \frac{a \, K_1(a \sqrt{1 + x^2})}{\pi \sqrt{1 + x^2}} \,
\exp(... | 11 | 1 | 12 | 1 | 9 | 2 | 2 | 0.71 | 1 | 3 | 1 | 0 | 8 | 0 | 8 | 85 | 145 | 26 | 70 | 34 | 59 | 50 | 64 | 34 | 53 | 5 | 2 | 2 | 18 |
322,233 | 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.pareto_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
from scipy.op... |
class pareto_gen(rv_continuous):
'''A Pareto continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `pareto` is:
.. math::
f(x, b) = \frac{b}{x^{b+1}}
for :math:`x \ge 1`, :math:`b > 0`.
`pareto` takes ``b`` as a shape parameter for :math:`... | 17 | 1 | 10 | 1 | 7 | 3 | 2 | 0.48 | 1 | 4 | 2 | 0 | 9 | 0 | 9 | 86 | 156 | 27 | 87 | 31 | 70 | 42 | 79 | 30 | 64 | 7 | 2 | 3 | 24 |
322,234 | 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.pearson3_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 pearson3_gen(rv_continuous):
'''A pearson type III continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `pearson3` is:
.. math::
f(x, \kappa) = \frac{|\beta|}{\Gamma(\alpha)}
(\beta (x - \zeta))^{\alpha - 1}
... | 14 | 1 | 12 | 1 | 8 | 3 | 1 | 0.66 | 1 | 4 | 1 | 0 | 11 | 0 | 11 | 88 | 194 | 42 | 92 | 43 | 76 | 61 | 80 | 42 | 68 | 3 | 2 | 2 | 15 |
322,235 | 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.powerlaw_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
from scipy._lib.doccer import extend_notes_in_docstring, replace_notes_in_docstring, inherit_docstring_from
import scipy.... |
class powerlaw_gen(rv_continuous):
'''A power-function continuous random variable.
%(before_notes)s
See Also
--------
pareto
Notes
-----
The probability density function for `powerlaw` is:
.. math::
f(x, a) = a x^{a-1}
for :math:`0 \le x \le 1`, :math:`a > 0`.
`power... | 24 | 1 | 15 | 2 | 8 | 5 | 2 | 0.75 | 1 | 5 | 2 | 0 | 12 | 0 | 12 | 89 | 280 | 52 | 131 | 53 | 102 | 98 | 111 | 52 | 89 | 17 | 2 | 2 | 40 |
322,236 | 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.powerlognorm_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 powerlognorm_gen(rv_continuous):
"""A power log-normal continuous random variable.
... |
class powerlognorm_gen(rv_continuous):
'''A power log-normal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `powerlognorm` is:
.. math::
f(x, c, s) = \frac{c}{x s} \phi(\log(x)/s)
(\Phi(-\log(x)/s))^{c-1}
where :mat... | 9 | 1 | 3 | 0 | 3 | 0 | 1 | 0.64 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 53 | 17 | 22 | 12 | 13 | 14 | 20 | 12 | 11 | 1 | 2 | 0 | 8 |
322,237 | 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.powernorm_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 powernorm_gen(rv_continuous):
"""A power normal continuous random variable.
%(... |
class powernorm_gen(rv_continuous):
'''A power normal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `powernorm` is:
.. math::
f(x, c) = c \phi(x) (\Phi(-x))^{c-1}
where :math:`\phi` is the normal pdf, :math:`\Phi` is the normal cdf,
... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 1.06 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 52 | 17 | 17 | 9 | 8 | 18 | 17 | 9 | 8 | 1 | 2 | 0 | 8 |
322,238 | 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.rayleigh_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 rayleigh_gen(rv_continuous):
'''A Rayleigh continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `rayleigh` is:
.. math::
f(x) = x \exp(-x^2/2)
for :math:`x \ge 0`.
`rayleigh` is a special case of `chi` with ``df=2``.
%(after_not... | 18 | 1 | 6 | 0 | 5 | 1 | 1 | 0.31 | 1 | 3 | 2 | 0 | 12 | 0 | 12 | 89 | 119 | 27 | 70 | 32 | 45 | 22 | 56 | 31 | 40 | 7 | 2 | 2 | 21 |
322,239 | 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.rdist_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 rdist_gen(rv_continuous):
"""An R-distributed (symmetric beta) continuous random va... |
class rdist_gen(rv_continuous):
'''An R-distributed (symmetric beta) continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `rdist` is:
.. math::
f(x, c) = \frac{(1-x^2)^{c/2-1}}{B(1/2, c/2)}
for :math:`-1 \le x \le 1`, :math:`c > 0`. `rdist` i... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 1.28 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 59 | 18 | 18 | 10 | 9 | 23 | 18 | 10 | 9 | 1 | 2 | 0 | 8 |
322,240 | 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.recipinvgauss_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
class recipinvgauss_gen(rv_continuous):
"""A reciprocal inverse Gaussian co... |
class recipinvgauss_gen(rv_continuous):
'''A reciprocal inverse Gaussian continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `recipinvgauss` is:
.. math::
f(x, \mu) = \frac{1}{\sqrt{2\pi x}}
\exp\left(\frac{-(1-\mu x)^2}{2\mu... | 7 | 1 | 4 | 0 | 4 | 0 | 1 | 0.65 | 1 | 1 | 1 | 0 | 6 | 0 | 6 | 83 | 52 | 14 | 23 | 13 | 16 | 15 | 19 | 13 | 12 | 1 | 2 | 0 | 6 |
322,241 | 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.reciprocal_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
from ._censor... |
class reciprocal_gen(rv_continuous):
'''A loguniform or reciprocal continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for this class is:
.. math::
f(x, a, b) = \frac{1}{x \log(b/a)}
for :math:`a \le x \le b`, :math:`b > a > 0`. This class takes... | 13 | 1 | 3 | 0 | 3 | 0 | 1 | 1.08 | 1 | 3 | 2 | 0 | 11 | 0 | 11 | 88 | 100 | 25 | 36 | 19 | 23 | 39 | 33 | 18 | 21 | 2 | 2 | 1 | 13 |
322,242 | 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.rel_breitwigner_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
from ._censor... |
class rel_breitwigner_gen(rv_continuous):
'''A relativistic Breit-Wigner random variable.
%(before_notes)s
See Also
--------
cauchy: Cauchy distribution, also known as the Breit-Wigner distribution.
Notes
-----
The probability density function for `rel_breitwigner` is
.. math::
... | 9 | 1 | 11 | 0 | 9 | 2 | 2 | 0.92 | 1 | 4 | 2 | 0 | 7 | 0 | 7 | 84 | 146 | 25 | 63 | 20 | 54 | 58 | 49 | 19 | 41 | 8 | 2 | 2 | 17 |
322,243 | 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.rice_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 rice_gen(rv_continuous):
"""A Rice continuous random variable.
%(before_notes)... |
class rice_gen(rv_continuous):
'''A Rice continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `rice` is:
.. math::
f(x, b) = x \exp(- \frac{x^2 + b^2}{2}) I_0(x b)
for :math:`x >= 0`, :math:`b > 0`. :math:`I_0` is the modified Bessel
func... | 8 | 1 | 4 | 0 | 3 | 1 | 1 | 1.25 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 61 | 16 | 20 | 12 | 12 | 25 | 19 | 12 | 11 | 1 | 2 | 0 | 7 |
322,244 | 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.rv_histogram | import scipy._lib.array_api_extra as xpx
from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
import numpy as np
import warnings
class rv_histogram(rv_continuous):
"""
Generates a dist... |
class rv_histogram(rv_continuous):
'''
Generates a distribution given by a histogram.
This is useful to generate a template distribution from a binned
datasample.
As a subclass of the `rv_continuous` class, `rv_histogram` inherits from it
a collection of generic methods (see `rv_continuous` for... | 8 | 8 | 11 | 0 | 6 | 5 | 2 | 2.26 | 1 | 4 | 0 | 0 | 7 | 8 | 7 | 84 | 179 | 26 | 47 | 23 | 39 | 106 | 43 | 21 | 35 | 5 | 2 | 1 | 11 |
322,245 | 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.semicircular_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 semicircular_gen(rv_continuous):
"""A semicircular continuous random variable.
... |
class semicircular_gen(rv_continuous):
'''A semicircular continuous random variable.
%(before_notes)s
See Also
--------
rdist
Notes
-----
The probability density function for `semicircular` is:
.. math::
f(x) = \frac{2}{\pi} \sqrt{1-x^2}
for :math:`-1 \le x \le 1`.
T... | 9 | 1 | 3 | 0 | 2 | 0 | 1 | 1.11 | 1 | 0 | 0 | 0 | 8 | 0 | 8 | 85 | 58 | 18 | 19 | 11 | 10 | 21 | 19 | 11 | 10 | 1 | 2 | 0 | 8 |
322,246 | 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.skewcauchy_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
from ._censored_data import CensoredData
class skewcauchy_gen(rv_continuous):
"""A skewed Cauchy random variable.
... |
class skewcauchy_gen(rv_continuous):
'''A skewed Cauchy random variable.
%(before_notes)s
See Also
--------
cauchy : Cauchy distribution
Notes
-----
The probability density function for `skewcauchy` is:
.. math::
f(x) = \frac{1}{\pi \left(\frac{x^2}{\left(a\, \text{sign}(x) ... | 8 | 1 | 4 | 0 | 3 | 0 | 1 | 1.04 | 1 | 2 | 2 | 0 | 7 | 0 | 7 | 84 | 65 | 18 | 23 | 10 | 15 | 24 | 19 | 10 | 11 | 2 | 2 | 1 | 8 |
322,247 | 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.skewnorm_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 skewnorm_gen(rv_continuous):
'''A skew-normal random variable.
%(before_notes)s
Notes
-----
The pdf is::
skewnorm.pdf(x, a) = 2 * norm.pdf(x) * norm.cdf(a*x)
`skewnorm` takes a real number :math:`a` as a skewness parameter
When ``a = 0`` the distribution is identical to a norm... | 18 | 1 | 11 | 1 | 9 | 2 | 2 | 0.37 | 1 | 5 | 2 | 0 | 13 | 0 | 13 | 90 | 223 | 31 | 142 | 48 | 113 | 53 | 91 | 35 | 75 | 14 | 2 | 2 | 34 |
322,248 | 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.studentized_range_gen | import numpy as np
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
from scipy import integrate
from . import _stats
class ... |
class studentized_range_gen(rv_continuous):
'''A studentized range continuous random variable.
%(before_notes)s
See Also
--------
t: Student's t distribution
Notes
-----
The probability density function for `studentized_range` is:
.. math::
f(x; k, \nu) = \frac{k(k-1)\nu^{\... | 10 | 1 | 15 | 2 | 11 | 2 | 1 | 1.53 | 1 | 6 | 2 | 0 | 6 | 0 | 6 | 83 | 193 | 46 | 58 | 37 | 48 | 89 | 56 | 37 | 46 | 2 | 2 | 1 | 11 |
322,249 | 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.t_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from scipy._lib._util import _lazyselect
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 t_gen... |
class t_gen(rv_continuous):
'''A Student's t continuous random variable.
For the noncentral t distribution, see `nct`.
%(before_notes)s
See Also
--------
nct
Notes
-----
The probability density function for `t` is:
.. math::
f(x, \nu) = \frac{\Gamma((\nu+1)/2)}
... | 15 | 1 | 6 | 1 | 5 | 1 | 1 | 0.41 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 87 | 112 | 30 | 58 | 25 | 43 | 24 | 43 | 25 | 28 | 2 | 2 | 1 | 15 |
322,250 | 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.trapezoid_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
from scipy._lib._util import _lazyselect
class trapezoid_gen(rv_continuous):
"""A trapezoidal continuous random vari... |
class trapezoid_gen(rv_continuous):
'''A trapezoidal continuous random variable.
%(before_notes)s
Notes
-----
The trapezoidal distribution can be represented with an up-sloping line
from ``loc`` to ``(loc + c*scale)``, then constant to ``(loc + d*scale)``
and then downsloping from ``(loc + ... | 8 | 1 | 9 | 0 | 6 | 3 | 1 | 1 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 84 | 104 | 16 | 44 | 16 | 36 | 44 | 24 | 16 | 16 | 1 | 2 | 0 | 7 |
322,251 | 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.triang_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
from scipy._lib._util import _lazyselect
class triang_gen(rv_continuous):
"""A triangular continuous random variable... |
class triang_gen(rv_continuous):
'''A triangular continuous random variable.
%(before_notes)s
Notes
-----
The triangular distribution can be represented with an up-sloping line from
``loc`` to ``(loc + c*scale)`` and then downsloping for ``(loc + c*scale)``
to ``(loc + scale)``.
`triang... | 9 | 1 | 5 | 0 | 5 | 1 | 1 | 0.5 | 1 | 1 | 1 | 0 | 8 | 0 | 8 | 85 | 71 | 14 | 38 | 11 | 29 | 19 | 19 | 11 | 10 | 1 | 2 | 0 | 8 |
322,252 | 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.truncexpon_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 truncexpon_gen(rv_continuous):
"""A truncated exponential continuous random variabl... |
class truncexpon_gen(rv_continuous):
'''A truncated exponential continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `truncexpon` is:
.. math::
f(x, b) = \frac{\exp(-x)}{1 - \exp(-b)}
for :math:`0 <= x <= b`.
`truncexpon` takes ``b`` as a... | 11 | 1 | 3 | 0 | 3 | 0 | 1 | 0.59 | 1 | 2 | 1 | 0 | 10 | 0 | 10 | 87 | 61 | 18 | 27 | 12 | 16 | 16 | 25 | 12 | 14 | 3 | 2 | 1 | 12 |
322,253 | 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.truncnorm_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
import scipy.special as sc
class trunc... |
class truncnorm_gen(rv_continuous):
'''A truncated normal continuous random variable.
%(before_notes)s
Notes
-----
This distribution is the normal distribution centered on ``loc`` (default
0), with standard deviation ``scale`` (default 1), and truncated at ``a``
and ``b`` *standard deviatio... | 22 | 2 | 11 | 1 | 8 | 1 | 1 | 0.52 | 1 | 5 | 2 | 0 | 15 | 0 | 15 | 92 | 236 | 45 | 127 | 72 | 105 | 66 | 115 | 72 | 93 | 3 | 2 | 1 | 29 |
322,254 | 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.truncpareto_gen | from scipy.optimize import root_scalar
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_note... |
class truncpareto_gen(rv_continuous):
'''An upper truncated Pareto continuous random variable.
%(before_notes)s
See Also
--------
pareto : Pareto distribution
Notes
-----
The probability density function for `truncpareto` is:
.. math::
f(x, b, c) = \frac{b}{1 - c^{-b}} \frac... | 28 | 1 | 12 | 1 | 9 | 2 | 2 | 0.39 | 1 | 5 | 3 | 0 | 15 | 0 | 15 | 92 | 315 | 51 | 192 | 63 | 164 | 74 | 171 | 62 | 145 | 29 | 2 | 4 | 58 |
322,255 | 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.truncweibull_min_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 truncweibull_min_gen(rv_continuous):
"""A doubly truncated Weibull minimum continuo... |
class truncweibull_min_gen(rv_continuous):
'''A doubly truncated Weibull minimum continuous random variable.
%(before_notes)s
See Also
--------
weibull_min, truncexpon
Notes
-----
The probability density function for `truncweibull_min` is:
.. math::
f(x, a, b, c) = \frac{c x... | 14 | 1 | 4 | 0 | 4 | 0 | 1 | 0.6 | 1 | 2 | 1 | 0 | 13 | 0 | 13 | 90 | 105 | 28 | 48 | 29 | 34 | 29 | 42 | 29 | 28 | 1 | 2 | 0 | 13 |
322,256 | 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.tukeylambda_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
from ._tukeylambda_stats import tukeylambda_variance ... |
class tukeylambda_gen(rv_continuous):
'''A Tukey-Lamdba continuous random variable.
%(before_notes)s
Notes
-----
A flexible distribution, able to represent and interpolate between the
following distributions:
- Cauchy (:math:`lambda = -1`)
- logistic (:math:`... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 0.56 | 1 | 2 | 1 | 0 | 8 | 0 | 8 | 85 | 57 | 15 | 27 | 14 | 17 | 15 | 25 | 14 | 15 | 1 | 2 | 1 | 9 |
322,257 | 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.uniform_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 uniform_gen(rv_continuous):
"""A uniform continuous random variable.
In the standard form, the distributi... |
class uniform_gen(rv_continuous):
'''A uniform continuous random variable.
In the standard form, the distribution is uniform on ``[0, 1]``. Using
the parameters ``loc`` and ``scale``, one obtains the uniform distribution
on ``[loc, loc + scale]``.
%(before_notes)s
%(example)s
'''
def _... | 10 | 2 | 20 | 3 | 5 | 12 | 2 | 2.34 | 1 | 5 | 2 | 0 | 8 | 0 | 8 | 85 | 181 | 34 | 44 | 15 | 34 | 103 | 40 | 14 | 31 | 8 | 2 | 3 | 15 |
322,258 | 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.vonmises_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 vonmises_gen(rv_continuous):
'''A Von Mises continuous random variable.
%(before_notes)s
See Also
--------
scipy.stats.vonmises_fisher : Von-Mises Fisher distribution on a
hypersphere
Notes
-----
The probability density function for `vonmises` and... | 19 | 1 | 13 | 1 | 6 | 6 | 2 | 1.64 | 1 | 4 | 1 | 0 | 11 | 0 | 11 | 88 | 240 | 47 | 74 | 31 | 47 | 121 | 54 | 24 | 39 | 5 | 2 | 2 | 24 |
322,259 | 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.wald_gen | from ._distn_infrastructure import _vectorize_rvs_over_shapes, get_distribution_names, _kurtosis, _isintegral, rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo
class wald_gen(invgauss_gen):
"""A Wald continuous random variable.
%(before_notes)s
Notes
-----
The probability dens... |
class wald_gen(invgauss_gen):
'''A Wald continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `wald` is:
.. math::
f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp(- \frac{ (x-1)^2 }{ 2x })
for :math:`x >= 0`.
`wald` is a special case of `invgauss` w... | 13 | 1 | 2 | 0 | 2 | 0 | 1 | 0.5 | 1 | 0 | 0 | 0 | 12 | 0 | 12 | 102 | 59 | 20 | 26 | 14 | 13 | 13 | 26 | 14 | 13 | 1 | 3 | 0 | 12 |
322,260 | 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.weibull_max_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
import s... |
class weibull_max_gen(rv_continuous):
'''Weibull maximum continuous random variable.
The Weibull Maximum Extreme Value distribution, from extreme value theory
(Fisher-Gnedenko theorem), is the limiting distribution of rescaled
maximum of iid random variables. This is the distribution of -X
if X is ... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 1 | 1 | 2 | 1 | 0 | 9 | 0 | 9 | 86 | 69 | 21 | 24 | 12 | 14 | 24 | 23 | 12 | 13 | 2 | 2 | 1 | 10 |
322,261 | 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.weibull_min_gen | from scipy.optimize import root_scalar
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, _isintegra... |
class weibull_min_gen(rv_continuous):
'''Weibull minimum continuous random variable.
The Weibull Minimum Extreme Value distribution, from extreme value theory
(Fisher-Gnedenko theorem), is also often simply called the Weibull
distribution. It arises as the limiting distribution of the rescaled
mini... | 14 | 1 | 9 | 1 | 6 | 2 | 2 | 0.65 | 1 | 3 | 2 | 0 | 11 | 0 | 11 | 88 | 159 | 34 | 77 | 34 | 56 | 50 | 61 | 26 | 48 | 14 | 2 | 2 | 25 |
322,262 | 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.wrapcauchy_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 wrapcauchy_gen(rv_continuous):
'''A wrapped Cauchy continuous random variable.
%(before_notes)s
Notes
-----
The probability density function for `wrapcauchy` is:
.. math::
f(x, c) = \frac{1-c^2}{2\pi (1+c^2 - 2c \cos(x))}
for :math:`0 \le x \le 2\pi`, :math:`0 < c < 1`.
`w... | 12 | 1 | 4 | 0 | 3 | 1 | 1 | 0.62 | 1 | 3 | 2 | 0 | 8 | 0 | 8 | 85 | 66 | 19 | 29 | 17 | 17 | 18 | 28 | 16 | 17 | 2 | 2 | 1 | 11 |
322,263 | 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/_covariance.py | scipy.stats._covariance.CovViaCholesky | from functools import cached_property
import numpy as np
from scipy import linalg
class CovViaCholesky(Covariance):
def __init__(self, cholesky):
L = self._validate_matrix(cholesky, 'cholesky')
self._factor = L
self._log_pdet = 2 * np.log(np.diag(self._factor)).sum(axis=-1)
self._r... |
class CovViaCholesky(Covariance):
def __init__(self, cholesky):
pass
@cached_property
def _covariance(self):
pass
def _whiten(self, x):
pass
def _colorize(self, x):
pass | 6 | 0 | 4 | 0 | 4 | 0 | 1 | 0.06 | 1 | 0 | 0 | 0 | 4 | 5 | 4 | 17 | 22 | 5 | 17 | 14 | 11 | 1 | 16 | 13 | 11 | 1 | 1 | 0 | 4 |
322,264 | 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/_covariance.py | scipy.stats._covariance.CovViaDiagonal | import numpy as np
class CovViaDiagonal(Covariance):
def __init__(self, diagonal):
diagonal = self._validate_vector(diagonal, 'diagonal')
i_zero = diagonal <= 0
positive_diagonal = np.array(diagonal, dtype=np.float64)
positive_diagonal[i_zero] = 1
self._log_pdet = np.sum(np... |
class CovViaDiagonal(Covariance):
def __init__(self, diagonal):
pass
def _whiten(self, x):
pass
def _colorize(self, x):
pass
def _support_mask(self, x):
'''
Check whether x lies in the support of the distribution.
'''
pass | 5 | 1 | 7 | 1 | 5 | 1 | 1 | 0.18 | 1 | 0 | 0 | 0 | 4 | 8 | 4 | 17 | 33 | 8 | 22 | 16 | 17 | 4 | 22 | 16 | 17 | 1 | 1 | 0 | 4 |
322,265 | 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/_covariance.py | scipy.stats._covariance.CovViaEigendecomposition | from scipy.stats import _multivariate
from functools import cached_property
import numpy as np
class CovViaEigendecomposition(Covariance):
def __init__(self, eigendecomposition):
eigenvalues, eigenvectors = eigendecomposition
eigenvalues = self._validate_vector(eigenvalues, 'eigenvalues')
... |
class CovViaEigendecomposition(Covariance):
def __init__(self, eigendecomposition):
pass
def _whiten(self, x):
pass
def _colorize(self, x):
pass
@cached_property
def _covariance(self):
pass
def _support_mask(self, x):
'''
Check whether x lies ... | 7 | 1 | 9 | 1 | 8 | 1 | 1 | 0.15 | 1 | 1 | 0 | 0 | 5 | 10 | 5 | 18 | 54 | 9 | 40 | 24 | 33 | 6 | 37 | 23 | 31 | 2 | 1 | 1 | 6 |
322,266 | 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/_covariance.py | scipy.stats._covariance.CovViaPSD | class CovViaPSD(Covariance):
"""
Representation of a covariance provided via an instance of _PSD
"""
def __init__(self, psd):
self._LP = psd.U
self._log_pdet = psd.log_pdet
self._rank = psd.rank
self._covariance = psd._M
self._shape = psd._M.shape
self._p... | class CovViaPSD(Covariance):
'''
Representation of a covariance provided via an instance of _PSD
'''
def __init__(self, psd):
pass
def _whiten(self, x):
pass
def _support_mask(self, x):
pass | 4 | 1 | 4 | 0 | 4 | 0 | 1 | 0.31 | 1 | 0 | 0 | 0 | 3 | 7 | 3 | 16 | 19 | 3 | 13 | 11 | 9 | 4 | 13 | 11 | 9 | 1 | 1 | 0 | 3 |
322,267 | 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/_covariance.py | scipy.stats._covariance.CovViaPrecision | import numpy as np
from scipy import linalg
from functools import cached_property
class CovViaPrecision(Covariance):
def __init__(self, precision, covariance=None):
precision = self._validate_matrix(precision, 'precision')
if covariance is not None:
covariance = self._validate_matrix(c... |
class CovViaPrecision(Covariance):
def __init__(self, precision, covariance=None):
pass
def _whiten(self, x):
pass
@cached_property
def _covariance(self):
pass
def _colorize(self, x):
pass | 6 | 0 | 6 | 0 | 6 | 0 | 2 | 0.04 | 1 | 1 | 0 | 0 | 4 | 7 | 4 | 17 | 31 | 5 | 26 | 17 | 20 | 1 | 24 | 16 | 19 | 3 | 1 | 2 | 7 |
322,268 | 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/_covariance.py | scipy.stats._covariance.Covariance | import numpy as np
class Covariance:
"""
Representation of a covariance matrix
Calculations involving covariance matrices (e.g. data whitening,
multivariate normal function evaluation) are often performed more
efficiently using a decomposition of the covariance matrix instead of the
covariance... |
class Covariance:
'''
Representation of a covariance matrix
Calculations involving covariance matrices (e.g. data whitening,
multivariate normal function evaluation) are often performed more
efficiently using a decomposition of the covariance matrix instead of the
covariance matrix itself. This... | 22 | 11 | 29 | 4 | 3 | 22 | 1 | 6.37 | 0 | 8 | 4 | 5 | 9 | 2 | 13 | 13 | 450 | 74 | 51 | 28 | 29 | 325 | 37 | 18 | 23 | 2 | 0 | 1 | 15 |
322,269 | 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/_discrete_distns.py | scipy.stats._discrete_distns._nchypergeom_gen | import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
from ._biasedurn import _PyFishersNCHypergeometric, _PyWalleniusNCHypergeometric, _PyStochasticLib3
class _nchypergeom_gen(rv_discrete):
"""A noncentral... |
class _nchypergeom_gen(rv_discrete):
'''A noncentral hypergeometric discrete random variable.
For subclassing by nchypergeom_fisher_gen and nchypergeom_wallenius_gen.
'''
def _shape_info(self):
pass
def _get_support(self, M, n, N, odds):
pass
def _argcheck(self, M, n, N, odds... | 13 | 1 | 9 | 1 | 8 | 0 | 2 | 0.09 | 1 | 3 | 1 | 2 | 6 | 0 | 6 | 70 | 77 | 16 | 58 | 33 | 45 | 5 | 51 | 30 | 41 | 2 | 2 | 1 | 14 |
322,270 | 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/_discrete_distns.py | scipy.stats._discrete_distns.bernoulli_gen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
class bernoulli_gen(binom_gen):
"""A Bernoulli discrete random variable.
%(before_notes)s
... |
class bernoulli_gen(binom_gen):
'''A Bernoulli discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `bernoulli` is:
.. math::
f(k) = \begin{cases}1-p &\text{if } k = 0\\
p &\text{if } k = 1\end{cases}
for :math:`k` in... | 13 | 1 | 2 | 0 | 2 | 0 | 1 | 0.72 | 1 | 1 | 1 | 0 | 12 | 0 | 12 | 88 | 63 | 20 | 25 | 13 | 12 | 18 | 25 | 13 | 12 | 1 | 3 | 0 | 12 |
322,271 | 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/_discrete_distns.py | scipy.stats._discrete_distns.betabinom_gen | from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
clas... |
class betabinom_gen(rv_discrete):
'''A beta-binomial discrete random variable.
%(before_notes)s
Notes
-----
The beta-binomial distribution is a binomial distribution with a
probability of success `p` that follows a beta distribution.
The probability mass function for `betabinom` is:
.. ... | 8 | 1 | 5 | 0 | 5 | 0 | 1 | 0.56 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 71 | 80 | 19 | 39 | 16 | 31 | 22 | 37 | 16 | 29 | 3 | 2 | 1 | 9 |
322,272 | 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/_discrete_distns.py | scipy.stats._discrete_distns.betanbinom_gen | from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
import scipy._lib.array_api_extra as xpx
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
from numpy import floor, ceil, log, exp, s... |
class betanbinom_gen(rv_discrete):
'''A beta-negative-binomial discrete random variable.
%(before_notes)s
Notes
-----
The beta-negative-binomial distribution is a negative binomial
distribution with a probability of success `p` that follows a
beta distribution.
The probability mass func... | 11 | 1 | 7 | 0 | 7 | 1 | 1 | 0.59 | 1 | 1 | 1 | 0 | 6 | 0 | 6 | 70 | 91 | 18 | 46 | 19 | 35 | 27 | 34 | 19 | 23 | 3 | 2 | 1 | 12 |
322,273 | 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/_discrete_distns.py | scipy.stats._discrete_distns.binom_gen | from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import scipy.special._ufuncs as scu
from scipy import special
from scipy.specia... |
class binom_gen(rv_discrete):
'''A binomial discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `binom` is:
.. math::
f(k) = \binom{n}{k} p^k (1-p)^{n-k}
for :math:`k \in \{0, 1, \dots, n\}`, :math:`0 \leq p \leq 1`
`binom` takes :math:`n` and... | 13 | 1 | 4 | 0 | 4 | 0 | 1 | 0.51 | 1 | 1 | 1 | 1 | 12 | 0 | 12 | 76 | 94 | 23 | 47 | 27 | 34 | 24 | 46 | 27 | 33 | 3 | 2 | 1 | 14 |
322,274 | 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/_discrete_distns.py | scipy.stats._discrete_distns.boltzmann_gen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
class boltzmann_gen(rv_discrete):
"""A Boltzmann (Truncated Discrete Expon... |
class boltzmann_gen(rv_discrete):
'''A Boltzmann (Truncated Discrete Exponential) random variable.
%(before_notes)s
Notes
-----
The probability mass function for `boltzmann` is:
.. math::
f(k) = (1-\exp(-\lambda)) \exp(-\lambda k) / (1-\exp(-\lambda N))
for :math:`k = 0,..., N-1`.
... | 8 | 1 | 5 | 0 | 4 | 0 | 1 | 0.44 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 71 | 61 | 15 | 32 | 22 | 24 | 14 | 31 | 22 | 23 | 1 | 2 | 0 | 7 |
322,275 | 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/_discrete_distns.py | scipy.stats._discrete_distns.dlaplace_gen | import scipy._lib.array_api_extra as xpx
import numpy as np
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class dlaplace_gen(rv_discrete):
... |
class dlaplace_gen(rv_discrete):
'''A Laplacian discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `dlaplace` is:
.. math::
f(k) = \tanh(a/2) \exp(-a |k|)
for integers :math:`k` and :math:`a > 0`.
`dlaplace` takes :math:`a` as shape paramet... | 10 | 1 | 6 | 0 | 4 | 2 | 1 | 0.9 | 1 | 1 | 1 | 0 | 7 | 0 | 7 | 71 | 78 | 19 | 31 | 20 | 21 | 28 | 29 | 20 | 19 | 1 | 2 | 0 | 9 |
322,276 | 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/_discrete_distns.py | scipy.stats._discrete_distns.geom_gen | from scipy import special
import numpy as np
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class geom_gen(rv_discrete):
"""A geometric disc... |
class geom_gen(rv_discrete):
'''A geometric discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `geom` is:
.. math::
f(k) = (1-p)^{k-1} p
for :math:`k \ge 1`, :math:`0 < p \leq 1`
`geom` takes :math:`p` as shape parameter,
where :math:`p`... | 12 | 1 | 3 | 0 | 3 | 0 | 1 | 0.68 | 1 | 2 | 1 | 0 | 11 | 0 | 11 | 75 | 79 | 22 | 34 | 23 | 22 | 23 | 34 | 23 | 22 | 1 | 2 | 0 | 11 |
322,277 | 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/_discrete_distns.py | scipy.stats._discrete_distns.hypergeom_gen | import numpy as np
from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
impor... |
class hypergeom_gen(rv_discrete):
'''A hypergeometric discrete random variable.
The hypergeometric distribution models drawing objects from a bin.
`M` is the total number of objects, `n` is total number of Type I objects.
The random variate represents the number of Type I objects in `N` drawn
witho... | 13 | 1 | 6 | 0 | 5 | 1 | 1 | 0.95 | 1 | 2 | 1 | 0 | 12 | 0 | 12 | 76 | 153 | 32 | 62 | 27 | 49 | 59 | 51 | 27 | 38 | 3 | 2 | 2 | 16 |
322,278 | 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/_discrete_distns.py | scipy.stats._discrete_distns.logser_gen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
from scipy import special
class logser_gen(rv_discrete):
"""A Logarithmic (Log-Series, Series) discrete random variable.
%(before_notes)s
Note... |
class logser_gen(rv_discrete):
'''A Logarithmic (Log-Series, Series) discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `logser` is:
.. math::
f(k) = - \frac{p^k}{k \log(1-p)}
for :math:`k \ge 1`, :math:`0 < p < 1`
`logser` takes :math:`p` a... | 6 | 1 | 5 | 0 | 4 | 1 | 1 | 0.77 | 1 | 1 | 1 | 0 | 5 | 0 | 5 | 69 | 54 | 15 | 22 | 16 | 16 | 17 | 21 | 16 | 15 | 1 | 2 | 0 | 5 |
322,279 | 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/_discrete_distns.py | scipy.stats._discrete_distns.nbinom_gen | from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
import scipy.special._ufuncs as scu
from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
from scipy import special
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _Shape... |
class nbinom_gen(rv_discrete):
'''A negative binomial discrete random variable.
%(before_notes)s
Notes
-----
Negative binomial distribution describes a sequence of i.i.d. Bernoulli
trials, repeated until a predefined, non-random number of successes occurs.
The probability mass function of t... | 13 | 1 | 4 | 0 | 4 | 0 | 1 | 0.98 | 1 | 2 | 1 | 0 | 11 | 0 | 11 | 75 | 115 | 30 | 44 | 20 | 31 | 43 | 38 | 20 | 25 | 1 | 2 | 1 | 12 |
322,280 | 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/_discrete_distns.py | scipy.stats._discrete_distns.nchypergeom_fisher_gen | from ._biasedurn import _PyFishersNCHypergeometric, _PyWalleniusNCHypergeometric, _PyStochasticLib3
class nchypergeom_fisher_gen(_nchypergeom_gen):
"""A Fisher's noncentral hypergeometric discrete random variable.
Fisher's noncentral hypergeometric distribution models drawing objects of
two types from a b... |
class nchypergeom_fisher_gen(_nchypergeom_gen):
'''A Fisher's noncentral hypergeometric discrete random variable.
Fisher's noncentral hypergeometric distribution models drawing objects of
two types from a bin. `M` is the total number of objects, `n` is the
number of Type I objects, and `odds` is the od... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 17.33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 76 | 21 | 3 | 3 | 2 | 52 | 3 | 3 | 2 | 0 | 3 | 0 | 0 |
322,281 | 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/_discrete_distns.py | scipy.stats._discrete_distns.nchypergeom_wallenius_gen | from ._biasedurn import _PyFishersNCHypergeometric, _PyWalleniusNCHypergeometric, _PyStochasticLib3
class nchypergeom_wallenius_gen(_nchypergeom_gen):
"""A Wallenius' noncentral hypergeometric discrete random variable.
Wallenius' noncentral hypergeometric distribution models drawing objects of
two types f... |
class nchypergeom_wallenius_gen(_nchypergeom_gen):
'''A Wallenius' noncentral hypergeometric discrete random variable.
Wallenius' noncentral hypergeometric distribution models drawing objects of
two types from a bin. `M` is the total number of objects, `n` is the
number of Type I objects, and `odds` is... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 17.33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 76 | 21 | 3 | 3 | 2 | 52 | 3 | 3 | 2 | 0 | 3 | 0 | 0 |
322,282 | 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/_discrete_distns.py | scipy.stats._discrete_distns.nhypergeom_gen | import scipy._lib.array_api_extra as xpx
from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
from numpy import floor, ceil, log, exp, s... |
class nhypergeom_gen(rv_discrete):
'''A negative hypergeometric discrete random variable.
Consider a box containing :math:`M` balls:, :math:`n` red and
:math:`M-n` blue. We randomly sample balls from the box, one
at a time and *without* replacement, until we have picked :math:`r`
blue balls. `nhype... | 10 | 1 | 7 | 1 | 6 | 1 | 1 | 2.08 | 1 | 3 | 2 | 0 | 7 | 0 | 7 | 71 | 156 | 36 | 39 | 19 | 29 | 81 | 30 | 18 | 21 | 2 | 2 | 1 | 9 |
322,283 | 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/_discrete_distns.py | scipy.stats._discrete_distns.planck_gen | import numpy as np
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class planck_gen(rv_discrete):
"""A Planck discrete exponential random var... |
class planck_gen(rv_discrete):
'''A Planck discrete exponential random variable.
%(before_notes)s
Notes
-----
The probability mass function for `planck` is:
.. math::
f(k) = (1-\exp(-\lambda)) \exp(-\lambda k)
for :math:`k \ge 0` and :math:`\lambda > 0`.
`planck` takes :math:`\l... | 11 | 1 | 3 | 0 | 3 | 0 | 1 | 0.56 | 1 | 1 | 1 | 0 | 10 | 0 | 10 | 74 | 69 | 19 | 32 | 22 | 21 | 18 | 32 | 22 | 21 | 1 | 2 | 0 | 10 |
322,284 | 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/_discrete_distns.py | scipy.stats._discrete_distns.poisson_binom_gen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
from ._stats_pythran import _poisson_binom
import numpy as np
class poisson_binom_gen(rv_discrete):
"""A Poisson Binomial discrete random variable.
%(before_notes)s
... |
class poisson_binom_gen(rv_discrete):
'''A Poisson Binomial discrete random variable.
%(before_notes)s
See Also
--------
binom
Notes
-----
The probability mass function for `poisson_binom` is:
.. math::
f(k; p_1, p_2, ..., p_n) = \sum_{A \in F_k} \prod_{i \in A} p_i \prod_{j \i... | 9 | 1 | 5 | 0 | 4 | 1 | 1 | 1.06 | 1 | 2 | 1 | 0 | 8 | 0 | 8 | 72 | 84 | 18 | 32 | 15 | 23 | 34 | 31 | 15 | 22 | 3 | 2 | 0 | 10 |
322,285 | 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/_discrete_distns.py | scipy.stats._discrete_distns.poisson_binomial_frozen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class poisson_binomial_frozen(rv_discrete_frozen):
def __init__(self, dist, *args, **kwds):
self.args = args
self.kwds = kwds
self.dist = dist.__c... |
class poisson_binomial_frozen(rv_discrete_frozen):
def __init__(self, dist, *args, **kwds):
pass
def expect(self, func=None, lb=None, ub=None, conditional=False, **kwds):
pass | 3 | 0 | 10 | 2 | 6 | 5 | 1 | 0.85 | 1 | 0 | 0 | 0 | 2 | 5 | 2 | 24 | 22 | 4 | 13 | 9 | 10 | 11 | 13 | 9 | 10 | 1 | 2 | 0 | 2 |
322,286 | 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/_discrete_distns.py | scipy.stats._discrete_distns.poisson_gen | from scipy.special import entr, logsumexp, betaln, gammaln as gamln, zeta
import scipy._lib.array_api_extra as xpx
from scipy import special
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize... |
class poisson_gen(rv_discrete):
'''A Poisson discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `poisson` is:
.. math::
f(k) = \exp(-\mu) \frac{\mu^k}{k!}
for :math:`k \ge 0`.
`poisson` takes :math:`\mu \geq 0` as shape parameter.
When :... | 10 | 1 | 3 | 0 | 3 | 0 | 1 | 0.53 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 73 | 64 | 18 | 30 | 21 | 20 | 16 | 30 | 21 | 20 | 1 | 2 | 0 | 9 |
322,287 | 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/_discrete_distns.py | scipy.stats._discrete_distns.randint_gen | from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from functools import partial
from scipy._lib._util import rng_integers
import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class ... |
class randint_gen(rv_discrete):
'''A uniform discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `randint` is:
.. math::
f(k) = \frac{1}{\texttt{high} - \texttt{low}}
for :math:`k \in \{\texttt{low}, \dots, \texttt{high} - 1\}`.
`randint` tak... | 10 | 2 | 5 | 0 | 4 | 1 | 1 | 1.29 | 1 | 4 | 1 | 0 | 9 | 0 | 9 | 73 | 117 | 30 | 38 | 22 | 28 | 49 | 36 | 22 | 26 | 3 | 2 | 1 | 11 |
322,288 | 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/_discrete_distns.py | scipy.stats._discrete_distns.skellam_gen | from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
import scipy.special._ufuncs as scu
class skellam_gen(rv_discrete):
"""A ... |
class skellam_gen(rv_discrete):
'''A Skellam discrete random variable.
%(before_notes)s
Notes
-----
Probability distribution of the difference of two correlated or
uncorrelated Poisson random variables.
Let :math:`k_1` and :math:`k_2` be two Poisson-distributed r.v. with
expected value... | 6 | 1 | 5 | 0 | 5 | 1 | 1 | 0.85 | 1 | 2 | 1 | 0 | 5 | 0 | 5 | 69 | 61 | 13 | 27 | 13 | 21 | 23 | 21 | 13 | 15 | 1 | 2 | 1 | 5 |
322,289 | 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/_discrete_distns.py | scipy.stats._discrete_distns.yulesimon_gen | from scipy import special
import numpy as np
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class yulesimon_gen(rv_discrete):
"""A Yule-Simo... |
class yulesimon_gen(rv_discrete):
'''A Yule-Simon discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for the `yulesimon` is:
.. math::
f(k) = \alpha B(k, \alpha+1)
for :math:`k=1,2,3,...`, where :math:`\alpha>0`.
Here :math:`B` refers to the `s... | 10 | 1 | 4 | 0 | 4 | 0 | 1 | 0.56 | 1 | 1 | 1 | 0 | 9 | 0 | 9 | 73 | 77 | 21 | 36 | 17 | 26 | 20 | 29 | 17 | 19 | 1 | 2 | 0 | 9 |
322,290 | 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/_discrete_distns.py | scipy.stats._discrete_distns.zipf_gen | from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
import numpy as np
from scipy import special
import scipy._lib.array_api_extra as xpx
class zipf_gen(rv_discrete):
"""A Zipf (Zeta) discrete random variable.
%(before... |
class zipf_gen(rv_discrete):
'''A Zipf (Zeta) discrete random variable.
%(before_notes)s
See Also
--------
zipfian
Notes
-----
The probability mass function for `zipf` is:
.. math::
f(k, a) = \frac{1}{\zeta(a) k^a}
for :math:`k \ge 1`, :math:`a > 1`.
`zipf` takes :ma... | 6 | 1 | 3 | 0 | 3 | 0 | 1 | 1.81 | 1 | 1 | 1 | 0 | 5 | 0 | 5 | 69 | 64 | 19 | 16 | 7 | 10 | 29 | 13 | 7 | 7 | 1 | 2 | 0 | 5 |
322,291 | 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/_discrete_distns.py | scipy.stats._discrete_distns.zipfian_gen | import numpy as np
from ._distn_infrastructure import rv_discrete, get_distribution_names, _vectorize_rvs_over_shapes, _ShapeInfo, _isintegral, rv_discrete_frozen
class zipfian_gen(rv_discrete):
"""A Zipfian discrete random variable.
%(before_notes)s
See Also
--------
zipf
Notes
-----
... |
class zipfian_gen(rv_discrete):
'''A Zipfian discrete random variable.
%(before_notes)s
See Also
--------
zipf
Notes
-----
The probability mass function for `zipfian` is:
.. math::
f(k, a, n) = \frac{1}{H_{n,a} k^a}
for :math:`k \in \{1, 2, \dots, n-1, n\}`, :math:`a \ge... | 8 | 1 | 5 | 0 | 5 | 1 | 1 | 1.06 | 1 | 2 | 1 | 0 | 7 | 0 | 7 | 71 | 88 | 21 | 33 | 19 | 25 | 35 | 30 | 19 | 22 | 1 | 2 | 0 | 7 |
322,292 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure._ShapeInfo | import numpy as np
class _ShapeInfo:
def __init__(self, name, integrality=False, domain=(-np.inf, np.inf), inclusive=(True, True)):
self.name = name
self.integrality = integrality
self.endpoints = domain
self.inclusive = inclusive
domain = list(domain)
if np.isfinit... |
class _ShapeInfo:
def __init__(self, name, integrality=False, domain=(-np.inf, np.inf), inclusive=(True, True)):
pass | 2 | 0 | 13 | 1 | 12 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 1 | 5 | 1 | 1 | 14 | 1 | 13 | 8 | 10 | 0 | 12 | 7 | 10 | 3 | 0 | 1 | 3 |
322,293 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_continuous | from scipy import optimize
from ._constants import _XMAX, _LOGXMAX
import sys
from scipy.special import comb, entr
from numpy import arange, putmask, ones, shape, ndarray, zeros, floor, logical_and, log, sqrt, place, argmax, vectorize, asarray, nan, inf, isinf, empty
import numpy as np
import scipy._lib.array_api_extra... |
class rv_continuous(rv_generic):
'''A generic continuous random variable class meant for subclassing.
`rv_continuous` is a base class to construct specific distribution classes
and instances for continuous random variables. It cannot be used
directly as a distribution.
Parameters
----------
... | 45 | 20 | 27 | 3 | 13 | 11 | 3 | 1.2 | 1 | 18 | 3 | 120 | 38 | 12 | 38 | 77 | 1,416 | 216 | 547 | 228 | 499 | 658 | 492 | 224 | 447 | 13 | 1 | 3 | 120 |
322,294 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_continuous_frozen | class rv_continuous_frozen(rv_frozen):
def pdf(self, x):
return self.dist.pdf(x, *self.args, **self.kwds)
def logpdf(self, x):
return self.dist.logpdf(x, *self.args, **self.kwds) | class rv_continuous_frozen(rv_frozen):
def pdf(self, x):
pass
def logpdf(self, x):
pass | 3 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 2 | 22 | 7 | 2 | 5 | 3 | 2 | 0 | 5 | 3 | 2 | 1 | 1 | 0 | 2 |
322,295 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_discrete | import sys
from scipy.special import comb, entr
from numpy import arange, putmask, ones, shape, ndarray, zeros, floor, logical_and, log, sqrt, place, argmax, vectorize, asarray, nan, inf, isinf, empty
import numpy as np
from scipy import stats
import types
from ._distr_params import distcont, distdiscrete
class rv_dis... |
class rv_discrete(rv_generic):
'''A generic discrete random variable class meant for subclassing.
`rv_discrete` is a base class to construct specific distribution classes
and instances for discrete random variables. It can also be used
to construct an arbitrary distribution defined by a list of support... | 28 | 13 | 22 | 2 | 11 | 9 | 2 | 1.16 | 1 | 11 | 2 | 20 | 25 | 13 | 25 | 64 | 764 | 104 | 306 | 135 | 273 | 356 | 282 | 129 | 254 | 6 | 1 | 2 | 60 |
322,296 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_discrete_frozen | class rv_discrete_frozen(rv_frozen):
def pmf(self, k):
return self.dist.pmf(k, *self.args, **self.kwds)
def logpmf(self, k):
return self.dist.logpmf(k, *self.args, **self.kwds) | class rv_discrete_frozen(rv_frozen):
def pmf(self, k):
pass
def logpmf(self, k):
pass | 3 | 0 | 2 | 0 | 2 | 1 | 1 | 0.2 | 1 | 0 | 0 | 1 | 2 | 0 | 2 | 22 | 7 | 2 | 5 | 3 | 2 | 1 | 5 | 3 | 2 | 1 | 1 | 0 | 2 |
322,297 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_frozen | from scipy._lib._util import check_random_state
class rv_frozen:
def __init__(self, dist, *args, **kwds):
self.args = args
self.kwds = kwds
self.dist = dist.__class__(**dist._updated_ctor_param())
shapes, _, _ = self.dist._parse_args(*args, **kwds)
self.a, self.b = self.dis... |
class rv_frozen:
def __init__(self, dist, *args, **kwds):
pass
@property
def random_state(self):
pass
@random_state.setter
def random_state(self):
pass
def cdf(self, x):
pass
def logcdf(self, x):
pass
def ppf(self, q):
pass
def is... | 23 | 0 | 3 | 0 | 3 | 0 | 1 | 0.09 | 0 | 1 | 1 | 2 | 20 | 5 | 20 | 20 | 83 | 22 | 56 | 31 | 33 | 5 | 52 | 29 | 31 | 2 | 0 | 1 | 21 |
322,298 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_generic | from scipy._lib._util import check_random_state
import types
import re
import keyword
from scipy._lib import doccer
from ._constants import _XMAX, _LOGXMAX
from numpy import arange, putmask, ones, shape, ndarray, zeros, floor, logical_and, log, sqrt, place, argmax, vectorize, asarray, nan, inf, isinf, empty
from scipy.... |
class rv_generic:
'''Class which encapsulates common functionality between rv_discrete
and rv_continuous.
'''
def __init__(self, seed=None):
pass
@property
def random_state(self):
'''Get or set the generator object for generating random variates.
If `random_state` is No... | 44 | 23 | 22 | 2 | 11 | 9 | 3 | 0.82 | 0 | 18 | 4 | 2 | 39 | 7 | 39 | 39 | 936 | 138 | 443 | 157 | 397 | 362 | 403 | 151 | 361 | 19 | 0 | 5 | 123 |
322,299 | 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/_distn_infrastructure.py | scipy.stats._distn_infrastructure.rv_sample | from numpy import arange, putmask, ones, shape, ndarray, zeros, floor, logical_and, log, sqrt, place, argmax, vectorize, asarray, nan, inf, isinf, empty
import numpy as np
from scipy import stats
class rv_sample(rv_discrete):
"""A 'sample' discrete distribution defined by the support and values.
The ctor igno... |
class rv_sample(rv_discrete):
'''A 'sample' discrete distribution defined by the support and values.
The ctor ignores most of the arguments, only needs the `values` argument.
'''
def __init__(self, a=0, b=inf, name=None, badvalue=None, moment_tol=1e-08, values=None, inc=1, longname=None, shapes=None, ... | 12 | 3 | 10 | 1 | 7 | 2 | 2 | 0.3 | 1 | 4 | 0 | 0 | 11 | 11 | 11 | 75 | 126 | 28 | 76 | 37 | 62 | 23 | 67 | 35 | 55 | 7 | 2 | 1 | 18 |
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