§ Vp«f-ã ó—ddlmZddlmZddlmZddlm Z ddl m Z m Z ddl mZmZmZmZdde d e d e d e d e f d „Zdde d e d e d e d e f d„Zdde d e d e d e d e f d„Zdde d e d e d e d e f d„Zdde d e d e d e d e f d„Zdde d e d e d e d e f d„ZdS)é)ÚlaxN)Ú_const)Úpromote_args_inexact)ÚArrayÚ ArrayLike)ÚgammalnÚxlogyÚgammaincÚ gammainccéÚxÚaÚlocÚscaleÚreturncó—td||||¦«\}}}}t|d¦«}tjtj||¦«|¦«}tjt tj||¦«|¦«|¦«}tjt|¦«tj|¦«¦«}tj||¦«}tj tj ||¦«tj |¦«S)a4Gamma log probability distribution function. JAX implementation of :obj:`scipy.stats.gamma` ``logpdf``. The Gamma probability distribution is given by .. math:: f(x, a) = \frac{1}{\Gamma(a)}x^{a-1}e^{-x} Where :math:`\Gamma(a)` is the :func:`~jax.scipy.special.gamma` function. It is defined for :math:`x \ge 0` and :math:`a > 0`. Args: x: arraylike, value at which to evaluate the PDF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of logpdf values. See Also: - :func:`jax.scipy.stats.gamma.cdf` - :func:`jax.scipy.stats.gamma.pdf` - :func:`jax.scipy.stats.gamma.sf` - :func:`jax.scipy.stats.gamma.logcdf` - :func:`jax.scipy.stats.gamma.logsf` z gamma.logpdfr ) rÚ _lax_constrÚdivÚsubr ÚaddrÚlogÚjnpÚwhereÚltÚinf) r rrrÚoneÚyÚlog_linear_termÚ shape_termsÚ log_probss úZ/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/scipy/stats/gamma.pyÚlogpdfr"sÀõ<*¨.¸!¸QÀÀUÑKÔKÑ€!€QˆˆUÝ1aÑÔ€#Ý „gcŒga˜‰oŒo˜uÑ%Ô%€!Ý”GE¥#¤'¨!¨S¡/¤/°1Ñ5Ô5°qÑ9Ô9€/Ý” ™ œ ¥C¤G¨E¡N¤NÑ3Ô3€+ÝŒgo {Ñ3Ô3€)Ý Œ•3”6˜!˜S‘>”>¥C¤G 8¨YÑ 7Ô 7Ð7ócóJ—tjt||||¦«¦«S)a-Gamma probability distribution function. JAX implementation of :obj:`scipy.stats.gamma` ``pdf``. The Gamma probability distribution is given by .. math:: f(x, a) = \frac{1}{\Gamma(a)}x^{a-1}e^{-x} Where :math:`\Gamma(a)` is the :func:`~jax.scipy.special.gamma` function. It is defined for :math:`x \ge 0` and :math:`a > 0`. Args: x: arraylike, value at which to evaluate the PDF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: - :func:`jax.scipy.stats.gamma.cdf` - :func:`jax.scipy.stats.gamma.sf` - :func:`jax.scipy.stats.gamma.logcdf` - :func:`jax.scipy.stats.gamma.logpdf` - :func:`jax.scipy.stats.gamma.logsf` )rÚexpr"©r rrrs r!Úpdfr'>s"€õ< Œ•˜˜1˜c 5Ñ)Ô)Ñ *Ô *Ð*r#c ó—td||||¦«\}}}}t|tjt |d¦«tjtj||¦«|¦«t |tj¦«¦«¦«S)aGamma cumulative distribution function. JAX implementation of :obj:`scipy.stats.gamma` ``cdf``. The cdf is defined as .. math:: f_{cdf}(x, a) = \int_{-\infty}^x f_{pdf}(y, a)\mathrm{d}y where :math:`f_{pdf}` is the probability density function, :func:`jax.scipy.stats.gamma.pdf`. Args: x: arraylike, value at which to evaluate the CDF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of cdf values. See Also: - :func:`jax.scipy.stats.gamma.pdf` - :func:`jax.scipy.stats.gamma.sf` - :func:`jax.scipy.stats.gamma.logcdf` - :func:`jax.scipy.stats.gamma.logpdf` - :func:`jax.scipy.stats.gamma.logsf` z gamma.cdfr) rr rÚclamprrrrrr&s r!Úcdfr*_s{€õ<*¨+°q¸!¸SÀ%ÑHÔHÑ€!€QˆˆUÝ ØÝ„IÝAÑÔÝ „gcŒga˜‰oŒo˜uÑ%Ô%Ý•C”GÑÔñôñ ô ðr#cóJ—tjt||||¦«¦«S)aGamma log cumulative distribution function. JAX implementation of :obj:`scipy.stats.gamma` ``logcdf``. The cdf is defined as .. math:: f_{cdf}(x, a) = \int_{-\infty}^x f_{pdf}(y, a)\mathrm{d}y where :math:`f_{pdf}` is the probability density function, :func:`jax.scipy.stats.gamma.pdf`. Args: x: arraylike, value at which to evaluate the CDF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of logcdf values. See Also: - :func:`jax.scipy.stats.gamma.cdf` - :func:`jax.scipy.stats.gamma.pdf` - :func:`jax.scipy.stats.gamma.sf` - :func:`jax.scipy.stats.gamma.logpdf` - :func:`jax.scipy.stats.gamma.logsf` )rrr*r&s r!Úlogcdfr,ˆs"€õ< Œ•Q˜˜3 Ñ&Ô&Ñ 'Ô 'Ð'r#c óž—td||||¦«\}}}}t|tjtj||¦«|¦«¦«S)aïGamma survival function. JAX implementation of :obj:`scipy.stats.gamma` ``sf``. The survival function is defined as .. math:: f_{sf}(x, k) = 1 - f_{cdf}(x, k) where :math:`f_{cdf}(x, k)` is the cumulative distribution function, :func:`jax.scipy.stats.gamma.cdf`. Args: x: arraylike, value at which to evaluate the SF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of sf values. See Also: - :func:`jax.scipy.stats.gamma.cdf` - :func:`jax.scipy.stats.gamma.pdf` - :func:`jax.scipy.stats.gamma.logcdf` - :func:`jax.scipy.stats.gamma.logpdf` - :func:`jax.scipy.stats.gamma.logsf` zgamma.sf)rr rrrr&s r!Úsfr.©sH€õ<*¨*°a¸¸CÀÑGÔGÑ€!€QˆˆUÝ 1•c”gcœg a¨™oœo¨uÑ5Ô5Ñ 6Ô 6Ð6r#cóJ—tjt||||¦«¦«S)aöGamma log survival function. JAX implementation of :obj:`scipy.stats.gamma` ``logsf``. The survival function is defined as .. math:: f_{sf}(x, k) = 1 - f_{cdf}(x, k) where :math:`f_{cdf}(x, k)` is the cumulative distribution function, :func:`jax.scipy.stats.gamma.cdf`. Args: x: arraylike, value at which to evaluate the SF a: arraylike, distribution shape parameter loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of logsf values. See Also: - :func:`jax.scipy.stats.gamma.cdf` - :func:`jax.scipy.stats.gamma.pdf` - :func:`jax.scipy.stats.gamma.sf` - :func:`jax.scipy.stats.gamma.logcdf` - :func:`jax.scipy.stats.gamma.logpdf` )rrr.r&s r!Úlogsfr0Ës"€õ< Œ•Aq˜#˜uÑ%Ô%Ñ &Ô &Ð&r#)rr )ÚjaxrÚ jax.numpyÚnumpyrÚjax._src.lax.laxrrÚjax._src.numpy.utilrÚjax._src.typingrrÚjax.scipy.specialrr r r r"r'r*r,r.r0©r#r!úr9sððÐÐÐÐÐØÐÐÐÐÐØ1Ð1Ð1Ð1Ð1Ð1Ø4Ð4Ð4Ð4Ð4Ð4Ø,Ð,Ð,Ð,Ð,Ð,Ð,Ð,ØAÐAÐAÐAÐAÐAÐAÐAÐAÐAÐAÐAð$8ð$8ˆið$8˜Ið$8¨Ið$8À)ð$8ÐTYð$8ð$8ð$8ð$8ðN+ð+ˆ9ð+˜ð+¨ð+¸yð+ÐQVð+ð+ð+ð+ðB&ð&ˆ9ð&˜ð&¨ð&¸yð&ÐQVð&ð&ð&ð&ðR(ð(ˆið(˜Ið(¨Ið(À)ð(ÐTYð(ð(ð(ð(ðB7ð7ˆ)ð7˜ ð7¨ ð7¸ið7ÐPUð7ð7ð7ð7ðD'ð'ˆYð'˜9ð'¨9ð'Àð'ÐSXð'ð'ð'ð'ð'ð'r#