Vpf ddlmZddlmZddlmZddlm Z ddl m Z m Z ddl mZmZdde de d e d e fd Zdde de d e d e fd Zde de d e fd Zde de d e fdZdS))laxN)_const)promote_args_inexact)Array ArrayLike)xlogyxlog1pykplocreturnc td|||\}}}t|d}t|d}tj||}t ||t tj||| z}t jt jtj ||tj ||t j |S)axBernoulli log probability mass function. JAX implementation of :obj:`scipy.stats.bernoulli` ``logpmf`` The Bernoulli probability mass function is defined as .. math:: f(k) = \begin{cases} 1 - p, & k = 0 \\ p, & k = 1 \\ 0, & \mathrm{otherwise} \end{cases} Args: k: arraylike, value at which to evaluate the PMF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of logpmf values See Also: - :func:`jax.scipy.stats.bernoulli.cdf` - :func:`jax.scipy.stats.bernoulli.pmf` - :func:`jax.scipy.stats.bernoulli.ppf` zbernoulli.logpmfr) r _lax_constrsubrr jnpwhere logical_orltgtinf)r r r zeroonex log_probss ^/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/scipy/stats/bernoulli.pylogpmfrs8##5q!SAA)!Q Aq  $1a# gaoo!AqkkGCGCOOaR888) 3>#&D//36!S>>BB7(I ' ''cHtjt|||S)aqBernoulli probability mass function. JAX implementation of :obj:`scipy.stats.bernoulli` ``pmf`` The Bernoulli probability mass function is defined as .. math:: f(k) = \begin{cases} 1 - p, & k = 0 \\ p, & k = 1 \\ 0, & \mathrm{otherwise} \end{cases} Args: k: arraylike, value at which to evaluate the PMF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of pmf values See Also: - :func:`jax.scipy.stats.bernoulli.cdf` - :func:`jax.scipy.stats.bernoulli.logpmf` - :func:`jax.scipy.stats.bernoulli.ppf` )rexpr)r r r s rpmfr!<s 8 1c"" # ##rc td||\}}t|dt|d}}tj|tj|z||kz||kzt j||tjt j||t j||t j||g}tj|||z |g}tj ||S)aBernoulli cumulative distribution function. JAX implementation of :obj:`scipy.stats.bernoulli` ``cdf`` The Bernoulli cumulative distribution function is defined as: .. math:: f_{cdf}(k, p) = \sum_{i=0}^k f_{pmf}(k, p) where :math:`f_{pmf}(k, p)` is the Bernoulli probability mass function :func:`jax.scipy.stats.bernoulli.pmf`. Args: k: arraylike, value at which to evaluate the CDF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of cdf values See Also: - :func:`jax.scipy.stats.bernoulli.logpmf` - :func:`jax.scipy.stats.bernoulli.pmf` - :func:`jax.scipy.stats.bernoulli.ppf` z bernoulli.cdfrr) rrrisnanrr logical_andgenanselect)r r rrcondsvalss rcdfr*[s6 oq! 4 4$!QA 1a 0 0$IaLL39Q<<1t8,C8F1dOOOCF1dOOSVAs^^44F1cNN  % '4q# &$ E4  rqc td||\}}t|dt|d}}tjtj|tj|z||kz||kz||kz||kztjtjt j|||z ||S)a.Bernoulli percent point function. JAX implementation of :obj:`scipy.stats.bernoulli` ``ppf`` The percent point function is the inverse of the cumulative distribution function, :func:`jax.scipy.stats.bernoulli.cdf`. Args: k: arraylike, value at which to evaluate the PPF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of ppf values See Also: - :func:`jax.scipy.stats.bernoulli.cdf` - :func:`jax.scipy.stats.bernoulli.logpmf` - :func:`jax.scipy.stats.bernoulli.pmf` z bernoulli.ppfrr)rrrrr#r&rle)r+r rrs rppfr.s* oq! 4 4$!QA 1a 0 0$ IaLL39Q<<1t8,C8AHESQGIcfQa  $,,  r)r)jaxr jax.numpynumpyrjax._src.lax.laxrrjax._src.numpy.utilrjax._src.typingrrjax.scipy.specialrr rr!r*r.rrr7sG111111444444,,,,,,,,,,,,,,,,"'"'i"'I"'I"'e"'"'"'"'J$$9$$$5$$$$>$!9$!$!u$!$!$!$!N9ur