uki ddlZddlmZddlmZddlmZddlm Z ddl m Z m Z ddl mZmZdded ed ed efd Zdded ed ed efd Zded ed efdZded ed efdZy)N)lax)numpy)_const)promote_args_inexact)xlogyxlog1py)Array ArrayLikekplocreturnc td|||\}}}t|d}t|d}tj||}t ||t tj||| z}t jt jtj||tj||tj |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_constrsubrrjnpwhere logical_orltgtnpinf)r r r zeroonex log_probss Y/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/_src/scipy/stats/bernoulli.pylogpmfrs8##5q!SA)!Q Aq $1a# ggao!AqkGCGGCOaR88) 3>>#&&D/366!S>B66'9 &&cBtjt|||S)aqBernoulli probability mass function. JAX implementation of :obj:`scipy.stats.bernoulli` ``pmf`` The Bernoulli probability mass function is defined as .. math:: f(k) = \begin{cases} 1 - p, & k = 0 \\ p, & k = 1 \\ 0, & \mathrm{otherwise} \end{cases} Args: k: arraylike, value at which to evaluate the PMF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of pmf values See Also: - :func:`jax.scipy.stats.bernoulli.cdf` - :func:`jax.scipy.stats.bernoulli.logpmf` - :func:`jax.scipy.stats.bernoulli.ppf` )rexpr)r r r s rpmfr#>s8 1c" ##r c td||\}}t|dt|d}}tj|tj|z||kz||kDzt j ||tj t j||t j ||t j||g}tj|||z |g}tj||S)aBernoulli cumulative distribution function. JAX implementation of :obj:`scipy.stats.bernoulli` ``cdf`` The Bernoulli cumulative distribution function is defined as: .. math:: f_{cdf}(k, p) = \sum_{i=0}^k f_{pmf}(k, p) where :math:`f_{pmf}(k, p)` is the Bernoulli probability mass function :func:`jax.scipy.stats.bernoulli.pmf`. Args: k: arraylike, value at which to evaluate the CDF p: arraylike, distribution shape parameter loc: arraylike, distribution offset Returns: array of cdf values See Also: - :func:`jax.scipy.stats.bernoulli.logpmf` - :func:`jax.scipy.stats.bernoulli.pmf` - :func:`jax.scipy.stats.bernoulli.ppf` z bernoulli.cdfrr) rrrisnanrr logical_andgernanselect)r r rrcondsvalss rcdfr,]s6 oq! 4$!QA 1a 0$IIaL399Q<1t8,C8FF1dOOOCFF1dOSVVAs^4FF1cN  % &&$a %$ E4  r qc vtd||\}}t|dt|d}}tjtj|tj|z||kz||kDz||kz||kDzt j tjtj|||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%rr(rle)r-r rrs rppfr0s* oq! 4$!QA 1a 0$ IIaL399Q<1t8,C8AHESQFFIIcffQa $, r )r)rrjax._srcrrjax._src.lax.laxrrjax._src.numpy.utilrjax._src.scipy.specialrrjax._src.typingr r rr#r,r0r rr7s!141,"&i"&I"&I"&e"&J$9$$$5$>$!9$!$!u$!N9ur