uki ddlZddlmZddlmZddlmZddlmZm Z dde de de d efd Z dde de de d efd Z dde de de d efd Z dde de de d efd Z dde de de d efdZdde de de d efdZdde de de d efdZy)N)lax)numpy)promote_args_inexact)Array ArrayLikexlocscalereturncjtd|||\}}}tj|}tjtj|||}tj tj ||}tjtj||tj |S)aExponential log probability distribution function. JAX implementation of :obj:`scipy.stats.expon` ``logpdf``. The Exponential probability distribution function is .. math:: f(x) = \begin{cases} e^{-x} & x \ge 0 \\ 0 & \mathrm{otherwise} \end{cases} Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of logpdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` z expon.logpdf) rrlogdivsubnegaddjnpwhereltnpinf)rr r log_scale linear_term log_probss U/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/_src/scipy/stats/expon.pylogpdfrs}>'~q#uE-!S%ggen)3/+ggcggk956) 366!S>BFF7I 66cBtjt|||S)aExponential probability distribution function. JAX implementation of :obj:`scipy.stats.expon` ``pdf``. The Exponential probability distribution function is .. math:: f(x) = \begin{cases} e^{-x} & x \ge 0 \\ 0 & \mathrm{otherwise} \end{cases} Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` )rexprrr r s rpdfr =s> 3& ''rc Btd|||\}}}tjtj|||}t j tj ||t j|tjtj|S)a(Exponential cumulative density function. JAX implementation of :obj:`scipy.stats.expon` ``cdf``. The cdf is defined as .. math:: f_{cdf}(x) = \int_{-\infty}^x f_{pdf}(y)\mathrm{d}y where :math:`f_{pdf}` is the exponential distribution probability density function, :func:`jax.scipy.stats.expon.pdf`. Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` z expon.cdf) rrrrrrr zeros_likerexpm1rr r neg_scaled_xs rcdfr&_ss>'{AsEB-!S%a%0, FF1cNNN< GGCIIl #$ rc htjtjt|||S)a/Exponential log cumulative density function. JAX implementation of :obj:`scipy.stats.expon` ``logcdf``. The cdf is defined as .. math:: f_{cdf}(x) = \int_{-\infty}^x f_{pdf}(y)\mathrm{d}y where :math:`f_{pdf}` is the exponential distribution probability density function, :func:`jax.scipy.stats.expon.pdf`. Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` )rlog1prsfrs rlogcdfr*s%> 3772ae,- ..rctd|||\}}}tjtj|||}t j tj ||t j||S)aExponential log survival function. JAX implementation of :obj:`scipy.stats.expon` ``logsf``. The survival function is defined as .. math:: f_{sf}(x) = 1 - f_{cdf}(x) where :math:`f_{cdf}(x)` is the exponential cumulative distribution function, :func:`jax.scipy.stats.expon.cdf`. Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` zexpon.sf)rrrrrrrr"r$s rlogsfr,sZ>'z1c5A-!S%a%0, 366!S>3>>,#? NNrcBtjt|||S)a Exponential survival function. JAX implementation of :obj:`scipy.stats.expon` ``sf``. The survival function is defined as .. math:: f_{sf}(x) = 1 - f_{cdf}(x) where :math:`f_{cdf}(x)` is the exponential cumulative distribution function, :func:`jax.scipy.stats.expon.cdf`. Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` )rrr,rs rr)r)s> q#u% &&rqc Ntd|||\}}}tjtj|||}t j t j ||dkz|dkDztjtjtj|S)aExponential survival function. JAX implementation of :obj:`scipy.stats.expon` ``ppf``. The percent point function is defined as the inverse of the cumulative distribution function, :func:`jax.scipy.stats.expon.cdf`. Args: x: arraylike, value at which to evaluate the PDF loc: arraylike, distribution offset parameter scale: arraylike, distribution scale parameter Returns: array of pdf values. See Also: :func:`jax.scipy.stats.expon.cdf` :func:`jax.scipy.stats.expon.pdf` :func:`jax.scipy.stats.expon.ppf` :func:`jax.scipy.stats.expon.sf` :func:`jax.scipy.stats.expon.logcdf` :func:`jax.scipy.stats.expon.logpdf` :func:`jax.scipy.stats.expon.logsf` z expon.ppfr) rrrrrrisnanrnanrr()r.r r neg_scaled_qs rppfr4s~2'{AsEB-!S%a%0, IIaLAEa!e$FFGGCIIl #$ r)rr0)rrjax._srcrrjax._src.numpy.utilrjax._src.typingrrrr r&r*r,r)r4rrr9s!4,#7i#7i#7I#7e#7L(9(9((5(D%9%9%%5%P/i/i/I/e/D!OY!OY!O9!OU!OH')')' '%'D995r