uki ddlZddlmZddlmZddlmZddlmZmZddl m Z m Z ddl m Z mZded ed ed e fd Zded ed ed e fd Zy)N)dtypes)lax)numpy)promote_args_inexactpromote_args_numeric)gammalnxlogy)Array ArrayLikexnpreturnchtd|\}td||\}}tj|jt j s%td|jd|j|j|j}|j|j}t|dztjt||t|dzz dz}tjtjtj|||t j S)aMultinomial log probability mass function. JAX implementation of :obj:`scipy.stats.multinomial` ``logpdf``. The multinomial probability distribution is given by .. math:: f(x, n, p) = n! \prod_{i=1}^k \frac{p_i^{x_i}}{x_i!} with :math:`n = \sum_i x_i`. Args: x: arraylike, value at which to evaluate the PMF n: arraylike, distribution shape parameter p: arraylike, distribution shape parameter Returns: array of logpmf values. See Also: :func:`jax.scipy.stats.multinomial.pmf` zmultinomial.logpmfz-x and n must be of integer type; got x.dtype=z , n.dtype=)axis)rrr issubdtypedtypenpinteger ValueErrorastyperjnpsumr whereequalinf)r r rlogprobss [/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/_src/scipy/stats/multinomial.pylogpmfr!s00!4"! 2Aq 9$!Q   177BJJ / DQWWIZXYX_X_W`a bbhhqww!hhqww! QU^cggeAqkGAEN&BL L( 399SWWQZ+Xw ??cBtjt|||S)aMultinomial probability mass function. JAX implementation of :obj:`scipy.stats.multinomial` ``pmf``. The multinomial probability distribution is given by .. math:: f(x, n, p) = n! \prod_{i=1}^k \frac{p_i^{x_i}}{x_i!} with :math:`n = \sum_i x_i`. Args: x: arraylike, value at which to evaluate the PMF n: arraylike, distribution shape parameter p: arraylike, distribution shape parameter Returns: array of pmf values See Also: :func:`jax.scipy.stats.multinomial.logpmf` )rexpr!)r r rs r pmfr%;s0 1a !!r")rrjax._srcrrrjax._src.numpy.utilrrjax._src.scipy.specialrr jax._src.typingr r r!r%r"r r+sd!J1,@i@I@)@@D"9""y"U"r"