Source code for jax._src.scipy.stats.nbinom

# Copyright 2021 The JAX Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     https://www.apache.org/licenses/LICENSE-2.0
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import scipy.stats as osp_stats

from jax import lax
import jax.numpy as jnp
from jax._src.lax.lax import _const as _lax_const
from jax._src.numpy.util import implements, promote_args_inexact
from jax._src.scipy.special import gammaln, xlogy
from jax._src.typing import Array, ArrayLike


[docs] @implements(osp_stats.nbinom.logpmf, update_doc=False) def logpmf(k: ArrayLike, n: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array: """JAX implementation of scipy.stats.nbinom.logpmf.""" k, n, p, loc = promote_args_inexact("nbinom.logpmf", k, n, p, loc) one = _lax_const(k, 1) y = lax.sub(k, loc) comb_term = lax.sub( lax.sub(gammaln(lax.add(y, n)), gammaln(n)), gammaln(lax.add(y, one)) ) log_linear_term = lax.add(xlogy(n, p), xlogy(y, lax.sub(one, p))) log_probs = lax.add(comb_term, log_linear_term) return jnp.where(lax.lt(k, loc), -jnp.inf, log_probs)
[docs] @implements(osp_stats.nbinom.pmf, update_doc=False) def pmf(k: ArrayLike, n: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array: """JAX implementation of scipy.stats.nbinom.pmf.""" return lax.exp(logpmf(k, n, p, loc))