41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
# Copyright 2022 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import scipy.stats as osp_stats
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from jax import lax
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import jax.numpy as jnp
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from jax._src.numpy.util import _wraps, promote_args_inexact, promote_args_numeric
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from jax._src.scipy.special import gammaln, xlogy
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from jax._src.typing import Array, ArrayLike
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@_wraps(osp_stats.multinomial.logpmf, update_doc=False)
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def logpmf(x: ArrayLike, n: ArrayLike, p: ArrayLike) -> Array:
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"""JAX implementation of scipy.stats.multinomial.logpmf."""
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p, = promote_args_inexact("multinomial.logpmf", p)
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x, n = promote_args_numeric("multinomial.logpmf", x, n)
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if not jnp.issubdtype(x.dtype, jnp.integer):
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raise ValueError(f"x and n must be of integer type; got x.dtype={x.dtype}, n.dtype={n.dtype}")
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x = x.astype(p.dtype)
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n = n.astype(p.dtype)
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logprobs = gammaln(n + 1) + jnp.sum(xlogy(x, p) - gammaln(x + 1), axis=-1)
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return jnp.where(jnp.equal(jnp.sum(x), n), logprobs, -jnp.inf)
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@_wraps(osp_stats.multinomial.pmf, update_doc=False)
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def pmf(x: ArrayLike, n: ArrayLike, p: ArrayLike) -> Array:
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"""JAX implementation of scipy.stats.multinomial.pmf."""
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return lax.exp(logpmf(x, n, p))
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