58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
# Copyright 2018 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.lax.lax import _const as _lax_const
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from jax._src.numpy.util import promote_dtypes_inexact, _wraps
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from jax.scipy.special import gammaln, xlogy
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from jax._src.typing import Array, ArrayLike
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def _is_simplex(x: Array) -> Array:
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x_sum = jnp.sum(x, axis=0)
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return jnp.all(x > 0, axis=0) & (abs(x_sum - 1) < 1E-6)
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@_wraps(osp_stats.dirichlet.logpdf, update_doc=False)
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def logpdf(x: ArrayLike, alpha: ArrayLike) -> Array:
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return _logpdf(*promote_dtypes_inexact(x, alpha))
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def _logpdf(x: Array, alpha: Array) -> Array:
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if alpha.ndim != 1:
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raise ValueError(
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f"`alpha` must be one-dimensional; got alpha.shape={alpha.shape}"
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)
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if x.shape[0] not in (alpha.shape[0], alpha.shape[0] - 1):
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raise ValueError(
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"`x` must have either the same number of entries as `alpha` "
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f"or one entry fewer; got x.shape={x.shape}, alpha.shape={alpha.shape}"
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)
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one = _lax_const(x, 1)
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if x.shape[0] != alpha.shape[0]:
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x = jnp.concatenate([x, lax.sub(one, x.sum(0, keepdims=True))], axis=0)
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normalize_term = jnp.sum(gammaln(alpha)) - gammaln(jnp.sum(alpha))
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if x.ndim > 1:
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alpha = lax.broadcast_in_dim(alpha, alpha.shape + (1,) * (x.ndim - 1), (0,))
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log_probs = lax.sub(jnp.sum(xlogy(lax.sub(alpha, one), x), axis=0), normalize_term)
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return jnp.where(_is_simplex(x), log_probs, -jnp.inf)
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@_wraps(osp_stats.dirichlet.pdf, update_doc=False)
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def pdf(x: ArrayLike, alpha: ArrayLike) -> Array:
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return lax.exp(logpdf(x, alpha))
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