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

# Copyright 2018 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     https://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import scipy.stats as osp_stats

from jax import lax
from jax._src.numpy import lax_numpy as jnp
from jax._src.numpy.util import _wraps
from jax.scipy.special import gammaln, xlogy
from jax._src.numpy.lax_numpy import _promote_dtypes_inexact


def _is_simplex(x):
  x_sum = jnp.sum(x, axis=0)
  return jnp.all(x > 0, axis=0) & (abs(x_sum - 1) < 1E-6)


[docs]@_wraps(osp_stats.dirichlet.logpdf, update_doc=False) def logpdf(x, alpha): x, alpha = _promote_dtypes_inexact(x, alpha) if alpha.ndim != 1: raise ValueError( f"`alpha` must be one-dimensional; got alpha.shape={alpha.shape}" ) if x.shape[0] not in (alpha.shape[0], alpha.shape[0] - 1): raise ValueError( "`x` must have either the same number of entries as `alpha` " f"or one entry fewer; got x.shape={x.shape}, alpha.shape={alpha.shape}" ) one = jnp._constant_like(x, 1) if x.shape[0] != alpha.shape[0]: x = jnp.concatenate([x, lax.sub(one, x.sum(0, keepdims=True))], axis=0) normalize_term = jnp.sum(gammaln(alpha)) - gammaln(jnp.sum(alpha)) if x.ndim > 1: alpha = lax.broadcast_in_dim(alpha, alpha.shape + (1,) * (x.ndim - 1), (0,)) log_probs = lax.sub(jnp.sum(xlogy(lax.sub(alpha, one), x), axis=0), normalize_term) return jnp.where(_is_simplex(x), log_probs, -jnp.inf)
[docs]@_wraps(osp_stats.dirichlet.pdf, update_doc=False) def pdf(x, alpha): return lax.exp(logpdf(x, alpha))