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

# Copyright 2021 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.
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#     https://www.apache.org/licenses/LICENSE-2.0
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import scipy
import scipy.stats as osp_stats

from jax import lax
from jax._src.numpy.util import _wraps
from jax._src.numpy.lax_numpy import _promote_args_inexact, _constant_like, where, inf, logical_or, nan
from jax._src.scipy.special import betaln

scipy_version = tuple(map(int, scipy.version.version.split('.')[:2]))


[docs]def logpmf(k, n, a, b, loc=0): """JAX implementation of scipy.stats.betabinom.logpmf.""" k, n, a, b, loc = _promote_args_inexact("betabinom.logpmf", k, n, a, b, loc) y = lax.sub(lax.floor(k), loc) one = _constant_like(y, 1) zero = _constant_like(y, 0) combiln = lax.neg(lax.add(lax.log1p(n), betaln(lax.add(lax.sub(n,y), one), lax.add(y,one)))) beta_lns = lax.sub(betaln(lax.add(y,a), lax.add(lax.sub(n,y),b)), betaln(a,b)) log_probs = lax.add(combiln, beta_lns) y_cond = logical_or(lax.lt(y, lax.neg(loc)), lax.gt(y, lax.sub(n, loc))) log_probs = where(y_cond, -inf, log_probs) n_a_b_cond = logical_or(logical_or(lax.lt(n, one), lax.lt(a, zero)), lax.lt(b, zero)) return where(n_a_b_cond, nan, log_probs)
[docs]def pmf(k, n, a, b, loc=0): """JAX implementation of scipy.stats.betabinom.pmf.""" return lax.exp(logpmf(k, n, a, b, loc))
# betabinom was added in scipy 1.4.0 if scipy_version >= (1, 4): logpmf = _wraps(osp_stats.betabinom.logpmf, update_doc=False)(logpmf) pmf = _wraps(osp_stats.betabinom.pmf, update_doc=False)(pmf)