48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
# Copyright 2021 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 _wraps, promote_args_inexact
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from jax._src.scipy.special import betaln
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from jax._src.typing import Array, ArrayLike
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@_wraps(osp_stats.betabinom.logpmf, update_doc=False)
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def logpmf(k: ArrayLike, n: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0) -> Array:
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"""JAX implementation of scipy.stats.betabinom.logpmf."""
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k, n, a, b, loc = promote_args_inexact("betabinom.logpmf", k, n, a, b, loc)
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y = lax.sub(lax.floor(k), loc)
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one = _lax_const(y, 1)
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zero = _lax_const(y, 0)
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combiln = lax.neg(lax.add(lax.log1p(n), betaln(lax.add(lax.sub(n,y), one), lax.add(y,one))))
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beta_lns = lax.sub(betaln(lax.add(y,a), lax.add(lax.sub(n,y),b)), betaln(a,b))
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log_probs = lax.add(combiln, beta_lns)
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y_cond = jnp.logical_or(lax.lt(y, lax.neg(loc)), lax.gt(y, lax.sub(n, loc)))
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log_probs = jnp.where(y_cond, -jnp.inf, log_probs)
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n_a_b_cond = jnp.logical_or(jnp.logical_or(lax.lt(n, one), lax.lt(a, zero)), lax.lt(b, zero))
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return jnp.where(n_a_b_cond, jnp.nan, log_probs)
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@_wraps(osp_stats.betabinom.pmf, update_doc=False)
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def pmf(k: ArrayLike, n: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0) -> Array:
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"""JAX implementation of scipy.stats.betabinom.pmf."""
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return lax.exp(logpmf(k, n, a, b, loc))
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