62 lines
2.2 KiB
Python
62 lines
2.2 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 _wraps, promote_args_inexact
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from jax._src.typing import Array, ArrayLike
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from jax.scipy.special import xlogy, xlog1py
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@_wraps(osp_stats.bernoulli.logpmf, update_doc=False)
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def logpmf(k: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array:
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k, p, loc = promote_args_inexact("bernoulli.logpmf", k, p, loc)
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zero = _lax_const(k, 0)
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one = _lax_const(k, 1)
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x = lax.sub(k, loc)
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log_probs = xlogy(x, p) + xlog1py(lax.sub(one, x), -p)
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return jnp.where(jnp.logical_or(lax.lt(x, zero), lax.gt(x, one)),
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-jnp.inf, log_probs)
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@_wraps(osp_stats.bernoulli.pmf, update_doc=False)
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def pmf(k: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array:
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return jnp.exp(logpmf(k, p, loc))
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@_wraps(osp_stats.bernoulli.cdf, update_doc=False)
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def cdf(k: ArrayLike, p: ArrayLike) -> Array:
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k, p = promote_args_inexact('bernoulli.cdf', k, p)
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zero, one = _lax_const(k, 0), _lax_const(k, 1)
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conds = [
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jnp.isnan(k) | jnp.isnan(p) | (p < zero) | (p > one),
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lax.lt(k, zero),
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jnp.logical_and(lax.ge(k, zero), lax.lt(k, one)),
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lax.ge(k, one)
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]
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vals = [jnp.nan, zero, one - p, one]
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return jnp.select(conds, vals)
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@_wraps(osp_stats.bernoulli.ppf, update_doc=False)
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def ppf(q: ArrayLike, p: ArrayLike) -> Array:
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q, p = promote_args_inexact('bernoulli.ppf', q, p)
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zero, one = _lax_const(q, 0), _lax_const(q, 1)
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return jnp.where(
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jnp.isnan(q) | jnp.isnan(p) | (p < zero) | (p > one) | (q < zero) | (q > one),
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jnp.nan,
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jnp.where(lax.le(q, one - p), zero, one)
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)
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