71 lines
2.5 KiB
Python
71 lines
2.5 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 betaln, betainc, xlogy, xlog1py
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@_wraps(osp_stats.beta.logpdf, update_doc=False)
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def logpdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, a, b, loc, scale = promote_args_inexact("beta.logpdf", x, a, b, loc, scale)
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one = _lax_const(x, 1)
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shape_term = lax.neg(betaln(a, b))
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y = lax.div(lax.sub(x, loc), scale)
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log_linear_term = lax.add(xlogy(lax.sub(a, one), y),
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xlog1py(lax.sub(b, one), lax.neg(y)))
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log_probs = lax.sub(lax.add(shape_term, log_linear_term), lax.log(scale))
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return jnp.where(jnp.logical_or(lax.gt(x, lax.add(loc, scale)),
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lax.lt(x, loc)), -jnp.inf, log_probs)
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@_wraps(osp_stats.beta.pdf, update_doc=False)
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def pdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return lax.exp(logpdf(x, a, b, loc, scale))
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@_wraps(osp_stats.beta.cdf, update_doc=False)
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def cdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, a, b, loc, scale = promote_args_inexact("beta.cdf", x, a, b, loc, scale)
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return betainc(
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a,
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b,
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lax.clamp(
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_lax_const(x, 0),
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lax.div(lax.sub(x, loc), scale),
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_lax_const(x, 1),
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)
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)
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@_wraps(osp_stats.beta.logcdf, update_doc=False)
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def logcdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return lax.log(cdf(x, a, b, loc, scale))
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@_wraps(osp_stats.beta.sf, update_doc=False)
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def sf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
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loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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cdf_result = cdf(x, a, b, loc, scale)
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return lax.sub(_lax_const(cdf_result, 1), cdf_result)
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