69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
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# 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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from typing import cast
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import numpy as np
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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 import special
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@_wraps(osp_stats.norm.logpdf, update_doc=False)
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def logpdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, loc, scale = promote_args_inexact("norm.logpdf", x, loc, scale)
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scale_sqrd = lax.square(scale)
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log_normalizer = lax.log(lax.mul(_lax_const(x, 2 * np.pi), scale_sqrd))
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quadratic = lax.div(lax.square(lax.sub(x, loc)), scale_sqrd)
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return lax.div(lax.add(log_normalizer, quadratic), _lax_const(x, -2))
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@_wraps(osp_stats.norm.pdf, update_doc=False)
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def pdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return lax.exp(logpdf(x, loc, scale))
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@_wraps(osp_stats.norm.cdf, update_doc=False)
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def cdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, loc, scale = promote_args_inexact("norm.cdf", x, loc, scale)
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return special.ndtr(lax.div(lax.sub(x, loc), scale))
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@_wraps(osp_stats.norm.logcdf, update_doc=False)
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def logcdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, loc, scale = promote_args_inexact("norm.logcdf", x, loc, scale)
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# Cast required because custom_jvp return type is broken.
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return cast(Array, special.log_ndtr(lax.div(lax.sub(x, loc), scale)))
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@_wraps(osp_stats.norm.ppf, update_doc=False)
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def ppf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return jnp.asarray(special.ndtri(q) * scale + loc, float)
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@_wraps(osp_stats.norm.sf, update_doc=False)
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def sf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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cdf_result = cdf(x, loc, scale)
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return lax.sub(_lax_const(cdf_result, 1), cdf_result)
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@_wraps(osp_stats.norm.isf, update_doc=False)
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def isf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return ppf(lax.sub(_lax_const(q, 1), q), loc, scale)
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