77 lines
2.8 KiB
Python
77 lines
2.8 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 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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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.numpy import arctan
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from jax._src.typing import Array, ArrayLike
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@_wraps(osp_stats.cauchy.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("cauchy.logpdf", x, loc, scale)
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pi = _lax_const(x, np.pi)
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scaled_x = lax.div(lax.sub(x, loc), scale)
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normalize_term = lax.log(lax.mul(pi, scale))
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return lax.neg(lax.add(normalize_term, lax.log1p(lax.mul(scaled_x, scaled_x))))
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@_wraps(osp_stats.cauchy.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.cauchy.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("cauchy.cdf", x, loc, scale)
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pi = _lax_const(x, np.pi)
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scaled_x = lax.div(lax.sub(x, loc), scale)
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return lax.add(_lax_const(x, 0.5), lax.mul(lax.div(_lax_const(x, 1.), pi), arctan(scaled_x)))
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@_wraps(osp_stats.cauchy.logcdf, update_doc=False)
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def logcdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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return lax.log(cdf(x, loc, scale))
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@_wraps(osp_stats.cauchy.sf, update_doc=False)
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def sf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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x, = promote_args_inexact("cauchy.sf", x)
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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.cauchy.isf, update_doc=False)
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def isf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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q, loc, scale = promote_args_inexact("cauchy.isf", q, loc, scale)
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pi = _lax_const(q, np.pi)
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half_pi = _lax_const(q, np.pi / 2)
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unscaled = lax.tan(lax.sub(half_pi, lax.mul(pi, q)))
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return lax.add(lax.mul(unscaled, scale), loc)
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@_wraps(osp_stats.cauchy.ppf, update_doc=False)
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def ppf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
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q, loc, scale = promote_args_inexact("cauchy.ppf", q, loc, scale)
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pi = _lax_const(q, np.pi)
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half_pi = _lax_const(q, np.pi / 2)
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unscaled = lax.tan(lax.sub(lax.mul(pi, q), half_pi))
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return lax.add(lax.mul(unscaled, scale), loc)
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