Source code for jax._src.scipy.stats.chi2

# Copyright 2021 The JAX Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     https://www.apache.org/licenses/LICENSE-2.0
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import scipy.stats as osp_stats

from jax import lax
import jax.numpy as jnp
from jax._src.lax.lax import _const as _lax_const
from jax._src.numpy.util import implements, promote_args_inexact
from jax._src.typing import Array, ArrayLike
from jax.scipy.special import gammainc, gammaincc


[docs] @implements(osp_stats.chi2.logpdf, update_doc=False) def logpdf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: x, df, loc, scale = promote_args_inexact("chi2.logpdf", x, df, loc, scale) one = _lax_const(x, 1) two = _lax_const(x, 2) y = lax.div(lax.sub(x, loc), scale) df_on_two = lax.div(df, two) kernel = lax.sub(lax.mul(lax.sub(df_on_two, one), lax.log(y)), lax.div(y,two)) nrml_cnst = lax.neg(lax.add(lax.lgamma(df_on_two),lax.div(lax.mul(lax.log(two), df),two))) log_probs = lax.add(lax.sub(nrml_cnst, lax.log(scale)), kernel) return jnp.where(lax.lt(x, loc), -jnp.inf, log_probs)
[docs] @implements(osp_stats.chi2.pdf, update_doc=False) def pdf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: return lax.exp(logpdf(x, df, loc, scale))
[docs] @implements(osp_stats.chi2.cdf, update_doc=False) def cdf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: x, df, loc, scale = promote_args_inexact("chi2.cdf", x, df, loc, scale) two = _lax_const(scale, 2) return gammainc( lax.div(df, two), lax.clamp( _lax_const(x, 0), lax.div( lax.sub(x, loc), lax.mul(scale, two), ), _lax_const(x, jnp.inf), ), )
[docs] @implements(osp_stats.chi2.logcdf, update_doc=False) def logcdf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: return lax.log(cdf(x, df, loc, scale))
[docs] @implements(osp_stats.chi2.sf, update_doc=False) def sf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: x, df, loc, scale = promote_args_inexact("chi2.sf", x, df, loc, scale) two = _lax_const(scale, 2) return gammaincc( lax.div(df, two), lax.clamp( _lax_const(x, 0), lax.div( lax.sub(x, loc), lax.mul(scale, two), ), _lax_const(x, jnp.inf), ), )
[docs] @implements(osp_stats.chi2.logsf, update_doc=False) def logsf(x: ArrayLike, df: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array: return lax.log(sf(x, df, loc, scale))