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

# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np
import scipy.stats as osp_stats

from jax import lax
from jax._src.numpy.util import _wraps
from jax._src.numpy.lax_numpy import _promote_args_inexact, _constant_like


[docs]@_wraps(osp_stats.t.logpdf, update_doc=False) def logpdf(x, df, loc=0, scale=1): x, df, loc, scale = _promote_args_inexact("t.logpdf", x, df, loc, scale) two = _constant_like(x, 2) scaled_x = lax.div(lax.sub(x, loc), scale) df_over_two = lax.div(df, two) df_plus_one_over_two = lax.add(df_over_two, _constant_like(x, 0.5)) normalize_term_const = lax.mul(lax.mul(scale, scale), _constant_like(x, np.pi)) normalize_term_tmp = lax.div(lax.log(lax.mul(normalize_term_const, df)), two) normalize_term = lax.sub(lax.add(lax.lgamma(df_over_two), normalize_term_tmp), lax.lgamma(df_plus_one_over_two)) quadratic = lax.div(lax.mul(scaled_x, scaled_x), df) return lax.neg(lax.add(normalize_term, lax.mul(df_plus_one_over_two, lax.log1p(quadratic))))
[docs]@_wraps(osp_stats.t.pdf, update_doc=False) def pdf(x, df, loc=0, scale=1): return lax.exp(logpdf(x, df, loc, scale))