torch/special/__init__.py

import sys

import torch
from torch._C import _add_docstr, _special  # type: ignore
from torch._torch_docs import common_args  # type: ignore

Tensor = torch.Tensor

gammaln = _add_docstr(_special.special_gammaln,
                      r"""
gammaln(input, *, out=None) -> Tensor

Computes the natural logarithm of the absolute value of the gamma function on :attr:`input`.

.. math::
    \text{out}_{i} = \ln \Gamma(|\text{input}_{i}|)
""" + """
Args:
    {input}

Keyword args:
    {out}

Example::

    >>> a = torch.arange(0.5, 2, 0.5)
    >>> torch.special.gammaln(a)
    tensor([ 0.5724,  0.0000, -0.1208])

""".format(**common_args))

erf = _add_docstr(_special.special_erf,
                  r"""
erf(input, *, out=None) -> Tensor

Computes the error function of :attr:`input`. The error function is defined as follows:

.. math::
    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
""" + r"""
Args:
    {input}

Keyword args:
    {out}

Example::

    >>> torch.erf(torch.tensor([0, -1., 10.]))
    tensor([ 0.0000, -0.8427,  1.0000])
""".format(**common_args))

erfc = _add_docstr(_special.special_erfc,
                   r"""
erfc(input, *, out=None) -> Tensor

Computes the complementary error function of :attr:`input`.
The complementary error function is defined as follows:

.. math::
    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
""" + r"""
Args:
    {input}

Keyword args:
    {out}

Example::

    >>> torch.erfc(torch.tensor([0, -1., 10.]))
    tensor([ 1.0000, 1.8427,  0.0000])
""".format(**common_args))

erfinv = _add_docstr(_special.special_erfinv,
                     r"""
erfinv(input, *, out=None) -> Tensor

Computes the inverse error function of :attr:`input`.
The inverse error function is defined in the range :math:`(-1, 1)` as:

.. math::
    \mathrm{erfinv}(\mathrm{erf}(x)) = x
""" + r"""

Args:
    {input}

Keyword args:
    {out}

Example::

    >>> torch.erfinv(torch.tensor([0, 0.5, -1.]))
    tensor([ 0.0000,  0.4769,    -inf])
""".format(**common_args))