mirrors/rocm_jax
1
0
Fork 0
mirror of https://github.com/ROCm/jax.git synced 2025-04-19 05:16:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix Typos and math rendering in jax.random docs

Browse Source
This commit is contained in:
rajasekharporeddy 2024-05-02 17:43:37 +05:30
parent 3146c2a3f6
commit a0b93153ca
1 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
jax/_src/random.py
View File

@ -1035,7 +1035,7 @@ def dirichlet(key: KeyArrayLike,
The values are distributed according to the probability density function:
.. math::
f(\{x_i\}; \{\alpha_i\}) = \propto \prod_{i=1}^k x_i^{\alpha_i - 1}
f(\{x_i\}; \{\alpha_i\}) \propto \prod_{i=1}^k x_i^{\alpha_i - 1}
Where :math:`k` is the dimension, and :math:`\{x_i\}` satisfies
@ -1760,7 +1760,7 @@ def chisquare(key: KeyArrayLike,
The values are distributed according to the probability density function:
.. math::
f(x; \nu) \propto x^{k/2 - 1}e^{-x/2}
f(x; \nu) \propto x^{\nu/2 - 1}e^{-x/2}
on the domain :math:`0 < x < \infty`, where :math:`\nu > 0` represents the
degrees of freedom, given by the parameter ``df``.
@ -1813,7 +1813,7 @@ def f(key: KeyArrayLike,
The values are distributed according to the probability density function:
.. math::
f(x; \nu) \propto x^{\nu_1/2 - 1}\left(1 + \frac{\nu_1}{\nu_2}x\right)^{
f(x; \nu_1, \nu_2) \propto x^{\nu_1/2 - 1}\left(1 + \frac{\nu_1}{\nu_2}x\right)^{
-(\nu_1 + \nu_2) / 2}
on the domain :math:`0 < x < \infty`. Here :math:`\nu_1` is the degrees of
@ -1877,7 +1877,7 @@ def rademacher(key: KeyArrayLike,
.. math::
f(k) = \frac{1}{2}(\delta(k - 1) + \delta(k + 1))
on the domain :math:`k \in \{-1, 1}`, where `\delta(x)` is the dirac delta function.
on the domain :math:`k \in \{-1, 1\}`, where :math:`\delta(x)` is the dirac delta function.
Args:
key: a PRNG key.
@ -2145,7 +2145,7 @@ def rayleigh(key: KeyArrayLike,
.. math::
f(x;\sigma) \propto xe^{-x^2/(2\sigma^2)}
on the domain :math:`-\infty < x < \infty`, and where `\sigma > 0` is the scale
on the domain :math:`-\infty < x < \infty`, and where :math:`\sigma > 0` is the scale
parameter of the distribution.
Args: