mlx/docs/build/doctrees/python/random.doctree

<EFBFBD><05><>H<00>sphinx.addnodes<65><73>document<6E><74><EFBFBD>)<29><>}<7D>(<28>	rawsource<63><65><00><>children<65>]<5D>(<28>docutils.nodes<65><73>target<65><74><EFBFBD>)<29><>}<7D>(h<05>.. _random:<3A>h]<5D><>
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<01>parent<6E>h<03>	_document<6E>h<03>source<63><65>6/Users/awnihannun/repos/mlx/docs/src/python/random.rst<73>ubh	<09>section<6F><6E><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>title<6C><65><EFBFBD>)<29><>}<7D>(h<05>Random<6F>h]<5D>h	<09>Text<78><74><EFBFBD><EFBFBD>Random<6F><6D><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h+h!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhh)h h&h!hh"h#hKubh	<09>	paragraph<70><68><EFBFBD>)<29><>}<7D>(h<05><>Random sampling functions in MLX use an implicit global PRNG state by default.
However, all function take an optional ``key`` keyword argument for when more
fine-grained control or explicit state management is needed.<2E>h]<5D>(h0<68>vRandom sampling functions in MLX use an implicit global PRNG state by default.
However, all function take an optional <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h=h!hh"NhNubh	<09>literal<61><6C><EFBFBD>)<29><>}<7D>(h<05>``key``<60>h]<5D>h0<68>key<65><79><EFBFBD><EFBFBD><EFBFBD>}<7D>(h hGh!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhhEh h=ubh0<68>\ keyword argument for when more
fine-grained control or explicit state management is needed.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h=h!hh"NhNubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhh;h"h#hKh h&h!hubh<)<29><>}<7D>(h<05>2For example, you can generate random numbers with:<3A>h]<5D>h0<68>2For example, you can generate random numbers with:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h_h!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhh;h"h#hK
h h&h!hubh	<09>
literal_block<63><6B><EFBFBD>)<29><>}<7D>(h<05>/for _ in range(3):
  print(mx.random.uniform())<29>h]<5D>h0<68>/for _ in range(3):
  print(mx.random.uniform())<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>h hosbah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D><>	xml:space<63><65>preserve<76><65>force<63><65><EFBFBD>language<67><65>python<6F><6E>highlight_args<67>}<7D>uhhmh"h#hKh h&h!hubh<)<29><>}<7D>(h<05>jwhich will print a sequence of unique pseudo random numbers. Alternatively you
can explicitly set the key:<3A>h]<5D>h0<68>jwhich will print a sequence of unique pseudo random numbers. Alternatively you
can explicitly set the key:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h<>h!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhh;h"h#hKh h&h!hubhn)<29><>}<7D>(h<05>Mkey = mx.random.key(0)
for _ in range(3):
  print(mx.random.uniform(key=key))<29>h]<5D>h0<68>Mkey = mx.random.key(0)
for _ in range(3):
  print(mx.random.uniform(key=key))<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>h h<>sbah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>h}h~h<68>h<EFBFBD><68>python<6F>h<EFBFBD>}<7D>uhhmh"h#hKh h&h!hubh<)<29><>}<7D>(h<05>Awhich will yield the same pseudo random number at each iteration.<2E>h]<5D>h0<68>Awhich will yield the same pseudo random number at each iteration.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h<>h!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D>uhh;h"h#hKh h&h!hubh<)<29><>}<7D>(h<05><>Following `JAX's PRNG design <https://jax.readthedocs.io/en/latest/jep/263-prng.html>`_
we use a splittable version of Threefry, which is a counter-based PRNG.<2E>h]<5D>(h0<68>
Following <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h<>h!hh"NhNubh	<09>	reference<63><65><EFBFBD>)<29><>}<7D>(h<05>M`JAX's PRNG design <https://jax.readthedocs.io/en/latest/jep/263-prng.html>`_<>h]<5D>h0<68>JAX’s PRNG design<67><6E><EFBFBD><EFBFBD><EFBFBD>}<7D>(h h<>h!hh"NhNubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D><>name<6D><65>JAX's PRNG design<67><6E>refuri<72><69>6https://jax.readthedocs.io/en/latest/jep/263-prng.html<6D>uhh<>h h<>ubh)<29><>}<7D>(h<05>9 <https://jax.readthedocs.io/en/latest/jep/263-prng.html><3E>h]<5D>h}<7D>(h]<5D><>jax-s-prng-design<67>ah]<5D>h]<5D><>jax's prng design<67>ah]<5D>h]<5D><>refuri<72>h<EFBFBD>uhh
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bernoulli([p, shape, key, stream])

Generate Bernoulli random values.

categorical(logits[, axis, shape, ...])

Sample from a categorical distribution.

gumbel([shape, dtype, stream, key])

Sample from the standard Gumbel distribution.

key(seed)

Get a PRNG key from a seed.

normal([shape, dtype, key, stream])

Generate normally distributed random numbers.

randint(low, high[, shape, dtype, key, stream])

Generate random integers from the given interval.

seed(seed)

Seed the global PRNG.

split(key[, num, stream])

Split a PRNG key into sub keys.

truncated_normal(lower, upper[, shape, ...])

Generate values from a truncated normal distribution.

uniform([low, high, shape, dtype, key, stream])

Generate uniformly distributed random numbers.<2E>h]<5D>h	<09>table<6C><65><EFBFBD>)<29><>}<7D>(hhh]<5D>h	<09>tgroup<75><70><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>colspec<65><63><EFBFBD>)<29><>}<7D>(hhh]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h]<5D><>colwidth<74>K
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