# mx.symbol.sample_uniform¶

## Description¶

Concurrent sampling from multiple uniform distributions on the intervals given by [low,high).

The parameters of the distributions are provided as input arrays. Let [s] be the shape of the input arrays, n be the dimension of [s], [t] be the shape specified as the parameter of the operator, and m be the dimension of [t]. Then the output will be a (n+m)-dimensional array with shape [s]x[t].

For any valid n-dimensional index i with respect to the input arrays, output[i] will be an m-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index i. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays.

Example:

low = [ 0.0, 2.5 ]
high = [ 1.0, 3.7 ]

// Draw a single sample for each distribution
sample_uniform(low, high) = [ 0.40451524,  3.18687344]

// Draw a vector containing two samples for each distribution
sample_uniform(low, high, shape=(2)) = [[ 0.40451524,  0.18017688],
[ 3.18687344,  3.68352246]]


## Usage¶

mx.symbol.sample_uniform(...)


## Arguments¶

Argument

Description

low

NDArray-or-Symbol.

Lower bounds of the distributions.

shape

Shape(tuple), optional, default=[].

Shape to be sampled from each random distribution.

dtype

{‘None’, ‘float16’, ‘float32’, ‘float64’},optional, default=’None’.

DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

high

NDArray-or-Symbol.

Upper bounds of the distributions.

name

string, optional.

Name of the resulting symbol.

## Value¶

out The result mx.symbol