# mx.nd.sample.exponential¶

## Description¶

Concurrent sampling from multiple exponential distributions with parameters lambda (rate).

The parameters of the distributions are provided as an input array. Let [s] be the shape of the input array, 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 array, output[i] will be an m-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input value 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 array.

Example:

lam = [ 1.0, 8.5 ]

// Draw a single sample for each distribution
sample_exponential(lam) = [ 0.51837951,  0.09994757]

// Draw a vector containing two samples for each distribution
sample_exponential(lam, shape=(2)) = [[ 0.51837951,  0.19866663],
[ 0.09994757,  0.50447971]]


## Arguments¶

Argument

Description

lam

NDArray-or-Symbol.

Lambda (rate) parameters 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).

## Value¶

out The result mx.ndarray