org.nd4j.linalg.sampling

Class Sampling

java.lang.Object

org.nd4j.linalg.sampling.Sampling

public class Sampling
extends java.lang.Object

Static methods for sampling from an ndarray

Constructor Summary

Constructors

Constructor and Description
Sampling()

Method Summary

Modifier and Type	Method and Description
static INDArray	binomial(INDArray p, int n, RandomGenerator rng) Generate a binomial distribution based on the given rng, a matrix of p values, and a max number.
static INDArray	normal(RandomGenerator rng, INDArray mean, double sigma) A uniform sample ranging from 0 to sigma.
static INDArray	normal(RandomGenerator rng, INDArray mean, INDArray sigma) A uniform sample ranging from 0 to sigma.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Sampling

public Sampling()

Method Detail

normal

public static INDArray normal(RandomGenerator rng,
                              INDArray mean,
                              INDArray sigma)

A uniform sample ranging from 0 to sigma.

Parameters:

rng - the rng to use

mean, - the matrix mean from which to generate values from

sigma - the standard deviation to use to generate the gaussian noise

Returns:

a uniform sample of the given shape and size

with numbers between 0 and 1

normal

public static INDArray normal(RandomGenerator rng,
                              INDArray mean,
                              double sigma)

A uniform sample ranging from 0 to sigma.

Parameters:

rng - the rng to use

mean, - the matrix mean from which to generate values from

sigma - the standard deviation to use to generate the gaussian noise

Returns:

a uniform sample of the given shape and size

with numbers between 0 and 1

binomial

public static INDArray binomial(INDArray p,
                                int n,
                                RandomGenerator rng)

Generate a binomial distribution based on the given rng, a matrix of p values, and a max number.

Parameters:

p - the p matrix to use

n - the n to use

rng - the rng to use

Returns:

a binomial distribution based on the one n, the passed in p values, and rng