org.nd4j.linalg.factory.ops

Class NDRandom

java.lang.Object

org.nd4j.linalg.factory.ops.NDRandom

public class NDRandom
extends Object

Constructor Summary

Constructors

Constructor and Description
NDRandom()

Method Summary

Modifier and Type	Method and Description
INDArray	bernoulli(double p, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution, with the specified probability.
INDArray	binomial(int nTrials, double p, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution, with the specified number of trials and probability.
INDArray	exponential(double lambda, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a exponential distribution: P(x) = lambda * exp(-lambda * x) Inputs must satisfy the following constraints: Must be positive: lambda > 0
INDArray	logNormal(double mean, double stddev, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution, i.e., log(x) ~ N(mean, stdev)
INDArray	normal(double mean, double stddev, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution, N(mean, stdev)
INDArray	normalTruncated(double mean, double stddev, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution, N(mean, stdev).
INDArray	uniform(double min, double max, DataType datatype, long... shape) Generate a new random INDArray, where values are randomly sampled according to a uniform distribution, U(min,max)

Methods inherited from class java.lang.Object

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

Constructor Detail

NDRandom

public NDRandom()

Method Detail

bernoulli

public INDArray bernoulli(double p,
                          DataType datatype,
                          long... shape)

Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
with the specified probability. Array values will have value 1 with probability P and value 0 with probability
1-P.

Parameters:

p - Probability of value 1

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

binomial

public INDArray binomial(int nTrials,
                         double p,
                         DataType datatype,
                         long... shape)

Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
with the specified number of trials and probability.

Parameters:

nTrials - Number of trials parameter for the binomial distribution

p - Probability of success for each trial

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

exponential

public INDArray exponential(double lambda,
                            DataType datatype,
                            long... shape)

Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
P(x) = lambda * exp(-lambda * x)
Inputs must satisfy the following constraints:
Must be positive: lambda > 0

Parameters:

lambda - lambda parameter

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

logNormal

public INDArray logNormal(double mean,
                          double stddev,
                          DataType datatype,
                          long... shape)

Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
i.e., log(x) ~ N(mean, stdev)

Parameters:

mean - Mean value for the random array

stddev - Standard deviation for the random array

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

normal

public INDArray normal(double mean,
                       double stddev,
                       DataType datatype,
                       long... shape)

Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev)

Parameters:

mean - Mean value for the random array

stddev - Standard deviation for the random array

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

normalTruncated

public INDArray normalTruncated(double mean,
                                double stddev,
                                DataType datatype,
                                long... shape)

Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev). However, any values more than 1 standard deviation from the mean are dropped and re-sampled

Parameters:

mean - Mean value for the random array

stddev - Standard deviation for the random array

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)

uniform

public INDArray uniform(double min,
                        double max,
                        DataType datatype,
                        long... shape)

Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
U(min,max)

Parameters:

min - Minimum value

max - Maximum value.

datatype - Data type of the output variable

shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

Returns:

output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)