distribution
Class LatinHypercube

java.lang.Object
  distribution.BasicMultivariateDistribution
      distribution.Uniform
          distribution.LatinHypercube

All Implemented Interfaces:

MultivariateDistribution

public class LatinHypercube
extends Uniform

Implements Latin Hypercube techniques for uniform random sampling.

Author:

dgorur

Field Summary

Fields inherited from class distribution.Uniform

uniform1D

Fields inherited from class distribution.BasicMultivariateDistribution

randomEngine

Constructor Summary

LatinHypercube()
Default constructor.

LatinHypercube(Hypercube bounds)
Constructs a LatinHypercube over the given bounds, which define a Hypercube.

Method Summary

cern.colt.matrix.DoubleMatrix2D	nextSample(cern.colt.matrix.DoubleMatrix2D mat) Generates the next m random samples of this distribution and places the result in the supplied matrix.
void	setInputDimension(int inputDimension) Sets the number of dimensions of this distribution

Methods inherited from class distribution.Uniform

assign, densityAt, densityAt, equals, getBounds, getInputDimension, maskWith, nextSample, setBounds, setSeed

Methods inherited from class distribution.BasicMultivariateDistribution

densityAt, densityAt, densityAt, makeNextSample, makeNextSample, nextSample, nextSample

Methods inherited from class java.lang.Object

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

Constructor Detail

LatinHypercube

public LatinHypercube()

Default constructor.

LatinHypercube

public LatinHypercube(Hypercube bounds)

Constructs a LatinHypercube over the given bounds, which define a Hypercube.

Parameters:

bounds - the given bounds.

Method Detail

nextSample

public cern.colt.matrix.DoubleMatrix2D nextSample(cern.colt.matrix.DoubleMatrix2D mat)

Description copied from interface: MultivariateDistribution

Generates the next m random samples of this distribution and places the result in the supplied matrix. Returns a pointer to the same matrix for convenience.

Specified by:

nextSample in interface MultivariateDistribution

Overrides:

nextSample in class Uniform

Parameters:

mat - matrix to hold the result. Cannot be null

Returns:

a pointer to the same matrix, for convenience. Use MultivariateDistribution.makeNextSample(int) to creates samples from scratch.

setInputDimension

public void setInputDimension(int inputDimension)

Description copied from interface: MultivariateDistribution

Sets the number of dimensions of this distribution

Specified by:

setInputDimension in interface MultivariateDistribution

Overrides:

setInputDimension in class Uniform

Parameters:

inputDimension - the given number of dimensions.