The CorrelatedRandomVectorGenerator.java Java example source code
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package org.apache.commons.math3.random;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RectangularCholeskyDecomposition;
/**
* A {@link RandomVectorGenerator} that generates vectors with with
* correlated components.
* <p>Random vectors with correlated components are built by combining
* the uncorrelated components of another random vector in such a way that
* the resulting correlations are the ones specified by a positive
* definite covariance matrix.</p>
* <p>The main use for correlated random vector generation is for Monte-Carlo
* simulation of physical problems with several variables, for example to
* generate error vectors to be added to a nominal vector. A particularly
* interesting case is when the generated vector should be drawn from a <a
* href="http://en.wikipedia.org/wiki/Multivariate_normal_distribution">
* Multivariate Normal Distribution</a>. The approach using a Cholesky
* decomposition is quite usual in this case. However, it can be extended
* to other cases as long as the underlying random generator provides
* {@link NormalizedRandomGenerator normalized values} like {@link
* GaussianRandomGenerator} or {@link UniformRandomGenerator}.</p>
* <p>Sometimes, the covariance matrix for a given simulation is not
* strictly positive definite. This means that the correlations are
* not all independent from each other. In this case, however, the non
* strictly positive elements found during the Cholesky decomposition
* of the covariance matrix should not be negative either, they
* should be null. Another non-conventional extension handling this case
* is used here. Rather than computing <code>C = U^{T}.U
* where <code>C is the covariance matrix and U
* is an upper-triangular matrix, we compute <code>C = B.B^{T}
* where <code>B is a rectangular matrix having
* more rows than columns. The number of columns of <code>B is
* the rank of the covariance matrix, and it is the dimension of the
* uncorrelated random vector that is needed to compute the component
* of the correlated vector. This class handles this situation
* automatically.</p>
*
* @since 1.2
*/
public class CorrelatedRandomVectorGenerator
implements RandomVectorGenerator {
/** Mean vector. */
private final double[] mean;
/** Underlying generator. */
private final NormalizedRandomGenerator generator;
/** Storage for the normalized vector. */
private final double[] normalized;
/** Root of the covariance matrix. */
private final RealMatrix root;
/**
* Builds a correlated random vector generator from its mean
* vector and covariance matrix.
*
* @param mean Expected mean values for all components.
* @param covariance Covariance matrix.
* @param small Diagonal elements threshold under which column are
* considered to be dependent on previous ones and are discarded
* @param generator underlying generator for uncorrelated normalized
* components.
* @throws org.apache.commons.math3.linear.NonPositiveDefiniteMatrixException
* if the covariance matrix is not strictly positive definite.
* @throws DimensionMismatchException if the mean and covariance
* arrays dimensions do not match.
*/
public CorrelatedRandomVectorGenerator(double[] mean,
RealMatrix covariance, double small,
NormalizedRandomGenerator generator) {
int order = covariance.getRowDimension();
if (mean.length != order) {
throw new DimensionMismatchException(mean.length, order);
}
this.mean = mean.clone();
final RectangularCholeskyDecomposition decomposition =
new RectangularCholeskyDecomposition(covariance, small);
root = decomposition.getRootMatrix();
this.generator = generator;
normalized = new double[decomposition.getRank()];
}
/**
* Builds a null mean random correlated vector generator from its
* covariance matrix.
*
* @param covariance Covariance matrix.
* @param small Diagonal elements threshold under which column are
* considered to be dependent on previous ones and are discarded.
* @param generator Underlying generator for uncorrelated normalized
* components.
* @throws org.apache.commons.math3.linear.NonPositiveDefiniteMatrixException
* if the covariance matrix is not strictly positive definite.
*/
public CorrelatedRandomVectorGenerator(RealMatrix covariance, double small,
NormalizedRandomGenerator generator) {
int order = covariance.getRowDimension();
mean = new double[order];
for (int i = 0; i < order; ++i) {
mean[i] = 0;
}
final RectangularCholeskyDecomposition decomposition =
new RectangularCholeskyDecomposition(covariance, small);
root = decomposition.getRootMatrix();
this.generator = generator;
normalized = new double[decomposition.getRank()];
}
/** Get the underlying normalized components generator.
* @return underlying uncorrelated components generator
*/
public NormalizedRandomGenerator getGenerator() {
return generator;
}
/** Get the rank of the covariance matrix.
* The rank is the number of independent rows in the covariance
* matrix, it is also the number of columns of the root matrix.
* @return rank of the square matrix.
* @see #getRootMatrix()
*/
public int getRank() {
return normalized.length;
}
/** Get the root of the covariance matrix.
* The root is the rectangular matrix <code>B such that
* the covariance matrix is equal to <code>B.B^{T}
* @return root of the square matrix
* @see #getRank()
*/
public RealMatrix getRootMatrix() {
return root;
}
/** Generate a correlated random vector.
* @return a random vector as an array of double. The returned array
* is created at each call, the caller can do what it wants with it.
*/
public double[] nextVector() {
// generate uncorrelated vector
for (int i = 0; i < normalized.length; ++i) {
normalized[i] = generator.nextNormalizedDouble();
}
// compute correlated vector
double[] correlated = new double[mean.length];
for (int i = 0; i < correlated.length; ++i) {
correlated[i] = mean[i];
for (int j = 0; j < root.getColumnDimension(); ++j) {
correlated[i] += root.getEntry(i, j) * normalized[j];
}
}
return correlated;
}
}
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