/*
* Copyright (c) 2009-2012, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* EJML is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* EJML is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with EJML. If not, see <http://www.gnu.org/licenses/>.
*/
package org.ejml.ops;
import org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionInner;
import org.ejml.data.DenseMatrix64F;
import java.util.Random;
import static org.ejml.ops.CommonOps.multAdd;
/**
* Generates random vectors based on a zero mean multivariate Gaussian distribution. The covariance
* matrix is provided in the contructor.
*/
public class CovarianceRandomDraw {
private DenseMatrix64F A;
private Random rand;
private DenseMatrix64F r;
/**
* Creates a random distribution with the specified mean and covariance. The references
* to the variables are not saved, their value are copied.
*
* @param rand Used to create the random numbers for the draw. Reference is saved.
* @param cov The covariance of the stribution. Not modified.
*/
public CovarianceRandomDraw( Random rand , DenseMatrix64F cov )
{
r = new DenseMatrix64F(cov.numRows,1);
CholeskyDecompositionInner choleky = new CholeskyDecompositionInner( true);
choleky.decompose(cov);
A = choleky.getT();
this.rand = rand;
}
/**
* Makes a draw on the distribution. The results are added to parameter 'x'
*/
public void next( DenseMatrix64F x )
{
for( int i = 0; i < r.numRows; i++ ) {
r.set(i,0,rand.nextGaussian());
}
multAdd(A,r,x);
}
/**
* Computes the likelihood of the random draw
*
* @return The likelihood.
*/
public double computeLikelihoodP() {
double ret = 1.0;
for( int i = 0; i < r.numRows; i++ ) {
double a = r.get(i,0);
ret *= Math.exp(-a*a/2.0);
}
return ret;
}
}