org.ejml.dense.row.CovarianceRandomDraw_DDRM Maven / Gradle / Ivy
/*
* Copyright (c) 2009-2017, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.ejml.dense.row;
import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.decomposition.chol.CholeskyDecompositionInner_DDRM;
import java.util.Random;
/**
* Generates random vectors based on a zero mean multivariate Gaussian distribution. The covariance
* matrix is provided in the constructor.
*/
public class CovarianceRandomDraw_DDRM {
private DMatrixRMaj A;
private Random rand;
private DMatrixRMaj 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 distribution. Not modified.
*/
public CovarianceRandomDraw_DDRM(Random rand , DMatrixRMaj cov )
{
r = new DMatrixRMaj(cov.numRows,1);
CholeskyDecompositionInner_DDRM cholesky = new CholeskyDecompositionInner_DDRM( true);
if( cholesky.inputModified() )
cov = cov.copy();
if( !cholesky.decompose(cov) )
throw new RuntimeException("Decomposition failed!");
A = cholesky.getT();
this.rand = rand;
}
/**
* Makes a draw on the distribution. The results are added to parameter 'x'
*/
public void next( DMatrixRMaj x )
{
for( int i = 0; i < r.numRows; i++ ) {
r.set(i,0, (double)rand.nextGaussian());
}
CommonOps_DDRM.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;
}
}