org.ejml.ops.CovarianceOps Maven / Gradle / Ivy
/*
* Copyright (c) 2009-2014, 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.ops;
import org.ejml.alg.dense.linsol.LinearSolverSafe;
import org.ejml.alg.dense.misc.UnrolledInverseFromMinor;
import org.ejml.data.DenseMatrix64F;
import org.ejml.factory.LinearSolverFactory;
import org.ejml.interfaces.linsol.LinearSolver;
import java.util.Random;
/**
* Contains operations specific to covariance matrices.
*
* @author Peter Abeles
*/
public class CovarianceOps {
public static double TOL = 1e-9;
/**
* This is a fairly light weight check to see of a covariance matrix is valid.
* It checks to see if the diagonal elements are all positive, which they should be
* if it is valid. Not all invalid covariance matrices will be caught by this method.
*
* @return true if valid and false if invalid
*/
public static boolean isValidFast( DenseMatrix64F cov ) {
return MatrixFeatures.isDiagonalPositive(cov);
}
/**
* Performs a variety of tests to see if the provided matrix is a valid
* covariance matrix.
*
* @return 0 = is valid 1 = failed positive diagonal, 2 = failed on symmetry, 2 = failed on positive definite
*/
public static int isValid( DenseMatrix64F cov ) {
if( !MatrixFeatures.isDiagonalPositive(cov) )
return 1;
if( !MatrixFeatures.isSymmetric(cov,TOL) )
return 2;
if( !MatrixFeatures.isPositiveSemidefinite(cov) )
return 3;
return 0;
}
/**
* Performs a matrix inversion operations that takes advantage of the special
* properties of a covariance matrix.
*
* @param cov On input it is a covariance matrix, on output it is the inverse. Modified.
* @return true if it could invert the matrix false if it could not.
*/
public static boolean invert( DenseMatrix64F cov ) {
return invert(cov,cov);
}
/**
* Performs a matrix inversion operations that takes advantage of the special
* properties of a covariance matrix.
*
* @param cov A covariance matrix. Not modified.
* @param cov_inv The inverse of cov. Modified.
* @return true if it could invert the matrix false if it could not.
*/
public static boolean invert( final DenseMatrix64F cov , final DenseMatrix64F cov_inv ) {
if( cov.numCols <= 4 ) {
if( cov.numCols != cov.numRows ) {
throw new IllegalArgumentException("Must be a square matrix.");
}
if( cov.numCols >= 2 )
UnrolledInverseFromMinor.inv(cov,cov_inv);
else
cov_inv.data[0] = 1.0/cov_inv.data[0];
} else {
LinearSolver solver = LinearSolverFactory.symmPosDef(cov.numRows);
// wrap it to make sure the covariance is not modified.
solver = new LinearSolverSafe(solver);
if( !solver.setA(cov) )
return false;
solver.invert(cov_inv);
}
return true;
}
/**
* Sets vector to a random value based upon a zero-mean multivariate Gaussian distribution with
* covariance 'cov'. If repeat calls are made to this class, consider using {@link CovarianceRandomDraw} instead.
*
* @param cov The distirbutions covariance. Not modified.
* @param vector The random vector. Modified.
* @param rand Random number generator.
*/
public static void randomVector( DenseMatrix64F cov ,
DenseMatrix64F vector ,
Random rand )
{
CovarianceRandomDraw rng = new CovarianceRandomDraw(rand,cov);
rng.next(vector);
}
}