TestCovarianceRandomDraw.java :  » Algebra » efficient-java-matrix-library » org » ejml » ops » Java Open Source

/*
 * Copyright (c) 2009-2011, 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.data.DenseMatrix64F;
import org.junit.Test;

import java.util.Random;

import static org.junit.Assert.assertEquals;


public class TestCovarianceRandomDraw
{
    public static int N = 6000;

    /**
     * Do a lot of draws on the distribution and see if a similar distribution is computed
     * in the end.
     */
    @Test
    public void testStatistics() {
        DenseMatrix64F orig_P = new DenseMatrix64F(new double[][]{{6,-2},{-2,10}});

        CovarianceRandomDraw dist = new CovarianceRandomDraw(new Random(0xfeed),orig_P);

        DenseMatrix64F draws[] = new DenseMatrix64F[N];

        // sample the distribution
        for( int i = 0; i < N; i++ ) {
            DenseMatrix64F x = new DenseMatrix64F(2,1);
            dist.next(x);
            draws[i] = x;
        }

        // compute the statistics
        double raw_comp_x[] = new double[2];

        // find the mean
        for( int i = 0; i < N; i++ ) {
            raw_comp_x[0] += draws[i].get(0,0);
            raw_comp_x[1] += draws[i].get(1,0);
        }

        raw_comp_x[0] /= N;
        raw_comp_x[1] /= N;

        assertEquals(0,raw_comp_x[0],0.1);
        assertEquals(0.0,raw_comp_x[1],0.1);

        // now the covariance
        DenseMatrix64F comp_P = new DenseMatrix64F(2,2);
        DenseMatrix64F temp = new DenseMatrix64F(2,1);

        for( int i = 0; i < N; i++ ) {
            temp.set(0,0,draws[i].get(0,0)-raw_comp_x[0]);
            temp.set(1,0,draws[i].get(1,0)-raw_comp_x[1]);

            CommonOps.multAddTransB(temp,temp,comp_P);
        }

        CommonOps.scale(1.0/N,comp_P);

        MatrixFeatures.isIdentical(comp_P,orig_P,0.3);
    }

}