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Java example source code file (StorelessCovarianceTest.java)

This example Java source code file (StorelessCovarianceTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

array2drowrealmatrix, isaacrandom, realmatrix, seed, storelessbivariatecovariance, storelesscovariance, storelesscovariancetest, test

The StorelessCovarianceTest.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.apache.commons.math3.stat.correlation;

import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.random.ISAACRandom;
import org.junit.Assert;
import org.junit.Test;

public class StorelessCovarianceTest {

    protected final double[] longleyData = new double[] {
            60323,83.0,234289,2356,1590,107608,1947,
            61122,88.5,259426,2325,1456,108632,1948,
            60171,88.2,258054,3682,1616,109773,1949,
            61187,89.5,284599,3351,1650,110929,1950,
            63221,96.2,328975,2099,3099,112075,1951,
            63639,98.1,346999,1932,3594,113270,1952,
            64989,99.0,365385,1870,3547,115094,1953,
            63761,100.0,363112,3578,3350,116219,1954,
            66019,101.2,397469,2904,3048,117388,1955,
            67857,104.6,419180,2822,2857,118734,1956,
            68169,108.4,442769,2936,2798,120445,1957,
            66513,110.8,444546,4681,2637,121950,1958,
            68655,112.6,482704,3813,2552,123366,1959,
            69564,114.2,502601,3931,2514,125368,1960,
            69331,115.7,518173,4806,2572,127852,1961,
            70551,116.9,554894,4007,2827,130081,1962
        };

    protected final double[] swissData = new double[] {
            80.2,17.0,15,12,9.96,
            83.1,45.1,6,9,84.84,
            92.5,39.7,5,5,93.40,
            85.8,36.5,12,7,33.77,
            76.9,43.5,17,15,5.16,
            76.1,35.3,9,7,90.57,
            83.8,70.2,16,7,92.85,
            92.4,67.8,14,8,97.16,
            82.4,53.3,12,7,97.67,
            82.9,45.2,16,13,91.38,
            87.1,64.5,14,6,98.61,
            64.1,62.0,21,12,8.52,
            66.9,67.5,14,7,2.27,
            68.9,60.7,19,12,4.43,
            61.7,69.3,22,5,2.82,
            68.3,72.6,18,2,24.20,
            71.7,34.0,17,8,3.30,
            55.7,19.4,26,28,12.11,
            54.3,15.2,31,20,2.15,
            65.1,73.0,19,9,2.84,
            65.5,59.8,22,10,5.23,
            65.0,55.1,14,3,4.52,
            56.6,50.9,22,12,15.14,
            57.4,54.1,20,6,4.20,
            72.5,71.2,12,1,2.40,
            74.2,58.1,14,8,5.23,
            72.0,63.5,6,3,2.56,
            60.5,60.8,16,10,7.72,
            58.3,26.8,25,19,18.46,
            65.4,49.5,15,8,6.10,
            75.5,85.9,3,2,99.71,
            69.3,84.9,7,6,99.68,
            77.3,89.7,5,2,100.00,
            70.5,78.2,12,6,98.96,
            79.4,64.9,7,3,98.22,
            65.0,75.9,9,9,99.06,
            92.2,84.6,3,3,99.46,
            79.3,63.1,13,13,96.83,
            70.4,38.4,26,12,5.62,
            65.7,7.7,29,11,13.79,
            72.7,16.7,22,13,11.22,
            64.4,17.6,35,32,16.92,
            77.6,37.6,15,7,4.97,
            67.6,18.7,25,7,8.65,
            35.0,1.2,37,53,42.34,
            44.7,46.6,16,29,50.43,
            42.8,27.7,22,29,58.33
        };

    protected final double[][] longleyDataSimple = {
        {60323, 83.0},
        {61122,88.5},
        {60171, 88.2},
        {61187, 89.5},
        {63221, 96.2},
        {63639, 98.1},
        {64989, 99.0},
        {63761, 100.0},
        {66019, 101.2},
        {67857, 104.6},
        {68169, 108.4},
        {66513, 110.8},
        {68655, 112.6},
        {69564, 114.2},
        {69331, 115.7},
        {70551, 116.9}
    };

    @Test
    public void testLonglySimpleVar(){
        double rCov = 12333921.73333333246;
        StorelessBivariateCovariance cov = new StorelessBivariateCovariance();
        for(int i=0;i<longleyDataSimple.length;i++){
            cov.increment(longleyDataSimple[i][0],longleyDataSimple[i][0]);
        }
        TestUtils.assertEquals("simple covariance test", rCov, cov.getResult(), 10E-7);
    }

    @Test
    public void testLonglySimpleCov(){
        double rCov = 36796.660000;
        StorelessBivariateCovariance cov = new StorelessBivariateCovariance();
        for(int i=0;i<longleyDataSimple.length;i++){
            cov.increment(longleyDataSimple[i][0], longleyDataSimple[i][1]);
        }
        TestUtils.assertEquals("simple covariance test", rCov, cov.getResult(), 10E-7);
    }

    /**
     * Test Longley dataset against R.
     * Data Source: J. Longley (1967) "An Appraisal of Least Squares
     * Programs for the Electronic Computer from the Point of View of the User"
     * Journal of the American Statistical Association, vol. 62. September,
     * pp. 819-841.
     *
     * Data are from NIST:
     * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
     */
    @Test
    public void testLonglyByRow() {
        RealMatrix matrix = createRealMatrix(longleyData, 16, 7);

        double[] rData = new double[] {
         12333921.73333333246, 3.679666000000000e+04, 343330206.333333313,
         1649102.666666666744, 1117681.066666666651, 23461965.733333334, 16240.93333333333248,
         36796.66000000000, 1.164576250000000e+02, 1063604.115416667,
         6258.666250000000, 3490.253750000000, 73503.000000000, 50.92333333333334,
         343330206.33333331347, 1.063604115416667e+06, 9879353659.329166412,
         56124369.854166664183, 30880428.345833335072, 685240944.600000024, 470977.90000000002328,
         1649102.66666666674, 6.258666250000000e+03, 56124369.854166664,
         873223.429166666698, -115378.762499999997, 4462741.533333333, 2973.03333333333330,
         1117681.06666666665, 3.490253750000000e+03, 30880428.345833335,
         -115378.762499999997, 484304.095833333326, 1764098.133333333, 1382.43333333333339,
         23461965.73333333433, 7.350300000000000e+04, 685240944.600000024,
         4462741.533333333209, 1764098.133333333302, 48387348.933333330, 32917.40000000000146,
         16240.93333333333, 5.092333333333334e+01, 470977.900000000,
         2973.033333333333, 1382.433333333333, 32917.40000000, 22.66666666666667
        };

        StorelessCovariance covMatrix = new StorelessCovariance(7);
        for(int i=0;i<matrix.getRowDimension();i++){
            covMatrix.increment(matrix.getRow(i));
        }

        RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();

        TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 7, 7), covarianceMatrix, 10E-7);

    }

    /**
     * Test R Swiss fertility dataset against R.
     * Data Source: R datasets package
     */
    @Test
    public void testSwissFertilityByRow() {
         RealMatrix matrix = createRealMatrix(swissData, 47, 5);

         double[] rData = new double[] {
           156.0424976873265, 100.1691489361702, -64.36692876965772, -79.7295097132285, 241.5632030527289,
           100.169148936170251, 515.7994172062905, -124.39283071230344, -139.6574005550416, 379.9043755781684,
           -64.3669287696577, -124.3928307123034, 63.64662349676226, 53.5758556891767, -190.5606105457909,
           -79.7295097132285, -139.6574005550416, 53.57585568917669, 92.4560592044403, -61.6988297872340,
            241.5632030527289, 379.9043755781684, -190.56061054579092, -61.6988297872340, 1739.2945371877890
         };

        StorelessCovariance covMatrix = new StorelessCovariance(5);
        for(int i=0;i<matrix.getRowDimension();i++){
            covMatrix.increment(matrix.getRow(i));
        }

        RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();

        TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 5, 5), covarianceMatrix, 10E-13);
    }

    /**
     * Test symmetry of the covariance matrix
     */
    @Test
    public void testSymmetry() {
        RealMatrix matrix = createRealMatrix(swissData, 47, 5);

        final int dimension = 5;
        StorelessCovariance storelessCov = new StorelessCovariance(dimension);
        for(int i=0;i<matrix.getRowDimension();i++){
            storelessCov.increment(matrix.getRow(i));
        }

        double[][] covMatrix = storelessCov.getData();
        for (int i = 0; i < dimension; i++) {
            for (int j = i; j < dimension; j++) {
                Assert.assertEquals(covMatrix[i][j], covMatrix[j][i], 10e-9);
            }
        }
    }

    /**
     * Test equality of covariance. chk: covariance of two
     * samples separately and adds them together. cov: computes
     * covariance of the combined sample showing both are equal.
     */
    @Test
    public void testEquivalence() {
        int num_sets = 2;
        StorelessBivariateCovariance cov = new StorelessBivariateCovariance();// covariance of the superset
        StorelessBivariateCovariance chk = new StorelessBivariateCovariance();// check covariance made by appending covariance of subsets

        ISAACRandom rand = new ISAACRandom(10L);// Seed can be changed
        for (int s = 0; s < num_sets; s++) {// loop through sets of samlpes
            StorelessBivariateCovariance covs = new StorelessBivariateCovariance();
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                double x = rand.nextDouble();
                double y = rand.nextDouble();
                covs.increment(x, y);// add sample to the subset
                cov.increment(x, y);// add sample to the superset
            }
           chk.append(covs);
        }

        TestUtils.assertEquals("covariance subset test", chk.getResult(), cov.getResult(), 10E-7);
    }

    protected RealMatrix createRealMatrix(double[] data, int nRows, int nCols) {
        double[][] matrixData = new double[nRows][nCols];
        int ptr = 0;
        for (int i = 0; i < nRows; i++) {
            System.arraycopy(data, ptr, matrixData[i], 0, nCols);
            ptr += nCols;
        }
        return new Array2DRowRealMatrix(matrixData);
    }


}

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