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Java example source code file (MultivariateSummaryStatisticsTest.java)

This example Java source code file (MultivariateSummaryStatisticsTest.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.

Learn more about this Java project at its project page.

Java - Java tags/keywords

array2drowrealmatrix, dimensionmismatchexception, expecting, illegalstateexception, locale, mean, multivariatesummarystatistics, multivariatesummarystatisticstest, storelessunivariatestatistic, string, test, util

The MultivariateSummaryStatisticsTest.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.descriptive;


import java.util.Locale;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.util.FastMath;

import org.junit.Test;
import org.junit.Assert;

/**
 * Test cases for the {@link MultivariateSummaryStatistics} class.
 *
 */

public class MultivariateSummaryStatisticsTest {

    protected MultivariateSummaryStatistics createMultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) {
        return new MultivariateSummaryStatistics(k, isCovarianceBiasCorrected);
    }

    @Test
    public void testSetterInjection() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
        u.setMeanImpl(new StorelessUnivariateStatistic[] {
                        new sumMean(), new sumMean()
                      });
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(4, u.getMean()[0], 1E-14);
        Assert.assertEquals(6, u.getMean()[1], 1E-14);
        u.clear();
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(4, u.getMean()[0], 1E-14);
        Assert.assertEquals(6, u.getMean()[1], 1E-14);
        u.clear();
        u.setMeanImpl(new StorelessUnivariateStatistic[] {
                        new Mean(), new Mean()
                      }); // OK after clear
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(2, u.getMean()[0], 1E-14);
        Assert.assertEquals(3, u.getMean()[1], 1E-14);
        Assert.assertEquals(2, u.getDimension());
    }

    @Test
    public void testSetterIllegalState() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        try {
            u.setMeanImpl(new StorelessUnivariateStatistic[] {
                            new sumMean(), new sumMean()
                          });
            Assert.fail("Expecting IllegalStateException");
        } catch (IllegalStateException ex) {
            // expected
        }
    }

    @Test
    public void testToString() {
        MultivariateSummaryStatistics stats = createMultivariateSummaryStatistics(2, true);
        stats.addValue(new double[] {1, 3});
        stats.addValue(new double[] {2, 2});
        stats.addValue(new double[] {3, 1});
        Locale d = Locale.getDefault();
        Locale.setDefault(Locale.US);
        final String suffix = System.getProperty("line.separator");
        Assert.assertEquals("MultivariateSummaryStatistics:" + suffix+
                     "n: 3" +suffix+
                     "min: 1.0, 1.0" +suffix+
                     "max: 3.0, 3.0" +suffix+
                     "mean: 2.0, 2.0" +suffix+
                     "geometric mean: 1.817..., 1.817..." +suffix+
                     "sum of squares: 14.0, 14.0" +suffix+
                     "sum of logarithms: 1.791..., 1.791..." +suffix+
                     "standard deviation: 1.0, 1.0" +suffix+
                     "covariance: Array2DRowRealMatrix{{1.0,-1.0},{-1.0,1.0}}" +suffix,
                     stats.toString().replaceAll("([0-9]+\\.[0-9][0-9][0-9])[0-9]+", "$1..."));
        Locale.setDefault(d);
    }

    @Test
    public void testShuffledStatistics() {
        // the purpose of this test is only to check the get/set methods
        // we are aware shuffling statistics like this is really not
        // something sensible to do in production ...
        MultivariateSummaryStatistics reference = createMultivariateSummaryStatistics(2, true);
        MultivariateSummaryStatistics shuffled  = createMultivariateSummaryStatistics(2, true);

        StorelessUnivariateStatistic[] tmp = shuffled.getGeoMeanImpl();
        shuffled.setGeoMeanImpl(shuffled.getMeanImpl());
        shuffled.setMeanImpl(shuffled.getMaxImpl());
        shuffled.setMaxImpl(shuffled.getMinImpl());
        shuffled.setMinImpl(shuffled.getSumImpl());
        shuffled.setSumImpl(shuffled.getSumsqImpl());
        shuffled.setSumsqImpl(shuffled.getSumLogImpl());
        shuffled.setSumLogImpl(tmp);

        for (int i = 100; i > 0; --i) {
            reference.addValue(new double[] {i, i});
            shuffled.addValue(new double[] {i, i});
        }

        TestUtils.assertEquals(reference.getMean(),          shuffled.getGeometricMean(), 1.0e-10);
        TestUtils.assertEquals(reference.getMax(),           shuffled.getMean(),          1.0e-10);
        TestUtils.assertEquals(reference.getMin(),           shuffled.getMax(),           1.0e-10);
        TestUtils.assertEquals(reference.getSum(),           shuffled.getMin(),           1.0e-10);
        TestUtils.assertEquals(reference.getSumSq(),         shuffled.getSum(),           1.0e-10);
        TestUtils.assertEquals(reference.getSumLog(),        shuffled.getSumSq(),         1.0e-10);
        TestUtils.assertEquals(reference.getGeometricMean(), shuffled.getSumLog(),        1.0e-10);

    }

    /**
     * Bogus mean implementation to test setter injection.
     * Returns the sum instead of the mean.
     */
    static class sumMean implements StorelessUnivariateStatistic {
        private double sum = 0;
        private long n = 0;
        public double evaluate(double[] values, int begin, int length) {
            return 0;
        }
        public double evaluate(double[] values) {
            return 0;
        }
        public void clear() {
          sum = 0;
          n = 0;
        }
        public long getN() {
            return n;
        }
        public double getResult() {
            return sum;
        }
        public void increment(double d) {
            sum += d;
            n++;
        }
        public void incrementAll(double[] values, int start, int length) {
        }
        public void incrementAll(double[] values) {
        }
        public StorelessUnivariateStatistic copy() {
            return new sumMean();
        }
    }

    @Test
    public void testDimension() {
        try {
            createMultivariateSummaryStatistics(2, true).addValue(new double[3]);
            Assert.fail("Expecting DimensionMismatchException");
        } catch (DimensionMismatchException dme) {
            // expected behavior
        }
    }

    /** test stats */
    @Test
    public void testStats() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
        Assert.assertEquals(0, u.getN());
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 2, 3 });
        u.addValue(new double[] { 2, 3 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals( 4, u.getN());
        Assert.assertEquals( 8, u.getSum()[0], 1.0e-10);
        Assert.assertEquals(12, u.getSum()[1], 1.0e-10);
        Assert.assertEquals(18, u.getSumSq()[0], 1.0e-10);
        Assert.assertEquals(38, u.getSumSq()[1], 1.0e-10);
        Assert.assertEquals( 1, u.getMin()[0], 1.0e-10);
        Assert.assertEquals( 2, u.getMin()[1], 1.0e-10);
        Assert.assertEquals( 3, u.getMax()[0], 1.0e-10);
        Assert.assertEquals( 4, u.getMax()[1], 1.0e-10);
        Assert.assertEquals(2.4849066497880003102, u.getSumLog()[0], 1.0e-10);
        Assert.assertEquals( 4.276666119016055311, u.getSumLog()[1], 1.0e-10);
        Assert.assertEquals( 1.8612097182041991979, u.getGeometricMean()[0], 1.0e-10);
        Assert.assertEquals( 2.9129506302439405217, u.getGeometricMean()[1], 1.0e-10);
        Assert.assertEquals( 2, u.getMean()[0], 1.0e-10);
        Assert.assertEquals( 3, u.getMean()[1], 1.0e-10);
        Assert.assertEquals(FastMath.sqrt(2.0 / 3.0), u.getStandardDeviation()[0], 1.0e-10);
        Assert.assertEquals(FastMath.sqrt(2.0 / 3.0), u.getStandardDeviation()[1], 1.0e-10);
        Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 0), 1.0e-10);
        Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 1), 1.0e-10);
        Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 0), 1.0e-10);
        Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 1), 1.0e-10);
        u.clear();
        Assert.assertEquals(0, u.getN());
    }

    @Test
    public void testN0andN1Conditions() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true);
        Assert.assertTrue(Double.isNaN(u.getMean()[0]));
        Assert.assertTrue(Double.isNaN(u.getStandardDeviation()[0]));

        /* n=1 */
        u.addValue(new double[] { 1 });
        Assert.assertEquals(1.0, u.getMean()[0], 1.0e-10);
        Assert.assertEquals(1.0, u.getGeometricMean()[0], 1.0e-10);
        Assert.assertEquals(0.0, u.getStandardDeviation()[0], 1.0e-10);

        /* n=2 */
        u.addValue(new double[] { 2 });
        Assert.assertTrue(u.getStandardDeviation()[0] > 0);

    }

    @Test
    public void testNaNContracts() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true);
        Assert.assertTrue(Double.isNaN(u.getMean()[0]));
        Assert.assertTrue(Double.isNaN(u.getMin()[0]));
        Assert.assertTrue(Double.isNaN(u.getStandardDeviation()[0]));
        Assert.assertTrue(Double.isNaN(u.getGeometricMean()[0]));

        u.addValue(new double[] { 1.0 });
        Assert.assertFalse(Double.isNaN(u.getMean()[0]));
        Assert.assertFalse(Double.isNaN(u.getMin()[0]));
        Assert.assertFalse(Double.isNaN(u.getStandardDeviation()[0]));
        Assert.assertFalse(Double.isNaN(u.getGeometricMean()[0]));

    }

    @Test
    public void testSerialization() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
        // Empty test
        TestUtils.checkSerializedEquality(u);
        MultivariateSummaryStatistics s = (MultivariateSummaryStatistics) TestUtils.serializeAndRecover(u);
        Assert.assertEquals(u, s);

        // Add some data
        u.addValue(new double[] { 2d, 1d });
        u.addValue(new double[] { 1d, 1d });
        u.addValue(new double[] { 3d, 1d });
        u.addValue(new double[] { 4d, 1d });
        u.addValue(new double[] { 5d, 1d });

        // Test again
        TestUtils.checkSerializedEquality(u);
        s = (MultivariateSummaryStatistics) TestUtils.serializeAndRecover(u);
        Assert.assertEquals(u, s);

    }

    @Test
    public void testEqualsAndHashCode() {
        MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
        MultivariateSummaryStatistics t = null;
        int emptyHash = u.hashCode();
        Assert.assertTrue(u.equals(u));
        Assert.assertFalse(u.equals(t));
        Assert.assertFalse(u.equals(Double.valueOf(0)));
        t = createMultivariateSummaryStatistics(2, true);
        Assert.assertTrue(t.equals(u));
        Assert.assertTrue(u.equals(t));
        Assert.assertEquals(emptyHash, t.hashCode());

        // Add some data to u
        u.addValue(new double[] { 2d, 1d });
        u.addValue(new double[] { 1d, 1d });
        u.addValue(new double[] { 3d, 1d });
        u.addValue(new double[] { 4d, 1d });
        u.addValue(new double[] { 5d, 1d });
        Assert.assertFalse(t.equals(u));
        Assert.assertFalse(u.equals(t));
        Assert.assertTrue(u.hashCode() != t.hashCode());

        //Add data in same order to t
        t.addValue(new double[] { 2d, 1d });
        t.addValue(new double[] { 1d, 1d });
        t.addValue(new double[] { 3d, 1d });
        t.addValue(new double[] { 4d, 1d });
        t.addValue(new double[] { 5d, 1d });
        Assert.assertTrue(t.equals(u));
        Assert.assertTrue(u.equals(t));
        Assert.assertEquals(u.hashCode(), t.hashCode());

        // Clear and make sure summaries are indistinguishable from empty summary
        u.clear();
        t.clear();
        Assert.assertTrue(t.equals(u));
        Assert.assertTrue(u.equals(t));
        Assert.assertEquals(emptyHash, t.hashCode());
        Assert.assertEquals(emptyHash, u.hashCode());
    }
}

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