home | career | drupal | java | mac | mysql | perl | scala | uml | unix  

Java example source code file (DescriptiveStatisticsTest.java)

This example Java source code file (DescriptiveStatisticsTest.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

descriptivestatistics, descriptivestatistics:\n, descriptivestatisticstest, expecting, illegalargumentexception, locale, nan\n, override, percentile, summarystatistics, test, univariatestatistic, util

The DescriptiveStatisticsTest.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.TestUtils;
import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.apache.commons.math3.stat.descriptive.moment.GeometricMean;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import org.apache.commons.math3.stat.descriptive.rank.Max;
import org.apache.commons.math3.stat.descriptive.rank.Min;
import org.apache.commons.math3.stat.descriptive.summary.Sum;
import org.apache.commons.math3.stat.descriptive.summary.SumOfSquares;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test cases for the {@link DescriptiveStatistics} class.
 */
public class DescriptiveStatisticsTest {

    protected DescriptiveStatistics createDescriptiveStatistics() {
        return new DescriptiveStatistics();
    }

    @Test
    public void testSetterInjection() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        stats.addValue(1);
        stats.addValue(3);
        Assert.assertEquals(2, stats.getMean(), 1E-10);
        // Now lets try some new math
        stats.setMeanImpl(new deepMean());
        Assert.assertEquals(42, stats.getMean(), 1E-10);
    }

    @Test
    public void testCopy() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        stats.addValue(1);
        stats.addValue(3);
        DescriptiveStatistics copy = new DescriptiveStatistics(stats);
        Assert.assertEquals(2, copy.getMean(), 1E-10);
        // Now lets try some new math
        stats.setMeanImpl(new deepMean());
        copy = stats.copy();
        Assert.assertEquals(42, copy.getMean(), 1E-10);
    }

    @Test
    public void testWindowSize() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        stats.setWindowSize(300);
        for (int i = 0; i < 100; ++i) {
            stats.addValue(i + 1);
        }
        int refSum = (100 * 101) / 2;
        Assert.assertEquals(refSum / 100.0, stats.getMean(), 1E-10);
        Assert.assertEquals(300, stats.getWindowSize());
        try {
            stats.setWindowSize(-3);
            Assert.fail("an exception should have been thrown");
        } catch (IllegalArgumentException iae) {
            // expected
        }
        Assert.assertEquals(300, stats.getWindowSize());
        stats.setWindowSize(50);
        Assert.assertEquals(50, stats.getWindowSize());
        int refSum2 = refSum - (50 * 51) / 2;
        Assert.assertEquals(refSum2 / 50.0, stats.getMean(), 1E-10);
    }

    @Test
    public void testGetValues() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        for (int i = 100; i > 0; --i) {
            stats.addValue(i);
        }
        int refSum = (100 * 101) / 2;
        Assert.assertEquals(refSum / 100.0, stats.getMean(), 1E-10);
        double[] v = stats.getValues();
        for (int i = 0; i < v.length; ++i) {
            Assert.assertEquals(100.0 - i, v[i], 1.0e-10);
        }
        double[] s = stats.getSortedValues();
        for (int i = 0; i < s.length; ++i) {
            Assert.assertEquals(i + 1.0, s[i], 1.0e-10);
        }
        Assert.assertEquals(12.0, stats.getElement(88), 1.0e-10);
    }

    @Test
    public void testQuadraticMean() {
        final double[] values = { 1.2, 3.4, 5.6, 7.89 };
        final DescriptiveStatistics stats = new DescriptiveStatistics(values);

        final int len = values.length;
        double expected = 0;
        for (int i = 0; i < len; i++) {
            final double v = values[i];
            expected += v * v / len;
        }
        expected = Math.sqrt(expected);

        Assert.assertEquals(expected, stats.getQuadraticMean(), Math.ulp(expected));
    }

    @Test
    public void testToString() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        stats.addValue(1);
        stats.addValue(2);
        stats.addValue(3);
        Locale d = Locale.getDefault();
        Locale.setDefault(Locale.US);
        Assert.assertEquals("DescriptiveStatistics:\n" +
                     "n: 3\n" +
                     "min: 1.0\n" +
                     "max: 3.0\n" +
                     "mean: 2.0\n" +
                     "std dev: 1.0\n" +
                     "median: 2.0\n" +
                     "skewness: 0.0\n" +
                     "kurtosis: NaN\n",  stats.toString());
        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 ...
        DescriptiveStatistics reference = createDescriptiveStatistics();
        DescriptiveStatistics shuffled  = createDescriptiveStatistics();

        UnivariateStatistic tmp = shuffled.getGeometricMeanImpl();
        shuffled.setGeometricMeanImpl(shuffled.getMeanImpl());
        shuffled.setMeanImpl(shuffled.getKurtosisImpl());
        shuffled.setKurtosisImpl(shuffled.getSkewnessImpl());
        shuffled.setSkewnessImpl(shuffled.getVarianceImpl());
        shuffled.setVarianceImpl(shuffled.getMaxImpl());
        shuffled.setMaxImpl(shuffled.getMinImpl());
        shuffled.setMinImpl(shuffled.getSumImpl());
        shuffled.setSumImpl(shuffled.getSumsqImpl());
        shuffled.setSumsqImpl(tmp);

        for (int i = 100; i > 0; --i) {
            reference.addValue(i);
            shuffled.addValue(i);
        }

        Assert.assertEquals(reference.getMean(),          shuffled.getGeometricMean(), 1.0e-10);
        Assert.assertEquals(reference.getKurtosis(),      shuffled.getMean(),          1.0e-10);
        Assert.assertEquals(reference.getSkewness(),      shuffled.getKurtosis(), 1.0e-10);
        Assert.assertEquals(reference.getVariance(),      shuffled.getSkewness(), 1.0e-10);
        Assert.assertEquals(reference.getMax(),           shuffled.getVariance(), 1.0e-10);
        Assert.assertEquals(reference.getMin(),           shuffled.getMax(), 1.0e-10);
        Assert.assertEquals(reference.getSum(),           shuffled.getMin(), 1.0e-10);
        Assert.assertEquals(reference.getSumsq(),         shuffled.getSum(), 1.0e-10);
        Assert.assertEquals(reference.getGeometricMean(), shuffled.getSumsq(), 1.0e-10);

    }

    @Test
    public void testPercentileSetter() {
        DescriptiveStatistics stats = createDescriptiveStatistics();
        stats.addValue(1);
        stats.addValue(2);
        stats.addValue(3);
        Assert.assertEquals(2, stats.getPercentile(50.0), 1E-10);

        // Inject wrapped Percentile impl
        stats.setPercentileImpl(new goodPercentile());
        Assert.assertEquals(2, stats.getPercentile(50.0), 1E-10);

        // Try "new math" impl
        stats.setPercentileImpl(new subPercentile());
        Assert.assertEquals(10.0, stats.getPercentile(10.0), 1E-10);

        // Try to set bad impl
        try {
            stats.setPercentileImpl(new badPercentile());
            Assert.fail("Expecting IllegalArgumentException");
        } catch (IllegalArgumentException ex) {
            // expected
        }
    }

    @Test
    public void test20090720() {
        DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(100);
        for (int i = 0; i < 161; i++) {
            descriptiveStatistics.addValue(1.2);
        }
        descriptiveStatistics.clear();
        descriptiveStatistics.addValue(1.2);
        Assert.assertEquals(1, descriptiveStatistics.getN());
    }

    @Test
    public void testRemoval() {

        final DescriptiveStatistics dstat = createDescriptiveStatistics();

        checkremoval(dstat, 1, 6.0, 0.0, Double.NaN);
        checkremoval(dstat, 3, 5.0, 3.0, 4.5);
        checkremoval(dstat, 6, 3.5, 2.5, 3.0);
        checkremoval(dstat, 9, 3.5, 2.5, 3.0);
        checkremoval(dstat, DescriptiveStatistics.INFINITE_WINDOW, 3.5, 2.5, 3.0);

    }

    @Test
    public void testSummaryConsistency() {
        final DescriptiveStatistics dstats = new DescriptiveStatistics();
        final SummaryStatistics sstats = new SummaryStatistics();
        final int windowSize = 5;
        dstats.setWindowSize(windowSize);
        final double tol = 1E-12;
        for (int i = 0; i < 20; i++) {
            dstats.addValue(i);
            sstats.clear();
            double[] values = dstats.getValues();
            for (int j = 0; j < values.length; j++) {
                sstats.addValue(values[j]);
            }
            TestUtils.assertEquals(dstats.getMean(), sstats.getMean(), tol);
            TestUtils.assertEquals(new Mean().evaluate(values), dstats.getMean(), tol);
            TestUtils.assertEquals(dstats.getMax(), sstats.getMax(), tol);
            TestUtils.assertEquals(new Max().evaluate(values), dstats.getMax(), tol);
            TestUtils.assertEquals(dstats.getGeometricMean(), sstats.getGeometricMean(), tol);
            TestUtils.assertEquals(new GeometricMean().evaluate(values), dstats.getGeometricMean(), tol);
            TestUtils.assertEquals(dstats.getMin(), sstats.getMin(), tol);
            TestUtils.assertEquals(new Min().evaluate(values), dstats.getMin(), tol);
            TestUtils.assertEquals(dstats.getStandardDeviation(), sstats.getStandardDeviation(), tol);
            TestUtils.assertEquals(dstats.getVariance(), sstats.getVariance(), tol);
            TestUtils.assertEquals(new Variance().evaluate(values), dstats.getVariance(), tol);
            TestUtils.assertEquals(dstats.getSum(), sstats.getSum(), tol);
            TestUtils.assertEquals(new Sum().evaluate(values), dstats.getSum(), tol);
            TestUtils.assertEquals(dstats.getSumsq(), sstats.getSumsq(), tol);
            TestUtils.assertEquals(new SumOfSquares().evaluate(values), dstats.getSumsq(), tol);
            TestUtils.assertEquals(dstats.getPopulationVariance(), sstats.getPopulationVariance(), tol);
            TestUtils.assertEquals(new Variance(false).evaluate(values), dstats.getPopulationVariance(), tol);
        }
    }

    @Test
    public void testMath1129(){
        final double[] data = new double[] {
            -0.012086732064244697,
            -0.24975668704012527,
            0.5706168483164684,
            -0.322111769955327,
            0.24166759508327315,
            Double.NaN,
            0.16698443218942854,
            -0.10427763937565114,
            -0.15595963093172435,
            -0.028075857595882995,
            -0.24137994506058857,
            0.47543170476574426,
            -0.07495595384947631,
            0.37445697625436497,
            -0.09944199541668033
        };

        final DescriptiveStatistics ds = new DescriptiveStatistics(data);

        final double t = ds.getPercentile(75);
        final double o = ds.getPercentile(25);

        final double iqr = t - o;
        // System.out.println(String.format("25th percentile %s 75th percentile %s", o, t));
        Assert.assertTrue(iqr >= 0);
    }

    public void checkremoval(DescriptiveStatistics dstat, int wsize,
                             double mean1, double mean2, double mean3) {

        dstat.setWindowSize(wsize);
        dstat.clear();

        for (int i = 1 ; i <= 6 ; ++i) {
            dstat.addValue(i);
        }

        Assert.assertTrue(Precision.equalsIncludingNaN(mean1, dstat.getMean()));
        dstat.replaceMostRecentValue(0);
        Assert.assertTrue(Precision.equalsIncludingNaN(mean2, dstat.getMean()));
        dstat.removeMostRecentValue();
        Assert.assertTrue(Precision.equalsIncludingNaN(mean3, dstat.getMean()));

    }

    // Test UnivariateStatistics impls for setter injection tests

    /**
     * A new way to compute the mean
     */
    static class deepMean implements UnivariateStatistic {

        public double evaluate(double[] values, int begin, int length) {
            return 42;
        }

        public double evaluate(double[] values) {
            return 42;
        }
        public UnivariateStatistic copy() {
            return new deepMean();
        }
    }

    /**
     * Test percentile implementation - wraps a Percentile
     */
    static class goodPercentile implements UnivariateStatistic {
        private Percentile percentile = new Percentile();
        public void setQuantile(double quantile) {
            percentile.setQuantile(quantile);
        }
        public double evaluate(double[] values, int begin, int length) {
            return percentile.evaluate(values, begin, length);
        }
        public double evaluate(double[] values) {
            return percentile.evaluate(values);
        }
        public UnivariateStatistic copy() {
            goodPercentile result = new goodPercentile();
            result.setQuantile(percentile.getQuantile());
            return result;
        }
    }

    /**
     * Test percentile subclass - another "new math" impl
     * Always returns currently set quantile
     */
    static class subPercentile extends Percentile {
        @Override
        public double evaluate(double[] values, int begin, int length) {
            return getQuantile();
        }
        @Override
        public double evaluate(double[] values) {
            return getQuantile();
        }
        private static final long serialVersionUID = 8040701391045914979L;
        @Override
        public Percentile copy() {
            subPercentile result = new subPercentile();
            return result;
        }
    }

    /**
     * "Bad" test percentile implementation - no setQuantile
     */
    static class badPercentile implements UnivariateStatistic {
        private Percentile percentile = new Percentile();
        public double evaluate(double[] values, int begin, int length) {
            return percentile.evaluate(values, begin, length);
        }
        public double evaluate(double[] values) {
            return percentile.evaluate(values);
        }
        public UnivariateStatistic copy() {
            return new badPercentile();
        }
    }

}

Other Java examples (source code examples)

Here is a short list of links related to this Java DescriptiveStatisticsTest.java source code file:



my book on functional programming

 

new blog posts

 

Copyright 1998-2019 Alvin Alexander, alvinalexander.com
All Rights Reserved.

A percentage of advertising revenue from
pages under the /java/jwarehouse URI on this website is
paid back to open source projects.