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Java example source code file (UnivariateStatisticAbstractTest.java)

This example Java source code file (UnivariateStatisticAbstractTest.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

arraylist, double, list, normaldistribution, realdistribution, test, uniformintegerdistribution, univariatestatistic, univariatestatisticabstracttest, util, weightedevaluation

The UnivariateStatisticAbstractTest.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
 *
s * 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.ArrayList;
import java.util.List;


import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.UniformIntegerDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test cases for the {@link UnivariateStatistic} class.
 */
public abstract class UnivariateStatisticAbstractTest {

    protected double mean = 12.404545454545455d;
    protected double geoMean = 12.070589161633011d;

    protected double var = 10.00235930735931d;
    protected double std = FastMath.sqrt(var);
    protected double skew = 1.437423729196190d;
    protected double kurt = 2.377191264804700d;

    protected double min = 8.2d;
    protected double max = 21d;
    protected double median = 12d;
    protected double percentile5 = 8.29d;
    protected double percentile95 = 20.82d;

    protected double product = 628096400563833396009676.9200400128d;
    protected double sumLog = 54.7969806116451507d;
    protected double sumSq = 3595.250d;
    protected double sum = 272.90d;
    protected double secondMoment = 210.04954545454547d;
    protected double thirdMoment = 868.0906859504136;
    protected double fourthMoment = 9244.080993773481;


    protected double weightedMean = 12.366995073891626d;
    protected double weightedVar =   9.974760968886391d;
    protected double weightedStd = FastMath.sqrt(weightedVar);
    protected double weightedProduct = 8517647448765288000000d;
    protected double weightedSum = 251.05d;

    protected double tolerance = 10E-12;

    protected double[] testArray =
        { 12.5, 12.0, 11.8, 14.2, 14.9, 14.5, 21.0,  8.2, 10.3, 11.3,
          14.1,  9.9, 12.2, 12.0, 12.1, 11.0, 19.8, 11.0, 10.0,  8.8,
           9.0, 12.3 };

    protected double[] testWeightsArray =
        {  1.5,  0.8,  1.2,  0.4,  0.8,  1.8,  1.2,  1.1,  1.0,  0.7,
           1.3,  0.6,  0.7,  1.3,  0.7,  1.0,  0.4,  0.1,  1.4,  0.9,
           1.1,  0.3 };

    protected double[] identicalWeightsArray =
        {  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,
           0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,
           0.5,  0.5 };

    protected double[] unitWeightsArray =
        {  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,
           1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,
           1.0,  1.0 };

    public abstract UnivariateStatistic getUnivariateStatistic();

    public abstract double expectedValue();

    public double getTolerance() {
        return tolerance;
    }

    @Test
    public void testEvaluation() {
        Assert.assertEquals(
            expectedValue(),
            getUnivariateStatistic().evaluate(testArray),
            getTolerance());
    }

    @Test
    public void testEvaluateArraySegment() {
        final UnivariateStatistic stat = getUnivariateStatistic();
        final double[] arrayZero = new double[5];
        System.arraycopy(testArray, 0, arrayZero, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayZero), stat.evaluate(testArray, 0, 5), 0);
        final double[] arrayOne = new double[5];
        System.arraycopy(testArray, 5, arrayOne, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayOne), stat.evaluate(testArray, 5, 5), 0);
        final double[] arrayEnd = new double[5];
        System.arraycopy(testArray, testArray.length - 5, arrayEnd, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayEnd), stat.evaluate(testArray, testArray.length - 5, 5), 0);
    }

    @Test
    public void testEvaluateArraySegmentWeighted() {
        // See if this statistic computes weighted statistics
        // If not, skip this test
        UnivariateStatistic statistic = getUnivariateStatistic();
        if (!(statistic instanceof WeightedEvaluation)) {
            return;
        }
        final WeightedEvaluation stat = (WeightedEvaluation) getUnivariateStatistic();
        final double[] arrayZero = new double[5];
        final double[] weightZero = new double[5];
        System.arraycopy(testArray, 0, arrayZero, 0, 5);
        System.arraycopy(testWeightsArray, 0, weightZero, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayZero, weightZero),
                stat.evaluate(testArray, testWeightsArray, 0, 5), 0);
        final double[] arrayOne = new double[5];
        final double[] weightOne = new double[5];
        System.arraycopy(testArray, 5, arrayOne, 0, 5);
        System.arraycopy(testWeightsArray, 5, weightOne, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayOne, weightOne),
                stat.evaluate(testArray, testWeightsArray, 5, 5), 0);
        final double[] arrayEnd = new double[5];
        final double[] weightEnd = new double[5];
        System.arraycopy(testArray, testArray.length - 5, arrayEnd, 0, 5);
        System.arraycopy(testWeightsArray, testArray.length - 5, weightEnd, 0, 5);
        Assert.assertEquals(stat.evaluate(arrayEnd, weightEnd),
                stat.evaluate(testArray, testWeightsArray, testArray.length - 5, 5), 0);
    }

    @Test
    public void testCopy() {
        UnivariateStatistic original = getUnivariateStatistic();
        UnivariateStatistic copy = original.copy();
        Assert.assertEquals(
                expectedValue(),
                copy.evaluate(testArray),
                getTolerance());
    }

    /**
     * Tests consistency of weighted statistic computation.
     * For statistics that support weighted evaluation, this test case compares
     * the result of direct computation on an array with repeated values with
     * a weighted computation on the corresponding (shorter) array with each
     * value appearing only once but with a weight value equal to its multiplicity
     * in the repeating array.
     */

    @Test
    public void testWeightedConsistency() {

        // See if this statistic computes weighted statistics
        // If not, skip this test
        UnivariateStatistic statistic = getUnivariateStatistic();
        if (!(statistic instanceof WeightedEvaluation)) {
            return;
        }

        // Create arrays of values and corresponding integral weights
        // and longer array with values repeated according to the weights
        final int len = 10;        // length of values array
        final double mu = 0;       // mean of test data
        final double sigma = 5;    // std dev of test data
        double[] values = new double[len];
        double[] weights = new double[len];

        // Fill weights array with random int values between 1 and 5
        int[] intWeights = new int[len];
        final IntegerDistribution weightDist = new UniformIntegerDistribution(1, 5);
        for (int i = 0; i < len; i++) {
            intWeights[i] = weightDist.sample();
            weights[i] = intWeights[i];
        }

        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        final RealDistribution valueDist = new NormalDistribution(mu, sigma);
        List<Double> valuesList = new ArrayList();
        for (int i = 0; i < len; i++) {
            double value = valueDist.sample();
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

        // Dump valuesList into repeatedValues array
        int sumWeights = valuesList.size();
        double[] repeatedValues = new double[sumWeights];
        for (int i = 0; i < sumWeights; i++) {
            repeatedValues[i] = valuesList.get(i);
        }

        // Compare result of weighted statistic computation with direct computation
        // on array of repeated values
        WeightedEvaluation weightedStatistic = (WeightedEvaluation) statistic;
        TestUtils.assertRelativelyEquals(statistic.evaluate(repeatedValues),
                weightedStatistic.evaluate(values, weights, 0, values.length),
                10E-12);

        // Check consistency of weighted evaluation methods
        Assert.assertEquals(weightedStatistic.evaluate(values, weights, 0, values.length),
                weightedStatistic.evaluate(values, weights), Double.MIN_VALUE);

    }

}

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