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Java example source code file (SemiVarianceTest.java)

This example Java source code file (SemiVarianceTest.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

bias, default, downside, mathillegalargumentexception, semivariance, semivariancetest, test, turn

The SemiVarianceTest.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.moment;

import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.stat.StatUtils;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.junit.Assert;
import org.junit.Test;


public class SemiVarianceTest {

    @Test
    public void testInsufficientData() {
        double[] nothing = null;
        SemiVariance sv = new SemiVariance();
        try {
            sv.evaluate(nothing);
            Assert.fail("null is not a valid data array.");
        } catch (MathIllegalArgumentException iae) {
        }

        try {
            sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
            sv.evaluate(nothing);
            Assert.fail("null is not a valid data array.");
        } catch (MathIllegalArgumentException iae) {
        }
        nothing = new double[] {};
        Assert.assertTrue(Double.isNaN(sv.evaluate(nothing)));
    }

    @Test
    public void testSingleDown() {
        SemiVariance sv = new SemiVariance();
        double[] values = { 50.0d };
        double singletest = sv.evaluate(values);
        Assert.assertEquals(0.0d, singletest, 0);
    }

    @Test
    public void testSingleUp() {
        SemiVariance sv = new SemiVariance(SemiVariance.UPSIDE_VARIANCE);
        double[] values = { 50.0d };
        double singletest = sv.evaluate(values);
        Assert.assertEquals(0.0d, singletest, 0);
    }

    @Test
    public void testSample() {
        final double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
        final int length = values.length;
        final double mean = StatUtils.mean(values); // 6.333...
        final SemiVariance sv = new SemiVariance();  // Default bias correction is true
        final double downsideSemiVariance = sv.evaluate(values); // Downside is the default
        Assert.assertEquals(TestUtils.sumSquareDev(new double[] {-2d, 2d, 4d, -2d, 3d, 5d}, mean) / (length - 1),
                downsideSemiVariance, 1E-14);

        sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
        final double upsideSemiVariance = sv.evaluate(values);
        Assert.assertEquals(TestUtils.sumSquareDev(new double[] {22d, 11d, 14d}, mean) / (length - 1),
                upsideSemiVariance, 1E-14);

        // Verify that upper + lower semivariance against the mean sum to variance
        Assert.assertEquals(StatUtils.variance(values), downsideSemiVariance + upsideSemiVariance, 10e-12);
    }

    @Test
    public void testPopulation() {
        double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
        SemiVariance sv = new SemiVariance(false);

        double singletest = sv.evaluate(values);
        Assert.assertEquals(19.556d, singletest, 0.01d);

        sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
        singletest = sv.evaluate(values);
        Assert.assertEquals(36.222d, singletest, 0.01d);
    }

    @Test
    public void testNonMeanCutoffs() {
        double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
        SemiVariance sv = new SemiVariance(false); // Turn off bias correction - use df = length

        double singletest = sv.evaluate(values, 1.0d, SemiVariance.DOWNSIDE_VARIANCE, false, 0, values.length);
        Assert.assertEquals(TestUtils.sumSquareDev(new double[] { -2d, -2d }, 1.0d) / values.length,
                singletest, 0.01d);

        singletest = sv.evaluate(values, 3.0d, SemiVariance.UPSIDE_VARIANCE, false, 0, values.length);
        Assert.assertEquals(TestUtils.sumSquareDev(new double[] { 4d, 22d, 11d, 14d, 5d }, 3.0d) / values.length, singletest,
                0.01d);
    }

    /**
     * Check that the lower + upper semivariance against the mean sum to the
     * variance.
     */
    @Test
    public void testVarianceDecompMeanCutoff() {
        double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
        double variance = StatUtils.variance(values);
        SemiVariance sv = new SemiVariance(true); // Bias corrected
        sv.setVarianceDirection(SemiVariance.DOWNSIDE_VARIANCE);
        final double lower = sv.evaluate(values);
        sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
        final double upper = sv.evaluate(values);
        Assert.assertEquals(variance, lower + upper, 10e-12);
    }

    /**
     * Check that upper and lower semivariances against a cutoff sum to the sum
     * of squared deviations of the full set of values against the cutoff
     * divided by df = length - 1 (assuming bias-corrected).
     */
    @Test
    public void testVarianceDecompNonMeanCutoff() {
        double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
        double target = 0;
        double totalSumOfSquares = TestUtils.sumSquareDev(values, target);
        SemiVariance sv = new SemiVariance(true); // Bias corrected
        sv.setVarianceDirection(SemiVariance.DOWNSIDE_VARIANCE);
        double lower = sv.evaluate(values, target);
        sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
        double upper = sv.evaluate(values, target);
        Assert.assertEquals(totalSumOfSquares / (values.length - 1), lower + upper, 10e-12);
    }

    @Test
    public void testNoVariance() {
        final double[] values = {100d, 100d, 100d, 100d};
        SemiVariance sv = new SemiVariance();
        Assert.assertEquals(0, sv.evaluate(values), 10E-12);
        Assert.assertEquals(0, sv.evaluate(values, 100d), 10E-12);
        Assert.assertEquals(0, sv.evaluate(values, 100d, SemiVariance.UPSIDE_VARIANCE, false, 0, values.length), 10E-12);
    }
}

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