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Commons Math example source code file (VarianceTest.java)
The Commons Math VarianceTest.java 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.math.stat.descriptive.moment; import org.apache.commons.math.stat.descriptive.StorelessUnivariateStatisticAbstractTest; import org.apache.commons.math.stat.descriptive.UnivariateStatistic; import org.apache.commons.math.util.MathUtils; /** * Test cases for the {@link UnivariateStatistic} class. * * @version $Revision: 902201 $ $Date: 2010-01-22 13:18:16 -0500 (Fri, 22 Jan 2010) $ */ public class VarianceTest extends StorelessUnivariateStatisticAbstractTest{ protected Variance stat; /** * @param name */ public VarianceTest(String name) { super(name); } /** * {@inheritDoc} */ @Override public UnivariateStatistic getUnivariateStatistic() { return new Variance(); } /** * {@inheritDoc} */ @Override public double expectedValue() { return this.var; } /**Expected value for the testArray defined in UnivariateStatisticAbstractTest */ public double expectedWeightedValue() { return this.weightedVar; } /** * Make sure Double.NaN is returned iff n = 0 * */ public void testNaN() { StandardDeviation std = new StandardDeviation(); assertTrue(Double.isNaN(std.getResult())); std.increment(1d); assertEquals(0d, std.getResult(), 0); } /** * Test population version of variance */ public void testPopulation() { double[] values = {-1.0d, 3.1d, 4.0d, -2.1d, 22d, 11.7d, 3d, 14d}; SecondMoment m = new SecondMoment(); m.evaluate(values); // side effect is to add values Variance v1 = new Variance(); v1.setBiasCorrected(false); assertEquals(populationVariance(values), v1.evaluate(values), 1E-14); v1.incrementAll(values); assertEquals(populationVariance(values), v1.getResult(), 1E-14); v1 = new Variance(false, m); assertEquals(populationVariance(values), v1.getResult(), 1E-14); v1 = new Variance(false); assertEquals(populationVariance(values), v1.evaluate(values), 1E-14); v1.incrementAll(values); assertEquals(populationVariance(values), v1.getResult(), 1E-14); } /** * Definitional formula for population variance */ protected double populationVariance(double[] v) { double mean = new Mean().evaluate(v); double sum = 0; for (int i = 0; i < v.length; i++) { sum += (v[i] - mean) * (v[i] - mean); } return sum / v.length; } public void testWeightedVariance() { Variance variance = new Variance(); assertEquals(expectedWeightedValue(), variance.evaluate(testArray, testWeightsArray, 0, testArray.length), getTolerance()); // All weights = 1 -> weighted variance = unweighted variance assertEquals(expectedValue(), variance.evaluate(testArray, unitWeightsArray, 0, testArray.length), getTolerance()); // All weights the same -> when weights are normalized to sum to the length of the values array, // weighted variance = unweighted value assertEquals(expectedValue(), variance.evaluate(testArray, MathUtils.normalizeArray(identicalWeightsArray, testArray.length), 0, testArray.length), getTolerance()); } } Other Commons Math examples (source code examples)Here is a short list of links related to this Commons Math VarianceTest.java source code file: |
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