001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.math.stat.descriptive.moment; 018 019 import junit.framework.Test; 020 import junit.framework.TestSuite; 021 022 import org.apache.commons.math.stat.descriptive.StorelessUnivariateStatisticAbstractTest; 023 import org.apache.commons.math.stat.descriptive.UnivariateStatistic; 024 025 /** 026 * Test cases for the {@link UnivariateStatistic} class. 027 * 028 * @version $Revision: 762118 $ $Date: 2009-04-05 12:55:59 -0400 (Sun, 05 Apr 2009) $ 029 */ 030 public class VarianceTest extends StorelessUnivariateStatisticAbstractTest{ 031 032 protected Variance stat; 033 034 /** 035 * @param name 036 */ 037 public VarianceTest(String name) { 038 super(name); 039 } 040 041 /** 042 * {@inheritDoc} 043 */ 044 @Override 045 public UnivariateStatistic getUnivariateStatistic() { 046 return new Variance(); 047 } 048 049 public static Test suite() { 050 TestSuite suite = new TestSuite(VarianceTest.class); 051 suite.setName("Variance Tests"); 052 return suite; 053 } 054 055 /** 056 * {@inheritDoc} 057 */ 058 @Override 059 public double expectedValue() { 060 return this.var; 061 } 062 063 /** 064 * Make sure Double.NaN is returned iff n = 0 065 * 066 */ 067 public void testNaN() { 068 StandardDeviation std = new StandardDeviation(); 069 assertTrue(Double.isNaN(std.getResult())); 070 std.increment(1d); 071 assertEquals(0d, std.getResult(), 0); 072 } 073 074 /** 075 * Test population version of variance 076 */ 077 public void testPopulation() { 078 double[] values = {-1.0d, 3.1d, 4.0d, -2.1d, 22d, 11.7d, 3d, 14d}; 079 SecondMoment m = new SecondMoment(); 080 m.evaluate(values); // side effect is to add values 081 Variance v1 = new Variance(); 082 v1.setBiasCorrected(false); 083 assertEquals(populationVariance(values), v1.evaluate(values), 1E-14); 084 v1.incrementAll(values); 085 assertEquals(populationVariance(values), v1.getResult(), 1E-14); 086 v1 = new Variance(false, m); 087 assertEquals(populationVariance(values), v1.getResult(), 1E-14); 088 v1 = new Variance(false); 089 assertEquals(populationVariance(values), v1.evaluate(values), 1E-14); 090 v1.incrementAll(values); 091 assertEquals(populationVariance(values), v1.getResult(), 1E-14); 092 } 093 094 /** 095 * Definitional formula for population variance 096 */ 097 protected double populationVariance(double[] v) { 098 double mean = new Mean().evaluate(v); 099 double sum = 0; 100 for (int i = 0; i < v.length; i++) { 101 sum += (v[i] - mean) * (v[i] - mean); 102 } 103 return sum / v.length; 104 } 105 106 }