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 StandardDeviationTest extends StorelessUnivariateStatisticAbstractTest{
031
032 protected StandardDeviation stat;
033
034 /**
035 * @param name
036 */
037 public StandardDeviationTest(String name) {
038 super(name);
039 }
040
041 /**
042 * {@inheritDoc}
043 */
044 @Override
045 public UnivariateStatistic getUnivariateStatistic() {
046 return new StandardDeviation();
047 }
048
049 public static Test suite() {
050 TestSuite suite = new TestSuite(StandardDeviationTest.class);
051 suite.setName("StandardDeviation Tests");
052 return suite;
053 }
054
055 /**
056 * {@inheritDoc}
057 */
058 @Override
059 public double expectedValue() {
060 return this.std;
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 double sigma = populationStandardDeviation(values);
080 SecondMoment m = new SecondMoment();
081 m.evaluate(values); // side effect is to add values
082 StandardDeviation s1 = new StandardDeviation();
083 s1.setBiasCorrected(false);
084 assertEquals(sigma, s1.evaluate(values), 1E-14);
085 s1.incrementAll(values);
086 assertEquals(sigma, s1.getResult(), 1E-14);
087 s1 = new StandardDeviation(false, m);
088 assertEquals(sigma, s1.getResult(), 1E-14);
089 s1 = new StandardDeviation(false);
090 assertEquals(sigma, s1.evaluate(values), 1E-14);
091 s1.incrementAll(values);
092 assertEquals(sigma, s1.getResult(), 1E-14);
093 }
094
095 /**
096 * Definitional formula for population standard deviation
097 */
098 protected double populationStandardDeviation(double[] v) {
099 double mean = new Mean().evaluate(v);
100 double sum = 0;
101 for (int i = 0; i < v.length; i++) {
102 sum += (v[i] - mean) * (v[i] - mean);
103 }
104 return Math.sqrt(sum / v.length);
105 }
106
107 }