/*
* 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.math4.stat.descriptive;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math4.TestUtils;
import org.apache.commons.math4.distribution.IntegerDistribution;
import org.apache.commons.math4.distribution.NormalDistribution;
import org.apache.commons.math4.distribution.RealDistribution;
import org.apache.commons.math4.distribution.UniformIntegerDistribution;
import org.apache.commons.math4.stat.descriptive.UnivariateStatistic;
import org.apache.commons.math4.stat.descriptive.WeightedEvaluation;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.rng.simple.RandomSource;
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.Sampler weightDist =
new UniformIntegerDistribution(1, 5).createSampler(RandomSource.create(RandomSource.WELL_512_A,
234878544L));
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.Sampler valueDist =
new NormalDistribution(mu, sigma).createSampler(RandomSource.create(RandomSource.WELL_512_A,
64925784252L));
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);
}
}