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* 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.math4.stat.descriptive;
import java.util.Locale;
import org.apache.commons.math4.TestUtils;
import org.apache.commons.math4.exception.MathIllegalArgumentException;
import org.apache.commons.math4.stat.descriptive.DescriptiveStatistics;
import org.apache.commons.math4.stat.descriptive.SummaryStatistics;
import org.apache.commons.math4.stat.descriptive.UnivariateStatistic;
import org.apache.commons.math4.stat.descriptive.moment.GeometricMean;
import org.apache.commons.math4.stat.descriptive.moment.Mean;
import org.apache.commons.math4.stat.descriptive.moment.Variance;
import org.apache.commons.math4.stat.descriptive.rank.Max;
import org.apache.commons.math4.stat.descriptive.rank.Min;
import org.apache.commons.math4.stat.descriptive.rank.Percentile;
import org.apache.commons.math4.stat.descriptive.summary.Sum;
import org.apache.commons.math4.stat.descriptive.summary.SumOfSquares;
import org.apache.commons.numbers.core.Precision;
import org.junit.Assert;
import org.junit.Test;
/**
* Test cases for the {@link DescriptiveStatistics} class.
*/
public class DescriptiveStatisticsTest {
protected DescriptiveStatistics createDescriptiveStatistics() {
return new DescriptiveStatistics();
}
@Test
public void testSetterInjection() {
DescriptiveStatistics stats = createDescriptiveStatistics();
stats.addValue(1);
stats.addValue(3);
Assert.assertEquals(2, stats.getMean(), 1E-10);
// Now lets try some new math
stats.setMeanImpl(new deepMean());
Assert.assertEquals(42, stats.getMean(), 1E-10);
}
@Test
public void testCopy() {
DescriptiveStatistics stats = createDescriptiveStatistics();
stats.addValue(1);
stats.addValue(3);
DescriptiveStatistics copy = new DescriptiveStatistics(stats);
Assert.assertEquals(2, copy.getMean(), 1E-10);
// Now lets try some new math
stats.setMeanImpl(new deepMean());
copy = stats.copy();
Assert.assertEquals(42, copy.getMean(), 1E-10);
}
@Test
public void testWindowSize() {
DescriptiveStatistics stats = createDescriptiveStatistics();
stats.setWindowSize(300);
for (int i = 0; i < 100; ++i) {
stats.addValue(i + 1);
}
int refSum = (100 * 101) / 2;
Assert.assertEquals(refSum / 100.0, stats.getMean(), 1E-10);
Assert.assertEquals(300, stats.getWindowSize());
try {
stats.setWindowSize(-3);
Assert.fail("an exception should have been thrown");
} catch (MathIllegalArgumentException iae) {
// expected
}
Assert.assertEquals(300, stats.getWindowSize());
stats.setWindowSize(50);
Assert.assertEquals(50, stats.getWindowSize());
int refSum2 = refSum - (50 * 51) / 2;
Assert.assertEquals(refSum2 / 50.0, stats.getMean(), 1E-10);
}
@Test
public void testGetValues() {
DescriptiveStatistics stats = createDescriptiveStatistics();
for (int i = 100; i > 0; --i) {
stats.addValue(i);
}
int refSum = (100 * 101) / 2;
Assert.assertEquals(refSum / 100.0, stats.getMean(), 1E-10);
double[] v = stats.getValues();
for (int i = 0; i < v.length; ++i) {
Assert.assertEquals(100.0 - i, v[i], 1.0e-10);
}
double[] s = stats.getSortedValues();
for (int i = 0; i < s.length; ++i) {
Assert.assertEquals(i + 1.0, s[i], 1.0e-10);
}
Assert.assertEquals(12.0, stats.getElement(88), 1.0e-10);
}
@Test
public void testQuadraticMean() {
final double[] values = { 1.2, 3.4, 5.6, 7.89 };
final DescriptiveStatistics stats = new DescriptiveStatistics(values);
final int len = values.length;
double expected = 0;
for (int i = 0; i < len; i++) {
final double v = values[i];
expected += v * v / len;
}
expected = Math.sqrt(expected);
Assert.assertEquals(expected, stats.getQuadraticMean(), Math.ulp(expected));
}
@Test
public void testToString() {
DescriptiveStatistics stats = createDescriptiveStatistics();
stats.addValue(1);
stats.addValue(2);
stats.addValue(3);
Locale d = Locale.getDefault();
Locale.setDefault(Locale.US);
Assert.assertEquals("DescriptiveStatistics:\n" +
"n: 3\n" +
"min: 1.0\n" +
"max: 3.0\n" +
"mean: 2.0\n" +
"std dev: 1.0\n" +
"median: 2.0\n" +
"skewness: 0.0\n" +
"kurtosis: NaN\n", stats.toString());
Locale.setDefault(d);
}
@Test
public void testShuffledStatistics() {
// the purpose of this test is only to check the get/set methods
// we are aware shuffling statistics like this is really not
// something sensible to do in production ...
DescriptiveStatistics reference = createDescriptiveStatistics();
DescriptiveStatistics shuffled = createDescriptiveStatistics();
UnivariateStatistic tmp = shuffled.getGeometricMeanImpl();
shuffled.setGeometricMeanImpl(shuffled.getMeanImpl());
shuffled.setMeanImpl(shuffled.getKurtosisImpl());
shuffled.setKurtosisImpl(shuffled.getSkewnessImpl());
shuffled.setSkewnessImpl(shuffled.getVarianceImpl());
shuffled.setVarianceImpl(shuffled.getMaxImpl());
shuffled.setMaxImpl(shuffled.getMinImpl());
shuffled.setMinImpl(shuffled.getSumImpl());
shuffled.setSumImpl(shuffled.getSumsqImpl());
shuffled.setSumsqImpl(tmp);
for (int i = 100; i > 0; --i) {
reference.addValue(i);
shuffled.addValue(i);
}
Assert.assertEquals(reference.getMean(), shuffled.getGeometricMean(), 1.0e-10);
Assert.assertEquals(reference.getKurtosis(), shuffled.getMean(), 1.0e-10);
Assert.assertEquals(reference.getSkewness(), shuffled.getKurtosis(), 1.0e-10);
Assert.assertEquals(reference.getVariance(), shuffled.getSkewness(), 1.0e-10);
Assert.assertEquals(reference.getMax(), shuffled.getVariance(), 1.0e-10);
Assert.assertEquals(reference.getMin(), shuffled.getMax(), 1.0e-10);
Assert.assertEquals(reference.getSum(), shuffled.getMin(), 1.0e-10);
Assert.assertEquals(reference.getSumsq(), shuffled.getSum(), 1.0e-10);
Assert.assertEquals(reference.getGeometricMean(), shuffled.getSumsq(), 1.0e-10);
}
@Test
public void testPercentileSetter() {
DescriptiveStatistics stats = createDescriptiveStatistics();
stats.addValue(1);
stats.addValue(2);
stats.addValue(3);
Assert.assertEquals(2, stats.getPercentile(50.0), 1E-10);
// Inject wrapped Percentile impl
stats.setPercentileImpl(new goodPercentile());
Assert.assertEquals(2, stats.getPercentile(50.0), 1E-10);
// Try "new math" impl
stats.setPercentileImpl(new subPercentile());
Assert.assertEquals(10.0, stats.getPercentile(10.0), 1E-10);
// Try to set bad impl
try {
stats.setPercentileImpl(new badPercentile());
Assert.fail("Expecting MathIllegalArgumentException");
} catch (MathIllegalArgumentException ex) {
// expected
}
}
@Test
public void test20090720() {
DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(100);
for (int i = 0; i < 161; i++) {
descriptiveStatistics.addValue(1.2);
}
descriptiveStatistics.clear();
descriptiveStatistics.addValue(1.2);
Assert.assertEquals(1, descriptiveStatistics.getN());
}
@Test
public void testRemoval() {
final DescriptiveStatistics dstat = createDescriptiveStatistics();
checkremoval(dstat, 1, 6.0, 0.0, Double.NaN);
checkremoval(dstat, 3, 5.0, 3.0, 4.5);
checkremoval(dstat, 6, 3.5, 2.5, 3.0);
checkremoval(dstat, 9, 3.5, 2.5, 3.0);
checkremoval(dstat, DescriptiveStatistics.INFINITE_WINDOW, 3.5, 2.5, 3.0);
}
@Test
public void testSummaryConsistency() {
final DescriptiveStatistics dstats = new DescriptiveStatistics();
final SummaryStatistics sstats = new SummaryStatistics();
final int windowSize = 5;
dstats.setWindowSize(windowSize);
final double tol = 1E-12;
for (int i = 0; i < 20; i++) {
dstats.addValue(i);
sstats.clear();
double[] values = dstats.getValues();
for (int j = 0; j < values.length; j++) {
sstats.addValue(values[j]);
}
TestUtils.assertEquals(dstats.getMean(), sstats.getMean(), tol);
TestUtils.assertEquals(new Mean().evaluate(values), dstats.getMean(), tol);
TestUtils.assertEquals(dstats.getMax(), sstats.getMax(), tol);
TestUtils.assertEquals(new Max().evaluate(values), dstats.getMax(), tol);
TestUtils.assertEquals(dstats.getGeometricMean(), sstats.getGeometricMean(), tol);
TestUtils.assertEquals(new GeometricMean().evaluate(values), dstats.getGeometricMean(), tol);
TestUtils.assertEquals(dstats.getMin(), sstats.getMin(), tol);
TestUtils.assertEquals(new Min().evaluate(values), dstats.getMin(), tol);
TestUtils.assertEquals(dstats.getStandardDeviation(), sstats.getStandardDeviation(), tol);
TestUtils.assertEquals(dstats.getVariance(), sstats.getVariance(), tol);
TestUtils.assertEquals(new Variance().evaluate(values), dstats.getVariance(), tol);
TestUtils.assertEquals(dstats.getSum(), sstats.getSum(), tol);
TestUtils.assertEquals(new Sum().evaluate(values), dstats.getSum(), tol);
TestUtils.assertEquals(dstats.getSumsq(), sstats.getSumsq(), tol);
TestUtils.assertEquals(new SumOfSquares().evaluate(values), dstats.getSumsq(), tol);
TestUtils.assertEquals(dstats.getPopulationVariance(), sstats.getPopulationVariance(), tol);
TestUtils.assertEquals(new Variance(false).evaluate(values), dstats.getPopulationVariance(), tol);
}
}
@Test
public void testMath1129(){
final double[] data = new double[] {
-0.012086732064244697,
-0.24975668704012527,
0.5706168483164684,
-0.322111769955327,
0.24166759508327315,
Double.NaN,
0.16698443218942854,
-0.10427763937565114,
-0.15595963093172435,
-0.028075857595882995,
-0.24137994506058857,
0.47543170476574426,
-0.07495595384947631,
0.37445697625436497,
-0.09944199541668033
};
final DescriptiveStatistics ds = new DescriptiveStatistics(data);
final double t = ds.getPercentile(75);
final double o = ds.getPercentile(25);
final double iqr = t - o;
// System.out.println(String.format("25th percentile %s 75th percentile %s", o, t));
Assert.assertTrue(iqr >= 0);
}
public void checkremoval(DescriptiveStatistics dstat, int wsize,
double mean1, double mean2, double mean3) {
dstat.setWindowSize(wsize);
dstat.clear();
for (int i = 1 ; i <= 6 ; ++i) {
dstat.addValue(i);
}
Assert.assertTrue(Precision.equalsIncludingNaN(mean1, dstat.getMean()));
dstat.replaceMostRecentValue(0);
Assert.assertTrue(Precision.equalsIncludingNaN(mean2, dstat.getMean()));
dstat.removeMostRecentValue();
Assert.assertTrue(Precision.equalsIncludingNaN(mean3, dstat.getMean()));
}
// Test UnivariateStatistics impls for setter injection tests
/**
* A new way to compute the mean
*/
static class deepMean implements UnivariateStatistic {
@Override
public double evaluate(double[] values, int begin, int length) {
return 42;
}
@Override
public double evaluate(double[] values) {
return 42;
}
@Override
public UnivariateStatistic copy() {
return new deepMean();
}
}
/**
* Test percentile implementation - wraps a Percentile
*/
static class goodPercentile implements UnivariateStatistic {
private final Percentile percentile = new Percentile();
public void setQuantile(double quantile) {
percentile.setQuantile(quantile);
}
@Override
public double evaluate(double[] values, int begin, int length) {
return percentile.evaluate(values, begin, length);
}
@Override
public double evaluate(double[] values) {
return percentile.evaluate(values);
}
@Override
public UnivariateStatistic copy() {
goodPercentile result = new goodPercentile();
result.setQuantile(percentile.getQuantile());
return result;
}
}
/**
* Test percentile subclass - another "new math" impl
* Always returns currently set quantile
*/
static class subPercentile extends Percentile {
@Override
public double evaluate(double[] values, int begin, int length) {
return getQuantile();
}
@Override
public double evaluate(double[] values) {
return getQuantile();
}
private static final long serialVersionUID = 8040701391045914979L;
@Override
public Percentile copy() {
subPercentile result = new subPercentile();
return result;
}
}
/**
* "Bad" test percentile implementation - no setQuantile
*/
static class badPercentile implements UnivariateStatistic {
private final Percentile percentile = new Percentile();
@Override
public double evaluate(double[] values, int begin, int length) {
return percentile.evaluate(values, begin, length);
}
@Override
public double evaluate(double[] values) {
return percentile.evaluate(values);
}
@Override
public UnivariateStatistic copy() {
return new badPercentile();
}
}
}