/*
* 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;
import java.util.Locale;
import junit.framework.TestCase;
import org.apache.commons.math.DimensionMismatchException;
import org.apache.commons.math.TestUtils;
import org.apache.commons.math.stat.descriptive.moment.Mean;
/**
* Test cases for the {@link MultivariateSummaryStatistics} class.
*
* @version $Revision: 902201 $ $Date: 2010-01-22 13:18:16 -0500 (Fri, 22 Jan 2010) $
*/
public class MultivariateSummaryStatisticsTest extends TestCase {
public MultivariateSummaryStatisticsTest(String name) {
super(name);
}
protected MultivariateSummaryStatistics createMultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) {
return new MultivariateSummaryStatistics(k, isCovarianceBiasCorrected);
}
public void testSetterInjection() throws Exception {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
u.setMeanImpl(new StorelessUnivariateStatistic[] {
new sumMean(), new sumMean()
});
u.addValue(new double[] { 1, 2 });
u.addValue(new double[] { 3, 4 });
assertEquals(4, u.getMean()[0], 1E-14);
assertEquals(6, u.getMean()[1], 1E-14);
u.clear();
u.addValue(new double[] { 1, 2 });
u.addValue(new double[] { 3, 4 });
assertEquals(4, u.getMean()[0], 1E-14);
assertEquals(6, u.getMean()[1], 1E-14);
u.clear();
u.setMeanImpl(new StorelessUnivariateStatistic[] {
new Mean(), new Mean()
}); // OK after clear
u.addValue(new double[] { 1, 2 });
u.addValue(new double[] { 3, 4 });
assertEquals(2, u.getMean()[0], 1E-14);
assertEquals(3, u.getMean()[1], 1E-14);
assertEquals(2, u.getDimension());
}
public void testSetterIllegalState() throws Exception {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
u.addValue(new double[] { 1, 2 });
u.addValue(new double[] { 3, 4 });
try {
u.setMeanImpl(new StorelessUnivariateStatistic[] {
new sumMean(), new sumMean()
});
fail("Expecting IllegalStateException");
} catch (IllegalStateException ex) {
// expected
}
}
public void testToString() throws DimensionMismatchException {
MultivariateSummaryStatistics stats = createMultivariateSummaryStatistics(2, true);
stats.addValue(new double[] {1, 3});
stats.addValue(new double[] {2, 2});
stats.addValue(new double[] {3, 1});
Locale d = Locale.getDefault();
Locale.setDefault(Locale.US);
assertEquals("MultivariateSummaryStatistics:\n" +
"n: 3\n" +
"min: 1.0, 1.0\n" +
"max: 3.0, 3.0\n" +
"mean: 2.0, 2.0\n" +
"geometric mean: 1.817..., 1.817...\n" +
"sum of squares: 14.0, 14.0\n" +
"sum of logarithms: 1.791..., 1.791...\n" +
"standard deviation: 1.0, 1.0\n" +
"covariance: Array2DRowRealMatrix{{1.0,-1.0},{-1.0,1.0}}\n",
stats.toString().replaceAll("([0-9]+\\.[0-9][0-9][0-9])[0-9]+", "$1..."));
Locale.setDefault(d);
}
public void testShuffledStatistics() throws DimensionMismatchException {
// 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 ...
MultivariateSummaryStatistics reference = createMultivariateSummaryStatistics(2, true);
MultivariateSummaryStatistics shuffled = createMultivariateSummaryStatistics(2, true);
StorelessUnivariateStatistic[] tmp = shuffled.getGeoMeanImpl();
shuffled.setGeoMeanImpl(shuffled.getMeanImpl());
shuffled.setMeanImpl(shuffled.getMaxImpl());
shuffled.setMaxImpl(shuffled.getMinImpl());
shuffled.setMinImpl(shuffled.getSumImpl());
shuffled.setSumImpl(shuffled.getSumsqImpl());
shuffled.setSumsqImpl(shuffled.getSumLogImpl());
shuffled.setSumLogImpl(tmp);
for (int i = 100; i > 0; --i) {
reference.addValue(new double[] {i, i});
shuffled.addValue(new double[] {i, i});
}
TestUtils.assertEquals(reference.getMean(), shuffled.getGeometricMean(), 1.0e-10);
TestUtils.assertEquals(reference.getMax(), shuffled.getMean(), 1.0e-10);
TestUtils.assertEquals(reference.getMin(), shuffled.getMax(), 1.0e-10);
TestUtils.assertEquals(reference.getSum(), shuffled.getMin(), 1.0e-10);
TestUtils.assertEquals(reference.getSumSq(), shuffled.getSum(), 1.0e-10);
TestUtils.assertEquals(reference.getSumLog(), shuffled.getSumSq(), 1.0e-10);
TestUtils.assertEquals(reference.getGeometricMean(), shuffled.getSumLog(), 1.0e-10);
}
/**
* Bogus mean implementation to test setter injection.
* Returns the sum instead of the mean.
*/
static class sumMean implements StorelessUnivariateStatistic {
private double sum = 0;
private long n = 0;
public double evaluate(double[] values, int begin, int length) {
return 0;
}
public double evaluate(double[] values) {
return 0;
}
public void clear() {
sum = 0;
n = 0;
}
public long getN() {
return n;
}
public double getResult() {
return sum;
}
public void increment(double d) {
sum += d;
n++;
}
public void incrementAll(double[] values, int start, int length) {
}
public void incrementAll(double[] values) {
}
public StorelessUnivariateStatistic copy() {
return new sumMean();
}
}
public void testDimension() {
try {
createMultivariateSummaryStatistics(2, true).addValue(new double[3]);
} catch (DimensionMismatchException dme) {
// expected behavior
} catch (Exception e) {
fail("wrong exception caught");
}
}
/** test stats */
public void testStats() throws DimensionMismatchException {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
assertEquals(0, u.getN());
u.addValue(new double[] { 1, 2 });
u.addValue(new double[] { 2, 3 });
u.addValue(new double[] { 2, 3 });
u.addValue(new double[] { 3, 4 });
assertEquals( 4, u.getN());
assertEquals( 8, u.getSum()[0], 1.0e-10);
assertEquals(12, u.getSum()[1], 1.0e-10);
assertEquals(18, u.getSumSq()[0], 1.0e-10);
assertEquals(38, u.getSumSq()[1], 1.0e-10);
assertEquals( 1, u.getMin()[0], 1.0e-10);
assertEquals( 2, u.getMin()[1], 1.0e-10);
assertEquals( 3, u.getMax()[0], 1.0e-10);
assertEquals( 4, u.getMax()[1], 1.0e-10);
assertEquals(2.4849066497880003102, u.getSumLog()[0], 1.0e-10);
assertEquals( 4.276666119016055311, u.getSumLog()[1], 1.0e-10);
assertEquals( 1.8612097182041991979, u.getGeometricMean()[0], 1.0e-10);
assertEquals( 2.9129506302439405217, u.getGeometricMean()[1], 1.0e-10);
assertEquals( 2, u.getMean()[0], 1.0e-10);
assertEquals( 3, u.getMean()[1], 1.0e-10);
assertEquals(Math.sqrt(2.0 / 3.0), u.getStandardDeviation()[0], 1.0e-10);
assertEquals(Math.sqrt(2.0 / 3.0), u.getStandardDeviation()[1], 1.0e-10);
assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 0), 1.0e-10);
assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 1), 1.0e-10);
assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 0), 1.0e-10);
assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 1), 1.0e-10);
u.clear();
assertEquals(0, u.getN());
}
public void testN0andN1Conditions() throws Exception {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true);
assertTrue(Double.isNaN(u.getMean()[0]));
assertTrue(Double.isNaN(u.getStandardDeviation()[0]));
/* n=1 */
u.addValue(new double[] { 1 });
assertEquals(1.0, u.getMean()[0], 1.0e-10);
assertEquals(1.0, u.getGeometricMean()[0], 1.0e-10);
assertEquals(0.0, u.getStandardDeviation()[0], 1.0e-10);
/* n=2 */
u.addValue(new double[] { 2 });
assertTrue(u.getStandardDeviation()[0] > 0);
}
public void testNaNContracts() throws DimensionMismatchException {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true);
assertTrue(Double.isNaN(u.getMean()[0]));
assertTrue(Double.isNaN(u.getMin()[0]));
assertTrue(Double.isNaN(u.getStandardDeviation()[0]));
assertTrue(Double.isNaN(u.getGeometricMean()[0]));
u.addValue(new double[] { 1.0 });
assertFalse(Double.isNaN(u.getMean()[0]));
assertFalse(Double.isNaN(u.getMin()[0]));
assertFalse(Double.isNaN(u.getStandardDeviation()[0]));
assertFalse(Double.isNaN(u.getGeometricMean()[0]));
}
public void testSerialization() throws DimensionMismatchException {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
// Empty test
TestUtils.checkSerializedEquality(u);
MultivariateSummaryStatistics s = (MultivariateSummaryStatistics) TestUtils.serializeAndRecover(u);
assertEquals(u, s);
// Add some data
u.addValue(new double[] { 2d, 1d });
u.addValue(new double[] { 1d, 1d });
u.addValue(new double[] { 3d, 1d });
u.addValue(new double[] { 4d, 1d });
u.addValue(new double[] { 5d, 1d });
// Test again
TestUtils.checkSerializedEquality(u);
s = (MultivariateSummaryStatistics) TestUtils.serializeAndRecover(u);
assertEquals(u, s);
}
public void testEqualsAndHashCode() throws DimensionMismatchException {
MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true);
MultivariateSummaryStatistics t = null;
int emptyHash = u.hashCode();
assertTrue(u.equals(u));
assertFalse(u.equals(t));
assertFalse(u.equals(Double.valueOf(0)));
t = createMultivariateSummaryStatistics(2, true);
assertTrue(t.equals(u));
assertTrue(u.equals(t));
assertEquals(emptyHash, t.hashCode());
// Add some data to u
u.addValue(new double[] { 2d, 1d });
u.addValue(new double[] { 1d, 1d });
u.addValue(new double[] { 3d, 1d });
u.addValue(new double[] { 4d, 1d });
u.addValue(new double[] { 5d, 1d });
assertFalse(t.equals(u));
assertFalse(u.equals(t));
assertTrue(u.hashCode() != t.hashCode());
//Add data in same order to t
t.addValue(new double[] { 2d, 1d });
t.addValue(new double[] { 1d, 1d });
t.addValue(new double[] { 3d, 1d });
t.addValue(new double[] { 4d, 1d });
t.addValue(new double[] { 5d, 1d });
assertTrue(t.equals(u));
assertTrue(u.equals(t));
assertEquals(u.hashCode(), t.hashCode());
// Clear and make sure summaries are indistinguishable from empty summary
u.clear();
t.clear();
assertTrue(t.equals(u));
assertTrue(u.equals(t));
assertEquals(emptyHash, t.hashCode());
assertEquals(emptyHash, u.hashCode());
}
}