| /* |
| * 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.math3.stat.descriptive; |
| |
| |
| import java.util.Locale; |
| |
| import org.apache.commons.math3.exception.DimensionMismatchException; |
| import org.apache.commons.math3.TestUtils; |
| import org.apache.commons.math3.stat.descriptive.moment.Mean; |
| import org.apache.commons.math3.util.FastMath; |
| |
| import org.junit.Test; |
| import org.junit.Assert; |
| |
| /** |
| * Test cases for the {@link MultivariateSummaryStatistics} class. |
| * |
| */ |
| |
| public class MultivariateSummaryStatisticsTest { |
| |
| protected MultivariateSummaryStatistics createMultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) { |
| return new MultivariateSummaryStatistics(k, isCovarianceBiasCorrected); |
| } |
| |
| @Test |
| public void testSetterInjection() { |
| 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 }); |
| Assert.assertEquals(4, u.getMean()[0], 1E-14); |
| Assert.assertEquals(6, u.getMean()[1], 1E-14); |
| u.clear(); |
| u.addValue(new double[] { 1, 2 }); |
| u.addValue(new double[] { 3, 4 }); |
| Assert.assertEquals(4, u.getMean()[0], 1E-14); |
| Assert.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 }); |
| Assert.assertEquals(2, u.getMean()[0], 1E-14); |
| Assert.assertEquals(3, u.getMean()[1], 1E-14); |
| Assert.assertEquals(2, u.getDimension()); |
| } |
| |
| @Test |
| public void testSetterIllegalState() { |
| 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() |
| }); |
| Assert.fail("Expecting IllegalStateException"); |
| } catch (IllegalStateException ex) { |
| // expected |
| } |
| } |
| |
| @Test |
| public void testToString() { |
| 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); |
| final String suffix = System.getProperty("line.separator"); |
| Assert.assertEquals("MultivariateSummaryStatistics:" + suffix+ |
| "n: 3" +suffix+ |
| "min: 1.0, 1.0" +suffix+ |
| "max: 3.0, 3.0" +suffix+ |
| "mean: 2.0, 2.0" +suffix+ |
| "geometric mean: 1.817..., 1.817..." +suffix+ |
| "sum of squares: 14.0, 14.0" +suffix+ |
| "sum of logarithms: 1.791..., 1.791..." +suffix+ |
| "standard deviation: 1.0, 1.0" +suffix+ |
| "covariance: Array2DRowRealMatrix{{1.0,-1.0},{-1.0,1.0}}" +suffix, |
| stats.toString().replaceAll("([0-9]+\\.[0-9][0-9][0-9])[0-9]+", "$1...")); |
| 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 ... |
| 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(); |
| } |
| } |
| |
| @Test |
| public void testDimension() { |
| try { |
| createMultivariateSummaryStatistics(2, true).addValue(new double[3]); |
| Assert.fail("Expecting DimensionMismatchException"); |
| } catch (DimensionMismatchException dme) { |
| // expected behavior |
| } |
| } |
| |
| /** test stats */ |
| @Test |
| public void testStats() { |
| MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true); |
| Assert.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 }); |
| Assert.assertEquals( 4, u.getN()); |
| Assert.assertEquals( 8, u.getSum()[0], 1.0e-10); |
| Assert.assertEquals(12, u.getSum()[1], 1.0e-10); |
| Assert.assertEquals(18, u.getSumSq()[0], 1.0e-10); |
| Assert.assertEquals(38, u.getSumSq()[1], 1.0e-10); |
| Assert.assertEquals( 1, u.getMin()[0], 1.0e-10); |
| Assert.assertEquals( 2, u.getMin()[1], 1.0e-10); |
| Assert.assertEquals( 3, u.getMax()[0], 1.0e-10); |
| Assert.assertEquals( 4, u.getMax()[1], 1.0e-10); |
| Assert.assertEquals(2.4849066497880003102, u.getSumLog()[0], 1.0e-10); |
| Assert.assertEquals( 4.276666119016055311, u.getSumLog()[1], 1.0e-10); |
| Assert.assertEquals( 1.8612097182041991979, u.getGeometricMean()[0], 1.0e-10); |
| Assert.assertEquals( 2.9129506302439405217, u.getGeometricMean()[1], 1.0e-10); |
| Assert.assertEquals( 2, u.getMean()[0], 1.0e-10); |
| Assert.assertEquals( 3, u.getMean()[1], 1.0e-10); |
| Assert.assertEquals(FastMath.sqrt(2.0 / 3.0), u.getStandardDeviation()[0], 1.0e-10); |
| Assert.assertEquals(FastMath.sqrt(2.0 / 3.0), u.getStandardDeviation()[1], 1.0e-10); |
| Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 0), 1.0e-10); |
| Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(0, 1), 1.0e-10); |
| Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 0), 1.0e-10); |
| Assert.assertEquals(2.0 / 3.0, u.getCovariance().getEntry(1, 1), 1.0e-10); |
| u.clear(); |
| Assert.assertEquals(0, u.getN()); |
| } |
| |
| @Test |
| public void testN0andN1Conditions() { |
| MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true); |
| Assert.assertTrue(Double.isNaN(u.getMean()[0])); |
| Assert.assertTrue(Double.isNaN(u.getStandardDeviation()[0])); |
| |
| /* n=1 */ |
| u.addValue(new double[] { 1 }); |
| Assert.assertEquals(1.0, u.getMean()[0], 1.0e-10); |
| Assert.assertEquals(1.0, u.getGeometricMean()[0], 1.0e-10); |
| Assert.assertEquals(0.0, u.getStandardDeviation()[0], 1.0e-10); |
| |
| /* n=2 */ |
| u.addValue(new double[] { 2 }); |
| Assert.assertTrue(u.getStandardDeviation()[0] > 0); |
| |
| } |
| |
| @Test |
| public void testNaNContracts() { |
| MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(1, true); |
| Assert.assertTrue(Double.isNaN(u.getMean()[0])); |
| Assert.assertTrue(Double.isNaN(u.getMin()[0])); |
| Assert.assertTrue(Double.isNaN(u.getStandardDeviation()[0])); |
| Assert.assertTrue(Double.isNaN(u.getGeometricMean()[0])); |
| |
| u.addValue(new double[] { 1.0 }); |
| Assert.assertFalse(Double.isNaN(u.getMean()[0])); |
| Assert.assertFalse(Double.isNaN(u.getMin()[0])); |
| Assert.assertFalse(Double.isNaN(u.getStandardDeviation()[0])); |
| Assert.assertFalse(Double.isNaN(u.getGeometricMean()[0])); |
| |
| } |
| |
| @Test |
| public void testSerialization() { |
| MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true); |
| // Empty test |
| TestUtils.checkSerializedEquality(u); |
| MultivariateSummaryStatistics s = (MultivariateSummaryStatistics) TestUtils.serializeAndRecover(u); |
| Assert.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); |
| Assert.assertEquals(u, s); |
| |
| } |
| |
| @Test |
| public void testEqualsAndHashCode() { |
| MultivariateSummaryStatistics u = createMultivariateSummaryStatistics(2, true); |
| MultivariateSummaryStatistics t = null; |
| int emptyHash = u.hashCode(); |
| Assert.assertTrue(u.equals(u)); |
| Assert.assertFalse(u.equals(t)); |
| Assert.assertFalse(u.equals(Double.valueOf(0))); |
| t = createMultivariateSummaryStatistics(2, true); |
| Assert.assertTrue(t.equals(u)); |
| Assert.assertTrue(u.equals(t)); |
| Assert.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 }); |
| Assert.assertFalse(t.equals(u)); |
| Assert.assertFalse(u.equals(t)); |
| Assert.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 }); |
| Assert.assertTrue(t.equals(u)); |
| Assert.assertTrue(u.equals(t)); |
| Assert.assertEquals(u.hashCode(), t.hashCode()); |
| |
| // Clear and make sure summaries are indistinguishable from empty summary |
| u.clear(); |
| t.clear(); |
| Assert.assertTrue(t.equals(u)); |
| Assert.assertTrue(u.equals(t)); |
| Assert.assertEquals(emptyHash, t.hashCode()); |
| Assert.assertEquals(emptyHash, u.hashCode()); |
| } |
| } |
