http://git-wip-us.apache.org/repos/asf/commons-math/blob/ccba0cfc/src/test/java/org/apache/commons/math4/random/EmpiricalDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/random/EmpiricalDistributionTest.java b/src/test/java/org/apache/commons/math4/random/EmpiricalDistributionTest.java deleted file mode 100644 index c23b477..0000000 --- a/src/test/java/org/apache/commons/math4/random/EmpiricalDistributionTest.java +++ /dev/null @@ -1,561 +0,0 @@ -/* - * 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.random; - -import java.io.BufferedReader; -import java.io.File; -import java.io.IOException; -import java.io.InputStreamReader; -import java.net.URL; -import java.util.ArrayList; -import java.util.Arrays; - -import org.apache.commons.math4.TestUtils; -import org.apache.commons.math4.analysis.UnivariateFunction; -import org.apache.commons.math4.analysis.integration.BaseAbstractUnivariateIntegrator; -import org.apache.commons.math4.analysis.integration.IterativeLegendreGaussIntegrator; -import org.apache.commons.math4.distribution.ConstantRealDistribution; -import org.apache.commons.math4.distribution.NormalDistribution; -import org.apache.commons.math4.distribution.RealDistribution; -import org.apache.commons.math4.distribution.RealDistributionAbstractTest; -import org.apache.commons.math4.distribution.UniformRealDistribution; -import org.apache.commons.math4.exception.MathIllegalStateException; -import org.apache.commons.math4.exception.NullArgumentException; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.apache.commons.math4.rng.RandomSource; -import org.apache.commons.math4.stat.descriptive.SummaryStatistics; -import org.apache.commons.math4.util.FastMath; -import org.junit.Assert; -import org.junit.Before; -import org.junit.Test; - -/** - * Test cases for the EmpiricalDistribution class - */ -public final class EmpiricalDistributionTest extends RealDistributionAbstractTest { - - protected EmpiricalDistribution empiricalDistribution = null; - protected EmpiricalDistribution empiricalDistribution2 = null; - protected File file = null; - protected URL url = null; - protected double[] dataArray = null; - protected final int n = 10000; - - @Override - @Before - public void setUp() { - super.setUp(); - empiricalDistribution = new EmpiricalDistribution(100); - url = getClass().getResource("testData.txt"); - final ArrayList<Double> list = new ArrayList<Double>(); - try { - empiricalDistribution2 = new EmpiricalDistribution(100); - BufferedReader in = - new BufferedReader(new InputStreamReader( - url.openStream())); - String str = null; - while ((str = in.readLine()) != null) { - list.add(Double.valueOf(str)); - } - in.close(); - in = null; - } catch (IOException ex) { - Assert.fail("IOException " + ex); - } - - dataArray = new double[list.size()]; - int i = 0; - for (Double data : list) { - dataArray[i] = data.doubleValue(); - i++; - } - } - - // MATH-1279 - @Test(expected=NotStrictlyPositiveException.class) - public void testPrecondition1() { - new EmpiricalDistribution(0); - } - - /** - * Test EmpiricalDistrbution.load() using sample data file.<br> - * Check that the sampleCount, mu and sigma match data in - * the sample data file. Also verify that load is idempotent. - */ - @Test - public void testLoad() throws Exception { - // Load from a URL - empiricalDistribution.load(url); - checkDistribution(); - - // Load again from a file (also verifies idempotency of load) - File file = new File(url.toURI()); - empiricalDistribution.load(file); - checkDistribution(); - } - - private void checkDistribution() { - // testData File has 10000 values, with mean ~ 5.0, std dev ~ 1 - // Make sure that loaded distribution matches this - Assert.assertEquals(empiricalDistribution.getSampleStats().getN(),1000,10E-7); - //TODO: replace with statistical tests - Assert.assertEquals(empiricalDistribution.getSampleStats().getMean(), - 5.069831575018909,10E-7); - Assert.assertEquals(empiricalDistribution.getSampleStats().getStandardDeviation(), - 1.0173699343977738,10E-7); - } - - /** - * Test EmpiricalDistrbution.load(double[]) using data taken from - * sample data file.<br> - * Check that the sampleCount, mu and sigma match data in - * the sample data file. - */ - @Test - public void testDoubleLoad() throws Exception { - empiricalDistribution2.load(dataArray); - // testData File has 10000 values, with mean ~ 5.0, std dev ~ 1 - // Make sure that loaded distribution matches this - Assert.assertEquals(empiricalDistribution2.getSampleStats().getN(),1000,10E-7); - //TODO: replace with statistical tests - Assert.assertEquals(empiricalDistribution2.getSampleStats().getMean(), - 5.069831575018909,10E-7); - Assert.assertEquals(empiricalDistribution2.getSampleStats().getStandardDeviation(), - 1.0173699343977738,10E-7); - - double[] bounds = empiricalDistribution2.getGeneratorUpperBounds(); - Assert.assertEquals(bounds.length, 100); - Assert.assertEquals(bounds[99], 1.0, 10e-12); - - } - - /** - * Generate 1000 random values and make sure they look OK.<br> - * Note that there is a non-zero (but very small) probability that - * these tests will fail even if the code is working as designed. - */ - @Test - public void testNext() throws Exception { - tstGen(0.1); - tstDoubleGen(0.1); - } - - /** - * Make sure exception thrown if sampling is attempted - * before loading empiricalDistribution. - */ - @Test - public void testNextFail1() { - try { - empiricalDistribution.createSampler(RandomSource.create(RandomSource.JDK)).sample(); - Assert.fail("Expecting MathIllegalStateException"); - } catch (MathIllegalStateException ex) { - // expected - } - } - - /** - * Make sure exception thrown if sampling is attempted - * before loading empiricalDistribution. - */ - @Test - public void testNextFail2() { - try { - empiricalDistribution2.createSampler(RandomSource.create(RandomSource.JDK)).sample(); - Assert.fail("Expecting MathIllegalStateException"); - } catch (MathIllegalStateException ex) { - // expected - } - } - - /** - * Make sure we can handle a grid size that is too fine - */ - @Test - public void testGridTooFine() throws Exception { - empiricalDistribution = new EmpiricalDistribution(1001); - tstGen(0.1); - empiricalDistribution2 = new EmpiricalDistribution(1001); - tstDoubleGen(0.1); - } - - /** - * How about too fat? - */ - @Test - public void testGridTooFat() throws Exception { - empiricalDistribution = new EmpiricalDistribution(1); - tstGen(5); // ridiculous tolerance; but ridiculous grid size - // really just checking to make sure we do not bomb - empiricalDistribution2 = new EmpiricalDistribution(1); - tstDoubleGen(5); - } - - /** - * Test bin index overflow problem (BZ 36450) - */ - @Test - public void testBinIndexOverflow() throws Exception { - double[] x = new double[] {9474.94326071674, 2080107.8865462579}; - new EmpiricalDistribution().load(x); - } - - @Test - public void testSerialization() { - // Empty - EmpiricalDistribution dist = new EmpiricalDistribution(); - EmpiricalDistribution dist2 = (EmpiricalDistribution) TestUtils.serializeAndRecover(dist); - verifySame(dist, dist2); - - // Loaded - empiricalDistribution2.load(dataArray); - dist2 = (EmpiricalDistribution) TestUtils.serializeAndRecover(empiricalDistribution2); - verifySame(empiricalDistribution2, dist2); - } - - @Test(expected=NullArgumentException.class) - public void testLoadNullDoubleArray() { - new EmpiricalDistribution().load((double[]) null); - } - - @Test(expected=NullArgumentException.class) - public void testLoadNullURL() throws Exception { - new EmpiricalDistribution().load((URL) null); - } - - @Test(expected=NullArgumentException.class) - public void testLoadNullFile() throws Exception { - new EmpiricalDistribution().load((File) null); - } - - /** - * MATH-298 - */ - @Test - public void testGetBinUpperBounds() { - double[] testData = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10}; - EmpiricalDistribution dist = new EmpiricalDistribution(5); - dist.load(testData); - double[] expectedBinUpperBounds = {2, 4, 6, 8, 10}; - double[] expectedGeneratorUpperBounds = {4d/13d, 7d/13d, 9d/13d, 11d/13d, 1}; - double tol = 10E-12; - TestUtils.assertEquals(expectedBinUpperBounds, dist.getUpperBounds(), tol); - TestUtils.assertEquals(expectedGeneratorUpperBounds, dist.getGeneratorUpperBounds(), tol); - } - - private void verifySame(EmpiricalDistribution d1, EmpiricalDistribution d2) { - Assert.assertEquals(d1.isLoaded(), d2.isLoaded()); - Assert.assertEquals(d1.getBinCount(), d2.getBinCount()); - Assert.assertEquals(d1.getSampleStats(), d2.getSampleStats()); - if (d1.isLoaded()) { - for (int i = 0; i < d1.getUpperBounds().length; i++) { - Assert.assertEquals(d1.getUpperBounds()[i], d2.getUpperBounds()[i], 0); - } - Assert.assertEquals(d1.getBinStats(), d2.getBinStats()); - } - } - - private void tstGen(double tolerance)throws Exception { - empiricalDistribution.load(url); - RealDistribution.Sampler sampler - = empiricalDistribution.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - SummaryStatistics stats = new SummaryStatistics(); - for (int i = 1; i < 1000; i++) { - stats.addValue(sampler.sample()); - } - Assert.assertEquals("mean", 5.069831575018909, stats.getMean(),tolerance); - Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(),tolerance); - } - - private void tstDoubleGen(double tolerance)throws Exception { - empiricalDistribution2.load(dataArray); - RealDistribution.Sampler sampler - = empiricalDistribution2.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - SummaryStatistics stats = new SummaryStatistics(); - for (int i = 1; i < 1000; i++) { - stats.addValue(sampler.sample()); - } - Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance); - Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(), tolerance); - } - - // Setup for distribution tests - - @Override - public RealDistribution makeDistribution() { - // Create a uniform distribution on [0, 10,000] - final double[] sourceData = new double[n + 1]; - for (int i = 0; i < n + 1; i++) { - sourceData[i] = i; - } - EmpiricalDistribution dist = new EmpiricalDistribution(); - dist.load(sourceData); - return dist; - } - - /** Uniform bin mass = 10/10001 == mass of all but the first bin */ - private final double binMass = 10d / (n + 1); - - /** Mass of first bin = 11/10001 */ - private final double firstBinMass = 11d / (n + 1); - - @Override - public double[] makeCumulativeTestPoints() { - final double[] testPoints = new double[] {9, 10, 15, 1000, 5004, 9999}; - return testPoints; - } - - - @Override - public double[] makeCumulativeTestValues() { - /* - * Bins should be [0, 10], (10, 20], ..., (9990, 10000] - * Kernels should be N(4.5, 3.02765), N(14.5, 3.02765)... - * Each bin should have mass 10/10000 = .001 - */ - final double[] testPoints = getCumulativeTestPoints(); - final double[] cumValues = new double[testPoints.length]; - final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution(); - final double[] binBounds = empiricalDistribution.getUpperBounds(); - for (int i = 0; i < testPoints.length; i++) { - final int bin = findBin(testPoints[i]); - final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() : - binBounds[bin - 1]; - final double upper = binBounds[bin]; - // Compute bMinus = sum or mass of bins below the bin containing the point - // First bin has mass 11 / 10000, the rest have mass 10 / 10000. - final double bMinus = bin == 0 ? 0 : (bin - 1) * binMass + firstBinMass; - final RealDistribution kernel = findKernel(lower, upper); - final double withinBinKernelMass = kernel.probability(lower, upper); - final double kernelCum = kernel.probability(lower, testPoints[i]); - cumValues[i] = bMinus + (bin == 0 ? firstBinMass : binMass) * kernelCum/withinBinKernelMass; - } - return cumValues; - } - - @Override - public double[] makeDensityTestValues() { - final double[] testPoints = getCumulativeTestPoints(); - final double[] densityValues = new double[testPoints.length]; - final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution(); - final double[] binBounds = empiricalDistribution.getUpperBounds(); - for (int i = 0; i < testPoints.length; i++) { - final int bin = findBin(testPoints[i]); - final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() : - binBounds[bin - 1]; - final double upper = binBounds[bin]; - final RealDistribution kernel = findKernel(lower, upper); - final double withinBinKernelMass = kernel.probability(lower, upper); - final double density = kernel.density(testPoints[i]); - densityValues[i] = density * (bin == 0 ? firstBinMass : binMass) / withinBinKernelMass; - } - return densityValues; - } - - /** - * Modify test integration bounds from the default. Because the distribution - * has discontinuities at bin boundaries, integrals spanning multiple bins - * will face convergence problems. Only test within-bin integrals and spans - * across no more than 3 bin boundaries. - */ - @Override - @Test - public void testDensityIntegrals() { - final RealDistribution distribution = makeDistribution(); - final double tol = 1.0e-9; - final BaseAbstractUnivariateIntegrator integrator = - new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10); - final UnivariateFunction d = new UnivariateFunction() { - @Override - public double value(double x) { - return distribution.density(x); - } - }; - final double[] lower = {0, 5, 1000, 5001, 9995}; - final double[] upper = {5, 12, 1030, 5010, 10000}; - for (int i = 1; i < 5; i++) { - Assert.assertEquals( - distribution.probability( - lower[i], upper[i]), - integrator.integrate( - 1000000, // Triangle integrals are very slow to converge - d, lower[i], upper[i]), tol); - } - } - - /** - * MATH-984 - * Verify that sampled values do not go outside of the range of the data. - */ - @Test - public void testSampleValuesRange() { - // Concentrate values near the endpoints of (0, 1). - // Unconstrained Gaussian kernel would generate values outside the interval. - final double[] data = new double[100]; - for (int i = 0; i < 50; i++) { - data[i] = 1 / ((double) i + 1); - } - for (int i = 51; i < 100; i++) { - data[i] = 1 - 1 / (100 - (double) i + 2); - } - EmpiricalDistribution dist = new EmpiricalDistribution(10); - dist.load(data); - RealDistribution.Sampler sampler - = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - for (int i = 0; i < 1000; i++) { - final double dev = sampler.sample(); - Assert.assertTrue(dev < 1); - Assert.assertTrue(dev > 0); - } - } - - /** - * MATH-1203, MATH-1208 - */ - @Test - public void testNoBinVariance() { - final double[] data = {0, 0, 1, 1}; - EmpiricalDistribution dist = new EmpiricalDistribution(2); - dist.load(data); - RealDistribution.Sampler sampler - = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - for (int i = 0; i < 1000; i++) { - final double dev = sampler.sample(); - Assert.assertTrue(dev == 0 || dev == 1); - } - Assert.assertEquals(0.5, dist.cumulativeProbability(0), Double.MIN_VALUE); - Assert.assertEquals(1.0, dist.cumulativeProbability(1), Double.MIN_VALUE); - Assert.assertEquals(0.5, dist.cumulativeProbability(0.5), Double.MIN_VALUE); - Assert.assertEquals(0.5, dist.cumulativeProbability(0.7), Double.MIN_VALUE); - } - - /** - * Find the bin that x belongs (relative to {@link #makeDistribution()}). - */ - private int findBin(double x) { - // Number of bins below x should be trunc(x/10) - final double nMinus = FastMath.floor(x / 10); - final int bin = (int) FastMath.round(nMinus); - // If x falls on a bin boundary, it is in the lower bin - return FastMath.floor(x / 10) == x / 10 ? bin - 1 : bin; - } - - /** - * Find the within-bin kernel for the bin with lower bound lower - * and upper bound upper. All bins other than the first contain 10 points - * exclusive of the lower bound and are centered at (lower + upper + 1) / 2. - * The first bin includes its lower bound, 0, so has different mean and - * standard deviation. - */ - private RealDistribution findKernel(double lower, double upper) { - if (lower < 1) { - return new NormalDistribution(5d, 3.3166247903554); - } else { - return new NormalDistribution((upper + lower + 1) / 2d, 3.0276503540974917); - } - } - - @Test - public void testKernelOverrideConstant() { - final EmpiricalDistribution dist = new ConstantKernelEmpiricalDistribution(5); - final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d}; - dist.load(data); - RealDistribution.Sampler sampler - = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - // Bin masses concentrated on 2, 5, 8, 11, 14 <- effectively discrete uniform distribution over these - double[] values = {2d, 5d, 8d, 11d, 14d}; - for (int i = 0; i < 20; i++) { - Assert.assertTrue(Arrays.binarySearch(values, sampler.sample()) >= 0); - } - final double tol = 10E-12; - Assert.assertEquals(0.0, dist.cumulativeProbability(1), tol); - Assert.assertEquals(0.2, dist.cumulativeProbability(2), tol); - Assert.assertEquals(0.6, dist.cumulativeProbability(10), tol); - Assert.assertEquals(0.8, dist.cumulativeProbability(12), tol); - Assert.assertEquals(0.8, dist.cumulativeProbability(13), tol); - Assert.assertEquals(1.0, dist.cumulativeProbability(15), tol); - - Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.1), tol); - Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.2), tol); - Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.3), tol); - Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.4), tol); - Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.5), tol); - Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.6), tol); - } - - @Test - public void testKernelOverrideUniform() { - final EmpiricalDistribution dist = new UniformKernelEmpiricalDistribution(5); - final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d}; - dist.load(data); - RealDistribution.Sampler sampler - = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); - // Kernels are uniform distributions on [1,3], [4,6], [7,9], [10,12], [13,15] - final double bounds[] = {3d, 6d, 9d, 12d}; - final double tol = 10E-12; - for (int i = 0; i < 20; i++) { - final double v = sampler.sample(); - // Make sure v is not in the excluded range between bins - that is (bounds[i], bounds[i] + 1) - for (int j = 0; j < bounds.length; j++) { - Assert.assertFalse(v > bounds[j] + tol && v < bounds[j] + 1 - tol); - } - } - Assert.assertEquals(0.0, dist.cumulativeProbability(1), tol); - Assert.assertEquals(0.1, dist.cumulativeProbability(2), tol); - Assert.assertEquals(0.6, dist.cumulativeProbability(10), tol); - Assert.assertEquals(0.8, dist.cumulativeProbability(12), tol); - Assert.assertEquals(0.8, dist.cumulativeProbability(13), tol); - Assert.assertEquals(1.0, dist.cumulativeProbability(15), tol); - - Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.1), tol); - Assert.assertEquals(3.0, dist.inverseCumulativeProbability(0.2), tol); - Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.3), tol); - Assert.assertEquals(6.0, dist.inverseCumulativeProbability(0.4), tol); - Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.5), tol); - Assert.assertEquals(9.0, dist.inverseCumulativeProbability(0.6), tol); - } - - - /** - * Empirical distribution using a constant smoothing kernel. - */ - private class ConstantKernelEmpiricalDistribution extends EmpiricalDistribution { - private static final long serialVersionUID = 1L; - public ConstantKernelEmpiricalDistribution(int i) { - super(i); - } - // Use constant distribution equal to bin mean within bin - @Override - protected RealDistribution getKernel(SummaryStatistics bStats) { - return new ConstantRealDistribution(bStats.getMean()); - } - } - - /** - * Empirical distribution using a uniform smoothing kernel. - */ - private class UniformKernelEmpiricalDistribution extends EmpiricalDistribution { - private static final long serialVersionUID = 2963149194515159653L; - public UniformKernelEmpiricalDistribution(int i) { - super(i); - } - @Override - protected RealDistribution getKernel(SummaryStatistics bStats) { - return new UniformRealDistribution(bStats.getMin(), bStats.getMax()); - } - } -}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/ccba0cfc/src/test/resources/org/apache/commons/math4/distribution/testData.txt ---------------------------------------------------------------------- diff --git a/src/test/resources/org/apache/commons/math4/distribution/testData.txt b/src/test/resources/org/apache/commons/math4/distribution/testData.txt new file mode 100644 index 0000000..4a10132 --- /dev/null +++ b/src/test/resources/org/apache/commons/math4/distribution/testData.txt @@ -0,0 +1,1000 @@ +4.038625496201205 +3.6485326248346936 +3.6651209675932845 +5.814896279561131 +5.384126469824717 +5.251190723365563 +4.465213440111648 +4.736608014129308 +5.566383814840726 +3.8872277480629114 +5.246598498086048 +3.7511487364188176 +6.733371385175343 +5.388632419618035 +6.036263402962769 +3.8105605069222905 +5.738599503606028 +4.994552792425298 +2.945504336988336 +4.095314381239862 +5.760924710543879 +3.889753944419315 +3.808861160991701 +5.084302950012555 +6.370292933705698 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