| /* |
| * 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.distribution; |
| |
| import org.apache.commons.math3.exception.NotStrictlyPositiveException; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * Test cases for {@link LogNormalDistribution}. Extends |
| * {@link RealDistributionAbstractTest}. See class javadoc of that class |
| * for details. |
| * |
| * @since 3.0 |
| */ |
| public class LogNormalDistributionTest extends RealDistributionAbstractTest { |
| |
| //-------------- Implementations for abstract methods ----------------------- |
| |
| /** Creates the default real distribution instance to use in tests. */ |
| @Override |
| public LogNormalDistribution makeDistribution() { |
| return new LogNormalDistribution(2.1, 1.4); |
| } |
| |
| /** Creates the default cumulative probability distribution test input values */ |
| @Override |
| public double[] makeCumulativeTestPoints() { |
| // quantiles computed using R |
| return new double[] { -2.226325228634938, -1.156887023657177, |
| -0.643949578356075, -0.2027950777320613, |
| 0.305827808237559, 6.42632522863494, |
| 5.35688702365718, 4.843949578356074, |
| 4.40279507773206, 3.89417219176244 }; |
| } |
| |
| /** Creates the default cumulative probability density test expected values */ |
| @Override |
| public double[] makeCumulativeTestValues() { |
| return new double[] { 0, 0, 0, 0, 0.00948199951485, 0.432056525076, |
| 0.381648158697, 0.354555726206, 0.329513316888, |
| 0.298422824228 }; |
| } |
| |
| /** Creates the default probability density test expected values */ |
| @Override |
| public double[] makeDensityTestValues() { |
| return new double[] { 0, 0, 0, 0, 0.0594218160072, 0.0436977691036, |
| 0.0508364857798, 0.054873528325, 0.0587182664085, |
| 0.0636229042785 }; |
| } |
| |
| /** |
| * Creates the default inverse cumulative probability distribution test |
| * input values. |
| */ |
| @Override |
| public double[] makeInverseCumulativeTestPoints() { |
| // Exclude the test points less than zero, as they have cumulative |
| // probability of zero, meaning the inverse returns zero, and not the |
| // points less than zero. |
| double[] points = makeCumulativeTestValues(); |
| double[] points2 = new double[points.length - 4]; |
| System.arraycopy(points, 4, points2, 0, points2.length - 4); |
| return points2; |
| //return Arrays.copyOfRange(points, 4, points.length - 4); |
| } |
| |
| /** |
| * Creates the default inverse cumulative probability test expected |
| * values. |
| */ |
| @Override |
| public double[] makeInverseCumulativeTestValues() { |
| // Exclude the test points less than zero, as they have cumulative |
| // probability of zero, meaning the inverse returns zero, and not the |
| // points less than zero. |
| double[] points = makeCumulativeTestPoints(); |
| double[] points2 = new double[points.length - 4]; |
| System.arraycopy(points, 4, points2, 0, points2.length - 4); |
| return points2; |
| //return Arrays.copyOfRange(points, 1, points.length - 4); |
| } |
| |
| // --------------------- Override tolerance -------------- |
| @Override |
| public void setUp() { |
| super.setUp(); |
| setTolerance(LogNormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); |
| } |
| |
| //---------------------------- Additional test cases ------------------------- |
| |
| private void verifyQuantiles() { |
| LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); |
| double mu = distribution.getScale(); |
| double sigma = distribution.getShape(); |
| setCumulativeTestPoints( new double[] { mu - 2 *sigma, mu - sigma, |
| mu, mu + sigma, mu + 2 * sigma, |
| mu + 3 * sigma,mu + 4 * sigma, |
| mu + 5 * sigma }); |
| verifyCumulativeProbabilities(); |
| } |
| |
| @Test |
| public void testQuantiles() { |
| setCumulativeTestValues(new double[] {0, 0.0396495152787, |
| 0.16601209243, 0.272533253269, |
| 0.357618409638, 0.426488363093, |
| 0.483255136841, 0.530823013877}); |
| setDensityTestValues(new double[] {0, 0.0873055825147, 0.0847676303432, |
| 0.0677935186237, 0.0544105523058, |
| 0.0444614628804, 0.0369750288945, |
| 0.0312206409653}); |
| verifyQuantiles(); |
| verifyDensities(); |
| |
| setDistribution(new LogNormalDistribution(0, 1)); |
| setCumulativeTestValues(new double[] {0, 0, 0, 0.5, 0.755891404214, |
| 0.864031392359, 0.917171480998, |
| 0.946239689548}); |
| setDensityTestValues(new double[] {0, 0, 0, 0.398942280401, |
| 0.156874019279, 0.07272825614, |
| 0.0381534565119, 0.0218507148303}); |
| verifyQuantiles(); |
| verifyDensities(); |
| |
| setDistribution(new LogNormalDistribution(0, 0.1)); |
| setCumulativeTestValues(new double[] {0, 0, 0, 1.28417563064e-117, |
| 1.39679883412e-58, |
| 1.09839325447e-33, |
| 2.52587961726e-20, |
| 2.0824223487e-12}); |
| setDensityTestValues(new double[] {0, 0, 0, 2.96247992535e-114, |
| 1.1283370232e-55, 4.43812313223e-31, |
| 5.85346445002e-18, |
| 2.9446618076e-10}); |
| verifyQuantiles(); |
| verifyDensities(); |
| } |
| |
| @Test |
| public void testInverseCumulativeProbabilityExtremes() { |
| setInverseCumulativeTestPoints(new double[] {0, 1}); |
| setInverseCumulativeTestValues( |
| new double[] {0, Double.POSITIVE_INFINITY}); |
| verifyInverseCumulativeProbabilities(); |
| } |
| |
| @Test |
| public void testGetScale() { |
| LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); |
| Assert.assertEquals(2.1, distribution.getScale(), 0); |
| } |
| |
| @Test |
| public void testGetShape() { |
| LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); |
| Assert.assertEquals(1.4, distribution.getShape(), 0); |
| } |
| |
| @Test(expected=NotStrictlyPositiveException.class) |
| public void testPreconditions() { |
| new LogNormalDistribution(1, 0); |
| } |
| |
| @Test |
| public void testDensity() { |
| double [] x = new double[]{-2, -1, 0, 1, 2}; |
| // R 2.13: print(dlnorm(c(-2,-1,0,1,2)), digits=10) |
| checkDensity(0, 1, x, new double[] { 0.0000000000, 0.0000000000, |
| 0.0000000000, 0.3989422804, |
| 0.1568740193 }); |
| // R 2.13: print(dlnorm(c(-2,-1,0,1,2), mean=1.1), digits=10) |
| checkDensity(1.1, 1, x, new double[] { 0.0000000000, 0.0000000000, |
| 0.0000000000, 0.2178521770, |
| 0.1836267118}); |
| } |
| |
| private void checkDensity(double scale, double shape, double[] x, |
| double[] expected) { |
| LogNormalDistribution d = new LogNormalDistribution(scale, shape); |
| for (int i = 0; i < x.length; i++) { |
| Assert.assertEquals(expected[i], d.density(x[i]), 1e-9); |
| } |
| } |
| |
| /** |
| * Check to make sure top-coding of extreme values works correctly. |
| * Verifies fixes for JIRA MATH-167, MATH-414 |
| */ |
| @Test |
| public void testExtremeValues() { |
| LogNormalDistribution d = new LogNormalDistribution(0, 1); |
| for (int i = 0; i < 1e5; i++) { // make sure no convergence exception |
| double upperTail = d.cumulativeProbability(i); |
| if (i <= 72) { // make sure not top-coded |
| Assert.assertTrue(upperTail < 1.0d); |
| } |
| else { // make sure top coding not reversed |
| Assert.assertTrue(upperTail > 0.99999); |
| } |
| } |
| |
| Assert.assertEquals(d.cumulativeProbability(Double.MAX_VALUE), 1, 0); |
| Assert.assertEquals(d.cumulativeProbability(-Double.MAX_VALUE), 0, 0); |
| Assert.assertEquals(d.cumulativeProbability(Double.POSITIVE_INFINITY), 1, 0); |
| Assert.assertEquals(d.cumulativeProbability(Double.NEGATIVE_INFINITY), 0, 0); |
| } |
| |
| @Test |
| public void testMeanVariance() { |
| final double tol = 1e-9; |
| LogNormalDistribution dist; |
| |
| dist = new LogNormalDistribution(0, 1); |
| Assert.assertEquals(dist.getNumericalMean(), 1.6487212707001282, tol); |
| Assert.assertEquals(dist.getNumericalVariance(), |
| 4.670774270471604, tol); |
| |
| dist = new LogNormalDistribution(2.2, 1.4); |
| Assert.assertEquals(dist.getNumericalMean(), 24.046753552064498, tol); |
| Assert.assertEquals(dist.getNumericalVariance(), |
| 3526.913651880464, tol); |
| |
| dist = new LogNormalDistribution(-2000.9, 10.4); |
| Assert.assertEquals(dist.getNumericalMean(), 0.0, tol); |
| Assert.assertEquals(dist.getNumericalVariance(), 0.0, tol); |
| } |
| |
| @Test |
| public void testTinyVariance() { |
| LogNormalDistribution dist = new LogNormalDistribution(0, 1e-9); |
| double t = dist.getNumericalVariance(); |
| Assert.assertEquals(1e-18, t, 1e-20); |
| } |
| |
| } |