/*
* 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
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package org.apache.commons.math3.distribution;
import java.util.ArrayList;
import java.util.Collections;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator;
import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
/**
* Abstract base class for {@link RealDistribution} tests.
* <p>
* To create a concrete test class for a continuous distribution
* implementation, first implement makeDistribution() to return a distribution
* instance to use in tests. Then implement each of the test data generation
* methods below. In each case, the test points and test values arrays
* returned represent parallel arrays of inputs and expected values for the
* distribution returned by makeDistribution(). Default implementations
* are provided for the makeInverseXxx methods that just invert the mapping
* defined by the arrays returned by the makeCumulativeXxx methods.
* <p>
* makeCumulativeTestPoints() -- arguments used to test cumulative probabilities
* makeCumulativeTestValues() -- expected cumulative probabilites
* makeDensityTestValues() -- expected density values at cumulativeTestPoints
* makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf
* makeInverseCumulativeTestValues() -- expected inverse cdf values
* <p>
* To implement additional test cases with different distribution instances and
* test data, use the setXxx methods for the instance data in test cases and
* call the verifyXxx methods to verify results.
* <p>
* Error tolerance can be overriden by implementing getTolerance().
* <p>
* Test data should be validated against reference tables or other packages
* where possible, and the source of the reference data and/or validation
* should be documented in the test cases. A framework for validating
* distribution data against R is included in the /src/test/R source tree.
* <p>
* See {@link NormalDistributionTest} and {@link ChiSquaredDistributionTest}
* for examples.
*
*/
public abstract class RealDistributionAbstractTest {
//-------------------- Private test instance data -------------------------
/** Distribution instance used to perform tests */
private RealDistribution distribution;
/** Tolerance used in comparing expected and returned values */
private double tolerance = 1E-4;
/** Arguments used to test cumulative probability density calculations */
private double[] cumulativeTestPoints;
/** Values used to test cumulative probability density calculations */
private double[] cumulativeTestValues;
/** Arguments used to test inverse cumulative probability density calculations */
private double[] inverseCumulativeTestPoints;
/** Values used to test inverse cumulative probability density calculations */
private double[] inverseCumulativeTestValues;
/** Values used to test density calculations */
private double[] densityTestValues;
/** Values used to test logarithmic density calculations */
private double[] logDensityTestValues;
//-------------------- Abstract methods -----------------------------------
/** Creates the default continuous distribution instance to use in tests. */
public abstract RealDistribution makeDistribution();
/** Creates the default cumulative probability test input values */
public abstract double[] makeCumulativeTestPoints();
/** Creates the default cumulative probability test expected values */
public abstract double[] makeCumulativeTestValues();
/** Creates the default density test expected values */
public abstract double[] makeDensityTestValues();
/** Creates the default logarithmic density test expected values.
* The default implementation simply computes the logarithm
* of each value returned by {@link #makeDensityTestValues()}.*/
public double[] makeLogDensityTestValues() {
final double[] densityTestValues = makeDensityTestValues();
final double[] logDensityTestValues = new double[densityTestValues.length];
for (int i = 0; i < densityTestValues.length; i++) {
logDensityTestValues[i] = FastMath.log(densityTestValues[i]);
}
return logDensityTestValues;
}
//---- Default implementations of inverse test data generation methods ----
/** Creates the default inverse cumulative probability test input values */
public double[] makeInverseCumulativeTestPoints() {
return makeCumulativeTestValues();
}
/** Creates the default inverse cumulative probability density test expected values */
public double[] makeInverseCumulativeTestValues() {
return makeCumulativeTestPoints();
}
//-------------------- Setup / tear down ----------------------------------
/**
* Setup sets all test instance data to default values
*/
@Before
public void setUp() {
distribution = makeDistribution();
cumulativeTestPoints = makeCumulativeTestPoints();
cumulativeTestValues = makeCumulativeTestValues();
inverseCumulativeTestPoints = makeInverseCumulativeTestPoints();
inverseCumulativeTestValues = makeInverseCumulativeTestValues();
densityTestValues = makeDensityTestValues();
logDensityTestValues = makeLogDensityTestValues();
}
/**
* Cleans up test instance data
*/
@After
public void tearDown() {
distribution = null;
cumulativeTestPoints = null;
cumulativeTestValues = null;
inverseCumulativeTestPoints = null;
inverseCumulativeTestValues = null;
densityTestValues = null;
logDensityTestValues = null;
}
//-------------------- Verification methods -------------------------------
/**
* Verifies that cumulative probability density calculations match expected values
* using current test instance data
*/
protected void verifyCumulativeProbabilities() {
// verify cumulativeProbability(double)
for (int i = 0; i < cumulativeTestPoints.length; i++) {
TestUtils.assertEquals("Incorrect cumulative probability value returned for "
+ cumulativeTestPoints[i], cumulativeTestValues[i],
distribution.cumulativeProbability(cumulativeTestPoints[i]),
getTolerance());
}
// verify cumulativeProbability(double, double)
// XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
for (int i = 0; i < cumulativeTestPoints.length; i++) {
for (int j = 0; j < cumulativeTestPoints.length; j++) {
if (cumulativeTestPoints[i] <= cumulativeTestPoints[j]) {
TestUtils.assertEquals(cumulativeTestValues[j] - cumulativeTestValues[i],
distribution.cumulativeProbability(cumulativeTestPoints[i], cumulativeTestPoints[j]),
getTolerance());
} else {
try {
distribution.cumulativeProbability(cumulativeTestPoints[i], cumulativeTestPoints[j]);
} catch (NumberIsTooLargeException e) {
continue;
}
Assert.fail("distribution.cumulativeProbability(double, double) should have thrown an exception that second argument is too large");
}
}
}
}
/**
* Verifies that inverse cumulative probability density calculations match expected values
* using current test instance data
*/
protected void verifyInverseCumulativeProbabilities() {
for (int i = 0; i < inverseCumulativeTestPoints.length; i++) {
TestUtils.assertEquals("Incorrect inverse cumulative probability value returned for "
+ inverseCumulativeTestPoints[i], inverseCumulativeTestValues[i],
distribution.inverseCumulativeProbability(inverseCumulativeTestPoints[i]),
getTolerance());
}
}
/**
* Verifies that density calculations match expected values
*/
protected void verifyDensities() {
for (int i = 0; i < cumulativeTestPoints.length; i++) {
TestUtils.assertEquals("Incorrect probability density value returned for "
+ cumulativeTestPoints[i], densityTestValues[i],
distribution.density(cumulativeTestPoints[i]),
getTolerance());
}
}
/**
* Verifies that logarithmic density calculations match expected values
*/
protected void verifyLogDensities() {
// FIXME: when logProbability methods are added to RealDistribution in 4.0, remove cast below
for (int i = 0; i < cumulativeTestPoints.length; i++) {
TestUtils.assertEquals("Incorrect probability density value returned for "
+ cumulativeTestPoints[i], logDensityTestValues[i],
((AbstractRealDistribution) distribution).logDensity(cumulativeTestPoints[i]),
getTolerance());
}
}
//------------------------ Default test cases -----------------------------
/**
* Verifies that cumulative probability density calculations match expected values
* using default test instance data
*/
@Test
public void testCumulativeProbabilities() {
verifyCumulativeProbabilities();
}
/**
* Verifies that inverse cumulative probability density calculations match expected values
* using default test instance data
*/
@Test
public void testInverseCumulativeProbabilities() {
verifyInverseCumulativeProbabilities();
}
/**
* Verifies that density calculations return expected values
* for default test instance data
*/
@Test
public void testDensities() {
verifyDensities();
}
/**
* Verifies that logarithmic density calculations return expected values
* for default test instance data
*/
@Test
public void testLogDensities() {
verifyLogDensities();
}
/**
* Verifies that probability computations are consistent
*/
@Test
public void testConsistency() {
for (int i=1; i < cumulativeTestPoints.length; i++) {
// check that cdf(x, x) = 0
// XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
TestUtils.assertEquals(0d,
distribution.cumulativeProbability
(cumulativeTestPoints[i], cumulativeTestPoints[i]), tolerance);
// check that P(a < X <= b) = P(X <= b) - P(X <= a)
double upper = FastMath.max(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
double lower = FastMath.min(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
double diff = distribution.cumulativeProbability(upper) -
distribution.cumulativeProbability(lower);
// XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
double direct = distribution.cumulativeProbability(lower, upper);
TestUtils.assertEquals("Inconsistent cumulative probabilities for ("
+ lower + "," + upper + ")", diff, direct, tolerance);
}
}
/**
* Verifies that illegal arguments are correctly handled
*/
@Test
public void testIllegalArguments() {
try {
// XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
distribution.cumulativeProbability(1, 0);
Assert.fail("Expecting MathIllegalArgumentException for bad cumulativeProbability interval");
} catch (MathIllegalArgumentException ex) {
// expected
}
try {
distribution.inverseCumulativeProbability(-1);
Assert.fail("Expecting MathIllegalArgumentException for p = -1");
} catch (MathIllegalArgumentException ex) {
// expected
}
try {
distribution.inverseCumulativeProbability(2);
Assert.fail("Expecting MathIllegalArgumentException for p = 2");
} catch (MathIllegalArgumentException ex) {
// expected
}
}
/**
* Test sampling
*/
@Test
public void testSampling() {
final int sampleSize = 1000;
distribution.reseedRandomGenerator(1000); // Use fixed seed
double[] sample = distribution.sample(sampleSize);
double[] quartiles = TestUtils.getDistributionQuartiles(distribution);
double[] expected = {250, 250, 250, 250};
long[] counts = new long[4];
for (int i = 0; i < sampleSize; i++) {
TestUtils.updateCounts(sample[i], counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
}
/**
* Verify that density integrals match the distribution.
* The (filtered, sorted) cumulativeTestPoints array is used to source
* integration limits. The integral of the density (estimated using a
* Legendre-Gauss integrator) is compared with the cdf over the same
* interval. Test points outside of the domain of the density function
* are discarded.
*/
@Test
public void testDensityIntegrals() {
final double tol = 1.0e-9;
final BaseAbstractUnivariateIntegrator integrator =
new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
final UnivariateFunction d = new UnivariateFunction() {
public double value(double x) {
return distribution.density(x);
}
};
final ArrayList<Double> integrationTestPoints = new ArrayList<Double>();
for (int i = 0; i < cumulativeTestPoints.length; i++) {
if (Double.isNaN(cumulativeTestValues[i]) ||
cumulativeTestValues[i] < 1.0e-5 ||
cumulativeTestValues[i] > 1 - 1.0e-5) {
continue; // exclude integrals outside domain.
}
integrationTestPoints.add(cumulativeTestPoints[i]);
}
Collections.sort(integrationTestPoints);
for (int i = 1; i < integrationTestPoints.size(); i++) {
Assert.assertEquals(
distribution.cumulativeProbability( // FIXME @4.0 when rename happens
integrationTestPoints.get(0), integrationTestPoints.get(i)),
integrator.integrate(
1000000, // Triangle integrals are very slow to converge
d, integrationTestPoints.get(0),
integrationTestPoints.get(i)), tol);
}
}
/**
* Verify that isSupportLowerBoundInclusvie returns true iff the lower bound
* is finite and density is non-NaN, non-infinite there.
*/
@Test
public void testIsSupportLowerBoundInclusive() {
final double lowerBound = distribution.getSupportLowerBound();
double result = Double.NaN;
result = distribution.density(lowerBound);
Assert.assertEquals(
!Double.isInfinite(lowerBound) && !Double.isNaN(result) &&
!Double.isInfinite(result),
distribution.isSupportLowerBoundInclusive());
}
/**
* Verify that isSupportUpperBoundInclusvie returns true iff the upper bound
* is finite and density is non-NaN, non-infinite there.
*/
@Test
public void testIsSupportUpperBoundInclusive() {
final double upperBound = distribution.getSupportUpperBound();
double result = Double.NaN;
result = distribution.density(upperBound);
Assert.assertEquals(
!Double.isInfinite(upperBound) && !Double.isNaN(result) &&
!Double.isInfinite(result),
distribution.isSupportUpperBoundInclusive());
}
@Test
public void testDistributionClone()
throws IOException,
ClassNotFoundException {
// Construct a distribution and initialize its internal random
// generator, using a fixed seed for deterministic results.
distribution.reseedRandomGenerator(123);
distribution.sample();
// Clone the distribution.
final RealDistribution cloned = deepClone();
// Make sure they still produce the same samples.
final double s1 = distribution.sample();
final double s2 = cloned.sample();
Assert.assertEquals(s1, s2, 0d);
}
//------------------ Getters / Setters for test instance data -----------
/**
* @return Returns the cumulativeTestPoints.
*/
protected double[] getCumulativeTestPoints() {
return cumulativeTestPoints;
}
/**
* @param cumulativeTestPoints The cumulativeTestPoints to set.
*/
protected void setCumulativeTestPoints(double[] cumulativeTestPoints) {
this.cumulativeTestPoints = cumulativeTestPoints;
}
/**
* @return Returns the cumulativeTestValues.
*/
protected double[] getCumulativeTestValues() {
return cumulativeTestValues;
}
/**
* @param cumulativeTestValues The cumulativeTestValues to set.
*/
protected void setCumulativeTestValues(double[] cumulativeTestValues) {
this.cumulativeTestValues = cumulativeTestValues;
}
protected double[] getDensityTestValues() {
return densityTestValues;
}
protected void setDensityTestValues(double[] densityTestValues) {
this.densityTestValues = densityTestValues;
}
/**
* @return Returns the distribution.
*/
protected RealDistribution getDistribution() {
return distribution;
}
/**
* @param distribution The distribution to set.
*/
protected void setDistribution(RealDistribution distribution) {
this.distribution = distribution;
}
/**
* @return Returns the inverseCumulativeTestPoints.
*/
protected double[] getInverseCumulativeTestPoints() {
return inverseCumulativeTestPoints;
}
/**
* @param inverseCumulativeTestPoints The inverseCumulativeTestPoints to set.
*/
protected void setInverseCumulativeTestPoints(double[] inverseCumulativeTestPoints) {
this.inverseCumulativeTestPoints = inverseCumulativeTestPoints;
}
/**
* @return Returns the inverseCumulativeTestValues.
*/
protected double[] getInverseCumulativeTestValues() {
return inverseCumulativeTestValues;
}
/**
* @param inverseCumulativeTestValues The inverseCumulativeTestValues to set.
*/
protected void setInverseCumulativeTestValues(double[] inverseCumulativeTestValues) {
this.inverseCumulativeTestValues = inverseCumulativeTestValues;
}
/**
* @return Returns the tolerance.
*/
protected double getTolerance() {
return tolerance;
}
/**
* @param tolerance The tolerance to set.
*/
protected void setTolerance(double tolerance) {
this.tolerance = tolerance;
}
/**
* Serialization and deserialization loop of the {@link #distribution}.
*/
private RealDistribution deepClone()
throws IOException,
ClassNotFoundException {
// Serialize to byte array.
final ByteArrayOutputStream bOut = new ByteArrayOutputStream();
final ObjectOutputStream oOut = new ObjectOutputStream(bOut);
oOut.writeObject(distribution);
final byte[] data = bOut.toByteArray();
// Deserialize from byte array.
final ByteArrayInputStream bIn = new ByteArrayInputStream(data);
final ObjectInputStream oIn = new ObjectInputStream(bIn);
final Object clone = oIn.readObject();
oIn.close();
return (RealDistribution) clone;
}
}