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commit 5fee542f820e233c59b796f01b737ae6a46964d7
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Sun Jul 11 02:44:10 2021 +0200
MATH-1615: Functionality is in "Commons RNG" (cf. RNG-137).
---
.../math4/legacy/random/StableRandomGenerator.java | 133 ---------------------
.../legacy/random/StableRandomGeneratorTest.java | 130 --------------------
2 files changed, 263 deletions(-)
diff --git
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/StableRandomGenerator.java
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/StableRandomGenerator.java
deleted file mode 100644
index cfb6b4f..0000000
---
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/StableRandomGenerator.java
+++ /dev/null
@@ -1,133 +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.legacy.random;
-
-import org.apache.commons.rng.UniformRandomProvider;
-import org.apache.commons.math4.legacy.exception.NullArgumentException;
-import org.apache.commons.math4.legacy.exception.OutOfRangeException;
-import org.apache.commons.math4.legacy.exception.util.LocalizedFormats;
-import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
-
-/**
- * <p>This class provides a stable normalized random generator. It samples
from a stable
- * distribution with location parameter 0 and scale 1.</p>
- *
- * <p>The implementation uses the Chambers-Mallows-Stuck method as described in
- * <i>Handbook of computational statistics: concepts and methods</i> by
- * James E. Gentle, Wolfgang Härdle, Yuichi Mori.</p>
- *
- * @since 3.0
- */
-public class StableRandomGenerator implements NormalizedRandomGenerator {
- /** Underlying generator. */
- private final UniformRandomProvider generator;
- /** stability parameter. */
- private final double alpha;
- /** skewness parameter. */
- private final double beta;
- /** cache of expression value used in generation. */
- private final double zeta;
-
- /**
- * Create a new generator.
- *
- * @param generator Underlying random generator
- * @param alpha Stability parameter. Must be in range (0, 2]
- * @param beta Skewness parameter. Must be in range [-1, 1]
- * @throws NullArgumentException if generator is null
- * @throws OutOfRangeException if {@code alpha <= 0} or {@code alpha > 2}
- * or {@code beta < -1} or {@code beta > 1}
- */
- public StableRandomGenerator(final UniformRandomProvider generator,
- final double alpha, final double beta)
- throws NullArgumentException, OutOfRangeException {
- if (generator == null) {
- throw new NullArgumentException();
- }
-
- if (!(alpha > 0d && alpha <= 2d)) {
- throw new OutOfRangeException(LocalizedFormats.OUT_OF_RANGE_LEFT,
- alpha, 0, 2);
- }
-
- if (!(beta >= -1d && beta <= 1d)) {
- throw new OutOfRangeException(LocalizedFormats.OUT_OF_RANGE_SIMPLE,
- beta, -1, 1);
- }
-
- this.generator = generator;
- this.alpha = alpha;
- this.beta = beta;
- if (alpha < 2d && beta != 0d) {
- zeta = beta * AccurateMath.tan(AccurateMath.PI * alpha / 2);
- } else {
- zeta = 0d;
- }
- }
-
- /**
- * Generate a random scalar with zero location and unit scale.
- *
- * @return a random scalar with zero location and unit scale
- */
- @Override
- public double nextNormalizedDouble() {
- // we need 2 uniform random numbers to calculate omega and phi
- double omega = -AccurateMath.log(generator.nextDouble());
- double phi = AccurateMath.PI * (generator.nextDouble() - 0.5);
-
- // Normal distribution case (Box-Muller algorithm)
- if (alpha == 2d) {
- return AccurateMath.sqrt(2d * omega) * AccurateMath.sin(phi);
- }
-
- double x;
- // when beta = 0, zeta is zero as well
- // Thus we can exclude it from the formula
- if (beta == 0d) {
- // Cauchy distribution case
- if (alpha == 1d) {
- x = AccurateMath.tan(phi);
- } else {
- x = AccurateMath.pow(omega * AccurateMath.cos((1 - alpha) *
phi),
- 1d / alpha - 1d) *
- AccurateMath.sin(alpha * phi) /
- AccurateMath.pow(AccurateMath.cos(phi), 1d / alpha);
- }
- } else {
- // Generic stable distribution
- double cosPhi = AccurateMath.cos(phi);
- // to avoid rounding errors around alpha = 1
- if (AccurateMath.abs(alpha - 1d) > 1e-8) {
- double alphaPhi = alpha * phi;
- double invAlphaPhi = phi - alphaPhi;
- x = (AccurateMath.sin(alphaPhi) + zeta *
AccurateMath.cos(alphaPhi)) / cosPhi *
- (AccurateMath.cos(invAlphaPhi) + zeta *
AccurateMath.sin(invAlphaPhi)) /
- AccurateMath.pow(omega * cosPhi, (1 - alpha) / alpha);
- } else {
- double betaPhi = AccurateMath.PI / 2 + beta * phi;
- x = 2d / AccurateMath.PI * (betaPhi * AccurateMath.tan(phi) -
beta *
- AccurateMath.log(AccurateMath.PI / 2d * omega * cosPhi /
betaPhi));
-
- if (alpha != 1d) {
- x += beta * AccurateMath.tan(AccurateMath.PI * alpha / 2);
- }
- }
- }
- return x;
- }
-}
diff --git
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/StableRandomGeneratorTest.java
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/StableRandomGeneratorTest.java
deleted file mode 100644
index 4f987eb..0000000
---
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/StableRandomGeneratorTest.java
+++ /dev/null
@@ -1,130 +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.legacy.random;
-
-import org.apache.commons.math4.legacy.exception.OutOfRangeException;
-import org.apache.commons.rng.UniformRandomProvider;
-import org.apache.commons.rng.simple.RandomSource;
-import org.apache.commons.math4.legacy.stat.StatUtils;
-import org.apache.commons.math4.legacy.stat.descriptive.DescriptiveStatistics;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * Tests for the class {@link StableRandomGenerator}.
- */
-public class StableRandomGeneratorTest {
- private final UniformRandomProvider rg =
RandomSource.create(RandomSource.WELL_19937_C, 100);
- private static final int sampleSize = 10000;
-
- /**
- * Run the double nextDouble() method test Due to leptokurtic property the
- * acceptance range is widened.
- *
- * TODO: verify that tolerance this wide is really OK
- */
- @Test
- public void testNextDouble() {
- StableRandomGenerator generator = new StableRandomGenerator(rg, 1.3,
- 0.1);
- double[] sample = new double[2 * sampleSize];
- for (int i = 0; i < sample.length; ++i) {
- sample[i] = generator.nextNormalizedDouble();
- }
- Assert.assertEquals(0.0, StatUtils.mean(sample), 0.3);
- }
-
- /**
- * If alpha = 2, than it must be Gaussian distribution
- */
- @Test
- public void testGaussianCase() {
- StableRandomGenerator generator = new StableRandomGenerator(rg, 2d,
0.0);
-
- double[] sample = new double[sampleSize];
- for (int i = 0; i < sample.length; ++i) {
- sample[i] = generator.nextNormalizedDouble();
- }
- Assert.assertEquals(0.0, StatUtils.mean(sample), 0.02);
- Assert.assertEquals(1.0, StatUtils.variance(sample), 0.02);
- }
-
- /**
- * If alpha = 1, than it must be Cauchy distribution
- */
- @Test
- public void testCauchyCase() {
- StableRandomGenerator generator = new StableRandomGenerator(rg, 1d,
0.0);
- DescriptiveStatistics summary = new DescriptiveStatistics();
-
- for (int i = 0; i < sampleSize; ++i) {
- double sample = generator.nextNormalizedDouble();
- summary.addValue(sample);
- }
-
- // Standard Cauchy distribution should have zero median and mode
- double median = summary.getPercentile(50);
- Assert.assertEquals(0.0, median, 0.2);
- }
-
- /**
- * Input parameter range tests
- */
- @Test
- public void testAlphaRangeBelowZero() {
- try {
- new StableRandomGenerator(rg,
- -1.0, 0.0);
- Assert.fail("Expected OutOfRangeException");
- } catch (OutOfRangeException e) {
- Assert.assertEquals(-1.0, e.getArgument());
- }
- }
-
- @Test
- public void testAlphaRangeAboveTwo() {
- try {
- new StableRandomGenerator(rg,
- 3.0, 0.0);
- Assert.fail("Expected OutOfRangeException");
- } catch (OutOfRangeException e) {
- Assert.assertEquals(3.0, e.getArgument());
- }
- }
-
- @Test
- public void testBetaRangeBelowMinusOne() {
- try {
- new StableRandomGenerator(rg,
- 1.0, -2.0);
- Assert.fail("Expected OutOfRangeException");
- } catch (OutOfRangeException e) {
- Assert.assertEquals(-2.0, e.getArgument());
- }
- }
-
- @Test
- public void testBetaRangeAboveOne() {
- try {
- new StableRandomGenerator(rg,
- 1.0, 2.0);
- Assert.fail("Expected OutOfRangeException");
- } catch (OutOfRangeException e) {
- Assert.assertEquals(2.0, e.getArgument());
- }
- }
-}
