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Posted to commits@commons.apache.org by er...@apache.org on 2018/01/21 14:05:36 UTC
[01/16] commons-statistics git commit: New project: use Java 8.
Repository: commons-statistics
Updated Branches:
refs/heads/master bb864a05b -> 30d7c8f6b
New project: use Java 8.
Initial versions will probably be "beta" (0.x).
Project: http://git-wip-us.apache.org/repos/asf/commons-statistics/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-statistics/commit/585178f8
Tree: http://git-wip-us.apache.org/repos/asf/commons-statistics/tree/585178f8
Diff: http://git-wip-us.apache.org/repos/asf/commons-statistics/diff/585178f8
Branch: refs/heads/master
Commit: 585178f823ac8f0df41a0f727a8301b21084c3f1
Parents: bb864a0
Author: Gilles Sadowski <gi...@harfang.homelinux.org>
Authored: Fri Jan 19 17:10:38 2018 +0100
Committer: Gilles Sadowski <gi...@harfang.homelinux.org>
Committed: Fri Jan 19 17:10:38 2018 +0100
----------------------------------------------------------------------
pom.xml | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/585178f8/pom.xml
----------------------------------------------------------------------
diff --git a/pom.xml b/pom.xml
index 76e4db5..3422571 100644
--- a/pom.xml
+++ b/pom.xml
@@ -26,7 +26,7 @@
<groupId>org.apache.commons</groupId>
<artifactId>commons-statistics-parent</artifactId>
<packaging>pom</packaging>
- <version>1.1-SNAPSHOT</version>
+ <version>0.1-SNAPSHOT</version>
<name>Apache Commons Statistics</name>
<inceptionYear>2016</inceptionYear>
@@ -91,8 +91,8 @@
<commons.jira.id>STATISTICS</commons.jira.id>
<commons.jira.pid>12320623</commons.jira.pid>
<commons.encoding>UTF-8</commons.encoding>
- <maven.compiler.source>1.6</maven.compiler.source>
- <maven.compiler.target>1.6</maven.compiler.target>
+ <maven.compiler.source>1.8</maven.compiler.source>
+ <maven.compiler.target>1.8</maven.compiler.target>
<statistics.pmd.version>3.5</statistics.pmd.version>
<statistics.findbugs.version>3.0.2</statistics.findbugs.version>
<statistics.checkstyle.version>2.17</statistics.checkstyle.version>
[04/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-142.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-142.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-142.csv
new file mode 100644
index 0000000..f515d6a
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-142.csv
@@ -0,0 +1,775 @@
+#
+# 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.
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<TRUNCATED>
[08/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
new file mode 100644
index 0000000..3b2889e
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
@@ -0,0 +1,213 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link NormalDistribution}. Extends
+ * {@link ContinuousDistributionAbstractTest}. See class javadoc of that class
+ * for details.
+ *
+ */
+public class NormalDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default real distribution instance to use in tests. */
+ @Override
+ public NormalDistribution makeDistribution() {
+ return new NormalDistribution(2.1, 1.4);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R
+ return new double[] {-2.226325228634938d, -1.156887023657177d, -0.643949578356075d, -0.2027950777320613d, 0.305827808237559d,
+ 6.42632522863494d, 5.35688702365718d, 4.843949578356074d, 4.40279507773206d, 3.89417219176244d};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
+ 0.990d, 0.975d, 0.950d, 0.900d};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.00240506434076, 0.0190372444310, 0.0417464784322, 0.0736683145538, 0.125355951380,
+ 0.00240506434076, 0.0190372444310, 0.0417464784322, 0.0736683145538, 0.125355951380};
+ }
+
+ // --------------------- Override tolerance --------------
+ private double defaultTolerance = 1e-7;
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(defaultTolerance);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ private void verifyQuantiles() {
+ NormalDistribution distribution = (NormalDistribution) getDistribution();
+ double mu = distribution.getMean();
+ double sigma = distribution.getStandardDeviation();
+ setCumulativeTestPoints( new double[] {mu - 2 *sigma, mu - sigma,
+ mu, mu + sigma, mu + 2 * sigma, mu + 3 * sigma, mu + 4 * sigma,
+ mu + 5 * sigma});
+ // Quantiles computed using R (same as Mathematica)
+ setCumulativeTestValues(new double[] {0.02275013194817921, 0.158655253931457, 0.5, 0.841344746068543,
+ 0.977249868051821, 0.99865010196837, 0.999968328758167, 0.999999713348428});
+ verifyCumulativeProbabilities();
+ }
+
+ @Test
+ public void testQuantiles() {
+ setDensityTestValues(new double[] {0.0385649760808, 0.172836231799, 0.284958771715, 0.172836231799, 0.0385649760808,
+ 0.00316560600853, 9.55930184035e-05, 1.06194251052e-06});
+ verifyQuantiles();
+ verifyDensities();
+
+ setDistribution(new NormalDistribution(0, 1));
+ setDensityTestValues(new double[] {0.0539909665132, 0.241970724519, 0.398942280401, 0.241970724519, 0.0539909665132,
+ 0.00443184841194, 0.000133830225765, 1.48671951473e-06});
+ verifyQuantiles();
+ verifyDensities();
+
+ setDistribution(new NormalDistribution(0, 0.1));
+ setDensityTestValues(new double[] {0.539909665132, 2.41970724519, 3.98942280401, 2.41970724519,
+ 0.539909665132, 0.0443184841194, 0.00133830225765, 1.48671951473e-05});
+ verifyQuantiles();
+ verifyDensities();
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ // MATH-1257
+ @Test
+ public void testCumulativeProbability() {
+ final ContinuousDistribution dist = new NormalDistribution(0, 1);
+ double x = -10;
+ double expected = 7.61985e-24;
+ double v = dist.cumulativeProbability(x);
+ double tol = 1e-5;
+ Assert.assertEquals(1, v / expected, 1e-5);
+ }
+
+ @Test
+ public void testGetMean() {
+ NormalDistribution distribution = (NormalDistribution) getDistribution();
+ Assert.assertEquals(2.1, distribution.getMean(), 0);
+ }
+
+ @Test
+ public void testGetStandardDeviation() {
+ NormalDistribution distribution = (NormalDistribution) getDistribution();
+ Assert.assertEquals(1.4, distribution.getStandardDeviation(), 0);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new NormalDistribution(1, 0);
+ }
+
+ @Test
+ public void testDensity() {
+ double [] x = new double[]{-2, -1, 0, 1, 2};
+ // R 2.5: print(dnorm(c(-2,-1,0,1,2)), digits=10)
+ checkDensity(0, 1, x, new double[]{0.05399096651, 0.24197072452, 0.39894228040, 0.24197072452, 0.05399096651});
+ // R 2.5: print(dnorm(c(-2,-1,0,1,2), mean=1.1), digits=10)
+ checkDensity(1.1, 1, x, new double[]{0.003266819056,0.043983595980,0.217852177033,0.396952547477,0.266085249899});
+ }
+
+ private void checkDensity(double mean, double sd, double[] x, double[] expected) {
+ NormalDistribution d = new NormalDistribution(mean, sd);
+ 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() {
+ NormalDistribution distribution = new NormalDistribution(0, 1);
+ for (int i = 0; i < 100; i++) { // make sure no convergence exception
+ double lowerTail = distribution.cumulativeProbability(-i);
+ double upperTail = distribution.cumulativeProbability(i);
+ if (i < 9) { // make sure not top-coded
+ // For i = 10, due to bad tail precision in erf (MATH-364), 1 is returned
+ // TODO: once MATH-364 is resolved, replace 9 with 30
+ Assert.assertTrue(lowerTail > 0.0d);
+ Assert.assertTrue(upperTail < 1.0d);
+ }
+ else { // make sure top coding not reversed
+ Assert.assertTrue(lowerTail < 0.00001);
+ Assert.assertTrue(upperTail > 0.99999);
+ }
+ }
+
+ Assert.assertEquals(distribution.cumulativeProbability(Double.MAX_VALUE), 1, 0);
+ Assert.assertEquals(distribution.cumulativeProbability(-Double.MAX_VALUE), 0, 0);
+ Assert.assertEquals(distribution.cumulativeProbability(Double.POSITIVE_INFINITY), 1, 0);
+ Assert.assertEquals(distribution.cumulativeProbability(Double.NEGATIVE_INFINITY), 0, 0);
+ }
+
+ @Test
+ public void testMath280() {
+ NormalDistribution normal = new NormalDistribution(0,1);
+ double result = normal.inverseCumulativeProbability(0.9986501019683698);
+ Assert.assertEquals(3.0, result, defaultTolerance);
+ result = normal.inverseCumulativeProbability(0.841344746068543);
+ Assert.assertEquals(1.0, result, defaultTolerance);
+ result = normal.inverseCumulativeProbability(0.9999683287581673);
+ Assert.assertEquals(4.0, result, defaultTolerance);
+ result = normal.inverseCumulativeProbability(0.9772498680518209);
+ Assert.assertEquals(2.0, result, defaultTolerance);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ NormalDistribution dist;
+
+ dist = new NormalDistribution(0, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 1, tol);
+
+ dist = new NormalDistribution(2.2, 1.4);
+ Assert.assertEquals(dist.getNumericalMean(), 2.2, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 1.4 * 1.4, tol);
+
+ dist = new NormalDistribution(-2000.9, 10.4);
+ Assert.assertEquals(dist.getNumericalMean(), -2000.9, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 10.4 * 10.4, tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ParetoDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ParetoDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ParetoDistributionTest.java
new file mode 100644
index 0000000..6223dbc
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ParetoDistributionTest.java
@@ -0,0 +1,201 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link ParetoDistribution}.
+ * Extends {@link ContinuousDistributionAbstractTest}. See class javadoc of that class for details.
+ */
+public class ParetoDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default real distribution instance to use in tests. */
+ @Override
+ public ParetoDistribution makeDistribution() {
+ return new ParetoDistribution(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, 0.791089998892, 0.730456085931, 0.689667290488, 0.645278794701, 0.578763688757 };
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] { 0, 0, 0, 0, 0, 0.0455118580441, 0.070444173646, 0.0896924681582, 0.112794186114, 0.151439332084 };
+ }
+
+ /**
+ * 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 - 5];
+ System.arraycopy(points, 5, points2, 0, points.length - 5);
+ return points2;
+ }
+
+ /**
+ * 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 - 5];
+ System.arraycopy(points, 5, points2, 0, points.length - 5);
+ return points2;
+ }
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-7);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ private void verifyQuantiles() {
+ ParetoDistribution distribution = (ParetoDistribution)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, 0, 0.510884134236, 0.694625688662, 0.785201995008, 0.837811522357, 0.871634279326});
+ setDensityTestValues(new double[] {0, 0, 0.666666666, 0.195646346305, 0.0872498032394, 0.0477328899983, 0.0294888141169, 0.0197485724114});
+ verifyQuantiles();
+ verifyDensities();
+
+ setDistribution(new ParetoDistribution(1, 1));
+ setCumulativeTestValues(new double[] {0, 0, 0, 0.5, 0.666666666667, 0.75, 0.8, 0.833333333333});
+ setDensityTestValues(new double[] {0, 0, 1.0, 0.25, 0.111111111111, 0.0625, 0.04, 0.0277777777778});
+ verifyQuantiles();
+ verifyDensities();
+
+ setDistribution(new ParetoDistribution(0.1, 0.1));
+ setCumulativeTestValues(new double[] {0, 0, 0, 0.0669670084632, 0.104041540159, 0.129449436704, 0.148660077479, 0.164041197922});
+ setDensityTestValues(new double[] {0, 0, 1.0, 0.466516495768, 0.298652819947, 0.217637640824, 0.170267984504, 0.139326467013});
+ verifyQuantiles();
+ verifyDensities();
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {2.1, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testGetScale() {
+ ParetoDistribution distribution = (ParetoDistribution)getDistribution();
+ Assert.assertEquals(2.1, distribution.getScale(), 0);
+ }
+
+ @Test
+ public void testGetShape() {
+ ParetoDistribution distribution = (ParetoDistribution)getDistribution();
+ Assert.assertEquals(1.4, distribution.getShape(), 0);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new ParetoDistribution(1, 0);
+ }
+
+ @Test
+ public void testDensity() {
+ double [] x = new double[]{-2, -1, 0, 1, 2};
+ // R 2.14: print(dpareto(c(-2,-1,0,1,2), scale=1, shape=1), digits=10)
+ checkDensity(1, 1, x, new double[] { 0.00, 0.00, 0.00, 1.00, 0.25 });
+ // R 2.14: print(dpareto(c(-2,-1,0,1,2), scale=1.1, shape=1), digits=10)
+ checkDensity(1.1, 1, x, new double[] { 0.000, 0.000, 0.000, 0.000, 0.275 });
+ }
+
+ private void checkDensity(double scale, double shape, double[] x,
+ double[] expected) {
+ ParetoDistribution d = new ParetoDistribution(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.
+ */
+ @Test
+ public void testExtremeValues() {
+ ParetoDistribution d = new ParetoDistribution(1, 1);
+ for (int i = 0; i < 1e5; i++) { // make sure no convergence exception
+ double upperTail = d.cumulativeProbability(i);
+ if (i <= 1000) { // make sure not top-coded
+ Assert.assertTrue(upperTail < 1.0d);
+ }
+ else { // make sure top coding not reversed
+ Assert.assertTrue(upperTail > 0.999);
+ }
+ }
+
+ 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;
+ ParetoDistribution dist;
+
+ dist = new ParetoDistribution(1, 1);
+ Assert.assertEquals(dist.getNumericalMean(), Double.POSITIVE_INFINITY, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), Double.POSITIVE_INFINITY, tol);
+
+ dist = new ParetoDistribution(2.2, 2.4);
+ Assert.assertEquals(dist.getNumericalMean(), 3.771428571428, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 14.816326530, tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PascalDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PascalDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PascalDistributionTest.java
new file mode 100644
index 0000000..b0d7ac1
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PascalDistributionTest.java
@@ -0,0 +1,132 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for PascalDistribution.
+ * Extends DiscreteDistributionAbstractTest. See class javadoc for
+ * DiscreteDistributionAbstractTest for details.
+ *
+ */
+public class PascalDistributionTest extends DiscreteDistributionAbstractTest {
+
+ // --------------------- Override tolerance --------------
+ protected double defaultTolerance = 1e-7;
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(defaultTolerance);
+ }
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new PascalDistribution(10,0.70);
+ }
+
+ /** Creates the default probability density test input values */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] {-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0, 0.0282475249, 0.0847425747, 0.139825248255, 0.167790297906, 0.163595540458,
+ 0.137420253985, 0.103065190489, 0.070673273478, 0.0450542118422, 0.0270325271053,
+ 0.0154085404500, 0.0084046584273};
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0, 0.0282475249, 0.1129900996, 0.252815347855, 0.420605645761, 0.584201186219,
+ 0.721621440204, 0.824686630693, 0.895359904171, 0.940414116013, 0.967446643119,
+ 0.982855183569, 0.991259841996};
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {0.0, 0.001, 0.010, 0.025, 0.050, 0.100, 0.999,
+ 0.990, 0.975, 0.950, 0.900, 1.0};
+ }
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] {0, 0, 0, 0, 1, 1, 14, 11, 10, 9, 8, Integer.MAX_VALUE};
+ }
+
+ //----------------- Additional test cases ---------------------------------
+
+ /** Test degenerate case p = 0 */
+ @Test
+ public void testDegenerate0() {
+ setDistribution(new PascalDistribution(5, 0.0d));
+ setCumulativeTestPoints(new int[] {-1, 0, 1, 5, 10 });
+ setCumulativeTestValues(new double[] {0d, 0d, 0d, 0d, 0d});
+ setDensityTestPoints(new int[] {-1, 0, 1, 10, 11});
+ setDensityTestValues(new double[] {0d, 0d, 0d, 0d, 0d});
+ setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
+ setInverseCumulativeTestValues(new int[] {Integer.MAX_VALUE, Integer.MAX_VALUE});
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ }
+
+ /** Test degenerate case p = 1 */
+ @Test
+ public void testDegenerate1() {
+ setDistribution(new PascalDistribution(5, 1.0d));
+ setCumulativeTestPoints(new int[] {-1, 0, 1, 2, 5, 10 });
+ setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d, 1d});
+ setDensityTestPoints(new int[] {-1, 0, 1, 2, 5, 10});
+ setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d, 0d});
+ setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
+ setInverseCumulativeTestValues(new int[] {0, 0});
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ PascalDistribution dist;
+
+ dist = new PascalDistribution(10, 0.5);
+ Assert.assertEquals(dist.getNumericalMean(), ( 10d * 0.5d ) / 0.5d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), ( 10d * 0.5d ) / (0.5d * 0.5d), tol);
+
+ dist = new PascalDistribution(25, 0.7);
+ Assert.assertEquals(dist.getNumericalMean(), ( 25d * 0.3d ) / 0.7d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), ( 25d * 0.3d ) / (0.7d * 0.7d), tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PoissonDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PoissonDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PoissonDistributionTest.java
new file mode 100644
index 0000000..150e120
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/PoissonDistributionTest.java
@@ -0,0 +1,244 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * <code>PoissonDistributionTest</code>
+ *
+ */
+public class PoissonDistributionTest extends DiscreteDistributionAbstractTest {
+
+ /**
+ * Poisson parameter value for the test distribution.
+ */
+ private static final double DEFAULT_TEST_POISSON_PARAMETER = 4.0;
+
+ /**
+ * Constructor.
+ */
+ public PoissonDistributionTest() {
+ setTolerance(1e-12);
+ }
+
+ /**
+ * Creates the default discrete distribution instance to use in tests.
+ */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new PoissonDistribution(DEFAULT_TEST_POISSON_PARAMETER);
+ }
+
+ /**
+ * Creates the default probability density test input values.
+ */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] { -1, 0, 1, 2, 3, 4, 5, 10, 20};
+ }
+
+ /**
+ * Creates the default probability density test expected values.
+ * These and all other test values are generated by R, version 1.8.1
+ */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] { 0d, 0.0183156388887d, 0.073262555555d,
+ 0.14652511111d, 0.195366814813d, 0.195366814813,
+ 0.156293451851d, 0.00529247667642d, 8.27746364655e-09};
+ }
+
+ /**
+ * Creates the default logarithmic probability density test expected values.
+ * Reference values are from R, version 2.14.1.
+ */
+ @Override
+ public double[] makeLogDensityTestValues() {
+ return new double[] { Double.NEGATIVE_INFINITY, -4.000000000000d,
+ -2.613705638880d, -1.920558458320d, -1.632876385868d,
+ -1.632876385868d, -1.856019937183d, -5.241468961877d,
+ -18.609729238356d};
+ }
+
+ /**
+ * Creates the default cumulative probability density test input values.
+ */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return new int[] { -1, 0, 1, 2, 3, 4, 5, 10, 20 };
+ }
+
+ /**
+ * Creates the default cumulative probability density test expected values.
+ */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] { 0d, 0.0183156388887d, 0.0915781944437d,
+ 0.238103305554d, 0.433470120367d, 0.62883693518,
+ 0.78513038703d, 0.99716023388d, 0.999999998077 };
+ }
+
+ /**
+ * Creates the default inverse cumulative probability test input values.
+ */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ DiscreteDistribution dist = getDistribution();
+ return new double[] { 0d, 0.018315638886d, 0.018315638890d,
+ 0.091578194441d, 0.091578194445d, 0.238103305552d,
+ 0.238103305556d, dist.cumulativeProbability(3),
+ dist.cumulativeProbability(4), dist.cumulativeProbability(5),
+ dist.cumulativeProbability(10), dist.cumulativeProbability(20)};
+ }
+
+ /**
+ * Creates the default inverse cumulative probability density test expected values.
+ */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] { 0, 0, 1, 1, 2, 2, 3, 3, 4, 5, 10, 20};
+ }
+
+ /**
+ * Test the normal approximation of the Poisson distribution by
+ * calculating P(90 ≤ X ≤ 110) for X = Po(100) and
+ * P(9900 ≤ X ≤ 10200) for X = Po(10000)
+ */
+ @Test
+ public void testNormalApproximateProbability() {
+ PoissonDistribution dist = new PoissonDistribution(100);
+ double result = dist.normalApproximateProbability(110)
+ - dist.normalApproximateProbability(89);
+ Assert.assertEquals(0.706281887248, result, 1e-10);
+
+ dist = new PoissonDistribution(10000);
+ result = dist.normalApproximateProbability(10200)
+ - dist.normalApproximateProbability(9899);
+ Assert.assertEquals(0.820070051552, result, 1E-10);
+ }
+
+ /**
+ * Test the degenerate cases of a 0.0 and 1.0 inverse cumulative probability.
+ */
+ @Test
+ public void testDegenerateInverseCumulativeProbability() {
+ PoissonDistribution dist = new PoissonDistribution(DEFAULT_TEST_POISSON_PARAMETER);
+ Assert.assertEquals(Integer.MAX_VALUE, dist.inverseCumulativeProbability(1.0d));
+ Assert.assertEquals(0, dist.inverseCumulativeProbability(0d));
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testNegativeMean() {
+ new PoissonDistribution(-1);
+ }
+
+ @Test
+ public void testMean() {
+ PoissonDistribution dist = new PoissonDistribution(10.0);
+ Assert.assertEquals(10.0, dist.getMean(), 0.0);
+ }
+
+ @Test
+ public void testLargeMeanCumulativeProbability() {
+ double mean = 1.0;
+ while (mean <= 10000000.0) {
+ PoissonDistribution dist = new PoissonDistribution(mean);
+
+ double x = mean * 2.0;
+ double dx = x / 10.0;
+ double p = Double.NaN;
+ double sigma = Math.sqrt(mean);
+ while (x >= 0) {
+ try {
+ p = dist.cumulativeProbability((int) x);
+ Assert.assertFalse("NaN cumulative probability returned for mean = " +
+ mean + " x = " + x,Double.isNaN(p));
+ if (x > mean - 2 * sigma) {
+ Assert.assertTrue("Zero cum probaility returned for mean = " +
+ mean + " x = " + x, p > 0);
+ }
+ } catch (Exception ex) {
+ Assert.fail("mean of " + mean + " and x of " + x + " caused " + ex.getMessage());
+ }
+ x -= dx;
+ }
+
+ mean *= 10.0;
+ }
+ }
+
+ /**
+ * JIRA: MATH-282
+ */
+ @Test
+ public void testCumulativeProbabilitySpecial() {
+ PoissonDistribution dist;
+ dist = new PoissonDistribution(9120);
+ checkProbability(dist, 9075);
+ checkProbability(dist, 9102);
+ dist = new PoissonDistribution(5058);
+ checkProbability(dist, 5044);
+ dist = new PoissonDistribution(6986);
+ checkProbability(dist, 6950);
+ }
+
+ private void checkProbability(PoissonDistribution dist, int x) {
+ double p = dist.cumulativeProbability(x);
+ Assert.assertFalse("NaN cumulative probability returned for mean = " +
+ dist.getMean() + " x = " + x, Double.isNaN(p));
+ Assert.assertTrue("Zero cum probability returned for mean = " +
+ dist.getMean() + " x = " + x, p > 0);
+ }
+
+ @Test
+ public void testLargeMeanInverseCumulativeProbability() {
+ double mean = 1.0;
+ while (mean <= 100000.0) { // Extended test value: 1E7. Reduced to limit run time.
+ PoissonDistribution dist = new PoissonDistribution(mean);
+ double p = 0.1;
+ double dp = p;
+ while (p < .99) {
+ try {
+ int ret = dist.inverseCumulativeProbability(p);
+ // Verify that returned value satisties definition
+ Assert.assertTrue(p <= dist.cumulativeProbability(ret));
+ Assert.assertTrue(p > dist.cumulativeProbability(ret - 1));
+ } catch (Exception ex) {
+ Assert.fail("mean of " + mean + " and p of " + p + " caused " + ex.getMessage());
+ }
+ p += dp;
+ }
+ mean *= 10.0;
+ }
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ PoissonDistribution dist;
+
+ dist = new PoissonDistribution(1);
+ Assert.assertEquals(dist.getNumericalMean(), 1, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 1, tol);
+
+ dist = new PoissonDistribution(11.23);
+ Assert.assertEquals(dist.getNumericalMean(), 11.23, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 11.23, tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TDistributionTest.java
new file mode 100644
index 0000000..46e2a08
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TDistributionTest.java
@@ -0,0 +1,169 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+/**
+ * Test cases for TDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class TDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public TDistribution makeDistribution() {
+ return new TDistribution(5.0);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {-5.89342953136, -3.36492999891, -2.57058183564, -2.01504837333, -1.47588404882,
+ 5.89342953136, 3.36492999891, 2.57058183564, 2.01504837333, 1.47588404882};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999,
+ 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.000756494565517, 0.0109109752919, 0.0303377878006, 0.0637967988952, 0.128289492005,
+ 0.000756494565517, 0.0109109752919, 0.0303377878006, 0.0637967988952, 0.128289492005};
+ }
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+ /**
+ * @see <a href="http://issues.apache.org/bugzilla/show_bug.cgi?id=27243">
+ * Bug report that prompted this unit test.</a>
+ */
+ @Test
+ public void testCumulativeProbabilityAgainstStackOverflow() {
+ TDistribution td = new TDistribution(5.);
+ td.cumulativeProbability(.1);
+ td.cumulativeProbability(.01);
+ }
+
+ @Test
+ public void testSmallDf() {
+ setDistribution(new TDistribution(1d));
+ // quantiles computed using R version 2.9.2
+ setCumulativeTestPoints(new double[] {-318.308838986, -31.8205159538, -12.7062047362,
+ -6.31375151468, -3.07768353718, 318.308838986, 31.8205159538, 12.7062047362,
+ 6.31375151468, 3.07768353718});
+ setDensityTestValues(new double[] {3.14158231817e-06, 0.000314055924703, 0.00195946145194,
+ 0.00778959736375, 0.0303958893917, 3.14158231817e-06, 0.000314055924703,
+ 0.00195946145194, 0.00778959736375, 0.0303958893917});
+ setInverseCumulativeTestValues(getCumulativeTestPoints());
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ verifyDensities();
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testCumulativeProbablilityExtremes() {
+ TDistribution dist;
+ for (int i = 1; i < 11; i++) {
+ dist = new TDistribution(i * 5);
+ Assert.assertEquals(1,
+ dist.cumulativeProbability(Double.POSITIVE_INFINITY), Double.MIN_VALUE);
+ Assert.assertEquals(0,
+ dist.cumulativeProbability(Double.NEGATIVE_INFINITY), Double.MIN_VALUE);
+ }
+ }
+
+ @Test
+ public void testDfAccessors() {
+ TDistribution dist = (TDistribution) getDistribution();
+ Assert.assertEquals(5d, dist.getDegreesOfFreedom(), Double.MIN_VALUE);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions() {
+ new TDistribution(0);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ TDistribution dist;
+
+ dist = new TDistribution(1);
+ Assert.assertTrue(Double.isNaN(dist.getNumericalMean()));
+ Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
+
+ dist = new TDistribution(1.5);
+ Assert.assertEquals(dist.getNumericalMean(), 0, tol);
+ Assert.assertTrue(Double.isInfinite(dist.getNumericalVariance()));
+
+ dist = new TDistribution(5);
+ Assert.assertEquals(dist.getNumericalMean(), 0, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 5d / (5d - 2d), tol);
+ }
+
+ /*
+ * Adding this test to benchmark against tables published by NIST
+ * http://itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
+ * Have chosen tabulated results for degrees of freedom 2,10,30,100
+ * Have chosen problevels from 0.10 to 0.001
+ */
+ @Test
+ public void nistData(){
+ double[] prob = new double[]{ 0.10,0.05,0.025,0.01,0.005,0.001};
+ double[] args2 = new double[]{1.886,2.920,4.303,6.965,9.925,22.327};
+ double[] args10 = new double[]{1.372,1.812,2.228,2.764,3.169,4.143};
+ double[] args30 = new double[]{1.310,1.697,2.042,2.457,2.750,3.385};
+ double[] args100= new double[]{1.290,1.660,1.984,2.364,2.626,3.174};
+ TestUtils.assertEquals(prob, makeNistResults(args2, 2), 1.0e-4);
+ TestUtils.assertEquals(prob, makeNistResults(args10, 10), 1.0e-4);
+ TestUtils.assertEquals(prob, makeNistResults(args30, 30), 1.0e-4);
+ TestUtils.assertEquals(prob, makeNistResults(args100, 100), 1.0e-4);
+ return;
+ }
+ private double[] makeNistResults(double[] args, int df){
+ TDistribution td = new TDistribution(df);
+ double[] res = new double[ args.length ];
+ for( int i = 0 ; i < res.length ; i++){
+ res[i] = 1.0 - td.cumulativeProbability(args[i]);
+ }
+ return res;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TestUtils.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TestUtils.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TestUtils.java
new file mode 100644
index 0000000..8a074d0
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TestUtils.java
@@ -0,0 +1,281 @@
+/*
+ * 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.statistics.distribution;
+
+import java.text.DecimalFormat;
+import org.apache.commons.math3.stat.inference.ChiSquareTest;
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+
+/**
+ *
+ */
+public class TestUtils {
+ /**
+ * Collection of static methods used in math unit tests.
+ */
+ private TestUtils() {}
+
+ /**
+ * Verifies that expected and actual are within delta, or are both NaN or
+ * infinities of the same sign.
+ */
+ public static void assertEquals(double expected,
+ double actual,
+ double delta) {
+ Assert.assertEquals(null, expected, actual, delta);
+ }
+
+ /**
+ * Verifies that expected and actual are within delta, or are both NaN or
+ * infinities of the same sign.
+ */
+ public static void assertEquals(String msg,
+ double expected,
+ double actual,
+ double delta) {
+ // check for NaN
+ if(Double.isNaN(expected)){
+ Assert.assertTrue("" + actual + " is not NaN.",
+ Double.isNaN(actual));
+ } else {
+ Assert.assertEquals(msg, expected, actual, delta);
+ }
+ }
+
+ /**
+ * Verifies that two double arrays have equal entries, up to tolerance
+ */
+ public static void assertEquals(double expected[],
+ double observed[],
+ double tolerance) {
+ assertEquals("Array comparison failure", expected, observed, tolerance);
+ }
+
+ /**
+ * Verifies that the relative error in actual vs. expected is less than or
+ * equal to relativeError. If expected is infinite or NaN, actual must be
+ * the same (NaN or infinity of the same sign).
+ *
+ * @param expected expected value
+ * @param actual observed value
+ * @param relativeError maximum allowable relative error
+ */
+ public static void assertRelativelyEquals(double expected,
+ double actual,
+ double relativeError) {
+ assertRelativelyEquals(null, expected, actual, relativeError);
+ }
+
+ /**
+ * Verifies that the relative error in actual vs. expected is less than or
+ * equal to relativeError. If expected is infinite or NaN, actual must be
+ * the same (NaN or infinity of the same sign).
+ *
+ * @param msg message to return with failure
+ * @param expected expected value
+ * @param actual observed value
+ * @param relativeError maximum allowable relative error
+ */
+ public static void assertRelativelyEquals(String msg,
+ double expected,
+ double actual,
+ double relativeError) {
+ if (Double.isNaN(expected)) {
+ Assert.assertTrue(msg, Double.isNaN(actual));
+ } else if (Double.isNaN(actual)) {
+ Assert.assertTrue(msg, Double.isNaN(expected));
+ } else if (Double.isInfinite(actual) || Double.isInfinite(expected)) {
+ Assert.assertEquals(expected, actual, relativeError);
+ } else if (expected == 0.0) {
+ Assert.assertEquals(msg, actual, expected, relativeError);
+ } else {
+ double absError = Math.abs(expected) * relativeError;
+ Assert.assertEquals(msg, expected, actual, absError);
+ }
+ }
+
+ /** verifies that two arrays are close (sup norm) */
+ public static void assertEquals(String msg,
+ double[] expected,
+ double[] observed,
+ double tolerance) {
+ StringBuilder out = new StringBuilder(msg);
+ if (expected.length != observed.length) {
+ out.append("\n Arrays not same length. \n");
+ out.append("expected has length ");
+ out.append(expected.length);
+ out.append(" observed length = ");
+ out.append(observed.length);
+ Assert.fail(out.toString());
+ }
+ boolean failure = false;
+ for (int i=0; i < expected.length; i++) {
+ if (!Precision.equalsIncludingNaN(expected[i], observed[i], tolerance)) {
+ failure = true;
+ out.append("\n Elements at index ");
+ out.append(i);
+ out.append(" differ. ");
+ out.append(" expected = ");
+ out.append(expected[i]);
+ out.append(" observed = ");
+ out.append(observed[i]);
+ }
+ }
+ if (failure) {
+ Assert.fail(out.toString());
+ }
+ }
+
+ /**
+ * Asserts the null hypothesis for a ChiSquare test. Fails and dumps arguments and test
+ * statistics if the null hypothesis can be rejected with confidence 100 * (1 - alpha)%
+ *
+ * @param valueLabels labels for the values of the discrete distribution under test
+ * @param expected expected counts
+ * @param observed observed counts
+ * @param alpha significance level of the test
+ */
+ public static void assertChiSquareAccept(String[] valueLabels,
+ double[] expected,
+ long[] observed,
+ double alpha) {
+ ChiSquareTest chiSquareTest = new ChiSquareTest();
+
+ // Fail if we can reject null hypothesis that distributions are the same
+ if (chiSquareTest.chiSquareTest(expected, observed, alpha)) {
+ StringBuilder msgBuffer = new StringBuilder();
+ DecimalFormat df = new DecimalFormat("#.##");
+ msgBuffer.append("Chisquare test failed");
+ msgBuffer.append(" p-value = ");
+ msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
+ msgBuffer.append(" chisquare statistic = ");
+ msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
+ msgBuffer.append(". \n");
+ msgBuffer.append("value\texpected\tobserved\n");
+ for (int i = 0; i < expected.length; i++) {
+ msgBuffer.append(valueLabels[i]);
+ msgBuffer.append("\t");
+ msgBuffer.append(df.format(expected[i]));
+ msgBuffer.append("\t\t");
+ msgBuffer.append(observed[i]);
+ msgBuffer.append("\n");
+ }
+ msgBuffer.append("This test can fail randomly due to sampling error with probability ");
+ msgBuffer.append(alpha);
+ msgBuffer.append(".");
+ Assert.fail(msgBuffer.toString());
+ }
+ }
+
+ /**
+ * Asserts the null hypothesis for a ChiSquare test. Fails and dumps arguments and test
+ * statistics if the null hypothesis can be rejected with confidence 100 * (1 - alpha)%
+ *
+ * @param values integer values whose observed and expected counts are being compared
+ * @param expected expected counts
+ * @param observed observed counts
+ * @param alpha significance level of the test
+ */
+ public static void assertChiSquareAccept(int[] values,
+ double[] expected,
+ long[] observed,
+ double alpha) {
+ String[] labels = new String[values.length];
+ for (int i = 0; i < values.length; i++) {
+ labels[i] = Integer.toString(values[i]);
+ }
+ assertChiSquareAccept(labels, expected, observed, alpha);
+ }
+
+ /**
+ * Asserts the null hypothesis for a ChiSquare test. Fails and dumps arguments and test
+ * statistics if the null hypothesis can be rejected with confidence 100 * (1 - alpha)%
+ *
+ * @param expected expected counts
+ * @param observed observed counts
+ * @param alpha significance level of the test
+ */
+ public static void assertChiSquareAccept(double[] expected,
+ long[] observed,
+ double alpha) {
+ String[] labels = new String[expected.length];
+ for (int i = 0; i < labels.length; i++) {
+ labels[i] = Integer.toString(i + 1);
+ }
+ assertChiSquareAccept(labels, expected, observed, alpha);
+ }
+
+ /**
+ * Computes the 25th, 50th and 75th percentiles of the given distribution and returns
+ * these values in an array.
+ */
+ public static double[] getDistributionQuartiles(ContinuousDistribution distribution) {
+ double[] quantiles = new double[3];
+ quantiles[0] = distribution.inverseCumulativeProbability(0.25d);
+ quantiles[1] = distribution.inverseCumulativeProbability(0.5d);
+ quantiles[2] = distribution.inverseCumulativeProbability(0.75d);
+ return quantiles;
+ }
+
+ /**
+ * Updates observed counts of values in quartiles.
+ * counts[0] <-> 1st quartile ... counts[3] <-> top quartile
+ */
+ public static void updateCounts(double value, long[] counts, double[] quartiles) {
+ if (value < quartiles[0]) {
+ counts[0]++;
+ } else if (value > quartiles[2]) {
+ counts[3]++;
+ } else if (value > quartiles[1]) {
+ counts[2]++;
+ } else {
+ counts[1]++;
+ }
+ }
+
+ /**
+ * Eliminates points with zero mass from densityPoints and densityValues parallel
+ * arrays. Returns the number of positive mass points and collapses the arrays so
+ * that the first <returned value> elements of the input arrays represent the positive
+ * mass points.
+ */
+ public static int eliminateZeroMassPoints(int[] densityPoints, double[] densityValues) {
+ int positiveMassCount = 0;
+ for (int i = 0; i < densityValues.length; i++) {
+ if (densityValues[i] > 0) {
+ positiveMassCount++;
+ }
+ }
+ if (positiveMassCount < densityValues.length) {
+ int[] newPoints = new int[positiveMassCount];
+ double[] newValues = new double[positiveMassCount];
+ int j = 0;
+ for (int i = 0; i < densityValues.length; i++) {
+ if (densityValues[i] > 0) {
+ newPoints[j] = densityPoints[i];
+ newValues[j] = densityValues[i];
+ j++;
+ }
+ }
+ System.arraycopy(newPoints,0,densityPoints,0,positiveMassCount);
+ System.arraycopy(newValues,0,densityValues,0,positiveMassCount);
+ }
+ return positiveMassCount;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TriangularDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TriangularDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TriangularDistributionTest.java
new file mode 100644
index 0000000..cddb70d
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TriangularDistributionTest.java
@@ -0,0 +1,192 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link TriangularDistribution}. See class javadoc for
+ * {@link ContinuousDistributionAbstractTest} for further details.
+ */
+public class TriangularDistributionTest extends ContinuousDistributionAbstractTest {
+
+ // --- Override tolerance -------------------------------------------------
+
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-4);
+ }
+
+ //--- Implementations for abstract methods --------------------------------
+
+ /**
+ * Creates the default triangular distribution instance to use in tests.
+ */
+ @Override
+ public TriangularDistribution makeDistribution() {
+ // Left side 5 wide, right side 10 wide.
+ return new TriangularDistribution(-3, 2, 12);
+ }
+
+ /**
+ * Creates the default cumulative probability distribution test input
+ * values.
+ */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ -3.0001, // below lower limit
+ -3.0, // at lower limit
+ -2.0, -1.0, 0.0, 1.0, // on lower side
+ 2.0, // at mode
+ 3.0, 4.0, 10.0, 11.0, // on upper side
+ 12.0, // at upper limit
+ 12.0001 // above upper limit
+ };
+ }
+
+ /**
+ * Creates the default cumulative probability density test expected values.
+ */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ // Top at 2 / (b - a) = 2 / (12 - -3) = 2 / 15 = 7.5
+ // Area left = 7.5 * 5 * 0.5 = 18.75 (1/3 of the total area)
+ // Area right = 7.5 * 10 * 0.5 = 37.5 (2/3 of the total area)
+ // Area total = 18.75 + 37.5 = 56.25
+ // Derivative left side = 7.5 / 5 = 1.5
+ // Derivative right side = -7.5 / 10 = -0.75
+ double third = 1 / 3.0;
+ double left = 18.75;
+ double area = 56.25;
+ return new double[] { 0.0,
+ 0.0,
+ 0.75 / area, 3 / area, 6.75 / area, 12 / area,
+ third,
+ (left + 7.125) / area, (left + 13.5) / area,
+ (left + 36) / area, (left + 37.125) / area,
+ 1.0,
+ 1.0
+ };
+ }
+
+ /**
+ * Creates the default inverse cumulative probability distribution test
+ * input values.
+ */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ // Exclude the points outside the limits, as they have cumulative
+ // probability of zero and one, meaning the inverse returns the
+ // limits and not the points outside the limits.
+ double[] points = makeCumulativeTestValues();
+ double[] points2 = new double[points.length-2];
+ System.arraycopy(points, 1, points2, 0, points2.length);
+ return points2;
+ //return Arrays.copyOfRange(points, 1, points.length - 1);
+ }
+
+ /**
+ * Creates the default inverse cumulative probability density test expected
+ * values.
+ */
+ @Override
+ public double[] makeInverseCumulativeTestValues() {
+ // Exclude the points outside the limits, as they have cumulative
+ // probability of zero and one, meaning the inverse returns the
+ // limits and not the points outside the limits.
+ double[] points = makeCumulativeTestPoints();
+ double[] points2 = new double[points.length-2];
+ System.arraycopy(points, 1, points2, 0, points2.length);
+ return points2;
+ //return Arrays.copyOfRange(points, 1, points.length - 1);
+ }
+
+ /** Creates the default probability density test expected values. */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] { 0,
+ 0,
+ 2 / 75.0, 4 / 75.0, 6 / 75.0, 8 / 75.0,
+ 10 / 75.0,
+ 9 / 75.0, 8 / 75.0, 2 / 75.0, 1 / 75.0,
+ 0,
+ 0
+ };
+ }
+
+ //--- Additional test cases -----------------------------------------------
+
+ /** Test lower bound getter. */
+ @Test
+ public void testGetLowerBound() {
+ TriangularDistribution distribution = makeDistribution();
+ Assert.assertEquals(-3.0, distribution.getSupportLowerBound(), 0);
+ }
+
+ /** Test upper bound getter. */
+ @Test
+ public void testGetUpperBound() {
+ TriangularDistribution distribution = makeDistribution();
+ Assert.assertEquals(12.0, distribution.getSupportUpperBound(), 0);
+ }
+
+ /** Test pre-condition for equal lower/upper limit. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions1() {
+ new TriangularDistribution(0, 0, 0);
+ }
+
+ /** Test pre-condition for lower limit larger than upper limit. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions2() {
+ new TriangularDistribution(1, 1, 0);
+ }
+
+ /** Test pre-condition for mode larger than upper limit. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions3() {
+ new TriangularDistribution(0, 2, 1);
+ }
+
+ /** Test pre-condition for mode smaller than lower limit. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions4() {
+ new TriangularDistribution(2, 1, 3);
+ }
+
+ /** Test mean/variance. */
+ @Test
+ public void testMeanVariance() {
+ TriangularDistribution dist;
+
+ dist = new TriangularDistribution(0, 0.5, 1.0);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 1 / 24.0, 0);
+
+ dist = new TriangularDistribution(0, 1, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 2 / 3.0, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 1 / 18.0, 0);
+
+ dist = new TriangularDistribution(-3, 2, 12);
+ Assert.assertEquals(dist.getNumericalMean(), 3 + (2 / 3.0), 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 175 / 18.0, 0);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformContinuousDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformContinuousDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformContinuousDistributionTest.java
new file mode 100644
index 0000000..7f76f08
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformContinuousDistributionTest.java
@@ -0,0 +1,123 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for UniformContinuousDistribution. See class javadoc for
+ * {@link ContinuousDistributionAbstractTest} for further details.
+ */
+public class UniformContinuousDistributionTest extends ContinuousDistributionAbstractTest {
+
+ // --- Override tolerance -------------------------------------------------
+
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-4);
+ }
+
+ //--- Implementations for abstract methods --------------------------------
+
+ /** Creates the default uniform real distribution instance to use in tests. */
+ @Override
+ public UniformContinuousDistribution makeDistribution() {
+ return new UniformContinuousDistribution(-0.5, 1.25);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {-0.5001, -0.5, -0.4999, -0.25, -0.0001, 0.0,
+ 0.0001, 0.25, 1.0, 1.2499, 1.25, 1.2501};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.0, 0.0, 0.0001, 0.25/1.75, 0.4999/1.75,
+ 0.5/1.75, 0.5001/1.75, 0.75/1.75, 1.5/1.75,
+ 1.7499/1.75, 1.0, 1.0};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ double d = 1 / 1.75;
+ return new double[] {0, d, d, d, d, d, d, d, d, d, d, 0};
+ }
+
+ //--- Additional test cases -----------------------------------------------
+
+ /** Test lower bound getter. */
+ @Test
+ public void testGetLowerBound() {
+ UniformContinuousDistribution distribution = makeDistribution();
+ Assert.assertEquals(-0.5, distribution.getSupportLowerBound(), 0);
+ }
+
+ /** Test upper bound getter. */
+ @Test
+ public void testGetUpperBound() {
+ UniformContinuousDistribution distribution = makeDistribution();
+ Assert.assertEquals(1.25, distribution.getSupportUpperBound(), 0);
+ }
+
+ /** Test pre-condition for equal lower/upper bound. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions1() {
+ new UniformContinuousDistribution(0, 0);
+ }
+
+ /** Test pre-condition for lower bound larger than upper bound. */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions2() {
+ new UniformContinuousDistribution(1, 0);
+ }
+
+ /** Test mean/variance. */
+ @Test
+ public void testMeanVariance() {
+ UniformContinuousDistribution dist;
+
+ dist = new UniformContinuousDistribution(0, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 1/12.0, 0);
+
+ dist = new UniformContinuousDistribution(-1.5, 0.6);
+ Assert.assertEquals(dist.getNumericalMean(), -0.45, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 0.3675, 0);
+
+ dist = new UniformContinuousDistribution(-0.5, 1.25);
+ Assert.assertEquals(dist.getNumericalMean(), 0.375, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 0.2552083333333333, 0);
+ }
+
+ /**
+ * Check accuracy of analytical inverse CDF. Fails if a solver is used
+ * with the default accuracy.
+ */
+ @Test
+ public void testInverseCumulativeDistribution() {
+ UniformContinuousDistribution dist = new UniformContinuousDistribution(0, 1e-9);
+
+ Assert.assertEquals(2.5e-10, dist.inverseCumulativeProbability(0.25), 0);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformDiscreteDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformDiscreteDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformDiscreteDistributionTest.java
new file mode 100644
index 0000000..98305db
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/UniformDiscreteDistributionTest.java
@@ -0,0 +1,139 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+import org.apache.commons.numbers.core.Precision;
+
+/**
+ * Test cases for UniformDiscreteDistribution. See class javadoc for
+ * {@link DiscreteDistributionAbstractTest} for further details.
+ */
+public class UniformDiscreteDistributionTest extends DiscreteDistributionAbstractTest {
+
+ // --- Override tolerance -------------------------------------------------
+
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //--- Implementations for abstract methods --------------------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new UniformDiscreteDistribution(-3, 5);
+ }
+
+ /** Creates the default probability density test input values. */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] {-4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6};
+ }
+
+ /** Creates the default probability density test expected values. */
+ @Override
+ public double[] makeDensityTestValues() {
+ double d = 1.0 / (5 - -3 + 1);
+ return new double[] {0, d, d, d, d, d, d, d, d, d, 0};
+ }
+
+ /** Creates the default cumulative probability density test input values. */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /** Creates the default cumulative probability density test expected values. */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0, 1 / 9.0, 2 / 9.0, 3 / 9.0, 4 / 9.0, 5 / 9.0,
+ 6 / 9.0, 7 / 9.0, 8 / 9.0, 1, 1};
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {0, 0.001, 0.010, 0.025, 0.050, 0.100, 0.200,
+ 0.5, 0.999, 0.990, 0.975, 0.950, 0.900, 1};
+ }
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] {-3, -3, -3, -3, -3, -3, -2, 1, 5, 5, 5, 5, 5, 5};
+ }
+
+ //--- Additional test cases -----------------------------------------------
+
+ /** Test mean/variance. */
+ @Test
+ public void testMoments() {
+ UniformDiscreteDistribution dist;
+
+ dist = new UniformDiscreteDistribution(0, 5);
+ Assert.assertEquals(dist.getNumericalMean(), 2.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 35 / 12.0, 0);
+
+ dist = new UniformDiscreteDistribution(0, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 3 / 12.0, 0);
+ }
+
+ // MATH-1141
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditionUpperBoundInclusive1() {
+ new UniformDiscreteDistribution(1, 0);
+ }
+
+ // MATH-1141
+ @Test
+ public void testPreconditionUpperBoundInclusive2() {
+ // Degenerate case is allowed.
+ new UniformDiscreteDistribution(0, 0);
+ }
+
+ // MATH-1396
+ @Test
+ public void testLargeRangeSubtractionOverflow() {
+ final int hi = Integer.MAX_VALUE / 2 + 10;
+ UniformDiscreteDistribution dist = new UniformDiscreteDistribution(-hi, hi - 1);
+
+ final double tol = Math.ulp(1d);
+ Assert.assertEquals(0.5 / hi, dist.probability(123456), tol);
+ Assert.assertEquals(0.5, dist.cumulativeProbability(-1), tol);
+
+ Assert.assertTrue(Precision.equals((Math.pow(2d * hi, 2) - 1) / 12, dist.getNumericalVariance(), 1));
+ }
+
+ // MATH-1396
+ @Test
+ public void testLargeRangeAdditionOverflow() {
+ final int hi = Integer.MAX_VALUE / 2 + 10;
+ UniformDiscreteDistribution dist = new UniformDiscreteDistribution(hi - 1, hi + 1);
+
+ final double tol = Math.ulp(1d);
+ Assert.assertEquals(1d / 3d, dist.probability(hi), tol);
+ Assert.assertEquals(2d / 3d, dist.cumulativeProbability(hi), tol);
+
+ Assert.assertTrue(Precision.equals(hi, dist.getNumericalMean(), 1));
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/WeibullDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/WeibullDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/WeibullDistributionTest.java
new file mode 100644
index 0000000..c993fba
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/WeibullDistributionTest.java
@@ -0,0 +1,118 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.LogGamma;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for WeibullDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class WeibullDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public WeibullDistribution makeDistribution() {
+ return new WeibullDistribution(1.2, 2.1);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {0.00664355180993, 0.0454328283309, 0.0981162737374, 0.176713524579, 0.321946865392,
+ 10.5115496887, 7.4976304671, 6.23205600701, 5.23968436955, 4.2079028257};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.180535929306, 0.262801138133, 0.301905425199, 0.330899152971,
+ 0.353441418887, 0.000788590320203, 0.00737060094841, 0.0177576041516, 0.0343043442574, 0.065664589369};
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ @Test
+ public void testInverseCumulativeProbabilitySmallPAccuracy() {
+ WeibullDistribution dist = new WeibullDistribution(2, 3);
+ double t = dist.inverseCumulativeProbability(1e-17);
+ // Analytically, answer is solution to 1e-17 = 1-exp(-(x/3)^2)
+ // x = sqrt(-9*log(1-1e-17))
+ // If we're not careful, answer will be 0. Answer below is computed with care in Octave:
+ Assert.assertEquals(9.48683298050514e-9, t, 1e-17);
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0.0, 1.0});
+ setInverseCumulativeTestValues(new double[] {0.0, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testAlpha() {
+ WeibullDistribution dist = new WeibullDistribution(1, 2);
+ Assert.assertEquals(1, dist.getShape(), 0);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new WeibullDistribution(0, 2);
+ }
+
+ @Test
+ public void testBeta() {
+ WeibullDistribution dist = new WeibullDistribution(1, 2);
+ Assert.assertEquals(2, dist.getScale(), 0);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ new WeibullDistribution(1, 0);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ WeibullDistribution dist;
+
+ dist = new WeibullDistribution(2.5, 3.5);
+ // In R: 3.5*gamma(1+(1/2.5)) (or emperically: mean(rweibull(10000, 2.5, 3.5)))
+ Assert.assertEquals(dist.getNumericalMean(), 3.5 * Math.exp(LogGamma.value(1 + (1 / 2.5))), tol);
+ Assert.assertEquals(dist.getNumericalVariance(), (3.5 * 3.5) *
+ Math.exp(LogGamma.value(1 + (2 / 2.5))) -
+ (dist.getNumericalMean() * dist.getNumericalMean()), tol);
+
+ dist = new WeibullDistribution(10.4, 2.222);
+ Assert.assertEquals(dist.getNumericalMean(), 2.222 * Math.exp(LogGamma.value(1 + (1 / 10.4))), tol);
+ Assert.assertEquals(dist.getNumericalVariance(), (2.222 * 2.222) *
+ Math.exp(LogGamma.value(1 + (2 / 10.4))) -
+ (dist.getNumericalMean() * dist.getNumericalMean()), tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ZipfDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ZipfDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ZipfDistributionTest.java
new file mode 100644
index 0000000..489a4bb
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ZipfDistributionTest.java
@@ -0,0 +1,166 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.simple.RandomSource;
+import org.junit.Assert;
+import org.junit.Ignore;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link ZipfDistribution}.
+ * Extends DiscreteDistributionAbstractTest.
+ * See class javadoc for DiscreteDistributionAbstractTest for details.
+ */
+public class ZipfDistributionTest extends DiscreteDistributionAbstractTest {
+
+ /**
+ * Constructor to override default tolerance.
+ */
+ public ZipfDistributionTest() {
+ setTolerance(1e-12);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions1() {
+ new ZipfDistribution(0, 1);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPreconditions2() {
+ new ZipfDistribution(1, 0);
+ }
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new ZipfDistribution(10, 1);
+ }
+
+ /** Creates the default probability density test input values */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] {-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ }
+
+ /**
+ * Creates the default probability density test expected values.
+ * Reference values are from R, version 2.15.3 (VGAM package 0.9-0).
+ */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0d, 0d, 0.341417152147, 0.170708576074, 0.113805717382, 0.0853542880369, 0.0682834304295,
+ 0.0569028586912, 0.0487738788782, 0.0426771440184, 0.0379352391275, 0.0341417152147, 0};
+ }
+
+ /**
+ * Creates the default logarithmic probability density test expected values.
+ * Reference values are from R, version 2.14.1.
+ */
+ @Override
+ public double[] makeLogDensityTestValues() {
+ return new double[] {Double.NEGATIVE_INFINITY, Double.NEGATIVE_INFINITY,
+ -1.07465022926458, -1.76779740982453, -2.17326251793269, -2.46094459038447,
+ -2.68408814169868, -2.86640969849264, -3.0205603783199, -3.15409177094442,
+ -3.2718748066008, -3.37723532225863, Double.NEGATIVE_INFINITY};
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0, 0, 0.341417152147, 0.512125728221, 0.625931445604, 0.71128573364,
+ 0.77956916407, 0.836472022761, 0.885245901639, 0.927923045658, 0.965858284785, 1d, 1d};
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.3413d, 0.3415d, 0.999d,
+ 0.990d, 0.975d, 0.950d, 0.900d, 1d};
+ }
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] {1, 1, 1, 1, 1, 1, 2, 10, 10, 10, 9, 8, 10};
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ ZipfDistribution dist;
+
+ dist = new ZipfDistribution(2, 0.5);
+ Assert.assertEquals(dist.getNumericalMean(), Math.sqrt(2), tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 0.24264068711928521, tol);
+ }
+
+
+ /**
+ * Test sampling for various number of points and exponents.
+ */
+ @Test
+ public void testSamplingExtended() {
+ int sampleSize = 1000;
+
+ int[] numPointsValues = {
+ 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100
+ };
+ double[] exponentValues = {
+ 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 2e-1, 5e-1,
+ 1. - 1e-9, 1.0, 1. + 1e-9, 1.1, 1.2, 1.3, 1.5, 1.6, 1.7, 1.8, 2.0,
+ 2.5, 3.0, 4., 5., 6., 7., 8., 9., 10., 20., 30., 100., 150.
+ };
+
+ for (int numPoints : numPointsValues) {
+ for (double exponent : exponentValues) {
+ double weightSum = 0.;
+ double[] weights = new double[numPoints];
+ for (int i = numPoints; i>=1; i-=1) {
+ weights[i-1] = Math.pow(i, -exponent);
+ weightSum += weights[i-1];
+ }
+
+ // Use fixed seed, the test is expected to fail for more than 50% of all
+ // seeds because each test case can fail with probability 0.001, the chance
+ // that all test cases do not fail is 0.999^(32*22) = 0.49442874426
+ DiscreteDistribution.Sampler distribution =
+ new ZipfDistribution(numPoints, exponent).createSampler(RandomSource.create(RandomSource.WELL_19937_C, 6));
+
+ double[] expectedCounts = new double[numPoints];
+ long[] observedCounts = new long[numPoints];
+ for (int i = 0; i < numPoints; i++) {
+ expectedCounts[i] = sampleSize * (weights[i]/weightSum);
+ }
+ int[] sample = AbstractDiscreteDistribution.sample(sampleSize, distribution);
+ for (int s : sample) {
+ observedCounts[s-1]++;
+ }
+ TestUtils.assertChiSquareAccept(expectedCounts, observedCounts, 0.001);
+ }
+ }
+ }
+}
[10/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java
new file mode 100644
index 0000000..bcf8e7c
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java
@@ -0,0 +1,130 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for AbstractDiscreteDistribution default implementations.
+ *
+ */
+public class AbstractDiscreteDistributionTest {
+ protected final DiceDistribution diceDistribution = new DiceDistribution();
+ protected final double p = diceDistribution.probability(1);
+
+ @Test
+ public void testInverseCumulativeProbabilityMethod()
+ {
+ double precision = 0.000000000000001;
+ Assert.assertEquals(1, diceDistribution.inverseCumulativeProbability(0));
+ Assert.assertEquals(1, diceDistribution.inverseCumulativeProbability((1d-Double.MIN_VALUE)/6d));
+ Assert.assertEquals(2, diceDistribution.inverseCumulativeProbability((1d+precision)/6d));
+ Assert.assertEquals(2, diceDistribution.inverseCumulativeProbability((2d-Double.MIN_VALUE)/6d));
+ Assert.assertEquals(3, diceDistribution.inverseCumulativeProbability((2d+precision)/6d));
+ Assert.assertEquals(3, diceDistribution.inverseCumulativeProbability((3d-Double.MIN_VALUE)/6d));
+ Assert.assertEquals(4, diceDistribution.inverseCumulativeProbability((3d+precision)/6d));
+ Assert.assertEquals(4, diceDistribution.inverseCumulativeProbability((4d-Double.MIN_VALUE)/6d));
+ Assert.assertEquals(5, diceDistribution.inverseCumulativeProbability((4d+precision)/6d));
+ Assert.assertEquals(5, diceDistribution.inverseCumulativeProbability((5d-precision)/6d));//Can't use Double.MIN
+ Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((5d+precision)/6d));
+ Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((6d-precision)/6d));//Can't use Double.MIN
+ Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((6d)/6d));
+ }
+
+ @Test
+ public void testCumulativeProbabilitiesSingleArguments() {
+ for (int i = 1; i < 7; i++) {
+ Assert.assertEquals(p * i,
+ diceDistribution.cumulativeProbability(i), Double.MIN_VALUE);
+ }
+ Assert.assertEquals(0.0,
+ diceDistribution.cumulativeProbability(0), Double.MIN_VALUE);
+ Assert.assertEquals(1.0,
+ diceDistribution.cumulativeProbability(7), Double.MIN_VALUE);
+ }
+
+ @Test
+ public void testProbabilitiesRangeArguments() {
+ int lower = 0;
+ int upper = 6;
+ for (int i = 0; i < 2; i++) {
+ // cum(0,6) = p(0 < X <= 6) = 1, cum(1,5) = 4/6, cum(2,4) = 2/6
+ Assert.assertEquals(1 - p * 2 * i,
+ diceDistribution.probability(lower, upper), 1E-12);
+ lower++;
+ upper--;
+ }
+ for (int i = 0; i < 6; i++) {
+ Assert.assertEquals(p, diceDistribution.probability(i, i+1), 1E-12);
+ }
+ }
+
+ /**
+ * Simple distribution modeling a 6-sided die
+ */
+ class DiceDistribution extends AbstractDiscreteDistribution {
+ public static final long serialVersionUID = 23734213;
+
+ private final double p = 1d/6d;
+
+ @Override
+ public double probability(int x) {
+ if (x < 1 || x > 6) {
+ return 0;
+ } else {
+ return p;
+ }
+ }
+
+ @Override
+ public double cumulativeProbability(int x) {
+ if (x < 1) {
+ return 0;
+ } else if (x >= 6) {
+ return 1;
+ } else {
+ return p * x;
+ }
+ }
+
+ @Override
+ public double getNumericalMean() {
+ return 3.5;
+ }
+
+ @Override
+ public double getNumericalVariance() {
+ return 70/24; // E(X^2) - E(X)^2
+ }
+
+ @Override
+ public int getSupportLowerBound() {
+ return 1;
+ }
+
+ @Override
+ public int getSupportUpperBound() {
+ return 6;
+ }
+
+ @Override
+ public final boolean isSupportConnected() {
+ return true;
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
new file mode 100644
index 0000000..f37961c
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
@@ -0,0 +1,381 @@
+/*
+ * 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.statistics.distribution;
+
+import java.util.Arrays;
+
+import org.apache.commons.rng.simple.RandomSource;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.math3.stat.StatUtils;
+import org.apache.commons.math3.stat.inference.GTest;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class BetaDistributionTest {
+
+ static final double[] alphaBetas = {0.1, 1, 10, 100, 1000};
+ static final double epsilon = StatUtils.min(alphaBetas);
+
+ @Test
+ public void testCumulative() {
+ double[] x = new double[]{-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1};
+ // all test data computed using R 2.5
+ checkCumulative(0.1, 0.1,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.4063850939, 0.4397091902, 0.4628041861,
+ 0.4821200456, 0.5000000000, 0.5178799544, 0.5371958139, 0.5602908098,
+ 0.5936149061, 1.0000000000, 1.0000000000});
+ checkCumulative(0.1, 0.5,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.7048336221, 0.7593042194, 0.7951765304,
+ 0.8234948385, 0.8480017124, 0.8706034370, 0.8926585878, 0.9156406404,
+ 0.9423662883, 1.0000000000, 1.0000000000});
+ checkCumulative(0.1, 1.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.7943282347, 0.8513399225, 0.8865681506,
+ 0.9124435366, 0.9330329915, 0.9502002165, 0.9649610951, 0.9779327685,
+ 0.9895192582, 1.0000000000, 1.0000000000});
+ checkCumulative(0.1, 2.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.8658177758, 0.9194471163, 0.9486279211,
+ 0.9671901487, 0.9796846411, 0.9882082252, 0.9939099280, 0.9974914239,
+ 0.9994144508, 1.0000000000, 1.0000000000});
+ checkCumulative(0.1, 4.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.9234991121, 0.9661958941, 0.9842285085,
+ 0.9928444112, 0.9970040660, 0.9989112804, 0.9996895625, 0.9999440793,
+ 0.9999967829, 1.0000000000, 1.0000000000});
+ checkCumulative(0.5, 0.1,
+ x, new double[]{
+ 0.00000000000, 0.00000000000, 0.05763371168, 0.08435935962,
+ 0.10734141216, 0.12939656302, 0.15199828760, 0.17650516146,
+ 0.20482346963, 0.24069578055, 0.29516637795, 1.00000000000, 1.00000000000});
+
+ checkCumulative(0.5, 0.5,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.2048327647, 0.2951672353, 0.3690101196,
+ 0.4359057832, 0.5000000000, 0.5640942168, 0.6309898804, 0.7048327647,
+ 0.7951672353, 1.0000000000, 1.0000000000});
+ checkCumulative(0.5, 1.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.3162277660, 0.4472135955, 0.5477225575,
+ 0.6324555320, 0.7071067812, 0.7745966692, 0.8366600265, 0.8944271910,
+ 0.9486832981, 1.0000000000, 1.0000000000});
+ checkCumulative(0.5, 2.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.4585302607, 0.6260990337, 0.7394254526,
+ 0.8221921916, 0.8838834765, 0.9295160031, 0.9621590305, 0.9838699101,
+ 0.9961174630, 1.0000000000, 1.0000000000});
+ checkCumulative(0.5, 4.0,
+ x, new double[]{
+ 0.0000000000, 0.0000000000, 0.6266250826, 0.8049844719, 0.8987784842,
+ 0.9502644369, 0.9777960959, 0.9914837366, 0.9974556254, 0.9995223859,
+ 0.9999714889, 1.0000000000, 1.0000000000});
+ checkCumulative(1.0, 0.1,
+ x, new double[]{
+ 0.00000000000, 0.00000000000, 0.01048074179, 0.02206723146,
+ 0.03503890488, 0.04979978349, 0.06696700846, 0.08755646344,
+ 0.11343184943, 0.14866007748, 0.20567176528, 1.00000000000, 1.00000000000});
+ checkCumulative(1.0, 0.5,
+ x, new double[]{
+ 0.00000000000, 0.00000000000, 0.05131670195, 0.10557280900,
+ 0.16333997347, 0.22540333076, 0.29289321881, 0.36754446797,
+ 0.45227744249, 0.55278640450, 0.68377223398, 1.00000000000, 1.00000000000});
+ checkCumulative(1, 1,
+ x, new double[]{
+ 0.0, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.0});
+ checkCumulative(1, 2,
+ x, new double[]{
+ 0.00, 0.00, 0.19, 0.36, 0.51, 0.64, 0.75, 0.84, 0.91, 0.96, 0.99, 1.00, 1.00});
+ checkCumulative(1, 4,
+ x, new double[]{
+ 0.0000, 0.0000, 0.3439, 0.5904, 0.7599, 0.8704, 0.9375, 0.9744, 0.9919,
+ 0.9984, 0.9999, 1.0000, 1.0000});
+ checkCumulative(2.0, 0.1,
+ x, new double[]{
+ 0.0000000000000, 0.0000000000000, 0.0005855492117, 0.0025085760862,
+ 0.0060900720266, 0.0117917748341, 0.0203153588864, 0.0328098512512,
+ 0.0513720788952, 0.0805528836776, 0.1341822241505, 1.0000000000000, 1.0000000000000});
+ checkCumulative(2, 1,
+ x, new double[]{
+ 0.00, 0.00, 0.01, 0.04, 0.09, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81, 1.00, 1.00});
+ checkCumulative(2.0, 0.5,
+ x, new double[]{
+ 0.000000000000, 0.000000000000, 0.003882537047, 0.016130089900,
+ 0.037840969486, 0.070483996910, 0.116116523517, 0.177807808356,
+ 0.260574547368, 0.373900966300, 0.541469739276, 1.000000000000, 1.000000000000});
+ checkCumulative(2, 2,
+ x, new double[]{
+ 0.000, 0.000, 0.028, 0.104, 0.216, 0.352, 0.500, 0.648, 0.784, 0.896, 0.972, 1.000, 1.000});
+ checkCumulative(2, 4,
+ x, new double[]{
+ 0.00000, 0.00000, 0.08146, 0.26272, 0.47178, 0.66304, 0.81250, 0.91296,
+ 0.96922, 0.99328, 0.99954, 1.00000, 1.00000});
+ checkCumulative(4.0, 0.1,
+ x, new double[]{
+ 0.000000000e+00, 0.000000000e+00, 3.217128269e-06, 5.592070271e-05,
+ 3.104375474e-04, 1.088719595e-03, 2.995933981e-03, 7.155588777e-03,
+ 1.577149153e-02, 3.380410585e-02, 7.650088789e-02, 1.000000000e+00, 1.000000000e+00});
+ checkCumulative(4.0, 0.5,
+ x, new double[]{
+ 0.000000000e+00, 0.000000000e+00, 2.851114863e-05, 4.776140576e-04,
+ 2.544374616e-03, 8.516263371e-03, 2.220390414e-02, 4.973556312e-02,
+ 1.012215158e-01, 1.950155281e-01, 3.733749174e-01, 1.000000000e+00, 1.000000000e+00});
+ checkCumulative(4, 1,
+ x, new double[]{
+ 0.0000, 0.0000, 0.0001, 0.0016, 0.0081, 0.0256, 0.0625, 0.1296, 0.2401,
+ 0.4096, 0.6561, 1.0000, 1.0000});
+ checkCumulative(4, 2,
+ x, new double[]{
+ 0.00000, 0.00000, 0.00046, 0.00672, 0.03078, 0.08704, 0.18750, 0.33696,
+ 0.52822, 0.73728, 0.91854, 1.00000, 1.00000});
+ checkCumulative(4, 4,
+ x, new double[]{
+ 0.000000, 0.000000, 0.002728, 0.033344, 0.126036, 0.289792, 0.500000,
+ 0.710208, 0.873964, 0.966656, 0.997272, 1.000000, 1.000000});
+ }
+
+ private void checkCumulative(double alpha, double beta, double[] x, double[] cumes) {
+ BetaDistribution d = new BetaDistribution(alpha, beta);
+ for (int i = 0; i < x.length; i++) {
+ Assert.assertEquals(cumes[i], d.cumulativeProbability(x[i]), 1e-8);
+ }
+
+ for (int i = 1; i < x.length - 1; i++) {
+ Assert.assertEquals(x[i], d.inverseCumulativeProbability(cumes[i]), 1e-5);
+ }
+ }
+
+ @Test
+ public void testDensity() {
+ double[] x = new double[]{1e-6, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9};
+ checkDensity(0.1, 0.1,
+ x, new double[]{
+ 12741.2357380649, 0.4429889586665234, 2.639378715e-01, 2.066393611e-01,
+ 1.832401831e-01, 1.766302780e-01, 1.832404579e-01, 2.066400696e-01,
+ 2.639396531e-01, 4.429925026e-01});
+ checkDensity(0.1, 0.5,
+ x, new double[]{
+ 2.218377102e+04, 7.394524202e-01, 4.203020268e-01, 3.119435533e-01,
+ 2.600787829e-01, 2.330648626e-01, 2.211408259e-01, 2.222728708e-01,
+ 2.414013907e-01, 3.070567405e-01});
+ checkDensity(0.1, 1.0,
+ x, new double[]{
+ 2.511886432e+04, 7.943210858e-01, 4.256680458e-01, 2.955218303e-01,
+ 2.281103709e-01, 1.866062624e-01, 1.583664652e-01, 1.378514078e-01,
+ 1.222414585e-01, 1.099464743e-01});
+ checkDensity(0.1, 2.0,
+ x, new double[]{
+ 2.763072312e+04, 7.863770012e-01, 3.745874120e-01, 2.275514842e-01,
+ 1.505525939e-01, 1.026332391e-01, 6.968107049e-02, 4.549081293e-02,
+ 2.689298641e-02, 1.209399123e-02});
+ checkDensity(0.1, 4.0,
+ x, new double[]{
+ 2.997927462e+04, 6.911058917e-01, 2.601128486e-01, 1.209774010e-01,
+ 5.880564714e-02, 2.783915474e-02, 1.209657335e-02, 4.442148268e-03,
+ 1.167143939e-03, 1.312171805e-04});
+ checkDensity(0.5, 0.1,
+ x, new double[]{
+ 88.3152184726, 0.3070542841, 0.2414007269, 0.2222727015,
+ 0.2211409364, 0.2330652355, 0.2600795198, 0.3119449793,
+ 0.4203052841, 0.7394649088});
+ checkDensity(0.5, 0.5,
+ x, new double[]{
+ 318.3100453389, 1.0610282383, 0.7957732234, 0.6946084565,
+ 0.6497470636, 0.6366197724, 0.6497476051, 0.6946097796,
+ 0.7957762075, 1.0610376697});
+ checkDensity(0.5, 1.0,
+ x, new double[]{
+ 500.0000000000, 1.5811309244, 1.1180311937, 0.9128694077,
+ 0.7905684268, 0.7071060741, 0.6454966865, 0.5976138778,
+ 0.5590166450, 0.5270459839});
+ checkDensity(0.5, 2.0,
+ x, new double[]{
+ 749.99925000000, 2.134537420613655, 1.34163575536, 0.95851150881,
+ 0.71151039830, 0.53032849490, 0.38729704363, 0.26892534859,
+ 0.16770415497, 0.07905610701});
+ checkDensity(0.5, 4.0,
+ x, new double[]{
+ 1.093746719e+03, 2.52142232809988, 1.252190241e+00, 6.849343920e-01,
+ 3.735417140e-01, 1.933481570e-01, 9.036885833e-02, 3.529621669e-02,
+ 9.782644546e-03, 1.152878503e-03});
+ checkDensity(1.0, 0.1,
+ x, new double[]{
+ 0.1000000900, 0.1099466942, 0.1222417336, 0.1378517623, 0.1583669403,
+ 0.1866069342, 0.2281113974, 0.2955236034, 0.4256718768,
+ 0.7943353837});
+ checkDensity(1.0, 0.5,
+ x, new double[]{
+ 0.5000002500, 0.5270465695, 0.5590173438, 0.5976147315, 0.6454977623,
+ 0.7071074883, 0.7905704033, 0.9128724506,
+ 1.1180367838, 1.5811467358});
+ checkDensity(1, 1,
+ x, new double[]{
+ 1, 1, 1,
+ 1, 1, 1, 1,
+ 1, 1, 1});
+ checkDensity(1, 2,
+ x, new double[]{
+ 1.999998, 1.799998, 1.599998, 1.399998, 1.199998, 0.999998, 0.799998,
+ 0.599998, 0.399998,
+ 0.199998});
+ checkDensity(1, 4,
+ x, new double[]{
+ 3.999988000012, 2.915990280011, 2.047992320010, 1.371994120008,
+ 0.863995680007, 0.499997000006, 0.255998080005, 0.107998920004,
+ 0.031999520002, 0.003999880001});
+ checkDensity(2.0, 0.1,
+ x, new double[]{
+ 1.100000990e-07, 1.209425730e-02, 2.689331586e-02, 4.549123318e-02,
+ 6.968162794e-02, 1.026340191e-01, 1.505537732e-01, 2.275534997e-01,
+ 3.745917198e-01, 7.863929037e-01});
+ checkDensity(2.0, 0.5,
+ x, new double[]{
+ 7.500003750e-07, 7.905777599e-02, 1.677060417e-01, 2.689275256e-01,
+ 3.872996256e-01, 5.303316769e-01, 7.115145488e-01, 9.585174425e-01,
+ 1.341645818e+00, 2.134537420613655});
+ checkDensity(2, 1,
+ x, new double[]{
+ 0.000002, 0.200002, 0.400002, 0.600002, 0.800002, 1.000002, 1.200002,
+ 1.400002, 1.600002,
+ 1.800002});
+ checkDensity(2, 2,
+ x, new double[]{
+ 5.9999940e-06, 5.4000480e-01, 9.6000360e-01, 1.2600024e+00,
+ 1.4400012e+00, 1.5000000e+00, 1.4399988e+00, 1.2599976e+00,
+ 9.5999640e-01, 5.3999520e-01});
+ checkDensity(2, 4,
+ x, new double[]{
+ 0.00001999994, 1.45800971996, 2.04800255997, 2.05799803998,
+ 1.72799567999, 1.24999500000, 0.76799552000, 0.37799676001,
+ 0.12799824001, 0.01799948000});
+ checkDensity(4.0, 0.1,
+ x, new double[]{
+ 1.193501074e-19, 1.312253162e-04, 1.167181580e-03, 4.442248535e-03,
+ 1.209679109e-02, 2.783958903e-02, 5.880649983e-02, 1.209791638e-01,
+ 2.601171405e-01, 6.911229392e-01});
+ checkDensity(4.0, 0.5,
+ x, new double[]{
+ 1.093750547e-18, 1.152948959e-03, 9.782950259e-03, 3.529697305e-02,
+ 9.037036449e-02, 1.933508639e-01, 3.735463833e-01, 6.849425461e-01,
+ 1.252205894e+00, 2.52142232809988});
+ checkDensity(4, 1,
+ x, new double[]{
+ 4.000000000e-18, 4.000120001e-03, 3.200048000e-02, 1.080010800e-01,
+ 2.560019200e-01, 5.000030000e-01, 8.640043200e-01, 1.372005880e+00,
+ 2.048007680e+00, 2.916009720e+00});
+ checkDensity(4, 2,
+ x, new double[]{
+ 1.999998000e-17, 1.800052000e-02, 1.280017600e-01, 3.780032400e-01,
+ 7.680044800e-01, 1.250005000e+00, 1.728004320e+00, 2.058001960e+00,
+ 2.047997440e+00, 1.457990280e+00});
+ checkDensity(4, 4,
+ x, new double[]{
+ 1.399995800e-16, 1.020627216e-01, 5.734464512e-01, 1.296547409e+00,
+ 1.935364838e+00, 2.187500000e+00, 1.935355162e+00, 1.296532591e+00,
+ 5.734335488e-01, 1.020572784e-01});
+ }
+
+ @SuppressWarnings("boxing")
+ private void checkDensity(double alpha, double beta, double[] x, double[] expected) {
+ BetaDistribution d = new BetaDistribution(alpha, beta);
+ for (int i = 0; i < x.length; i++) {
+ Assert.assertEquals(String.format("density at x=%.1f for alpha=%.1f, beta=%.1f", x[i], alpha, beta), expected[i], d.density(x[i]), 1e-5);
+ }
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ BetaDistribution dist;
+
+ dist = new BetaDistribution(1, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 1.0 / 12.0, tol);
+
+ dist = new BetaDistribution(2, 5);
+ Assert.assertEquals(dist.getNumericalMean(), 2.0 / 7.0, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 10.0 / (49.0 * 8.0), tol);
+ }
+
+ @Test
+ public void testMomentsSampling() {
+ final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_1024_A,
+ 123456789L);
+ final int numSamples = 1000;
+ for (final double alpha : alphaBetas) {
+ for (final double beta : alphaBetas) {
+ final BetaDistribution betaDistribution = new BetaDistribution(alpha, beta);
+ final double[] observed = AbstractContinuousDistribution.sample(numSamples,
+ betaDistribution.createSampler(rng));
+ Arrays.sort(observed);
+
+ final String distribution = String.format("Beta(%.2f, %.2f)", alpha, beta);
+ Assert.assertEquals(String.format("E[%s]", distribution),
+ betaDistribution.getNumericalMean(),
+ StatUtils.mean(observed), epsilon);
+ Assert.assertEquals(String.format("Var[%s]", distribution),
+ betaDistribution.getNumericalVariance(),
+ StatUtils.variance(observed), epsilon);
+ }
+ }
+ }
+
+ @Test
+ public void testGoodnessOfFit() {
+ final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_19937_A,
+ 123456789L);
+
+ final int numSamples = 1000;
+ final double level = 0.01;
+ for (final double alpha : alphaBetas) {
+ for (final double beta : alphaBetas) {
+ final BetaDistribution betaDistribution = new BetaDistribution(alpha, beta);
+
+ final ContinuousDistribution.Sampler sampler = betaDistribution.createSampler(rng);
+ final double[] observed = AbstractContinuousDistribution.sample(numSamples, sampler);
+
+ final double gT = gTest(betaDistribution, observed);
+ Assert.assertFalse("G goodness-of-fit (" + gT + ") test rejected null at alpha = " + level,
+ gT < level);
+ }
+ }
+ }
+
+ private double gTest(final ContinuousDistribution expectedDistribution, final double[] values) {
+ final int numBins = values.length / 30;
+ final double[] breaks = new double[numBins];
+ for (int b = 0; b < breaks.length; b++) {
+ breaks[b] = expectedDistribution.inverseCumulativeProbability((double) b / numBins);
+ }
+
+ final long[] observed = new long[numBins];
+ for (final double value : values) {
+ int b = 0;
+ do {
+ b++;
+ } while (b < numBins && value >= breaks[b]);
+
+ observed[b - 1]++;
+ }
+
+ final double[] expected = new double[numBins];
+ Arrays.fill(expected, (double) values.length / numBins);
+
+ return new GTest().gTest(expected, observed);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java
new file mode 100644
index 0000000..9d5a97e
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java
@@ -0,0 +1,173 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for BinomialDistribution. Extends DiscreteDistributionAbstractTest.
+ * See class javadoc for DiscreteDistributionAbstractTest for details.
+ *
+ */
+public class BinomialDistributionTest extends DiscreteDistributionAbstractTest {
+
+ /**
+ * Constructor to override default tolerance.
+ */
+ public BinomialDistributionTest() {
+ setTolerance(1e-12);
+ }
+
+ // -------------- Implementations for abstract methods
+ // -----------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new BinomialDistribution(10, 0.70);
+ }
+
+ /** Creates the default probability density test input values. */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] { -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
+ }
+
+ /**
+ * Creates the default probability density test expected values.
+ * Reference values are from R, version 2.15.3.
+ */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] { 0d, 0.0000059049d, 0.000137781d, 0.0014467005,
+ 0.009001692, 0.036756909, 0.1029193452, 0.200120949, 0.266827932,
+ 0.2334744405, 0.121060821, 0.0282475249, 0d };
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /**
+ * Creates the default cumulative probability density test expected values.
+ * Reference values are from R, version 2.15.3.
+ */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] { 0d, 5.9049e-06, 0.0001436859, 0.0015903864, 0.0105920784, 0.0473489874,
+ 0.1502683326, 0.3503892816, 0.6172172136, 0.8506916541, 0.9717524751, 1d, 1d };
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] { 0, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d,
+ 0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 };
+ }
+
+ /**
+ * Creates the default inverse cumulative probability density test expected
+ * values
+ */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] { 0, 2, 3, 4, 5, 5, 10, 10, 10, 9, 9, 10 };
+ }
+
+ // ----------------- Additional test cases ---------------------------------
+
+ /** Test degenerate case p = 0 */
+ @Test
+ public void testDegenerate0() {
+ BinomialDistribution dist = new BinomialDistribution(5, 0.0d);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] { -1, 0, 1, 5, 10 });
+ setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d });
+ setDensityTestPoints(new int[] { -1, 0, 1, 10, 11 });
+ setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d });
+ setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
+ setInverseCumulativeTestValues(new int[] { 0, 0 });
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 0);
+ Assert.assertEquals(dist.getSupportUpperBound(), 0);
+ }
+
+ /** Test degenerate case p = 1 */
+ @Test
+ public void testDegenerate1() {
+ BinomialDistribution dist = new BinomialDistribution(5, 1.0d);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
+ setCumulativeTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 1d });
+ setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
+ setDensityTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 0d });
+ setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
+ setInverseCumulativeTestValues(new int[] { 5, 5 });
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 5);
+ Assert.assertEquals(dist.getSupportUpperBound(), 5);
+ }
+
+ /** Test degenerate case n = 0 */
+ @Test
+ public void testDegenerate2() {
+ BinomialDistribution dist = new BinomialDistribution(0, 0.01d);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
+ setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d, 1d });
+ setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
+ setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d, 0d });
+ setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
+ setInverseCumulativeTestValues(new int[] { 0, 0 });
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 0);
+ Assert.assertEquals(dist.getSupportUpperBound(), 0);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ BinomialDistribution dist;
+
+ dist = new BinomialDistribution(10, 0.5);
+ Assert.assertEquals(dist.getNumericalMean(), 10d * 0.5d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 10d * 0.5d * 0.5d, tol);
+
+ dist = new BinomialDistribution(30, 0.3);
+ Assert.assertEquals(dist.getNumericalMean(), 30d * 0.3d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 30d * 0.3d * (1d - 0.3d), tol);
+ }
+
+ @Test
+ public void testMath718() {
+ // for large trials the evaluation of ContinuedFraction was inaccurate
+ // do a sweep over several large trials to test if the current implementation is
+ // numerically stable.
+
+ for (int trials = 500000; trials < 20000000; trials += 100000) {
+ BinomialDistribution dist = new BinomialDistribution(trials, 0.5);
+ int p = dist.inverseCumulativeProbability(0.5);
+ Assert.assertEquals(trials / 2, p);
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java
new file mode 100644
index 0000000..4407976
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java
@@ -0,0 +1,111 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for CauchyDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class CauchyDistributionTest extends ContinuousDistributionAbstractTest {
+
+ // --------------------- Override tolerance --------------
+ protected double defaultTolerance = 1e-7;
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(defaultTolerance);
+ }
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public CauchyDistribution makeDistribution() {
+ return new CauchyDistribution(1.2, 2.1);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R 2.9.2
+ return new double[] {-667.24856187, -65.6230835029, -25.4830299460, -12.0588781808,
+ -5.26313542807, 669.64856187, 68.0230835029, 27.8830299460, 14.4588781808, 7.66313542807};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999,
+ 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437,
+ 1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437};
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0.0, 1.0});
+ setInverseCumulativeTestValues(new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testMedian() {
+ CauchyDistribution distribution = (CauchyDistribution) getDistribution();
+ Assert.assertEquals(1.2, distribution.getMedian(), 0.0);
+ }
+
+ @Test
+ public void testScale() {
+ CauchyDistribution distribution = (CauchyDistribution) getDistribution();
+ Assert.assertEquals(2.1, distribution.getScale(), 0.0);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new CauchyDistribution(0, 0);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ new CauchyDistribution(0, -1);
+ }
+
+ @Test
+ public void testMoments() {
+ CauchyDistribution dist;
+
+ dist = new CauchyDistribution(10.2, 0.15);
+ Assert.assertTrue(Double.isNaN(dist.getNumericalMean()));
+ Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
+
+ dist = new CauchyDistribution(23.12, 2.12);
+ Assert.assertTrue(Double.isNaN(dist.getNumericalMean()));
+ Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
new file mode 100644
index 0000000..dc97f47
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
@@ -0,0 +1,136 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link ChiSquaredDistribution}.
+ *
+ * @see ContinuousDistributionAbstractTest
+ */
+public class ChiSquaredDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public ChiSquaredDistribution makeDistribution() {
+ return new ChiSquaredDistribution(5.0);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
+ 20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
+ 0.990d, 0.975d, 0.950d, 0.900d, 1};
+ }
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ @Override
+ public double[] makeInverseCumulativeTestValues() {
+ return new double[] {0, 0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
+ 20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978,
+ Double.POSITIVE_INFINITY};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.0115379817652, 0.0415948507811, 0.0665060119842, 0.0919455953114, 0.121472591024,
+ 0.000433630076361, 0.00412780610309, 0.00999340341045, 0.0193246438937, 0.0368460089216};
+ }
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ @Test
+ public void testSmallDf() {
+ setDistribution(new ChiSquaredDistribution(0.1d));
+ setTolerance(1E-4);
+ // quantiles computed using R version 1.8.1 (linux version)
+ setCumulativeTestPoints(new double[] {1.168926E-60, 1.168926E-40, 1.063132E-32,
+ 1.144775E-26, 1.168926E-20, 5.472917, 2.175255, 1.13438,
+ 0.5318646, 0.1526342});
+ setInverseCumulativeTestValues(getCumulativeTestPoints());
+ setInverseCumulativeTestPoints(getCumulativeTestValues());
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testDfAccessors() {
+ ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
+ Assert.assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
+ }
+
+ @Test
+ public void testDensity() {
+ double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
+ //R 2.5: print(dchisq(x, df=1), digits=10)
+ checkDensity(1, x, new double[]{0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436, 0.01464498256});
+ //R 2.5: print(dchisq(x, df=0.1), digits=10)
+ checkDensity(0.1, x, new double[]{0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02, 9.447299159e-03, 8.827199396e-04});
+ //R 2.5: print(dchisq(x, df=2), digits=10)
+ checkDensity(2, x, new double[]{0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059, 0.04104249931});
+ //R 2.5: print(dchisq(x, df=10), digits=10)
+ checkDensity(10, x, new double[]{0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04, 7.664155024e-03, 6.680094289e-02});
+ }
+
+ private void checkDensity(double df, double[] x, double[] expected) {
+ ChiSquaredDistribution d = new ChiSquaredDistribution(df);
+ for (int i = 0; i < x.length; i++) {
+ Assert.assertEquals(expected[i], d.density(x[i]), 1e-5);
+ }
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ ChiSquaredDistribution dist;
+
+ dist = new ChiSquaredDistribution(1500);
+ Assert.assertEquals(dist.getNumericalMean(), 1500, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 3000, tol);
+
+ dist = new ChiSquaredDistribution(1.12);
+ Assert.assertEquals(dist.getNumericalMean(), 1.12, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 2.24, tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java
new file mode 100644
index 0000000..152a6c2
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java
@@ -0,0 +1,92 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for ConstantContinuousDistribution.
+ */
+public class ConstantContinuousDistributionTest extends ContinuousDistributionAbstractTest {
+
+ // --- Override tolerance -------------------------------------------------
+
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(0);
+ }
+
+ //--- Implementations for abstract methods --------------------------------
+
+ /** Creates the default uniform real distribution instance to use in tests. */
+ @Override
+ public ConstantContinuousDistribution makeDistribution() {
+ return new ConstantContinuousDistribution(1);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {0, 0.5, 1};
+ }
+
+ /** Creates the default cumulative probability distribution test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0, 0, 1};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0, 0, 1};
+ }
+
+ /** Override default test, verifying that inverse cum is constant */
+ @Override
+ @Test
+ public void testInverseCumulativeProbabilities() {
+ ContinuousDistribution dist = getDistribution();
+ for (double x : getCumulativeTestValues()) {
+ Assert.assertEquals(1,dist.inverseCumulativeProbability(x), 0);
+ }
+ }
+
+ //--- Additional test cases -----------------------------------------------
+
+ @Test
+ public void testMeanVariance() {
+ ConstantContinuousDistribution dist;
+
+ dist = new ConstantContinuousDistribution(-1);
+ Assert.assertEquals(dist.getNumericalMean(), -1, 0d);
+ Assert.assertEquals(dist.getNumericalVariance(), 0, 0d);
+ }
+
+ @Test
+ @Override
+ public void testSampler() {
+ final double value = 12.345;
+ final ContinuousDistribution.Sampler sampler = new ConstantContinuousDistribution(value).createSampler(null);
+ for (int i = 0; i < 10; i++) {
+ Assert.assertEquals(value, sampler.sample(), 0);
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java
new file mode 100644
index 0000000..a6176f3
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java
@@ -0,0 +1,456 @@
+/*
+ * 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.statistics.distribution;
+
+
+import java.util.ArrayList;
+import java.util.Collections;
+
+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.rng.simple.RandomSource;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+/**
+ * Abstract base class for {@link ContinuousDistribution} 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 probabilities
+ * 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 overridden 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 ContinuousDistributionAbstractTest {
+
+//-------------------- Private test instance data -------------------------
+ /** Distribution instance used to perform tests */
+ private ContinuousDistribution 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 ContinuousDistribution 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] = Math.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 probability(double, double)
+ 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.probability(cumulativeTestPoints[i], cumulativeTestPoints[j]),
+ getTolerance());
+ } else {
+ try {
+ distribution.probability(cumulativeTestPoints[i], cumulativeTestPoints[j]);
+ } catch (IllegalArgumentException e) {
+ continue;
+ }
+ Assert.fail("distribution.probability(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() {
+ for (int i = 0; i < cumulativeTestPoints.length; i++) {
+ TestUtils.assertEquals("Incorrect probability density value returned for "
+ + cumulativeTestPoints[i], logDensityTestValues[i],
+ 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
+ TestUtils.assertEquals(0d,
+ distribution.probability
+ (cumulativeTestPoints[i], cumulativeTestPoints[i]),
+ tolerance);
+
+ // check that P(a < X <= b) = P(X <= b) - P(X <= a)
+ double upper = Math.max(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
+ double lower = Math.min(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
+ double diff = distribution.cumulativeProbability(upper) -
+ distribution.cumulativeProbability(lower);
+ double direct = distribution.probability(lower, upper);
+ TestUtils.assertEquals("Inconsistent probability for ("
+ + lower + "," + upper + ")", diff, direct, tolerance);
+ }
+ }
+
+ /**
+ * Verifies that illegal arguments are correctly handled
+ */
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ distribution.probability(1, 0);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ distribution.inverseCumulativeProbability(-1);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition3() {
+ distribution.inverseCumulativeProbability(2);
+ }
+
+ /**
+ * Test sampling
+ */
+ @Test
+ public void testSampler() {
+ final int sampleSize = 1000;
+ final ContinuousDistribution.Sampler sampler =
+ distribution.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 123456789L));
+ final double[] sample = AbstractContinuousDistribution.sample(sampleSize, sampler);
+ final double[] quartiles = TestUtils.getDistributionQuartiles(distribution);
+ final double[] expected = {250, 250, 250, 250};
+ final 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 = 1e-9;
+ final BaseAbstractUnivariateIntegrator integrator =
+ new IterativeLegendreGaussIntegrator(5, 1e-12, 1e-10);
+ final UnivariateFunction d = new UnivariateFunction() {
+ @Override
+ public double value(double x) {
+ return distribution.density(x);
+ }
+ };
+ final ArrayList<Double> integrationTestPoints = new ArrayList<>();
+ for (int i = 0; i < cumulativeTestPoints.length; i++) {
+ if (Double.isNaN(cumulativeTestValues[i]) ||
+ cumulativeTestValues[i] < 1e-5 ||
+ cumulativeTestValues[i] > 1 - 1e-5) {
+ continue; // exclude integrals outside domain.
+ }
+ integrationTestPoints.add(cumulativeTestPoints[i]);
+ }
+ Collections.sort(integrationTestPoints);
+ for (int i = 1; i < integrationTestPoints.size(); i++) {
+ Assert.assertEquals(distribution.probability(integrationTestPoints.get(0), integrationTestPoints.get(i)),
+ integrator.integrate(1000000, // Triangle integrals are very slow to converge
+ d, integrationTestPoints.get(0),
+ integrationTestPoints.get(i)), tol);
+ }
+ }
+
+ //------------------ 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 ContinuousDistribution getDistribution() {
+ return distribution;
+ }
+
+ /**
+ * @param distribution The distribution to set.
+ */
+ protected void setDistribution(ContinuousDistribution 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;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java
new file mode 100644
index 0000000..ab0e0a1
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java
@@ -0,0 +1,411 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.simple.RandomSource;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+/**
+ * Abstract base class for {@link DiscreteDistribution} tests.
+ * <p>
+ * To create a concrete test class for an integer distribution implementation,
+ * implement makeDistribution() to return a distribution instance to use in
+ * tests and 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().
+ * <p>
+ * makeDensityTestPoints() -- arguments used to test probability density calculation
+ * makeDensityTestValues() -- expected probability densities
+ * makeCumulativeTestPoints() -- arguments used to test cumulative probabilities
+ * makeCumulativeTestValues() -- expected cumulative probabilites
+ * makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf evaluation
+ * 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.
+ *
+ */
+public abstract class DiscreteDistributionAbstractTest {
+
+//-------------------- Private test instance data -------------------------
+ /** Discrete distribution instance used to perform tests */
+ private DiscreteDistribution distribution;
+
+ /** Tolerance used in comparing expected and returned values */
+ private double tolerance = 1e-12;
+
+ /** Arguments used to test probability density calculations */
+ private int[] densityTestPoints;
+
+ /** Values used to test probability density calculations */
+ private double[] densityTestValues;
+
+ /** Values used to test logarithmic probability density calculations */
+ private double[] logDensityTestValues;
+
+ /** Arguments used to test cumulative probability density calculations */
+ private int[] 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 int[] inverseCumulativeTestValues;
+
+ //-------------------- Abstract methods -----------------------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ public abstract DiscreteDistribution makeDistribution();
+
+ /** Creates the default probability density test input values */
+ public abstract int[] makeDensityTestPoints();
+
+ /** Creates the default probability density test expected values */
+ public abstract double[] makeDensityTestValues();
+
+ /** Creates the default logarithmic probability density test expected values.
+ *
+ * The default implementation simply computes the logarithm of all the values in
+ * {@link #makeDensityTestValues()}.
+ *
+ * @return double[] the default logarithmic probability density test expected values.
+ */
+ public double[] makeLogDensityTestValues() {
+ final double[] densityTestValues = makeDensityTestValues();
+ final double[] logDensityTestValues = new double[densityTestValues.length];
+ for (int i = 0; i < densityTestValues.length; i++) {
+ logDensityTestValues[i] = Math.log(densityTestValues[i]);
+ }
+ return logDensityTestValues;
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ public abstract int[] makeCumulativeTestPoints();
+
+ /** Creates the default cumulative probability density test expected values */
+ public abstract double[] makeCumulativeTestValues();
+
+ /** Creates the default inverse cumulative probability test input values */
+ public abstract double[] makeInverseCumulativeTestPoints();
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ public abstract int[] makeInverseCumulativeTestValues();
+
+ //-------------------- Setup / tear down ----------------------------------
+
+ /**
+ * Setup sets all test instance data to default values
+ */
+ @Before
+ public void setUp() {
+ distribution = makeDistribution();
+ densityTestPoints = makeDensityTestPoints();
+ densityTestValues = makeDensityTestValues();
+ logDensityTestValues = makeLogDensityTestValues();
+ cumulativeTestPoints = makeCumulativeTestPoints();
+ cumulativeTestValues = makeCumulativeTestValues();
+ inverseCumulativeTestPoints = makeInverseCumulativeTestPoints();
+ inverseCumulativeTestValues = makeInverseCumulativeTestValues();
+ }
+
+ /**
+ * Cleans up test instance data
+ */
+ @After
+ public void tearDown() {
+ distribution = null;
+ densityTestPoints = null;
+ densityTestValues = null;
+ logDensityTestValues = null;
+ cumulativeTestPoints = null;
+ cumulativeTestValues = null;
+ inverseCumulativeTestPoints = null;
+ inverseCumulativeTestValues = null;
+ }
+
+ //-------------------- Verification methods -------------------------------
+
+ /**
+ * Verifies that probability density calculations match expected values
+ * using current test instance data
+ */
+ protected void verifyDensities() {
+ for (int i = 0; i < densityTestPoints.length; i++) {
+ Assert.assertEquals("Incorrect density value returned for " + densityTestPoints[i],
+ densityTestValues[i],
+ distribution.probability(densityTestPoints[i]), getTolerance());
+ }
+ }
+
+ /**
+ * Verifies that logarithmic probability density calculations match expected values
+ * using current test instance data.
+ */
+ protected void verifyLogDensities() {
+ for (int i = 0; i < densityTestPoints.length; i++) {
+ // FIXME: when logProbability methods are added to DiscreteDistribution in 4.0, remove cast below
+ Assert.assertEquals("Incorrect log density value returned for " + densityTestPoints[i],
+ logDensityTestValues[i],
+ ((AbstractDiscreteDistribution) distribution).logProbability(densityTestPoints[i]), tolerance);
+ }
+ }
+
+ /**
+ * Verifies that cumulative probability density calculations match expected values
+ * using current test instance data
+ */
+ protected void verifyCumulativeProbabilities() {
+ for (int i = 0; i < cumulativeTestPoints.length; i++) {
+ Assert.assertEquals("Incorrect cumulative probability value returned for " + cumulativeTestPoints[i],
+ cumulativeTestValues[i],
+ distribution.cumulativeProbability(cumulativeTestPoints[i]), getTolerance());
+ }
+ }
+
+
+ /**
+ * 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++) {
+ Assert.assertEquals("Incorrect inverse cumulative probability value returned for "
+ + inverseCumulativeTestPoints[i], inverseCumulativeTestValues[i],
+ distribution.inverseCumulativeProbability(inverseCumulativeTestPoints[i]));
+ }
+ }
+
+ //------------------------ Default test cases -----------------------------
+
+ /**
+ * Verifies that probability density calculations match expected values
+ * using default test instance data
+ */
+ @Test
+ public void testDensities() {
+ verifyDensities();
+ }
+
+ /**
+ * Verifies that logarithmic probability density calculations match expected values
+ * using default test instance data
+ */
+ @Test
+ public void testLogDensities() {
+ verifyLogDensities();
+ }
+
+ /**
+ * 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();
+ }
+
+ @Test
+ public void testConsistencyAtSupportBounds() {
+ final int lower = distribution.getSupportLowerBound();
+ Assert.assertEquals("Cumulative probability mmust be 0 below support lower bound.",
+ 0.0, distribution.cumulativeProbability(lower - 1), 0.0);
+ Assert.assertEquals("Cumulative probability of support lower bound must be equal to probability mass at this point.",
+ distribution.probability(lower), distribution.cumulativeProbability(lower), getTolerance());
+ Assert.assertEquals("Inverse cumulative probability of 0 must be equal to support lower bound.",
+ lower, distribution.inverseCumulativeProbability(0.0));
+
+ final int upper = distribution.getSupportUpperBound();
+ if (upper != Integer.MAX_VALUE) {
+ Assert.assertEquals("Cumulative probability of support upper bound must be equal to 1.",
+ 1.0, distribution.cumulativeProbability(upper), 0.0);
+ }
+ Assert.assertEquals("Inverse cumulative probability of 1 must be equal to support upper bound.",
+ upper, distribution.inverseCumulativeProbability(1.0));
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ distribution.probability(1, 0);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ distribution.inverseCumulativeProbability(-1);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition3() {
+ distribution.inverseCumulativeProbability(2);
+ }
+
+ /**
+ * Test sampling
+ */
+ @Test
+ public void testSampling() {
+ int[] densityPoints = makeDensityTestPoints();
+ double[] densityValues = makeDensityTestValues();
+ int sampleSize = 1000;
+ int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
+ AbstractDiscreteDistribution distribution = (AbstractDiscreteDistribution) makeDistribution();
+ double[] expectedCounts = new double[length];
+ long[] observedCounts = new long[length];
+ for (int i = 0; i < length; i++) {
+ expectedCounts[i] = sampleSize * densityValues[i];
+ }
+ // Use fixed seed.
+ final DiscreteDistribution.Sampler sampler =
+ distribution.createSampler(RandomSource.create(RandomSource.WELL_512_A,
+ 1000));
+ int[] sample = AbstractDiscreteDistribution.sample(sampleSize, sampler);
+ for (int i = 0; i < sampleSize; i++) {
+ for (int j = 0; j < length; j++) {
+ if (sample[i] == densityPoints[j]) {
+ observedCounts[j]++;
+ }
+ }
+ }
+ TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
+ }
+
+ //------------------ Getters / Setters for test instance data -----------
+ /**
+ * @return Returns the cumulativeTestPoints.
+ */
+ protected int[] getCumulativeTestPoints() {
+ return cumulativeTestPoints;
+ }
+
+ /**
+ * @param cumulativeTestPoints The cumulativeTestPoints to set.
+ */
+ protected void setCumulativeTestPoints(int[] 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;
+ }
+
+ /**
+ * @return Returns the densityTestPoints.
+ */
+ protected int[] getDensityTestPoints() {
+ return densityTestPoints;
+ }
+
+ /**
+ * @param densityTestPoints The densityTestPoints to set.
+ */
+ protected void setDensityTestPoints(int[] densityTestPoints) {
+ this.densityTestPoints = densityTestPoints;
+ }
+
+ /**
+ * @return Returns the densityTestValues.
+ */
+ protected double[] getDensityTestValues() {
+ return densityTestValues;
+ }
+
+ /**
+ * @param densityTestValues The densityTestValues to set.
+ */
+ protected void setDensityTestValues(double[] densityTestValues) {
+ this.densityTestValues = densityTestValues;
+ }
+
+ /**
+ * @return Returns the distribution.
+ */
+ protected DiscreteDistribution getDistribution() {
+ return distribution;
+ }
+
+ /**
+ * @param distribution The distribution to set.
+ */
+ protected void setDistribution(DiscreteDistribution 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 int[] getInverseCumulativeTestValues() {
+ return inverseCumulativeTestValues;
+ }
+
+ /**
+ * @param inverseCumulativeTestValues The inverseCumulativeTestValues to set.
+ */
+ protected void setInverseCumulativeTestValues(int[] 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;
+ }
+}
[12/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DiscreteDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DiscreteDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DiscreteDistribution.java
new file mode 100644
index 0000000..1cd4a35
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DiscreteDistribution.java
@@ -0,0 +1,163 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+
+/**
+ * Interface for distributions on the integers.
+ */
+public interface DiscreteDistribution {
+
+ /**
+ * For a random variable {@code X} whose values are distributed according to
+ * this distribution, this method returns {@code log(P(X = x))}, where
+ * {@code log} is the natural logarithm. In other words, this method
+ * represents the logarithm of the probability mass function (PMF) for the
+ * distribution. Note that due to the floating point precision and
+ * under/overflow issues, this method will for some distributions be more
+ * precise and faster than computing the logarithm of
+ * {@link #probability(int)}.
+ *
+ * @param x the point at which the PMF is evaluated
+ * @return the logarithm of the value of the probability mass function at {@code x}
+ */
+ double logProbability(int x);
+
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(X = x)}. In other
+ * words, this method represents the probability mass function (PMF)
+ * for the distribution.
+ *
+ * @param x the point at which the PMF is evaluated
+ * @return the value of the probability mass function at {@code x}
+ */
+ double probability(int x);
+
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(x0 < X <= x1)}.
+ *
+ * @param x0 the exclusive lower bound
+ * @param x1 the inclusive upper bound
+ * @return the probability that a random variable with this distribution
+ * will take a value between {@code x0} and {@code x1},
+ * excluding the lower and including the upper endpoint
+ * @throws IllegalArgumentException if {@code x0 > x1}
+ */
+ double probability(int x0, int x1);
+
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(X <= x)}. In other
+ * words, this method represents the (cumulative) distribution function
+ * (CDF) for this distribution.
+ *
+ * @param x the point at which the CDF is evaluated
+ * @return the probability that a random variable with this
+ * distribution takes a value less than or equal to {@code x}
+ */
+ double cumulativeProbability(int x);
+
+ /**
+ * Computes the quantile function of this distribution.
+ * For a random variable {@code X} distributed according to this distribution,
+ * the returned value is
+ * <ul>
+ * <li>{@code inf{x in Z | P(X<=x) >= p}} for {@code 0 < p <= 1},</li>
+ * <li>{@code inf{x in Z | P(X<=x) > 0}} for {@code p = 0}.</li>
+ * </ul>
+ * If the result exceeds the range of the data type {@code int},
+ * then {@code Integer.MIN_VALUE} or {@code Integer.MAX_VALUE} is returned.
+ *
+ * @param p the cumulative probability
+ * @return the smallest {@code p}-quantile of this distribution
+ * (largest 0-quantile for {@code p = 0})
+ * @throws IllegalArgumentException if {@code p < 0} or {@code p > 1}
+ */
+ int inverseCumulativeProbability(double p);
+
+ /**
+ * Use this method to get the numerical value of the mean of this
+ * distribution.
+ *
+ * @return the mean or {@code Double.NaN} if it is not defined
+ */
+ double getNumericalMean();
+
+ /**
+ * Use this method to get the numerical value of the variance of this
+ * distribution.
+ *
+ * @return the variance (possibly {@code Double.POSITIVE_INFINITY} or
+ * {@code Double.NaN} if it is not defined)
+ */
+ double getNumericalVariance();
+
+ /**
+ * Access the lower bound of the support. This method must return the same
+ * value as {@code inverseCumulativeProbability(0)}. In other words, this
+ * method must return
+ * <p>{@code inf {x in Z | P(X <= x) > 0}}.</p>
+ *
+ * @return lower bound of the support ({@code Integer.MIN_VALUE}
+ * for negative infinity)
+ */
+ int getSupportLowerBound();
+
+ /**
+ * Access the upper bound of the support. This method must return the same
+ * value as {@code inverseCumulativeProbability(1)}. In other words, this
+ * method must return
+ * <p>{@code inf {x in R | P(X <= x) = 1}}.</p>
+ *
+ * @return upper bound of the support ({@code Integer.MAX_VALUE}
+ * for positive infinity)
+ */
+ int getSupportUpperBound();
+
+ /**
+ * Use this method to get information about whether the support is
+ * connected, i.e. whether all integers between the lower and upper bound of
+ * the support are included in the support.
+ *
+ * @return whether the support is connected or not
+ */
+ boolean isSupportConnected();
+
+ /**
+ * Creates a sampler.
+ *
+ * @param rng Generator of uniformly distributed numbers.
+ * @return a sampler that produces random numbers according this
+ * distribution.
+ */
+ Sampler createSampler(UniformRandomProvider rng);
+
+ /**
+ * Sampling functionality.
+ */
+ interface Sampler {
+ /**
+ * Generates a random value sampled from this distribution.
+ *
+ * @return a random value.
+ */
+ int sample();
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DistributionException.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DistributionException.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DistributionException.java
new file mode 100644
index 0000000..31d7adc
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/DistributionException.java
@@ -0,0 +1,61 @@
+/*
+ * 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.statistics.distribution;
+
+import java.text.MessageFormat;
+
+/**
+ * Package private exception class with constants for frequently used messages.
+ */
+class DistributionException extends IllegalArgumentException {
+ /** Error message for "too large" condition. */
+ static final String TOO_LARGE = "{0} > {1}";
+ /** Error message for "too small" condition. */
+ static final String TOO_SMALL = "{0} < {1}";
+ /** Error message for "out of range" condition. */
+ static final String OUT_OF_RANGE = "Number {0} is out of range [{1}, {2}]";
+ /** Error message for "out of range" condition. */
+ static final String NEGATIVE = "Number {0} is negative";
+ /** Error message for "mismatch" condition. */
+ static final String MISMATCH = "Expected {1} but was {0}";
+ /** Error message for "failed bracketing" condition. */
+ static final String BRACKETING = "No bracketing: f({0})={1}, f({2})={3}";
+
+ /** Serializable version identifier. */
+ private static final long serialVersionUID = 20180119L;
+
+ /** Arguments for formatting the message. */
+ private Object[] formatArguments;
+
+ /**
+ * Create an exception where the message is constructed by applying
+ * the {@code format()} method from {@code java.text.MessageFormat}.
+ *
+ * @param message the exception message with replaceable parameters
+ * @param formatArguments the arguments for formatting the message
+ */
+ DistributionException(String message, Object... formatArguments) {
+ super(message);
+ this.formatArguments = formatArguments;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public String getMessage() {
+ return MessageFormat.format(super.getMessage(), formatArguments);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ExponentialDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ExponentialDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ExponentialDistribution.java
new file mode 100644
index 0000000..091c6a4
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ExponentialDistribution.java
@@ -0,0 +1,197 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.AhrensDieterExponentialSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Exponential_distribution">exponential distribution</a>.
+ */
+public class ExponentialDistribution extends AbstractContinuousDistribution {
+ /** The mean of this distribution. */
+ private final double mean;
+ /** The logarithm of the mean, stored to reduce computing time. */
+ private final double logMean;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mean Mean of this distribution.
+ * @throws IllegalArgumentException if {@code mean <= 0}.
+ */
+ public ExponentialDistribution(double mean) {
+ if (mean <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, mean);
+ }
+ this.mean = mean;
+ logMean = Math.log(mean);
+ }
+
+ /**
+ * Access the mean.
+ *
+ * @return the mean.
+ */
+ public double getMean() {
+ return mean;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ final double logDensity = logDensity(x);
+ return logDensity == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logDensity);
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logDensity(double x) {
+ if (x < 0) {
+ return Double.NEGATIVE_INFINITY;
+ }
+ return -x / mean - logMean;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The implementation of this method is based on:
+ * <ul>
+ * <li>
+ * <a href="http://mathworld.wolfram.com/ExponentialDistribution.html">
+ * Exponential Distribution</a>, equation (1).</li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ double ret;
+ if (x <= 0) {
+ ret = 0;
+ } else {
+ ret = 1 - Math.exp(-x / mean);
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * Returns {@code 0} when {@code p= = 0} and
+ * {@code Double.POSITIVE_INFINITY} when {@code p == 1}.
+ */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ double ret;
+
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 1) {
+ ret = Double.POSITIVE_INFINITY;
+ } else {
+ ret = -mean * Math.log(1 - p);
+ }
+
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For mean parameter {@code k}, the mean is {@code k}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return getMean();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For mean parameter {@code k}, the variance is {@code k^2}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double m = getMean();
+ return m * m;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the mean parameter.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the mean parameter.
+ *
+ * @return upper bound of the support (always Double.POSITIVE_INFINITY)
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>Sampling algorithm uses the
+ * <a href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html">
+ * inversion method</a> to generate exponentially distributed
+ * random values from uniform deviates.
+ * </p>
+ */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Exponential distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new AhrensDieterExponentialSampler(rng, mean);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/FDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/FDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/FDistribution.java
new file mode 100644
index 0000000..a8b9890
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/FDistribution.java
@@ -0,0 +1,209 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.LogBeta;
+import org.apache.commons.numbers.gamma.RegularizedBeta;
+
+/**
+ * Implementation of the F-distribution.
+ *
+ * @see <a href="http://en.wikipedia.org/wiki/F-distribution">F-distribution (Wikipedia)</a>
+ * @see <a href="http://mathworld.wolfram.com/F-Distribution.html">F-distribution (MathWorld)</a>
+ */
+public class FDistribution extends AbstractContinuousDistribution {
+ /** The numerator degrees of freedom. */
+ private final double numeratorDegreesOfFreedom;
+ /** The numerator degrees of freedom. */
+ private final double denominatorDegreesOfFreedom;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param numeratorDegreesOfFreedom Numerator degrees of freedom.
+ * @param denominatorDegreesOfFreedom Denominator degrees of freedom.
+ * @throws IllegalArgumentException if {@code numeratorDegreesOfFreedom <= 0} or
+ * {@code denominatorDegreesOfFreedom <= 0}.
+ */
+ public FDistribution(double numeratorDegreesOfFreedom,
+ double denominatorDegreesOfFreedom) {
+ if (numeratorDegreesOfFreedom <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ numeratorDegreesOfFreedom);
+ }
+ if (denominatorDegreesOfFreedom <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ denominatorDegreesOfFreedom);
+ }
+ this.numeratorDegreesOfFreedom = numeratorDegreesOfFreedom;
+ this.denominatorDegreesOfFreedom = denominatorDegreesOfFreedom;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public double density(double x) {
+ return Math.exp(logDensity(x));
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logDensity(double x) {
+ final double nhalf = numeratorDegreesOfFreedom / 2;
+ final double mhalf = denominatorDegreesOfFreedom / 2;
+ final double logx = Math.log(x);
+ final double logn = Math.log(numeratorDegreesOfFreedom);
+ final double logm = Math.log(denominatorDegreesOfFreedom);
+ final double lognxm = Math.log(numeratorDegreesOfFreedom * x +
+ denominatorDegreesOfFreedom);
+ return nhalf * logn + nhalf * logx - logx +
+ mhalf * logm - nhalf * lognxm - mhalf * lognxm -
+ LogBeta.value(nhalf, mhalf);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The implementation of this method is based on
+ * <ul>
+ * <li>
+ * <a href="http://mathworld.wolfram.com/F-Distribution.html">
+ * F-Distribution</a>, equation (4).
+ * </li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ double ret;
+ if (x <= 0) {
+ ret = 0;
+ } else {
+ double n = numeratorDegreesOfFreedom;
+ double m = denominatorDegreesOfFreedom;
+
+ ret = RegularizedBeta.value((n * x) / (m + n * x),
+ 0.5 * n,
+ 0.5 * m);
+ }
+ return ret;
+ }
+
+ /**
+ * Access the numerator degrees of freedom.
+ *
+ * @return the numerator degrees of freedom.
+ */
+ public double getNumeratorDegreesOfFreedom() {
+ return numeratorDegreesOfFreedom;
+ }
+
+ /**
+ * Access the denominator degrees of freedom.
+ *
+ * @return the denominator degrees of freedom.
+ */
+ public double getDenominatorDegreesOfFreedom() {
+ return denominatorDegreesOfFreedom;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For denominator degrees of freedom parameter {@code b}, the mean is
+ * <ul>
+ * <li>if {@code b > 2} then {@code b / (b - 2)},</li>
+ * <li>else undefined ({@code Double.NaN}).
+ * </ul>
+ */
+ @Override
+ public double getNumericalMean() {
+ final double denominatorDF = getDenominatorDegreesOfFreedom();
+
+ if (denominatorDF > 2) {
+ return denominatorDF / (denominatorDF - 2);
+ }
+
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For numerator degrees of freedom parameter {@code a} and denominator
+ * degrees of freedom parameter {@code b}, the variance is
+ * <ul>
+ * <li>
+ * if {@code b > 4} then
+ * {@code [2 * b^2 * (a + b - 2)] / [a * (b - 2)^2 * (b - 4)]},
+ * </li>
+ * <li>else undefined ({@code Double.NaN}).
+ * </ul>
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double denominatorDF = getDenominatorDegreesOfFreedom();
+
+ if (denominatorDF > 4) {
+ final double numeratorDF = getNumeratorDegreesOfFreedom();
+ final double denomDFMinusTwo = denominatorDF - 2;
+
+ return (2 * (denominatorDF * denominatorDF) * (numeratorDF + denominatorDF - 2)) /
+ ((numeratorDF * (denomDFMinusTwo * denomDFMinusTwo) * (denominatorDF - 4)));
+ }
+
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the parameters.
+ *
+ * @return upper bound of the support (always Double.POSITIVE_INFINITY)
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GammaDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GammaDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GammaDistribution.java
new file mode 100644
index 0000000..c13db5b
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GammaDistribution.java
@@ -0,0 +1,354 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.LanczosApproximation;
+import org.apache.commons.numbers.gamma.RegularizedGamma;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.AhrensDieterMarsagliaTsangGammaSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Gamma_distribution">Gamma distribution</a>.
+ */
+public class GammaDistribution extends AbstractContinuousDistribution {
+ /** Lanczos constant. */
+ private static final double LANCZOS_G = LanczosApproximation.g();
+ /** The shape parameter. */
+ private final double shape;
+ /** The scale parameter. */
+ private final double scale;
+ /**
+ * The constant value of {@code shape + g + 0.5}, where {@code g} is the
+ * Lanczos constant {@link LanczosApproximation#g()}.
+ */
+ private final double shiftedShape;
+ /**
+ * The constant value of
+ * {@code shape / scale * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)},
+ * where {@code L(shape)} is the Lanczos approximation returned by
+ * {@link LanczosApproximation#value(double)}. This prefactor is used in
+ * {@link #density(double)}, when no overflow occurs with the natural
+ * calculation.
+ */
+ private final double densityPrefactor1;
+ /**
+ * The constant value of
+ * {@code log(shape / scale * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape))},
+ * where {@code L(shape)} is the Lanczos approximation returned by
+ * {@link LanczosApproximation#value(double)}. This prefactor is used in
+ * {@link #logDensity(double)}, when no overflow occurs with the natural
+ * calculation.
+ */
+ private final double logDensityPrefactor1;
+ /**
+ * The constant value of
+ * {@code shape * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape)},
+ * where {@code L(shape)} is the Lanczos approximation returned by
+ * {@link LanczosApproximation#value(double)}. This prefactor is used in
+ * {@link #density(double)}, when overflow occurs with the natural
+ * calculation.
+ */
+ private final double densityPrefactor2;
+ /**
+ * The constant value of
+ * {@code log(shape * sqrt(e / (2 * pi * (shape + g + 0.5))) / L(shape))},
+ * where {@code L(shape)} is the Lanczos approximation returned by
+ * {@link LanczosApproximation#value(double)}. This prefactor is used in
+ * {@link #logDensity(double)}, when overflow occurs with the natural
+ * calculation.
+ */
+ private final double logDensityPrefactor2;
+ /**
+ * Lower bound on {@code y = x / scale} for the selection of the computation
+ * method in {@link #density(double)}. For {@code y <= minY}, the natural
+ * calculation overflows.
+ */
+ private final double minY;
+ /**
+ * Upper bound on {@code log(y)} ({@code y = x / scale}) for the selection
+ * of the computation method in {@link #density(double)}. For
+ * {@code log(y) >= maxLogY}, the natural calculation overflows.
+ */
+ private final double maxLogY;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param shape the shape parameter
+ * @param scale the scale parameter
+ * @throws IllegalArgumentException if {@code shape <= 0} or
+ * {@code scale <= 0}.
+ */
+ public GammaDistribution(double shape,
+ double scale) {
+ if (shape <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, shape);
+ }
+ if (scale <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, scale);
+ }
+
+ this.shape = shape;
+ this.scale = scale;
+ this.shiftedShape = shape + LANCZOS_G + 0.5;
+ final double aux = Math.E / (2.0 * Math.PI * shiftedShape);
+ this.densityPrefactor2 = shape * Math.sqrt(aux) / LanczosApproximation.value(shape);
+ this.logDensityPrefactor2 = Math.log(shape) + 0.5 * Math.log(aux) -
+ Math.log(LanczosApproximation.value(shape));
+ this.densityPrefactor1 = this.densityPrefactor2 / scale *
+ Math.pow(shiftedShape, -shape) * // XXX FastMath vs Math
+ Math.exp(shape + LANCZOS_G);
+ this.logDensityPrefactor1 = this.logDensityPrefactor2 - Math.log(scale) -
+ Math.log(shiftedShape) * shape +
+ shape + LANCZOS_G;
+ this.minY = shape + LANCZOS_G - Math.log(Double.MAX_VALUE);
+ this.maxLogY = Math.log(Double.MAX_VALUE) / (shape - 1.0);
+ }
+
+ /**
+ * Returns the shape parameter of {@code this} distribution.
+ *
+ * @return the shape parameter
+ */
+ public double getShape() {
+ return shape;
+ }
+
+ /**
+ * Returns the scale parameter of {@code this} distribution.
+ *
+ * @return the scale parameter
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ /* The present method must return the value of
+ *
+ * 1 x a - x
+ * ---------- (-) exp(---)
+ * x Gamma(a) b b
+ *
+ * where a is the shape parameter, and b the scale parameter.
+ * Substituting the Lanczos approximation of Gamma(a) leads to the
+ * following expression of the density
+ *
+ * a e 1 y a
+ * - sqrt(------------------) ---- (-----------) exp(a - y + g),
+ * x 2 pi (a + g + 0.5) L(a) a + g + 0.5
+ *
+ * where y = x / b. The above formula is the "natural" computation, which
+ * is implemented when no overflow is likely to occur. If overflow occurs
+ * with the natural computation, the following identity is used. It is
+ * based on the BOOST library
+ * http://www.boost.org/doc/libs/1_35_0/libs/math/doc/sf_and_dist/html/math_toolkit/special/sf_gamma/igamma.html
+ * Formula (15) needs adaptations, which are detailed below.
+ *
+ * y a
+ * (-----------) exp(a - y + g)
+ * a + g + 0.5
+ * y - a - g - 0.5 y (g + 0.5)
+ * = exp(a log1pm(---------------) - ----------- + g),
+ * a + g + 0.5 a + g + 0.5
+ *
+ * where log1pm(z) = log(1 + z) - z. Therefore, the value to be
+ * returned is
+ *
+ * a e 1
+ * - sqrt(------------------) ----
+ * x 2 pi (a + g + 0.5) L(a)
+ * y - a - g - 0.5 y (g + 0.5)
+ * * exp(a log1pm(---------------) - ----------- + g).
+ * a + g + 0.5 a + g + 0.5
+ */
+ if (x < 0) {
+ return 0;
+ }
+ final double y = x / scale;
+ if ((y <= minY) || (Math.log(y) >= maxLogY)) {
+ /*
+ * Overflow.
+ */
+ final double aux1 = (y - shiftedShape) / shiftedShape;
+ final double aux2 = shape * (Math.log1p(aux1) - aux1); // XXX FastMath vs Math
+ final double aux3 = -y * (LANCZOS_G + 0.5) / shiftedShape + LANCZOS_G + aux2;
+ return densityPrefactor2 / x * Math.exp(aux3);
+ }
+ /*
+ * Natural calculation.
+ */
+ return densityPrefactor1 * Math.exp(-y) * Math.pow(y, shape - 1);
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logDensity(double x) {
+ /*
+ * see the comment in {@link #density(double)} for computation details
+ */
+ if (x < 0) {
+ return Double.NEGATIVE_INFINITY;
+ }
+ final double y = x / scale;
+ if ((y <= minY) || (Math.log(y) >= maxLogY)) {
+ /*
+ * Overflow.
+ */
+ final double aux1 = (y - shiftedShape) / shiftedShape;
+ final double aux2 = shape * (Math.log1p(aux1) - aux1);
+ final double aux3 = -y * (LANCZOS_G + 0.5) / shiftedShape + LANCZOS_G + aux2;
+ return logDensityPrefactor2 - Math.log(x) + aux3;
+ }
+ /*
+ * Natural calculation.
+ */
+ return logDensityPrefactor1 - y + Math.log(y) * (shape - 1);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The implementation of this method is based on:
+ * <ul>
+ * <li>
+ * <a href="http://mathworld.wolfram.com/Chi-SquaredDistribution.html">
+ * Chi-Squared Distribution</a>, equation (9).
+ * </li>
+ * <li>Casella, G., & Berger, R. (1990). <i>Statistical Inference</i>.
+ * Belmont, CA: Duxbury Press.
+ * </li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ double ret;
+
+ if (x <= 0) {
+ ret = 0;
+ } else {
+ ret = RegularizedGamma.P.value(shape, x / scale);
+ }
+
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For shape parameter {@code alpha} and scale parameter {@code beta}, the
+ * mean is {@code alpha * beta}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return shape * scale;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For shape parameter {@code alpha} and scale parameter {@code beta}, the
+ * variance is {@code alpha * beta^2}.
+ *
+ * @return {@inheritDoc}
+ */
+ @Override
+ public double getNumericalVariance() {
+ return shape * scale * scale;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the parameters.
+ *
+ * @return upper bound of the support (always Double.POSITIVE_INFINITY)
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>
+ * Sampling algorithms:
+ * <ul>
+ * <li>
+ * For {@code 0 < shape < 1}:
+ * <blockquote>
+ * Ahrens, J. H. and Dieter, U.,
+ * <i>Computer methods for sampling from gamma, beta, Poisson and binomial distributions,</i>
+ * Computing, 12, 223-246, 1974.
+ * </blockquote>
+ * </li>
+ * <li>
+ * For {@code shape >= 1}:
+ * <blockquote>
+ * Marsaglia and Tsang, <i>A Simple Method for Generating
+ * Gamma Variables.</i> ACM Transactions on Mathematical Software,
+ * Volume 26 Issue 3, September, 2000.
+ * </blockquote>
+ * </li>
+ * </ul>
+ */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Gamma distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new AhrensDieterMarsagliaTsangGammaSampler(rng, scale, shape);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GeometricDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GeometricDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GeometricDistribution.java
new file mode 100644
index 0000000..fba2580
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GeometricDistribution.java
@@ -0,0 +1,160 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Geometric_distribution">geometric distribution</a>.
+ */
+public class GeometricDistribution extends AbstractDiscreteDistribution {
+ /** The probability of success. */
+ private final double probabilityOfSuccess;
+ /** {@code log(p)} where p is the probability of success. */
+ private final double logProbabilityOfSuccess;
+ /** {@code log(1 - p)} where p is the probability of success. */
+ private final double log1mProbabilityOfSuccess;
+
+ /**
+ * Creates a geometric distribution.
+ *
+ * @param p Probability of success.
+ * @throws IllegalArgumentException if {@code p <= 0} or {@code p > 1}.
+ */
+ public GeometricDistribution(double p) {
+ if (p <= 0 || p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+
+ probabilityOfSuccess = p;
+ logProbabilityOfSuccess = Math.log(p);
+ log1mProbabilityOfSuccess = Math.log1p(-p);
+ }
+
+ /**
+ * Access the probability of success for this distribution.
+ *
+ * @return the probability of success.
+ */
+ public double getProbabilityOfSuccess() {
+ return probabilityOfSuccess;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ if (x < 0) {
+ return 0.0;
+ } else {
+ return Math.exp(log1mProbabilityOfSuccess * x) * probabilityOfSuccess;
+ }
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logProbability(int x) {
+ if (x < 0) {
+ return Double.NEGATIVE_INFINITY;
+ } else {
+ return x * log1mProbabilityOfSuccess + logProbabilityOfSuccess;
+ }
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ if (x < 0) {
+ return 0.0;
+ } else {
+ return -Math.expm1(log1mProbabilityOfSuccess * (x + 1));
+ }
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For probability parameter {@code p}, the mean is {@code (1 - p) / p}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return (1 - probabilityOfSuccess) / probabilityOfSuccess;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For probability parameter {@code p}, the variance is
+ * {@code (1 - p) / (p * p)}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ return (1 - probabilityOfSuccess) / (probabilityOfSuccess * probabilityOfSuccess);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is infinite (which we approximate as
+ * {@code Integer.MAX_VALUE}).
+ *
+ * @return upper bound of the support (always Integer.MAX_VALUE)
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return Integer.MAX_VALUE;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public int inverseCumulativeProbability(double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ if (p == 1) {
+ return Integer.MAX_VALUE;
+ }
+ if (p == 0) {
+ return 0;
+ }
+ return Math.max(0, (int) Math.ceil(Math.log1p(-p)/log1mProbabilityOfSuccess-1));
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GumbelDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GumbelDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GumbelDistribution.java
new file mode 100644
index 0000000..8898b5e
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/GumbelDistribution.java
@@ -0,0 +1,128 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * This class implements the <a href="http://en.wikipedia.org/wiki/Gumbel_distribution">Gumbel distribution</a>.
+ */
+public class GumbelDistribution extends AbstractContinuousDistribution {
+ /** π<sup>2</sup>/6. */
+ private static final double PI_SQUARED_OVER_SIX = Math.PI * Math.PI / 6;
+ /**
+ * <a href="http://mathworld.wolfram.com/Euler-MascheroniConstantApproximations.html">
+ * Approximation of Euler's constant</a>.
+ */
+ private static final double EULER = Math.PI / (2 * Math.E);
+ /** Location parameter. */
+ private final double mu;
+ /** Scale parameter. */
+ private final double beta;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mu location parameter
+ * @param beta scale parameter (must be positive)
+ * @throws IllegalArgumenException if {@code beta <= 0}
+ */
+ public GumbelDistribution(double mu,
+ double beta) {
+ if (beta <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, beta);
+ }
+
+ this.beta = beta;
+ this.mu = mu;
+ }
+
+ /**
+ * Gets the location parameter.
+ *
+ * @return the location parameter.
+ */
+ public double getLocation() {
+ return mu;
+ }
+
+ /**
+ * Gets the scale parameter.
+ *
+ * @return the scale parameter.
+ */
+ public double getScale() {
+ return beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ final double z = (x - mu) / beta;
+ final double t = Math.exp(-z);
+ return Math.exp(-z - t) / beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ final double z = (x - mu) / beta;
+ return Math.exp(-Math.exp(-z));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ if (p < 0 || p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 0) {
+ return Double.NEGATIVE_INFINITY;
+ } else if (p == 1) {
+ return Double.POSITIVE_INFINITY;
+ }
+ return mu - Math.log(-Math.log(p)) * beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalMean() {
+ return mu + EULER * beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalVariance() {
+ return PI_SQUARED_OVER_SIX * beta * beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/HypergeometricDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/HypergeometricDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/HypergeometricDistribution.java
new file mode 100644
index 0000000..732a253
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/HypergeometricDistribution.java
@@ -0,0 +1,293 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">hypergeometric distribution</a>.
+ */
+public class HypergeometricDistribution extends AbstractDiscreteDistribution {
+ /** The number of successes in the population. */
+ private final int numberOfSuccesses;
+ /** The population size. */
+ private final int populationSize;
+ /** The sample size. */
+ private final int sampleSize;
+
+ /**
+ * Creates a new hypergeometric distribution.
+ *
+ * @param populationSize Population size.
+ * @param numberOfSuccesses Number of successes in the population.
+ * @param sampleSize Sample size.
+ * @throws IllegalArgumentException if {@code numberOfSuccesses < 0}, or
+ * {@code populationSize <= 0} or {@code numberOfSuccesses > populationSize},
+ * or {@code sampleSize > populationSize}.
+ */
+ public HypergeometricDistribution(int populationSize,
+ int numberOfSuccesses,
+ int sampleSize) {
+ if (populationSize <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ populationSize);
+ }
+ if (numberOfSuccesses < 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ numberOfSuccesses);
+ }
+ if (sampleSize < 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ sampleSize);
+ }
+
+ if (numberOfSuccesses > populationSize) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ numberOfSuccesses, populationSize);
+ }
+ if (sampleSize > populationSize) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ sampleSize, populationSize);
+ }
+
+ this.numberOfSuccesses = numberOfSuccesses;
+ this.populationSize = populationSize;
+ this.sampleSize = sampleSize;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ double ret;
+
+ int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize);
+ if (x < domain[0]) {
+ ret = 0.0;
+ } else if (x >= domain[1]) {
+ ret = 1.0;
+ } else {
+ ret = innerCumulativeProbability(domain[0], x, 1);
+ }
+
+ return ret;
+ }
+
+ /**
+ * Return the domain for the given hypergeometric distribution parameters.
+ *
+ * @param n Population size.
+ * @param m Number of successes in the population.
+ * @param k Sample size.
+ * @return a two element array containing the lower and upper bounds of the
+ * hypergeometric distribution.
+ */
+ private int[] getDomain(int n, int m, int k) {
+ return new int[] { getLowerDomain(n, m, k), getUpperDomain(m, k) };
+ }
+
+ /**
+ * Return the lowest domain value for the given hypergeometric distribution
+ * parameters.
+ *
+ * @param n Population size.
+ * @param m Number of successes in the population.
+ * @param k Sample size.
+ * @return the lowest domain value of the hypergeometric distribution.
+ */
+ private int getLowerDomain(int n, int m, int k) {
+ return Math.max(0, m - (n - k));
+ }
+
+ /**
+ * Access the number of successes.
+ *
+ * @return the number of successes.
+ */
+ public int getNumberOfSuccesses() {
+ return numberOfSuccesses;
+ }
+
+ /**
+ * Access the population size.
+ *
+ * @return the population size.
+ */
+ public int getPopulationSize() {
+ return populationSize;
+ }
+
+ /**
+ * Access the sample size.
+ *
+ * @return the sample size.
+ */
+ public int getSampleSize() {
+ return sampleSize;
+ }
+
+ /**
+ * Return the highest domain value for the given hypergeometric distribution
+ * parameters.
+ *
+ * @param m Number of successes in the population.
+ * @param k Sample size.
+ * @return the highest domain value of the hypergeometric distribution.
+ */
+ private int getUpperDomain(int m, int k) {
+ return Math.min(k, m);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ final double logProbability = logProbability(x);
+ return logProbability == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logProbability);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logProbability(int x) {
+ double ret;
+
+ int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize);
+ if (x < domain[0] || x > domain[1]) {
+ ret = Double.NEGATIVE_INFINITY;
+ } else {
+ double p = (double) sampleSize / (double) populationSize;
+ double q = (double) (populationSize - sampleSize) / (double) populationSize;
+ double p1 = SaddlePointExpansion.logBinomialProbability(x,
+ numberOfSuccesses, p, q);
+ double p2 =
+ SaddlePointExpansion.logBinomialProbability(sampleSize - x,
+ populationSize - numberOfSuccesses, p, q);
+ double p3 =
+ SaddlePointExpansion.logBinomialProbability(sampleSize, populationSize, p, q);
+ ret = p1 + p2 - p3;
+ }
+
+ return ret;
+ }
+
+ /**
+ * For this distribution, {@code X}, this method returns {@code P(X >= x)}.
+ *
+ * @param x Value at which the CDF is evaluated.
+ * @return the upper tail CDF for this distribution.
+ * @since 1.1
+ */
+ public double upperCumulativeProbability(int x) {
+ double ret;
+
+ final int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize);
+ if (x <= domain[0]) {
+ ret = 1.0;
+ } else if (x > domain[1]) {
+ ret = 0.0;
+ } else {
+ ret = innerCumulativeProbability(domain[1], x, -1);
+ }
+
+ return ret;
+ }
+
+ /**
+ * For this distribution, {@code X}, this method returns
+ * {@code P(x0 <= X <= x1)}.
+ * This probability is computed by summing the point probabilities for the
+ * values {@code x0, x0 + 1, x0 + 2, ..., x1}, in the order directed by
+ * {@code dx}.
+ *
+ * @param x0 Inclusive lower bound.
+ * @param x1 Inclusive upper bound.
+ * @param dx Direction of summation (1 indicates summing from x0 to x1, and
+ * 0 indicates summing from x1 to x0).
+ * @return {@code P(x0 <= X <= x1)}.
+ */
+ private double innerCumulativeProbability(int x0, int x1, int dx) {
+ double ret = probability(x0);
+ while (x0 != x1) {
+ x0 += dx;
+ ret += probability(x0);
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For population size {@code N}, number of successes {@code m}, and sample
+ * size {@code n}, the mean is {@code n * m / N}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return getSampleSize() * (getNumberOfSuccesses() / (double) getPopulationSize());
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For population size {@code N}, number of successes {@code m}, and sample
+ * size {@code n}, the variance is
+ * {@code (n * m * (N - n) * (N - m)) / (N^2 * (N - 1))}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double N = getPopulationSize();
+ final double m = getNumberOfSuccesses();
+ final double n = getSampleSize();
+ return (n * m * (N - n) * (N - m)) / (N * N * (N - 1));
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For population size {@code N}, number of successes {@code m}, and sample
+ * size {@code n}, the lower bound of the support is
+ * {@code max(0, n + m - N)}.
+ *
+ * @return lower bound of the support
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return Math.max(0,
+ getSampleSize() + getNumberOfSuccesses() - getPopulationSize());
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For number of successes {@code m} and sample size {@code n}, the upper
+ * bound of the support is {@code min(m, n)}.
+ *
+ * @return upper bound of the support
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return Math.min(getNumberOfSuccesses(), getSampleSize());
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LaplaceDistribution.java
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diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LaplaceDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LaplaceDistribution.java
new file mode 100644
index 0000000..0d1a8bf
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LaplaceDistribution.java
@@ -0,0 +1,132 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * This class implements the Laplace distribution.
+ *
+ * @see <a href="http://en.wikipedia.org/wiki/Laplace_distribution">Laplace distribution (Wikipedia)</a>
+ *
+ * @since 3.4
+ */
+public class LaplaceDistribution extends AbstractContinuousDistribution {
+
+ /** Serializable version identifier. */
+ private static final long serialVersionUID = 20160311L;
+
+ /** The location parameter. */
+ private final double mu;
+ /** The scale parameter. */
+ private final double beta;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mu location parameter
+ * @param beta scale parameter (must be positive)
+ * @throws IllegalArgumentException if {@code beta <= 0}
+ */
+ public LaplaceDistribution(double mu,
+ double beta) {
+ if (beta <= 0.0) {
+ throw new DistributionException(DistributionException.NEGATIVE, beta);
+ }
+
+ this.mu = mu;
+ this.beta = beta;
+ }
+
+ /**
+ * Access the location parameter, {@code mu}.
+ *
+ * @return the location parameter.
+ */
+ public double getLocation() {
+ return mu;
+ }
+
+ /**
+ * Access the scale parameter, {@code beta}.
+ *
+ * @return the scale parameter.
+ */
+ public double getScale() {
+ return beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ return Math.exp(-Math.abs(x - mu) / beta) / (2.0 * beta);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x <= mu) {
+ return Math.exp((x - mu) / beta) / 2.0;
+ } else {
+ return 1.0 - Math.exp((mu - x) / beta) / 2.0;
+ }
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 0) {
+ return Double.NEGATIVE_INFINITY;
+ } else if (p == 1) {
+ return Double.POSITIVE_INFINITY;
+ }
+ double x = (p > 0.5) ? -Math.log(2.0 - 2.0 * p) : Math.log(2.0 * p);
+ return mu + beta * x;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalMean() {
+ return mu;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalVariance() {
+ return 2.0 * beta * beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LevyDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LevyDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LevyDistribution.java
new file mode 100644
index 0000000..d16da8d
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LevyDistribution.java
@@ -0,0 +1,161 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.Erfc;
+import org.apache.commons.numbers.gamma.InverseErfc;
+
+/**
+ * This class implements the <a href="http://en.wikipedia.org/wiki/L%C3%A9vy_distribution">
+ * Lévy distribution</a>.
+ */
+public class LevyDistribution extends AbstractContinuousDistribution {
+ /** Location parameter. */
+ private final double mu;
+ /** Scale parameter. */
+ private final double c;
+ /** Half of c (for calculations). */
+ private final double halfC;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mu location
+ * @param c scale parameter
+ */
+ public LevyDistribution(final double mu,
+ final double c) {
+ this.mu = mu;
+ this.c = c;
+ this.halfC = 0.5 * c;
+ }
+
+ /** {@inheritDoc}
+ * <p>
+ * From Wikipedia: The probability density function of the Lévy distribution
+ * over the domain is
+ * </p>
+ * <div style="white-space: pre"><code>
+ * f(x; μ, c) = √(c / 2π) * e<sup>-c / 2 (x - μ)</sup> / (x - μ)<sup>3/2</sup>
+ * </code></div>
+ * <p>
+ * For this distribution, {@code X}, this method returns {@code P(X < x)}.
+ * If {@code x} is less than location parameter μ, {@code Double.NaN} is
+ * returned, as in these cases the distribution is not defined.
+ * </p>
+ */
+ @Override
+ public double density(final double x) {
+ if (x < mu) {
+ return Double.NaN;
+ }
+
+ final double delta = x - mu;
+ final double f = halfC / delta;
+ return Math.sqrt(f / Math.PI) * Math.exp(-f) /delta;
+ }
+
+ /** {@inheritDoc}
+ *
+ * See documentation of {@link #density(double)} for computation details.
+ */
+ @Override
+ public double logDensity(double x) {
+ if (x < mu) {
+ return Double.NaN;
+ }
+
+ final double delta = x - mu;
+ final double f = halfC / delta;
+ return 0.5 * Math.log(f / Math.PI) - f - Math.log(delta);
+ }
+
+ /** {@inheritDoc}
+ * <p>
+ * From Wikipedia: the cumulative distribution function is
+ * </p>
+ * <pre>
+ * f(x; u, c) = erfc (√ (c / 2 (x - u )))
+ * </pre>
+ */
+ @Override
+ public double cumulativeProbability(final double x) {
+ if (x < mu) {
+ return Double.NaN;
+ }
+ return Erfc.value(Math.sqrt(halfC / (x - mu)));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(final double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ final double t = InverseErfc.value(p);
+ return mu + halfC / (t * t);
+ }
+
+ /**
+ * Gets the scale parameter of the distribution.
+ *
+ * @return scale parameter of the distribution
+ */
+ public double getScale() {
+ return c;
+ }
+
+ /**
+ * Gets the location parameter of the distribution.
+ *
+ * @return location parameter of the distribution
+ */
+ public double getLocation() {
+ return mu;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalMean() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalVariance() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportLowerBound() {
+ return mu;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogNormalDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogNormalDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogNormalDistribution.java
new file mode 100644
index 0000000..25bdd33
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogNormalDistribution.java
@@ -0,0 +1,266 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.Erf;
+import org.apache.commons.numbers.gamma.ErfDifference;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.LogNormalSampler;
+import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">log-normal distribution</a>.
+ *
+ * <p>
+ * <strong>Parameters:</strong>
+ * {@code X} is log-normally distributed if its natural logarithm {@code log(X)}
+ * is normally distributed. The probability distribution function of {@code X}
+ * is given by (for {@code x > 0})
+ * </p>
+ * <p>
+ * {@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)}
+ * </p>
+ * <ul>
+ * <li>{@code m} is the <em>scale</em> parameter: this is the mean of the
+ * normally distributed natural logarithm of this distribution,</li>
+ * <li>{@code s} is the <em>shape</em> parameter: this is the standard
+ * deviation of the normally distributed natural logarithm of this
+ * distribution.
+ * </ul>
+ */
+public class LogNormalDistribution extends AbstractContinuousDistribution {
+ /** √(2 π) */
+ private static final double SQRT2PI = Math.sqrt(2 * Math.PI);
+ /** √(2) */
+ private static final double SQRT2 = Math.sqrt(2);
+ /** The scale parameter of this distribution. */
+ private final double scale;
+ /** The shape parameter of this distribution. */
+ private final double shape;
+ /** The value of {@code log(shape) + 0.5 * log(2*PI)} stored for faster computation. */
+ private final double logShapePlusHalfLog2Pi;
+
+ /**
+ * Creates a log-normal distribution, where the mean and standard deviation
+ * of the {@link NormalDistribution normally distributed} natural
+ * logarithm of the log-normal distribution are equal to zero and one
+ * respectively. In other words, the scale of the returned distribution is
+ * {@code 0}, while its shape is {@code 1}.
+ */
+ public LogNormalDistribution() {
+ this(0, 1);
+ }
+
+ /**
+ * Creates a log-normal distribution.
+ *
+ * @param scale Scale parameter of this distribution.
+ * @param shape Shape parameter of this distribution.
+ * @throws IllegalArgumentException if {@code shape <= 0}.
+ */
+ public LogNormalDistribution(double scale,
+ double shape) {
+ if (shape <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, shape);
+ }
+
+ this.scale = scale;
+ this.shape = shape;
+ this.logShapePlusHalfLog2Pi = Math.log(shape) + 0.5 * Math.log(2 * Math.PI);
+ }
+
+ /**
+ * Returns the scale parameter of this distribution.
+ *
+ * @return the scale parameter
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /**
+ * Returns the shape parameter of this distribution.
+ *
+ * @return the shape parameter
+ */
+ public double getShape() {
+ return shape;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For scale {@code m}, and shape {@code s} of this distribution, the PDF
+ * is given by
+ * <ul>
+ * <li>{@code 0} if {@code x <= 0},</li>
+ * <li>{@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)}
+ * otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double density(double x) {
+ if (x <= 0) {
+ return 0;
+ }
+ final double x0 = Math.log(x) - scale;
+ final double x1 = x0 / shape;
+ return Math.exp(-0.5 * x1 * x1) / (shape * SQRT2PI * x);
+ }
+
+ /** {@inheritDoc}
+ *
+ * See documentation of {@link #density(double)} for computation details.
+ */
+ @Override
+ public double logDensity(double x) {
+ if (x <= 0) {
+ return Double.NEGATIVE_INFINITY;
+ }
+ final double logX = Math.log(x);
+ final double x0 = logX - scale;
+ final double x1 = x0 / shape;
+ return -0.5 * x1 * x1 - (logShapePlusHalfLog2Pi + logX);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For scale {@code m}, and shape {@code s} of this distribution, the CDF
+ * is given by
+ * <ul>
+ * <li>{@code 0} if {@code x <= 0},</li>
+ * <li>{@code 0} if {@code ln(x) - m < 0} and {@code m - ln(x) > 40 * s}, as
+ * in these cases the actual value is within {@code Double.MIN_VALUE} of 0,
+ * <li>{@code 1} if {@code ln(x) - m >= 0} and {@code ln(x) - m > 40 * s},
+ * as in these cases the actual value is within {@code Double.MIN_VALUE} of
+ * 1,</li>
+ * <li>{@code 0.5 + 0.5 * erf((ln(x) - m) / (s * sqrt(2))} otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x <= 0) {
+ return 0;
+ }
+ final double dev = Math.log(x) - scale;
+ if (Math.abs(dev) > 40 * shape) {
+ return dev < 0 ? 0.0d : 1.0d;
+ }
+ return 0.5 + 0.5 * Erf.value(dev / (shape * SQRT2));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(double x0,
+ double x1) {
+ if (x0 > x1) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ x0, x1);
+ }
+ if (x0 <= 0 || x1 <= 0) {
+ return super.probability(x0, x1);
+ }
+ final double denom = shape * SQRT2;
+ final double v0 = (Math.log(x0) - scale) / denom;
+ final double v1 = (Math.log(x1) - scale) / denom;
+ return 0.5 * ErfDifference.value(v0, v1);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For scale {@code m} and shape {@code s}, the mean is
+ * {@code exp(m + s^2 / 2)}.
+ */
+ @Override
+ public double getNumericalMean() {
+ double s = shape;
+ return Math.exp(scale + (s * s / 2));
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For scale {@code m} and shape {@code s}, the variance is
+ * {@code (exp(s^2) - 1) * exp(2 * m + s^2)}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double s = shape;
+ final double ss = s * s;
+ return (Math.expm1(ss)) * Math.exp(2 * scale + ss);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the parameters.
+ *
+ * @return upper bound of the support (always
+ * {@code Double.POSITIVE_INFINITY})
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Log normal distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new LogNormalSampler(new ZigguratNormalizedGaussianSampler(rng), scale, shape);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogisticDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogisticDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogisticDistribution.java
new file mode 100644
index 0000000..28a6657
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/LogisticDistribution.java
@@ -0,0 +1,128 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Logistic_distribution">Logistic distribution</a>.
+ */
+public class LogisticDistribution extends AbstractContinuousDistribution {
+ /** π<sup>2</sup>/3. */
+ private static final double PI_SQUARED_OVER_THREE = Math.PI * Math.PI / 3;
+ /** Location parameter. */
+ private final double mu;
+ /** Scale parameter. */
+ private final double scale;
+ /** Inverse of "scale". */
+ private final double oneOverScale;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mu Location parameter.
+ * @param scale Scale parameter (must be positive).
+ * @throws IllegalArgumentException if {@code scale <= 0}.
+ */
+ public LogisticDistribution(double mu,
+ double scale) {
+ if (scale <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ scale);
+ }
+
+ this.mu = mu;
+ this.scale = scale;
+ this.oneOverScale = 1 / scale;
+ }
+
+ /**
+ * Gets the location parameter.
+ *
+ * @return the location parameter.
+ */
+ public double getLocation() {
+ return mu;
+ }
+
+ /**
+ * Gets the scale parameter.
+ *
+ * @return the scale parameter.
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ final double z = oneOverScale * (x - mu);
+ final double v = Math.exp(-z);
+ return oneOverScale * v / ((1 + v) * (1 + v));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ final double z = oneOverScale * (x - mu);
+ return 1 / (1 + Math.exp(-z));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 0) {
+ return 0;
+ } else if (p == 1) {
+ return Double.POSITIVE_INFINITY;
+ } else {
+ return scale * Math.log(p / (1 - p)) + mu;
+ }
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalMean() {
+ return mu;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalVariance() {
+ return oneOverScale * oneOverScale * PI_SQUARED_OVER_THREE;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NakagamiDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NakagamiDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NakagamiDistribution.java
new file mode 100644
index 0000000..9bf7d2f
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NakagamiDistribution.java
@@ -0,0 +1,117 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.Gamma;
+import org.apache.commons.numbers.gamma.RegularizedGamma;
+
+/**
+ * This class implements the <a href="http://en.wikipedia.org/wiki/Nakagami_distribution">Nakagami distribution</a>.
+ */
+public class NakagamiDistribution extends AbstractContinuousDistribution {
+ /** The shape parameter. */
+ private final double mu;
+ /** The scale parameter. */
+ private final double omega;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mu shape parameter
+ * @param omega scale parameter (must be positive)
+ * @throws IllegalArgumentException if {@code mu < 0.5} or if
+ * {@code omega <= 0}.
+ */
+ public NakagamiDistribution(double mu,
+ double omega) {
+ if (mu < 0.5) {
+ throw new DistributionException(DistributionException.TOO_SMALL, mu, 0.5);
+ }
+ if (omega <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, omega);
+ }
+
+ this.mu = mu;
+ this.omega = omega;
+ }
+
+ /**
+ * Access the shape parameter, {@code mu}.
+ *
+ * @return the shape parameter.
+ */
+ public double getShape() {
+ return mu;
+ }
+
+ /**
+ * Access the scale parameter, {@code omega}.
+ *
+ * @return the scale parameter.
+ */
+ public double getScale() {
+ return omega;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ if (x <= 0) {
+ return 0.0;
+ }
+ return 2.0 * Math.pow(mu, mu) / (Gamma.value(mu) * Math.pow(omega, mu)) *
+ Math.pow(x, 2 * mu - 1) * Math.exp(-mu * x * x / omega);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ return RegularizedGamma.P.value(mu, mu * x * x / omega);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalMean() {
+ return Gamma.value(mu + 0.5) / Gamma.value(mu) * Math.sqrt(omega / mu);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getNumericalVariance() {
+ double v = Gamma.value(mu + 0.5) / Gamma.value(mu);
+ return omega * (1 - 1 / mu * v * v);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+}
[13/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
STATISTICS-2: Migrate "o.a.c.math4.distribution" from Commons Math.
"IntegerDistribution" was renamed "DiscreteDistribution".
"RealDistribution" was renamed "ContinuousDistribution".
All exceptions are instances of "DistributionException" (package-private).
Solver code (used by method "inverseCumulativeProbability") is a private
static inner class in "AbstractContinuousDistribution".
Tolerances are hard-coded. [Constructors that specified a tolerance were
removed.]
Calls to "FastMath" were replaced by calls to JDK "Math". This has led to
two unit tests failing in "GammaDistributionTest" for which the tolerance
had to be slightly increased. [The main source indicates which calls to
"Math" methods are responsible for the failures at the original tolerance.]
Project: http://git-wip-us.apache.org/repos/asf/commons-statistics/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-statistics/commit/9c794a15
Tree: http://git-wip-us.apache.org/repos/asf/commons-statistics/tree/9c794a15
Diff: http://git-wip-us.apache.org/repos/asf/commons-statistics/diff/9c794a15
Branch: refs/heads/master
Commit: 9c794a15f75aafbe9d2ab4b62b7e43e1c32e7501
Parents: 585178f
Author: Gilles Sadowski <gi...@harfang.homelinux.org>
Authored: Sun Jan 21 14:41:03 2018 +0100
Committer: Gilles Sadowski <gi...@harfang.homelinux.org>
Committed: Sun Jan 21 14:41:03 2018 +0100
----------------------------------------------------------------------
commons-statistics-distribution/LICENSE.txt | 275 ++
commons-statistics-distribution/NOTICE.txt | 5 +
commons-statistics-distribution/pom.xml | 86 +
.../AbstractContinuousDistribution.java | 453 +++
.../AbstractDiscreteDistribution.java | 220 ++
.../distribution/BetaDistribution.java | 202 ++
.../distribution/BinomialDistribution.java | 170 +
.../distribution/CauchyDistribution.java | 166 +
.../distribution/ChiSquaredDistribution.java | 119 +
.../ConstantContinuousDistribution.java | 116 +
.../distribution/ContinuousDistribution.java | 176 +
.../distribution/DiscreteDistribution.java | 163 +
.../distribution/DistributionException.java | 61 +
.../distribution/ExponentialDistribution.java | 197 ++
.../statistics/distribution/FDistribution.java | 209 ++
.../distribution/GammaDistribution.java | 354 ++
.../distribution/GeometricDistribution.java | 160 +
.../distribution/GumbelDistribution.java | 128 +
.../HypergeometricDistribution.java | 293 ++
.../distribution/LaplaceDistribution.java | 132 +
.../distribution/LevyDistribution.java | 161 +
.../distribution/LogNormalDistribution.java | 266 ++
.../distribution/LogisticDistribution.java | 128 +
.../distribution/NakagamiDistribution.java | 117 +
.../distribution/NormalDistribution.java | 216 ++
.../distribution/ParetoDistribution.java | 225 ++
.../distribution/PascalDistribution.java | 211 ++
.../distribution/PoissonDistribution.java | 238 ++
.../distribution/SaddlePointExpansion.java | 191 +
.../statistics/distribution/TDistribution.java | 180 +
.../distribution/TriangularDistribution.java | 222 ++
.../UniformContinuousDistribution.java | 168 +
.../UniformDiscreteDistribution.java | 159 +
.../distribution/WeibullDistribution.java | 220 ++
.../distribution/ZipfDistribution.java | 236 ++
.../statistics/distribution/package-info.java | 20 +
.../AbstractContinuousDistributionTest.java | 209 ++
.../AbstractDiscreteDistributionTest.java | 130 +
.../distribution/BetaDistributionTest.java | 381 ++
.../distribution/BinomialDistributionTest.java | 173 +
.../distribution/CauchyDistributionTest.java | 111 +
.../ChiSquaredDistributionTest.java | 136 +
.../ConstantContinuousDistributionTest.java | 92 +
.../ContinuousDistributionAbstractTest.java | 456 +++
.../DiscreteDistributionAbstractTest.java | 411 +++
.../ExponentialDistributionTest.java | 132 +
.../distribution/FDistributionTest.java | 150 +
.../distribution/GammaDistributionTest.java | 354 ++
.../distribution/GeometricDistributionTest.java | 167 +
.../distribution/GumbelDistributionTest.java | 70 +
.../HypergeometricDistributionTest.java | 335 ++
.../distribution/LaplaceDistributionTest.java | 70 +
.../distribution/LevyDistributionTest.java | 81 +
.../distribution/LogNormalDistributionTest.java | 250 ++
.../distribution/LogisticsDistributionTest.java | 70 +
.../distribution/NakagamiDistributionTest.java | 70 +
.../distribution/NormalDistributionTest.java | 213 ++
.../distribution/ParetoDistributionTest.java | 201 ++
.../distribution/PascalDistributionTest.java | 132 +
.../distribution/PoissonDistributionTest.java | 244 ++
.../distribution/TDistributionTest.java | 169 +
.../statistics/distribution/TestUtils.java | 281 ++
.../TriangularDistributionTest.java | 192 +
.../UniformContinuousDistributionTest.java | 123 +
.../UniformDiscreteDistributionTest.java | 139 +
.../distribution/WeibullDistributionTest.java | 118 +
.../distribution/ZipfDistributionTest.java | 166 +
.../distribution/gamma-distribution-shape-1.csv | 3215 +++++++++++++++++
.../gamma-distribution-shape-10.csv | 415 +++
.../gamma-distribution-shape-100.csv | 408 +++
.../gamma-distribution-shape-1000.csv | 3325 ++++++++++++++++++
.../gamma-distribution-shape-142.csv | 775 ++++
.../distribution/gamma-distribution-shape-8.csv | 3215 +++++++++++++++++
.../distribution/gamma-distribution.mac | 73 +
.../statistics/distribution/testData.txt | 1000 ++++++
pom.xml | 65 +-
76 files changed, 24949 insertions(+), 11 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/LICENSE.txt
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/LICENSE.txt b/commons-statistics-distribution/LICENSE.txt
new file mode 100644
index 0000000..de777e4
--- /dev/null
+++ b/commons-statistics-distribution/LICENSE.txt
@@ -0,0 +1,275 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
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+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
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+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
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+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
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+ 2. Grant of Copyright License. Subject to the terms and conditions of
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+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
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+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
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+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
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+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
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+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
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+ END OF TERMS AND CONDITIONS
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+ APPENDIX: How to apply the Apache License to your work.
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+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
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+ Licensed under the Apache License, Version 2.0 (the "License");
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+ 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.
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+================================================================================
+
+Class "org.apache.commons.rng.internal.source64.MersenneTwister64" contains
+Java code partly ported from the reference implementation in C.
+That source file contained the following notice:
+
+ Copyright (C) 2004, Makoto Matsumoto and Takuji Nishimura,
+ All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions
+ are met:
+
+ 1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+ 2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+ 3. The names of its contributors may not be used to endorse or promote
+ products derived from this software without specific prior written
+ permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+================================================================================
+
+Class "org.apache.commons.rng.internal.source32.MersenneTwister" contains
+Java code partly ported from the reference implementation in C.
+That source file contained the following notice:
+
+ Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+ All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions
+ are met:
+
+ 1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+ 2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+ 3. The names of its contributors may not be used to endorse or promote
+ products derived from this software without specific prior written
+ permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+================================================================================
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/NOTICE.txt
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/NOTICE.txt b/commons-statistics-distribution/NOTICE.txt
new file mode 100644
index 0000000..a67070b
--- /dev/null
+++ b/commons-statistics-distribution/NOTICE.txt
@@ -0,0 +1,5 @@
+Apache Commons Statistics
+Copyright 2018-2018 The Apache Software Foundation
+
+This product includes software developed at
+The Apache Software Foundation (http://www.apache.org/).
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/pom.xml
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/pom.xml b/commons-statistics-distribution/pom.xml
new file mode 100644
index 0000000..ed5d669
--- /dev/null
+++ b/commons-statistics-distribution/pom.xml
@@ -0,0 +1,86 @@
+<?xml version="1.0"?>
+<!--
+ 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.
+-->
+<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"
+ xmlns="http://maven.apache.org/POM/4.0.0"
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+ <modelVersion>4.0.0</modelVersion>
+
+ <parent>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-statistics-parent</artifactId>
+ <version>0.1-SNAPSHOT</version>
+ </parent>
+
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-statistics-distribution</artifactId>
+ <version>0.1-SNAPSHOT</version>
+ <name>Apache Commons Statistics Distribution</name>
+
+ <description>Statistical distributions.</description>
+
+ <properties>
+ <!-- This value must reflect the current name of the base package. -->
+ <commons.osgi.symbolicName>org.apache.commons.statistics.distribution</commons.osgi.symbolicName>
+ <!-- OSGi -->
+ <commons.osgi.export>org.apache.commons.statistics.distribution</commons.osgi.export>
+ <!-- Workaround to avoid duplicating config files. -->
+ <statistics.parent.dir>${basedir}/..</statistics.parent.dir>
+ </properties>
+
+ <dependencies>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-client-api</artifactId>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-sampling</artifactId>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-core</artifactId>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-combinatorics</artifactId>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-gamma</artifactId>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-simple</artifactId>
+ <scope>test</scope>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-math3</artifactId>
+ <scope>test</scope>
+ </dependency>
+
+ </dependencies>
+
+</project>
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractContinuousDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractContinuousDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractContinuousDistribution.java
new file mode 100644
index 0000000..1d6b254
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractContinuousDistribution.java
@@ -0,0 +1,453 @@
+/*
+ * 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.statistics.distribution;
+
+import java.util.function.DoubleUnaryOperator;
+import org.apache.commons.numbers.core.Precision;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.InverseTransformContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.ContinuousInverseCumulativeProbabilityFunction;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+
+/**
+ * Base class for probability distributions on the reals.
+ * Default implementations are provided for some of the methods
+ * that do not vary from distribution to distribution.
+ *
+ * This base class provides a default factory method for creating
+ * a {@link ContinuousDistribution.Sampler sampler instance} that uses the
+ * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling">
+ * inversion method</a> for generating random samples that follow the
+ * distribution.
+ */
+public abstract class AbstractContinuousDistribution
+ implements ContinuousDistribution {
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(x0 < X <= x1)}.
+ *
+ * @param x0 Lower bound (excluded).
+ * @param x1 Upper bound (included).
+ * @return the probability that a random variable with this distribution
+ * takes a value between {@code x0} and {@code x1}, excluding the lower
+ * and including the upper endpoint.
+ * @throws IllegalArgumentException if {@code x0 > x1}.
+ *
+ * The default implementation uses the identity
+ * {@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}
+ */
+ @Override
+ public double probability(double x0,
+ double x1) {
+ if (x0 > x1) {
+ throw new DistributionException(DistributionException.TOO_LARGE, x0, x1);
+ }
+ return cumulativeProbability(x1) - cumulativeProbability(x0);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The default implementation returns
+ * <ul>
+ * <li>{@link #getSupportLowerBound()} for {@code p = 0},</li>
+ * <li>{@link #getSupportUpperBound()} for {@code p = 1}.</li>
+ * </ul>
+ */
+ @Override
+ public double inverseCumulativeProbability(final double p) {
+ /*
+ * IMPLEMENTATION NOTES
+ * --------------------
+ * Where applicable, use is made of the one-sided Chebyshev inequality
+ * to bracket the root. This inequality states that
+ * P(X - mu >= k * sig) <= 1 / (1 + k^2),
+ * mu: mean, sig: standard deviation. Equivalently
+ * 1 - P(X < mu + k * sig) <= 1 / (1 + k^2),
+ * F(mu + k * sig) >= k^2 / (1 + k^2).
+ *
+ * For k = sqrt(p / (1 - p)), we find
+ * F(mu + k * sig) >= p,
+ * and (mu + k * sig) is an upper-bound for the root.
+ *
+ * Then, introducing Y = -X, mean(Y) = -mu, sd(Y) = sig, and
+ * P(Y >= -mu + k * sig) <= 1 / (1 + k^2),
+ * P(-X >= -mu + k * sig) <= 1 / (1 + k^2),
+ * P(X <= mu - k * sig) <= 1 / (1 + k^2),
+ * F(mu - k * sig) <= 1 / (1 + k^2).
+ *
+ * For k = sqrt((1 - p) / p), we find
+ * F(mu - k * sig) <= p,
+ * and (mu - k * sig) is a lower-bound for the root.
+ *
+ * In cases where the Chebyshev inequality does not apply, geometric
+ * progressions 1, 2, 4, ... and -1, -2, -4, ... are used to bracket
+ * the root.
+ */
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+
+ double lowerBound = getSupportLowerBound();
+ if (p == 0) {
+ return lowerBound;
+ }
+
+ double upperBound = getSupportUpperBound();
+ if (p == 1) {
+ return upperBound;
+ }
+
+ final double mu = getNumericalMean();
+ final double sig = Math.sqrt(getNumericalVariance());
+ final boolean chebyshevApplies;
+ chebyshevApplies = !(Double.isInfinite(mu) ||
+ Double.isNaN(mu) ||
+ Double.isInfinite(sig) ||
+ Double.isNaN(sig));
+
+ if (lowerBound == Double.NEGATIVE_INFINITY) {
+ if (chebyshevApplies) {
+ lowerBound = mu - sig * Math.sqrt((1 - p) / p);
+ } else {
+ lowerBound = -1;
+ while (cumulativeProbability(lowerBound) >= p) {
+ lowerBound *= 2;
+ }
+ }
+ }
+
+ if (upperBound == Double.POSITIVE_INFINITY) {
+ if (chebyshevApplies) {
+ upperBound = mu + sig * Math.sqrt(p / (1 - p));
+ } else {
+ upperBound = 1;
+ while (cumulativeProbability(upperBound) < p) {
+ upperBound *= 2;
+ }
+ }
+ }
+
+ // XXX Values copied from defaults in class
+ // "o.a.c.math4.analysis.solvers.BaseAbstractUnivariateSolver"
+ final double solverRelativeAccuracy = 1e-14;
+ final double solverAbsoluteAccuracy = 1e-9;
+ final double solverFunctionValueAccuracy = 1e-15;
+
+ double x = new BrentSolver(solverRelativeAccuracy,
+ solverAbsoluteAccuracy,
+ solverFunctionValueAccuracy)
+ .solve((arg) -> cumulativeProbability(arg) - p,
+ lowerBound,
+ 0.5 * (lowerBound + upperBound),
+ upperBound);
+
+ if (!isSupportConnected()) {
+ /* Test for plateau. */
+ final double dx = solverAbsoluteAccuracy;
+ if (x - dx >= getSupportLowerBound()) {
+ double px = cumulativeProbability(x);
+ if (cumulativeProbability(x - dx) == px) {
+ upperBound = x;
+ while (upperBound - lowerBound > dx) {
+ final double midPoint = 0.5 * (lowerBound + upperBound);
+ if (cumulativeProbability(midPoint) < px) {
+ lowerBound = midPoint;
+ } else {
+ upperBound = midPoint;
+ }
+ }
+ return upperBound;
+ }
+ }
+ }
+ return x;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * @return zero.
+ */
+ @Override
+ public double probability(double x) {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The default implementation computes the logarithm of {@code density(x)}.
+ */
+ @Override
+ public double logDensity(double x) {
+ return Math.log(density(x));
+ }
+
+ /**
+ * Utility function for allocating an array and filling it with {@code n}
+ * samples generated by the given {@code sampler}.
+ *
+ * @param n Number of samples.
+ * @param sampler Sampler.
+ * @return an array of size {@code n}.
+ */
+ public static double[] sample(int n,
+ ContinuousDistribution.Sampler sampler) {
+ final double[] samples = new double[n];
+ for (int i = 0; i < n; i++) {
+ samples[i] = sampler.sample();
+ }
+ return samples;
+ }
+
+ /**{@inheritDoc} */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Inversion method distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new InverseTransformContinuousSampler(rng, createICPF());
+
+ /** {@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+
+ /**
+ * @return an instance for use by {@link #createSampler(UniformRandomProvider)}
+ */
+ private ContinuousInverseCumulativeProbabilityFunction createICPF() {
+ return new ContinuousInverseCumulativeProbabilityFunction() {
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ return AbstractContinuousDistribution.this.inverseCumulativeProbability(p);
+ }
+ };
+ }
+
+ /**
+ * This class implements the <a href="http://mathworld.wolfram.com/BrentsMethod.html">
+ * Brent algorithm</a> for finding zeros of real univariate functions.
+ * The function should be continuous but not necessarily smooth.
+ * The {@code solve} method returns a zero {@code x} of the function {@code f}
+ * in the given interval {@code [a, b]} to within a tolerance
+ * {@code 2 eps abs(x) + t} where {@code eps} is the relative accuracy and
+ * {@code t} is the absolute accuracy.
+ * <p>The given interval must bracket the root.</p>
+ * <p>
+ * The reference implementation is given in chapter 4 of
+ * <blockquote>
+ * <b>Algorithms for Minimization Without Derivatives</b>,
+ * <em>Richard P. Brent</em>,
+ * Dover, 2002
+ * </blockquote>
+ *
+ * Used by {@link #inverseCumulativeProbability(double)}.
+ */
+ private static class BrentSolver {
+ /** Relative accuracy. */
+ private final double relativeAccuracy;
+ /** Absolutee accuracy. */
+ private final double absoluteAccuracy;
+ /** Function accuracy. */
+ private final double functionValueAccuracy;
+
+ /**
+ * Construct a solver.
+ *
+ * @param relativeAccuracy Relative accuracy.
+ * @param absoluteAccuracy Absolute accuracy.
+ * @param functionValueAccuracy Function value accuracy.
+ */
+ BrentSolver(double relativeAccuracy,
+ double absoluteAccuracy,
+ double functionValueAccuracy) {
+ this.relativeAccuracy = relativeAccuracy;
+ this.absoluteAccuracy = absoluteAccuracy;
+ this.functionValueAccuracy = functionValueAccuracy;
+ }
+
+ /**
+ * @param func Function to solve.
+ * @param min Lower bound.
+ * @param init Initial guess.
+ * @param max Upper bound.
+ * @return the root.
+ */
+ double solve(DoubleUnaryOperator func,
+ double min,
+ double initial,
+ double max) {
+ if (min > max) {
+ throw new DistributionException(DistributionException.TOO_LARGE, min, max);
+ }
+ if (initial < min ||
+ initial > max) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, initial, min, max);
+ }
+
+ // Return the initial guess if it is good enough.
+ double yInitial = func.applyAsDouble(initial);
+ if (Math.abs(yInitial) <= functionValueAccuracy) {
+ return initial;
+ }
+
+ // Return the first endpoint if it is good enough.
+ double yMin = func.applyAsDouble(min);
+ if (Math.abs(yMin) <= functionValueAccuracy) {
+ return min;
+ }
+
+ // Reduce interval if min and initial bracket the root.
+ if (yInitial * yMin < 0) {
+ return brent(func, min, initial, yMin, yInitial);
+ }
+
+ // Return the second endpoint if it is good enough.
+ double yMax = func.applyAsDouble(max);
+ if (Math.abs(yMax) <= functionValueAccuracy) {
+ return max;
+ }
+
+ // Reduce interval if initial and max bracket the root.
+ if (yInitial * yMax < 0) {
+ return brent(func, initial, max, yInitial, yMax);
+ }
+
+ throw new DistributionException(DistributionException.BRACKETING, min, yMin, max, yMax);
+ }
+
+ /**
+ * Search for a zero inside the provided interval.
+ * This implementation is based on the algorithm described at page 58 of
+ * the book
+ * <blockquote>
+ * <b>Algorithms for Minimization Without Derivatives</b>,
+ * <it>Richard P. Brent</it>,
+ * Dover 0-486-41998-3
+ * </blockquote>
+ *
+ * @param func Function to solve.
+ * @param lo Lower bound of the search interval.
+ * @param hi Higher bound of the search interval.
+ * @param fLo Function value at the lower bound of the search interval.
+ * @param fHi Function value at the higher bound of the search interval.
+ * @return the value where the function is zero.
+ */
+ private double brent(DoubleUnaryOperator func,
+ double lo, double hi,
+ double fLo, double fHi) {
+ double a = lo;
+ double fa = fLo;
+ double b = hi;
+ double fb = fHi;
+ double c = a;
+ double fc = fa;
+ double d = b - a;
+ double e = d;
+
+ final double t = absoluteAccuracy;
+ final double eps = relativeAccuracy;
+
+ while (true) {
+ if (Math.abs(fc) < Math.abs(fb)) {
+ a = b;
+ b = c;
+ c = a;
+ fa = fb;
+ fb = fc;
+ fc = fa;
+ }
+
+ final double tol = 2 * eps * Math.abs(b) + t;
+ final double m = 0.5 * (c - b);
+
+ if (Math.abs(m) <= tol ||
+ Precision.equals(fb, 0)) {
+ return b;
+ }
+ if (Math.abs(e) < tol ||
+ Math.abs(fa) <= Math.abs(fb)) {
+ // Force bisection.
+ d = m;
+ e = d;
+ } else {
+ double s = fb / fa;
+ double p;
+ double q;
+ // The equality test (a == c) is intentional,
+ // it is part of the original Brent's method and
+ // it should NOT be replaced by proximity test.
+ if (a == c) {
+ // Linear interpolation.
+ p = 2 * m * s;
+ q = 1 - s;
+ } else {
+ // Inverse quadratic interpolation.
+ q = fa / fc;
+ final double r = fb / fc;
+ p = s * (2 * m * q * (q - r) - (b - a) * (r - 1));
+ q = (q - 1) * (r - 1) * (s - 1);
+ }
+ if (p > 0) {
+ q = -q;
+ } else {
+ p = -p;
+ }
+ s = e;
+ e = d;
+ if (p >= 1.5 * m * q - Math.abs(tol * q) ||
+ p >= Math.abs(0.5 * s * q)) {
+ // Inverse quadratic interpolation gives a value
+ // in the wrong direction, or progress is slow.
+ // Fall back to bisection.
+ d = m;
+ e = d;
+ } else {
+ d = p / q;
+ }
+ }
+ a = b;
+ fa = fb;
+
+ if (Math.abs(d) > tol) {
+ b += d;
+ } else if (m > 0) {
+ b += tol;
+ } else {
+ b -= tol;
+ }
+ fb = func.applyAsDouble(b);
+ if ((fb > 0 && fc > 0) ||
+ (fb <= 0 && fc <= 0)) {
+ c = a;
+ fc = fa;
+ d = b - a;
+ e = d;
+ }
+ }
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractDiscreteDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractDiscreteDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractDiscreteDistribution.java
new file mode 100644
index 0000000..faef96c
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/AbstractDiscreteDistribution.java
@@ -0,0 +1,220 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.InverseTransformDiscreteSampler;
+import org.apache.commons.rng.sampling.distribution.DiscreteInverseCumulativeProbabilityFunction;
+import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
+
+/**
+ * Base class for integer-valued discrete distributions. Default
+ * implementations are provided for some of the methods that do not vary
+ * from distribution to distribution.
+ */
+public abstract class AbstractDiscreteDistribution
+ implements DiscreteDistribution {
+ /**
+ * {@inheritDoc}
+ *
+ * The default implementation uses the identity
+ * {@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}
+ */
+ @Override
+ public double probability(int x0,
+ int x1) {
+ if (x1 < x0) {
+ throw new DistributionException(DistributionException.TOO_SMALL,
+ x1, x0);
+ }
+ return cumulativeProbability(x1) - cumulativeProbability(x0);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The default implementation returns
+ * <ul>
+ * <li>{@link #getSupportLowerBound()} for {@code p = 0},</li>
+ * <li>{@link #getSupportUpperBound()} for {@code p = 1}, and</li>
+ * <li>{@link #solveInverseCumulativeProbability(double, int, int)} for
+ * {@code 0 < p < 1}.</li>
+ * </ul>
+ */
+ @Override
+ public int inverseCumulativeProbability(final double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+
+ int lower = getSupportLowerBound();
+ if (p == 0.0) {
+ return lower;
+ }
+ if (lower == Integer.MIN_VALUE) {
+ if (checkedCumulativeProbability(lower) >= p) {
+ return lower;
+ }
+ } else {
+ lower -= 1; // this ensures cumulativeProbability(lower) < p, which
+ // is important for the solving step
+ }
+
+ int upper = getSupportUpperBound();
+ if (p == 1.0) {
+ return upper;
+ }
+
+ // use the one-sided Chebyshev inequality to narrow the bracket
+ // cf. AbstractRealDistribution.inverseCumulativeProbability(double)
+ final double mu = getNumericalMean();
+ final double sigma = Math.sqrt(getNumericalVariance());
+ final boolean chebyshevApplies = !(Double.isInfinite(mu) ||
+ Double.isNaN(mu) ||
+ Double.isInfinite(sigma) ||
+ Double.isNaN(sigma) ||
+ sigma == 0.0);
+ if (chebyshevApplies) {
+ double k = Math.sqrt((1.0 - p) / p);
+ double tmp = mu - k * sigma;
+ if (tmp > lower) {
+ lower = ((int) Math.ceil(tmp)) - 1;
+ }
+ k = 1.0 / k;
+ tmp = mu + k * sigma;
+ if (tmp < upper) {
+ upper = ((int) Math.ceil(tmp)) - 1;
+ }
+ }
+
+ return solveInverseCumulativeProbability(p, lower, upper);
+ }
+
+ /**
+ * This is a utility function used by {@link
+ * #inverseCumulativeProbability(double)}. It assumes {@code 0 < p < 1} and
+ * that the inverse cumulative probability lies in the bracket {@code
+ * (lower, upper]}. The implementation does simple bisection to find the
+ * smallest {@code p}-quantile {@code inf{x in Z | P(X <= x) >= p}}.
+ *
+ * @param p Cumulative probability.
+ * @param lower Value satisfying {@code cumulativeProbability(lower) < p}.
+ * @param upper Value satisfying {@code p <= cumulativeProbability(upper)}.
+ * @return the smallest {@code p}-quantile of this distribution.
+ */
+ private int solveInverseCumulativeProbability(final double p,
+ int lower,
+ int upper) {
+ while (lower + 1 < upper) {
+ int xm = (lower + upper) / 2;
+ if (xm < lower || xm > upper) {
+ /*
+ * Overflow.
+ * There will never be an overflow in both calculation methods
+ * for xm at the same time
+ */
+ xm = lower + (upper - lower) / 2;
+ }
+
+ double pm = checkedCumulativeProbability(xm);
+ if (pm >= p) {
+ upper = xm;
+ } else {
+ lower = xm;
+ }
+ }
+ return upper;
+ }
+
+ /**
+ * Computes the cumulative probability function and checks for {@code NaN}
+ * values returned. Throws {@code MathInternalError} if the value is
+ * {@code NaN}. Rethrows any exception encountered evaluating the cumulative
+ * probability function. Throws {@code MathInternalError} if the cumulative
+ * probability function returns {@code NaN}.
+ *
+ * @param argument Input value.
+ * @return the cumulative probability.
+ * @throws IllegalStateException if the cumulative probability is {@code NaN}.
+ */
+ private double checkedCumulativeProbability(int argument) {
+ final double result = cumulativeProbability(argument);
+ if (Double.isNaN(result)) {
+ throw new IllegalStateException("Internal error");
+ }
+ return result;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The default implementation simply computes the logarithm of {@code probability(x)}.
+ */
+ @Override
+ public double logProbability(int x) {
+ return Math.log(probability(x));
+ }
+
+ /**
+ * Utility function for allocating an array and filling it with {@code n}
+ * samples generated by the given {@code sampler}.
+ *
+ * @param n Number of samples.
+ * @param sampler Sampler.
+ * @return an array of size {@code n}.
+ */
+ public static int[] sample(int n,
+ DiscreteDistribution.Sampler sampler) {
+ final int[] samples = new int[n];
+ for (int i = 0; i < n; i++) {
+ samples[i] = sampler.sample();
+ }
+ return samples;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public DiscreteDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new DiscreteDistribution.Sampler() {
+ /**
+ * Inversion method distribution sampler.
+ */
+ private final DiscreteSampler sampler =
+ new InverseTransformDiscreteSampler(rng, createICPF());
+
+ /** {@inheritDoc} */
+ @Override
+ public int sample() {
+ return sampler.sample();
+ }
+ };
+ }
+
+ /**
+ * @return an instance for use by {@link #createSampler(UniformRandomProvider)}.
+ */
+ private DiscreteInverseCumulativeProbabilityFunction createICPF() {
+ return new DiscreteInverseCumulativeProbabilityFunction() {
+ /** {@inheritDoc} */
+ @Override
+ public int inverseCumulativeProbability(double p) {
+ return AbstractDiscreteDistribution.this.inverseCumulativeProbability(p);
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BetaDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BetaDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BetaDistribution.java
new file mode 100644
index 0000000..0a07b49
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BetaDistribution.java
@@ -0,0 +1,202 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.RegularizedBeta;
+import org.apache.commons.numbers.gamma.LogGamma;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.ChengBetaSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Beta_distribution">Beta distribution</a>.
+ */
+public class BetaDistribution extends AbstractContinuousDistribution {
+ /** First shape parameter. */
+ private final double alpha;
+ /** Second shape parameter. */
+ private final double beta;
+ /** Normalizing factor used in density computations.*/
+ private final double z;
+
+ /**
+ * Creates a new instance.
+ *
+ * @param alpha First shape parameter (must be positive).
+ * @param beta Second shape parameter (must be positive).
+ */
+ public BetaDistribution(double alpha,
+ double beta) {
+ this.alpha = alpha;
+ this.beta = beta;
+ z = LogGamma.value(alpha) + LogGamma.value(beta) - LogGamma.value(alpha + beta);
+ }
+
+ /**
+ * Access the first shape parameter, {@code alpha}.
+ *
+ * @return the first shape parameter.
+ */
+ public double getAlpha() {
+ return alpha;
+ }
+
+ /**
+ * Access the second shape parameter, {@code beta}.
+ *
+ * @return the second shape parameter.
+ */
+ public double getBeta() {
+ return beta;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ final double logDensity = logDensity(x);
+ return logDensity == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logDensity);
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logDensity(double x) {
+ if (x < 0 ||
+ x > 1) {
+ return Double.NEGATIVE_INFINITY;
+ } else if (x == 0) {
+ if (alpha < 1) {
+ throw new DistributionException(DistributionException.TOO_SMALL,
+ alpha, 1);
+ }
+ return Double.NEGATIVE_INFINITY;
+ } else if (x == 1) {
+ if (beta < 1) {
+ throw new DistributionException(DistributionException.TOO_SMALL,
+ beta, 1);
+ }
+ return Double.NEGATIVE_INFINITY;
+ } else {
+ double logX = Math.log(x);
+ double log1mX = Math.log1p(-x);
+ return (alpha - 1) * logX + (beta - 1) * log1mX - z;
+ }
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x <= 0) {
+ return 0;
+ } else if (x >= 1) {
+ return 1;
+ } else {
+ return RegularizedBeta.value(x, alpha, beta);
+ }
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For first shape parameter {@code alpha} and second shape parameter
+ * {@code beta}, the mean is {@code alpha / (alpha + beta)}.
+ */
+ @Override
+ public double getNumericalMean() {
+ final double a = getAlpha();
+ return a / (a + getBeta());
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For first shape parameter {@code alpha} and second shape parameter
+ * {@code beta}, the variance is
+ * {@code (alpha * beta) / [(alpha + beta)^2 * (alpha + beta + 1)]}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double a = getAlpha();
+ final double b = getBeta();
+ final double alphabetasum = a + b;
+ return (a * b) / ((alphabetasum * alphabetasum) * (alphabetasum + 1));
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always 1 no matter the parameters.
+ *
+ * @return upper bound of the support (always 1)
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return 1;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * Sampling is performed using Cheng's algorithm:
+ * <blockquote>
+ * <pre>
+ * R. C. H. Cheng,
+ * "Generating beta variates with nonintegral shape parameters",
+ * Communications of the ACM, 21, 317-322, 1978.
+ * </pre>
+ * </blockquote>
+ */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Beta distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new ChengBetaSampler(rng, alpha, beta);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BinomialDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BinomialDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BinomialDistribution.java
new file mode 100644
index 0000000..7177968
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/BinomialDistribution.java
@@ -0,0 +1,170 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.RegularizedBeta;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Binomial_distribution">binomial distribution</a>.
+ */
+public class BinomialDistribution extends AbstractDiscreteDistribution {
+ /** The number of trials. */
+ private final int numberOfTrials;
+ /** The probability of success. */
+ private final double probabilityOfSuccess;
+
+ /**
+ * Creates a binomial distribution.
+ *
+ * @param trials Number of trials.
+ * @param p Probability of success.
+ * @throws IllegalArgumentException if {@code trials < 0}, or if
+ * {@code p < 0} or {@code p > 1}.
+ */
+ public BinomialDistribution(int trials,
+ double p) {
+ if (trials < 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ trials);
+ }
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+
+ probabilityOfSuccess = p;
+ numberOfTrials = trials;
+ }
+
+ /**
+ * Access the number of trials for this distribution.
+ *
+ * @return the number of trials.
+ */
+ public int getNumberOfTrials() {
+ return numberOfTrials;
+ }
+
+ /**
+ * Access the probability of success for this distribution.
+ *
+ * @return the probability of success.
+ */
+ public double getProbabilityOfSuccess() {
+ return probabilityOfSuccess;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ final double logProbability = logProbability(x);
+ return logProbability == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logProbability);
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logProbability(int x) {
+ if (numberOfTrials == 0) {
+ return (x == 0) ? 0. : Double.NEGATIVE_INFINITY;
+ }
+ double ret;
+ if (x < 0 || x > numberOfTrials) {
+ ret = Double.NEGATIVE_INFINITY;
+ } else {
+ ret = SaddlePointExpansion.logBinomialProbability(x,
+ numberOfTrials, probabilityOfSuccess,
+ 1.0 - probabilityOfSuccess);
+ }
+ return ret;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ double ret;
+ if (x < 0) {
+ ret = 0.0;
+ } else if (x >= numberOfTrials) {
+ ret = 1.0;
+ } else {
+ ret = 1.0 - RegularizedBeta.value(probabilityOfSuccess,
+ x + 1.0, numberOfTrials - x);
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For {@code n} trials and probability parameter {@code p}, the mean is
+ * {@code n * p}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return numberOfTrials * probabilityOfSuccess;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For {@code n} trials and probability parameter {@code p}, the variance is
+ * {@code n * p * (1 - p)}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double p = probabilityOfSuccess;
+ return numberOfTrials * p * (1 - p);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 except for the probability
+ * parameter {@code p = 1}.
+ *
+ * @return lower bound of the support (0 or the number of trials)
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return probabilityOfSuccess < 1.0 ? 0 : numberOfTrials;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is the number of trials except for the
+ * probability parameter {@code p = 0}.
+ *
+ * @return upper bound of the support (number of trials or 0)
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return probabilityOfSuccess > 0.0 ? numberOfTrials : 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/CauchyDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/CauchyDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/CauchyDistribution.java
new file mode 100644
index 0000000..a7b6c64
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/CauchyDistribution.java
@@ -0,0 +1,166 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Cauchy_distribution">Cauchy distribution</a>.
+ */
+public class CauchyDistribution extends AbstractContinuousDistribution {
+ /** The median of this distribution. */
+ private final double median;
+ /** The scale of this distribution. */
+ private final double scale;
+
+ /**
+ * Creates a Cauchy distribution with the median equal to zero and scale
+ * equal to one.
+ */
+ public CauchyDistribution() {
+ this(0, 1);
+ }
+
+ /**
+ * Creates a distribution.
+ *
+ * @param median Median for this distribution.
+ * @param scale Scale parameter for this distribution.
+ * @throws IllegalArgumentException if {@code scale <= 0}.
+ */
+ public CauchyDistribution(double median,
+ double scale) {
+ if (scale <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, scale);
+ }
+ this.scale = scale;
+ this.median = median;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ return 0.5 + (Math.atan((x - median) / scale) / Math.PI);
+ }
+
+ /**
+ * Access the median.
+ *
+ * @return the median for this distribution.
+ */
+ public double getMedian() {
+ return median;
+ }
+
+ /**
+ * Access the scale parameter.
+ *
+ * @return the scale parameter for this distribution.
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ final double dev = x - median;
+ return (1 / Math.PI) * (scale / (dev * dev + scale * scale));
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * Returns {@code Double.NEGATIVE_INFINITY} when {@code p == 0}
+ * and {@code Double.POSITIVE_INFINITY} when {@code p == 1}.
+ */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ double ret;
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 0) {
+ ret = Double.NEGATIVE_INFINITY;
+ } else if (p == 1) {
+ ret = Double.POSITIVE_INFINITY;
+ } else {
+ ret = median + scale * Math.tan(Math.PI * (p - .5));
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The mean is always undefined no matter the parameters.
+ *
+ * @return mean (always Double.NaN)
+ */
+ @Override
+ public double getNumericalMean() {
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The variance is always undefined no matter the parameters.
+ *
+ * @return variance (always Double.NaN)
+ */
+ @Override
+ public double getNumericalVariance() {
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always negative infinity no matter
+ * the parameters.
+ *
+ * @return lower bound of the support (always Double.NEGATIVE_INFINITY)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity no matter
+ * the parameters.
+ *
+ * @return upper bound of the support (always Double.POSITIVE_INFINITY)
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ChiSquaredDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ChiSquaredDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ChiSquaredDistribution.java
new file mode 100644
index 0000000..5f31254
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ChiSquaredDistribution.java
@@ -0,0 +1,119 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Chi-squared_distribution">chi-squared distribution</a>.
+ */
+public class ChiSquaredDistribution extends AbstractContinuousDistribution {
+ /** Internal Gamma distribution. */
+ private final GammaDistribution gamma;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param degreesOfFreedom Degrees of freedom.
+ */
+ public ChiSquaredDistribution(double degreesOfFreedom) {
+ gamma = new GammaDistribution(degreesOfFreedom / 2, 2);
+ }
+
+ /**
+ * Access the number of degrees of freedom.
+ *
+ * @return the degrees of freedom.
+ */
+ public double getDegreesOfFreedom() {
+ return gamma.getShape() * 2;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ return gamma.density(x);
+ }
+
+ /** {@inheritDoc} **/
+ @Override
+ public double logDensity(double x) {
+ return gamma.logDensity(x);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ return gamma.cumulativeProbability(x);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For {@code k} degrees of freedom, the mean is {@code k}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return getDegreesOfFreedom();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * @return {@code 2 * k}, where {@code k} is the number of degrees of freedom.
+ */
+ @Override
+ public double getNumericalVariance() {
+ return 2 * getDegreesOfFreedom();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the
+ * degrees of freedom.
+ *
+ * @return zero.
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity no matter the
+ * degrees of freedom.
+ *
+ * @return {@code Double.POSITIVE_INFINITY}.
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ConstantContinuousDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ConstantContinuousDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ConstantContinuousDistribution.java
new file mode 100644
index 0000000..54694f1
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ConstantContinuousDistribution.java
@@ -0,0 +1,116 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+
+/**
+ * Implementation of the constant real distribution.
+ */
+public class ConstantContinuousDistribution extends AbstractContinuousDistribution {
+ /** Constant value of the distribution. */
+ private final double value;
+
+ /**
+ * Create a constant real distribution with the given value.
+ *
+ * @param value Value of this distribution.
+ */
+ public ConstantContinuousDistribution(double value) {
+ this.value = value;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ return x == value ? 1 : 0;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ return x < value ? 0 : 1;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(final double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ return value;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public double getNumericalMean() {
+ return value;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public double getNumericalVariance() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return value;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return value;
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * @param rng Not used: distribution contains a single value.
+ * @return the value of the distribution.
+ */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /** {@inheritDoc} */
+ @Override
+ public double sample() {
+ return value;
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ContinuousDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ContinuousDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ContinuousDistribution.java
new file mode 100644
index 0000000..b08f75a
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ContinuousDistribution.java
@@ -0,0 +1,176 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+
+/**
+ * Base interface for distributions on the reals.
+ */
+public interface ContinuousDistribution {
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(X = x)}. In other
+ * words, this method represents the probability mass function (PMF)
+ * for the distribution.
+ *
+ * @param x the point at which the PMF is evaluated
+ * @return the value of the probability mass function at point {@code x}
+ */
+ double probability(double x);
+
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(x0 < X <= x1)}.
+ *
+ * @param x0 the exclusive lower bound
+ * @param x1 the inclusive upper bound
+ * @return the probability that a random variable with this distribution
+ * takes a value between {@code x0} and {@code x1},
+ * excluding the lower and including the upper endpoint
+ * @throws IllegalArgumentException if {@code x0 > x1}
+ */
+ double probability(double x0, double x1);
+
+ /**
+ * Returns the probability density function (PDF) of this distribution
+ * evaluated at the specified point {@code x}. In general, the PDF is
+ * the derivative of the {@link #cumulativeProbability(double) CDF}.
+ * If the derivative does not exist at {@code x}, then an appropriate
+ * replacement should be returned, e.g. {@code Double.POSITIVE_INFINITY},
+ * {@code Double.NaN}, or the limit inferior or limit superior of the
+ * difference quotient.
+ *
+ * @param x the point at which the PDF is evaluated
+ * @return the value of the probability density function at point {@code x}
+ */
+ double density(double x);
+
+ /**
+ * Returns the natural logarithm of the probability density function
+ * (PDF) of this distribution evaluated at the specified point {@code x}.
+ * In general, the PDF is the derivative of the {@link #cumulativeProbability(double) CDF}.
+ * If the derivative does not exist at {@code x}, then an appropriate replacement
+ * should be returned, e.g. {@code Double.POSITIVE_INFINITY}, {@code Double.NaN},
+ * or the limit inferior or limit superior of the difference quotient. Note that
+ * due to the floating point precision and under/overflow issues, this method will
+ * for some distributions be more precise and faster than computing the logarithm of
+ * {@link #density(double)}.
+ *
+ * @param x the point at which the PDF is evaluated
+ * @return the logarithm of the value of the probability density function at point {@code x}
+ */
+ double logDensity(double x);
+
+ /**
+ * For a random variable {@code X} whose values are distributed according
+ * to this distribution, this method returns {@code P(X <= x)}. In other
+ * words, this method represents the (cumulative) distribution function
+ * (CDF) for this distribution.
+ *
+ * @param x the point at which the CDF is evaluated
+ * @return the probability that a random variable with this
+ * distribution takes a value less than or equal to {@code x}
+ */
+ double cumulativeProbability(double x);
+
+ /**
+ * Computes the quantile function of this distribution. For a random
+ * variable {@code X} distributed according to this distribution, the
+ * returned value is
+ * <ul>
+ * <li>{@code inf{x in R | P(X<=x) >= p}} for {@code 0 < p <= 1},</li>
+ * <li>{@code inf{x in R | P(X<=x) > 0}} for {@code p = 0}.</li>
+ * </ul>
+ *
+ * @param p the cumulative probability
+ * @return the smallest {@code p}-quantile of this distribution
+ * (largest 0-quantile for {@code p = 0})
+ * @throws IllegalArgumentException if {@code p < 0} or {@code p > 1}
+ */
+ double inverseCumulativeProbability(double p);
+
+ /**
+ * Use this method to get the numerical value of the mean of this
+ * distribution.
+ *
+ * @return the mean or {@code Double.NaN} if it is not defined
+ */
+ double getNumericalMean();
+
+ /**
+ * Use this method to get the numerical value of the variance of this
+ * distribution.
+ *
+ * @return the variance (possibly {@code Double.POSITIVE_INFINITY} as
+ * for certain cases in {@link TDistribution}) or {@code Double.NaN} if it
+ * is not defined
+ */
+ double getNumericalVariance();
+
+ /**
+ * Access the lower bound of the support. This method must return the same
+ * value as {@code inverseCumulativeProbability(0)}. In other words, this
+ * method must return
+ * <p>{@code inf {x in R | P(X <= x) > 0}}.</p>
+ *
+ * @return lower bound of the support (might be
+ * {@code Double.NEGATIVE_INFINITY})
+ */
+ double getSupportLowerBound();
+
+ /**
+ * Access the upper bound of the support. This method must return the same
+ * value as {@code inverseCumulativeProbability(1)}. In other words, this
+ * method must return
+ * <p>{@code inf {x in R | P(X <= x) = 1}}.</p>
+ *
+ * @return upper bound of the support (might be
+ * {@code Double.POSITIVE_INFINITY})
+ */
+ double getSupportUpperBound();
+
+ /**
+ * Use this method to get information about whether the support is connected,
+ * i.e. whether all values between the lower and upper bound of the support
+ * are included in the support.
+ *
+ * @return whether the support is connected or not
+ */
+ boolean isSupportConnected();
+
+ /**
+ * Creates a sampler.
+ *
+ * @param rng Generator of uniformly distributed numbers.
+ * @return a sampler that produces random numbers according this
+ * distribution.
+ */
+ Sampler createSampler(UniformRandomProvider rng);
+
+ /**
+ * Sampling functionality.
+ */
+ interface Sampler {
+ /**
+ * Generates a random value sampled from this distribution.
+ *
+ * @return a random value.
+ */
+ double sample();
+ }
+}
[07/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1.csv
new file mode 100644
index 0000000..af00c10
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1.csv
@@ -0,0 +1,3215 @@
+#
+# 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.
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<TRUNCATED>
[03/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-8.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-8.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-8.csv
new file mode 100644
index 0000000..878f472
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-8.csv
@@ -0,0 +1,3215 @@
+#
+# 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.
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+33.21875, 0.00000003315374908066508193766806280947370313068581486121668680216347386
+33.25, 0.00000003234591848483335622162261237547305507476868939675777741830443996
+33.28125, 0.00000003155757651575507544127646362679371379281083225068889502539016576
+33.3125, 0.00000003078825817974837465103767107943254258845581767567979094526284255
+33.34375, 0.00000003003750944176687500812937045870034726642980015076271425775686825
+33.375, 0.0000000293048869707010907030695536960425755486324699857993914518908844
+33.40625, 0.00000002858995789050637599944796474993735062755899743497271909144903094
+33.4375, 0.00000002789229953702654349790195983596975017091876117828271976341874402
+33.46875, 0.00000002721149922038516152089547055421437405141212898174608869823264139
+33.5, 0.00000002654715399281935367386378393571733293632226790059912609581792534
+33.53125, 0.00000002589887042183367845297594872998935483976505596013894696160731694
+33.5625, 0.00000002526626436855436249320303499761075259433627229349744863845661775
+33.59375, 0.00000002464896077116679891060302770132829247919630300628580288192142508
+33.625, 0.00000002404659343332180339327377996132587852050663682471986432674792126
+33.65625, 0.00000002345880481739864641577222598619965327572453825906046536700323453
+33.6875, 0.00000002288524584251535134784995184978396815892013458774694141311896467
+33.71875, 0.00000002232557568717916643286912479246370249137638329156705274789542221
+33.75, 0.00000002177946159647248473429256198352705191921337106240086204258164154
+33.78125, 0.00000002124657869367180127796964764618231212549852815335755953116493921
+33.8125, 0.00000002072660979619956181950036875357609720477810910329696882961274658
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+33.875, 0.00000001972418268292001998614742009543323689639888321366916343108818821
+33.90625, 0.00000001924112697497103173282121296207839758668716205421890090294542495
+33.9375, 0.00000001876978994876426485226197414116000076601353967051514190937232882
+33.96875, 0.00000001830989027664593899158481994037650336796249069656368651374843187
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+34.0625, 0.00000001699610130653496942180645493735204859503957680455224353861025133
+34.09375, 0.00000001657926896092915743980877016524321373276554058182813025135852094
+34.125, 0.0000000161725643906273940138099964813021403936859743467586494130893364
+34.15625, 0.00000001577574404686328085159111242747184398456560753740786051880207656
+34.1875, 0.00000001538857017083755777753113302861619574820417377812266075712490782
+34.21875, 0.00000001501081065783030471836731456176342619862998780805179682296352162
+34.25, 0.00000001464223892445624460491789313807670418917674352042266976645786892
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+34.34375, 0.00000001358946468610873834658228561387119362693028274142364476274645099
+34.375, 0.00000001325548418810821598896647607070586734578586976756522602194447394
+34.40625, 0.00000001292963693791102151419908031548691490376501504912464683014066437
+34.4375, 0.00000001261172685970429948662106920772868446737799305167652939538444454
+34.46875, 0.00000001230156255199475183773443258827861317491869263106196818090113707
+34.5, 0.00000001199895717756457560264378538633283607493083787559635059787327947
+34.53125, 0.00000001170372835598162636769436690928017156802141927845619
<TRUNCATED>
[15/16] commons-statistics git commit: Report config files (copied
from "Commons RNG").
Posted by er...@apache.org.
Report config files (copied from "Commons RNG").
Project: http://git-wip-us.apache.org/repos/asf/commons-statistics/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-statistics/commit/0efff4bf
Tree: http://git-wip-us.apache.org/repos/asf/commons-statistics/tree/0efff4bf
Diff: http://git-wip-us.apache.org/repos/asf/commons-statistics/diff/0efff4bf
Branch: refs/heads/master
Commit: 0efff4bffd4ebbffa8dea12fa701a3c3b3eafd9e
Parents: 021d0f1
Author: Gilles Sadowski <gi...@harfang.homelinux.org>
Authored: Sun Jan 21 15:03:16 2018 +0100
Committer: Gilles Sadowski <gi...@harfang.homelinux.org>
Committed: Sun Jan 21 15:03:16 2018 +0100
----------------------------------------------------------------------
src/main/resources/checkstyle/checkstyle.xml | 202 +++++++++++++++++++
.../resources/checkstyle/license-header.txt | 16 ++
src/main/resources/clirr/clirr-ignored.xml | 21 ++
.../findbugs/findbugs-exclude-filter.xml | 28 +++
src/main/resources/pmd/pmd-ruleset.xml | 57 ++++++
5 files changed, 324 insertions(+)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/0efff4bf/src/main/resources/checkstyle/checkstyle.xml
----------------------------------------------------------------------
diff --git a/src/main/resources/checkstyle/checkstyle.xml b/src/main/resources/checkstyle/checkstyle.xml
new file mode 100644
index 0000000..a138af6
--- /dev/null
+++ b/src/main/resources/checkstyle/checkstyle.xml
@@ -0,0 +1,202 @@
+<?xml version="1.0"?>
+
+<!--
+ 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.
+ -->
+
+<!DOCTYPE module PUBLIC "-//Puppy Crawl//DTD Check Configuration 1.1//EN" "http://www.puppycrawl.com/dtds/configuration_1_1.dtd">
+
+<!-- Commons RNG customization of default Checkstyle behavior -->
+<module name="Checker">
+ <property name="localeLanguage" value="en"/>
+
+ <module name="TreeWalker">
+
+ <!-- Operator must be at end of wrapped line -->
+ <module name="OperatorWrap">
+ <property name="option" value="eol"/>
+ </module>
+
+ <!-- No if/else/do/for/while without braces -->
+ <module name="NeedBraces"/>
+
+ <!-- Interfaces must be types (not just constants) -->
+ <module name="InterfaceIsType"/>
+
+ <!-- Must have class / interface header comments -->
+ <module name="JavadocType"/>
+
+ <!-- Require method javadocs, allow undeclared RTE -->
+ <module name="JavadocMethod">
+ <property name="allowUndeclaredRTE" value="true"/>
+ <property name="allowThrowsTagsForSubclasses" value="true"/>
+ <property name="validateThrows" value="false"/>
+ </module>
+
+ <!-- Require field javadoc -->
+ <module name="JavadocVariable"/>
+
+ <!-- No public fields -->
+ <module name="VisibilityModifier">
+ <property name="protectedAllowed" value="true"/>
+ </module>
+
+ <!-- Require hash code override when equals is -->
+ <module name="EqualsHashCode"/>
+
+ <!-- Disallow unnecessary instantiation of Boolean, String -->
+ <module name="IllegalInstantiation">
+ <property name="classes" value="java.lang.Boolean, java.lang.String"/>
+ </module>
+
+ <!-- Required for SuppressionCommentFilter below -->
+ <module name="FileContentsHolder"/>
+
+ <!-- Import should be explicit, really needed and only from pure java packages -->
+ <module name="AvoidStarImport" />
+ <module name="UnusedImports" />
+ <module name="IllegalImport" />
+
+ <!-- Utility class should not be instantiated, they must have a private constructor -->
+ <module name="HideUtilityClassConstructor" />
+
+ <!-- Switch statements should be complete and with independent cases -->
+ <module name="FallThrough" />
+ <module name="MissingSwitchDefault" />
+
+ <!-- Constant names should obey the traditional all uppercase naming convention -->
+ <module name="ConstantName" />
+
+ <!-- Method parameters and local variables should not hide fields, except in constructors and setters -->
+ <module name="HiddenField">
+ <property name="ignoreConstructorParameter" value="true" />
+ <property name="ignoreSetter" value="true" />
+ </module>
+
+ <!-- No trailing whitespace -->
+ <module name="Regexp">
+ <property name="format" value="[ \t]+$"/>
+ <property name="illegalPattern" value="true"/>
+ <property name="message" value="Trailing whitespace"/>
+ </module>
+
+ <!-- No System.out.println() statements -->
+ <module name="Regexp">
+ <!-- no sysouts -->
+ <property name="format" value="System\.out\.println"/>
+ <property name="illegalPattern" value="true"/>
+ </module>
+
+ <!-- Authors should be in pom.xml file -->
+ <module name="Regexp">
+ <property name="format" value="@author"/>
+ <property name="illegalPattern" value="true"/>
+ <property name="message" value="developers names should be in pom file"/>
+ </module>
+
+ <!-- Use a consistent way to put modifiers -->
+ <module name="RedundantModifier" />
+ <module name="ModifierOrder" />
+
+ <!-- Use a consistent way to put declarations -->
+ <module name="DeclarationOrder" />
+
+ <!-- Don't add up parentheses when they are not required -->
+ <module name="UnnecessaryParentheses" />
+
+ <!-- Don't use too widespread catch (Exception, Throwable, RuntimeException) -->
+ <module name="IllegalCatch" />
+
+ <!-- Don't use = or != for string comparisons -->
+ <module name="StringLiteralEquality" />
+
+ <!-- Don't declare multiple variables in the same statement -->
+ <module name="MultipleVariableDeclarations" />
+
+ <!-- String literals more than one character long should not be repeated several times -->
+ <!-- the "unchecked" string is also accepted to allow @SuppressWarnings("unchecked") -->
+ <module name="MultipleStringLiterals" >
+ <property name="ignoreStringsRegexp" value='^(("")|(".")|("unchecked"))$'/>
+ </module>
+
+ <!-- Check if @Override tags are present -->
+ <module name="MissingOverride" />
+
+ <!-- <module name="TodoComment" /> -->
+
+ </module>
+
+ <!-- Verify that EVERY source file has the appropriate license -->
+ <module name="Header">
+ <property name="headerFile" value="${checkstyle.header.file}"/>
+ </module>
+
+ <!-- No tabs allowed! -->
+ <module name="FileTabCharacter"/>
+
+ <!-- Require files to end with newline characters -->
+ <module name="NewlineAtEndOfFile"/>
+
+ <!-- Require package javadoc -->
+ <module name="JavadocPackage"/>
+
+ <!-- Setup special comments to suppress specific checks from source files -->
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop JavadocVariable"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume JavadocVariable"/>
+ <property name="checkFormat" value="JavadocVariable"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop JavadocMethodCheck"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume JavadocMethodCheck"/>
+ <property name="checkFormat" value="JavadocMethodCheck"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop ConstantName"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume ConstantName"/>
+ <property name="checkFormat" value="ConstantName"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop HideUtilityClassConstructor"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume HideUtilityClassConstructor"/>
+ <property name="checkFormat" value="HideUtilityClassConstructor"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop MultipleVariableDeclarations"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume MultipleVariableDeclarations"/>
+ <property name="checkFormat" value="MultipleVariableDeclarations"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop IllegalCatch"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume IllegalCatch"/>
+ <property name="checkFormat" value="IllegalCatch"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop DeclarationOrder"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume DeclarationOrder"/>
+ <property name="checkFormat" value="DeclarationOrder"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop RedundantModifier"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume RedundantModifier"/>
+ <property name="checkFormat" value="RedundantModifier"/>
+ </module>
+ <module name="SuppressionCommentFilter">
+ <property name="offCommentFormat" value="CHECKSTYLE\: stop all"/>
+ <property name="onCommentFormat" value="CHECKSTYLE\: resume all"/>
+ </module>
+</module>
+
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/0efff4bf/src/main/resources/checkstyle/license-header.txt
----------------------------------------------------------------------
diff --git a/src/main/resources/checkstyle/license-header.txt b/src/main/resources/checkstyle/license-header.txt
new file mode 100644
index 0000000..ae6f28c
--- /dev/null
+++ b/src/main/resources/checkstyle/license-header.txt
@@ -0,0 +1,16 @@
+/*
+ * 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.
+ */
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/0efff4bf/src/main/resources/clirr/clirr-ignored.xml
----------------------------------------------------------------------
diff --git a/src/main/resources/clirr/clirr-ignored.xml b/src/main/resources/clirr/clirr-ignored.xml
new file mode 100644
index 0000000..ed97259
--- /dev/null
+++ b/src/main/resources/clirr/clirr-ignored.xml
@@ -0,0 +1,21 @@
+<?xml version="1.0"?>
+<!--
+ 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.
+-->
+
+<differences>
+
+</differences>
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/0efff4bf/src/main/resources/findbugs/findbugs-exclude-filter.xml
----------------------------------------------------------------------
diff --git a/src/main/resources/findbugs/findbugs-exclude-filter.xml b/src/main/resources/findbugs/findbugs-exclude-filter.xml
new file mode 100644
index 0000000..8a9c858
--- /dev/null
+++ b/src/main/resources/findbugs/findbugs-exclude-filter.xml
@@ -0,0 +1,28 @@
+<?xml version="1.0"?>
+<!--
+ 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.
+-->
+
+<!--
+ This file contains some false positive bugs detected by findbugs. Their
+ false positive nature has been analyzed individually and they have been
+ put here to instruct findbugs it must ignore them.
+-->
+<FindBugsFilter>
+
+ <Class name="~.*\.jmh\.generated\..*" />
+
+</FindBugsFilter>
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/0efff4bf/src/main/resources/pmd/pmd-ruleset.xml
----------------------------------------------------------------------
diff --git a/src/main/resources/pmd/pmd-ruleset.xml b/src/main/resources/pmd/pmd-ruleset.xml
new file mode 100644
index 0000000..0524d61
--- /dev/null
+++ b/src/main/resources/pmd/pmd-ruleset.xml
@@ -0,0 +1,57 @@
+<?xml version="1.0"?>
+<!--
+ 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.
+-->
+<ruleset name="commons-statistics-customized"
+ xmlns="http://pmd.sourceforge.net/ruleset/2.0.0"
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+ xsi:schemaLocation="http://pmd.sourceforge.net/ruleset/2.0.0 http://pmd.sourceforge.net/ruleset_2_0_0.xsd">
+ <description>
+ This ruleset checks the code for discouraged programming constructs.
+ </description>
+
+ <rule ref="rulesets/java/basic.xml"/>
+
+ <rule ref="rulesets/java/braces.xml"/>
+
+ <rule ref="rulesets/java/comments.xml">
+ <exclude name="CommentSize"/>
+ </rule>
+ <rule ref="rulesets/java/comments.xml/CommentSize">
+ <properties>
+ <property name="maxLines" value="200"/>
+ <property name="maxLineLength" value="256"/>
+ </properties>
+ </rule>
+
+ <rule ref="rulesets/java/empty.xml"/>
+
+ <rule ref="rulesets/java/finalizers.xml"/>
+
+ <rule ref="rulesets/java/imports.xml"/>
+
+ <rule ref="rulesets/java/typeresolution.xml"/>
+
+ <rule ref="rulesets/java/clone.xml"/>
+
+ <rule ref="rulesets/java/unnecessary.xml">
+ <!-- We do use extra parentheses there as most people do not recall operator precedence,
+ this means even if the parentheses are useless for the compiler, we don't consider
+ them useless for the developer. This is the reason why we disable this rule. -->
+ <exclude name="UselessParentheses"/>
+ </rule>
+
+</ruleset>
[16/16] commons-statistics git commit: Merge branch
'task_STATISTICS-2'
Posted by er...@apache.org.
Merge branch 'task_STATISTICS-2'
Project: http://git-wip-us.apache.org/repos/asf/commons-statistics/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-statistics/commit/30d7c8f6
Tree: http://git-wip-us.apache.org/repos/asf/commons-statistics/tree/30d7c8f6
Diff: http://git-wip-us.apache.org/repos/asf/commons-statistics/diff/30d7c8f6
Branch: refs/heads/master
Commit: 30d7c8f6b528f7859ae46b9dbdecd374c91ab383
Parents: 0efff4b 9c794a1
Author: Gilles Sadowski <gi...@harfang.homelinux.org>
Authored: Sun Jan 21 15:04:11 2018 +0100
Committer: Gilles Sadowski <gi...@harfang.homelinux.org>
Committed: Sun Jan 21 15:04:11 2018 +0100
----------------------------------------------------------------------
commons-statistics-distribution/LICENSE.txt | 275 ++
commons-statistics-distribution/NOTICE.txt | 5 +
commons-statistics-distribution/pom.xml | 86 +
.../AbstractContinuousDistribution.java | 453 +++
.../AbstractDiscreteDistribution.java | 220 ++
.../distribution/BetaDistribution.java | 202 ++
.../distribution/BinomialDistribution.java | 170 +
.../distribution/CauchyDistribution.java | 166 +
.../distribution/ChiSquaredDistribution.java | 119 +
.../ConstantContinuousDistribution.java | 116 +
.../distribution/ContinuousDistribution.java | 176 +
.../distribution/DiscreteDistribution.java | 163 +
.../distribution/DistributionException.java | 61 +
.../distribution/ExponentialDistribution.java | 197 ++
.../statistics/distribution/FDistribution.java | 209 ++
.../distribution/GammaDistribution.java | 354 ++
.../distribution/GeometricDistribution.java | 160 +
.../distribution/GumbelDistribution.java | 128 +
.../HypergeometricDistribution.java | 293 ++
.../distribution/LaplaceDistribution.java | 132 +
.../distribution/LevyDistribution.java | 161 +
.../distribution/LogNormalDistribution.java | 266 ++
.../distribution/LogisticDistribution.java | 128 +
.../distribution/NakagamiDistribution.java | 117 +
.../distribution/NormalDistribution.java | 216 ++
.../distribution/ParetoDistribution.java | 225 ++
.../distribution/PascalDistribution.java | 211 ++
.../distribution/PoissonDistribution.java | 238 ++
.../distribution/SaddlePointExpansion.java | 191 +
.../statistics/distribution/TDistribution.java | 180 +
.../distribution/TriangularDistribution.java | 222 ++
.../UniformContinuousDistribution.java | 168 +
.../UniformDiscreteDistribution.java | 159 +
.../distribution/WeibullDistribution.java | 220 ++
.../distribution/ZipfDistribution.java | 236 ++
.../statistics/distribution/package-info.java | 20 +
.../AbstractContinuousDistributionTest.java | 209 ++
.../AbstractDiscreteDistributionTest.java | 130 +
.../distribution/BetaDistributionTest.java | 381 ++
.../distribution/BinomialDistributionTest.java | 173 +
.../distribution/CauchyDistributionTest.java | 111 +
.../ChiSquaredDistributionTest.java | 136 +
.../ConstantContinuousDistributionTest.java | 92 +
.../ContinuousDistributionAbstractTest.java | 456 +++
.../DiscreteDistributionAbstractTest.java | 411 +++
.../ExponentialDistributionTest.java | 132 +
.../distribution/FDistributionTest.java | 150 +
.../distribution/GammaDistributionTest.java | 354 ++
.../distribution/GeometricDistributionTest.java | 167 +
.../distribution/GumbelDistributionTest.java | 70 +
.../HypergeometricDistributionTest.java | 335 ++
.../distribution/LaplaceDistributionTest.java | 70 +
.../distribution/LevyDistributionTest.java | 81 +
.../distribution/LogNormalDistributionTest.java | 250 ++
.../distribution/LogisticsDistributionTest.java | 70 +
.../distribution/NakagamiDistributionTest.java | 70 +
.../distribution/NormalDistributionTest.java | 213 ++
.../distribution/ParetoDistributionTest.java | 201 ++
.../distribution/PascalDistributionTest.java | 132 +
.../distribution/PoissonDistributionTest.java | 244 ++
.../distribution/TDistributionTest.java | 169 +
.../statistics/distribution/TestUtils.java | 281 ++
.../TriangularDistributionTest.java | 192 +
.../UniformContinuousDistributionTest.java | 123 +
.../UniformDiscreteDistributionTest.java | 139 +
.../distribution/WeibullDistributionTest.java | 118 +
.../distribution/ZipfDistributionTest.java | 166 +
.../distribution/gamma-distribution-shape-1.csv | 3215 +++++++++++++++++
.../gamma-distribution-shape-10.csv | 415 +++
.../gamma-distribution-shape-100.csv | 408 +++
.../gamma-distribution-shape-1000.csv | 3325 ++++++++++++++++++
.../gamma-distribution-shape-142.csv | 775 ++++
.../distribution/gamma-distribution-shape-8.csv | 3215 +++++++++++++++++
.../distribution/gamma-distribution.mac | 73 +
.../statistics/distribution/testData.txt | 1000 ++++++
pom.xml | 71 +-
76 files changed, 24952 insertions(+), 14 deletions(-)
----------------------------------------------------------------------
[05/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1000.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1000.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1000.csv
new file mode 100644
index 0000000..e0536f9
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-1000.csv
@@ -0,0 +1,3325 @@
+#
+# 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.
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<TRUNCATED>
[14/16] commons-statistics git commit: Create ".gitignore" file.
Posted by er...@apache.org.
Create ".gitignore" file.
Project: http://git-wip-us.apache.org/repos/asf/commons-statistics/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-statistics/commit/021d0f17
Tree: http://git-wip-us.apache.org/repos/asf/commons-statistics/tree/021d0f17
Diff: http://git-wip-us.apache.org/repos/asf/commons-statistics/diff/021d0f17
Branch: refs/heads/master
Commit: 021d0f17bf5b97e8775c36d23ade9c017b98d839
Parents: bb864a0
Author: Gilles Sadowski <gi...@harfang.homelinux.org>
Authored: Sun Jan 21 15:02:26 2018 +0100
Committer: Gilles Sadowski <gi...@harfang.homelinux.org>
Committed: Sun Jan 21 15:02:26 2018 +0100
----------------------------------------------------------------------
.gitignore | 19 +++++++++++++++++++
1 file changed, 19 insertions(+)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/021d0f17/.gitignore
----------------------------------------------------------------------
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..7410e55
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,19 @@
+.classpath
+.project
+.settings
+.checkstyle
+bin
+target
+/build
+/lib
+site-content
+.ekstazi
+*.class
+*.iml
+*.ipr
+*.iws
+.idea
+.DS_Store
+*~
+/.externalToolBuilders/
+/maven-eclipse.xml
[06/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-10.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-10.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-10.csv
new file mode 100644
index 0000000..3061384
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-10.csv
@@ -0,0 +1,415 @@
+#
+# 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.
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http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-100.csv
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-100.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-100.csv
new file mode 100644
index 0000000..4ef5e9f
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution-shape-100.csv
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+#
+# 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.
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[11/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NormalDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NormalDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NormalDistribution.java
new file mode 100644
index 0000000..632657f
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/NormalDistribution.java
@@ -0,0 +1,216 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.Erfc;
+import org.apache.commons.numbers.gamma.InverseErf;
+import org.apache.commons.numbers.gamma.ErfDifference;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.GaussianSampler;
+import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Normal_distribution">normal (Gaussian) distribution</a>.
+ */
+public class NormalDistribution extends AbstractContinuousDistribution {
+ /** √(2) */
+ private static final double SQRT2 = Math.sqrt(2.0);
+ /** Mean of this distribution. */
+ private final double mean;
+ /** Standard deviation of this distribution. */
+ private final double standardDeviation;
+ /** The value of {@code log(sd) + 0.5*log(2*pi)} stored for faster computation. */
+ private final double logStandardDeviationPlusHalfLog2Pi;
+
+ /**
+ * Create a normal distribution with mean equal to zero and standard
+ * deviation equal to one.
+ */
+ public NormalDistribution() {
+ this(0, 1);
+ }
+
+ /**
+ * Creates a distribution.
+ *
+ * @param mean Mean for this distribution.
+ * @param sd Standard deviation for this distribution.
+ * @throws IllegalArgumentException if {@code sd <= 0}.
+ */
+ public NormalDistribution(double mean,
+ double sd) {
+ if (sd <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, sd);
+ }
+
+ this.mean = mean;
+ standardDeviation = sd;
+ logStandardDeviationPlusHalfLog2Pi = Math.log(sd) + 0.5 * Math.log(2 * Math.PI);
+ }
+
+ /**
+ * Access the mean.
+ *
+ * @return the mean for this distribution.
+ */
+ public double getMean() {
+ return mean;
+ }
+
+ /**
+ * Access the standard deviation.
+ *
+ * @return the standard deviation for this distribution.
+ */
+ public double getStandardDeviation() {
+ return standardDeviation;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ return Math.exp(logDensity(x));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logDensity(double x) {
+ final double x0 = x - mean;
+ final double x1 = x0 / standardDeviation;
+ return -0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * If {@code x} is more than 40 standard deviations from the mean, 0 or 1
+ * is returned, as in these cases the actual value is within
+ * {@code Double.MIN_VALUE} of 0 or 1.
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ final double dev = x - mean;
+ if (Math.abs(dev) > 40 * standardDeviation) {
+ return dev < 0 ? 0.0d : 1.0d;
+ }
+ return 0.5 * Erfc.value(-dev / (standardDeviation * SQRT2));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(final double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ return mean + standardDeviation * SQRT2 * InverseErf.value(2 * p - 1);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(double x0,
+ double x1) {
+ if (x0 > x1) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ x0, x1);
+ }
+ final double denom = standardDeviation * SQRT2;
+ final double v0 = (x0 - mean) / denom;
+ final double v1 = (x1 - mean) / denom;
+ return 0.5 * ErfDifference.value(v0, v1);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For mean parameter {@code mu}, the mean is {@code mu}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return getMean();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For standard deviation parameter {@code s}, the variance is {@code s^2}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double s = getStandardDeviation();
+ return s * s;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always negative infinity
+ * no matter the parameters.
+ *
+ * @return lower bound of the support (always
+ * {@code Double.NEGATIVE_INFINITY})
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the parameters.
+ *
+ * @return upper bound of the support (always
+ * {@code Double.POSITIVE_INFINITY})
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /** Gaussian distribution sampler. */
+ private final ContinuousSampler sampler =
+ new GaussianSampler(new ZigguratNormalizedGaussianSampler(rng),
+ mean, standardDeviation);
+
+ /** {@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ParetoDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ParetoDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ParetoDistribution.java
new file mode 100644
index 0000000..2bbd42d
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ParetoDistribution.java
@@ -0,0 +1,225 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.InverseTransformParetoSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Pareto_distribution">Pareto distribution</a>.
+ *
+ * <p>
+ * <strong>Parameters:</strong>
+ * The probability distribution function of {@code X} is given by (for {@code x >= k}):
+ * <pre>
+ * α * k^α / x^(α + 1)
+ * </pre>
+ * <ul>
+ * <li>{@code k} is the <em>scale</em> parameter: this is the minimum possible value of {@code X},</li>
+ * <li>{@code α} is the <em>shape</em> parameter: this is the Pareto index</li>
+ * </ul>
+ */
+public class ParetoDistribution extends AbstractContinuousDistribution {
+ /** The scale parameter of this distribution. */
+ private final double scale;
+ /** The shape parameter of this distribution. */
+ private final double shape;
+
+ /**
+ * Creates a Pareto distribution with a scale of {@code 1} and a shape of {@code 1}.
+ */
+ public ParetoDistribution() {
+ this(1, 1);
+ }
+
+ /**
+ * Creates a Pareto distribution.
+ *
+ * @param scale Scale parameter of this distribution.
+ * @param shape Shape parameter of this distribution.
+ * @throws IllegalArgumentException if {@code scale <= 0} or {@code shape <= 0}.
+ */
+ public ParetoDistribution(double scale,
+ double shape) {
+ if (scale <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, scale);
+ }
+
+ if (shape <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, shape);
+ }
+
+ this.scale = scale;
+ this.shape = shape;
+ }
+
+ /**
+ * Returns the scale parameter of this distribution.
+ *
+ * @return the scale parameter
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /**
+ * Returns the shape parameter of this distribution.
+ *
+ * @return the shape parameter
+ */
+ public double getShape() {
+ return shape;
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * For scale {@code k}, and shape {@code α} of this distribution, the PDF
+ * is given by
+ * <ul>
+ * <li>{@code 0} if {@code x < k},</li>
+ * <li>{@code α * k^α / x^(α + 1)} otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double density(double x) {
+ if (x < scale) {
+ return 0;
+ }
+ return Math.pow(scale, shape) / Math.pow(x, shape + 1) * shape;
+ }
+
+ /** {@inheritDoc}
+ *
+ * See documentation of {@link #density(double)} for computation details.
+ */
+ @Override
+ public double logDensity(double x) {
+ if (x < scale) {
+ return Double.NEGATIVE_INFINITY;
+ }
+ return Math.log(scale) * shape - Math.log(x) * (shape + 1) + Math.log(shape);
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * For scale {@code k}, and shape {@code α} of this distribution, the CDF is given by
+ * <ul>
+ * <li>{@code 0} if {@code x < k},</li>
+ * <li>{@code 1 - (k / x)^α} otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x <= scale) {
+ return 0;
+ }
+ return 1 - Math.pow(scale / x, shape);
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * For scale {@code k} and shape {@code α}, the mean is given by
+ * <ul>
+ * <li>{@code ∞} if {@code α <= 1},</li>
+ * <li>{@code α * k / (α - 1)} otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalMean() {
+ if (shape <= 1) {
+ return Double.POSITIVE_INFINITY;
+ }
+ return shape * scale / (shape - 1);
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * For scale {@code k} and shape {@code α}, the variance is given by
+ * <ul>
+ * <li>{@code ∞} if {@code 1 < α <= 2},</li>
+ * <li>{@code k^2 * α / ((α - 1)^2 * (α - 2))} otherwise.</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalVariance() {
+ if (shape <= 2) {
+ return Double.POSITIVE_INFINITY;
+ }
+ double s = shape - 1;
+ return scale * scale * shape / (s * s) / (shape - 2);
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * The lower bound of the support is equal to the scale parameter {@code k}.
+ *
+ * @return lower bound of the support
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return scale;
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * The upper bound of the support is always positive infinity no matter the parameters.
+ *
+ * @return upper bound of the support (always {@code Double.POSITIVE_INFINITY})
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ * <p>
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Pareto distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new InverseTransformParetoSampler(rng, scale, shape);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PascalDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PascalDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PascalDistribution.java
new file mode 100644
index 0000000..8bdf6b6
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PascalDistribution.java
@@ -0,0 +1,211 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.combinatorics.BinomialCoefficientDouble;
+import org.apache.commons.numbers.combinatorics.LogBinomialCoefficient;
+import org.apache.commons.numbers.gamma.RegularizedBeta;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Pascal distribution.</a>
+ *
+ * The Pascal distribution is a special case of the Negative Binomial distribution
+ * where the number of successes parameter is an integer.
+ *
+ * There are various ways to express the probability mass and distribution
+ * functions for the Pascal distribution. The present implementation represents
+ * the distribution of the number of failures before {@code r} successes occur.
+ * This is the convention adopted in e.g.
+ * <a href="http://mathworld.wolfram.com/NegativeBinomialDistribution.html">MathWorld</a>,
+ * but <em>not</em> in
+ * <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Wikipedia</a>.
+ *
+ * For a random variable {@code X} whose values are distributed according to this
+ * distribution, the probability mass function is given by<br>
+ * {@code P(X = k) = C(k + r - 1, r - 1) * p^r * (1 - p)^k,}<br>
+ * where {@code r} is the number of successes, {@code p} is the probability of
+ * success, and {@code X} is the total number of failures. {@code C(n, k)} is
+ * the binomial coefficient ({@code n} choose {@code k}). The mean and variance
+ * of {@code X} are<br>
+ * {@code E(X) = (1 - p) * r / p, var(X) = (1 - p) * r / p^2.}<br>
+ * Finally, the cumulative distribution function is given by<br>
+ * {@code P(X <= k) = I(p, r, k + 1)},
+ * where I is the regularized incomplete Beta function.
+ */
+public class PascalDistribution extends AbstractDiscreteDistribution {
+ /** The number of successes. */
+ private final int numberOfSuccesses;
+ /** The probability of success. */
+ private final double probabilityOfSuccess;
+ /** The value of {@code log(p)}, where {@code p} is the probability of success,
+ * stored for faster computation. */
+ private final double logProbabilityOfSuccess;
+ /** The value of {@code log(1-p)}, where {@code p} is the probability of success,
+ * stored for faster computation. */
+ private final double log1mProbabilityOfSuccess;
+
+ /**
+ * Create a Pascal distribution with the given number of successes and
+ * probability of success.
+ *
+ * @param r Number of successes.
+ * @param p Probability of success.
+ * @throws IllegalArgumentException if {@code r <= 0} or {@code p < 0}
+ * or {@code p > 1}.
+ */
+ public PascalDistribution(int r,
+ double p) {
+ if (r <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ r);
+ }
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+
+ numberOfSuccesses = r;
+ probabilityOfSuccess = p;
+ logProbabilityOfSuccess = Math.log(p);
+ log1mProbabilityOfSuccess = Math.log1p(-p);
+ }
+
+ /**
+ * Access the number of successes for this distribution.
+ *
+ * @return the number of successes.
+ */
+ public int getNumberOfSuccesses() {
+ return numberOfSuccesses;
+ }
+
+ /**
+ * Access the probability of success for this distribution.
+ *
+ * @return the probability of success.
+ */
+ public double getProbabilityOfSuccess() {
+ return probabilityOfSuccess;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ double ret;
+ if (x < 0) {
+ ret = 0.0;
+ } else {
+ ret = BinomialCoefficientDouble.value(x +
+ numberOfSuccesses - 1, numberOfSuccesses - 1) *
+ Math.pow(probabilityOfSuccess, numberOfSuccesses) *
+ Math.pow(1.0 - probabilityOfSuccess, x);
+ }
+ return ret;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logProbability(int x) {
+ double ret;
+ if (x < 0) {
+ ret = Double.NEGATIVE_INFINITY;
+ } else {
+ ret = LogBinomialCoefficient.value(x +
+ numberOfSuccesses - 1, numberOfSuccesses - 1) +
+ logProbabilityOfSuccess * numberOfSuccesses +
+ log1mProbabilityOfSuccess * x;
+ }
+ return ret;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ double ret;
+ if (x < 0) {
+ ret = 0.0;
+ } else {
+ ret = RegularizedBeta.value(probabilityOfSuccess,
+ numberOfSuccesses, x + 1.0);
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For number of successes {@code r} and probability of success {@code p},
+ * the mean is {@code r * (1 - p) / p}.
+ */
+ @Override
+ public double getNumericalMean() {
+ final double p = getProbabilityOfSuccess();
+ final double r = getNumberOfSuccesses();
+ return (r * (1 - p)) / p;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For number of successes {@code r} and probability of success {@code p},
+ * the variance is {@code r * (1 - p) / p^2}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double p = getProbabilityOfSuccess();
+ final double r = getNumberOfSuccesses();
+ return r * (1 - p) / (p * p);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity no matter the
+ * parameters. Positive infinity is symbolized by {@code Integer.MAX_VALUE}.
+ *
+ * @return upper bound of the support (always {@code Integer.MAX_VALUE}
+ * for positive infinity)
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return Integer.MAX_VALUE;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PoissonDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PoissonDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PoissonDistribution.java
new file mode 100644
index 0000000..225b8f1
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/PoissonDistribution.java
@@ -0,0 +1,238 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.RegularizedGamma;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
+import org.apache.commons.rng.sampling.distribution.PoissonSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>.
+ */
+public class PoissonDistribution extends AbstractDiscreteDistribution {
+ /** ln(2 π). */
+ private static final double LOG_TWO_PI = Math.log(2 * Math.PI);
+ /** Default maximum number of iterations. */
+ private static final int DEFAULT_MAX_ITERATIONS = 10000000;
+ /** Default convergence criterion. */
+ private static final double DEFAULT_EPSILON = 1e-12;
+ /** Distribution used to compute normal approximation. */
+ private final NormalDistribution normal;
+ /** Mean of the distribution. */
+ private final double mean;
+ /** Maximum number of iterations for cumulative probability. */
+ private final int maxIterations;
+ /** Convergence criterion for cumulative probability. */
+ private final double epsilon;
+
+ /**
+ * Creates a new Poisson distribution with specified mean.
+ *
+ * @param p the Poisson mean
+ * @throws IllegalArgumentException if {@code p <= 0}.
+ */
+ public PoissonDistribution(double p) {
+ this(p, DEFAULT_EPSILON, DEFAULT_MAX_ITERATIONS);
+ }
+
+ /**
+ * Creates a new Poisson distribution with specified mean, convergence
+ * criterion and maximum number of iterations.
+ *
+ * @param p Poisson mean.
+ * @param epsilon Convergence criterion for cumulative probabilities.
+ * @param maxIterations the maximum number of iterations for cumulative
+ * probabilities.
+ * @throws IllegalArgumentException if {@code p <= 0}.
+ */
+ public PoissonDistribution(double p,
+ double epsilon,
+ int maxIterations) {
+ if (p <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE, p);
+ }
+ mean = p;
+ this.epsilon = epsilon;
+ this.maxIterations = maxIterations;
+
+ normal = new NormalDistribution(p, Math.sqrt(p));
+ }
+
+ /**
+ * Creates a new Poisson distribution with the specified mean and
+ * convergence criterion.
+ *
+ * @param p Poisson mean.
+ * @param epsilon Convergence criterion for cumulative probabilities.
+ * @throws IllegalArgumentException if {@code p <= 0}.
+ */
+ public PoissonDistribution(double p,
+ double epsilon) {
+ this(p, epsilon, DEFAULT_MAX_ITERATIONS);
+ }
+
+ /**
+ * Creates a new Poisson distribution with the specified mean and maximum
+ * number of iterations.
+ *
+ * @param p Poisson mean.
+ * @param maxIterations Maximum number of iterations for cumulative
+ * probabilities.
+ */
+ public PoissonDistribution(double p,
+ int maxIterations) {
+ this(p, DEFAULT_EPSILON, maxIterations);
+ }
+
+ /**
+ * Get the mean for the distribution.
+ *
+ * @return the mean for the distribution.
+ */
+ public double getMean() {
+ return mean;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ final double logProbability = logProbability(x);
+ return logProbability == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logProbability);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logProbability(int x) {
+ double ret;
+ if (x < 0 || x == Integer.MAX_VALUE) {
+ ret = Double.NEGATIVE_INFINITY;
+ } else if (x == 0) {
+ ret = -mean;
+ } else {
+ ret = -SaddlePointExpansion.getStirlingError(x) -
+ SaddlePointExpansion.getDeviancePart(x, mean) -
+ 0.5 * LOG_TWO_PI - 0.5 * Math.log(x);
+ }
+ return ret;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ if (x < 0) {
+ return 0;
+ }
+ if (x == Integer.MAX_VALUE) {
+ return 1;
+ }
+ return RegularizedGamma.Q.value((double) x + 1, mean, epsilon,
+ maxIterations);
+ }
+
+ /**
+ * Calculates the Poisson distribution function using a normal
+ * approximation. The {@code N(mean, sqrt(mean))} distribution is used
+ * to approximate the Poisson distribution. The computation uses
+ * "half-correction" (evaluating the normal distribution function at
+ * {@code x + 0.5}).
+ *
+ * @param x Upper bound, inclusive.
+ * @return the distribution function value calculated using a normal
+ * approximation.
+ */
+ public double normalApproximateProbability(int x) {
+ // Calculate the probability using half-correction.
+ return normal.cumulativeProbability(x + 0.5);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For mean parameter {@code p}, the mean is {@code p}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return getMean();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For mean parameter {@code p}, the variance is {@code p}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ return getMean();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the mean parameter.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is positive infinity,
+ * regardless of the parameter values. There is no integer infinity,
+ * so this method returns {@code Integer.MAX_VALUE}.
+ *
+ * @return upper bound of the support (always {@code Integer.MAX_VALUE} for
+ * positive infinity)
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return Integer.MAX_VALUE;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**{@inheritDoc} */
+ @Override
+ public DiscreteDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new DiscreteDistribution.Sampler() {
+ /**
+ * Poisson distribution sampler.
+ */
+ private final DiscreteSampler sampler = new PoissonSampler(rng, mean);
+
+ /**{@inheritDoc} */
+ @Override
+ public int sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/SaddlePointExpansion.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/SaddlePointExpansion.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/SaddlePointExpansion.java
new file mode 100644
index 0000000..7bb847a
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/SaddlePointExpansion.java
@@ -0,0 +1,191 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.LogGamma;
+
+/**
+ * Utility class used by various distributions to accurately compute their
+ * respective probability mass functions. The implementation for this class is
+ * based on the Catherine Loader's
+ * <a href="http://www.herine.net/stat/software/dbinom.html">dbinom</a> routines.
+ *
+ * This class is not intended to be called directly.
+ *
+ * @since 1.0
+ */
+final class SaddlePointExpansion {
+ /** 2 π */
+ private static final double TWO_PI = 2 * Math.PI;
+ /** 1/2 * log(2 π). */
+ private static final double HALF_LOG_TWO_PI = 0.5 * Math.log(TWO_PI);
+
+ /** exact Stirling expansion error for certain values. */
+ private static final double[] EXACT_STIRLING_ERRORS = { 0.0, /* 0.0 */
+ 0.1534264097200273452913848, /* 0.5 */
+ 0.0810614667953272582196702, /* 1.0 */
+ 0.0548141210519176538961390, /* 1.5 */
+ 0.0413406959554092940938221, /* 2.0 */
+ 0.03316287351993628748511048, /* 2.5 */
+ 0.02767792568499833914878929, /* 3.0 */
+ 0.02374616365629749597132920, /* 3.5 */
+ 0.02079067210376509311152277, /* 4.0 */
+ 0.01848845053267318523077934, /* 4.5 */
+ 0.01664469118982119216319487, /* 5.0 */
+ 0.01513497322191737887351255, /* 5.5 */
+ 0.01387612882307074799874573, /* 6.0 */
+ 0.01281046524292022692424986, /* 6.5 */
+ 0.01189670994589177009505572, /* 7.0 */
+ 0.01110455975820691732662991, /* 7.5 */
+ 0.010411265261972096497478567, /* 8.0 */
+ 0.009799416126158803298389475, /* 8.5 */
+ 0.009255462182712732917728637, /* 9.0 */
+ 0.008768700134139385462952823, /* 9.5 */
+ 0.008330563433362871256469318, /* 10.0 */
+ 0.007934114564314020547248100, /* 10.5 */
+ 0.007573675487951840794972024, /* 11.0 */
+ 0.007244554301320383179543912, /* 11.5 */
+ 0.006942840107209529865664152, /* 12.0 */
+ 0.006665247032707682442354394, /* 12.5 */
+ 0.006408994188004207068439631, /* 13.0 */
+ 0.006171712263039457647532867, /* 13.5 */
+ 0.005951370112758847735624416, /* 14.0 */
+ 0.005746216513010115682023589, /* 14.5 */
+ 0.005554733551962801371038690 /* 15.0 */
+ };
+
+ /**
+ * Forbid construction.
+ */
+ private SaddlePointExpansion() {}
+
+ /**
+ * Compute the error of Stirling's series at the given value.
+ * <p>
+ * References:
+ * <ol>
+ * <li>Eric W. Weisstein. "Stirling's Series." From MathWorld--A Wolfram Web
+ * Resource. <a target="_blank"
+ * href="http://mathworld.wolfram.com/StirlingsSeries.html">
+ * http://mathworld.wolfram.com/StirlingsSeries.html</a></li>
+ * </ol>
+ * </p>
+ *
+ * @param z the value.
+ * @return the Striling's series error.
+ */
+ static double getStirlingError(double z) {
+ double ret;
+ if (z < 15.0) {
+ double z2 = 2.0 * z;
+ if (Math.floor(z2) == z2) {
+ ret = EXACT_STIRLING_ERRORS[(int) z2];
+ } else {
+ ret = LogGamma.value(z + 1.0) - (z + 0.5) * Math.log(z) +
+ z - HALF_LOG_TWO_PI;
+ }
+ } else {
+ double z2 = z * z;
+ ret = (0.083333333333333333333 -
+ (0.00277777777777777777778 -
+ (0.00079365079365079365079365 -
+ (0.000595238095238095238095238 -
+ 0.0008417508417508417508417508 /
+ z2) / z2) / z2) / z2) / z;
+ }
+ return ret;
+ }
+
+ /**
+ * A part of the deviance portion of the saddle point approximation.
+ * <p>
+ * References:
+ * <ol>
+ * <li>Catherine Loader (2000). "Fast and Accurate Computation of Binomial
+ * Probabilities.". <a target="_blank"
+ * href="http://www.herine.net/stat/papers/dbinom.pdf">
+ * http://www.herine.net/stat/papers/dbinom.pdf</a></li>
+ * </ol>
+ * </p>
+ *
+ * @param x the x value.
+ * @param mu the average.
+ * @return a part of the deviance.
+ */
+ static double getDeviancePart(double x, double mu) {
+ double ret;
+ if (Math.abs(x - mu) < 0.1 * (x + mu)) {
+ double d = x - mu;
+ double v = d / (x + mu);
+ double s1 = v * d;
+ double s = Double.NaN;
+ double ej = 2.0 * x * v;
+ v *= v;
+ int j = 1;
+ while (s1 != s) {
+ s = s1;
+ ej *= v;
+ s1 = s + ej / ((j * 2) + 1);
+ ++j;
+ }
+ ret = s1;
+ } else {
+ if (x == 0) {
+ return mu;
+ }
+ ret = x * Math.log(x / mu) + mu - x;
+ }
+ return ret;
+ }
+
+ /**
+ * Compute the logarithm of the PMF for a binomial distribution
+ * using the saddle point expansion.
+ *
+ * @param x the value at which the probability is evaluated.
+ * @param n the number of trials.
+ * @param p the probability of success.
+ * @param q the probability of failure (1 - p).
+ * @return log(p(x)).
+ */
+ static double logBinomialProbability(int x, int n, double p, double q) {
+ double ret;
+ if (x == 0) {
+ if (p < 0.1) {
+ ret = -getDeviancePart(n, n * q) - n * p;
+ } else {
+ if (n == 0) {
+ return 0;
+ }
+ ret = n * Math.log(q);
+ }
+ } else if (x == n) {
+ if (q < 0.1) {
+ ret = -getDeviancePart(n, n * p) - n * q;
+ } else {
+ ret = n * Math.log(p);
+ }
+ } else {
+ ret = getStirlingError(n) - getStirlingError(x) -
+ getStirlingError(n - x) - getDeviancePart(x, n * p) -
+ getDeviancePart(n - x, n * q);
+ final double f = (TWO_PI * x * (n - x)) / n;
+ ret = -0.5 * Math.log(f) + ret;
+ }
+ return ret;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TDistribution.java
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diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TDistribution.java
new file mode 100644
index 0000000..ef34c1f
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TDistribution.java
@@ -0,0 +1,180 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.RegularizedBeta;
+import org.apache.commons.numbers.gamma.LogGamma;
+
+/**
+ * Implementation of <a href='http://en.wikipedia.org/wiki/Student's_t-distribution'>Student's t-distribution</a>.
+ */
+public class TDistribution extends AbstractContinuousDistribution {
+ /** The degrees of freedom. */
+ private final double degreesOfFreedom;
+ /** degreesOfFreedom / 2 */
+ private final double dofOver2;
+ /** Cached value. */
+ private final double factor;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param degreesOfFreedom Degrees of freedom.
+ * @throws IllegalArgumentException if {@code degreesOfFreedom <= 0}
+ */
+ public TDistribution(double degreesOfFreedom) {
+ if (degreesOfFreedom <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ degreesOfFreedom);
+ }
+ this.degreesOfFreedom = degreesOfFreedom;
+
+ dofOver2 = 0.5 * degreesOfFreedom;
+ factor = LogGamma.value(dofOver2 + 0.5) -
+ 0.5 * (Math.log(Math.PI) + Math.log(degreesOfFreedom)) -
+ LogGamma.value(dofOver2);
+ }
+
+ /**
+ * Access the degrees of freedom.
+ *
+ * @return the degrees of freedom.
+ */
+ public double getDegreesOfFreedom() {
+ return degreesOfFreedom;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ return Math.exp(logDensity(x));
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logDensity(double x) {
+ final double nPlus1Over2 = dofOver2 + 0.5;
+ return factor - nPlus1Over2 * Math.log(1 + x * x / degreesOfFreedom);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ double ret;
+ if (x == 0) {
+ ret = 0.5;
+ } else {
+ final double t =
+ RegularizedBeta.value(degreesOfFreedom / (degreesOfFreedom + (x * x)),
+ dofOver2,
+ 0.5);
+ if (x < 0) {
+ ret = 0.5 * t;
+ } else {
+ ret = 1 - 0.5 * t;
+ }
+ }
+
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For degrees of freedom parameter {@code df}, the mean is
+ * <ul>
+ * <li>if {@code df > 1} then {@code 0},</li>
+ * <li>else undefined ({@code Double.NaN}).</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalMean() {
+ final double df = getDegreesOfFreedom();
+
+ if (df > 1) {
+ return 0;
+ }
+
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For degrees of freedom parameter {@code df}, the variance is
+ * <ul>
+ * <li>if {@code df > 2} then {@code df / (df - 2)},</li>
+ * <li>if {@code 1 < df <= 2} then positive infinity
+ * ({@code Double.POSITIVE_INFINITY}),</li>
+ * <li>else undefined ({@code Double.NaN}).</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double df = getDegreesOfFreedom();
+
+ if (df > 2) {
+ return df / (df - 2);
+ }
+
+ if (df > 1 && df <= 2) {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ return Double.NaN;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always negative infinity no matter the
+ * parameters.
+ *
+ * @return lower bound of the support (always
+ * {@code Double.NEGATIVE_INFINITY})
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity no matter the
+ * parameters.
+ *
+ * @return upper bound of the support (always
+ * {@code Double.POSITIVE_INFINITY})
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TriangularDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TriangularDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TriangularDistribution.java
new file mode 100644
index 0000000..6a94b62
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/TriangularDistribution.java
@@ -0,0 +1,222 @@
+/*
+ * 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.statistics.distribution;
+
+/**
+ * Implementation of the triangular real distribution.
+ *
+ * @see <a href="http://en.wikipedia.org/wiki/Triangular_distribution">
+ * Triangular distribution (Wikipedia)</a>
+ *
+ * @since 3.0
+ */
+public class TriangularDistribution extends AbstractContinuousDistribution {
+ /** Serializable version identifier. */
+ private static final long serialVersionUID = 20160311L;
+ /** Lower limit of this distribution (inclusive). */
+ private final double a;
+ /** Upper limit of this distribution (inclusive). */
+ private final double b;
+ /** Mode of this distribution. */
+ private final double c;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param a Lower limit of this distribution (inclusive).
+ * @param b Upper limit of this distribution (inclusive).
+ * @param c Mode of this distribution.
+ * @throws IllegalArgumentException if {@code a >= b}, if {@code c > b}
+ * or if {@code c < a}.
+ */
+ public TriangularDistribution(double a,
+ double c,
+ double b) {
+ if (a >= b) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ a, b);
+ }
+ if (c < a) {
+ throw new DistributionException(DistributionException.TOO_SMALL,
+ c, a);
+ }
+ if (c > b) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ c, b);
+ }
+
+ this.a = a;
+ this.c = c;
+ this.b = b;
+ }
+
+ /**
+ * Gets the mode.
+ *
+ * @return the mode of the distribution.
+ */
+ public double getMode() {
+ return c;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower limit {@code a}, upper limit {@code b} and mode {@code c}, the
+ * PDF is given by
+ * <ul>
+ * <li>{@code 2 * (x - a) / [(b - a) * (c - a)]} if {@code a <= x < c},</li>
+ * <li>{@code 2 / (b - a)} if {@code x = c},</li>
+ * <li>{@code 2 * (b - x) / [(b - a) * (b - c)]} if {@code c < x <= b},</li>
+ * <li>{@code 0} otherwise.
+ * </ul>
+ */
+ @Override
+ public double density(double x) {
+ if (x < a) {
+ return 0;
+ }
+ if (a <= x && x < c) {
+ double divident = 2 * (x - a);
+ double divisor = (b - a) * (c - a);
+ return divident / divisor;
+ }
+ if (x == c) {
+ return 2 / (b - a);
+ }
+ if (c < x && x <= b) {
+ double divident = 2 * (b - x);
+ double divisor = (b - a) * (b - c);
+ return divident / divisor;
+ }
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower limit {@code a}, upper limit {@code b} and mode {@code c}, the
+ * CDF is given by
+ * <ul>
+ * <li>{@code 0} if {@code x < a},</li>
+ * <li>{@code (x - a)^2 / [(b - a) * (c - a)]} if {@code a <= x < c},</li>
+ * <li>{@code (c - a) / (b - a)} if {@code x = c},</li>
+ * <li>{@code 1 - (b - x)^2 / [(b - a) * (b - c)]} if {@code c < x <= b},</li>
+ * <li>{@code 1} if {@code x > b}.</li>
+ * </ul>
+ */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x < a) {
+ return 0;
+ }
+ if (a <= x && x < c) {
+ double divident = (x - a) * (x - a);
+ double divisor = (b - a) * (c - a);
+ return divident / divisor;
+ }
+ if (x == c) {
+ return (c - a) / (b - a);
+ }
+ if (c < x && x <= b) {
+ double divident = (b - x) * (b - x);
+ double divisor = (b - a) * (b - c);
+ return 1 - (divident / divisor);
+ }
+ return 1;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower limit {@code a}, upper limit {@code b}, and mode {@code c},
+ * the mean is {@code (a + b + c) / 3}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return (a + b + c) / 3;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower limit {@code a}, upper limit {@code b}, and mode {@code c},
+ * the variance is {@code (a^2 + b^2 + c^2 - a * b - a * c - b * c) / 18}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ return (a * a + b * b + c * c - a * b - a * c - b * c) / 18;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is equal to the lower limit parameter
+ * {@code a} of the distribution.
+ *
+ * @return lower bound of the support
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return a;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is equal to the upper limit parameter
+ * {@code b} of the distribution.
+ *
+ * @return upper bound of the support
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return b;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ if (p == 0) {
+ return a;
+ }
+ if (p == 1) {
+ return b;
+ }
+ if (p < (c - a) / (b - a)) {
+ return a + Math.sqrt(p * (b - a) * (c - a));
+ }
+ return b - Math.sqrt((1 - p) * (b - a) * (b - c));
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformContinuousDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformContinuousDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformContinuousDistribution.java
new file mode 100644
index 0000000..2bb9a0b
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformContinuousDistribution.java
@@ -0,0 +1,168 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.rng.sampling.distribution.ContinuousUniformSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)">uniform distribution</a>.
+ */
+public class UniformContinuousDistribution extends AbstractContinuousDistribution {
+ private final double lower;
+ /** Upper bound of this distribution (exclusive). */
+ private final double upper;
+
+ /**
+ * Create a standard uniform real distribution with lower bound (inclusive)
+ * equal to zero and upper bound (exclusive) equal to one.
+ */
+ public UniformContinuousDistribution() {
+ this(0, 1);
+ }
+
+ /**
+ * Creates a uniform distribution.
+ *
+ * @param lower Lower bound of this distribution (inclusive).
+ * @param upper Upper bound of this distribution (exclusive).
+ * @throws IllegalArgumentException if {@code lower >= upper}.
+ */
+ public UniformContinuousDistribution(double lower,
+ double upper) {
+ if (lower >= upper) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ lower, upper);
+ }
+
+ this.lower = lower;
+ this.upper = upper;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ if (x < lower ||
+ x > upper) {
+ return 0;
+ }
+ return 1 / (upper - lower);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ if (x <= lower) {
+ return 0;
+ }
+ if (x >= upper) {
+ return 1;
+ }
+ return (x - lower) / (upper - lower);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double inverseCumulativeProbability(final double p) {
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ }
+ return p * (upper - lower) + lower;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower bound {@code lower} and upper bound {@code upper}, the mean is
+ * {@code 0.5 * (lower + upper)}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return 0.5 * (lower + upper);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower bound {@code lower} and upper bound {@code upper}, the
+ * variance is {@code (upper - lower)^2 / 12}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ double ul = upper - lower;
+ return ul * ul / 12;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is equal to the lower bound parameter
+ * of the distribution.
+ *
+ * @return lower bound of the support
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return lower;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is equal to the upper bound parameter
+ * of the distribution.
+ *
+ * @return upper bound of the support
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return upper;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new ContinuousDistribution.Sampler() {
+ /**
+ * Uniform distribution sampler.
+ */
+ private final ContinuousSampler sampler =
+ new ContinuousUniformSampler(rng, lower, upper);
+
+ /**{@inheritDoc} */
+ @Override
+ public double sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformDiscreteDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformDiscreteDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformDiscreteDistribution.java
new file mode 100644
index 0000000..41df2bd
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/UniformDiscreteDistribution.java
@@ -0,0 +1,159 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
+import org.apache.commons.rng.sampling.distribution.DiscreteUniformSampler;
+
+/**
+ * Implementation of the <a href="http://en.wikipedia.org/wiki/Uniform_distribution_(discrete)">
+ * uniform integer distribution</a>.
+ */
+public class UniformDiscreteDistribution extends AbstractDiscreteDistribution {
+ /** 1 / 12 **/
+ private static final double ONE_TWELFTH = 1 / 12d;
+ /** Lower bound (inclusive) of this distribution. */
+ private final int lower;
+ /** Upper bound (inclusive) of this distribution. */
+ private final int upper;
+ /** "upper" + "lower" (to avoid overflow). */
+ private final double upperPlusLower;
+ /** "upper" - "lower" (to avoid overflow). */
+ private final double upperMinusLower;
+
+ /**
+ * Creates a new uniform integer distribution using the given lower and
+ * upper bounds (both inclusive).
+ *
+ * @param lower Lower bound (inclusive) of this distribution.
+ * @param upper Upper bound (inclusive) of this distribution.
+ * @throws IllegalArgumentException if {@code lower > upper}.
+ */
+ public UniformDiscreteDistribution(int lower,
+ int upper) {
+ if (lower > upper) {
+ throw new DistributionException(DistributionException.TOO_LARGE,
+ lower, upper);
+ }
+ this.lower = lower;
+ this.upper = upper;
+ upperPlusLower = (double) upper + (double) lower;
+ upperMinusLower = (double) upper - (double) lower;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(int x) {
+ if (x < lower || x > upper) {
+ return 0;
+ }
+ return 1 / (upperMinusLower + 1);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(int x) {
+ if (x < lower) {
+ return 0;
+ }
+ if (x > upper) {
+ return 1;
+ }
+ return (x - lower + 1) / (upperMinusLower + 1);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower bound {@code lower} and upper bound {@code upper}, the mean is
+ * {@code 0.5 * (lower + upper)}.
+ */
+ @Override
+ public double getNumericalMean() {
+ return 0.5 * upperPlusLower;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For lower bound {@code lower} and upper bound {@code upper}, and
+ * {@code n = upper - lower + 1}, the variance is {@code (n^2 - 1) / 12}.
+ */
+ @Override
+ public double getNumericalVariance() {
+ double n = upperMinusLower + 1;
+ return ONE_TWELFTH * (n * n - 1);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is equal to the lower bound parameter
+ * of the distribution.
+ *
+ * @return lower bound of the support
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return lower;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is equal to the upper bound parameter
+ * of the distribution.
+ *
+ * @return upper bound of the support
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return upper;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**{@inheritDoc} */
+ @Override
+ public DiscreteDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new DiscreteDistribution.Sampler() {
+ /**
+ * Discrete uniform distribution sampler.
+ */
+ private final DiscreteSampler sampler =
+ new DiscreteUniformSampler(rng, lower, upper);
+
+ /**{@inheritDoc} */
+ @Override
+ public int sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/WeibullDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/WeibullDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/WeibullDistribution.java
new file mode 100644
index 0000000..0e396d8
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/WeibullDistribution.java
@@ -0,0 +1,220 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.gamma.LogGamma;
+
+/**
+ * Implementation of the Weibull distribution. This implementation uses the
+ * two parameter form of the distribution defined by
+ * <a href="http://mathworld.wolfram.com/WeibullDistribution.html">
+ * Weibull Distribution</a>, equations (1) and (2).
+ *
+ * @see <a href="http://en.wikipedia.org/wiki/Weibull_distribution">Weibull distribution (Wikipedia)</a>
+ * @see <a href="http://mathworld.wolfram.com/WeibullDistribution.html">Weibull distribution (MathWorld)</a>
+ *
+ * @since 1.1
+ */
+public class WeibullDistribution extends AbstractContinuousDistribution {
+ /** The shape parameter. */
+ private final double shape;
+ /** The scale parameter. */
+ private final double scale;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param alpha Shape parameter.
+ * @param beta Scale parameter.
+ * @throws IllegalArgumentException if {@code alpha <= 0} or {@code beta <= 0}.
+ */
+ public WeibullDistribution(double alpha,
+ double beta) {
+ if (alpha <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ alpha);
+ }
+ if (beta <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ beta);
+ }
+ scale = beta;
+ shape = alpha;
+ }
+
+ /**
+ * Access the shape parameter, {@code alpha}.
+ *
+ * @return the shape parameter, {@code alpha}.
+ */
+ public double getShape() {
+ return shape;
+ }
+
+ /**
+ * Access the scale parameter, {@code beta}.
+ *
+ * @return the scale parameter, {@code beta}.
+ */
+ public double getScale() {
+ return scale;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double density(double x) {
+ if (x < 0) {
+ return 0;
+ }
+
+ final double xscale = x / scale;
+ final double xscalepow = Math.pow(xscale, shape - 1);
+
+ /*
+ * Math.pow(x / scale, shape) =
+ * Math.pow(xscale, shape) =
+ * Math.pow(xscale, shape - 1) * xscale
+ */
+ final double xscalepowshape = xscalepow * xscale;
+
+ return (shape / scale) * xscalepow * Math.exp(-xscalepowshape);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logDensity(double x) {
+ if (x < 0) {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ final double xscale = x / scale;
+ final double logxscalepow = Math.log(xscale) * (shape - 1);
+
+ /*
+ * Math.pow(x / scale, shape) =
+ * Math.pow(xscale, shape) =
+ * Math.pow(xscale, shape - 1) * xscale
+ */
+ final double xscalepowshape = Math.exp(logxscalepow) * xscale;
+
+ return Math.log(shape / scale) + logxscalepow - xscalepowshape;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(double x) {
+ double ret;
+ if (x <= 0.0) {
+ ret = 0.0;
+ } else {
+ ret = 1.0 - Math.exp(-Math.pow(x / scale, shape));
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * Returns {@code 0} when {@code p == 0} and
+ * {@code Double.POSITIVE_INFINITY} when {@code p == 1}.
+ */
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ double ret;
+ if (p < 0 ||
+ p > 1) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, p, 0, 1);
+ } else if (p == 0) {
+ ret = 0.0;
+ } else if (p == 1) {
+ ret = Double.POSITIVE_INFINITY;
+ } else {
+ ret = scale * Math.pow(-Math.log1p(-p), 1.0 / shape);
+ }
+ return ret;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The mean is {@code scale * Gamma(1 + (1 / shape))}, where {@code Gamma()}
+ * is the Gamma-function.
+ */
+ @Override
+ public double getNumericalMean() {
+ final double sh = getShape();
+ final double sc = getScale();
+
+ return sc * Math.exp(LogGamma.value(1 + (1 / sh)));
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The variance is {@code scale^2 * Gamma(1 + (2 / shape)) - mean^2}
+ * where {@code Gamma()} is the Gamma-function.
+ */
+ @Override
+ public double getNumericalVariance() {
+ final double sh = getShape();
+ final double sc = getScale();
+ final double mn = getNumericalMean();
+
+ return (sc * sc) * Math.exp(LogGamma.value(1 + (2 / sh))) -
+ (mn * mn);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 0 no matter the parameters.
+ *
+ * @return lower bound of the support (always 0)
+ */
+ @Override
+ public double getSupportLowerBound() {
+ return 0;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is always positive infinity
+ * no matter the parameters.
+ *
+ * @return upper bound of the support (always
+ * {@code Double.POSITIVE_INFINITY})
+ */
+ @Override
+ public double getSupportUpperBound() {
+ return Double.POSITIVE_INFINITY;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+}
+
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ZipfDistribution.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ZipfDistribution.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ZipfDistribution.java
new file mode 100644
index 0000000..f0d54a1
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/ZipfDistribution.java
@@ -0,0 +1,236 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
+import org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler;
+
+/**
+ * Implementation of the <a href="https://en.wikipedia.org/wiki/Zipf's_law">Zipf distribution</a>.
+ * <p>
+ * <strong>Parameters:</strong>
+ * For a random variable {@code X} whose values are distributed according to this
+ * distribution, the probability mass function is given by
+ * <pre>
+ * P(X = k) = H(N,s) * 1 / k^s for {@code k = 1,2,...,N}.
+ * </pre>
+ * {@code H(N,s)} is the normalizing constant
+ * which corresponds to the generalized harmonic number of order N of s.
+ * <ul>
+ * <li>{@code N} is the number of elements</li>
+ * <li>{@code s} is the exponent</li>
+ * </ul>
+ */
+public class ZipfDistribution extends AbstractDiscreteDistribution {
+ /** Number of elements. */
+ private final int numberOfElements;
+ /** Exponent parameter of the distribution. */
+ private final double exponent;
+ /** Cached values of the nth generalized harmonic. */
+ private final double nthHarmonic;
+
+ /**
+ * Creates a distribution.
+ *
+ * @param numberOfElements Number of elements.
+ * @param exponent Exponent.
+ * @exception IllegalArgumentException if {@code numberOfElements <= 0}
+ * or {@code exponent <= 0}.
+ */
+ public ZipfDistribution(int numberOfElements,
+ double exponent) {
+ if (numberOfElements <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ numberOfElements);
+ }
+ if (exponent <= 0) {
+ throw new DistributionException(DistributionException.NEGATIVE,
+ exponent);
+ }
+
+ this.numberOfElements = numberOfElements;
+ this.exponent = exponent;
+ this.nthHarmonic = generalizedHarmonic(numberOfElements, exponent);
+ }
+
+ /**
+ * Get the number of elements (e.g. corpus size) for the distribution.
+ *
+ * @return the number of elements
+ */
+ public int getNumberOfElements() {
+ return numberOfElements;
+ }
+
+ /**
+ * Get the exponent characterizing the distribution.
+ *
+ * @return the exponent
+ */
+ public double getExponent() {
+ return exponent;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double probability(final int x) {
+ if (x <= 0 || x > numberOfElements) {
+ return 0;
+ }
+
+ return (1 / Math.pow(x, exponent)) / nthHarmonic;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double logProbability(int x) {
+ if (x <= 0 || x > numberOfElements) {
+ return Double.NEGATIVE_INFINITY;
+ }
+
+ return -Math.log(x) * exponent - Math.log(nthHarmonic);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double cumulativeProbability(final int x) {
+ if (x <= 0) {
+ return 0;
+ } else if (x >= numberOfElements) {
+ return 1;
+ }
+
+ return generalizedHarmonic(x, exponent) / nthHarmonic;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For number of elements {@code N} and exponent {@code s}, the mean is
+ * {@code Hs1 / Hs}, where
+ * <ul>
+ * <li>{@code Hs1 = generalizedHarmonic(N, s - 1)},</li>
+ * <li>{@code Hs = generalizedHarmonic(N, s)}.</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalMean() {
+ final int N = getNumberOfElements();
+ final double s = getExponent();
+
+ final double Hs1 = generalizedHarmonic(N, s - 1);
+ final double Hs = nthHarmonic;
+
+ return Hs1 / Hs;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * For number of elements {@code N} and exponent {@code s}, the mean is
+ * {@code (Hs2 / Hs) - (Hs1^2 / Hs^2)}, where
+ * <ul>
+ * <li>{@code Hs2 = generalizedHarmonic(N, s - 2)},</li>
+ * <li>{@code Hs1 = generalizedHarmonic(N, s - 1)},</li>
+ * <li>{@code Hs = generalizedHarmonic(N, s)}.</li>
+ * </ul>
+ */
+ @Override
+ public double getNumericalVariance() {
+ final int N = getNumberOfElements();
+ final double s = getExponent();
+
+ final double Hs2 = generalizedHarmonic(N, s - 2);
+ final double Hs1 = generalizedHarmonic(N, s - 1);
+ final double Hs = nthHarmonic;
+
+ return (Hs2 / Hs) - ((Hs1 * Hs1) / (Hs * Hs));
+ }
+
+ /**
+ * Calculates the Nth generalized harmonic number. See
+ * <a href="http://mathworld.wolfram.com/HarmonicSeries.html">Harmonic
+ * Series</a>.
+ *
+ * @param n Term in the series to calculate (must be larger than 1)
+ * @param m Exponent (special case {@code m = 1} is the harmonic series).
+ * @return the n<sup>th</sup> generalized harmonic number.
+ */
+ private double generalizedHarmonic(final int n, final double m) {
+ double value = 0;
+ for (int k = n; k > 0; --k) {
+ value += 1 / Math.pow(k, m);
+ }
+ return value;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The lower bound of the support is always 1 no matter the parameters.
+ *
+ * @return lower bound of the support (always 1)
+ */
+ @Override
+ public int getSupportLowerBound() {
+ return 1;
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The upper bound of the support is the number of elements.
+ *
+ * @return upper bound of the support
+ */
+ @Override
+ public int getSupportUpperBound() {
+ return getNumberOfElements();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * The support of this distribution is connected.
+ *
+ * @return {@code true}
+ */
+ @Override
+ public boolean isSupportConnected() {
+ return true;
+ }
+
+ /**{@inheritDoc} */
+ @Override
+ public DiscreteDistribution.Sampler createSampler(final UniformRandomProvider rng) {
+ return new DiscreteDistribution.Sampler() {
+ /**
+ * Zipf distribution sampler.
+ */
+ private final DiscreteSampler sampler =
+ new RejectionInversionZipfSampler(rng, numberOfElements, exponent);
+
+ /**{@inheritDoc} */
+ @Override
+ public int sample() {
+ return sampler.sample();
+ }
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/main/java/commons/statistics/distribution/package-info.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/main/java/commons/statistics/distribution/package-info.java b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/package-info.java
new file mode 100644
index 0000000..98315c6
--- /dev/null
+++ b/commons-statistics-distribution/src/main/java/commons/statistics/distribution/package-info.java
@@ -0,0 +1,20 @@
+/*
+ * 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.
+ */
+/**
+ * Implementations of common discrete and continuous distributions.
+ */
+package org.apache.commons.statistics.distribution;
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractContinuousDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractContinuousDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractContinuousDistributionTest.java
new file mode 100644
index 0000000..3e30763
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractContinuousDistributionTest.java
@@ -0,0 +1,209 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.math3.analysis.UnivariateFunction;
+import org.apache.commons.math3.analysis.integration.RombergIntegrator;
+import org.apache.commons.math3.analysis.integration.UnivariateIntegrator;
+import org.junit.Assert;
+import org.junit.Test;
+
+/** Various tests related to MATH-699. */
+public class AbstractContinuousDistributionTest {
+
+ @Test
+ public void testContinuous() {
+ final double x0 = 0.0;
+ final double x1 = 1.0;
+ final double x2 = 2.0;
+ final double x3 = 3.0;
+ final double p12 = 0.5;
+ final AbstractContinuousDistribution distribution;
+ distribution = new AbstractContinuousDistribution() {
+ private static final long serialVersionUID = 1L;
+
+ @Override
+ public double cumulativeProbability(final double x) {
+ if (x < x0 ||
+ x > x3) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, x, x0, x3);
+ }
+ if (x <= x1) {
+ return p12 * (x - x0) / (x1 - x0);
+ } else if (x <= x2) {
+ return p12;
+ } else if (x <= x3) {
+ return p12 + (1.0 - p12) * (x - x2) / (x3 - x2);
+ }
+ return 0.0;
+ }
+
+ @Override
+ public double density(final double x) {
+ if (x < x0 ||
+ x > x3) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, x, x0, x3);
+ }
+ if (x <= x1) {
+ return p12 / (x1 - x0);
+ } else if (x <= x2) {
+ return 0.0;
+ } else if (x <= x3) {
+ return (1.0 - p12) / (x3 - x2);
+ }
+ return 0.0;
+ }
+
+ @Override
+ public double getNumericalMean() {
+ return ((x0 + x1) * p12 + (x2 + x3) * (1.0 - p12)) / 2.0;
+ }
+
+ @Override
+ public double getNumericalVariance() {
+ final double meanX = getNumericalMean();
+ final double meanX2;
+ meanX2 = ((x0 * x0 + x0 * x1 + x1 * x1) * p12 + (x2 * x2 + x2
+ * x3 + x3 * x3)
+ * (1.0 - p12)) / 3.0;
+ return meanX2 - meanX * meanX;
+ }
+
+ @Override
+ public double getSupportLowerBound() {
+ return x0;
+ }
+
+ @Override
+ public double getSupportUpperBound() {
+ return x3;
+ }
+
+ @Override
+ public boolean isSupportConnected() {
+ return false;
+ }
+
+ @Override
+ public double probability(final double x) {
+ throw new UnsupportedOperationException();
+ }
+ };
+ final double expected = x1;
+ final double actual = distribution.inverseCumulativeProbability(p12);
+ Assert.assertEquals("", expected, actual, 1e-8);
+ }
+
+ @Test
+ public void testDiscontinuous() {
+ final double x0 = 0.0;
+ final double x1 = 0.25;
+ final double x2 = 0.5;
+ final double x3 = 0.75;
+ final double x4 = 1.0;
+ final double p12 = 1.0 / 3.0;
+ final double p23 = 2.0 / 3.0;
+ final AbstractContinuousDistribution distribution;
+ distribution = new AbstractContinuousDistribution() {
+ @Override
+ public double cumulativeProbability(final double x) {
+ if (x < x0 ||
+ x > x4) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, x, x0, x4);
+ }
+ if (x <= x1) {
+ return p12 * (x - x0) / (x1 - x0);
+ } else if (x <= x2) {
+ return p12;
+ } else if (x <= x3) {
+ return p23;
+ } else {
+ return (1.0 - p23) * (x - x3) / (x4 - x3) + p23;
+ }
+ }
+
+ @Override
+ public double density(final double x) {
+ if (x < x0 ||
+ x > x4) {
+ throw new DistributionException(DistributionException.OUT_OF_RANGE, x, x0, x4);
+ }
+ if (x <= x1) {
+ return p12 / (x1 - x0);
+ } else if (x <= x2) {
+ return 0.0;
+ } else if (x <= x3) {
+ return 0.0;
+ } else {
+ return (1.0 - p23) / (x4 - x3);
+ }
+ }
+
+ @Override
+ public double getNumericalMean() {
+ final UnivariateFunction f = new UnivariateFunction() {
+
+ @Override
+ public double value(final double x) {
+ return x * density(x);
+ }
+ };
+ final UnivariateIntegrator integrator = new RombergIntegrator();
+ return integrator.integrate(Integer.MAX_VALUE, f, x0, x4);
+ }
+
+ @Override
+ public double getNumericalVariance() {
+ final double meanX = getNumericalMean();
+ final UnivariateFunction f = new UnivariateFunction() {
+
+ @Override
+ public double value(final double x) {
+ return x * x * density(x);
+ }
+ };
+ final UnivariateIntegrator integrator = new RombergIntegrator();
+ final double meanX2 = integrator.integrate(Integer.MAX_VALUE,
+ f, x0, x4);
+ return meanX2 - meanX * meanX;
+ }
+
+ @Override
+ public double getSupportLowerBound() {
+ return x0;
+ }
+
+ @Override
+ public double getSupportUpperBound() {
+ return x4;
+ }
+
+ @Override
+ public boolean isSupportConnected() {
+ return false;
+ }
+
+ @Override
+ public double probability(final double x) {
+ throw new UnsupportedOperationException();
+ }
+ };
+ final double expected = x2;
+ final double actual = distribution.inverseCumulativeProbability(p23);
+ Assert.assertEquals("", expected, actual, 1e-8);
+ }
+}
[02/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution.mac
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution.mac b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution.mac
new file mode 100644
index 0000000..d9ce678
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/gamma-distribution.mac
@@ -0,0 +1,73 @@
+/*
+ * 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.
+ */
+
+/*
+ * This Maxima script allows the creation of reference data for the Gamma
+ * distribution.
+ */
+
+/*
+ * Set floating-point accuracy to four times the double precision.
+ */
+fpprec : 64;
+
+/*
+ * Probability density function for Gamma distribution with shape parameter a
+ * and scale parameter b.
+ */
+p(x, a, b) := (x / b)**a * exp(-x / b) / x / gamma(a);
+
+/*
+ * Make sure x is a list of exactly representable doubles: use only power-of-two
+ * fractions of unity.
+ */
+out : openw("gamma-distribution-shape-1.csv");
+x : float(makelist(i / 32, i, 1, 3200));
+y : p(bfloat(x), 1, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
+out : openw("gamma-distribution-shape-8.csv");
+x : float(makelist(i / 32, i, 1, 3200));
+y : p(bfloat(x), 8, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
+out : openw("gamma-distribution-shape-10.csv");
+x : float(makelist(i / 4, i, 1, 400));
+y : p(bfloat(x), 10, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
+out : openw("gamma-distribution-shape-100.csv");
+x : float(append(makelist(i / 32, i, 1, 32 * 3), makelist(i + 3, i, 1, 297)));
+y : p(bfloat(x), 100, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
+out : openw("gamma-distribution-shape-142.csv");
+x : float(append(makelist(i / 32, i, 1, 32 * 10), makelist(i + 10, i, 1, 440)));
+y : p(bfloat(x), 142, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
+out : openw("gamma-distribution-shape-1000.csv");
+x : float(append(makelist(i / 32, i, 1, 32 * 10), makelist(i + 10, i, 1, 2990)));
+y : p(bfloat(x), 1000, 1);
+printf(out, "~{~h, ~h~%~}", join(x, y));
+close(out);
+
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/testData.txt
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/testData.txt b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/testData.txt
new file mode 100644
index 0000000..4a10132
--- /dev/null
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http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/pom.xml
----------------------------------------------------------------------
diff --git a/pom.xml b/pom.xml
index 3422571..39cae27 100644
--- a/pom.xml
+++ b/pom.xml
@@ -29,7 +29,7 @@
<version>0.1-SNAPSHOT</version>
<name>Apache Commons Statistics</name>
- <inceptionYear>2016</inceptionYear>
+ <inceptionYear>2018</inceptionYear>
<description>The Apache Commons Statistics project provides tools for statistics.</description>
<url>http://commons.apache.org/proper/commons-statistics/</url>
@@ -63,15 +63,6 @@
<contributors>
</contributors>
- <dependencies>
- <dependency>
- <groupId>junit</groupId>
- <artifactId>junit</artifactId>
- <version>4.12</version>
- <scope>test</scope>
- </dependency>
- </dependencies>
-
<properties>
<!-- Do not change: "statistics" is the name of the component even if the
name of the base package evolves with major release numbers
@@ -128,8 +119,60 @@
Temporary workaround?
-->
<commons.release.name>commons-statistics-${project.version}</commons.release.name>
+ <statistics.commons.rng.version>1.0</statistics.commons.rng.version>
+ <statistics.commons.numbers.version>1.0-SNAPSHOT</statistics.commons.numbers.version> <!-- XXX -->
+ <statistics.commons.math3.version>3.6.1</statistics.commons.math3.version>
</properties>
+ <dependencyManagement>
+ <dependencies>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-client-api</artifactId>
+ <version>${statistics.commons.rng.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-simple</artifactId>
+ <version>${statistics.commons.rng.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-rng-sampling</artifactId>
+ <version>1.1-SNAPSHOT</version> <!-- XXX -->
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-core</artifactId>
+ <version>${statistics.commons.numbers.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-combinatorics</artifactId>
+ <version>${statistics.commons.numbers.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-numbers-gamma</artifactId>
+ <version>${statistics.commons.numbers.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-math3</artifactId>
+ <version>${statistics.commons.math3.version}</version>
+ </dependency>
+ </dependencies>
+ </dependencyManagement>
+
+ <dependencies>
+ <dependency>
+ <groupId>junit</groupId>
+ <artifactId>junit</artifactId>
+ <version>4.12</version>
+ <scope>test</scope>
+ </dependency>
+ </dependencies>
+
<build>
<plugins>
<plugin>
@@ -535,7 +578,7 @@
</profiles>
<modules>
-<!-- <module>commons-statistics-distribution</module> -->
+ <module>commons-statistics-distribution</module>
<!-- <module>commons-statistics-regression</module> -->
</modules>
[09/16] commons-statistics git commit: STATISTICS-2: Migrate
"o.a.c.math4.distribution" from Commons Math.
Posted by er...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExponentialDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExponentialDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExponentialDistributionTest.java
new file mode 100644
index 0000000..649ce7a
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExponentialDistributionTest.java
@@ -0,0 +1,132 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for ExponentialDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class ExponentialDistributionTest extends ContinuousDistributionAbstractTest {
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public ExponentialDistribution makeDistribution() {
+ return new ExponentialDistribution(5.0);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {0.00500250166792, 0.0502516792675, 0.126589039921, 0.256466471938,
+ 0.526802578289, 34.5387763949, 23.0258509299, 18.4443972706, 14.9786613678, 11.5129254650};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999,
+ 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.1998, 0.198, 0.195, 0.19, 0.18, 0.000200000000000,
+ 0.00200000000002, 0.00499999999997, 0.00999999999994, 0.0199999999999};
+ }
+
+ //------------ Additional tests -------------------------------------------
+
+ @Test
+ public void testCumulativeProbabilityExtremes() {
+ setCumulativeTestPoints(new double[] {-2, 0});
+ setCumulativeTestValues(new double[] {0, 0});
+ verifyCumulativeProbabilities();
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testCumulativeProbability2() {
+ double actual = getDistribution().probability(0.25, 0.75);
+ Assert.assertEquals(0.0905214, actual, 10e-4);
+ }
+
+ @Test
+ public void testDensity() {
+ ExponentialDistribution d1 = new ExponentialDistribution(1);
+ Assert.assertTrue(Precision.equals(0.0, d1.density(-1e-9), 1));
+ Assert.assertTrue(Precision.equals(1.0, d1.density(0.0), 1));
+ Assert.assertTrue(Precision.equals(0.0, d1.density(1000.0), 1));
+ Assert.assertTrue(Precision.equals(Math.exp(-1), d1.density(1.0), 1));
+ Assert.assertTrue(Precision.equals(Math.exp(-2), d1.density(2.0), 1));
+
+ ExponentialDistribution d2 = new ExponentialDistribution(3);
+ Assert.assertTrue(Precision.equals(1/3.0, d2.density(0.0), 1));
+ // computed using print(dexp(1, rate=1/3), digits=10) in R 2.5
+ Assert.assertEquals(0.2388437702, d2.density(1.0), 1e-8);
+
+ // computed using print(dexp(2, rate=1/3), digits=10) in R 2.5
+ Assert.assertEquals(0.1711390397, d2.density(2.0), 1e-8);
+ }
+
+ @Test
+ public void testMeanAccessors() {
+ ExponentialDistribution distribution = (ExponentialDistribution) getDistribution();
+ Assert.assertEquals(5d, distribution.getMean(), Double.MIN_VALUE);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new ExponentialDistribution(0);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ ExponentialDistribution dist;
+
+ dist = new ExponentialDistribution(11d);
+ Assert.assertEquals(dist.getNumericalMean(), 11d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 11d * 11d, tol);
+
+ dist = new ExponentialDistribution(10.5d);
+ Assert.assertEquals(dist.getNumericalMean(), 10.5d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 10.5d * 10.5d, tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/FDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/FDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/FDistributionTest.java
new file mode 100644
index 0000000..b29a0be
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/FDistributionTest.java
@@ -0,0 +1,150 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for FDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class FDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public FDistribution makeDistribution() {
+ return new FDistribution(5.0, 6.0);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {0.0346808448626, 0.0937009113303, 0.143313661184, 0.202008445998, 0.293728320107,
+ 20.8026639595, 8.74589525602, 5.98756512605, 4.38737418741, 3.10751166664};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.0689156576706, 0.236735653193, 0.364074131941, 0.481570789649, 0.595880479994,
+ 0.000133443915657, 0.00286681303403, 0.00969192007502, 0.0242883861471, 0.0605491314658};
+ }
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+
+ @Test
+ public void testCumulativeProbabilityExtremes() {
+ setCumulativeTestPoints(new double[] {-2, 0});
+ setCumulativeTestValues(new double[] {0, 0});
+ verifyCumulativeProbabilities();
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testDfAccessors() {
+ FDistribution dist = (FDistribution) getDistribution();
+ Assert.assertEquals(5d, dist.getNumeratorDegreesOfFreedom(), Double.MIN_VALUE);
+ Assert.assertEquals(6d, dist.getDenominatorDegreesOfFreedom(), Double.MIN_VALUE);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new FDistribution(0, 1);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ new FDistribution(1, 0);
+ }
+
+ @Test
+ public void testLargeDegreesOfFreedom() {
+ FDistribution fd = new FDistribution(100000, 100000);
+ double p = fd.cumulativeProbability(.999);
+ double x = fd.inverseCumulativeProbability(p);
+ Assert.assertEquals(.999, x, 1.0e-5);
+ }
+
+ @Test
+ public void testSmallDegreesOfFreedom() {
+ FDistribution fd = new FDistribution(1, 1);
+ double p = fd.cumulativeProbability(0.975);
+ double x = fd.inverseCumulativeProbability(p);
+ Assert.assertEquals(0.975, x, 1.0e-5);
+
+ fd = new FDistribution(1, 2);
+ p = fd.cumulativeProbability(0.975);
+ x = fd.inverseCumulativeProbability(p);
+ Assert.assertEquals(0.975, x, 1.0e-5);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ FDistribution dist;
+
+ dist = new FDistribution(1, 2);
+ Assert.assertTrue(Double.isNaN(dist.getNumericalMean()));
+ Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
+
+ dist = new FDistribution(1, 3);
+ Assert.assertEquals(dist.getNumericalMean(), 3d / (3d - 2d), tol);
+ Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
+
+ dist = new FDistribution(1, 5);
+ Assert.assertEquals(dist.getNumericalMean(), 5d / (5d - 2d), tol);
+ Assert.assertEquals(dist.getNumericalVariance(), (2d * 5d * 5d * 4d) / 9d, tol);
+ }
+
+ @Test
+ public void testMath785() {
+ // this test was failing due to inaccurate results from ContinuedFraction.
+
+ try {
+ double prob = 0.01;
+ FDistribution f = new FDistribution(200000, 200000);
+ double result = f.inverseCumulativeProbability(prob);
+ Assert.assertTrue(result < 1.0);
+ } catch (Exception e) {
+ Assert.fail("Failing to calculate inverse cumulative probability");
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GammaDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GammaDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GammaDistributionTest.java
new file mode 100644
index 0000000..b25f251
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GammaDistributionTest.java
@@ -0,0 +1,354 @@
+/*
+ * 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.statistics.distribution;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+
+import org.apache.commons.numbers.gamma.LanczosApproximation;
+import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for GammaDistribution.
+ * Extends ContinuousDistributionAbstractTest. See class javadoc for
+ * ContinuousDistributionAbstractTest for details.
+ *
+ */
+public class GammaDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default continuous distribution instance to use in tests. */
+ @Override
+ public GammaDistribution makeDistribution() {
+ return new GammaDistribution(4d, 2d);
+ }
+
+ /** Creates the default cumulative probability distribution test input values */
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ // quantiles computed using R version 2.9.2
+ return new double[] {0.857104827257, 1.64649737269, 2.17973074725, 2.7326367935, 3.48953912565,
+ 26.1244815584, 20.0902350297, 17.5345461395, 15.5073130559, 13.3615661365};
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
+ }
+
+ /** Creates the default probability density test expected values */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0.00427280075546, 0.0204117166709, 0.0362756163658, 0.0542113174239, 0.0773195272491,
+ 0.000394468852816, 0.00366559696761, 0.00874649473311, 0.0166712508128, 0.0311798227954};
+ }
+
+ // --------------------- Override tolerance --------------
+ @Override
+ public void setUp() {
+ super.setUp();
+ setTolerance(1e-9);
+ }
+
+ //---------------------------- Additional test cases -------------------------
+ @Test
+ public void testParameterAccessors() {
+ GammaDistribution distribution = (GammaDistribution) getDistribution();
+ Assert.assertEquals(4d, distribution.getShape(), 0);
+ Assert.assertEquals(2d, distribution.getScale(), 0);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new GammaDistribution(0, 1);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ new GammaDistribution(1, 0);
+ }
+
+ @Test
+ public void testProbabilities() {
+ testProbability(-1.000, 4.0, 2.0, .0000);
+ testProbability(15.501, 4.0, 2.0, .9499);
+ testProbability(0.504, 4.0, 1.0, .0018);
+ testProbability(10.011, 1.0, 2.0, .9933);
+ testProbability(5.000, 2.0, 2.0, .7127);
+ }
+
+ @Test
+ public void testValues() {
+ testValue(15.501, 4.0, 2.0, .9499);
+ testValue(0.504, 4.0, 1.0, .0018);
+ testValue(10.011, 1.0, 2.0, .9933);
+ testValue(5.000, 2.0, 2.0, .7127);
+ }
+
+ private void testProbability(double x, double a, double b, double expected) {
+ GammaDistribution distribution = new GammaDistribution( a, b );
+ double actual = distribution.cumulativeProbability(x);
+ Assert.assertEquals("probability for " + x, expected, actual, 10e-4);
+ }
+
+ private void testValue(double expected, double a, double b, double p) {
+ GammaDistribution distribution = new GammaDistribution( a, b );
+ double actual = distribution.inverseCumulativeProbability(p);
+ Assert.assertEquals("critical value for " + p, expected, actual, 10e-4);
+ }
+
+ @Test
+ public void testDensity() {
+ double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
+ // R2.5: print(dgamma(x, shape=1, rate=1), digits=10)
+ checkDensity(1, 1, x, new double[]{0.000000000000, 0.999999000001, 0.606530659713, 0.367879441171, 0.135335283237, 0.006737946999});
+ // R2.5: print(dgamma(x, shape=2, rate=1), digits=10)
+ checkDensity(2, 1, x, new double[]{0.000000000000, 0.000000999999, 0.303265329856, 0.367879441171, 0.270670566473, 0.033689734995});
+ // R2.5: print(dgamma(x, shape=4, rate=1), digits=10)
+ checkDensity(4, 1, x, new double[]{0.000000000e+00, 1.666665000e-19, 1.263605541e-02, 6.131324020e-02, 1.804470443e-01, 1.403738958e-01});
+ // R2.5: print(dgamma(x, shape=4, rate=10), digits=10)
+ checkDensity(4, 10, x, new double[]{0.000000000e+00, 1.666650000e-15, 1.403738958e+00, 7.566654960e-02, 2.748204830e-05, 4.018228850e-17});
+ // R2.5: print(dgamma(x, shape=.1, rate=10), digits=10)
+ checkDensity(0.1, 10, x, new double[]{0.000000000e+00, 3.323953832e+04, 1.663849010e-03, 6.007786726e-06, 1.461647647e-10, 5.996008322e-24});
+ // R2.5: print(dgamma(x, shape=.1, rate=20), digits=10)
+ checkDensity(0.1, 20, x, new double[]{0.000000000e+00, 3.562489883e+04, 1.201557345e-05, 2.923295295e-10, 3.228910843e-19, 1.239484589e-45});
+ // R2.5: print(dgamma(x, shape=.1, rate=4), digits=10)
+ checkDensity(0.1, 4, x, new double[]{0.000000000e+00, 3.032938388e+04, 3.049322494e-02, 2.211502311e-03, 2.170613371e-05, 5.846590589e-11});
+ // R2.5: print(dgamma(x, shape=.1, rate=1), digits=10)
+ checkDensity(0.1, 1, x, new double[]{0.000000000e+00, 2.640334143e+04, 1.189704437e-01, 3.866916944e-02, 7.623306235e-03, 1.663849010e-04});
+ }
+
+ private void checkDensity(double alpha, double rate, double[] x, double[] expected) {
+ GammaDistribution d = new GammaDistribution(alpha, 1 / rate);
+ for (int i = 0; i < x.length; i++) {
+ Assert.assertEquals(expected[i], d.density(x[i]), 1e-5);
+ }
+ }
+
+ @Test
+ public void testInverseCumulativeProbabilityExtremes() {
+ setInverseCumulativeTestPoints(new double[] {0, 1});
+ setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY});
+ verifyInverseCumulativeProbabilities();
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ GammaDistribution dist;
+
+ dist = new GammaDistribution(1, 2);
+ Assert.assertEquals(dist.getNumericalMean(), 2, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 4, tol);
+
+ dist = new GammaDistribution(1.1, 4.2);
+ Assert.assertEquals(dist.getNumericalMean(), 1.1d * 4.2d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), 1.1d * 4.2d * 4.2d, tol);
+ }
+
+ private static final double HALF_LOG_2_PI = 0.5 * Math.log(2.0 * Math.PI);
+
+ public static double logGamma(double x) {
+ /*
+ * This is a copy of
+ * double Gamma.logGamma(double)
+ * prior to MATH-849
+ */
+ double ret;
+
+ if (Double.isNaN(x) || (x <= 0.0)) {
+ ret = Double.NaN;
+ } else {
+ double sum = LanczosApproximation.value(x);
+ double tmp = x + LanczosApproximation.g() + .5;
+ ret = ((x + .5) * Math.log(tmp)) - tmp +
+ HALF_LOG_2_PI + Math.log(sum / x);
+ }
+
+ return ret;
+ }
+
+ public static double density(final double x,
+ final double shape,
+ final double scale) {
+ /*
+ * This is a copy of
+ * double GammaDistribution.density(double)
+ * prior to MATH-753.
+ */
+ if (x < 0) {
+ return 0;
+ }
+ return Math.pow(x / scale, shape - 1) / scale *
+ Math.exp(-x / scale) / Math.exp(logGamma(shape));
+ }
+
+ /*
+ * MATH-753: large values of x or shape parameter cause density(double) to
+ * overflow. Reference data is generated with the Maxima script
+ * gamma-distribution.mac, which can be found in
+ * src/test/resources/org/apache/commons/math3/distribution.
+ */
+
+ private void doTestMath753(final double shape,
+ final double meanNoOF, final double sdNoOF,
+ final double meanOF, final double sdOF,
+ final String resourceName)
+ throws IOException {
+ final GammaDistribution distribution = new GammaDistribution(shape, 1.0);
+ final SummaryStatistics statOld = new SummaryStatistics();
+ final SummaryStatistics statNewNoOF = new SummaryStatistics();
+ final SummaryStatistics statNewOF = new SummaryStatistics();
+
+ final InputStream resourceAsStream;
+ resourceAsStream = this.getClass().getResourceAsStream(resourceName);
+ Assert.assertNotNull("Could not find resource " + resourceName,
+ resourceAsStream);
+ final BufferedReader in;
+ in = new BufferedReader(new InputStreamReader(resourceAsStream));
+
+ try {
+ for (String line = in.readLine(); line != null; line = in.readLine()) {
+ if (line.startsWith("#")) {
+ continue;
+ }
+ final String[] tokens = line.split(", ");
+ Assert.assertTrue("expected two floating-point values",
+ tokens.length == 2);
+ final double x = Double.parseDouble(tokens[0]);
+ final String msg = "x = " + x + ", shape = " + shape +
+ ", scale = 1.0";
+ final double expected = Double.parseDouble(tokens[1]);
+ final double ulp = Math.ulp(expected);
+ final double actualOld = density(x, shape, 1.0);
+ final double actualNew = distribution.density(x);
+ final double errOld, errNew;
+ errOld = Math.abs((actualOld - expected) / ulp);
+ errNew = Math.abs((actualNew - expected) / ulp);
+
+ if (Double.isNaN(actualOld) || Double.isInfinite(actualOld)) {
+ Assert.assertFalse(msg, Double.isNaN(actualNew));
+ Assert.assertFalse(msg, Double.isInfinite(actualNew));
+ statNewOF.addValue(errNew);
+ } else {
+ statOld.addValue(errOld);
+ statNewNoOF.addValue(errNew);
+ }
+ }
+ if (statOld.getN() != 0) {
+ /*
+ * If no overflow occurs, check that new implementation is
+ * better than old one.
+ */
+ final StringBuilder sb = new StringBuilder("shape = ");
+ sb.append(shape);
+ sb.append(", scale = 1.0\n");
+ sb.append("Old implementation\n");
+ sb.append("------------------\n");
+ sb.append(statOld.toString());
+ sb.append("New implementation\n");
+ sb.append("------------------\n");
+ sb.append(statNewNoOF.toString());
+ final String msg = sb.toString();
+
+ final double oldMin = statOld.getMin();
+ final double newMin = statNewNoOF.getMin();
+ Assert.assertTrue(msg, newMin <= oldMin);
+
+ final double oldMax = statOld.getMax();
+ final double newMax = statNewNoOF.getMax();
+ Assert.assertTrue(msg, newMax <= oldMax);
+
+ final double oldMean = statOld.getMean();
+ final double newMean = statNewNoOF.getMean();
+ Assert.assertTrue(msg, newMean <= oldMean);
+
+ final double oldSd = statOld.getStandardDeviation();
+ final double newSd = statNewNoOF.getStandardDeviation();
+ Assert.assertTrue(msg, newSd <= oldSd);
+
+ Assert.assertTrue(msg, newMean <= meanNoOF);
+ Assert.assertTrue(msg, newSd <= sdNoOF);
+ }
+ if (statNewOF.getN() != 0) {
+ final double newMean = statNewOF.getMean();
+ final double newSd = statNewOF.getStandardDeviation();
+
+ final StringBuilder sb = new StringBuilder("shape = ");
+ sb.append(shape);
+ sb.append(", scale = 1.0");
+ sb.append(", max. mean error (ulps) = ");
+ sb.append(meanOF);
+ sb.append(", actual mean error (ulps) = ");
+ sb.append(newMean);
+ sb.append(", max. sd of error (ulps) = ");
+ sb.append(sdOF);
+ sb.append(", actual sd of error (ulps) = ");
+ sb.append(newSd);
+ final String msg = sb.toString();
+
+ Assert.assertTrue(msg, newMean <= meanOF);
+ Assert.assertTrue(msg, newSd <= sdOF);
+ }
+ } catch (IOException e) {
+ Assert.fail(e.getMessage());
+ } finally {
+ in.close();
+ }
+ }
+
+
+ @Test
+ public void testMath753Shape1() throws IOException {
+ doTestMath753(1.0, 1.5, 0.5, 0.0, 0.0, "gamma-distribution-shape-1.csv");
+ }
+
+ @Test
+ public void testMath753Shape8() throws IOException {
+ doTestMath753(8.0, 1.5, 1.0, 0.0, 0.0, "gamma-distribution-shape-8.csv");
+ }
+
+ @Test
+ public void testMath753Shape10() throws IOException {
+ doTestMath753(10.0, 1.0, 1.0, 0.0, 0.0, "gamma-distribution-shape-10.csv");
+ }
+
+ @Test
+ public void testMath753Shape100() throws IOException {
+ // XXX Increased tolerance ("1.5" -> "2.0") to make test pass with JDK "Math"
+ // where CM used "FastMath" (cf. "XXX" comment in main source code).
+ doTestMath753(100.0, 2.0, 1.0, 0.0, 0.0, "gamma-distribution-shape-100.csv");
+ }
+
+ @Test
+ public void testMath753Shape142() throws IOException {
+ doTestMath753(142.0, 3.3, 1.6, 40.0, 40.0, "gamma-distribution-shape-142.csv");
+ }
+
+ @Test
+ public void testMath753Shape1000() throws IOException {
+ // XXX Increased tolerance ("220.0" -> "230.0") to make test pass with JDK "Math"
+ // where CM used "FastMath" (cf. "XXX" comment in main source code).
+ doTestMath753(1000.0, 1.0, 1.0, 160.0, 230.0, "gamma-distribution-shape-1000.csv");
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GeometricDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GeometricDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GeometricDistributionTest.java
new file mode 100644
index 0000000..25cf6ed
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GeometricDistributionTest.java
@@ -0,0 +1,167 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for GeometricDistribution.
+ * See class javadoc for DiscreteDistributionAbstractTest for details.
+ */
+public class GeometricDistributionTest extends DiscreteDistributionAbstractTest {
+
+ /**
+ * Constructor to override default tolerance.
+ */
+ public GeometricDistributionTest() {
+ setTolerance(1e-12);
+ }
+
+ // -------------- Implementations for abstract methods --------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new GeometricDistribution(0.40);
+ }
+
+ /** Creates the default probability density test input values */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] { -1, 0, 1, 2, 3, 4, 5, 6, 7, 8,
+ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+ 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 };
+ }
+
+ /**
+ * Creates the default probability density test expected values.
+ * Reference values are from R, version version 2.15.3.
+ */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {
+ 0d, 0.4, 0.24, 0.144, 0.0864, 0.05184, 0.031104, 0.0186624,
+ 0.01119744, 0.006718464, 0.0040310784, 0.00241864704,
+ 0.001451188224,0.0008707129344, 0.00052242776064, 0.000313456656384,
+ 0.00018807399383, 0.000112844396298, 6.77066377789e-05, 4.06239826674e-05,
+ 2.43743896004e-05, 1.46246337603e-05, 8.77478025615e-06, 5.26486815369e-06,
+ 3.15892089221e-06, 1.89535253533e-06, 1.1372115212e-06, 6.82326912718e-07,
+ 4.09396147631e-07, 2.45637688579e-07
+ };
+ }
+
+ /**
+ * Creates the default log probability density test expected values.
+ * Reference values are from R, version version 2.14.1.
+ */
+ @Override
+ public double[] makeLogDensityTestValues() {
+ return new double[] {
+ Double.NEGATIVE_INFINITY, -0.916290731874155, -1.42711635564015, -1.93794197940614,
+ -2.44876760317213, -2.95959322693812, -3.47041885070411, -3.9812444744701,
+ -4.49207009823609, -5.00289572200208, -5.51372134576807, -6.02454696953406,
+ -6.53537259330005, -7.04619821706604, -7.55702384083203, -8.06784946459802,
+ -8.57867508836402, -9.08950071213001, -9.600326335896, -10.111151959662,
+ -10.621977583428, -11.132803207194, -11.64362883096, -12.154454454726,
+ -12.6652800784919, -13.1761057022579, -13.6869313260239, -14.1977569497899,
+ -14.7085825735559, -15.2194081973219
+ };
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /** Creates the default cumulative probability density test expected values */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ final double[] densities = makeDensityTestValues();
+ final int n = densities.length;
+ final double[] ret = new double[n];
+ ret[0] = densities[0];
+ for (int i = 1; i < n; i++) {
+ ret[i] = ret[i - 1] + densities[i];
+ }
+ return ret;
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {
+ 0.000, 0.005, 0.010, 0.015, 0.020, 0.025, 0.030, 0.035, 0.040,
+ 0.045, 0.050, 0.055, 0.060, 0.065, 0.070, 0.075, 0.080, 0.085,
+ 0.090, 0.095, 0.100, 0.105, 0.110, 0.115, 0.120, 0.125, 0.130,
+ 0.135, 0.140, 0.145, 0.150, 0.155, 0.160, 0.165, 0.170, 0.175,
+ 0.180, 0.185, 0.190, 0.195, 0.200, 0.205, 0.210, 0.215, 0.220,
+ 0.225, 0.230, 0.235, 0.240, 0.245, 0.250, 0.255, 0.260, 0.265,
+ 0.270, 0.275, 0.280, 0.285, 0.290, 0.295, 0.300, 0.305, 0.310,
+ 0.315, 0.320, 0.325, 0.330, 0.335, 0.340, 0.345, 0.350, 0.355,
+ 0.360, 0.365, 0.370, 0.375, 0.380, 0.385, 0.390, 0.395, 0.400,
+ 0.405, 0.410, 0.415, 0.420, 0.425, 0.430, 0.435, 0.440, 0.445,
+ 0.450, 0.455, 0.460, 0.465, 0.470, 0.475, 0.480, 0.485, 0.490,
+ 0.495, 0.500, 0.505, 0.510, 0.515, 0.520, 0.525, 0.530, 0.535,
+ 0.540, 0.545, 0.550, 0.555, 0.560, 0.565, 0.570, 0.575, 0.580,
+ 0.585, 0.590, 0.595, 0.600, 0.605, 0.610, 0.615, 0.620, 0.625,
+ 0.630, 0.635, 0.640, 0.645, 0.650, 0.655, 0.660, 0.665, 0.670,
+ 0.675, 0.680, 0.685, 0.690, 0.695, 0.700, 0.705, 0.710, 0.715,
+ 0.720, 0.725, 0.730, 0.735, 0.740, 0.745, 0.750, 0.755, 0.760,
+ 0.765, 0.770, 0.775, 0.780, 0.785, 0.790, 0.795, 0.800, 0.805,
+ 0.810, 0.815, 0.820, 0.825, 0.830, 0.835, 0.840, 0.845, 0.850,
+ 0.855, 0.860, 0.865, 0.870, 0.875, 0.880, 0.885, 0.890, 0.895,
+ 0.900, 0.905, 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940,
+ 0.945, 0.950, 0.955, 0.960, 0.965, 0.970, 0.975, 0.980, 0.985,
+ 0.990, 0.995, 1.000
+ };
+ }
+
+ /**
+ * Creates the default inverse cumulative probability density test expected
+ * values
+ */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] {
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
+ 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,
+ 5, 5, 6, 6, 6, 6, 7, 7, 8, 9, 10, Integer.MAX_VALUE
+ };
+ }
+
+ // ----------------- Additional test cases ---------------------------------
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ GeometricDistribution dist;
+
+ dist = new GeometricDistribution(0.5);
+ Assert.assertEquals(dist.getNumericalMean(), (1.0d - 0.5d) / 0.5d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), (1.0d - 0.5d) / (0.5d * 0.5d), tol);
+
+ dist = new GeometricDistribution(0.3);
+ Assert.assertEquals(dist.getNumericalMean(), (1.0d - 0.3d) / 0.3d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), (1.0d - 0.3d) / (0.3d * 0.3d), tol);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GumbelDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GumbelDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GumbelDistributionTest.java
new file mode 100644
index 0000000..7722942
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/GumbelDistributionTest.java
@@ -0,0 +1,70 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for GumbelDistribution.
+ */
+public class GumbelDistributionTest extends ContinuousDistributionAbstractTest {
+
+ @Test
+ public void testParameters() {
+ GumbelDistribution d = makeDistribution();
+ Assert.assertEquals(0.5, d.getLocation(), Precision.EPSILON);
+ Assert.assertEquals(2, d.getScale(), Precision.EPSILON);
+ }
+
+ @Test
+ public void testSupport() {
+ GumbelDistribution d = makeDistribution();
+ Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound()));
+ Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+ Assert.assertTrue(d.isSupportConnected());
+ }
+
+ @Override
+ public GumbelDistribution makeDistribution() {
+ return new GumbelDistribution(0.5, 2);
+ }
+
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5
+ };
+ }
+
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {
+ 1.258262e-06, 3.594689e-04, 9.115766e-03, 5.321100e-02, 1.274352e-01, 1.777864e-01,
+ 1.787177e-01, 1.472662e-01, 1.075659e-01, 7.302736e-02, 4.742782e-02
+ };
+ }
+
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {
+ 1.608760e-07, 7.577548e-05, 3.168165e-03, 3.049041e-02, 1.203923e-01, 2.769203e-01,
+ 4.589561e-01, 6.235249e-01, 7.508835e-01, 8.404869e-01, 8.999652e-01
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/HypergeometricDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/HypergeometricDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/HypergeometricDistributionTest.java
new file mode 100644
index 0000000..95be226
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/HypergeometricDistributionTest.java
@@ -0,0 +1,335 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.apache.commons.rng.simple.RandomSource;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for HyperGeometriclDistribution.
+ * Extends DiscreteDistributionAbstractTest. See class javadoc for
+ * DiscreteDistributionAbstractTest for details.
+ *
+ */
+public class HypergeometricDistributionTest extends DiscreteDistributionAbstractTest {
+
+ /**
+ * Constructor to override default tolerance.
+ */
+ public HypergeometricDistributionTest() {
+ setTolerance(1e-12);
+ }
+
+ //-------------- Implementations for abstract methods -----------------------
+
+ /** Creates the default discrete distribution instance to use in tests. */
+ @Override
+ public DiscreteDistribution makeDistribution() {
+ return new HypergeometricDistribution(10, 5, 5);
+ }
+
+ /** Creates the default probability density test input values */
+ @Override
+ public int[] makeDensityTestPoints() {
+ return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
+ }
+
+ /**
+ * Creates the default probability density test expected values
+ * Reference values are from R, version 2.15.3.
+ */
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {0d, 0.00396825396825, 0.0992063492063, 0.396825396825, 0.396825396825,
+ 0.0992063492063, 0.00396825396825, 0d};
+ }
+
+ /**
+ * Creates the default probability log density test expected values
+ * Reference values are from R, version 2.14.1.
+ */
+ @Override
+ public double[] makeLogDensityTestValues() {
+ //-Inf -Inf
+ return new double[] {Double.NEGATIVE_INFINITY, -5.52942908751142, -2.31055326264322, -0.924258901523332,
+ -0.924258901523332, -2.31055326264322, -5.52942908751142, Double.NEGATIVE_INFINITY};
+ }
+
+ /** Creates the default cumulative probability density test input values */
+ @Override
+ public int[] makeCumulativeTestPoints() {
+ return makeDensityTestPoints();
+ }
+
+ /**
+ * Creates the default cumulative probability density test expected values
+ * Reference values are from R, version 2.15.3.
+ */
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {0d, 0.00396825396825, 0.103174603175, .5, 0.896825396825, 0.996031746032,
+ 1, 1};
+ }
+
+ /** Creates the default inverse cumulative probability test input values */
+ @Override
+ public double[] makeInverseCumulativeTestPoints() {
+ return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
+ 0.990d, 0.975d, 0.950d, 0.900d, 1d};
+ }
+
+ /** Creates the default inverse cumulative probability density test expected values */
+ @Override
+ public int[] makeInverseCumulativeTestValues() {
+ return new int[] {0, 0, 1, 1, 1, 1, 5, 4, 4, 4, 4, 5};
+ }
+
+ //-------------------- Additional test cases ------------------------------
+
+ /** Verify that if there are no failures, mass is concentrated on sampleSize */
+ @Test
+ public void testDegenerateNoFailures() {
+ HypergeometricDistribution dist = new HypergeometricDistribution(5,5,3);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
+ setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
+ setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
+ setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
+ setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
+ setInverseCumulativeTestValues(new int[] {3, 3});
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 3);
+ Assert.assertEquals(dist.getSupportUpperBound(), 3);
+ }
+
+ /** Verify that if there are no successes, mass is concentrated on 0 */
+ @Test
+ public void testDegenerateNoSuccesses() {
+ HypergeometricDistribution dist = new HypergeometricDistribution(5,0,3);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
+ setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
+ setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
+ setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
+ setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
+ setInverseCumulativeTestValues(new int[] {0, 0});
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 0);
+ Assert.assertEquals(dist.getSupportUpperBound(), 0);
+ }
+
+ /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
+ @Test
+ public void testDegenerateFullSample() {
+ HypergeometricDistribution dist = new HypergeometricDistribution(5,3,5);
+ setDistribution(dist);
+ setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
+ setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
+ setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
+ setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
+ setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
+ setInverseCumulativeTestValues(new int[] {3, 3});
+ verifyDensities();
+ verifyCumulativeProbabilities();
+ verifyInverseCumulativeProbabilities();
+ Assert.assertEquals(dist.getSupportLowerBound(), 3);
+ Assert.assertEquals(dist.getSupportUpperBound(), 3);
+ }
+
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ new HypergeometricDistribution(0, 3, 5);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition2() {
+ new HypergeometricDistribution(5, -1, 5);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition3() {
+ new HypergeometricDistribution(5, 3, -1);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition4() {
+ new HypergeometricDistribution(5, 6, 5);
+ }
+ @Test(expected=IllegalArgumentException.class)
+ public void testPrecondition5() {
+ new HypergeometricDistribution(5, 3, 6);
+ }
+
+ @Test
+ public void testAccessors() {
+ HypergeometricDistribution dist = new HypergeometricDistribution(5, 3, 4);
+ Assert.assertEquals(5, dist.getPopulationSize());
+ Assert.assertEquals(3, dist.getNumberOfSuccesses());
+ Assert.assertEquals(4, dist.getSampleSize());
+ }
+
+ @Test
+ public void testLargeValues() {
+ int populationSize = 3456;
+ int sampleSize = 789;
+ int numberOfSucceses = 101;
+ double[][] data = {
+ {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0},
+ {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244},
+ {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673},
+ {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792},
+ {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375},
+ {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203},
+ {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781},
+ {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701},
+ {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381},
+ {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199},
+ {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718},
+ {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418},
+ {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57},
+ {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59},
+ {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61},
+ {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63},
+ {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65},
+ {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68},
+ };
+
+ testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
+ }
+
+ private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) {
+ HypergeometricDistribution dist = new HypergeometricDistribution(populationSize, numberOfSucceses, sampleSize);
+ for (int i = 0; i < data.length; ++i) {
+ int x = (int)data[i][0];
+ double pmf = data[i][1];
+ double actualPmf = dist.probability(x);
+ TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> pmf",pmf, actualPmf, 1.0e-9);
+
+ double cdf = data[i][2];
+ double actualCdf = dist.cumulativeProbability(x);
+ TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf",cdf, actualCdf, 1.0e-9);
+
+ double cdf1 = data[i][3];
+ double actualCdf1 = dist.upperCumulativeProbability(x);
+ TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf1",cdf1, actualCdf1, 1.0e-9);
+ }
+ }
+
+ @Test
+ public void testMoreLargeValues() {
+ int populationSize = 26896;
+ int sampleSize = 895;
+ int numberOfSucceses = 55;
+ double[][] data = {
+ {0.0, 0.155168304750504, 0.155168304750504, 1.0},
+ {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496},
+ {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036},
+ {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033},
+ {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247},
+ {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237},
+ {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16},
+ {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17},
+ {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18},
+ {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20},
+ {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21},
+ {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23},
+ {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69},
+ {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71},
+ {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74},
+ {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76},
+ {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79},
+ {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82},
+ };
+ testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
+ }
+
+ @Test
+ public void testMoments() {
+ final double tol = 1e-9;
+ HypergeometricDistribution dist;
+
+ dist = new HypergeometricDistribution(1500, 40, 100);
+ Assert.assertEquals(dist.getNumericalMean(), 40d * 100d / 1500d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), ( 100d * 40d * (1500d - 100d) * (1500d - 40d) ) / ( (1500d * 1500d * 1499d) ), tol);
+
+ dist = new HypergeometricDistribution(3000, 55, 200);
+ Assert.assertEquals(dist.getNumericalMean(), 55d * 200d / 3000d, tol);
+ Assert.assertEquals(dist.getNumericalVariance(), ( 200d * 55d * (3000d - 200d) * (3000d - 55d) ) / ( (3000d * 3000d * 2999d) ), tol);
+ }
+
+ @Test
+ public void testMath644() {
+ int N = 14761461; // population
+ int m = 1035; // successes in population
+ int n = 1841; // number of trials
+
+ int k = 0;
+ final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n);
+
+ Assert.assertTrue(Precision.compareTo(1.0, dist.upperCumulativeProbability(k), 1) == 0);
+ Assert.assertTrue(Precision.compareTo(dist.cumulativeProbability(k), 0.0, 1) > 0);
+
+ // another way to calculate the upper cumulative probability
+ double upper = 1.0 - dist.cumulativeProbability(k) + dist.probability(k);
+ Assert.assertTrue(Precision.compareTo(1.0, upper, 1) == 0);
+ }
+
+ @Test
+ public void testZeroTrial() {
+ final int n = 11; // population
+ final int m = 4; // successes in population
+ final int s = 0; // number of trials
+
+ final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, 0);
+
+ for (int i = 1; i <= n; i++) {
+ final double p = dist.probability(i);
+ Assert.assertEquals("p=" + p, 0, p, 0d);
+ }
+ }
+
+ @Test
+ public void testMath1356() {
+ final int n = 11; // population
+ final int m = 11; // successes in population
+
+ for (int s = 0; s <= n; s++) {
+ final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, s);
+ final double p = dist.probability(s);
+ Assert.assertEquals("p=" + p, 1, p, 0d);
+ }
+ }
+
+ @Test
+ public void testMath1021() {
+ final int N = 43130568;
+ final int m = 42976365;
+ final int n = 50;
+ final DiscreteDistribution.Sampler dist =
+ new HypergeometricDistribution(N, m, n).createSampler(RandomSource.create(RandomSource.WELL_512_A));
+
+ for (int i = 0; i < 100; i++) {
+ final int sample = dist.sample();
+ Assert.assertTrue("sample=" + sample, 0 <= sample);
+ Assert.assertTrue("sample=" + sample, sample <= n);
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LaplaceDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LaplaceDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LaplaceDistributionTest.java
new file mode 100644
index 0000000..d935aca
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LaplaceDistributionTest.java
@@ -0,0 +1,70 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for LaplaceDistribution.
+ */
+public class LaplaceDistributionTest extends ContinuousDistributionAbstractTest {
+
+ @Test
+ public void testParameters() {
+ LaplaceDistribution d = makeDistribution();
+ Assert.assertEquals(0, d.getLocation(), Precision.EPSILON);
+ Assert.assertEquals(1, d.getScale(), Precision.EPSILON);
+ }
+
+ @Test
+ public void testSupport() {
+ LaplaceDistribution d = makeDistribution();
+ Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound()));
+ Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+ Assert.assertTrue(d.isSupportConnected());
+ }
+
+ @Override
+ public LaplaceDistribution makeDistribution() {
+ return new LaplaceDistribution(0, 1);
+ }
+
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5
+ };
+ }
+
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {
+ 0.003368973, 0.009157819, 0.024893534, 0.067667642, 0.183939721,
+ 0.500000000, 0.183939721, 0.067667642, 0.024893534, 0.009157819, 0.003368973
+ };
+ }
+
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {
+ 0.003368973, 0.009157819, 0.024893534, 0.067667642, 0.183939721,
+ 0.500000000, 0.816060279, 0.932332358, 0.975106466, 0.990842181, 0.996631027
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LevyDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LevyDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LevyDistributionTest.java
new file mode 100644
index 0000000..78b210c
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LevyDistributionTest.java
@@ -0,0 +1,81 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class LevyDistributionTest extends ContinuousDistributionAbstractTest {
+
+ @Test
+ public void testParameters() {
+ LevyDistribution d = makeDistribution();
+ Assert.assertEquals(1.2, d.getLocation(), Precision.EPSILON);
+ Assert.assertEquals(0.4, d.getScale(), Precision.EPSILON);
+ }
+
+ @Test
+ public void testSupport() {
+ LevyDistribution d = makeDistribution();
+ Assert.assertEquals(d.getLocation(), d.getSupportLowerBound(), Precision.EPSILON);
+ Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+ Assert.assertTrue(d.isSupportConnected());
+ }
+
+ @Override
+ public LevyDistribution makeDistribution() {
+ return new LevyDistribution(1.2, 0.4);
+ }
+
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ 1.2001, 1.21, 1.225, 1.25, 1.3, 1.9, 3.4, 5.6
+ };
+ }
+
+ @Override
+ public double[] makeCumulativeTestValues() {
+ // values computed with R and function plevy from rmutil package
+ return new double[] {
+ 0, 2.53962850749e-10, 6.33424836662e-05, 0.00467773498105,
+ 0.0455002638964, 0.449691797969, 0.669815357599, 0.763024600553
+ };
+ }
+
+ @Override
+ public double[] makeDensityTestValues() {
+ // values computed with R and function dlevy from rmutil package
+ return new double[] {
+ 0, 5.20056373765e-07, 0.0214128361224, 0.413339707082, 1.07981933026,
+ 0.323749319161, 0.0706032550094, 0.026122839884
+ };
+ }
+
+ /**
+ * Creates the default logarithmic probability density test expected values.
+ * Reference values are from R, version 2.14.1.
+ */
+ @Override
+ public double[] makeLogDensityTestValues() {
+ return new double[] {
+ -1987.561573341398d, -14.469328620160d, -3.843764717971d,
+ -0.883485488811d, 0.076793740349d, -1.127785768948d,
+ -2.650679030597d, -3.644945255983d};
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogNormalDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogNormalDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogNormalDistributionTest.java
new file mode 100644
index 0000000..43b2fdd
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogNormalDistributionTest.java
@@ -0,0 +1,250 @@
+/*
+ * 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.statistics.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for {@link LogNormalDistribution}. Extends
+ * {@link ContinuousDistributionAbstractTest}. See class javadoc of that class
+ * for details.
+ */
+public class LogNormalDistributionTest extends ContinuousDistributionAbstractTest {
+
+ //-------------- 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(1e-7);
+ }
+
+ //---------------------------- 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=IllegalArgumentException.class)
+ public void testPrecondition1() {
+ 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);
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogisticsDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogisticsDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogisticsDistributionTest.java
new file mode 100644
index 0000000..cfdf6d8
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/LogisticsDistributionTest.java
@@ -0,0 +1,70 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for LogisticsDistribution.
+ */
+public class LogisticsDistributionTest extends ContinuousDistributionAbstractTest {
+
+ @Test
+ public void testParameters() {
+ LogisticDistribution d = makeDistribution();
+ Assert.assertEquals(2, d.getLocation(), Precision.EPSILON);
+ Assert.assertEquals(5, d.getScale(), Precision.EPSILON);
+ }
+
+ @Test
+ public void testSupport() {
+ LogisticDistribution d = makeDistribution();
+ Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound()));
+ Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+ Assert.assertTrue(d.isSupportConnected());
+ }
+
+ @Override
+ public LogisticDistribution makeDistribution() {
+ return new LogisticDistribution(2, 5);
+ }
+
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5
+ };
+ }
+
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {
+ 0.03173698, 0.03557889, 0.03932239, 0.04278194, 0.04575685, 0.04805215,
+ 0.04950331, 0.05000000, 0.04950331, 0.04805215, 0.04575685
+ };
+ }
+
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {
+ 0.1978161, 0.2314752, 0.2689414, 0.3100255, 0.3543437, 0.4013123,
+ 0.4501660, 0.5000000, 0.5498340, 0.5986877, 0.6456563
+ };
+ }
+}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NakagamiDistributionTest.java
----------------------------------------------------------------------
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NakagamiDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NakagamiDistributionTest.java
new file mode 100644
index 0000000..6aab2dd
--- /dev/null
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NakagamiDistributionTest.java
@@ -0,0 +1,70 @@
+/*
+ * 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.statistics.distribution;
+
+import org.apache.commons.numbers.core.Precision;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for NakagamiDistribution.
+ */
+public class NakagamiDistributionTest extends ContinuousDistributionAbstractTest {
+
+ @Test
+ public void testParameters() {
+ NakagamiDistribution d = makeDistribution();
+ Assert.assertEquals(0.5, d.getShape(), Precision.EPSILON);
+ Assert.assertEquals(1, d.getScale(), Precision.EPSILON);
+ }
+
+ @Test
+ public void testSupport() {
+ NakagamiDistribution d = makeDistribution();
+ Assert.assertEquals(d.getSupportLowerBound(), 0, Precision.EPSILON);
+ Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+ Assert.assertTrue(d.isSupportConnected());
+ }
+
+ @Override
+ public NakagamiDistribution makeDistribution() {
+ return new NakagamiDistribution(0.5, 1);
+ }
+
+ @Override
+ public double[] makeCumulativeTestPoints() {
+ return new double[] {
+ 0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2
+ };
+ }
+
+ @Override
+ public double[] makeDensityTestValues() {
+ return new double[] {
+ 0.0000000, 0.7820854, 0.7365403, 0.6664492, 0.5793831, 0.4839414,
+ 0.3883721, 0.2994549, 0.2218417, 0.1579003, 0.1079819
+ };
+ }
+
+ @Override
+ public double[] makeCumulativeTestValues() {
+ return new double[] {
+ 0.0000000, 0.1585194, 0.3108435, 0.4514938, 0.5762892, 0.6826895,
+ 0.7698607, 0.8384867, 0.8904014, 0.9281394, 0.9544997
+ };
+ }
+}