lower,upper) (i.e., endpoints excluded).
+ * {@code (lower, upper)} (i.e., endpoints excluded).
*
* Definition:
*
@@ -211,11 +211,6 @@ public interface RandomData {
* upper - lower are the
*
* location and scale parameters, respectively.
- * Preconditions:
lower < upper (otherwise an IllegalArgumentException
- * is thrown.)
+ * Definition:
+ *
+ * Uniform Distribution lower and
+ * upper - lower are the
+ *
+ * location and scale parameters, respectively.
k whose entries
* are selected randomly, without repetition, from the integers
diff --git a/src/main/java/org/apache/commons/math/random/RandomDataImpl.java b/src/main/java/org/apache/commons/math/random/RandomDataImpl.java
index 78e883518..f47c38f86 100644
--- a/src/main/java/org/apache/commons/math/random/RandomDataImpl.java
+++ b/src/main/java/org/apache/commons/math/random/RandomDataImpl.java
@@ -593,9 +593,34 @@ public class RandomDataImpl implements RandomData, Serializable {
* or either bound is infinite or NaN
*/
public double nextUniform(double lower, double upper) {
+ return nextUniform(lower, upper, false);
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * Algorithm Description: if the lower bound is excluded,
+ * scales the output of Random.nextDouble(), but rejects 0 values (i.e.,
+ * will generate another random double if Random.nextDouble() returns 0).
+ * This is necessary to provide a symmetric output interval (both
+ * endpoints excluded).
+ *
+ *
+ * @param lower
+ * the lower bound.
+ * @param upper
+ * the upper bound.
+ * @param lowerInclusive
+ * whether the lower bound is included in the interval
+ * @return a uniformly distributed random value from the interval (lower,
+ * upper)
+ * @throws NumberIsTooLargeException if {@code lower >= upper}.
+ * @since 3.0
+ */
+ public double nextUniform(double lower, double upper, boolean lowerInclusive) {
if (lower >= upper) {
- throw new MathIllegalArgumentException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
- lower, upper);
+ throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
+ lower, upper, false);
}
if (Double.isInfinite(lower) || Double.isInfinite(upper)) {
@@ -610,7 +635,7 @@ public class RandomDataImpl implements RandomData, Serializable {
// ensure nextDouble() isn't 0.0
double u = generator.nextDouble();
- while (u <= 0.0) {
+ while (!lowerInclusive && u <= 0.0) {
u = generator.nextDouble();
}
diff --git a/src/test/java/org/apache/commons/math/distribution/UniformIntegerDistributionTest.java b/src/test/java/org/apache/commons/math/distribution/UniformIntegerDistributionTest.java
new file mode 100644
index 000000000..a828df918
--- /dev/null
+++ b/src/test/java/org/apache/commons/math/distribution/UniformIntegerDistributionTest.java
@@ -0,0 +1,99 @@
+/*
+ * 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.math.distribution;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for UniformIntegerDistribution. See class javadoc for
+ * {@link IntegerDistributionAbstractTest} for further details.
+ */
+public class UniformIntegerDistributionTest extends IntegerDistributionAbstractTest {
+
+ // --- 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 IntegerDistribution makeDistribution() {
+ return new UniformIntegerDistribution(-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() {
+ UniformIntegerDistribution dist;
+
+ dist = new UniformIntegerDistribution(0, 5);
+ Assert.assertEquals(dist.getNumericalMean(), 2.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 35 / 12.0, 0);
+
+ dist = new UniformIntegerDistribution(0, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 3 / 12.0, 0);
+ }
+}
diff --git a/src/test/java/org/apache/commons/math/distribution/UniformRealDistributionTest.java b/src/test/java/org/apache/commons/math/distribution/UniformRealDistributionTest.java
new file mode 100644
index 000000000..821e05c18
--- /dev/null
+++ b/src/test/java/org/apache/commons/math/distribution/UniformRealDistributionTest.java
@@ -0,0 +1,113 @@
+/*
+ * 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.math.distribution;
+
+import org.apache.commons.math.exception.NumberIsTooLargeException;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Test cases for UniformRealDistribution. See class javadoc for
+ * {@link RealDistributionAbstractTest} for further details.
+ */
+public class UniformRealDistributionTest extends RealDistributionAbstractTest {
+
+ // --- Override tolerance -------------------------------------------------
+
+ @Override
+ public void setUp() throws Exception {
+ super.setUp();
+ setTolerance(1e-4);
+ }
+
+ //--- Implementations for abstract methods --------------------------------
+
+ /** Creates the default uniform real distribution instance to use in tests. */
+ @Override
+ public UniformRealDistribution makeDistribution() {
+ return new UniformRealDistribution(-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() {
+ UniformRealDistribution distribution = makeDistribution();
+ Assert.assertEquals(-0.5, distribution.getSupportLowerBound(), 0);
+ }
+
+ /** Test upper bound getter. */
+ @Test
+ public void testGetUpperBound() {
+ UniformRealDistribution distribution = makeDistribution();
+ Assert.assertEquals(1.25, distribution.getSupportUpperBound(), 0);
+ }
+
+ /** Test pre-condition for equal lower/upper bound. */
+ @Test(expected=NumberIsTooLargeException.class)
+ public void testPreconditions1() {
+ new UniformRealDistribution(0, 0);
+ }
+
+ /** Test pre-condition for lower bound larger than upper bound. */
+ @Test(expected=NumberIsTooLargeException.class)
+ public void testPreconditions2() {
+ new UniformRealDistribution(1, 0);
+ }
+
+ /** Test mean/variance. */
+ @Test
+ public void testMeanVariance() {
+ UniformRealDistribution dist;
+
+ dist = new UniformRealDistribution(0, 1);
+ Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 1/12.0, 0);
+
+ dist = new UniformRealDistribution(-1.5, 0.6);
+ Assert.assertEquals(dist.getNumericalMean(), -0.45, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 0.3675, 0);
+
+ dist = new UniformRealDistribution(-0.5, 1.25);
+ Assert.assertEquals(dist.getNumericalMean(), 0.375, 0);
+ Assert.assertEquals(dist.getNumericalVariance(), 0.2552083333333333, 0);
+ }
+}