/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.distribution;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathArithmeticException;
import org.apache.commons.math3.exception.NotANumberException;
import org.apache.commons.math3.exception.NotFiniteNumberException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.util.Pair;
/**
* <p>Implementation of an integer-valued {@link EnumeratedDistribution}.</p>
*
* <p>Values with zero-probability are allowed but they do not extend the
* support.<br/>
* Duplicate values are allowed. Probabilities of duplicate values are combined
* when computing cumulative probabilities and statistics.</p>
*
* @since 3.2
*/
public class EnumeratedIntegerDistribution extends AbstractIntegerDistribution {
/** Serializable UID. */
private static final long serialVersionUID = 20130308L;
/**
* {@link EnumeratedDistribution} instance (using the {@link Integer} wrapper)
* used to generate the pmf.
*/
protected final EnumeratedDistribution<Integer> innerDistribution;
/**
* Create a discrete distribution using the given probability mass function
* definition.
*
* @param singletons array of random variable values.
* @param probabilities array of probabilities.
* @throws DimensionMismatchException if
* {@code singletons.length != probabilities.length}
* @throws NotPositiveException if any of the probabilities are negative.
* @throws NotFiniteNumberException if any of the probabilities are infinite.
* @throws NotANumberException if any of the probabilities are NaN.
* @throws MathArithmeticException all of the probabilities are 0.
*/
public EnumeratedIntegerDistribution(final int[] singletons, final double[] probabilities)
throws DimensionMismatchException, NotPositiveException, MathArithmeticException,
NotFiniteNumberException, NotANumberException{
this(new Well19937c(), singletons, probabilities);
}
/**
* Create a discrete distribution using the given random number generator
* and probability mass function definition.
*
* @param rng random number generator.
* @param singletons array of random variable values.
* @param probabilities array of probabilities.
* @throws DimensionMismatchException if
* {@code singletons.length != probabilities.length}
* @throws NotPositiveException if any of the probabilities are negative.
* @throws NotFiniteNumberException if any of the probabilities are infinite.
* @throws NotANumberException if any of the probabilities are NaN.
* @throws MathArithmeticException all of the probabilities are 0.
*/
public EnumeratedIntegerDistribution(final RandomGenerator rng,
final int[] singletons, final double[] probabilities)
throws DimensionMismatchException, NotPositiveException, MathArithmeticException,
NotFiniteNumberException, NotANumberException {
super(rng);
if (singletons.length != probabilities.length) {
throw new DimensionMismatchException(probabilities.length, singletons.length);
}
final List<Pair<Integer, Double>> samples = new ArrayList<Pair<Integer, Double>>(singletons.length);
for (int i = 0; i < singletons.length; i++) {
samples.add(new Pair<Integer, Double>(singletons[i], probabilities[i]));
}
innerDistribution = new EnumeratedDistribution<Integer>(rng, samples);
}
/**
* {@inheritDoc}
*/
public double probability(final int x) {
return innerDistribution.probability(x);
}
/**
* {@inheritDoc}
*/
public double cumulativeProbability(final int x) {
double probability = 0;
for (final Pair<Integer, Double> sample : innerDistribution.getPmf()) {
if (sample.getKey() <= x) {
probability += sample.getValue();
}
}
return probability;
}
/**
* {@inheritDoc}
*
* @return {@code sum(singletons[i] * probabilities[i])}
*/
public double getNumericalMean() {
double mean = 0;
for (final Pair<Integer, Double> sample : innerDistribution.getPmf()) {
mean += sample.getValue() * sample.getKey();
}
return mean;
}
/**
* {@inheritDoc}
*
* @return {@code sum((singletons[i] - mean) ^ 2 * probabilities[i])}
*/
public double getNumericalVariance() {
double mean = 0;
double meanOfSquares = 0;
for (final Pair<Integer, Double> sample : innerDistribution.getPmf()) {
mean += sample.getValue() * sample.getKey();
meanOfSquares += sample.getValue() * sample.getKey() * sample.getKey();
}
return meanOfSquares - mean * mean;
}
/**
* {@inheritDoc}
*
* Returns the lowest value with non-zero probability.
*
* @return the lowest value with non-zero probability.
*/
public int getSupportLowerBound() {
int min = Integer.MAX_VALUE;
for (final Pair<Integer, Double> sample : innerDistribution.getPmf()) {
if (sample.getKey() < min && sample.getValue() > 0) {
min = sample.getKey();
}
}
return min;
}
/**
* {@inheritDoc}
*
* Returns the highest value with non-zero probability.
*
* @return the highest value with non-zero probability.
*/
public int getSupportUpperBound() {
int max = Integer.MIN_VALUE;
for (final Pair<Integer, Double> sample : innerDistribution.getPmf()) {
if (sample.getKey() > max && sample.getValue() > 0) {
max = sample.getKey();
}
}
return max;
}
/**
* {@inheritDoc}
*
* The support of this distribution is connected.
*
* @return {@code true}
*/
public boolean isSupportConnected() {
return true;
}
/**
* {@inheritDoc}
*/
@Override
public int sample() {
return innerDistribution.sample();
}
}