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Diffstat (limited to 'src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java')
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diff --git a/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java b/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java new file mode 100644 index 0000000..37daf57 --- /dev/null +++ b/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java @@ -0,0 +1,274 @@ +/* + * 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 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; + +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Map.Entry; + +/** + * Implementation of an integer-valued {@link EnumeratedDistribution}. + * + * <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. + * + * @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. + * + * <p><b>Note:</b> this constructor will implicitly create an instance of {@link Well19937c} as + * random generator to be used for sampling only (see {@link #sample()} and {@link + * #sample(int)}). In case no sampling is needed for the created distribution, it is advised to + * pass {@code null} as random generator via the appropriate constructors to avoid the + * additional initialisation overhead. + * + * @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); + innerDistribution = + new EnumeratedDistribution<Integer>( + rng, createDistribution(singletons, probabilities)); + } + + /** + * Create a discrete integer-valued distribution from the input data. Values are assigned mass + * based on their frequency. + * + * @param rng random number generator used for sampling + * @param data input dataset + * @since 3.6 + */ + public EnumeratedIntegerDistribution(final RandomGenerator rng, final int[] data) { + super(rng); + final Map<Integer, Integer> dataMap = new HashMap<Integer, Integer>(); + for (int value : data) { + Integer count = dataMap.get(value); + if (count == null) { + count = 0; + } + dataMap.put(value, ++count); + } + final int massPoints = dataMap.size(); + final double denom = data.length; + final int[] values = new int[massPoints]; + final double[] probabilities = new double[massPoints]; + int index = 0; + for (Entry<Integer, Integer> entry : dataMap.entrySet()) { + values[index] = entry.getKey(); + probabilities[index] = entry.getValue().intValue() / denom; + index++; + } + innerDistribution = + new EnumeratedDistribution<Integer>(rng, createDistribution(values, probabilities)); + } + + /** + * Create a discrete integer-valued distribution from the input data. Values are assigned mass + * based on their frequency. For example, [0,1,1,2] as input creates a distribution with values + * 0, 1 and 2 having probability masses 0.25, 0.5 and 0.25 respectively, + * + * @param data input dataset + * @since 3.6 + */ + public EnumeratedIntegerDistribution(final int[] data) { + this(new Well19937c(), data); + } + + /** + * Create the list of Pairs representing the distribution from singletons and probabilities. + * + * @param singletons values + * @param probabilities probabilities + * @return list of value/probability pairs + */ + private static List<Pair<Integer, Double>> createDistribution( + int[] singletons, double[] probabilities) { + 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])); + } + return 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} + * + * <p>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} + * + * <p>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} + * + * <p>The support of this distribution is connected. + * + * @return {@code true} + */ + public boolean isSupportConnected() { + return true; + } + + /** {@inheritDoc} */ + @Override + public int sample() { + return innerDistribution.sample(); + } +} |