AbstractIntegerDistribution.java
- /*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
- package org.apache.commons.math4.legacy.distribution;
- import org.apache.commons.statistics.distribution.DiscreteDistribution;
- import org.apache.commons.math4.legacy.exception.MathInternalError;
- import org.apache.commons.math4.legacy.exception.NumberIsTooLargeException;
- import org.apache.commons.math4.legacy.exception.OutOfRangeException;
- import org.apache.commons.math4.legacy.exception.util.LocalizedFormats;
- import org.apache.commons.rng.UniformRandomProvider;
- import org.apache.commons.rng.sampling.distribution.InverseTransformDiscreteSampler;
- import org.apache.commons.math4.core.jdkmath.JdkMath;
- /**
- * 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 AbstractIntegerDistribution
- implements DiscreteDistribution {
- /**
- * {@inheritDoc}
- *
- * The default implementation uses the identity
- * <p>{@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}</p>
- *
- * @since 4.0, was previously named cumulativeProbability
- */
- @Override
- public double probability(int x0, int x1) throws NumberIsTooLargeException {
- if (x1 < x0) {
- throw new NumberIsTooLargeException(LocalizedFormats.LOWER_ENDPOINT_ABOVE_UPPER_ENDPOINT,
- x0, x1, true);
- }
- 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) throws OutOfRangeException {
- if (p < 0.0 || p > 1.0) {
- throw new OutOfRangeException(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 = getMean();
- final double sigma = JdkMath.sqrt(getVariance());
- final boolean chebyshevApplies = !(Double.isInfinite(mu) || Double.isNaN(mu) ||
- Double.isInfinite(sigma) || Double.isNaN(sigma) || sigma == 0.0);
- if (chebyshevApplies) {
- double k = JdkMath.sqrt((1.0 - p) / p);
- double tmp = mu - k * sigma;
- if (tmp > lower) {
- lower = ((int) JdkMath.ceil(tmp)) - 1;
- }
- k = 1.0 / k;
- tmp = mu + k * sigma;
- if (tmp < upper) {
- upper = ((int) JdkMath.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 the cumulative probability
- * @param lower a value satisfying {@code cumulativeProbability(lower) < p}
- * @param upper a value satisfying {@code p <= cumulativeProbability(upper)}
- * @return the smallest {@code p}-quantile of this distribution
- */
- protected 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 MathInternalError if the cumulative probability is {@code NaN}
- */
- private double checkedCumulativeProbability(int argument)
- throws MathInternalError {
- final double result = cumulativeProbability(argument);
- if (Double.isNaN(result)) {
- throw new MathInternalError(LocalizedFormats
- .DISCRETE_CUMULATIVE_PROBABILITY_RETURNED_NAN, argument);
- }
- return result;
- }
- /**
- * {@inheritDoc}
- * <p>
- * The default implementation simply computes the logarithm of {@code probability(x)}.
- */
- @Override
- public double logProbability(int x) {
- return JdkMath.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) {
- // Inversion method distribution sampler.
- return InverseTransformDiscreteSampler.of(rng, this::inverseCumulativeProbability)::sample;
- }
- }