/*
* 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.distribution;
import org.apache.commons.math4.exception.NumberIsTooSmallException;
import org.apache.commons.math4.exception.util.LocalizedFormats;
import org.apache.commons.numbers.gamma.RegularizedBeta;
import org.apache.commons.numbers.gamma.LogGamma;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
import org.apache.commons.rng.sampling.distribution.ChengBetaSampler;
/**
* Implements the Beta distribution.
*
* @see <a href="http://en.wikipedia.org/wiki/Beta_distribution">Beta distribution</a>
* @since 2.0 (changed to concrete class in 3.0)
*/
public class BetaDistribution extends AbstractRealDistribution {
/**
* Default inverse cumulative probability accuracy.
* @since 2.1
*/
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
/** Serializable version identifier. */
private static final long serialVersionUID = 20160311L;
/** First shape parameter. */
private final double alpha;
/** Second shape parameter. */
private final double beta;
/** Normalizing factor used in density computations.*/
private final double z;
/** Inverse cumulative probability accuracy. */
private final double solverAbsoluteAccuracy;
/**
* 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, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
}
/**
* Creates a new instance.
*
* @param alpha First shape parameter (must be positive).
* @param beta Second shape parameter (must be positive).
* @param inverseCumAccuracy Maximum absolute error in inverse
* cumulative probability estimates (defaults to
* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
*
* @since 3.1
*/
public BetaDistribution(double alpha,
double beta,
double inverseCumAccuracy) {
this.alpha = alpha;
this.beta = beta;
z = LogGamma.value(alpha) + LogGamma.value(beta) - LogGamma.value(alpha + beta);
solverAbsoluteAccuracy = inverseCumAccuracy;
}
/**
* 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 : FastMath.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 NumberIsTooSmallException(LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_0_FOR_SOME_ALPHA,
alpha, 1, false);
}
return Double.NEGATIVE_INFINITY;
} else if (x == 1) {
if (beta < 1) {
throw new NumberIsTooSmallException(LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_1_FOR_SOME_BETA,
beta, 1, false);
}
return Double.NEGATIVE_INFINITY;
} else {
double logX = FastMath.log(x);
double log1mX = FastMath.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);
}
}
/**
* Return the absolute accuracy setting of the solver used to estimate
* inverse cumulative probabilities.
*
* @return the solver absolute accuracy.
* @since 2.1
*/
@Override
protected double getSolverAbsoluteAccuracy() {
return solverAbsoluteAccuracy;
}
/**
* {@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 RealDistribution.Sampler createSampler(final UniformRandomProvider rng) {
return new RealDistribution.Sampler() {
/**
* Beta distribution sampler.
*/
private final ContinuousSampler sampler =
new ChengBetaSampler(rng, alpha, beta);
/**{@inheritDoc} */
@Override
public double sample() {
return sampler.sample();
}
};
}
}