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Diffstat (limited to 'src/main/java/org/apache/commons/math/distribution/ExponentialDistributionImpl.java')
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diff --git a/src/main/java/org/apache/commons/math/distribution/ExponentialDistributionImpl.java b/src/main/java/org/apache/commons/math/distribution/ExponentialDistributionImpl.java new file mode 100644 index 0000000..25d81f4 --- /dev/null +++ b/src/main/java/org/apache/commons/math/distribution/ExponentialDistributionImpl.java @@ -0,0 +1,319 @@ +/* + * 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 java.io.Serializable; + +import org.apache.commons.math.MathException; +import org.apache.commons.math.MathRuntimeException; +import org.apache.commons.math.exception.util.LocalizedFormats; +import org.apache.commons.math.util.FastMath; + +/** + * The default implementation of {@link ExponentialDistribution}. + * + * @version $Revision: 1055914 $ $Date: 2011-01-06 16:34:34 +0100 (jeu. 06 janv. 2011) $ + */ +public class ExponentialDistributionImpl extends AbstractContinuousDistribution + implements ExponentialDistribution, Serializable { + + /** + * 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 = 2401296428283614780L; + + /** The mean of this distribution. */ + private double mean; + + /** Inverse cumulative probability accuracy */ + private final double solverAbsoluteAccuracy; + + /** + * Create a exponential distribution with the given mean. + * @param mean mean of this distribution. + */ + public ExponentialDistributionImpl(double mean) { + this(mean, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); + } + + /** + * Create a exponential distribution with the given mean. + * @param mean mean of this distribution. + * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates + * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}) + * @since 2.1 + */ + public ExponentialDistributionImpl(double mean, double inverseCumAccuracy) { + super(); + setMeanInternal(mean); + solverAbsoluteAccuracy = inverseCumAccuracy; + } + + /** + * Modify the mean. + * @param mean the new mean. + * @throws IllegalArgumentException if <code>mean</code> is not positive. + * @deprecated as of 2.1 (class will become immutable in 3.0) + */ + @Deprecated + public void setMean(double mean) { + setMeanInternal(mean); + } + /** + * Modify the mean. + * @param newMean the new mean. + * @throws IllegalArgumentException if <code>newMean</code> is not positive. + */ + private void setMeanInternal(double newMean) { + if (newMean <= 0.0) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.NOT_POSITIVE_MEAN, newMean); + } + this.mean = newMean; + } + + /** + * Access the mean. + * @return the mean. + */ + public double getMean() { + return mean; + } + + /** + * Return the probability density for a particular point. + * + * @param x The point at which the density should be computed. + * @return The pdf at point x. + * @deprecated - use density(double) + */ + @Deprecated + public double density(Double x) { + return density(x.doubleValue()); + } + + /** + * Return the probability density for a particular point. + * + * @param x The point at which the density should be computed. + * @return The pdf at point x. + * @since 2.1 + */ + @Override + public double density(double x) { + if (x < 0) { + return 0; + } + return FastMath.exp(-x / mean) / mean; + } + + /** + * For this distribution, X, this method returns P(X < x). + * + * The implementation of this method is based on: + * <ul> + * <li> + * <a href="http://mathworld.wolfram.com/ExponentialDistribution.html"> + * Exponential Distribution</a>, equation (1).</li> + * </ul> + * + * @param x the value at which the CDF is evaluated. + * @return CDF for this distribution. + * @throws MathException if the cumulative probability can not be + * computed due to convergence or other numerical errors. + */ + public double cumulativeProbability(double x) throws MathException{ + double ret; + if (x <= 0.0) { + ret = 0.0; + } else { + ret = 1.0 - FastMath.exp(-x / mean); + } + return ret; + } + + /** + * For this distribution, X, this method returns the critical point x, such + * that P(X < x) = <code>p</code>. + * <p> + * Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY</code> for p=1.</p> + * + * @param p the desired probability + * @return x, such that P(X < x) = <code>p</code> + * @throws MathException if the inverse cumulative probability can not be + * computed due to convergence or other numerical errors. + * @throws IllegalArgumentException if p < 0 or p > 1. + */ + @Override + public double inverseCumulativeProbability(double p) throws MathException { + double ret; + + if (p < 0.0 || p > 1.0) { + throw MathRuntimeException.createIllegalArgumentException( + LocalizedFormats.OUT_OF_RANGE_SIMPLE, p, 0.0, 1.0); + } else if (p == 1.0) { + ret = Double.POSITIVE_INFINITY; + } else { + ret = -mean * FastMath.log(1.0 - p); + } + + return ret; + } + + /** + * Generates a random value sampled from this distribution. + * + * <p><strong>Algorithm Description</strong>: Uses the <a + * href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion + * Method</a> to generate exponentially distributed random values from + * uniform deviates. </p> + * + * @return random value + * @since 2.2 + * @throws MathException if an error occurs generating the random value + */ + @Override + public double sample() throws MathException { + return randomData.nextExponential(mean); + } + + /** + * Access the domain value lower bound, based on <code>p</code>, used to + * bracket a CDF root. + * + * @param p the desired probability for the critical value + * @return domain value lower bound, i.e. + * P(X < <i>lower bound</i>) < <code>p</code> + */ + @Override + protected double getDomainLowerBound(double p) { + return 0; + } + + /** + * Access the domain value upper bound, based on <code>p</code>, used to + * bracket a CDF root. + * + * @param p the desired probability for the critical value + * @return domain value upper bound, i.e. + * P(X < <i>upper bound</i>) > <code>p</code> + */ + @Override + protected double getDomainUpperBound(double p) { + // NOTE: exponential is skewed to the left + // NOTE: therefore, P(X < μ) > .5 + + if (p < .5) { + // use mean + return mean; + } else { + // use max + return Double.MAX_VALUE; + } + } + + /** + * Access the initial domain value, based on <code>p</code>, used to + * bracket a CDF root. + * + * @param p the desired probability for the critical value + * @return initial domain value + */ + @Override + protected double getInitialDomain(double p) { + // TODO: try to improve on this estimate + // TODO: what should really happen here is not derive from AbstractContinuousDistribution + // TODO: because the inverse cumulative distribution is simple. + // Exponential is skewed to the left, therefore, P(X < μ) > .5 + if (p < .5) { + // use 1/2 mean + return mean * .5; + } else { + // use mean + return mean; + } + } + + /** + * 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; + } + + /** + * Returns the lower bound of the support for the distribution. + * + * The lower bound of the support is always 0, regardless of the mean. + * + * @return lower bound of the support (always 0) + * @since 2.2 + */ + public double getSupportLowerBound() { + return 0; + } + + /** + * Returns the upper bound of the support for the distribution. + * + * The upper bound of the support is always positive infinity, + * regardless of the mean. + * + * @return upper bound of the support (always Double.POSITIVE_INFINITY) + * @since 2.2 + */ + public double getSupportUpperBound() { + return Double.POSITIVE_INFINITY; + } + + /** + * Returns the mean of the distribution. + * + * For mean parameter <code>k</code>, the mean is + * <code>k</code> + * + * @return the mean + * @since 2.2 + */ + public double getNumericalMean() { + return getMean(); + } + + /** + * Returns the variance of the distribution. + * + * For mean parameter <code>k</code>, the variance is + * <code>k^2</code> + * + * @return the variance + * @since 2.2 + */ + public double getNumericalVariance() { + final double m = getMean(); + return m * m; + } + +} |