| /* |
| * 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.MathRuntimeException; |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.special.Gamma; |
| import org.apache.commons.math.util.FastMath; |
| |
| /** |
| * Default implementation of |
| * {@link org.apache.commons.math.distribution.WeibullDistribution}. |
| * |
| * @since 1.1 |
| * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $ |
| */ |
| public class WeibullDistributionImpl extends AbstractContinuousDistribution |
| implements WeibullDistribution, 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 = 8589540077390120676L; |
| |
| /** The shape parameter. */ |
| private double shape; |
| |
| /** The scale parameter. */ |
| private double scale; |
| |
| /** Inverse cumulative probability accuracy */ |
| private final double solverAbsoluteAccuracy; |
| |
| /** Cached numerical mean */ |
| private double numericalMean = Double.NaN; |
| |
| /** Whether or not the numerical mean has been calculated */ |
| private boolean numericalMeanIsCalculated = false; |
| |
| /** Cached numerical variance */ |
| private double numericalVariance = Double.NaN; |
| |
| /** Whether or not the numerical variance has been calculated */ |
| private boolean numericalVarianceIsCalculated = false; |
| |
| /** |
| * Creates weibull distribution with the given shape and scale and a |
| * location equal to zero. |
| * @param alpha the shape parameter. |
| * @param beta the scale parameter. |
| */ |
| public WeibullDistributionImpl(double alpha, double beta){ |
| this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); |
| } |
| |
| /** |
| * Creates weibull distribution with the given shape, scale and inverse |
| * cumulative probability accuracy and a location equal to zero. |
| * @param alpha the shape parameter. |
| * @param beta the scale parameter. |
| * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates |
| * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}) |
| * @since 2.1 |
| */ |
| public WeibullDistributionImpl(double alpha, double beta, double inverseCumAccuracy){ |
| super(); |
| setShapeInternal(alpha); |
| setScaleInternal(beta); |
| solverAbsoluteAccuracy = inverseCumAccuracy; |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(X < <code>x</code>). |
| * @param x the value at which the CDF is evaluated. |
| * @return CDF evaluated at <code>x</code>. |
| */ |
| public double cumulativeProbability(double x) { |
| double ret; |
| if (x <= 0.0) { |
| ret = 0.0; |
| } else { |
| ret = 1.0 - FastMath.exp(-FastMath.pow(x / scale, shape)); |
| } |
| return ret; |
| } |
| |
| /** |
| * Access the shape parameter. |
| * @return the shape parameter. |
| */ |
| public double getShape() { |
| return shape; |
| } |
| |
| /** |
| * Access the scale parameter. |
| * @return the scale parameter. |
| */ |
| public double getScale() { |
| return scale; |
| } |
| |
| /** |
| * Returns 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; |
| } |
| |
| final double xscale = x / scale; |
| final double xscalepow = FastMath.pow(xscale, shape - 1); |
| |
| /* |
| * FastMath.pow(x / scale, shape) = |
| * FastMath.pow(xscale, shape) = |
| * FastMath.pow(xscale, shape - 1) * xscale |
| */ |
| final double xscalepowshape = xscalepow * xscale; |
| |
| return (shape / scale) * xscalepow * FastMath.exp(-xscalepowshape); |
| } |
| |
| /** |
| * For this distribution, X, this method returns the critical point x, such |
| * that P(X < x) = <code>p</code>. |
| * <p> |
| * Returns <code>Double.NEGATIVE_INFINITY</code> 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 IllegalArgumentException if <code>p</code> is not a valid |
| * probability. |
| */ |
| @Override |
| public double inverseCumulativeProbability(double p) { |
| 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 == 0) { |
| ret = 0.0; |
| } else if (p == 1) { |
| ret = Double.POSITIVE_INFINITY; |
| } else { |
| ret = scale * FastMath.pow(-FastMath.log(1.0 - p), 1.0 / shape); |
| } |
| return ret; |
| } |
| |
| /** |
| * Modify the shape parameter. |
| * @param alpha the new shape parameter value. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setShape(double alpha) { |
| setShapeInternal(alpha); |
| invalidateParameterDependentMoments(); |
| } |
| /** |
| * Modify the shape parameter. |
| * @param alpha the new shape parameter value. |
| */ |
| private void setShapeInternal(double alpha) { |
| if (alpha <= 0.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_SHAPE, |
| alpha); |
| } |
| this.shape = alpha; |
| } |
| |
| /** |
| * Modify the scale parameter. |
| * @param beta the new scale parameter value. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setScale(double beta) { |
| setScaleInternal(beta); |
| invalidateParameterDependentMoments(); |
| } |
| /** |
| * Modify the scale parameter. |
| * @param beta the new scale parameter value. |
| */ |
| private void setScaleInternal(double beta) { |
| if (beta <= 0.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_SCALE, |
| beta); |
| } |
| this.scale = beta; |
| } |
| |
| /** |
| * Access the domain value lower bound, based on <code>p</code>, used to |
| * bracket a CDF root. This method is used by |
| * {@link #inverseCumulativeProbability(double)} to find critical values. |
| * |
| * @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 0.0; |
| } |
| |
| /** |
| * Access the domain value upper bound, based on <code>p</code>, used to |
| * bracket a CDF root. This method is used by |
| * {@link #inverseCumulativeProbability(double)} to find critical values. |
| * |
| * @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) { |
| return Double.MAX_VALUE; |
| } |
| |
| /** |
| * Access the initial domain value, based on <code>p</code>, used to |
| * bracket a CDF root. This method is used by |
| * {@link #inverseCumulativeProbability(double)} to find critical values. |
| * |
| * @param p the desired probability for the critical value |
| * @return initial domain value |
| */ |
| @Override |
| protected double getInitialDomain(double p) { |
| // use median |
| return FastMath.pow(scale * FastMath.log(2.0), 1.0 / shape); |
| } |
| |
| /** |
| * 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 no matter the parameters. |
| * |
| * @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 |
| * no matter the parameters. |
| * |
| * @return upper bound of the support (always Double.POSITIVE_INFINITY) |
| * @since 2.2 |
| */ |
| public double getSupportUpperBound() { |
| return Double.POSITIVE_INFINITY; |
| } |
| |
| /** |
| * Calculates the mean. |
| * |
| * The mean is <code>scale * Gamma(1 + (1 / shape))</code> |
| * where <code>Gamma(...)</code> is the Gamma-function |
| * |
| * @return the mean |
| * @since 2.2 |
| */ |
| protected double calculateNumericalMean() { |
| final double sh = getShape(); |
| final double sc = getScale(); |
| |
| return sc * FastMath.exp(Gamma.logGamma(1 + (1 / sh))); |
| } |
| |
| /** |
| * Calculates the variance. |
| * |
| * The variance is |
| * <code>scale^2 * Gamma(1 + (2 / shape)) - mean^2</code> |
| * where <code>Gamma(...)</code> is the Gamma-function |
| * |
| * @return the variance |
| * @since 2.2 |
| */ |
| private double calculateNumericalVariance() { |
| final double sh = getShape(); |
| final double sc = getScale(); |
| final double mn = getNumericalMean(); |
| |
| return (sc * sc) * |
| FastMath.exp(Gamma.logGamma(1 + (2 / sh))) - |
| (mn * mn); |
| } |
| |
| /** |
| * Returns the mean of the distribution. |
| * |
| * @return the mean or Double.NaN if it's not defined |
| * @since 2.2 |
| */ |
| public double getNumericalMean() { |
| if (!numericalMeanIsCalculated) { |
| numericalMean = calculateNumericalMean(); |
| numericalMeanIsCalculated = true; |
| } |
| |
| return numericalMean; |
| } |
| |
| /** |
| * Returns the variance of the distribution. |
| * |
| * @return the variance (possibly Double.POSITIVE_INFINITY as |
| * for certain cases in {@link TDistributionImpl}) or |
| * Double.NaN if it's not defined |
| * @since 2.2 |
| */ |
| public double getNumericalVariance() { |
| if (!numericalVarianceIsCalculated) { |
| numericalVariance = calculateNumericalVariance(); |
| numericalVarianceIsCalculated = true; |
| } |
| |
| return numericalVariance; |
| } |
| |
| /** |
| * Invalidates the cached mean and variance. |
| */ |
| private void invalidateParameterDependentMoments() { |
| numericalMeanIsCalculated = false; |
| numericalVarianceIsCalculated = false; |
| } |
| } |