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Diffstat (limited to 'src/main/java/org/apache/commons/math/distribution/WeibullDistributionImpl.java')
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diff --git a/src/main/java/org/apache/commons/math/distribution/WeibullDistributionImpl.java b/src/main/java/org/apache/commons/math/distribution/WeibullDistributionImpl.java new file mode 100644 index 0000000..c52caac --- /dev/null +++ b/src/main/java/org/apache/commons/math/distribution/WeibullDistributionImpl.java @@ -0,0 +1,378 @@ +/* + * 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; + } +} |