001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    
018    package org.apache.commons.math.distribution;
019    
020    import java.io.Serializable;
021    
022    import org.apache.commons.math.MathRuntimeException;
023    
024    /**
025     * Default implementation of
026     * {@link org.apache.commons.math.distribution.WeibullDistribution}.
027     *
028     * @since 1.1
029     * @version $Revision: 925812 $ $Date: 2010-03-21 11:49:31 -0400 (Sun, 21 Mar 2010) $
030     */
031    public class WeibullDistributionImpl extends AbstractContinuousDistribution
032            implements WeibullDistribution, Serializable {
033    
034        /**
035         * Default inverse cumulative probability accuracy
036         * @since 2.1
037         */
038        public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
039    
040        /** Serializable version identifier */
041        private static final long serialVersionUID = 8589540077390120676L;
042    
043        /** The shape parameter. */
044        private double shape;
045    
046        /** The scale parameter. */
047        private double scale;
048    
049        /** Inverse cumulative probability accuracy */
050        private final double solverAbsoluteAccuracy;
051    
052        /**
053         * Creates weibull distribution with the given shape and scale and a
054         * location equal to zero.
055         * @param alpha the shape parameter.
056         * @param beta the scale parameter.
057         */
058        public WeibullDistributionImpl(double alpha, double beta){
059            this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
060        }
061    
062        /**
063         * Creates weibull distribution with the given shape, scale and inverse
064         * cumulative probability accuracy and a location equal to zero.
065         * @param alpha the shape parameter.
066         * @param beta the scale parameter.
067         * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
068         * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
069         * @since 2.1
070         */
071        public WeibullDistributionImpl(double alpha, double beta, double inverseCumAccuracy){
072            super();
073            setShapeInternal(alpha);
074            setScaleInternal(beta);
075            solverAbsoluteAccuracy = inverseCumAccuracy;
076        }
077    
078        /**
079         * For this distribution, X, this method returns P(X &lt; <code>x</code>).
080         * @param x the value at which the CDF is evaluated.
081         * @return CDF evaluted at <code>x</code>.
082         */
083        public double cumulativeProbability(double x) {
084            double ret;
085            if (x <= 0.0) {
086                ret = 0.0;
087            } else {
088                ret = 1.0 - Math.exp(-Math.pow(x / scale, shape));
089            }
090            return ret;
091        }
092    
093        /**
094         * Access the shape parameter.
095         * @return the shape parameter.
096         */
097        public double getShape() {
098            return shape;
099        }
100    
101        /**
102         * Access the scale parameter.
103         * @return the scale parameter.
104         */
105        public double getScale() {
106            return scale;
107        }
108    
109        /**
110         * Returns the probability density for a particular point.
111         *
112         * @param x The point at which the density should be computed.
113         * @return The pdf at point x.
114         * @since 2.1
115         */
116        @Override
117        public double density(double x) {
118            if (x < 0) {
119                return 0;
120            }
121    
122            final double xscale = x / scale;
123            final double xscalepow = Math.pow(xscale, shape - 1);
124    
125            /*
126             * Math.pow(x / scale, shape) =
127             * Math.pow(xscale, shape) =
128             * Math.pow(xscale, shape - 1) * xscale
129             */
130            final double xscalepowshape = xscalepow * xscale;
131    
132            return (shape / scale) * xscalepow * Math.exp(-xscalepowshape);
133        }
134    
135        /**
136         * For this distribution, X, this method returns the critical point x, such
137         * that P(X &lt; x) = <code>p</code>.
138         * <p>
139         * Returns <code>Double.NEGATIVE_INFINITY</code> for p=0 and
140         * <code>Double.POSITIVE_INFINITY</code> for p=1.</p>
141         *
142         * @param p the desired probability
143         * @return x, such that P(X &lt; x) = <code>p</code>
144         * @throws IllegalArgumentException if <code>p</code> is not a valid
145         *         probability.
146         */
147        @Override
148        public double inverseCumulativeProbability(double p) {
149            double ret;
150            if (p < 0.0 || p > 1.0) {
151                throw MathRuntimeException.createIllegalArgumentException(
152                      "{0} out of [{1}, {2}] range", p, 0.0, 1.0);
153            } else if (p == 0) {
154                ret = 0.0;
155            } else  if (p == 1) {
156                ret = Double.POSITIVE_INFINITY;
157            } else {
158                ret = scale * Math.pow(-Math.log(1.0 - p), 1.0 / shape);
159            }
160            return ret;
161        }
162    
163        /**
164         * Modify the shape parameter.
165         * @param alpha the new shape parameter value.
166         * @deprecated as of 2.1 (class will become immutable in 3.0)
167         */
168        @Deprecated
169        public void setShape(double alpha) {
170            setShapeInternal(alpha);
171        }
172        /**
173         * Modify the shape parameter.
174         * @param alpha the new shape parameter value.
175         */
176        private void setShapeInternal(double alpha) {
177            if (alpha <= 0.0) {
178                throw MathRuntimeException.createIllegalArgumentException(
179                      "shape must be positive ({0})",
180                      alpha);
181            }
182            this.shape = alpha;
183        }
184    
185        /**
186         * Modify the scale parameter.
187         * @param beta the new scale parameter value.
188         * @deprecated as of 2.1 (class will become immutable in 3.0)
189         */
190        @Deprecated
191        public void setScale(double beta) {
192            setScaleInternal(beta);
193        }
194        /**
195         * Modify the scale parameter.
196         * @param beta the new scale parameter value.
197         */
198        private void setScaleInternal(double beta) {
199            if (beta <= 0.0) {
200                throw MathRuntimeException.createIllegalArgumentException(
201                      "scale must be positive ({0})",
202                      beta);
203            }
204            this.scale = beta;
205        }
206    
207        /**
208         * Access the domain value lower bound, based on <code>p</code>, used to
209         * bracket a CDF root.  This method is used by
210         * {@link #inverseCumulativeProbability(double)} to find critical values.
211         *
212         * @param p the desired probability for the critical value
213         * @return domain value lower bound, i.e.
214         *         P(X &lt; <i>lower bound</i>) &lt; <code>p</code>
215         */
216        @Override
217        protected double getDomainLowerBound(double p) {
218            return 0.0;
219        }
220    
221        /**
222         * Access the domain value upper bound, based on <code>p</code>, used to
223         * bracket a CDF root.  This method is used by
224         * {@link #inverseCumulativeProbability(double)} to find critical values.
225         *
226         * @param p the desired probability for the critical value
227         * @return domain value upper bound, i.e.
228         *         P(X &lt; <i>upper bound</i>) &gt; <code>p</code>
229         */
230        @Override
231        protected double getDomainUpperBound(double p) {
232            return Double.MAX_VALUE;
233        }
234    
235        /**
236         * Access the initial domain value, based on <code>p</code>, used to
237         * bracket a CDF root.  This method is used by
238         * {@link #inverseCumulativeProbability(double)} to find critical values.
239         *
240         * @param p the desired probability for the critical value
241         * @return initial domain value
242         */
243        @Override
244        protected double getInitialDomain(double p) {
245            // use median
246            return Math.pow(scale * Math.log(2.0), 1.0 / shape);
247        }
248    
249        /**
250         * Return the absolute accuracy setting of the solver used to estimate
251         * inverse cumulative probabilities.
252         *
253         * @return the solver absolute accuracy
254         * @since 2.1
255         */
256        @Override
257        protected double getSolverAbsoluteAccuracy() {
258            return solverAbsoluteAccuracy;
259        }
260    }