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Java > Open Source Codes > org > apache > commons > math > distribution > WeibullDistributionImpl


1 /*
2  * Copyright 2005 The Apache Software Foundation.
3  *
4  * Licensed under the Apache License, Version 2.0 (the "License");
5  * you may not use this file except in compliance with the License.
6  * You may obtain a copy of the License at
7  *
8  * http://www.apache.org/licenses/LICENSE-2.0
9  *
10  * Unless required by applicable law or agreed to in writing, software
11  * distributed under the License is distributed on an "AS IS" BASIS,
12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13  * See the License for the specific language governing permissions and
14  * limitations under the License.
15  */
16
17 package org.apache.commons.math.distribution;
18
19 import java.io.Serializable JavaDoc;
20
21 /**
22  * Default implementation of
23  * {@link org.apache.commons.math.distribution.WeibullDistribution}.
24  *
25  * @since 1.1
26  * @version $Revision: 1.13 $ $Date: 2004-07-24 16:41:37 -0500 (Sat, 24 Jul 2004) $
27  */
28 public class WeibullDistributionImpl extends AbstractContinuousDistribution
29         implements WeibullDistribution, Serializable JavaDoc {
30     
31     /** Serializable version identifier */
32     static final long serialVersionUID = 8589540077390120676L;
33     
34     /** The shape parameter. */
35     private double alpha;
36     
37     /** The scale parameter. */
38     private double beta;
39     
40     /**
41      * Creates weibull distribution with the given shape and scale and a
42      * location equal to zero.
43      * @param alpha the shape parameter.
44      * @param beta the scale parameter.
45      */
46     public WeibullDistributionImpl(double alpha, double beta){
47         super();
48         setShape(alpha);
49         setScale(beta);
50     }
51
52     /**
53      * For this disbution, X, this method returns P(X &lt; <code>x</code>).
54      * @param x the value at which the CDF is evaluated.
55      * @return CDF evaluted at <code>x</code>.
56      */
57     public double cumulativeProbability(double x) {
58         double ret;
59         if (x <= 0.0) {
60             ret = 0.0;
61         } else {
62             ret = 1.0 - Math.exp(-Math.pow(x / getScale(), getShape()));
63         }
64         return ret;
65     }
66
67     /**
68      * Access the shape parameter.
69      * @return the shape parameter.
70      */
71     public double getShape() {
72         return alpha;
73     }
74     
75     /**
76      * Access the scale parameter.
77      * @return the scale parameter.
78      */
79     public double getScale() {
80         return beta;
81     }
82     
83     /**
84      * For this distribution, X, this method returns the critical point x, such
85      * that P(X &lt; x) = <code>p</code>.
86      * <p>
87      * Returns <code>Double.NEGATIVE_INFINITY</code> for p=0 and
88      * <code>Double.POSITIVE_INFINITY</code> for p=1.
89      *
90      * @param p the desired probability
91      * @return x, such that P(X &lt; x) = <code>p</code>
92      * @throws IllegalArgumentException if <code>p</code> is not a valid
93      * probability.
94      */
95     public double inverseCumulativeProbability(double p) {
96         double ret;
97         if (p < 0.0 || p > 1.0) {
98             throw new IllegalArgumentException JavaDoc
99                 ("probability argument must be between 0 and 1 (inclusive)");
100         } else if (p == 0) {
101             ret = 0.0;
102         } else if (p == 1) {
103             ret = Double.POSITIVE_INFINITY;
104         } else {
105             ret = getScale() * Math.pow(-Math.log(1.0 - p), 1.0 / getShape());
106         }
107         return ret;
108     }
109     
110     /**
111      * Modify the shape parameter.
112      * @param alpha the new shape parameter value.
113      */
114     public void setShape(double alpha) {
115         if (alpha <= 0.0) {
116             throw new IllegalArgumentException JavaDoc(
117                 "Shape must be positive.");
118         }
119         this.alpha = alpha;
120     }
121     
122     /**
123      * Modify the scale parameter.
124      * @param beta the new scale parameter value.
125      */
126     public void setScale(double beta) {
127         if (beta <= 0.0) {
128             throw new IllegalArgumentException JavaDoc(
129                 "Scale must be positive.");
130         }
131         this.beta = beta;
132     }
133
134     /**
135      * Access the domain value lower bound, based on <code>p</code>, used to
136      * bracket a CDF root. This method is used by
137      * {@link #inverseCumulativeProbability(double)} to find critical values.
138      *
139      * @param p the desired probability for the critical value
140      * @return domain value lower bound, i.e.
141      * P(X &lt; <i>lower bound</i>) &lt; <code>p</code>
142      */
143     protected double getDomainLowerBound(double p) {
144         return 0.0;
145     }
146
147     /**
148      * Access the domain value upper bound, based on <code>p</code>, used to
149      * bracket a CDF root. This method is used by
150      * {@link #inverseCumulativeProbability(double)} to find critical values.
151      *
152      * @param p the desired probability for the critical value
153      * @return domain value upper bound, i.e.
154      * P(X &lt; <i>upper bound</i>) &gt; <code>p</code>
155      */
156     protected double getDomainUpperBound(double p) {
157         return Double.MAX_VALUE;
158     }
159
160     /**
161      * Access the initial domain value, based on <code>p</code>, used to
162      * bracket a CDF root. This method is used by
163      * {@link #inverseCumulativeProbability(double)} to find critical values.
164      *
165      * @param p the desired probability for the critical value
166      * @return initial domain value
167      */
168     protected double getInitialDomain(double p) {
169         // use median
170 return Math.pow(getScale() * Math.log(2.0), 1.0 / getShape());
171     }
172 }
173
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