Coverage Report - org.apache.commons.math.distribution.ExponentialDistributionImpl

Classes in this File Line Coverage Branch Coverage Complexity
ExponentialDistributionImpl
72% 
67% 
2.25

 1  
 /*
 2  
  * Copyright 2003-2004 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  
 package org.apache.commons.math.distribution;
 17  
 
 18  
 import java.io.Serializable;
 19  
 
 20  
 import org.apache.commons.math.MathException;
 21  
 
 22  
 /**
 23  
  * The default implementation of {@link ExponentialDistribution}
 24  
  *
 25  
  * @version $Revision$ $Date: 2005-02-26 05:11:52 -0800 (Sat, 26 Feb 2005) $
 26  
  */
 27  
 public class ExponentialDistributionImpl extends AbstractContinuousDistribution
 28  
     implements ExponentialDistribution, Serializable {
 29  
 
 30  
     /** Serializable version identifier */
 31  
     static final long serialVersionUID = 2401296428283614780L;
 32  
     
 33  
     /** The mean of this distribution. */
 34  
     private double mean;
 35  
     
 36  
     /**
 37  
      * Create a exponential distribution with the given mean.
 38  
      * @param mean mean of this distribution.
 39  
      */
 40  
     public ExponentialDistributionImpl(double mean) {
 41  22
         super();
 42  22
         setMean(mean);
 43  18
     }
 44  
 
 45  
     /**
 46  
      * Modify the mean.
 47  
      * @param mean the new mean.
 48  
      * @throws IllegalArgumentException if <code>mean</code> is not positive.
 49  
      */
 50  
     public void setMean(double mean) {
 51  26
         if (mean <= 0.0) {
 52  6
             throw new IllegalArgumentException("mean must be positive.");
 53  
         }
 54  20
         this.mean = mean;
 55  20
     }
 56  
 
 57  
     /**
 58  
      * Access the mean.
 59  
      * @return the mean.
 60  
      */
 61  
     public double getMean() {
 62  158
         return mean;
 63  
     }
 64  
 
 65  
     /**
 66  
      * For this disbution, X, this method returns P(X &lt; x).
 67  
      * 
 68  
      * The implementation of this method is based on:
 69  
      * <ul>
 70  
      * <li>
 71  
      * <a href="http://mathworld.wolfram.com/ExponentialDistribution.html">
 72  
      * Exponential Distribution</a>, equation (1).</li>
 73  
      * </ul>
 74  
      * 
 75  
      * @param x the value at which the CDF is evaluated.
 76  
      * @return CDF for this distribution.
 77  
      * @throws MathException if the cumulative probability can not be
 78  
      *            computed due to convergence or other numerical errors.
 79  
      */
 80  
     public double cumulativeProbability(double x) throws MathException{
 81  
         double ret;
 82  136
         if (x <= 0.0) {
 83  4
             ret = 0.0;
 84  
         } else {
 85  132
             ret = 1.0 - Math.exp(-x / getMean());
 86  
         }
 87  136
         return ret;
 88  
     }
 89  
     
 90  
     /**
 91  
      * For this distribution, X, this method returns the critical point x, such
 92  
      * that P(X &lt; x) = <code>p</code>.
 93  
      * <p>
 94  
      * Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY</code> for p=1.
 95  
      * 
 96  
      * @param p the desired probability
 97  
      * @return x, such that P(X &lt; x) = <code>p</code>
 98  
      * @throws MathException if the inverse cumulative probability can not be
 99  
      *            computed due to convergence or other numerical errors.
 100  
      * @throws IllegalArgumentException if p < 0 or p > 1.
 101  
      */
 102  
     public double inverseCumulativeProbability(double p) throws MathException {
 103  
         double ret;
 104  
         
 105  28
         if (p < 0.0 || p > 1.0) {
 106  4
             throw new IllegalArgumentException
 107  
                 ("probability argument must be between 0 and 1 (inclusive)");
 108  24
         } else if (p == 1.0) {
 109  2
             ret = Double.POSITIVE_INFINITY;
 110  
         } else {
 111  22
             ret = -getMean() * Math.log(1.0 - p);
 112  
         }
 113  
         
 114  24
         return ret;
 115  
     }
 116  
     
 117  
     /**
 118  
      * Access the domain value lower bound, based on <code>p</code>, used to
 119  
      * bracket a CDF root.   
 120  
      * 
 121  
      * @param p the desired probability for the critical value
 122  
      * @return domain value lower bound, i.e.
 123  
      *         P(X &lt; <i>lower bound</i>) &lt; <code>p</code>
 124  
      */
 125  
     protected double getDomainLowerBound(double p) {
 126  0
         return 0;
 127  
     }
 128  
     
 129  
     /**
 130  
      * Access the domain value upper bound, based on <code>p</code>, used to
 131  
      * bracket a CDF root.   
 132  
      * 
 133  
      * @param p the desired probability for the critical value
 134  
      * @return domain value upper bound, i.e.
 135  
      *         P(X &lt; <i>upper bound</i>) &gt; <code>p</code> 
 136  
      */
 137  
     protected double getDomainUpperBound(double p) {
 138  
         // NOTE: exponential is skewed to the left
 139  
         // NOTE: therefore, P(X < &mu;) > .5
 140  
 
 141  0
         if (p < .5) {
 142  
             // use mean
 143  0
             return getMean();
 144  
         } else {
 145  
             // use max
 146  0
             return Double.MAX_VALUE;
 147  
         }
 148  
     }
 149  
     
 150  
     /**
 151  
      * Access the initial domain value, based on <code>p</code>, used to
 152  
      * bracket a CDF root.   
 153  
      * 
 154  
      * @param p the desired probability for the critical value
 155  
      * @return initial domain value
 156  
      */
 157  
     protected double getInitialDomain(double p) {
 158  
         // TODO: try to improve on this estimate
 159  
         // Exponential is skewed to the left, therefore, P(X < &mu;) > .5
 160  0
         if (p < .5) {
 161  
             // use 1/2 mean
 162  0
             return getMean() * .5;
 163  
         } else {
 164  
             // use mean
 165  0
             return getMean();
 166  
         }
 167  
     }
 168  
 }