Coverage Report - org.apache.commons.math.stat.descriptive.moment.StandardDeviation

Classes in this File Line Coverage Branch Coverage Complexity
StandardDeviation
73% 
N/A 
1

 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.stat.descriptive.moment;
 17  
 
 18  
 import java.io.Serializable;
 19  
 
 20  
 import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
 21  
 
 22  
 /**
 23  
  * Computes the sample standard deviation.  The standard deviation
 24  
  * is the positive square root of the variance.  This implementation wraps a
 25  
  * {@link Variance} instance.  The <code>isBiasCorrected</code> property of the
 26  
  * wrapped Variance instance is exposed, so that this class can be used to
 27  
  * compute both the "sample standard deviation" (the square root of the 
 28  
  * bias-corrected "sample variance") or the "population standard deviation"
 29  
  * (the square root of the non-bias-corrected "population variance"). See 
 30  
  * {@link Variance} for more information.  
 31  
  * <p>
 32  
  * <strong>Note that this implementation is not synchronized.</strong> If 
 33  
  * multiple threads access an instance of this class concurrently, and at least
 34  
  * one of the threads invokes the <code>increment()</code> or 
 35  
  * <code>clear()</code> method, it must be synchronized externally.
 36  
  * 
 37  
  * @version $Revision$ $Date: 2005-02-26 05:11:52 -0800 (Sat, 26 Feb 2005) $
 38  
  */
 39  
 public class StandardDeviation extends AbstractStorelessUnivariateStatistic
 40  
     implements Serializable {
 41  
 
 42  
     /** Serializable version identifier */
 43  
     static final long serialVersionUID = 5728716329662425188L;  
 44  
     
 45  
     /** Wrapped Variance instance */
 46  24
     private Variance variance = null;
 47  
 
 48  
     /**
 49  
      * Constructs a StandardDeviation.  Sets the underlying {@link Variance}
 50  
      * instance's <code>isBiasCorrected</code> property to true.
 51  
      */
 52  20
     public StandardDeviation() {
 53  20
         variance = new Variance();
 54  20
     }
 55  
 
 56  
     /**
 57  
      * Constructs a StandardDeviation from an external second moment.
 58  
      * 
 59  
      * @param m2 the external moment
 60  
      */
 61  0
     public StandardDeviation(final SecondMoment m2) {
 62  0
         variance = new Variance(m2);
 63  0
     }
 64  
     
 65  
     /**
 66  
      * Contructs a StandardDeviation with the specified value for the
 67  
      * <code>isBiasCorrected</code> property.  If this property is set to 
 68  
      * <code>true</code>, the {@link Variance} used in computing results will
 69  
      * use the bias-corrected, or "sample" formula.  See {@link Variance} for
 70  
      * details.
 71  
      * 
 72  
      * @param isBiasCorrected  whether or not the variance computation will use
 73  
      * the bias-corrected formula
 74  
      */
 75  2
     public StandardDeviation(boolean isBiasCorrected) {
 76  2
         variance = new Variance(isBiasCorrected);
 77  2
     }
 78  
     
 79  
     /**
 80  
      * Contructs a StandardDeviation with the specified value for the
 81  
      * <code>isBiasCorrected</code> property and the supplied external moment.
 82  
      * If <code>isBiasCorrected</code> is set to <code>true</code>, the
 83  
      * {@link Variance} used in computing results will use the bias-corrected,
 84  
      * or "sample" formula.  See {@link Variance} for details.
 85  
      * 
 86  
      * @param isBiasCorrected  whether or not the variance computation will use
 87  
      * the bias-corrected formula
 88  
       * @param m2 the external moment
 89  
      */
 90  2
     public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
 91  2
         variance = new Variance(isBiasCorrected, m2);
 92  2
     }
 93  
 
 94  
     /**
 95  
      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#increment(double)
 96  
      */
 97  
     public void increment(final double d) {
 98  196
         variance.increment(d);
 99  196
     }
 100  
     
 101  
     /**
 102  
      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getN()
 103  
      */
 104  
     public long getN() {
 105  58
         return variance.getN();
 106  
     }
 107  
 
 108  
     /**
 109  
      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getResult()
 110  
      */
 111  
     public double getResult() {
 112  100
         return Math.sqrt(variance.getResult());
 113  
     }
 114  
 
 115  
     /**
 116  
      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
 117  
      */
 118  
     public void clear() {
 119  22
         variance.clear();
 120  22
     }
 121  
 
 122  
     /**
 123  
      * Returns the Standard Deviation of the entries in the input array, or 
 124  
      * <code>Double.NaN</code> if the array is empty.
 125  
      * <p>
 126  
      * Returns 0 for a single-value (i.e. length = 1) sample.
 127  
      * <p>
 128  
      * Throws <code>IllegalArgumentException</code> if the array is null.
 129  
      * <p>
 130  
      * Does not change the internal state of the statistic.
 131  
      * 
 132  
      * @param values the input array
 133  
      * @return the standard deviation of the values or Double.NaN if length = 0
 134  
      * @throws IllegalArgumentException if the array is null
 135  
      */  
 136  
     public double evaluate(final double[] values)  {
 137  18
         return Math.sqrt(variance.evaluate(values));
 138  
     }
 139  
     
 140  
     /**
 141  
      * Returns the Standard Deviation of the entries in the specified portion of
 142  
      * the input array, or <code>Double.NaN</code> if the designated subarray
 143  
      * is empty.
 144  
      * <p>
 145  
      * Returns 0 for a single-value (i.e. length = 1) sample.
 146  
      * <p>
 147  
      * Throws <code>IllegalArgumentException</code> if the array is null.
 148  
      * <p>
 149  
      * Does not change the internal state of the statistic.
 150  
      * 
 151  
      * @param values the input array
 152  
      * @param begin index of the first array element to include
 153  
      * @param length the number of elements to include
 154  
      * @return the standard deviation of the values or Double.NaN if length = 0
 155  
      * @throws IllegalArgumentException if the array is null or the array index
 156  
      *  parameters are not valid
 157  
      */
 158  
     public double evaluate(final double[] values, final int begin, final int length)  {
 159  0
        return Math.sqrt(variance.evaluate(values, begin, length));
 160  
     }
 161  
     
 162  
     /**
 163  
      * Returns the Standard Deviation of the entries in the specified portion of
 164  
      * the input array, using the precomputed mean value.  Returns
 165  
      * <code>Double.NaN</code> if the designated subarray is empty.
 166  
      * <p>
 167  
      * Returns 0 for a single-value (i.e. length = 1) sample.
 168  
      * <p>
 169  
      * The formula used assumes that the supplied mean value is the arithmetic
 170  
      * mean of the sample data, not a known population parameter.  This method
 171  
      * is supplied only to save computation when the mean has already been
 172  
      * computed.
 173  
      * <p>
 174  
      * Throws <code>IllegalArgumentException</code> if the array is null.
 175  
      * <p>
 176  
      * Does not change the internal state of the statistic.
 177  
      * 
 178  
      * @param values the input array
 179  
      * @param mean the precomputed mean value
 180  
      * @param begin index of the first array element to include
 181  
      * @param length the number of elements to include
 182  
      * @return the standard deviation of the values or Double.NaN if length = 0
 183  
      * @throws IllegalArgumentException if the array is null or the array index
 184  
      *  parameters are not valid
 185  
      */
 186  
     public double evaluate(final double[] values, final double mean,
 187  
             final int begin, final int length)  {
 188  0
         return Math.sqrt(variance.evaluate(values, mean, begin, length));
 189  
     }
 190  
     
 191  
     /**
 192  
      * Returns the Standard Deviation of the entries in the input array, using
 193  
      * the precomputed mean value.  Returns
 194  
      * <code>Double.NaN</code> if the designated subarray is empty.
 195  
      * <p>
 196  
      * Returns 0 for a single-value (i.e. length = 1) sample.
 197  
      * <p>
 198  
      * The formula used assumes that the supplied mean value is the arithmetic
 199  
      * mean of the sample data, not a known population parameter.  This method
 200  
      * is supplied only to save computation when the mean has already been
 201  
      * computed.
 202  
      * <p>
 203  
      * Throws <code>IllegalArgumentException</code> if the array is null.
 204  
      * <p>
 205  
      * Does not change the internal state of the statistic.
 206  
      * 
 207  
      * @param values the input array
 208  
      * @param mean the precomputed mean value
 209  
      * @return the standard deviation of the values or Double.NaN if length = 0
 210  
      * @throws IllegalArgumentException if the array is null
 211  
      */
 212  
     public double evaluate(final double[] values, final double mean)  {
 213  0
         return Math.sqrt(variance.evaluate(values, mean));
 214  
     }
 215  
     
 216  
     /**
 217  
      * @return Returns the isBiasCorrected.
 218  
      */
 219  
     public boolean isBiasCorrected() {
 220  0
         return variance.isBiasCorrected();
 221  
     }
 222  
 
 223  
     /**
 224  
      * @param isBiasCorrected The isBiasCorrected to set.
 225  
      */
 226  
     public void setBiasCorrected(boolean isBiasCorrected) {
 227  2
         variance.setBiasCorrected(isBiasCorrected);
 228  2
     }
 229  
 }