001 /*
002 * Java Genetic Algorithm Library (jenetics-1.5.0).
003 * Copyright (c) 2007-2013 Franz Wilhelmstötter
004 *
005 * Licensed under the Apache License, Version 2.0 (the "License");
006 * you may not use this file except in compliance with the License.
007 * 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 * Author:
018 * Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
019 */
020 package org.jenetics.stat;
021
022 import static java.lang.Double.NaN;
023 import static java.lang.String.format;
024 import static org.jenetics.util.object.eq;
025 import static org.jenetics.util.object.hashCodeOf;
026
027
028 /**
029 * <p>Calculate the variance from a finite sample of <i>N</i> observations.</p>
030 * <p><img src="doc-files/variance.gif"
031 * alt="s^2_{N-1}=\frac{1}{N-1}\sum_{i=1}^{N}\left ( x_i - \bar{x} \right )^2"
032 * />
033 * </p>
034 *
035 * <p/>
036 * <strong>Note that this implementation is not synchronized.</strong> If
037 * multiple threads access this object concurrently, and at least one of the
038 * threads modifies it, it must be synchronized externally.
039 *
040 * @see <a href="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance" >
041 * Wikipedia: Algorithms for calculating variance</a>
042 * @see <a href="http://mathworld.wolfram.com/Variance.html">
043 * Wolfram MathWorld: Variance</a>
044 *
045 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
046 * @since 1.0
047 * @version 1.0 — <em>$Date: 2013-09-01 $</em>
048 */
049 public class Variance<N extends Number> extends Mean<N> {
050
051 private double _m2 = NaN;
052
053 public Variance() {
054 }
055
056 /**
057 * Return the variance of the accumulated values.
058 * <p><img src="doc-files/variance.gif" alt="Variance" /></p>
059 *
060 * @return the variance of the accumulated values, or {@link java.lang.Double#NaN}
061 * if {@code getSamples() == 0}.
062 */
063 public double getVariance() {
064 double variance = NaN;
065
066 if (_samples == 1) {
067 variance = _m2;
068 } else if (_samples > 1) {
069 variance = _m2/(_samples - 1);
070 }
071
072 return variance;
073 }
074
075 /**
076 * @throws NullPointerException if the given {@code value} is {@code null}.
077 */
078 @Override
079 public void accumulate(final N value) {
080 if (_samples == 0) {
081 _mean = 0;
082 _m2 = 0;
083 }
084
085 final double data = value.doubleValue();
086 final double delta = data - _mean;
087
088 _mean += delta/(++_samples);
089 _m2 += delta*(data - _mean);
090 }
091
092 @Override
093 public int hashCode() {
094 return hashCodeOf(getClass()).and(super.hashCode()).and(_m2).value();
095 }
096
097 @Override
098 public boolean equals(final Object obj) {
099 if (obj == this) {
100 return true;
101 }
102 if (obj == null || getClass() != obj.getClass()) {
103 return false;
104 }
105
106 final Variance<?> variance = (Variance<?>)obj;
107 return eq(_m2, variance._m2) && super.equals(variance);
108 }
109
110 @Override
111 public String toString() {
112 return format(
113 "%s[samples=%d, mean=%f, stderr=%f, var=%f]",
114 getClass().getSimpleName(),
115 getSamples(),
116 getMean(),
117 getStandardError(),
118 getVariance()
119 );
120 }
121
122 @Override
123 public Variance<N> clone() {
124 return (Variance<N>)super.clone();
125 }
126
127 }
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