001 /*
002 * Java Genetic Algorithm Library (jenetics-1.6.0).
003 * Copyright (c) 2007-2014 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.internal.util.object.eq;
025
026 import org.jenetics.internal.util.HashBuilder;
027
028
029 /**
030 * <p>Calculate the variance from a finite sample of <i>N</i> observations.</p>
031 * <p><img src="doc-files/variance.gif"
032 * alt="s^2_{N-1}=\frac{1}{N-1}\sum_{i=1}^{N}\left ( x_i - \bar{x} \right )^2"
033 * />
034 * </p>
035 *
036 * <p/>
037 * <strong>Note that this implementation is not synchronized.</strong> If
038 * multiple threads access this object concurrently, and at least one of the
039 * threads modifies it, it must be synchronized externally.
040 *
041 * @see <a href="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance" >
042 * Wikipedia: Algorithms for calculating variance</a>
043 * @see <a href="http://mathworld.wolfram.com/Variance.html">
044 * Wolfram MathWorld: Variance</a>
045 *
046 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
047 * @since 1.0
048 * @version 1.0 — <em>$Date: 2014-03-01 $</em>
049 */
050 public class Variance<N extends Number> extends Mean<N> {
051
052 private double _m2 = NaN;
053
054 public Variance() {
055 }
056
057 /**
058 * Return the variance of the accumulated values.
059 * <p><img src="doc-files/variance.gif" alt="Variance" /></p>
060 *
061 * @return the variance of the accumulated values, or {@link java.lang.Double#NaN}
062 * if {@code getSamples() == 0}.
063 */
064 public double getVariance() {
065 double variance = NaN;
066
067 if (_samples == 1) {
068 variance = _m2;
069 } else if (_samples > 1) {
070 variance = _m2/(_samples - 1);
071 }
072
073 return variance;
074 }
075
076 /**
077 * @throws NullPointerException if the given {@code value} is {@code null}.
078 */
079 @Override
080 public void accumulate(final N value) {
081 if (_samples == 0) {
082 _mean = 0;
083 _m2 = 0;
084 }
085
086 final double data = value.doubleValue();
087 final double delta = data - _mean;
088
089 _mean += delta/(++_samples);
090 _m2 += delta*(data - _mean);
091 }
092
093 @Override
094 public int hashCode() {
095 return HashBuilder.of(getClass()).and(super.hashCode()).and(_m2).value();
096 }
097
098 @Override
099 public boolean equals(final Object obj) {
100 if (obj == this) {
101 return true;
102 }
103 if (obj == null || getClass() != obj.getClass()) {
104 return false;
105 }
106
107 final Variance<?> variance = (Variance<?>)obj;
108 return eq(_m2, variance._m2) && super.equals(variance);
109 }
110
111 @Override
112 public String toString() {
113 return format(
114 "%s[samples=%d, mean=%f, stderr=%f, var=%f]",
115 getClass().getSimpleName(),
116 getSamples(),
117 getMean(),
118 getStandardError(),
119 getVariance()
120 );
121 }
122
123 @Override
124 public Variance<N> clone() {
125 return (Variance<N>)super.clone();
126 }
127
128 }
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