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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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package org.apache.commons.math.stat.descriptive.moment;
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import java.io.Serializable;
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import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
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* Computes the skewness of the available values.
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* We use the following (unbiased) formula to define skewness:</p>
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* skewness = [n / (n -1) (n - 2)] sum[(x_i - mean)^3] / std^3 </p>
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* where n is the number of values, mean is the {@link Mean} and std is the
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* {@link StandardDeviation} </p>
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* <strong>Note that this implementation is not synchronized.</strong> If
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* multiple threads access an instance of this class concurrently, and at least
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* one of the threads invokes the <code>increment()</code> or
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* <code>clear()</code> method, it must be synchronized externally. </p>
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* @version $Revision: 617953 $ $Date: 2008-02-02 22:54:00 -0700 (Sat, 02 Feb 2008) $
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public class Skewness extends AbstractStorelessUnivariateStatistic implements Serializable {
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/** Serializable version identifier */
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private static final long serialVersionUID = 7101857578996691352L;
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/** Third moment on which this statistic is based */
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protected ThirdMoment moment = null;
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* Determines whether or not this statistic can be incremented or cleared.
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* Statistics based on (constructed from) external moments cannot
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* be incremented or cleared.</p>
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protected boolean incMoment;
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* Constructs a Skewness
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moment = new ThirdMoment();
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* Constructs a Skewness with an external moment
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* @param m3 external moment
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public Skewness(final ThirdMoment m3) {
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* @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#increment(double)
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public void increment(final double d) {
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* Returns the value of the statistic based on the values that have been added.
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* See {@link Skewness} for the definition used in the computation.</p>
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* @return the skewness of the available values.
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public double getResult() {
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double variance = moment.m2 / (double) (moment.n - 1);
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if (variance < 10E-20) {
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double n0 = (double) moment.getN();
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return (n0 * moment.m3) /
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((n0 - 1) * (n0 -2) * Math.sqrt(variance) * variance);
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* @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getN()
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return moment.getN();
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* @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
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public void clear() {
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* Returns the Skewness of the entries in the specifed portion of the
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* See {@link Skewness} for the definition used in the computation.</p>
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* Throws <code>IllegalArgumentException</code> if the array is null.</p>
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* @param values the input array
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* @param begin the index of the first array element to include
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* @param length the number of elements to include
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* @return the skewness of the values or Double.NaN if length is less than
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* @throws IllegalArgumentException if the array is null or the array index
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* parameters are not valid
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public double evaluate(final double[] values,final int begin,
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// Initialize the skewness
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double skew = Double.NaN;
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if (test(values, begin, length) && length > 2 ){
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Mean mean = new Mean();
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// Get the mean and the standard deviation
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double m = mean.evaluate(values, begin, length);
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// Calc the std, this is implemented here instead
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// of using the standardDeviation method eliminate
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// a duplicate pass to get the mean
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for (int i = begin; i < begin + length; i++) {
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accum += Math.pow((values[i] - m), 2.0);
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accum2 += (values[i] - m);
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double stdDev = Math.sqrt((accum - (Math.pow(accum2, 2) / ((double) length))) /
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(double) (length - 1));
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for (int i = begin; i < begin + length; i++) {
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accum3 += Math.pow(values[i] - m, 3.0d);
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accum3 /= Math.pow(stdDev, 3.0d);
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// Calculate skewness
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skew = (n0 / ((n0 - 1) * (n0 - 2))) * accum3;