As this is now a sensitive subject, I'm not going to commit this until I receive +/- input from the group. Please review the following UnivariateImpl and submit your opinion, This version of UnivariateImpl no longer extends AbstractStoredUnivariate to accomplish delegation, but does delegate methods to an implementation of the static statistical functions in StatUtils, it no longer directly instantiates runtime exceptions.
-Mark
/* ==================================================================== * The Apache Software License, Version 1.1 * * Copyright (c) 2003 The Apache Software Foundation. All rights * reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * * 3. The end-user documentation included with the redistribution, if * any, must include the following acknowlegement: * "This product includes software developed by the * Apache Software Foundation (http://www.apache.org/)." * Alternately, this acknowlegement may appear in the software itself, * if and wherever such third-party acknowlegements normally appear. * * 4. The names "The Jakarta Project", "Commons", and "Apache Software * Foundation" must not be used to endorse or promote products derived * from this software without prior written permission. For written * permission, please contact [EMAIL PROTECTED] * * 5. Products derived from this software may not be called "Apache" * nor may "Apache" appear in their names without prior written * permission of the Apache Software Foundation. * * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR * ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF * SUCH DAMAGE. * ==================================================================== * * This software consists of voluntary contributions made by many * individuals on behalf of the Apache Software Foundation. For more * information on the Apache Software Foundation, please see * <http://www.apache.org/>. */ package org.apache.commons.math.stat;
import java.io.Serializable; import org.apache.commons.math.DoubleArray; import org.apache.commons.math.FixedDoubleArray; /** * * Accumulates univariate statistics for values fed in * through the addValue() method. Does not store raw data values. * All data are represented internally as doubles. * Integers, floats and longs can be added, but they will be converted * to doubles by addValue(). * * @author Phil Steitz * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a> * @author <a href="mailto:[EMAIL PROTECTED]">Mark Diggory</a> * @author Brent Worden * @author <a href="mailto:[EMAIL PROTECTED]">Albert Davidson Chou</a> * @version $Revision: 1.8 $ $Date: 2003/06/16 21:38:15 $ * */ public class UnivariateImpl implements Univariate, Serializable { /** hold the window size **/ private int windowSize = Univariate.INFINITE_WINDOW; /** Just in case the windowSize is not infinite, we need to * keep an array to remember values 0 to N */ private DoubleArray doubleArray; /** count of values that have been added */ private int n = 0; /** sum of values that have been added */ private double sum = Double.NaN; /** sum of the square of each value that has been added */ private double sumsq = Double.NaN; /** sum of the Cube of each value that has been added */ private double sumCube = Double.NaN; /** sum of the Quadrate of each value that has been added */ private double sumQuad = Double.NaN; /** min of values that have been added */ private double min = Double.NaN; /** max of values that have been added */ private double max = Double.NaN; /** product of values that have been added */ private double product = Double.NaN; /** mean of values that have been added */ private double mean = Double.NaN; /** running ( variance * (n - 1) ) of values that have been added */ private double pre_variance = Double.NaN; /** variance of values that have been added */ private double variance = Double.NaN; /** Creates new univariate with an infinite window */ public UnivariateImpl() { super(); } /** Creates a new univariate with a fixed window **/ public UnivariateImpl(int window) { super(); setWindowSize(window); } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getN() */ public int getN() { return n; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getSum() */ public double getSum() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.sum(doubleArray.getElements()); } return sum; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getSumsq() */ public double getSumsq() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.sumSq(doubleArray.getElements()); } return sumsq; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getMean() */ public double getMean() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.mean(doubleArray.getElements()); } return mean; } /** * Returns the standard deviation for this collection of values * @see org.apache.commons.math.stat.Univariate#getStandardDeviation() */ public double getStandardDeviation() { double stdDev = Double.NaN; if (getN() != 0) { stdDev = Math.sqrt(getVariance()); } return (stdDev); } /** * Returns the variance of the values that have been added via West's * algorithm as described by * <a href="http://doi.acm.org/10.1145/359146.359152";>Chan, T. F. and * J. G. Lewis 1979, <i>Communications of the ACM</i>, * vol. 22 no. 9, pp. 526-531.</a>. * * @return The variance of a set of values. Double.NaN is returned for * an empty set of values and 0.0 is returned for a <= 1 value set. */ public double getVariance() { double variance = Double.NaN; if (windowSize != Univariate.INFINITE_WINDOW) { variance = StatUtils.variance(doubleArray.getElements()); } return variance < 0 ? 0.0 : variance; } /** * Returns the skewness of the values that have been added as described by * <a href="http://mathworld.wolfram.com/k-Statistic.html";>Equation (6) for k-Statistics</a>. * * @return The skew of a set of values. Double.NaN is returned for * an empty set of values and 0.0 is returned for a <= 2 value set. */ public double getSkewness() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.skewness(doubleArray.getElements()); } if (n == 0) { return Double.NaN; } if (n <= 2) { /* if n <= 2, skewness to 0.0 */ return 0.0; } else { /* else calc the skewness */ return ( 2 * Math.pow(sum, 3) - 3 * sum * sumsq + ((double) (n * n)) * sumCube) / ((double) (n * (n - 1) * (n - 2))); } } /** * Returns the kurtosis of the values that have been added as described by * <a href="http://mathworld.wolfram.com/k-Statistic.html";>Equation (7) for k-Statistics</a>. * * @return The kurtosis of a set of values. Double.NaN is returned for * an empty set of values and 0.0 is returned for a <= 3 value set. */ public double getKurtosis() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.kurtosis(doubleArray.getElements()); } if (n == 0) { return Double.NaN; } if (n <= 3) { /* if n <= 3, kurtosis to 0.0 */ return 0.0; } else { /* calc the kurtosis */ double x1 = -6 * Math.pow(sum, 4); double x2 = 12 * ((double) n) * Math.pow(sum, 2) * sumsq; double x3 = -3 * ((double) (n * (n - 1))) * Math.pow(sumsq, 2); double x4 = -4 * ((double) (n * (n + 1))) * sum * sumCube; double x5 = Math.pow(((double) n), 2) * ((double) (n + 1)) * sumQuad; return (x1 + x2 + x3 + x4 + x5) / ((double) (n * (n - 1) * (n - 2) * (n - 3))); } } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getMax() */ public double getMax() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.max(doubleArray.getElements()); } return max; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getMin() */ public double getMin() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.min(doubleArray.getElements()); } return min; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getProduct() */ public double getProduct() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.product(doubleArray.getElements()); } return product; } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#getGeometricMean() */ public double getGeometricMean() { if (windowSize != Univariate.INFINITE_WINDOW) { return StatUtils.geometricMean(doubleArray.getElements()); } if ((product <= 0.0) || (n == 0)) { return Double.NaN; } else { return Math.pow(product, (1.0 / (double) n)); } } /* (non-Javadoc) * @see org.apache.commons.math.stat.Univariate#addValue(double) */ public void addValue(double v) { if (windowSize != Univariate.INFINITE_WINDOW) { /* then all getters deligate to AbstractStoreUnivariate * and this clause simply adds/rolls a value in the storage array */ if (windowSize == n) { doubleArray.addElementRolling(v); } else { n++; doubleArray.addElement(v); } } else { /* If the windowSize is infinite don't store any values and there * is no need to discard the influence of any single item. */ n++; if (n <= 1) { /* if n <= 1, initialize the product, min, max, mean, variance and pre-variance */ product = 1.0; sum = min = max = mean = v; sumsq = Math.pow(v, 2); sumCube = Math.pow(v, 3); sumQuad = Math.pow(v, 4); variance = pre_variance = 0.0; } else { /* otherwise calc these values */ product *= v; sum += v; sumsq += Math.pow(v, 2); sumCube += Math.pow(v, 3); sumQuad += Math.pow(v, 4); min = Math.min(min, v); max = Math.max(max, v); double deviationFromMean = v - mean; double deviationFromMean_overN = deviationFromMean / n; mean += deviationFromMean_overN; pre_variance += (n - 1) * deviationFromMean * deviationFromMean_overN; variance = pre_variance / (n - 1); } } } /** * Generates a text report displaying * univariate statistics from values that * have been added. * @return String with line feeds displaying statistics */ public String toString() { StringBuffer outBuffer = new StringBuffer(); outBuffer.append("UnivariateImpl:\n"); outBuffer.append("n: " + n + "\n"); outBuffer.append("min: " + min + "\n"); outBuffer.append("max: " + max + "\n"); outBuffer.append("mean: " + getMean() + "\n"); outBuffer.append("std dev: " + getStandardDeviation() + "\n"); outBuffer.append("skewness: " + getSkewness() + "\n"); outBuffer.append("kurtosis: " + getKurtosis() + "\n"); return outBuffer.toString(); } /* (non-Javadoc) * @see org.apache.commons.math.Univariate#clear() */ public void clear() { this.n = 0; this.min = this.max = Double.NaN; this.product = this.mean = Double.NaN; this.variance = this.pre_variance = Double.NaN; if (doubleArray != null) doubleArray = new FixedDoubleArray(windowSize); } /* (non-Javadoc) * @see org.apache.commons.math.Univariate#getWindowSize() */ public int getWindowSize() { return windowSize; } /* (non-Javadoc) * @see org.apache.commons.math.Univariate#setWindowSize(int) */ public void setWindowSize(int windowSize) { clear(); this.windowSize = windowSize; doubleArray = new FixedDoubleArray(windowSize); } }
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