Last commit got formated with tabs, this is formated with spaces
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140925 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Mark R. Diggory
parent
202a38df83
commit
283716c077
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@ -18,7 +18,7 @@
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*
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* 3. The end-user documentation included with the redistribution, if
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* any, must include the following acknowlegement:
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* "This sumLog includes software developed by the
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* "This product includes software developed by the
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* Apache Software Foundation (http://www.apache.org/)."
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* Alternately, this acknowlegement may appear in the software itself,
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* if and wherever such third-party acknowlegements normally appear.
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@ -71,323 +71,329 @@ import org.apache.commons.math.FixedDoubleArray;
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* @author <a href="mailto:mdiggory@apache.org">Mark Diggory</a>
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* @author Brent Worden
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* @author <a href="mailto:HotFusionMan@Yahoo.com">Albert Davidson Chou</a>
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* @version $Revision: 1.10 $ $Date: 2003/06/18 13:47:35 $
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* @version $Revision: 1.11 $ $Date: 2003/06/18 13:57:24 $
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*
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*/
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public class UnivariateImpl implements Univariate, Serializable {
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/** hold the window size **/
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private int windowSize = Univariate.INFINITE_WINDOW;
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/** hold the window size **/
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private int windowSize = Univariate.INFINITE_WINDOW;
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/** Just in case the windowSize is not infinite, we need to
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* keep an array to remember values 0 to N
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*/
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private DoubleArray doubleArray;
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/** Just in case the windowSize is not infinite, we need to
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* keep an array to remember values 0 to N
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*/
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private DoubleArray doubleArray;
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/** count of values that have been added */
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private int n = 0;
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/** count of values that have been added */
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private int n = 0;
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/** sum of values that have been added */
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private double sum = Double.NaN;
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/** sum of values that have been added */
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private double sum = Double.NaN;
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/** sum of the square of each value that has been added */
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private double sumsq = Double.NaN;
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/** sum of the square of each value that has been added */
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private double sumsq = Double.NaN;
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/** min of values that have been added */
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private double min = Double.NaN;
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/** min of values that have been added */
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private double min = Double.NaN;
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/** max of values that have been added */
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private double max = Double.NaN;
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/** max of values that have been added */
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private double max = Double.NaN;
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/** sumLog of values that have been added */
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private double sumLog = Double.NaN;
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/** sumLog of values that have been added */
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private double sumLog = Double.NaN;
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/** mean of values that have been added */
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private double mean = Double.NaN;
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/** mean of values that have been added */
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private double mean = Double.NaN;
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/** second moment of values that have been added */
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private double s2 = Double.NaN;
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/** second moment of values that have been added */
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private double s2 = Double.NaN;
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/** third moment of values that have been added */
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private double s3 = Double.NaN;
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/** third moment of values that have been added */
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private double s3 = Double.NaN;
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/** fourth moment of values that have been added */
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private double s4 = Double.NaN;
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/** fourth moment of values that have been added */
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private double s4 = Double.NaN;
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/** variance of values that have been added */
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private double variance = Double.NaN;
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/** variance of values that have been added */
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private double variance = Double.NaN;
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/** skewness of values that have been added */
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private double skewness = Double.NaN;
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/** skewness of values that have been added */
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private double skewness = Double.NaN;
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/** kurtosis of values that have been added */
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private double kurtosis = Double.NaN;
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/** kurtosis of values that have been added */
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private double kurtosis = Double.NaN;
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/** Creates new univariate with an infinite window */
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public UnivariateImpl() {
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}
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/** Creates new univariate with an infinite window */
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public UnivariateImpl() {
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}
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/** Creates a new univariate with a fixed window **/
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public UnivariateImpl(int window) {
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setWindowSize(window);
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}
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/** Creates a new univariate with a fixed window **/
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public UnivariateImpl(int window) {
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setWindowSize(window);
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getN()
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*/
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public int getN() {
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return n;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getN()
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*/
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public int getN() {
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return n;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getSum()
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*/
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public double getSum() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.sum(doubleArray.getElements());
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getSum()
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*/
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public double getSum() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.sum(doubleArray.getElements());
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}
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return sum;
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}
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return sum;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getSumsq()
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*/
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public double getSumsq() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.sumSq(doubleArray.getElements());
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getSumsq()
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*/
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public double getSumsq() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.sumSq(doubleArray.getElements());
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}
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return sumsq;
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}
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return sumsq;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMean()
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*/
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public double getMean() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.mean(doubleArray.getElements());
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMean()
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*/
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public double getMean() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.mean(doubleArray.getElements());
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}
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return mean;
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}
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return mean;
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}
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/**
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* Returns the standard deviation for this collection of values
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* @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
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*/
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public double getStandardDeviation() {
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double stdDev = Double.NaN;
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if (getN() != 0) {
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stdDev = Math.sqrt(getVariance());
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}
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return (stdDev);
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}
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/**
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* Returns the standard deviation for this collection of values
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* @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
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*/
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public double getStandardDeviation() {
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double stdDev = Double.NaN;
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if (getN() != 0) {
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stdDev = Math.sqrt(getVariance());
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}
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return (stdDev);
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}
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/**
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* Returns the variance of the values that have been added via West's
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* algorithm as described by
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* <a href="http://doi.acm.org/10.1145/359146.359152">Chan, T. F. and
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* J. G. Lewis 1979, <i>Communications of the ACM</i>,
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* vol. 22 no. 9, pp. 526-531.</a>.
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*
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* @return The variance of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 1 value set.
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*/
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public double getVariance() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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variance = StatUtils.variance(doubleArray.getElements());
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}
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return variance;
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}
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/**
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* Returns the variance of the values that have been added via West's
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* algorithm as described by
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* <a href="http://doi.acm.org/10.1145/359146.359152">Chan, T. F. and
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* J. G. Lewis 1979, <i>Communications of the ACM</i>,
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* vol. 22 no. 9, pp. 526-531.</a>.
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*
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* @return The variance of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 1 value set.
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*/
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public double getVariance() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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variance = StatUtils.variance(doubleArray.getElements());
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}
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return variance;
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}
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/**
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* Returns the skewness of the values that have been added as described by
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* <a href="http://mathworld.wolfram.com/k-Statistic.html">Equation (6) for k-Statistics</a>.
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*
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* @return The skew of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 2 value set.
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*/
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public double getSkewness() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.skewness(doubleArray.getElements());
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}
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return skewness;
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}
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/**
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* Returns the skewness of the values that have been added as described by
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* <a href="http://mathworld.wolfram.com/k-Statistic.html">Equation (6) for k-Statistics</a>.
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*
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* @return The skew of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 2 value set.
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*/
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public double getSkewness() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.skewness(doubleArray.getElements());
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}
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return skewness;
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}
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/**
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* Returns the kurtosis of the values that have been added as described by
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* <a href="http://mathworld.wolfram.com/k-Statistic.html">Equation (7) for k-Statistics</a>.
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*
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* @return The kurtosis of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 3 value set.
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*/
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public double getKurtosis() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.kurtosis(doubleArray.getElements());
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}
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return kurtosis;
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}
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/**
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* Returns the kurtosis of the values that have been added as described by
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* <a href="http://mathworld.wolfram.com/k-Statistic.html">Equation (7) for k-Statistics</a>.
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*
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* @return The kurtosis of a set of values. Double.NaN is returned for
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* an empty set of values and 0.0 is returned for a <= 3 value set.
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*/
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public double getKurtosis() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.kurtosis(doubleArray.getElements());
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}
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return kurtosis;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMax()
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*/
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public double getMax() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.max(doubleArray.getElements());
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}
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return max;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMax()
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*/
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public double getMax() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.max(doubleArray.getElements());
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}
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return max;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMin()
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*/
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public double getMin() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.min(doubleArray.getElements());
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}
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return min;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getMin()
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*/
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public double getMin() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.min(doubleArray.getElements());
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}
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return min;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getProduct()
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*/
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public double getProduct() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.product(doubleArray.getElements());
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getProduct()
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*/
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public double getProduct() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.product(doubleArray.getElements());
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}
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return sumLog;
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}
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return sumLog;
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}
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getGeometricMean()
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*/
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public double getGeometricMean() {
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/* (non-Javadoc)
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* @see org.apache.commons.math.stat.Univariate#getGeometricMean()
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*/
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public double getGeometricMean() {
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.geometricMean(doubleArray.getElements());
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}
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if (windowSize != Univariate.INFINITE_WINDOW) {
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return StatUtils.geometricMean(doubleArray.getElements());
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}
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if (n == 0) {
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return Double.NaN;
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} else {
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return Math.exp(sumLog / (double) n);
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}
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}
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if (n == 0) {
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return Double.NaN;
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} else {
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return Math.exp(sumLog / (double) n);
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}
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}
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/* If windowSize is set to Infinite, moments are calculated using the following
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* <a href="http://www.spss.com/tech/stat/Algorithms/11.5/descriptives.pdf">
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/* If windowSize is set to Infinite, moments are calculated using the following
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* <a href="http://www.spss.com/tech/stat/Algorithms/11.5/descriptives.pdf">
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* recursive strategy
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* </a>.
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* Otherwise, stat methods delegate to StatUtils.
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* @see org.apache.commons.math.stat.Univariate#addValue(double)
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*/
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public void addValue(double value) {
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* @see org.apache.commons.math.stat.Univariate#addValue(double)
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*/
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public void addValue(double value) {
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if (windowSize != Univariate.INFINITE_WINDOW) {
|
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/* then all getters deligate to StatUtils
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* and this clause simply adds/rolls a value in the storage array
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*/
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if (windowSize == n) {
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doubleArray.addElementRolling(value);
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} else {
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n++;
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doubleArray.addElement(value);
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}
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if (windowSize != Univariate.INFINITE_WINDOW) {
|
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/* then all getters deligate to StatUtils
|
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* and this clause simply adds/rolls a value in the storage array
|
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*/
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if (windowSize == n) {
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doubleArray.addElementRolling(value);
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} else {
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n++;
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doubleArray.addElement(value);
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}
|
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|
||||
} else {
|
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/* If the windowSize is infinite don't store any values and there
|
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* is no need to discard the influence of any single item.
|
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*/
|
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n++;
|
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} else {
|
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/* If the windowSize is infinite don't store any values and there
|
||||
* is no need to discard the influence of any single item.
|
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*/
|
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n++;
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|
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if (n <= 1) {
|
||||
/* if n <= 1, initialize the sumLog, min, max, mean, variance and pre-variance */
|
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sumLog = 0.0;
|
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sum = min = max = mean = value;
|
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sumsq = Math.pow(value, 2);
|
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variance = s2 = 0.0;
|
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skewness = kurtosis = 0.0;
|
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if (n <= 1) {
|
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/* if n <= 1, initialize the sumLog, min, max, mean, variance and pre-variance */
|
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sumLog = 0.0;
|
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sum = min = max = mean = value;
|
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sumsq = Math.pow(value, 2);
|
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variance = s2 = 0.0;
|
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skewness = kurtosis = 0.0;
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} else {
|
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/* otherwise calc these values */
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sumLog += Math.log(value);
|
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sum += value;
|
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sumsq += Math.pow(value, 2);
|
||||
min = Math.min(min, value);
|
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max = Math.max(max, value);
|
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} else {
|
||||
/* otherwise calc these values */
|
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sumLog += Math.log(value);
|
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sum += value;
|
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sumsq += Math.pow(value, 2);
|
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min = Math.min(min, value);
|
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max = Math.max(max, value);
|
||||
|
||||
double dev = value - mean;
|
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double v = dev / ((double) n);
|
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double v2 = Math.pow(v, 2);
|
||||
double n1 = ((double) n - 1);
|
||||
double dev = value - mean;
|
||||
double v = dev / ((double) n);
|
||||
double v2 = Math.pow(v, 2);
|
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double n1 = ((double) n - 1);
|
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|
||||
s4 += v
|
||||
* (
|
||||
- 4.0 * s3
|
||||
+ v * (6.0 * s2 + n1 * (1 + Math.pow((double) n, 3)) * v2));
|
||||
s4 += v
|
||||
* (
|
||||
- 4.0 * s3
|
||||
+ v
|
||||
* (6.0 * s2
|
||||
+ n1 * (1 + Math.pow((double) n, 3)) * v2));
|
||||
|
||||
s3 += v * (-3.0 * s2 + (double) n * n1 * (n - 2) * Math.pow(v, 2));
|
||||
s2 += n1 * dev * v;
|
||||
s3 += v
|
||||
* (-3.0 * s2 + (double) n * n1 * (n - 2) * Math.pow(v, 2));
|
||||
s2 += n1 * dev * v;
|
||||
|
||||
mean += v;
|
||||
variance =
|
||||
(n <= 1) ? 0.0 : s2 / n1;
|
||||
skewness =
|
||||
(n <= 2) ? 0.0 : s3 / ((double) n * Math.sqrt(variance) * variance);
|
||||
kurtosis =
|
||||
(n <= 3) ? 0.0 : s4 / ((double) n * Math.pow(variance, 2)) - 3;
|
||||
}
|
||||
}
|
||||
}
|
||||
mean += v;
|
||||
variance = (n <= 1) ? 0.0 : s2 / n1;
|
||||
skewness =
|
||||
(n <= 2)
|
||||
? 0.0
|
||||
: s3 / ((double) n * Math.sqrt(variance) * variance);
|
||||
kurtosis =
|
||||
(n <= 3)
|
||||
? 0.0
|
||||
: s4 / ((double) n * Math.pow(variance, 2)) - 3;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 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();
|
||||
}
|
||||
/**
|
||||
* 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.sumLog = this.mean = Double.NaN;
|
||||
this.variance = this.skewness = this.kurtosis = Double.NaN;
|
||||
/* (non-Javadoc)
|
||||
* @see org.apache.commons.math.Univariate#clear()
|
||||
*/
|
||||
public void clear() {
|
||||
this.n = 0;
|
||||
this.min = this.max = Double.NaN;
|
||||
this.sumLog = this.mean = Double.NaN;
|
||||
this.variance = this.skewness = this.kurtosis = Double.NaN;
|
||||
this.s2 = this.s3 = this.s4 = Double.NaN;
|
||||
if (doubleArray != null)
|
||||
doubleArray = new FixedDoubleArray(windowSize);
|
||||
}
|
||||
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#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);
|
||||
}
|
||||
/* (non-Javadoc)
|
||||
* @see org.apache.commons.math.Univariate#setWindowSize(int)
|
||||
*/
|
||||
public void setWindowSize(int windowSize) {
|
||||
clear();
|
||||
this.windowSize = windowSize;
|
||||
doubleArray = new FixedDoubleArray(windowSize);
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue