-1 on that specific commit.
As the originial contributor, I believe you have voting rights for this.
I'd concur on this one. UnivariateImpl is an optimized, performance
oriented "storage-less" implementation, StoreUnivariate captures another
set of requirements entirely.
Tim
On Tue, 17 Jun 2003, Phil Steitz wrote:
> I would like to request that these changes be rolled back. I would like to
> keep UnivariateImpl separate and lightweight, with improved updating formulas
> and good array-based computations when the window in finite. I see the full
> stored versions (as I thought Tim did when he defined them) as separate. The
> cleanest way to get the best computations would be to encapsulate the simple
> vector computations in AbstractStoreUnivariate and make them available to both
> AbstratStoreUnivariate and UnivariateImpl.
>
> --- [EMAIL PROTECTED] wrote:
> > mdiggory 2003/06/16 13:42:25
> >
> > Modified: math/src/java/org/apache/commons/math/stat
> > UnivariateImpl.java
> > Log:
> > This house-cleaning improves UnivariateImpl, in a number of ways.
> >
> > 1.) insertValue is abolished and its contents are now in addValue
> >
> > 2.) UnivariateImpl now extends AbstractStoredUnivariate to deligate to
> > those methods directly for calculating statistics when storage is active,
> > all methods
> > deligate to AbstractStoreUniv when the DoubleArray is no longer null. This
> > also means
> > that a majority of the StoreUnivariate interface is now implemented in
> > Univariate to provide
> > deligates when storage is active, and to throw runtime exceptions when its
> > not (this at least until
> > we establish rolling implementations for those methods). We should consider
> > consolidating the
> > StoreUnivariate interface into the Univariate interface
> >
> > 3.) Calculations in addValue have been reorganized, only calculations for
> > the storageless solution are now present in this class. otherwise the value
> > is
> > added/Rolling to the Double array when appropriate.
> >
> > I'm satisfied that it passes all Unit tests.
> >
> >
> > Revision Changes Path
> > 1.6 +383 -347
> >
> jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/UnivariateImpl.java
> >
> > Index: UnivariateImpl.java
> > ===================================================================
> > RCS file:
> >
> /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/UnivariateImpl.java,v
> > retrieving revision 1.5
> > retrieving revision 1.6
> > diff -u -r1.5 -r1.6
> > --- UnivariateImpl.java 16 Jun 2003 14:29:30 -0000 1.5
> > +++ UnivariateImpl.java 16 Jun 2003 20:42:24 -0000 1.6
> > @@ -74,349 +74,385 @@
> > * @version $Revision$ $Date$
> > *
> > */
> > -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;
> > -
> > - /** min of values that have been added */
> > - private double min = Double.MAX_VALUE;
> > -
> > - /** max of values that have been added */
> > - private double max = Double.MIN_VALUE;
> > -
> > - /** 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 ;
> > -
> > - /** running sum of values that have been added */
> > - private double sum = 0.0;
> > -
> > - /** running sum of squares that have been added */
> > - private double sumsq = 0.0;
> > -
> > - /** running sum of 3rd powers that have been added */
> > - private double sumCube = 0.0;
> > -
> > - /** running sum of 4th powers that have been added */
> > - private double sumQuad = 0.0;
> > -
> > - /** Creates new univariate with an infinite window */
> > - public UnivariateImpl() {
> > - clear();
> > - }
> > -
> > - /** Creates a new univariate with a fixed window **/
> > - public UnivariateImpl(int window) {
> > - windowSize = window;
> > - doubleArray = new FixedDoubleArray( window );
> > - }
> > -
> > - /**
> > - * @see org.apache.commons.math.stat.Univariate#addValue(double)
> > - */
> > - public void addValue(double v) {
> > - insertValue(v);
> > - }
> > -
> > - /**
> > - * @see org.apache.commons.math.stat.Univariate#getMean()
> > - */
> > - public double getMean() {
> > - return mean ;
> > - }
> > -
> > - /**
> > - * @see org.apache.commons.math.stat.Univariate#getGeometricMean()
> > - */
> > - public double getGeometricMean() {
> > - if ((product <= 0.0) || (n == 0)) {
> > - return Double.NaN;
> > - } else {
> > - return Math.pow(product,( 1.0 / (double) n ) );
> > - }
> > - }
> > -
> > - /**
> > - * @see org.apache.commons.math.stat.Univariate#getProduct()
> > - */
> > - public double getProduct() {
> > - return product;
> > - }
> > -
> > - /**
> > - * @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
> > - */
> > - public double getStandardDeviation() {
> > - double variance = getVariance();
> > -
> > - if ((variance == 0.0) || (variance == Double.NaN)) {
> > - return variance;
> > - } else {
> > - return Math.sqrt(variance);
> > - }
> > - }
> > -
> > - /**
> > - * 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() {
> > - return 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( n < 1) return Double.NaN;
> > - if( n <= 2 ) return 0.0;
> > -
> > - 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( n < 1) return Double.NaN;
> > - if( n <= 3 ) return 0.0;
> > -
> > - 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)) );
> > - }
> > -
> > - /**
> > - * Called in "addValue" to insert a new value into the statistic.
> > - * @param v The value to be added.
> > - */
> > - private void insertValue(double v) {
> > - // The default value of product is NaN, if you
> > - // try to retrieve the product for a univariate with
> > - // no values, we return NaN.
> > - //
> > - // If this is the first call to insertValue, we want
> > - // to set product to 1.0, so that our first element
> > - // is not "cancelled" out by the NaN.
> > - //
> > - // For the first value added, the mean is that value,
> > - // and the variance is zero.
> > - if( n == 0 ) {
> > - product = 1.0 ;
> > - mean = v ;
> > - pre_variance = 0.0 ;
> > - variance = 0.0 ;
> > - }
> > -
> > - if( windowSize != Univariate.INFINITE_WINDOW ) {
> > - if( windowSize == n ) {
> > - double discarded = doubleArray.addElementRolling( v );
> > -
> > - // Remove the influence of the discarded
> > - sum -= discarded;
> > - sumsq -= discarded * discarded;
> > - sumCube -= Math.pow(discarded, 3);
> > - sumQuad -= Math.pow(discarded, 4);
> > -
> > - if(discarded == min) {
> > - min = doubleArray.getMin();
> > - } else if(discarded == max){
> > - max = doubleArray.getMax();
> > - }
> > -
> > - if(product != 0.0){
> > - // can safely remove discarded value
> > - product *= v / discarded;
> > - } else if(discarded == 0.0){
> > - // need to recompute product
> > - product = 1.0;
> > - double[] elements = doubleArray.getElements();
> > - for( int i = 0; i < elements.length; i++ ) {
> > - product *= elements[i];
> > - }
> > - } // else product = 0 and will still be 0 after discard
> > -
> > - } else {
> > - doubleArray.addElement( v );
> > - n += 1 ;
> > - if (v < min) {
> > - min = v;
> > - }
> > - if (v > max) {
> > - max = v;
> > - }
> > - product *= v;
> > - }
> > - } else {
> > - // If the windowSize is infinite please don't take the time to
> > - // worry about storing any values. We don't need to discard
> > the
> > - // influence of any single item.
> > - n += 1 ;
> > - if (v < min) {
> > - min = v;
> > - }
> > - if (v > max) {
> > - max = v;
> > - }
> > - product *= v;
> > -
> > - if ( n > 1 )
> > - {
> > - double deviationFromMean = v - mean ;
> > - double deviationFromMean_overN = deviationFromMean / n ;
> > - mean += deviationFromMean_overN ;
> > - pre_variance += (n - 1) * deviationFromMean *
> > deviationFromMean_overN ;
> > - variance = pre_variance / (n - 1) ;
> > - }
> > - }
> > -
> > - sum += v;
> > - sumsq += v * v;
> > - sumCube += Math.pow(v,3);
> > - sumQuad += Math.pow(v,4);
> > - }
> > -
> > - /** Getter for property max.
> > - * @return Value of property max.
> > - */
> > - public double getMax() {
> > - if (n == 0) {
> > - return Double.NaN;
> > - } else {
> > - return max;
> > - }
> > - }
> > -
> > - /** Getter for property min.
> > - * @return Value of property min.
> > - */
> > - public double getMin() {
> > - if (n == 0) {
> > - return Double.NaN;
> > - } else {
> > - return min;
> > - }
> > - }
> > -
> > - /** Getter for property n.
> > - * @return Value of property n.
> > - */
> > - public int getN() {
> > - return n;
> > - }
> > -
> > - /** Getter for property sum.
> > - * @return Value of property sum.
> > - */
> > - public double getSum() {
> > - return sum;
> > - }
> > -
> > - /** Getter for property sumsq.
> > - * @return Value of property sumsq.
> > - */
> > - public double getSumsq() {
> > - return sumsq;
> > - }
> > -
> > - /** Getter for property sumCube.
> > - * @return Value of property sumCube.
> > - */
> > - public double getSumCube() {
> > - return sumCube;
> > - }
> > -
> > - /** Getter for property sumQuad.
> > - * @return Value of property sumQuad.
> > - */
> > - public double getSumQuad() {
> > - return sumQuad;
> > - }
> > -
> > - /**
> > - * 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();
> > - }
> > -
> > - /**
> > - * Resets all sums, product, mean, and variance to 0; resets min and
> > max.
> > - */
> > - public void clear() {
> > - this.sum = this.sumsq = this.sumCube = this.sumQuad = 0.0;
> > - this.n = 0;
> > - this.min = Double.MAX_VALUE;
> > - this.max = Double.MIN_VALUE;
> > - this.product = Double.NaN;
> > - this.mean = Double.NaN ;
> > - this.variance = this.pre_variance = Double.NaN ;
> > - }
> > -
> > - /* (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) {
> > - String msg = "A fixed window size must be set via the " +
> > - "UnivariateImpl constructor";
> > - throw new RuntimeException( msg );
> > - }
> > -}
> > +public class UnivariateImpl
> > + extends AbstractStoreUnivariate
> > + 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);
> > + }
> > +
> > + /** Getter for property n.
> > + * @return Value of property n.
> > + */
> > + public int getN() {
> > + return n;
> > + }
> > +
> > + /**
> > + * Returns the sum of all values contained herein
> > + * @see org.apache.commons.math.stat.Univariate#getSum()
> > + */
> > + public double getSum() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getSum();
> > + }
> > +
> > + return sum;
> > + }
> > +
> > + /**
> > + * Returns the sun of the squares of all values contained herein
> > + * @see org.apache.commons.math.stat.Univariate#getSumsq()
> > + */
> > + public double getSumsq() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getSumsq();
> > + }
> > +
> > + return sumsq;
> > + }
> > +
> > + /**
> > + * @see org.apache.commons.math.stat.Univariate#getMean()
> > + */
> > + public double getMean() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getMean();
> > + }
> > +
> > + return mean;
> > + }
> > +
> > + /**
> > + * 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() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getVariance();
> > + }
> > +
> > + return 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 super.getSkewness();
> > + }
> > +
> > + 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 super.getKurtosis();
> > + }
> > +
> > + 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)));
> > + }
> > + }
> > +
> > + /** Getter for property max.
> > + * @return Value of property max.
> > + */
> > + public double getMax() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getMax();
> > + }
> > +
> > + return max;
> > + }
> > +
> > + /** Getter for property min.
> > + * @return Value of property min.
> > + */
> > + public double getMin() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getMin();
> > + }
> > +
> > + return min;
> > + }
> > +
> > + /**
> > + * @see org.apache.commons.math.stat.Univariate#getProduct()
> > + */
> > + public double getProduct() {
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getProduct();
> > + }
> > +
> > + return product;
> > + }
> > +
> > + /**
> > + * @see org.apache.commons.math.stat.Univariate#getGeometricMean()
> > + */
> > + public double getGeometricMean() {
> > +
> > + if (windowSize != Univariate.INFINITE_WINDOW) {
> > + return super.getGeometricMean();
> > + }
> > +
> > + 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.StoreUnivariate#getMode()
> > + */
> > + public double getMode() {
> > + if (windowSize == Univariate.INFINITE_WINDOW) {
> > + throw new RuntimeException("Mode is only available if
> > windowSize is
> > fixed");
> > + }
> > +
> > + return super.getMode();
> > + }
> > +
> > + /* (non-Javadoc)
> > + * @see
> > org.apache.commons.math.stat.StoreUnivariate#getPercentile(double)
> > + */
> > + public double getPercentile(double p) {
> > + if (windowSize == Univariate.INFINITE_WINDOW) {
> > + throw new RuntimeException("Percentiles are only available if
> > windowSize is fixed");
> > + }
> > +
> > + return super.getPercentile(p);
> > +
> > + }
> > +
> > + /**
> > + * @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();
> > + }
> > +
> > + /**
> > + * Resets all stats to NaN. Reinitializes the Double Array
> > + */
> > + 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);
> > + }
> > +
> > + /* (non-Javadoc)
> > + * @see org.apache.commons.math.stat.StoreUnivariate#getValues()
> > + */
> > + public double[] getValues() {
> > + if (windowSize == Univariate.INFINITE_WINDOW) {
> > + throw new RuntimeException("Values are only available if
> > windowSize is
> > fixed");
> > + }
> > +
> > + return this.doubleArray.getElements();
> > + }
> > +
> > + /* (non-Javadoc)
> > + * @see org.apache.commons.math.stat.StoreUnivariate#getElement(int)
> > + */
> > + public double getElement(int index) {
> > + if (windowSize == Univariate.INFINITE_WINDOW) {
> > + throw new RuntimeException("Elements are only available if
> > windowSize
> > is fixed");
> > + }
> > +
> > + return this.doubleArray.getElement(index);
> > + }
> > +
> > + /* (non-Javadoc)
> > + * @see org.apache.commons.math.stat.StoreUnivariate#getSortedValues()
> > + */
> > + public double[] getSortedValues() {
> > + if (windowSize == Univariate.INFINITE_WINDOW) {
> > + throw new RuntimeException("SortedValues are only available if
> > windowSize is fixed");
> > + }
> > +
> > + return super.getSortedValues();
> > + }
> > +}
> > \ No newline at end of file
> >
> >
> >
> >
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----------------------
Tim O'Brien
Evanston, IL
(847) 863-7045
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