Examples of org.apache.commons.math.stat.descriptive.summary.SumOfSquares

Package org.apache.commons.math.stat.descriptive.summary

Examples of org.apache.commons.math.stat.descriptive.summary.SumOfSquares

org.apache.commons.math.stat.descriptive.summary.SumOfSquares
Returns the sum of the squares of the available values.
If there are no values in the dataset, or any of the values are NaN, then NaN is returned.

Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.
@version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $

        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

View Full Code Here

        }


        int dfwg = 0;
        double sswg = 0;
        Sum totsum = new Sum();
        SumOfSquares totsumsq = new SumOfSquares();
        int totnum = 0;


        for (double[] data : categoryData) {


            Sum sum = new Sum();
            SumOfSquares sumsq = new SumOfSquares();
            int num = 0;


            for (int i = 0; i < data.length; i++) {
                double val = data[i];


                // within category
                num++;
                sum.increment(val);
                sumsq.increment(val);


                // for all categories
                totnum++;
                totsum.increment(val);
                totsumsq.increment(val);
            }
            dfwg += num - 1;
            double ss = sumsq.getResult() - sum.getResult() * sum.getResult() / num;
            sswg += ss;
        }
        double sst = totsumsq.getResult() - totsum.getResult() *
            totsum.getResult()/totnum;
        double ssbg = sst - sswg;

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    /**
     * Construct a SummaryStatistics
     */
    public SummaryStatisticsImpl() {
        sum = new Sum();
        sumsq = new SumOfSquares();
        min = new Min();
        max = new Max();
        sumLog = new SumOfLogs();
        geoMean = new GeometricMean();
        secondMoment = new SecondMoment();

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     * Returns the sum of the squares of the available values.
     * @return The sum of the squares or Double.NaN if no 
     * values have been added.
     */
    public double getSumsq() {
        return apply(new SumOfSquares());
    }

View Full Code Here

        }


        int dfwg = 0;
        double sswg = 0;
        Sum totsum = new Sum();
        SumOfSquares totsumsq = new SumOfSquares();
        int totnum = 0;
        
        for (double[] data : categoryData) {


            Sum sum = new Sum();
            SumOfSquares sumsq = new SumOfSquares();
            int num = 0;


            for (int i = 0; i < data.length; i++) {
                double val = data[i];


                // within category
                num++;
                sum.increment(val);
                sumsq.increment(val);


                // for all categories
                totnum++;
                totsum.increment(val);
                totsumsq.increment(val);
            }
            dfwg += num - 1;
            double ss = sumsq.getResult() - sum.getResult() * sum.getResult() / num;
            sswg += ss;
        }
        double sst = totsumsq.getResult() - totsum.getResult() * 
            totsum.getResult()/totnum;
        double ssbg = sst - sswg;

View Full Code Here

        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

View Full Code Here

    /**
     * Construct a SummaryStatistics
     */
    public SummaryStatisticsImpl() {
        sum = new Sum();
        sumsq = new SumOfSquares();
        min = new Min();
        max = new Max();
        sumLog = new SumOfLogs();
        geoMean = new GeometricMean();
        secondMoment = new SecondMoment();

View Full Code Here

     * Returns the sum of the squares of the available values.
     * @return The sum of the squares or Double.NaN if no 
     * values have been added.
     */
    public double getSumsq() {
        return apply(new SumOfSquares());
    }

View Full Code Here

        }


        int dfwg = 0;
        double sswg = 0;
        Sum totsum = new Sum();
        SumOfSquares totsumsq = new SumOfSquares();
        int totnum = 0;


        for (double[] data : categoryData) {


            Sum sum = new Sum();
            SumOfSquares sumsq = new SumOfSquares();
            int num = 0;


            for (int i = 0; i < data.length; i++) {
                double val = data[i];


                // within category
                num++;
                sum.increment(val);
                sumsq.increment(val);


                // for all categories
                totnum++;
                totsum.increment(val);
                totsumsq.increment(val);
            }
            dfwg += num - 1;
            double ss = sumsq.getResult() - sum.getResult() * sum.getResult() / num;
            sswg += ss;
        }
        double sst = totsumsq.getResult() - totsum.getResult() *
            totsum.getResult()/totnum;
        double ssbg = sst - sswg;

View Full Code Here

        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

View Full Code Here

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org.apache.commons.math.stat.descriptive.DescriptiveStatistics

org.apache.commons.math.stat.descriptive.MultivariateSummaryStatistics

org.apache.commons.math.stat.descriptive.SummaryStatisticsImpl

org.apache.commons.math.stat.inference.OneWayAnovaImpl

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