Package org.apache.commons.math3.stat.descriptive.summary

Examples of org.apache.commons.math3.stat.descriptive.summary.Sum.increment()

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                // compute the distance variance of the current cluster
                final Clusterable center = cluster.getCenter();
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(distance(point, center));
                }
                final double variance = stat.getResult();

                // select the cluster with the largest variance
                if (variance > maxVariance) {
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                final Clusterable center = centroidOf(cluster);

                // compute the distance variance of the current cluster
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(distance(point, center));
                }
                varianceSum += stat.getResult();

            }
        }
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                    // compute the distance variance of the current cluster
                    final T center = cluster.getCenter();
                    final Variance stat = new Variance();
                    for (final T point : cluster.getPoints()) {
                        stat.increment(point.distanceFrom(center));
                    }
                    varianceSum += stat.getResult();

                }
            }
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                // compute the distance variance of the current cluster
                final T center = cluster.getCenter();
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(point.distanceFrom(center));
                }
                final double variance = stat.getResult();

                // select the cluster with the largest variance
                if (variance > maxVariance) {
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                    // compute the distance variance of the current cluster
                    final Clusterable center = cluster.getCenter();
                    final Variance stat = new Variance();
                    for (final T point : cluster.getPoints()) {
                        stat.increment(distance(point, center));
                    }
                    varianceSum += stat.getResult();

                }
            }
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                // compute the distance variance of the current cluster
                final Clusterable center = cluster.getCenter();
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(distance(point, center));
                }
                final double variance = stat.getResult();

                // select the cluster with the largest variance
                if (variance > maxVariance) {
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                    // compute the distance variance of the current cluster
                    final T center = cluster.getCenter();
                    final Variance stat = new Variance();
                    for (final T point : cluster.getPoints()) {
                        stat.increment(point.distanceFrom(center));
                    }
                    varianceSum += stat.getResult();

                }
            }
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                // compute the distance variance of the current cluster
                final T center = cluster.getCenter();
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(point.distanceFrom(center));
                }
                final double variance = stat.getResult();

                // select the cluster with the largest variance
                if (variance > maxVariance) {
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        VectorialMean meanStat = new VectorialMean(mean.length);
        VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
        for (int i = 0; i < 5000; ++i) {
            double[] v = generator.nextVector();
            meanStat.increment(v);
            covStat.increment(v);
        }

        double[] estimatedMean = meanStat.getResult();
        RealMatrix estimatedCovariance = covStat.getResult();
        for (int i = 0; i < estimatedMean.length; ++i) {
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        VectorialMean meanStat = new VectorialMean(mean.length);
        VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
        for (int i = 0; i < 10000; ++i) {
            double[] v = generator.nextVector();
            meanStat.increment(v);
            covStat.increment(v);
        }

        double[] estimatedMean = meanStat.getResult();
        double scale;
        RealMatrix estimatedCorrelation = covStat.getResult();
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