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

Examples of org.apache.commons.math3.stat.descriptive.moment.Mean.increment()

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    public void testMomentSmallSamples() {
        UnivariateStatistic stat = getUnivariateStatistic();
        if (stat instanceof SecondMoment) {
            SecondMoment moment = (SecondMoment) getUnivariateStatistic();
            Assert.assertTrue(Double.isNaN(moment.getResult()));
            moment.increment(1d);
            Assert.assertEquals(0d, moment.getResult(), 0);
        }
    }

    /**
 
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                if (sample.timestamp + sample.duration > finish) {
                    finish = sample.timestamp + sample.duration;
                }
                mean.increment(sample.duration);
                stdDev.increment(sample.duration);
                skewness.increment(sample.duration);
            }
        }
       
        double throughPut = (double)n * (double)TimeUnit.SECONDS.toNanos(1) / (double)(finish - start);
       
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                }
                if (sample.timestamp + sample.duration > finish) {
                    finish = sample.timestamp + sample.duration;
                }
                mean.increment(sample.duration);
                stdDev.increment(sample.duration);
                skewness.increment(sample.duration);
            }
        }
       
        double throughPut = (double)n * (double)TimeUnit.SECONDS.toNanos(1) / (double)(finish - start);
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      for (int j = 0; j < numSamples; j++) {
        final List<Double> times = generator.generate(rng);

        final double dyn = Metrics.measureDynamism(times, lengthOfScenario);
        dynamismValues.add(dyn);
        sd.increment(dyn);
        sum += dyn;
        max = Math.max(max, dyn);
        if (j < 3) {
          // System.out.printf("%1.3f%% %d%n", dyn * 100, times.size());
          Analysis.writeTimes(
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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));
                }
                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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