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

Examples of org.apache.commons.math3.stat.descriptive.moment.Variance

Note that adding values using increment or incrementAll and then executing getResult will sometimes give a different, less accurate, result than executing evaluate with the full array of values. The former approach should only be used when the full array of values is not available.

The "population variance" ( sum((x_i - mean)^2) / n ) can also be computed using this statistic. The isBiasCorrected property determines whether the "population" or "sample" value is returned by the evaluate and getResult methods. To compute population variances, set this property to false.

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.

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            if (!cluster.getPoints().isEmpty()) {

                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();

            }
        }
        return varianceSum;
    }
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     */
    @Test
    public void testOverrideVarianceWithMathClass() {
        double[] scores = {1, 2, 3, 4};
        SummaryStatistics stats = new SummaryStatistics();
        stats.setVarianceImpl(new Variance(false)); //use "population variance"
        for(double i : scores) {
          stats.addValue(i);
        }
        Assert.assertEquals((new Variance(false)).evaluate(scores),stats.getVariance(), 0);
    }
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            TestUtils.assertEquals(new GeometricMean().evaluate(values), dstats.getGeometricMean(), tol);
            TestUtils.assertEquals(dstats.getMin(), sstats.getMin(), tol);
            TestUtils.assertEquals(new Min().evaluate(values), dstats.getMin(), tol);
            TestUtils.assertEquals(dstats.getStandardDeviation(), sstats.getStandardDeviation(), tol);
            TestUtils.assertEquals(dstats.getVariance(), sstats.getVariance(), tol);
            TestUtils.assertEquals(new Variance().evaluate(values), dstats.getVariance(), tol);
            TestUtils.assertEquals(dstats.getSum(), sstats.getSum(), tol);
            TestUtils.assertEquals(new Sum().evaluate(values), dstats.getSum(), tol);
            TestUtils.assertEquals(dstats.getSumsq(), sstats.getSumsq(), tol);
            TestUtils.assertEquals(new SumOfSquares().evaluate(values), dstats.getSumsq(), tol);
            TestUtils.assertEquals(dstats.getPopulationVariance(), sstats.getPopulationVariance(), tol);
            TestUtils.assertEquals(new Variance(false).evaluate(values), dstats.getPopulationVariance(), tol);
        }
    }
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     * <p>Double.NaN is returned if no values have been added.</p>
     *
     * @return the population variance
     */
    public double getPopulationVariance() {
        Variance populationVariance = new Variance(secondMoment);
        populationVariance.setBiasCorrected(false);
        return populationVariance.getResult();
    }
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        dest.secondMoment = source.secondMoment.copy();
        dest.n = source.n;

        // Keep commons-math supplied statistics with embedded moments in synch
        if (source.getVarianceImpl() instanceof Variance) {
            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);
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     * Calculates the variance of the y values.
     *
     * @return Y variance
     */
    protected double calculateYVariance() {
        return new Variance().evaluate(yVector.toArray());
    }
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            for (final Cluster<T> cluster : clusters) {
                if (!cluster.getPoints().isEmpty()) {

                    // 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();

                }
            }

            if (varianceSum <= bestVarianceSum) {
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        for (final Cluster<T> cluster : clusters) {
            if (!cluster.getPoints().isEmpty()) {

                // 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) {
                    maxVariance = variance;
                    selected = cluster;
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     *
     * @return The population variance, Double.NaN if no values have been added,
     * or 0.0 for a single value set.
     */
    public double getPopulationVariance() {
        return apply(new Variance(false));
    }
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     * @param values the input array
     * @return the population variance of the values or Double.NaN if the array is empty
     * @throws IllegalArgumentException if the array is null
     */
    public static double populationVariance(final double[] values) {
        return new Variance(false).evaluate(values);
    }
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  • com.facebook.presto.operator.aggregation.TestDoubleVariancePopAggregation
  • com.facebook.presto.operator.aggregation.TestLongVarianceAggregation
  • com.facebook.presto.operator.aggregation.TestLongVariancePopAggregation
  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
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