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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            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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     * Calculates the variance of the y values.
     *
     * @return Y variance
     */
    protected double calculateYVariance() {
        return new Variance().evaluate(yVector.toArray());
    }
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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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    public void testConsistency() {
        final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
        final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();

        // Variances on the diagonal
        Variance variance = new Variance();
        for (int i = 0; i < 5; i++) {
            Assert.assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
        }

        // Symmetry, column-consistency
        Assert.assertEquals(covarianceMatrix.getEntry(2, 3),
                new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
        Assert.assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);

        // All columns same -> all entries = column variance
        RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
        for (int i = 0; i < 3; i++) {
            repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
        }
        RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
        double columnVariance = variance.evaluate(matrix.getColumn(0));
        for (int i = 0; i < 3; i++) {
            for (int j = 0; j < 3; j++) {
                Assert.assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
            }
        }
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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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        }
        return this.mean;
    }
    private Variance _getVariance() {
        if (this.variance == null) {
            this.variance = new Variance(this._getSecondMoment());
        }
        return this.variance;
    }
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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(_getSecondMoment());
        populationVariance.setBiasCorrected(false);
        return populationVariance.getResult();
    }
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        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }

        Variance variance = new Variance(false);
        return variance.evaluate(values);
    }
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