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.


    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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            for (final CentroidCluster<T> cluster : clusters) {
                if (!cluster.getPoints().isEmpty()) {

                    // 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();

                }
            }

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

                // 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) {
                    maxVariance = variance;
                    selected = cluster;
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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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     * @return the population variance of the values or Double.NaN if the array is empty
     * @throws MathIllegalArgumentException if the array is null
     */
    public static double populationVariance(final double[] values)
    throws MathIllegalArgumentException {
        return new Variance(false).evaluate(values);
    }
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     * @throws MathIllegalArgumentException if the array is null or the array index
     *  parameters are not valid
     */
    public static double populationVariance(final double[] values, final int begin,
            final int length) throws MathIllegalArgumentException {
        return new Variance(false).evaluate(values, begin, length);
    }
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     * @throws MathIllegalArgumentException if the array is null or the array index
     *  parameters are not valid
     */
    public static double populationVariance(final double[] values, final double mean,
            final int begin, final int length) throws MathIllegalArgumentException {
        return new Variance(false).evaluate(values, mean, begin, length);
    }
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     * @return the population variance of the values or Double.NaN if the array is empty
     * @throws MathIllegalArgumentException if the array is null
     */
    public static double populationVariance(final double[] values, final double mean)
    throws MathIllegalArgumentException {
        return new Variance(false).evaluate(values, mean);
    }
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     * @throws MathIllegalArgumentException if the matrix does not contain sufficient data
     */
    protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected)
    throws MathIllegalArgumentException {
        int dimension = matrix.getColumnDimension();
        Variance variance = new Variance(biasCorrected);
        RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
        for (int i = 0; i < dimension; i++) {
            for (int j = 0; j < i; j++) {
              double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
              outMatrix.setEntry(i, j, cov);
              outMatrix.setEntry(j, i, cov);
            }
            outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
        }
        return outMatrix;
    }
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