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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     * @throws IllegalArgumentException 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) {
        return new Variance(false).evaluate(values, begin, length);
    }
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     * @throws IllegalArgumentException 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) {
        return new Variance(false).evaluate(values, mean, begin, length);
    }
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     * @param mean the precomputed mean value
     * @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, final double mean) {
        return new Variance(false).evaluate(values, mean);
    }
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     * @param biasCorrected determines whether or not covariance estimates are bias-corrected
     * @return covariance matrix
     */
    protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
        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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        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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     * Calculates the variance of the y values.
     *
     * @return Y variance
     */
    protected double calculateYVariance() {
        return new Variance().evaluate(yVector.toArray());
    }
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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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     * @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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