Examples of org.apache.commons.math3.stat.descriptive.rank.Median.evaluate()

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

Class org.apache.commons.math3.stat.descriptive.rank.Median

Examples of org.apache.commons.math3.stat.descriptive.rank.Median.evaluate()

org.apache.commons.math3.stat.descriptive.rank.Median.evaluate()
Returns the result of evaluating the statistic over the input array, using the supplied weights. @param values input array @param weights array of weights @return the value of the weighted statistic applied to the input array @throws MathIllegalArgumentException if either array is null, lengthsdo not match, weights contain NaN, negative or infinite values, or weights does not include at least on positive value

        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


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


    @Override
    protected String getFunctionName()
    {

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            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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        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);

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        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 (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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        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);

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        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 (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


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

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        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


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

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        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


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

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