Examples of org.apache.commons.math3.stat.descriptive.summary.Sum.evaluate()

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

Class org.apache.commons.math3.stat.descriptive.summary.Sum

Examples of org.apache.commons.math3.stat.descriptive.summary.Sum.evaluate()

org.apache.commons.math3.stat.descriptive.summary.Sum.evaluate()
The sum of the entries in the specified portion of the input array, or 0 if the designated subarray is empty.
Throws MathIllegalArgumentException if the array is null.
@param values the input array @param begin index of the first array element to include @param length the number of elements to include @return the sum of the values or 0 if length = 0 @throws MathIllegalArgumentException if the array is null or the array indexparameters are not valid

        for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
            reset (50, e);
            final UnivariateStatistic percentile = getUnivariateStatistic();
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray),
                    0);
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray,
                    0, 0),
                    0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 5), 0);
            Assert.assertEquals(Double.NaN,

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                    0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 5), 0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 100), 0);
            Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray,
                    0, 0)));
        }
    }


    @Test

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    public void testAllTechniquesSpecialValues() {
        reset(50d, Percentile.EstimationType.LEGACY);
        final UnivariateStatistic percentile = getUnivariateStatistic();
        double[] specialValues =
                new double[] { 0d, 1d, 2d, 3d, 4d, Double.NaN };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_6, 2.0 },
                { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 }}, 50d, 0d);

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                { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 }}, 50d, 0d);


        specialValues =
                new double[] { Double.NEGATIVE_INFINITY, 1d, 2d, 3d,
                        Double.NaN, Double.POSITIVE_INFINITY };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_7, 2.0 },
                { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 } }, 50d, 0d);

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                { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 } }, 50d, 0d);


        specialValues =
                new double[] { 1d, 1d, Double.POSITIVE_INFINITY,
                        Double.POSITIVE_INFINITY };
        Assert.assertTrue(Double.isInfinite(percentile.evaluate(specialValues)));


        testAssertMappedValues(specialValues, new Object[][] {
                // This is one test not matching with R results.
                { Percentile.EstimationType.LEGACY, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_1,/* 1.0 */Double.NaN },

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                { Percentile.EstimationType.R_7, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_8, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_9, Double.POSITIVE_INFINITY }, }, 50d, 0d);


        specialValues = new double[] { 1d, 1d, Double.NaN, Double.NaN };
        Assert.assertTrue(Double.isNaN(percentile.evaluate(specialValues)));
        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, Double.NaN }, { Percentile.EstimationType.R_1, 1.0 }, { Percentile.EstimationType.R_2, 1.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.0 }, { Percentile.EstimationType.R_5, 1.0 },{ Percentile.EstimationType.R_6, 1.0 },{ Percentile.EstimationType.R_7, 1.0 },
                { Percentile.EstimationType.R_8, 1.0 }, { Percentile.EstimationType.R_9, 1.0 },}, 50d, 0d);

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                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);

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        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);

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        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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