Examples of org.apache.commons.math3.stat.descriptive.moment.Variance.evaluate()

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

Class org.apache.commons.math3.stat.descriptive.moment.Variance

Examples of org.apache.commons.math3.stat.descriptive.moment.Variance.evaluate()

org.apache.commons.math3.stat.descriptive.moment.Variance.evaluate()
Returns the variance of the entries in the input array, or Double.NaN if the array is empty.
See {@link Variance} for details on the computing algorithm.
Returns 0 for a single-value (i.e. length = 1) sample.

Throws MathIllegalArgumentException if the array is null.

Does not change the internal state of the statistic.
@param values the input array @return the variance of the values or Double.NaN if length = 0 @throws MathIllegalArgumentException if the array is null

                           final int begin, final int length) {
        if (test(values, weights, begin, length)) {
            Sum sum = new Sum();


            // Compute initial estimate using definitional formula
            double sumw = sum.evaluate(weights,begin,length);
            double xbarw = sum.evaluate(values, weights, begin, length) / sumw;


            // Compute correction factor in second pass
            double correction = 0;
            for (int i = begin; i < begin + length; i++) {

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        if (test(values, weights, begin, length)) {
            Sum sum = new Sum();


            // Compute initial estimate using definitional formula
            double sumw = sum.evaluate(weights,begin,length);
            double xbarw = sum.evaluate(values, weights, begin, length) / sumw;


            // Compute correction factor in second pass
            double correction = 0;
            for (int i = begin; i < begin + length; i++) {
                correction += weights[i] * (values[i] - xbarw);

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        if (test(values, begin, length)) {
            Sum sum = new Sum();
            double sampleSize = length;


            // Compute initial estimate using definitional formula
            double xbar = sum.evaluate(values, begin, length) / sampleSize;


            // Compute correction factor in second pass
            double correction = 0;
            for (int i = begin; i < begin + length; i++) {
                correction += values[i] - xbar;

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                           final int begin, final int length) throws MathIllegalArgumentException {
        if (test(values, weights, begin, length)) {
            Sum sum = new Sum();


            // Compute initial estimate using definitional formula
            double sumw = sum.evaluate(weights,begin,length);
            double xbarw = sum.evaluate(values, weights, begin, length) / sumw;


            // Compute correction factor in second pass
            double correction = 0;
            for (int i = begin; i < begin + length; i++) {

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        if (test(values, weights, begin, length)) {
            Sum sum = new Sum();


            // Compute initial estimate using definitional formula
            double sumw = sum.evaluate(weights,begin,length);
            double xbarw = sum.evaluate(values, weights, begin, length) / sumw;


            // Compute correction factor in second pass
            double correction = 0;
            for (int i = begin; i < begin + length; i++) {
                correction += weights[i] * (values[i] - xbarw);

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                    return 1.0;
                }
            };
            ret = FastMath.exp((a * FastMath.log(x)) + (b * FastMath.log1p(-x)) -
                FastMath.log(a) - logBeta(a, b)) *
                1.0 / fraction.evaluate(x, epsilon, maxIterations);
        }


        return ret;
    }

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                protected double getB(int n, double x) {
                    return n * (a - n);
                }
            };


            ret = 1.0 / cf.evaluate(x, epsilon, maxIterations);
            ret = FastMath.exp(-x + (a * FastMath.log(x)) - logGamma(a)) * ret;
        }


        return ret;
    }

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                    return 1.0;
                }
            };
            ret = FastMath.exp((a * FastMath.log(x)) + (b * FastMath.log(1.0 - x)) -
                FastMath.log(a) - logBeta(a, b, epsilon, maxIterations)) *
                1.0 / fraction.evaluate(x, epsilon, maxIterations);
        }


        return ret;
    }

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                protected double getB(int n, double x) {
                    return n * (a - n);
                }
            };


            ret = 1.0 / cf.evaluate(x, epsilon, maxIterations);
            ret = FastMath.exp(-x + (a * FastMath.log(x)) - logGamma(a)) * ret;
        }


        return ret;
    }

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                    return 1.0;
                }
            };
            ret = FastMath.exp((a * FastMath.log(x)) + (b * FastMath.log1p(-x)) -
                FastMath.log(a) - logBeta(a, b)) *
                1.0 / fraction.evaluate(x, epsilon, maxIterations);
        }


        return ret;
    }

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