Examples of org.apache.commons.math3.util.ContinuedFraction.evaluate()

Class org.apache.commons.math3.util.ContinuedFraction

Examples of org.apache.commons.math3.util.ContinuedFraction.evaluate()

org.apache.commons.math3.util.ContinuedFraction.evaluate()
Evaluates the continued fraction at the value x. @param x the evaluation point. @return the value of the continued fraction evaluated at x. @throws ConvergenceException if the algorithm fails to converge.

        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(false);
        return variance.evaluate(values);
    }
}

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        double              values[] = new double[ count ];


        for ( int i = 0; i < count; i++)
        { values[ i ] = ((Double)arraylist.get(i)).doubleValue(); }


        return median.evaluate( values );
    }


    /**
     * <p>
     * Format a double as a percentage, suitable for printing.

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    Percentile distribution = new Percentile();
    distribution.setData(values);
    double[] percentiles = new double[MAX_NUMERIC_SPLITS];
    for (int i = 0 ; i < percentiles.length; i++) {
      double p = 100.0 * ((i + 1.0) / (MAX_NUMERIC_SPLITS + 1.0));
      percentiles[i] = distribution.evaluate(p);
    }
    return percentiles;
  }


  private static double[] chooseCategoricalSplitPoints(double[] values) {

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    @Test
    public void testRandomGenerator() {
        
        Percentile p = new Percentile(35);
        p.setData(new double[]{35});
        System.out.println(p.evaluate(5));
        
        Map<String, Object> data = new HashMap<String, Object>();
        data.put(SimulationConstants.DISTRIBUTION_TYPE, "random");
        data.put(SimulationConstants.MIN, 500L);
        data.put(SimulationConstants.MAX, 40000L);

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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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        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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