Example usage for org.apache.commons.math.distribution GammaDistributionImpl inverseCumulativeProbability

Introduction

In this page you can find the example usage for org.apache.commons.math.distribution GammaDistributionImpl inverseCumulativeProbability.

Prototype

@Override
public double inverseCumulativeProbability(final double p) throws MathException 

Source Link

Document

For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

Usage

From source file:adams.gui.visualization.stats.paintlet.Gamma.java

/**
 * For calculating the dimensions of the plot area.
 *//*from w  w w  .  j av a 2  s  . co m*/
@Override
public void calculateDimensions() {
    double median;
    GammaDistributionImpl gam = new GammaDistributionImpl(m_Shape, m_Scale);
    m_Sorted = SpreadSheetUtils.getNumericColumn(m_Data, m_Index);
    m_TransformedY = new double[m_Sorted.length];
    Arrays.sort(m_Sorted);
    for (int i = 0; i < m_Sorted.length; i++) {
        median = ((i + 1) - 0.3) / (m_Sorted.length + 0.4);
        try {
            m_TransformedY[i] = gam.inverseCumulativeProbability(median);
        } catch (MathException e) {
            e.printStackTrace();
        }
    }
    //If axis can handle the data
    if (m_AxisBottom.getType().canHandle(m_Sorted[0], m_Sorted[m_Sorted.length - 1])) {
        m_AxisBottom.setMinimum(m_Sorted[0]);
        m_AxisBottom.setMaximum(m_Sorted[m_Sorted.length - 1]);
    } else {
        getLogger().severe("errors in plotting");
    }
    if (m_AxisLeft.getType().canHandle(m_TransformedY[0], m_TransformedY[m_TransformedY.length - 1])) {
        m_AxisLeft.setMinimum(m_TransformedY[0]);
        m_AxisLeft.setMaximum(m_TransformedY[m_TransformedY.length - 1]);
    } else {
        getLogger().severe("errors in plotting");
    }
    m_AxisBottom.setAxisName(m_Data.getColumnName(m_Index) + ")");
    m_AxisLeft.setAxisName("Inverse Gamma");
}

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From source file:de.tum.bgu.msm.syntheticPopulationGenerator.kagawa.SyntheticPopJP.java

private static double translateIncome(int incomeClass, double[] incomeThresholds, GammaDistributionImpl q)
        throws MathException, MathException {
    //provide the income value for each person give the income class.
    //income follows a gamma distribution that was calibrated using the microdata. Income thresholds are calculated for the stiches
    double income;
    int finish = 0;
    double low = 0;
    double high = 1;
    if (incomeClass == 90) {
        income = 0; // kein Einkommen
        /*        } else if (incomeClass == 50) {
                    income = 0; // Selbstndige/r Landwirt/in in der Hauptttigkeit
                } else if (incomeClass == 99) {
                    income = -1; //keine Angabe*/
    } else {//from www  .j a  va 2s . c  o m
        if (incomeClass == 1) {
            low = 0;
            high = incomeThresholds[0];
        } else if (incomeClass == 50) { // Selbstndige/r Landwirt/in in der Hauptttigkeit
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == 99) { //keine Angabe
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == incomeThresholds.length + 1) {
            low = incomeThresholds[incomeThresholds.length - 1];
            high = 1;
        } else {
            int i = 2;
            while (finish == 0) {
                if (incomeClass > i) {
                    i++;
                } else {
                    finish = 1;
                    low = incomeThresholds[i - 2];
                    high = incomeThresholds[i - 1];
                }
            }
        }
        Random rnd = new Random();
        double cummulativeProb = rnd.nextDouble() * (high - low) + low;
        income = q.inverseCumulativeProbability(cummulativeProb);
    }
    return income;
}

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From source file:de.tum.bgu.msm.syntheticPopulationGenerator.capeTown.SyntheticPopCT.java

private static double translateIncome(int incomeClass, double[] incomeThresholds, GammaDistributionImpl q)
        throws MathException {
    //provide the income value for each person give the income class.
    //income follows a gamma distribution that was calibrated using the microdata. Income thresholds are calculated for the stiches
    double income;
    int finish = 0;
    double low = 0;
    double high = 1;
    if (incomeClass == 90) {
        income = 0; // kein Einkommen
        /*        } else if (incomeClass == 50) {
                    income = 0; // Selbstndige/r Landwirt/in in der Hauptttigkeit
                } else if (incomeClass == 99) {
                    income = -1; //keine Angabe*/
    } else {/* w  w  w.j  a  v  a  2  s.com*/
        if (incomeClass == 1) {
            low = 0;
            high = incomeThresholds[0];
        } else if (incomeClass == 50) { // Selbstndige/r Landwirt/in in der Hauptttigkeit
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == 99) { //keine Angabe
            low = 0; //give them a random income following the distribution
            high = 1;
        } else if (incomeClass == incomeThresholds.length + 1) {
            low = incomeThresholds[incomeThresholds.length - 1];
            high = 1;
        } else {
            int i = 2;
            while (finish == 0) {
                if (incomeClass > i) {
                    i++;
                } else {
                    finish = 1;
                    low = incomeThresholds[i - 2];
                    high = incomeThresholds[i - 1];
                }
            }
        }
        double cummulativeProb = SiloUtil.getRandomNumberAsDouble() * (high - low) + low;
        income = q.inverseCumulativeProbability(cummulativeProb);
    }
    return income;
}

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