Example usage for org.apache.commons.math3.distribution MultivariateNormalDistribution sample

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution MultivariateNormalDistribution sample.

Prototype

public double[][] sample(final int sampleSize) 

Source Link

Usage

From source file:org.apache.hadoop.hive.ql.abm.simulation.MCSimNode.java

public List<SimulationResult> simulate(IntArrayList[] requests) {
    init(requests);//  w  w  w . j  av a 2 s  .c  o m

    if (dimension == 0) {
        return null;
    }

    if (parent == null) {
        return defaultSimulate();
    }

    List<SimulationResult> parRet = parent.simulate(targets);

    if (parRet == null) {
        return defaultSimulate();
    }

    List<SimulationResult> ret = new ArrayList<SimulationResult>();

    for (SimulationResult res : parRet) {
        ArrayList<IntArrayList> condIds = res.lastCondId;

        boolean[] fake = new boolean[dimension];
        double[] mu = new double[dimension];
        double[][] A = new double[dimension][dimension];
        double[][] B = new double[res.invSigma.getColumnDimension()][dimension];
        double[] zero = new double[dimension];

        for (int i = 0; i < within1.length; ++i) {
            IntArrayList cIds = condIds.get(i);
            within1[i].fill(cIds, fake, mu, A);
            InterDistOracle[] w2s = within2[i];
            for (int j = i + 1; j < within2.length; ++j) {
                w2s[j].fillSym(cIds, condIds.get(j), fake, mu, A);
            }
        }

        int dif = between.size() - res.condIds.size();
        double[] pmu = res.getMean();
        for (int i = 0; i < targets.length; ++i) {
            int cum = 0;
            IntArrayList cIds = condIds.get(i);
            for (int k = 0; k < res.condIds.size(); ++k) {
                ArrayList<IntArrayList> condIds2 = res.condIds.get(k);
                InterDistOracle[] os = between.get(k + dif)[i];
                for (int j = 0; j < os.length; ++j) {
                    cum += os[j].fillAsym(cIds, condIds2.get(j), fake, mu, pmu, B, cum);
                }
            }
        }

        for (int k = 0, cum = 0; k < res.condIds.size(); ++k) {
            ArrayList<IntArrayList> pCIds = res.condIds.get(k);
            InterDistOracle[][] oss = between.get(k + dif);
            for (int j = 0; j < oss.length; ++j) {
                IntArrayList cIds = pCIds.get(j);
                InterDistOracle[] os = oss[j];
                int off = 0;
                for (int i = 0; i < os.length; ++i) {
                    off = os[i].fillAsym(cIds, condIds.get(i), fake, pmu, mu, B, cum);
                }
                cum += off;
            }
        }

        Array2DRowRealMatrix a = new Array2DRowRealMatrix(A);
        Array2DRowRealMatrix b = new Array2DRowRealMatrix(B);
        Array2DRowRealMatrix c = (Array2DRowRealMatrix) b.transpose();
        Array2DRowRealMatrix tmp = c.multiply(res.invSigma);

        Array2DRowRealMatrix sigma = a.subtract(tmp.multiply(b));
        double[] scale = correct(sigma.getDataRef());

        MultivariateNormalDistribution dist = new MultivariateNormalDistribution(zero, sigma.getDataRef());

        double[][] smpls = dist.sample(res.samples.size());

        int pos = 0;
        for (double[] smpl : smpls) {
            if (scale != null) {
                restore(smpl, scale);
            }
            fixFake(fake, mu, smpl);
            double[] s = res.getSample(pos);
            subtract(s, pmu);
            add(smpl, tmp.operate(s));
            res.samples.get(pos)[level] = smpl;
            ++pos;
        }
        res.means.add(mu);
        res.invSigma = new Array2DRowRealMatrix(
                new LUDecomposition(concat(a, c, b, res.invSigma)).getSolver().getInverse().getData());

        dispatch(res, ret);
    }

    return ret;
}

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From source file:org.apache.hadoop.hive.ql.abm.simulation.MCSimNode.java

private List<SimulationResult> defaultSimulate() {
    List<SimulationResult> ret = new ArrayList<SimulationResult>();

    boolean[] fake = new boolean[dimension];
    double[] mu = new double[dimension];
    double[][] A = new double[dimension][dimension];
    double[] zero = new double[dimension];

    ArrayList<IntArrayList> condIds = zeroCondIds;

    for (int i = 0; i < within1.length; ++i) {
        IntArrayList cIds = condIds.get(i);
        within1[i].fill(cIds, fake, mu, A);
        InterDistOracle[] w2s = within2[i];
        for (int j = i + 1; j < within2.length; ++j) {
            w2s[j].fillSym(cIds, condIds.get(j), fake, mu, A);
        }//from ww  w. jav  a  2 s  . com
    }

    double[] scale = correct(A);

    MultivariateNormalDistribution dist = new MultivariateNormalDistribution(zero, A);
    double[][] smpls = dist.sample(NUM_SIMULATIONS);

    SimulationResult res = new SimulationResult();
    for (double[] smpl : smpls) {
        if (scale != null) {
            restore(smpl, scale);
        }
        fixFake(fake, mu, smpl);
        double[][] samples = new double[NUM_LEVEL][];
        samples[level] = smpl;
        res.samples.add(samples);
    }
    res.means.add(mu);
    res.invSigma = new Array2DRowRealMatrix(
            new LUDecomposition(new Array2DRowRealMatrix(A)).getSolver().getInverse().getData());

    dispatch(res, ret);

    return ret;
}

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