Example usage for org.apache.commons.math3.stat.correlation StorelessCovariance getData

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.correlation StorelessCovariance getData.

Prototype

public double[][] getData() throws NumberIsTooSmallException 

Source Link

Document

Return the covariance matrix as two-dimensional array.

Usage

From source file:joinery.impl.Aggregation.java

public static <V> DataFrame<Number> cov(final DataFrame<V> df) {
    DataFrame<Number> num = df.numeric();
    StorelessCovariance cov = new StorelessCovariance(num.size());

    // row-wise copy to double array and increment
    double[] data = new double[num.size()];
    for (List<Number> row : num) {
        for (int i = 0; i < row.size(); i++) {
            data[i] = row.get(i).doubleValue();
        }/*  w  w w .java  2  s  . co  m*/
        cov.increment(data);
    }

    // row-wise copy results into new data frame
    double[][] result = cov.getData();
    DataFrame<Number> r = new DataFrame<>(num.columns());
    List<Number> row = new ArrayList<>(num.size());
    for (int i = 0; i < result.length; i++) {
        row.clear();
        for (int j = 0; j < result[i].length; j++) {
            row.add(result[i][j]);
        }
        r.append(row);
    }

    return r;
}

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From source file:org.drugis.mtc.summary.MCMCMultivariateNormalSummary.java

private void calculateResults() {
    if (!isReady()) {
        return;//from  w  w w. ja  v  a2 s  .c  o m
    }
    List<List<Double>> sampleCache = new ArrayList<List<Double>>();
    for (int i = 0; i < getParameters().length; ++i) {
        List<Double> samples = SummaryUtil.getAllChainsLastHalfSamples(d_results, getParameters()[i]);
        sampleCache.add(samples);
        d_means[i] = SummaryUtil.evaluate(new Mean(), samples);
    }
    StorelessCovariance cov = new StorelessCovariance(getParameters().length);
    double[] rowData = new double[getParameters().length];
    for (int row = 0; row < sampleCache.get(0).size(); ++row) {
        for (int col = 0; col < getParameters().length; ++col) {
            rowData[col] = sampleCache.get(col).get(row);
        }
        cov.increment(rowData);
    }
    d_covMatrix = cov.getData();
    boolean wasDefined = d_isDefined;
    d_isDefined = true;
    firePropertyChange(PROPERTY_DEFINED, wasDefined, d_isDefined);
    firePropertyChange(PROPERTY_MEAN_VECTOR, null, d_means);
    firePropertyChange(PROPERTY_COVARIANCE_MATRIX, null, d_covMatrix);
}

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