Examples with Matrix Jama.Matrix used on opensource projects

Example 1 with Matrix

use of Jama.Matrix in project h2o-2 by h2oai.

the class CholTest method test.

public void test() {
    Log.info("CholTest::test enter");
    for (int sz = 6000; sz < 10000; sz += 2000) {
        Log.info("CholTest::test sz is " + sz);
        DataSetup data = new DataSetup(sz, 12345);
        long start = System.currentTimeMillis();
        CholeskyDecomposition jamaChol = new Matrix(data.xx).chol();
        Log.info("JAMA CHOLESKY [N = " + sz + "] TAKES " + (System.currentTimeMillis() - start) + " MILLISECONDS.");
        if (!jamaChol.isSPD())
            continue;
        ForkJoinPool fjp = new ForkJoinPool(32);
        for (int t = 2; t <= 32; t += 2) {
            for (int step : STEPS) fjp.invoke(new TestSetup(new DataSetup(data.xx), jamaChol.getL().getArray(), step, t));
        }
    }
    Log.info("CholTest::test exit");
}

Example 2 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class LogisticRegressionContent method getZScoreMatrix.

/**
 * Computes the Wald's statistic.
 */
private Matrix getZScoreMatrix() {
    Matrix stdErr = getStdErrorMatrix();
    // Wald's statistic
    Matrix waldStat = new Matrix(1, m_covMat.getRowDimension());
    for (int i = 0; i < m_covMat.getRowDimension(); i++) {
        waldStat.set(0, i, m_beta.get(0, i) / stdErr.get(0, i));
    }
    return waldStat;
}

Example 3 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class LogisticRegressionContent method getPValueMatrix.

/**
 * Computes the two-tailed p-values of the z-test, which can be calculated
 * by 2*Phi(-|Z|), where Phi is the standard normal cumulative
 * distribution function.
 */
private Matrix getPValueMatrix() {
    Matrix zScore = getZScoreMatrix();
    // p-value
    Matrix pvalue = new Matrix(1, m_covMat.getRowDimension());
    for (int i = 0; i < m_covMat.getRowDimension(); i++) {
        double absZ = Math.abs(zScore.get(0, i));
        pvalue.set(0, i, 2 * Gaussian.Phi(-absZ));
    }
    return pvalue;
}

Example 4 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class EigenValue method createSortedList.

/**
 * create list of {@link EigenValue}s sorted by absolute value
 * @param eigenVectors matrix of eigenvector (in columns)
 * @param eigenvalues eigenvalues, same order as columns of eigenVectors
 * @return sorted list of {@link EigenValue}s
 */
public static List<EigenValue> createSortedList(final double[][] eigenVectors, final double[] eigenvalues) {
    final int[] rowindices = new int[eigenvalues.length];
    final Matrix v = new Matrix(eigenVectors);
    for (int i = 0; i < rowindices.length; i++) {
        rowindices[i] = i;
    }
    final LinkedList<EigenValue> list = new LinkedList<EigenValue>();
    for (int i = 0; i < eigenvalues.length; i++) {
        list.add(new EigenValue(i, eigenvalues[i], v.getMatrix(rowindices, new int[] { i })));
    }
    Collections.sort(list);
    return list;
}

Example 5 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class PCAComputeNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    if (!(inData[DATA_INPORT] instanceof BufferedDataTable)) {
        throw new IllegalArgumentException("Datatable as input expected");
    }
    final BufferedDataTable dataTable = (BufferedDataTable) inData[DATA_INPORT];
    if (dataTable.size() == 0) {
        throw new IllegalArgumentException("Input table is empty!");
    }
    final double[] meanVector = PCANodeModel.getMeanVector(dataTable, m_inputColumnIndices, m_failOnMissingValues.getBooleanValue(), exec.createSubExecutionContext(0.4));
    final double[][] m = new double[m_inputColumnIndices.length][m_inputColumnIndices.length];
    exec.checkCanceled();
    final int missingValues = PCANodeModel.getCovarianceMatrix(exec.createSubExecutionContext(0.4), dataTable, m_inputColumnIndices, meanVector, m);
    if (missingValues > 0) {
        if (m_failOnMissingValues.getBooleanValue()) {
            throw new IllegalArgumentException("missing, infinite or impossible values in table");
        }
        setWarningMessage(missingValues + " rows ignored because of missing, " + "infinite or impossible values");
    }
    exec.checkCanceled();
    final Matrix covarianceMatrix = new Matrix(m);
    exec.setProgress("calculation of spectral decomposition");
    final EigenvalueDecomposition evd = covarianceMatrix.eig();
    exec.checkCanceled();
    exec.setProgress(0.9);
    final Matrix d = evd.getD();
    final double[] evs = new double[d.getRowDimension()];
    for (int i = 0; i < evs.length; i++) {
        evs[i] = d.get(i, i);
    }
    exec.checkCanceled();
    return new PortObject[] { PCANodeModel.createCovarianceTable(exec, m, m_inputColumnNames), PCANodeModel.createDecompositionOutputTable(exec.createSubExecutionContext(0.1), evd, m_inputColumnNames), new PCAModelPortObject(evd.getV().getArray(), evs, m_inputColumnNames, meanVector) };
}