Examples with SingularValueDecomposition Jama.SingularValueDecomposition used on opensource projects

Example 1 with SingularValueDecomposition

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

the class PCA method buildModel.

public PCAModel buildModel(DataInfo dinfo, GramTask tsk) {
    logStart();
    // X'X/n where n = num rows
    Matrix myGram = new Matrix(tsk._gram.getXX());
    SingularValueDecomposition mySVD = myGram.svd();
    // Extract eigenvalues and eigenvectors
    // Note: Singular values ordered in weakly descending order by algorithm
    double[] Sval = mySVD.getSingularValues();
    // rows = features, cols = principal components
    double[][] eigVec = mySVD.getV().getArray();
    assert Sval.length == eigVec.length;
    // DKV.put(EigenvectorMatrix.makeKey(input("source"), destination_key), new EigenvectorMatrix(eigVec));
    // Compute standard deviation
    double[] sdev = new double[Sval.length];
    double totVar = 0;
    double dfcorr = dinfo._adaptedFrame.numRows() / (dinfo._adaptedFrame.numRows() - 1.0);
    for (int i = 0; i < Sval.length; i++) {
        // if(standardize)
        // Correct since degrees of freedom = n-1
        Sval[i] = dfcorr * Sval[i];
        sdev[i] = Math.sqrt(Sval[i]);
        totVar += Sval[i];
    }
    // Proportion of total variance
    double[] propVar = new double[Sval.length];
    // Cumulative proportion of total variance
    double[] cumVar = new double[Sval.length];
    for (int i = 0; i < Sval.length; i++) {
        propVar[i] = Sval[i] / totVar;
        cumVar[i] = i == 0 ? propVar[0] : cumVar[i - 1] + propVar[i];
    }
    Key dataKey = input("source") == null ? null : Key.make(input("source"));
    int ncomp = Math.min(getNumPC(sdev, tolerance), max_pc);
    return new PCAModel(this, destination_key, dataKey, dinfo, tsk, sdev, propVar, cumVar, eigVec, mySVD.rank(), ncomp);
}