protected void computeStatsFillModel(
PCAModel pca, DataInfo dinfo, SingularValueDecomposition svd, Gram gram, long nobs) {
// Save adapted frame info for scoring later
pca._output._normSub = dinfo._normSub == null ? new double[dinfo._nums] : dinfo._normSub;
if (dinfo._normMul == null) {
pca._output._normMul = new double[dinfo._nums];
Arrays.fill(pca._output._normMul, 1.0);
} else pca._output._normMul = dinfo._normMul;
pca._output._permutation = dinfo._permutation;
pca._output._nnums = dinfo._nums;
pca._output._ncats = dinfo._cats;
pca._output._catOffsets = dinfo._catOffsets;
double dfcorr = nobs / (nobs - 1.0);
double[] sval = svd.getSingularValues();
pca._output._std_deviation = new double[_parms._k]; // Only want first k standard deviations
for (int i = 0; i < _parms._k; i++) {
sval[i] =
dfcorr
* sval[
i]; // Degrees of freedom = n-1, where n = nobs = # row observations processed
pca._output._std_deviation[i] = Math.sqrt(sval[i]);
}
double[][] eigvec = svd.getV().getArray();
pca._output._eigenvectors_raw =
new double[eigvec.length][_parms._k]; // Only want first k eigenvectors
for (int i = 0; i < eigvec.length; i++)
System.arraycopy(eigvec[i], 0, pca._output._eigenvectors_raw[i], 0, _parms._k);
pca._output._total_variance =
dfcorr * gram.diagSum(); // Since gram = X'X/n, but variance requires n-1 in denominator
buildTables(pca, dinfo.coefNames());
}
