private void calculatePCs() {
// square values in the distance matrix, then double center them
DoubleMatrix dm = SquaredDoubleMatrixFromDistanceMatrix();
int n = dm.numberOfRows();
// double center the matrix
for (int r = 0; r < n; r++) {
double mean = dm.rowSum(r) / n;
for (int c = 0; c < n; c++) {
dm.set(r, c, dm.get(r, c) - mean);
}
}
for (int c = 0; c < n; c++) {
double mean = dm.columnSum(c) / n;
for (int r = 0; r < n; r++) {
dm.set(r, c, dm.get(r, c) - mean);
}
}
// finally multiply by -1/2
dm.scalarMultEquals(-0.5);
// get an eigenvalue decomposition
eigenDecomp = dm.getEigenvalueDecomposition();
// calculate PC's for positive eigenvalues
numberOfPositiveEigenvalues = 0;
double[] eval = eigenDecomp.getEigenvalues();
for (int i = 0; i < n; i++) if (eval[i] > tol) numberOfPositiveEigenvalues++;
eigenVectors = eigenDecomp.getEigenvectors();
// determine the sort order
int nEigenvalues = eval.length;
eigenSort =
IntStream.range(0, nEigenvalues)
.boxed()
.sorted(
(a, b) -> {
if (eval[a] > eval[b]) return -1;
if (eval[a] < eval[b]) return 1;
return 0;
})
.mapToInt(I -> I.intValue())
.toArray();
eigenVectors.column(280);
}
public double[] getPrincipalCoordinate(int index) {
if (index > numberOfPositiveEigenvalues - 1) return null;
double eval = Math.sqrt(eigenDecomp.getEigenvalue(eigenSort[index]));
int ntaxa = myDistanceMatrix.numberOfTaxa();
double[] pc = new double[ntaxa];
for (int i = 0; i < ntaxa; i++) pc[i] = eigenVectors.get(i, eigenSort[index]) * eval;
return pc;
}
public double getEigenvalue(int index) {
return eigenDecomp.getEigenvalue(eigenSort[index]);
}
