package jhplot.math.pca.covmatrixevd;



import java.lang.Math;
import jhplot.math.pca.*;
import Jama.Matrix;
import Jama.SingularValueDecomposition;
/**
 * SVD-based covariance matrix eigenvalue decomposition
 * @author Mateusz Kobos
 *
 */
public class SVDBased implements CovarianceMatrixEVDCalculator{
	@Override
	public EVDResult run(Matrix centeredData) {
		int m = centeredData.getRowDimension();
		int n = centeredData.getColumnDimension();
		SingularValueDecomposition svd = centeredData.svd();
		double[] singularValues = svd.getSingularValues();
		Matrix d = Matrix.identity(n, n);
		for(int i = 0; i < n; i++){
			/** TODO: This is true according to SVD properties in my notes*/
			double val;
			if(i < m) val = singularValues[i];
			else val = 0;
			
			d.set(i, i, 1.0/(m-1) * Math.pow(val, 2));
		}
		Matrix v = svd.getV();
		return new EVDResult(d, v);
	}
}