net.maizegenetics.stats.PCA.ClassicMds Maven / Gradle / Ivy

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TASSEL 6 is a software package to evaluate traits association. Feature Tables are at the heart of the package where, a feature is a range of positions or a single position. Row in the that table are taxon.

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package net.maizegenetics.stats.PCA;

import java.util.stream.IntStream;

import net.maizegenetics.matrixalgebra.Matrix.DoubleMatrix;
import net.maizegenetics.matrixalgebra.Matrix.DoubleMatrixFactory;
import net.maizegenetics.matrixalgebra.Matrix.DoubleMatrixFactory.FactoryType;
import net.maizegenetics.matrixalgebra.decomposition.EigenvalueDecomposition;
import net.maizegenetics.taxa.distance.DistanceMatrix;

public class ClassicMds {
	//The method implemented in this class was adapted from R source code for the function cmdscale()
	//expected input is a distance matrix
	
	private DistanceMatrix myDistanceMatrix;
	private EigenvalueDecomposition eigenDecomp;
	private DoubleMatrix eigenVectors;
	private int numberOfPositiveEigenvalues;
	private double tol = 1e-8 ;
	private int[] eigenSort;
	
	public ClassicMds(DistanceMatrix dm) {
		myDistanceMatrix = dm;
		testDMforMissing();
		calculatePCs();
	}
	
	public int maximumNumberOfPCs() {
		return numberOfPositiveEigenvalues;
	}
	
	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]);
	}

	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();
	}
	
	private DoubleMatrix SquaredDoubleMatrixFromDistanceMatrix() {
		int n = myDistanceMatrix.getSize();
		DoubleMatrix dm = DoubleMatrixFactory.DEFAULT.make(n, n);
		for (int r = 0; r < n; r++) {
			for (int c = 0; c < n; c++) {
				double val = myDistanceMatrix.getDistance(r, c);
				val *= val;
				dm.set(r, c, val);
			}
		}
		return dm;	
	}
	
	private void testDMforMissing() {
		int n = myDistanceMatrix.getSize();
		for (int r = 0; r < n; r++) {
			for (int c = 0; c < n; c++) {
				if (!Double.isFinite(myDistanceMatrix.getDistance(r, c))) {
					throw new RuntimeException("Distance matrix contains missing values in ClassicMds.");
				}
			}
		}
	}
}