Example usage for org.apache.commons.math.linear EigenDecompositionImpl getEigenvector

Introduction

In this page you can find the example usage for org.apache.commons.math.linear EigenDecompositionImpl getEigenvector.

Prototype

public RealVector getEigenvector(final int i) throws InvalidMatrixException, ArrayIndexOutOfBoundsException 

Source Link

Usage

From source file:org.caleydo.view.bicluster.elem.layout.MDSLayout.java

@Override
public boolean doLayout(List<? extends IGLLayoutElement> children, float w_p, float h_p,
        IGLLayoutElement parent, int deltaTimeMs) {
    final int n = children.size();
    RealMatrix a = MatrixUtils.createRealMatrix(n, n);

    // Sind als Startwerte anstatt Distanzen hnlichkeitsmae c_{ij} zwischen Objekten gegeben, so lassen sich diese
    // durch die Transformation
    ////w ww. ja v  a2 s  . co m
    // d_{ij} = \sqrt{c_{ii}+c_{jj}-2c_{ij}}
    //
    // in Distanzen berfhren.

    for (int i = 0; i < n; ++i) {
        ClusterElement ci = (ClusterElement) children.get(i).asElement();
        int c_ii = ci.getDimSize() + ci.getRecSize();

        for (int j = i + 1; j < n; ++j) {
            ClusterElement cj = (ClusterElement) children.get(j).asElement();
            int recOverlap = ci.getRecOverlap(cj);
            int dimOverlap = ci.getDimOverlap(cj);

            int c_jj = cj.getDimSize() + cj.getRecSize();
            int c_ij = recOverlap + dimOverlap;

            double d_ij = Math.sqrt(c_ii + c_jj - 2 * c_ij);

            a.setEntry(i, j, d_ij);
            a.setEntry(j, i, d_ij);
        }
    }

    //#1. negative squared dissimilarity matrix Q
    //q = as.matrix( -0.5 * d ** 2 )
    RealMatrix q = a.copy();
    for (int i = 0; i < n; ++i) {
        q.getRowVector(i).mapPowToSelf(2);
    }
    q = q.scalarMultiply(-0.5);

    //#2. centering matrix H
    //h = diag(n) - 1/n * 1
    RealMatrix h = MatrixUtils.createRealMatrix(n, n);
    for (int i = 0; i < n; ++i)
        h.setEntry(i, i, n - 1. / n * 1);
    //#3. double-center matrix B
    //b = h %*% q %*% h
    RealMatrix b = h.copy().multiply(q).multiply(h);
    // #4. eigen decomposition of B
    // eig = eigen(b)
    EigenDecompositionImpl eig = new EigenDecompositionImpl(b, 0);
    // #5. use top k values/vectors to compute projected points
    // points = eig$vectors[,1:k] %*% diag(sqrt(eig$values[1:k]))
    for (int i = 0; i < n; ++i) {
        RealVector v = eig.getEigenvector(i).getSubVector(0, 2);
        double x = v.getEntry(0) * eig.getRealEigenvalue(0);
        double y = v.getEntry(1) * eig.getRealEigenvalue(1);
        IGLLayoutElement child = children.get(i);
        child.setLocation((float) x, (float) y);
    }

    return false;
}

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