Example usage for org.apache.commons.math.linear RealMatrix getRowVector

Introduction

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

Prototype

RealVector getRowVector(int row) throws MatrixIndexException;

Source Link

Document

Returns the entries in row number row as a vector.

Usage

From source file:fi.smaa.libror.MaximalVectorComputation.java

private static LinkedList<RealVector> matrixToListOfRows(RealMatrix mat) {
    LinkedList<RealVector> list = new LinkedList<RealVector>();
    for (int i = 0; i < mat.getRowDimension(); i++) {
        list.add(mat.getRowVector(i));
    }/*  w ww  .  j  a v a 2  s  . co m*/
    return list;
}

---

From source file:fi.smaa.libror.MaximalVectorComputation.java

/**
 * Implements the Best algorithm as described in Godfrey & al., VLDB Journal, 2007.
 * //from w w w.  j a  v  a 2s.com
 * Returns indices of the rows from the original matrix.
 * 
 * @param mat The matrix of values (each row = 1 vector of input)
 * 
 * @return Matrix containing rows from the input s.t. none are dominated
 */
public static int[] computeBESTindices(RealMatrix mat) {
    LinkedList<Integer> list = matrixToListOfIndices(mat);
    LinkedList<Integer> results = new LinkedList<Integer>();

    while (list.size() > 0) {
        Iterator<Integer> iter = list.iterator();
        Integer b = iter.next(); // Get the first
        iter.remove();
        while (iter.hasNext()) { // Find a max
            Integer t = iter.next();
            if (dominates(mat.getRowVector(b), mat.getRowVector(t))) {
                iter.remove();
            } else if (dominates(mat.getRowVector(t), mat.getRowVector(b))) {
                iter.remove();
                b = t;
            }
        }
        results.add(b);
        iter = list.iterator();
        while (iter.hasNext()) { // Clean up
            Integer t = iter.next();
            if (dominates(mat.getRowVector(b), mat.getRowVector(t))) {
                iter.remove();
            }
        }
    }

    return listOfIntegersToIntArray(results);
}

---

From source file:fi.smaa.libror.eff.FastROR.java

private boolean isNecessarilyPreferred(int i, int j) {
    RealMatrix mat = ror.getPerfMatrix().getMatrix();
    if (MaximalVectorComputation.dominates(mat.getRowVector(i), mat.getRowVector(j))) {
        return true;
    }/*  ww  w .  j  av  a 2 s  .c om*/
    return false;
}

---

From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents2.java

/**
 * {@inheritDoc}/*www  . j a va 2 s  .  co m*/
 */
@Override
public double getExpectedLogNormalizer(Map<Variable, MomentParameters> momentParents) {
    CompoundVector globalNaturalParameters = (CompoundVector) this.naturalParameters;
    double logNorm = -0.5 * Math.log(-2 * globalNaturalParameters.getTheta_Minus1());

    double[] Yarray = new double[nOfParents];
    double[] YYarray = new double[nOfParents];

    for (int i = 0; i < nOfParents; i++) {
        Yarray[i] = momentParents.get(this.getConditioningVariables().get(i)).get(0);
        YYarray[i] = momentParents.get(this.getConditioningVariables().get(i)).get(1);
    }
    RealVector Y = new ArrayRealVector(Yarray);

    logNorm -= globalNaturalParameters.getTheta_beta0BetaRV().dotProduct(new ArrayRealVector(Y));

    RealMatrix YY = Y.outerProduct(Y);
    for (int i = 0; i < nOfParents; i++) {
        YY.setEntry(i, i, YYarray[i]);
    }

    logNorm -= IntStream.range(0, nOfParents).mapToDouble(p -> {
        return globalNaturalParameters.getTheta_BetaBetaRM().getRowVector(p).dotProduct(YY.getRowVector(p));
    }).sum();

    logNorm -= Math.pow(globalNaturalParameters.getTheta_beta0(), 2)
            / (4 * globalNaturalParameters.getTheta_Minus1());

    return logNorm;
}

---

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
    ///*from ww  w .j  av a 2 s.c  om*/
    // 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;
}

---