Example usage for org.apache.commons.math3.linear RealMatrix getRowDimension

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealMatrix getRowDimension.

Prototype

int getRowDimension();

Source Link

Document

Returns the number of rows in the matrix.

Usage

From source file:org.eclipse.january.dataset.LinearAlgebra.java

private static Dataset createDataset(RealMatrix m) {
    DoubleDataset r = DatasetFactory.zeros(DoubleDataset.class, m.getRowDimension(), m.getColumnDimension());
    if (m instanceof Array2DRowRealMatrix) {
        double[][] data = ((Array2DRowRealMatrix) m).getDataRef();
        IndexIterator it = r.getIterator(true);
        int[] pos = it.getPos();
        while (it.hasNext()) {
            r.setAbs(it.index, data[pos[0]][pos[1]]);
        }//from w w  w.j  a  v  a  2 s .  c  o m
    } else {
        IndexIterator it = r.getIterator(true);
        int[] pos = it.getPos();
        while (it.hasNext()) {
            r.setAbs(it.index, m.getEntry(pos[0], pos[1]));
        }
    }
    return r;
}

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From source file:org.grouplens.samantha.modeler.solver.RandomInitializer.java

public void randInitMatrix(RealMatrix mat, boolean normalize) {
    int len = mat.getRowDimension();
    RealVector vec = MatrixUtils.createRealVector(new double[mat.getColumnDimension()]);
    for (int i = 0; i < len; i++) {
        randInitVector(vec, normalize);//from ww  w.ja  v  a 2 s .  c o  m
        mat.setRowVector(i, vec);
    }
}

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From source file:org.interpss.opf.dc.impl.EqIneqMatrixBuilder.java

public Array2DRowRealMatrix formG() {
    RealMatrix U_matrix = formU();
    ;//from   w ww .jav  a  2s .c o  m
    RealMatrix Wrr_matrix = formReducedADWMatrix();
    int gcnt = U_matrix.getRowDimension() + Wrr_matrix.getRowDimension();
    Array2DRowRealMatrix G = new Array2DRowRealMatrix(gcnt, gcnt);
    G.setSubMatrix(U_matrix.getData(), 0, 0);
    G.setSubMatrix(Wrr_matrix.getData(), U_matrix.getRowDimension(), U_matrix.getColumnDimension());
    return G;
}

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From source file:org.knime.al.util.noveltydetection.knfst.KNFST.java

public static RealMatrix projection(final RealMatrix kernelMatrix, final String[] labels)
        throws KNFSTException {

    final ClassWrapper[] classes = ClassWrapper.classes(labels);

    // check labels
    if (classes.length == 1) {
        throw new IllegalArgumentException(
                "not able to calculate a nullspace from data of a single class using KNFST (input variable \"labels\" only contains a single value)");
    }/*  ww w  . ja v  a 2  s. c o m*/

    // check kernel matrix
    if (!kernelMatrix.isSquare()) {
        throw new IllegalArgumentException("The KernelMatrix must be quadratic!");
    }

    // calculate weights of orthonormal basis in kernel space
    final RealMatrix centeredK = centerKernelMatrix(kernelMatrix);

    final EigenDecomposition eig = new EigenDecomposition(centeredK);
    final double[] eigVals = eig.getRealEigenvalues();
    final ArrayList<Integer> nonZeroEigValIndices = new ArrayList<Integer>();
    for (int i = 0; i < eigVals.length; i++) {
        if (eigVals[i] > 1e-12) {
            nonZeroEigValIndices.add(i);
        }
    }

    int eigIterator = 0;
    final RealMatrix eigVecs = eig.getV();
    RealMatrix basisvecs = null;
    try {
        basisvecs = MatrixUtils.createRealMatrix(eigVecs.getRowDimension(), nonZeroEigValIndices.size());
    } catch (final Exception e) {
        throw new KNFSTException("Something went wrong. Try different parameters or a different kernel.");
    }

    for (final Integer index : nonZeroEigValIndices) {
        final double normalizer = 1 / Math.sqrt(eigVals[index]);
        final RealVector basisVec = eigVecs.getColumnVector(eigIterator).mapMultiply(normalizer);
        basisvecs.setColumnVector(eigIterator++, basisVec);
    }

    // calculate transformation T of within class scatter Sw:
    // T= B'*K*(I-L) and L a block matrix
    final RealMatrix L = kernelMatrix.createMatrix(kernelMatrix.getRowDimension(),
            kernelMatrix.getColumnDimension());
    int start = 0;
    for (final ClassWrapper cl : classes) {
        final int count = cl.getCount();
        L.setSubMatrix(MatrixFunctions.ones(count, count).scalarMultiply(1.0 / count).getData(), start, start);
        start += count;
    }

    // need Matrix M with all entries 1/m to modify basisvecs which allows
    // usage of
    // uncentered kernel values (eye(size(M)).M)*basisvecs
    final RealMatrix M = MatrixFunctions
            .ones(kernelMatrix.getColumnDimension(), kernelMatrix.getColumnDimension())
            .scalarMultiply(1.0 / kernelMatrix.getColumnDimension());
    final RealMatrix I = MatrixUtils.createRealIdentityMatrix(M.getColumnDimension());

    // compute helper matrix H
    final RealMatrix H = ((I.subtract(M)).multiply(basisvecs)).transpose().multiply(kernelMatrix)
            .multiply(I.subtract(L));

    // T = H*H' = B'*Sw*B with B=basisvecs
    final RealMatrix T = H.multiply(H.transpose());

    // calculate weights for null space
    RealMatrix eigenvecs = MatrixFunctions.nullspace(T);

    if (eigenvecs == null) {
        final EigenDecomposition eigenComp = new EigenDecomposition(T);
        final double[] eigenvals = eigenComp.getRealEigenvalues();
        eigenvecs = eigenComp.getV();
        final int minId = MatrixFunctions.argmin(MatrixFunctions.abs(eigenvals));
        final double[] eigenvecsData = eigenvecs.getColumn(minId);
        eigenvecs = MatrixUtils.createColumnRealMatrix(eigenvecsData);
    }

    // System.out.println("eigenvecs:");
    // test.printMatrix(eigenvecs);

    // calculate null space projection
    final RealMatrix proj = ((I.subtract(M)).multiply(basisvecs)).multiply(eigenvecs);

    return proj;
}

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From source file:org.knime.al.util.noveltydetection.knfst.KNFST.java

private static RealMatrix centerKernelMatrix(final RealMatrix kernelMatrix) {
    // get size of kernelMatrix
    final int n = kernelMatrix.getRowDimension();

    // get mean values for each row/column
    final RealVector columnMeans = MatrixFunctions.columnMeans(kernelMatrix);
    final double matrixMean = MatrixFunctions.mean(kernelMatrix);

    RealMatrix centeredKernelMatrix = kernelMatrix.copy();

    for (int k = 0; k < n; k++) {
        centeredKernelMatrix.setRowVector(k, centeredKernelMatrix.getRowVector(k).subtract(columnMeans));
        centeredKernelMatrix.setColumnVector(k, centeredKernelMatrix.getColumnVector(k).subtract(columnMeans));
    }/* w  w w .j a  v  a  2s .  c o  m*/

    centeredKernelMatrix = centeredKernelMatrix.scalarAdd(matrixMean);

    return centeredKernelMatrix;
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealMatrix nullspace(final RealMatrix matrix) {
    final SingularValueDecomposition svd = new SingularValueDecomposition(matrix);
    final int rank = svd.getRank();
    final RealMatrix V = svd.getV();
    RealMatrix nullspace = null;/*from  w w  w .  j  a v a 2  s  . co  m*/
    if (rank < matrix.getColumnDimension()) {
        nullspace = V.getSubMatrix(0, V.getRowDimension() - 1, rank, V.getColumnDimension() - 1);
    }

    return nullspace;
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealVector rowMins(final RealMatrix matrix) {
    final RealVector rowMins = MatrixUtils.createRealVector(new double[matrix.getRowDimension()]);

    for (int r = 0; r < matrix.getRowDimension(); r++) {
        rowMins.setEntry(r, matrix.getRowVector(r).getMinValue());
    }/*from w ww .  jav a2  s .co m*/

    return rowMins;
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealMatrix sqrt(final RealMatrix matrix) {
    final double[][] data = matrix.getData();
    for (int r = 0; r < matrix.getRowDimension(); r++) {
        for (int c = 0; c < matrix.getColumnDimension(); c++) {
            data[r][c] = Math.sqrt(data[r][c]);
        }/*from  w  w w. ja  va2s .  c om*/
    }
    return MatrixUtils.createRealMatrix(data);
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealMatrix multiplyElementWise(final RealMatrix matrix1, final RealMatrix matrix2) {
    if (matrix1.getRowDimension() != matrix2.getRowDimension()
            || matrix1.getColumnDimension() != matrix2.getColumnDimension()) {
        throw new IllegalArgumentException("The matrices must be of the same dimensions!");
    }//from   w w  w .j  ava 2s. c  om

    final RealMatrix result = matrix1.createMatrix(matrix1.getRowDimension(), matrix1.getColumnDimension());

    for (int r = 0; r < matrix1.getRowDimension(); r++) {
        for (int c = 0; c < matrix1.getColumnDimension(); c++) {
            result.setEntry(r, c, matrix1.getEntry(r, c) * matrix2.getEntry(r, c));
        }
    }

    return result;
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealVector rowSums(final RealMatrix matrix) {
    final RealVector rowSums = MatrixUtils.createRealVector(new double[matrix.getRowDimension()]);
    for (int r = 0; r < matrix.getRowDimension(); r++) {
        final double[] row = matrix.getRow(r);
        for (final double cell : row) {
            rowSums.addToEntry(r, cell);
        }/*from  w  ww. j  a  v  a 2 s . c  om*/
    }
    return rowSums;
}

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