Example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix.

Prototype

public Array2DRowRealMatrix(final double[][] d, final boolean copyArray)
        throws DimensionMismatchException, NoDataException, NullArgumentException 

Source Link

Document

Create a new RealMatrix using the input array as the underlying data array.

Usage

From source file:com.analog.lyric.math.MoreMatrixUtils.java

/**
 * Return row-real matrix that uses specified underlying representation.
 * <p>//from   w w  w .  ja v a 2 s . c  o m
 * Simply shorthand for invoking {@link Array2DRowRealMatrix#Array2DRowRealMatrix(double[][], boolean)}
 * constructor with {@code copyArray} argument set to false.
 * @since 0.08
 */
public static Array2DRowRealMatrix wrapRealMatrix(double[][] data) {
    return new Array2DRowRealMatrix(data, false);
}

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From source file:com.cloudera.oryx.common.math.VectorMath.java

/**
 * @param M tall, skinny matrix//ww w  .  jav  a 2s . co m
 * @return MT * M as a dense matrix
 */
public static RealMatrix transposeTimesSelf(Collection<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }
    int features = 0;
    RealMatrix result = null;
    for (float[] vector : M) {
        if (result == null) {
            features = vector.length;
            result = new Array2DRowRealMatrix(features, features);
        }
        for (int row = 0; row < features; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < features; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    return result;
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.MatrixUtils.java

/**
 * Elementwise multiplication of two matrices.
 *
 * @param A a matrix that is multiplied by the elements of B
 * @param B another matrix//w  ww. j a v a 2s  .  co m
 * @throws DimensionMismatchException
 */
public static void multiplyElementsBy(RealMatrix A, RealMatrix B) throws DimensionMismatchException {
    int nrow = A.getRowDimension();
    int ncol = A.getColumnDimension();
    if (nrow != B.getRowDimension()) {
        throw new DimensionMismatchException(nrow, B.getRowDimension());
    }
    if (ncol != B.getColumnDimension()) {
        throw new DimensionMismatchException(ncol, B.getColumnDimension());
    }

    RealMatrix M = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            A.multiplyEntry(i, j, B.getEntry(i, j));
        }
    }
}

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From source file:com.clust4j.metrics.pairwise.HaversineTest.java

@Test
public void test1() {

    final Array2DRowRealMatrix mat = new Array2DRowRealMatrix(coordinates, false);
    StandardScaler scaler = new StandardScaler().fit(mat);

    AbstractCentroidClusterer km;//from   w  w w . jav a  2  s. c om
    CentroidClustererParameters<? extends AbstractCentroidClusterer> planner;

    planner = new KMeansParameters(2).setVerbose(true).setMetric(Distance.HAVERSINE.MI).setVerbose(true);
    km = planner.fitNewModel(scaler.transform(mat));

    int[] kmlabels = km.getLabels();
    assertTrue(kmlabels[0] == kmlabels[1] && kmlabels[1] == kmlabels[2]);
    assertTrue(kmlabels[1] != kmlabels[3] && kmlabels[3] == kmlabels[4]);
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.ObliminCriteria.java

public void computeValues(RealMatrix L) {
    final int k = L.getColumnDimension();
    final int p = L.getRowDimension();
    RealMatrix I = new IdentityMatrix(p);
    RealMatrix L2 = MatrixUtils.multiplyElements(L, L);

    RealMatrix N = new Array2DRowRealMatrix(k, k);
    N.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override// w  w w .j  ava 2  s. c  o m
        public double visit(int row, int column, double value) {
            if (row == column)
                return 0.0;
            return 1.0;
        }
    });

    RealMatrix C = new Array2DRowRealMatrix(p, p);
    C.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return gam / (double) p;
        }
    });

    RealMatrix X = I.subtract(C).multiply(L2).multiply(N);
    double sum = MatrixUtils.sumMatrix(MatrixUtils.multiplyElements(L2, X));
    functionValue = sum / 4.0;
    gradient = MatrixUtils.multiplyElements(L, X);

}

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From source file:com.github.thorbenlindhauer.math.MathUtil.java

public RealMatrix invert() {
    if (!matrix.isSquare()) {
        throw new FactorOperationException("Cannot invert non-square matrix");
    }/*w  ww  . jav a 2 s .c  om*/
    ensureLUDecompositionInitialized();

    int matrixDimension = matrix.getRowDimension();

    RealMatrix inverseMatrix = new Array2DRowRealMatrix(matrixDimension, matrixDimension);
    RealMatrix identityMatrix = MatrixUtils.createRealIdentityMatrix(matrixDimension);

    DecompositionSolver solver = luDecomposition.getSolver();

    for (int i = 0; i < matrixDimension; i++) {
        RealVector identityColumn = identityMatrix.getColumnVector(i);
        RealVector inverseColumn = solver.solve(identityColumn);

        inverseMatrix.setColumnVector(i, inverseColumn);
    }

    return inverseMatrix;

}

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From source file:edu.oregonstate.eecs.mcplan.ml.WekaGlue.java

public static SequentialProjectionHashLearner createSequentialProjectionHashLearner(final RandomGenerator rng,
        final Instances labeled, final Instances unlabeled, final int K, final double eta, final double alpha) {
    assert (labeled.classIndex() >= 0);
    final int Nfeatures = labeled.numAttributes() - 1;

    final RealMatrix X = new Array2DRowRealMatrix(Nfeatures, labeled.size() + unlabeled.size());
    final RealMatrix XL = new Array2DRowRealMatrix(Nfeatures, labeled.size() * 2);
    final RealMatrix S = new Array2DRowRealMatrix(XL.getColumnDimension(), XL.getColumnDimension());

    for (int j = 0; j < labeled.size(); ++j) {
        final Instance inst = labeled.get(j);
        for (int i = 0; i < XL.getRowDimension(); ++i) {
            X.setEntry(i, j, inst.value(i));
            XL.setEntry(i, j, inst.value(i));
        }/* ww w . j ava  2  s.  c o m*/

        int sj = -1;
        Instance s = null;
        do {
            sj = rng.nextInt(labeled.size());
            s = labeled.get(sj);
        } while (s == inst || s.classValue() != inst.classValue());
        S.setEntry(j, sj, 1);

        int dj = -1;
        Instance d = null;
        do {
            dj = rng.nextInt(labeled.size());
            d = labeled.get(dj);
        } while (d == inst || d.classValue() == inst.classValue());
        S.setEntry(j, dj, -1);
    }

    for (int j = 0; j < unlabeled.size(); ++j) {
        final Instance inst = unlabeled.get(j);
        for (int i = 0; i < X.getRowDimension(); ++i) {
            X.setEntry(i, labeled.size() + j, inst.value(i));
        }
    }

    return new SequentialProjectionHashLearner(X, XL, S, K, eta, alpha);
}

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From source file:eagle.security.userprofile.impl.UserProfileAnomalyEigenEvaluator.java

private RealMatrix normalizeData(RealMatrix matrix, UserProfileEigenModel model) {
    RealMatrix normalizedData = new Array2DRowRealMatrix(matrix.getRowDimension(), matrix.getColumnDimension());
    if (LOG.isDebugEnabled())
        LOG.debug("model statistics size: " + model.statistics().length);
    for (int i = 0; i < matrix.getRowDimension(); i++) {
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            double value = (matrix.getEntry(i, j) - model.statistics()[j].getMean())
                    / model.statistics()[j].getStddev();
            normalizedData.setEntry(i, j, value);
        }//from www.ja va  2 s.  c om
    }
    return normalizedData;
}

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From source file:de.biomedical_imaging.traj.math.RadiusGyrationTensor2D.java

/**
 * Calculates the radius of gyration tensor according to formula (6.3) in
 * /*from  w  w  w  .j a  v a 2  s. c  o m*/
 * ELEMENTS OF THE RANDOM WALK by Rudnick and Gaspari
 * 
 * @return Radius of gyration tensor
 */
public static Array2DRowRealMatrix getRadiusOfGyrationTensor(Trajectory t) {
    double meanx = 0;
    double meany = 0;
    for (int i = 0; i < t.size(); i++) {
        meanx += t.get(i).x;
        meany += t.get(i).y;
    }
    meanx = meanx / t.size();
    meany = meany / t.size();

    double e11 = 0;
    double e12 = 0;
    double e21 = 0;
    double e22 = 0;

    for (int i = 0; i < t.size(); i++) {
        e11 += Math.pow(t.get(i).x - meanx, 2);
        e12 += (t.get(i).x - meanx) * (t.get(i).y - meany);
        e22 += Math.pow(t.get(i).y - meany, 2);
    }
    e11 = e11 / t.size();
    e12 = e12 / t.size();
    e21 = e12;
    e22 = e22 / t.size();
    int rows = 2;
    int columns = 2;
    Array2DRowRealMatrix gyr = new Array2DRowRealMatrix(rows, columns);

    gyr.addToEntry(0, 0, e11);
    gyr.addToEntry(0, 1, e12);
    gyr.addToEntry(1, 0, e21);
    gyr.addToEntry(1, 1, e22);

    return gyr;
}

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From source file:com.joptimizer.solvers.KKTSolver.java

public void setHMatrix(double[][] HMatrix) {
    this.H = new Array2DRowRealMatrix(HMatrix, false);
}

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