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

Introduction

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

Prototype

void setEntry(int row, int column, double value) throws OutOfRangeException;

Source Link

Document

Set the entry in the specified row and column.

Usage

From source file:lsafunctions.LSA.java

private static RealMatrix normalizeMatrix(RealMatrix M) {
    double sumColumn = 0;
    //        Matrix row = new Matrix(1, M.getColumnDimension());
    RealMatrix row = MatrixUtils.createRealMatrix(1, M.getColumnDimension());
    for (int j = 0; j < M.getColumnDimension(); j++) {
        sumColumn = 0;//from ww  w  .  ja  v a2s .  com
        for (int k = 0; k < M.getRowDimension(); k++) {
            sumColumn += Math.pow(M.getEntry(k, j), 2);
        }
        sumColumn = Math.sqrt(sumColumn);
        row.setEntry(0, j, sumColumn);
    }
    for (int j = 0; j < M.getColumnDimension(); j++) {
        for (int k = 0; k < M.getRowDimension(); k++) {
            M.setEntry(k, j, M.getEntry(k, j) / row.getEntry(0, j));
        }
    }
    return M;
}

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From source file:edu.washington.gs.skyline.model.quantification.LinearModel.java

private static RealMatrix computeMatrixCrossproduct(RealMatrix matrix, int[] columnIndexes) {
    RealMatrix result = MatrixUtils.createRealMatrix(columnIndexes.length, columnIndexes.length);
    for (int iRow = 0; iRow < columnIndexes.length; iRow++) {
        for (int iCol = 0; iCol < columnIndexes.length; iCol++) {
            double dotProduct = matrix.getColumnVector(iRow).dotProduct(matrix.getColumnVector(iCol));
            result.setEntry(iRow, iCol, dotProduct);
        }//  ww  w  .  ja  v a  2s  .co  m
    }
    return result;
}

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From source file:com.yahoo.egads.utilities.SpectralMethods.java

public static TimeSeries.DataSequence mFilter(TimeSeries.DataSequence data, int windowSize,
        FilteringMethod method, double methodParameter) {

    TimeSeries.DataSequence result = new TimeSeries.DataSequence();
    RealMatrix dataMat = MatrixUtils.createRealMatrix(data.size(), 1);

    int i = 0;/*  ww w  . j  a  va 2s.  co  m*/
    for (TimeSeries.Entry e : data) {
        dataMat.setEntry(i, 0, e.value);
        i++;
        TimeSeries.Entry eCopy = new TimeSeries.Entry(e);
        result.add(eCopy);
    }

    RealMatrix resultMat = SpectralMethods.mFilter(dataMat, windowSize, method, methodParameter);

    i = 0;
    for (TimeSeries.Entry e : result) {
        e.value = (float) resultMat.getEntry(i, 0);
        i++;
    }

    return result;
}

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From source file:net.myrrix.common.math.MatrixUtils.java

public static RealMatrix multiplyXYT(FastByIDMap<float[]> X, FastByIDMap<float[]> Y) {
    int Ysize = Y.size();
    int Xsize = X.size();
    RealMatrix result = new Array2DRowRealMatrix(Xsize, Ysize);
    for (int row = 0; row < Xsize; row++) {
        for (int col = 0; col < Ysize; col++) {
            result.setEntry(row, col, SimpleVectorMath.dot(X.get(row), Y.get(col)));
        }/*from   w  ww. j a v  a2  s .  c  om*/
    }
    return result;
}

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From source file:edu.cudenver.bios.matrix.MatrixUtilities.java

/**
 * Force a square RealMatrix to be symmetric.
 *
 * @param rm The RealMatrix./*w  ww. ja v a 2  s.  com*/
 *
 * @return The same RealMatrix, modified if necessary
 *         to be symmetric.
 *
 * @throws NonSquareMatrixException if the RealMatrix is
 *                                  not square.
 */
public static RealMatrix forceSymmetric(RealMatrix rm) {
    int m = rm.getRowDimension();
    int n = rm.getColumnDimension();

    if (m != n) {
        throw new NonSquareMatrixException(m, n);
    }

    for (int i = 0; i < m; ++i) {
        for (int j = i + 1; j < n; ++j) {
            double value = (rm.getEntry(i, j) + rm.getEntry(j, i)) / 2;
            rm.setEntry(i, j, value);
            rm.setEntry(j, i, value);
        }
    }

    return rm;
}

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

public static RealMatrix makePositiveDefinite(final RealMatrix M, final double eps) {
    assert (eps > 0.0);
    final SingularValueDecomposition svd = new SingularValueDecomposition(M);
    final RealMatrix Sigma = svd.getS().copy();
    final int N = Math.min(Sigma.getColumnDimension(), Sigma.getRowDimension());
    for (int i = 0; i < N; ++i) {
        final double lambda = Sigma.getEntry(i, i);
        System.out.println("lambda_" + i + " = " + lambda);
        if (Math.abs(lambda) < eps) {
            System.out.println("Corrected " + i);
            Sigma.setEntry(i, i, eps);
        } else if (lambda < 0.0) {
            throw new NonPositiveDefiniteMatrixException(lambda, i, eps);
        } else {/*from  w w w  .j ava 2 s  .c  o  m*/
            Sigma.setEntry(i, i, lambda);
        }
    }
    return svd.getU().multiply(Sigma).multiply(svd.getVT());
}

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

/**
 * Computes the inverse of a matrix using the singular value decomposition.
 * //ww  w.j  av  a 2s  .  c  o  m
 * The input matrix M is assumed to be positive definite up to numerical
 * precision 'eps'. That is, for all eigenvalues lambda of M, it must be
 * the case that lambda + eps > 0. For eigenvalues with |lambda| < eps, the
 * eigenvalue is set to 'eps' before inverting. Throws an exception if
 * any lambda < -eps.
 * @param M
 * @param eps
 * @return
 */
public static RealMatrix robustInversePSD(final RealMatrix M, final double eps) {
    assert (eps > 0.0);
    final SingularValueDecomposition svd = new SingularValueDecomposition(M);
    final RealMatrix Sigma = svd.getS().copy();
    final int N = Math.min(Sigma.getColumnDimension(), Sigma.getRowDimension());
    for (int i = 0; i < N; ++i) {
        final double lambda = Sigma.getEntry(i, i);
        System.out.println("lambda_" + i + " = " + lambda);
        if (Math.abs(lambda) < eps) {
            System.out.println("Corrected " + i);
            Sigma.setEntry(i, i, 1.0 / eps);
        } else if (lambda < 0.0) {
            throw new IllegalArgumentException("Negative eigenvalue " + lambda);
        } else {
            Sigma.setEntry(i, i, 1.0 / lambda);
        }
    }
    return svd.getV().multiply(Sigma.transpose()).multiply(svd.getUT());
}

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From source file:ch.zhaw.iamp.rct.weights.Weights.java

private static RealMatrix addNoise(final RealMatrix A) {
    RealMatrix buffer = A.copy();//from w w w.  ja  va2  s .c o m
    RealMatrix noise = MatrixUtils.createRealMatrix(A.getRowDimension(), A.getColumnDimension());

    for (int i = 0; i < A.getRowDimension(); ++i) {
        for (int j = 0; j < A.getColumnDimension(); ++j) {
            double noiseSign = Math.random() > 0.5 ? 1 : -1;
            double noiseAmplitude = 1;
            noise.setEntry(i, j, noiseSign * Math.random() * noiseAmplitude);
        }
    }

    buffer = buffer.add(noise);
    return buffer;
}

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From source file:lsafunctions.LSA.java

public static RealMatrix calculateTfIdf(RealMatrix M) {
    int tf;//from  w  w  w .ja  va2s  . c  o  m
    double idf;
    int df;
    double ndf;
    for (int j = 0; j < M.getRowDimension(); j++) {

        df = calcDf(j, M);
        // System.out.println("J:"+j+"  df:"+df);

        for (int k = 0; k < M.getColumnDimension(); k++) {
            tf = (int) M.getEntry(j, k);
            ndf = M.getColumnDimension() / df;
            idf = Math.log(ndf) / Math.log(2);
            M.setEntry(j, k, idf * tf);
        }
    }
    //M.print(NumberFormat.INTEGER_FIELD, M.getColumnDimension());
    M = normalizeMatrix(M);
    return M;
}

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From source file:ch.unil.genescore.vegas.LinkageDisequilibrium.java

/** Compute rectangular 'cross'-correlation (LD) r values for the two given snp lists( */
static public RealMatrix computeCrossCorrelationMatrix(ArrayList<Snp> geneSnps1, ArrayList<Snp> geneSnps2) {

    // The correlation matrix for the given set of snps
    RealMatrix corr = MatrixUtils.createRealMatrix(geneSnps1.size(), geneSnps2.size());
    // For each snp pair
    for (int i = 0; i < geneSnps1.size(); i++) {
        for (int j = 0; j < geneSnps2.size(); j++) {
            Snp snp_i = geneSnps1.get(i);
            Snp snp_j = geneSnps2.get(j);
            double r = computeCorrelation(snp_i, snp_j);
            corr.setEntry(i, j, r);
        }/*from   w  w w. j a  v  a  2  s.  c o  m*/
    }
    return corr;
}

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