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

Introduction

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

Prototype

int getColumnDimension();

Source Link

Document

Returns the number of columns in the matrix.

Usage

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

private static RealMatrix checkMatrix(RealMatrix m) {
    for (int i = 0; i < m.getRowDimension(); i++) {
        for (int j = 0; j < m.getColumnDimension(); j++) {
            if (Double.isNaN(m.getEntry(i, j)) || Double.isInfinite(m.getEntry(i, j))) {
                throw new DimpleException("cannot do SVD on matrix that contains NaN or infinite");
            }//from  ww  w . j  a v  a 2  s  .  c  om
        }
    }
    return m;
}

From source file:lsafunctions.LSA.java

private static int calcDf(int nRow, RealMatrix M) {
    int df = 0;//from  www .j  a v a 2  s .c  om
    for (int j = 0; j < M.getColumnDimension(); j++) {
        if (M.getEntry(nRow, j) != 0) {
            df++;
        }
    }
    return df;
}

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);/*from  w  ww.  ja v  a 2 s  . c om*/
        } else if (lambda < 0.0) {
            throw new NonPositiveDefiniteMatrixException(lambda, i, eps);
        } else {
            Sigma.setEntry(i, i, lambda);
        }
    }
    return svd.getU().multiply(Sigma).multiply(svd.getVT());
}

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

/**
 * Computes the inverse of a matrix using the singular value decomposition.
 * //from   ww  w .  ja v  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());
}

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

/**
 * Force a square RealMatrix to be symmetric.
 *
 * @param rm The RealMatrix.//from w ww . j  av a 2s.  co m
 *
 * @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;
}

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

public static double inner_prod(final double[] x, final RealMatrix M, final double[] y) {
    double s = 0.0;
    for (int i = 0; i < M.getRowDimension(); ++i) {
        for (int j = 0; j < M.getColumnDimension(); ++j) {
            s += x[i] * M.getEntry(i, j) * y[j];
        }/*from   www .ja v a2  s  . com*/
    }
    return s;
}

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

public static double sumMatrix(RealMatrix X) {
    double sum = 0.0;
    for (int i = 0; i < X.getRowDimension(); i++) {
        for (int j = 0; j < X.getColumnDimension(); j++) {
            sum += X.getEntry(i, j);/*from  ww w  .j  a  va2s .  c o m*/
        }
    }
    return sum;
}

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//from  w w  w  . j  a v a2s  .  c o  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));
        }
    }
}

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

/**
 * Elementwise multiplication of elements in two arrays. This is equivalent to
 * using A*B in R when both A and B are matrices.
 *
 * @param A a matrix//from  w  ww . ja v a 2s .  c om
 * @param B a matrix of the same dimension as A
 * @return a matrix with elements that are the produce of elements in A and B.
 * @throws org.apache.commons.math3.exception.DimensionMismatchException
 */
public static RealMatrix multiplyElements(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++) {
            M.setEntry(i, j, A.getEntry(i, j) * B.getEntry(i, j));
        }
    }
    return M;
}

From source file:edu.byu.nlp.stats.GammaDistribution.java

/**
 * Samples a new Gamma distributed random variate for each parameter setting specified as elements
 * of the shapes matrix./*from   ww w  .j  a  va2  s.  com*/
 */
public static double[][] sample(RealMatrix shapes, RandomGenerator rnd) {
    double[][] gammas = new double[shapes.getRowDimension()][shapes.getColumnDimension()];
    for (int i = 0; i < shapes.getRowDimension(); i++) {
        for (int j = 0; j < shapes.getColumnDimension(); j++) {
            gammas[i][j] = sample(shapes.getEntry(i, j), rnd);
        }
    }
    return gammas;
}