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:movierecommend.MovieRecommend.java

public static void main(String[] args) throws ClassNotFoundException, SQLException {
    String url = "jdbc:sqlserver://localhost;databaseName=MovieDB;integratedSecurity=true";
    Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver");
    Connection conn = DriverManager.getConnection(url);

    Statement stm = conn.createStatement();
    ResultSet rsRecnik = stm.executeQuery("SELECT Recnik FROM Recnik WHERE (ID_Zanra = 1)"); //citam recnik iz baze za odredjeni zanr
    String recnik[] = null;//from ww w. ja v a2  s. c o  m

    while (rsRecnik.next()) {
        recnik = rsRecnik.getString("Recnik").split(","); //delim recnik na reci

    }

    ResultSet rsFilmovi = stm.executeQuery(
            "SELECT TOP (200) Naziv_Filma, LemmaPlots, " + "ID_Filma FROM Film WHERE (ID_Zanra = 1)");
    List<Film> listaFilmova = new ArrayList<>();
    Film f = null;
    int rb = 0;
    while (rsFilmovi.next()) {
        f = new Film(rb, Integer.parseInt(rsFilmovi.getString("ID_Filma")), rsFilmovi.getString("Naziv_Filma"),
                rsFilmovi.getString("LemmaPlots"));
        listaFilmova.add(f);
        rb++;

    }
    //kreiranje vektorskog modela
    M = MatrixUtils.createRealMatrix(recnik.length, listaFilmova.size());
    System.out.println("Prva tezinska matrica");

    for (int i = 0; i < recnik.length; i++) {
        String recBaza = recnik[i];
        for (Film film : listaFilmova) {
            for (String lemmaRec : film.getPlotLema()) {
                if (recBaza.equals(lemmaRec)) {
                    M.setEntry(i, film.getRb(), M.getEntry(i, film.getRb()) + 1);
                }
            }
        }
    }
    //racunanje tf-idf
    System.out.println("td-idf");
    M = LSA.calculateTfIdf(M);
    System.out.println("SVD");
    //SVD
    SingularValueDecomposition svd = new SingularValueDecomposition(M);
    RealMatrix V = svd.getV();
    RealMatrix Vk = V.getSubMatrix(0, V.getRowDimension() - 1, 0, brojDimenzija - 1); //dimenzija je poslednji argument
    //kosinusna slicnost
    System.out.println("Cosin simmilarity");
    CallableStatement stmTop = conn.prepareCall("{call Dodaj_TopList(?,?,?)}");

    for (int j = 0; j < listaFilmova.size(); j++) {
        Film fl = listaFilmova.get(j);
        List<Film> lFilmova1 = new ArrayList<>();
        lFilmova1.add(listaFilmova.get(j));
        double sim = 0.0;
        for (int k = 0; k < listaFilmova.size(); k++) {
            // System.out.println(listaFilmova.size());                
            sim = LSA.cosinSim(j, k, Vk.transpose());
            listaFilmova.get(k).setSimilarity(sim);
            lFilmova1.add(listaFilmova.get(k));
        }
        Collections.sort(lFilmova1);
        for (int k = 2; k < 13; k++) {
            stmTop.setString(1, fl.getID() + "");
            stmTop.setString(2, lFilmova1.get(k).getID() + "");
            stmTop.setString(3, lFilmova1.get(k).getSimilarity() + "");
            stmTop.execute();
        }

    }

    stm.close();
    rsRecnik.close();
    rsFilmovi.close();
    conn.close();

}

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");
            }//  w  w  w  .  ja  va 2  s  .  c  o  m
        }
    }
    return m;
}

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];
        }//  w ww  .  jav a  2 s . co  m
    }
    return s;
}

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

/**
 * Force a square RealMatrix to be symmetric.
 *
 * @param rm The RealMatrix.// w  ww  .  ja v  a2s.  c o 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: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  w  ww  . ja v  a  2s  .  co  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// w  w w .  ja v a 2s  .c om
 * @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/* w w  w  . j a  v  a  2 s .  c  o m*/
 * @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.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   ww  w.j  a v  a  2  s.c  o  m*/
        } 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.HilbertSpace.java

public static double inner_prod(final RealVector x, final RealMatrix M, final RealVector y) {
    // return x.dotProduct( M.operate( y ) );
    double s = 0.0;
    for (int i = 0; i < M.getRowDimension(); ++i) {
        for (int j = 0; j < M.getColumnDimension(); ++j) {
            s += x.getEntry(i) * M.getEntry(i, j) * y.getEntry(j);
        }//from w ww  .j  a va  2s  .com
    }
    return s;
}

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

/**
 * Computes the inverse of a matrix using the singular value decomposition.
 * //w w  w  .jav a  2 s .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());
}