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

List of usage examples for org.apache.commons.math3.linear RealMatrix transpose

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Introduction

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

Prototype

RealMatrix transpose();

Source Link

Document

Returns the transpose of this 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  w w  w.ja va2s .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:net.myrrix.online.generation.MergeModels.java

public static void merge(File model1File, File model2File, File mergedModelFile) throws IOException {

    Generation model1 = GenerationSerializer.readGeneration(model1File);
    Generation model2 = GenerationSerializer.readGeneration(model2File);

    FastByIDMap<float[]> x1 = model1.getX();
    FastByIDMap<float[]> y1 = model1.getY();
    FastByIDMap<float[]> x2 = model2.getX();
    FastByIDMap<float[]> y2 = model2.getY();

    RealMatrix translation = multiply(y1, x2);

    FastByIDMap<float[]> xMerged = MatrixUtils.multiply(translation.transpose(), x1);

    FastIDSet emptySet = new FastIDSet();
    FastByIDMap<FastIDSet> knownItems = new FastByIDMap<FastIDSet>();
    LongPrimitiveIterator it = xMerged.keySetIterator();
    while (it.hasNext()) {
        knownItems.put(it.nextLong(), emptySet);
    }/*from  w  ww . jav  a2 s . c  o  m*/

    FastIDSet x1ItemTagIDs = model1.getItemTagIDs();
    FastIDSet y2UserTagIDs = model2.getUserTagIDs();

    Generation merged = new Generation(knownItems, xMerged, y2, x1ItemTagIDs, y2UserTagIDs);
    GenerationSerializer.writeGeneration(merged, mergedModelFile);
}

From source file:com.google.location.lbs.gnss.gps.pseudorange.EcefToTopocentricConverter.java

/**
 * Transformation of {@code inputVectorMeters} with origin at {@code originECEFMeters} into
 * topocentric coordinate system. The result is {@code TopocentricAEDValues} containing azimuth
 * from north +ve clockwise, radians; elevation angle, radians; distance, vector length meters
 *
 * <p>Source: http://www.navipedia.net/index.php/Transformations_between_ECEF_and_ENU_coordinates
 * http://kom.aau.dk/~borre/life-l99/topocent.m
 *
 *///from ww  w . java  2s  .  c om
public static TopocentricAEDValues convertCartesianToTopocentericRadMeters(final double[] originECEFMeters,
        final double[] inputVectorMeters) {

    GeodeticLlaValues latLngAlt = Ecef2LlaConverter.convertECEFToLLACloseForm(originECEFMeters[0],
            originECEFMeters[1], originECEFMeters[2]);

    RealMatrix rotationMatrix = Ecef2EnuConverter
            .getRotationMatrix(latLngAlt.latitudeRadians, latLngAlt.longitudeRadians).transpose();
    double[] eastNorthUpVectorMeters = GpsMathOperations
            .matrixByColVectMultiplication(rotationMatrix.transpose().getData(), inputVectorMeters);
    double eastMeters = eastNorthUpVectorMeters[EAST_IDX];
    double northMeters = eastNorthUpVectorMeters[NORTH_IDX];
    double upMeters = eastNorthUpVectorMeters[UP_IDX];

    // calculate azimuth, elevation and height from the ENU values
    double horizontalDistanceMeters = Math.hypot(eastMeters, northMeters);
    double azimuthRadians;
    double elevationRadians;

    if (horizontalDistanceMeters < MIN_DISTANCE_MAGNITUDE_METERS) {
        elevationRadians = Math.PI / 2.0;
        azimuthRadians = 0;
    } else {
        elevationRadians = Math.atan2(upMeters, horizontalDistanceMeters);
        azimuthRadians = Math.atan2(eastMeters, northMeters);
    }
    if (azimuthRadians < 0) {
        azimuthRadians += 2 * Math.PI;
    }

    double distanceMeters = Math.sqrt(Math.pow(inputVectorMeters[0], 2) + Math.pow(inputVectorMeters[1], 2)
            + Math.pow(inputVectorMeters[2], 2));
    return new TopocentricAEDValues(elevationRadians, azimuthRadians, distanceMeters);
}

From source file:com.rapidminer.tools.math.LinearRegression.java

/** Calculates the coefficients of linear ridge regression. */
public static double[] performRegression(Matrix a, Matrix b, double ridge) {
    RealMatrix x = MatrixUtils.createRealMatrix(a.getArray());
    RealMatrix y = MatrixUtils.createRealMatrix(b.getArray());
    int numberOfColumns = x.getColumnDimension();
    double[] coefficients = new double[numberOfColumns];
    RealMatrix xTransposed = x.transpose();
    Matrix result;//from  ww  w . j av a  2 s .  c  om
    boolean finished = false;
    while (!finished) {
        RealMatrix xTx = xTransposed.multiply(x);

        for (int i = 0; i < numberOfColumns; i++) {
            xTx.addToEntry(i, i, ridge);
        }

        RealMatrix xTy = xTransposed.multiply(y);
        coefficients = xTy.getColumn(0);

        try {
            // do not use Apache LUDecomposition for solve instead because it creates different
            // results
            result = new Matrix(xTx.getData()).solve(new Matrix(coefficients, coefficients.length));
            for (int i = 0; i < numberOfColumns; i++) {
                coefficients[i] = result.get(i, 0);
            }
            finished = true;
        } catch (Exception ex) {
            double ridgeOld = ridge;
            if (ridge > 0) {
                ridge *= 10;
            } else {
                ridge = 0.0000001;
            }
            finished = false;
            logger.warning("Error during calculation: " + ex.getMessage() + ": Increasing ridge factor from "
                    + ridgeOld + " to " + ridge);
        }
    }
    return coefficients;
}

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

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

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

public static double[] weighted(double[][] x, double[] y, double[] weights, boolean intercept) {
    RealMatrix predictor;
    if (intercept) {
        int nRows = x.length;
        int nCols = x[0].length + 1;
        predictor = MatrixUtils.createRealMatrix(nRows, nCols);
        for (int iRow = 0; iRow < nRows; iRow++) {
            predictor.setEntry(iRow, 0, 1.0);
            for (int iCol = 1; iCol < nCols; iCol++) {
                predictor.setEntry(iRow, iCol, x[iRow][iCol - 1]);
            }//from   w  w  w.ja va2  s.c o  m
        }
    } else {
        predictor = MatrixUtils.createRealMatrix(x);
    }
    RealVector responseVector = MatrixUtils.createRealVector(y);
    RealMatrix weightsMatrix = MatrixUtils.createRealDiagonalMatrix(weights);
    RealMatrix predictorTransposed = predictor.transpose();
    RealMatrix predictorTransposedTimesWeights = predictorTransposed
            .multiply(weightsMatrix.multiply(predictor));
    CholeskyDecomposition choleskyDecomposition = new CholeskyDecomposition(predictorTransposedTimesWeights);
    RealVector vectorToSolve = predictorTransposed.operate(weightsMatrix.operate(responseVector));
    RealVector result = choleskyDecomposition.getSolver().solve(vectorToSolve);
    return result.toArray();
}

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * Lanczos tridiagonalization for a symmetric matrix C to make s * s tridiagonal matrix T.
 *
 * @see http://www.cas.mcmaster.ca/~qiao/publications/spie05.pdf
 * @param C target symmetric matrix/*from   w  w  w. j  a va2  s .c  o  m*/
 * @param a initial vector
 * @param T result is stored here
 */
public static void lanczosTridiagonalization(@Nonnull final RealMatrix C, @Nonnull final double[] a,
        @Nonnull final RealMatrix T) {
    Preconditions.checkArgument(Arrays.deepEquals(C.getData(), C.transpose().getData()),
            "Target matrix C must be a symmetric matrix");
    Preconditions.checkArgument(C.getColumnDimension() == a.length,
            "Column size of A and length of a should be same");
    Preconditions.checkArgument(T.getRowDimension() == T.getColumnDimension(), "T must be a square matrix");

    int s = T.getRowDimension();

    // initialize T with zeros
    T.setSubMatrix(new double[s][s], 0, 0);

    RealVector a0 = new ArrayRealVector(a.length);
    RealVector r = new ArrayRealVector(a);

    double beta0 = 1.d;

    for (int i = 0; i < s; i++) {
        RealVector a1 = r.mapDivide(beta0);
        RealVector Ca1 = C.operate(a1);

        double alpha1 = a1.dotProduct(Ca1);

        r = Ca1.add(a1.mapMultiply(-1.d * alpha1)).add(a0.mapMultiply(-1.d * beta0));

        double beta1 = r.getNorm();

        T.setEntry(i, i, alpha1);
        if (i - 1 >= 0) {
            T.setEntry(i, i - 1, beta0);
        }
        if (i + 1 < s) {
            T.setEntry(i, i + 1, beta1);
        }

        a0 = a1.copy();
        beta0 = beta1;
    }
}

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * Find eigenvalues and eigenvectors of given tridiagonal matrix T.
 *
 * @see http://web.csulb.edu/~tgao/math423/s94.pdf
 * @see http://stats.stackexchange.com/questions/20643/finding-matrix-eigenvectors-using-qr-
 *      decomposition/*w  w w  . j ava  2  s.c  om*/
 * @param T target tridiagonal matrix
 * @param nIter number of iterations for the QR method
 * @param eigvals eigenvalues are stored here
 * @param eigvecs eigenvectors are stored here
 */
public static void tridiagonalEigen(@Nonnull final RealMatrix T, @Nonnull final int nIter,
        @Nonnull final double[] eigvals, @Nonnull final RealMatrix eigvecs) {
    Preconditions.checkArgument(Arrays.deepEquals(T.getData(), T.transpose().getData()),
            "Target matrix T must be a symmetric (tridiagonal) matrix");
    Preconditions.checkArgument(eigvecs.getRowDimension() == eigvecs.getColumnDimension(),
            "eigvecs must be a square matrix");
    Preconditions.checkArgument(T.getRowDimension() == eigvecs.getRowDimension(),
            "T and eigvecs must be the same shape");
    Preconditions.checkArgument(eigvals.length == eigvecs.getRowDimension(),
            "Number of eigenvalues and eigenvectors must be same");

    int nEig = eigvals.length;

    // initialize eigvecs as an identity matrix
    eigvecs.setSubMatrix(eye(nEig), 0, 0);

    RealMatrix T_ = T.copy();

    for (int i = 0; i < nIter; i++) {
        // QR decomposition for the tridiagonal matrix T
        RealMatrix R = new Array2DRowRealMatrix(nEig, nEig);
        RealMatrix Qt = new Array2DRowRealMatrix(nEig, nEig);
        tridiagonalQR(T_, R, Qt);

        RealMatrix Q = Qt.transpose();
        T_ = R.multiply(Q);
        eigvecs.setSubMatrix(eigvecs.multiply(Q).getData(), 0, 0);
    }

    // diagonal elements correspond to the eigenvalues
    for (int i = 0; i < nEig; i++) {
        eigvals[i] = T_.getEntry(i, i);
    }
}

From source file:gamlss.utilities.MatrixFunctions.java

/**
* <p>Compute the "hat" matrix.//from w  w  w.  j  av a2 s .c  o m
* </p>
* <p>The hat matrix is defined in terms of the design matrix X
*  by X(X<sup>T</sup>X)<sup>-1</sup>X<sup>T</sup>
* </p>
* <p>The implementation here uses the QR decomposition to compute the
* hat matrix as Q I<sub>p</sub>Q<sup>T</sup> where I<sub>p</sub> is the
* p-dimensional identity matrix augmented by 0's.  This computational
* formula is from "The Hat Matrix in Regression and ANOVA",
* David C. Hoaglin and Roy E. Welsch,
* <i>The American Statistician</i>, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
*@param m - matrix
* @return the hat matrix
*/
public static RealMatrix calculateHat(final BlockRealMatrix m) {
    QRDecomposition qr = new QRDecomposition(m);
    // Create augmented identity matrix
    RealMatrix qM = qr.getQ();
    final int p = qr.getR().getColumnDimension();
    final int n = qM.getColumnDimension();
    Array2DRowRealMatrix augI = new Array2DRowRealMatrix(n, n);
    double[][] augIData = augI.getDataRef();
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            if (i == j && i < p) {
                augIData[i][j] = 1d;
            } else {
                augIData[i][j] = 0d;
            }
        }
    }
    // Compute and return Hat matrix
    return qM.multiply(augI).multiply(qM.transpose());
}

From source file:io.warp10.script.functions.TRANSPOSE.java

@Override
public Object apply(WarpScriptStack stack) throws WarpScriptException {

    Object o = stack.pop();//from w  ww.j  a va2  s .c o  m

    if (o instanceof RealMatrix) {
        RealMatrix matrix = (RealMatrix) o;

        stack.push(matrix.transpose());
    } else {
        throw new WarpScriptException(getName() + " expects a matrix on top of the stack.");
    }

    return stack;
}