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

List of usage examples for org.apache.commons.math3.linear Array2DRowRealMatrix transpose

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Introduction

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

Prototype

public RealMatrix transpose()

Source Link

Usage

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java

@Override
public void deleteAlternative(Alternative alt) {
    final int n = alternatives.size();
    final int index = alternatives.indexOf(alt);
    alternatives.remove(alt);//from  w w  w. j av  a  2 s .c o  m

    double select[][] = new double[n - 1][n];
    for (int i = 0; i < n; ++i) {
        if (i < index) {
            select[i][i] = 1;
        } else if (i > index) {
            select[i - 1][i] = 1;
        }
    }
    Array2DRowRealMatrix m = new Array2DRowRealMatrix(select);

    final RealVector newMeanVector = m.operate(meanVector);
    final RealMatrix newCovarianceMatrix = covarianceMatrix.multiply(m.transpose()).preMultiply(m);
    fireEvents(setMeanVectorInternal(newMeanVector), setCovarianceMatrixInternal(newCovarianceMatrix));
}

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java

@Override
public void reorderAlternatives(List<Alternative> alts) {
    final int n = alternatives.size();

    double permute[][] = new double[n][n];
    for (int i = 0; i < n; ++i) {
        int j = alternatives.indexOf(alts.get(i));
        permute[i][j] = 1;/* w w  w .  ja va2  s  .  c  om*/
    }
    Array2DRowRealMatrix m = new Array2DRowRealMatrix(permute);

    alternatives.clear();
    alternatives.addAll(alts);

    final RealVector newMeanVector = m.operate(meanVector);
    final RealMatrix newCovarianceMatrix = covarianceMatrix.multiply(m.transpose()).preMultiply(m);
    fireEvents(setMeanVectorInternal(newMeanVector), setCovarianceMatrixInternal(newCovarianceMatrix));
}

From source file:MultivariateNormalDistribution.java

/**
 * Creates a multivariate normal distribution with the given mean vector and
 * covariance matrix./*from  w  ww . j  a  v a2s.  c  o m*/
 * <br/>
 * The number of dimensions is equal to the length of the mean vector
 * and to the number of rows and columns of the covariance matrix.
 * It is frequently written as "p" in formulae.
 *
 * @param rng Random Number Generator.
 * @param means Vector of means.
 * @param covariances Covariance matrix.
 * @throws DimensionMismatchException if the arrays length are
 * inconsistent.
 * @throws SingularMatrixException if the eigenvalue decomposition cannot
 * be performed on the provided covariance matrix.
 * @throws NonPositiveDefiniteMatrixException if any of the eigenvalues is
 * negative.
 */
public MultivariateNormalDistribution(RandomGenerator rng, final double[] means, final double[][] covariances)
        throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException {
    super(rng, means.length);

    final int dim = means.length;

    if (covariances.length != dim) {
        throw new DimensionMismatchException(covariances.length, dim);
    }

    for (int i = 0; i < dim; i++) {
        if (dim != covariances[i].length) {
            throw new DimensionMismatchException(covariances[i].length, dim);
        }
    }

    this.means = MathArrays.copyOf(means);

    covarianceMatrix = new Array2DRowRealMatrix(covariances);

    // Covariance matrix eigen decomposition.
    final EigenDecomposition covMatDec = new EigenDecomposition(covarianceMatrix);

    // Compute and store the inverse.
    covarianceMatrixInverse = covMatDec.getSolver().getInverse();
    // Compute and store the determinant.
    covarianceMatrixDeterminant = covMatDec.getDeterminant();

    // Eigenvalues of the covariance matrix.
    final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();

    for (int i = 0; i < covMatEigenvalues.length; i++) {
        if (covMatEigenvalues[i] < 0) {
            throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
        }
    }

    // Matrix where each column is an eigenvector of the covariance matrix.
    final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
    for (int v = 0; v < dim; v++) {
        final double[] evec = covMatDec.getEigenvector(v).toArray();
        covMatEigenvectors.setColumn(v, evec);
    }

    final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

    // Scale each eigenvector by the square root of its eigenvalue.
    for (int row = 0; row < dim; row++) {
        final double factor = FastMath.sqrt(covMatEigenvalues[row]);
        for (int col = 0; col < dim; col++) {
            tmpMatrix.multiplyEntry(row, col, factor);
        }
    }

    samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
}

From source file:com.clust4j.utils.MatTests.java

@Test
public void testTrans() {
    final double[][] mat = new double[][] { new double[] { -1.000, -1.000, -1.000 },
            new double[] { 10.000, 10.000, 10.000 }, new double[] { 90.000, 90.000, 90.000 } };

    Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(mat);
    assertTrue(MatUtils.equalsExactly(MatUtils.transpose(mat), matrix.transpose().getData()));
}

From source file:org.apache.solr.client.solrj.io.eval.TransposeEvaluator.java

@Override
public Object doWork(Object value) throws IOException {
    if (null == value) {
        return null;
    } else if (value instanceof Matrix) {
        Matrix matrix = (Matrix) value;/*ww w .  j  a va2 s.com*/
        double[][] data = matrix.getData();
        Array2DRowRealMatrix amatrix = new Array2DRowRealMatrix(data);
        Array2DRowRealMatrix tmatrix = (Array2DRowRealMatrix) amatrix.transpose();
        return new Matrix(tmatrix.getData());
    } else {
        throw new IOException("matrix parameter expected for transpose function");
    }
}

From source file:org.drugis.mtc.yadas.MultivariateGaussian.java

public double compute(double[] x, double[] mu, double[][] sigma) {
    int d = x.length;
    if (d != mu.length || d != sigma.length) {
        throw new IllegalArgumentException("All arguments need to be of equal length");
    }/*w w w. j ava 2 s  .c o m*/

    Array2DRowRealMatrix sigmaM = new Array2DRowRealMatrix(sigma);
    Array2DRowRealMatrix xM = new Array2DRowRealMatrix(x);
    Array2DRowRealMatrix muM = new Array2DRowRealMatrix(mu);

    // Return the log of:
    // 1/sqrt(2pi^d * det(sigma)) * e^(-.5 (x - mu)' inv(sigma) (x - mu))
    // Which is:
    // -log(sqrt(2pi^d * det(sigma))) + -.5 (x - mu)' inv(sigma) (x - mu) 
    Array2DRowRealMatrix dM = xM.subtract(muM);
    LUDecomposition sigmaD = new LUDecomposition(sigmaM, Precision.SAFE_MIN);
    try {
        RealMatrix sigmaInv = sigmaD.getSolver().getInverse();
        return -0.5 * (Math.log(2 * Math.PI) * d + Math.log(sigmaD.getDeterminant())
                + dM.transpose().multiply(sigmaInv).multiply(dM).getEntry(0, 0));
    } catch (RuntimeException e) {
        System.out.println(sigmaM);
        throw e;
    }
    /*      RealMatrix sigmaInv = sigmaM.inverse();
          return -0.5 * (
             Math.log(2 * Math.PI) * d + Math.log(sigmaM.getDeterminant()) +
             dM.transpose().multiply(sigmaInv).multiply(dM).getEntry(0, 0)); */
}

From source file:org.interpss.opf.dc.impl.EqIneqMatrixBuilder.java

public Array2DRowRealMatrix formCeq() {
    int numOfBus = opfNet.getNoActiveBus();
    int numOfGen = opfNet.getNoOfGen();

    Array2DRowRealMatrix genBusAdjacent = new Array2DRowRealMatrix(numOfBus, numOfGen);
    int genCount = 0;
    for (Bus b : opfNet.getBusList()) {
        // set the elements in genBusAdjacent matrix to ONE if there is a
        // generator ;
        /*/* ww w . j a v a  2s  .c  o m*/
         * in the case of multi-gen ,all are treated as a union;
         * not suitable for a case with multi-generators connected to one
         * bus;
         */
        if (opfNet.isOpfGenBus(b)) {
            genBusAdjacent.setEntry(b.getSortNumber(), genCount, 1);
            genCount++;
        }
    }
    Array2DRowRealMatrix BrTranspose = (Array2DRowRealMatrix) this.getReducedBusAdmittance().transpose();

    Array2DRowRealMatrix CeqTranspose = new Array2DRowRealMatrix(numOfBus, numOfBus + numOfGen - 1);
    CeqTranspose.setSubMatrix(genBusAdjacent.getData(), 0, 0); // Ceq'={genBusAdjacent,BrTranspose}
    CeqTranspose.setSubMatrix(BrTranspose.scalarMultiply(-1).getData(), 0, numOfGen);
    return (Array2DRowRealMatrix) CeqTranspose.transpose();
}

From source file:org.interpss.opf.dc.impl.EqIneqMatrixBuilder.java

public Array2DRowRealMatrix formCiq() {
    int numOfBranch = opfNet.getNoActiveBranch();
    int numOfBus = opfNet.getNoActiveBus();
    int numOfGen = opfNet.getNoOfGen();

    double bij = DEFAULT_BIJ;

    int braIndex = 0;
    Array2DRowRealMatrix braAdmDiag = new Array2DRowRealMatrix(numOfBranch, numOfBranch);
    Array2DRowRealMatrix braBusAdjacent = new Array2DRowRealMatrix(numOfBranch, numOfBus - 1);
    int swingIndex = this.getSwingBusIndex();
    for (Branch bra : opfNet.getBranchList()) {
        //if (bra.isAclfBranch()) {
        DclfOpfBranch aclfBra = (DclfOpfBranch) bra;
        // create branch admittance matrix
        bij = (aclfBra.getZ().getImaginary() > 0.00001) ? 1 / aclfBra.getZ().getImaginary() : DEFAULT_BIJ; // in case x=0;
        braAdmDiag.setEntry(braIndex, braIndex, bij);

        // create branch-bus connected or adjacent matrix;
        if (!((DclfOpfBus) bra.getFromBus()).isSwing()) {
            int fromBusIndex = bra.getFromBus().getSortNumber();
            fromBusIndex = (fromBusIndex < swingIndex) ? fromBusIndex : fromBusIndex - 1; // insure nonswing bus;
            braBusAdjacent.setEntry(braIndex, fromBusIndex, 1);
        }/* w w  w.  j av a  2 s  .  c  o m*/
        if (!((DclfOpfBus) bra.getToBus()).isSwing()) {
            int toBusIndex = bra.getToBus().getSortNumber();
            toBusIndex = (toBusIndex < swingIndex) ? toBusIndex : toBusIndex - 1;
            braBusAdjacent.setEntry(braIndex, toBusIndex, -1);
        }
        braIndex++;
        //}
    }

    int genIndex = 0;
    Array2DRowRealMatrix eyeGenP = new Array2DRowRealMatrix(numOfGen, numOfGen);
    for (Bus bus : opfNet.getBusList()) {
        if (opfNet.isOpfGenBus(bus)) {
            eyeGenP.setEntry(genIndex, genIndex, 1);
            genIndex++;
        }
    }
    Array2DRowRealMatrix DAr = braAdmDiag.multiply(braBusAdjacent);
    // Ciq'={Ot,-braAdmDiag*braBusAdjacent;
    //     -Ot,braAdmDiag*braBusAdjacent;
    //      eyeGenP,Op;
    //      -eyeGenP,-Op}

    Array2DRowRealMatrix CiqTranspose = new Array2DRowRealMatrix(numOfBranch * 2 + numOfGen * 2,
            numOfGen + numOfBus - 1);
    CiqTranspose.setSubMatrix(DAr.scalarMultiply(-1).getData(), 0, numOfGen);
    CiqTranspose.setSubMatrix(DAr.getData(), numOfBranch, numOfGen);
    CiqTranspose.setSubMatrix(eyeGenP.getData(), 2 * numOfBranch, 0);
    CiqTranspose.setSubMatrix(eyeGenP.scalarMultiply(-1).getData(), 2 * numOfBranch + numOfGen, 0);
    // Ciq=CiqTranspose'
    return (Array2DRowRealMatrix) CiqTranspose.transpose();
}

From source file:org.pmad.gmm.MyMND.java

/**
 * Creates a multivariate normal distribution with the given mean vector and
 * covariance matrix.//from   ww w  .  ja  va  2  s. c o  m
 * <br/>
 * The number of dimensions is equal to the length of the mean vector
 * and to the number of rows and columns of the covariance matrix.
 * It is frequently written as "p" in formulae.
 *
 * @param rng Random Number Generator.
 * @param means Vector of means.
 * @param covariances Covariance matrix.
 * @throws DimensionMismatchException if the arrays length are
 * inconsistent.
 * @throws SingularMatrixException if the eigenvalue decomposition cannot
 * be performed on the provided covariance matrix.
 * @throws NonPositiveDefiniteMatrixException if any of the eigenvalues is
 * negative.
 */
public MyMND(RandomGenerator rng, final double[] means, final double[][] covariances)
        throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException {
    super(rng, means.length);

    final int dim = means.length;

    if (covariances.length != dim) {
        throw new DimensionMismatchException(covariances.length, dim);
    }

    for (int i = 0; i < dim; i++) {
        if (dim != covariances[i].length) {
            throw new DimensionMismatchException(covariances[i].length, dim);
        }
    }

    this.means = MathArrays.copyOf(means);
    double msum = 0;
    for (int i = 0; i < covariances.length; i++) {
        for (int j = 0; j < covariances.length; j++) {
            msum += covariances[i][j];
        }
    }
    msum /= covariances.length * covariances.length;
    //        System.out.print("in");
    MyEDC covMatDec = null;
    double a = -1;
    while (true) {
        try {

            covarianceMatrix = new Array2DRowRealMatrix(covariances);

            covMatDec = new MyEDC(covarianceMatrix);

            // Compute and store the inverse.
            covarianceMatrixInverse = covMatDec.getSolver().getInverse();
            a *= -1;
            break;
        } catch (NoDataException e) {
            e.printStackTrace();
        } catch (NullArgumentException e) {
            e.printStackTrace();
        } catch (MathArithmeticException e) {
            e.printStackTrace();
        } catch (SingularMatrixException e) {
            //            System.out.print("S");
            for (int i = 0; i < covariances.length; i++) {
                double add = covariances[i][i] == 0 ? msum : covariances[i][i];
                covariances[i][i] += new Random().nextDouble() * add * 0.01;
            }
        }
        //         catch (MaxCountExceededException e) {
        ////            e.printStackTrace();
        ////            System.out.print("M"+msum);
        //            for (int i = 0; i < covariances.length; i++) {
        //               for (int j = i; j < covariances.length; j++) {
        //                  double add = covariances[i][j] == 0?msum:covariances[i][j];
        //                  add = new Random().nextDouble()*add*0.1*a;
        //                  covariances[i][j] += add;
        //                  covariances[j][i] += add;
        //               }
        //            }
        ////            break;
        //         }
    }
    // Compute and store the determinant.
    covarianceMatrixDeterminant = covMatDec.getDeterminant();

    // Eigenvalues of the covariance matrix.
    final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();

    for (int i = 0; i < covMatEigenvalues.length; i++) {
        if (covMatEigenvalues[i] < 0) {
            throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
        }
    }

    // Matrix where each column is an eigenvector of the covariance matrix.
    final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
    for (int v = 0; v < dim; v++) {
        final double[] evec = covMatDec.getEigenvector(v).toArray();
        covMatEigenvectors.setColumn(v, evec);
    }

    final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

    // Scale each eigenvector by the square root of its eigenvalue.
    for (int row = 0; row < dim; row++) {
        final double factor = FastMath.sqrt(covMatEigenvalues[row]);
        for (int col = 0; col < dim; col++) {
            tmpMatrix.multiplyEntry(row, col, factor);
        }
    }

    samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
}

From source file:outlineDescriptor.ODUtils.java

/**
 * Generates a 2D covariance matrix for a distribution function
 *
 * @param vX  X component/*from w w  w. ja  v a 2s .c  o  m*/
 * @param vY  Y component
 * @param eV1 First eigenvalue
 * @param eV2 Second eigenvalue
 *
 * @return Resulting covariance matrix
 */
public static Array2DRowRealMatrix getDistributionCovMatrix(double vX, double vY, double eV1, double eV2) {

    double multiplier = 1 / (vX * vX + vY * vY);

    Array2DRowRealMatrix vectMat = new Array2DRowRealMatrix(2, 2);

    Array2DRowRealMatrix valMat = new Array2DRowRealMatrix(2, 2);

    valMat.setEntry(0, 0, eV1);
    valMat.setEntry(1, 1, eV2);

    vectMat.setEntry(0, 0, vX);
    vectMat.setEntry(0, 1, vY);

    vectMat.setEntry(1, 0, -vY);
    vectMat.setEntry(1, 1, vX);

    Array2DRowRealMatrix vE = vectMat.multiply(valMat);
    Array2DRowRealMatrix tempCov = vE.multiply((Array2DRowRealMatrix) vectMat.transpose());

    return (Array2DRowRealMatrix) tempCov.scalarMultiply(multiplier);

}