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:com.itemanalysis.psychometrics.factoranalysis.GPArotation.java

private RealMatrix getNormalizingWeights(RealMatrix A, boolean normalize) {
    int nrow = A.getRowDimension();
    int ncol = A.getColumnDimension();
    final double[] w = new double[nrow];

    RealMatrix W = new Array2DRowRealMatrix(nrow, ncol);
    if (!normalize) {
        W.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
            @Override/*from w  w  w . j  av a 2  s .c o  m*/
            public double visit(int row, int column, double value) {
                return 1.0;
            }
        });
        return W;
    }

    //compute row sum of squared loadings
    A.walkInRowOrder(new DefaultRealMatrixPreservingVisitor() {
        @Override
        public void visit(int row, int column, double value) {
            w[row] += value * value;
        }
    });

    //compute normalizing weights for the matrix
    W.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return Math.sqrt(w[row]);
        }
    });
    return W;
}

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From source file:edu.cudenver.bios.power.test.paper.TestConditionalOrthogonalPolynomial2Factor.java

/**
 * //from w w  w . j av a 2 s .  c o m
 * @param checker
 * @param outputFilename
 * @param params
 */
private void checkPower(PowerChecker checker, String title, String description, String outputFilename,
        GLMMPowerParameters params) {
    /* 
     * get orthogonal contrasts for within subject factors
     * Log base 2 spacing Clip (2,4,16) and Region(2,8,32) 
     */
    ArrayList<Factor> factorList = new ArrayList<Factor>();
    factorList.add(factorA);
    factorList.add(factorB);
    OrthogonalPolynomialContrastCollection collection = OrthogonalPolynomials.withinSubjectContrast(factorList);
    params.setWithinSubjectContrast(collection.getInteractionContrast(factorList).getContrastMatrix());

    // theta critical matrix used to back-tranform beta from U
    //    THETA = {.25}#{.5 1 -1 .5}; * =Theta(cr) from 1st sentence *after* 
    //  equation 7, Coffey and Muller (2003); 
    double[][] thetaData = { { 0.5, 1, -1, 0.5 } };
    RealMatrix thetaCr = (new Array2DRowRealMatrix(thetaData)).scalarMultiply(0.25);

    // loop over the various sigma matrices
    //Test[] testList = Test.values();
    Test[] testList = { Test.UNIREP, Test.UNIREP_BOX, Test.UNIREP_GEISSER_GREENHOUSE, Test.UNIREP_HUYNH_FELDT };
    for (Test test : testList) {
        params.clearTestList();
        params.addTest(test);

        for (RealMatrix sigStar : sigmaStars) {
            RealMatrix U = params.getWithinSubjectContrast();
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *;
            RealMatrix sigmaTemp = U.multiply(sigStar).multiply(U.transpose());
            int dimension = sigmaTemp.getRowDimension();
            RealMatrix Uother = MatrixUtils.getRealMatrixWithFilledValue(dimension, 1,
                    1 / Math.sqrt(U.getColumnDimension()));
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorA).getContrastMatrix());
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorB).getContrastMatrix());
            double varianceMean = (double) sigStar.getTrace() / (double) sigStar.getColumnDimension();
            RealMatrix sigmaError = sigmaTemp
                    .add(Uother.multiply(Uother.transpose()).scalarMultiply(varianceMean));

            if (verbose)
                printMatrix("Sigma Error", sigmaError);
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *; 
            RealMatrix beta = thetaCr.multiply(U.transpose());
            params.setSigmaError(sigmaError);
            params.setBeta(new FixedRandomMatrix(beta.getData(), null, false));

            checker.checkPower(params);
        }

    }

    // output the results
    try {
        ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
        reportBuilder.createValidationReportAsStdout(checker, title, false);
        reportBuilder.createValidationReportAsLaTex(outputFilename, title, AUTHOR, description, params,
                checker);
    } catch (Exception e) {
        System.err.println(e.getMessage());
    }

    assertTrue(checker.isSASDeviationBelowTolerance());
    checker.reset();
}

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From source file:edu.cudenver.bios.matrix.test.TestMatrixOrthonormalization.java

/**
 * Verify that the Q'Q = I for the Q matrix produced by the
 * orthonormalization//from   w  ww  . j a va  2  s  .com
 */
public void testQQisIdentity() {
    RealMatrix Q = norm.getQ();

    // verify that Q'Q = identity
    RealMatrix shouldBeIdentityMatrix = Q.transpose().multiply(Q);
    // make sure the matrix is sqaure
    if (!shouldBeIdentityMatrix.isSquare()) {
        fail();
    }
    // make sure the diagonal elements are one (within tolerance), and off diagonals
    // are zero (within tolerance)
    for (int r = 0; r < shouldBeIdentityMatrix.getRowDimension(); r++) {
        for (int c = 0; c < shouldBeIdentityMatrix.getColumnDimension(); c++) {
            double shouldBeValue = (r == c) ? 1 : 0;
            if (Precision.compareTo(shouldBeIdentityMatrix.getEntry(r, c), shouldBeValue, TOLERANCE) != 0)
                fail();
        }
    }
    assertTrue(true);
}

---

From source file:edu.cudenver.bios.power.test.paper.TestConditionalOrthogonalPolynomial2Factor.java

/**
 * Write the matrix to std out/*  w ww .  java 2  s  .  c  o m*/
 * @param m
 */
private void printMatrix(String title, RealMatrix m) {
    System.out.println(title);
    DecimalFormat Number = new DecimalFormat("#0.00000000000000000000");
    for (int row = 0; row < m.getRowDimension(); row++) {
        for (int col = 0; col < m.getColumnDimension(); col++) {
            System.out.print(Number.format(m.getEntry(row, col)) + "\t");
        }
        System.out.print("\n");
    }
}

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From source file:hello.UserProfileEigenModeler.java

public void generate(String site, String user, RealMatrix matrix) {
    LOG.info(String.format("Receive aggregated user activity matrix: %s size: %s x %s", user,
            matrix.getRowDimension(), matrix.getColumnDimension()));
    computeStats(matrix);/*from  ww w  .ja va2  s .c o  m*/
    RealMatrix normalizedInputMatrix = normalizeData(matrix);
    int lowVariantColumnCount = 0;
    for (int j = 0; j < normalizedInputMatrix.getColumnDimension(); j++) {
        if (statistics[j].isLowVariant()) {
            lowVariantColumnCount++;
        }
    }

    if (normalizedInputMatrix.getColumnDimension() == lowVariantColumnCount) {
        LOG.info("found user: " + user + " with all features being low variant. Nothing to do...");
        return;
    } else {
        computeCovarianceAndSVD(normalizedInputMatrix, lowVariantColumnCount);//??
        computeDimensionWithMaxVariance();
        computePrincipalComponents();
        maximumL2Norm = new ArrayRealVector(principalComponents.length);
        minimumL2Norm = new ArrayRealVector(principalComponents.length);

        for (int i = 0; i < principalComponents.length; i++) {
            RealMatrix trainingDataTranspose = computeMaxDistanceOnPCs(i);
        }

        return;
    }
}

---

From source file:com.clust4j.algo.DBSCAN.java

/**
 * Constructs an instance of DBSCAN from the provided builder
 * @param builder//from   www. j ava 2 s  .com
 * @param data
 */
protected DBSCAN(final RealMatrix data, final DBSCANParameters planner) {
    super(data, planner);
    this.m = data.getRowDimension();
    this.eps = planner.getEps();

    // Error handle...
    if (this.eps <= 0.0)
        error(new IllegalArgumentException("eps " + "must be greater than 0.0"));

    if (!isValidMetric(this.dist_metric)) {
        warn(this.dist_metric.getName() + " is not valid for " + getName() + ". "
                + "Falling back to default Euclidean dist");
        setSeparabilityMetric(DEF_DIST);
    }

    logModelSummary();
}

---

From source file:ellipsoidFit.FitPoints.java

/**
 * Fit points to the polynomial expression Ax^2 + By^2 + Cz^2 + 2Dxy + 2Exz
 * + 2Fyz + 2Gx + 2Hy + 2Iz = 1 and determine the center and radii of the
 * fit ellipsoid.//  w ww.ja v  a 2s. co m
 * 
 * @param points
 *            the points to be fit to the ellipsoid.
 */
public void fitEllipsoid(ArrayList<ThreeSpacePoint> points) {
    // Fit the points to Ax^2 + By^2 + Cz^2 + 2Dxy + 2Exz
    // + 2Fyz + 2Gx + 2Hy + 2Iz = 1 and solve the system.
    // v = (( d' * d )^-1) * ( d' * ones.mapAddToSelf(1));
    RealVector v = solveSystem(points);

    // Form the algebraic form of the ellipsoid.
    RealMatrix a = formAlgebraicMatrix(v);

    // Find the center of the ellipsoid.
    center = findCenter(a);

    // Translate the algebraic form of the ellipsoid to the center.
    RealMatrix r = translateToCenter(center, a);

    // Generate a submatrix of r.
    RealMatrix subr = r.getSubMatrix(0, 2, 0, 2);

    // subr[i][j] = subr[i][j] / -r[3][3]).
    double divr = -r.getEntry(3, 3);
    for (int i = 0; i < subr.getRowDimension(); i++) {
        for (int j = 0; j < subr.getRowDimension(); j++) {
            subr.setEntry(i, j, subr.getEntry(i, j) / divr);
        }
    }

    // Get the eigenvalues and eigenvectors.
    EigenDecomposition ed = new EigenDecomposition(subr, 0);
    evals = ed.getRealEigenvalues();
    evecs = ed.getEigenvector(0);
    evecs1 = ed.getEigenvector(1);
    evecs2 = ed.getEigenvector(2);

    // Find the radii of the ellipsoid.
    radii = findRadii(evals);
}

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From source file:com.clust4j.algo.preprocess.BoxCoxTransformer.java

/**
 * Inverse transform your matrix. Note: this suffers some
 * accuracy issues due to the log base// ww  w .  jav  a 2s . co m
 */
@Override
public RealMatrix inverseTransform(RealMatrix X) {
    checkFit();

    final int m = X.getRowDimension();
    final int n = X.getColumnDimension();

    if (n != shift.length)
        throw new DimensionMismatchException(n, shift.length);

    double[][] x = X.getData();
    for (int j = 0; j < n; j++) {
        double lam = lambdas[j];
        double ool = 1.0 / lam;

        for (int i = 0; i < m; i++) {
            // If the lambda is near zero, exp to reverse the log:
            if (lam < zero) {
                x[i][j] = FastMath.exp(x[i][j]);
            } else {
                x[i][j] *= lam;
                x[i][j] += 1;
                x[i][j] = FastMath.pow(x[i][j], ool);
            }

            // Add back the shift value:
            x[i][j] += shift[j];
        }
    }

    // Implicit copy in the getData()
    return new Array2DRowRealMatrix(x, false);
}

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From source file:gamlss.utilities.WLSMultipleLinearRegression.java

/**
 * //w w  w  .  j a va  2  s . c o m
 * @param y
 * @param x
 * @param w
 */
private void newSampleDataNoCopy(ArrayRealVector y, RealMatrix x, ArrayRealVector w) {
    for (int row = 0; row < x.getRowDimension(); row++) {
        x.setRowVector(row, x.getRowVector(row).mapMultiplyToSelf(w.getEntry(row)));
    }
    //double[][] xw=x.getData();
    //double[] yw= y.ebeMultiply(w).getDataRef();
    //validateSampleData(xw, yw); //we have already checked this in the gamlss algorithm. 
    newYSampleData(y.ebeMultiply(w));
    newXSampleData(x.getData(), w);
}

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From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

/**
 * Create the barrier function for the Phase I.
 * It is an instance of this class for the constraints: 
 * <br>||Ai.x+bi|| < ci.x+di+t, i=1,...,m
 * @see "S.Boyd and L.Vandenberghe, Convex Optimization, 11.6.2"
 *//*from www  . j ava2  s .co  m*/
public BarrierFunction createPhase1BarrierFunction() {

    final int dimPh1 = dim + 1;
    List<SOCPConstraintParameters> socpConstraintParametersPh1List = new ArrayList<SOCPConstraintParameters>();
    SOCPLogarithmicBarrier bfPh1 = new SOCPLogarithmicBarrier(socpConstraintParametersPh1List, dimPh1);

    for (int i = 0; i < socpConstraintParametersList.size(); i++) {
        SOCPConstraintParameters param = socpConstraintParametersList.get(i);
        RealMatrix A = param.getA();
        RealVector b = param.getB();
        RealVector c = param.getC();
        double d = param.getD();

        RealMatrix APh1 = MatrixUtils.createRealMatrix(A.getRowDimension(), dimPh1);
        APh1.setSubMatrix(A.getData(), 0, 0);
        RealVector bPh1 = b;
        RealVector cPh1 = new ArrayRealVector(c.getDimension() + 1);
        cPh1.setSubVector(0, c);
        cPh1.setEntry(c.getDimension(), 1);
        double dPh1 = d;

        SOCPConstraintParameters paramsPh1 = new SOCPConstraintParameters(APh1.getData(), bPh1.toArray(),
                cPh1.toArray(), dPh1);
        socpConstraintParametersPh1List.add(socpConstraintParametersPh1List.size(), paramsPh1);
    }

    return bfPh1;
}

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