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.github.thorbenlindhauer.cluster.ep.TruncatedGaussianPotentialResolverTest.java

@Test
public void testTwoSidedTruncatedGaussianApproximation() {

    ContinuousVariable variable = new ContinuousVariable("A");
    Scope scope = new Scope(Collections.singleton(variable));

    GaussianFactor factor = StandaloneGaussiaFactorBuilder.withVariables(variable).scope("A").momentForm()
            .parameters(new ArrayRealVector(new double[] { 2.0d }),
                    new Array2DRowRealMatrix(new double[] { 4.0d }));

    TruncatedGaussianPotentialResolver resolver = new TruncatedGaussianPotentialResolver(variable, 0.5d, 6.0d);

    FactorSet<GaussianFactor> factorSet = new FactorSet<GaussianFactor>(Collections.singleton(factor));
    FactorSet<GaussianFactor> approximation = resolver.project(factorSet, scope);

    assertThat(approximation.getFactors()).hasSize(1);

    GaussianFactor approximationFactor = approximation.getFactors().iterator().next();
    RealVector meanVector = approximationFactor.getMeanVector();
    RealMatrix covarianceMatrix = approximationFactor.getCovarianceMatrix();

    assertThat(meanVector.getDimension()).isEqualTo(1);
    assertThat(meanVector.getEntry(0)).isEqualTo(2.658510664d, TestConstants.DOUBLE_VALUE_TOLERANCE);

    assertThat(covarianceMatrix.isSquare());
    assertThat(covarianceMatrix.getColumnDimension()).isEqualTo(1);

    assertThat(covarianceMatrix.getEntry(0, 0)).isEqualTo(1.787386921d, TestConstants.DOUBLE_VALUE_TOLERANCE);

}

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

@Test
public void testM255GeominOblique() {
    System.out.println("Oblique Geomin rotation test: m255 data");

    /**//w w  w.  jav a  2  s .c o  m
     * True result obtained form R using GPArotation package
     */
    double[][] true_Geomin_oblique = { { 0.814401032, -0.07246248, -0.02746737 },
            { 0.831474480, -0.01326628, 0.02443654 }, { 0.646042722, 0.11864079, -0.05620512 },
            { 0.604841472, 0.16035608, 0.04246413 }, { 0.429130847, 0.32919710, -0.11951339 },
            { 0.068651498, 0.74475428, -0.04555539 }, { -0.026759588, 0.84183379, 0.08633271 },
            { 0.111079049, 0.44919958, -0.05420552 }, { 0.130427210, 0.37116164, -0.31191986 },
            { -0.051932818, 0.47010788, -0.26735188 }, { 0.036623600, 0.01140911, -0.87276177 },
            { 0.002120624, 0.01879531, -0.73637231 }, };

    double[][] true_Phi = { { 1.0000000, 0.5565973, -0.7280542 }, { 0.5565973, 1.0000000, -0.6808553 },
            { -0.7280542, -0.6808553, 1.0000000 } };

    RealMatrix L = new Array2DRowRealMatrix(m255MINRESLoadings);
    GPArotation gpa = new GPArotation();
    RotationResults R = gpa.rotate(L, RotationMethod.GEOMIN_Q, false, 500, 1e-5);
    RealMatrix Lr = R.getFactorLoadings();
    //        System.out.println(R.toString());

    for (int i = 0; i < Lr.getRowDimension(); i++) {
        for (int j = 0; j < Lr.getColumnDimension(); j++) {
            assertEquals("  loading: ", Precision.round(true_Geomin_oblique[i][j], 4),
                    Precision.round(Lr.getEntry(i, j), 5), 1e-4);
        }
    }

    RealMatrix Phi = R.getPhi();
    for (int i = 0; i < Phi.getRowDimension(); i++) {
        for (int j = 0; j < Phi.getColumnDimension(); j++) {
            assertEquals("  factor correlation: ", Precision.round(true_Phi[i][j], 4),
                    Precision.round(Phi.getEntry(i, j), 5), 1e-4);
        }
    }

}

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From source file:com.itemanalysis.psychometrics.cfa.AbstractConfirmatoryFactorAnalysisEstimator.java

public double sumSquaredElements(RealMatrix matrix) {
    double sum = 0.0;
    double v = 0.0;
    for (int i = 0; i < matrix.getRowDimension(); i++) {
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            v = matrix.getEntry(i, j);/* w w  w .  j  a v  a2 s .c o  m*/
            sum += (v * v);
        }
    }
    return sum;
}

---

From source file:eagle.security.userprofile.model.eigen.UserProfileEigenModeler.java

private RealMatrix normalizeData(RealMatrix matrix) {

    RealMatrix normalizedData = new Array2DRowRealMatrix(matrix.getRowDimension(), matrix.getColumnDimension());
    for (int i = 0; i < matrix.getRowDimension(); i++) {
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            // TODO:  statistics[j].getStddev() == 0 what should the value be if stddev is o?
            double value = statistics[j].getStddev() == 0 ? 0
                    : (matrix.getEntry(i, j) - statistics[j].getMean()) / statistics[j].getStddev();
            normalizedData.setEntry(i, j, value);
        }/*from  ww  w . j a v  a 2 s.c  om*/
    }
    return normalizedData;
}

---

From source file:ellipsoidFit.FitPoints.java

/**
 * Find the center of the ellipsoid.//  ww  w . j av  a2 s.c o  m
 * 
 * @param a
 *            the algebraic from of the polynomial.
 * @return a vector containing the center of the ellipsoid.
 */
private RealVector findCenter(RealMatrix a) {
    RealMatrix subA = a.getSubMatrix(0, 2, 0, 2);

    for (int q = 0; q < subA.getRowDimension(); q++) {
        for (int s = 0; s < subA.getColumnDimension(); s++) {
            subA.multiplyEntry(q, s, -1.0);
        }
    }

    RealVector subV = a.getRowVector(3).getSubVector(0, 3);

    // inv (dtd)
    DecompositionSolver solver = new SingularValueDecomposition(subA).getSolver();
    RealMatrix subAi = solver.getInverse();

    return subAi.operate(subV);
}

---

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

/**
 * Update the random columns in the specified design matrix with a new realization
 * of random values//from w w  w  .j a v  a 2s. c o  m
 *
 * @param fullDesignMatrix an instance of the full design matrix
 */
public void updateRandomColumns(RealMatrix fullDesignMatrix) throws IllegalArgumentException {
    if (fullDesignMatrix.getColumnDimension() != combinedMatrix.getColumnDimension())
        throw new IllegalArgumentException("Design matrix instance has invalid #columns");
    int fullDesignColumn = 0;
    if (fixedMatrix != null)
        fullDesignColumn = fixedMatrix.getColumnDimension();

    if (randomMatrix != null) {
        for (int col = 0; col < randomMatrix.getColumnDimension(); col++, fullDesignColumn++) {
            fillRandomColumn(col, fullDesignColumn, fullDesignMatrix);
        }
    }

}

---

From source file:edu.ucdenver.bios.powersvc.resource.PowerResourceHelper.java

/**
 * Create a null hypothesis matrix from the study design.
 * @param studyDesign study design object
 * @return theta null matrix/*from w w w .j a va  2s  .c  o m*/
 */
public static RealMatrix thetaNullMatrixFromStudyDesign(StudyDesign studyDesign, FixedRandomMatrix C,
        RealMatrix U) {
    if (studyDesign.getViewTypeEnum() == StudyDesignViewTypeEnum.MATRIX_MODE) {
        return toRealMatrix(studyDesign.getNamedMatrix(PowerConstants.MATRIX_THETA_NULL));
    } else {
        RealMatrix thetaNull = toRealMatrix(studyDesign.getNamedMatrix(PowerConstants.MATRIX_THETA_NULL));
        if (thetaNull == null) {
            if (C != null && C.getFixedMatrix() != null && U != null) {
                int rows = C.getFixedMatrix().getRowDimension();
                int columns = U.getColumnDimension();
                thetaNull = MatrixUtils.getRealMatrixWithFilledValue(rows, columns, 0);
            }
        }
        return thetaNull;
    }
}

---

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

@Override
public void run() {
    final RealMatrix cov_reg = MatrixUtils.createRealIdentityMatrix(X.getRowDimension())
            .scalarMultiply(shrinkage);//from  w ww .  jav a2s  .c  o m
    for (int k = 0; k < K; ++k) {
        System.out.println("k = " + k);
        System.out.println("\tCovariance");
        final RealMatrix cov = Xi_.multiply(Xi_.transpose()).add(cov_reg);
        //         System.out.println( cov );
        System.out.println("\tM");
        final RealMatrix M = cov; // XL.multiply( Si_ ).multiply( XLt ).add( cov.scalarMultiply( eta ) );
        // TODO: You only need the largest eigenvalue, so the full
        // decomposition is a ton of unnecessary work.
        System.out.println("\tM[" + M.getRowDimension() + "x" + M.getColumnDimension() + "]");
        final EigenDecomposition evd = new EigenDecomposition(M);
        final RealVector w = evd.getEigenvector(0);
        w.mapMultiplyToSelf(1.0 / w.getNorm());
        //         if( Math.abs( w.getNorm() - 1.0 ) > 1e-2 ) {
        //            System.out.println( "! Non-unit eigenvector: ||w|| = " + w.getNorm() );
        //            System.out.println( Math.abs( w.getNorm() - 1.0 ) );
        //            assert( false );
        //         }
        W.add(w);
        final RealMatrix w_wt = w.outerProduct(w);
        final RealMatrix S_tilde = XLt.multiply(w_wt).multiply(XL);
        T(S_tilde, Si_);
        Si_ = Si_.subtract(S_tilde.scalarMultiply(alpha));
        Xi_ = Xi_.subtract(w_wt.multiply(Xi_));
    }
}

---

From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFit.java

/**
* Calculates the leverages of data points for least squares fitting (assuming equal variances).
* 
* @param indVarValues The values of the independent variable used for the fitting.
* @return a RealVector containing a leverage value for each independent variable value.
*//*from   w w  w  .j  a  va  2 s .  c  om*/
protected RealVector calculateLeverages(RealVector indVarValues) {

    RealMatrix indVarMatrix = null;

    if (this.noIntercept) {
        indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 1);
    } else {
        indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 2);
    }

    indVarMatrix.setColumnVector(0, indVarValues);

    if (!this.noIntercept) {
        indVarMatrix.setColumnVector(1, indVarValues.mapMultiply(0).mapAdd(1));
    }

    RealVector leverages = new ArrayRealVector(indVarValues.getDimension());

    QRDecomposition xQR = new QRDecomposition(indVarMatrix);

    RealMatrix xR = xQR.getR();

    int smallerDim = xR.getRowDimension() < xR.getColumnDimension() ? xR.getRowDimension()
            : xR.getColumnDimension();

    RealMatrix xRSq = xR.getSubMatrix(0, smallerDim - 1, 0, smallerDim - 1);

    QRDecomposition xRQR = new QRDecomposition(xRSq);

    RealMatrix xRInv = xRQR.getSolver().getInverse();

    RealMatrix xxRInv = indVarMatrix.multiply(xRInv);

    for (int i = 0; i < indVarValues.getDimension(); i++) {
        double sum = 0;
        for (int j = 0; j < xxRInv.getColumnDimension(); j++) {
            sum += Math.pow(xxRInv.getEntry(i, j), 2);
        }
        leverages.setEntry(i, sum);
    }

    return leverages;

}

---

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);// ww  w.j  av  a  2  s. co 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;
    }
}

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