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.FactorAnalysisTest.java

@Test
public void eigenValues() {
    RealMatrix R = new Array2DRowRealMatrix(readHarman74Data());

    int nFactors = 2;
    int nVariables = 24;
    double[] x = new double[nVariables];
    for (int i = 0; i < nVariables; i++) {
        x[i] = .5;//from   w  w w . java 2 s .  com
    }

    for (int i = 0; i < nVariables; i++) {
        R.setEntry(i, i, 1.0 - x[i]);
    }

    EigenDecomposition eigen = new EigenDecomposition(R);
    RealMatrix eigenVectors = eigen.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);

    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(eigen.getRealEigenvalue(i));
    }
    DiagonalMatrix evMatrix = new DiagonalMatrix(ev);// USE Apache version
    // of Diagonal
    // matrix when
    // upgrade to
    // version 3.2
    RealMatrix LAMBDA = eigenVectors.multiply(evMatrix);
    RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
    RealMatrix RESID = R.subtract(SIGMA);

    double sum = 0.0;
    double squared = 0.0;
    for (int i = 0; i < RESID.getRowDimension(); i++) {
        for (int j = 0; j < RESID.getColumnDimension(); j++) {
            if (i != j) {
                sum += Math.pow(RESID.getEntry(i, j), 2);
            }
        }
    }

    System.out.println(sum);

    // RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
    //
    // System.out.println("SIGMA");
    // for(int i=0;i<SIGMA.getRowDimension();i++){
    // for(int j=0;j<SIGMA.getColumnDimension();j++){
    // System.out.print(SIGMA.getEntry(i, j) + " ");
    // }
    // System.out.println();
    // }
}

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

/**
 * Perform Gram Schmidt Orthonormalization on the specified 
 * matrix. The matrix A (mxn) is decomposed into two matrices 
 * Q (mxn), R (nxn) such that//from   www  .j  a  v  a2 s  . com
 * <ul>
 * <li>A = QR
 * <li>Q'Q = Identity
 * <li>R is upper triangular
 * </ul> 
 * The resulting Q, R matrices can be retrieved with the getQ()
 * and getR() functions.
 * 
 * @param matrix
 */
public GramSchmidtOrthonormalization(RealMatrix matrix) {
    if (matrix == null)
        throw new IllegalArgumentException("Null matrix");

    // create the Q, R matrices
    int m = matrix.getRowDimension();
    int n = matrix.getColumnDimension();
    Q = MatrixUtils.createRealMatrix(m, n);
    R = MatrixUtils.createRealMatrix(n, n);

    // perform Gram Schmidt process using the following algorithm
    // let w<n> be the resulting orthonormal column vectors
    // let v<n> be the columns of the incoming matrix
    // w1 = (1/norm(v1))*v1
    // ...
    // wj = 1/norm(vj - projectionVj-1Vj)*[vj - projectionVj-1Vj]
    // where projectionVj-1Vj = (w1 * vj) * w1 + (w2 * vj) * w2 + ... + (wj-1 * vj) * wj-1
    //
    for (int i = 0; i < n; i++) {
        RealMatrix v = matrix.getColumnMatrix(i);
        for (int j = 0; j < i; j++) {
            RealMatrix Qj = Q.getColumnMatrix(j);
            double value = Qj.transpose().multiply(v).getEntry(0, 0);
            R.setEntry(j, i, value);
            v = v.subtract(Qj.scalarMultiply(value));
        }
        double norm = v.getFrobeniusNorm();
        R.setEntry(i, i, norm);
        Q.setColumnMatrix(i, v.scalarMultiply(1 / norm));
    }
}

---

From source file:eagle.security.userprofile.impl.UserProfileAnomalyEigenEvaluator.java

private RealMatrix normalizeData(RealMatrix matrix, UserProfileEigenModel model) {
    RealMatrix normalizedData = new Array2DRowRealMatrix(matrix.getRowDimension(), matrix.getColumnDimension());
    if (LOG.isDebugEnabled())
        LOG.debug("model statistics size: " + model.statistics().length);
    for (int i = 0; i < matrix.getRowDimension(); i++) {
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            double value = (matrix.getEntry(i, j) - model.statistics()[j].getMean())
                    / model.statistics()[j].getStddev();
            normalizedData.setEntry(i, j, value);
        }//from   www  . ja va  2s. co  m
    }
    return normalizedData;
}

---

From source file:edu.cudenver.bios.power.glmm.GLMMTestHotellingLawley.java

/**
 * Compute a Hotelling-Lawley Trace statistic
 *
 * @param H hypothesis sum of squares matrix
 * @param E error sum of squares matrix/*from   w  w w.  j  a v  a2  s . c  o  m*/
 * @returns F statistic
 */
private double getHotellingLawleyTrace(RealMatrix H, RealMatrix E) throws IllegalArgumentException {
    if (!H.isSquare() || !E.isSquare() || H.getColumnDimension() != E.getRowDimension())
        throw new IllegalArgumentException(
                "Failed to compute Hotelling-Lawley Trace: hypothesis and error matrices must be square and same dimensions");

    RealMatrix inverseE = new LUDecomposition(E).getSolver().getInverse();
    RealMatrix HinverseE = H.multiply(inverseE);

    return HinverseE.getTrace();
}

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From source file:com.cloudera.oryx.common.math.RRQRDecomposition.java

/**
 * Return the effective numerical matrix rank.
 * <p>The effective numerical rank is the number of non-negligible
 * singular values.</p>//from  w ww .j  a  v a  2 s . c o  m
 * <p>This implementation looks at Frobenius norms of the sequence of
 * bottom right submatrices.  When a large fall in norm is seen,
 * the rank is returned. The drop is computed as:</p>
 * {@code (thisNorm/lastNorm) * rNorm < dropThreshold }
 * <p>
 * where thisNorm is the Frobenius norm of the current submatrix,
 * lastNorm is the Frobenius norm of the previous submatrix,
 * rNorm is is the Frobenius norm of the complete matrix
 * </p>
 *
 * @param dropThreshold threshold triggering rank computation
 * @return effective numerical matrix rank
 */
public int getRank(final double dropThreshold) {
    RealMatrix r = getR();
    int rows = r.getRowDimension();
    int columns = r.getColumnDimension();
    int rank = 1;
    double lastNorm = r.getFrobeniusNorm();
    double rNorm = lastNorm;
    while (rank < Math.min(rows, columns)) {
        double thisNorm = r.getSubMatrix(rank, rows - 1, rank, columns - 1).getFrobeniusNorm();
        if (thisNorm == 0 || (thisNorm / lastNorm) * rNorm < dropThreshold) {
            break;
        }
        lastNorm = thisNorm;
        rank++;
    }
    return rank;
}

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

public void setGradient(RealMatrix factorLoadingFirstDerivative, RealMatrix errorVarianceFirstDerivative,
        double[] gradient) {
    double sum = 0.0;
    for (int i = 0; i < factorLoadingFirstDerivative.getRowDimension(); i++) {
        sum += factorLoadingFirstDerivative.getEntry(i, 0);
    }/*from  w ww .  j a v  a  2s .c o m*/
    gradient[0] = sum;

    for (int i = 0; i < errorVariance.length; i++) {
        gradient[i + 1] = errorVarianceFirstDerivative.getEntry(i, i);
    }

}

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From source file:edu.cudenver.bios.power.test.general.TestDataAnalysis.java

private void checkFit(Test test, ModelFit fit) {
    System.out.println("Test: " + test + " Ndf: " + number.format(fit.numeratorDF) + " Ddf: "
            + number.format(fit.denominatorDF) + " F-crit: " + number.format(fit.Fvalue) + " p-value: "
            + number.format(fit.Pvalue));
    RealMatrix beta = fit.beta;
    System.out.println("Parameter estimates:");
    for (int r = 0; r < beta.getRowDimension(); r++) {
        for (int c = 0; c < beta.getColumnDimension(); c++) {
            System.out.println("Beta[" + r + "," + c + "]=" + beta.getEntry(r, c));
        }//from w ww  .ja va 2 s.  c  om
    }
}

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

private void printMatrix(RealMatrix x, String title) {
    System.out.println("PRINTING MATRIX: " + title);
    for (int i = 0; i < x.getRowDimension(); i++) {
        for (int j = 0; j < x.getColumnDimension(); j++) {
            System.out.print(x.getEntry(i, j) + "  ");
        }/*ww w .j  a  v  a  2s. c  o  m*/
        System.out.println();
    }
}

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

public void setGradient(RealMatrix factorLoadingFirstDerivative, RealMatrix errorVarianceFirstDerivative,
        double[] gradient) {
    double sumL = 0.0, sumE = 0.0;

    for (int i = 0; i < factorLoadingFirstDerivative.getRowDimension(); i++) {
        sumL += factorLoadingFirstDerivative.getEntry(i, 0);
    }/*from  w  w  w.  j  a v a 2 s . c o  m*/
    gradient[0] = sumL;

    for (int i = 0; i < errorVariance.length; i++) {
        sumE += errorVarianceFirstDerivative.getEntry(i, i);
    }
    gradient[1] = sumE;
}

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From source file:com.mothsoft.alexis.engine.numeric.CorrelationCalculatorTest.java

@Test
public void testCommonsMathCorrelationIdentityMatrix() {
    final PearsonsCorrelation correlation = new PearsonsCorrelation();

    final double[][] xy = new double[4][2];
    xy[0][0] = 0.0;// w w w  . j  av a 2 s .c o  m
    xy[1][0] = 1.0;
    xy[2][0] = 2.3;
    xy[3][0] = 3.4;

    xy[0][1] = 0.0;
    xy[1][1] = 1.0;
    xy[2][1] = 2.3;
    xy[3][1] = 3.4;

    final RealMatrix matrix = correlation.computeCorrelationMatrix(xy);
    assertEquals(2, matrix.getColumnDimension());
    assertEquals(2, matrix.getRowDimension());
    assertEquals(1.0, matrix.getEntry(0, 1), 0.000001);
    assertEquals(1.0, matrix.getEntry(1, 0), 0.000001);
}

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