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

Introduction

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

Prototype

double getTrace() throws NonSquareMatrixException;

Source Link

Document

Returns the <a href="http://mathworld.wolfram.com/MatrixTrace.html"> trace</a> of the matrix (the sum of the elements on the main diagonal).

Usage

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

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

    Object o = stack.pop();/*from   ww w.  j  av a  2s .c  om*/

    if (!(o instanceof RealMatrix)) {
        throw new WarpScriptException(getName() + " expects a matrix on top of the stack.");
    }

    RealMatrix matrix = (RealMatrix) o;

    stack.push(matrix.getTrace());

    return stack;
}

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

/**
* Empty method - artifact of interface/*ww w .jav  a2  s. c om*/
*
* @param argument
* @return
*/
public double value(double[] argument) {
    SIGMA = model.getImpliedCovariance(argument);
    RealMatrix D = varcov.subtract(SIGMA);
    RealMatrix D2 = D.multiply(D);
    F = 0.5 * D2.getTrace();
    return F;
}

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

/**
 * /* www .  j  a va2 s  .  com*/
 *
 * @param argument
 * @return
 */
public double value(double[] argument) {
    SIGMA = model.getImpliedCovariance(argument);
    RealMatrix D = (varcov.subtract(SIGMA)).multiply(VCinv);
    RealMatrix D2 = D.multiply(D);
    F = 0.5 * D2.getTrace();
    return F;
}

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

public double gfi() {
    double fit = 0.0;
    double q = Double.valueOf(model.getNumberOfItems()).doubleValue();
    RealMatrix I = new IdentityMatrix(nItems);
    RealMatrix P = SIGMA.multiply(VCinv);
    RealMatrix D = I.subtract(P);//from   w  w w  .jav  a  2  s . c om
    RealMatrix D2 = D.multiply(D);
    fit = 1.0 - D2.getTrace() / q;
    return fit;
}

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

public double gfi() {
    double fit = 0.0;
    double q = Double.valueOf(model.getNumberOfItems()).doubleValue();
    RealMatrix D = varcov.subtract(SIGMA);
    RealMatrix D2 = D.multiply(D);
    RealMatrix V2 = varcov.multiply(varcov);
    fit = 1.0 - D2.getTrace() / V2.getTrace();
    return fit;//from   ww  w .  j av  a 2  s .  c om
}

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

/**
 * Computes the function value for varimax rotation.
 *
 * @param L matrix of factor loadings.//from w w w  . j a v  a  2  s.c  om
 */
public void computeValues(RealMatrix L) {
    //initialize dimensions and column mean array
    int nrow = L.getRowDimension();
    int ncol = L.getColumnDimension();
    Mean[] colMean = new Mean[ncol];
    for (int i = 0; i < ncol; i++) {
        colMean[i] = new Mean();
    }

    //square each element in matrix
    RealMatrix L2 = L.copy();
    double value = 0.0;
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = L.getEntry(i, j);
            value *= value;
            L2.setEntry(i, j, value);
            colMean[j].increment(value);
        }
    }

    double dif = 0.0;
    RealMatrix QL = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            dif = L2.getEntry(i, j) - colMean[j].getResult();
            QL.setEntry(i, j, dif);
        }
    }

    //compute gradientAt
    gradient = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = -L.getEntry(i, j) * QL.getEntry(i, j);
            gradient.setEntry(i, j, value);
        }
    }

    //compute function value
    RealMatrix B = QL.transpose().multiply(QL);
    double sum = B.getTrace();
    functionValue = -sum / 4.0;

}

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

public double value(double[] argument) {
    model.setParameters(argument);/*w ww . j av a2  s .c o m*/

    //            Linesearch method in QNMinimizer is causing NaN values after repeated calls to here
    //            Next libe is for monitoring values when called from line search
    //            No problem occurs with CGMinimizer
    //            System.out.println("valueAt: " + argument[0] + " " + argument[1]);
    SIGMA = model.getImpliedCovariance(argument);

    //compute determinant of SIGMA
    LUDecomposition SLUD = new LUDecomposition(SIGMA);
    double detSig = SLUD.getDeterminant();

    //compute inverse of SIGMA
    RealMatrix SIGMAinv = SLUD.getSolver().getInverse();
    RealMatrix VC_SIGMA_INV = varcov.multiply(SIGMAinv);
    double trace = VC_SIGMA_INV.getTrace();

    //convert number of items to double
    double p = Double.valueOf(model.getNumberOfItems()).doubleValue();

    //compute objective function
    F = Math.log(detSig) + trace - Math.log(detVc) - p;
    return F;
}

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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 ww.  j ava  2s . co  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:edu.ucdenver.bios.statisticaltest.HotellingLawleyTraceTest.java

/**
 * Compute a Hotelling-Lawley Trace statistic
 * // ww w . java 2 s . com
 * @param H hypothesis sum of squares matrix
 * @param E error sum of squares matrix
 * @returns F statistic
 */
private double getHotellingLawleyTrace() throws IllegalArgumentException {
    RealMatrix inverseE = new LUDecomposition(this.errorSumOfSquares).getSolver().getInverse();
    RealMatrix HinverseE = this.hypothesisSumOfSquares.multiply(inverseE);

    return HinverseE.getTrace();
}

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From source file:com.opengamma.strata.math.impl.matrix.CommonsMatrixAlgebra.java

@Override
public double getTrace(Matrix m) {
    ArgChecker.notNull(m, "m");
    if (m instanceof DoubleMatrix) {
        RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix) m);
        return temp.getTrace();
    }//from   w  ww .ja va 2  s . co  m
    throw new IllegalArgumentException("Can only find trace of DoubleMatrix; have " + m.getClass());
}

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