Example usage for org.apache.commons.math3.linear LUDecomposition LUDecomposition

Introduction

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

Prototype

public LUDecomposition(RealMatrix matrix)

Source Link

Document

Calculates the LU-decomposition of the given matrix.

Usage

From source file:Matrix_Operations.java

public static void main(String[] args) {

    Scanner keyboard = new Scanner(System.in);

    //Allow user to enter number of columns
    int rows = getRowsNumberFromUser();
    int columns = getColumnNumberFromUser();
    double matrixA[][] = new double[rows][columns];

    //Enter values for matrix
    System.out.println("Enter values for each position in the matrix below. ");
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < columns; j++) {
            matrixA[i][j] = keyboard.nextDouble();

        }/*from   www .j av a  2  s.  c o m*/
        System.out.println("");
    }

    showMatrix(matrixA);
    System.out.println("");
    RealMatrix A = MatrixUtils.createRealMatrix(matrixA);

    LUDecomposition lu = new LUDecomposition(A);

    showMatrix(matrixA);

    System.out.println(lu.getDeterminant());

}

From source file:com.opengamma.strata.math.impl.linearalgebra.LUDecompositionCommons.java

@Override
public LUDecompositionResult apply(DoubleMatrix x) {
    ArgChecker.notNull(x, "x");
    RealMatrix temp = CommonsMathWrapper.wrap(x);
    LUDecomposition lu = new LUDecomposition(temp);
    return new LUDecompositionCommonsResult(lu);
}

From source file:com.itemanalysis.psychometrics.polycor.PolychoricTwoStepVariance.java

public double[][] variance(double[] x) {
    RealMatrix m = this.hessianAt(x);
    LUDecomposition SLUD = new LUDecomposition(m);
    RealMatrix inv = SLUD.getSolver().getInverse();
    return inv.getData();
}

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

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

    Object o = stack.pop();//  ww  w  .ja  v  a  2s  .  com

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

    RealMatrix matrix = (RealMatrix) o;

    stack.push(new LUDecomposition(matrix).getSolver().getInverse());

    return stack;
}

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

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

    Object o = stack.pop();// www . ja v a 2  s . c o m

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

    RealMatrix matrix = (RealMatrix) o;

    stack.push(new LUDecomposition(matrix).getDeterminant());

    return stack;
}

From source file:com.github.thorbenlindhauer.math.MathUtil.java

protected void ensureLUDecompositionInitialized() {
    if (luDecomposition == null) {
        luDecomposition = new LUDecomposition(matrix);
    }
}

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

public MaximumLikelihoodEstimation(ConfirmatoryFactorAnalysisModel model, RealMatrix varcov,
        double numberOfExaminees) {
    super(model, varcov, numberOfExaminees);
    CVLUD = new LUDecomposition(varcov);
    detVc = CVLUD.getDeterminant();//from  ww w  . ja v  a 2s  .  co m
}

From source file:imagingbook.lib.math.MahalanobisDistance.java

public MahalanobisDistance(double[][] samples) {
    N = samples.length;/*  w w w . j ava  2 s  .  com*/
    K = samples[0].length;
    Covariance cov = new Covariance(samples);
    RealMatrix S = cov.getCovarianceMatrix();
    // condition the covariance matrix to avoid singularity 
    // (add a small quantity to the diagonal)
    for (int i = 0; i < K; i++) {
        S.addToEntry(i, i, 0.0001);
    }
    // get the inverse covariance matrix
    RealMatrix iSM = new LUDecomposition(S).getSolver().getInverse();
    iS = iSM.getData();
}

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

@Override
public double getDeterminant(Matrix m) {
    ArgChecker.notNull(m, "m");
    if (m instanceof DoubleMatrix) {
        RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix) m);
        LUDecomposition lud = new LUDecomposition(temp);
        return lud.getDeterminant();
    }//www .  j av a  2s.c o  m
    throw new IllegalArgumentException("Can only find determinant of DoubleMatrix; have " + m.getClass());
}

From source file:com.itemanalysis.psychometrics.mixture.MvNormalComponentDistribution.java

/**
 *
 * @param x a matrix of dimension 1 x k, where k is the number of variables
 * @return/*from  w  w  w  .java2  s  . co m*/
 */
public double density(RealMatrix x) throws SingularMatrixException {
    double prob = 0.0;
    RealMatrix xTran = x.transpose();
    int d = xTran.getRowDimension();
    double det = new LUDecomposition(sigma).getDeterminant();
    double nconst = 1.0 / Math.sqrt(det * Math.pow(2.0 * Math.PI, d));
    RealMatrix Sinv = new LUDecomposition(sigma).getSolver().getInverse();
    RealMatrix delta = xTran.subtract(mu);
    RealMatrix dist = (delta.transpose().multiply(Sinv).multiply(delta));
    prob = nconst * Math.exp(-0.5 * dist.getEntry(0, 0));
    return prob;
}