List of det() Examples

Examples of det()

Jama.Matrix.det()
Matrix determinant @return determinant
com.nr.la.Bandec.det()
Using the stored decomposition, return the determinant of the matrix A. @return
com.nr.la.LUdcmp.det()
Using the stored LU decomposition, return the determinant of the matrix A. @return
com.numb3r3.common.math.Matrix.det()
de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.det()
Matrix determinant @return determinant
edu.gmu.seor.prognos.unbbayesplugin.cps.Jama.Matrix.det()
Matrix determinant @return determinant
org.jamesii.core.math.Matrix.det()
Calculates the determinant value of the matrix. This is currently done with Laplacian expansion by minors which can be slow on large matrices.
TODO: Laplacian expansion is horribly slow for large (> 6×6) matrices. Better to use Gaussian elimination here. @return The determinant of the matrix @exception MatrixException if the matrix is not square

Examples of Jama.Matrix.det()


        float rgbDet = (float) RGB.det();


        return new float[] {(R[1] * (float) WGB.det()) / (W[1] * rgbDet),
                            (G[1] * (float) RWB.det()) / (W[1] * rgbDet),
                            (B[1] * (float) RGW.det()) / (W[1] * rgbDet)};
    }


    /*
        8.2 - HSI, HSL, HSV, and related color spaces

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Examples of Jama.Matrix.det()

                    subsolution.set(1, 0, solution.get(3, 0));
                    subsolution.set(1, 1, solution.get(4, 0));


                    // Symmetry: MathUtils.round(subsolution.det()) == -1) 
                    // Inverse: MathUtils.isIdentity(subsolution.times(subsolution.transpose()))
                    if (MathUtils.round(subsolution.det()) == 0)
                        continue;


                    // we round transformations to 3 decimal places
                    outTransform.setTransform(
                        solution.get(0, 0),

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Examples of Jama.Matrix.det()

            prepareNewMatrixSize(random);
            final int numRow = getNumRow();
            final int numCol = getNumCol();
            double[] elements = createRandomPositiveValues(numRow * numCol);
            final Matrix reference = new Matrix(elements, numCol).transpose();
            if (!(reference.det() >= DETERMINANT_THRESHOLD)) {
                continue; // To close to a singular matrix - search an other one.
            }
            final MatrixSIS matrix = Matrices.create(numRow, numCol, elements);
            /*
             * Computes new random value for the argument. We mix positive and negative values,

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Examples of Jama.Matrix.det()

            prepareNewMatrixSize(random);
            final int numRow = getNumRow();
            final int numCol = getNumCol();
            final double[] elements = createRandomPositiveValues(numRow * numCol);
            final Matrix reference = new Matrix(elements, numCol).transpose();
            if (!(reference.det() >= DETERMINANT_THRESHOLD)) {
                continue; // To close to a singular matrix - search an other one.
            }
            final MatrixSIS matrix = Matrices.create(numRow, numCol, elements);
            assertEqualsJAMA(reference.inverse(), matrix.inverse(), TOLERANCE);
        }

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Examples of com.nr.la.Bandec.det()

    
    double[] b = new double[]{4.0, -4.0, -3.0,1.0};
    double[] x = new double[4];
    bdc.solve(b, x);
    System.out.println(NRUtil.toString(x));
    System.out.println(bdc.det());
    
  }
}

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Examples of com.nr.la.LUdcmp.det()

    }


    // Test determinant
    LUdcmp ainvlu = new LUdcmp(ainv);
    sbeps = 5.e-15;
    localflag = (alu.det()*ainvlu.det()-1.) > sbeps;
    globalflag = globalflag || localflag;
    if (localflag) {
      fail("*** ludcmp: Test of determinant failed");
      
    }

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Examples of com.numb3r3.common.math.Matrix.det()

        Sigma.put(0, 0, 2.3);
        Sigma.put(1, 1, 1.5);
        Sigma.put(2, 2, 1.7);
        Sigma.put(3, 3, 2.0);


        System.out.println("Det: " + Sigma.det());
        System.out.println("Normal density: " + Gaussian.mphi(x, mu, Sigma));
        // System.out.println("PDF: " + Gaussian.pdf());


        double y = 0.1;
        double mean = 1.5;

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.det()


    int dimensionality = means.get(0).getDimensionality();
    for(int i = 0; i < k; i++) {
      Matrix m = Matrix.identity(dimensionality, dimensionality);
      covarianceMatrices.add(m);
      normDistrFactor.add(1.0 / Math.sqrt(Math.pow(MathUtil.TWOPI, dimensionality) * m.det()));
      invCovMatr.add(m.inverse());
      clusterWeights.add(1.0 / k);
      if(logger.isDebuggingFinest()) {
        StringBuffer msg = new StringBuffer();
        msg.append(" model ").append(i).append(":\n");

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Examples of edu.gmu.seor.prognos.unbbayesplugin.cps.Jama.Matrix.det()

        
        // G component
        double d = 0;
        k11 = new Matrix(getK11(ghkPot.getKValue(i).getkMatrix(),ghkPot));
        h1 = new Matrix(getH1(ghkPot.getHValue(i).gethMatrix(), ghkPot));
        double logValue = k11.det() > 0 ? Math.log(k11.det()):k11.det();
        System.out.println("Calculated values are "+ghkPot.getGValue(i)+" and "+ (0.5* (y1.size() * Math.log(2 * Math.PI)- logValue + (h1.transpose().times(matInverse(k11)).times(h1)).det())));
        d = ghkPot.getGValue(i)
            + 0.5
            * (y1.size() * Math.log(2 * Math.PI)
                - logValue + (h1.transpose().times(matInverse(k11)).times(h1)).det());

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Examples of edu.gmu.seor.prognos.unbbayesplugin.cps.Jama.Matrix.det()

        
        // G component
        double d = 0;
        k11 = new Matrix(getK11(ghkPot.getKValue(i).getkMatrix(),ghkPot));
        h1 = new Matrix(getH1(ghkPot.getHValue(i).gethMatrix(), ghkPot));
        double logValue = k11.det() > 0 ? Math.log(k11.det()):k11.det();
        System.out.println("Calculated values are "+ghkPot.getGValue(i)+" and "+ (0.5* (y1.size() * Math.log(2 * Math.PI)- logValue + (h1.transpose().times(matInverse(k11)).times(h1)).det())));
        d = ghkPot.getGValue(i)
            + 0.5
            * (y1.size() * Math.log(2 * Math.PI)
                - logValue + (h1.transpose().times(matInverse(k11)).times(h1)).det());

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