Example usage for org.apache.commons.math3.exception NoDataException printStackTrace

Introduction

In this page you can find the example usage for org.apache.commons.math3.exception NoDataException printStackTrace.

Prototype

public void printStackTrace() 

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Document

Prints this throwable and its backtrace to the standard error stream.

Usage

From source file:org.pmad.gmm.MyMND.java

/**
 * Creates a multivariate normal distribution with the given mean vector and
 * covariance matrix.//from   w w  w. ja va 2 s.co  m
 * <br/>
 * The number of dimensions is equal to the length of the mean vector
 * and to the number of rows and columns of the covariance matrix.
 * It is frequently written as "p" in formulae.
 *
 * @param rng Random Number Generator.
 * @param means Vector of means.
 * @param covariances Covariance matrix.
 * @throws DimensionMismatchException if the arrays length are
 * inconsistent.
 * @throws SingularMatrixException if the eigenvalue decomposition cannot
 * be performed on the provided covariance matrix.
 * @throws NonPositiveDefiniteMatrixException if any of the eigenvalues is
 * negative.
 */
public MyMND(RandomGenerator rng, final double[] means, final double[][] covariances)
        throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException {
    super(rng, means.length);

    final int dim = means.length;

    if (covariances.length != dim) {
        throw new DimensionMismatchException(covariances.length, dim);
    }

    for (int i = 0; i < dim; i++) {
        if (dim != covariances[i].length) {
            throw new DimensionMismatchException(covariances[i].length, dim);
        }
    }

    this.means = MathArrays.copyOf(means);
    double msum = 0;
    for (int i = 0; i < covariances.length; i++) {
        for (int j = 0; j < covariances.length; j++) {
            msum += covariances[i][j];
        }
    }
    msum /= covariances.length * covariances.length;
    //        System.out.print("in");
    MyEDC covMatDec = null;
    double a = -1;
    while (true) {
        try {

            covarianceMatrix = new Array2DRowRealMatrix(covariances);

            covMatDec = new MyEDC(covarianceMatrix);

            // Compute and store the inverse.
            covarianceMatrixInverse = covMatDec.getSolver().getInverse();
            a *= -1;
            break;
        } catch (NoDataException e) {
            e.printStackTrace();
        } catch (NullArgumentException e) {
            e.printStackTrace();
        } catch (MathArithmeticException e) {
            e.printStackTrace();
        } catch (SingularMatrixException e) {
            //            System.out.print("S");
            for (int i = 0; i < covariances.length; i++) {
                double add = covariances[i][i] == 0 ? msum : covariances[i][i];
                covariances[i][i] += new Random().nextDouble() * add * 0.01;
            }
        }
        //         catch (MaxCountExceededException e) {
        ////            e.printStackTrace();
        ////            System.out.print("M"+msum);
        //            for (int i = 0; i < covariances.length; i++) {
        //               for (int j = i; j < covariances.length; j++) {
        //                  double add = covariances[i][j] == 0?msum:covariances[i][j];
        //                  add = new Random().nextDouble()*add*0.1*a;
        //                  covariances[i][j] += add;
        //                  covariances[j][i] += add;
        //               }
        //            }
        ////            break;
        //         }
    }
    // Compute and store the determinant.
    covarianceMatrixDeterminant = covMatDec.getDeterminant();

    // Eigenvalues of the covariance matrix.
    final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();

    for (int i = 0; i < covMatEigenvalues.length; i++) {
        if (covMatEigenvalues[i] < 0) {
            throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
        }
    }

    // Matrix where each column is an eigenvector of the covariance matrix.
    final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
    for (int v = 0; v < dim; v++) {
        final double[] evec = covMatDec.getEigenvector(v).toArray();
        covMatEigenvectors.setColumn(v, evec);
    }

    final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

    // Scale each eigenvector by the square root of its eigenvalue.
    for (int row = 0; row < dim; row++) {
        final double factor = FastMath.sqrt(covMatEigenvalues[row]);
        for (int col = 0; col < dim; col++) {
            tmpMatrix.multiplyEntry(row, col, factor);
        }
    }

    samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
}

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