Package org.apache.commons.math3.exception

Examples of org.apache.commons.math3.exception.ConvergenceException


                for (int j = k; j < nR; ++j) {
                    double aki = weightedJacobian[j][permutation[i]];
                    norm2 += aki * aki;
                }
                if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
                    throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
                                                   nR, nC);
                }
                if (norm2 > ak2) {
                    nextColumn = i;
                    ak2        = norm2;
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                // tests for termination and stringent tolerances
                if (FastMath.abs(actRed) <= TWO_EPS &&
                    preRed <= TWO_EPS &&
                    ratio <= 2.0) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_COST_RELATIVE_TOLERANCE,
                                                   costRelativeTolerance);
                } else if (delta <= TWO_EPS * xNorm) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE,
                                                   parRelativeTolerance);
                } else if (maxCosine <= TWO_EPS) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_ORTHOGONALITY_TOLERANCE,
                                                   orthoTolerance);
                }
            }
        }
    }
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                for (int j = k; j < nR; ++j) {
                    double aki = weightedJacobian[j][permutation[i]];
                    norm2 += aki * aki;
                }
                if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
                    throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
                                                   nR, nC);
                }
                if (norm2 > ak2) {
                    nextColumn = i;
                    ak2        = norm2;
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                // update the estimated parameters
                for (int i = 0; i < nC; ++i) {
                    currentPoint[i] += dX[i];
                }
            } catch (SingularMatrixException e) {
                throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
            }

            // Check convergence.
            if (previous != null) {
                converged = checker.converged(getIterations(), previous, current);
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                                                                    newCovMatArrays);
        }

        if (Math.abs(previousLogLikelihood - logLikelihood) > threshold) {
            // Did not converge before the maximum number of iterations
            throw new ConvergenceException();
        }
    }
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                            break;
                        case FARTHEST_POINT :
                            newCenter = getFarthestPoint(clusters);
                            break;
                        default :
                            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
                    }
                    emptyCluster = true;
                } else {
                    newCenter = centroidOf(cluster.getPoints(), cluster.getCenter().getPoint().length);
                }
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            }
        }

        // did we find at least one non-empty cluster ?
        if (selected == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        // extract a random point from the cluster
        final List<T> selectedPoints = selected.getPoints();
        return selectedPoints.remove(random.nextInt(selectedPoints.size()));
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        }

        // did we find at least one non-empty cluster ?
        if (selected == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        // extract a random point from the cluster
        final List<T> selectedPoints = selected.getPoints();
        return selectedPoints.remove(random.nextInt(selectedPoints.size()));
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        }

        // did we find at least one non-empty cluster ?
        if (selectedCluster == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        return selectedCluster.getPoints().remove(selectedPoint);

    }
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                    break;
                case REGULA_FALSI:
                    // Detect early that algorithm is stuck, instead of waiting
                    // for the maximum number of iterations to be exceeded.
                    if (x == x1) {
                        throw new ConvergenceException();
                    }
                    break;
                default:
                    // Should never happen.
                    throw new MathInternalError();
View Full Code Here

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