Example usage for org.apache.commons.math3.optim.nonlinear.vector.jacobian LevenbergMarquardtOptimizer getChiSquare

Introduction

In this page you can find the example usage for org.apache.commons.math3.optim.nonlinear.vector.jacobian LevenbergMarquardtOptimizer getChiSquare.

Prototype

public double getChiSquare() 

Source Link

Document

Get a Chi-Square-like value assuming the N residuals follow N distinct normal distributions centered on 0 and whose variances are the reciprocal of the weights.

Usage

From source file:gdsc.smlm.fitting.nonlinear.ApacheLVMFitter.java

public FitStatus fit(int n, double[] y, double[] y_fit, double[] a, double[] a_dev, double[] error,
        double noise) {
    numberOfFittedPoints = n;/*w w w  .ja v  a 2  s. c o m*/

    try {
        // Different convergence thresholds seem to have no effect on the resulting fit, only the number of
        // iterations for convergence
        final double initialStepBoundFactor = 100;
        final double costRelativeTolerance = 1e-10;
        final double parRelativeTolerance = 1e-10;
        final double orthoTolerance = 1e-10;
        final double threshold = Precision.SAFE_MIN;

        // Extract the parameters to be fitted
        final double[] initialSolution = getInitialSolution(a);

        // TODO - Pass in more advanced stopping criteria.

        // Create the target and weight arrays
        final double[] yd = new double[n];
        final double[] w = new double[n];
        for (int i = 0; i < n; i++) {
            yd[i] = y[i];
            w[i] = 1;
        }

        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(initialStepBoundFactor,
                costRelativeTolerance, parRelativeTolerance, orthoTolerance, threshold);
        PointVectorValuePair optimum = optimizer.optimize(new MaxIter(getMaxEvaluations()),
                new MaxEval(Integer.MAX_VALUE),
                new ModelFunctionJacobian(new MultivariateMatrixFunctionWrapper(f, a, n)),
                new ModelFunction(new MultivariateVectorFunctionWrapper(f, a, n)), new Target(yd),
                new Weight(w), new InitialGuess(initialSolution));

        final double[] parameters = optimum.getPoint();
        setSolution(a, parameters);
        iterations = optimizer.getIterations();
        evaluations = optimizer.getEvaluations();
        if (a_dev != null) {
            double[][] covar = optimizer.computeCovariances(parameters, threshold);
            setDeviations(a_dev, covar);
        }
        // Compute fitted function if desired 
        if (y_fit != null) {
            f.initialise(a);
            for (int i = 0; i < n; i++)
                y_fit[i] = f.eval(i);
        }

        residualSumOfSquares = error[0] = optimizer.getChiSquare();
        totalSumOfSquares = getSumOfSquares(n, y);
    } catch (TooManyEvaluationsException e) {
        return FitStatus.FAILED_TO_CONVERGE;
    } catch (ConvergenceException e) {
        // Occurs when QR decomposition fails - mark as a singular non-linear model (no solution)
        return FitStatus.SINGULAR_NON_LINEAR_MODEL;
    } catch (Exception e) {
        // TODO - Find out the other exceptions from the fitter and add return values to match. 
        return FitStatus.UNKNOWN;
    }

    return FitStatus.OK;
}

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From source file:org.eclipse.dawnsci.analysis.dataset.roi.fitting.CircleCoordinatesFunction.java

@Override
public void geometricFit(IDataset x, IDataset y, double[] init) {
    residual = Double.NaN;/*from   w  ww  . j  av  a2 s .c  om*/
    if (x.getSize() < PARAMETERS || y.getSize() < PARAMETERS) {
        throw new IllegalArgumentException("Need " + PARAMETERS + " or more points");
    }

    if (x.getSize() == PARAMETERS) {
        init = quickfit(x, y);
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = init[i];
        residual = 0;
        return;
    }

    if (init == null)
        init = quickfit(x, y);
    else if (init.length < PARAMETERS)
        throw new IllegalArgumentException("Need " + PARAMETERS + " parameters");

    CircleCoordinatesFunction f = (CircleCoordinatesFunction) getFitFunction(x, y);
    LevenbergMarquardtOptimizer opt = new LevenbergMarquardtOptimizer();

    try {
        PointVectorValuePair result = opt.optimize(new ModelFunction(f),
                new ModelFunctionJacobian(f.jacobian()), f.getTarget(), f.getWeight(),
                f.calcAllInitValues(init), new MaxEval(MAX_EVALUATIONS));

        double[] point = result.getPointRef();
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = point[i];

        residual = opt.getRMS();
        logger.trace("Circle fit: rms = {}, x^2 = {}", opt.getRMS(), opt.getChiSquare());
    } catch (DimensionMismatchException e) {
        // cannot happen
    } catch (IllegalArgumentException e) {
        // should not happen!
    } catch (TooManyEvaluationsException e) {
        throw new IllegalArgumentException("Problem with optimizer converging");
    }
}

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From source file:org.eclipse.dawnsci.analysis.dataset.roi.fitting.EllipseCoordinatesFunction.java

@Override
public void geometricFit(IDataset x, IDataset y, double[] init) {
    residual = Double.NaN;/*from  w  w w .j  av  a  2  s .c  o  m*/
    if (x.getSize() < PARAMETERS || y.getSize() < PARAMETERS) {
        throw new IllegalArgumentException("Need " + PARAMETERS + " or more points");
    }

    if (init == null)
        init = quickfit(x, y);
    else if (init.length < PARAMETERS)
        throw new IllegalArgumentException("Need " + PARAMETERS + " parameters");

    EllipseCoordinatesFunction f = (EllipseCoordinatesFunction) getFitFunction(x, y);
    LevenbergMarquardtOptimizer opt = new LevenbergMarquardtOptimizer();

    try {
        PointVectorValuePair result = opt.optimize(new ModelFunction(f),
                new ModelFunctionJacobian(f.jacobian()), f.getTarget(), f.getWeight(),
                f.calcAllInitValues(init), new MaxEval(MAX_EVALUATIONS));

        double[] point = result.getPointRef();
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = point[i];

        residual = opt.getRMS();

        logger.trace("Ellipse fit: rms = {}, x^2 = {}", residual, opt.getChiSquare());
    } catch (DimensionMismatchException e) {
        // cannot happen
    } catch (IllegalArgumentException e) {
        // should not happen!
    } catch (TooManyEvaluationsException e) {
        throw new IllegalArgumentException("Problem with optimizer converging");
    }
}

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