Examples with ApacheLVMFitter gdsc.smlm.fitting.nonlinear.ApacheLVMFitter used on opensource projects

Example 1 with ApacheLVMFitter

use of gdsc.smlm.fitting.nonlinear.ApacheLVMFitter in project GDSC-SMLM by aherbert.

the class FitConfiguration method createFunctionSolver.

private BaseFunctionSolver createFunctionSolver() {
    if (gaussianFunction == null) {
        // Other code may want to call getFunctionSolver() to see if exceptions are thrown
        // so create a dummy function so we can return a function solver.
        gaussianFunction = createGaussianFunction(1, 1, 1, null);
    }
    // Remove noise model
    gaussianFunction.setNoiseModel(null);
    NonLinearFit nlinfit;
    switch(fitSolver) {
        case MLE:
            // Only the Poisson likelihood function supports gradients
            if (searchMethod.usesGradients() && modelCamera) {
                throw new IllegalArgumentException(String.format("The derivative based search method '%s' can only be used with the " + "'%s' likelihood function, i.e. no model camera noise", searchMethod, MaximumLikelihoodFitter.LikelihoodFunction.POISSON));
            }
            MaximumLikelihoodFitter fitter = new MaximumLikelihoodFitter(gaussianFunction);
            fitter.setRelativeThreshold(relativeThreshold);
            fitter.setAbsoluteThreshold(absoluteThreshold);
            fitter.setMaxEvaluations(maxFunctionEvaluations);
            fitter.setMaxIterations(maxIterations);
            fitter.setSearchMethod(searchMethod);
            fitter.setGradientLineMinimisation(gradientLineMinimisation);
            // Specify the likelihood function to use
            if (modelCamera) {
                // Set the camera read noise
                fitter.setSigma(readNoise);
                if (emCCD) {
                    // EMCCD = Poisson+Gamma+Gaussian
                    fitter.setLikelihoodFunction(MaximumLikelihoodFitter.LikelihoodFunction.POISSON_GAMMA_GAUSSIAN);
                } else {
                    // CCD = Poisson+Gaussian
                    fitter.setLikelihoodFunction(MaximumLikelihoodFitter.LikelihoodFunction.POISSON_GAUSSIAN);
                }
            } else {
                fitter.setLikelihoodFunction(MaximumLikelihoodFitter.LikelihoodFunction.POISSON);
            }
            // All models use the amplification gain (i.e. how many ADUs/electron)
            if (amplification <= 0) {
                throw new IllegalArgumentException("The amplification is required for the " + fitSolver.getName());
            }
            fitter.setAlpha(1.0 / amplification);
            return fitter;
        case BOUNDED_LVM_WEIGHTED:
            gaussianFunction.setNoiseModel(getNoiseModel());
        case BOUNDED_LVM:
        case LVM_MLE:
            if (fitSolver == FitSolver.LVM_MLE && gain <= 0) {
                throw new IllegalArgumentException("The gain is required for the " + fitSolver.getName());
            }
            if (useClamping) {
                bounds = new ParameterBounds(gaussianFunction);
                setClampValues(bounds);
            }
            BoundedNonLinearFit bnlinfit = new BoundedNonLinearFit(gaussianFunction, getStoppingCriteria(), bounds);
            nlinfit = bnlinfit;
            break;
        case LVM_QUASI_NEWTON:
            // This only works with a Gaussian2DFunction
            if (gaussianFunction instanceof Gaussian2DFunction) {
                ApacheLVMFitter apacheNLinFit = new ApacheLVMFitter((Gaussian2DFunction) gaussianFunction);
                apacheNLinFit.setMaxEvaluations(maxIterations);
                // TODO - Configure stopping criteria ...
                return apacheNLinFit;
            }
        case LVM_WEIGHTED:
        case LVM:
        default:
            // Only set the weighting function if necessary
            if (fitSolver == FitSolver.LVM_WEIGHTED)
                gaussianFunction.setNoiseModel(getNoiseModel());
            nlinfit = new NonLinearFit(gaussianFunction, getStoppingCriteria());
    }
    nlinfit.setInitialLambda(getLambda());
    if (fitSolver == FitSolver.LVM_MLE) {
        nlinfit.setMLE(true);
    }
    return nlinfit;
}