Examples with NonLinearFunction gdsc.smlm.function.NonLinearFunction used on opensource projects

Example 1 with NonLinearFunction

use of gdsc.smlm.function.NonLinearFunction in project GDSC-SMLM by aherbert.

the class ApacheLVMFitter method computeFit.

public FitStatus computeFit(double[] y, final double[] y_fit, double[] a, double[] a_dev) {
    int n = y.length;
    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);
        //@formatter:off
        LeastSquaresBuilder builder = new LeastSquaresBuilder().maxEvaluations(Integer.MAX_VALUE).maxIterations(getMaxEvaluations()).start(initialSolution).target(yd).weight(new DiagonalMatrix(w));
        if (f instanceof ExtendedNonLinearFunction && ((ExtendedNonLinearFunction) f).canComputeValuesAndJacobian()) {
            // Compute together, or each individually
            builder.model(new ValueAndJacobianFunction() {

                final ExtendedNonLinearFunction fun = (ExtendedNonLinearFunction) f;

                public Pair<RealVector, RealMatrix> value(RealVector point) {
                    final double[] p = point.toArray();
                    final Pair<double[], double[][]> result = fun.computeValuesAndJacobian(p);
                    return new Pair<RealVector, RealMatrix>(new ArrayRealVector(result.getFirst(), false), new Array2DRowRealMatrix(result.getSecond(), false));
                }

                public RealVector computeValue(double[] params) {
                    return new ArrayRealVector(fun.computeValues(params), false);
                }

                public RealMatrix computeJacobian(double[] params) {
                    return new Array2DRowRealMatrix(fun.computeJacobian(params), false);
                }
            });
        } else {
            // Compute separately
            builder.model(new MultivariateVectorFunctionWrapper((NonLinearFunction) f, a, n), new MultivariateMatrixFunctionWrapper((NonLinearFunction) f, a, n));
        }
        LeastSquaresProblem problem = builder.build();
        Optimum optimum = optimizer.optimize(problem);
        final double[] parameters = optimum.getPoint().toArray();
        setSolution(a, parameters);
        iterations = optimum.getIterations();
        evaluations = optimum.getEvaluations();
        if (a_dev != null) {
            try {
                double[][] covar = optimum.getCovariances(threshold).getData();
                setDeviationsFromMatrix(a_dev, covar);
            } catch (SingularMatrixException e) {
                // Matrix inversion failed. In order to return a solution 
                // return the reciprocal of the diagonal of the Fisher information 
                // for a loose bound on the limit 
                final int[] gradientIndices = f.gradientIndices();
                final int nparams = gradientIndices.length;
                GradientCalculator calculator = GradientCalculatorFactory.newCalculator(nparams);
                double[][] alpha = new double[nparams][nparams];
                double[] beta = new double[nparams];
                calculator.findLinearised(nparams, y, a, alpha, beta, (NonLinearFunction) f);
                FisherInformationMatrix m = new FisherInformationMatrix(alpha);
                setDeviations(a_dev, m.crlb(true));
            }
        }
        // Compute function value
        if (y_fit != null) {
            Gaussian2DFunction f = (Gaussian2DFunction) this.f;
            f.initialise0(a);
            f.forEach(new ValueProcedure() {

                int i = 0;

                public void execute(double value) {
                    y_fit[i] = value;
                }
            });
        }
        // As this is unweighted then we can do this to get the sum of squared residuals
        // This is the same as optimum.getCost() * optimum.getCost(); The getCost() function
        // just computes the dot product anyway.
        value = optimum.getResiduals().dotProduct(optimum.getResiduals());
    } catch (TooManyEvaluationsException e) {
        return FitStatus.TOO_MANY_EVALUATIONS;
    } catch (TooManyIterationsException e) {
        return FitStatus.TOO_MANY_ITERATIONS;
    } 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;
}

Example 2 with NonLinearFunction

use of gdsc.smlm.function.NonLinearFunction in project GDSC-SMLM by aherbert.

the class ApacheLVMFitter method computeValue.

@Override
public boolean computeValue(double[] y, double[] y_fit, double[] a) {
    final int nparams = f.gradientIndices().length;
    GradientCalculator calculator = GradientCalculatorFactory.newCalculator(nparams, false);
    // Since we know the function is a Gaussian2DFunction
    value = calculator.findLinearised(y.length, y, y_fit, a, (NonLinearFunction) f);
    return true;
}

Example 3 with NonLinearFunction

use of gdsc.smlm.function.NonLinearFunction in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method mleGradientCalculatorComputesLikelihood.

@Test
public void mleGradientCalculatorComputesLikelihood() {
    //@formatter:off
    NonLinearFunction func = new NonLinearFunction() {

        double u;

        public void initialise(double[] a) {
            u = a[0];
        }

        public int[] gradientIndices() {
            return null;
        }

        public double eval(int x, double[] dyda) {
            return 0;
        }

        public double eval(int x) {
            return u;
        }

        public double eval(int x, double[] dyda, double[] w) {
            return 0;
        }

        public double evalw(int x, double[] w) {
            return 0;
        }

        public boolean canComputeWeights() {
            return false;
        }

        public int getNumberOfGradients() {
            return 0;
        }
    };
    //@formatter:on
    int[] xx = Utils.newArray(100, 0, 1);
    double[] xxx = Utils.newArray(100, 0, 1.0);
    for (double u : new double[] { 0.79, 2.5, 5.32 }) {
        double ll = 0;
        PoissonDistribution pd = new PoissonDistribution(u);
        for (int x : xx) {
            double o = MLEGradientCalculator.likelihood(u, x);
            double e = pd.probability(x);
            Assert.assertEquals("likelihood", e, o, e * 1e-10);
            o = MLEGradientCalculator.logLikelihood(u, x);
            e = pd.logProbability(x);
            Assert.assertEquals("log likelihood", e, o, Math.abs(e) * 1e-10);
            ll += e;
        }
        MLEGradientCalculator gc = new MLEGradientCalculator(1);
        double o = gc.logLikelihood(xxx, new double[] { u }, func);
        Assert.assertEquals("sum log likelihood", ll, o, Math.abs(ll) * 1e-10);
    }
}