Examples with PoissonGradientProcedure uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient.PoissonGradientProcedure used on opensource projects

Example 6 with PoissonGradientProcedure

use of uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient.PoissonGradientProcedure in project GDSC-SMLM by aherbert.

the class MleLvmSteppingFunctionSolver method computeFunctionFisherInformationMatrix.

@Override
protected FisherInformationMatrix computeFunctionFisherInformationMatrix(double[] y, double[] a) {
    // Compute and invert a matrix related to the Poisson log-likelihood.
    // This assumes this does achieve the maximum likelihood estimate for a
    // Poisson process.
    // We must wrap the gradient function if weights are present.
    Gradient1Function localF1 = (Gradient1Function) function;
    if (weights != null) {
        localF1 = OffsetGradient1Function.wrapGradient1Function(localF1, weights);
    }
    final PoissonGradientProcedure p = PoissonGradientProcedureUtils.create(localF1);
    p.computeFisherInformation(a);
    if (p.isNaNGradients()) {
        throw new FunctionSolverException(FitStatus.INVALID_GRADIENTS);
    }
    return new FisherInformationMatrix(p.getLinear(), function.getNumberOfGradients());
}

Example 7 with PoissonGradientProcedure

use of uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient.PoissonGradientProcedure in project GDSC-SMLM by aherbert.

the class UnivariateLikelihoodFisherInformationCalculatorTest method computePoissonFisherInformation.

private static void computePoissonFisherInformation(UniformRandomProvider rng, Model model) {
    // Create function
    final Gaussian2DFunction func = GaussianFunctionFactory.create2D(1, 10, 10, GaussianFunctionFactory.FIT_ERF_CIRCLE, null);
    final double[] params = new double[1 + Gaussian2DFunction.PARAMETERS_PER_PEAK];
    params[Gaussian2DFunction.BACKGROUND] = nextUniform(rng, 0.1, 0.3);
    params[Gaussian2DFunction.SIGNAL] = nextUniform(rng, 100, 300);
    params[Gaussian2DFunction.X_POSITION] = nextUniform(rng, 4, 6);
    params[Gaussian2DFunction.Y_POSITION] = nextUniform(rng, 4, 6);
    params[Gaussian2DFunction.X_SD] = nextUniform(rng, 1, 1.3);
    Gradient1Function f1 = func;
    FisherInformation fi;
    switch(model) {
        // Get a variance
        case POISSON_GAUSSIAN:
            final double var = 0.9 + 0.2 * rng.nextDouble();
            fi = new PoissonGaussianApproximationFisherInformation(Math.sqrt(var));
            f1 = (Gradient1Function) OffsetFunctionFactory.wrapFunction(func, SimpleArrayUtils.newDoubleArray(func.size(), var));
            break;
        case POISSON:
            fi = new PoissonFisherInformation();
            break;
        case HALF_POISSON:
            fi = new HalfPoissonFisherInformation();
            break;
        default:
            throw new IllegalStateException();
    }
    // This introduces a dependency on a different package, and relies on that
    // computing the correct answer. However that code predates this and so the
    // test ensures that the FisherInformationCalculator functions correctly.
    final PoissonGradientProcedure p1 = PoissonGradientProcedureUtils.create(f1);
    p1.computeFisherInformation(params);
    final double[] e = p1.getLinear();
    final FisherInformationCalculator calc = new UnivariateLikelihoodFisherInformationCalculator(func, fi);
    final FisherInformationMatrix I = calc.compute(params);
    final double[] o = I.getMatrix().data;
    final boolean emCcd = model == Model.HALF_POISSON;
    if (emCcd) {
        // Assumes half the poisson fisher information
        SimpleArrayUtils.multiply(e, 0.5);
    }
    Assertions.assertArrayEquals(e, o, 1e-6);
    final DoubleDoubleBiPredicate predicate = TestHelper.doublesAreClose(5e-2, 0);
    if (model == Model.POISSON || model == Model.HALF_POISSON) {
        // Get the Mortensen approximation for fitting Poisson data with a Gaussian.
        // Set a to 100 for the square pixel adjustment.
        final double a = 100;
        final double s = params[Gaussian2DFunction.X_SD] * a;
        final double N = params[Gaussian2DFunction.SIGNAL];
        final double b2 = params[Gaussian2DFunction.BACKGROUND];
        double var = Gaussian2DPeakResultHelper.getMLVarianceX(a, s, N, b2, emCcd);
        // Convert expected variance to pixels
        var /= (a * a);
        // Get the limits by inverting the Fisher information
        final double[] crlb = I.crlb();
        TestAssertions.assertTest(var, crlb[2], predicate);
        TestAssertions.assertTest(var, crlb[3], predicate);
    }
}