Examples with SingleEllipticalGaussian2DFunction gdsc.smlm.function.gaussian.SingleEllipticalGaussian2DFunction used on opensource projects

Example 1 with SingleEllipticalGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleEllipticalGaussian2DFunction in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method gradientCalculatorComputesGradient.

private void gradientCalculatorComputesGradient(GradientCalculator calc) {
    int nparams = calc.nparams;
    Gaussian2DFunction func = new SingleEllipticalGaussian2DFunction(blockWidth, blockWidth);
    // Check the function is the correct size
    Assert.assertEquals(nparams, func.gradientIndices().length);
    int iter = 100;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    double[] beta = new double[nparams];
    double[] beta2 = new double[nparams];
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    int[] x = createData(1, iter, paramsList, yList, true);
    double delta = 1e-3;
    DoubleEquality eq = new DoubleEquality(1e-3, 1e-3);
    for (int i = 0; i < paramsList.size(); i++) {
        double[] y = yList.get(i);
        double[] a = paramsList.get(i);
        double[] a2 = a.clone();
        //double s = 
        calc.evaluate(x, y, a, beta, func);
        for (int j = 0; j < nparams; j++) {
            double d = Precision.representableDelta(a[j], (a[j] == 0) ? 1e-3 : a[j] * delta);
            a2[j] = a[j] + d;
            double s1 = calc.evaluate(x, y, a2, beta2, func);
            a2[j] = a[j] - d;
            double s2 = calc.evaluate(x, y, a2, beta2, func);
            a2[j] = a[j];
            double gradient = (s1 - s2) / (2 * d);
            //System.out.printf("[%d,%d] %f  (%s %f+/-%f)  %f  ?=  %f\n", i, j, s, func.getName(j), a[j], d, beta[j],
            //		gradient);
            Assert.assertTrue("Not same gradient @ " + j, eq.almostEqualRelativeOrAbsolute(beta[j], gradient));
        }
    }
}

Example 2 with SingleEllipticalGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleEllipticalGaussian2DFunction in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method gradientCalculatorComputesSameOutputWithBias.

@Test
public void gradientCalculatorComputesSameOutputWithBias() {
    Gaussian2DFunction func = new SingleEllipticalGaussian2DFunction(blockWidth, blockWidth);
    int nparams = func.getNumberOfGradients();
    GradientCalculator calc = new GradientCalculator(nparams);
    int n = func.size();
    int iter = 100;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    ArrayList<double[][]> alphaList = new ArrayList<double[][]>(iter);
    ArrayList<double[]> betaList = new ArrayList<double[]>(iter);
    ArrayList<double[]> xList = new ArrayList<double[]>(iter);
    // Manipulate the background
    double defaultBackground = Background;
    try {
        Background = 1e-2;
        createData(1, iter, paramsList, yList, true);
        EJMLLinearSolver solver = new EJMLLinearSolver(1e-5, 1e-6);
        for (int i = 0; i < paramsList.size(); i++) {
            double[] y = yList.get(i);
            double[] a = paramsList.get(i);
            double[][] alpha = new double[nparams][nparams];
            double[] beta = new double[nparams];
            calc.findLinearised(n, y, a, alpha, beta, func);
            alphaList.add(alpha);
            betaList.add(beta.clone());
            for (int j = 0; j < nparams; j++) {
                if (Math.abs(beta[j]) < 1e-6)
                    System.out.printf("[%d] Tiny beta %s %g\n", i, func.getName(j), beta[j]);
            }
            // Solve
            if (!solver.solve(alpha, beta))
                throw new AssertionError();
            xList.add(beta);
        //System.out.println(Arrays.toString(beta));
        }
        double[][] alpha = new double[nparams][nparams];
        double[] beta = new double[nparams];
        //for (int b = 1; b < 1000; b *= 2)
        for (double b : new double[] { -500, -100, -10, -1, -0.1, 0, 0.1, 1, 10, 100, 500 }) {
            Statistics[] rel = new Statistics[nparams];
            Statistics[] abs = new Statistics[nparams];
            for (int i = 0; i < nparams; i++) {
                rel[i] = new Statistics();
                abs[i] = new Statistics();
            }
            for (int i = 0; i < paramsList.size(); i++) {
                double[] y = add(yList.get(i), b);
                double[] a = paramsList.get(i).clone();
                a[0] += b;
                calc.findLinearised(n, y, a, alpha, beta, func);
                double[][] alpha2 = alphaList.get(i);
                double[] beta2 = betaList.get(i);
                double[] x2 = xList.get(i);
                Assert.assertArrayEquals("Beta", beta2, beta, 1e-10);
                for (int j = 0; j < nparams; j++) {
                    Assert.assertArrayEquals("Alpha", alpha2[j], alpha[j], 1e-10);
                }
                // Solve
                solver.solve(alpha, beta);
                Assert.assertArrayEquals("X", x2, beta, 1e-10);
                for (int j = 0; j < nparams; j++) {
                    rel[j].add(DoubleEquality.relativeError(x2[j], beta[j]));
                    abs[j].add(Math.abs(x2[j] - beta[j]));
                }
            }
            for (int i = 0; i < nparams; i++) System.out.printf("Bias = %.2f : %s : Rel %g +/- %g: Abs %g +/- %g\n", b, func.getName(i), rel[i].getMean(), rel[i].getStandardDeviation(), abs[i].getMean(), abs[i].getStandardDeviation());
        }
    } finally {
        Background = defaultBackground;
    }
}

Example 3 with SingleEllipticalGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleEllipticalGaussian2DFunction in project GDSC-SMLM by aherbert.

the class SingleEllipticalGaussian2DFunctionTest method init.

protected void init() {
    flags = GaussianFunctionFactory.FIT_SIMPLE_ELLIPTICAL;
    f1 = new SingleEllipticalGaussian2DFunction(maxx, maxx);
}