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

Example 1 with SingleEllipticalGaussian2DFunction

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

the class GradientCalculatorSpeedTest method gradientCalculatorComputesSameOutputWithBias.

@SeededTest
void gradientCalculatorComputesSameOutputWithBias(RandomSeed seed) {
    final Gaussian2DFunction func = new SingleEllipticalGaussian2DFunction(blockWidth, blockWidth);
    final int nparams = func.getNumberOfGradients();
    final GradientCalculator calc = new GradientCalculator(nparams);
    final int n = func.size();
    final int iter = 50;
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    final ArrayList<double[][]> alphaList = new ArrayList<>(iter);
    final ArrayList<double[]> betaList = new ArrayList<>(iter);
    final ArrayList<double[]> xList = new ArrayList<>(iter);
    // Manipulate the background
    final double defaultBackground = background;
    final boolean report = logger.isLoggable(Level.INFO);
    try {
        background = 1e-2;
        createData(RngUtils.create(seed.getSeed()), 1, iter, paramsList, yList, true);
        final EjmlLinearSolver solver = new EjmlLinearSolver(1e-5, 1e-6);
        for (int i = 0; i < paramsList.size(); i++) {
            final double[] y = yList.get(i);
            final double[] a = paramsList.get(i);
            final double[][] alpha = new double[nparams][nparams];
            final 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) {
                    logger.info(FunctionUtils.getSupplier("[%d] Tiny beta %s %g", i, func.getGradientParameterName(j), beta[j]));
                }
            }
            // Solve
            if (!solver.solve(alpha, beta)) {
                throw new AssertionError();
            }
            xList.add(beta);
        // System.out.println(Arrays.toString(beta));
        }
        final double[][] alpha = new double[nparams][nparams];
        final double[] beta = new double[nparams];
        final Statistics[] rel = new Statistics[nparams];
        final Statistics[] abs = new Statistics[nparams];
        for (int i = 0; i < nparams; i++) {
            rel[i] = new Statistics();
            abs[i] = new Statistics();
        }
        final DoubleDoubleBiPredicate predicate = TestHelper.doublesAreClose(1e-10, 0);
        // for (double b : new double[] { -500, -100, -10, -1, -0.1, 0.1, 1, 10, 100, 500 })
        for (final double b : new double[] { -10, -1, -0.1, 0.1, 1, 10 }) {
            if (report) {
                for (int i = 0; i < nparams; i++) {
                    rel[i].reset();
                    abs[i].reset();
                }
            }
            for (int i = 0; i < paramsList.size(); i++) {
                final double[] y = add(yList.get(i), b);
                final double[] a = paramsList.get(i).clone();
                a[0] += b;
                calc.findLinearised(n, y, a, alpha, beta, func);
                final double[][] alpha2 = alphaList.get(i);
                final double[] beta2 = betaList.get(i);
                final double[] x2 = xList.get(i);
                TestAssertions.assertArrayTest(beta2, beta, predicate, "Beta");
                TestAssertions.assertArrayTest(alpha2, alpha, predicate, "Alpha");
                // Solve
                solver.solve(alpha, beta);
                Assertions.assertArrayEquals(x2, beta, 1e-10, "X");
                if (report) {
                    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]));
                    }
                }
            }
            if (report) {
                for (int i = 0; i < nparams; i++) {
                    logger.info(FunctionUtils.getSupplier("Bias = %.2f : %s : Rel %g +/- %g: Abs %g +/- %g", b, func.getGradientParameterName(i), rel[i].getMean(), rel[i].getStandardDeviation(), abs[i].getMean(), abs[i].getStandardDeviation()));
                }
            }
        }
    } finally {
        background = defaultBackground;
    }
}

Example 2 with SingleEllipticalGaussian2DFunction

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

the class GradientCalculatorSpeedTest method gradientCalculatorComputesGradient.

private void gradientCalculatorComputesGradient(RandomSeed seed, GradientCalculator calc) {
    final int nparams = calc.nparams;
    final Gaussian2DFunction func = new SingleEllipticalGaussian2DFunction(blockWidth, blockWidth);
    // Check the function is the correct size
    final int[] indices = func.gradientIndices();
    Assertions.assertEquals(nparams, indices.length);
    final int iter = 50;
    final double[] beta = new double[nparams];
    final double[] beta2 = new double[nparams];
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    final int[] x = createData(RngUtils.create(seed.getSeed()), 1, iter, paramsList, yList, true);
    final double delta = 1e-3;
    final DoubleEquality eq = new DoubleEquality(1e-3, 1e-3);
    final IntArrayFormatSupplier msg = new IntArrayFormatSupplier("[%d] Not same gradient @ %d", 2);
    for (int i = 0; i < paramsList.size(); i++) {
        msg.set(0, i);
        final double[] y = yList.get(i);
        final double[] a = paramsList.get(i);
        final double[] a2 = a.clone();
        // double s =
        calc.evaluate(x, y, a, beta, func);
        for (int k = 0; k < nparams; k++) {
            final int j = indices[k];
            final double d = Precision.representableDelta(a[j], (a[j] == 0) ? 1e-3 : a[j] * delta);
            a2[j] = a[j] + d;
            final double s1 = calc.evaluate(x, y, a2, beta2, func);
            a2[j] = a[j] - d;
            final double s2 = calc.evaluate(x, y, a2, beta2, func);
            a2[j] = a[j];
            final double gradient = (s1 - s2) / (2 * d);
            // logger.fine(FunctionUtils.getSupplier("[%d,%d] %f (%s %f+/-%f) %f ?= %f", i, j, s,
            // func.getName(j), a[j], d, beta[k],
            // gradient));
            Assertions.assertTrue(eq.almostEqualRelativeOrAbsolute(beta[k], gradient), msg.set(1, j));
        }
    }
}