Examples with SingleFreeCircularGaussian2DFunction gdsc.smlm.function.gaussian.SingleFreeCircularGaussian2DFunction used on opensource projects

Example 1 with SingleFreeCircularGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleFreeCircularGaussian2DFunction in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method gradientCalculatorAssumedXIsFasterThanGradientCalculator.

@Test
public void gradientCalculatorAssumedXIsFasterThanGradientCalculator() {
    org.junit.Assume.assumeTrue(speedTests || TestSettings.RUN_SPEED_TESTS);
    int iter = 10000;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    double[][] alpha = new double[7][7];
    double[] beta = new double[7];
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    int[] x = createData(1, iter, paramsList, yList);
    GradientCalculator calc = new GradientCalculator6();
    GradientCalculator calc2 = new GradientCalculator6();
    SingleFreeCircularGaussian2DFunction func = new SingleFreeCircularGaussian2DFunction(blockWidth, blockWidth);
    int n = x.length;
    for (int i = 0; i < paramsList.size(); i++) calc.findLinearised(x, yList.get(i), paramsList.get(i), alpha, beta, func);
    for (int i = 0; i < paramsList.size(); i++) calc2.findLinearised(n, yList.get(i), paramsList.get(i), alpha, beta, func);
    long start1 = System.nanoTime();
    for (int i = 0; i < paramsList.size(); i++) calc.findLinearised(x, yList.get(i), paramsList.get(i), alpha, beta, func);
    start1 = System.nanoTime() - start1;
    long start2 = System.nanoTime();
    for (int i = 0; i < paramsList.size(); i++) calc2.findLinearised(n, yList.get(i), paramsList.get(i), alpha, beta, func);
    start2 = System.nanoTime() - start2;
    log("GradientCalculator = %d : GradientCalculatorAssumed = %d : %fx\n", start1, start2, (1.0 * start1) / start2);
    if (TestSettings.ASSERT_SPEED_TESTS)
        Assert.assertTrue(start2 < start1);
}

Example 2 with SingleFreeCircularGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleFreeCircularGaussian2DFunction in project GDSC-SMLM by aherbert.

the class SingleFreeCircularGaussian2DFunctionTest method init.

protected void init() {
    flags = GaussianFunctionFactory.FIT_SIMPLE_FREE_CIRCLE;
    f1 = new SingleFreeCircularGaussian2DFunction(maxx, maxx);
}

Example 3 with SingleFreeCircularGaussian2DFunction

use of gdsc.smlm.function.gaussian.SingleFreeCircularGaussian2DFunction in project GDSC-SMLM by aherbert.

the class SolverSpeedTest method createData.

private boolean createData(float[][] alpha, float[] beta, boolean positiveDifinite) {
    // Generate a 2D Gaussian
    SingleFreeCircularGaussian2DFunction func = new SingleFreeCircularGaussian2DFunction(10, 10);
    double[] a = new double[] { // Background, Amplitude, Angle, Xpos, Ypos, Xwidth, yWidth
    20 + rand.nextDouble() * 5, 10 + rand.nextDouble() * 5, 0, 5 + rand.nextDouble() * 2, 5 + rand.nextDouble() * 2, 5 + rand.nextDouble() * 2, 5 + rand.nextDouble() * 2 };
    int[] x = new int[100];
    double[] y = new double[100];
    func.initialise(a);
    for (int i = 0; i < x.length; i++) {
        // Add random noise
        y[i] = func.eval(i) + ((rand.nextDouble() < 0.5) ? -rand.nextDouble() * 5 : rand.nextDouble() * 5);
    }
    // Randomise parameters
    for (int i = 0; i < a.length; i++) a[i] += (rand.nextDouble() < 0.5) ? -rand.nextDouble() : rand.nextDouble();
    // Compute the Hessian and parameter gradient vector
    GradientCalculator calc = new GradientCalculator(6);
    double[][] alpha2 = new double[6][6];
    double[] beta2 = new double[6];
    calc.findLinearised(y.length, y, a, alpha2, beta2, func);
    // Update the Hessian using a lambda shift
    double lambda = 1.001;
    for (int i = 0; i < alpha2.length; i++) alpha2[i][i] *= lambda;
    // Copy back
    for (int i = 0; i < beta.length; i++) {
        beta[i] = (float) beta2[i];
        for (int j = 0; j < beta.length; j++) {
            alpha[i][j] = (float) alpha2[i][j];
        }
    }
    // Check for a positive definite matrix
    if (positiveDifinite) {
        EJMLLinearSolver solver = new EJMLLinearSolver();
        return solver.solveCholeskyLDLT(copydouble(alpha), copydouble(beta));
    }
    return true;
}