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

Example 16 with FakeGradientFunction

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

the class FastMLEGradient2ProcedureTest method gradientProcedureIsNotSlowerThanGradientCalculator.

private void gradientProcedureIsNotSlowerThanGradientCalculator(final int nparams) {
    org.junit.Assume.assumeTrue(speedTests || TestSettings.RUN_SPEED_TESTS);
    final int iter = 1000;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    final ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    final ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    createFakeData(nparams, iter, paramsList, yList);
    final FakeGradientFunction func = new FakeGradientFunction(blockWidth, nparams);
    MLEGradientCalculator calc = (MLEGradientCalculator) GradientCalculatorFactory.newCalculator(nparams, true);
    for (int i = 0; i < paramsList.size(); i++) calc.logLikelihood(yList.get(i), paramsList.get(i), func);
    for (int i = 0; i < paramsList.size(); i++) {
        FastMLEGradient2Procedure p = FastMLEGradient2ProcedureFactory.createUnrolled(yList.get(i), func);
        p.computeLogLikelihood(paramsList.get(i));
    }
    // Realistic loops for an optimisation
    final int loops = 15;
    // Run till stable timing
    Timer t1 = new Timer() {

        @Override
        void run() {
            for (int i = 0, k = 0; i < iter; i++) {
                MLEGradientCalculator calc = (MLEGradientCalculator) GradientCalculatorFactory.newCalculator(nparams, true);
                for (int j = loops; j-- > 0; ) calc.logLikelihood(yList.get(i), paramsList.get(k++ % iter), func);
            }
        }
    };
    long time1 = t1.getTime();
    Timer t2 = new Timer(t1.loops) {

        @Override
        void run() {
            for (int i = 0, k = 0; i < iter; i++) {
                FastMLEGradient2Procedure p = FastMLEGradient2ProcedureFactory.createUnrolled(yList.get(i), func);
                for (int j = loops; j-- > 0; ) p.computeLogLikelihood(paramsList.get(k++ % iter));
            }
        }
    };
    long time2 = t2.getTime();
    log("GradientCalculator = %d : FastMLEGradient2Procedure %d = %d : %fx\n", time1, nparams, time2, (1.0 * time1) / time2);
    if (TestSettings.ASSERT_SPEED_TESTS) {
        // Add contingency
        Assert.assertTrue(time2 < time1 * 1.5);
    }
}

Example 17 with FakeGradientFunction

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

the class PoissonGradientProcedureTest method gradientProcedureUnrolledComputesSameAsGradientProcedure.

private void gradientProcedureUnrolledComputesSameAsGradientProcedure(int nparams, boolean precomputed) {
    int iter = 10;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    createFakeParams(nparams, iter, paramsList);
    Gradient1Function func = new FakeGradientFunction(blockWidth, nparams);
    if (precomputed)
        func = PrecomputedGradient1Function.wrapGradient1Function(func, Utils.newArray(func.size(), 0.1, 1.3));
    String name = String.format("[%d]", nparams);
    for (int i = 0; i < paramsList.size(); i++) {
        PoissonGradientProcedure p1 = new PoissonGradientProcedure(func);
        p1.computeFisherInformation(paramsList.get(i));
        PoissonGradientProcedure p2 = PoissonGradientProcedureFactory.create(func);
        p2.computeFisherInformation(paramsList.get(i));
        // Exactly the same ...
        Assert.assertArrayEquals(name + " Observations: Not same alpha @ " + i, p1.getLinear(), p2.getLinear(), 0);
        double[][] am1 = p1.getMatrix();
        double[][] am2 = p2.getMatrix();
        for (int j = 0; j < nparams; j++) Assert.assertArrayEquals(name + " Observations: Not same alpha @ " + i, am1[j], am2[j], 0);
    }
}