Examples with Well19937c org.apache.commons.math3.random.Well19937c used on opensource projects

Example 6 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class FilterTest method canCompareMultiFilter2.

@Test
public void canCompareMultiFilter2() {
    RandomGenerator randomGenerator = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this));
    MultiFilter2 f = new MultiFilter2(0, 0, 0, 0, 0, 0, 0);
    for (int i = 1000; i-- > 0; ) {
        MultiFilter2 f1 = (MultiFilter2) f.create(random(f.getNumberOfParameters(), randomGenerator));
        MultiFilter2 f2 = (MultiFilter2) f.create(random(f.getNumberOfParameters(), randomGenerator));
        int e = f1.weakest((Filter) f2);
        int o = f1.weakest(f2);
        Assert.assertEquals(e, o);
    }
}

Example 7 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class BinomialFitter method fitBinomial.

/**
	 * Fit the binomial distribution (n,p) to the cumulative histogram. Performs fitting assuming a fixed n value and
	 * attempts to optimise p.
	 * 
	 * @param histogram
	 *            The input histogram
	 * @param mean
	 *            The histogram mean (used to estimate p). Calculated if NaN.
	 * @param n
	 *            The n to evaluate
	 * @param zeroTruncated
	 *            True if the model should ignore n=0 (zero-truncated binomial)
	 * @return The best fit (n, p)
	 * @throws IllegalArgumentException
	 *             If any of the input data values are negative
	 * @throws IllegalArgumentException
	 *             If any fitting a zero truncated binomial and there are no values above zero
	 */
public PointValuePair fitBinomial(double[] histogram, double mean, int n, boolean zeroTruncated) {
    if (Double.isNaN(mean))
        mean = getMean(histogram);
    if (zeroTruncated && histogram[0] > 0) {
        log("Fitting zero-truncated histogram but there are zero values - Renormalising to ignore zero");
        double cumul = 0;
        for (int i = 1; i < histogram.length; i++) cumul += histogram[i];
        if (cumul == 0)
            throw new IllegalArgumentException("Fitting zero-truncated histogram but there are no non-zero values");
        histogram[0] = 0;
        for (int i = 1; i < histogram.length; i++) histogram[i] /= cumul;
    }
    int nFittedPoints = Math.min(histogram.length, n + 1) - ((zeroTruncated) ? 1 : 0);
    if (nFittedPoints < 1) {
        log("No points to fit (%d): Histogram.length = %d, n = %d, zero-truncated = %b", nFittedPoints, histogram.length, n, zeroTruncated);
        return null;
    }
    // The model is only fitting the probability p
    // For a binomial n*p = mean => p = mean/n
    double[] initialSolution = new double[] { FastMath.min(mean / n, 1) };
    // Create the function
    BinomialModelFunction function = new BinomialModelFunction(histogram, n, zeroTruncated);
    double[] lB = new double[1];
    double[] uB = new double[] { 1 };
    SimpleBounds bounds = new SimpleBounds(lB, uB);
    // Fit
    // CMAESOptimizer or BOBYQAOptimizer support bounds
    // CMAESOptimiser based on Matlab code:
    // https://www.lri.fr/~hansen/cmaes.m
    // Take the defaults from the Matlab documentation
    int maxIterations = 2000;
    //Double.NEGATIVE_INFINITY;
    double stopFitness = 0;
    boolean isActiveCMA = true;
    int diagonalOnly = 0;
    int checkFeasableCount = 1;
    RandomGenerator random = new Well19937c();
    boolean generateStatistics = false;
    ConvergenceChecker<PointValuePair> checker = new SimpleValueChecker(1e-6, 1e-10);
    // The sigma determines the search range for the variables. It should be 1/3 of the initial search region.
    OptimizationData sigma = new CMAESOptimizer.Sigma(new double[] { (uB[0] - lB[0]) / 3 });
    OptimizationData popSize = new CMAESOptimizer.PopulationSize((int) (4 + Math.floor(3 * Math.log(2))));
    try {
        PointValuePair solution = null;
        boolean noRefit = maximumLikelihood;
        if (n == 1 && zeroTruncated) {
            // No need to fit
            solution = new PointValuePair(new double[] { 1 }, 0);
            noRefit = true;
        } else {
            GoalType goalType = (maximumLikelihood) ? GoalType.MAXIMIZE : GoalType.MINIMIZE;
            // Iteratively fit
            CMAESOptimizer opt = new CMAESOptimizer(maxIterations, stopFitness, isActiveCMA, diagonalOnly, checkFeasableCount, random, generateStatistics, checker);
            for (int iteration = 0; iteration <= fitRestarts; iteration++) {
                try {
                    // Start from the initial solution
                    PointValuePair result = opt.optimize(new InitialGuess(initialSolution), new ObjectiveFunction(function), goalType, bounds, sigma, popSize, new MaxIter(maxIterations), new MaxEval(maxIterations * 2));
                    //		opt.getEvaluations());
                    if (solution == null || result.getValue() < solution.getValue()) {
                        solution = result;
                    }
                } catch (TooManyEvaluationsException e) {
                } catch (TooManyIterationsException e) {
                }
                if (solution == null)
                    continue;
                try {
                    // Also restart from the current optimum
                    PointValuePair result = opt.optimize(new InitialGuess(solution.getPointRef()), new ObjectiveFunction(function), goalType, bounds, sigma, popSize, new MaxIter(maxIterations), new MaxEval(maxIterations * 2));
                    //		opt.getEvaluations());
                    if (result.getValue() < solution.getValue()) {
                        solution = result;
                    }
                } catch (TooManyEvaluationsException e) {
                } catch (TooManyIterationsException e) {
                }
            }
            if (solution == null)
                return null;
        }
        if (noRefit) {
            // Although we fit the log-likelihood, return the sum-of-squares to allow 
            // comparison across different n
            double p = solution.getPointRef()[0];
            double ss = 0;
            double[] obs = function.p;
            double[] exp = function.getP(p);
            for (int i = 0; i < obs.length; i++) ss += (obs[i] - exp[i]) * (obs[i] - exp[i]);
            return new PointValuePair(solution.getPointRef(), ss);
        } else // We can do a LVM refit if the number of fitted points is more than 1
        if (nFittedPoints > 1) {
            // Improve SS fit with a gradient based LVM optimizer
            LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
            try {
                final BinomialModelFunctionGradient gradientFunction = new BinomialModelFunctionGradient(histogram, n, zeroTruncated);
                //@formatter:off
                LeastSquaresProblem problem = new LeastSquaresBuilder().maxEvaluations(Integer.MAX_VALUE).maxIterations(3000).start(solution.getPointRef()).target(gradientFunction.p).weight(new DiagonalMatrix(gradientFunction.getWeights())).model(gradientFunction, new MultivariateMatrixFunction() {

                    public double[][] value(double[] point) throws IllegalArgumentException {
                        return gradientFunction.jacobian(point);
                    }
                }).build();
                //@formatter:on
                Optimum lvmSolution = optimizer.optimize(problem);
                // Check the pValue is valid since the LVM is not bounded.
                double p = lvmSolution.getPoint().getEntry(0);
                if (p <= 1 && p >= 0) {
                    // True if the weights are 1
                    double ss = lvmSolution.getResiduals().dotProduct(lvmSolution.getResiduals());
                    //	ss += (obs[i] - exp[i]) * (obs[i] - exp[i]);
                    if (ss < solution.getValue()) {
                        //		Utils.rounded(100 * (solution.getValue() - ss) / solution.getValue(), 4));
                        return new PointValuePair(lvmSolution.getPoint().toArray(), ss);
                    }
                }
            } catch (TooManyIterationsException e) {
                log("Failed to re-fit: Too many iterations: %s", e.getMessage());
            } catch (ConvergenceException e) {
                log("Failed to re-fit: %s", e.getMessage());
            } catch (Exception e) {
            // Ignore this ...
            }
        }
        return solution;
    } catch (Exception e) {
        log("Failed to fit Binomial distribution with N=%d : %s", n, e.getMessage());
    }
    return null;
}

Example 8 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class CMOSAnalysis method simulate.

private void simulate() {
    // Create the offset, variance and gain for each pixel
    int n = size * size;
    float[] pixelOffset = new float[n];
    float[] pixelVariance = new float[n];
    float[] pixelGain = new float[n];
    IJ.showStatus("Creating random per-pixel readout");
    long start = System.currentTimeMillis();
    RandomGenerator rg = new Well19937c();
    PoissonDistribution pd = new PoissonDistribution(rg, offset, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    ExponentialDistribution ed = new ExponentialDistribution(rg, variance, ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    totalProgress = n;
    stepProgress = Utils.getProgressInterval(totalProgress);
    for (int i = 0; i < n; i++) {
        if (i % n == 0)
            IJ.showProgress(i, n);
        // Q. Should these be clipped to a sensible range?
        pixelOffset[i] = (float) pd.sample();
        pixelVariance[i] = (float) ed.sample();
        pixelGain[i] = (float) (gain + rg.nextGaussian() * gainSD);
    }
    IJ.showProgress(1);
    // Avoid all the file saves from updating the progress bar and status line
    Utils.setShowStatus(false);
    Utils.setShowProgress(false);
    JLabel statusLine = Utils.getStatusLine();
    progressBar = Utils.getProgressBar();
    // Save to the directory as a stack
    ImageStack simulationStack = new ImageStack(size, size);
    simulationStack.addSlice("Offset", pixelOffset);
    simulationStack.addSlice("Variance", pixelVariance);
    simulationStack.addSlice("Gain", pixelGain);
    simulationImp = new ImagePlus("PerPixel", simulationStack);
    // Only the info property is saved to the TIFF file
    simulationImp.setProperty("Info", String.format("Offset=%s; Variance=%s; Gain=%s +/- %s", Utils.rounded(offset), Utils.rounded(variance), Utils.rounded(gain), Utils.rounded(gainSD)));
    IJ.save(simulationImp, new File(directory, "perPixelSimulation.tif").getPath());
    // Create thread pool and workers
    ExecutorService executor = Executors.newFixedThreadPool(getThreads());
    TurboList<Future<?>> futures = new TurboList<Future<?>>(nThreads);
    // Simulate the zero exposure input.
    // Simulate 20 - 200 photon images.
    int[] photons = new int[] { 0, 20, 50, 100, 200 };
    totalProgress = photons.length * frames;
    stepProgress = Utils.getProgressInterval(totalProgress);
    progress = 0;
    progressBar.show(0);
    // For saving stacks
    int blockSize = 10;
    int nPerThread = (int) Math.ceil((double) frames / nThreads);
    // Convert to fit the block size
    nPerThread = (int) Math.ceil((double) nPerThread / blockSize) * blockSize;
    long seed = start;
    for (int p : photons) {
        statusLine.setText("Simulating " + Utils.pleural(p, "photon"));
        // Create the directory
        File out = new File(directory, String.format("photon%03d", p));
        if (!out.exists())
            out.mkdir();
        for (int from = 0; from < frames; ) {
            int to = Math.min(from + nPerThread, frames);
            futures.add(executor.submit(new SimulationWorker(seed++, out.getPath(), simulationStack, from, to, blockSize, p)));
            from = to;
        }
        // Wait for all to finish
        for (int t = futures.size(); t-- > 0; ) {
            try {
                // The future .get() method will block until completed
                futures.get(t).get();
            } catch (Exception e) {
                // This should not happen. 
                e.printStackTrace();
            }
        }
        futures.clear();
    }
    Utils.setShowStatus(true);
    Utils.setShowProgress(true);
    IJ.showProgress(1);
    executor.shutdown();
    Utils.log("Simulation time = " + Utils.timeToString(System.currentTimeMillis() - start));
}

Example 9 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class EJMLLinearSolverTest method runInversionSpeedTest.

private void runInversionSpeedTest(int flags) {
    final Gaussian2DFunction f0 = GaussianFunctionFactory.create2D(1, 10, 10, flags, null);
    int n = f0.size();
    final double[] y = new double[n];
    final TurboList<DenseMatrix64F> aList = new TurboList<DenseMatrix64F>();
    double[] testbackground = new double[] { 0.2, 0.7 };
    double[] testsignal1 = new double[] { 30, 100, 300 };
    double[] testcx1 = new double[] { 4.9, 5.3 };
    double[] testcy1 = new double[] { 4.8, 5.2 };
    double[] testw1 = new double[] { 1.1, 1.2, 1.5 };
    int np = f0.getNumberOfGradients();
    GradientCalculator calc = GradientCalculatorFactory.newCalculator(np);
    final RandomDataGenerator rdg = new RandomDataGenerator(new Well19937c(30051977));
    //double lambda = 10;
    for (double background : testbackground) // Peak 1
    for (double signal1 : testsignal1) for (double cx1 : testcx1) for (double cy1 : testcy1) for (double w1 : testw1) {
        double[] p = new double[] { background, signal1, 0, cx1, cy1, w1, w1 };
        f0.initialise(p);
        f0.forEach(new ValueProcedure() {

            int i = 0;

            public void execute(double value) {
                // Poisson data 
                y[i++] = rdg.nextPoisson(value);
            }
        });
        double[][] alpha = new double[np][np];
        double[] beta = new double[np];
        //double ss = 
        calc.findLinearised(n, y, p, alpha, beta, f0);
        //System.out.printf("SS = %f\n", ss);
        // As per the LVM algorithm
        //for (int i = 0; i < np; i++)
        //	alpha[i][i] *= lambda;
        aList.add(EJMLLinearSolver.toA(alpha));
    }
    DenseMatrix64F[] a = aList.toArray(new DenseMatrix64F[aList.size()]);
    boolean[] ignore = new boolean[a.length];
    double[][] answer = new double[a.length][];
    int runs = 100000 / a.length;
    TimingService ts = new TimingService(runs);
    TurboList<InversionTimingTask> tasks = new TurboList<InversionTimingTask>();
    tasks.add(new PseudoInverseInversionTimingTask(a, ignore, answer));
    tasks.add(new CholeskyInversionTimingTask(a, ignore, answer));
    tasks.add(new LinearInversionTimingTask(a, ignore, answer));
    tasks.add(new CholeskyLDLTInversionTimingTask(a, ignore, answer));
    tasks.add(new DirectInversionInversionTimingTask(a, ignore, answer));
    tasks.add(new DiagonalDirectInversionInversionTimingTask(a, ignore, answer));
    for (InversionTimingTask task : tasks) if (!task.badSolver)
        ts.execute(task);
    ts.repeat();
    ts.report();
}

Example 10 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class FisherInformationMatrixTest method createFisherInformationMatrix.

private FisherInformationMatrix createFisherInformationMatrix(int n, int k) {
    int maxx = 10;
    int size = maxx * maxx;
    RandomGenerator randomGenerator = new Well19937c(30051977);
    RandomDataGenerator rdg = new RandomDataGenerator(randomGenerator);
    // Use a real Gaussian function here to compute the Fisher information.
    // The matrix may be sensitive to the type of equation used.
    int npeaks = 1;
    while (1 + npeaks * 6 < n) npeaks++;
    Gaussian2DFunction f = GaussianFunctionFactory.create2D(npeaks, maxx, maxx, GaussianFunctionFactory.FIT_ELLIPTICAL, null);
    double[] a = new double[1 + npeaks * 6];
    a[Gaussian2DFunction.BACKGROUND] = rdg.nextUniform(1, 5);
    for (int i = 0, j = 0; i < npeaks; i++, j += 6) {
        a[j + Gaussian2DFunction.SIGNAL] = rdg.nextUniform(100, 300);
        a[j + Gaussian2DFunction.SHAPE] = rdg.nextUniform(-Math.PI, Math.PI);
        // Non-overlapping peaks otherwise the CRLB are poor
        a[j + Gaussian2DFunction.X_POSITION] = rdg.nextUniform(2 + i * 2, 4 + i * 2);
        a[j + Gaussian2DFunction.Y_POSITION] = rdg.nextUniform(2 + i * 2, 4 + i * 2);
        a[j + Gaussian2DFunction.X_SD] = rdg.nextUniform(1.5, 2);
        a[j + Gaussian2DFunction.Y_SD] = rdg.nextUniform(1.5, 2);
    }
    f.initialise(a);
    GradientCalculator c = GradientCalculatorFactory.newCalculator(a.length);
    double[][] I = c.fisherInformationMatrix(size, a, f);
    //System.out.printf("n=%d, k=%d, I=\n", n, k);
    //for (int i = 0; i < I.length; i++)
    //	System.out.println(Arrays.toString(I[i]));
    // Reduce to the desired size
    I = Arrays.copyOf(I, n);
    for (int i = 0; i < n; i++) I[i] = Arrays.copyOf(I[i], n);
    // Zero selected columns
    if (k > 0) {
        int[] zero = new RandomDataGenerator(randomGenerator).nextPermutation(n, k);
        for (int i : zero) {
            for (int j = 0; j < n; j++) {
                I[i][j] = I[j][i] = 0;
            }
        }
    }
    // Create matrix
    return new FisherInformationMatrix(I, 1e-3);
}