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

Example 11 with RandomDataGenerator

use of org.apache.commons.math3.random.RandomDataGenerator 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);
}

Example 12 with RandomDataGenerator

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

the class BenchmarkFilterAnalysis method filterAnalysis.

private int filterAnalysis(FilterSet filterSet, int setNumber, DirectFilter currentOptimum, double rangeReduction) {
    // Check if the filters are the same so allowing optimisation
    final boolean allSameType = filterSet.allSameType();
    this.ga_resultsList = resultsList;
    Chromosome<FilterScore> best = null;
    String algorithm = "";
    // All the search algorithms use search dimensions.
    // Create search dimensions if needed (these are used for testing if the optimum is at the limit).
    ss_filter = null;
    ss_lower = null;
    ss_upper = null;
    FixedDimension[] originalDimensions = null;
    boolean rangeInput = false;
    boolean[] disabled = null;
    double[][] seed = null;
    boolean nonInteractive = false;
    if (allSameType) {
        // There should always be 1 filter
        ss_filter = (DirectFilter) filterSet.getFilters().get(0);
        int n = ss_filter.getNumberOfParameters();
        // Option to configure a range
        rangeInput = filterSet.getName().contains("Range");
        double[] range = new double[n];
        if (rangeInput && filterSet.size() == 4) {
            originalDimensions = new FixedDimension[n];
            // This is used as min/lower/upper/max
            final Filter minF = ss_filter;
            final Filter lowerF = filterSet.getFilters().get(1);
            final Filter upperF = filterSet.getFilters().get(2);
            final Filter maxF = filterSet.getFilters().get(3);
            for (int i = 0; i < n; i++) {
                double min = minF.getParameterValue(i);
                double lower = lowerF.getParameterValue(i);
                double upper = upperF.getParameterValue(i);
                range[i] = upper - lower;
                double max = maxF.getParameterValue(i);
                double minIncrement = ss_filter.getParameterIncrement(i);
                try {
                    originalDimensions[i] = new FixedDimension(min, max, minIncrement, lower, upper);
                } catch (IllegalArgumentException e) {
                    Utils.log(TITLE + " : Unable to configure dimension [%d] %s: " + e.getMessage(), i, ss_filter.getParameterName(i));
                    originalDimensions = null;
                    rangeInput = false;
                    break;
                }
            }
        }
        if (rangeInput && (filterSet.size() == 3 || filterSet.size() == 2)) {
            originalDimensions = new FixedDimension[n];
            // This is used as lower/upper[/increment]
            final Filter lowerF = ss_filter;
            final Filter upperF = filterSet.getFilters().get(1);
            for (int i = 0; i < n; i++) {
                // Do not disable if the increment is not set. This is left to the user to decide.
                //					if (incF.getParameterValue(i) == incF.getDisabledParameterValue(i) ||
                //							Double.isInfinite(incF.getParameterValue(i)))
                //					{
                //						// Not enabled
                //						dimensions[i] = new SearchDimension(incF.getDisabledParameterValue(i));
                //						continue;
                //					}
                double lower = lowerF.getParameterValue(i);
                double upper = upperF.getParameterValue(i);
                range[i] = upper - lower;
                ParameterType type = ss_filter.getParameterType(i);
                double min = BenchmarkSpotFit.getMin(type);
                double max = BenchmarkSpotFit.getMax(type);
                double minIncrement = ss_filter.getParameterIncrement(i);
                try {
                    originalDimensions[i] = new FixedDimension(min, max, minIncrement, lower, upper);
                } catch (IllegalArgumentException e) {
                    Utils.log(TITLE + " : Unable to configure dimension [%d] %s: " + e.getMessage(), i, ss_filter.getParameterName(i));
                    originalDimensions = null;
                    rangeInput = false;
                    break;
                }
            }
        }
        // Get the dimensions from the filters
        if (originalDimensions == null) {
            originalDimensions = new FixedDimension[n];
            // Allow inputing a filter set (e.g. saved from previous optimisation)
            // Find the limits in the current scores
            final double[] lower = ss_filter.getParameters().clone();
            final double[] upper = lower.clone();
            // Allow the SearchSpace algorithms to be seeded with an initial population 
            // for the first evaluation of the optimum. This is done when the input filter 
            // set is not a range.
            seed = new double[filterSet.size()][];
            int c = 0;
            for (Filter f : filterSet.getFilters()) {
                final double[] point = f.getParameters();
                seed[c++] = point;
                for (int j = 0; j < lower.length; j++) {
                    if (lower[j] > point[j])
                        lower[j] = point[j];
                    if (upper[j] < point[j])
                        upper[j] = point[j];
                }
            }
            // Min/max must be set using values from BenchmarkSpotFit.
            for (int i = 0; i < n; i++) {
                if (lower[i] == upper[i]) {
                    // Not enabled
                    originalDimensions[i] = new FixedDimension(lower[i]);
                    continue;
                }
                ParameterType type = ss_filter.getParameterType(i);
                double min = BenchmarkSpotFit.getMin(type);
                double max = BenchmarkSpotFit.getMax(type);
                double minIncrement = ss_filter.getParameterIncrement(i);
                if (min > lower[i])
                    min = lower[i];
                if (max < upper[i])
                    max = upper[i];
                try {
                    originalDimensions[i] = new FixedDimension(min, max, minIncrement, lower[i], upper[i]);
                } catch (IllegalArgumentException e) {
                    Utils.log(TITLE + " : Unable to configure dimension [%d] %s: " + e.getMessage(), i, ss_filter.getParameterName(i));
                    originalDimensions = null;
                    break;
                }
            }
            if (originalDimensions == null) {
                // Failed to work out the dimensions. No optimisation will be possible.
                // Sort so that the filters are in a nice order for reporting
                filterSet.sort();
                // This will not be used when the dimensions are null
                seed = null;
            }
        }
        if (originalDimensions != null) {
            // Use the current optimum if we are doing a range optimisation
            if (currentOptimum != null && rangeInput && currentOptimum.getType().equals(ss_filter.getType()) && evolve != 0) {
                // Suppress dialogs and use the current settings
                nonInteractive = true;
                double[] p = currentOptimum.getParameters();
                // Range search uses SearchDimension and we must centre on the optimum after creation.							
                for (int i = 0; i < originalDimensions.length; i++) {
                    double centre = p[i];
                    double r = 0;
                    if (originalDimensions[i].isActive()) {
                        // Set the range around the centre.
                        // This uses the range for each param when we read the filters.
                        r = range[i];
                        // Optionally reduce the width of the dimensions. 
                        if (rangeReduction > 0 && rangeReduction < 1)
                            r *= rangeReduction;
                    }
                    double lower = centre - r * 0.5;
                    double upper = centre + r * 0.5;
                    originalDimensions[i] = originalDimensions[i].create(lower, upper);
                }
            }
            // Store the dimensions so we can do an 'at limit' check
            disabled = new boolean[originalDimensions.length];
            ss_lower = new double[originalDimensions.length];
            ss_upper = new double[originalDimensions.length];
            for (int i = 0; i < disabled.length; i++) {
                disabled[i] = !originalDimensions[i].isActive();
                ss_lower[i] = originalDimensions[i].lower;
                ss_upper[i] = originalDimensions[i].upper;
            }
        }
    } else {
        // Sort so that the filters are in a nice order for reporting
        filterSet.sort();
    }
    analysisStopWatch = StopWatch.createStarted();
    if (evolve == 1 && originalDimensions != null) {
        // Collect parameters for the genetic algorithm
        pauseFilterTimer();
        // Remember the step size settings
        double[] stepSize = stepSizeMap.get(setNumber);
        if (stepSize == null || stepSize.length != ss_filter.length()) {
            stepSize = ss_filter.mutationStepRange().clone();
            for (int j = 0; j < stepSize.length; j++) stepSize[j] *= delta;
            // See if the same number of parameters have been optimised in other algorithms
            boolean[] enabled = searchRangeMap.get(setNumber);
            if (enabled != null && enabled.length == stepSize.length) {
                for (int j = 0; j < stepSize.length; j++) if (!enabled[j])
                    stepSize[j] *= -1;
            }
        }
        GenericDialog gd = null;
        int[] indices = ss_filter.getChromosomeParameters();
        boolean runAlgorithm = nonInteractive;
        if (!nonInteractive) {
            // Ask the user for the mutation step parameters.
            gd = new GenericDialog(TITLE);
            String prefix = setNumber + "_";
            gd.addMessage("Configure the genetic algorithm for [" + setNumber + "] " + filterSet.getName());
            gd.addNumericField(prefix + "Population_size", populationSize, 0);
            gd.addNumericField(prefix + "Failure_limit", failureLimit, 0);
            gd.addNumericField(prefix + "Tolerance", tolerance, -1);
            gd.addNumericField(prefix + "Converged_count", convergedCount, 0);
            gd.addSlider(prefix + "Mutation_rate", 0.05, 1, mutationRate);
            gd.addSlider(prefix + "Crossover_rate", 0.05, 1, crossoverRate);
            gd.addSlider(prefix + "Mean_children", 0.05, 3, meanChildren);
            gd.addSlider(prefix + "Selection_fraction", 0.05, 0.5, selectionFraction);
            gd.addCheckbox(prefix + "Ramped_selection", rampedSelection);
            gd.addCheckbox(prefix + "Save_option", saveOption);
            gd.addMessage("Configure the step size for each parameter");
            for (int j = 0; j < indices.length; j++) {
                // Do not mutate parameters that were not expanded, i.e. the input did not vary them.
                final double step = (originalDimensions[indices[j]].isActive()) ? stepSize[j] : 0;
                gd.addNumericField(getDialogName(prefix, ss_filter, indices[j]), step, 2);
            }
            gd.showDialog();
            runAlgorithm = !gd.wasCanceled();
        }
        if (runAlgorithm) {
            // Used to create random sample
            FixedDimension[] dimensions = Arrays.copyOf(originalDimensions, originalDimensions.length);
            if (!nonInteractive) {
                populationSize = (int) Math.abs(gd.getNextNumber());
                if (populationSize < 10)
                    populationSize = 10;
                failureLimit = (int) Math.abs(gd.getNextNumber());
                tolerance = gd.getNextNumber();
                // Allow negatives
                convergedCount = (int) gd.getNextNumber();
                mutationRate = Math.abs(gd.getNextNumber());
                crossoverRate = Math.abs(gd.getNextNumber());
                meanChildren = Math.abs(gd.getNextNumber());
                selectionFraction = Math.abs(gd.getNextNumber());
                rampedSelection = gd.getNextBoolean();
                saveOption = gd.getNextBoolean();
                for (int j = 0; j < indices.length; j++) {
                    stepSize[j] = gd.getNextNumber();
                }
                // Store for repeat analysis
                stepSizeMap.put(setNumber, stepSize);
            }
            for (int j = 0; j < indices.length; j++) {
                // A zero step size will keep the parameter but prevent range mutation.
                if (stepSize[j] < 0) {
                    dimensions[indices[j]] = new FixedDimension(ss_filter.getDisabledParameterValue(indices[j]));
                    disabled[indices[j]] = true;
                }
            }
            //				// Reset negatives to zero
            //				stepSize = stepSize.clone();
            //				for (int j = 0; j < stepSize.length; j++)
            //					if (stepSize[j] < 0)
            //						stepSize[j] = 0;
            // Create the genetic algorithm
            RandomDataGenerator random = new RandomDataGenerator(new Well44497b());
            SimpleMutator<FilterScore> mutator = new SimpleMutator<FilterScore>(random, mutationRate);
            // Override the settings with the step length, a min of zero and the configured upper
            double[] upper = ss_filter.upperLimit();
            mutator.overrideChromosomeSettings(stepSize, new double[stepSize.length], upper);
            Recombiner<FilterScore> recombiner = new SimpleRecombiner<FilterScore>(random, crossoverRate, meanChildren);
            SelectionStrategy<FilterScore> selectionStrategy;
            // If the initial population is huge ensure that the first selection culls to the correct size
            final int selectionMax = (int) (selectionFraction * populationSize);
            if (rampedSelection)
                selectionStrategy = new RampedSelectionStrategy<FilterScore>(random, selectionFraction, selectionMax);
            else
                selectionStrategy = new SimpleSelectionStrategy<FilterScore>(random, selectionFraction, selectionMax);
            ToleranceChecker<FilterScore> ga_checker = new InterruptChecker(tolerance, tolerance * 1e-3, convergedCount);
            // Create new random filters if the population is initially below the population size
            List<Filter> filters = filterSet.getFilters();
            if (filterSet.size() < populationSize) {
                filters = new ArrayList<Filter>(populationSize);
                // Add the existing filters if they are not a range input file
                if (!rangeInput)
                    filters.addAll(filterSet.getFilters());
                // Add current optimum to seed
                if (nonInteractive)
                    filters.add(currentOptimum);
                // The GA does not use the min interval grid so sample without rounding
                double[][] sample = SearchSpace.sampleWithoutRounding(dimensions, populationSize - filters.size(), null);
                filters.addAll(searchSpaceToFilters(sample));
            }
            ga_population = new Population<FilterScore>(filters);
            ga_population.setPopulationSize(populationSize);
            ga_population.setFailureLimit(failureLimit);
            selectionStrategy.setTracker(this);
            // Evolve
            algorithm = EVOLVE[evolve];
            ga_statusPrefix = algorithm + " [" + setNumber + "] " + filterSet.getName() + " ... ";
            ga_iteration = 0;
            ga_population.setTracker(this);
            createGAWindow();
            resumeFilterTimer();
            best = ga_population.evolve(mutator, recombiner, this, selectionStrategy, ga_checker);
            if (best != null) {
                // In case optimisation was stopped
                IJ.resetEscape();
                // The GA may produce coordinates off the min interval grid
                best = enumerateMinInterval(best, stepSize, indices);
                // Now update the filter set for final assessment
                filterSet = new FilterSet(filterSet.getName(), populationToFilters(ga_population.getIndividuals()));
                // Option to save the filters
                if (saveOption)
                    saveFilterSet(filterSet, setNumber, !nonInteractive);
            }
        } else
            resumeFilterTimer();
    }
    if ((evolve == 2 || evolve == 4) && originalDimensions != null) {
        // Collect parameters for the range search algorithm
        pauseFilterTimer();
        boolean isStepSearch = evolve == 4;
        // The step search should use a multi-dimension refinement and no range reduction
        SearchSpace.RefinementMode myRefinementMode = SearchSpace.RefinementMode.MULTI_DIMENSION;
        // Remember the enabled settings
        boolean[] enabled = searchRangeMap.get(setNumber);
        int n = ss_filter.getNumberOfParameters();
        if (enabled == null || enabled.length != n) {
            enabled = new boolean[n];
            Arrays.fill(enabled, true);
            // See if the same number of parameters have been optimised in other algorithms
            double[] stepSize = stepSizeMap.get(setNumber);
            if (stepSize != null && enabled.length == stepSize.length) {
                for (int j = 0; j < stepSize.length; j++) if (stepSize[j] < 0)
                    enabled[j] = false;
            }
        }
        GenericDialog gd = null;
        boolean runAlgorithm = nonInteractive;
        if (!nonInteractive) {
            // Ask the user for the search parameters.
            gd = new GenericDialog(TITLE);
            String prefix = setNumber + "_";
            gd.addMessage("Configure the " + EVOLVE[evolve] + " algorithm for [" + setNumber + "] " + filterSet.getName());
            gd.addSlider(prefix + "Width", 1, 5, rangeSearchWidth);
            if (!isStepSearch) {
                gd.addCheckbox(prefix + "Save_option", saveOption);
                gd.addNumericField(prefix + "Max_iterations", maxIterations, 0);
                String[] modes = SettingsManager.getNames((Object[]) SearchSpace.RefinementMode.values());
                gd.addSlider(prefix + "Reduce", 0.01, 0.99, rangeSearchReduce);
                gd.addChoice("Refinement", modes, modes[refinementMode]);
            }
            gd.addNumericField(prefix + "Seed_size", seedSize, 0);
            // Add choice of fields to optimise
            for (int i = 0; i < n; i++) gd.addCheckbox(getDialogName(prefix, ss_filter, i), enabled[i]);
            gd.showDialog();
            runAlgorithm = !gd.wasCanceled();
        }
        if (runAlgorithm) {
            SearchDimension[] dimensions = new SearchDimension[n];
            if (!nonInteractive) {
                rangeSearchWidth = (int) gd.getNextNumber();
                if (!isStepSearch) {
                    saveOption = gd.getNextBoolean();
                    maxIterations = (int) gd.getNextNumber();
                    refinementMode = gd.getNextChoiceIndex();
                    rangeSearchReduce = gd.getNextNumber();
                }
                seedSize = (int) gd.getNextNumber();
                for (int i = 0; i < n; i++) enabled[i] = gd.getNextBoolean();
                searchRangeMap.put(setNumber, enabled);
            }
            if (!isStepSearch)
                myRefinementMode = SearchSpace.RefinementMode.values()[refinementMode];
            for (int i = 0; i < n; i++) {
                if (enabled[i]) {
                    try {
                        dimensions[i] = originalDimensions[i].create(rangeSearchWidth);
                        dimensions[i].setPad(true);
                        // Prevent range reduction so that the step search just does a single refinement step
                        dimensions[i].setReduceFactor((isStepSearch) ? 1 : rangeSearchReduce);
                        // Centre on current optimum
                        if (nonInteractive)
                            dimensions[i].setCentre(currentOptimum.getParameterValue(i));
                    } catch (IllegalArgumentException e) {
                        IJ.error(TITLE, String.format("Unable to configure dimension [%d] %s: " + e.getMessage(), i, ss_filter.getParameterName(i)));
                        return -1;
                    }
                } else {
                    dimensions[i] = new SearchDimension(ss_filter.getDisabledParameterValue(i));
                }
            }
            for (int i = 0; i < disabled.length; i++) disabled[i] = !dimensions[i].active;
            // Check the number of combinations is OK
            long combinations = SearchSpace.countCombinations(dimensions);
            if (!nonInteractive && combinations > 10000) {
                gd = new GenericDialog(TITLE);
                gd.addMessage(String.format("%d combinations for the configured dimensions.\n \nClick 'Yes' to optimise.", combinations));
                gd.enableYesNoCancel();
                gd.hideCancelButton();
                gd.showDialog();
                if (!gd.wasOKed()) {
                    combinations = 0;
                }
            }
            if (combinations == 0) {
                resumeFilterTimer();
            } else {
                algorithm = EVOLVE[evolve] + " " + rangeSearchWidth;
                ga_statusPrefix = algorithm + " [" + setNumber + "] " + filterSet.getName() + " ... ";
                ga_iteration = 0;
                es_optimum = null;
                SearchSpace ss = new SearchSpace();
                ss.setTracker(this);
                if (seedSize > 0) {
                    double[][] sample;
                    // Add current optimum to seed
                    if (nonInteractive) {
                        sample = new double[1][];
                        sample[0] = currentOptimum.getParameters();
                        seed = merge(seed, sample);
                    }
                    int size = (seed == null) ? 0 : seed.length;
                    // Sample without rounding as the seed will be rounded
                    sample = SearchSpace.sampleWithoutRounding(dimensions, seedSize - size, null);
                    seed = merge(seed, sample);
                }
                // Note: If we have an optimum and we are not seeding this should not matter as the dimensions 
                // have been centred on the current optimum					
                ss.seed(seed);
                ConvergenceChecker<FilterScore> checker = new InterruptConvergenceChecker(0, 0, maxIterations);
                createGAWindow();
                resumeFilterTimer();
                SearchResult<FilterScore> optimum = ss.search(dimensions, this, checker, myRefinementMode);
                if (optimum != null) {
                    // In case optimisation was stopped
                    IJ.resetEscape();
                    best = ((SimpleFilterScore) optimum.score).r.filter;
                    if (seedSize > 0) {
                    // Not required as the search now respects the min interval
                    // The optimum may be off grid if it was from the seed
                    //best = enumerateMinInterval(best, enabled);
                    }
                    // Now update the filter set for final assessment
                    filterSet = new FilterSet(filterSet.getName(), searchSpaceToFilters((DirectFilter) best, ss.getSearchSpace()));
                    // Option to save the filters
                    if (saveOption)
                        saveFilterSet(filterSet, setNumber, !nonInteractive);
                }
            }
        } else
            resumeFilterTimer();
    }
    if (evolve == 3 && originalDimensions != null) {
        // Collect parameters for the enrichment search algorithm
        pauseFilterTimer();
        boolean[] enabled = searchRangeMap.get(setNumber);
        int n = ss_filter.getNumberOfParameters();
        if (enabled == null || enabled.length != n) {
            enabled = new boolean[n];
            Arrays.fill(enabled, true);
            // See if the same number of parameters have been optimised in other algorithms
            double[] stepSize = stepSizeMap.get(setNumber);
            if (stepSize != null && enabled.length == stepSize.length) {
                for (int j = 0; j < stepSize.length; j++) if (stepSize[j] < 0)
                    enabled[j] = false;
            }
        }
        GenericDialog gd = null;
        boolean runAlgorithm = nonInteractive;
        if (!nonInteractive) {
            // Ask the user for the search parameters.
            gd = new GenericDialog(TITLE);
            String prefix = setNumber + "_";
            gd.addMessage("Configure the enrichment search algorithm for [" + setNumber + "] " + filterSet.getName());
            gd.addCheckbox(prefix + "Save_option", saveOption);
            gd.addNumericField(prefix + "Max_iterations", maxIterations, 0);
            gd.addNumericField(prefix + "Converged_count", convergedCount, 0);
            gd.addNumericField(prefix + "Samples", enrichmentSamples, 0);
            gd.addSlider(prefix + "Fraction", 0.01, 0.99, enrichmentFraction);
            gd.addSlider(prefix + "Padding", 0, 0.99, enrichmentPadding);
            // Add choice of fields to optimise
            for (int i = 0; i < n; i++) gd.addCheckbox(getDialogName(prefix, ss_filter, i), enabled[i]);
            gd.showDialog();
            runAlgorithm = !gd.wasCanceled();
        }
        if (runAlgorithm) {
            FixedDimension[] dimensions = Arrays.copyOf(originalDimensions, originalDimensions.length);
            if (!nonInteractive) {
                saveOption = gd.getNextBoolean();
                maxIterations = (int) gd.getNextNumber();
                convergedCount = (int) gd.getNextNumber();
                enrichmentSamples = (int) gd.getNextNumber();
                enrichmentFraction = gd.getNextNumber();
                enrichmentPadding = gd.getNextNumber();
                for (int i = 0; i < n; i++) enabled[i] = gd.getNextBoolean();
                searchRangeMap.put(setNumber, enabled);
            }
            for (int i = 0; i < n; i++) {
                if (!enabled[i])
                    dimensions[i] = new FixedDimension(ss_filter.getDisabledParameterValue(i));
            }
            for (int i = 0; i < disabled.length; i++) disabled[i] = !dimensions[i].active;
            algorithm = EVOLVE[evolve];
            ga_statusPrefix = algorithm + " [" + setNumber + "] " + filterSet.getName() + " ... ";
            ga_iteration = 0;
            es_optimum = null;
            SearchSpace ss = new SearchSpace();
            ss.setTracker(this);
            // Add current optimum to seed
            if (nonInteractive) {
                double[][] sample = new double[1][];
                sample[0] = currentOptimum.getParameters();
                seed = merge(seed, sample);
            }
            ss.seed(seed);
            ConvergenceChecker<FilterScore> checker = new InterruptConvergenceChecker(0, 0, maxIterations, convergedCount);
            createGAWindow();
            resumeFilterTimer();
            SearchResult<FilterScore> optimum = ss.enrichmentSearch(dimensions, this, checker, enrichmentSamples, enrichmentFraction, enrichmentPadding);
            if (optimum != null) {
                // In case optimisation was stopped
                IJ.resetEscape();
                best = ((SimpleFilterScore) optimum.score).r.filter;
                // Not required as the search now respects the min interval
                // Enumerate on the min interval to produce the final filter
                //best = enumerateMinInterval(best, enabled);
                // Now update the filter set for final assessment
                filterSet = new FilterSet(filterSet.getName(), searchSpaceToFilters((DirectFilter) best, ss.getSearchSpace()));
                // Option to save the filters
                if (saveOption)
                    saveFilterSet(filterSet, setNumber, !nonInteractive);
            }
        } else
            resumeFilterTimer();
    }
    IJ.showStatus("Analysing [" + setNumber + "] " + filterSet.getName() + " ...");
    // Do not support plotting if we used optimisation
    double[] xValues = (best != null || isHeadless || (plotTopN == 0)) ? null : new double[filterSet.size()];
    double[] yValues = (xValues == null) ? null : new double[xValues.length];
    SimpleFilterScore max = null;
    // It can just assess the top 1 required for the summary.
    if (best != null) {
        // Only assess the top 1 filter for the summary
        List<Filter> list = new ArrayList<Filter>();
        list.add((DirectFilter) best);
        filterSet = new FilterSet(filterSet.getName(), list);
    }
    // Score the filters and report the results if configured.
    FilterScoreResult[] scoreResults = scoreFilters(setUncomputedStrength(filterSet), showResultsTable);
    if (scoreResults == null)
        return -1;
    analysisStopWatch.stop();
    for (int index = 0; index < scoreResults.length; index++) {
        final FilterScoreResult scoreResult = scoreResults[index];
        if (xValues != null) {
            xValues[index] = scoreResult.filter.getNumericalValue();
            yValues[index] = scoreResult.score;
        }
        final SimpleFilterScore result = new SimpleFilterScore(scoreResult, allSameType, scoreResult.criteria >= minCriteria);
        if (result.compareTo(max) < 0) {
            max = result;
        }
    }
    if (showResultsTable) {
        BufferedTextWindow tw = null;
        if (resultsWindow != null) {
            tw = new BufferedTextWindow(resultsWindow);
            tw.setIncrement(Integer.MAX_VALUE);
        }
        for (int index = 0; index < scoreResults.length; index++) addToResultsWindow(tw, scoreResults[index].text);
        if (resultsWindow != null)
            resultsWindow.getTextPanel().updateDisplay();
    }
    // Check the top filter against the limits of the original dimensions
    char[] atLimit = null;
    if (allSameType && originalDimensions != null) {
        DirectFilter filter = max.r.filter;
        int[] indices = filter.getChromosomeParameters();
        atLimit = new char[indices.length];
        StringBuilder sb = new StringBuilder(200);
        for (int j = 0; j < indices.length; j++) {
            atLimit[j] = ComplexFilterScore.WITHIN;
            final int p = indices[j];
            if (disabled[p])
                continue;
            final double value = filter.getParameterValue(p);
            double lowerLimit = originalDimensions[p].getLower();
            double upperLimit = originalDimensions[p].getUpper();
            int c1 = Double.compare(value, lowerLimit);
            if (c1 <= 0) {
                atLimit[j] = ComplexFilterScore.FLOOR;
                sb.append(" : ").append(filter.getParameterName(p)).append(' ').append(atLimit[j]).append('[').append(Utils.rounded(value));
                if (c1 == -1) {
                    atLimit[j] = ComplexFilterScore.BELOW;
                    sb.append("<").append(Utils.rounded(lowerLimit));
                }
                sb.append("]");
            } else {
                int c2 = Double.compare(value, upperLimit);
                if (c2 >= 0) {
                    atLimit[j] = ComplexFilterScore.CEIL;
                    sb.append(" : ").append(filter.getParameterName(p)).append(' ').append(atLimit[j]).append('[').append(Utils.rounded(value));
                    if (c2 == 1) {
                        atLimit[j] = ComplexFilterScore.ABOVE;
                        sb.append(">").append(Utils.rounded(upperLimit));
                    }
                    sb.append("]");
                }
            }
        }
        if (sb.length() > 0) {
            if (max.criteriaPassed) {
                Utils.log("Warning: Top filter (%s @ %s|%s) [%s] at the limit of the expanded range%s", filter.getName(), Utils.rounded((invertScore) ? -max.score : max.score), Utils.rounded((invertCriteria) ? -minCriteria : minCriteria), limitFailCount + limitRange, sb.toString());
            } else {
                Utils.log("Warning: Top filter (%s @ -|%s) [%s] at the limit of the expanded range%s", filter.getName(), Utils.rounded((invertCriteria) ? -max.criteria : max.criteria), limitFailCount + limitRange, sb.toString());
            }
        }
    }
    // Note that max should never be null since this method is not run with an empty filter set
    // We may have no filters that pass the criteria
    String type = max.r.filter.getType();
    if (!max.criteriaPassed) {
        Utils.log("Warning: Filter does not pass the criteria: %s : Best = %s using %s", type, Utils.rounded((invertCriteria) ? -max.criteria : max.criteria), max.r.filter.getName());
        return 0;
    }
    // This could be an option?
    boolean allowDuplicates = true;
    // XXX - Commented out the requirement to be the same type to store for later analysis. 
    // This may break the code, however I think that all filter sets should be able to have a best filter
    // irrespective of whether they were the same type or not.
    //if (allSameType)
    //{
    ComplexFilterScore newFilterScore = new ComplexFilterScore(max.r, atLimit, algorithm, analysisStopWatch.getTime(), "", 0);
    addBestFilter(type, allowDuplicates, newFilterScore);
    // Add spacer at end of each result set
    if (isHeadless) {
        if (showResultsTable && filterSet.size() > 1)
            IJ.log("");
    } else {
        if (showResultsTable && filterSet.size() > 1)
            resultsWindow.append("");
        if (plotTopN > 0 && xValues != null) {
            // Check the xValues are unique. Since the filters have been sorted by their
            // numeric value we only need to compare adjacent entries.
            boolean unique = true;
            for (int ii = 0; ii < xValues.length - 1; ii++) {
                if (xValues[ii] == xValues[ii + 1]) {
                    unique = false;
                    break;
                }
            }
            String xAxisName = filterSet.getValueName();
            if (unique) {
                // Check the values all refer to the same property
                for (Filter filter : filterSet.getFilters()) {
                    if (!xAxisName.equals(filter.getNumericalValueName())) {
                        unique = false;
                        break;
                    }
                }
            }
            if (!unique) {
                // If not unique then renumber them and use an arbitrary label
                xAxisName = "Filter";
                for (int ii = 0; ii < xValues.length; ii++) xValues[ii] = ii + 1;
            }
            String title = filterSet.getName();
            // Check if a previous filter set had the same name, update if necessary
            NamedPlot p = getNamedPlot(title);
            if (p == null)
                plots.add(new NamedPlot(title, xAxisName, xValues, yValues));
            else
                p.updateValues(xAxisName, xValues, yValues);
            if (plots.size() > plotTopN) {
                Collections.sort(plots);
                p = plots.remove(plots.size() - 1);
            }
        }
    }
    return 0;
}

Example 13 with RandomDataGenerator

use of org.apache.commons.math3.random.RandomDataGenerator 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 14 with RandomDataGenerator

use of org.apache.commons.math3.random.RandomDataGenerator 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 15 with RandomDataGenerator

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

the class LSQLVMGradientProcedureTest method gradientProcedureComputesSameOutputWithBias.

@Test
public void gradientProcedureComputesSameOutputWithBias() {
    ErfGaussian2DFunction func = new SingleFreeCircularErfGaussian2DFunction(blockWidth, blockWidth);
    int nparams = func.getNumberOfGradients();
    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);
            BaseLSQLVMGradientProcedure p = LSQLVMGradientProcedureFactory.create(y, func);
            p.gradient(a);
            double[] beta = p.beta;
            alphaList.add(p.getAlphaLinear());
            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(p.getAlphaMatrix(), beta))
                throw new AssertionError();
            xList.add(beta);
        //System.out.println(Arrays.toString(beta));
        }
        //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;
                BaseLSQLVMGradientProcedure p = LSQLVMGradientProcedureFactory.create(y, func);
                p.gradient(a);
                double[] beta = p.beta;
                double[] alpha2 = alphaList.get(i);
                double[] beta2 = betaList.get(i);
                double[] x2 = xList.get(i);
                Assert.assertArrayEquals("Beta", beta2, beta, 1e-10);
                Assert.assertArrayEquals("Alpha", alpha2, p.getAlphaLinear(), 1e-10);
                // Solve
                solver.solve(p.getAlphaMatrix(), 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;
    }
}