Examples with Sum org.apache.commons.math3.stat.descriptive.summary.Sum used on opensource projects

Example 41 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project GDSC-SMLM by aherbert.

the class EMGainAnalysis method simulateFromPDF.

/**
	 * Random sample from the cumulative probability distribution function that is used during fitting
	 * 
	 * @return The histogram
	 */
private int[] simulateFromPDF() {
    final double[] g = pdf(0, _photons, _gain, _noise, (int) _bias);
    // Debug this
    double[] x = Utils.newArray(g.length, 0, 1.0);
    Utils.display(TITLE + " PDF", new Plot(TITLE + " PDF", "ADU", "P", x, Arrays.copyOf(g, g.length)));
    // Get cumulative probability
    double sum = 0;
    for (int i = 0; i < g.length; i++) {
        final double p = g[i];
        g[i] += sum;
        sum += p;
    }
    for (int i = 0; i < g.length; i++) g[i] /= sum;
    // Ensure value of 1 at the end
    g[g.length - 1] = 1;
    // Randomly sample
    RandomGenerator random = new Well44497b(System.currentTimeMillis() + System.identityHashCode(this));
    int[] h = new int[g.length];
    final int steps = simulationSize;
    for (int n = 0; n < steps; n++) {
        if (n % 64 == 0)
            IJ.showProgress(n, steps);
        final double p = random.nextDouble();
        for (int i = 0; i < g.length; i++) if (p <= g[i]) {
            h[i]++;
            break;
        }
    }
    return h;
}

Example 42 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method plotJumpDistances.

/**
	 * Plot a cumulative histogram and standard histogram of the jump distances.
	 *
	 * @param title
	 *            the title
	 * @param jumpDistances
	 *            the jump distances
	 * @param dimensions
	 *            the number of dimensions for the jumps
	 * @param steps
	 *            the steps
	 */
private void plotJumpDistances(String title, DoubleData jumpDistances, int dimensions) {
    // Cumulative histogram
    // --------------------
    double[] values = jumpDistances.values();
    String title2 = title + " Cumulative Jump Distance " + dimensions + "D";
    double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(values);
    Plot2 jdPlot = new Plot2(title2, "Distance (um^2)", "Cumulative Probability", jdHistogram[0], jdHistogram[1]);
    PlotWindow pw2 = Utils.display(title2, jdPlot);
    if (Utils.isNewWindow())
        idList[idCount++] = pw2.getImagePlus().getID();
    // Plot the expected function
    // This is the Chi-squared distribution: The sum of the squares of k independent
    // standard normal random variables with k = dimensions. It is a special case of
    // the gamma distribution. If the normals have non-unit variance the distribution 
    // is scaled.
    // Chi       ~ Gamma(k/2, 2)      // using the scale parameterisation of the gamma
    // s^2 * Chi ~ Gamma(k/2, 2*s^2)
    // So if s^2 = 2D:
    // 2D * Chi  ~ Gamma(k/2, 4D)
    double estimatedD = simpleD * simpleSteps;
    double max = Maths.max(values);
    double[] x = Utils.newArray(1000, 0, max / 1000);
    double k = dimensions / 2.0;
    double mean = 4 * estimatedD;
    GammaDistribution dist = new GammaDistribution(k, mean);
    double[] y = new double[x.length];
    for (int i = 0; i < x.length; i++) y[i] = dist.cumulativeProbability(x[i]);
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(title2, jdPlot);
    // Histogram
    // ---------
    title2 = title + " Jump " + dimensions + "D";
    int plotId = Utils.showHistogram(title2, jumpDistances, "Distance (um^2)", 0, 0, Math.max(20, values.length / 1000));
    if (Utils.isNewWindow())
        idList[idCount++] = plotId;
    // Recompute the expected function
    for (int i = 0; i < x.length; i++) y[i] = dist.density(x[i]);
    // Scale to have the same area
    if (Utils.xValues.length > 1) {
        final double area1 = jumpDistances.size() * (Utils.xValues[1] - Utils.xValues[0]);
        final double area2 = dist.cumulativeProbability(x[x.length - 1]);
        final double scaleFactor = area1 / area2;
        for (int i = 0; i < y.length; i++) y[i] *= scaleFactor;
    }
    jdPlot = Utils.plot;
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(WindowManager.getImage(plotId).getTitle(), jdPlot);
}

Example 43 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method reportResults.

private ComplexFilterScore reportResults(boolean newResults, List<ComplexFilterScore> filters) {
    if (filters.isEmpty()) {
        IJ.log("Warning: No filters pass the criteria");
        return null;
    }
    getCoordinateStore();
    Collections.sort(filters);
    FractionClassificationResult topFilterClassificationResult = null;
    ArrayList<FractionalAssignment[]> topFilterResults = null;
    String topFilterSummary = null;
    if (showSummaryTable || saveTemplate) {
        createSummaryWindow();
        int n = 0;
        final double range = (summaryDepth / simulationParameters.a) * 0.5;
        int np = 0;
        for (double depth : depthStats) {
            if (Math.abs(depth) < range)
                np++;
        }
        for (ComplexFilterScore fs : filters) {
            final ArrayList<FractionalAssignment[]> list = new ArrayList<FractionalAssignment[]>(resultsList.length);
            final FractionClassificationResult r = scoreFilter(fs.getFilter(), minimalFilter, resultsList, list, coordinateStore);
            final StringBuilder sb = createResult(fs.getFilter(), r);
            if (topFilterResults == null) {
                topFilterResults = list;
                topFilterClassificationResult = r;
            }
            // Show the recall at the specified depth. Sum the distance and signal factor of all scored spots.
            int scored = 0;
            double tp = 0, d = 0, sf = 0, rmsd = 0;
            SimpleRegression regression = new SimpleRegression(false);
            for (FractionalAssignment[] assignments : list) {
                if (assignments == null)
                    continue;
                for (int i = 0; i < assignments.length; i++) {
                    final CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
                    if (Math.abs(c.peak.error) <= range)
                        tp += c.getScore();
                    d += c.d;
                    sf += c.getSignalFactor();
                    rmsd += c.d * c.d;
                    regression.addData(c.peakResult.getSignal(), c.peak.getSignal());
                }
                scored += assignments.length;
            }
            final double slope = regression.getSlope();
            sb.append('\t');
            sb.append(Utils.rounded((double) tp / np)).append('\t');
            sb.append(Utils.rounded(d / scored)).append('\t');
            sb.append(Utils.rounded(sf / scored)).append('\t');
            sb.append(Utils.rounded(Math.sqrt(rmsd / scored))).append('\t');
            sb.append(Utils.rounded(slope)).append('\t');
            if (fs.atLimit() != null)
                sb.append(fs.atLimit());
            String text = sb.toString();
            if (topFilterSummary == null) {
                topFilterSummary = text;
                if (!showSummaryTable)
                    break;
            }
            if (fs.time != 0) {
                sb.append('\t');
                sb.append(fs.algorithm);
                sb.append('\t');
                sb.append(org.apache.commons.lang3.time.DurationFormatUtils.formatDurationHMS(fs.time));
            } else
                sb.append("\t\t");
            if (fs.paramTime != 0) {
                sb.append('\t');
                sb.append(fs.getParamAlgorithm());
                sb.append('\t');
                sb.append(org.apache.commons.lang3.time.DurationFormatUtils.formatDurationHMS(fs.paramTime));
            } else
                sb.append("\t\t");
            text = sb.toString();
            if (isHeadless)
                IJ.log(text);
            else
                summaryWindow.append(text);
            n++;
            if (summaryTopN > 0 && n >= summaryTopN)
                break;
        }
        // Add a spacer to the summary table if we have multiple results
        if (n > 1 && showSummaryTable) {
            if (isHeadless)
                IJ.log("");
            else
                summaryWindow.append("");
        }
    }
    DirectFilter bestFilter = filters.get(0).getFilter();
    if (saveBestFilter)
        saveFilter(bestFilter);
    if (topFilterClassificationResult == null) {
        topFilterResults = new ArrayList<FractionalAssignment[]>(resultsList.length);
        topFilterClassificationResult = scoreFilter(bestFilter, minimalFilter, resultsList, topFilterResults, coordinateStore);
    }
    if (newResults || scores.isEmpty()) {
        scores.add(new FilterResult(failCount, residualsThreshold, duplicateDistance, filters.get(0)));
    }
    if (saveTemplate)
        saveTemplate(topFilterSummary);
    showPlots();
    calculateSensitivity();
    topFilterResults = depthAnalysis(topFilterResults, bestFilter);
    topFilterResults = scoreAnalysis(topFilterResults, bestFilter);
    componentAnalysis(topFilterClassificationResult, filters.get(0));
    PreprocessedPeakResult[] filterResults = null;
    if (isShowOverlay())
        filterResults = showOverlay(topFilterResults, bestFilter);
    saveResults(filterResults, bestFilter);
    wo.tile();
    return filters.get(0);
}

Example 44 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project lucene-solr by apache.

the class EmpiricalDistributionEvaluator method evaluate.

public Tuple evaluate(Tuple tuple) throws IOException {
    if (subEvaluators.size() != 1) {
        throw new IOException("Empirical dist expects 1 column as a parameters");
    }
    StreamEvaluator colEval1 = subEvaluators.get(0);
    List<Number> numbers1 = (List<Number>) colEval1.evaluate(tuple);
    double[] column1 = new double[numbers1.size()];
    for (int i = 0; i < numbers1.size(); i++) {
        column1[i] = numbers1.get(i).doubleValue();
    }
    Arrays.sort(column1);
    EmpiricalDistribution empiricalDistribution = new EmpiricalDistribution();
    empiricalDistribution.load(column1);
    Map map = new HashMap();
    StatisticalSummary statisticalSummary = empiricalDistribution.getSampleStats();
    map.put("max", statisticalSummary.getMax());
    map.put("mean", statisticalSummary.getMean());
    map.put("min", statisticalSummary.getMin());
    map.put("stdev", statisticalSummary.getStandardDeviation());
    map.put("sum", statisticalSummary.getSum());
    map.put("N", statisticalSummary.getN());
    map.put("var", statisticalSummary.getVariance());
    return new EmpiricalDistributionTuple(empiricalDistribution, column1, map);
}

Example 45 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project lucene-solr by apache.

the class HistogramEvaluator method evaluate.

public List<Map> evaluate(Tuple tuple) throws IOException {
    StreamEvaluator colEval1 = subEvaluators.get(0);
    List<Number> numbers1 = (List<Number>) colEval1.evaluate(tuple);
    double[] column1 = new double[numbers1.size()];
    for (int i = 0; i < numbers1.size(); i++) {
        column1[i] = numbers1.get(i).doubleValue();
    }
    int bins = 10;
    if (subEvaluators.size() == 2) {
        StreamEvaluator binsEval = subEvaluators.get(1);
        Number binsNum = (Number) binsEval.evaluate(tuple);
        bins = binsNum.intValue();
    }
    EmpiricalDistribution empiricalDistribution = new EmpiricalDistribution(bins);
    empiricalDistribution.load(column1);
    List<Map> binList = new ArrayList();
    List<SummaryStatistics> summaries = empiricalDistribution.getBinStats();
    for (SummaryStatistics statisticalSummary : summaries) {
        Map map = new HashMap();
        map.put("max", statisticalSummary.getMax());
        map.put("mean", statisticalSummary.getMean());
        map.put("min", statisticalSummary.getMin());
        map.put("stdev", statisticalSummary.getStandardDeviation());
        map.put("sum", statisticalSummary.getSum());
        map.put("N", statisticalSummary.getN());
        map.put("var", statisticalSummary.getVariance());
        binList.add(map);
    }
    return binList;
}