Example usage for org.apache.commons.math3.random EmpiricalDistribution getBinStats

Introduction

In this page you can find the example usage for org.apache.commons.math3.random EmpiricalDistribution getBinStats.

Prototype

public List<SummaryStatistics> getBinStats() 

Source Link

Document

Returns a List of SummaryStatistics instances containing statistics describing the values in each of the bins.

Usage

From source file:edu.wisc.ssec.mcidasv.data.hydra.Statistics.java

public static Long[] histogram(FlatField field, int bins) throws VisADException {
    Long[] histogram = new Long[bins];
    EmpiricalDistribution distribution = new EmpiricalDistribution(bins);
    distribution.load(field.getValues(false)[0]);
    int k = 0;/*  w w w  . j a  v  a 2s.co m*/
    for (SummaryStatistics stats : distribution.getBinStats()) {
        histogram[k++] = stats.getN();
    }
    return histogram;
}

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From source file:ijfx.ui.plugin.panel.OverlayPanel.java

protected XYChart.Series<Double, Double> getOverlayHistogram(Overlay overlay) {

    Timer timer = timerService.getTimer(this.getClass());
    timer.start();/*from w w  w  .  ja va 2 s  . c om*/
    Double[] valueList = statsService.getValueList(currentDisplay(), overlay);
    timer.elapsed("Getting the stats");
    SummaryStatistics sumup = new SummaryStatistics();
    for (Double v : valueList) {
        sumup.addValue(v);
    }
    timer.elapsed("Building the sumup");

    double min = sumup.getMin();
    double max = sumup.getMax();
    double range = max - min;
    int bins = 100;//new Double(max - min).intValue();

    EmpiricalDistribution distribution = new EmpiricalDistribution(bins);

    double[] values = ArrayUtils.toPrimitive(valueList);
    Arrays.parallelSort(values);
    distribution.load(values);

    timer.elapsed("Sort and distrubution repartition up");

    XYChart.Series<Double, Double> serie = new XYChart.Series<>();
    ArrayList<Data<Double, Double>> data = new ArrayList<>(bins);
    double k = min;
    for (SummaryStatistics st : distribution.getBinStats()) {
        data.add(new Data<Double, Double>(k, new Double(st.getN())));
        k += range / bins;
    }

    serie.getData().clear();
    serie.getData().addAll(data);
    timer.elapsed("Creating charts");
    return serie;
}

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From source file:ijfx.ui.utils.ChartUpdater.java

public void updateChart() {

    final double min; // minimum value
    final double max; // maximum value
    double range; // max - min
    final double binSize;
    // int maximumBinNumber = 30;
    int finalBinNumber;

    int differentValuesCount = possibleValues.stream().filter(n -> Double.isFinite(n.doubleValue()))
            .collect(Collectors.toSet()).size();
    if (differentValuesCount < maximumBinNumber) {
        finalBinNumber = differentValuesCount;
    } else {/* ww  w .j  a  v  a 2 s .c o m*/
        finalBinNumber = maximumBinNumber;
    }

    EmpiricalDistribution distribution = new EmpiricalDistribution(finalBinNumber);

    Double[] values = possibleValues.parallelStream().filter(n -> Double.isFinite(n.doubleValue()))
            .map(v -> v.doubleValue()).sorted()
            //.toArray();
            .toArray(size -> new Double[size]);
    distribution.load(ArrayUtils.toPrimitive(values));

    min = values[0];
    max = values[values.length - 1];
    range = max - min;
    binSize = range / (finalBinNumber - 1);

    //System.out.println(String.format("min = %.0f, max = %.0f, range = %.0f, bin size = %.0f, bin number = %d", min, max, range, binSize, finalBinNumber));

    XYChart.Series<Double, Double> serie = new XYChart.Series<>();
    ArrayList<XYChart.Data<Double, Double>> data = new ArrayList<>();
    double k = min;
    for (SummaryStatistics st : distribution.getBinStats()) {
        data.add(new XYChart.Data<>(k, new Double(st.getN())));
        k += binSize;
    }

    Platform.runLater(() -> {
        serie.getData().addAll(data);
        areaChart.getData().clear();
        areaChart.getData().add(serie);
    });

}

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From source file:ijfx.ui.filter.DefaultNumberFilter.java

public void updateChart() {

    final double min; // minimum value
    final double max; // maximum value
    double range; // max - min
    final double binSize;
    int maximumBinNumber = 30;
    int finalBinNumber;

    int differentValuesCount = possibleValues.stream().filter(n -> Double.isFinite(n.doubleValue()))
            .collect(Collectors.toSet()).size();
    if (differentValuesCount < maximumBinNumber) {
        finalBinNumber = differentValuesCount;
    } else {//  w ww .ja  va2 s  .c  om
        finalBinNumber = maximumBinNumber;
    }

    EmpiricalDistribution distribution = new EmpiricalDistribution(finalBinNumber);

    double[] values = possibleValues.parallelStream().filter(n -> Double.isFinite(n.doubleValue()))
            .mapToDouble(v -> v.doubleValue()).sorted().toArray();
    distribution.load(values);

    min = values[0];
    max = values[values.length - 1];
    range = max - min;
    binSize = range / (finalBinNumber - 1);

    XYChart.Series<Double, Double> serie = new XYChart.Series<>();
    ArrayList<Data<Double, Double>> data = new ArrayList<>();
    double k = min;
    for (SummaryStatistics st : distribution.getBinStats()) {
        data.add(new Data<>(k, new Double(st.getN())));
        k += binSize;
    }

    Platform.runLater(() -> {

        serie.getData().addAll(data);
        areaChart.getData().clear();

        areaChart.getData().add(serie);

        updateSlider(min, max, finalBinNumber);
    });
}

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From source file:ijfx.ui.plugin.overlay.OverlayPanel.java

protected XYChart.Series<Double, Double> getOverlayHistogram(Overlay overlay) {

    Timer timer = timerService.getTimer(this.getClass());
    timer.start();//from www .j  ava  2  s . c om
    Double[] valueList = statsService.getValueListFromImageDisplay(currentDisplay(), overlay);
    timer.elapsed("Getting the stats");
    SummaryStatistics sumup = new SummaryStatistics();
    for (Double v : valueList) {
        sumup.addValue(v);
    }
    timer.elapsed("Building the sumup");

    double min = sumup.getMin();
    double max = sumup.getMax();
    double range = max - min;
    int bins = 100;//new Double(max - min).intValue();

    EmpiricalDistribution distribution = new EmpiricalDistribution(bins);

    double[] values = ArrayUtils.toPrimitive(valueList);
    Arrays.parallelSort(values);
    distribution.load(values);

    timer.elapsed("Sort and distrubution repartition up");

    XYChart.Series<Double, Double> serie = new XYChart.Series<>();
    ArrayList<Data<Double, Double>> data = new ArrayList<>(bins);
    double k = min;
    for (SummaryStatistics st : distribution.getBinStats()) {
        data.add(new Data<Double, Double>(k, new Double(st.getN())));
        k += range / bins;
    }

    serie.getData().clear();
    serie.getData().addAll(data);
    timer.elapsed("Creating charts");
    return serie;
}

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From source file:org.apache.solr.client.solrj.io.eval.HistogramEvaluator.java

@Override
public Object doWork(Object... values) throws IOException {
    if (Arrays.stream(values).anyMatch(item -> null == item)) {
        return null;
    }/*from  w  ww.j  ava 2 s  .  c o m*/

    List<?> sourceValues;
    Integer bins = 10;

    if (values.length >= 1) {
        sourceValues = values[0] instanceof List<?> ? (List<?>) values[0] : Arrays.asList(values[0]);

        if (values.length >= 2) {
            if (values[1] instanceof Number) {
                bins = ((Number) values[1]).intValue();
            } else {
                throw new IOException(String.format(Locale.ROOT,
                        "Invalid expression %s - if second parameter is provided then it must be a valid number but found %s instead",
                        toExpression(constructingFactory), values[1].getClass().getSimpleName()));
            }
        }
    } else {
        throw new IOException(
                String.format(Locale.ROOT, "Invalid expression %s - expecting at least one value but found %d",
                        toExpression(constructingFactory), containedEvaluators.size()));
    }

    EmpiricalDistribution distribution = new EmpiricalDistribution(bins);
    distribution.load(
            ((List<?>) sourceValues).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray());
    ;

    List<Tuple> histogramBins = new ArrayList<>();
    for (SummaryStatistics binSummary : distribution.getBinStats()) {
        Map<String, Number> map = new HashMap<>();
        map.put("max", binSummary.getMax());
        map.put("mean", binSummary.getMean());
        map.put("min", binSummary.getMin());
        map.put("stdev", binSummary.getStandardDeviation());
        map.put("sum", binSummary.getSum());
        map.put("N", binSummary.getN());
        map.put("var", binSummary.getVariance());
        map.put("cumProb", distribution.cumulativeProbability(binSummary.getMean()));
        map.put("prob", distribution.probability(binSummary.getMin(), binSummary.getMax()));
        histogramBins.add(new Tuple(map));
    }

    return histogramBins;
}

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From source file:tech.tablesaw.columns.numbers.NumberMapFunctions.java

default DoubleColumn bin(int binCount) {
    double[] histogram = new double[binCount];
    EmpiricalDistribution distribution = new EmpiricalDistribution(binCount);
    distribution.load(asDoubleArray());//  w w w. j a  v  a  2s  . com
    int k = 0;
    for (SummaryStatistics stats : distribution.getBinStats()) {
        histogram[k++] = stats.getN();
    }
    return DoubleColumn.create(name() + "[binned]", histogram);
}

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From source file:util.Statistics.java

public Statistics(List<Integer> list) {
    scores = intsToDoubles(list);//from   ww w  . j  ava 2 s  .co m
    DescriptiveStatistics dStats = new DescriptiveStatistics(scores);

    summaryStatistics.put("min", dStats.getMin()); // Minimum
    summaryStatistics.put("q1", dStats.getPercentile(25)); // Lower Quartile (Q1)
    summaryStatistics.put("q2", dStats.getPercentile(50)); // Middle Quartile (Median - Q2)
    summaryStatistics.put("q3", dStats.getPercentile(75)); // High Quartile (Q3)
    summaryStatistics.put("max", dStats.getMax()); // Maxiumum

    summaryStatistics.put("mean", dStats.getMean()); // Mean
    summaryStatistics.put("sd", dStats.getStandardDeviation()); // Standard Deviation

    EmpiricalDistribution distribution = new EmpiricalDistribution(NUM_BINS);
    distribution.load(scores);
    List<SummaryStatistics> binStats = distribution.getBinStats();
    double[] upperBounds = distribution.getUpperBounds();

    Double lastUpperBound = upperBounds[0];
    bins.add(new Pair<Pair<Double, Double>, Long>(
            new Pair<Double, Double>(summaryStatistics.get("min"), lastUpperBound), binStats.get(0).getN()));
    for (int i = 1; i < binStats.size(); i++) {
        bins.add(new Pair<Pair<Double, Double>, Long>(new Pair<Double, Double>(lastUpperBound, upperBounds[i]),
                binStats.get(i).getN()));
        lastUpperBound = upperBounds[i];
    }

    if (list.size() > 5 && dStats.getStandardDeviation() > 0) // Only remove outliers if relatively normal
    {
        double mean = dStats.getMean();
        double stDev = dStats.getStandardDeviation();
        NormalDistribution normalDistribution = new NormalDistribution(mean, stDev);

        Iterator<Integer> listIterator = list.iterator();
        double significanceLevel = .50 / list.size(); // Chauvenet's Criterion for Outliers
        while (listIterator.hasNext()) {
            int num = listIterator.next();
            double pValue = normalDistribution.cumulativeProbability(num);
            if (pValue < significanceLevel) {
                outliers.add(num);
                listIterator.remove();
            }
        }

        if (list.size() != dStats.getN()) // If and only if outliers have been removed
        {
            double[] significantData = intsToDoubles(list);
            dStats = new DescriptiveStatistics(significantData);

            summaryStatistics.put("min", dStats.getMin());
            summaryStatistics.put("max", dStats.getMax());
            summaryStatistics.put("mean", dStats.getMean());
            summaryStatistics.put("sd", dStats.getStandardDeviation());
        }
    }
}

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