Example usage for org.apache.commons.math3.stat.descriptive.rank Percentile Percentile

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.rank Percentile Percentile.

Prototype

public Percentile() 

Source Link

Document

Constructs a Percentile with a default quantile value of 50.0.

Usage

From source file:edu.umd.umiacs.clip.tools.classifier.ConfusionMatrix.java

public Pair<Float, Float> getPrecisionCI() {
    Percentile percentile = new Percentile();
    percentile.setData(/*from   w  w  w. j  av  a  2  s. com*/
            Stream.of(sampleFromPosterior()).parallel().mapToDouble(cm -> cm.getPrecision()).toArray());
    double alpha = (1 - CONF_LEVEL) / 2;
    return Pair.of((float) percentile.evaluate(100 * alpha), (float) percentile.evaluate(100 * (1 - alpha)));
}

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From source file:ch.unil.genescore.pathway.GeneSetLibrary.java

License:asdf

public void computeApproxChi2Values() {

    Percentile perc = new Percentile();
    EffTestCalculator effCalc = new EffTestCalculator();
    effCalc.addGeneIds(genes_);/*from w w w . j a va  2s  .  c  o  m*/
    effCalc.addMetaGeneIds(metaGenes_);
    effCalc.setGeneSets(geneSets_);
    effCalc.initializeGeneVals();

    int nrOfLoops = 10000;
    double[] mins = new double[nrOfLoops];
    for (int i = 0; i < nrOfLoops; i++) {
        mins[i] = effCalc.calcMinVal();
    }
    perc.setData(mins);
    double p1 = perc.evaluate(1);
    double p5 = perc.evaluate(5);
    double p10 = perc.evaluate(10);
    double p15 = perc.evaluate(15);
    //double w1 = perc.evaluate(1.0/50);
    System.out.println(p1);
    System.out.println(p5);
    System.out.println(p10);
    System.out.println(p15);
    System.out.println("asdf");

}

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From source file:edu.jhuapl.bsp.detector.OpenMath.java

public static double prctile(double[] in, double p) {
    Percentile prc = new Percentile();
    double in2[] = copya(in);
    Arrays.sort(in2);/*from w w  w.  j a  v a2 s . c o m*/
    prc.setData(in2);
    double result = prc.evaluate(p);
    return result;
}

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From source file:coolmap.canvas.datarenderer.renderer.impl.NumberToBoxPlot.java

private double[] boxPlotValues(CoolMapObject object, VNode rowNode, VNode columnNode) {
    if (rowNode == null || columnNode == null || rowNode.isSingleNode() && columnNode.isSingleNode()) {
        return null;
    } else {//from   w  ww  . j a v  a 2  s .  co m

        Integer[] rowIndices;
        Integer[] colIndices;
        if (rowNode.isGroupNode()) {
            rowIndices = rowNode.getBaseIndicesFromCOntology((CMatrix) object.getBaseCMatrices().get(0),
                    COntology.ROW);
        } else {
            rowIndices = new Integer[] {
                    ((CMatrix) object.getBaseCMatrices().get(0)).getIndexOfRowName(rowNode.getName()) };
        }
        if (columnNode.isGroupNode()) {
            colIndices = columnNode.getBaseIndicesFromCOntology((CMatrix) object.getBaseCMatrices().get(0),
                    COntology.COLUMN);
        } else {
            colIndices = new Integer[] {
                    ((CMatrix) object.getBaseCMatrices().get(0)).getIndexOfColName(columnNode.getName()) };
        }

        //A box plot across all matrices
        List<CMatrix> matrices = object.getBaseCMatrices();
        Double value;
        ArrayList<Double> values = new ArrayList<Double>();

        //add values
        for (Integer i : rowIndices) {
            if (i == null || i < 0) {
                continue;
            }

            for (Integer j : colIndices) {

                if (j == null || j < 0) {
                    continue;
                }
                //Double value = (Double) getCoolMapObject().getViewValue(i, j);
                //This is wrong. it should eb the base matrix value, not the view values
                for (CMatrix<Double> matrix : matrices) {

                    value = matrix.getValue(i, j);

                    if (value == null || value.isNaN()) {
                        continue;
                    } else {
                        //System.out.println(i + " " + j + " " + v);
                        values.add(value);
                    }
                }

            }
        }

        if (values.isEmpty()) {
            return null;
        }

        Collections.sort(values);
        int size = values.size();
        double min = values.get(0);
        double max = values.get(values.size() - 1);

        double median;
        if (size % 2 == 0) {
            median = (values.get(size / 2) + values.get(size / 2 - 1)) / 2;
        } else {
            median = (values.get(size / 2));
        }
        double[] valueArray = new double[values.size()];
        int c = 0;
        for (Double d : values) {
            valueArray[c++] = d.doubleValue();
        }

        Arrays.sort(valueArray);

        Percentile percentile = new Percentile();
        double q1 = percentile.evaluate(valueArray, 25);
        double q3 = percentile.evaluate(valueArray, 75);
        //            double median = percentile.evaluate(valueArray, 50);
        return new double[] { min, q1, median, q3, max };

    }
}

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From source file:org.apache.mahout.classifier.df.split.OptIgSplit.java

/**
 * @return an array of values to split the numeric feature's values on when
 *  building candidate splits. When input size is <= MAX_NUMERIC_SPLITS + 1, it will
 *  return the averages between success values as split points. When larger, it will
 *  return MAX_NUMERIC_SPLITS approximate percentiles through the data.
 *//*w  w w. j  a  v a 2  s.c o  m*/
private static double[] chooseNumericSplitPoints(double[] values) {
    if (values.length <= 1) {
        return values;
    }
    if (values.length <= MAX_NUMERIC_SPLITS + 1) {
        double[] splitPoints = new double[values.length - 1];
        for (int i = 1; i < values.length; i++) {
            splitPoints[i - 1] = (values[i] + values[i - 1]) / 2.0;
        }
        return splitPoints;
    }
    Percentile distribution = new Percentile();
    distribution.setData(values);
    double[] percentiles = new double[MAX_NUMERIC_SPLITS];
    for (int i = 0; i < percentiles.length; i++) {
        double p = 100.0 * ((i + 1.0) / (MAX_NUMERIC_SPLITS + 1.0));
        percentiles[i] = distribution.evaluate(p);
    }
    return percentiles;
}

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

@Override
public Object doWork(Object first, Object second) throws IOException {
    if (null == first) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the first value", toExpression(constructingFactory)));
    }//from  ww  w  .  j a  v a 2 s.  c o  m
    if (null == second) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the second value", toExpression(constructingFactory)));
    }
    if (!(first instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the first value, expecting a List",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }
    if (!(second instanceof Number)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the second value, expecting a Number",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }

    List<?> values = (List<?>) first;
    int window = ((Number) second).intValue();

    List<Number> moving = new ArrayList<>();
    DescriptiveStatistics slider = new DescriptiveStatistics(window);
    Percentile percentile = new Percentile();
    for (Object value : values) {
        slider.addValue(((Number) value).doubleValue());
        if (slider.getN() >= window) {
            double median = percentile.evaluate(slider.getValues(), 50);
            moving.add(median);
        }
    }

    return moving;
}

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

@Override
public Object doWork(Object first, Object second) throws IOException {
    if (null == first) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the first value", toExpression(constructingFactory)));
    }//from   w  w w. j av a  2 s. com
    if (null == second) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the second value", toExpression(constructingFactory)));
    }
    if (!(first instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the first value, expecting a List",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }
    if (!(second instanceof Number)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the second value, expecting a Number",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }

    Percentile percentile = new Percentile();
    percentile
            .setData(((List<?>) first).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray());
    return percentile.evaluate(((Number) second).doubleValue());
}

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From source file:org.drugis.mtc.summary.QuantileSummary.java

private Percentile getSamples() {
    List<Double> list = SummaryUtil.getAllChainsLastHalfSamples(d_results, d_parameter);
    double[] arr = new double[list.size()];
    for (int i = 0; i < list.size(); ++i) {
        arr[i] = list.get(i);//from w ww  .  j a  v a2 s  .co  m
    }

    Percentile percentile = new Percentile();
    percentile.setData(arr);
    return percentile;
}

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From source file:org.hawkular.metrics.core.impl.cassandra.GaugeBucketedOutputMapper.java

@Override
protected GaugeBucketDataPoint newPointInstance(long from, long to, List<GaugeData> gaugeDatas) {
    double[] values = new double[gaugeDatas.size()];
    for (ListIterator<GaugeData> iterator = gaugeDatas.listIterator(); iterator.hasNext();) {
        GaugeData gaugeData = iterator.next();
        values[iterator.previousIndex()] = gaugeData.getValue();
    }//from w  ww  .  j  av  a  2  s .c om

    Percentile percentile = new Percentile();
    percentile.setData(values);

    return new GaugeBucketDataPoint.Builder(from, to).setMin(new Min().evaluate(values))
            .setAvg(new Mean().evaluate(values)).setMedian(percentile.evaluate(50.0))
            .setMax(new Max().evaluate(values)).setPercentile95th(percentile.evaluate(95.0)).build();
}

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From source file:org.hawkular.metrics.core.impl.cassandra.NumericBucketedOutputMapper.java

@Override
protected NumericBucketDataPoint newPointInstance(long from, long to, List<NumericData> numericDatas) {
    double[] values = new double[numericDatas.size()];
    for (ListIterator<NumericData> iterator = numericDatas.listIterator(); iterator.hasNext();) {
        NumericData numericData = iterator.next();
        values[iterator.previousIndex()] = numericData.getValue();
    }/*from w ww  . ja  v a2 s. co  m*/

    Percentile percentile = new Percentile();
    percentile.setData(values);

    return new NumericBucketDataPoint.Builder(from, to).setMin(new Min().evaluate(values))
            .setAvg(new Mean().evaluate(values)).setMedian(percentile.evaluate(50.0))
            .setMax(new Max().evaluate(values)).setPercentile95th(percentile.evaluate(95.0)).build();
}

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