Example usage for org.apache.commons.math3.stat.descriptive SummaryStatistics addValue

Introduction

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

Prototype

public void addValue(double value) 

Source Link

Document

Add a value to the data

Usage

From source file:edu.washington.gs.skyline.model.quantification.RegressionFit.java

public Double computeRSquared(CalibrationCurve curve, List<WeightedObservedPoint> points) {
    SummaryStatistics yValues = new SummaryStatistics();
    SummaryStatistics residuals = new SummaryStatistics();
    for (WeightedObservedPoint point : points) {
        Double yFitted = curve.getY(point.getX());
        if (yFitted == null) {
            continue;
        }//from   ww  w.ja v  a  2 s .c  om
        yValues.addValue(point.getY());
        residuals.addValue(point.getY() - yFitted);
    }
    if (0 == residuals.getN()) {
        return null;
    }
    double yMean = yValues.getMean();
    double totalSumOfSquares = points.stream().mapToDouble(p -> (p.getY() - yMean) * (p.getY() - yMean)).sum();
    double sumOfSquaresOfResiduals = residuals.getSumsq();
    double rSquared = 1 - sumOfSquaresOfResiduals / totalSumOfSquares;
    return rSquared;
}

---

From source file:fr.gael.drb.cortex.topic.sentinel3.jai.operator.QuicklookSlstrRIF.java

private BufferedImage toGrayScale(Raster in, PixelCorrection c, boolean invertColors, boolean ignoreBadStats) {
    int width = in.getWidth();
    int height = in.getHeight();
    // compute stats
    SummaryStatistics stats = new SummaryStatistics();
    for (int j = 0; j < height; j++) {
        for (int i = 0; i < width; i++) {
            int pixel = checkAndApplyCorrection(in.getSample(i, j, 0), c);
            if (pixel != c.nodata)
                stats.addValue(pixel);
        }//from  w  w  w  .  j  av a 2  s  . co  m
    }
    double lowerBound = Math.max(stats.getMin(), stats.getMean() - 3 * stats.getStandardDeviation());
    double upperBound = Math.min(stats.getMax(), stats.getMean() + 3 * stats.getStandardDeviation());

    if (!ignoreBadStats)
        if (Double.isNaN(stats.getMean()) || Double.isNaN(stats.getStandardDeviation())
                || stats.getStandardDeviation() < 1)
            throw new IllegalStateException("Ugly band stats. Acquired during night?");

    return toGrayScale(in, c, invertColors, lowerBound, upperBound);
}

---

From source file:cl.usach.managedbeans.CreditosManagedBean.java

public double buscarPromedioSprintAsignatura(SprintAsignatura sprintA) {
    List<SprintGrupos> spgs = buscarSprintGrupos(sprintA);
    SummaryStatistics stats = new SummaryStatistics();
    int a;//from ww w.j ava 2  s.  c o m
    for (SprintGrupos sprintGrupos : spgs) {
        List<Equipo> eqs = buscarEquipos(sprintGrupos);
        for (Equipo equipo : eqs) {
            a = buscarTiempoTareas(equipo);
            stats.addValue(a);
        }
    }
    double mean = stats.getMean();
    mean = (double) Math.round(mean * 10) / 10;
    return mean;
}

---

From source file:cl.usach.managedbeans.CreditosManagedBean.java

public double buscarDesviacionEstandarSprintAsignatura(SprintAsignatura sprintA) {
    List<SprintGrupos> spgs = buscarSprintGrupos(sprintA);
    SummaryStatistics stats = new SummaryStatistics();
    int a;//from  ww w . j  a va 2 s. c om
    for (SprintGrupos sprintGrupos : spgs) {
        List<Equipo> eqs = buscarEquipos(sprintGrupos);
        for (Equipo equipo : eqs) {
            a = buscarTiempoTareas(equipo);
            stats.addValue(a);
        }
    }

    double dv = stats.getStandardDeviation();
    dv = (double) Math.round(dv * 10) / 10;
    return dv;
}

---

From source file:ijfx.core.stats.DefaultImageStatisticsService.java

@Override
public SummaryStatistics getSummaryStatistics(Dataset dataset) {
    SummaryStatistics summary = new SummaryStatistics();
    Cursor<RealType<?>> cursor = dataset.cursor();
    cursor.reset();/*from  w w  w. j  a v  a  2  s.  c  o m*/

    while (cursor.hasNext()) {
        cursor.fwd();
        double value = cursor.get().getRealDouble();
        summary.addValue(value);

    }
    return summary;
}

---

From source file:model.experiments.stickyprices.StickyPricesCSVPrinter.java

private static void oneHundredLearningCompetitive(File file) throws IOException {

    CSVWriter writer = new CSVWriter(new FileWriter(file));
    writer.writeNext(new String[] { "production", "price" });

    //this will take a looong time

    //run the test 5 times!
    for (long i = 0; i < 100; i++) {

        //create the run
        MacroII macroII = new MacroII(i);
        TripolistScenario scenario = new TripolistScenario(macroII);
        macroII.setScenario(scenario);//from  w w  w .  ja v  a2s  . co  m
        scenario.setAdditionalCompetitors(4);
        //set the demand
        scenario.setAskPricingStrategy(InventoryBufferSalesControl.class);
        scenario.setControlType(
                MonopolistScenario.MonopolistScenarioIntegratedControlEnum.MARGINAL_PLANT_CONTROL);

        //start it and have one step
        macroII.start();
        macroII.schedule.step(macroII);

        //now set the right parameters
        for (Firm firm : scenario.getCompetitors()) {
            final SalesDepartment salesDepartment = firm.getSalesDepartment(UndifferentiatedGoodType.GENERIC);
            //learning
            assert salesDepartment.getPredictorStrategy() instanceof RecursiveSalePredictor;
        }

        //run the model
        for (int j = 0; j < 5000; j++) {
            macroII.schedule.step(macroII);
            MarginalMaximizerPIDTuning.printProgressBar(5000, j, 100);
        }

        //average over the last 500 steps
        SummaryStatistics prices = new SummaryStatistics();
        SummaryStatistics quantities = new SummaryStatistics();
        for (int j = 0; j < 500; j++) {
            macroII.schedule.step(macroII);
            assert !Float.isNaN(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayAveragePrice());
            prices.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayAveragePrice());
            quantities.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayVolume());

        }

        String[] resultString = new String[2];
        resultString[0] = String.valueOf(quantities.getMean());
        resultString[1] = String.valueOf(prices.getMean());
        System.out.println(Arrays.toString(resultString));
        writer.writeNext(resultString);
        writer.flush();
    }

    writer.close();

}

---

From source file:com.hurence.logisland.sampling.AverageSampler.java

/**
 * divide the points sequence into equally sized buckets
 * and select the first point of each bucket
 *
 * @param inputRecords the iput list//w w  w  .  j  av  a  2  s  .  c  om
 * @return
 */
@Override
public List<Record> sample(List<Record> inputRecords) {

    SummaryStatistics stats = new SummaryStatistics();

    // simple average to 100 data points
    final int realBucketSize = SamplingUtils.fitBucketSize(inputRecords, bucketSize);
    return SamplingUtils.grouped(inputRecords, realBucketSize).map(bucket -> {

        bucket.forEach(record -> {
            final Double recordValue = getRecordValue(record);
            if (recordValue != null)
                stats.addValue(recordValue);
        });

        final double meanValue = stats.getMean();
        final Record sampleRecord = getTimeValueRecord(bucket.get(0));
        final FieldType fieldType = bucket.get(0).getField(valueFieldName).getType();
        switch (fieldType) {
        case INT:
            sampleRecord.setField(valueFieldName, fieldType, (int) Math.round(meanValue));
            break;
        case LONG:
            sampleRecord.setField(valueFieldName, fieldType, Math.round(meanValue));
            break;
        case FLOAT:
            sampleRecord.setField(valueFieldName, fieldType, (float) meanValue);
            break;
        case DOUBLE:
            sampleRecord.setField(valueFieldName, fieldType, meanValue);
            break;
        }
        return sampleRecord;
    }).collect(Collectors.toList());
}

---

From source file:net.lizalab.util.RdRandRandomTest.java

/**
 * Tests RdRandRandom by verifying the average of the distribution of digits 0-9
 * over 100 million values. Also runs the test for Random and SecureRandom for
 * reference./*  ww w .j  a  va 2 s.  c om*/
 * Based on Mean Test outlined in <i>Beautiful Testing</i> published by O'Reilly.
 * @throws GeneralSecurityException 
 * @throws SeedException 
 */
@Test
public final void testRdRandRandomMean() throws GeneralSecurityException, SeedException {
    final String methodName = "testRdRandRandom : ";

    SummaryStatistics stats = new SummaryStatistics();
    // Initialize the array
    ndigits = new int[10];
    for (int i = 0; i < 10; i++) {
        ndigits[i] = 0;
        stats.addValue(i);
    }

    // Calculate the confidence intervals to assert.
    mean = stats.getMean();
    stdDev = stats.getStandardDeviation();
    var = stats.getVariance();
    LOGGER.info("{} Normal mean: {}", methodName, mean);
    LOGGER.info("{} Normal std: {}", methodName, stdDev);
    LOGGER.info("{} Normal var: {}", methodName, var);
    // 99.7% CI is within 3 std.
    double expectedDev3SD = 3 * stdDev / Math.sqrt(values);
    smLowerRng3SD = mean - expectedDev3SD;
    smUpperRng3SD = mean + expectedDev3SD;
    // 95% CI is within 2 std.
    double expectedDev2SD = 2 * stdDev / Math.sqrt(values);
    smLowerRng2SD = mean - expectedDev2SD;
    smUpperRng2SD = mean + expectedDev2SD;
    LOGGER.info("{} Generating {} values.", methodName, values);
    LOGGER.info("{} Sample mean expected in range {} - {} 99.7% of the times.", methodName, smLowerRng3SD,
            smUpperRng3SD);
    LOGGER.info("{} Sample mean expected in range {} - {} 95% of the times.", methodName, smLowerRng2SD,
            smUpperRng2SD);

    LOGGER.info("{} Running for Random..", methodName);
    Random random = new Random();
    meanTest(random, false);

    LOGGER.info("{} Running for RdRand..", methodName);
    random = new RdRandRandom();
    meanTest(random, true);

    LOGGER.info("{} Running for SecureRandom..", methodName);
    random = new SecureRandom();
    meanTest(random, false);

    LOGGER.info("{} Running Uncommons Maths AESCounterRNG using RdRandSeedGenerator..", methodName);
    random = new AESCounterRNG(new RdRandSeedGenerator());
    meanTest(random, false);
}

---

From source file:model.experiments.stickyprices.StickyPricesCSVPrinter.java

private static void competitiveSweepRun(int additionalCompetitors) throws IOException {

    CSVWriter writer = new CSVWriter(new FileWriter(
            Paths.get("runs", "rawdata", "competitivePeriodSweep" + additionalCompetitors + ".csv").toFile()));
    writer.writeNext(new String[] { "speed", "distance", "finaldistance", "variance" });

    for (int speed = 1; speed < 30; speed++) {
        SummaryStatistics distance = new SummaryStatistics();
        SummaryStatistics finalDistance = new SummaryStatistics();
        SummaryStatistics variance = new SummaryStatistics();
        for (int seed = 0; seed < 50; seed++) {

            final double[] result = competitiveSweepRun(seed, 101, 1, 1, 14, .1f, .1f, speed, 58, null,
                    additionalCompetitors);
            distance.addValue(result[0]);
            finalDistance.addValue(result[1]);
            variance.addValue(result[2]);

        }//from  ww w . j ava  2s.co  m

        final String[] nextLine = { String.valueOf(speed), String.valueOf(distance.getMean()),
                String.valueOf(finalDistance.getMean()), String.valueOf(variance.getMean()) };
        System.out.println(Arrays.toString(nextLine));
        writer.writeNext(nextLine);
        writer.flush();
    }

    writer.close();

}

---

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 www .j  av  a 2  s .com*/
    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;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• »