Example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics DescriptiveStatistics

Introduction

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

Prototype

public DescriptiveStatistics(DescriptiveStatistics original) throws NullArgumentException 

Source Link

Document

Copy constructor.

Usage

From source file:com.loadtesting.core.data.TimeSerieData.java

public TimeSerieData(String name, List<TimeSample> samples, CapturerConfig config) {
    this.name = name;
    this.unit = config.getUnit();
    this.volume = samples.size();
    if (volume > 0) {
        TimeSample first = samples.get(0);
        this.unit = first.getTimeUnit();
        this.opening = first.getTime(unit);
        TimeSample last = samples.get(volume - 1);
        this.closing = last.getTime(unit);
        this.samples = config.getFilter().filter(samples);

        DescriptiveStatistics stats = new DescriptiveStatistics(volume);
        for (TimeSample timeSample : samples) {
            stats.addValue(timeSample.getTime(unit));
        }/*from   w  ww  .  j  a  v a 2s  .co  m*/
        this.high = stats.getMax();
        this.low = stats.getMin();
        this.median = (high + low) / 2;
        this.typical = (high + low + closing) / 3;
        this.weightedClose = (high + low + closing + closing) / 4;
        this.sma = stats.getMean();
        this.variance = stats.getVariance();
        this.sd = stats.getStandardDeviation();
        this.sum = stats.getSum();
        this.sumsq = stats.getSumsq();
        this.skewness = stats.getSkewness();
        this.kurtosis = stats.getKurtosis();
        this.geometricMean = stats.getGeometricMean();
        this.populationVariance = stats.getPopulationVariance();
    } else {
        this.samples = samples;
    }
}

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From source file:com.vsthost.rnd.jdeoptim.diagnostics.Diagnostics.java

/**
 * Constructs a new diagnostics instance.
 *
 * @param logging Indicates if logging is enabled or not.
 * @param statistics Indicates if detailed statistics are to be computed or not.
 *///from w  w  w  . ja v a 2  s . c o m
public Diagnostics(final boolean logging, final boolean statistics) {
    // Define the log format:
    this.logFormat = "%02d [%.6f] %s";

    // Are we logging?
    this.logging = logging;

    // Are we collecting statistics?
    this.statistics = statistics;

    // Set default observers:
    this.addObserver(new Listener() {
        @Override
        public void evolutionStarted() {
            // Mark the started datetime.
            timeStarted = System.currentTimeMillis();
        }

        @Override
        public void iterationStarted(int iteration) {
            // Nothing to be done...
        }

        @Override
        public void iterationFinished(int iteration, Population population) {
            // Are we logging? If yes log it:
            if (logging) {
                System.err.println(String.format(logFormat, iteration, population.getBestScore(),
                        Arrays.toString(population.getBestMember())));
            }

            if (statistics) {
                // Calculate the statistics:
                DescriptiveStatistics stats = new DescriptiveStatistics(population.getScores());

                // Add the log entry to the evolution book:
                entries.add(
                        new Diagnostics.Entry(population.getBestScore(), population.getBestMember(), stats));
            } else {
                // Add the log entry to the evolution book:
                entries.add(new Diagnostics.Entry(population.getBestScore(), population.getBestMember(), null));
            }
        }

        @Override
        public void evolutionFinished(Population population) {
            // Mark the end datetime:
            timeFinished = System.currentTimeMillis();

            // Set the best member:
            bestMember = population.getBestMember();

            // Set the best score:
            bestScore = population.getBestScore();
        }
    });
}

---

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

public Statistics(FlatField fltFld) throws VisADException {
    rngVals = fltFld.getValues(false);/* w  w  w. jav  a 2s. c  om*/
    rngTupLen = rngVals.length;
    numPoints = fltFld.getDomainSet().getLength();
    numGoodPoints = new int[rngTupLen];

    values_x = new double[rngTupLen][];

    for (int k = 0; k < rngTupLen; k++) {
        values_x[k] = removeMissing(rngVals[k]);
        numGoodPoints[k] = values_x[k].length;
    }

    descriptiveStats = new DescriptiveStatistics[rngTupLen];
    for (int k = 0; k < rngTupLen; k++) {
        descriptiveStats[k] = new DescriptiveStatistics(values_x[k]);
    }

    MathType rangeType = ((FunctionType) fltFld.getType()).getRange();

    if (rangeType instanceof RealTupleType) {
        RealType[] rttypes = ((TupleType) rangeType).getRealComponents();
        if (rngTupLen > 1) {
            statType = new RealTupleType(rttypes);
        } else {
            statType = rttypes[0];
        }
    } else if (rangeType instanceof RealType) {
        statType = rangeType;
    } else {
        throw new VisADException("fltFld must be RealTupleType or RealType");
    }

    pCorrelation = new PearsonsCorrelation();
}

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From source file:mimir.ControlChart.java

public void dataReset(double[] newData) {
    this.data = new DescriptiveStatistics(newData);
    this.statsReset();
    return;//from  ww  w. j a v a  2 s  .c  o  m
}

---

From source file:algorithm.NQueens.java

/**
 * Calculates statistics for the current data in the fitness, similarity, and mutation rate
 * buffers. After the statistics have been calculated they are added to the appropriate
 * fitnessStats, similarityStats, and mutationStats class members.
 * //w ww  . jav a  2s.  c o  m
 * @param units The units for generations such as thousands (1000) or millions.
 */
@SuppressWarnings("serial")
public static void calcStatistics(int units) {
    /* Calculate fitness stats */
    DescriptiveStatistics descStats = new DescriptiveStatistics(Doubles.toArray(fitnessBuffer));
    fitnessStats.put((int) Math.ceil(numGenerations / (double) units), descStats.getMean());

    /* Calculate chromosome similarity stats */
    descStats = new DescriptiveStatistics(Doubles.toArray(similarityBuffer));
    similarityStats.put((int) Math.ceil(numGenerations / (double) units), descStats.getMean());

    /* Calculate mutation rate stats for variable mutation */
    if (mutation == null) {
        descStats = new DescriptiveStatistics(Doubles.toArray(mutationBuffer));
        mutationStats.put((int) Math.ceil(numGenerations / (double) units), descStats.getMean());
    }

    /* Calculate the duplicate solutions statistics */
    if (duplicateBuffer.size() > 0) {
        final DescriptiveStatistics descDupStats = new DescriptiveStatistics(
                Doubles.toArray(duplicateBuffer.values()));
        duplicateStats.put((int) Math.ceil(numGenerations / (double) units), new ArrayList<Double>() {
            {
                add((double) duplicateBuffer.size());
                add(descDupStats.getMean());
            }
        });
    } else {
        duplicateStats.put((int) Math.ceil(numGenerations / (double) units), new ArrayList<Double>() {
            {
                add(0.0);
                add(0.0);
            }
        });
    }

    /* Clear the buffers */
    mutationBuffer.clear();
    fitnessBuffer.clear();
    similarityBuffer.clear();
    duplicateBuffer.clear();
}

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From source file:es.csic.iiia.planes.util.InverseWishartDistributionTest.java

private double testGaussian(double scale) {
    double[] means = new double[] { 0, 0 };
    double[][] covariances = new double[][] { { scale, 0 }, { 0, scale } };
    MultivariateNormalDistribution uniform = new MultivariateNormalDistribution(means, covariances);
    DescriptiveStatistics stats = new DescriptiveStatistics(NORMAL_SAMPLES);
    for (int i = 0; i < NORMAL_SAMPLES; i++) {
        double[] xy = uniform.sample();
        final double x = xy[0];
        final double y = xy[1];
        stats.addValue(Math.sqrt(x * x + y * y));
    }/*from w w  w  .j  a  v  a  2s .  co m*/

    return stats.getPercentile(90);
}

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From source file:com.github.jessemull.microflexdouble.stat.NTest.java

/**
 * Tests the plate statistics method.//from w  ww .  j a  v a  2s .c o  m
 */
@Test
public void testPlate() {

    for (Plate plate : array) {

        Map<Well, Integer> resultMap = new TreeMap<Well, Integer>();
        Map<Well, Integer> returnedMap = n.plate(plate);

        for (Well well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double db : well) {
                input[index++] = db;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getN();

            resultMap.put(well, (int) result);
        }

        for (Well well : plate) {

            int result = resultMap.get(well);
            int returned = returnedMap.get(well);

            assertEquals(result, returned);
        }
    }
}

---

From source file:gobblin.util.limiter.stressTest.RateComputingLimiterContainer.java

private @Nullable DescriptiveStatistics getNormalizedStatistics(String key,
        Collection<? extends Number> values) {
    long now = System.currentTimeMillis();

    long deltaTime = 0;
    if (this.lastReportTimes.containsKey(key)) {
        deltaTime = now - this.lastReportTimes.get(key);
    }//from   w  w  w .  j a v  a2 s  .  c o m
    this.lastReportTimes.put(key, now);

    if (deltaTime == 0) {
        return null;
    }

    double[] normalizedValues = new double[values.size()];
    int i = 0;
    for (Number value : values) {
        normalizedValues[i++] = 1000 * value.doubleValue() / deltaTime;
    }

    return new DescriptiveStatistics(normalizedValues);
}

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From source file:com.github.jessemull.microflexbigdecimal.stat.NTest.java

/**
 * Tests the plate statistics method./* www .ja v a  2 s .c o m*/
 */
@Test
public void testPlate() {

    for (Plate plate : array) {

        Map<Well, Integer> resultMap = new TreeMap<Well, Integer>();
        Map<Well, Integer> returnedMap = n.plate(plate);

        for (Well well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (BigDecimal bd : well) {
                input[index++] = bd.doubleValue();
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getN();

            resultMap.put(well, (int) result);
        }

        for (Well well : plate) {

            int result = resultMap.get(well);
            int returned = returnedMap.get(well);

            assertEquals(result, returned);
        }
    }
}

---

From source file:com.github.jessemull.microflex.stat.statdouble.NDoubleTest.java

/**
 * Tests the plate statistics method./*  w  w  w . j a v a 2  s  .  c  o  m*/
 */
@Test
public void testPlate() {

    for (PlateDouble plate : array) {

        Map<WellDouble, Integer> resultMap = new TreeMap<WellDouble, Integer>();
        Map<WellDouble, Integer> returnedMap = n.plate(plate);

        for (WellDouble well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double db : well) {
                input[index++] = db;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getN();

            resultMap.put(well, (int) result);
        }

        for (WellDouble well : plate) {

            int result = resultMap.get(well);
            int returned = returnedMap.get(well);

            assertEquals(result, returned);
        }
    }
}

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