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

Introduction

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

Prototype

public void addValue(double v) 

Source Link

Document

Adds the value to the dataset.

Usage

From source file:com.linkedin.pinot.perf.ForwardIndexReaderBenchmark.java

public static void singleValuedReadBenchMarkV2(File file, int numDocs, int numBits) throws Exception {
    boolean signed = false;
    boolean isMmap = false;
    long start, end;
    boolean fullScan = true;

    boolean batchRead = true;
    boolean singleRead = true;

    PinotDataBuffer heapBuffer = PinotDataBuffer.fromFile(file, ReadMode.heap, FileChannel.MapMode.READ_ONLY,
            "benchmarking");
    com.linkedin.pinot.core.io.reader.impl.v2.FixedBitSingleValueReader reader = new com.linkedin.pinot.core.io.reader.impl.v2.FixedBitSingleValueReader(
            heapBuffer, numDocs, numBits, signed);

    if (fullScan) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        ByteBuffer buffer = ByteBuffer.allocateDirect((int) file.length());
        RandomAccessFile raf = new RandomAccessFile(file, "r");
        raf.getChannel().read(buffer);/*from  w w  w . j a v a  2s .c  o m*/
        raf.close();
        int[] input = new int[numBits];
        int[] output = new int[32];
        int numBatches = (numDocs + 31) / 32;
        for (int run = 0; run < MAX_RUNS; run++) {
            start = System.currentTimeMillis();
            for (int i = 0; i < numBatches; i++) {
                for (int j = 0; j < numBits; j++) {
                    input[j] = buffer.getInt(i * numBits * 4 + j * 4);
                }
                BitPacking.fastunpack(input, 0, output, 0, numBits);
            }
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println(" v2 full scan stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    }
    if (singleRead) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        // sequential read
        for (int run = 0; run < MAX_RUNS; run++) {
            start = System.currentTimeMillis();
            for (int i = 0; i < numDocs; i++) {
                int value = reader.getInt(i);
            }
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println(" v2 sequential single read for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    }
    if (batchRead) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        int batchSize = Math.min(5000, numDocs);
        int[] output = new int[batchSize];
        int[] rowIds = new int[batchSize];

        // sequential read
        for (int run = 0; run < MAX_RUNS; run++) {
            start = System.currentTimeMillis();
            int rowId = 0;
            while (rowId < numDocs) {
                int length = Math.min(batchSize, numDocs - rowId);
                for (int i = 0; i < length; i++) {
                    rowIds[i] = rowId + i;
                }
                reader.getIntBatch(rowIds, output, length);
                rowId = rowId + length;
            }
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println("v2 sequential batch read stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    }
    reader.close();

}

---

From source file:cz.cuni.mff.d3s.tools.perfdoc.server.measuring.statistics.Statistics.java

public long computeMedian() {
    if (measurementResults.isEmpty()) {
        return -1;
    }/*from www  . ja  v a2  s  .  co  m*/

    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (Long l : measurementResults) {
        stats.addValue(l);
    }
    return (long) stats.getMean();
}

---

From source file:cz.cuni.mff.d3s.tools.perfdoc.server.measuring.statistics.Statistics.java

public long computeMean() {
    if (measurementResults.isEmpty()) {
        return -1;
    }/*from   w  w  w. ja  v a 2 s  .c  o  m*/

    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (Long l : measurementResults) {
        stats.addValue(l);
    }

    return (long) stats.getPercentile(50);
}

---

From source file:de.tudarmstadt.ukp.dkpro.core.performance.ThroughputTestAE.java

public String getPerformanceaAnalysis() {
    StringBuilder sb = new StringBuilder();

    long sumMillis = 0;
    for (double timeValue : times) {
        sumMillis += timeValue;/*from w w w  .j  a v  a2s.c o m*/
    }

    DescriptiveStatistics statTimes = new DescriptiveStatistics();
    for (Long timeValue : times) {
        statTimes.addValue((double) timeValue / 1000);
    }

    sb.append("Estimate after processing " + times.size() + " documents.");
    sb.append(LF);

    Formatter formatter = new Formatter(sb, Locale.US);

    formatter.format("Time / Document:       %,.3f (%,.3f)\n", statTimes.getMean(),
            statTimes.getStandardDeviation());
    formatter.format("Time / 10^4 Token:     %,.3f\n", getNormalizedTime(sumMillis, nrofTokens, 1000));
    formatter.format("Time / 10^4 Sentences: %,.3f\n", getNormalizedTime(sumMillis, nrofSentences, 1000));

    formatter.close();

    return sb.toString();
}

---

From source file:gr.iti.mklab.reveal.forensics.util.Util.java

public static double[][] blockNoiseVar(double[][] inputMap, int blockSize) {
    // Calculate the block variance of a noise map
    int blockedWidth = (int) Math.floor(inputMap.length / blockSize) * blockSize;
    int blockedHeight = (int) Math.floor(inputMap[0].length / blockSize) * blockSize;
    double[][] blockedIm = new double[blockedWidth / blockSize][blockedHeight / blockSize];
    DescriptiveStatistics blockValues;
    for (int ii = 0; ii < blockedWidth; ii = ii + blockSize) {
        for (int jj = 0; jj < blockedHeight; jj = jj + blockSize) {
            blockValues = new DescriptiveStatistics();
            for (int B_ii = ii; B_ii < ii + blockSize; B_ii++) {
                for (int B_jj = jj; B_jj < jj + blockSize; B_jj++) {
                    blockValues.addValue(Math.abs(inputMap[B_ii][B_jj]));
                }// ww  w.  j  a  v  a 2 s .  co  m
            }
            blockedIm[ii / blockSize][jj / blockSize] = Math.sqrt(blockValues.getPercentile(50) / 0.6745);
        }
    }
    return blockedIm;
}

---

From source file:gr.iti.mklab.reveal.forensics.util.Util.java

public static double[][] blockVar(double[][] inputIm, int blockSize) {
    // block variance of input int image
    int blockedWidth = (int) Math.floor(inputIm.length / blockSize) * blockSize;
    int blockedHeight = (int) Math.floor(inputIm[0].length / blockSize) * blockSize;
    double[][] blockedIm = new double[blockedWidth / blockSize][blockedHeight / blockSize];
    DescriptiveStatistics blockValues;
    for (int ii = 0; ii < blockedWidth; ii = ii + blockSize) {
        for (int jj = 0; jj < blockedHeight; jj = jj + blockSize) {
            blockValues = new DescriptiveStatistics();
            for (int B_ii = ii; B_ii < ii + blockSize; B_ii++) {
                for (int B_jj = jj; B_jj < jj + blockSize; B_jj++) {
                    blockValues.addValue(Math.abs(inputIm[B_ii][B_jj]));
                }//from   w  w w  .  j  a  v  a2  s.  c o m
            }
            blockedIm[ii / blockSize][jj / blockSize] = blockValues.getPopulationVariance();
        }
    }
    return blockedIm;
}

---

From source file:iac_soap.statsq.NormVerdService.java

@Override
public NormVerdResponse calculateNormVerd(List<Double> data) throws MyFault {

    //Service Requirements 
    if (data.isEmpty()) {
        throw new MyFault("No data is provided");
    } else if (data.size() < 2) {
        throw new MyFault("A minimum of two data elements is required.");
    }/* ww  w. j av a  2s  .c o  m*/

    //Declaring Apache Commons DescriptiveStatistics
    DescriptiveStatistics stats = new DescriptiveStatistics();

    //Filling DescriptiveStatistics class with the provided dataset
    for (int i = 0; i < data.size(); i++) {
        stats.addValue(data.get(i));
    }

    //Let the DescriptiveStatistics class calculate the mean and standard deviation
    double mean = stats.getMean();
    double std = stats.getStandardDeviation();

    //Implementing the KolmogorovSmirnov test & calculating the kurtosis and skewness
    NormalDistribution x = new NormalDistribution(mean, std);
    double p_value = TestUtils.kolmogorovSmirnovTest(x, stats.getValues(), false);
    double kurtosis = stats.getKurtosis();
    double skewness = stats.getSkewness();
    boolean result = false;

    //Check if the dataset is a normal distribution:
    //KolmogorovSmirnov p_value should be >= 0.05
    //Both kurtosis and skewness should be between -2.0 and 2.0
    if (kurtosis < 2.0 && kurtosis > -2.0 && skewness < 2.0 && skewness > -2.0 && p_value >= 0.05) {
        result = true;
    }

    //Response message:
    NormVerdResponse nvr = new NormVerdResponse(result, p_value, kurtosis, skewness);

    return nvr;
}

---

From source file:io.yields.math.framework.DomainTest.java

@Explore(name = "Test Variable Distribution", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class)
@Exploration(name = "Test Function", context = FunctionExplorerContext.class, group = "domain")
public void testVariableDistribution(Explorer<Double> explorer) {

    assertThat(explorer.all().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.valid().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.invalid().count()).isEqualTo(0);
    assertThat(explorer.propertyError().count()).isEqualTo(0);

    DescriptiveStatistics stats = new DescriptiveStatistics();
    explorer.all().forEach(result -> stats.addValue(result.getFunctionOutcome().orElse(0d)));

    assertThat(stats.getMean()).isEqualTo(0, delta(0.1));
    assertThat(stats.getMax()).isEqualTo(1, delta(0.1));
    assertThat(stats.getMin()).isEqualTo(-1, delta(0.1));
}

---

From source file:io.yields.math.framework.DomainTest.java

@Explore(name = "Test Variable Distribution with multiple properties", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class)
@Exploration(name = "Test Function", context = FunctionExplorerMultiplePropertiesContext.class, group = "domain")
public void testVariableDistributionMultipleProperties(Explorer<Double> explorer) {

    assertThat(explorer.all().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.valid().count()).isEqualTo(explorer.valid().count());
    assertThat(explorer.invalid().count()).isEqualTo(0);
    assertThat(explorer.propertyError().count()).isEqualTo(0);

    DescriptiveStatistics stats = new DescriptiveStatistics();
    explorer.all().forEach(result -> stats.addValue(result.getFunctionOutcome().orElse(0d)));

    assertThat(stats.getMean()).isEqualTo(0, delta(0.1));
    assertThat(stats.getMax()).isEqualTo(1, delta(0.1));
    assertThat(stats.getMin()).isEqualTo(-1, delta(0.1));
}

---

From source file:mase.stat.FitnessStat.java

/**
 * Prints out the statistics, but does not end with a println -- this lets
 * overriding methods print additional statistics on the same line
 *//*from  ww  w. j a v a  2 s .com*/
@Override
public void postEvaluationStatistics(final EvolutionState state) {
    super.postEvaluationStatistics(state);

    int subpops = state.population.subpops.length; // number of supopulations
    DescriptiveStatistics[] fitness = new DescriptiveStatistics[subpops];
    for (int i = 0; i < subpops; i++) {
        fitness[i] = new DescriptiveStatistics();
    }
    int evals = state.evaluator.p_problem instanceof MaseProblem
            ? ((MaseProblem) state.evaluator.p_problem).getTotalEvaluations()
            : 0;

    // gather per-subpopulation statistics
    for (int x = 0; x < subpops; x++) {
        for (int y = 0; y < state.population.subpops[x].individuals.length; y++) {
            if (state.population.subpops[x].individuals[y].evaluated) {// he's got a valid fitness
                // update fitness
                double f = ((ExpandedFitness) state.population.subpops[x].individuals[y].fitness)
                        .getFitnessScore();
                bestSoFar[x] = Math.max(bestSoFar[x], f);
                absoluteBest = Math.max(absoluteBest, f);
                fitness[x].addValue(f);
            }
        }
        // print out fitness information
        if (doSubpops) {
            state.output.println(state.generation + " " + evals + " " + x + " " + fitness[x].getN() + " "
                    + fitness[x].getMin() + " " + fitness[x].getMean() + " " + fitness[x].getMax() + " "
                    + bestSoFar[x], statisticslog);
        }
    }

    // Now gather global statistics
    DescriptiveStatistics global = new DescriptiveStatistics();
    for (DescriptiveStatistics ds : fitness) {
        for (double v : ds.getValues()) {
            global.addValue(v);
        }
    }

    state.output.println(state.generation + " " + evals + " NA " + global.getN() + " " + global.getMin() + " "
            + global.getMean() + " " + global.getMax() + " " + absoluteBest, statisticslog);
}

---

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