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

Introduction

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

Prototype

public double[] getValues() 

Source Link

Document

Returns the current set of values in an array of double primitives.

Usage

From source file:async.nio2.Main.java

private static DescriptiveStatistics combine(DescriptiveStatistics stats1, DescriptiveStatistics stats2) {
    Arrays.stream(stats2.getValues()).forEach(d -> stats1.addValue(d));
    stats2.clear();// w w w  .  j  a  v  a2 s.c  o  m
    return stats1;
}

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From source file:net.adamjak.thomas.graph.application.commons.StatisticsUtils.java

public static DescriptiveStatistics statisticsWithoutExtremes(DescriptiveStatistics inputStatistics,
        GrubbsLevel grubbsLevel) throws IllegalArgumentException {
    if (inputStatistics == null || grubbsLevel == null)
        throw new IllegalArgumentException("Params inputStatistics and grubbsLevel can not be null.");

    int countInput = inputStatistics.getValues().length;
    Double avgInput = inputStatistics.getMean();
    Double stdInput = inputStatistics.getStandardDeviation();
    Double s = stdInput * Math.sqrt((countInput - 1.0) / countInput);
    Double criticalValue = grubbsLevel.getCriticalValue(countInput);

    DescriptiveStatistics outputStatistic = new DescriptiveStatistics();

    for (double inpVal : inputStatistics.getValues()) {
        double test = Math.abs(inpVal - avgInput) / s;

        if (test <= criticalValue) {
            outputStatistic.addValue(inpVal);
        }//from ww  w. j  a v  a2 s .  c  o  m
    }

    return outputStatistic;
}

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From source file:net.adamjak.thomas.graph.application.commons.StatisticsUtils.java

public static double getConfidenceInterval(DescriptiveStatistics inputStatistics, NormCritical uAlpha)
        throws IllegalArgumentException {
    if (inputStatistics == null || uAlpha == null)
        throw new IllegalArgumentException("Params inputStatistics or uAlpha can not be null!");

    return (inputStatistics.getStandardDeviation() * uAlpha.getCriticalValue())
            / Math.sqrt(inputStatistics.getValues().length);
}

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

public static void singleValuedReadBenchMarkV1(File file, int numDocs, int columnSizeInBits) throws Exception {
    boolean signed = false;
    boolean isMmap = false;
    PinotDataBuffer heapBuffer = PinotDataBuffer.fromFile(file, ReadMode.heap, FileChannel.MapMode.READ_ONLY,
            "benchmark");
    BaseSingleColumnSingleValueReader reader = new com.linkedin.pinot.core.io.reader.impl.v1.FixedBitSingleValueReader(
            heapBuffer, numDocs, columnSizeInBits, signed);
    // sequential read
    long start, end;
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (int run = 0; run < MAX_RUNS; run++) {
        start = System.currentTimeMillis();
        for (int i = 0; i < numDocs; i++) {
            int value = reader.getInt(i);
        }//from ww  w .  ja v a 2  s .c  o m
        end = System.currentTimeMillis();
        stats.addValue(end - start);
    }
    System.out.println(" v1 sequential read stats for " + file.getName());
    System.out.println(stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    reader.close();
    heapBuffer.close();
}

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

public static void multiValuedReadBenchMarkV2(File file, int numDocs, int totalNumValues, int maxEntriesPerDoc,
        int columnSizeInBits) throws Exception {
    boolean signed = false;
    boolean isMmap = false;
    boolean readOneEachTime = true;
    PinotDataBuffer heapBuffer = PinotDataBuffer.fromFile(file, ReadMode.heap, FileChannel.MapMode.READ_ONLY,
            "benchmarking");
    com.linkedin.pinot.core.io.reader.impl.v2.FixedBitMultiValueReader reader = new com.linkedin.pinot.core.io.reader.impl.v2.FixedBitMultiValueReader(
            heapBuffer, numDocs, totalNumValues, columnSizeInBits, signed);

    int[] intArray = new int[maxEntriesPerDoc];
    long start, end;

    // read one entry at a time
    if (readOneEachTime) {
        DescriptiveStatistics stats = new DescriptiveStatistics();

        for (int run = 0; run < MAX_RUNS; run++) {
            start = System.currentTimeMillis();
            for (int i = 0; i < numDocs; i++) {
                int length = reader.getIntArray(i, intArray);
            }//w  w w . j a v a2  s.c om
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println("v2 multi value sequential read one stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    }
    reader.close();
    heapBuffer.close();
}

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From source file:com.fpuna.preproceso.PreprocesoTS.java

private static TrainingSetFeature calculoFeaturesMagnitud(List<Registro> muestras, String activity) {

    TrainingSetFeature Feature = new TrainingSetFeature();
    DescriptiveStatistics stats_m = new DescriptiveStatistics();

    double[] fft_m;
    double[] AR_4;

    muestras = Util.calcMagnitud(muestras);

    for (int i = 0; i < muestras.size(); i++) {
        stats_m.addValue(muestras.get(i).getM_1());
    }/* w w  w .jav  a  2 s.  com*/

    //********* FFT *********
    //fft_m = Util.transform(stats_m.getValues());
    fft_m = FFTMixedRadix.fftPowerSpectrum(stats_m.getValues());

    //******************* Calculos Magnitud *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_m.getMean() + ",");
    Feature.setMeanX((float) stats_m.getMean());

    //std(s) - Standard deviation
    System.out.print(stats_m.getStandardDeviation() + ",");
    Feature.setStdX((float) stats_m.getStandardDeviation());

    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_m.getMax() + ",");
    Feature.setMaxX((float) stats_m.getMax());

    //min(s) - Smallest value in array
    System.out.print(stats_m.getMin() + ",");
    Feature.setMinX((float) stats_m.getMin());

    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_m.getSkewness() + ",");
    Feature.setSkewnessX((float) stats_m.getSkewness());

    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_m.getKurtosis() + ",");
    Feature.setKurtosisX((float) stats_m.getKurtosis());

    //energy(s) - Average sum of the squares
    System.out.print(stats_m.getSumsq() / stats_m.getN() + ",");
    Feature.setEnergyX((float) (stats_m.getSumsq() / stats_m.getN()));

    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_m) + ",");
    Feature.setEntropyX(Util.calculateShannonEntropy(fft_m).floatValue());

    //iqr (s) Interquartile range
    System.out.print(stats_m.getPercentile(75) - stats_m.getPercentile(25) + ",");
    Feature.setIqrX((float) (stats_m.getPercentile(75) - stats_m.getPercentile(25)));

    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_m.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
        Feature.setArX1((float) AR_4[0]);
        Feature.setArX2((float) AR_4[1]);
        Feature.setArX3((float) AR_4[2]);
        Feature.setArX4((float) AR_4[3]);
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_m, stats_m.getValues()) + ",");
    Feature.setMeanFreqx((float) Util.meanFreq(fft_m, stats_m.getValues()));

    //******************* Actividad *******************/
    System.out.print(activity);
    System.out.print("\n");
    Feature.setEtiqueta(activity);

    return Feature;
}

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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   w w w  . j a  v a 2  s  . c  o  m
@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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From source file:com.linkedin.pinot.perf.ForwardIndexReaderBenchmark.java

public static void multiValuedReadBenchMarkV1(File file, int numDocs, int totalNumValues, int maxEntriesPerDoc,
        int columnSizeInBits) throws Exception {
    System.out.println("******************************************************************");
    System.out.println("Analyzing " + file.getName() + " numDocs:" + numDocs + ", totalNumValues:"
            + totalNumValues + ", maxEntriesPerDoc:" + maxEntriesPerDoc + ", numBits:" + columnSizeInBits);
    long start, end;
    boolean readFile = true;
    boolean randomRead = true;
    boolean contextualRead = true;
    boolean signed = false;
    boolean isMmap = false;
    PinotDataBuffer heapBuffer = PinotDataBuffer.fromFile(file, ReadMode.mmap, FileChannel.MapMode.READ_ONLY,
            "benchmarking");
    BaseSingleColumnMultiValueReader reader = new com.linkedin.pinot.core.io.reader.impl.v1.FixedBitMultiValueReader(
            heapBuffer, numDocs, totalNumValues, columnSizeInBits, signed);

    int[] intArray = new int[maxEntriesPerDoc];
    File outfile = new File("/tmp/" + file.getName() + ".raw");
    FileWriter fw = new FileWriter(outfile);
    for (int i = 0; i < numDocs; i++) {
        int length = reader.getIntArray(i, intArray);
        StringBuilder sb = new StringBuilder();
        String delim = "";
        for (int j = 0; j < length; j++) {
            sb.append(delim);/*w  w  w .j  a  v a 2  s  .  com*/
            sb.append(intArray[j]);
            delim = ",";
        }
        fw.write(sb.toString());
        fw.write("\n");
    }
    fw.close();

    // sequential read
    if (readFile) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        RandomAccessFile raf = new RandomAccessFile(file, "rw");
        ByteBuffer buffer = ByteBuffer.allocateDirect((int) file.length());
        raf.getChannel().read(buffer);
        for (int run = 0; run < MAX_RUNS; run++) {
            long length = file.length();
            start = System.currentTimeMillis();
            for (int i = 0; i < length; i++) {
                byte b = buffer.get(i);
            }
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println("v1 multi value read bytes stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));

        raf.close();
    }
    if (randomRead) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (int run = 0; run < MAX_RUNS; run++) {
            start = System.currentTimeMillis();
            for (int i = 0; i < numDocs; i++) {
                int length = reader.getIntArray(i, intArray);
            }
            end = System.currentTimeMillis();
            stats.addValue((end - start));
        }
        System.out.println("v1 multi value sequential read one stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));
    }

    if (contextualRead) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (int run = 0; run < MAX_RUNS; run++) {
            MultiValueReaderContext context = (MultiValueReaderContext) reader.createContext();
            start = System.currentTimeMillis();
            for (int i = 0; i < numDocs; i++) {
                int length = reader.getIntArray(i, intArray, context);
            }
            end = System.currentTimeMillis();
            // System.out.println("RUN:" + run + "Time:" + (end-start));
            stats.addValue((end - start));
        }
        System.out.println("v1 multi value sequential read one with context stats for " + file.getName());
        System.out.println(
                stats.toString().replaceAll("\n", ", ") + " raw:" + Arrays.toString(stats.getValues()));

    }
    reader.close();
    heapBuffer.close();
    System.out.println("******************************************************************");

}

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From source file:com.insightml.data.features.stats.FeatureStatistics.java

private int getDistinct(final String feature) {
    final DescriptiveStatistics stat = stats.get(feature);
    if (stat == null) {
        return 0;
    }/*from   ww  w  . j  av a  2  s  . c om*/
    final Set<Double> values = Sets.create((int) stat.getN());
    for (final double val : stat.getValues()) {
        values.add(val);
    }
    return values.size();
}

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From source file:com.fpuna.preproceso.PreprocesoTS.java

private static void calculoFeatures(Registro[] muestras, String activity) {

    DescriptiveStatistics stats_x = new DescriptiveStatistics();
    DescriptiveStatistics stats_y = new DescriptiveStatistics();
    DescriptiveStatistics stats_z = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m1 = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m2 = new DescriptiveStatistics();
    double[] fft_x;
    double[] fft_y;
    double[] fft_z;
    double[] AR_4;

    for (int i = 0; i < muestras.length; i++) {
        stats_x.addValue(muestras[i].getValor_x());
        stats_y.addValue(muestras[i].getValor_y());
        stats_z.addValue(muestras[i].getValor_z());
    }/*  w w w .ja va 2s  . c o m*/

    //********* FFT *********
    fft_x = Util.transform(stats_x.getValues());
    fft_y = Util.transform(stats_y.getValues());
    fft_z = Util.transform(stats_z.getValues());

    //******************* Eje X *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_x.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_x.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_x.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_x.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_x.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_x.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_x.getSumsq() / stats_x.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_x) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_x.getPercentile(75) - stats_x.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_x.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_x, stats_x.getValues()) + ",");

    //******************* Eje Y *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_y.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_y.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_y.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_y.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_y.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_y.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_y.getSumsq() / stats_y.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_y) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_y.getPercentile(75) - stats_y.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_y.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_y, stats_y.getValues()) + ",");

    //******************* Eje Z *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_z.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_z.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_z.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_z.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_z.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_z.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_z.getSumsq() / stats_z.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_z) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_z.getPercentile(75) - stats_z.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_z.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_z, stats_z.getValues()) + ",");

    //******************* Feature combinados *******************/
    //sma(s1; s2; s3) - Signal magnitude area
    System.out.print(Util.sma(stats_x.getValues(), stats_y.getValues(), stats_z.getValues()) + ",");
    //correlation(s1; s2) - Pearson Correlation coefficient
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_y.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_z.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_y.getValues(), stats_z.getValues()) + ",");

    //******************* Actividad *******************/
    System.out.print(activity);
    System.out.print("\n");
}

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