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

List of usage examples for org.apache.commons.math.stat.descriptive DescriptiveStatistics getValues

  1. HOME
  2. Java
  3. org.apache.commons
  4. org.apache.commons.math.stat.descriptive.*
  5. DescriptiveStatistics
  6. getValues

Introduction

In this page you can find the example usage for org.apache.commons.math.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:playground.johannes.gsv.synPop.analysis.AnalyzerTask.java

protected void writeHistograms(DescriptiveStatistics stats, String name, int bins, int minsize)
        throws IOException {
    double[] values = stats.getValues();
    if (values.length > 0) {
        if (overwrite) {
            logger.warn("Overwriting stratification!");
            bins = overwriteBins;// ww w  .  j a  va2s  .  c  om
            minsize = overwriteMinsize;
        }

        TDoubleDoubleHashMap hist = Histogram.createHistogram(stats,
                FixedSampleSizeDiscretizer.create(values, minsize, bins), true);
        Histogram.normalize(hist);
        TXTWriter.writeMap(hist, name, "p", String.format("%1$s/%2$s.strat.txt", getOutputDirectory(), name));
    } else {
        logger.debug(String.format("Cannot create histogram: No samples for %s.", name));
    }
}

From source file:playground.johannes.gsv.synPop.sim3.HamiltonianLogger.java

@Override
public void afterStep(Collection<ProxyPerson> population, Collection<ProxyPerson> mutations, boolean accepted) {
    if (iter.get() % logInterval == 0) {
        long iterNow = iter.get();
        double[] values = new double[population.size()];
        int i = 0;
        for (ProxyPerson person : population) {
            values[i] = h.evaluate(person);
            i++;/*from ww w.  ja  va  2s.  c  o  m*/
        }
        DescriptiveStatistics stats = new DescriptiveStatistics(values);
        double sum = stats.getSum();
        double avr = stats.getMean();
        //         double med = stats.getPercentile(50);
        double max = stats.getMax();
        double min = stats.getMin();

        StringBuilder builder = new StringBuilder();
        builder.append("Statistics for ");
        builder.append(h.getClass().getSimpleName());
        builder.append(String.format(Locale.US, ": Sum = %.4f, ", sum));
        builder.append(String.format(Locale.US, ": Avr = %.4f, ", avr));
        //         builder.append(String.format(Locale.US, ": Med = %.4f, ", med));
        builder.append(String.format(Locale.US, ": Max = %.4f, ", max));
        builder.append(String.format(Locale.US, ": Min = %.4f", min));

        logger.info(builder.toString());

        if (writer != null) {
            try {
                writer.write(String.valueOf(iterNow));
                writer.write(TAB);
                writer.write(String.valueOf(sum));
                writer.write(TAB);
                writer.write(String.valueOf(avr));
                writer.write(TAB);
                //               writer.write(String.valueOf(med));
                //               writer.write(TAB);
                writer.write(String.valueOf(min));
                writer.write(TAB);
                writer.write(String.valueOf(max));
                writer.newLine();
                writer.flush();
            } catch (IOException e) {
                e.printStackTrace();
            }
        }

        TDoubleDoubleHashMap hist = Histogram.createHistogram(stats,
                FixedSampleSizeDiscretizer.create(stats.getValues(), 1, 100), true);
        Histogram.normalize(hist);
        //         String file = String.format("%s/%s.%s.txt", outdir, h.getClass().getSimpleName(), iterNow);
        String file = String.format("%s/%s", outdir, h.getClass().getSimpleName());
        File afile = new File(file);
        afile.mkdirs();
        try {
            TXTWriter.writeMap(hist, "value", "frequency", String.format("%s/%s.txt", file, iterNow));
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    iter.incrementAndGet();
}

From source file:playground.johannes.sna.graph.analysis.AnalyzerTask.java

protected TDoubleDoubleHashMap writeHistograms(DescriptiveStatistics stats, String name, int bins, int minsize)
        throws IOException {
    double[] values = stats.getValues();
    if (values.length > 0) {
        TDoubleDoubleHashMap hist = Histogram.createHistogram(stats,
                FixedSampleSizeDiscretizer.create(values, minsize, bins), true);
        TXTWriter.writeMap(hist, name, "p",
                String.format("%1$s/%2$s.n%3$s.nonorm.txt", getOutputDirectory(), name, values.length / bins));
        Histogram.normalize(hist);/*ww  w .java  2 s .  co  m*/
        TXTWriter.writeMap(hist, name, "p",
                String.format("%1$s/%2$s.n%3$s.txt", getOutputDirectory(), name, values.length / bins));

        return hist;
    } else {
        logger.warn("Cannot create histogram. No samples.");
        return null;
    }
}

From source file:playground.johannes.sna.graph.analysis.AnalyzerTask.java

protected TDoubleDoubleHashMap writeCumulativeHistograms(DescriptiveStatistics stats, String name, int bins,
        int minsize) throws IOException {
    double[] values = stats.getValues();
    if (values.length > 0) {
        TDoubleDoubleHashMap hist = Histogram.createHistogram(stats,
                FixedSampleSizeDiscretizer.create(values, minsize, bins), false, false);
        hist = Histogram.createCumulativeHistogram(hist);
        Histogram.normalizeCumulative(hist);
        Histogram.complementary(hist);//ww  w  .  ja  v a  2  s. c om
        TXTWriter.writeMap(hist, name, "P",
                String.format("%1$s/%2$s.n%3$s.cum.txt", getOutputDirectory(), name, values.length / bins));

        return hist;
    } else {
        logger.warn("Cannot create histogram. No samples.");
        return null;
    }
}

From source file:playground.johannes.sna.math.Histogram.java

public static TDoubleDoubleHashMap createHistogram(DescriptiveStatistics stats, Discretizer discretizer,
        boolean reweight, boolean reverse) {
    if (stats instanceof DescriptivePiStatistics)
        return createHistogram((DescriptivePiStatistics) stats, discretizer, reweight, reverse);
    else//from w ww .  j  a va 2s .  c  om
        return createHistogram(stats.getValues(), discretizer, reweight, reverse);
}

From source file:playground.johannes.snowball2.Clustering.java

@SuppressWarnings("unchecked")
@Override/* w  w  w .  j a  v a2s  .  c  o  m*/
public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    Map<Vertex, Double> values = GraphStatistics.clusteringCoefficients(g);
    DescriptiveStatistics stats = new DescriptiveStatistics();

    TIntDoubleHashMap degreeClustering = new TIntDoubleHashMap();
    TIntIntHashMap numDegree = new TIntIntHashMap();

    double sum = 0;
    double wsum = 0;

    if (g instanceof SampledGraph) {
        for (Vertex v : values.keySet()) {
            int k = v.degree();

            if (!((SampledVertex) v).isAnonymous()) {
                double cc = degreeClustering.get(k);

                if (v.degree() == 1) {
                    stats.addValue(0.0);
                    //                  sum += (cc / ((SampledVertex)v).getSampleProbability());
                } else {
                    double C = values.get(v);
                    stats.addValue(C);
                    cc += C;
                    sum += (C / ((SampledVertex) v).getSampleProbability());
                }
                degreeClustering.put(k, cc);
                numDegree.put(k, numDegree.get(k) + 1);

                wsum += (1 / ((SampledVertex) v).getSampleProbability());
            }
        }
    } else {
        for (Vertex v : values.keySet()) {
            int k = v.degree();
            double cc = degreeClustering.get(k);

            wsum++;

            if (v.degree() == 1)
                stats.addValue(0.0);
            else {
                double C = values.get(v);
                stats.addValue(C);
                cc += C;
                sum += C;
            }

            degreeClustering.put(k, cc);
            numDegree.put(k, numDegree.get(k) + 1);
        }
    }

    wMean = sum / wsum;

    try {
        BufferedWriter writer = IOUtils
                .getBufferedWriter(String.format("%1$s/%2$s.degreeDependency.txt", outputDir, iteration));
        int[] keys = numDegree.keys();
        Arrays.sort(keys);
        for (int k : keys) {
            double bc = degreeClustering.get(k);
            int numV = numDegree.get(k);

            writer.write(String.valueOf(k));
            writer.write("\t");
            writer.write(String.valueOf(bc / (double) numV));
            writer.newLine();
        }
        writer.close();
    } catch (Exception e) {
        e.printStackTrace();
    }

    dumpStatistics(getStatisticsMap(stats), iteration);

    if (reference != null) {
        Histogram hist = new Histogram(100, reference.getMin(), reference.getMax());
        plotHistogram(stats.getValues(), hist, iteration);
    } else {
        plotHistogram(stats.getValues(), new Histogram(100), iteration);
    }

    return stats;
}

From source file:playground.johannes.socialnets.NetworkGenerator2.java

private static void dumpStats(SocialNetwork socialNet, String histfile, int it)
        throws FileNotFoundException, IOException {
    int numEdges = socialNet.getEdges().size();
    int numVertices = socialNet.getVertices().size();
    logger.info(String.format("%1$s vertices, %2$s edges.", numVertices, numEdges));

    int isolates = 0;
    for (Vertex v : socialNet.getVertices())
        if (v.getEdges().size() == 0)
            isolates++;/*from w w w.j a va  2 s.c o  m*/
    logger.info(String.format("%1$s isolates.", isolates));

    DescriptiveStatistics stats = GraphStatistics.getDegreeStatistics(socialNet);
    double meanDegree = stats.getMean();
    logger.info(String.format("Mean degree is %1$s.", meanDegree));
    WeightedStatistics wstats = new WeightedStatistics();
    wstats.addAll(stats.getValues());
    WeightedStatistics.writeHistogram(wstats.absoluteDistribution(),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".degreehist.txt");

    double clustering = GraphStatistics.getClusteringStatistics(socialNet).getMean();
    logger.info(String.format("Mean clustering coefficient is %1$s.", clustering));

    double mutuality = GraphStatistics.getMutuality(socialNet);
    logger.info(String.format("Mutuality is %1$s.", mutuality));

    double dcorrelation = GraphStatistics.getDegreeCorrelation(socialNet);
    logger.info(String.format("Degree correlation is %1$s.", dcorrelation));

    //      logger.info(String.format("Closeness is %1$s.", GraphStatistics.getCentrality(socialNet).getGraphCloseness()));
    logger.info("Calculating distance distribution...");
    WeightedStatistics stats3 = new WeightedStatistics();
    //      HashMap<Ego, TDoubleDoubleHashMap> egoHist = new HashMap<Ego, TDoubleDoubleHashMap>();
    double binsize = 1000;
    for (Ego ego : socialNet.getVertices()) {
        TDoubleDoubleHashMap hist = new TDoubleDoubleHashMap();
        Coord c1 = ego.getPerson().getSelectedPlan().getFirstActivity().getCoord();
        for (Ego p2 : socialNet.getVertices()) {

            Coord c2 = p2.getPerson().getSelectedPlan().getFirstActivity().getCoord();
            double d = c1.calcDistance(c2);
            double bin = Math.floor(d / binsize);
            double val = hist.get(bin);
            val++;
            hist.put(bin, val);
        }
        //         egoHist.put(ego, hist);

        for (Vertex n : ego.getNeighbours()) {
            Coord c2 = ((Ego) n).getPerson().getSelectedPlan().getFirstActivity().getCoord();
            double dist = c1.calcDistance(c2);
            stats3.add(dist, 1 / hist.get(Math.floor(dist / binsize)));
        }
    }
    //      WeightedStatistics stats3 = new WeightedStatistics();
    //      for(Object o : socialNet.getEdges()) {
    //         Edge e = (Edge)o;
    //         Ego e1 = (Ego) e.getVertices().getFirst();
    //         Ego e2 = (Ego) e.getVertices().getSecond();
    //         Coord c1 = e1.getPerson().getSelectedPlan().getFirstActivity().getCoord();
    //         Coord c2 = e2.getPerson().getSelectedPlan().getFirstActivity().getCoord();
    //         double dist = c1.calcDistance(c2);
    //         stats3.add(dist);
    //      }
    WeightedStatistics.writeHistogram(stats3.absoluteDistribution(1000),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".edgelength.txt");

    PajekVisWriter pWriter = new PajekVisWriter();
    pWriter.write(socialNet,
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".socialnet.net");

    WeightedStatistics stats4 = new WeightedStatistics();
    for (Object o : socialNet.getEdges()) {
        Edge e = (Edge) o;
        Ego e1 = (Ego) e.getVertices().getFirst();
        Ego e2 = (Ego) e.getVertices().getSecond();
        int age1 = e1.getPerson().getAge();
        int age2 = e2.getPerson().getAge();
        double dAge = 0;
        if (age1 > age2)
            dAge = age1 / (double) age2;
        else
            dAge = age2 / (double) age1;
        stats4.add(dAge);
    }
    WeightedStatistics.writeHistogram(stats4.absoluteDistribution(0.05),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".agedist.txt");

    logger.info("Random link selection = " + rndLinkCount);

    logger.info("Number of components = " + GraphStatistics.getComponents(socialNet).size());
}

From source file:playground.johannes.socialnets.SpatialNetworkGenerator.java

private static void dumpStats(SocialNetwork socialNet, String histfile, int it)
        throws FileNotFoundException, IOException {
    int numEdges = socialNet.getEdges().size();
    int numVertices = socialNet.getVertices().size();
    logger.info(String.format("%1$s vertices, %2$s edges.", numVertices, numEdges));

    int isolates = 0;
    for (Vertex v : socialNet.getVertices())
        if (v.getEdges().size() == 0)
            isolates++;/*from  ww  w  .  j av  a  2  s .  c  o m*/
    logger.info(String.format("%1$s isolates.", isolates));

    DescriptiveStatistics stats = GraphStatistics.getDegreeStatistics(socialNet);
    double meanDegree = stats.getMean();
    logger.info(String.format("Mean degree is %1$s.", meanDegree));
    WeightedStatistics wstats = new WeightedStatistics();
    wstats.addAll(stats.getValues());
    WeightedStatistics.writeHistogram(wstats.absoluteDistribution(),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".degreehist.txt");

    double clustering = GraphStatistics.getClusteringStatistics(socialNet).getMean();
    logger.info(String.format("Mean clustering coefficient is %1$s.", clustering));

    double mutuality = GraphStatistics.getMutuality(socialNet);
    logger.info(String.format("Mutuality is %1$s.", mutuality));

    double dcorrelation = GraphStatistics.getDegreeCorrelation(socialNet);
    logger.info(String.format("Degree correlation is %1$s.", dcorrelation));

    //      logger.info(String.format("Closeness is %1$s.", GraphStatistics.getCentrality(socialNet).getGraphCloseness()));

    WeightedStatistics stats3 = new WeightedStatistics();
    double binsize = 1000;
    for (Ego ego : socialNet.getVertices()) {
        TDoubleDoubleHashMap hist = new TDoubleDoubleHashMap();
        Coord c1 = ego.getPerson().getSelectedPlan().getFirstActivity().getCoord();
        for (Ego p2 : socialNet.getVertices()) {

            Coord c2 = p2.getPerson().getSelectedPlan().getFirstActivity().getCoord();
            double d = c1.calcDistance(c2);
            double bin = Math.floor(d / binsize);
            double val = hist.get(bin);
            val++;
            hist.put(bin, val);
        }
        //         egoHist.put(ego, hist);

        for (Vertex n : ego.getNeighbours()) {
            Coord c2 = ((Ego) n).getPerson().getSelectedPlan().getFirstActivity().getCoord();
            double dist = c1.calcDistance(c2);
            stats3.add(dist, 1 / hist.get(Math.floor(dist / binsize)));
        }
    }
    //      for(Object o : socialNet.getEdges()) {
    //         Edge e = (Edge)o;
    //         Ego e1 = (Ego) e.getVertices().getFirst();
    //         Ego e2 = (Ego) e.getVertices().getSecond();
    //         Coord c1 = e1.getPerson().getSelectedPlan().getFirstActivity().getCoord();
    //         Coord c2 = e2.getPerson().getSelectedPlan().getFirstActivity().getCoord();
    //         double dist = c1.calcDistance(c2);
    //         stats3.add(dist);
    //      }
    WeightedStatistics.writeHistogram(stats3.absoluteDistribution(1000),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".edgelength.txt");

    PajekVisWriter pWriter = new PajekVisWriter();
    pWriter.write(socialNet,
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".socialnet.net");

    WeightedStatistics stats4 = new WeightedStatistics();
    for (Object o : socialNet.getEdges()) {
        Edge e = (Edge) o;
        Ego e1 = (Ego) e.getVertices().getFirst();
        Ego e2 = (Ego) e.getVertices().getSecond();
        int age1 = e1.getPerson().getAge();
        int age2 = e2.getPerson().getAge();
        double dAge = 0;
        if (age1 > age2)
            dAge = age1 / (double) age2;
        else
            dAge = age2 / (double) age1;
        stats4.add(dAge);
    }
    WeightedStatistics.writeHistogram(stats4.absoluteDistribution(0.05),
            "/Users/fearonni/vsp-work/socialnets/data-analysis/socialnetgenerator/" + it + ".agedist.txt");
}

From source file:playground.johannes.socialnetworks.graph.spatial.analysis.AcceptanceProbaDegreeTask.java

@Override
public void analyze(Graph graph, Map<String, DescriptiveStatistics> results) {
    TObjectDoubleHashMap<Vertex> normValues = ObservedDegree.getInstance().values(graph.getVertices());

    AttributePartition partitioner = new AttributePartition(
            FixedSampleSizeDiscretizer.create(normValues.getValues(), 1, 2));
    TDoubleObjectHashMap<?> partitions = partitioner.partition(normValues);
    TDoubleObjectIterator<?> it = partitions.iterator();

    AcceptanceProbability proba = new ObservedAcceptanceProbability();

    Map<String, TDoubleDoubleHashMap> histograms = new HashMap<String, TDoubleDoubleHashMap>();
    Map<String, DescriptiveStatistics> distributions = new HashMap<String, DescriptiveStatistics>();
    double sum = 0;

    for (int i = 0; i < partitions.size(); i++) {
        it.advance();/* w ww  .j a va  2 s.  co m*/
        double key = it.key();
        Set<SpatialVertex> partition = (Set<SpatialVertex>) it.value();

        System.out.println("Partition size = " + partition.size() + "; key = " + key);

        DescriptiveStatistics distr = proba.distribution(partition, destinations);

        double[] values = distr.getValues();

        System.out.println("Num samples = " + values.length);

        if (values.length > 0) {
            TDoubleDoubleHashMap hist = Histogram.createHistogram(distr,
                    FixedSampleSizeDiscretizer.create(values, 1, 50), true);
            sum += Histogram.sum(hist);
            histograms.put(String.format("p_accept-k%1$.4f", key), hist);
            distributions.put(String.format("p_accept-k%1$.4f", key), distr);
        }
    }

    for (Entry<String, TDoubleDoubleHashMap> entry : histograms.entrySet()) {
        String key = entry.getKey();
        TDoubleDoubleHashMap histogram = entry.getValue();
        Histogram.normalize(histogram, sum);
        try {
            TXTWriter.writeMap(histogram, "d", "p", String.format("%1$s/%2$s.txt", getOutputDirectory(), key));
        } catch (IOException e) {
            e.printStackTrace();
        }

        histogram = Histogram.createCumulativeHistogram(histogram);
        Histogram.complementary(histogram);
        try {
            TXTWriter.writeMap(histogram, "d", "p",
                    String.format("%1$s/%2$s.cum.txt", getOutputDirectory(), key));
        } catch (IOException e) {
            e.printStackTrace();
        }

        DescriptiveStatistics stats = distributions.get(key);
        writeRawData(stats, key);
    }
}

From source file:playground.johannes.socialnetworks.graph.spatial.analysis.AcceptancePropaCategoryTask.java

@Override
public void analyze(Graph graph, Map<String, DescriptiveStatistics> statsMap) {
    Accessibility access = module;//from   w  w  w .jav  a  2 s  . co m
    //      access.setTargets(destinations);

    //      destinations = new HashSet<Point>();
    Set<Vertex> vertices = new HashSet<Vertex>();
    for (Vertex v : graph.getVertices()) {
        Point p = ((SpatialVertex) v).getPoint();
        if (p != null) {
            //            if (boundary.contains(p)) {
            vertices.add(v);
            //               destinations.add(((SpatialVertex) v).getPoint());
            //            }
        }
    }
    //      access.setTargets(destinations);

    TObjectDoubleHashMap<Vertex> normValues = access.values(vertices);
    //      TObjectDoubleHashMap<Vertex> normValues = ObservedDegree.getInstance().values(vertices);
    //      TObjectDoubleHashMap<Vertex> normValues = ObservedAge.getInstance().values(vertices);

    //      AttributePartition partitioner = new AttributePartition(new FixedBordersDiscretizer(normValues.getValues(), new double[]{21.0}));
    AttributePartition partitioner = new AttributePartition(
            FixedSampleSizeDiscretizer.create(normValues.getValues(), 1, 2));
    TDoubleObjectHashMap<?> partitions = partitioner.partition(normValues);
    TDoubleObjectIterator<?> it = partitions.iterator();

    //      AcceptanceProbability propa = new ObservedAcceptanceProbability();
    AcceptanceProbability propa = new AcceptanceProbability();

    Map<String, TDoubleDoubleHashMap> histograms = new HashMap<String, TDoubleDoubleHashMap>();
    Map<String, DescriptiveStatistics> distributions = new HashMap<String, DescriptiveStatistics>();
    double sum = 0;

    for (int i = 0; i < partitions.size(); i++) {
        it.advance();
        double key = it.key();
        Set<SpatialVertex> partition = (Set<SpatialVertex>) it.value();
        System.out.println("Partition size = " + partition.size() + "; key = " + key);
        DescriptiveStatistics distr = propa.distribution(partition, destinations);
        try {
            double[] values = distr.getValues();
            System.out.println("Num samples = " + values.length);
            if (values.length > 0) {
                TDoubleDoubleHashMap hist = Histogram.createHistogram(distr,
                        FixedSampleSizeDiscretizer.create(values, 1, 50), true);
                sum += Histogram.sum(hist);
                histograms.put(String.format("p_accept-cat%1$.4f", key), hist);
                distributions.put(String.format("p_accept-cat%1$.4f", key), distr);
            }
            writeHistograms(distr, new LinLogDiscretizer(1000.0, 2),
                    String.format("p_accept-cat%1$.4f.log", key), true);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    for (Entry<String, TDoubleDoubleHashMap> entry : histograms.entrySet()) {
        String key = entry.getKey();
        TDoubleDoubleHashMap histogram = entry.getValue();
        Histogram.normalize(histogram, sum);
        try {
            TXTWriter.writeMap(histogram, "d", "p", String.format("%1$s/%2$s.txt", getOutputDirectory(), key));
        } catch (IOException e) {
            e.printStackTrace();
        }

        histogram = Histogram.createCumulativeHistogram(histogram);
        Histogram.complementary(histogram);
        try {
            TXTWriter.writeMap(histogram, "d", "p",
                    String.format("%1$s/%2$s.cum.txt", getOutputDirectory(), key));
        } catch (IOException e) {
            e.printStackTrace();
        }

        DescriptiveStatistics stats = distributions.get(key);
        writeRawData(stats, key);
    }
}