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

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

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Introduction

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

Prototype

public double getMean()

Source Link

Document

Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm"> arithmetic mean </a> of the available values

Usage

From source file:playground.johannes.sna.util.TXTWriter.java

public static void writeStatistics(TDoubleObjectHashMap<DescriptiveStatistics> statsMap, String xLab,
        String file) throws IOException {
    double[] keys = statsMap.keys();
    Arrays.sort(keys);//from   www.j a v a2  s  .  com

    BufferedWriter writer = new BufferedWriter(new FileWriter(file));

    writer.write(xLab);
    writer.write(TAB);
    writer.write("mean");
    writer.write(TAB);
    writer.write("median");
    writer.write(TAB);
    writer.write("min");
    writer.write(TAB);
    writer.write("max");
    writer.write(TAB);
    writer.write("n");
    writer.newLine();

    for (double key : keys) {
        DescriptiveStatistics stats = statsMap.get(key);

        writer.write(String.valueOf(key));
        writer.write(TAB);
        writer.write(String.valueOf(stats.getMean()));
        writer.write(TAB);
        writer.write(String.valueOf(stats.getPercentile(50)));
        writer.write(TAB);
        writer.write(String.valueOf(stats.getMin()));
        writer.write(TAB);
        writer.write(String.valueOf(stats.getMax()));
        writer.write(TAB);
        writer.write(String.valueOf(stats.getN()));
        writer.newLine();
    }

    writer.close();
}

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

protected TObjectDoubleHashMap<String> getStatisticsMap(DescriptiveStatistics stats) {
    TObjectDoubleHashMap<String> statsMap = new TObjectDoubleHashMap<String>();
    statsMap.put(MIN_KEY, stats.getMin());
    statsMap.put(MAX_KEY, stats.getMax());
    statsMap.put(MEAN_KEY, stats.getMean());
    statsMap.put(VARIANCE_KEY, stats.getVariance());
    statsMap.put(SKEWNESS_KEY, stats.getSkewness());
    statsMap.put(KURTOSIS_KEY, stats.getKurtosis());
    return statsMap;
}

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

private TObjectDoubleHashMap<String> computeStatistics(Graph g, Map<String, GraphStatistic> statistics,
        Map<String, DescriptiveStatistics> references, int iteration) {
    TObjectDoubleHashMap<String> meanValues = new TObjectDoubleHashMap<String>();
    for (String key : statisticsKeys) {
        logger.info(String.format("Calculating statistics... %1$s.", key));
        GraphStatistic s = statistics.get(key);
        if (s != null) {
            DescriptiveStatistics ref = references.get(key);
            DescriptiveStatistics stats = s.calculate(g, iteration, ref);
            if (ref == null && iteration == -1)
                references.put(key, stats);

            meanValues.put(key, stats.getMean());
        } else {/*w ww.  j  a  v  a  2s . c  o  m*/
            meanValues.put(key, Double.NaN);
        }
    }

    return meanValues;
}

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

public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {

    Set<Vertex> vertices = g.getVertices();
    Map<String, Collection<Vertex>> clusters = new HashMap<String, Collection<Vertex>>();
    for (Vertex v : vertices) {
        String type = (String) v.getUserDatum(UserDataKeys.TYPE_KEY);
        if (type != null) {
            Collection<Vertex> cluster = clusters.get(type);
            if (cluster == null) {
                cluster = new LinkedList<Vertex>();
                clusters.put(type, cluster);
            }//ww  w.ja v a  2s .c  om
            cluster.add(v);
        }
    }

    Map<String, Graph> subGraphs = new HashMap<String, Graph>();
    for (String type : clusters.keySet()) {
        subGraphs.put(type, extractCluster(clusters.get(type)));
    }

    DescriptiveStatistics degree;
    DescriptiveStatistics clustering;
    if (g instanceof SampledGraph) {
        degree = SampledGraphStatistics.getDegreeStatistics((SampledGraph) g);
        clustering = SampledGraphStatistics.getClusteringStatistics((SampledGraph) g);
    } else {
        degree = GraphStatistics.getDegreeStatistics(g);
        clustering = GraphStatistics.getClusteringStatistics(g);
    }
    //      CountComponents cc = new CountComponents(null, null);
    for (String type : clusters.keySet()) {
        Graph subGraph = subGraphs.get(type);
        float z = (float) degree.getMean();
        float c = (float) clustering.getMean();
        int components = GraphStatistics.getDisconnectedComponents(subGraph).size();

        System.out.println(String.format(
                "Subgraph of type %1$s has %2$s vertices, %3$s components, mean degree %4$s and mean clustering %5$s.",
                type, subGraph.numVertices(), components, z, c));
    }

    return new DescriptiveStatistics();
}

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  v a 2 s  .com*/
    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  ava  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.survey.ivt2009.graph.io.GraphBuilder.java

private void loadSociogramData(Collection<VertexRecord> records, SQLDumpReader sqlData) {
    logger.info("Loading sociogram data...");
    Map<String, VertexRecord> map = sqlData.getFullAlterKeyMappping(records);

    TObjectIntHashMap<Vertex> rawDegrees = new TObjectIntHashMap<Vertex>();
    for (Vertex v : proj.getVertices()) {
        rawDegrees.put(v, v.getNeighbours().size());
    }//  w  w w .j  a va2s .c  om

    int edgecnt = 0;
    int doublecnt = 0;
    int egoEdge = 0;

    Set<Vertex> notOkVertices = new HashSet<Vertex>();
    Set<Vertex> okVertices = new HashSet<Vertex>();
    DescriptiveStatistics notOkStats = new DescriptiveStatistics();
    DescriptiveStatistics okStats = new DescriptiveStatistics();

    DescriptiveStatistics numDistr = new DescriptiveStatistics();
    DescriptiveStatistics numDistrNoZero = new DescriptiveStatistics();
    DescriptiveStatistics sizeDistr = new DescriptiveStatistics();

    TDoubleArrayList sizeValues = new TDoubleArrayList();
    TDoubleArrayList kSizeValues = new TDoubleArrayList();
    TDoubleArrayList numValues = new TDoubleArrayList();
    TDoubleArrayList numValues2 = new TDoubleArrayList();
    TDoubleArrayList kNumValues = new TDoubleArrayList();

    for (VertexRecord record : records) {
        if (record.isEgo) {
            List<Set<String>> cliques = sqlData.getCliques(record);
            numDistr.addValue(cliques.size());

            Vertex v = idMap.get(record.id);
            numValues.add(cliques.size());
            kNumValues.add(v.getNeighbours().size());

            if (!cliques.isEmpty())
                numDistrNoZero.addValue(cliques.size());

            for (Set<String> clique : cliques) {
                sizeDistr.addValue(clique.size());
                sizeValues.add(clique.size());
                kSizeValues.add(rawDegrees.get(projMap.get(v)));
                numValues2.add(cliques.size());
                List<SocialSparseVertex> vertices = new ArrayList<SocialSparseVertex>(clique.size());
                for (String alter : clique) {
                    VertexRecord r = map.get(record.egoSQLId + alter);
                    if (r != null) {
                        SocialSparseVertex vertex = idMap.get(r.id);
                        if (vertex != null) {
                            vertices.add(vertex);
                        } else {
                            logger.warn("Vertex not found.");
                        }
                    } else {
                        logger.warn("Record not found.");
                    }
                }

                for (int i = 0; i < vertices.size(); i++) {
                    for (int j = i + 1; j < vertices.size(); j++) {
                        SampledVertexDecorator<SocialSparseVertex> vProj1 = projMap.get(vertices.get(i));
                        SampledVertexDecorator<SocialSparseVertex> vProj2 = projMap.get(vertices.get(j));
                        if (!vProj1.isSampled() && !vProj2.isSampled()) {

                            if (Math.random() < 0.62) {
                                SocialSparseEdge socialEdge = builder.addEdge(graph, vertices.get(i),
                                        vertices.get(j));
                                if (socialEdge != null) {
                                    projBuilder.addEdge(proj, vProj1, vProj2, socialEdge);
                                    edgecnt++;

                                    if (vProj1.isSampled() || vProj2.isSampled()) {
                                        egoEdge++;
                                        if (vProj1.isSampled())
                                            notOkVertices.add(vProj1);
                                        else
                                            notOkVertices.add(vProj2);
                                    }

                                } else {
                                    doublecnt++;
                                    if (vProj1.isSampled())
                                        okVertices.add(vProj1);
                                    else if (vProj2.isSampled())
                                        okVertices.add(vProj2);
                                }
                            }
                        }
                    }
                }
            }
        }
    }

    for (Vertex v : okVertices)
        okStats.addValue(rawDegrees.get(v));

    for (Vertex v : notOkVertices)
        notOkStats.addValue(rawDegrees.get(v));
    try {

        TDoubleDoubleHashMap hist = Histogram.createHistogram(okStats, new LinearDiscretizer(1), false);
        TXTWriter.writeMap(hist, "k", "n",
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_ok.txt");

        TDoubleDoubleHashMap hist2 = Histogram.createHistogram(notOkStats, new LinearDiscretizer(1), false);
        TXTWriter.writeMap(hist2, "k", "n",
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_notok.txt");

        TDoubleDoubleHashMap ratio = new TDoubleDoubleHashMap();
        double[] keys = hist.keys();
        for (double k : keys) {
            double val1 = hist2.get(k);
            double val2 = hist.get(k);

            ratio.put(k, val1 / (val2 + val1));
        }
        TXTWriter.writeMap(ratio, "k", "p",
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/k_ratio.txt");

        logger.info("Mean num of cliques: " + numDistrNoZero.getMean());
        logger.info("Mean size: " + sizeDistr.getMean());
        logger.info("Median num of cliques: " + StatUtils.percentile(numDistrNoZero.getValues(), 50));
        logger.info("Median size: " + StatUtils.percentile(sizeDistr.getValues(), 50));

        TDoubleDoubleHashMap histNum = Histogram.createHistogram(numDistrNoZero,
                FixedSampleSizeDiscretizer.create(numDistrNoZero.getValues(), 2, 20), true);
        Histogram.normalize(histNum);
        TXTWriter.writeMap(histNum, "num", "freq",
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/numCliques.txt");

        TDoubleDoubleHashMap histSize = Histogram.createHistogram(sizeDistr,
                FixedSampleSizeDiscretizer.create(sizeDistr.getValues(), 2, 20), true);
        Histogram.normalize(histSize);
        TXTWriter.writeMap(histSize, "size", "freq",
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/numPersons.txt");

        Discretizer discretizer = FixedSampleSizeDiscretizer.create(kSizeValues.toNativeArray(), 20, 20);
        TDoubleArrayList valuesX = new TDoubleArrayList();
        for (int i = 0; i < kSizeValues.size(); i++) {
            valuesX.add(discretizer.discretize(kSizeValues.get(i)));
        }

        Correlations.writeToFile(Correlations.mean(valuesX.toNativeArray(), sizeValues.toNativeArray()),
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/size_k.txt", "k", "size");

        discretizer = FixedSampleSizeDiscretizer.create(kNumValues.toNativeArray(), 20, 20);
        valuesX = new TDoubleArrayList();
        for (int i = 0; i < kNumValues.size(); i++) {
            valuesX.add(discretizer.discretize(kNumValues.get(i)));
        }

        Correlations.writeToFile(Correlations.mean(valuesX.toNativeArray(), numValues.toNativeArray()),
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/num_k.txt", "k", "n");

        Correlations.writeToFile(Correlations.mean(numValues2.toNativeArray(), sizeValues.toNativeArray()),
                "/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/augmented/size_num.txt", "num",
                "size");
    } catch (FileNotFoundException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    } catch (IOException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    logger.info(String.format("Inserted %1$s edges, %2$s edges already present.", edgecnt, doublecnt));
    logger.info(String.format("Inserted %1$s edges between at least one ego.", egoEdge));
}

From source file:playground.johannes.studies.coopsim.SweepMerge1D.java

/**
 * @param args/*from   w  ww . ja  v  a 2s  .  c  om*/
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
    File root = new File("/Volumes/cluster.math.tu-berlin.de/net/ils2/jillenberger/leisure/runs/run272/");
    //      String property = "score_join_culture";
    String property = "d_trip_culture";
    int valIdx = 1;
    //      String parameterKey = "beta_join";
    //      String parameterKey = "performing";
    String parameterKey = "alterProba_culture";

    int dumps = 1;

    File analysis = new File(String.format("%1$s/analysis/", root.getAbsolutePath()));
    analysis.mkdirs();

    TDoubleDoubleHashMap values = new TDoubleDoubleHashMap();

    File tasks = new File(String.format("%1$s/tasks/", root.getAbsolutePath()));
    for (File file : tasks.listFiles()) {
        if (file.isDirectory()) {
            if (!file.getName().equals("analysis")) {
                File output = new File(String.format("%1$s/output/", file.getAbsolutePath()));
                String[] dirs = output.list();
                if (dirs.length > 0) {

                    Arrays.sort(dirs, new Comparator<String>() {
                        @Override
                        public int compare(String o1, String o2) {
                            return Double.compare(Double.parseDouble(o1), Double.parseDouble(o2));
                        }
                    });
                    /*
                     * get parameter value
                     */
                    Config config = new Config();
                    ConfigReader creader = new ConfigReader(config);
                    creader.readFile(String.format("%1$s/config.xml", file.getAbsolutePath()));
                    double paramValue = Double.parseDouble(config.findParam("socialnets", parameterKey));
                    //               double paramValue = Double.parseDouble(config.findParam("planCalcScore", parameterKey));

                    int start = dirs.length - dumps;
                    start = Math.max(0, start);
                    if (dirs.length < dumps) {
                        logger.warn(String.format("Less than %1$s samples.", dumps));
                    }
                    DescriptiveStatistics stat = new DescriptiveStatistics();
                    for (int i = start; i < dirs.length; i++) {
                        //               for(int i = 12; i < 13; i++) {
                        File statsFile = new File(
                                String.format("%1$s/%2$s/statistics.txt", output.getAbsolutePath(), dirs[i]));
                        if (statsFile.exists()) {
                            /*
                             * get property value
                             */
                            BufferedReader reader = new BufferedReader(new FileReader(statsFile));
                            String line = reader.readLine();

                            while ((line = reader.readLine()) != null) {
                                String[] tokens = line.split("\t");
                                String key = tokens[0];
                                double val = Double.parseDouble(tokens[valIdx]);

                                if (key.equals(property)) {
                                    stat.addValue(val);
                                    break;
                                }
                            }
                        }

                    }
                    values.put(paramValue, stat.getMean());
                } else {
                    logger.warn("No samples.");
                }
            }
        }
    }

    TXTWriter.writeMap(values, parameterKey, property,
            String.format("%1$s/%2$s.txt", analysis.getAbsolutePath(), property));
}

From source file:playground.johannes.studies.coopsim.SweepMerge2D.java

/**
 * @param args//from   ww w . j  av a 2 s .  c  om
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
    File root = new File("/Volumes/cluster.math.tu-berlin.de/net/ils2/jillenberger/leisure/runs/run205/");
    String property = "d_trip_home";
    int valIdx = 1;
    String parameterKey1 = "traveling";
    String parameterKey2 = "performing";

    File analysis = new File(String.format("%1$s/analysis/", root.getAbsolutePath()));
    analysis.mkdirs();

    KeyMatrix<Double> matrix = new KeyMatrix<Double>();

    File tasks = new File(String.format("%1$s/tasks/", root.getAbsolutePath()));
    for (File file : tasks.listFiles()) {
        if (file.isDirectory()) {
            if (!file.getName().equals("analysis")) {
                File output = new File(String.format("%1$s/output/", file.getAbsolutePath()));
                String[] dirs = output.list();
                if (dirs.length > 0) {

                    Arrays.sort(dirs, new Comparator<String>() {
                        @Override
                        public int compare(String o1, String o2) {
                            return Double.compare(Double.parseDouble(o1), Double.parseDouble(o2));
                        }
                    });
                    /*
                     * get parameter value
                     */
                    Config config = new Config();
                    ConfigReader creader = new ConfigReader(config);
                    creader.readFile(String.format("%1$s/config.xml", file.getAbsolutePath()));
                    double paramValue1 = Double.parseDouble(config.findParam("planCalcScore", parameterKey1));
                    double paramValue2 = Double.parseDouble(config.findParam("planCalcScore", parameterKey2));

                    int start = dirs.length - 10;
                    start = Math.max(0, start);
                    if (dirs.length < 10) {
                        logger.warn("Less than 10 samples.");
                    }
                    DescriptiveStatistics stat = new DescriptiveStatistics();
                    for (int i = start; i < dirs.length; i++) {
                        File statsFile = new File(
                                String.format("%1$s/%2$s/statistics.txt", output.getAbsolutePath(), dirs[i]));
                        if (statsFile.exists()) {
                            /*
                             * get property value
                             */
                            BufferedReader reader = new BufferedReader(new FileReader(statsFile));
                            String line = reader.readLine();

                            while ((line = reader.readLine()) != null) {
                                String[] tokens = line.split("\t");
                                String key = tokens[0];
                                double val = Double.parseDouble(tokens[valIdx]);

                                if (key.equals(property)) {
                                    stat.addValue(val);
                                    break;
                                }
                            }
                        }

                    }
                    matrix.putValue(stat.getMean(), Math.abs(paramValue1), Math.abs(paramValue2));
                } else {
                    logger.warn("No samples.");
                }
            }
        }
    }

    matrix.write(String.format("%1$s/%2$s.matrix.txt", analysis.getAbsolutePath(), property));
}

From source file:playground.johannes.studies.ivt.WebdiaryDistCalc.java

/**
 * @param args//from   w w  w  .  j  ava 2 s .  co  m
 * @throws IOException 
 * @throws NumberFormatException 
 */
public static void main(String[] args) throws NumberFormatException, IOException {
    GeometryFactory factory = new GeometryFactory();
    DistanceCalculator distCalc = new WGS84DistanceCalculator();

    TDoubleDoubleHashMap correl = new TDoubleDoubleHashMap();
    TDoubleArrayList xvals = new TDoubleArrayList();
    TDoubleArrayList yvals = new TDoubleArrayList();

    SocialSampledGraphProjection<SocialSparseGraph, SocialSparseVertex, SocialSparseEdge> graph = GraphReaderFacade
            .read("/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/graph/graph.graphml");

    Map<String, SocialSparseVertex> map = new HashMap<String, SocialSparseVertex>();
    for (SocialSampledVertexDecorator<SocialSparseVertex> v : graph.getVertices()) {
        map.put(v.getPerson().getPerson().getId().toString(), v.getDelegate());
    }

    List<String> files = new ArrayList<String>();
    files.add("/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/raw/alters1.txt");
    files.add("/Users/jillenberger/Work/socialnets/data/ivt2009/11-2011/raw/alters2.txt");
    TIntIntHashMap idMapping = idMapping(files);

    BufferedReader reader = new BufferedReader(
            new FileReader("/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/webdiary.xy.csv"));
    String line = reader.readLine();
    String[] tokens = line.split(";");

    int userIdx = getIndex("\"User_ID\"", tokens);
    int typeIdx = getIndex("\"Zweck_ID\"", tokens);
    int latIdx = getIndex("\"lat\"", tokens);
    int longIdx = getIndex("\"long\"", tokens);
    int membersIdx = getIndex("\"Aktivitaet_Andere_Mitglieder\"", tokens);

    int notfound = 0;
    int valid = 0;
    int nomembers = 0;
    while ((line = reader.readLine()) != null) {
        tokens = line.split(";");

        int type = Integer.parseInt(tokens[typeIdx]);
        if (type == 8) {
            //            double lat = Double.parseDouble(tokens[latIdx]);
            double lat = Double.parseDouble(tokens[tokens.length - 2]);
            double lon = Double.parseDouble(tokens[tokens.length - 1]);

            int id = Integer.parseInt(tokens[userIdx]);
            if (idMapping.contains(id)) {
                int id2 = idMapping.get(id);

                SocialVertex v = map.get(String.valueOf(id2));
                Point p1 = v.getPoint();
                if (p1 != null) {
                    Point p2 = factory.createPoint(new Coordinate(lon, lat));
                    double d = distCalc.distance(p1, p2);

                    int members = -1;
                    if (!tokens[membersIdx].equals("\"NULL\"")) {
                        try {
                            members = Integer.parseInt(tokens[membersIdx]);
                        } catch (Exception e) {
                            e.printStackTrace();
                        }
                    } else {
                        members = 0;
                        nomembers++;
                    }

                    if (members >= 0 && d < 300000) {
                        //                  xvals.add(v.getNeighbours().size());
                        //                  yvals.add(members);
                        xvals.add(members);
                        yvals.add(d);
                        valid++;
                    }
                }
            } else {
                //               System.err.println("Ego not found.");
                notfound++;
            }
        }
    }
    System.out.println("Valid samples = " + valid);
    System.err.println("No members = " + nomembers);
    System.err.println("Not found = " + notfound);
    Discretizer disc = new FixedBordersDiscretizer(new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 60 });
    //      correl = Correlations.mean(xvals.toNativeArray(), yvals.toNativeArray(), FixedSampleSizeDiscretizer.create(xvals.toNativeArray(), 20, 20));
    correl = Correlations.mean(xvals.toNativeArray(), yvals.toNativeArray(), disc);
    //      TXTWriter.writeMap(correl, "members", "disr", "/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/members_k.txt");
    TXTWriter.writeMap(correl, "members", "disr",
            "/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/d_members.txt");

    TDoubleObjectHashMap<DescriptiveStatistics> correl2 = Correlations.statistics(xvals.toNativeArray(),
            yvals.toNativeArray(), disc);
    //      TXTWriter.writeBoxplotStats(correl2,"/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/d_members.stats.txt");
    TXTWriter.writeStatistics(correl2, "members",
            "/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/d_members.stats.txt");
    DescriptiveStatistics stats = new DescriptiveStatistics(xvals.toNativeArray());
    TDoubleDoubleHashMap hist = Histogram.createHistogram(stats, new DummyDiscretizer(), false);
    TXTWriter.writeMap(hist, "members", "n",
            "/Users/jillenberger/Work/socialnets/data/ivt2009/webdiary/members.txt");

    DescriptiveStatistics dists = new DescriptiveStatistics(yvals.toNativeArray());
    System.out.println("Mean dist = " + dists.getMean());
}