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

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

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Introduction

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

Prototype

public void addValue(double v)

Source Link

Document

Adds the value to the dataset.

Usage

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

@SuppressWarnings("unchecked")
@Override/*  w w w . jav  a 2  s  .  co 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.snowball2.CountComponents.java

public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    stats.addValue(GraphStatistics.getDisconnectedComponents(g).size());
    return stats;
}

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

@SuppressWarnings("unchecked")
@Override//from  w  w  w  .  j  a  v a 2  s. co  m
public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    Set<Vertex> vertices = g.getVertices();
    int count = 0;
    for (Vertex v : vertices)
        if (v.degree() == 0)
            count++;
    stats.addValue(count);
    return stats;
}

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

@SuppressWarnings("unchecked")
@Override//from   w  w  w . ja va 2  s  .  c om
public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    TDoubleArrayList values = new TDoubleArrayList(g.numVertices());
    TDoubleArrayList weights = new TDoubleArrayList(g.numVertices());
    TDoubleArrayList normWeights = new TDoubleArrayList(g.numVertices());

    if (g instanceof SampledGraph) {
        Set<SampledVertex> vertices = g.getVertices();

        double wsum = 0.0;
        for (SampledVertex v : vertices) {
            if (!v.isAnonymous()) {
                values.add(v.degree());
                if (biasCorrection) {
                    weights.add(1 / v.getSampleProbability());
                    wsum += 1 / v.getSampleProbability();
                } else {
                    weights.add(1.0);
                    wsum++;
                }
            }
        }
        double k = values.size() / wsum;
        for (int i = 0; i < weights.size(); i++) {
            normWeights.add(weights.getQuick(i) * k);
            stats.addValue(values.getQuick(i) * normWeights.getQuick(i));
        }

    } else {
        Set<Vertex> vertices = g.getVertices();
        for (Vertex v : vertices) {
            stats.addValue(v.degree());
            values.add(v.degree());
            weights.add(1.0);
        }
    }

    gamma = calcGammaExponent(values.toNativeArray(), weights.toNativeArray(), 1.0, 0);

    dumpStatistics(getStatisticsMap(stats), iteration);

    if (reference != null) {
        Histogram hist = new Histogram(1.0, reference.getMin(), reference.getMax());
        plotHistogram(values.toNativeArray(), weights.toNativeArray(), hist, iteration);
    } else {
        plotHistogram(values.toNativeArray(), weights.toNativeArray(), new Histogram(1.0), iteration);
    }

    return stats;
}

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

public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    double product = 0;
    double sum = 0;
    double wdegreeSum = 0;
    double squareSum = 0;
    //      double wsquareSum = 0;
    double edges = 0;

    boolean sampled = false;
    if (g instanceof SampledGraph)
        sampled = true;//from   w  w w .j a  v  a 2 s .co  m

    /*
     * Calculate normalization constant
     */
    double wconst = 1;
    if (sampled) {
        double wsum = 0;
        int count = 0;
        for (Object v : g.getVertices()) {
            if (!((SampledVertex) v).isAnonymous()) {
                wsum += 1 / ((SampledVertex) v).getSampleProbability();
                count++;
            }
        }
        wconst = count / wsum;
    }

    double probaSum = 0;
    double probaSquareSum = 0;
    double probaProductSum = 0;
    for (Object e : g.getEdges()) {
        Pair p = ((Edge) e).getEndpoints();
        Vertex v1 = (Vertex) p.getFirst();
        Vertex v2 = (Vertex) p.getSecond();

        if (sampled) {
            //            if (!((SampledVertex)v1).isAnonymous() && !((SampledVertex)v2).isAnonymous()) {
            //               int d_v1 = v1.degree();
            //               int d_v2 = v2.degree();
            ////               double d_v1 = v1.degree() * 1/((SampledVertex)v1).getSampleProbability();// * wconst;
            ////               double d_v2 = v2.degree() * 1/((SampledVertex)v2).getSampleProbability();// * wconst;
            //
            //               sum += d_v1 + d_v2;
            ////               wdegreeSum += (d_v1 * 1/((SampledVertex)v1).getSampleProbability() * wconst)
            ////                        + (d_v2 * 1/((SampledVertex)v2).getSampleProbability() * wconst);
            //               squareSum += Math.pow(d_v1, 2) + Math.pow(d_v2, 2);
            ////               wsquareSum += Math.pow(d_v1 * 1/((SampledVertex)v1).getSampleProbability() * wconst, 2)
            ////                        + Math.pow(d_v2 * 1/((SampledVertex)v2).getSampleProbability() * wconst, 2);
            ////               product += d_v1 * 1/((SampledVertex)v1).getSampleProbability() * wconst
            ////                      * d_v2 * 1/((SampledVertex)v2).getSampleProbability() * wconst;
            //               product += d_v1 * d_v2;
            //               
            //               edges++;
            //            }

            if (!((SampledVertex) v1).isAnonymous() && !((SampledVertex) v2).isAnonymous()) {
                int d_v1 = v1.degree();
                int d_v2 = v2.degree();
                double p_v1 = ((SampledVertex) v1).getSampleProbability();// * wconst;
                double p_v2 = ((SampledVertex) v2).getSampleProbability();// * wconst;
                double proba = p_v1 * p_v2;

                sum += 0.5 * (d_v1 + d_v2) / proba;
                probaSum += 1 / proba;

                squareSum += 0.5 * (Math.pow(d_v1, 2) + Math.pow(d_v2, 2)) / proba;
                probaSquareSum = 1 / proba;

                product += (d_v1 * d_v2) / proba;
                probaProductSum += 1 / proba;

                edges++;
            }
        } else {
            int d_v1 = v1.degree();
            int d_v2 = v2.degree();

            sum += 0.5 * (d_v1 + d_v2);
            wdegreeSum = sum;
            squareSum += 0.5 * (Math.pow(d_v1, 2) + Math.pow(d_v2, 2));
            product += d_v1 * d_v2;

            probaSum++;
            probaSquareSum++;
            probaProductSum++;
            edges++;
        }
    }
    double M_minus1 = 1 / (double) edges;
    double normSumSquare = Math.pow((1 / probaSum * sum), 2);
    //      double wSumSquare = Math.pow((M_minus1 * 0.5 * wdegreeSum), 2);
    double numerator = (1 / probaProductSum * product) - normSumSquare;
    double denumerator = (1 / probaSquareSum * squareSum) - normSumSquare;

    double result = numerator / denumerator;

    DescriptiveStatistics stats = new DescriptiveStatistics();
    stats.addValue(result);
    return stats;
}

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

@SuppressWarnings("unchecked")
public DescriptiveStatistics calculate(Graph g, int iteration, DescriptiveStatistics reference) {
    Map<Vertex, Double> clustering = GraphStatistics.clusteringCoefficients(g);
    Set<Vertex> vertices = g.getVertices();
    double z = 0;
    double m = 0;

    boolean isSampled = false;
    if (g instanceof SampledGraph)
        isSampled = true;//from www. j a  va 2s.  c o m

    for (Vertex v : vertices) {
        if (isSampled) {
            if (!((SampledVertex) v).isAnonymous()) {
                z += v.degree();
                double c = clustering.get(v);
                m += v.degree() / (double) (1 + Math.pow(c, 2) * (v.degree() - 1));
            }
        } else {
            z += v.degree();
            double c = clustering.get(v);
            m += v.degree() / (double) (1 + Math.pow(c, 2) * (v.degree() - 1));
        }
    }
    z = z / (double) vertices.size();
    m = m / (double) vertices.size();

    double result = m / z;

    DescriptiveStatistics stats = new DescriptiveStatistics();
    stats.addValue(result);
    return stats;
}

From source file:playground.johannes.socialnetworks.graph.social.analysis.AgeTask.java

@SuppressWarnings("unchecked")
@Override//from w w  w . j a va 2  s . c  o  m
public void analyze(Graph graph, Map<String, DescriptiveStatistics> statsMap) {
    DescriptiveStatistics stats = module.statistics(graph.getVertices());
    statsMap.put(key, stats);
    printStats(stats, key);

    if (outputDirectoryNotNull()) {
        try {
            writeHistograms(stats, new LinearDiscretizer(1.0), key, false);

            TXTWriter.writeMap(module.correlation((Set<? extends SocialVertex>) graph.getVertices()), "age",
                    "age_mean", getOutputDirectory() + "/age_age.mean.txt");

            TDoubleObjectHashMap<DescriptiveStatistics> stat = module
                    .boxplot((Set<? extends SocialVertex>) graph.getVertices());
            TXTWriter.writeBoxplotStats(stat, getOutputDirectory() + "age_age.table.txt");
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    stats = new DescriptiveStatistics();
    stats.addValue(module.correlationCoefficient((Set<? extends SocialEdge>) graph.getEdges()));
    statsMap.put("r_" + key, stats);
    printStats(stats, "r_" + key);
}

From source file:playground.johannes.socialnetworks.graph.social.analysis.GenderTask.java

@Override
public void analyze(Graph g, Map<String, DescriptiveStatistics> statsMap) {
    SocialGraph graph = (SocialGraph) g;

    Map<SocialVertex, String> values = module.values(graph.getVertices());
    TObjectDoubleHashMap<String> hist = LinguisticHistogram.create(values.values());

    DescriptiveStatistics male = new DescriptiveStatistics();
    male.addValue(hist.get(Gender.MALE));
    String key = "n_male";
    statsMap.put(key, male);//from   w w  w.j  a  v  a2 s .  c o  m
    printStats(male, key);

    DescriptiveStatistics female = new DescriptiveStatistics();
    female.addValue(hist.get(Gender.FEMALE));
    key = "n_female";
    statsMap.put(key, female);
    printStats(female, key);

    DescriptiveStatistics r = new DescriptiveStatistics();
    r.addValue(module.correlation(graph.getEdges()));
    key = "r_gender";
    statsMap.put(key, r);
    printStats(r, key);

    if (outputDirectoryNotNull()) {
        try {
            TXTWriter.writeMap(hist, "gender", "n", String.format("%1$s/gender.txt", getOutputDirectory()));

            SocioMatrix<String> m = module.countsMatrix(graph.getVertices());
            m.toFile(String.format("%1$s/gender.countsMatrix.txt", getOutputDirectory()));

            SocioMatrixBuilder.normalizeTotalSum(m);
            m.toFile(String.format("%1$s/gender.countsMatrix.normTotal.txt", getOutputDirectory()));

            m = module.countsMatrix(graph.getVertices());
            SocioMatrixBuilder.normalizeRowSum(m);
            m.toFile(String.format("%1$s/gender.countsMatrix.normRow.txt", getOutputDirectory()));

            m = module.probaMatrix(graph.getVertices());
            m.toFile(String.format("%1$s/gender.probaMatrix.txt", getOutputDirectory()));
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

From source file:playground.johannes.socialnetworks.snowball2.sim.postprocess.ConfidenceInterval.java

/**
 * @param args/*from  w ww. jav a 2 s  .co m*/
 * @throws IOException 
 */
public static void main(String[] args) throws IOException {
    String rootDir = args[0];

    int dumpStart = Integer.parseInt(args[1]);
    int dumpEnd = Integer.parseInt(args[2]);
    ;
    int dumpStep = Integer.parseInt(args[3]);

    String constParamKey = args[4];
    int constParam = Integer.parseInt(args[5]);
    String property = args[6];
    String dumpProperty = args[7];
    String output = args[8];

    final double mean = Double.parseDouble(args[9]);
    double confProba = Double.parseDouble(args[10]);
    String mode = args[11];

    TIntObjectHashMap<TIntDoubleHashMap> table = new TIntObjectHashMap<TIntDoubleHashMap>();
    SortedSet<Integer> dumpKeys = new TreeSet<Integer>();

    for (int dumpKey = dumpStart; dumpKey <= dumpEnd; dumpKey += dumpStep) {
        TIntDoubleHashMap row = new TIntDoubleHashMap();

        BufferedReader valueReader;
        BufferedReader dumpReader;
        if (constParamKey.equalsIgnoreCase("alpha")) {
            String path = String.format("%1$s/seed.%2$s/alpha.%3$s/%4$s.txt", rootDir, dumpKey, constParam,
                    property);
            valueReader = new BufferedReader(new FileReader(path));
            System.out.println("Loading file " + path);

            path = String.format("%1$s/seed.%2$s/alpha.%3$s/%4$s.avr.txt", rootDir, dumpKey, constParam,
                    dumpProperty);
            dumpReader = new BufferedReader(new FileReader(path));
            System.out.println("Loading file " + path);
        } else if (constParamKey.equalsIgnoreCase("seed")) {
            String path = String.format("%1$s/seed.%2$s/alpha.%3$s/%4$s.txt", rootDir, constParam, dumpKey,
                    property);
            valueReader = new BufferedReader(new FileReader(path));
            System.out.println("Loading file " + path);

            path = String.format("%1$s/seed.%2$s/alpha.%3$s/%4$s.avr.txt", rootDir, constParam, dumpKey,
                    dumpProperty);
            dumpReader = new BufferedReader(new FileReader(path));
            System.out.println("Loading file " + path);
        } else
            throw new IllegalArgumentException(
                    String.format("Constant parameter %1$s unknown.", constParamKey));

        String header = valueReader.readLine();
        String keys[] = header.split("\t");
        int cols = keys.length;
        String valueLine;
        Map<String, TDoubleArrayList> matrix = new HashMap<String, TDoubleArrayList>();
        while ((valueLine = valueReader.readLine()) != null) {
            String[] tokens = valueLine.split("\t");
            for (int i = 0; i < cols; i++) {
                TDoubleArrayList list = matrix.get(keys[i]);
                if (list == null) {
                    list = new TDoubleArrayList();
                    matrix.put(keys[i], list);
                }

                list.add(Double.parseDouble(tokens[i]));
            }
        }

        String dumpLine;
        Map<String, String> dumpMapping = new HashMap<String, String>();
        while ((dumpLine = dumpReader.readLine()) != null) {
            String[] tokens = dumpLine.split("\t");
            dumpMapping.put(tokens[0], tokens[1]);
        }

        for (Entry<String, TDoubleArrayList> entry : matrix.entrySet()) {
            DescriptiveStatistics stats = new DescriptiveStatistics();

            double vals[] = entry.getValue().toNativeArray();
            for (double val : vals) {
                if (!Double.isNaN(val)) {
                    double relerr;
                    if (mode.equals("abs")) {
                        relerr = Math.abs((val - mean) / mean);
                    } else {
                        relerr = (val - mean) / mean;
                    }
                    stats.addValue(relerr);

                }
            }
            if (stats.getN() < 50) {
                System.err.println("Less than 50 samples. Ignoring dump.");
            } else {
                double conf;
                if (mode.equals("abs"))
                    conf = stats.getPercentile(confProba);
                else if (mode.equals("pos")) {
                    confProba = (100 - confProba) / 2.0;
                    conf = stats.getPercentile(100 - confProba);
                } else if (mode.equals("neg")) {
                    confProba = (100 - confProba) / 2.0;
                    conf = stats.getPercentile(confProba);
                } else {
                    throw new IllegalArgumentException(String.format("Mode %1$s unknown.", mode));
                }
                // int key = Integer.parseInt(keys[i]);
                String keyStr = entry.getKey();
                if (!dumpMapping.get(keyStr).equals("null")) {
                    int key = (int) Double.parseDouble(dumpMapping.get(keyStr));
                    row.put(key, conf);
                    dumpKeys.add(key);
                } else {
                    System.err.println("Null key");
                }
            }
        }
        table.put(dumpKey, row);
    }

    write(table, output, dumpKeys);
}

From source file:playground.johannes.socialnetworks.snowball2.sim.postprocess.PercentileTest.java

/**
 * @param args/*from   w  w w . j  av  a  2 s.c  o m*/
 */
public static void main(String[] args) {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (int i = 0; i < 20; i++) {
        stats.addValue(i);
    }

    System.out.println(stats.getPercentile(10));

}