Ejemplo n.º 1
0
  @Override
  public Clustering<E, C> reduce(
      Clustering[] clusts, Algorithm<E, C> alg, ColorGenerator cg, Props props) {
    int k = props.getInt(KMeans.K);

    Clustering<E, C> result = new ClusterList<>(k); // reducer - find consensus
    // vote about final result
    E curr;
    Iterator<E> it = clusts[0].instancesIterator();
    Cluster<E> cluster;
    int[][] mapping = findMapping(clusts, k, alg.getDistanceFunction());

    if (cg != null) {
      cg.reset();
    }

    int idx;
    while (it.hasNext()) {
      curr = it.next();
      int[] assign = new int[k];
      for (int i = 0; i < clusts.length; i++) {
        cluster = clusts[i].assignedCluster(curr);
        if (i > 0) {
          assign[map(mapping, i, cluster.getClusterId())]++;
        } else {
          assign[cluster.getClusterId()]++;
        }
      }
      idx = findMax(assign);
      // check if cluster already exists
      if (!result.hasAt(idx)) {
        result.createCluster(idx);
        if (cg != null) {
          result.get(idx).setColor(cg.next());
        }
      }
      // final cluster assignment
      result.get(idx).add(curr);
    }
    result.compact();

    return result;
  }
Ejemplo n.º 2
0
  @Override
  public String runMetis(Graph graph, int k, Props params) {
    long current = System.currentTimeMillis();
    File file = new File("inputGraph-" + current);
    String filename = file.getName();
    Process p;
    try {
      graph.hMetisExport(file, false);
      if (metisFile == null) {
        // fetch the file just once
        metisFile = getBinary();
        System.out.println("metis path: " + metisFile.getAbsolutePath());
      }

      // run metis
      String space = " ";
      StringBuilder sb = new StringBuilder(metisFile.getAbsolutePath());
      sb.append(" ")
          .append(filename)
          .append(" ")
          .append(String.valueOf(k))
          .append(space)
          .append("-ufactor=")
          .append(String.valueOf(params.getDouble(UFACTOR, 5.0)))
          .append(space)
          .append("-nruns=")
          .append(String.valueOf(params.getInt(NRUNS, 10)))
          .append(space)
          .append("-ptype=")
          .append(String.valueOf(params.get(PTYPE, "rb")))
          .append(space)
          .append("-otype=")
          .append(String.valueOf(params.get(OTYPE, "cut")))
          .append(space);
      if (params.containsKey(CTYPE)) {
        sb.append("-ctype=").append(params.get(CTYPE)).append(space);
      }
      if (params.containsKey(RTYPE)) {
        sb.append("-rtype=").append(params.get(RTYPE)).append(space);
      }

      // sb.append(String.valueOf(vcycle)).append(space);
      p = Runtime.getRuntime().exec(sb.toString());
      p.waitFor();
      if (debug) {
        System.out.println("cmd: " + sb.toString());
        System.out.println("exit code: " + p.exitValue());
        if (p.exitValue() != 1) {
          // System.out.println(ExtBinHelper.readFile(file));
        }

        helper.readStdout(p);
        helper.readStderr(p);
      }
      file.delete();
    } catch (FileNotFoundException | UnsupportedEncodingException ex) {
      Exceptions.printStackTrace(ex);
    } catch (IOException | InterruptedException ex) {
      Exceptions.printStackTrace(ex);
    }
    return filename;
  }
Ejemplo n.º 3
0
  @Override
  public Clustering<E, C> reduce(
      Clustering[] clusts, Algorithm<E, C> alg, ColorGenerator cg, Props props) {
    Graph graph = createGraph(clusts);

    // degree of freedom
    double df;
    double w, attain;
    EdgeIterable neigh;
    PriorityQueue<DoubleElem> pq = new PriorityQueue<>(graph.getNodeCount());
    DoubleElem<Node> elem;
    // for each node compute attainment score
    for (Node node : graph.getNodes()) {
      neigh = graph.getEdges(node);
      df = neigh.size();
      w = 0.0;
      for (Edge ne : neigh) {
        w += ne.getWeight();
      }
      attain = w / df;
      elem = new DoubleElem<>(node, attain);
      pq.add(elem);
    }

    // number of clusters is just a hint
    int k = props.getInt(KMeans.K, 5);
    double relax = props.getDouble(RELAX, 0.5);
    Clustering<E, C> result = new ClusterList(k);
    Dataset<? extends Instance> dataset = clusts[0].getLookup().lookup(Dataset.class);
    result.lookupAdd(dataset);
    ObjectOpenHashSet<Node> blacklist = new ObjectOpenHashSet();
    Node node, other;
    Cluster curr;
    double maxW;
    while (!pq.isEmpty()) {
      elem = pq.poll();
      node = elem.getElem();
      if (!blacklist.contains(node)) {
        blacklist.add(node);
        curr = result.createCluster();
        if (cg != null) {
          curr.setColor(cg.next());
        }
        curr.add(node.getInstance());

        EdgeIterable iter = graph.getEdges(node);
        maxW = -1;
        for (Edge ne : iter) {
          if (ne.getWeight() > maxW) {
            maxW = ne.getWeight();
          }
        }
        // add immediate neighbours with max weight to same cluster
        if (maxW >= 0.0) {
          for (Edge ne : iter) {
            // when relax set to 0.0, only items with maximum weight
            // will be added to the same cluster
            w = ne.getWeight() + relax * ne.getWeight();
            if (w >= maxW) {
              if (!node.equals(ne.getSource())) {
                other = ne.getSource();
              } else {
                other = ne.getTarget();
              }
              if (!blacklist.contains(other)) {
                curr.add(other.getInstance());
                blacklist.add(other);
              }
            }
          }
        }
      }
    }
    // TODO merge some clusters

    return result;
  }