Example #1
0
  public void newmanCluster(ItemRegistry registry) {

    DoubleMatrix2D distance_matrix =
        DoubleFactory2D.sparse.make(
            registry.getGraph().getNodeCount(), registry.getGraph().getNodeCount());
    DoubleMatrix1D a_matrix = DoubleFactory1D.dense.make(registry.getGraph().getNodeCount(), 0.);

    Map<String, Cluster> cluster_map = new HashMap<String, Cluster>();

    // construct the leaf node distance matrix

    Iterator edge_iter = registry.getGraph().getEdges();
    m_total_distances = 0.;
    while (edge_iter.hasNext()) {
      Edge edge = (Edge) edge_iter.next();
      Cluster clust1 = (Cluster) edge.getFirstNode();
      Cluster clust2 = (Cluster) edge.getSecondNode();
      if (cluster_map.get(clust1.getAttribute("id")) == null) {
        cluster_map.put(clust1.getAttribute("id"), clust1);
      }
      if (cluster_map.get(clust2.getAttribute("id")) == null) {
        cluster_map.put(clust2.getAttribute("id"), clust2);
      }
      int n = Integer.parseInt(clust1.getAttribute("id"));
      int m = Integer.parseInt(clust2.getAttribute("id"));
      // make reciprocal (big values = good in newman, but not in our case)
      double dist = 1 / clust1.getCenter().distance(clust2.getCenter());
      distance_matrix.set(Math.max(n, m), Math.min(n, m), dist);
      //            m_total_distances += dist;
      //            a_matrix.setQuick( n, a_matrix.getQuick( n ) + dist );
      //            a_matrix.setQuick( m, a_matrix.getQuick( m ) + dist );
      m_total_distances += 1;
      a_matrix.setQuick(n, a_matrix.getQuick(n) + 1);
      a_matrix.setQuick(m, a_matrix.getQuick(m) + 1);
    }

    //        System.out.println(distance_matrix);
    //        System.out.println(count_matrix);
    // agglomerate nodes until we reach a root node (or nodes)
    boolean done = false;
    int trash = 0;
    QFinder qfinder = new QFinder();
    qfinder.a_matrix = a_matrix;
    QMerger qmerger = new QMerger();
    while (!done) {
      // find the minimum cluster distance

      qfinder.reset();

      distance_matrix.forEachNonZero(qfinder);

      //            done = true;

      //            System.out.println(distance_matrix);
      //            System.out.println(count_matrix);
      if (qfinder.getVal() == -Double.MAX_VALUE) {
        break;
      }

      // add a parent cluster to the graph

      Cluster clust1 = cluster_map.get("" + qfinder.getM());
      Cluster clust2 = cluster_map.get("" + qfinder.getN());
      while (clust1.getParent() != null) {
        clust1 = clust1.getParent();
      }
      while (clust2.getParent() != null) {
        clust2 = clust2.getParent();
      }
      trash++;
      double dist = Math.max(clust1.getHeight(), clust2.getHeight());
      Cluster new_cluster =
          new DefaultCluster(
              (float) (clust1.getCenter().getX() + clust2.getCenter().getX()) / 2.f,
              (float) (clust1.getCenter().getY() + clust2.getCenter().getY()) / 2.f,
              (float)
                  Math.sqrt(
                      clust1.getRadius() * clust1.getRadius()
                          + clust2.getRadius() * clust2.getRadius()),
              clust1,
              clust2,
              dist);
      registry.getGraph().addNode(new_cluster);

      // merge the clusters distances / counts

      int M = Math.max(qfinder.getM(), qfinder.getN());
      int N = Math.min(qfinder.getM(), qfinder.getN());
      a_matrix.set(N, a_matrix.getQuick(M) + a_matrix.getQuick(N));
      a_matrix.set(M, 0);
      //            System.out.println("M = "+M+" N = "+N + " VAL=" + minfinder.getVal() );
      qmerger.setM(M);
      qmerger.setN(N);
      qmerger.setParent(distance_matrix);
      qmerger.setMode(true);
      //            System.out.println(distance_matrix.viewPart( 0, M, distance_matrix.rows(), 1));
      distance_matrix.viewPart(0, M, distance_matrix.rows(), 1).forEachNonZero(qmerger);
      qmerger.setMode(false);
      //            System.out.println(distance_matrix.viewPart( M, 0, 1, M ));
      distance_matrix.viewPart(M, 0, 1, M).forEachNonZero(qmerger);

      //            System.out.println(distance_matrix);
      //            System.out.println(count_matrix);
      // free any superfluous memory randomly ~ (1/20) times

      if (Math.random() > 0.95) {
        distance_matrix.trimToSize();
      }
    }
  }
Example #2
0
  public void shortestDistance(ItemRegistry registry) {
    // System.out.println(""+registry.getGraph().getNodeCount());
    DoubleMatrix2D distance_matrix =
        DoubleFactory2D.sparse.make(
            registry.getGraph().getNodeCount(), registry.getGraph().getNodeCount());
    DoubleMatrix2D count_matrix =
        DoubleFactory2D.sparse.make(
            registry.getGraph().getNodeCount(), registry.getGraph().getNodeCount());

    Map<String, Cluster> cluster_map = new HashMap<String, Cluster>();

    // construct the leaf node distance matrix

    Iterator edge_iter = registry.getGraph().getEdges();
    while (edge_iter.hasNext()) {
      Edge edge = (Edge) edge_iter.next();
      Cluster clust1 = (Cluster) edge.getFirstNode();
      Cluster clust2 = (Cluster) edge.getSecondNode();
      if (cluster_map.get(clust1.getAttribute("id")) == null) {
        cluster_map.put(clust1.getAttribute("id"), clust1);
      }
      if (cluster_map.get(clust2.getAttribute("id")) == null) {
        cluster_map.put(clust2.getAttribute("id"), clust2);
      }
      int n = Integer.parseInt(clust1.getAttribute("id"));
      int m = Integer.parseInt(clust2.getAttribute("id"));
      double dist = clust1.getCenter().distance(clust2.getCenter());
      distance_matrix.set(Math.max(n, m), Math.min(n, m), dist);
      count_matrix.set(Math.max(n, m), Math.min(n, m), 1);
    }

    //        System.out.println(distance_matrix);
    //        System.out.println(count_matrix);
    // agglomerate nodes until we reach a root node (or nodes)
    boolean done = false;
    MinFinder minfinder = new MinFinder();
    minfinder.count_matrix = count_matrix;
    Merger merger = new Merger();
    while (!done) {

      // find the minimum cluster distance

      minfinder.reset();

      distance_matrix.forEachNonZero(minfinder);

      //            done = true;

      //            System.out.println(distance_matrix);
      //            System.out.println(count_matrix);
      if (minfinder.getVal() == Double.MAX_VALUE) {
        break;
      }

      // add a parent cluster to the graph

      Cluster clust1 = cluster_map.get("" + minfinder.getM());
      Cluster clust2 = cluster_map.get("" + minfinder.getN());
      while (clust1.getParent() != null) {
        clust1 = clust1.getParent();
      }
      while (clust2.getParent() != null) {
        clust2 = clust2.getParent();
      }
      //            System.out.println("HERE!");
      Cluster new_cluster =
          new DefaultCluster(
              (float) (clust1.getCenter().getX() + clust2.getCenter().getX()) / 2.f,
              (float) (clust1.getCenter().getY() + clust2.getCenter().getY()) / 2.f,
              (float)
                  Math.sqrt(
                      clust1.getRadius() * clust1.getRadius()
                          + clust2.getRadius() * clust2.getRadius()),
              clust1,
              clust2,
              minfinder.getVal());
      registry.getGraph().addNode(new_cluster);

      // merge the clusters distances / counts

      int M = Math.max(minfinder.getM(), minfinder.getN());
      int N = Math.min(minfinder.getM(), minfinder.getN());
      //            System.out.println("M = "+M+" N = "+N + " VAL=" + minfinder.getVal() );
      merger.setM(M);
      merger.setN(N);
      merger.setParent(distance_matrix);
      merger.setMode(true);
      //            System.out.println(distance_matrix.viewPart( 0, M, distance_matrix.rows(), 1));
      distance_matrix.viewPart(0, M, distance_matrix.rows(), 1).forEachNonZero(merger);
      merger.setMode(false);
      //            System.out.println(distance_matrix.viewPart( M, 0, 1, M ));
      distance_matrix.viewPart(M, 0, 1, M).forEachNonZero(merger);
      merger.setParent(count_matrix);
      merger.setMode(true);
      count_matrix.viewPart(0, M, count_matrix.rows(), 1).forEachNonZero(merger);
      merger.setMode(false);
      count_matrix.viewPart(M, 0, 1, M).forEachNonZero(merger);

      //            System.out.println(distance_matrix);
      //            System.out.println(count_matrix);
      // free any superfluous memory randomly ~ (1/20) times

      if (Math.random() > 0.95) {
        distance_matrix.trimToSize();
      }
    }
  }