コード例 #1
0
ファイル: GesConcurrent.java プロジェクト: renjiey/tetrad
  /** Returns true iif the given set forms a clique in the given graph. */
  private static boolean isClique(List<Node> nodes, Graph graph) {
    for (int i = 0; i < nodes.size() - 1; i++) {
      for (int j = i + 1; j < nodes.size(); j++) {
        if (!graph.isAdjacentTo(nodes.get(i), nodes.get(j))) {
          return false;
        }
      }
    }

    return true;
  }
コード例 #2
0
ファイル: TestMimbuild3.java プロジェクト: jdramsey/tetrad
  private Graph changeLatentNames(Graph full, Clusters measurements, List<String> latentVarList) {
    Graph g2 = null;

    try {
      g2 = (Graph) new MarshalledObject(full).get();
    } catch (IOException e) {
      e.printStackTrace();
    } catch (ClassNotFoundException e) {
      e.printStackTrace();
    }

    for (int i = 0; i < measurements.getNumClusters(); i++) {
      List<String> d = measurements.getCluster(i);
      String latentName = latentVarList.get(i);

      for (Node node : full.getNodes()) {
        if (!(node.getNodeType() == NodeType.LATENT)) {
          continue;
        }

        List<Node> _children = full.getChildren(node);

        _children.removeAll(ReidentifyVariables.getLatents(full));

        List<String> childNames = getNames(_children);

        if (new HashSet<String>(childNames).equals(new HashSet<String>(d))) {
          g2.getNode(node.getName()).setName(latentName);
        }
      }
    }

    return g2;
  }
コード例 #3
0
ファイル: GesConcurrent.java プロジェクト: renjiey/tetrad
  // ===========================SCORING METHODS===================//
  public double scoreDag(Graph graph) {
    Graph dag = new EdgeListGraphSingleConnections(graph);
    buildIndexing(graph);

    double score = 0.0;

    for (Node y : dag.getNodes()) {
      Set<Node> parents = new HashSet<Node>(dag.getParents(y));
      int nextIndex = -1;
      for (int i = 0; i < getVariables().size(); i++) {
        nextIndex = hashIndices.get(variables.get(i));
      }
      int parentIndices[] = new int[parents.size()];
      Iterator<Node> pi = parents.iterator();
      int count = 0;
      while (pi.hasNext()) {
        Node nextParent = pi.next();
        parentIndices[count++] = hashIndices.get(nextParent);
      }

      if (this.isDiscrete()) {
        score += localDiscreteScore(nextIndex, parentIndices);
      } else {
        score += localSemScore(nextIndex, parentIndices);
      }
    }
    return score;
  }