Esempio n. 1
0
  private void calculateArrowsForward(Node x, Node y, Graph graph) {
    clearArrow(x, y);

    if (!knowledgeEmpty()) {
      if (getKnowledge().isForbidden(x.getName(), y.getName())) {
        return;
      }
    }

    List<Node> naYX = getNaYX(x, y, graph);
    List<Node> t = getTNeighbors(x, y, graph);

    DepthChoiceGenerator gen = new DepthChoiceGenerator(t.size(), t.size());
    int[] choice;

    while ((choice = gen.next()) != null) {
      List<Node> s = GraphUtils.asList(choice, t);

      if (!knowledgeEmpty()) {
        if (!validSetByKnowledge(y, s)) {
          continue;
        }
      }

      double bump = insertEval(x, y, s, naYX, graph);

      if (bump > 0.0) {
        Arrow arrow = new Arrow(bump, x, y, s, naYX);
        sortedArrows.add(arrow);
        addLookupArrow(x, y, arrow);
      }
    }
  }
Esempio n. 2
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  private void ruleR1(Graph skeleton, Graph graph, List<Node> nodes) {
    for (Node node : nodes) {
      SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();

      List<Node> adj = skeleton.getAdjacentNodes(node);

      DepthChoiceGenerator gen = new DepthChoiceGenerator(adj.size(), adj.size());
      int[] choice;
      double maxScore = Double.NEGATIVE_INFINITY;
      List<Node> parents = null;

      while ((choice = gen.next()) != null) {
        List<Node> _parents = GraphUtils.asList(choice, adj);

        double score = score(node, _parents);
        scoreReports.put(-score, _parents.toString());

        if (score > maxScore) {
          maxScore = score;
          parents = _parents;
        }
      }

      for (double score : scoreReports.keySet()) {
        TetradLogger.getInstance()
            .log(
                "score",
                "For " + node + " parents = " + scoreReports.get(score) + " score = " + -score);
      }

      TetradLogger.getInstance().log("score", "");

      if (parents == null) {
        continue;
      }

      if (normal(node, parents)) continue;

      for (Node _node : adj) {
        if (parents.contains(_node)) {
          Edge parentEdge = Edges.directedEdge(_node, node);

          if (!graph.containsEdge(parentEdge)) {
            graph.addEdge(parentEdge);
          }
        }
      }
    }
  }
Esempio n. 3
0
  // Invalid if then nodes or graph changes.
  private void calculateArrowsBackward(Node x, Node y, Graph graph) {
    if (x == y) {
      return;
    }

    if (!graph.isAdjacentTo(x, y)) {
      return;
    }

    if (!knowledgeEmpty()) {
      if (!getKnowledge().noEdgeRequired(x.getName(), y.getName())) {
        return;
      }
    }

    List<Node> naYX = getNaYX(x, y, graph);

    clearArrow(x, y);

    List<Node> _naYX = new ArrayList<Node>(naYX);
    DepthChoiceGenerator gen = new DepthChoiceGenerator(_naYX.size(), _naYX.size());
    int[] choice;

    while ((choice = gen.next()) != null) {
      List<Node> H = GraphUtils.asList(choice, _naYX);

      if (!knowledgeEmpty()) {
        if (!validSetByKnowledge(y, H)) {
          continue;
        }
      }

      double bump = deleteEval(x, y, H, naYX, graph);

      if (bump > 0.0) {
        Arrow arrow = new Arrow(bump, x, y, H, naYX);
        sortedArrows.add(arrow);
        addLookupArrow(x, y, arrow);
      }
    }
  }
  /**
   * Determines whether variable x is independent of variable y given a list of conditioning
   * variables z.
   *
   * @param x the one variable being compared.
   * @param y the second variable being compared.
   * @param z the list of conditioning variables.
   * @return true iff x _||_ y | z.
   * @throws RuntimeException if a matrix singularity is encountered.
   */
  public boolean isIndependent(Node x, Node y, List<Node> z) {
    TetradMatrix submatrix = subMatrix(x, y, z);
    double r = 0;

    try {
      r = StatUtils.partialCorrelation(submatrix);

      if (Double.isNaN((r)) || r < -1. || r > 1.) throw new RuntimeException();
    } catch (Exception e) {
      DepthChoiceGenerator gen = new DepthChoiceGenerator(z.size(), z.size());
      int[] choice;

      while ((choice = gen.next()) != null) {
        try {
          List<Node> z2 = new ArrayList<Node>(z);
          z2.removeAll(GraphUtils.asList(choice, z));
          submatrix = subMatrix(x, y, z2);
          r = StatUtils.partialCorrelation(submatrix);
        } catch (Exception e2) {
          continue;
        }

        //                if (Double.isNaN(r)) continue;
        //
        //                if (r > 1.) r = 1.;
        //                 if (r < -1.) r = -1.;

        if (Double.isNaN(r) || r < -1. || r > 1.) continue;

        break;
      }
    }

    // Either dividing by a zero standard deviation (in which case it's dependent) or doing a
    // regression
    // (effectively) with a multicolliarity
    if (Double.isNaN(r)) {
      int[] _z = new int[z.size()];
      //            for (int i = 0; i < _z.length; i++) _z[i] = i + 2;
      //
      ////            double varx = StatUtils.partialVariance(submatrix, 0, _z); // submatrix.get(0,
      // 0);
      ////            double vary = StatUtils.partialVariance(submatrix, 1, _z); //submatrix.get(1,
      // 1);
      //
      //            double varx = submatrix.get(0, 0);
      //            double vary = submatrix.get(1, 1);
      //
      //            if (varx * vary == 0) {
      return true;
      //            }
    }

    if (r > 1.) r = 1.;
    if (r < -1.) r = -1.;

    this.fisherZ =
        Math.sqrt(sampleSize() - z.size() - 3.0) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));

    if (Double.isNaN(this.fisherZ)) {
      throw new IllegalArgumentException(
          "The Fisher's Z "
              + "score for independence fact "
              + x
              + " _||_ "
              + y
              + " | "
              + z
              + " is undefined. r = "
              + r);
    }

    boolean independent = getPValue() > alpha;

    if (independent) {
      TetradLogger.getInstance()
          .log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
    } else {
      TetradLogger.getInstance()
          .log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, getPValue()));
    }

    return independent;
  }
Esempio n. 5
0
  private void resolveOneEdgeMax(Graph graph, Node x, Node y, boolean strong, Graph oldGraph) {
    if (RandomUtil.getInstance().nextDouble() > 0.5) {
      Node temp = x;
      x = y;
      y = temp;
    }

    TetradLogger.getInstance().log("info", "\nEDGE " + x + " --- " + y);

    SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();

    List<Node> neighborsx = graph.getAdjacentNodes(x);
    neighborsx.remove(y);

    double max = Double.NEGATIVE_INFINITY;
    boolean left = false;
    boolean right = false;

    DepthChoiceGenerator genx = new DepthChoiceGenerator(neighborsx.size(), neighborsx.size());
    int[] choicex;

    while ((choicex = genx.next()) != null) {
      List<Node> condxMinus = GraphUtils.asList(choicex, neighborsx);

      List<Node> condxPlus = new ArrayList<Node>(condxMinus);
      condxPlus.add(y);

      double xPlus = score(x, condxPlus);
      double xMinus = score(x, condxMinus);

      List<Node> neighborsy = graph.getAdjacentNodes(y);
      neighborsy.remove(x);

      DepthChoiceGenerator geny = new DepthChoiceGenerator(neighborsy.size(), neighborsy.size());
      int[] choicey;

      while ((choicey = geny.next()) != null) {
        List<Node> condyMinus = GraphUtils.asList(choicey, neighborsy);

        //                List<Node> parentsY = oldGraph.getParents(y);
        //                parentsY.remove(x);
        //                if (!condyMinus.containsAll(parentsY)) {
        //                    continue;
        //                }

        List<Node> condyPlus = new ArrayList<Node>(condyMinus);
        condyPlus.add(x);

        double yPlus = score(y, condyPlus);
        double yMinus = score(y, condyMinus);

        // Checking them all at once is expensive but avoids lexical ordering problems in the
        // algorithm.
        if (normal(y, condyPlus)
            || normal(x, condxMinus)
            || normal(x, condxPlus)
            || normal(y, condyMinus)) {
          continue;
        }

        double delta = 0.0;

        if (strong) {
          if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(xPlus, yMinus);

            if (yPlus <= yMinus + delta && xMinus <= xPlus + delta) {
              StringBuilder builder = new StringBuilder();

              builder.append("\nStrong " + y + "->" + x + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());

              if (score > max) {
                max = score;
                left = true;
                right = false;
              }
            } else {
              StringBuilder builder = new StringBuilder();

              builder.append("\nNo directed edge " + x + "--" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());
            }
          } else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            if (yMinus <= yPlus + delta && xPlus <= xMinus + delta) {
              StringBuilder builder = new StringBuilder();

              builder.append("\nStrong " + x + "->" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());

              if (score > max) {
                max = score;
                left = false;
                right = true;
              }
            } else {
              StringBuilder builder = new StringBuilder();

              builder.append("\nNo directed edge " + x + "--" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());
            }
          } else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          } else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          }
        } else {
          if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(xPlus, yMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nWeak " + y + "->" + x + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());

            if (score > max) {
              max = score;
              left = true;
              right = false;
            }
          } else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nWeak " + x + "->" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());

            if (score > max) {
              max = score;
              left = false;
              right = true;
            }
          } else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          } else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          }
        }
      }
    }

    for (double score : scoreReports.keySet()) {
      TetradLogger.getInstance().log("info", scoreReports.get(score));
    }

    graph.removeEdges(x, y);

    if (left) {
      graph.addDirectedEdge(y, x);
    }

    if (right) {
      graph.addDirectedEdge(x, y);
    }

    if (!graph.isAdjacentTo(x, y)) {
      graph.addUndirectedEdge(x, y);
    }
  }