示例#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);
      }
    }
  }
示例#2
0
 /**
  * Use background knowledge to decide if an insert or delete operation does not orient edges in a
  * forbidden direction according to prior knowledge. If some orientation is forbidden in the
  * subset, the whole subset is forbidden.
  */
 private boolean validSetByKnowledge(Node y, List<Node> subset) {
   for (Node node : subset) {
     if (getKnowledge().isForbidden(node.getName(), y.getName())) {
       return false;
     }
   }
   return true;
 }
示例#3
0
文件: Lofs.java 项目: jdramsey/tetrad
  private double pValue(Node node, List<Node> parents) {
    List<Double> _residuals = new ArrayList<Double>();

    Node _target = node;
    List<Node> _regressors = parents;
    Node target = getVariable(variables, _target.getName());
    List<Node> regressors = new ArrayList<Node>();

    for (Node _regressor : _regressors) {
      Node variable = getVariable(variables, _regressor.getName());
      regressors.add(variable);
    }

    DATASET:
    for (int m = 0; m < dataSets.size(); m++) {
      RegressionResult result = regressions.get(m).regress(target, regressors);
      TetradVector residualsSingleDataset = result.getResiduals();

      for (int h = 0; h < residualsSingleDataset.size(); h++) {
        if (Double.isNaN(residualsSingleDataset.get(h))) {
          continue DATASET;
        }
      }

      DoubleArrayList _residualsSingleDataset =
          new DoubleArrayList(residualsSingleDataset.toArray());

      double mean = Descriptive.mean(_residualsSingleDataset);
      double std =
          Descriptive.standardDeviation(
              Descriptive.variance(
                  _residualsSingleDataset.size(),
                  Descriptive.sum(_residualsSingleDataset),
                  Descriptive.sumOfSquares(_residualsSingleDataset)));

      for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
        //                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) /
        // std);
        if (isMeanCenterResiduals()) {
          _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
        }
        //                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)));
      }

      for (int k = 0; k < _residualsSingleDataset.size(); k++) {
        _residuals.add(_residualsSingleDataset.get(k));
      }
    }

    double[] _f = new double[_residuals.size()];

    for (int k = 0; k < _residuals.size(); k++) {
      _f[k] = _residuals.get(k);
    }

    return new AndersonDarlingTest(_f).getP();
  }
示例#4
0
文件: Lofs.java 项目: jdramsey/tetrad
  private Node getVariable(List<Node> variables, String name) {
    for (Node node : variables) {
      if (name.equals(node.getName())) {
        return node;
      }
    }

    return null;
  }
示例#5
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);
      }
    }
  }
示例#6
0
  private void initializeArrowsBackward(Graph graph) {
    sortedArrows.clear();
    lookupArrows.clear();

    for (Edge edge : graph.getEdges()) {
      Node x = edge.getNode1();
      Node y = edge.getNode2();

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

      if (Edges.isDirectedEdge(edge)) {
        calculateArrowsBackward(x, y, graph);
      } else {
        calculateArrowsBackward(x, y, graph);
        calculateArrowsBackward(y, x, graph);
      }
    }
  }
示例#7
0
  private void addRequiredEdges(Graph graph) {
    if (true) return;
    if (knowledgeEmpty()) return;

    for (Iterator<KnowledgeEdge> it = getKnowledge().requiredEdgesIterator(); it.hasNext(); ) {
      KnowledgeEdge next = it.next();

      Node nodeA = graph.getNode(next.getFrom());
      Node nodeB = graph.getNode(next.getTo());

      if (!graph.isAncestorOf(nodeB, nodeA)) {
        graph.removeEdges(nodeA, nodeB);
        graph.addDirectedEdge(nodeA, nodeB);
        TetradLogger.getInstance()
            .log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeA, nodeB));
      }
    }
    for (Edge edge : graph.getEdges()) {
      final String A = edge.getNode1().getName();
      final String B = edge.getNode2().getName();

      if (knowledge.isForbidden(A, B)) {
        Node nodeA = edge.getNode1();
        Node nodeB = edge.getNode2();

        if (nodeA != null
            && nodeB != null
            && graph.isAdjacentTo(nodeA, nodeB)
            && !graph.isChildOf(nodeA, nodeB)) {
          if (!graph.isAncestorOf(nodeA, nodeB)) {
            graph.removeEdges(nodeA, nodeB);
            graph.addDirectedEdge(nodeB, nodeA);
            TetradLogger.getInstance()
                .log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
          }
        }
        if (!graph.isChildOf(nodeA, nodeB)
            && getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
          if (!graph.isAncestorOf(nodeA, nodeB)) {
            graph.removeEdges(nodeA, nodeB);
            graph.addDirectedEdge(nodeB, nodeA);
            TetradLogger.getInstance()
                .log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
          }
        }
      } else if (knowledge.isForbidden(B, A)) {
        Node nodeA = edge.getNode2();
        Node nodeB = edge.getNode1();

        if (nodeA != null
            && nodeB != null
            && graph.isAdjacentTo(nodeA, nodeB)
            && !graph.isChildOf(nodeA, nodeB)) {
          if (!graph.isAncestorOf(nodeA, nodeB)) {
            graph.removeEdges(nodeA, nodeB);
            graph.addDirectedEdge(nodeB, nodeA);
            TetradLogger.getInstance()
                .log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
          }
        }
        if (!graph.isChildOf(nodeA, nodeB)
            && getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
          if (!graph.isAncestorOf(nodeA, nodeB)) {
            graph.removeEdges(nodeA, nodeB);
            graph.addDirectedEdge(nodeB, nodeA);
            TetradLogger.getInstance()
                .log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
          }
        }
      }
    }
  }
示例#8
0
  /** Do an actual deletion (Definition 13 from Chickering, 2002). */
  private void delete(Node x, Node y, List<Node> subset, Graph graph, double bump) {

    Edge trueEdge = null;

    if (trueGraph != null) {
      Node _x = trueGraph.getNode(x.getName());
      Node _y = trueGraph.getNode(y.getName());
      trueEdge = trueGraph.getEdge(_x, _y);
    }

    if (log && verbose) {
      Edge oldEdge = graph.getEdge(x, y);

      String label = trueGraph != null && trueEdge != null ? "*" : "";
      TetradLogger.getInstance()
          .log(
              "deletedEdges",
              (graph.getNumEdges() - 1)
                  + ". DELETE "
                  + oldEdge
                  + " "
                  + subset
                  + " ("
                  + bump
                  + ") "
                  + label);
      out.println(
          (graph.getNumEdges() - 1)
              + ". DELETE "
              + oldEdge
              + " "
              + subset
              + " ("
              + bump
              + ") "
              + label);
    } else {
      int numEdges = graph.getNumEdges() - 1;
      if (numEdges % 50 == 0) out.println(numEdges);
    }

    graph.removeEdge(x, y);

    for (Node h : subset) {
      Edge oldEdge = graph.getEdge(y, h);

      graph.removeEdge(y, h);
      graph.addDirectedEdge(y, h);

      if (log) {
        TetradLogger.getInstance()
            .log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
      }

      if (verbose) {
        out.println("--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
      }

      if (Edges.isUndirectedEdge(graph.getEdge(x, h))) {
        if (!graph.isAdjacentTo(x, h))
          throw new IllegalArgumentException("Not adjacent: " + x + ", " + h);
        oldEdge = graph.getEdge(x, h);

        graph.removeEdge(x, h);
        graph.addDirectedEdge(x, h);

        if (log) {
          TetradLogger.getInstance()
              .log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
        }

        if (verbose) {
          out.println("--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
        }
      }
    }
  }
示例#9
0
  // serial.
  private void insert(Node x, Node y, List<Node> t, Graph graph, double bump) {
    if (graph.isAdjacentTo(x, y)) {
      return; // The initial graph may already have put this edge in the graph.
      //            throw new IllegalArgumentException(x + " and " + y + " are already adjacent in
      // the graph.");
    }

    Edge trueEdge = null;

    if (trueGraph != null) {
      Node _x = trueGraph.getNode(x.getName());
      Node _y = trueGraph.getNode(y.getName());
      trueEdge = trueGraph.getEdge(_x, _y);
    }

    graph.addDirectedEdge(x, y);

    if (log) {
      String label = trueGraph != null && trueEdge != null ? "*" : "";
      TetradLogger.getInstance()
          .log(
              "insertedEdges",
              graph.getNumEdges()
                  + ". INSERT "
                  + graph.getEdge(x, y)
                  + " "
                  + t
                  + " "
                  + bump
                  + " "
                  + label);
    } else {
      int numEdges = graph.getNumEdges() - 1;
      if (verbose) {
        if (numEdges % 50 == 0) out.println(numEdges);
      }
    }

    if (verbose) {
      String label = trueGraph != null && trueEdge != null ? "*" : "";
      out.println(
          graph.getNumEdges()
              + ". INSERT "
              + graph.getEdge(x, y)
              + " "
              + t
              + " "
              + bump
              + " "
              + label);
    } else {
      int numEdges = graph.getNumEdges() - 1;
      if (verbose) {
        if (numEdges % 50 == 0) out.println(numEdges);
      }
    }

    for (Node _t : t) {
      Edge oldEdge = graph.getEdge(_t, y);

      if (oldEdge == null) throw new IllegalArgumentException("Not adjacent: " + _t + ", " + y);

      graph.removeEdge(_t, y);
      graph.addDirectedEdge(_t, y);

      if (log && verbose) {
        TetradLogger.getInstance()
            .log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
        out.println("--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
      }
    }
  }
示例#10
0
文件: Lofs.java 项目: jdramsey/tetrad
  private double andersonDarlingPASquareStarB(Node node, List<Node> parents) {
    List<Double> _residuals = new ArrayList<Double>();

    Node _target = node;
    List<Node> _regressors = parents;
    Node target = getVariable(variables, _target.getName());
    List<Node> regressors = new ArrayList<Node>();

    for (Node _regressor : _regressors) {
      Node variable = getVariable(variables, _regressor.getName());
      regressors.add(variable);
    }

    double sum = 0.0;

    DATASET:
    for (int m = 0; m < dataSets.size(); m++) {
      RegressionResult result = regressions.get(m).regress(target, regressors);
      TetradVector residualsSingleDataset = result.getResiduals();

      for (int h = 0; h < residualsSingleDataset.size(); h++) {
        if (Double.isNaN(residualsSingleDataset.get(h))) {
          continue DATASET;
        }
      }

      DoubleArrayList _residualsSingleDataset =
          new DoubleArrayList(residualsSingleDataset.toArray());

      double mean = Descriptive.mean(_residualsSingleDataset);
      double std =
          Descriptive.standardDeviation(
              Descriptive.variance(
                  _residualsSingleDataset.size(),
                  Descriptive.sum(_residualsSingleDataset),
                  Descriptive.sumOfSquares(_residualsSingleDataset)));

      // By centering the individual residual columns, all moments of the mixture become weighted
      // averages of the moments
      // of the individual columns.
      // http://en.wikipedia.org/wiki/Mixture_distribution#Finite_and_countable_mixtures
      for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
        //                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) /
        // std);
        //                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)) / std);
        if (isMeanCenterResiduals()) {
          _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
        }
      }

      double[] _f = new double[_residuals.size()];

      for (int k = 0; k < _residuals.size(); k++) {
        _f[k] = _residuals.get(k);
      }

      sum += new AndersonDarlingTest(_f).getASquaredStar();
    }

    return sum / dataSets.size();
  }
示例#11
0
文件: Lofs.java 项目: jdramsey/tetrad
  private double localScoreB(Node node, List<Node> parents) {

    double score = 0.0;
    double maxScore = Double.NEGATIVE_INFINITY;

    Node _target = node;
    List<Node> _regressors = parents;
    Node target = getVariable(variables, _target.getName());
    List<Node> regressors = new ArrayList<Node>();

    for (Node _regressor : _regressors) {
      Node variable = getVariable(variables, _regressor.getName());
      regressors.add(variable);
    }

    DATASET:
    for (int m = 0; m < dataSets.size(); m++) {
      RegressionResult result = regressions.get(m).regress(target, regressors);
      TetradVector residualsSingleDataset = result.getResiduals();
      DoubleArrayList _residualsSingleDataset =
          new DoubleArrayList(residualsSingleDataset.toArray());

      for (int h = 0; h < residualsSingleDataset.size(); h++) {
        if (Double.isNaN(residualsSingleDataset.get(h))) {
          continue DATASET;
        }
      }

      double mean = Descriptive.mean(_residualsSingleDataset);
      double std =
          Descriptive.standardDeviation(
              Descriptive.variance(
                  _residualsSingleDataset.size(),
                  Descriptive.sum(_residualsSingleDataset),
                  Descriptive.sumOfSquares(_residualsSingleDataset)));

      for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
        _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) / std);
      }

      double[] _f = new double[_residualsSingleDataset.size()];

      for (int k = 0; k < _residualsSingleDataset.size(); k++) {
        _f[k] = _residualsSingleDataset.get(k);
      }

      DoubleArrayList f = new DoubleArrayList(_f);

      for (int k = 0; k < f.size(); k++) {
        f.set(k, Math.abs(f.get(k)));
      }

      double _mean = Descriptive.mean(f);
      double diff = _mean - Math.sqrt(2.0 / Math.PI);
      score += diff * diff;

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

    double avg = score / dataSets.size();

    return avg;
  }
示例#12
0
  /**
   * Calculates the error variance for the given error node, given all of the coefficient values in
   * the model.
   *
   * @param error An error term in the model--i.e. a variable with NodeType.ERROR.
   * @return The value of the error variance, or Double.NaN is the value is undefined.
   */
  private double calculateErrorVarianceFromParams(Node error) {
    error = semGraph.getNode(error.getName());

    Node child = semGraph.getChildren(error).get(0);
    List<Node> parents = semGraph.getParents(child);

    double otherVariance = 0;

    for (Node parent : parents) {
      if (parent == error) continue;
      double coef = getEdgeCoefficient(parent, child);
      otherVariance += coef * coef;
    }

    if (parents.size() >= 2) {
      ChoiceGenerator gen = new ChoiceGenerator(parents.size(), 2);
      int[] indices;

      while ((indices = gen.next()) != null) {
        Node node1 = parents.get(indices[0]);
        Node node2 = parents.get(indices[1]);

        double coef1, coef2;

        if (node1.getNodeType() != NodeType.ERROR) {
          coef1 = getEdgeCoefficient(node1, child);
        } else {
          coef1 = 1;
        }

        if (node2.getNodeType() != NodeType.ERROR) {
          coef2 = getEdgeCoefficient(node2, child);
        } else {
          coef2 = 1;
        }

        List<List<Node>> treks = GraphUtils.treksIncludingBidirected(semGraph, node1, node2);

        double cov = 0.0;

        for (List<Node> trek : treks) {
          double product = 1.0;

          for (int i = 1; i < trek.size(); i++) {
            Node _node1 = trek.get(i - 1);
            Node _node2 = trek.get(i);

            Edge edge = semGraph.getEdge(_node1, _node2);
            double factor;

            if (Edges.isBidirectedEdge(edge)) {
              factor = edgeParameters.get(edge);
            } else if (!edgeParameters.containsKey(edge)) {
              factor = 1;
            } else if (semGraph.isParentOf(_node1, _node2)) {
              factor = getEdgeCoefficient(_node1, _node2);
            } else {
              factor = getEdgeCoefficient(_node2, _node1);
            }

            product *= factor;
          }

          cov += product;
        }

        otherVariance += 2 * coef1 * coef2 * cov;
      }
    }

    return 1.0 - otherVariance <= 0 ? Double.NaN : 1.0 - otherVariance;
  }