code: ///////////////////////////////////////////////////////////////////////////////
  // For information as to what this class does, see the Javadoc, below.       //
  // Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,       //
  // 2007, 2008, 2009, 2010, 2014, 2015 by Peter Spirtes, Richard Scheines, Joseph   //
  // Ramsey, and Clark Glymour.                                                //
  //                                                                           //
  // This program is free software; you can redistribute it and/or modify      //
  // it under the terms of the GNU General Public License as published by      //
  // the Free Software Foundation; either version 2 of the License, or         //
  // (at your option) any later version.                                       //
  //                                                                           //
  // This program is distributed in the hope that it will be useful,           //
  // but WITHOUT ANY WARRANTY; without even the implied warranty of            //
  // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the             //
  // GNU General Public License for more details.                              //
  //                                                                           //
  // You should have received a copy of the GNU General Public License         //
  // along with this program; if not, write to the Free Software               //
  // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA //
  ///////////////////////////////////////////////////////////////////////////////
  
  package edu.cmu.tetrad.search;
  
  import edu.cmu.tetrad.data.IKnowledge;
  import edu.cmu.tetrad.data.Knowledge2;
  import edu.cmu.tetrad.graph.*;
  import edu.cmu.tetrad.util.TetradLogger;
  
  import java.util.*;
  
  /**
   * Implements a modification of the the PC ("Peter/Clark") algorithm, as specified in Chapter 6 of
   * Spirtes, Glymour, and Scheines, Causation, Prediction, and Search, 2nd edition, using the rule set
   * in step D due to Chris Meek. For the modified rule set, see Chris Meek (1995), "Causal inference and
   * causal explanation with background knowledge." The modification is to replace the collider orientation
   * step by a scoring step.
   *
   * @author Joseph Ramsey.
   */
  public class PcStableMax implements GraphSearch {
  
      /**
       * The independence test used for the PC search. This can  be a test based on a score.
       */
      private IndependenceTest independenceTest;
  
      /**
       * Forbidden and required edges for the search.
       */
      private IKnowledge knowledge = new Knowledge2();
  
      /**
       * The maximum number of nodes conditioned on in the search. The default unlimited (1000).
       */
      private int depth = -1;
  
      /**
       * The graph that's constructed during the search.
       */
      private Graph graph;
  
      /**
       * Elapsed time of the most recent search.
       */
      private long elapsedTime;
  
      /**
       * The logger for this class. The config needs to be set.
       */
      private TetradLogger logger = TetradLogger.getInstance();
  
      /**
       * The initial graph for the Fast Adjacency Search, or null if there is none.
       */
      private Graph initialGraph = null;
  
      /**
       * True if verbose output should be printed.
       */
      private boolean verbose = false;
      private boolean useHeuristic = false;
      private int maxPathLength;
  
      //=============================CONSTRUCTORS==========================//
  
      /**
       * Constructs a new PC search using the given independence test as oracle.
       *
       * @param independenceTest The oracle for conditional independence facts. This does not make a copy of the
       *                         independence test, for fear of duplicating the data set!
       */
      public PcStableMax(IndependenceTest independenceTest) {
          if (independenceTest == null) {
              throw new NullPointerException();
          }
  
          this.independenceTest = independenceTest;
      }
  
      //==============================PUBLIC METHODS========================//
  
      /**
       * @return the independence test being used in the search.
       */
      public IndependenceTest getIndependenceTest() {
          return independenceTest;
      }
  
      /**
       * @return the knowledge specification used in the search. Non-null.
       */
      public IKnowledge getKnowledge() {
          return knowledge;
      }
  
      /**
       * Sets the knowledge specification to be used in the search. May not be null.
       */
      public void setKnowledge(IKnowledge knowledge) {
          if (knowledge == null) {
              throw new NullPointerException();
          }
  
          this.knowledge = knowledge;
      }
  
      /**
       * @return the current depth of search--that is, the maximum number of conditioning nodes for any conditional
       * independence checked.
       */
      public int getDepth() {
          return depth;
      }
  
      /**
       * Sets the depth of the search--that is, the maximum number of conditioning nodes for any conditional independence
       * checked.
       *
       * @param depth The depth of the search. The default is 1000. A value of -1 may be used to indicate that the depth
       *              should be high (1000). A value of Integer.MAX_VALUE may not be used, due to a bug on multi-core
       *              machines.
       */
      public void setDepth(int depth) {
          if (depth < -1) {
              throw new IllegalArgumentException("Depth must be -1 or >= 0: " + depth);
          }
  
          if (depth > 1000) {
              throw new IllegalArgumentException("Depth must be <= 1000.");
          }
  
          this.depth = depth;
      }
  
      /**
       * Runs PC search, returning the output pattern.
       */
      public Graph search() {
          return search(independenceTest.getVariables());
      }
  
      /**
       * Runs PC search, returning the output pattern, over the given nodes.
       */
      public Graph search(List<Node> nodes) {
          this.logger.log("info", "Starting PC algorithm");
          this.logger.log("info", "Independence test = " + getIndependenceTest() + ".");
  
          long startTime = System.currentTimeMillis();
  
          if (getIndependenceTest() == null) {
              throw new NullPointerException();
          }
  
          List<Node> allNodes = getIndependenceTest().getVariables();
  
          if (!allNodes.containsAll(nodes)) {
              throw new IllegalArgumentException("All of the given nodes must " +
                      "be in the domain of the independence test provided.");
          }
  
          FasStable fas = new FasStable(initialGraph, getIndependenceTest());
          fas.setKnowledge(getKnowledge());
          fas.setDepth(getDepth());
          fas.setVerbose(verbose);
  //        fas.setRecordSepsets(false);
          graph = fas.search();
  
          SearchGraphUtils.pcOrientbk(knowledge, graph, nodes);
  
          final OrientCollidersMaxP orientCollidersMaxP = new OrientCollidersMaxP(independenceTest);
          orientCollidersMaxP.setKnowledge(knowledge);
          orientCollidersMaxP.setUseHeuristic(useHeuristic);
          orientCollidersMaxP.setMaxPathLength(maxPathLength);
          orientCollidersMaxP.orient(graph);
  
          MeekRules rules = new MeekRules();
          rules.setKnowledge(knowledge);
          rules.orientImplied(graph);
  
          this.logger.log("graph", "\nReturning this graph: " + graph);
  
          this.elapsedTime = System.currentTimeMillis() - startTime;
  
          this.logger.log("info", "Elapsed time = " + (elapsedTime) / 1000. + " s");
          this.logger.log("info", "Finishing PC Algorithm.");
          this.logger.flush();
  
          return graph;
      }
  
      /**
       * @return the elapsed time of the search, in milliseconds.
       */
      public long getElapsedTime() {
          return elapsedTime;
      }
  
      public Set<Edge> getAdjacencies() {
          Set<Edge> adjacencies = new HashSet<>();
          for (Edge edge : graph.getEdges()) {
              adjacencies.add(edge);
          }
          return adjacencies;
      }
  
      public Set<Edge> getNonadjacencies() {
          Graph complete = GraphUtils.completeGraph(graph);
          Set<Edge> nonAdjacencies = complete.getEdges();
          Graph undirected = GraphUtils.undirectedGraph(graph);
          nonAdjacencies.removeAll(undirected.getEdges());
          return new HashSet<>(nonAdjacencies);
      }
  
      public List<Node> getNodes() {
          return graph.getNodes();
      }
  
      public void setInitialGraph(Graph initialGraph) {
          this.initialGraph = initialGraph;
      }
  
      public boolean isVerbose() {
          return verbose;
      }
  
      public void setVerbose(boolean verbose) {
          this.verbose = verbose;
      }
  
      public void setUseHeuristic(boolean useHeuristic) {
          this.useHeuristic = useHeuristic;
      }
  
      public boolean isUseHeuristic() {
          return useHeuristic;
      }
  
      public void setMaxPathLength(int maxPathLength) {
          this.maxPathLength = maxPathLength;
      }
  
      public int getMaxPathLength() {
          return maxPathLength;
      }
  }
  
  
  
  