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.study;
  
  import edu.cmu.tetrad.algcomparison.Comparison;
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithms;
  import edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.*;
  import edu.cmu.tetrad.algcomparison.graph.RandomForward;
  import edu.cmu.tetrad.algcomparison.score.FisherZScore;
  import edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel;
  import edu.cmu.tetrad.algcomparison.simulation.Simulations;
  import edu.cmu.tetrad.algcomparison.statistic.*;
  import edu.cmu.tetrad.util.Parameters;
  
  /**
   * An example script to simulate data and run a comparison analysis on it.
   *
   * @author jdramsey
   */
  public class ExampleCompareSimulation {
      public static void main(String... args) {
          if (args.length > 0) {
              int numMeasures = Integer.parseInt(args[0]);
              int avgDegree = Integer.parseInt(args[1]);
  
              Parameters parameters = new Parameters();
  
  //        parameters.set("minCategories", 3);
  //        parameters.set("maxCategories", 3);
  
              parameters.set("numRuns", 2);
              parameters.set("differentGraphs", true);
              parameters.set("sampleSize", 1000);
  
              parameters.set("numMeasures", numMeasures);
              parameters.set("numLatents", 0);
              parameters.set("avgDegree", avgDegree);
              parameters.set("maxDegree", 100);
              parameters.set("maxIndegree", 100);
              parameters.set("maxOutdegree", 100);
              parameters.set("connected", false);
  
              parameters.set("coefLow", 0.2);
              parameters.set("coefHigh", 0.9);
              parameters.set("varLow", 1);
              parameters.set("varHigh", 3);
              parameters.set("verbose", false);
              parameters.set("coefSymmetric", true);
              parameters.set("numRuns", 1);
              parameters.set("percentDiscrete", 0);
              parameters.set("numCategories", 3);
              parameters.set("differentGraphs", true);
              parameters.set("sampleSize", 1000);
              parameters.set("intervalBetweenShocks", 10);
              parameters.set("intervalBetweenRecordings", 10);
              parameters.set("fisherEpsilon", 0.001);
              parameters.set("randomizeColumns", true);
  
              parameters.set("alpha", 1e-8);
              parameters.set("depth", -1);
              parameters.set("penaltyDiscount", 4);
  
              parameters.set("useMaxPOrientationHeuristic", false);
              parameters.set("maxPOrientationMaxPathLength", 3);
              parameters.set("verbose", false);
  
              parameters.set("scaleFreeAlpha", 0.00001);
              parameters.set("scaleFreeBeta", 0.4);
              parameters.set("scaleFreeDeltaIn", .1);
              parameters.set("scaleFreeDeltaOut", 3);
  
              parameters.set("symmetricFirstStep", false);
              parameters.set("faithfulnessAssumed", true);
              parameters.set("maxDegree", 100);
  
              Statistics statistics = new Statistics();
  
              statistics.add(new ParameterColumn("numMeasures"));
              statistics.add(new ParameterColumn("avgDegree"));
  
              statistics.add(new AdjacencyPrecision());
              statistics.add(new AdjacencyRecall());
              statistics.add(new ArrowheadPrecision());
              statistics.add(new ArrowheadRecall());
  //        statistics.add(new NumBidirectedEdges());
  //        statistics.add(new MathewsCorrAdj());
  //        statistics.add(new MathewsCorrArrow());
  //        statistics.add(new F1Adj());
  //        statistics.add(new F1Arrow());
  //        statistics.add(new SHD());
              statistics.add(new ElapsedTime());
  
              statistics.setWeight("AP", 0.25);
              statistics.setWeight("AR", 0.25);
              statistics.setWeight("AHP", 0.25);
              statistics.setWeight("AHR", 0.25);
  
              Algorithms algorithms = new Algorithms();
  
  //        algorithms.add(new Pc(new FisherZ()));
  //        algorithms.add(new Pc(new SemBicTest()));
  //        algorithms.add(new Cpc(new FisherZ()));
  //        algorithms.add(new PcStable(new FisherZ()));
  //        algorithms.add(new CpcStable(new FisherZ()));
  //        algorithms.add(new PcStableMax(new FisherZ(), false));
  //        algorithms.add(new PcStableMax(new SemBicTest(), false));
              algorithms.add(new Fges(new FisherZScore(), false));
  //        algorithms.add(new Fges(new SemBicScore(), false));
  
              Simulations simulations = new Simulations();
  
              simulations.add(new LinearFisherModel(new RandomForward()));
  
              Comparison comparison = new Comparison();
  
              comparison.setShowAlgorithmIndices(true);
              comparison.setShowSimulationIndices(true);
              comparison.setSortByUtility(false);
              comparison.setShowUtilities(true);
  
              comparison.compareFromSimulations("comparisonJoe", simulations, algorithms, statistics, parameters);
          }
      }
  }
  
  
  
  
  