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.CovarianceMatrix;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DataUtils;
  import edu.cmu.tetrad.data.ICovarianceMatrix;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.TetradLogger;
  import edu.cmu.tetrad.util.TetradMatrix;
  
  import java.util.ArrayList;
  import java.util.Arrays;
  import java.util.Collections;
  import java.util.List;
  
  /**
   * Tests independence using a bootstrapped Fisher Z method.
   */
  public class IndTestFisherZBootstrap implements IndependenceTest {
  
      /**
       * The variables of the correlation matrix, in order. (Unmodifiable list.)
       */
      private final List<Node> variables;
  
      /**
       * The significance level of the independence tests.
       */
      private double alpha = 0.05;
  
      private DataSet dataSet;
      private int numBootstrapSamples;
      private IndependenceTest[] tests;
      private boolean verbose = false;
  
      public IndTestFisherZBootstrap(DataSet dataSet, double alpha, int numBootstrapSamples, int bootstrapSampleSize) {
          if (!(dataSet.isContinuous())) {
              throw new IllegalArgumentException("Data set must be continuous.");
          }
  
          if (!(alpha >= 0 && alpha <= 1)) {
              throw new IllegalArgumentException("Alpha mut be in [0, 1]");
          }
  
          ICovarianceMatrix covMatrix = new CovarianceMatrix(dataSet);
          this.dataSet = dataSet;
  
          this.variables = Collections.unmodifiableList(covMatrix.getVariables());
          setAlpha(alpha);
  
          this.numBootstrapSamples = numBootstrapSamples;
          TetradMatrix[] bootstrapSamples = new TetradMatrix[numBootstrapSamples];
          this.tests = new IndependenceTest[numBootstrapSamples];
  
          for (int i = 0; i < numBootstrapSamples; i++) {
              TetradMatrix fullData = dataSet.getDoubleData();
              bootstrapSamples[i] = DataUtils.getBootstrapSample(fullData, bootstrapSampleSize);
              tests[i] = new IndTestFisherZ(bootstrapSamples[i], dataSet.getVariables(), alpha);
  
          }
  
      }
  
      public IndependenceTest indTestSubset(List<Node> vars) {
          return null;
      }
  
      public boolean isIndependent(Node x, Node y, List<Node> z) {
          int[] independentGuys = new int[numBootstrapSamples];
  
          for (int i = 0; i < numBootstrapSamples; i++) {
              boolean independent = tests[i].isIndependent(x, y, z);
              independentGuys[i] = independent ? 1 : 0;
          }
  
          int sum = 0;
          for (int i = 0; i < numBootstrapSamples; i++) sum += independentGuys[i];
          boolean independent = sum > numBootstrapSamples / 2;
  
          if (verbose) {
              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;
      }
  
      public boolean isIndependent(Node x, Node y, Node... z) {
          List<Node> zList = Arrays.asList(z);
          return isIndependent(x, y, zList);
      }
  
      public boolean isDependent(Node x, Node y, List<Node> z) {
          return !isIndependent(x, y, z);
      }
  
      public boolean isDependent(Node x, Node y, Node... z) {
          List<Node> zList = Arrays.asList(z);
          return isDependent(x, y, zList);
      }
  
      public double getPValue() {
          return 0;
      }
  
      /**
       * @return the list of variables over which this independence checker is capable of determinine independence
       * relations-- that is, all the variables in the given graph or the given data set.
       */
      public List<Node> getVariables() {
          return this.variables;
      }
  
      /**
       * @return the variable with the given name.
       */
      public Node getVariable(String name) {
          for (int i = 0; i < getVariables().size(); i++) {
              Node variable = getVariables().get(i);
              if (variable.getName().equals(name)) {
                  return variable;
              }
          }
  
          return null;
      }
  
      /**
       * @return the list of variable varNames.
       */
      public List<String> getVariableNames() {
          List<Node> variables = getVariables();
          List<String> variableNames = new ArrayList<>();
          for (Node variable1 : variables) {
              variableNames.add(variable1.getName());
          }
          return variableNames;
      }
  
      public boolean determines(List<Node> z, Node x1) {
          return false;
      }
  
      public double getAlpha() {
          return alpha;
      }
  
      public void setAlpha(double alpha) {
          this.alpha = alpha;
      }
  
      public String toString() {
          return "Fisher's Z Bootstrap";
      }
  
  
      public DataSet getData() {
          return dataSet;
      }
  
      @Override
      public ICovarianceMatrix getCov() {
          return null;
      }
  
      @Override
      public List<DataSet> getDataSets() {
          return null;
      }
  
      @Override
      public int getSampleSize() {
          return 0;
      }
  
      @Override
      public List<TetradMatrix> getCovMatrices() {
          return null;
      }
  
      @Override
      public double getScore() {
          return -(getPValue() - getAlpha());
      }
  
      public boolean isVerbose() {
          return verbose;
      }
  
      public void setVerbose(boolean verbose) {
          this.verbose = verbose;
      }
  }
  
  
  