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.DataSet;
  import edu.cmu.tetrad.data.ICovarianceMatrix;
  import edu.cmu.tetrad.graph.IndependenceFact;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.NumberFormatUtil;
  import edu.cmu.tetrad.util.TetradLogger;
  import edu.cmu.tetrad.util.TetradMatrix;
  
  import java.text.NumberFormat;
  import java.util.*;
  
  import static edu.cmu.tetrad.util.MathUtils.logChoose;
  import static java.lang.Math.exp;
  import static java.lang.Math.log;
  
  /**
   * Checks conditional independence of variable in a continuous data set using a conditional correlation test
   * for the nonlinear nonGaussian case.
   *
   * @author Joseph Ramsey
   */
  public final class IndTestConditionalCorrelation implements IndependenceTest, ScoreForFact {
  
      /**
       * The instance of CCI that is wrapped.
       */
      private final ConditionalCorrelationIndependence cci;
  
      /**
       * The variables of the covariance data, in order. (Unmodifiable list.)
       */
      private final List<Node> variables;
  
      /**
       * The significance level of the independence tests.
       */
      private double alpha;
  
      /**
       * Formats as 0.0000.
       */
      private static NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
  
      /**
       * Stores a reference to the data set passed in through the constructor.
       */
      private final DataSet dataSet;
  
      /**
       * Map from nodes to the indices.
       */
      private final Map<Node, Integer> indices;
  
      /**
       * True iff the fast FDR adjustment should be made.
       */
      private boolean fastFDR = false;
  
      /**
       * True if verbose output should be printed.
       */
      private boolean verbose = false;
      private double score = Double.NaN;
  
      //==========================CONSTRUCTORS=============================//
  
      /**
       * Constructs a new Independence test which checks independence facts based on the correlation data implied by the
       * given data set (must be continuous). The given significance level is used.
       *
       * @param dataSet A data set containing only continuous columns.
       * @param alpha   The q level of the test.
       */
      public IndTestConditionalCorrelation(DataSet dataSet, double alpha) {
          if (!(dataSet.isContinuous())) {
              throw new IllegalArgumentException("Data set must be continuous.");
          }
  
          if (!(alpha >= 0 && alpha <= 1)) {
              throw new IllegalArgumentException("Q mut be in [0, 1]");
          }
  
          List<Node> nodes = dataSet.getVariables();
  
          this.variables = Collections.unmodifiableList(nodes);
  
          this.cci = new ConditionalCorrelationIndependence(dataSet, alpha);
          this.alpha = alpha;
  
          indices = new HashMap<>();
  
          for (int i = 0; i < nodes.size(); i++) {
              indices.put(nodes.get(i), i);
          }
  
          this.dataSet = dataSet;
      }
  
      //==========================PUBLIC METHODS=============================//
  
      /**
       * Creates a new IndTestCramerT instance for a subset of the variables.
       */
      public IndependenceTest indTestSubset(List<Node> vars) {
          throw new UnsupportedOperationException();
      }
  
      public boolean isIndependent(Node x, Node y, List<Node> z) {
          String _x = x.getName();
          String _y = y.getName();
          List<String> _z = new ArrayList<>();
          for (Node node : z) _z.add(node.getName());
  
          if(fastFDR) {
              final int d1 = 0; // reference
              final int d2 = z.size();
              final int v = variables.size() - 2;
  
              double alpha2 = (exp(log(alpha) + logChoose(v, d1) - logChoose(v, d2)));
  
              cci.setAlpha(alpha2);
              double p = cci.isIndependent(_x, _y, _z);
              this.score = alpha2 - p;// cci.getScore();
  
              if (verbose) {
                  IndependenceFact fact = new IndependenceFact(x, y, z);
  
                  if (p > alpha2) {
                      final String s = fact + " INDEPENDENT p = " + p;
                      System.out.println(s);
                      TetradLogger.getInstance().log("info", s);
  
                  } else {
                      final String s = fact + " dependent p = " + p;
                      System.out.println(s);
                      TetradLogger.getInstance().log("info", s);
                  }
              }
  
              return p > alpha2;
          } else {
              cci.setAlpha(alpha);
              double p = cci.isIndependent(_x, _y, _z);
              this.score = alpha - p;// cci.getScore();
  
              if (verbose) {
                  IndependenceFact fact = new IndependenceFact(x, y, z);
  
                  if (p > alpha) {
                      final String s = fact + " INDEPENDENT p = " + p;
                      System.out.println(s);
                      TetradLogger.getInstance().log("info", s);
  
                  } else {
                      final String s = fact + " dependent p = " + p;
                      System.out.println(s);
                      TetradLogger.getInstance().log("info", s);
                  }
              }
  
              return p > alpha;
          }
      }
  
      public boolean isIndependent(Node x, Node y, Node... z) {
          return isIndependent(x, y, Arrays.asList(z));
      }
  
      public boolean isDependent(Node x, Node y, List<Node> z) {
          String _x = x.getName();
          String _y = y.getName();
          List<String> _z = new ArrayList<>();
          for (Node node : z) _z.add(node.getName());
          double p = cci.isIndependent(_x, _y, _z);
  
  //        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 p < alpha;
      }
  
      public boolean isDependent(Node x, Node y, Node... z) {
          List<Node> zList = Arrays.asList(z);
          return isDependent(x, y, zList);
      }
  
      public double getPValue() {
          return cci.getPValue();
      }
  
      /**
       * Sets the significance level at which independence judgments should be made.  Affects the cutoff for partial
       * correlations to be considered statistically equal to zero.
       */
      public void setAlpha(double alpha) {
          if (alpha < 0.0 || alpha > 1.0) {
              throw new IllegalArgumentException("Significance out of range.");
          }
  
          this.alpha = alpha;
          cci.setAlpha(alpha);
      }
  
      /**
       * Gets the getModel significance level.
       */
      public double getAlpha() {
          return this.alpha;
      }
  
      /**
       * @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 (Node node : variables) {
              if (node.getName().equals(name)) return node;
          }
  
          throw new IllegalArgumentException();
      }
  
      /**
       * @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;
      }
  
      /**
       * If <code>isDeterminismAllowed()</code>, deters to IndTestFisherZD; otherwise throws
       * UnsupportedOperationException.
       */
      public boolean determines(List<Node> z, Node x) throws UnsupportedOperationException {
          throw new UnsupportedOperationException();
      }
  
      /**
       * @return the data set being analyzed.
       */
      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 score;
      }
  
      @Override
      public double getScoreForFact(IndependenceFact fact) {
          List<String> zNames = new ArrayList<>();
          for (Node node : fact.getZ()) zNames.add(node.getName());
          double p = cci.isIndependent(fact.getX().getName(), fact.getY().getName(), zNames);
          double score = alpha - p;// cci.getScore();
          this.score = score;
          return score;
      }
  
      /**
       * @return a string representation of this test.
       */
      public String toString() {
          return "Conditional Correlation, q = " + nf.format(getAlpha());
      }
  
      public boolean isVerbose() {
          return verbose;
      }
  
      public void setVerbose(boolean verbose) {
          this.verbose = verbose;
      }
  
      /**
       * Number of functions to use in (truncated) basis.
       */
      public int getNumFunctions() {
          return this.cci.getNumFunctions();
      }
  
      public void setNumFunctions(int numFunctions) {
          this.cci.setNumFunctions(numFunctions);
      }
  
      public double getWeight() {
          return this.cci.getWidth();
      }
  
      public void setKernelMultiplier(double multiplier) {
          this.cci.setWidth(multiplier);
      }
  
      public void setKernel(ConditionalCorrelationIndependence.Kernel kernel) {
          cci.setKernelMultiplier(kernel);
      }
  
      public void setBasis(ConditionalCorrelationIndependence.Basis basis) {
          cci.setBasis(basis);
      }
  
      public void setFastFDR(boolean fastFDR) {
          this.fastFDR = fastFDR;
      }
  
      public void setKernelRegressionSampleSize(int i) {
          cci.setKernelRegressionSampleSize(i);
      }
  
      public void setEarlyReturn(boolean b) {
          cci.setEarlyReturn(b);
      }
  
      public void setNumDependenceSpotChecks(int numDependenceSpotChecks) {
          cci.setNumDependenceSpotChecks(numDependenceSpotChecks);
      }
  }
  
  
  
  