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.Node;
  import edu.cmu.tetrad.util.NumberFormatUtil;
  import edu.cmu.tetrad.util.TetradMatrix;
  import edu.cmu.tetrad.util.TetradVector;
  
  import java.io.PrintStream;
  import java.text.NumberFormat;
  import java.util.ArrayList;
  import java.util.Arrays;
  import java.util.List;
  import java.util.Map;
  
  /**
   * Checks conditional independence of variable in a continuous data set using Fisher's Z test. See Spirtes, Glymour, and
   * Scheines, "Causation, Prediction and Search," 2nd edition, page 94.
   *
   * @author Joseph Ramsey
   * @author Frank Wimberly adapted IndTestCramerT for Fisher's Z
   */
  public final class IndTestDirichletScore implements IndependenceTest {
  
      /**
       * The covariance matrix.
       */
      private ICovarianceMatrix covMatrix;
  
      /**
       * The variables of the covariance matrix, in order. (Unmodifiable list.)
       */
      private List<Node> variables;
  
      /**
       * Formats as 0.0000.
       */
      private static NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
  
      /**
       * Stores a reference to the dataset being analyzed.
       */
      private DataSet dataSet;
  
      private PrintStream pValueLogger;
      private Map<Node, Integer> indexMap;
      private Map<String, Node> nameMap;
      private boolean verbose = true;
  
      private double bump;
      private DirichletScore score;
      private double samplePrior = 1;
      private double structurePrior = 1;
  
      // Legacy
      private double alpha;
  
  
      //==========================CONSTRUCTORS=============================//
  
      /**
       * Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the
       * given data set (must be continuous). The given significance level is used.
       *
       * @param dataSet A data set containing only continuous columns.
       */
      public IndTestDirichletScore(DataSet dataSet, double samplePrior, double structurePrior) {
          if (!(dataSet.isDiscrete())) {
              throw new IllegalArgumentException("Data set must be discrete.");
          }
          this.dataSet = dataSet;
          this.samplePrior = samplePrior;
          this.structurePrior = structurePrior;
          this.score = new DirichletScore(dataSet);
          this.score.setSamplePrior(samplePrior);
          this.score.setStructurePrior(structurePrior);
          this.variables = this.score.getVariables();
      }
  
      //==========================PUBLIC METHODS=============================//
  
      /**
       * Creates a new independence test instance for a subset of the variables.
       */
      public IndependenceTest indTestSubset(List<Node> vars) {
          if (vars.isEmpty()) {
              throw new IllegalArgumentException("Subset may not be empty.");
          }
  
          for (Node var : vars) {
              if (!variables.contains(var)) {
                  throw new IllegalArgumentException(
                          "All vars must be original vars");
              }
          }
  
          int[] indices = new int[vars.size()];
  
          for (int i = 0; i < indices.length; i++) {
              indices[i] = indexMap.get(vars.get(i));
          }
  
          DataSet newDataSet = dataSet.subsetColumns(indices);
          return new IndTestDirichletScore(newDataSet, getSamplePrior(), getStructurePrior());
      }
  
      /**
       * Determines whether variable x is independent of variable y given a list of conditioning variables z.
       *
       * @param x the one variable being compared.
       * @param y the second variable being compared.
       * @param z the list of conditioning variables.
       * @return true iff x _||_ y | z.
       * @throws RuntimeException if a matrix singularity is encountered.
       */
      public boolean isIndependent(Node x, Node y, List<Node> z) {
          double v = -this.score.localScoreDiff(variables.indexOf(x), variables.indexOf(y), varIndices(z));
          this.bump = v;
          return v > 0;
      }
  
      private int[] varIndices(List<Node> z) {
          int[] indices = new int[z.size()];
  
          for (int i = 0; i < z.size(); i++) {
              indices[i] = variables.indexOf(z.get(i));
          }
  
          return indices;
      }
  
      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) {
          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);
      }
  
      /**
       * @return the probability associated with the most recently computed independence test.
       */
      public double getPValue() {
          throw new UnsupportedOperationException();
      }
  
      /**
       * @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) {
          return nameMap.get(name);
      }
  
      /**
       * @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 {
          int[] parents = new int[z.size()];
  
          for (int j = 0; j < parents.length; j++) {
              parents[j] = covMatrix.getVariables().indexOf(z.get(j));
          }
  
          int i = covMatrix.getVariables().indexOf(x);
  
          double variance = covMatrix.getValue(i, i);
  
          if (parents.length > 0) {
  
              // Regress z onto i, yielding regression coefficients b.
              TetradMatrix Czz = covMatrix.getSelection(parents, parents);
              TetradMatrix inverse;
  
              try {
                  inverse = Czz.inverse();
              } catch (Exception e) {
                  return true;
              }
  
              TetradVector Cyz = covMatrix.getSelection(parents, new int[]{i}).getColumn(0);
              TetradVector b = inverse.times(Cyz);
  
              variance -= Cyz.dotProduct(b);
          }
  
          return variance < 1e-20;
      }
  
      @Override
      public double getAlpha() {
          return this.alpha;
      }
  
      @Override
      public void setAlpha(double alpha) {
          this.alpha = alpha;
      }
  
      /**
       * @return the data set being analyzed.
       */
      public DataSet getData() {
          return dataSet;
      }
  
      /**
       * @return a string representation of this test.
       */
      public String toString() {
          return "Fisher's Z, alpha = " + nf.format(getAlpha());
      }
  
      //==========================PRIVATE METHODS============================//
  
      public void setVariables(List<Node> variables) {
          if (variables.size() != this.variables.size()) throw new IllegalArgumentException("Wrong # of variables.");
          this.variables = new ArrayList<>(variables);
          covMatrix.setVariables(variables);
      }
  
      public ICovarianceMatrix getCov() {
          return covMatrix;
      }
  
      @Override
      public List<DataSet> getDataSets() {
  
          List<DataSet> dataSets = new ArrayList<>();
  
          dataSets.add(dataSet);
  
          return dataSets;
      }
  
      @Override
      public int getSampleSize() {
          return covMatrix.getSampleSize();
      }
  
      @Override
      public List<TetradMatrix> getCovMatrices() {
          return null;
      }
  
      public boolean isVerbose() {
          return verbose;
      }
  
      public void setVerbose(boolean verbose) {
          this.verbose = verbose;
      }
  
      @Override
      public double getScore() {
          return this.bump;
      }
  
      public double getSamplePrior() {
          return samplePrior;
      }
  
      public void setSamplePrior(double samplePrior) {
          this.samplePrior = samplePrior;
      }
  
      public double getStructurePrior() {
          return structurePrior;
      }
  
      public void setStructurePrior(double structurePrior) {
          this.structurePrior = structurePrior;
      }
  }
  
  
  
  
  