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.data;
  
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.MatrixUtils;
  import edu.cmu.tetrad.util.TetradMatrix;
  import edu.cmu.tetrad.util.TetradSerializable;
  
  import java.util.Collections;
  import java.util.LinkedList;
  import java.util.List;
  
  /**
   * Stores a correlation matrix together with variable names and sample size;
   * intended as a representation of a data set.
   *
   * @author Joseph Ramsey jdramsey@andrew.cmu.edu
   */
  public final class CorrelationMatrix extends CovarianceMatrix
          implements TetradSerializable {
      static final long serialVersionUID = 23L;
  
      //=============================CONSTRUCTORS=========================//
  
      /**
       * Constructs a new correlation matrix using the covariances in the given
       * covariance matrix.
       */
      public CorrelationMatrix(ICovarianceMatrix matrix) {
          this(matrix.getVariables(), matrix.getMatrix().copy(), matrix.getSampleSize());
      }
  
      /**
       * Constructs a new correlation matrix using the covariances in the given
       * covariance matrix.
       */
      public CorrelationMatrix(ICovarianceMatrix matrix, boolean inPlace) {
          this(matrix.getVariables(), (inPlace ? matrix.getMatrix() : new CovarianceMatrix(matrix).getMatrix()),
                  matrix.getSampleSize(), inPlace);
      }
  
      /**
       * Constructs a new correlation matrix from the the given DataSet.
       */
      public CorrelationMatrix(DataSet dataSet) {
          super(Collections.unmodifiableList(dataSet.getVariables()),
                  dataSet.getCorrelationMatrix(), dataSet.getNumRows());
          if (!dataSet.isContinuous()) {
              throw new IllegalArgumentException("Data set not continuous.");
          }
  
          if (DataUtils.containsMissingValue(dataSet)) {
              throw new IllegalArgumentException("Please remove or impute missing values.");
          }
      }
  
      /**
       * Constructs a correlation matrix data set using the given information. The
       * matrix matrix is internally converted to a correlation matrix.
       */
      private CorrelationMatrix(List<Node> variables, TetradMatrix matrix,
                                int sampleSize) {
          super(variables, MatrixUtils.convertCovToCorr(matrix).copy(), sampleSize);
      }
  
      /**
       * Constructs a correlation matrix data set using the given information. The
       * matrix matrix is internally converted to a correlation matrix.
       */
      private CorrelationMatrix(List<Node> variables, TetradMatrix matrix,
                                int sampleSize, boolean inPlace) {
          super(variables, inPlace ? MatrixUtils.convertCovToCorr(matrix) :
                  MatrixUtils.convertCovToCorr(matrix.copy()), sampleSize);
      }
  
      /**
       * Generates a simple exemplar of this class to test serialization.
       */
      public static CorrelationMatrix serializableInstance() {
          return new CorrelationMatrix(new LinkedList<Node>(),
                  new TetradMatrix(0, 0), 1);
      }
  
      //=================================PUBLIC METHODS======================//
  
      public final void setMatrix(TetradMatrix matrix) {
          if (!matrix.isSquare()) {
              throw new IllegalArgumentException("Matrix must be square.");
          }
  
          for (int i = 0; i < matrix.rows(); i++) {
              if (Math.abs(matrix.get(i, i) - 1.0) > 1.e-5) {
                  throw new IllegalArgumentException(
                          "For a correlation matrix, " +
                                  "variances (diagonal elements) must be 1.0");
              }
          }
  
          super.setMatrix(matrix);
      }
  
      @Override
      public TetradMatrix getSelection(int[] rows, int[] cols) {
          return getMatrix().getSelection(rows, cols);
      }
  
      /**
       * @return a submatrix, returning as a correlation matrix, with variables
       * in the given order.
       */
      public CorrelationMatrix getSubCorrMatrix(String[] submatrixVarNames) {
          ICovarianceMatrix covarianceMatrix = getSubmatrix(submatrixVarNames);
          return new CorrelationMatrix(covarianceMatrix);
      }
  
      /**
       * @return a submatrix, returning as a correlation matrix, with variables
       * in the given order.
       */
      public CorrelationMatrix getSubCorrMatrix(int[] indices) {
          ICovarianceMatrix covarianceMatrix = getSubmatrix(indices);
          return new CorrelationMatrix(covarianceMatrix);
      }
  }
  
  
  
  
  
  