Beispiel #1
0
  public static void testManualDiscretize() {
    Node x = new ContinuousVariable("X");
    List<Node> nodes = Collections.singletonList(x);
    DataSet data = new ColtDataSet(9, nodes);

    data.setDouble(0, 0, 13.0);
    data.setDouble(1, 0, 1.2);
    data.setDouble(2, 0, 2.2);
    data.setDouble(3, 0, 4.5);
    data.setDouble(4, 0, 12.005);
    data.setDouble(5, 0, 5.5);
    data.setDouble(6, 0, 10.1);
    data.setDouble(7, 0, 7.5);
    data.setDouble(8, 0, 3.4);

    System.out.println(data);

    Discretizer discretizer = new Discretizer(data);
    discretizer.setVariablesCopied(true);

    discretizer.equalCounts(x, 3);
    DataSet discretized = discretizer.discretize();

    System.out.println(discretized);

    assertEquals(discretized.getInt(0, 0), 2);
    assertEquals(discretized.getInt(1, 0), 0);
    assertEquals(discretized.getInt(2, 0), 0);
    assertEquals(discretized.getInt(3, 0), 1);
    assertEquals(discretized.getInt(4, 0), 2);
    assertEquals(discretized.getInt(5, 0), 1);
    assertEquals(discretized.getInt(6, 0), 2);
    assertEquals(discretized.getInt(7, 0), 1);
    assertEquals(discretized.getInt(8, 0), 0);
  }
Beispiel #2
0
  /*
   * @param dataSet A discrete data set.
   * @param column the column in question.
   * @return the max value in that column.
   */
  private int maxInColumn(DataSet dataSet, int column) {
    int max = -1;

    for (int i = 0; i < dataSet.getNumRows(); i++) {
      int value = dataSet.getInt(i, column);
      if (value > max) max = value;
    }

    return max;
  }
Beispiel #3
0
  /**
   * @return the estimated conditional probability for the given assertion conditional on the given
   *     condition.
   */
  public double getConditionalProb(Proposition assertion, Proposition condition) {
    if (assertion.getVariableSource() != condition.getVariableSource()) {
      throw new IllegalArgumentException(
          "Assertion and condition must be " + "for the same Bayes IM.");
    }

    List<Node> assertionVars = assertion.getVariableSource().getVariables();
    List<Node> dataVars = dataSet.getVariables();

    assertionVars = GraphUtils.replaceNodes(assertionVars, dataVars);

    if (!new HashSet<Node>(assertionVars).equals(new HashSet<Node>(dataVars))) {
      throw new IllegalArgumentException(
          "Assertion variable and data variables"
              + " are either different or in a different order: "
              + "\n\tAssertion vars: "
              + assertionVars
              + "\n\tData vars: "
              + dataVars);
    }

    int[] point = new int[dims.length];
    int count1 = 0;
    int count2 = 0;
    this.missingValueCaseFound = false;

    point:
    for (int i = 0; i < numRows; i++) {
      for (int j = 0; j < dims.length; j++) {
        point[j] = dataSet.getInt(i, j);

        if (point[j] == DiscreteVariable.MISSING_VALUE) {
          continue point;
        }
      }

      if (condition.isPermissibleCombination(point)) {
        count1++;

        if (assertion.isPermissibleCombination(point)) {
          count2++;
        }
      }
    }

    return count2 / (double) count1;
  }
  private boolean isMissing(Node x, int i) {
    int j = internalData.getColumn(x);

    if (x instanceof DiscreteVariable) {
      int v = internalData.getInt(i, j);

      if (v == -99) {
        return true;
      }
    }

    if (x instanceof ContinuousVariable) {
      double v = internalData.getDouble(i, j);

      if (Double.isNaN(v)) {
        return true;
      }
    }

    return false;
  }
Beispiel #5
0
  /** @return the estimated probability of the given proposition. */
  public double getProb(Proposition assertion) {
    int[] point = new int[dims.length];
    int count = 0;

    this.missingValueCaseFound = false;

    point:
    for (int i = 0; i < numRows; i++) {
      for (int j = 0; j < dims.length; j++) {
        point[j] = dataSet.getInt(i, j);

        if (point[j] == DiscreteVariable.MISSING_VALUE) {
          this.missingValueCaseFound = true;
          continue point;
        }
      }

      if (assertion.isPermissibleCombination(point)) {
        count++;
      }
    }

    return count / (double) this.numRows;
  }
Beispiel #6
0
  /**
   * @return the estimated probability for the given cell. The order of the variable values is the
   *     order of the variables in getVariable().
   */
  public double getCellProb(int[] variableValues) {
    int[] point = new int[dims.length];
    int count = 0;

    this.missingValueCaseFound = false;

    point:
    for (int i = 0; i < numRows; i++) {
      for (int j = 0; j < dims.length; j++) {
        point[j] = dataSet.getInt(i, j);

        if (point[j] == DiscreteVariable.MISSING_VALUE) {
          this.missingValueCaseFound = true;
          continue point;
        }
      }

      if (Arrays.equals(point, variableValues)) {
        count++;
      }
    }

    return count / (double) this.numRows;
  }
  private void createDiscreteTimeSeriesData() {

    // GIVEN: Continuous data set D, maximum lag m.
    Node[] dataVars = dataSet.getVariables().toArray(new Node[0]);
    int n = dataVars.length;
    int m = getNumLags();

    // LetXi, i = 0,...,n-1, be the variables from the data. Let Xi(t) be
    // the variable Xi at time lag t (before 0), t = 0,...,m.
    Node[][] laggedVars = new Node[m + 1][n];
    Knowledge knowledge = new Knowledge();

    for (int s = 0; s <= m; s++) {
      for (int j = 0; j < n; j++) {
        String name1 = dataVars[j].getName();
        String name2 = name1 + "." + (s + 1);
        laggedVars[s][j] = new DiscreteVariable((DiscreteVariable) dataVars[j]);
        laggedVars[s][j].setName(name2);
        laggedVars[s][j].setCenter(80 * j + 50, 80 * (m - s) + 50);
        knowledge.addToTier(s, laggedVars[s][j].getName());
      }
    }

    // 2. Prepare the data the way you did.
    List<Node> variables = new LinkedList<Node>();

    for (int s = 0; s <= m; s++) {
      for (int i = 0; i < n; i++) {
        int[] rawData = new int[dataSet.getNumRows()];

        for (int j = 0; j < dataSet.getNumRows(); j++) {
          rawData[j] = dataSet.getInt(j, i);
        }

        int size = dataSet.getNumRows();

        int[] laggedRaw = new int[size - m + 1];
        System.arraycopy(rawData, m - s, laggedRaw, 0, size - m + 1);
        variables.add(laggedVars[s][i]);
      }
    }

    DataSet _laggedData = new ColtDataSet(dataSet.getNumRows() - m + 1, variables);

    for (int s = 0; s <= m; s++) {
      for (int i = 0; i < n; i++) {
        int[] rawData = new int[dataSet.getNumRows()];

        for (int j = 0; j < dataSet.getNumRows(); j++) {
          rawData[j] = dataSet.getInt(j, i);
        }

        int size = dataSet.getNumRows();

        int[] laggedRaw = new int[size - m + 1];
        System.arraycopy(rawData, m - s, laggedRaw, 0, size - m + 1);
        int _col = _laggedData.getColumn(laggedVars[s][i]);

        for (int j = 0; j < dataSet.getNumRows(); j++) {
          _laggedData.setInt(j, _col, laggedRaw[j]);
        }
      }
    }

    knowledge.setDefaultToKnowledgeLayout(true);
    _laggedData.setKnowledge(knowledge);
    DataModelList list = new DataModelList();
    list.add(_laggedData);
    getDataEditor().reset(list);
    getDataEditor().selectLastTab();
  }
  /**
   * This method takes an instantiated Bayes net (BayesIm) whose graph include all the variables
   * (observed and latent) and computes estimated counts using the data in the DataSet mixedData.
   * The counts that are estimated correspond to cells in the conditional probability tables of the
   * Bayes net. The outermost loop (indexed by j) is over the set of variables. If the variable has
   * no parents, each case in the dataset is examined and the count for the observed value of the
   * variables is increased by 1.0; if the value of the variable is missing the marginal
   * probabilities its values given the values of the variables that are available for that case are
   * used to increment the corresponding estimated counts. If a variable has parents then there is a
   * loop which steps through all possible sets of values of its parents. This loop is indexed by
   * the variable "row". Each case in the dataset is examined. It the variable and all its parents
   * have values in the case the corresponding estimated counts are incremented by 1.0. If the
   * variable or any of its parents have missing values, the joint marginal is computed for the
   * variable and the set of values of its parents corresponding to "row" and the corresponding
   * estimated counts are incremented by the appropriate probability. The estimated counts are
   * stored in the double[][][] array estimatedCounts. The count (possibly fractional) of the number
   * of times each combination of parent values occurs is stored in the double[][] array
   * estimatedCountsDenom. These two arrays are used to compute the estimated conditional
   * probabilities of the output Bayes net.
   */
  private BayesIm expectation(BayesIm inputBayesIm) {
    // System.out.println("Entered method expectation.");

    int numCases = mixedData.getNumRows();
    // StoredCellEstCounts estCounts = new StoredCellEstCounts(variables);

    int numVariables = allVariables.size();
    RowSummingExactUpdater rseu = new RowSummingExactUpdater(inputBayesIm);

    for (int j = 0; j < numVariables; j++) {
      DiscreteVariable var = (DiscreteVariable) allVariables.get(j);
      String varName = var.getName();
      Node varNode = graph.getNode(varName);
      int varIndex = inputBayesIm.getNodeIndex(varNode);
      int[] parentVarIndices = inputBayesIm.getParents(varIndex);
      // System.out.println("graph = " + graph);

      // for(int col = 0; col < var.getNumSplits(); col++)
      //    System.out.println("Category " + col + " = " + var.getCategory(col));

      // System.out.println("Updating estimated counts for node " + varName);
      // This segment is for variables with no parents:
      if (parentVarIndices.length == 0) {
        // System.out.println("No parents");
        for (int col = 0; col < var.getNumCategories(); col++) {
          estimatedCounts[j][0][col] = 0.0;
        }

        for (int i = 0; i < numCases; i++) {
          // System.out.println("Case " + i);
          // If this case has a value for var
          if (mixedData.getInt(i, j) != -99) {
            estimatedCounts[j][0][mixedData.getInt(i, j)] += 1.0;
            // System.out.println("Adding 1.0 to " + varName +
            //        " row 0 category " + mixedData[j][i]);
          } else {
            // find marginal probability, given obs data in this case, p(v=0)
            Evidence evidenceThisCase = Evidence.tautology(inputBayesIm);
            boolean existsEvidence = false;

            // Define evidence for updating by using the values of the other vars.
            for (int k = 0; k < numVariables; k++) {
              if (k == j) {
                continue;
              }
              Node otherVar = allVariables.get(k);
              if (mixedData.getInt(i, k) == -99) {
                continue;
              }
              existsEvidence = true;
              String otherVarName = otherVar.getName();
              Node otherNode = graph.getNode(otherVarName);
              int otherIndex = inputBayesIm.getNodeIndex(otherNode);

              evidenceThisCase.getProposition().setCategory(otherIndex, mixedData.getInt(i, k));
            }

            if (!existsEvidence) {
              continue; // No other variable contained useful data
            }

            rseu.setEvidence(evidenceThisCase);

            for (int m = 0; m < var.getNumCategories(); m++) {
              estimatedCounts[j][0][m] += rseu.getMarginal(varIndex, m);
              // System.out.println("Adding " + p + " to " + varName +
              //        " row 0 category " + m);

              // find marginal probability, given obs data in this case, p(v=1)
              // estimatedCounts[j][0][1] += 0.5;
            }
          }
        }

        // Print estimated counts:
        // System.out.println("Estimated counts:  ");

        // Print counts for each value of this variable with no parents.
        // for(int m = 0; m < var.getNumSplits(); m++)
        //    System.out.print("    " + m + " " + estimatedCounts[j][0][m]);
        // System.out.println();
      } else { // For variables with parents:
        int numRows = inputBayesIm.getNumRows(varIndex);
        for (int row = 0; row < numRows; row++) {
          int[] parValues = inputBayesIm.getParentValues(varIndex, row);
          estimatedCountsDenom[varIndex][row] = 0.0;
          for (int col = 0; col < var.getNumCategories(); col++) {
            estimatedCounts[varIndex][row][col] = 0.0;
          }

          for (int i = 0; i < numCases; i++) {
            // for a case where the parent values = parValues increment the estCount

            boolean parentMatch = true;

            for (int p = 0; p < parentVarIndices.length; p++) {
              if (parValues[p] != mixedData.getInt(i, parentVarIndices[p])
                  && mixedData.getInt(i, parentVarIndices[p]) != -99) {
                parentMatch = false;
                break;
              }
            }

            if (!parentMatch) {
              continue; // Not a matching case; go to next.
            }

            boolean parentMissing = false;
            for (int parentVarIndice : parentVarIndices) {
              if (mixedData.getInt(i, parentVarIndice) == -99) {
                parentMissing = true;
                break;
              }
            }

            if (mixedData.getInt(i, j) != -99 && !parentMissing) {
              estimatedCounts[j][row][mixedData.getInt(i, j)] += 1.0;
              estimatedCountsDenom[j][row] += 1.0;
              continue; // Next case
            }

            // for a case with missing data (either var or one of its parents)
            // compute the joint marginal
            // distribution for var & this combination of values of its parents
            // and update the estCounts accordingly

            // To compute marginals create the evidence
            boolean existsEvidence = false;

            Evidence evidenceThisCase = Evidence.tautology(inputBayesIm);

            // "evidenceVars" not used.
            //                        List<String> evidenceVars = new LinkedList<String>();
            //                        for (int k = 0; k < numVariables; k++) {
            //                            //if(k == j) continue;
            //                            Variable otherVar = allVariables.get(k);
            //                            if (mixedData.getInt(i, k) == -99) {
            //                                continue;
            //                            }
            //                            existsEvidence = true;
            //                            String otherVarName = otherVar.getName();
            //                            Node otherNode = graph.getNode(otherVarName);
            //                            int otherIndex = inputBayesIm.getNodeIndex(
            //                                    otherNode);
            //                            evidenceThisCase.getProposition().setCategory(
            //                                    otherIndex, mixedData.getInt(i, k));
            //                            evidenceVars.add(otherVarName);
            //                        }

            if (!existsEvidence) {
              continue;
            }

            rseu.setEvidence(evidenceThisCase);

            estimatedCountsDenom[j][row] += rseu.getJointMarginal(parentVarIndices, parValues);

            int[] parPlusChildIndices = new int[parentVarIndices.length + 1];
            int[] parPlusChildValues = new int[parentVarIndices.length + 1];

            parPlusChildIndices[0] = varIndex;
            for (int pc = 1; pc < parPlusChildIndices.length; pc++) {
              parPlusChildIndices[pc] = parentVarIndices[pc - 1];
              parPlusChildValues[pc] = parValues[pc - 1];
            }

            for (int m = 0; m < var.getNumCategories(); m++) {

              parPlusChildValues[0] = m;

              /*
              if(varName.equals("X1") && i == 0 ) {
                  System.out.println("Calling getJointMarginal with parvalues");
                  for(int k = 0; k < parPlusChildIndices.length; k++) {
                      int pIndex = parPlusChildIndices[k];
                      Node pNode = inputBayesIm.getNode(pIndex);
                      String pName = pNode.getName();
                      System.out.println(pName + " " + parPlusChildValues[k]);
                  }
              }
              */

              /*
              if(varName.equals("X1") && i == 0 ) {
                  System.out.println("Evidence = " + evidenceThisCase);
                  //int[] vars = {l1Index, x1Index};
                  Node nodex1 = inputBayesIm.getNode("X1");
                  int x1Index = inputBayesIm.getNodeIndex(nodex1);
                  Node nodel1 = inputBayesIm.getNode("L1");
                  int l1Index = inputBayesIm.getNodeIndex(nodel1);

                  int[] vars = {l1Index, x1Index};
                  int[] vals = {0, 0};
                  double ptest = rseu.getJointMarginal(vars, vals);
                  System.out.println("Joint marginal (X1=0, L1 = 0) = " + p);
              }
              */

              estimatedCounts[j][row][m] +=
                  rseu.getJointMarginal(parPlusChildIndices, parPlusChildValues);

              // System.out.println("Case " + i + " parent values ");
              // for (int pp = 0; pp < parentVarIndices.length; pp++) {
              //    Variable par = (Variable) allVariables.get(parentVarIndices[pp]);
              //    System.out.print("    " + par.getName() + " " + parValues[pp]);
              // }

              // System.out.println();
              // System.out.println("Adding " + p + " to " + varName +
              //        " row " + row + " category " + m);

            }
            // }
          }

          // Print estimated counts:
          // System.out.println("Estimated counts:  ");
          // System.out.println("    Parent values:  ");
          // for (int i = 0; i < parentVarIndices.length; i++) {
          //    Variable par = (Variable) allVariables.get(parentVarIndices[i]);
          //    System.out.print("    " + par.getName() + " " + parValues[i] + "    ");
          // }
          // System.out.println();

          // for(int m = 0; m < var.getNumSplits(); m++)
          //    System.out.print("    " + m + " " + estimatedCounts[j][row][m]);
          // System.out.println();

        }
      } // else
    } // j < numVariables

    BayesIm outputBayesIm = new MlBayesIm(bayesPm);

    for (int j = 0; j < nodes.length; j++) {

      DiscreteVariable var = (DiscreteVariable) allVariables.get(j);
      String varName = var.getName();
      Node varNode = graph.getNode(varName);
      int varIndex = inputBayesIm.getNodeIndex(varNode);
      //            int[] parentVarIndices = inputBayesIm.getParents(varIndex);

      int numRows = inputBayesIm.getNumRows(j);
      // System.out.println("Conditional probabilities for variable " + varName);

      int numCols = inputBayesIm.getNumColumns(j);
      if (numRows == 1) {
        double sum = 0.0;
        for (int m = 0; m < numCols; m++) {
          sum += estimatedCounts[j][0][m];
        }

        for (int m = 0; m < numCols; m++) {
          condProbs[j][0][m] = estimatedCounts[j][0][m] / sum;
          // System.out.print("  " + condProbs[j][0][m]);
          outputBayesIm.setProbability(varIndex, 0, m, condProbs[j][0][m]);
        }
        // System.out.println();
      } else {

        for (int row = 0; row < numRows; row++) {
          //                    int[] parValues = inputBayesIm.getParentValues(varIndex,
          //                            row);
          // int numCols = inputBayesIm.getNumColumns(j);

          // for (int p = 0; p < parentVarIndices.length; p++) {
          //    Variable par = (Variable) allVariables.get(parentVarIndices[p]);
          //    System.out.print("    " + par.getName() + " " + parValues[p]);
          // }

          // double sum = 0.0;
          // for(int m = 0; m < numCols; m++)
          //    sum += estimatedCounts[j][row][m];

          for (int m = 0; m < numCols; m++) {
            if (estimatedCountsDenom[j][row] != 0.0) {
              condProbs[j][row][m] = estimatedCounts[j][row][m] / estimatedCountsDenom[j][row];
            } else {
              condProbs[j][row][m] = Double.NaN;
            }
            // System.out.print("  " + condProbs[j][row][m]);
            outputBayesIm.setProbability(varIndex, row, m, condProbs[j][row][m]);
          }
          // System.out.println();

        }
      }
    }

    return outputBayesIm;
  }
  private void initialize() {
    DirichletBayesIm prior = DirichletBayesIm.symmetricDirichletIm(bayesPmObs, 0.5);
    observedIm = DirichletEstimator.estimate(prior, dataSet);

    //        MLBayesEstimator dirichEst = new MLBayesEstimator();
    //        observedIm = dirichEst.estimate(bayesPmObs, dataSet);

    //        System.out.println("Estimated Bayes IM for Measured Variables:  ");
    //        System.out.println(observedIm);

    // mixedData should be ddsNm with new columns for the latent variables.
    // Each such column should contain missing data for each case.

    int numFullCases = dataSet.getNumRows();
    List<Node> variables = new LinkedList<Node>();

    for (Node node : nodes) {
      if (node.getNodeType() == NodeType.LATENT) {
        int numCategories = bayesPm.getNumCategories(node);
        DiscreteVariable latentVar = new DiscreteVariable(node.getName(), numCategories);
        variables.add(latentVar);
      } else {
        String name = bayesPm.getVariable(node).getName();
        Node variable = dataSet.getVariable(name);
        variables.add(variable);
      }
    }

    DataSet dsMixed = new ColtDataSet(numFullCases, variables);

    for (int j = 0; j < nodes.length; j++) {
      if (nodes[j].getNodeType() == NodeType.LATENT) {
        for (int i = 0; i < numFullCases; i++) {
          dsMixed.setInt(i, j, -99);
        }
      } else {
        String name = bayesPm.getVariable(nodes[j]).getName();
        Node variable = dataSet.getVariable(name);
        int index = dataSet.getColumn(variable);

        for (int i = 0; i < numFullCases; i++) {
          dsMixed.setInt(i, j, dataSet.getInt(i, index));
        }
      }
    }

    //        System.out.println(dsMixed);

    mixedData = dsMixed;
    allVariables = mixedData.getVariables();

    // Find the bayes net which is parameterized using mixedData or set randomly when that's
    // not possible.
    estimateIM(bayesPm, mixedData);

    // The following DEBUG section tests a case specified by P. Spirtes
    // DEBUG TAIL:   For use with embayes_l1x1x2x3V3.dat
    /*
    Node l1Node = graph.getNode("L1");
    //int l1Index = bayesImMixed.getNodeIndex(l1Node);
    int l1index = estimatedIm.getNodeIndex(l1Node);
    Node x1Node = graph.getNode("X1");
    //int x1Index = bayesImMixed.getNodeIndex(x1Node);
    int x1Index = estimatedIm.getNodeIndex(x1Node);
    Node x2Node = graph.getNode("X2");
    //int x2Index = bayesImMixed.getNodeIndex(x2Node);
    int x2Index = estimatedIm.getNodeIndex(x2Node);
    Node x3Node = graph.getNode("X3");
    //int x3Index = bayesImMixed.getNodeIndex(x3Node);
    int x3Index = estimatedIm.getNodeIndex(x3Node);

    estimatedIm.setProbability(l1index, 0, 0, 0.5);
    estimatedIm.setProbability(l1index, 0, 1, 0.5);

    //bayesImMixed.setProbability(x1Index, 0, 0, 0.33333);
    //bayesImMixed.setProbability(x1Index, 0, 1, 0.66667);
    estimatedIm.setProbability(x1Index, 0, 0, 0.6);      //p(x1 = 0 | l1 = 0)
    estimatedIm.setProbability(x1Index, 0, 1, 0.4);      //p(x1 = 1 | l1 = 0)
    estimatedIm.setProbability(x1Index, 1, 0, 0.4);      //p(x1 = 0 | l1 = 1)
    estimatedIm.setProbability(x1Index, 1, 1, 0.6);      //p(x1 = 1 | l1 = 1)

    //bayesImMixed.setProbability(x2Index, 1, 0, 0.66667);
    //bayesImMixed.setProbability(x2Index, 1, 1, 0.33333);
    estimatedIm.setProbability(x2Index, 1, 0, 0.4);      //p(x2 = 0 | l1 = 1)
    estimatedIm.setProbability(x2Index, 1, 1, 0.6);      //p(x2 = 1 | l1 = 1)
    estimatedIm.setProbability(x2Index, 0, 0, 0.6);      //p(x2 = 0 | l1 = 0)
    estimatedIm.setProbability(x2Index, 0, 1, 0.4);      //p(x2 = 1 | l1 = 0)

    //bayesImMixed.setProbability(x3Index, 1, 0, 0.66667);
    //bayesImMixed.setProbability(x3Index, 1, 1, 0.33333);
    estimatedIm.setProbability(x3Index, 1, 0, 0.4);      //p(x3 = 0 | l1 = 1)
    estimatedIm.setProbability(x3Index, 1, 1, 0.6);      //p(x3 = 1 | l1 = 1)
    estimatedIm.setProbability(x3Index, 0, 0, 0.6);      //p(x3 = 0 | l1 = 0)
    estimatedIm.setProbability(x3Index, 0, 1, 0.4);      //p(x3 = 1 | l1 = 0)
    */
    // END of TAIL

    // System.out.println("bayes IM estimated by estimateIM");
    // System.out.println(bayesImMixed);
    // System.out.println(estimatedIm);

    estimatedCounts = new double[nodes.length][][];
    estimatedCountsDenom = new double[nodes.length][];
    condProbs = new double[nodes.length][][];

    for (int i = 0; i < nodes.length; i++) {
      // int numRows = bayesImMixed.getNumRows(i);
      int numRows = estimatedIm.getNumRows(i);
      estimatedCounts[i] = new double[numRows][];
      estimatedCountsDenom[i] = new double[numRows];
      condProbs[i] = new double[numRows][];
      // for(int j = 0; j < bayesImMixed.getNumRows(i); j++) {
      for (int j = 0; j < estimatedIm.getNumRows(i); j++) {
        // int numCols = bayesImMixed.getNumColumns(i);
        int numCols = estimatedIm.getNumColumns(i);
        estimatedCounts[i][j] = new double[numCols];
        condProbs[i][j] = new double[numCols];
      }
    }
  }
  public final DataSet filter(DataSet dataSet) {

    // Why does it have to be discrete? Why can't we simply expand
    // whatever discrete columns are there and leave the continuous
    // ones untouched? jdramsey 7/4/2005
    //        if (!(dataSet.isDiscrete())) {
    //            throw new IllegalArgumentException("Data set must be discrete.");
    //        }

    List<Node> variables = new LinkedList<>();

    // Add all of the variables to the new data set.
    for (int j = 0; j < dataSet.getNumColumns(); j++) {
      Node _var = dataSet.getVariable(j);

      if (!(_var instanceof DiscreteVariable)) {
        variables.add(_var);
        continue;
      }

      DiscreteVariable variable = (DiscreteVariable) _var;

      String oldName = variable.getName();
      List<String> oldCategories = variable.getCategories();
      List<String> newCategories = new LinkedList<>(oldCategories);

      String newCategory = "Missing";
      int _j = 0;

      while (oldCategories.contains(newCategory)) {
        newCategory = "Missing" + (++_j);
      }

      newCategories.add(newCategory);
      String newName = oldName + "+";
      DiscreteVariable newVariable = new DiscreteVariable(newName, newCategories);

      variables.add(newVariable);
    }

    DataSet newDataSet = new ColtDataSet(dataSet.getNumRows(), variables);

    // Copy old values to new data set, replacing missing values with new
    // "MissingValue" categories.
    for (int j = 0; j < dataSet.getNumColumns(); j++) {
      Node _var = dataSet.getVariable(j);

      if (_var instanceof ContinuousVariable) {
        for (int i = 0; i < dataSet.getNumRows(); i++) {
          newDataSet.setDouble(i, j, dataSet.getDouble(i, j));
        }
      } else if (_var instanceof DiscreteVariable) {
        DiscreteVariable variable = (DiscreteVariable) _var;
        int numCategories = variable.getNumCategories();

        for (int i = 0; i < dataSet.getNumRows(); i++) {
          int value = dataSet.getInt(i, j);

          if (value == DiscreteVariable.MISSING_VALUE) {
            newDataSet.setInt(i, j, numCategories);
          } else {
            newDataSet.setInt(i, j, value);
          }
        }
      }
    }

    return newDataSet;
  }