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);
}
/*
* @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;
}
/**
* @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;
}
/** @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;
}
/**
* @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;
}