public String toString() {
StringBuffer buf = new StringBuffer();
List<Node> variables = getVariableSource().getVariables();
buf.append("\n");
for (int i = 0; i < getNumVariables(); i++) {
DiscreteVariable variable = (DiscreteVariable) variables.get(i);
String name = variable.getName();
buf.append(name);
for (int j = name.length(); j < 5; j++) {
buf.append(" ");
}
buf.append("\t");
}
for (int i = 0; i < getMaxNumCategories(); i++) {
buf.append("\n");
for (int j = 0; j < getNumVariables(); j++) {
if (i < getNumCategories(j)) {
boolean allowed = isAllowed(j, i);
buf.append(allowed ? "true" : "* ").append("\t");
} else {
buf.append(" \t");
}
}
}
return buf.toString();
}
private String reportIfDiscrete(Graph dag, DataSet dataSet) {
List vars = dataSet.getVariables();
Map<String, DiscreteVariable> nodesToVars = new HashMap<String, DiscreteVariable>();
for (int i = 0; i < dataSet.getNumColumns(); i++) {
DiscreteVariable var = (DiscreteVariable) vars.get(i);
String name = var.getName();
Node node = new GraphNode(name);
nodesToVars.put(node.getName(), var);
}
BayesPm bayesPm = new BayesPm(new Dag(dag));
List<Node> nodes = bayesPm.getDag().getNodes();
for (Node node : nodes) {
Node var = nodesToVars.get(node.getName());
if (var instanceof DiscreteVariable) {
DiscreteVariable var2 = nodesToVars.get(node.getName());
int numCategories = var2.getNumCategories();
List<String> categories = new ArrayList<String>();
for (int j = 0; j < numCategories; j++) {
categories.add(var2.getCategory(j));
}
bayesPm.setCategories(node, categories);
}
}
BayesProperties properties = new BayesProperties(dataSet, dag);
properties.setGraph(dag);
NumberFormat nf = NumberFormat.getInstance();
nf.setMaximumFractionDigits(4);
StringBuilder buf = new StringBuilder();
buf.append("\nP-value = ").append(properties.getLikelihoodRatioP());
buf.append("\nDf = ").append(properties.getPValueDf());
buf.append("\nChi square = ").append(nf.format(properties.getPValueChisq()));
buf.append("\nBIC score = ").append(nf.format(properties.getBic()));
buf.append("\n\nH0: Completely disconnected graph.");
return buf.toString();
}
/**
* 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;
}
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;
}