Added Daniel's Bayes Net code. Converted example code to unit tests. Minor code clean-up.

src/dkohl/bayes/inference/EnumerateAll.java

@@ -0,0 +1,118 @@
+package dkohl.bayes.inference;
+
+import java.util.LinkedList;
+import java.util.List;
+
+import dkohl.bayes.bayesnet.BayesNet;
+import dkohl.bayes.probability.Assignment;
+import dkohl.bayes.probability.ProbabilityAssignment;
+import dkohl.bayes.probability.Variable;
+
+/**
+ * Enumeration Algorithm: Exact inference in Baysian Networks.
+ * 
+ * @author Daniel Kohlsdorf
+ * 
+ */
+public class EnumerateAll {
+
+	/**
+	 * The probability distribution of a variable.
+	 * 
+	 * @param query
+	 *            the variable
+	 * @param net
+	 *            the bayes net defining the independence
+	 * @param assignments
+	 *            a set of assignments in this net.
+	 * @return
+	 */
+	public static LinkedList<ProbabilityAssignment> enumerateAsk(
+			Variable query, BayesNet net, LinkedList<Assignment> assignments) {
+		LinkedList<Variable> variables = net.getVariables();
+		LinkedList<ProbabilityAssignment> result = new LinkedList<ProbabilityAssignment>();
+
+		// Evaluate probability for each possible
+		// value of the variable by enumeration
+		for (String value : query.getDomain()) {
+			LinkedList<Assignment> temp = new LinkedList<Assignment>();
+			temp.addAll(assignments);
+			temp.add(new Assignment(query, value));
+			double prob = enumerateAll(net, variables, temp);
+			result.add(new ProbabilityAssignment(query, value, prob));
+		}
+		return result;
+	}
+
+	/**
+	 * Decides if a variable is hidden or not. A variable is hidden if it is not
+	 * assigned.
+	 * 
+	 * @param variable
+	 *            the variable in question.
+	 * @param assignments
+	 *            all the assignments
+	 * @return true if not assigned
+	 */
+	private static boolean hidden(Variable variable,
+			LinkedList<Assignment> assignments) {
+		for (Assignment assignment : assignments) {
+			if (assignment.getVariable().getName().equals(variable.getName())) {
+				return false;
+			}
+		}
+		return true;
+	}
+
+	/**
+	 * Recursively evaluate probability of an assignment.
+	 * 
+	 * Can be seen as depth first search + backtracking (branching on hidden
+	 * nodes)
+	 * 
+	 * @param net
+	 *            a bayes net defining independence
+	 * @param variables
+	 *            the variables left to evaluate
+	 * @param assignments
+	 *            all assignments
+	 * @return
+	 */
+	public static double enumerateAll(BayesNet net, List<Variable> variables,
+			LinkedList<Assignment> assignments) {
+		// if no variables left to evaluate,
+		// leaf node reached.
+		if (variables.isEmpty()) {
+			return 1;
+		}
+		// evaluate variable, recurse on rest
+		// PROLOG: [Variable|Rest].
+		Variable variable = variables.get(0);
+		List<Variable> rest = variables.subList(1, variables.size());
+		// if current variable is hidden
+		if (hidden(variable, assignments)) {
+			// sum out all possible values for that variable
+			double sumOut = 0;
+			for (String value : variable.getDomain()) {
+				// by temporarily adding each value to the asigned variable set
+				LinkedList<Assignment> temp = new LinkedList<Assignment>();
+				temp.addAll(assignments);
+				temp.add(new Assignment(variable, value));
+
+				// then evaluate this variable
+				double val = net.getNodes().get(variable.getName()).eval(temp)
+						.getProbability();
+				// and all that depend on it
+				val *= enumerateAll(net, rest, temp);
+
+				sumOut += val;
+			}
+			return sumOut;
+		}
+		// if not just evaluate variable and continue.
+		return net.getNodes().get(variable.getName()).eval(assignments)
+				.getProbability()
+				* enumerateAll(net, rest, assignments);
+	}
+
+}