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

src/dkohl/bayes/estimation/MaximumLikelihoodEstimation.java

@@ -0,0 +1,119 @@
+package dkohl.bayes.estimation;
+
+import java.util.LinkedList;
+
+import com.google.common.base.Preconditions;
+
+import dkohl.bayes.bayesnet.BayesNet;
+import dkohl.bayes.probability.Assignment;
+import dkohl.bayes.probability.Probability;
+import dkohl.bayes.probability.Variable;
+import dkohl.bayes.probability.distribution.ContinousDistribution;
+import dkohl.bayes.probability.distribution.ProbabilityDistribution;
+import dkohl.bayes.probability.distribution.ProbabilityTable;
+import dkohl.bayes.probability.distribution.ProbabilityTree;
+import dkohl.bayes.statistic.DataSet;
+
+/**
+ * Works for fully observable bayes nets
+ * 
+ * @author Daniel Kohlsdorf
+ */
+public class MaximumLikelihoodEstimation {
+
+	/**
+	 * Enumerates all possible assignments for a set of variables using depth
+	 * first + back tracking. Estimates the probability for each assignment
+	 * given data and inserts the values in a probability function.
+	 * 
+	 * @param target
+	 *            The target variable
+	 * @param assignments
+	 *            A list built by traversing the tree. Adding an assignment on
+	 *            each layer
+	 * @param variables
+	 *            All variables to assign
+	 * @param current
+	 *            The current variable
+	 * @param data
+	 *            the data for estimation
+	 * @param table
+	 *            the probability table
+	 */
+	private static void enumerate(Assignment target,
+			LinkedList<Assignment> assignments, LinkedList<Variable> variables,
+			int current, DataSet data, ProbabilityDistribution dist) {
+
+		if (assignments.size() == variables.size()) {
+			if (dist instanceof ProbabilityTable) {
+				double likelihood = data.prob(target, assignments)
+						.getProbability();
+				assignments.add(target);
+				ProbabilityTable table = (ProbabilityTable) dist;
+				table.setProbabilityForAssignment(assignments, new Probability(
+						likelihood));
+			}
+			if (dist instanceof ProbabilityTree) {
+				double likelihood = data.prob(target, assignments)
+						.getProbability();
+				assignments.add(target);
+
+				ProbabilityTree tree = (ProbabilityTree) dist;
+				tree.setProbabilityForAssignment(assignments, new Probability(
+						likelihood));
+			}
+			if (dist instanceof ContinousDistribution) {
+				ContinousDistribution pdf = (ContinousDistribution) dist;
+				for (LinkedList<Assignment> assignment : data
+						.getAssignmentMatchesForQuery(assignments)) {
+					pdf.pushAssignment(assignment);
+				}
+			}
+			return;
+		}
+		Variable variable = variables.get(assignments.size());
+		for (String value : variable.getDomain()) {
+			LinkedList<Assignment> new_assignments = new LinkedList<Assignment>(
+					assignments);
+			new_assignments.add(new Assignment(variable, value));
+			enumerate(target, new_assignments, variables, current + 1, data,
+					dist);
+		}
+	}
+
+	public static void estimate(DataSet data, BayesNet net, String targetName) {
+		Variable target = null;
+		// Search parent variables
+		LinkedList<String> parentNames = net.getParents(targetName);
+		LinkedList<Variable> allVariables = net.getVariables();
+		LinkedList<Variable> parentVariables = new LinkedList<Variable>();
+		for (Variable variable : allVariables) {
+			if (parentNames.contains(variable.getName())) {
+				parentVariables.add(variable);
+			}
+			if (variable.getName().equals(targetName)) {
+				target = variable;
+			}
+		}
+		Preconditions.checkState(target != null, "MLE: variable " + targetName
+				+ "Not in net");
+
+		/**
+		 * For all assignments of this variable list all assignments of it's
+		 * parents and estimate the probability for each assignment using data.
+		 */
+		ProbabilityDistribution dist = net.getNodes().get(targetName);
+		if (dist instanceof ContinousDistribution) {
+			enumerate(null, new LinkedList<Assignment>(), parentVariables, 0,
+					data, dist);
+		} else {
+			for (String value : target.getDomain()) {
+				enumerate(new Assignment(target, value),
+						new LinkedList<Assignment>(), parentVariables, 0, data,
+						dist);
+			}
+		}
+		net.updateDistribution(target, dist);
+	}
+
+}