checking in changed heursitic code

Author: magicindian

src/aima/search/demos/MapDemo.java

@@ -128,7 +128,7 @@ private static void mapWithRecursiveBestFirstSearch() {
 				new AStarEvaluationFunction()),
 				new String[] { SimplifiedRoadMapOfPartOfRomania.BUCHAREST });
 		ma.setHeuristicFunction(new HeuristicFunction() {
-			public int getHeuristicValue(Object state) {
+			public double getHeuristicValue(Object state) {
 				return SimplifiedRoadMapOfPartOfRomania
 						.getStraightLineDistancesToBucharest().getDistance(
 								(String) state,

src/aima/search/eightpuzzle/ManhattanHeuristicFunction.java

@@ -14,7 +14,7 @@
 
 public class ManhattanHeuristicFunction implements HeuristicFunction {
 
-	public int getHeuristicValue(Object state) {
+	public double getHeuristicValue(Object state) {
 		EightPuzzleBoard board = (EightPuzzleBoard) state;
 		int retVal = 0;
 		for (int i = 1; i < 9; i++) {

src/aima/search/eightpuzzle/MisplacedTilleHeuristicFunction.java

@@ -14,7 +14,7 @@
 
 public class MisplacedTilleHeuristicFunction implements HeuristicFunction {
 
-	public int getHeuristicValue(Object state) {
+	public double getHeuristicValue(Object state) {
 		EightPuzzleBoard board = (EightPuzzleBoard) state;
 		return getNumberOfMisplacedTiles(board);
 

src/aima/search/framework/DefaultHeuristicFunction.java

@@ -11,7 +11,7 @@
 
 public class DefaultHeuristicFunction implements HeuristicFunction {
 
-	public int getHeuristicValue(Object state) {
+	public double getHeuristicValue(Object state) {
 		throw new IllegalStateException(
 				"Should not be depending on the DefaultHeuristicFunction.");
 		// return 1;

src/aima/search/framework/HeuristicFunction.java

@@ -6,6 +6,6 @@
  */
 public interface HeuristicFunction {
 
-	int getHeuristicValue(Object state);
+	double getHeuristicValue(Object state);
 
 }

src/aima/search/informed/HillClimbingSearch.java

@@ -86,11 +86,11 @@ public Object getLastSearchState() {
 	}
 
 	private Node getHighestValuedNodeFrom(List children, Problem p) {
-		int highestValue = Integer.MIN_VALUE;
+		double highestValue = Double.MIN_VALUE;
 		Node nodeWithHighestValue = null;
 		for (int i = 0; i < children.size(); i++) {
 			Node child = (Node) children.get(i);
-			int value = getValue(p, child);
+			double value = getValue(p, child);
 			if (value > highestValue) {
 				highestValue = value;
 				nodeWithHighestValue = child;
@@ -99,12 +99,12 @@ private Node getHighestValuedNodeFrom(List children, Problem p) {
 		return nodeWithHighestValue;
 	}
 
-	private int getValue(Problem p, Node n) {
+	private double getValue(Problem p, Node n) {
 		return -1 * getHeuristic(p, n); // assumption greater heuristic value =>
 		// HIGHER on hill; 0 == goal state;
 	}
 
-	private int getHeuristic(Problem p, Node aNode) {
+	private double getHeuristic(Problem p, Node aNode) {
 		return p.getHeuristicFunction().getHeuristicValue(aNode.getState());
 	}
 }

src/aima/search/informed/SimulatedAnnealingSearch.java

@@ -98,7 +98,7 @@ public List<String> search(Problem p) throws Exception {
 				// next <- a randomly selected successor of current
 				next = Util.selectRandomlyFromList(children);
 				// /\E <- VALUE[next] - VALUE[current]
-				int deltaE = getValue(p, next) - getValue(p, current);
+				double deltaE = getValue(p, next) - getValue(p, current);
 
 				if (shouldAccept(temperature, deltaE)) {
 					current = next;
@@ -111,13 +111,13 @@ public List<String> search(Problem p) throws Exception {
 
 	// if /\E > 0 then current <- next
 	// else current <- next only with probablity e^(/\E/T)
-	private boolean shouldAccept(double temperature, int deltaE) {
+	private boolean shouldAccept(double temperature, double deltaE) {
 		return (deltaE > 0.0)
 				|| (new Random().nextDouble() <= probabilityOfAcceptance(
 						temperature, deltaE));
 	}
 
-	public double probabilityOfAcceptance(double temperature, int deltaE) {
+	public double probabilityOfAcceptance(double temperature, double deltaE) {
 		return Math.exp(deltaE / temperature);
 	}
 
@@ -129,13 +129,13 @@ public Object getLastSearchState() {
 		return lastState;
 	}
 
-	private int getValue(Problem p, Node n) {
+	private double getValue(Problem p, Node n) {
 		return -1 * getHeuristic(p, n); // assumption greater heuristic value =>
 		// HIGHER on hill; 0 == goal state;
 		// SA deals with gardient DESCENT
 	}
 
-	private int getHeuristic(Problem p, Node aNode) {
+	private double getHeuristic(Problem p, Node aNode) {
 		return p.getHeuristicFunction().getHeuristicValue(aNode.getState());
 	}
 }

src/aima/search/nqueens/QueensToBePlacedHeuristic.java

@@ -8,7 +8,7 @@
  */
 
 public class QueensToBePlacedHeuristic implements HeuristicFunction {
-	public int getHeuristicValue(Object state) {
+	public double getHeuristicValue(Object state) {
 		NQueensBoard board = (NQueensBoard) state;
 		return board.size - board.getNumberOfQueensOnBoard();
 	}