Implement project 4 q1-q5.

p4_tracking/bustersAgents.py

@@ -162,5 +162,27 @@ class GreedyBustersAgent(BustersAgent):
         livingGhostPositionDistributions = \
             [beliefs for i, beliefs in enumerate(self.ghostBeliefs)
              if livingGhosts[i+1]]
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+
+        def getMaxProbPos(distribution):
+            return max([(prob, pos) for pos, prob in distribution.items()])[1]
+
+        closestGhostPosition = None
+        closestGhostDist = float("inf")
+        for distribution in livingGhostPositionDistributions:
+            ghostPosition = getMaxProbPos(distribution)
+            dist = self.distancer.getDistance(pacmanPosition, ghostPosition)
+            if dist < closestGhostDist:
+                closestGhostPosition = ghostPosition
+                closestGhostDist = dist
+
+        if closestGhostPosition is None:
+            return 'Stop'
+
+        distanceActionTuples = [
+            (self.distancer.getDistance(
+                Actions.getSuccessor(pacmanPosition, action),
+                closestGhostPosition),
+             action)
+            for action in legal]
+        
+        return min(distanceActionTuples)[1]

p4_tracking/inference.py

@@ -149,17 +149,18 @@ class ExactInference(InferenceModule):
         pacmanPosition = gameState.getPacmanPosition()
 
         "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
-
-        # Replace this code with a correct observation update
-        # Be sure to handle the "jail" edge case where the ghost is eaten
-        # and noisyDistance is None
         allPossible = util.Counter()
-        for p in self.legalPositions:
-            trueDistance = util.manhattanDistance(p, pacmanPosition)
-            if emissionModel[trueDistance] > 0:
-                allPossible[p] = 1.0
-
+        if noisyDistance is None:
+            # Ghost is in jail.
+            for p in self.legalPositions:
+                allPossible[p] = 0.0
+            jailPosition = self.getJailPosition()
+            allPossible[jailPosition]= 1.0
+        else:
+            for p in self.legalPositions:
+                trueDistance = util.manhattanDistance(p, pacmanPosition)
+                pNoisy = emissionModel[trueDistance]
+                allPossible[p] = pNoisy * self.beliefs[p]
         "*** END YOUR CODE HERE ***"
 
         allPossible.normalize()
@@ -218,8 +219,18 @@ class ExactInference(InferenceModule):
         are used and how they combine to give us a belief distribution over new
         positions after a time update from a particular position.
         """
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        pacmanPosition = gameState.getPacmanPosition()
+        allPossible = util.Counter()
+        for pos in self.legalPositions:
+            allPossible[pos] = 0.0
+
+        for oldPos in self.legalPositions:
+            newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
+            for newPos, prob in newPosDist.items():
+                allPossible[newPos] += self.beliefs[oldPos] * prob
+
+        allPossible.normalize()
+        self.beliefs = allPossible
 
     def getBeliefDistribution(self):
         return self.beliefs
@@ -253,7 +264,9 @@ class ParticleFilter(InferenceModule):
         Storing your particles as a Counter (where there could be an associated
         weight with each position) is incorrect and may produce errors.
         """
-        "*** YOUR CODE HERE ***"
+        numLegalPositions = len(self.legalPositions)
+        self.particles = [self.legalPositions[i % numLegalPositions]
+                          for i in range(self.numParticles)]
 
     def observe(self, observation, gameState):
         """
@@ -283,10 +296,28 @@ class ParticleFilter(InferenceModule):
         distance between a particle and Pacman's position.
         """
         noisyDistance = observation
-        emissionModel = busters.getObservationDistribution(noisyDistance)
+
+        # 1)
+        if noisyDistance is None:
+            self.particles = [self.getJailPosition()
+                              for _ in range(self.numParticles)]
+            return
+
         pacmanPosition = gameState.getPacmanPosition()
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        emissionModel = busters.getObservationDistribution(noisyDistance)
+        distribution, samples = [], []
+        for particle in self.particles:
+            trueDistance = util.manhattanDistance(particle, pacmanPosition)
+            probability = emissionModel[trueDistance]
+            distribution.append(probability)
+            samples.append(particle)
+
+        # 2)
+        if not [p for p in distribution if p > 0.0]:
+            self.initializeUniformly(gameState)
+            return
+
+        self.particles = util.nSample(distribution, samples, self.numParticles)
 
     def elapseTime(self, gameState):
         """
@@ -303,7 +334,12 @@ class ParticleFilter(InferenceModule):
         a belief distribution.
         """
         "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        newParticles = []
+        for oldPos in self.particles:
+            newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
+            newParticle = util.sample(newPosDist)
+            newParticles.append(newParticle)
+        self.particles = newParticles
 
     def getBeliefDistribution(self):
         """
@@ -312,8 +348,12 @@ class ParticleFilter(InferenceModule):
         essentially converts a list of particles into a belief distribution (a
         Counter object)
         """
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        allPossible = util.Counter()
+        for particle in self.particles:
+            allPossible[particle] += 1
+        allPossible.normalize()
+        return allPossible
+
 
 class MarginalInference(InferenceModule):
     """