Add project 5 sources.

p5_classification/perceptron_pacman.py

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+# perceptron_pacman.py
+# --------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# Perceptron implementation for apprenticeship learning
+import util
+from perceptron import PerceptronClassifier
+from pacman import GameState
+
+PRINT = True
+
+
+class PerceptronClassifierPacman(PerceptronClassifier):
+    def __init__(self, legalLabels, maxIterations):
+        PerceptronClassifier.__init__(self, legalLabels, maxIterations)
+        self.weights = util.Counter()
+
+    def classify(self, data ):
+        """
+        Data contains a list of (datum, legal moves)
+        
+        Datum is a Counter representing the features of each GameState.
+        legalMoves is a list of legal moves for that GameState.
+        """
+        guesses = []
+        for datum, legalMoves in data:
+            vectors = util.Counter()
+            for l in legalMoves:
+                vectors[l] = self.weights * datum[l] #changed from datum to datum[l]
+            guesses.append(vectors.argMax())
+        return guesses
+
+
+    def train( self, trainingData, trainingLabels, validationData, validationLabels ):
+        self.features = trainingData[0][0]['Stop'].keys() # could be useful later
+        # DO NOT ZERO OUT YOUR WEIGHTS BEFORE STARTING TRAINING, OR
+        # THE AUTOGRADER WILL LIKELY DEDUCT POINTS.
+
+        for iteration in range(self.max_iterations):
+            print "Starting iteration ", iteration, "..."
+            for i in range(len(trainingData)):
+                "*** YOUR CODE HERE ***"
+                util.raiseNotDefined()