Add project 5 sources.

p5_classification/VERSION

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+v1.001

p5_classification/answers.py

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+# answers.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).
+
+
+def q2():
+    "*** YOUR CODE HERE ***"

p5_classification/autograder.py

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+# autograder.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).
+
+
+# imports from python standard library
+import grading
+import imp
+import optparse
+import os
+import re
+import sys
+import projectParams
+import random
+random.seed(0)
+try: 
+    from pacman import GameState
+except:
+    pass
+
+# register arguments and set default values
+def readCommand(argv):
+    parser = optparse.OptionParser(description = 'Run public tests on student code')
+    parser.set_defaults(generateSolutions=False, edxOutput=False, muteOutput=False, printTestCase=False, noGraphics=False)
+    parser.add_option('--test-directory',
+                      dest = 'testRoot',
+                      default = 'test_cases',
+                      help = 'Root test directory which contains subdirectories corresponding to each question')
+    parser.add_option('--student-code',
+                      dest = 'studentCode',
+                      default = projectParams.STUDENT_CODE_DEFAULT,
+                      help = 'comma separated list of student code files')
+    parser.add_option('--code-directory',
+                    dest = 'codeRoot',
+                    default = "",
+                    help = 'Root directory containing the student and testClass code')
+    parser.add_option('--test-case-code',
+                      dest = 'testCaseCode',
+                      default = projectParams.PROJECT_TEST_CLASSES,
+                      help = 'class containing testClass classes for this project')
+    parser.add_option('--generate-solutions',
+                      dest = 'generateSolutions',
+                      action = 'store_true',
+                      help = 'Write solutions generated to .solution file')
+    parser.add_option('--edx-output',
+                    dest = 'edxOutput',
+                    action = 'store_true',
+                    help = 'Generate edX output files')
+    parser.add_option('--mute',
+                    dest = 'muteOutput',
+                    action = 'store_true',
+                    help = 'Mute output from executing tests')
+    parser.add_option('--print-tests', '-p',
+                    dest = 'printTestCase',
+                    action = 'store_true',
+                    help = 'Print each test case before running them.')
+    parser.add_option('--test', '-t',
+                      dest = 'runTest',
+                      default = None,
+                      help = 'Run one particular test.  Relative to test root.')
+    parser.add_option('--question', '-q',
+                    dest = 'gradeQuestion',
+                    default = None,
+                    help = 'Grade one particular question.')
+    parser.add_option('--no-graphics',
+                    dest = 'noGraphics',
+                    action = 'store_true',
+                    help = 'No graphics display for pacman games.')
+    (options, args) = parser.parse_args(argv)
+    return options
+
+
+# confirm we should author solution files
+def confirmGenerate():
+    print 'WARNING: this action will overwrite any solution files.'
+    print 'Are you sure you want to proceed? (yes/no)'
+    while True:
+        ans = sys.stdin.readline().strip()
+        if ans == 'yes':
+            break
+        elif ans == 'no':
+            sys.exit(0)
+        else:
+            print 'please answer either "yes" or "no"'
+
+
+# TODO: Fix this so that it tracebacks work correctly
+# Looking at source of the traceback module, presuming it works
+# the same as the intepreters, it uses co_filename.  This is,
+# however, a readonly attribute.
+def setModuleName(module, filename):
+    functionType = type(confirmGenerate)
+    classType = type(optparse.Option)
+
+    for i in dir(module):
+        o = getattr(module, i)
+        if hasattr(o, '__file__'): continue
+
+        if type(o) == functionType:
+            setattr(o, '__file__', filename)
+        elif type(o) == classType:
+            setattr(o, '__file__', filename)
+            # TODO: assign member __file__'s?
+        #print i, type(o)
+
+
+#from cStringIO import StringIO
+
+def loadModuleString(moduleSource):
+    # Below broken, imp doesn't believe its being passed a file:
+    #    ValueError: load_module arg#2 should be a file or None
+    #
+    #f = StringIO(moduleCodeDict[k])
+    #tmp = imp.load_module(k, f, k, (".py", "r", imp.PY_SOURCE))
+    tmp = imp.new_module(k)
+    exec moduleCodeDict[k] in tmp.__dict__
+    setModuleName(tmp, k)
+    return tmp
+
+import py_compile
+
+def loadModuleFile(moduleName, filePath):
+    with open(filePath, 'r') as f:
+        return imp.load_module(moduleName, f, "%s.py" % moduleName, (".py", "r", imp.PY_SOURCE))
+
+
+def readFile(path, root=""):
+    "Read file from disk at specified path and return as string"
+    with open(os.path.join(root, path), 'r') as handle:
+        return handle.read()
+
+
+#######################################################################
+# Error Hint Map
+#######################################################################
+
+# TODO: use these
+ERROR_HINT_MAP = {
+  'q1': {
+    "<type 'exceptions.IndexError'>": """
+      We noticed that your project threw an IndexError on q1.
+      While many things may cause this, it may have been from
+      assuming a certain number of successors from a state space
+      or assuming a certain number of actions available from a given
+      state. Try making your code more general (no hardcoded indices)
+      and submit again!
+    """
+  },
+  'q3': {
+      "<type 'exceptions.AttributeError'>": """
+        We noticed that your project threw an AttributeError on q3.
+        While many things may cause this, it may have been from assuming
+        a certain size or structure to the state space. For example, if you have
+        a line of code assuming that the state is (x, y) and we run your code
+        on a state space with (x, y, z), this error could be thrown. Try
+        making your code more general and submit again!
+
+    """
+  }
+}
+
+import pprint
+
+def splitStrings(d):
+    d2 = dict(d)
+    for k in d:
+        if k[0:2] == "__":
+            del d2[k]
+            continue
+        if d2[k].find("\n") >= 0:
+            d2[k] = d2[k].split("\n")
+    return d2
+
+
+def printTest(testDict, solutionDict):
+    pp = pprint.PrettyPrinter(indent=4)
+    print "Test case:"
+    for line in testDict["__raw_lines__"]:
+        print "   |", line
+    print "Solution:"
+    for line in solutionDict["__raw_lines__"]:
+        print "   |", line
+
+
+def runTest(testName, moduleDict, printTestCase=False, display=None):
+    import testParser
+    import testClasses
+    for module in moduleDict:
+        setattr(sys.modules[__name__], module, moduleDict[module])
+
+    testDict = testParser.TestParser(testName + ".test").parse()
+    solutionDict = testParser.TestParser(testName + ".solution").parse()
+    test_out_file = os.path.join('%s.test_output' % testName)
+    testDict['test_out_file'] = test_out_file
+    testClass = getattr(projectTestClasses, testDict['class'])
+
+    questionClass = getattr(testClasses, 'Question')
+    question = questionClass({'max_points': 0}, display)
+    testCase = testClass(question, testDict)
+
+    if printTestCase:
+        printTest(testDict, solutionDict)
+
+    # This is a fragile hack to create a stub grades object
+    grades = grading.Grades(projectParams.PROJECT_NAME, [(None,0)])
+    testCase.execute(grades, moduleDict, solutionDict)
+
+
+# returns all the tests you need to run in order to run question
+def getDepends(testParser, testRoot, question):
+    allDeps = [question]
+    questionDict = testParser.TestParser(os.path.join(testRoot, question, 'CONFIG')).parse()
+    if 'depends' in questionDict:
+        depends = questionDict['depends'].split()
+        for d in depends:
+            # run dependencies first
+            allDeps = getDepends(testParser, testRoot, d) + allDeps
+    return allDeps
+
+# get list of questions to grade
+def getTestSubdirs(testParser, testRoot, questionToGrade):
+    problemDict = testParser.TestParser(os.path.join(testRoot, 'CONFIG')).parse()
+    if questionToGrade != None:
+        questions = getDepends(testParser, testRoot, questionToGrade)
+        if len(questions) > 1:
+            print 'Note: due to dependencies, the following tests will be run: %s' % ' '.join(questions)
+        return questions
+    if 'order' in problemDict:
+        return problemDict['order'].split()
+    return sorted(os.listdir(testRoot))
+
+
+# evaluate student code
+def evaluate(generateSolutions, testRoot, moduleDict, exceptionMap=ERROR_HINT_MAP, edxOutput=False, muteOutput=False,
+            printTestCase=False, questionToGrade=None, display=None):
+    # imports of testbench code.  note that the testClasses import must follow
+    # the import of student code due to dependencies
+    import testParser
+    import testClasses
+    for module in moduleDict:
+        setattr(sys.modules[__name__], module, moduleDict[module])
+
+    questions = []
+    questionDicts = {}
+    test_subdirs = getTestSubdirs(testParser, testRoot, questionToGrade)
+    for q in test_subdirs:
+        subdir_path = os.path.join(testRoot, q)
+        if not os.path.isdir(subdir_path) or q[0] == '.':
+            continue
+
+        # create a question object
+        questionDict = testParser.TestParser(os.path.join(subdir_path, 'CONFIG')).parse()
+        questionClass = getattr(testClasses, questionDict['class'])
+        question = questionClass(questionDict, display)
+        questionDicts[q] = questionDict
+
+        # load test cases into question
+        tests = filter(lambda t: re.match('[^#~.].*\.test\Z', t), os.listdir(subdir_path))
+        tests = map(lambda t: re.match('(.*)\.test\Z', t).group(1), tests)
+        for t in sorted(tests):
+            test_file = os.path.join(subdir_path, '%s.test' % t)
+            solution_file = os.path.join(subdir_path, '%s.solution' % t)
+            test_out_file = os.path.join(subdir_path, '%s.test_output' % t)
+            testDict = testParser.TestParser(test_file).parse()
+            if testDict.get("disabled", "false").lower() == "true":
+                continue
+            testDict['test_out_file'] = test_out_file
+            testClass = getattr(projectTestClasses, testDict['class'])
+            testCase = testClass(question, testDict)
+            def makefun(testCase, solution_file):
+                if generateSolutions:
+                    # write solution file to disk
+                    return lambda grades: testCase.writeSolution(moduleDict, solution_file)
+                else:
+                    # read in solution dictionary and pass as an argument
+                    testDict = testParser.TestParser(test_file).parse()
+                    solutionDict = testParser.TestParser(solution_file).parse()
+                    if printTestCase:
+                        return lambda grades: printTest(testDict, solutionDict) or testCase.execute(grades, moduleDict, solutionDict)
+                    else:
+                        return lambda grades: testCase.execute(grades, moduleDict, solutionDict)
+            question.addTestCase(testCase, makefun(testCase, solution_file))
+
+        # Note extra function is necessary for scoping reasons
+        def makefun(question):
+            return lambda grades: question.execute(grades)
+        setattr(sys.modules[__name__], q, makefun(question))
+        questions.append((q, question.getMaxPoints()))
+
+    grades = grading.Grades(projectParams.PROJECT_NAME, questions, edxOutput=edxOutput, muteOutput=muteOutput)
+    if questionToGrade == None:
+        for q in questionDicts:
+            for prereq in questionDicts[q].get('depends', '').split():
+                grades.addPrereq(q, prereq)
+
+    grades.grade(sys.modules[__name__], bonusPic = projectParams.BONUS_PIC)
+    return grades.points
+
+
+
+def getDisplay(graphicsByDefault, options=None):
+    graphics = graphicsByDefault
+    if options is not None and options.noGraphics:
+        graphics = False
+    if graphics:
+        try:
+            import graphicsDisplay
+            return graphicsDisplay.PacmanGraphics(1, frameTime=.05)
+        except ImportError:
+            pass
+    import textDisplay
+    return textDisplay.NullGraphics()
+
+
+
+
+if __name__ == '__main__':
+    options = readCommand(sys.argv)
+    if options.generateSolutions:
+        confirmGenerate()
+    codePaths = options.studentCode.split(',')
+    # moduleCodeDict = {}
+    # for cp in codePaths:
+    #     moduleName = re.match('.*?([^/]*)\.py', cp).group(1)
+    #     moduleCodeDict[moduleName] = readFile(cp, root=options.codeRoot)
+    # moduleCodeDict['projectTestClasses'] = readFile(options.testCaseCode, root=options.codeRoot)
+    # moduleDict = loadModuleDict(moduleCodeDict)
+
+    moduleDict = {}
+    for cp in codePaths:
+        moduleName = re.match('.*?([^/]*)\.py', cp).group(1)
+        moduleDict[moduleName] = loadModuleFile(moduleName, os.path.join(options.codeRoot, cp))
+    moduleName = re.match('.*?([^/]*)\.py', options.testCaseCode).group(1)
+    moduleDict['projectTestClasses'] = loadModuleFile(moduleName, os.path.join(options.codeRoot, options.testCaseCode))
+
+
+    if options.runTest != None:
+        runTest(options.runTest, moduleDict, printTestCase=options.printTestCase, display=getDisplay(True, options))
+    else:
+        evaluate(options.generateSolutions, options.testRoot, moduleDict,
+            edxOutput=options.edxOutput, muteOutput=options.muteOutput, printTestCase=options.printTestCase,
+            questionToGrade=options.gradeQuestion, display=getDisplay(options.gradeQuestion!=None, options))

p5_classification/classificationAgents.py

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+# classificationAgents.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).
+
+
+# pacmanAgents.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://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html
+# 
+# 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).
+
+
+from pacman import Directions
+from game import Agent
+
+import random
+import game
+import util
+
+class DummyOptions:
+    def __init__(self):
+        self.data = "pacman"
+        self.training = 25000
+        self.test = 100
+        self.odds = False
+        self.weights = False
+
+
+import perceptron_pacman
+
+class ClassifierAgent(Agent):
+    def __init__(self, trainingData=None, validationData=None, classifierType="perceptron", agentToClone=None, numTraining=3):
+        from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
+        legalLabels = ['Stop', 'West', 'East', 'North', 'South']
+        if(classifierType == "perceptron"):
+            classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,numTraining)
+        self.classifier = classifier
+        self.featureFunction = enhancedFeatureExtractorPacman
+        args = {'featureFunction': self.featureFunction,
+                'classifier':self.classifier,
+                'printImage':None,
+                'trainingData':trainingData,
+                'validationData':validationData,
+                'agentToClone': agentToClone,
+        }
+        options = DummyOptions()
+        options.classifier = classifierType
+        runClassifier(args, options)
+    def getAction(self, state):
+        from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
+        features = self.featureFunction(state)
+        
+        action =  self.classifier.classify([features])[0]
+
+        return action
+
+def scoreEvaluation(state):
+    return state.getScore()

p5_classification/classificationMethod.py

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+# classificationMethod.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).
+
+
+# This file contains the abstract class ClassificationMethod
+
+class ClassificationMethod:
+    """
+    ClassificationMethod is the abstract superclass of
+     - MostFrequentClassifier
+     - NaiveBayesClassifier
+     - PerceptronClassifier
+     - MiraClassifier
+
+    As such, you need not add any code to this file.  You can write
+    all of your implementation code in the files for the individual
+    classification methods listed above.
+    """
+    def __init__(self, legalLabels):
+        """
+        For digits dataset, the set of legal labels will be 0,1,..,9
+        For faces dataset, the set of legal labels will be 0 (non-face) or 1 (face)
+        """
+        self.legalLabels = legalLabels
+
+
+    def train(self, trainingData, trainingLabels, validationData, validationLabels):
+        """
+        This is the supervised training function for the classifier.  Two sets of
+        labeled data are passed in: a large training set and a small validation set.
+
+        Many types of classifiers have a common training structure in practice: using
+        training data for the main supervised training loop but tuning certain parameters
+        with a small held-out validation set.
+
+        For some classifiers (naive Bayes, MIRA), you will need to return the parameters'
+        values after training and tuning step.
+
+        To make the classifier generic to multiple problems, the data should be represented
+        as lists of Counters containing feature descriptions and their counts.
+        """
+        abstract
+
+    def classify(self, data):
+        """
+        This function returns a list of labels, each drawn from the set of legal labels
+        provided to the classifier upon construction.
+
+        To make the classifier generic to multiple problems, the data should be represented
+        as lists of Counters containing feature descriptions and their counts.
+        """
+        abstract

p5_classification/classificationTestClasses.py

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+# classificationTestClasses.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).
+
+
+from hashlib import sha1
+import testClasses
+# import json
+
+from collections import defaultdict
+from pprint import PrettyPrinter
+pp = PrettyPrinter()
+
+# from game import Agent
+from pacman import GameState
+# from ghostAgents import RandomGhost, DirectionalGhost
+import random, math, traceback, sys, os
+# import layout, pacman
+# import autograder
+# import grading
+
+import dataClassifier, samples
+
+VERBOSE = False
+
+
+
+# Data sets
+# ---------
+
+EVAL_MULTIPLE_CHOICE=True
+
+numTraining = 100
+TEST_SET_SIZE = 100
+DIGIT_DATUM_WIDTH=28
+DIGIT_DATUM_HEIGHT=28
+
+def readDigitData(trainingSize=100, testSize=100):
+    rootdata = 'digitdata/'
+    # loading digits data
+    rawTrainingData = samples.loadDataFile(rootdata + 'trainingimages', trainingSize,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+    trainingLabels = samples.loadLabelsFile(rootdata + "traininglabels", trainingSize)
+    rawValidationData = samples.loadDataFile(rootdata + "validationimages", TEST_SET_SIZE,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+    validationLabels = samples.loadLabelsFile(rootdata + "validationlabels", TEST_SET_SIZE)
+    rawTestData = samples.loadDataFile("digitdata/testimages", testSize,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+    testLabels = samples.loadLabelsFile("digitdata/testlabels", testSize)
+    try:
+        print "Extracting features..."
+        featureFunction = dataClassifier.basicFeatureExtractorDigit
+        trainingData = map(featureFunction, rawTrainingData)
+        validationData = map(featureFunction, rawValidationData)
+        testData = map(featureFunction, rawTestData)
+    except:
+        display("An exception was raised while extracting basic features: \n %s" % getExceptionTraceBack())
+    return (trainingData, trainingLabels, validationData, validationLabels, rawTrainingData, rawValidationData, testData, testLabels, rawTestData)
+
+def readSuicideData(trainingSize=100, testSize=100):
+    rootdata = 'pacmandata'
+    rawTrainingData, trainingLabels = samples.loadPacmanData(rootdata + '/suicide_training.pkl', trainingSize)
+    rawValidationData, validationLabels = samples.loadPacmanData(rootdata + '/suicide_validation.pkl', testSize)
+    rawTestData, testLabels = samples.loadPacmanData(rootdata + '/suicide_test.pkl', testSize)
+    trainingData = []
+    validationData = []
+    testData = []
+    return (trainingData, trainingLabels, validationData, validationLabels, rawTrainingData, rawValidationData, testData, testLabels, rawTestData)
+
+def readContestData(trainingSize=100, testSize=100):
+    rootdata = 'pacmandata'
+    rawTrainingData, trainingLabels = samples.loadPacmanData(rootdata + '/contest_training.pkl', trainingSize)
+    rawValidationData, validationLabels = samples.loadPacmanData(rootdata + '/contest_validation.pkl', testSize)
+    rawTestData, testLabels = samples.loadPacmanData(rootdata + '/contest_test.pkl', testSize)
+    trainingData = []
+    validationData = []
+    testData = []
+    return (trainingData, trainingLabels, validationData, validationLabels, rawTrainingData, rawValidationData, testData, testLabels, rawTestData)
+
+
+smallDigitData = readDigitData(20)
+bigDigitData = readDigitData(1000)
+
+suicideData = readSuicideData(1000)
+contestData = readContestData(1000)
+
+def tinyDataSet():
+    def count(m,b,h):
+        c = util.Counter();
+        c['m'] = m;
+        c['b'] = b;
+        c['h'] = h;
+        return c;
+
+    training = [count(0,0,0), count(1,0,0), count(1,1,0), count(0,1,1), count(1,0,1), count(1,1,1)]
+    trainingLabels = [1,        1,            1           , 1           ,      -1     ,      -1]
+
+    validation = [count(1,0,1)]
+    validationLabels = [ 1]
+
+    test = [count(1,0,1)]
+    testLabels = [-1]
+
+    return (training,trainingLabels,validation,validationLabels,test,testLabels);
+
+
+def tinyDataSetPeceptronAndMira():
+    def count(m,b,h):
+        c = util.Counter();
+        c['m'] = m;
+        c['b'] = b;
+        c['h'] = h;
+        return c;
+
+    training = [count(1,0,0), count(1,1,0), count(0,1,1), count(1,0,1), count(1,1,1)]
+    trainingLabels = [1,            1,            1,          -1      ,      -1]
+
+    validation = [count(1,0,1)]
+    validationLabels = [ 1]
+
+    test = [count(1,0,1)]
+    testLabels = [-1]
+
+    return (training,trainingLabels,validation,validationLabels,test,testLabels);
+
+
+DATASETS = {
+    "smallDigitData": lambda: smallDigitData,
+    "bigDigitData": lambda: bigDigitData,
+    "tinyDataSet": tinyDataSet,
+    "tinyDataSetPeceptronAndMira": tinyDataSetPeceptronAndMira,
+    "suicideData": lambda: suicideData,
+    "contestData": lambda: contestData
+}
+
+DATASETS_LEGAL_LABELS = {
+    "smallDigitData": range(10),
+    "bigDigitData": range(10),
+    "tinyDataSet": [-1,1],
+    "tinyDataSetPeceptronAndMira": [-1,1],
+    "suicideData": ["EAST", 'WEST', 'NORTH', 'SOUTH', 'STOP'],
+    "contestData": ["EAST", 'WEST', 'NORTH', 'SOUTH', 'STOP']
+}
+
+
+# Test classes
+# ------------
+
+def getAccuracy(data, classifier, featureFunction=dataClassifier.basicFeatureExtractorDigit):
+    trainingData, trainingLabels, validationData, validationLabels, rawTrainingData, rawValidationData, testData, testLabels, rawTestData = data
+    if featureFunction != dataClassifier.basicFeatureExtractorDigit:
+        trainingData = map(featureFunction, rawTrainingData)
+        validationData = map(featureFunction, rawValidationData)
+        testData = map(featureFunction, rawTestData)
+    classifier.train(trainingData, trainingLabels, validationData, validationLabels)
+    guesses = classifier.classify(testData)
+    correct = [guesses[i] == testLabels[i] for i in range(len(testLabels))].count(True)
+    acc = 100.0 * correct / len(testLabels)
+    serialized_guesses = ", ".join([str(guesses[i]) for i in range(len(testLabels))])
+    print str(correct), ("correct out of " + str(len(testLabels)) + " (%.1f%%).") % (acc)
+    return acc, serialized_guesses
+
+
+class GradeClassifierTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(GradeClassifierTest, self).__init__(question, testDict)
+
+        self.classifierModule = testDict['classifierModule']
+        self.classifierClass = testDict['classifierClass']
+        self.datasetName = testDict['datasetName']
+
+        self.accuracyScale = int(testDict['accuracyScale'])
+        self.accuracyThresholds = [int(s) for s in testDict.get('accuracyThresholds','').split()]
+        self.exactOutput = testDict['exactOutput'].lower() == "true"
+
+        self.automaticTuning = testDict['automaticTuning'].lower() == "true" if 'automaticTuning' in testDict else None
+        self.max_iterations = int(testDict['max_iterations']) if 'max_iterations' in testDict else None
+        self.featureFunction = testDict['featureFunction'] if 'featureFunction' in testDict else 'basicFeatureExtractorDigit'
+
+        self.maxPoints = len(self.accuracyThresholds) * self.accuracyScale
+
+
+    def grade_classifier(self, moduleDict):
+        featureFunction = getattr(dataClassifier, self.featureFunction)
+        data = DATASETS[self.datasetName]()
+        legalLabels = DATASETS_LEGAL_LABELS[self.datasetName]
+
+        classifierClass = getattr(moduleDict[self.classifierModule], self.classifierClass)
+
+        if self.max_iterations != None:
+            classifier = classifierClass(legalLabels, self.max_iterations)
+        else:
+            classifier = classifierClass(legalLabels)
+
+        if self.automaticTuning != None:
+            classifier.automaticTuning = self.automaticTuning
+
+        return getAccuracy(data, classifier, featureFunction=featureFunction)
+
+
+    def execute(self, grades, moduleDict, solutionDict):
+        accuracy, guesses = self.grade_classifier(moduleDict)
+
+        # Either grade them on the accuracy of their classifer,
+        # or their exact
+        if self.exactOutput:
+            gold_guesses = solutionDict['guesses']
+            if guesses == gold_guesses:
+                totalPoints = self.maxPoints
+            else:
+                self.addMessage("Incorrect classification after training:")
+                self.addMessage("  student classifications: " + guesses)
+                self.addMessage("  correct classifications: " + gold_guesses)
+                totalPoints = 0
+        else:
+            # Grade accuracy
+            totalPoints = 0
+            for threshold in self.accuracyThresholds:
+                if accuracy >= threshold:
+                    totalPoints += self.accuracyScale
+
+            # Print grading schedule
+            self.addMessage("%s correct (%s of %s points)" % (accuracy, totalPoints, self.maxPoints))
+            self.addMessage("    Grading scheme:")
+            self.addMessage("     < %s:  0 points" % (self.accuracyThresholds[0],))
+            for idx, threshold in enumerate(self.accuracyThresholds):
+                self.addMessage("    >= %s:  %s points" % (threshold, (idx+1)*self.accuracyScale))
+
+        return self.testPartial(grades, totalPoints, self.maxPoints)
+
+    def writeSolution(self, moduleDict, filePath):
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+
+        if self.exactOutput:
+            _, guesses = self.grade_classifier(moduleDict)
+            handle.write('guesses: "%s"' % (guesses,))
+
+        handle.close()
+        return True
+
+
+
+
+class MultipleChoiceTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(MultipleChoiceTest, self).__init__(question, testDict)
+        self.ans = testDict['result']
+        self.question = testDict['question']
+
+    def execute(self, grades, moduleDict, solutionDict):
+        studentSolution = str(getattr(moduleDict['answers'], self.question)())
+        encryptedSolution = sha1(studentSolution.strip().lower()).hexdigest()
+        if encryptedSolution == self.ans:
+            return self.testPass(grades)
+        else:
+            self.addMessage("Solution is not correct.")
+            self.addMessage("Student solution: %s" % studentSolution)
+            return self.testFail(grades)
+
+    def writeSolution(self, moduleDict, filePath):
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+        handle.write('# File intentionally blank.\n')
+        handle.close()
+        return True
+
+

p5_classification/dataClassifier.py

@@ -0,0 +1,431 @@
+# dataClassifier.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).
+
+
+# This file contains feature extraction methods and harness
+# code for data classification
+
+import mostFrequent
+import naiveBayes
+import perceptron
+import perceptron_pacman
+import mira
+import samples
+import sys
+import util
+from pacman import GameState
+
+TEST_SET_SIZE = 100
+DIGIT_DATUM_WIDTH=28
+DIGIT_DATUM_HEIGHT=28
+FACE_DATUM_WIDTH=60
+FACE_DATUM_HEIGHT=70
+
+
+def basicFeatureExtractorDigit(datum):
+    """
+    Returns a set of pixel features indicating whether
+    each pixel in the provided datum is white (0) or gray/black (1)
+    """
+    a = datum.getPixels()
+
+    features = util.Counter()
+    for x in range(DIGIT_DATUM_WIDTH):
+        for y in range(DIGIT_DATUM_HEIGHT):
+            if datum.getPixel(x, y) > 0:
+                features[(x,y)] = 1
+            else:
+                features[(x,y)] = 0
+    return features
+
+def basicFeatureExtractorFace(datum):
+    """
+    Returns a set of pixel features indicating whether
+    each pixel in the provided datum is an edge (1) or no edge (0)
+    """
+    a = datum.getPixels()
+
+    features = util.Counter()
+    for x in range(FACE_DATUM_WIDTH):
+        for y in range(FACE_DATUM_HEIGHT):
+            if datum.getPixel(x, y) > 0:
+                features[(x,y)] = 1
+            else:
+                features[(x,y)] = 0
+    return features
+
+def enhancedFeatureExtractorDigit(datum):
+    """
+    Your feature extraction playground.
+
+    You should return a util.Counter() of features
+    for this datum (datum is of type samples.Datum).
+
+    ## DESCRIBE YOUR ENHANCED FEATURES HERE...
+
+    ##
+    """
+    features =  basicFeatureExtractorDigit(datum)
+
+    "*** YOUR CODE HERE ***"
+    util.raiseNotDefined()
+
+    return features
+
+
+
+def basicFeatureExtractorPacman(state):
+    """
+    A basic feature extraction function.
+
+    You should return a util.Counter() of features
+    for each (state, action) pair along with a list of the legal actions
+
+    ##
+    """
+    features = util.Counter()
+    for action in state.getLegalActions():
+        successor = state.generateSuccessor(0, action)
+        foodCount = successor.getFood().count()
+        featureCounter = util.Counter()
+        featureCounter['foodCount'] = foodCount
+        features[action] = featureCounter
+    return features, state.getLegalActions()
+
+def enhancedFeatureExtractorPacman(state):
+    """
+    Your feature extraction playground.
+
+    You should return a util.Counter() of features
+    for each (state, action) pair along with a list of the legal actions
+
+    ##
+    """
+
+    features = basicFeatureExtractorPacman(state)[0]
+    for action in state.getLegalActions():
+        features[action] = util.Counter(features[action], **enhancedPacmanFeatures(state, action))
+    return features, state.getLegalActions()
+
+def enhancedPacmanFeatures(state, action):
+    """
+    For each state, this function is called with each legal action.
+    It should return a counter with { <feature name> : <feature value>, ... }
+    """
+    features = util.Counter()
+    "*** YOUR CODE HERE ***"
+    util.raiseNotDefined()
+    return features
+
+
+def contestFeatureExtractorDigit(datum):
+    """
+    Specify features to use for the minicontest
+    """
+    features =  basicFeatureExtractorDigit(datum)
+    return features
+
+def enhancedFeatureExtractorFace(datum):
+    """
+    Your feature extraction playground for faces.
+    It is your choice to modify this.
+    """
+    features =  basicFeatureExtractorFace(datum)
+    return features
+
+def analysis(classifier, guesses, testLabels, testData, rawTestData, printImage):
+    """
+    This function is called after learning.
+    Include any code that you want here to help you analyze your results.
+
+    Use the printImage(<list of pixels>) function to visualize features.
+
+    An example of use has been given to you.
+
+    - classifier is the trained classifier
+    - guesses is the list of labels predicted by your classifier on the test set
+    - testLabels is the list of true labels
+    - testData is the list of training datapoints (as util.Counter of features)
+    - rawTestData is the list of training datapoints (as samples.Datum)
+    - printImage is a method to visualize the features
+    (see its use in the odds ratio part in runClassifier method)
+
+    This code won't be evaluated. It is for your own optional use
+    (and you can modify the signature if you want).
+    """
+
+    # Put any code here...
+    # Example of use:
+    # for i in range(len(guesses)):
+    #     prediction = guesses[i]
+    #     truth = testLabels[i]
+    #     if (prediction != truth):
+    #         print "==================================="
+    #         print "Mistake on example %d" % i
+    #         print "Predicted %d; truth is %d" % (prediction, truth)
+    #         print "Image: "
+    #         print rawTestData[i]
+    #         break
+
+
+## =====================
+## You don't have to modify any code below.
+## =====================
+
+
+class ImagePrinter:
+    def __init__(self, width, height):
+        self.width = width
+        self.height = height
+
+    def printImage(self, pixels):
+        """
+        Prints a Datum object that contains all pixels in the
+        provided list of pixels.  This will serve as a helper function
+        to the analysis function you write.
+
+        Pixels should take the form
+        [(2,2), (2, 3), ...]
+        where each tuple represents a pixel.
+        """
+        image = samples.Datum(None,self.width,self.height)
+        for pix in pixels:
+            try:
+            # This is so that new features that you could define which
+            # which are not of the form of (x,y) will not break
+            # this image printer...
+                x,y = pix
+                image.pixels[x][y] = 2
+            except:
+                print "new features:", pix
+                continue
+        print image
+
+def default(str):
+    return str + ' [Default: %default]'
+
+USAGE_STRING = """
+  USAGE:      python dataClassifier.py <options>
+  EXAMPLES:   (1) python dataClassifier.py
+                  - trains the default mostFrequent classifier on the digit dataset
+                  using the default 100 training examples and
+                  then test the classifier on test data
+              (2) python dataClassifier.py -c naiveBayes -d digits -t 1000 -f -o -1 3 -2 6 -k 2.5
+                  - would run the naive Bayes classifier on 1000 training examples
+                  using the enhancedFeatureExtractorDigits function to get the features
+                  on the faces dataset, would use the smoothing parameter equals to 2.5, would
+                  test the classifier on the test data and performs an odd ratio analysis
+                  with label1=3 vs. label2=6
+                 """
+
+
+def readCommand( argv ):
+    "Processes the command used to run from the command line."
+    from optparse import OptionParser
+    parser = OptionParser(USAGE_STRING)
+
+    parser.add_option('-c', '--classifier', help=default('The type of classifier'), choices=['mostFrequent', 'nb', 'naiveBayes', 'perceptron', 'mira', 'minicontest'], default='mostFrequent')
+    parser.add_option('-d', '--data', help=default('Dataset to use'), choices=['digits', 'faces', 'pacman'], default='digits')
+    parser.add_option('-t', '--training', help=default('The size of the training set'), default=100, type="int")
+    parser.add_option('-f', '--features', help=default('Whether to use enhanced features'), default=False, action="store_true")
+    parser.add_option('-o', '--odds', help=default('Whether to compute odds ratios'), default=False, action="store_true")
+    parser.add_option('-1', '--label1', help=default("First label in an odds ratio comparison"), default=0, type="int")
+    parser.add_option('-2', '--label2', help=default("Second label in an odds ratio comparison"), default=1, type="int")
+    parser.add_option('-w', '--weights', help=default('Whether to print weights'), default=False, action="store_true")
+    parser.add_option('-k', '--smoothing', help=default("Smoothing parameter (ignored when using --autotune)"), type="float", default=2.0)
+    parser.add_option('-a', '--autotune', help=default("Whether to automatically tune hyperparameters"), default=False, action="store_true")
+    parser.add_option('-i', '--iterations', help=default("Maximum iterations to run training"), default=3, type="int")
+    parser.add_option('-s', '--test', help=default("Amount of test data to use"), default=TEST_SET_SIZE, type="int")
+    parser.add_option('-g', '--agentToClone', help=default("Pacman agent to copy"), default=None, type="str")
+
+    options, otherjunk = parser.parse_args(argv)
+    if len(otherjunk) != 0: raise Exception('Command line input not understood: ' + str(otherjunk))
+    args = {}
+
+    # Set up variables according to the command line input.
+    print "Doing classification"
+    print "--------------------"
+    print "data:\t\t" + options.data
+    print "classifier:\t\t" + options.classifier
+    if not options.classifier == 'minicontest':
+        print "using enhanced features?:\t" + str(options.features)
+    else:
+        print "using minicontest feature extractor"
+    print "training set size:\t" + str(options.training)
+    if(options.data=="digits"):
+        printImage = ImagePrinter(DIGIT_DATUM_WIDTH, DIGIT_DATUM_HEIGHT).printImage
+        if (options.features):
+            featureFunction = enhancedFeatureExtractorDigit
+        else:
+            featureFunction = basicFeatureExtractorDigit
+        if (options.classifier == 'minicontest'):
+            featureFunction = contestFeatureExtractorDigit
+    elif(options.data=="faces"):
+        printImage = ImagePrinter(FACE_DATUM_WIDTH, FACE_DATUM_HEIGHT).printImage
+        if (options.features):
+            featureFunction = enhancedFeatureExtractorFace
+        else:
+            featureFunction = basicFeatureExtractorFace
+    elif(options.data=="pacman"):
+        printImage = None
+        if (options.features):
+            featureFunction = enhancedFeatureExtractorPacman
+        else:
+            featureFunction = basicFeatureExtractorPacman
+    else:
+        print "Unknown dataset", options.data
+        print USAGE_STRING
+        sys.exit(2)
+
+    if(options.data=="digits"):
+        legalLabels = range(10)
+    else:
+        legalLabels = ['Stop', 'West', 'East', 'North', 'South']
+
+    if options.training <= 0:
+        print "Training set size should be a positive integer (you provided: %d)" % options.training
+        print USAGE_STRING
+        sys.exit(2)
+
+    if options.smoothing <= 0:
+        print "Please provide a positive number for smoothing (you provided: %f)" % options.smoothing
+        print USAGE_STRING
+        sys.exit(2)
+
+    if options.odds:
+        if options.label1 not in legalLabels or options.label2 not in legalLabels:
+            print "Didn't provide a legal labels for the odds ratio: (%d,%d)" % (options.label1, options.label2)
+            print USAGE_STRING
+            sys.exit(2)
+
+    if(options.classifier == "mostFrequent"):
+        classifier = mostFrequent.MostFrequentClassifier(legalLabels)
+    elif(options.classifier == "naiveBayes" or options.classifier == "nb"):
+        classifier = naiveBayes.NaiveBayesClassifier(legalLabels)
+        classifier.setSmoothing(options.smoothing)
+        if (options.autotune):
+            print "using automatic tuning for naivebayes"
+            classifier.automaticTuning = True
+        else:
+            print "using smoothing parameter k=%f for naivebayes" %  options.smoothing
+    elif(options.classifier == "perceptron"):
+        if options.data != 'pacman':
+            classifier = perceptron.PerceptronClassifier(legalLabels,options.iterations)
+        else:
+            classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,options.iterations)
+    elif(options.classifier == "mira"):
+        if options.data != 'pacman':
+            classifier = mira.MiraClassifier(legalLabels, options.iterations)
+        if (options.autotune):
+            print "using automatic tuning for MIRA"
+            classifier.automaticTuning = True
+        else:
+            print "using default C=0.001 for MIRA"
+    elif(options.classifier == 'minicontest'):
+        import minicontest
+        classifier = minicontest.contestClassifier(legalLabels)
+    else:
+        print "Unknown classifier:", options.classifier
+        print USAGE_STRING
+
+        sys.exit(2)
+
+    args['agentToClone'] = options.agentToClone
+
+    args['classifier'] = classifier
+    args['featureFunction'] = featureFunction
+    args['printImage'] = printImage
+
+    return args, options
+
+# Dictionary containing full path to .pkl file that contains the agent's training, validation, and testing data.
+MAP_AGENT_TO_PATH_OF_SAVED_GAMES = {
+    'FoodAgent': ('pacmandata/food_training.pkl','pacmandata/food_validation.pkl','pacmandata/food_test.pkl' ),
+    'StopAgent': ('pacmandata/stop_training.pkl','pacmandata/stop_validation.pkl','pacmandata/stop_test.pkl' ),
+    'SuicideAgent': ('pacmandata/suicide_training.pkl','pacmandata/suicide_validation.pkl','pacmandata/suicide_test.pkl' ),
+    'GoodReflexAgent': ('pacmandata/good_reflex_training.pkl','pacmandata/good_reflex_validation.pkl','pacmandata/good_reflex_test.pkl' ),
+    'ContestAgent': ('pacmandata/contest_training.pkl','pacmandata/contest_validation.pkl', 'pacmandata/contest_test.pkl' )
+}
+# Main harness code
+
+
+
+def runClassifier(args, options):
+    featureFunction = args['featureFunction']
+    classifier = args['classifier']
+    printImage = args['printImage']
+    
+    # Load data
+    numTraining = options.training
+    numTest = options.test
+
+    if(options.data=="pacman"):
+        agentToClone = args.get('agentToClone', None)
+        trainingData, validationData, testData = MAP_AGENT_TO_PATH_OF_SAVED_GAMES.get(agentToClone, (None, None, None))
+        trainingData = trainingData or args.get('trainingData', False) or MAP_AGENT_TO_PATH_OF_SAVED_GAMES['ContestAgent'][0]
+        validationData = validationData or args.get('validationData', False) or MAP_AGENT_TO_PATH_OF_SAVED_GAMES['ContestAgent'][1]
+        testData = testData or MAP_AGENT_TO_PATH_OF_SAVED_GAMES['ContestAgent'][2]
+        rawTrainingData, trainingLabels = samples.loadPacmanData(trainingData, numTraining)
+        rawValidationData, validationLabels = samples.loadPacmanData(validationData, numTest)
+        rawTestData, testLabels = samples.loadPacmanData(testData, numTest)
+    else:
+        rawTrainingData = samples.loadDataFile("digitdata/trainingimages", numTraining,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+        trainingLabels = samples.loadLabelsFile("digitdata/traininglabels", numTraining)
+        rawValidationData = samples.loadDataFile("digitdata/validationimages", numTest,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+        validationLabels = samples.loadLabelsFile("digitdata/validationlabels", numTest)
+        rawTestData = samples.loadDataFile("digitdata/testimages", numTest,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
+        testLabels = samples.loadLabelsFile("digitdata/testlabels", numTest)
+
+
+    # Extract features
+    print "Extracting features..."
+    trainingData = map(featureFunction, rawTrainingData)
+    validationData = map(featureFunction, rawValidationData)
+    testData = map(featureFunction, rawTestData)
+
+    # Conduct training and testing
+    print "Training..."
+    classifier.train(trainingData, trainingLabels, validationData, validationLabels)
+    print "Validating..."
+    guesses = classifier.classify(validationData)
+    correct = [guesses[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
+    print str(correct), ("correct out of " + str(len(validationLabels)) + " (%.1f%%).") % (100.0 * correct / len(validationLabels))
+    print "Testing..."
+    guesses = classifier.classify(testData)
+    correct = [guesses[i] == testLabels[i] for i in range(len(testLabels))].count(True)
+    print str(correct), ("correct out of " + str(len(testLabels)) + " (%.1f%%).") % (100.0 * correct / len(testLabels))
+    analysis(classifier, guesses, testLabels, testData, rawTestData, printImage)
+
+    # do odds ratio computation if specified at command line
+    if((options.odds) & (options.classifier == "naiveBayes" or (options.classifier == "nb")) ):
+        label1, label2 = options.label1, options.label2
+        features_odds = classifier.findHighOddsFeatures(label1,label2)
+        if(options.classifier == "naiveBayes" or options.classifier == "nb"):
+            string3 = "=== Features with highest odd ratio of label %d over label %d ===" % (label1, label2)
+        else:
+            string3 = "=== Features for which weight(label %d)-weight(label %d) is biggest ===" % (label1, label2)
+
+        print string3
+        printImage(features_odds)
+
+    if((options.weights) & (options.classifier == "perceptron")):
+        for l in classifier.legalLabels:
+            features_weights = classifier.findHighWeightFeatures(l)
+            print ("=== Features with high weight for label %d ==="%l)
+            printImage(features_weights)
+
+if __name__ == '__main__':
+    # Read input
+    args, options = readCommand( sys.argv[1:] )
+    # Run classifier
+    runClassifier(args, options)

p5_classification/game.py

@@ -0,0 +1,729 @@
+# game.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).
+
+
+# game.py
+# -------
+# Licensing Information: Please do not distribute or publish solutions to this
+# project. You are free to use and extend these projects for educational
+# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
+# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
+
+from util import *
+import time, os
+import traceback
+import sys
+
+#######################
+# Parts worth reading #
+#######################
+
+class Agent:
+    """
+    An agent must define a getAction method, but may also define the
+    following methods which will be called if they exist:
+
+    def registerInitialState(self, state): # inspects the starting state
+    """
+    def __init__(self, index=0):
+        self.index = index
+
+    def getAction(self, state):
+        """
+        The Agent will receive a GameState (from either {pacman, capture, sonar}.py) and
+        must return an action from Directions.{North, South, East, West, Stop}
+        """
+        raiseNotDefined()
+
+class Directions:
+    NORTH = 'North'
+    SOUTH = 'South'
+    EAST = 'East'
+    WEST = 'West'
+    STOP = 'Stop'
+
+    LEFT =       {NORTH: WEST,
+                   SOUTH: EAST,
+                   EAST:  NORTH,
+                   WEST:  SOUTH,
+                   STOP:  STOP}
+
+    RIGHT =      dict([(y,x) for x, y in LEFT.items()])
+
+    REVERSE = {NORTH: SOUTH,
+               SOUTH: NORTH,
+               EAST: WEST,
+               WEST: EAST,
+               STOP: STOP}
+
+class Configuration:
+    """
+    A Configuration holds the (x,y) coordinate of a character, along with its
+    traveling direction.
+
+    The convention for positions, like a graph, is that (0,0) is the lower left corner, x increases
+    horizontally and y increases vertically.  Therefore, north is the direction of increasing y, or (0,1).
+    """
+
+    def __init__(self, pos, direction):
+        self.pos = pos
+        self.direction = direction
+
+    def getPosition(self):
+        return (self.pos)
+
+    def getDirection(self):
+        return self.direction
+
+    def isInteger(self):
+        x,y = self.pos
+        return x == int(x) and y == int(y)
+
+    def __eq__(self, other):
+        if other == None: return False
+        return (self.pos == other.pos and self.direction == other.direction)
+
+    def __hash__(self):
+        x = hash(self.pos)
+        y = hash(self.direction)
+        return hash(x + 13 * y)
+
+    def __str__(self):
+        return "(x,y)="+str(self.pos)+", "+str(self.direction)
+
+    def generateSuccessor(self, vector):
+        """
+        Generates a new configuration reached by translating the current
+        configuration by the action vector.  This is a low-level call and does
+        not attempt to respect the legality of the movement.
+
+        Actions are movement vectors.
+        """
+        x, y= self.pos
+        dx, dy = vector
+        direction = Actions.vectorToDirection(vector)
+        if direction == Directions.STOP:
+            direction = self.direction # There is no stop direction
+        return Configuration((x + dx, y+dy), direction)
+
+class AgentState:
+    """
+    AgentStates hold the state of an agent (configuration, speed, scared, etc).
+    """
+
+    def __init__( self, startConfiguration, isPacman ):
+        self.start = startConfiguration
+        self.configuration = startConfiguration
+        self.isPacman = isPacman
+        self.scaredTimer = 0
+        self.numCarrying = 0
+        self.numReturned = 0
+
+    def __str__( self ):
+        if self.isPacman:
+            return "Pacman: " + str( self.configuration )
+        else:
+            return "Ghost: " + str( self.configuration )
+
+    def __eq__( self, other ):
+        if other == None:
+            return False
+        return self.configuration == other.configuration and self.scaredTimer == other.scaredTimer
+
+    def __hash__(self):
+        return hash(hash(self.configuration) + 13 * hash(self.scaredTimer))
+
+    def copy( self ):
+        state = AgentState( self.start, self.isPacman )
+        state.configuration = self.configuration
+        state.scaredTimer = self.scaredTimer
+        state.numCarrying = self.numCarrying
+        state.numReturned = self.numReturned
+        return state
+
+    def getPosition(self):
+        if self.configuration == None: return None
+        return self.configuration.getPosition()
+
+    def getDirection(self):
+        return self.configuration.getDirection()
+
+class Grid:
+    """
+    A 2-dimensional array of objects backed by a list of lists.  Data is accessed
+    via grid[x][y] where (x,y) are positions on a Pacman map with x horizontal,
+    y vertical and the origin (0,0) in the bottom left corner.
+
+    The __str__ method constructs an output that is oriented like a pacman board.
+    """
+    def __init__(self, width, height, initialValue=False, bitRepresentation=None):
+        if initialValue not in [False, True]: raise Exception('Grids can only contain booleans')
+        self.CELLS_PER_INT = 30
+
+        self.width = width
+        self.height = height
+        self.data = [[initialValue for y in range(height)] for x in range(width)]
+        if bitRepresentation:
+            self._unpackBits(bitRepresentation)
+
+    def __getitem__(self, i):
+        return self.data[i]
+
+    def __setitem__(self, key, item):
+        self.data[key] = item
+
+    def __str__(self):
+        out = [[str(self.data[x][y])[0] for x in range(self.width)] for y in range(self.height)]
+        out.reverse()
+        return '\n'.join([''.join(x) for x in out])
+
+    def __eq__(self, other):
+        if other == None: return False
+        return self.data == other.data
+
+    def __hash__(self):
+        # return hash(str(self))
+        base = 1
+        h = 0
+        for l in self.data:
+            for i in l:
+                if i:
+                    h += base
+                base *= 2
+        return hash(h)
+
+    def copy(self):
+        g = Grid(self.width, self.height)
+        g.data = [x[:] for x in self.data]
+        return g
+
+    def deepCopy(self):
+        return self.copy()
+
+    def shallowCopy(self):
+        g = Grid(self.width, self.height)
+        g.data = self.data
+        return g
+
+    def count(self, item =True ):
+        return sum([x.count(item) for x in self.data])
+
+    def asList(self, key = True):
+        list = []
+        for x in range(self.width):
+            for y in range(self.height):
+                if self[x][y] == key: list.append( (x,y) )
+        return list
+
+    def packBits(self):
+        """
+        Returns an efficient int list representation
+
+        (width, height, bitPackedInts...)
+        """
+        bits = [self.width, self.height]
+        currentInt = 0
+        for i in range(self.height * self.width):
+            bit = self.CELLS_PER_INT - (i % self.CELLS_PER_INT) - 1
+            x, y = self._cellIndexToPosition(i)
+            if self[x][y]:
+                currentInt += 2 ** bit
+            if (i + 1) % self.CELLS_PER_INT == 0:
+                bits.append(currentInt)
+                currentInt = 0
+        bits.append(currentInt)
+        return tuple(bits)
+
+    def _cellIndexToPosition(self, index):
+        x = index / self.height
+        y = index % self.height
+        return x, y
+
+    def _unpackBits(self, bits):
+        """
+        Fills in data from a bit-level representation
+        """
+        cell = 0
+        for packed in bits:
+            for bit in self._unpackInt(packed, self.CELLS_PER_INT):
+                if cell == self.width * self.height: break
+                x, y = self._cellIndexToPosition(cell)
+                self[x][y] = bit
+                cell += 1
+
+    def _unpackInt(self, packed, size):
+        bools = []
+        if packed < 0: raise ValueError, "must be a positive integer"
+        for i in range(size):
+            n = 2 ** (self.CELLS_PER_INT - i - 1)
+            if packed >= n:
+                bools.append(True)
+                packed -= n
+            else:
+                bools.append(False)
+        return bools
+
+def reconstituteGrid(bitRep):
+    if type(bitRep) is not type((1,2)):
+        return bitRep
+    width, height = bitRep[:2]
+    return Grid(width, height, bitRepresentation= bitRep[2:])
+
+####################################
+# Parts you shouldn't have to read #
+####################################
+
+class Actions:
+    """
+    A collection of static methods for manipulating move actions.
+    """
+    # Directions
+    _directions = {Directions.NORTH: (0, 1),
+                   Directions.SOUTH: (0, -1),
+                   Directions.EAST:  (1, 0),
+                   Directions.WEST:  (-1, 0),
+                   Directions.STOP:  (0, 0)}
+
+    _directionsAsList = _directions.items()
+
+    TOLERANCE = .001
+
+    def reverseDirection(action):
+        if action == Directions.NORTH:
+            return Directions.SOUTH
+        if action == Directions.SOUTH:
+            return Directions.NORTH
+        if action == Directions.EAST:
+            return Directions.WEST
+        if action == Directions.WEST:
+            return Directions.EAST
+        return action
+    reverseDirection = staticmethod(reverseDirection)
+
+    def vectorToDirection(vector):
+        dx, dy = vector
+        if dy > 0:
+            return Directions.NORTH
+        if dy < 0:
+            return Directions.SOUTH
+        if dx < 0:
+            return Directions.WEST
+        if dx > 0:
+            return Directions.EAST
+        return Directions.STOP
+    vectorToDirection = staticmethod(vectorToDirection)
+
+    def directionToVector(direction, speed = 1.0):
+        dx, dy =  Actions._directions[direction]
+        return (dx * speed, dy * speed)
+    directionToVector = staticmethod(directionToVector)
+
+    def getPossibleActions(config, walls):
+        possible = []
+        x, y = config.pos
+        x_int, y_int = int(x + 0.5), int(y + 0.5)
+
+        # In between grid points, all agents must continue straight
+        if (abs(x - x_int) + abs(y - y_int)  > Actions.TOLERANCE):
+            return [config.getDirection()]
+
+        for dir, vec in Actions._directionsAsList:
+            dx, dy = vec
+            next_y = y_int + dy
+            next_x = x_int + dx
+            if not walls[next_x][next_y]: possible.append(dir)
+
+        return possible
+
+    getPossibleActions = staticmethod(getPossibleActions)
+
+    def getLegalNeighbors(position, walls):
+        x,y = position
+        x_int, y_int = int(x + 0.5), int(y + 0.5)
+        neighbors = []
+        for dir, vec in Actions._directionsAsList:
+            dx, dy = vec
+            next_x = x_int + dx
+            if next_x < 0 or next_x == walls.width: continue
+            next_y = y_int + dy
+            if next_y < 0 or next_y == walls.height: continue
+            if not walls[next_x][next_y]: neighbors.append((next_x, next_y))
+        return neighbors
+    getLegalNeighbors = staticmethod(getLegalNeighbors)
+
+    def getSuccessor(position, action):
+        dx, dy = Actions.directionToVector(action)
+        x, y = position
+        return (x + dx, y + dy)
+    getSuccessor = staticmethod(getSuccessor)
+
+class GameStateData:
+    """
+
+    """
+    def __init__( self, prevState = None ):
+        """
+        Generates a new data packet by copying information from its predecessor.
+        """
+        if prevState != None:
+            self.food = prevState.food.shallowCopy()
+            self.capsules = prevState.capsules[:]
+            self.agentStates = self.copyAgentStates( prevState.agentStates )
+            self.layout = prevState.layout
+            self._eaten = prevState._eaten
+            self.score = prevState.score
+
+        self._foodEaten = None
+        self._foodAdded = None
+        self._capsuleEaten = None
+        self._agentMoved = None
+        self._lose = False
+        self._win = False
+        self.scoreChange = 0
+
+    def deepCopy( self ):
+        state = GameStateData( self )
+        state.food = self.food.deepCopy()
+        state.layout = self.layout.deepCopy()
+        state._agentMoved = self._agentMoved
+        state._foodEaten = self._foodEaten
+        state._foodAdded = self._foodAdded
+        state._capsuleEaten = self._capsuleEaten
+        return state
+
+    def copyAgentStates( self, agentStates ):
+        copiedStates = []
+        for agentState in agentStates:
+            copiedStates.append( agentState.copy() )
+        return copiedStates
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        if other == None: return False
+        # TODO Check for type of other
+        if not self.agentStates == other.agentStates: return False
+        if not self.food == other.food: return False
+        if not self.capsules == other.capsules: return False
+        if not self.score == other.score: return False
+        return True
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        for i, state in enumerate( self.agentStates ):
+            try:
+                int(hash(state))
+            except TypeError, e:
+                print e
+                #hash(state)
+        return int((hash(tuple(self.agentStates)) + 13*hash(self.food) + 113* hash(tuple(self.capsules)) + 7 * hash(self.score)) % 1048575 )
+
+    def __str__( self ):
+        width, height = self.layout.width, self.layout.height
+        map = Grid(width, height)
+        if type(self.food) == type((1,2)):
+            self.food = reconstituteGrid(self.food)
+        for x in range(width):
+            for y in range(height):
+                food, walls = self.food, self.layout.walls
+                map[x][y] = self._foodWallStr(food[x][y], walls[x][y])
+
+        for agentState in self.agentStates:
+            if agentState == None: continue
+            if agentState.configuration == None: continue
+            x,y = [int( i ) for i in nearestPoint( agentState.configuration.pos )]
+            agent_dir = agentState.configuration.direction
+            if agentState.isPacman:
+                map[x][y] = self._pacStr( agent_dir )
+            else:
+                map[x][y] = self._ghostStr( agent_dir )
+
+        for x, y in self.capsules:
+            map[x][y] = 'o'
+
+        return str(map) + ("\nScore: %d\n" % self.score)
+
+    def _foodWallStr( self, hasFood, hasWall ):
+        if hasFood:
+            return '.'
+        elif hasWall:
+            return '%'
+        else:
+            return ' '
+
+    def _pacStr( self, dir ):
+        if dir == Directions.NORTH:
+            return 'v'
+        if dir == Directions.SOUTH:
+            return '^'
+        if dir == Directions.WEST:
+            return '>'
+        return '<'
+
+    def _ghostStr( self, dir ):
+        return 'G'
+        if dir == Directions.NORTH:
+            return 'M'
+        if dir == Directions.SOUTH:
+            return 'W'
+        if dir == Directions.WEST:
+            return '3'
+        return 'E'
+
+    def initialize( self, layout, numGhostAgents ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.food = layout.food.copy()
+        #self.capsules = []
+        self.capsules = layout.capsules[:]
+        self.layout = layout
+        self.score = 0
+        self.scoreChange = 0
+
+        self.agentStates = []
+        numGhosts = 0
+        for isPacman, pos in layout.agentPositions:
+            if not isPacman:
+                if numGhosts == numGhostAgents: continue # Max ghosts reached already
+                else: numGhosts += 1
+            self.agentStates.append( AgentState( Configuration( pos, Directions.STOP), isPacman) )
+        self._eaten = [False for a in self.agentStates]
+
+try:
+    import boinc
+    _BOINC_ENABLED = True
+except:
+    _BOINC_ENABLED = False
+
+class Game:
+    """
+    The Game manages the control flow, soliciting actions from agents.
+    """
+
+    def __init__( self, agents, display, rules, startingIndex=0, muteAgents=False, catchExceptions=False ):
+        self.agentCrashed = False
+        self.agents = agents
+        self.display = display
+        self.rules = rules
+        self.startingIndex = startingIndex
+        self.gameOver = False
+        self.muteAgents = muteAgents
+        self.catchExceptions = catchExceptions
+        self.moveHistory = []
+        self.totalAgentTimes = [0 for agent in agents]
+        self.totalAgentTimeWarnings = [0 for agent in agents]
+        self.agentTimeout = False
+        import cStringIO
+        self.agentOutput = [cStringIO.StringIO() for agent in agents]
+
+    def getProgress(self):
+        if self.gameOver:
+            return 1.0
+        else:
+            return self.rules.getProgress(self)
+
+    def _agentCrash( self, agentIndex, quiet=False):
+        "Helper method for handling agent crashes"
+        if not quiet: traceback.print_exc()
+        self.gameOver = True
+        self.agentCrashed = True
+        self.rules.agentCrash(self, agentIndex)
+
+    OLD_STDOUT = None
+    OLD_STDERR = None
+
+    def mute(self, agentIndex):
+        if not self.muteAgents: return
+        global OLD_STDOUT, OLD_STDERR
+        import cStringIO
+        OLD_STDOUT = sys.stdout
+        OLD_STDERR = sys.stderr
+        sys.stdout = self.agentOutput[agentIndex]
+        sys.stderr = self.agentOutput[agentIndex]
+
+    def unmute(self):
+        if not self.muteAgents: return
+        global OLD_STDOUT, OLD_STDERR
+        # Revert stdout/stderr to originals
+        sys.stdout = OLD_STDOUT
+        sys.stderr = OLD_STDERR
+
+
+    def run( self ):
+        """
+        Main control loop for game play.
+        """
+        self.display.initialize(self.state.data)
+        self.numMoves = 0
+
+        ###self.display.initialize(self.state.makeObservation(1).data)
+        # inform learning agents of the game start
+        for i in range(len(self.agents)):
+            agent = self.agents[i]
+            if not agent:
+                self.mute(i)
+                # this is a null agent, meaning it failed to load
+                # the other team wins
+                print >>sys.stderr, "Agent %d failed to load" % i
+                self.unmute()
+                self._agentCrash(i, quiet=True)
+                return
+            if ("registerInitialState" in dir(agent)):
+                self.mute(i)
+                if self.catchExceptions:
+                    try:
+                        timed_func = TimeoutFunction(agent.registerInitialState, int(self.rules.getMaxStartupTime(i)))
+                        try:
+                            start_time = time.time()
+                            timed_func(self.state.deepCopy())
+                            time_taken = time.time() - start_time
+                            self.totalAgentTimes[i] += time_taken
+                        except TimeoutFunctionException:
+                            print >>sys.stderr, "Agent %d ran out of time on startup!" % i
+                            self.unmute()
+                            self.agentTimeout = True
+                            self._agentCrash(i, quiet=True)
+                            return
+                    except Exception,data:
+                        self._agentCrash(i, quiet=False)
+                        self.unmute()
+                        return
+                else:
+                    agent.registerInitialState(self.state.deepCopy())
+                ## TODO: could this exceed the total time
+                self.unmute()
+
+        agentIndex = self.startingIndex
+        numAgents = len( self.agents )
+
+        while not self.gameOver:
+            # Fetch the next agent
+            agent = self.agents[agentIndex]
+            move_time = 0
+            skip_action = False
+            # Generate an observation of the state
+            if 'observationFunction' in dir( agent ):
+                self.mute(agentIndex)
+                if self.catchExceptions:
+                    try:
+                        timed_func = TimeoutFunction(agent.observationFunction, int(self.rules.getMoveTimeout(agentIndex)))
+                        try:
+                            start_time = time.time()
+                            observation = timed_func(self.state.deepCopy())
+                        except TimeoutFunctionException:
+                            skip_action = True
+                        move_time += time.time() - start_time
+                        self.unmute()
+                    except Exception,data:
+                        self._agentCrash(agentIndex, quiet=False)
+                        self.unmute()
+                        return
+                else:
+                    observation = agent.observationFunction(self.state.deepCopy())
+                self.unmute()
+            else:
+                observation = self.state.deepCopy()
+
+            # Solicit an action
+            action = None
+            self.mute(agentIndex)
+            if self.catchExceptions:
+                try:
+                    timed_func = TimeoutFunction(agent.getAction, int(self.rules.getMoveTimeout(agentIndex)) - int(move_time))
+                    try:
+                        start_time = time.time()
+                        if skip_action:
+                            raise TimeoutFunctionException()
+                        action = timed_func( observation )
+                    except TimeoutFunctionException:
+                        print >>sys.stderr, "Agent %d timed out on a single move!" % agentIndex
+                        self.agentTimeout = True
+                        self._agentCrash(agentIndex, quiet=True)
+                        self.unmute()
+                        return
+
+                    move_time += time.time() - start_time
+
+                    if move_time > self.rules.getMoveWarningTime(agentIndex):
+                        self.totalAgentTimeWarnings[agentIndex] += 1
+                        print >>sys.stderr, "Agent %d took too long to make a move! This is warning %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
+                        if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
+                            print >>sys.stderr, "Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
+                            self.agentTimeout = True
+                            self._agentCrash(agentIndex, quiet=True)
+                            self.unmute()
+                            return
+
+                    self.totalAgentTimes[agentIndex] += move_time
+                    #print "Agent: %d, time: %f, total: %f" % (agentIndex, move_time, self.totalAgentTimes[agentIndex])
+                    if self.totalAgentTimes[agentIndex] > self.rules.getMaxTotalTime(agentIndex):
+                        print >>sys.stderr, "Agent %d ran out of time! (time: %1.2f)" % (agentIndex, self.totalAgentTimes[agentIndex])
+                        self.agentTimeout = True
+                        self._agentCrash(agentIndex, quiet=True)
+                        self.unmute()
+                        return
+                    self.unmute()
+                except Exception,data:
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+            else:
+                action = agent.getAction(observation)
+            self.unmute()
+
+            # Execute the action
+            self.moveHistory.append( (agentIndex, action) )
+            if self.catchExceptions:
+                try:
+                    self.state = self.state.generateSuccessor( agentIndex, action )
+                except Exception,data:
+                    self.mute(agentIndex)
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+            else:
+                self.state = self.state.generateSuccessor( agentIndex, action )
+
+            # Change the display
+            self.display.update( self.state.data )
+            ###idx = agentIndex - agentIndex % 2 + 1
+            ###self.display.update( self.state.makeObservation(idx).data )
+
+            # Allow for game specific conditions (winning, losing, etc.)
+            self.rules.process(self.state, self)
+            # Track progress
+            if agentIndex == numAgents + 1: self.numMoves += 1
+            # Next agent
+            agentIndex = ( agentIndex + 1 ) % numAgents
+
+            if _BOINC_ENABLED:
+                boinc.set_fraction_done(self.getProgress())
+
+        # inform a learning agent of the game result
+        for agentIndex, agent in enumerate(self.agents):
+            if "final" in dir( agent ) :
+                try:
+                    self.mute(agentIndex)
+                    agent.final( self.state )
+                    self.unmute()
+                except Exception,data:
+                    if not self.catchExceptions: raise
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+        self.display.finish()

p5_classification/ghostAgents.py

@@ -0,0 +1,81 @@
+# ghostAgents.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).
+
+
+from game import Agent
+from game import Actions
+from game import Directions
+import random
+from util import manhattanDistance
+import util
+
+class GhostAgent( Agent ):
+    def __init__( self, index ):
+        self.index = index
+
+    def getAction( self, state ):
+        dist = self.getDistribution(state)
+        if len(dist) == 0:
+            return Directions.STOP
+        else:
+            return util.chooseFromDistribution( dist )
+
+    def getDistribution(self, state):
+        "Returns a Counter encoding a distribution over actions from the provided state."
+        util.raiseNotDefined()
+
+class RandomGhost( GhostAgent ):
+    "A ghost that chooses a legal action uniformly at random."
+    def getDistribution( self, state ):
+        dist = util.Counter()
+        for a in state.getLegalActions( self.index ): dist[a] = 1.0
+        dist.normalize()
+        return dist
+
+class DirectionalGhost( GhostAgent ):
+    "A ghost that prefers to rush Pacman, or flee when scared."
+    def __init__( self, index, prob_attack=0.8, prob_scaredFlee=0.8 ):
+        self.index = index
+        self.prob_attack = prob_attack
+        self.prob_scaredFlee = prob_scaredFlee
+
+    def getDistribution( self, state ):
+        # Read variables from state
+        ghostState = state.getGhostState( self.index )
+        legalActions = state.getLegalActions( self.index )
+        pos = state.getGhostPosition( self.index )
+        isScared = ghostState.scaredTimer > 0
+
+        speed = 1
+        if isScared: speed = 0.5
+
+        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
+        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
+        pacmanPosition = state.getPacmanPosition()
+
+        # Select best actions given the state
+        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
+        if isScared:
+            bestScore = max( distancesToPacman )
+            bestProb = self.prob_scaredFlee
+        else:
+            bestScore = min( distancesToPacman )
+            bestProb = self.prob_attack
+        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]
+
+        # Construct distribution
+        dist = util.Counter()
+        for a in bestActions: dist[a] = bestProb / len(bestActions)
+        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
+        dist.normalize()
+        return dist

p5_classification/grading.py

@@ -0,0 +1,282 @@
+# grading.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).
+
+
+"Common code for autograders"
+
+import cgi
+import time
+import sys
+import traceback
+import pdb
+from collections import defaultdict
+import util
+
+class Grades:
+  "A data structure for project grades, along with formatting code to display them"
+  def __init__(self, projectName, questionsAndMaxesList, edxOutput=False, muteOutput=False):
+    """
+    Defines the grading scheme for a project
+      projectName: project name
+      questionsAndMaxesDict: a list of (question name, max points per question)
+    """
+    self.questions = [el[0] for el in questionsAndMaxesList]
+    self.maxes = dict(questionsAndMaxesList)
+    self.points = Counter()
+    self.messages = dict([(q, []) for q in self.questions])
+    self.project = projectName
+    self.start = time.localtime()[1:6]
+    self.sane = True # Sanity checks
+    self.currentQuestion = None # Which question we're grading
+    self.edxOutput = edxOutput
+    self.mute = muteOutput
+    self.prereqs = defaultdict(set)
+
+    #print 'Autograder transcript for %s' % self.project
+    print 'Starting on %d-%d at %d:%02d:%02d' % self.start
+
+  def addPrereq(self, question, prereq):
+    self.prereqs[question].add(prereq)
+
+  def grade(self, gradingModule, exceptionMap = {}, bonusPic = False):
+    """
+    Grades each question
+      gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])
+    """
+
+    completedQuestions = set([])
+    for q in self.questions:
+      print '\nQuestion %s' % q
+      print '=' * (9 + len(q))
+      print
+      self.currentQuestion = q
+
+      incompleted = self.prereqs[q].difference(completedQuestions)
+      if len(incompleted) > 0:
+          prereq = incompleted.pop()
+          print \
+"""*** NOTE: Make sure to complete Question %s before working on Question %s,
+*** because Question %s builds upon your answer for Question %s.
+""" % (prereq, q, q, prereq)
+          continue
+
+      if self.mute: util.mutePrint()
+      try:
+        util.TimeoutFunction(getattr(gradingModule, q),300)(self) # Call the question's function
+        #TimeoutFunction(getattr(gradingModule, q),1200)(self) # Call the question's function
+      except Exception, inst:
+        self.addExceptionMessage(q, inst, traceback)
+        self.addErrorHints(exceptionMap, inst, q[1])
+      except:
+        self.fail('FAIL: Terminated with a string exception.')
+      finally:
+        if self.mute: util.unmutePrint()
+
+      if self.points[q] >= self.maxes[q]:
+        completedQuestions.add(q)
+
+      print '\n### Question %s: %d/%d ###\n' % (q, self.points[q], self.maxes[q])
+
+
+    print '\nFinished at %d:%02d:%02d' % time.localtime()[3:6]
+    print "\nProvisional grades\n=================="
+
+    for q in self.questions:
+      print 'Question %s: %d/%d' % (q, self.points[q], self.maxes[q])
+    print '------------------'
+    print 'Total: %d/%d' % (self.points.totalCount(), sum(self.maxes.values()))
+    if bonusPic and self.points.totalCount() == 25:
+      print """
+
+                     ALL HAIL GRANDPAC.
+              LONG LIVE THE GHOSTBUSTING KING.
+
+                  ---      ----      ---
+                  |  \    /  + \    /  |
+                  | + \--/      \--/ + |
+                  |   +     +          |
+                  | +     +        +   |
+                @@@@@@@@@@@@@@@@@@@@@@@@@@
+              @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            \   @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+             \ /  @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+              V   \   @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+                   \ /  @@@@@@@@@@@@@@@@@@@@@@@@@@
+                    V     @@@@@@@@@@@@@@@@@@@@@@@@
+                            @@@@@@@@@@@@@@@@@@@@@@
+                    /\      @@@@@@@@@@@@@@@@@@@@@@
+                   /  \  @@@@@@@@@@@@@@@@@@@@@@@@@
+              /\  /    @@@@@@@@@@@@@@@@@@@@@@@@@@@
+             /  \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            /    @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+              @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+                @@@@@@@@@@@@@@@@@@@@@@@@@@
+                    @@@@@@@@@@@@@@@@@@
+
+"""
+    print """
+Your grades are NOT yet registered.  To register your grades, make sure
+to follow your instructor's guidelines to receive credit on your project.
+"""
+
+    if self.edxOutput:
+        self.produceOutput()
+
+  def addExceptionMessage(self, q, inst, traceback):
+    """
+    Method to format the exception message, this is more complicated because
+    we need to cgi.escape the traceback but wrap the exception in a <pre> tag
+    """
+    self.fail('FAIL: Exception raised: %s' % inst)
+    self.addMessage('')
+    for line in traceback.format_exc().split('\n'):
+        self.addMessage(line)
+
+  def addErrorHints(self, exceptionMap, errorInstance, questionNum):
+    typeOf = str(type(errorInstance))
+    questionName = 'q' + questionNum
+    errorHint = ''
+
+    # question specific error hints
+    if exceptionMap.get(questionName):
+      questionMap = exceptionMap.get(questionName)
+      if (questionMap.get(typeOf)):
+        errorHint = questionMap.get(typeOf)
+    # fall back to general error messages if a question specific
+    # one does not exist
+    if (exceptionMap.get(typeOf)):
+      errorHint = exceptionMap.get(typeOf)
+
+    # dont include the HTML if we have no error hint
+    if not errorHint:
+      return ''
+
+    for line in errorHint.split('\n'):
+      self.addMessage(line)
+
+  def produceOutput(self):
+    edxOutput = open('edx_response.html', 'w')
+    edxOutput.write("<div>")
+
+    # first sum
+    total_possible = sum(self.maxes.values())
+    total_score = sum(self.points.values())
+    checkOrX = '<span class="incorrect"/>'
+    if (total_score >= total_possible):
+        checkOrX = '<span class="correct"/>'
+    header = """
+        <h3>
+            Total score ({total_score} / {total_possible})
+        </h3>
+    """.format(total_score = total_score,
+      total_possible = total_possible,
+      checkOrX = checkOrX
+    )
+    edxOutput.write(header)
+
+    for q in self.questions:
+      if len(q) == 2:
+          name = q[1]
+      else:
+          name = q
+      checkOrX = '<span class="incorrect"/>'
+      if (self.points[q] == self.maxes[q]):
+        checkOrX = '<span class="correct"/>'
+      #messages = '\n<br/>\n'.join(self.messages[q])
+      messages = "<pre>%s</pre>" % '\n'.join(self.messages[q])
+      output = """
+        <div class="test">
+          <section>
+          <div class="shortform">
+            Question {q} ({points}/{max}) {checkOrX}
+          </div>
+        <div class="longform">
+          {messages}
+        </div>
+        </section>
+      </div>
+      """.format(q = name,
+        max = self.maxes[q],
+        messages = messages,
+        checkOrX = checkOrX,
+        points = self.points[q]
+      )
+      # print "*** output for Question %s " % q[1]
+      # print output
+      edxOutput.write(output)
+    edxOutput.write("</div>")
+    edxOutput.close()
+    edxOutput = open('edx_grade', 'w')
+    edxOutput.write(str(self.points.totalCount()))
+    edxOutput.close()
+
+  def fail(self, message, raw=False):
+    "Sets sanity check bit to false and outputs a message"
+    self.sane = False
+    self.assignZeroCredit()
+    self.addMessage(message, raw)
+
+  def assignZeroCredit(self):
+    self.points[self.currentQuestion] = 0
+
+  def addPoints(self, amt):
+    self.points[self.currentQuestion] += amt
+
+  def deductPoints(self, amt):
+    self.points[self.currentQuestion] -= amt
+
+  def assignFullCredit(self, message="", raw=False):
+    self.points[self.currentQuestion] = self.maxes[self.currentQuestion]
+    if message != "":
+      self.addMessage(message, raw)
+
+  def addMessage(self, message, raw=False):
+    if not raw:
+        # We assume raw messages, formatted for HTML, are printed separately
+        if self.mute: util.unmutePrint()
+        print '*** ' + message
+        if self.mute: util.mutePrint()
+        message = cgi.escape(message)
+    self.messages[self.currentQuestion].append(message)
+
+  def addMessageToEmail(self, message):
+    print "WARNING**** addMessageToEmail is deprecated %s" % message
+    for line in message.split('\n'):
+      pass
+      #print '%%% ' + line + ' %%%'
+      #self.messages[self.currentQuestion].append(line)
+
+
+
+
+
+class Counter(dict):
+  """
+  Dict with default 0
+  """
+  def __getitem__(self, idx):
+    try:
+      return dict.__getitem__(self, idx)
+    except KeyError:
+      return 0
+
+  def totalCount(self):
+    """
+    Returns the sum of counts for all keys.
+    """
+    return sum(self.values())
+

p5_classification/graphicsDisplay.py

@@ -0,0 +1,679 @@
+# graphicsDisplay.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).
+
+
+from graphicsUtils import *
+import math, time
+from game import Directions
+
+###########################
+#  GRAPHICS DISPLAY CODE  #
+###########################
+
+# Most code by Dan Klein and John Denero written or rewritten for cs188, UC Berkeley.
+# Some code from a Pacman implementation by LiveWires, and used / modified with permission.
+
+DEFAULT_GRID_SIZE = 30.0
+INFO_PANE_HEIGHT = 35
+BACKGROUND_COLOR = formatColor(0,0,0)
+WALL_COLOR = formatColor(0.0/255.0, 51.0/255.0, 255.0/255.0)
+INFO_PANE_COLOR = formatColor(.4,.4,0)
+SCORE_COLOR = formatColor(.9, .9, .9)
+PACMAN_OUTLINE_WIDTH = 2
+PACMAN_CAPTURE_OUTLINE_WIDTH = 4
+
+GHOST_COLORS = []
+GHOST_COLORS.append(formatColor(.9,0,0)) # Red
+GHOST_COLORS.append(formatColor(0,.3,.9)) # Blue
+GHOST_COLORS.append(formatColor(.98,.41,.07)) # Orange
+GHOST_COLORS.append(formatColor(.1,.75,.7)) # Green
+GHOST_COLORS.append(formatColor(1.0,0.6,0.0)) # Yellow
+GHOST_COLORS.append(formatColor(.4,0.13,0.91)) # Purple
+
+TEAM_COLORS = GHOST_COLORS[:2]
+
+GHOST_SHAPE = [
+    ( 0,    0.3 ),
+    ( 0.25, 0.75 ),
+    ( 0.5,  0.3 ),
+    ( 0.75, 0.75 ),
+    ( 0.75, -0.5 ),
+    ( 0.5,  -0.75 ),
+    (-0.5,  -0.75 ),
+    (-0.75, -0.5 ),
+    (-0.75, 0.75 ),
+    (-0.5,  0.3 ),
+    (-0.25, 0.75 )
+  ]
+GHOST_SIZE = 0.65
+SCARED_COLOR = formatColor(1,1,1)
+
+GHOST_VEC_COLORS = map(colorToVector, GHOST_COLORS)
+
+PACMAN_COLOR = formatColor(255.0/255.0,255.0/255.0,61.0/255)
+PACMAN_SCALE = 0.5
+#pacman_speed = 0.25
+
+# Food
+FOOD_COLOR = formatColor(1,1,1)
+FOOD_SIZE = 0.1
+
+# Laser
+LASER_COLOR = formatColor(1,0,0)
+LASER_SIZE = 0.02
+
+# Capsule graphics
+CAPSULE_COLOR = formatColor(1,1,1)
+CAPSULE_SIZE = 0.25
+
+# Drawing walls
+WALL_RADIUS = 0.15
+
+class InfoPane:
+    def __init__(self, layout, gridSize):
+        self.gridSize = gridSize
+        self.width = (layout.width) * gridSize
+        self.base = (layout.height + 1) * gridSize
+        self.height = INFO_PANE_HEIGHT
+        self.fontSize = 24
+        self.textColor = PACMAN_COLOR
+        self.drawPane()
+
+    def toScreen(self, pos, y = None):
+        """
+          Translates a point relative from the bottom left of the info pane.
+        """
+        if y == None:
+            x,y = pos
+        else:
+            x = pos
+
+        x = self.gridSize + x # Margin
+        y = self.base + y
+        return x,y
+
+    def drawPane(self):
+        self.scoreText = text( self.toScreen(0, 0  ), self.textColor, "SCORE:    0", "Times", self.fontSize, "bold")
+
+    def initializeGhostDistances(self, distances):
+        self.ghostDistanceText = []
+
+        size = 20
+        if self.width < 240:
+            size = 12
+        if self.width < 160:
+            size = 10
+
+        for i, d in enumerate(distances):
+            t = text( self.toScreen(self.width/2 + self.width/8 * i, 0), GHOST_COLORS[i+1], d, "Times", size, "bold")
+            self.ghostDistanceText.append(t)
+
+    def updateScore(self, score):
+        changeText(self.scoreText, "SCORE: % 4d" % score)
+
+    def setTeam(self, isBlue):
+        text = "RED TEAM"
+        if isBlue: text = "BLUE TEAM"
+        self.teamText = text( self.toScreen(300, 0  ), self.textColor, text, "Times", self.fontSize, "bold")
+
+    def updateGhostDistances(self, distances):
+        if len(distances) == 0: return
+        if 'ghostDistanceText' not in dir(self): self.initializeGhostDistances(distances)
+        else:
+            for i, d in enumerate(distances):
+                changeText(self.ghostDistanceText[i], d)
+
+    def drawGhost(self):
+        pass
+
+    def drawPacman(self):
+        pass
+
+    def drawWarning(self):
+        pass
+
+    def clearIcon(self):
+        pass
+
+    def updateMessage(self, message):
+        pass
+
+    def clearMessage(self):
+        pass
+
+
+class PacmanGraphics:
+    def __init__(self, zoom=1.0, frameTime=0.0, capture=False):
+        self.have_window = 0
+        self.currentGhostImages = {}
+        self.pacmanImage = None
+        self.zoom = zoom
+        self.gridSize = DEFAULT_GRID_SIZE * zoom
+        self.capture = capture
+        self.frameTime = frameTime
+
+    def checkNullDisplay(self):
+        return False
+
+    def initialize(self, state, isBlue = False):
+        self.isBlue = isBlue
+        self.startGraphics(state)
+
+        # self.drawDistributions(state)
+        self.distributionImages = None  # Initialized lazily
+        self.drawStaticObjects(state)
+        self.drawAgentObjects(state)
+
+        # Information
+        self.previousState = state
+
+    def startGraphics(self, state):
+        self.layout = state.layout
+        layout = self.layout
+        self.width = layout.width
+        self.height = layout.height
+        self.make_window(self.width, self.height)
+        self.infoPane = InfoPane(layout, self.gridSize)
+        self.currentState = layout
+
+    def drawDistributions(self, state):
+        walls = state.layout.walls
+        dist = []
+        for x in range(walls.width):
+            distx = []
+            dist.append(distx)
+            for y in range(walls.height):
+                ( screen_x, screen_y ) = self.to_screen( (x, y) )
+                block = square( (screen_x, screen_y),
+                                0.5 * self.gridSize,
+                                color = BACKGROUND_COLOR,
+                                filled = 1, behind=2)
+                distx.append(block)
+        self.distributionImages = dist
+
+    def drawStaticObjects(self, state):
+        layout = self.layout
+        self.drawWalls(layout.walls)
+        self.food = self.drawFood(layout.food)
+        self.capsules = self.drawCapsules(layout.capsules)
+        refresh()
+
+    def drawAgentObjects(self, state):
+        self.agentImages = [] # (agentState, image)
+        for index, agent in enumerate(state.agentStates):
+            if agent.isPacman:
+                image = self.drawPacman(agent, index)
+                self.agentImages.append( (agent, image) )
+            else:
+                image = self.drawGhost(agent, index)
+                self.agentImages.append( (agent, image) )
+        refresh()
+
+    def swapImages(self, agentIndex, newState):
+        """
+          Changes an image from a ghost to a pacman or vis versa (for capture)
+        """
+        prevState, prevImage = self.agentImages[agentIndex]
+        for item in prevImage: remove_from_screen(item)
+        if newState.isPacman:
+            image = self.drawPacman(newState, agentIndex)
+            self.agentImages[agentIndex] = (newState, image )
+        else:
+            image = self.drawGhost(newState, agentIndex)
+            self.agentImages[agentIndex] = (newState, image )
+        refresh()
+
+    def update(self, newState):
+        agentIndex = newState._agentMoved
+        agentState = newState.agentStates[agentIndex]
+
+        if self.agentImages[agentIndex][0].isPacman != agentState.isPacman: self.swapImages(agentIndex, agentState)
+        prevState, prevImage = self.agentImages[agentIndex]
+        if agentState.isPacman:
+            self.animatePacman(agentState, prevState, prevImage)
+        else:
+            self.moveGhost(agentState, agentIndex, prevState, prevImage)
+        self.agentImages[agentIndex] = (agentState, prevImage)
+
+        if newState._foodEaten != None:
+            self.removeFood(newState._foodEaten, self.food)
+        if newState._capsuleEaten != None:
+            self.removeCapsule(newState._capsuleEaten, self.capsules)
+        self.infoPane.updateScore(newState.score)
+        if 'ghostDistances' in dir(newState):
+            self.infoPane.updateGhostDistances(newState.ghostDistances)
+
+    def make_window(self, width, height):
+        grid_width = (width-1) * self.gridSize
+        grid_height = (height-1) * self.gridSize
+        screen_width = 2*self.gridSize + grid_width
+        screen_height = 2*self.gridSize + grid_height + INFO_PANE_HEIGHT
+
+        begin_graphics(screen_width,
+                       screen_height,
+                       BACKGROUND_COLOR,
+                       "CS188 Pacman")
+
+    def drawPacman(self, pacman, index):
+        position = self.getPosition(pacman)
+        screen_point = self.to_screen(position)
+        endpoints = self.getEndpoints(self.getDirection(pacman))
+
+        width = PACMAN_OUTLINE_WIDTH
+        outlineColor = PACMAN_COLOR
+        fillColor = PACMAN_COLOR
+
+        if self.capture:
+            outlineColor = TEAM_COLORS[index % 2]
+            fillColor = GHOST_COLORS[index]
+            width = PACMAN_CAPTURE_OUTLINE_WIDTH
+
+        return [circle(screen_point, PACMAN_SCALE * self.gridSize,
+                       fillColor = fillColor, outlineColor = outlineColor,
+                       endpoints = endpoints,
+                       width = width)]
+
+    def getEndpoints(self, direction, position=(0,0)):
+        x, y = position
+        pos = x - int(x) + y - int(y)
+        width = 30 + 80 * math.sin(math.pi* pos)
+
+        delta = width / 2
+        if (direction == 'West'):
+            endpoints = (180+delta, 180-delta)
+        elif (direction == 'North'):
+            endpoints = (90+delta, 90-delta)
+        elif (direction == 'South'):
+            endpoints = (270+delta, 270-delta)
+        else:
+            endpoints = (0+delta, 0-delta)
+        return endpoints
+
+    def movePacman(self, position, direction, image):
+        screenPosition = self.to_screen(position)
+        endpoints = self.getEndpoints( direction, position )
+        r = PACMAN_SCALE * self.gridSize
+        moveCircle(image[0], screenPosition, r, endpoints)
+        refresh()
+
+    def animatePacman(self, pacman, prevPacman, image):
+        if self.frameTime < 0:
+            print 'Press any key to step forward, "q" to play'
+            keys = wait_for_keys()
+            if 'q' in keys:
+                self.frameTime = 0.1
+        if self.frameTime > 0.01 or self.frameTime < 0:
+            start = time.time()
+            fx, fy = self.getPosition(prevPacman)
+            px, py = self.getPosition(pacman)
+            frames = 4.0
+            for i in range(1,int(frames) + 1):
+                pos = px*i/frames + fx*(frames-i)/frames, py*i/frames + fy*(frames-i)/frames
+                self.movePacman(pos, self.getDirection(pacman), image)
+                refresh()
+                sleep(abs(self.frameTime) / frames)
+        else:
+            self.movePacman(self.getPosition(pacman), self.getDirection(pacman), image)
+        refresh()
+
+    def getGhostColor(self, ghost, ghostIndex):
+        if ghost.scaredTimer > 0:
+            return SCARED_COLOR
+        else:
+            return GHOST_COLORS[ghostIndex]
+
+    def drawGhost(self, ghost, agentIndex):
+        pos = self.getPosition(ghost)
+        dir = self.getDirection(ghost)
+        (screen_x, screen_y) = (self.to_screen(pos) )
+        coords = []
+        for (x, y) in GHOST_SHAPE:
+            coords.append((x*self.gridSize*GHOST_SIZE + screen_x, y*self.gridSize*GHOST_SIZE + screen_y))
+
+        colour = self.getGhostColor(ghost, agentIndex)
+        body = polygon(coords, colour, filled = 1)
+        WHITE = formatColor(1.0, 1.0, 1.0)
+        BLACK = formatColor(0.0, 0.0, 0.0)
+
+        dx = 0
+        dy = 0
+        if dir == 'North':
+            dy = -0.2
+        if dir == 'South':
+            dy = 0.2
+        if dir == 'East':
+            dx = 0.2
+        if dir == 'West':
+            dx = -0.2
+        leftEye = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
+        rightEye = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
+        leftPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
+        rightPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
+        ghostImageParts = []
+        ghostImageParts.append(body)
+        ghostImageParts.append(leftEye)
+        ghostImageParts.append(rightEye)
+        ghostImageParts.append(leftPupil)
+        ghostImageParts.append(rightPupil)
+
+        return ghostImageParts
+
+    def moveEyes(self, pos, dir, eyes):
+        (screen_x, screen_y) = (self.to_screen(pos) )
+        dx = 0
+        dy = 0
+        if dir == 'North':
+            dy = -0.2
+        if dir == 'South':
+            dy = 0.2
+        if dir == 'East':
+            dx = 0.2
+        if dir == 'West':
+            dx = -0.2
+        moveCircle(eyes[0],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
+        moveCircle(eyes[1],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
+        moveCircle(eyes[2],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
+        moveCircle(eyes[3],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
+
+    def moveGhost(self, ghost, ghostIndex, prevGhost, ghostImageParts):
+        old_x, old_y = self.to_screen(self.getPosition(prevGhost))
+        new_x, new_y = self.to_screen(self.getPosition(ghost))
+        delta = new_x - old_x, new_y - old_y
+
+        for ghostImagePart in ghostImageParts:
+            move_by(ghostImagePart, delta)
+        refresh()
+
+        if ghost.scaredTimer > 0:
+            color = SCARED_COLOR
+        else:
+            color = GHOST_COLORS[ghostIndex]
+        edit(ghostImageParts[0], ('fill', color), ('outline', color))
+        self.moveEyes(self.getPosition(ghost), self.getDirection(ghost), ghostImageParts[-4:])
+        refresh()
+
+    def getPosition(self, agentState):
+        if agentState.configuration == None: return (-1000, -1000)
+        return agentState.getPosition()
+
+    def getDirection(self, agentState):
+        if agentState.configuration == None: return Directions.STOP
+        return agentState.configuration.getDirection()
+
+    def finish(self):
+        end_graphics()
+
+    def to_screen(self, point):
+        ( x, y ) = point
+        #y = self.height - y
+        x = (x + 1)*self.gridSize
+        y = (self.height  - y)*self.gridSize
+        return ( x, y )
+
+    # Fixes some TK issue with off-center circles
+    def to_screen2(self, point):
+        ( x, y ) = point
+        #y = self.height - y
+        x = (x + 1)*self.gridSize
+        y = (self.height  - y)*self.gridSize
+        return ( x, y )
+
+    def drawWalls(self, wallMatrix):
+        wallColor = WALL_COLOR
+        for xNum, x in enumerate(wallMatrix):
+            if self.capture and (xNum * 2) < wallMatrix.width: wallColor = TEAM_COLORS[0]
+            if self.capture and (xNum * 2) >= wallMatrix.width: wallColor = TEAM_COLORS[1]
+
+            for yNum, cell in enumerate(x):
+                if cell: # There's a wall here
+                    pos = (xNum, yNum)
+                    screen = self.to_screen(pos)
+                    screen2 = self.to_screen2(pos)
+
+                    # draw each quadrant of the square based on adjacent walls
+                    wIsWall = self.isWall(xNum-1, yNum, wallMatrix)
+                    eIsWall = self.isWall(xNum+1, yNum, wallMatrix)
+                    nIsWall = self.isWall(xNum, yNum+1, wallMatrix)
+                    sIsWall = self.isWall(xNum, yNum-1, wallMatrix)
+                    nwIsWall = self.isWall(xNum-1, yNum+1, wallMatrix)
+                    swIsWall = self.isWall(xNum-1, yNum-1, wallMatrix)
+                    neIsWall = self.isWall(xNum+1, yNum+1, wallMatrix)
+                    seIsWall = self.isWall(xNum+1, yNum-1, wallMatrix)
+
+                    # NE quadrant
+                    if (not nIsWall) and (not eIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (0,91), 'arc')
+                    if (nIsWall) and (not eIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
+                    if (not nIsWall) and (eIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                    if (nIsWall) and (eIsWall) and (not neIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (180,271), 'arc')
+                        line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
+
+                    # NW quadrant
+                    if (not nIsWall) and (not wIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (90,181), 'arc')
+                    if (nIsWall) and (not wIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
+                    if (not nIsWall) and (wIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                    if (nIsWall) and (wIsWall) and (not nwIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (270,361), 'arc')
+                        line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
+
+                    # SE quadrant
+                    if (not sIsWall) and (not eIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (270,361), 'arc')
+                    if (sIsWall) and (not eIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
+                    if (not sIsWall) and (eIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                    if (sIsWall) and (eIsWall) and (not seIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (90,181), 'arc')
+                        line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
+
+                    # SW quadrant
+                    if (not sIsWall) and (not wIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (180,271), 'arc')
+                    if (sIsWall) and (not wIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
+                    if (not sIsWall) and (wIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                    if (sIsWall) and (wIsWall) and (not swIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (0,91), 'arc')
+                        line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
+
+    def isWall(self, x, y, walls):
+        if x < 0 or y < 0:
+            return False
+        if x >= walls.width or y >= walls.height:
+            return False
+        return walls[x][y]
+
+    def drawFood(self, foodMatrix ):
+        foodImages = []
+        color = FOOD_COLOR
+        for xNum, x in enumerate(foodMatrix):
+            if self.capture and (xNum * 2) <= foodMatrix.width: color = TEAM_COLORS[0]
+            if self.capture and (xNum * 2) > foodMatrix.width: color = TEAM_COLORS[1]
+            imageRow = []
+            foodImages.append(imageRow)
+            for yNum, cell in enumerate(x):
+                if cell: # There's food here
+                    screen = self.to_screen((xNum, yNum ))
+                    dot = circle( screen,
+                                  FOOD_SIZE * self.gridSize,
+                                  outlineColor = color, fillColor = color,
+                                  width = 1)
+                    imageRow.append(dot)
+                else:
+                    imageRow.append(None)
+        return foodImages
+
+    def drawCapsules(self, capsules ):
+        capsuleImages = {}
+        for capsule in capsules:
+            ( screen_x, screen_y ) = self.to_screen(capsule)
+            dot = circle( (screen_x, screen_y),
+                              CAPSULE_SIZE * self.gridSize,
+                              outlineColor = CAPSULE_COLOR,
+                              fillColor = CAPSULE_COLOR,
+                              width = 1)
+            capsuleImages[capsule] = dot
+        return capsuleImages
+
+    def removeFood(self, cell, foodImages ):
+        x, y = cell
+        remove_from_screen(foodImages[x][y])
+
+    def removeCapsule(self, cell, capsuleImages ):
+        x, y = cell
+        remove_from_screen(capsuleImages[(x, y)])
+
+    def drawExpandedCells(self, cells):
+        """
+        Draws an overlay of expanded grid positions for search agents
+        """
+        n = float(len(cells))
+        baseColor = [1.0, 0.0, 0.0]
+        self.clearExpandedCells()
+        self.expandedCells = []
+        for k, cell in enumerate(cells):
+            screenPos = self.to_screen( cell)
+            cellColor = formatColor(*[(n-k) * c * .5 / n + .25 for c in baseColor])
+            block = square(screenPos,
+                     0.5 * self.gridSize,
+                     color = cellColor,
+                     filled = 1, behind=2)
+            self.expandedCells.append(block)
+            if self.frameTime < 0:
+                refresh()
+
+    def clearExpandedCells(self):
+        if 'expandedCells' in dir(self) and len(self.expandedCells) > 0:
+            for cell in self.expandedCells:
+                remove_from_screen(cell)
+
+
+    def updateDistributions(self, distributions):
+        "Draws an agent's belief distributions"
+        # copy all distributions so we don't change their state
+        distributions = map(lambda x: x.copy(), distributions)
+        if self.distributionImages == None:
+            self.drawDistributions(self.previousState)
+        for x in range(len(self.distributionImages)):
+            for y in range(len(self.distributionImages[0])):
+                image = self.distributionImages[x][y]
+                weights = [dist[ (x,y) ] for dist in distributions]
+
+                if sum(weights) != 0:
+                    pass
+                # Fog of war
+                color = [0.0,0.0,0.0]
+                colors = GHOST_VEC_COLORS[1:] # With Pacman
+                if self.capture: colors = GHOST_VEC_COLORS
+                for weight, gcolor in zip(weights, colors):
+                    color = [min(1.0, c + 0.95 * g * weight ** .3) for c,g in zip(color, gcolor)]
+                changeColor(image, formatColor(*color))
+        refresh()
+
+class FirstPersonPacmanGraphics(PacmanGraphics):
+    def __init__(self, zoom = 1.0, showGhosts = True, capture = False, frameTime=0):
+        PacmanGraphics.__init__(self, zoom, frameTime=frameTime)
+        self.showGhosts = showGhosts
+        self.capture = capture
+
+    def initialize(self, state, isBlue = False):
+
+        self.isBlue = isBlue
+        PacmanGraphics.startGraphics(self, state)
+        # Initialize distribution images
+        walls = state.layout.walls
+        dist = []
+        self.layout = state.layout
+
+        # Draw the rest
+        self.distributionImages = None  # initialize lazily
+        self.drawStaticObjects(state)
+        self.drawAgentObjects(state)
+
+        # Information
+        self.previousState = state
+
+    def lookAhead(self, config, state):
+        if config.getDirection() == 'Stop':
+            return
+        else:
+            pass
+            # Draw relevant ghosts
+            allGhosts = state.getGhostStates()
+            visibleGhosts = state.getVisibleGhosts()
+            for i, ghost in enumerate(allGhosts):
+                if ghost in visibleGhosts:
+                    self.drawGhost(ghost, i)
+                else:
+                    self.currentGhostImages[i] = None
+
+    def getGhostColor(self, ghost, ghostIndex):
+        return GHOST_COLORS[ghostIndex]
+
+    def getPosition(self, ghostState):
+        if not self.showGhosts and not ghostState.isPacman and ghostState.getPosition()[1] > 1:
+            return (-1000, -1000)
+        else:
+            return PacmanGraphics.getPosition(self, ghostState)
+
+def add(x, y):
+    return (x[0] + y[0], x[1] + y[1])
+
+
+# Saving graphical output
+# -----------------------
+# Note: to make an animated gif from this postscript output, try the command:
+# convert -delay 7 -loop 1 -compress lzw -layers optimize frame* out.gif
+# convert is part of imagemagick (freeware)
+
+SAVE_POSTSCRIPT = False
+POSTSCRIPT_OUTPUT_DIR = 'frames'
+FRAME_NUMBER = 0
+import os
+
+def saveFrame():
+    "Saves the current graphical output as a postscript file"
+    global SAVE_POSTSCRIPT, FRAME_NUMBER, POSTSCRIPT_OUTPUT_DIR
+    if not SAVE_POSTSCRIPT: return
+    if not os.path.exists(POSTSCRIPT_OUTPUT_DIR): os.mkdir(POSTSCRIPT_OUTPUT_DIR)
+    name = os.path.join(POSTSCRIPT_OUTPUT_DIR, 'frame_%08d.ps' % FRAME_NUMBER)
+    FRAME_NUMBER += 1
+    writePostscript(name) # writes the current canvas

p5_classification/graphicsUtils.py

@@ -0,0 +1,398 @@
+# graphicsUtils.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).
+
+
+import sys
+import math
+import random
+import string
+import time
+import types
+import Tkinter
+
+_Windows = sys.platform == 'win32'  # True if on Win95/98/NT
+
+_root_window = None      # The root window for graphics output
+_canvas = None      # The canvas which holds graphics
+_canvas_xs = None      # Size of canvas object
+_canvas_ys = None
+_canvas_x = None      # Current position on canvas
+_canvas_y = None
+_canvas_col = None      # Current colour (set to black below)
+_canvas_tsize = 12
+_canvas_tserifs = 0
+
+def formatColor(r, g, b):
+    return '#%02x%02x%02x' % (int(r * 255), int(g * 255), int(b * 255))
+
+def colorToVector(color):
+    return map(lambda x: int(x, 16) / 256.0, [color[1:3], color[3:5], color[5:7]])
+
+if _Windows:
+    _canvas_tfonts = ['times new roman', 'lucida console']
+else:
+    _canvas_tfonts = ['times', 'lucidasans-24']
+    pass # XXX need defaults here
+
+def sleep(secs):
+    global _root_window
+    if _root_window == None:
+        time.sleep(secs)
+    else:
+        _root_window.update_idletasks()
+        _root_window.after(int(1000 * secs), _root_window.quit)
+        _root_window.mainloop()
+
+def begin_graphics(width=640, height=480, color=formatColor(0, 0, 0), title=None):
+
+    global _root_window, _canvas, _canvas_x, _canvas_y, _canvas_xs, _canvas_ys, _bg_color
+
+    # Check for duplicate call
+    if _root_window is not None:
+        # Lose the window.
+        _root_window.destroy()
+
+    # Save the canvas size parameters
+    _canvas_xs, _canvas_ys = width - 1, height - 1
+    _canvas_x, _canvas_y = 0, _canvas_ys
+    _bg_color = color
+
+    # Create the root window
+    _root_window = Tkinter.Tk()
+    _root_window.protocol('WM_DELETE_WINDOW', _destroy_window)
+    _root_window.title(title or 'Graphics Window')
+    _root_window.resizable(0, 0)
+
+    # Create the canvas object
+    try:
+        _canvas = Tkinter.Canvas(_root_window, width=width, height=height)
+        _canvas.pack()
+        draw_background()
+        _canvas.update()
+    except:
+        _root_window = None
+        raise
+
+    # Bind to key-down and key-up events
+    _root_window.bind( "<KeyPress>", _keypress )
+    _root_window.bind( "<KeyRelease>", _keyrelease )
+    _root_window.bind( "<FocusIn>", _clear_keys )
+    _root_window.bind( "<FocusOut>", _clear_keys )
+    _root_window.bind( "<Button-1>", _leftclick )
+    _root_window.bind( "<Button-2>", _rightclick )
+    _root_window.bind( "<Button-3>", _rightclick )
+    _root_window.bind( "<Control-Button-1>", _ctrl_leftclick)
+    _clear_keys()
+
+_leftclick_loc = None
+_rightclick_loc = None
+_ctrl_leftclick_loc = None
+
+def _leftclick(event):
+    global _leftclick_loc
+    _leftclick_loc = (event.x, event.y)
+
+def _rightclick(event):
+    global _rightclick_loc
+    _rightclick_loc = (event.x, event.y)
+
+def _ctrl_leftclick(event):
+    global _ctrl_leftclick_loc
+    _ctrl_leftclick_loc = (event.x, event.y)
+
+def wait_for_click():
+    while True:
+        global _leftclick_loc
+        global _rightclick_loc
+        global _ctrl_leftclick_loc
+        if _leftclick_loc != None:
+            val = _leftclick_loc
+            _leftclick_loc = None
+            return val, 'left'
+        if _rightclick_loc != None:
+            val = _rightclick_loc
+            _rightclick_loc = None
+            return val, 'right'
+        if _ctrl_leftclick_loc != None:
+            val = _ctrl_leftclick_loc
+            _ctrl_leftclick_loc = None
+            return val, 'ctrl_left'
+        sleep(0.05)
+
+def draw_background():
+    corners = [(0,0), (0, _canvas_ys), (_canvas_xs, _canvas_ys), (_canvas_xs, 0)]
+    polygon(corners, _bg_color, fillColor=_bg_color, filled=True, smoothed=False)
+
+def _destroy_window(event=None):
+    sys.exit(0)
+#    global _root_window
+#    _root_window.destroy()
+#    _root_window = None
+    #print "DESTROY"
+
+def end_graphics():
+    global _root_window, _canvas, _mouse_enabled
+    try:
+        try:
+            sleep(1)
+            if _root_window != None:
+                _root_window.destroy()
+        except SystemExit, e:
+            print 'Ending graphics raised an exception:', e
+    finally:
+        _root_window = None
+        _canvas = None
+        _mouse_enabled = 0
+        _clear_keys()
+
+def clear_screen(background=None):
+    global _canvas_x, _canvas_y
+    _canvas.delete('all')
+    draw_background()
+    _canvas_x, _canvas_y = 0, _canvas_ys
+
+def polygon(coords, outlineColor, fillColor=None, filled=1, smoothed=1, behind=0, width=1):
+    c = []
+    for coord in coords:
+        c.append(coord[0])
+        c.append(coord[1])
+    if fillColor == None: fillColor = outlineColor
+    if filled == 0: fillColor = ""
+    poly = _canvas.create_polygon(c, outline=outlineColor, fill=fillColor, smooth=smoothed, width=width)
+    if behind > 0:
+        _canvas.tag_lower(poly, behind) # Higher should be more visible
+    return poly
+
+def square(pos, r, color, filled=1, behind=0):
+    x, y = pos
+    coords = [(x - r, y - r), (x + r, y - r), (x + r, y + r), (x - r, y + r)]
+    return polygon(coords, color, color, filled, 0, behind=behind)
+
+def circle(pos, r, outlineColor, fillColor, endpoints=None, style='pieslice', width=2):
+    x, y = pos
+    x0, x1 = x - r - 1, x + r
+    y0, y1 = y - r - 1, y + r
+    if endpoints == None:
+        e = [0, 359]
+    else:
+        e = list(endpoints)
+    while e[0] > e[1]: e[1] = e[1] + 360
+
+    return _canvas.create_arc(x0, y0, x1, y1, outline=outlineColor, fill=fillColor,
+                              extent=e[1] - e[0], start=e[0], style=style, width=width)
+
+def image(pos, file="../../blueghost.gif"):
+    x, y = pos
+    # img = PhotoImage(file=file)
+    return _canvas.create_image(x, y, image = Tkinter.PhotoImage(file=file), anchor = Tkinter.NW)
+
+
+def refresh():
+    _canvas.update_idletasks()
+
+def moveCircle(id, pos, r, endpoints=None):
+    global _canvas_x, _canvas_y
+
+    x, y = pos
+#    x0, x1 = x - r, x + r + 1
+#    y0, y1 = y - r, y + r + 1
+    x0, x1 = x - r - 1, x + r
+    y0, y1 = y - r - 1, y + r
+    if endpoints == None:
+        e = [0, 359]
+    else:
+        e = list(endpoints)
+    while e[0] > e[1]: e[1] = e[1] + 360
+
+    edit(id, ('start', e[0]), ('extent', e[1] - e[0]))
+    move_to(id, x0, y0)
+
+def edit(id, *args):
+    _canvas.itemconfigure(id, **dict(args))
+
+def text(pos, color, contents, font='Helvetica', size=12, style='normal', anchor="nw"):
+    global _canvas_x, _canvas_y
+    x, y = pos
+    font = (font, str(size), style)
+    return _canvas.create_text(x, y, fill=color, text=contents, font=font, anchor=anchor)
+
+def changeText(id, newText, font=None, size=12, style='normal'):
+    _canvas.itemconfigure(id, text=newText)
+    if font != None:
+        _canvas.itemconfigure(id, font=(font, '-%d' % size, style))
+
+def changeColor(id, newColor):
+    _canvas.itemconfigure(id, fill=newColor)
+
+def line(here, there, color=formatColor(0, 0, 0), width=2):
+    x0, y0 = here[0], here[1]
+    x1, y1 = there[0], there[1]
+    return _canvas.create_line(x0, y0, x1, y1, fill=color, width=width)
+
+##############################################################################
+### Keypress handling ########################################################
+##############################################################################
+
+# We bind to key-down and key-up events.
+
+_keysdown = {}
+_keyswaiting = {}
+# This holds an unprocessed key release.  We delay key releases by up to
+# one call to keys_pressed() to get round a problem with auto repeat.
+_got_release = None
+
+def _keypress(event):
+    global _got_release
+    #remap_arrows(event)
+    _keysdown[event.keysym] = 1
+    _keyswaiting[event.keysym] = 1
+#    print event.char, event.keycode
+    _got_release = None
+
+def _keyrelease(event):
+    global _got_release
+    #remap_arrows(event)
+    try:
+        del _keysdown[event.keysym]
+    except:
+        pass
+    _got_release = 1
+
+def remap_arrows(event):
+    # TURN ARROW PRESSES INTO LETTERS (SHOULD BE IN KEYBOARD AGENT)
+    if event.char in ['a', 's', 'd', 'w']:
+        return
+    if event.keycode in [37, 101]: # LEFT ARROW (win / x)
+        event.char = 'a'
+    if event.keycode in [38, 99]: # UP ARROW
+        event.char = 'w'
+    if event.keycode in [39, 102]: # RIGHT ARROW
+        event.char = 'd'
+    if event.keycode in [40, 104]: # DOWN ARROW
+        event.char = 's'
+
+def _clear_keys(event=None):
+    global _keysdown, _got_release, _keyswaiting
+    _keysdown = {}
+    _keyswaiting = {}
+    _got_release = None
+
+def keys_pressed(d_o_e=Tkinter.tkinter.dooneevent,
+                 d_w=Tkinter.tkinter.DONT_WAIT):
+    d_o_e(d_w)
+    if _got_release:
+        d_o_e(d_w)
+    return _keysdown.keys()
+
+def keys_waiting():
+    global _keyswaiting
+    keys = _keyswaiting.keys()
+    _keyswaiting = {}
+    return keys
+
+# Block for a list of keys...
+
+def wait_for_keys():
+    keys = []
+    while keys == []:
+        keys = keys_pressed()
+        sleep(0.05)
+    return keys
+
+def remove_from_screen(x,
+                       d_o_e=Tkinter.tkinter.dooneevent,
+                       d_w=Tkinter.tkinter.DONT_WAIT):
+    _canvas.delete(x)
+    d_o_e(d_w)
+
+def _adjust_coords(coord_list, x, y):
+    for i in range(0, len(coord_list), 2):
+        coord_list[i] = coord_list[i] + x
+        coord_list[i + 1] = coord_list[i + 1] + y
+    return coord_list
+
+def move_to(object, x, y=None,
+            d_o_e=Tkinter.tkinter.dooneevent,
+            d_w=Tkinter.tkinter.DONT_WAIT):
+    if y is None:
+        try: x, y = x
+        except: raise  'incomprehensible coordinates'
+
+    horiz = True
+    newCoords = []
+    current_x, current_y = _canvas.coords(object)[0:2] # first point
+    for coord in  _canvas.coords(object):
+        if horiz:
+            inc = x - current_x
+        else:
+            inc = y - current_y
+        horiz = not horiz
+
+        newCoords.append(coord + inc)
+
+    _canvas.coords(object, *newCoords)
+    d_o_e(d_w)
+
+def move_by(object, x, y=None,
+            d_o_e=Tkinter.tkinter.dooneevent,
+            d_w=Tkinter.tkinter.DONT_WAIT, lift=False):
+    if y is None:
+        try: x, y = x
+        except: raise Exception, 'incomprehensible coordinates'
+
+    horiz = True
+    newCoords = []
+    for coord in  _canvas.coords(object):
+        if horiz:
+            inc = x
+        else:
+            inc = y
+        horiz = not horiz
+
+        newCoords.append(coord + inc)
+
+    _canvas.coords(object, *newCoords)
+    d_o_e(d_w)
+    if lift:
+        _canvas.tag_raise(object)
+
+def writePostscript(filename):
+    "Writes the current canvas to a postscript file."
+    psfile = file(filename, 'w')
+    psfile.write(_canvas.postscript(pageanchor='sw',
+                     y='0.c',
+                     x='0.c'))
+    psfile.close()
+
+ghost_shape = [
+    (0, - 0.5),
+    (0.25, - 0.75),
+    (0.5, - 0.5),
+    (0.75, - 0.75),
+    (0.75, 0.5),
+    (0.5, 0.75),
+    (- 0.5, 0.75),
+    (- 0.75, 0.5),
+    (- 0.75, - 0.75),
+    (- 0.5, - 0.5),
+    (- 0.25, - 0.75)
+  ]
+
+if __name__ == '__main__':
+    begin_graphics()
+    clear_screen()
+    ghost_shape = [(x * 10 + 20, y * 10 + 20) for x, y in ghost_shape]
+    g = polygon(ghost_shape, formatColor(1, 1, 1))
+    move_to(g, (50, 50))
+    circle((150, 150), 20, formatColor(0.7, 0.3, 0.0), endpoints=[15, - 15])
+    sleep(2)

p5_classification/keyboardAgents.py

@@ -0,0 +1,84 @@
+# keyboardAgents.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).
+
+
+from game import Agent
+from game import Directions
+import random
+
+class KeyboardAgent(Agent):
+    """
+    An agent controlled by the keyboard.
+    """
+    # NOTE: Arrow keys also work.
+    WEST_KEY  = 'a'
+    EAST_KEY  = 'd'
+    NORTH_KEY = 'w'
+    SOUTH_KEY = 's'
+    STOP_KEY = 'q'
+
+    def __init__( self, index = 0 ):
+
+        self.lastMove = Directions.STOP
+        self.index = index
+        self.keys = []
+
+    def getAction( self, state):
+        from graphicsUtils import keys_waiting
+        from graphicsUtils import keys_pressed
+        keys = keys_waiting() + keys_pressed()
+        if keys != []:
+            self.keys = keys
+
+        legal = state.getLegalActions(self.index)
+        move = self.getMove(legal)
+
+        if move == Directions.STOP:
+            # Try to move in the same direction as before
+            if self.lastMove in legal:
+                move = self.lastMove
+
+        if (self.STOP_KEY in self.keys) and Directions.STOP in legal: move = Directions.STOP
+
+        if move not in legal:
+            move = random.choice(legal)
+
+        self.lastMove = move
+        return move
+
+    def getMove(self, legal):
+        move = Directions.STOP
+        if   (self.WEST_KEY in self.keys or 'Left' in self.keys) and Directions.WEST in legal:  move = Directions.WEST
+        if   (self.EAST_KEY in self.keys or 'Right' in self.keys) and Directions.EAST in legal: move = Directions.EAST
+        if   (self.NORTH_KEY in self.keys or 'Up' in self.keys) and Directions.NORTH in legal:   move = Directions.NORTH
+        if   (self.SOUTH_KEY in self.keys or 'Down' in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
+        return move
+
+class KeyboardAgent2(KeyboardAgent):
+    """
+    A second agent controlled by the keyboard.
+    """
+    # NOTE: Arrow keys also work.
+    WEST_KEY  = 'j'
+    EAST_KEY  = "l"
+    NORTH_KEY = 'i'
+    SOUTH_KEY = 'k'
+    STOP_KEY = 'u'
+
+    def getMove(self, legal):
+        move = Directions.STOP
+        if   (self.WEST_KEY in self.keys) and Directions.WEST in legal:  move = Directions.WEST
+        if   (self.EAST_KEY in self.keys) and Directions.EAST in legal: move = Directions.EAST
+        if   (self.NORTH_KEY in self.keys) and Directions.NORTH in legal:   move = Directions.NORTH
+        if   (self.SOUTH_KEY in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
+        return move

p5_classification/layout.py

@@ -0,0 +1,149 @@
+# layout.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).
+
+
+from util import manhattanDistance
+from game import Grid
+import os
+import random
+
+VISIBILITY_MATRIX_CACHE = {}
+
+class Layout:
+    """
+    A Layout manages the static information about the game board.
+    """
+
+    def __init__(self, layoutText):
+        self.width = len(layoutText[0])
+        self.height= len(layoutText)
+        self.walls = Grid(self.width, self.height, False)
+        self.food = Grid(self.width, self.height, False)
+        self.capsules = []
+        self.agentPositions = []
+        self.numGhosts = 0
+        self.processLayoutText(layoutText)
+        self.layoutText = layoutText
+        self.totalFood = len(self.food.asList())
+        # self.initializeVisibilityMatrix()
+
+    def getNumGhosts(self):
+        return self.numGhosts
+
+    def initializeVisibilityMatrix(self):
+        global VISIBILITY_MATRIX_CACHE
+        if reduce(str.__add__, self.layoutText) not in VISIBILITY_MATRIX_CACHE:
+            from game import Directions
+            vecs = [(-0.5,0), (0.5,0),(0,-0.5),(0,0.5)]
+            dirs = [Directions.NORTH, Directions.SOUTH, Directions.WEST, Directions.EAST]
+            vis = Grid(self.width, self.height, {Directions.NORTH:set(), Directions.SOUTH:set(), Directions.EAST:set(), Directions.WEST:set(), Directions.STOP:set()})
+            for x in range(self.width):
+                for y in range(self.height):
+                    if self.walls[x][y] == False:
+                        for vec, direction in zip(vecs, dirs):
+                            dx, dy = vec
+                            nextx, nexty = x + dx, y + dy
+                            while (nextx + nexty) != int(nextx) + int(nexty) or not self.walls[int(nextx)][int(nexty)] :
+                                vis[x][y][direction].add((nextx, nexty))
+                                nextx, nexty = x + dx, y + dy
+            self.visibility = vis
+            VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)] = vis
+        else:
+            self.visibility = VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)]
+
+    def isWall(self, pos):
+        x, col = pos
+        return self.walls[x][col]
+
+    def getRandomLegalPosition(self):
+        x = random.choice(range(self.width))
+        y = random.choice(range(self.height))
+        while self.isWall( (x, y) ):
+            x = random.choice(range(self.width))
+            y = random.choice(range(self.height))
+        return (x,y)
+
+    def getRandomCorner(self):
+        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
+        return random.choice(poses)
+
+    def getFurthestCorner(self, pacPos):
+        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
+        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
+        return pos
+
+    def isVisibleFrom(self, ghostPos, pacPos, pacDirection):
+        row, col = [int(x) for x in pacPos]
+        return ghostPos in self.visibility[row][col][pacDirection]
+
+    def __str__(self):
+        return "\n".join(self.layoutText)
+
+    def deepCopy(self):
+        return Layout(self.layoutText[:])
+
+    def processLayoutText(self, layoutText):
+        """
+        Coordinates are flipped from the input format to the (x,y) convention here
+
+        The shape of the maze.  Each character
+        represents a different type of object.
+         % - Wall
+         . - Food
+         o - Capsule
+         G - Ghost
+         P - Pacman
+        Other characters are ignored.
+        """
+        maxY = self.height - 1
+        for y in range(self.height):
+            for x in range(self.width):
+                layoutChar = layoutText[maxY - y][x]
+                self.processLayoutChar(x, y, layoutChar)
+        self.agentPositions.sort()
+        self.agentPositions = [ ( i == 0, pos) for i, pos in self.agentPositions]
+
+    def processLayoutChar(self, x, y, layoutChar):
+        if layoutChar == '%':
+            self.walls[x][y] = True
+        elif layoutChar == '.':
+            self.food[x][y] = True
+        elif layoutChar == 'o':
+            self.capsules.append((x, y))
+        elif layoutChar == 'P':
+            self.agentPositions.append( (0, (x, y) ) )
+        elif layoutChar in ['G']:
+            self.agentPositions.append( (1, (x, y) ) )
+            self.numGhosts += 1
+        elif layoutChar in  ['1', '2', '3', '4']:
+            self.agentPositions.append( (int(layoutChar), (x,y)))
+            self.numGhosts += 1
+def getLayout(name, back = 2):
+    if name.endswith('.lay'):
+        layout = tryToLoad('layouts/' + name)
+        if layout == None: layout = tryToLoad(name)
+    else:
+        layout = tryToLoad('layouts/' + name + '.lay')
+        if layout == None: layout = tryToLoad(name + '.lay')
+    if layout == None and back >= 0:
+        curdir = os.path.abspath('.')
+        os.chdir('..')
+        layout = getLayout(name, back -1)
+        os.chdir(curdir)
+    return layout
+
+def tryToLoad(fullname):
+    if(not os.path.exists(fullname)): return None
+    f = open(fullname)
+    try: return Layout([line.strip() for line in f])
+    finally: f.close()

p5_classification/layouts/capsuleClassic.lay

@@ -0,0 +1,7 @@
+%%%%%%%%%%%%%%%%%%%
+%G.       G   ....%
+%.% % %%%%%% %.%%.%
+%.%o% %   o% %.o%.%
+%.%%%.%  %%% %..%.%
+%.....  P    %..%G%
+%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/contestClassic.lay

@@ -0,0 +1,9 @@
+%%%%%%%%%%%%%%%%%%%%
+%o...%........%...o%
+%.%%.%.%%..%%.%.%%.%
+%...... G GG%......%
+%.%.%%.%% %%%.%%.%.%
+%.%....% ooo%.%..%.%
+%.%.%%.% %% %.%.%%.%
+%o%......P....%....%
+%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/mediumClassic.lay

@@ -0,0 +1,11 @@
+%%%%%%%%%%%%%%%%%%%%
+%o...%........%....%
+%.%%.%.%%%%%%.%.%%.%
+%.%..............%.%
+%.%.%%.%%  %%.%%.%.%
+%......%G  G%......%
+%.%.%%.%%%%%%.%%.%.%
+%.%..............%.%
+%.%%.%.%%%%%%.%.%%.%
+%....%...P....%...o%
+%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/minimaxClassic.lay

@@ -0,0 +1,5 @@
+%%%%%%%%%
+%.P    G% 
+% %.%G%%%  
+%G    %%% 
+%%%%%%%%%

p5_classification/layouts/openClassic.lay

@@ -0,0 +1,9 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%
+%.. P  ....      ....   %
+%..  ...  ...  ...  ... %
+%..  ...  ...  ...  ... %
+%..    ....      .... G %
+%..  ...  ...  ...  ... %
+%..  ...  ...  ...  ... %
+%..    ....      ....  o%
+%%%%%%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/originalClassic.lay

@@ -0,0 +1,27 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%............%%............%
+%.%%%%.%%%%%.%%.%%%%%.%%%%.%
+%o%%%%.%%%%%.%%.%%%%%.%%%%o%
+%.%%%%.%%%%%.%%.%%%%%.%%%%.%
+%..........................%
+%.%%%%.%%.%%%%%%%%.%%.%%%%.%
+%.%%%%.%%.%%%%%%%%.%%.%%%%.%
+%......%%....%%....%%......%
+%%%%%%.%%%%% %% %%%%%.%%%%%%
+%%%%%%.%%%%% %% %%%%%.%%%%%%
+%%%%%%.%            %.%%%%%%
+%%%%%%.% %%%%  %%%% %.%%%%%%
+%     .  %G  GG  G%  .     %
+%%%%%%.% %%%%%%%%%% %.%%%%%%
+%%%%%%.%            %.%%%%%%
+%%%%%%.% %%%%%%%%%% %.%%%%%%
+%............%%............%
+%.%%%%.%%%%%.%%.%%%%%.%%%%.%
+%.%%%%.%%%%%.%%.%%%%%.%%%%.%
+%o..%%.......  .......%%..o%
+%%%.%%.%%.%%%%%%%%.%%.%%.%%%
+%%%.%%.%%.%%%%%%%%.%%.%%.%%%
+%......%%....%%....%%......%
+%.%%%%%%%%%%.%%.%%%%%%%%%%.%
+%.............P............%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/smallClassic.lay

@@ -0,0 +1,7 @@
+%%%%%%%%%%%%%%%%%%%%
+%......%G  G%......%
+%.%%...%%  %%...%%.%
+%.%o.%........%.o%.%
+%.%%.%.%%%%%%.%.%%.%
+%........P.........%
+%%%%%%%%%%%%%%%%%%%%

p5_classification/layouts/testClassic.lay

@@ -0,0 +1,10 @@
+%%%%%
+% . %
+%.G.%
+% . %
+%. .%
+%   %
+%  .%
+%   %
+%P .%
+%%%%%

p5_classification/layouts/trappedClassic.lay

@@ -0,0 +1,5 @@
+%%%%%%%%
+%   P G%
+%G%%%%%%
+%....  %
+%%%%%%%%

p5_classification/layouts/trickyClassic.lay

@@ -0,0 +1,13 @@
+%%%%%%%%%%%%%%%%%%%%
+%o...%........%...o%
+%.%%.%.%%..%%.%.%%.%
+%.%.....%..%.....%.%
+%.%.%%.%%  %%.%%.%.%
+%...... GGGG%.%....%
+%.%....%%%%%%.%..%.%
+%.%....%  oo%.%..%.%
+%.%....% %%%%.%..%.%
+%.%...........%..%.%
+%.%%.%.%%%%%%.%.%%.%
+%o...%...P....%...o%
+%%%%%%%%%%%%%%%%%%%%

p5_classification/mira.py

@@ -0,0 +1,81 @@
+# mira.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).
+
+
+# Mira implementation
+import util
+PRINT = True
+
+class MiraClassifier:
+    """
+    Mira classifier.
+
+    Note that the variable 'datum' in this code refers to a counter of features
+    (not to a raw samples.Datum).
+    """
+    def __init__( self, legalLabels, max_iterations):
+        self.legalLabels = legalLabels
+        self.type = "mira"
+        self.automaticTuning = False
+        self.C = 0.001
+        self.legalLabels = legalLabels
+        self.max_iterations = max_iterations
+        self.initializeWeightsToZero()
+
+    def initializeWeightsToZero(self):
+        "Resets the weights of each label to zero vectors"
+        self.weights = {}
+        for label in self.legalLabels:
+            self.weights[label] = util.Counter() # this is the data-structure you should use
+
+    def train(self, trainingData, trainingLabels, validationData, validationLabels):
+        "Outside shell to call your method. Do not modify this method."
+
+        self.features = trainingData[0].keys() # this could be useful for your code later...
+
+        if (self.automaticTuning):
+            Cgrid = [0.002, 0.004, 0.008]
+        else:
+            Cgrid = [self.C]
+
+        return self.trainAndTune(trainingData, trainingLabels, validationData, validationLabels, Cgrid)
+
+    def trainAndTune(self, trainingData, trainingLabels, validationData, validationLabels, Cgrid):
+        """
+        This method sets self.weights using MIRA.  Train the classifier for each value of C in Cgrid,
+        then store the weights that give the best accuracy on the validationData.
+
+        Use the provided self.weights[label] data structure so that
+        the classify method works correctly. Also, recall that a
+        datum is a counter from features to values for those features
+        representing a vector of values.
+        """
+        "*** YOUR CODE HERE ***"
+        util.raiseNotDefined()
+
+    def classify(self, data ):
+        """
+        Classifies each datum as the label that most closely matches the prototype vector
+        for that label.  See the project description for details.
+
+        Recall that a datum is a util.counter...
+        """
+        guesses = []
+        for datum in data:
+            vectors = util.Counter()
+            for l in self.legalLabels:
+                vectors[l] = self.weights[l] * datum
+            guesses.append(vectors.argMax())
+        return guesses
+
+

p5_classification/mostFrequent.py

@@ -0,0 +1,40 @@
+# mostFrequent.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).
+
+
+import util
+import classificationMethod
+
+class MostFrequentClassifier(classificationMethod.ClassificationMethod):
+    """
+    The MostFrequentClassifier is a very simple classifier: for
+    every test instance presented to it, the classifier returns
+    the label that was seen most often in the training data.
+    """
+    def __init__(self, legalLabels):
+        self.guess = None
+        self.type = "mostfrequent"
+
+    def train(self, data, labels, validationData, validationLabels):
+        """
+        Find the most common label in the training data.
+        """
+        counter = util.Counter()
+        counter.incrementAll(labels, 1)
+        self.guess = counter.argMax()
+
+    def classify(self, testData):
+        """
+        Classify all test data as the most common label.
+        """
+        return [self.guess for i in testData]

p5_classification/naiveBayes.py

@@ -0,0 +1,175 @@
+# naiveBayes.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).
+
+
+import util
+import classificationMethod
+import math
+
+class NaiveBayesClassifier(classificationMethod.ClassificationMethod):
+    """
+    See the project description for the specifications of the Naive Bayes classifier.
+
+    Note that the variable 'datum' in this code refers to a counter of features
+    (not to a raw samples.Datum).
+    """
+    def __init__(self, legalLabels):
+        self.legalLabels = legalLabels
+        self.type = "naivebayes"
+        self.k = 1 # this is the smoothing parameter, ** use it in your train method **
+        self.automaticTuning = False # Look at this flag to decide whether to choose k automatically ** use this in your train method **
+
+    def setSmoothing(self, k):
+        """
+        This is used by the main method to change the smoothing parameter before training.
+        Do not modify this method.
+        """
+        self.k = k
+
+    def train(self, trainingData, trainingLabels, validationData, validationLabels):
+        """
+        Outside shell to call your method. Do not modify this method.
+        """
+
+        # might be useful in your code later...
+        # this is a list of all features in the training set.
+        self.features = list(set([ f for datum in trainingData for f in datum.keys() ]));
+
+        if (self.automaticTuning):
+            kgrid = [0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 20, 50]
+        else:
+            kgrid = [self.k]
+
+        self.trainAndTune(trainingData, trainingLabels, validationData, validationLabels, kgrid)
+
+    def trainAndTune(self, trainingData, trainingLabels, validationData, validationLabels, kgrid):
+        """
+        Trains the classifier by collecting counts over the training data, and
+        stores the Laplace smoothed estimates so that they can be used to classify.
+        Evaluate each value of k in kgrid to choose the smoothing parameter
+        that gives the best accuracy on the held-out validationData.
+
+        trainingData and validationData are lists of feature Counters.  The corresponding
+        label lists contain the correct label for each datum.
+
+        To get the list of all possible features or labels, use self.features and
+        self.legalLabels.
+        """
+
+        bestAccuracyCount = -1 # best accuracy so far on validation set
+
+        # Common training - get all counts from training data
+        # We only do it once - save computation in tuning smoothing parameter
+        commonPrior = util.Counter() # probability over labels
+        commonConditionalProb = util.Counter() # Conditional probability of feature feat being 1
+                                      # indexed by (feat, label)
+        commonCounts = util.Counter() # how many time I have seen feature feat with label y
+                                    # whatever inactive or active
+
+        for i in range(len(trainingData)):
+            datum = trainingData[i]
+            label = trainingLabels[i]
+            commonPrior[label] += 1
+            for feat, value in datum.items():
+                commonCounts[(feat,label)] += 1
+                if value > 0: # assume binary value
+                    commonConditionalProb[(feat, label)] += 1
+
+        for k in kgrid: # Smoothing parameter tuning loop!
+            prior = util.Counter()
+            conditionalProb = util.Counter()
+            counts = util.Counter()
+
+            # get counts from common training step
+            for key, val in commonPrior.items():
+                prior[key] += val
+            for key, val in commonCounts.items():
+                counts[key] += val
+            for key, val in commonConditionalProb.items():
+                conditionalProb[key] += val
+
+            # smoothing:
+            for label in self.legalLabels:
+                for feat in self.features:
+                    conditionalProb[ (feat, label)] +=  k
+                    counts[(feat, label)] +=  2*k # 2 because both value 0 and 1 are smoothed
+
+            # normalizing:
+            prior.normalize()
+            for x, count in conditionalProb.items():
+                conditionalProb[x] = count * 1.0 / counts[x]
+
+            self.prior = prior
+            self.conditionalProb = conditionalProb
+
+            # evaluating performance on validation set
+            predictions = self.classify(validationData)
+            accuracyCount =  [predictions[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
+
+            print "Performance on validation set for k=%f: (%.1f%%)" % (k, 100.0*accuracyCount/len(validationLabels))
+            if accuracyCount > bestAccuracyCount:
+                bestParams = (prior, conditionalProb, k)
+                bestAccuracyCount = accuracyCount
+            # end of automatic tuning loop
+        self.prior, self.conditionalProb, self.k = bestParams
+
+    def classify(self, testData):
+        """
+        Classify the data based on the posterior distribution over labels.
+
+        You shouldn't modify this method.
+        """
+        guesses = []
+        self.posteriors = [] # Log posteriors are stored for later data analysis (autograder).
+        for datum in testData:
+            posterior = self.calculateLogJointProbabilities(datum)
+            guesses.append(posterior.argMax())
+            self.posteriors.append(posterior)
+        return guesses
+
+    def calculateLogJointProbabilities(self, datum):
+        """
+        Returns the log-joint distribution over legal labels and the datum.
+        Each log-probability should be stored in the log-joint counter, e.g.
+        logJoint[3] = <Estimate of log( P(Label = 3, datum) )>
+
+        To get the list of all possible features or labels, use self.features and
+        self.legalLabels.
+        """
+        logJoint = util.Counter()
+
+        for label in self.legalLabels:
+            logJoint[label] = math.log(self.prior[label])
+            for feat, value in datum.items():
+                if value > 0:
+                    logJoint[label] += math.log(self.conditionalProb[feat,label])
+                else:
+                    logJoint[label] += math.log(1-self.conditionalProb[feat,label])
+
+        return logJoint
+
+    def findHighOddsFeatures(self, label1, label2):
+        """
+        Returns the 100 best features for the odds ratio:
+                P(feature=1 | label1)/P(feature=1 | label2)
+
+        Note: you may find 'self.features' a useful way to loop through all possible features
+        """
+        featuresOdds = []
+
+        for feat in self.features:
+            featuresOdds.append((self.conditionalProb[feat, label1]/self.conditionalProb[feat, label2], feat))
+        featuresOdds.sort()
+        featuresOdds = [feat for val, feat in featuresOdds[-100:]]
+
+        return featuresOdds

p5_classification/pacman.py

@@ -0,0 +1,684 @@
+# 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).
+
+
+"""
+Pacman.py holds the logic for the classic pacman game along with the main
+code to run a game.  This file is divided into three sections:
+
+  (i)  Your interface to the pacman world:
+          Pacman is a complex environment.  You probably don't want to
+          read through all of the code we wrote to make the game runs
+          correctly.  This section contains the parts of the code
+          that you will need to understand in order to complete the
+          project.  There is also some code in game.py that you should
+          understand.
+
+  (ii)  The hidden secrets of pacman:
+          This section contains all of the logic code that the pacman
+          environment uses to decide who can move where, who dies when
+          things collide, etc.  You shouldn't need to read this section
+          of code, but you can if you want.
+
+  (iii) Framework to start a game:
+          The final section contains the code for reading the command
+          you use to set up the game, then starting up a new game, along with
+          linking in all the external parts (agent functions, graphics).
+          Check this section out to see all the options available to you.
+
+To play your first game, type 'python pacman.py' from the command line.
+The keys are 'a', 's', 'd', and 'w' to move (or arrow keys).  Have fun!
+"""
+from game import GameStateData
+from game import Game
+from game import Directions
+from game import Actions
+from util import nearestPoint
+from util import manhattanDistance
+import util, layout
+import sys, types, time, random, os
+
+###################################################
+# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
+###################################################
+
+class GameState:
+    """
+    A GameState specifies the full game state, including the food, capsules,
+    agent configurations and score changes.
+
+    GameStates are used by the Game object to capture the actual state of the game and
+    can be used by agents to reason about the game.
+
+    Much of the information in a GameState is stored in a GameStateData object.  We
+    strongly suggest that you access that data via the accessor methods below rather
+    than referring to the GameStateData object directly.
+
+    Note that in classic Pacman, Pacman is always agent 0.
+    """
+
+    ####################################################
+    # Accessor methods: use these to access state data #
+    ####################################################
+
+    # static variable keeps track of which states have had getLegalActions called
+    explored = set()
+    def getAndResetExplored():
+        tmp = GameState.explored.copy()
+        GameState.explored = set()
+        return tmp
+    getAndResetExplored = staticmethod(getAndResetExplored)
+
+    def getLegalActions( self, agentIndex=0 ):
+        """
+        Returns the legal actions for the agent specified.
+        """
+#        GameState.explored.add(self)
+        if self.isWin() or self.isLose(): return []
+
+        if agentIndex == 0:  # Pacman is moving
+            return PacmanRules.getLegalActions( self )
+        else:
+            return GhostRules.getLegalActions( self, agentIndex )
+
+    def generateSuccessor( self, agentIndex, action):
+        """
+        Returns the successor state after the specified agent takes the action.
+        """
+        # Check that successors exist
+        if self.isWin() or self.isLose(): raise Exception('Can\'t generate a successor of a terminal state.')
+
+        # Copy current state
+        state = GameState(self)
+
+        # Let agent's logic deal with its action's effects on the board
+        if agentIndex == 0:  # Pacman is moving
+            state.data._eaten = [False for i in range(state.getNumAgents())]
+            PacmanRules.applyAction( state, action )
+        else:                # A ghost is moving
+            GhostRules.applyAction( state, action, agentIndex )
+
+        # Time passes
+        if agentIndex == 0:
+            state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
+        else:
+            GhostRules.decrementTimer( state.data.agentStates[agentIndex] )
+
+        # Resolve multi-agent effects
+        GhostRules.checkDeath( state, agentIndex )
+
+        # Book keeping
+        state.data._agentMoved = agentIndex
+        state.data.score += state.data.scoreChange
+        GameState.explored.add(self)
+        GameState.explored.add(state)
+        return state
+
+    def getLegalPacmanActions( self ):
+        return self.getLegalActions( 0 )
+
+    def generatePacmanSuccessor( self, action ):
+        """
+        Generates the successor state after the specified pacman move
+        """
+        return self.generateSuccessor( 0, action )
+
+    def getPacmanState( self ):
+        """
+        Returns an AgentState object for pacman (in game.py)
+
+        state.pos gives the current position
+        state.direction gives the travel vector
+        """
+        return self.data.agentStates[0].copy()
+
+    def getPacmanPosition( self ):
+        return self.data.agentStates[0].getPosition()
+
+    def getGhostStates( self ):
+        return self.data.agentStates[1:]
+
+    def getGhostState( self, agentIndex ):
+        if agentIndex == 0 or agentIndex >= self.getNumAgents():
+            raise Exception("Invalid index passed to getGhostState")
+        return self.data.agentStates[agentIndex]
+
+    def getGhostPosition( self, agentIndex ):
+        if agentIndex == 0:
+            raise Exception("Pacman's index passed to getGhostPosition")
+        return self.data.agentStates[agentIndex].getPosition()
+
+    def getGhostPositions(self):
+        return [s.getPosition() for s in self.getGhostStates()]
+
+    def getNumAgents( self ):
+        return len( self.data.agentStates )
+
+    def getScore( self ):
+        return float(self.data.score)
+
+    def getCapsules(self):
+        """
+        Returns a list of positions (x,y) of the remaining capsules.
+        """
+        return self.data.capsules
+
+    def getNumFood( self ):
+        return self.data.food.count()
+
+    def getFood(self):
+        """
+        Returns a Grid of boolean food indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        currentFood = state.getFood()
+        if currentFood[x][y] == True: ...
+        """
+        return self.data.food
+
+    def getWalls(self):
+        """
+        Returns a Grid of boolean wall indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is a wall at (x,y), just call
+
+        walls = state.getWalls()
+        if walls[x][y] == True: ...
+        """
+        return self.data.layout.walls
+
+    def hasFood(self, x, y):
+        return self.data.food[x][y]
+
+    def hasWall(self, x, y):
+        return self.data.layout.walls[x][y]
+
+    def isLose( self ):
+        return self.data._lose
+
+    def isWin( self ):
+        return self.data._win
+
+    #############################################
+    #             Helper methods:               #
+    # You shouldn't need to call these directly #
+    #############################################
+
+    def __init__( self, prevState = None ):
+        """
+        Generates a new state by copying information from its predecessor.
+        """
+        if prevState != None: # Initial state
+            self.data = GameStateData(prevState.data)
+        else:
+            self.data = GameStateData()
+
+    def deepCopy( self ):
+        state = GameState( self )
+        state.data = self.data.deepCopy()
+        return state
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        return hasattr(other, 'data') and self.data == other.data
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        return hash( self.data )
+
+    def __str__( self ):
+
+        return str(self.data)
+
+    def initialize( self, layout, numGhostAgents=1000 ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.data.initialize(layout, numGhostAgents)
+
+############################################################################
+#                     THE HIDDEN SECRETS OF PACMAN                         #
+#                                                                          #
+# You shouldn't need to look through the code in this section of the file. #
+############################################################################
+
+SCARED_TIME = 40    # Moves ghosts are scared
+COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
+TIME_PENALTY = 1 # Number of points lost each round
+
+class ClassicGameRules:
+    """
+    These game rules manage the control flow of a game, deciding when
+    and how the game starts and ends.
+    """
+    def __init__(self, timeout=30):
+        self.timeout = timeout
+
+    def newGame( self, layout, pacmanAgent, ghostAgents, display, quiet = False, catchExceptions=False):
+        agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
+        initState = GameState()
+        initState.initialize( layout, len(ghostAgents) )
+        game = Game(agents, display, self, catchExceptions=catchExceptions)
+        game.state = initState
+        self.initialState = initState.deepCopy()
+        self.quiet = quiet
+        return game
+
+    def process(self, state, game):
+        """
+        Checks to see whether it is time to end the game.
+        """
+        if state.isWin(): self.win(state, game)
+        if state.isLose(): self.lose(state, game)
+
+    def win( self, state, game ):
+        if not self.quiet: print "Pacman emerges victorious! Score: %d" % state.data.score
+        game.gameOver = True
+
+    def lose( self, state, game ):
+        if not self.quiet: print "Pacman died! Score: %d" % state.data.score
+        game.gameOver = True
+
+    def getProgress(self, game):
+        return float(game.state.getNumFood()) / self.initialState.getNumFood()
+
+    def agentCrash(self, game, agentIndex):
+        if agentIndex == 0:
+            print "Pacman crashed"
+        else:
+            print "A ghost crashed"
+
+    def getMaxTotalTime(self, agentIndex):
+        return self.timeout
+
+    def getMaxStartupTime(self, agentIndex):
+        return self.timeout
+
+    def getMoveWarningTime(self, agentIndex):
+        return self.timeout
+
+    def getMoveTimeout(self, agentIndex):
+        return self.timeout
+
+    def getMaxTimeWarnings(self, agentIndex):
+        return 0
+
+class PacmanRules:
+    """
+    These functions govern how pacman interacts with his environment under
+    the classic game rules.
+    """
+    PACMAN_SPEED=1
+
+    def getLegalActions( state ):
+        """
+        Returns a list of possible actions.
+        """
+        return Actions.getPossibleActions( state.getPacmanState().configuration, state.data.layout.walls )
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action ):
+        """
+        Edits the state to reflect the results of the action.
+        """
+        legal = PacmanRules.getLegalActions( state )
+        if action not in legal:
+            raise Exception("Illegal action " + str(action))
+
+        pacmanState = state.data.agentStates[0]
+
+        # Update Configuration
+        vector = Actions.directionToVector( action, PacmanRules.PACMAN_SPEED )
+        pacmanState.configuration = pacmanState.configuration.generateSuccessor( vector )
+
+        # Eat
+        next = pacmanState.configuration.getPosition()
+        nearest = nearestPoint( next )
+        if manhattanDistance( nearest, next ) <= 0.5 :
+            # Remove food
+            PacmanRules.consume( nearest, state )
+    applyAction = staticmethod( applyAction )
+
+    def consume( position, state ):
+        x,y = position
+        # Eat food
+        if state.data.food[x][y]:
+            state.data.scoreChange += 10
+            state.data.food = state.data.food.copy()
+            state.data.food[x][y] = False
+            state.data._foodEaten = position
+            # TODO: cache numFood?
+            numFood = state.getNumFood()
+            if numFood == 0 and not state.data._lose:
+                state.data.scoreChange += 500
+                state.data._win = True
+        # Eat capsule
+        if( position in state.getCapsules() ):
+            state.data.capsules.remove( position )
+            state.data._capsuleEaten = position
+            # Reset all ghosts' scared timers
+            for index in range( 1, len( state.data.agentStates ) ):
+                state.data.agentStates[index].scaredTimer = SCARED_TIME
+    consume = staticmethod( consume )
+
+class GhostRules:
+    """
+    These functions dictate how ghosts interact with their environment.
+    """
+    GHOST_SPEED=1.0
+    def getLegalActions( state, ghostIndex ):
+        """
+        Ghosts cannot stop, and cannot turn around unless they
+        reach a dead end, but can turn 90 degrees at intersections.
+        """
+        conf = state.getGhostState( ghostIndex ).configuration
+        possibleActions = Actions.getPossibleActions( conf, state.data.layout.walls )
+        reverse = Actions.reverseDirection( conf.direction )
+        if Directions.STOP in possibleActions:
+            possibleActions.remove( Directions.STOP )
+        if reverse in possibleActions and len( possibleActions ) > 1:
+            possibleActions.remove( reverse )
+        return possibleActions
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action, ghostIndex):
+
+        legal = GhostRules.getLegalActions( state, ghostIndex )
+        if action not in legal:
+            raise Exception("Illegal ghost action " + str(action))
+
+        ghostState = state.data.agentStates[ghostIndex]
+        speed = GhostRules.GHOST_SPEED
+        if ghostState.scaredTimer > 0: speed /= 2.0
+        vector = Actions.directionToVector( action, speed )
+        ghostState.configuration = ghostState.configuration.generateSuccessor( vector )
+    applyAction = staticmethod( applyAction )
+
+    def decrementTimer( ghostState):
+        timer = ghostState.scaredTimer
+        if timer == 1:
+            ghostState.configuration.pos = nearestPoint( ghostState.configuration.pos )
+        ghostState.scaredTimer = max( 0, timer - 1 )
+    decrementTimer = staticmethod( decrementTimer )
+
+    def checkDeath( state, agentIndex):
+        pacmanPosition = state.getPacmanPosition()
+        if agentIndex == 0: # Pacman just moved; Anyone can kill him
+            for index in range( 1, len( state.data.agentStates ) ):
+                ghostState = state.data.agentStates[index]
+                ghostPosition = ghostState.configuration.getPosition()
+                if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                    GhostRules.collide( state, ghostState, index )
+        else:
+            ghostState = state.data.agentStates[agentIndex]
+            ghostPosition = ghostState.configuration.getPosition()
+            if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                GhostRules.collide( state, ghostState, agentIndex )
+    checkDeath = staticmethod( checkDeath )
+
+    def collide( state, ghostState, agentIndex):
+        if ghostState.scaredTimer > 0:
+            state.data.scoreChange += 200
+            GhostRules.placeGhost(state, ghostState)
+            ghostState.scaredTimer = 0
+            # Added for first-person
+            state.data._eaten[agentIndex] = True
+        else:
+            if not state.data._win:
+                state.data.scoreChange -= 500
+                state.data._lose = True
+    collide = staticmethod( collide )
+
+    def canKill( pacmanPosition, ghostPosition ):
+        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE
+    canKill = staticmethod( canKill )
+
+    def placeGhost(state, ghostState):
+        ghostState.configuration = ghostState.start
+    placeGhost = staticmethod( placeGhost )
+
+#############################
+# FRAMEWORK TO START A GAME #
+#############################
+
+def default(str):
+    return str + ' [Default: %default]'
+
+def parseAgentArgs(str):
+    if str == None: return {}
+    pieces = str.split(',')
+    opts = {}
+    for p in pieces:
+        if '=' in p:
+            key, val = p.split('=')
+        else:
+            key,val = p, 1
+        opts[key] = val
+    return opts
+
+def readCommand( argv ):
+    """
+    Processes the command used to run pacman from the command line.
+    """
+    from optparse import OptionParser
+    usageStr = """
+    USAGE:      python pacman.py <options>
+    EXAMPLES:   (1) python pacman.py
+                    - starts an interactive game
+                (2) python pacman.py --layout smallClassic --zoom 2
+                OR  python pacman.py -l smallClassic -z 2
+                    - starts an interactive game on a smaller board, zoomed in
+    """
+    parser = OptionParser(usageStr)
+
+    parser.add_option('-n', '--numGames', dest='numGames', type='int',
+                      help=default('the number of GAMES to play'), metavar='GAMES', default=1)
+    parser.add_option('-l', '--layout', dest='layout',
+                      help=default('the LAYOUT_FILE from which to load the map layout'),
+                      metavar='LAYOUT_FILE', default='mediumClassic')
+    parser.add_option('-p', '--pacman', dest='pacman',
+                      help=default('the agent TYPE in the pacmanAgents module to use'),
+                      metavar='TYPE', default='KeyboardAgent')
+    parser.add_option('-t', '--textGraphics', action='store_true', dest='textGraphics',
+                      help='Display output as text only', default=False)
+    parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
+                      help='Generate minimal output and no graphics', default=False)
+    parser.add_option('-g', '--ghosts', dest='ghost',
+                      help=default('the ghost agent TYPE in the ghostAgents module to use'),
+                      metavar = 'TYPE', default='RandomGhost')
+    parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
+                      help=default('The maximum number of ghosts to use'), default=4)
+    parser.add_option('-z', '--zoom', type='float', dest='zoom',
+                      help=default('Zoom the size of the graphics window'), default=1.0)
+    parser.add_option('-f', '--fixRandomSeed', action='store_true', dest='fixRandomSeed',
+                      help='Fixes the random seed to always play the same game', default=False)
+    parser.add_option('-r', '--recordActions', action='store_true', dest='record',
+                      help='Writes game histories to a file (named by the time they were played)', default=False)
+    parser.add_option('--replay', dest='gameToReplay',
+                      help='A recorded game file (pickle) to replay', default=None)
+    parser.add_option('-a','--agentArgs',dest='agentArgs',
+                      help='Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
+    parser.add_option('-x', '--numTraining', dest='numTraining', type='int',
+                      help=default('How many episodes are training (suppresses output)'), default=0)
+    parser.add_option('--frameTime', dest='frameTime', type='float',
+                      help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
+    parser.add_option('-c', '--catchExceptions', action='store_true', dest='catchExceptions',
+                      help='Turns on exception handling and timeouts during games', default=False)
+    parser.add_option('--timeout', dest='timeout', type='int',
+                      help=default('Maximum length of time an agent can spend computing in a single game'), default=30)
+
+    options, otherjunk = parser.parse_args(argv)
+    if len(otherjunk) != 0:
+        raise Exception('Command line input not understood: ' + str(otherjunk))
+    args = dict()
+
+    # Fix the random seed
+    if options.fixRandomSeed: random.seed('cs188')
+
+    # Choose a layout
+    args['layout'] = layout.getLayout( options.layout )
+    if args['layout'] == None: raise Exception("The layout " + options.layout + " cannot be found")
+
+    # Choose a Pacman agent
+    noKeyboard = options.gameToReplay == None and (options.textGraphics or options.quietGraphics)
+    pacmanType = loadAgent(options.pacman, noKeyboard)
+    agentOpts = parseAgentArgs(options.agentArgs)
+    if options.numTraining > 0:
+        args['numTraining'] = options.numTraining
+        if 'numTraining' not in agentOpts: agentOpts['numTraining'] = options.numTraining
+    pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
+    args['pacman'] = pacman
+
+    # Don't display training games
+    if 'numTrain' in agentOpts:
+        options.numQuiet = int(agentOpts['numTrain'])
+        options.numIgnore = int(agentOpts['numTrain'])
+
+    # Choose a ghost agent
+    ghostType = loadAgent(options.ghost, noKeyboard)
+    args['ghosts'] = [ghostType( i+1 ) for i in range( options.numGhosts )]
+
+    # Choose a display format
+    if options.quietGraphics:
+        import textDisplay
+        args['display'] = textDisplay.NullGraphics()
+    elif options.textGraphics:
+        import textDisplay
+        textDisplay.SLEEP_TIME = options.frameTime
+        args['display'] = textDisplay.PacmanGraphics()
+    else:
+        import graphicsDisplay
+        args['display'] = graphicsDisplay.PacmanGraphics(options.zoom, frameTime = options.frameTime)
+    args['numGames'] = options.numGames
+    args['record'] = options.record
+    args['catchExceptions'] = options.catchExceptions
+    args['timeout'] = options.timeout
+
+    # Special case: recorded games don't use the runGames method or args structure
+    if options.gameToReplay != None:
+        print 'Replaying recorded game %s.' % options.gameToReplay
+        import cPickle
+        f = open(options.gameToReplay)
+        try: recorded = cPickle.load(f)
+        finally: f.close()
+        recorded['display'] = args['display']
+        replayGame(**recorded)
+        sys.exit(0)
+
+    return args
+
+def loadAgent(pacman, nographics):
+    # Looks through all pythonPath Directories for the right module,
+    pythonPathStr = os.path.expandvars("$PYTHONPATH")
+    if pythonPathStr.find(';') == -1:
+        pythonPathDirs = pythonPathStr.split(':')
+    else:
+        pythonPathDirs = pythonPathStr.split(';')
+    pythonPathDirs.append('.')
+
+    for moduleDir in pythonPathDirs:
+        if not os.path.isdir(moduleDir): continue
+        moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
+        for modulename in moduleNames:
+            try:
+                module = __import__(modulename[:-3])
+            except ImportError:
+                continue
+            if pacman in dir(module):
+                if nographics and modulename == 'keyboardAgents.py':
+                    raise Exception('Using the keyboard requires graphics (not text display)')
+                return getattr(module, pacman)
+    raise Exception('The agent ' + pacman + ' is not specified in any *Agents.py.')
+
+def replayGame( layout, actions, display ):
+    import pacmanAgents, ghostAgents
+    rules = ClassicGameRules()
+    agents = [pacmanAgents.GreedyAgent()] + [ghostAgents.RandomGhost(i+1) for i in range(layout.getNumGhosts())]
+    game = rules.newGame( layout, agents[0], agents[1:], display )
+    state = game.state
+    display.initialize(state.data)
+
+    for action in actions:
+            # Execute the action
+        state = state.generateSuccessor( *action )
+        # Change the display
+        display.update( state.data )
+        # Allow for game specific conditions (winning, losing, etc.)
+        rules.process(state, game)
+
+    display.finish()
+
+def runGames( layout, pacman, ghosts, display, numGames, record, numTraining = 0, catchExceptions=False, timeout=30 ):
+    import __main__
+    __main__.__dict__['_display'] = display
+
+    rules = ClassicGameRules(timeout)
+    games = []
+
+    for i in range( numGames ):
+        beQuiet = i < numTraining
+        if beQuiet:
+                # Suppress output and graphics
+            import textDisplay
+            gameDisplay = textDisplay.NullGraphics()
+            rules.quiet = True
+        else:
+            gameDisplay = display
+            rules.quiet = False
+        game = rules.newGame( layout, pacman, ghosts, gameDisplay, beQuiet, catchExceptions)
+        game.run()
+        if not beQuiet: games.append(game)
+
+        if record:
+            import time, cPickle
+            fname = ('recorded-game-%d' % (i + 1)) +  '-'.join([str(t) for t in time.localtime()[1:6]])
+            f = file(fname, 'w')
+            components = {'layout': layout, 'actions': game.moveHistory}
+            cPickle.dump(components, f)
+            f.close()
+
+    if (numGames-numTraining) > 0:
+        scores = [game.state.getScore() for game in games]
+        wins = [game.state.isWin() for game in games]
+        winRate = wins.count(True)/ float(len(wins))
+        print 'Average Score:', sum(scores) / float(len(scores))
+        print 'Scores:       ', ', '.join([str(score) for score in scores])
+        print 'Win Rate:      %d/%d (%.2f)' % (wins.count(True), len(wins), winRate)
+        print 'Record:       ', ', '.join([ ['Loss', 'Win'][int(w)] for w in wins])
+
+    return games
+
+if __name__ == '__main__':
+    """
+    The main function called when pacman.py is run
+    from the command line:
+
+    > python pacman.py
+
+    See the usage string for more details.
+
+    > python pacman.py --help
+    """
+    args = readCommand( sys.argv[1:] ) # Get game components based on input
+    runGames( **args )
+
+    # import cProfile
+    # cProfile.run("runGames( **args )")
+    pass

p5_classification/pacmanAgents.py

@@ -0,0 +1,52 @@
+# pacmanAgents.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).
+
+
+from pacman import Directions
+from game import Agent
+import random
+import game
+import util
+
+class LeftTurnAgent(game.Agent):
+    "An agent that turns left at every opportunity"
+
+    def getAction(self, state):
+        legal = state.getLegalPacmanActions()
+        current = state.getPacmanState().configuration.direction
+        if current == Directions.STOP: current = Directions.NORTH
+        left = Directions.LEFT[current]
+        if left in legal: return left
+        if current in legal: return current
+        if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
+        if Directions.LEFT[left] in legal: return Directions.LEFT[left]
+        return Directions.STOP
+
+class GreedyAgent(Agent):
+    def __init__(self, evalFn="scoreEvaluation"):
+        self.evaluationFunction = util.lookup(evalFn, globals())
+        assert self.evaluationFunction != None
+
+    def getAction(self, state):
+        # Generate candidate actions
+        legal = state.getLegalPacmanActions()
+        if Directions.STOP in legal: legal.remove(Directions.STOP)
+
+        successors = [(state.generateSuccessor(0, action), action) for action in legal]
+        scored = [(self.evaluationFunction(state), action) for state, action in successors]
+        bestScore = max(scored)[0]
+        bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
+        return random.choice(bestActions)
+
+def scoreEvaluation(state):
+    return state.getScore()

p5_classification/perceptron.py

@@ -0,0 +1,85 @@
+# perceptron.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
+import util
+PRINT = True
+
+class PerceptronClassifier:
+    """
+    Perceptron classifier.
+
+    Note that the variable 'datum' in this code refers to a counter of features
+    (not to a raw samples.Datum).
+    """
+    def __init__( self, legalLabels, max_iterations):
+        self.legalLabels = legalLabels
+        self.type = "perceptron"
+        self.max_iterations = max_iterations
+        self.weights = {}
+        for label in legalLabels:
+            self.weights[label] = util.Counter() # this is the data-structure you should use
+
+    def setWeights(self, weights):
+        assert len(weights) == len(self.legalLabels);
+        self.weights = weights;
+
+    def train( self, trainingData, trainingLabels, validationData, validationLabels ):
+        """
+        The training loop for the perceptron passes through the training data several
+        times and updates the weight vector for each label based on classification errors.
+        See the project description for details.
+
+        Use the provided self.weights[label] data structure so that
+        the classify method works correctly. Also, recall that a
+        datum is a counter from features to values for those features
+        (and thus represents a vector a values).
+        """
+
+        self.features = trainingData[0].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()
+
+    def classify(self, data ):
+        """
+        Classifies each datum as the label that most closely matches the prototype vector
+        for that label.  See the project description for details.
+
+        Recall that a datum is a util.counter...
+        """
+        guesses = []
+        for datum in data:
+            vectors = util.Counter()
+            for l in self.legalLabels:
+                vectors[l] = self.weights[l] * datum
+            guesses.append(vectors.argMax())
+        return guesses
+
+
+    def findHighWeightFeatures(self, label):
+        """
+        Returns a list of the 100 features with the greatest weight for some label
+        """
+        featuresWeights = []
+
+        "*** YOUR CODE HERE ***"
+        util.raiseNotDefined()
+
+        return featuresWeights

p5_classification/perceptron_pacman.py

@@ -0,0 +1,53 @@
+# 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()

p5_classification/projectParams.py

@@ -0,0 +1,18 @@
+# projectParams.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).
+
+
+STUDENT_CODE_DEFAULT = 'naiveBayes.py,perceptron.py,mira.py,dataClassifier.py,answers.py,perceptron_pacman.py'
+PROJECT_TEST_CLASSES = 'classificationTestClasses.py'
+PROJECT_NAME = 'Project 5: Classification'
+BONUS_PIC = False

p5_classification/samples.py

@@ -0,0 +1,213 @@
+# samples.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).
+
+
+import util
+
+## Constants
+DATUM_WIDTH = 0 # in pixels
+DATUM_HEIGHT = 0 # in pixels
+
+## Module Classes
+
+class Datum:
+    """
+    A datum is a pixel-level encoding of digits or face/non-face edge maps.
+
+    Digits are from the MNIST dataset and face images are from the
+    easy-faces and background categories of the Caltech 101 dataset.
+
+
+    Each digit is 28x28 pixels, and each face/non-face image is 60x74
+    pixels, each pixel can take the following values:
+      0: no edge (blank)
+      1: gray pixel (+) [used for digits only]
+      2: edge [for face] or black pixel [for digit] (#)
+
+    Pixel data is stored in the 2-dimensional array pixels, which
+    maps to pixels on a plane according to standard euclidean axes
+    with the first dimension denoting the horizontal and the second
+    the vertical coordinate:
+
+      28 # # # #      #  #
+      27 # # # #      #  #
+       .
+       .
+       .
+       3 # # + #      #  #
+       2 # # # #      #  #
+       1 # # # #      #  #
+       0 # # # #      #  #
+         0 1 2 3 ... 27 28
+
+    For example, the + in the above diagram is stored in pixels[2][3], or
+    more generally pixels[column][row].
+
+    The contents of the representation can be accessed directly
+    via the getPixel and getPixels methods.
+    """
+    def __init__(self, data,width,height):
+        """
+        Create a new datum from file input (standard MNIST encoding).
+        """
+        DATUM_HEIGHT = height
+        DATUM_WIDTH=width
+        self.height = DATUM_HEIGHT
+        self.width = DATUM_WIDTH
+        if data == None:
+            data = [[' ' for i in range(DATUM_WIDTH)] for j in range(DATUM_HEIGHT)]
+        self.pixels = util.arrayInvert(convertToInteger(data))
+
+    def getPixel(self, column, row):
+        """
+        Returns the value of the pixel at column, row as 0, or 1.
+        """
+        return self.pixels[column][row]
+
+    def getPixels(self):
+        """
+        Returns all pixels as a list of lists.
+        """
+        return self.pixels
+
+    def getAsciiString(self):
+        """
+        Renders the data item as an ascii image.
+        """
+        rows = []
+        data = util.arrayInvert(self.pixels)
+        for row in data:
+            ascii = map(asciiGrayscaleConversionFunction, row)
+            rows.append( "".join(ascii) )
+        return "\n".join(rows)
+
+    def __str__(self):
+        return self.getAsciiString()
+
+
+
+# Data processing, cleanup and display functions
+
+def loadDataFile(filename, n,width,height):
+    """
+    Reads n data images from a file and returns a list of Datum objects.
+
+    (Return less then n items if the end of file is encountered).
+    """
+    DATUM_WIDTH=width
+    DATUM_HEIGHT=height
+    fin = readlines(filename)
+    fin.reverse()
+    items = []
+    for i in range(n):
+        data = []
+        for j in range(height):
+            data.append(list(fin.pop()))
+        if len(data[0]) < DATUM_WIDTH-1:
+            # we encountered end of file...
+            print "Truncating at %d examples (maximum)" % i
+            break
+        items.append(Datum(data,DATUM_WIDTH,DATUM_HEIGHT))
+    return items
+
+import zipfile
+import os
+def readlines(filename):
+    "Opens a file or reads it from the zip archive data.zip"
+    if(os.path.exists(filename)):
+        return [l[:-1] for l in open(filename).readlines()]
+    else:
+        z = zipfile.ZipFile('data.zip')
+        return z.read(filename).split('\n')
+
+def loadLabelsFile(filename, n):
+    """
+    Reads n labels from a file and returns a list of integers.
+    """
+    fin = readlines(filename)
+    labels = []
+    for line in fin[:min(n, len(fin))]:
+        if line == '':
+            break
+        labels.append(int(line))
+    return labels
+
+def loadPacmanStatesFile(filename, n):
+    f = open(filename, 'r')
+    result = cPickle.load(f)
+    f.close()
+    return result
+
+import cPickle
+import pacmanAgents
+import ghostAgents
+import textDisplay
+from pacman import ClassicGameRules, GameState
+def loadPacmanData(filename, n):
+    """
+    Return game states from specified recorded games as data, and actions taken as labels
+    """
+    components = loadPacmanStatesFile(filename, n)
+    return components['states'][:n], components['actions'][:n]
+
+def asciiGrayscaleConversionFunction(value):
+    """
+    Helper function for display purposes.
+    """
+    if(value == 0):
+        return ' '
+    elif(value == 1):
+        return '+'
+    elif(value == 2):
+        return '#'
+
+def IntegerConversionFunction(character):
+    """
+    Helper function for file reading.
+    """
+    if(character == ' '):
+        return 0
+    elif(character == '+'):
+        return 1
+    elif(character == '#'):
+        return 2
+
+def convertToInteger(data):
+    """
+    Helper function for file reading.
+    """
+    if type(data) != type([]):
+        return IntegerConversionFunction(data)
+    else:
+        return map(convertToInteger, data)
+
+# Testing
+
+def _test():
+    import doctest
+    doctest.testmod() # Test the interactive sessions in function comments
+    n = 1
+#  items = loadDataFile("facedata/facedatatrain", n,60,70)
+#  labels = loadLabelsFile("facedata/facedatatrainlabels", n)
+    items = loadDataFile("digitdata/trainingimages", n,28,28)
+    labels = loadLabelsFile("digitdata/traininglabels", n)
+    for i in range(1):
+        print items[i]
+        print items[i]
+        print (items[i].height)
+        print (items[i].width)
+        print dir(items[i])
+        print items[i].getPixels()
+
+if __name__ == "__main__":
+    _test()

p5_classification/testClasses.py

@@ -0,0 +1,189 @@
+# testClasses.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).
+
+
+# import modules from python standard library
+import inspect
+import re
+import sys
+
+
+# Class which models a question in a project.  Note that questions have a
+# maximum number of points they are worth, and are composed of a series of
+# test cases
+class Question(object):
+
+    def raiseNotDefined(self):
+        print 'Method not implemented: %s' % inspect.stack()[1][3]
+        sys.exit(1)
+
+    def __init__(self, questionDict, display):
+        self.maxPoints = int(questionDict['max_points'])
+        self.testCases = []
+        self.display = display
+
+    def getDisplay(self):
+        return self.display
+
+    def getMaxPoints(self):
+        return self.maxPoints
+
+    # Note that 'thunk' must be a function which accepts a single argument,
+    # namely a 'grading' object
+    def addTestCase(self, testCase, thunk):
+        self.testCases.append((testCase, thunk))
+
+    def execute(self, grades):
+        self.raiseNotDefined()
+
+# Question in which all test cases must be passed in order to receive credit
+class PassAllTestsQuestion(Question):
+
+    def execute(self, grades):
+        # TODO: is this the right way to use grades?  The autograder doesn't seem to use it.
+        testsFailed = False
+        grades.assignZeroCredit()
+        for _, f in self.testCases:
+            if not f(grades):
+                testsFailed = True
+        if testsFailed:
+            grades.fail("Tests failed.")
+        else:
+            grades.assignFullCredit()
+
+
+# Question in which predict credit is given for test cases with a ``points'' property.
+# All other tests are mandatory and must be passed.
+class HackedPartialCreditQuestion(Question):
+
+    def execute(self, grades):
+        # TODO: is this the right way to use grades?  The autograder doesn't seem to use it.
+        grades.assignZeroCredit()
+
+        points = 0
+        passed = True
+        for testCase, f in self.testCases:
+            testResult = f(grades)
+            if "points" in testCase.testDict:
+                if testResult: points += float(testCase.testDict["points"])
+            else:
+                passed = passed and testResult
+
+        ## FIXME: Below terrible hack to match q3's logic
+        if int(points) == self.maxPoints and not passed:
+            grades.assignZeroCredit()
+        else:
+            grades.addPoints(int(points))
+
+
+class Q6PartialCreditQuestion(Question):
+    """Fails any test which returns False, otherwise doesn't effect the grades object.
+    Partial credit tests will add the required points."""
+
+    def execute(self, grades):
+        grades.assignZeroCredit()
+
+        results = []
+        for _, f in self.testCases:
+            results.append(f(grades))
+        if False in results:
+            grades.assignZeroCredit()
+
+class PartialCreditQuestion(Question):
+    """Fails any test which returns False, otherwise doesn't effect the grades object.
+    Partial credit tests will add the required points."""
+
+    def execute(self, grades):
+        grades.assignZeroCredit()
+
+        for _, f in self.testCases:
+            if not f(grades):
+                grades.assignZeroCredit()
+                grades.fail("Tests failed.")
+                return False
+
+
+
+class NumberPassedQuestion(Question):
+    """Grade is the number of test cases passed."""
+
+    def execute(self, grades):
+        grades.addPoints([f(grades) for _, f in self.testCases].count(True))
+
+
+
+
+
+# Template modeling a generic test case
+class TestCase(object):
+
+    def raiseNotDefined(self):
+        print 'Method not implemented: %s' % inspect.stack()[1][3]
+        sys.exit(1)
+
+    def getPath(self):
+        return self.path
+
+    def __init__(self, question, testDict):
+        self.question = question
+        self.testDict = testDict
+        self.path = testDict['path']
+        self.messages = []
+
+    def __str__(self):
+        self.raiseNotDefined()
+
+    def execute(self, grades, moduleDict, solutionDict):
+        self.raiseNotDefined()
+
+    def writeSolution(self, moduleDict, filePath):
+        self.raiseNotDefined()
+        return True
+
+    # Tests should call the following messages for grading
+    # to ensure a uniform format for test output.
+    #
+    # TODO: this is hairy, but we need to fix grading.py's interface
+    # to get a nice hierarchical project - question - test structure,
+    # then these should be moved into Question proper.
+    def testPass(self, grades):
+        grades.addMessage('PASS: %s' % (self.path,))
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+        return True
+
+    def testFail(self, grades):
+        grades.addMessage('FAIL: %s' % (self.path,))
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+        return False
+
+    # This should really be question level?
+    #
+    def testPartial(self, grades, points, maxPoints):
+        grades.addPoints(points)
+        extraCredit = max(0, points - maxPoints)
+        regularCredit = points - extraCredit
+
+        grades.addMessage('%s: %s (%s of %s points)' % ("PASS" if points >= maxPoints else "FAIL", self.path, regularCredit, maxPoints))
+        if extraCredit > 0:
+            grades.addMessage('EXTRA CREDIT: %s points' % (extraCredit,))
+
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+
+        return True
+
+    def addMessage(self, message):
+        self.messages.extend(message.split('\n'))
+

p5_classification/testParser.py

@@ -0,0 +1,85 @@
+# testParser.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).
+
+
+import re
+import sys
+
+class TestParser(object):
+
+    def __init__(self, path):
+        # save the path to the test file
+        self.path = path
+
+    def removeComments(self, rawlines):
+        # remove any portion of a line following a '#' symbol
+        fixed_lines = []
+        for l in rawlines:
+            idx = l.find('#')
+            if idx == -1:
+                fixed_lines.append(l)
+            else:
+                fixed_lines.append(l[0:idx])
+        return '\n'.join(fixed_lines)
+
+    def parse(self):
+        # read in the test case and remove comments
+        test = {}
+        with open(self.path) as handle:
+            raw_lines = handle.read().split('\n')
+
+        test_text = self.removeComments(raw_lines)
+        test['__raw_lines__'] = raw_lines
+        test['path'] = self.path
+        test['__emit__'] = []
+        lines = test_text.split('\n')
+        i = 0
+        # read a property in each loop cycle
+        while(i < len(lines)):
+            # skip blank lines
+            if re.match('\A\s*\Z', lines[i]):
+                test['__emit__'].append(("raw", raw_lines[i]))
+                i += 1
+                continue
+            m = re.match('\A([^"]*?):\s*"([^"]*)"\s*\Z', lines[i])
+            if m:
+                test[m.group(1)] = m.group(2)
+                test['__emit__'].append(("oneline", m.group(1)))
+                i += 1
+                continue
+            m = re.match('\A([^"]*?):\s*"""\s*\Z', lines[i])
+            if m:
+                msg = []
+                i += 1
+                while(not re.match('\A\s*"""\s*\Z', lines[i])):
+                    msg.append(raw_lines[i])
+                    i += 1
+                test[m.group(1)] = '\n'.join(msg)
+                test['__emit__'].append(("multiline", m.group(1)))
+                i += 1
+                continue
+            print 'error parsing test file: %s' % self.path
+            sys.exit(1)
+        return test
+
+
+def emitTestDict(testDict, handle):
+    for kind, data in testDict['__emit__']:
+        if kind == "raw":
+            handle.write(data + "\n")
+        elif kind == "oneline":
+            handle.write('%s: "%s"\n' % (data, testDict[data]))
+        elif kind == "multiline":
+            handle.write('%s: """\n%s\n"""\n' % (data, testDict[data]))
+        else:
+            raise Exception("Bad __emit__")

p5_classification/test_cases/CONFIG

@@ -0,0 +1 @@
+order: "q1 q2 q3 q4 q5 q6"

p5_classification/test_cases/q1/CONFIG

@@ -0,0 +1,2 @@
+max_points: "4"
+class: "PartialCreditQuestion"

p5_classification/test_cases/q1/grade.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q1/grade.test.

p5_classification/test_cases/q1/grade.test

@@ -0,0 +1,14 @@
+class: "GradeClassifierTest"
+classifierModule: "perceptron"
+classifierClass: "PerceptronClassifier"
+
+max_iterations: "4"
+
+datasetName: "bigDigitData"
+
+# our solution: 75%
+exactOutput: "False"
+accuracyScale: "4"
+accuracyThresholds: "70"
+
+

p5_classification/test_cases/q2/CONFIG

@@ -0,0 +1,2 @@
+max_points: "1"
+class: "PassAllTestsQuestion"

p5_classification/test_cases/q2/grade.solution

@@ -0,0 +1,2 @@
+# This is the solution file for test_cases/q2/grade.test.
+# File intentionally blank.

p5_classification/test_cases/q2/grade.test

@@ -0,0 +1,6 @@
+class: "MultipleChoiceTest"
+question: "q2"
+result: "86f7e437faa5a7fce15d1ddcb9eaeaea377667b8"
+
+
+

p5_classification/test_cases/q3/CONFIG

@@ -0,0 +1,2 @@
+max_points: "6"
+class: "PartialCreditQuestion"

p5_classification/test_cases/q3/grade.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q3/grade.test.

p5_classification/test_cases/q3/grade.test

@@ -0,0 +1,12 @@
+class: "GradeClassifierTest"
+classifierModule: "mira"
+classifierClass: "MiraClassifier"
+
+max_iterations: "5"
+
+datasetName: "bigDigitData"
+
+# our solution: 87%
+exactOutput: "False"
+accuracyScale: "6"
+accuracyThresholds: "80"

p5_classification/test_cases/q4/CONFIG

@@ -0,0 +1,2 @@
+max_points: "6"
+class: "PartialCreditQuestion"

p5_classification/test_cases/q4/grade.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q4/grade.test.

p5_classification/test_cases/q4/grade.test

@@ -0,0 +1,14 @@
+class: "GradeClassifierTest"
+classifierModule: "naiveBayes"
+classifierClass: "NaiveBayesClassifier"
+
+automaticTuning: "True"
+featureFunction: "enhancedFeatureExtractorDigit"
+
+datasetName: "bigDigitData"
+
+# baseline is 79%
+# our solution is 85%
+exactOutput: "False"
+accuracyScale: "3"
+accuracyThresholds: "80 84"

p5_classification/test_cases/q5/CONFIG

@@ -0,0 +1,2 @@
+max_points: "4"
+class: "PartialCreditQuestion"

p5_classification/test_cases/q5/contest.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q5/contest.test.

p5_classification/test_cases/q5/contest.test

@@ -0,0 +1,13 @@
+class: "GradeClassifierTest"
+classifierModule: "perceptron_pacman"
+classifierClass: "PerceptronClassifierPacman"
+
+max_iterations: "5"
+
+datasetName: "contestData"
+
+featureFunction: "basicFeatureExtractorPacman"
+
+exactOutput: "False"
+accuracyScale: "2"
+accuracyThresholds: "70"

p5_classification/test_cases/q5/suicide.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q5/suicide.test.

p5_classification/test_cases/q5/suicide.test

@@ -0,0 +1,13 @@
+class: "GradeClassifierTest"
+classifierModule: "perceptron_pacman"
+classifierClass: "PerceptronClassifierPacman"
+
+max_iterations: "5"
+
+datasetName: "suicideData"
+
+featureFunction: "basicFeatureExtractorPacman"
+
+exactOutput: "False"
+accuracyScale: "2"
+accuracyThresholds: "70"

p5_classification/test_cases/q6/CONFIG

@@ -0,0 +1,2 @@
+max_points: "4"
+class: "PartialCreditQuestion"

p5_classification/test_cases/q6/contest.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q6/contest.test.

p5_classification/test_cases/q6/contest.test

@@ -0,0 +1,14 @@
+class: "GradeClassifierTest"
+classifierModule: "perceptron_pacman"
+classifierClass: "PerceptronClassifierPacman"
+
+automaticTuning: "True"
+featureFunction: "enhancedFeatureExtractorPacman"
+
+max_iterations: "4"
+
+datasetName: "contestData"
+
+exactOutput: "False"
+accuracyScale: "2"
+accuracyThresholds: "90"

p5_classification/test_cases/q6/suicide.solution

@@ -0,0 +1 @@
+# This is the solution file for test_cases/q6/suicide.test.

p5_classification/test_cases/q6/suicide.test

@@ -0,0 +1,14 @@
+class: "GradeClassifierTest"
+classifierModule: "perceptron_pacman"
+classifierClass: "PerceptronClassifierPacman"
+
+automaticTuning: "True"
+featureFunction: "enhancedFeatureExtractorPacman"
+
+max_iterations: "4"
+
+datasetName: "suicideData"
+
+exactOutput: "False"
+accuracyScale: "2"
+accuracyThresholds: "80"

p5_classification/textDisplay.py

@@ -0,0 +1,81 @@
+# textDisplay.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).
+
+
+import time
+try: 
+    import pacman
+except:
+    pass
+
+DRAW_EVERY = 1
+SLEEP_TIME = 0 # This can be overwritten by __init__
+DISPLAY_MOVES = False
+QUIET = False # Supresses output
+
+class NullGraphics:
+    def initialize(self, state, isBlue = False):
+        pass
+
+    def update(self, state):
+        pass
+
+    def checkNullDisplay(self):
+        return True
+
+    def pause(self):
+        time.sleep(SLEEP_TIME)
+
+    def draw(self, state):
+        print state
+
+    def updateDistributions(self, dist):
+        pass
+
+    def finish(self):
+        pass
+
+class PacmanGraphics:
+    def __init__(self, speed=None):
+        if speed != None:
+            global SLEEP_TIME
+            SLEEP_TIME = speed
+
+    def initialize(self, state, isBlue = False):
+        self.draw(state)
+        self.pause()
+        self.turn = 0
+        self.agentCounter = 0
+
+    def update(self, state):
+        numAgents = len(state.agentStates)
+        self.agentCounter = (self.agentCounter + 1) % numAgents
+        if self.agentCounter == 0:
+            self.turn += 1
+            if DISPLAY_MOVES:
+                ghosts = [pacman.nearestPoint(state.getGhostPosition(i)) for i in range(1, numAgents)]
+                print "%4d) P: %-8s" % (self.turn, str(pacman.nearestPoint(state.getPacmanPosition()))),'| Score: %-5d' % state.score,'| Ghosts:', ghosts
+            if self.turn % DRAW_EVERY == 0:
+                self.draw(state)
+                self.pause()
+        if state._win or state._lose:
+            self.draw(state)
+
+    def pause(self):
+        time.sleep(SLEEP_TIME)
+
+    def draw(self, state):
+        print state
+
+    def finish(self):
+        pass

p5_classification/util.py

@@ -0,0 +1,653 @@
+# util.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).
+
+
+import sys
+import inspect
+import heapq, random
+import cStringIO
+
+
+class FixedRandom:
+    def __init__(self):
+        fixedState = (3, (2147483648L, 507801126L, 683453281L, 310439348L, 2597246090L, \
+            2209084787L, 2267831527L, 979920060L, 3098657677L, 37650879L, 807947081L, 3974896263L, \
+            881243242L, 3100634921L, 1334775171L, 3965168385L, 746264660L, 4074750168L, 500078808L, \
+            776561771L, 702988163L, 1636311725L, 2559226045L, 157578202L, 2498342920L, 2794591496L, \
+            4130598723L, 496985844L, 2944563015L, 3731321600L, 3514814613L, 3362575829L, 3038768745L, \
+            2206497038L, 1108748846L, 1317460727L, 3134077628L, 988312410L, 1674063516L, 746456451L, \
+            3958482413L, 1857117812L, 708750586L, 1583423339L, 3466495450L, 1536929345L, 1137240525L, \
+            3875025632L, 2466137587L, 1235845595L, 4214575620L, 3792516855L, 657994358L, 1241843248L, \
+            1695651859L, 3678946666L, 1929922113L, 2351044952L, 2317810202L, 2039319015L, 460787996L, \
+            3654096216L, 4068721415L, 1814163703L, 2904112444L, 1386111013L, 574629867L, 2654529343L, \
+            3833135042L, 2725328455L, 552431551L, 4006991378L, 1331562057L, 3710134542L, 303171486L, \
+            1203231078L, 2670768975L, 54570816L, 2679609001L, 578983064L, 1271454725L, 3230871056L, \
+            2496832891L, 2944938195L, 1608828728L, 367886575L, 2544708204L, 103775539L, 1912402393L, \
+            1098482180L, 2738577070L, 3091646463L, 1505274463L, 2079416566L, 659100352L, 839995305L, \
+            1696257633L, 274389836L, 3973303017L, 671127655L, 1061109122L, 517486945L, 1379749962L, \
+            3421383928L, 3116950429L, 2165882425L, 2346928266L, 2892678711L, 2936066049L, 1316407868L, \
+            2873411858L, 4279682888L, 2744351923L, 3290373816L, 1014377279L, 955200944L, 4220990860L, \
+            2386098930L, 1772997650L, 3757346974L, 1621616438L, 2877097197L, 442116595L, 2010480266L, \
+            2867861469L, 2955352695L, 605335967L, 2222936009L, 2067554933L, 4129906358L, 1519608541L, \
+            1195006590L, 1942991038L, 2736562236L, 279162408L, 1415982909L, 4099901426L, 1732201505L, \
+            2934657937L, 860563237L, 2479235483L, 3081651097L, 2244720867L, 3112631622L, 1636991639L, \
+            3860393305L, 2312061927L, 48780114L, 1149090394L, 2643246550L, 1764050647L, 3836789087L, \
+            3474859076L, 4237194338L, 1735191073L, 2150369208L, 92164394L, 756974036L, 2314453957L, \
+            323969533L, 4267621035L, 283649842L, 810004843L, 727855536L, 1757827251L, 3334960421L, \
+            3261035106L, 38417393L, 2660980472L, 1256633965L, 2184045390L, 811213141L, 2857482069L, \
+            2237770878L, 3891003138L, 2787806886L, 2435192790L, 2249324662L, 3507764896L, 995388363L, \
+            856944153L, 619213904L, 3233967826L, 3703465555L, 3286531781L, 3863193356L, 2992340714L, \
+            413696855L, 3865185632L, 1704163171L, 3043634452L, 2225424707L, 2199018022L, 3506117517L, \
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+            2372035079L, 3945715164L, 2029501215L, 3362012634L, 2007375355L, 4074709820L, 631485888L, \
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+            3119920613L, 531400869L, 1626487579L, 1099320497L, 407414753L, 2438623324L, 99073255L, \
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+            230984053L, 719791226L, 2718891946L, 624L), None)
+        self.random = random.Random()
+        self.random.setstate(fixedState)
+
+"""
+ Data structures useful for implementing SearchAgents
+"""
+
+class Stack:
+    "A container with a last-in-first-out (LIFO) queuing policy."
+    def __init__(self):
+        self.list = []
+
+    def push(self,item):
+        "Push 'item' onto the stack"
+        self.list.append(item)
+
+    def pop(self):
+        "Pop the most recently pushed item from the stack"
+        return self.list.pop()
+
+    def isEmpty(self):
+        "Returns true if the stack is empty"
+        return len(self.list) == 0
+
+class Queue:
+    "A container with a first-in-first-out (FIFO) queuing policy."
+    def __init__(self):
+        self.list = []
+
+    def push(self,item):
+        "Enqueue the 'item' into the queue"
+        self.list.insert(0,item)
+
+    def pop(self):
+        """
+          Dequeue the earliest enqueued item still in the queue. This
+          operation removes the item from the queue.
+        """
+        return self.list.pop()
+
+    def isEmpty(self):
+        "Returns true if the queue is empty"
+        return len(self.list) == 0
+
+class PriorityQueue:
+    """
+      Implements a priority queue data structure. Each inserted item
+      has a priority associated with it and the client is usually interested
+      in quick retrieval of the lowest-priority item in the queue. This
+      data structure allows O(1) access to the lowest-priority item.
+
+      Note that this PriorityQueue does not allow you to change the priority
+      of an item.  However, you may insert the same item multiple times with
+      different priorities.
+    """
+    def  __init__(self):
+        self.heap = []
+        self.count = 0
+
+    def push(self, item, priority):
+        # FIXME: restored old behaviour to check against old results better
+        # FIXED: restored to stable behaviour
+        entry = (priority, self.count, item)
+        # entry = (priority, item)
+        heapq.heappush(self.heap, entry)
+        self.count += 1
+
+    def pop(self):
+        (_, _, item) = heapq.heappop(self.heap)
+        #  (_, item) = heapq.heappop(self.heap)
+        return item
+
+    def isEmpty(self):
+        return len(self.heap) == 0
+
+class PriorityQueueWithFunction(PriorityQueue):
+    """
+    Implements a priority queue with the same push/pop signature of the
+    Queue and the Stack classes. This is designed for drop-in replacement for
+    those two classes. The caller has to provide a priority function, which
+    extracts each item's priority.
+    """
+    def  __init__(self, priorityFunction):
+        "priorityFunction (item) -> priority"
+        self.priorityFunction = priorityFunction      # store the priority function
+        PriorityQueue.__init__(self)        # super-class initializer
+
+    def push(self, item):
+        "Adds an item to the queue with priority from the priority function"
+        PriorityQueue.push(self, item, self.priorityFunction(item))
+
+
+def manhattanDistance( xy1, xy2 ):
+    "Returns the Manhattan distance between points xy1 and xy2"
+    return abs( xy1[0] - xy2[0] ) + abs( xy1[1] - xy2[1] )
+
+"""
+  Data structures and functions useful for various course projects
+
+  The search project should not need anything below this line.
+"""
+
+class Counter(dict):
+    """
+    A counter keeps track of counts for a set of keys.
+
+    The counter class is an extension of the standard python
+    dictionary type.  It is specialized to have number values
+    (integers or floats), and includes a handful of additional
+    functions to ease the task of counting data.  In particular,
+    all keys are defaulted to have value 0.  Using a dictionary:
+
+    a = {}
+    print a['test']
+
+    would give an error, while the Counter class analogue:
+
+    >>> a = Counter()
+    >>> print a['test']
+    0
+
+    returns the default 0 value. Note that to reference a key
+    that you know is contained in the counter,
+    you can still use the dictionary syntax:
+
+    >>> a = Counter()
+    >>> a['test'] = 2
+    >>> print a['test']
+    2
+
+    This is very useful for counting things without initializing their counts,
+    see for example:
+
+    >>> a['blah'] += 1
+    >>> print a['blah']
+    1
+
+    The counter also includes additional functionality useful in implementing
+    the classifiers for this assignment.  Two counters can be added,
+    subtracted or multiplied together.  See below for details.  They can
+    also be normalized and their total count and arg max can be extracted.
+    """
+    def __getitem__(self, idx):
+        self.setdefault(idx, 0)
+        return dict.__getitem__(self, idx)
+
+    def incrementAll(self, keys, count):
+        """
+        Increments all elements of keys by the same count.
+
+        >>> a = Counter()
+        >>> a.incrementAll(['one','two', 'three'], 1)
+        >>> a['one']
+        1
+        >>> a['two']
+        1
+        """
+        for key in keys:
+            self[key] += count
+
+    def argMax(self):
+        """
+        Returns the key with the highest value.
+        """
+        if len(self.keys()) == 0: return None
+        all = self.items()
+        values = [x[1] for x in all]
+        maxIndex = values.index(max(values))
+        return all[maxIndex][0]
+
+    def sortedKeys(self):
+        """
+        Returns a list of keys sorted by their values.  Keys
+        with the highest values will appear first.
+
+        >>> a = Counter()
+        >>> a['first'] = -2
+        >>> a['second'] = 4
+        >>> a['third'] = 1
+        >>> a.sortedKeys()
+        ['second', 'third', 'first']
+        """
+        sortedItems = self.items()
+        compare = lambda x, y:  sign(y[1] - x[1])
+        sortedItems.sort(cmp=compare)
+        return [x[0] for x in sortedItems]
+
+    def totalCount(self):
+        """
+        Returns the sum of counts for all keys.
+        """
+        return sum(self.values())
+
+    def normalize(self):
+        """
+        Edits the counter such that the total count of all
+        keys sums to 1.  The ratio of counts for all keys
+        will remain the same. Note that normalizing an empty
+        Counter will result in an error.
+        """
+        total = float(self.totalCount())
+        if total == 0: return
+        for key in self.keys():
+            self[key] = self[key] / total
+
+    def divideAll(self, divisor):
+        """
+        Divides all counts by divisor
+        """
+        divisor = float(divisor)
+        for key in self:
+            self[key] /= divisor
+
+    def copy(self):
+        """
+        Returns a copy of the counter
+        """
+        return Counter(dict.copy(self))
+
+    def __mul__(self, y ):
+        """
+        Multiplying two counters gives the dot product of their vectors where
+        each unique label is a vector element.
+
+        >>> a = Counter()
+        >>> b = Counter()
+        >>> a['first'] = -2
+        >>> a['second'] = 4
+        >>> b['first'] = 3
+        >>> b['second'] = 5
+        >>> a['third'] = 1.5
+        >>> a['fourth'] = 2.5
+        >>> a * b
+        14
+        """
+        sum = 0
+        x = self
+        if len(x) > len(y):
+            x,y = y,x
+        for key in x:
+            if key not in y:
+                continue
+            sum += x[key] * y[key]
+        return sum
+
+    def __radd__(self, y):
+        """
+        Adding another counter to a counter increments the current counter
+        by the values stored in the second counter.
+
+        >>> a = Counter()
+        >>> b = Counter()
+        >>> a['first'] = -2
+        >>> a['second'] = 4
+        >>> b['first'] = 3
+        >>> b['third'] = 1
+        >>> a += b
+        >>> a['first']
+        1
+        """
+        for key, value in y.items():
+            self[key] += value
+
+    def __add__( self, y ):
+        """
+        Adding two counters gives a counter with the union of all keys and
+        counts of the second added to counts of the first.
+
+        >>> a = Counter()
+        >>> b = Counter()
+        >>> a['first'] = -2
+        >>> a['second'] = 4
+        >>> b['first'] = 3
+        >>> b['third'] = 1
+        >>> (a + b)['first']
+        1
+        """
+        addend = Counter()
+        for key in self:
+            if key in y:
+                addend[key] = self[key] + y[key]
+            else:
+                addend[key] = self[key]
+        for key in y:
+            if key in self:
+                continue
+            addend[key] = y[key]
+        return addend
+
+    def __sub__( self, y ):
+        """
+        Subtracting a counter from another gives a counter with the union of all keys and
+        counts of the second subtracted from counts of the first.
+
+        >>> a = Counter()
+        >>> b = Counter()
+        >>> a['first'] = -2
+        >>> a['second'] = 4
+        >>> b['first'] = 3
+        >>> b['third'] = 1
+        >>> (a - b)['first']
+        -5
+        """
+        addend = Counter()
+        for key in self:
+            if key in y:
+                addend[key] = self[key] - y[key]
+            else:
+                addend[key] = self[key]
+        for key in y:
+            if key in self:
+                continue
+            addend[key] = -1 * y[key]
+        return addend
+
+def raiseNotDefined():
+    fileName = inspect.stack()[1][1]
+    line = inspect.stack()[1][2]
+    method = inspect.stack()[1][3]
+
+    print "*** Method not implemented: %s at line %s of %s" % (method, line, fileName)
+    sys.exit(1)
+
+def normalize(vectorOrCounter):
+    """
+    normalize a vector or counter by dividing each value by the sum of all values
+    """
+    normalizedCounter = Counter()
+    if type(vectorOrCounter) == type(normalizedCounter):
+        counter = vectorOrCounter
+        total = float(counter.totalCount())
+        if total == 0: return counter
+        for key in counter.keys():
+            value = counter[key]
+            normalizedCounter[key] = value / total
+        return normalizedCounter
+    else:
+        vector = vectorOrCounter
+        s = float(sum(vector))
+        if s == 0: return vector
+        return [el / s for el in vector]
+
+def nSample(distribution, values, n):
+    if sum(distribution) != 1:
+        distribution = normalize(distribution)
+    rand = [random.random() for i in range(n)]
+    rand.sort()
+    samples = []
+    samplePos, distPos, cdf = 0,0, distribution[0]
+    while samplePos < n:
+        if rand[samplePos] < cdf:
+            samplePos += 1
+            samples.append(values[distPos])
+        else:
+            distPos += 1
+            cdf += distribution[distPos]
+    return samples
+
+def sample(distribution, values = None):
+    if type(distribution) == Counter:
+        items = sorted(distribution.items())
+        distribution = [i[1] for i in items]
+        values = [i[0] for i in items]
+    if sum(distribution) != 1:
+        distribution = normalize(distribution)
+    choice = random.random()
+    i, total= 0, distribution[0]
+    while choice > total:
+        i += 1
+        total += distribution[i]
+    return values[i]
+
+def sampleFromCounter(ctr):
+    items = sorted(ctr.items())
+    return sample([v for k,v in items], [k for k,v in items])
+
+def getProbability(value, distribution, values):
+    """
+      Gives the probability of a value under a discrete distribution
+      defined by (distributions, values).
+    """
+    total = 0.0
+    for prob, val in zip(distribution, values):
+        if val == value:
+            total += prob
+    return total
+
+def flipCoin( p ):
+    r = random.random()
+    return r < p
+
+def chooseFromDistribution( distribution ):
+    "Takes either a counter or a list of (prob, key) pairs and samples"
+    if type(distribution) == dict or type(distribution) == Counter:
+        return sample(distribution)
+    r = random.random()
+    base = 0.0
+    for prob, element in distribution:
+        base += prob
+        if r <= base: return element
+
+def nearestPoint( pos ):
+    """
+    Finds the nearest grid point to a position (discretizes).
+    """
+    ( current_row, current_col ) = pos
+
+    grid_row = int( current_row + 0.5 )
+    grid_col = int( current_col + 0.5 )
+    return ( grid_row, grid_col )
+
+def sign( x ):
+    """
+    Returns 1 or -1 depending on the sign of x
+    """
+    if( x >= 0 ):
+        return 1
+    else:
+        return -1
+
+def arrayInvert(array):
+    """
+    Inverts a matrix stored as a list of lists.
+    """
+    result = [[] for i in array]
+    for outer in array:
+        for inner in range(len(outer)):
+            result[inner].append(outer[inner])
+    return result
+
+def matrixAsList( matrix, value = True ):
+    """
+    Turns a matrix into a list of coordinates matching the specified value
+    """
+    rows, cols = len( matrix ), len( matrix[0] )
+    cells = []
+    for row in range( rows ):
+        for col in range( cols ):
+            if matrix[row][col] == value:
+                cells.append( ( row, col ) )
+    return cells
+
+def lookup(name, namespace):
+    """
+    Get a method or class from any imported module from its name.
+    Usage: lookup(functionName, globals())
+    """
+    dots = name.count('.')
+    if dots > 0:
+        moduleName, objName = '.'.join(name.split('.')[:-1]), name.split('.')[-1]
+        module = __import__(moduleName)
+        return getattr(module, objName)
+    else:
+        modules = [obj for obj in namespace.values() if str(type(obj)) == "<type 'module'>"]
+        options = [getattr(module, name) for module in modules if name in dir(module)]
+        options += [obj[1] for obj in namespace.items() if obj[0] == name ]
+        if len(options) == 1: return options[0]
+        if len(options) > 1: raise Exception, 'Name conflict for %s'
+        raise Exception, '%s not found as a method or class' % name
+
+def pause():
+    """
+    Pauses the output stream awaiting user feedback.
+    """
+    print "<Press enter/return to continue>"
+    raw_input()
+
+
+# code to handle timeouts
+#
+# FIXME
+# NOTE: TimeoutFuncton is NOT reentrant.  Later timeouts will silently
+# disable earlier timeouts.  Could be solved by maintaining a global list
+# of active time outs.  Currently, questions which have test cases calling
+# this have all student code so wrapped.
+#
+import signal
+import time
+class TimeoutFunctionException(Exception):
+    """Exception to raise on a timeout"""
+    pass
+
+
+class TimeoutFunction:
+    def __init__(self, function, timeout):
+        self.timeout = timeout
+        self.function = function
+
+    def handle_timeout(self, signum, frame):
+        raise TimeoutFunctionException()
+
+    def __call__(self, *args, **keyArgs):
+        # If we have SIGALRM signal, use it to cause an exception if and
+        # when this function runs too long.  Otherwise check the time taken
+        # after the method has returned, and throw an exception then.
+        if hasattr(signal, 'SIGALRM'):
+            old = signal.signal(signal.SIGALRM, self.handle_timeout)
+            signal.alarm(self.timeout)
+            try:
+                result = self.function(*args, **keyArgs)
+            finally:
+                signal.signal(signal.SIGALRM, old)
+            signal.alarm(0)
+        else:
+            startTime = time.time()
+            result = self.function(*args, **keyArgs)
+            timeElapsed = time.time() - startTime
+            if timeElapsed >= self.timeout:
+                self.handle_timeout(None, None)
+        return result
+
+
+
+_ORIGINAL_STDOUT = None
+_ORIGINAL_STDERR = None
+_MUTED = False
+
+class WritableNull:
+    def write(self, string):
+        pass
+
+def mutePrint():
+    global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
+    if _MUTED:
+        return
+    _MUTED = True
+
+    _ORIGINAL_STDOUT = sys.stdout
+    #_ORIGINAL_STDERR = sys.stderr
+    sys.stdout = WritableNull()
+    #sys.stderr = WritableNull()
+
+def unmutePrint():
+    global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
+    if not _MUTED:
+        return
+    _MUTED = False
+
+    sys.stdout = _ORIGINAL_STDOUT
+    #sys.stderr = _ORIGINAL_STDERR
+