From f2dd2c1bea3846fa316d11096b785f372119218c Mon Sep 17 00:00:00 2001 From: Austin Date: Fri, 10 Feb 2017 01:15:40 -0500 Subject: [PATCH] Initial commit --- README.txt | 14 + VERSION | 1 + answers.py | 17 + answers.pyc | Bin 0 -> 228 bytes autograder.py | 351 + classificationAgents.py | 75 + classificationMethod.py | 62 + classificationMethod.pyc | Bin 0 -> 2621 bytes classificationTestClasses.py | 276 + classificationTestClasses.pyc | Bin 0 -> 9444 bytes data.zip | Bin 0 -> 3310421 bytes dataClassifier.py | 451 + dataClassifier.pyc | Bin 0 -> 15447 bytes game.py | 729 + game.pyc | Bin 0 -> 24441 bytes ghostAgents.py | 81 + ghostAgents.pyc | Bin 0 -> 3531 bytes grading.py | 282 + grading.pyc | Bin 0 -> 9828 bytes graphicsDisplay.py | 679 + graphicsDisplay.pyc | Bin 0 -> 25682 bytes graphicsUtils.py | 398 + graphicsUtils.pyc | Bin 0 -> 13798 bytes images/2002_08_07_img_1578.0.jpg | Bin 0 -> 5536 bytes images/2002_09_05_img_12098.0.jpg | Bin 0 -> 2076 bytes 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pacmandata/food_training.pkl | 259524 ++++ pacmandata/food_validation.pkl | 116686 ++ pacmandata/stop_test.pkl | 102756 ++ pacmandata/stop_training.pkl | 122149 ++ pacmandata/stop_validation.pkl | 65184 + pacmandata/suicide_test.pkl | 37184 + pacmandata/suicide_training.pkl | 84007 ++ pacmandata/suicide_validation.pkl | 105325 ++ perceptron.py | 84 + perceptron.pyc | Bin 0 -> 2929 bytes perceptron_pacman.py | 57 + perceptron_pacman.pyc | Bin 0 -> 2054 bytes projectParams.py | 18 + projectParams.pyc | Bin 0 -> 424 bytes samples.py | 213 + samples.pyc | Bin 0 -> 7061 bytes testClasses.py | 189 + testClasses.pyc | Bin 0 -> 7657 bytes testParser.py | 85 + testParser.pyc | Bin 0 -> 2561 bytes test_cases/CONFIG | 1 + test_cases/q1/CONFIG | 2 + test_cases/q1/grade.solution | 1 + test_cases/q1/grade.test | 14 + test_cases/q2/CONFIG | 2 + test_cases/q2/grade.solution | 2 + test_cases/q2/grade.test | 6 + test_cases/q3/CONFIG | 2 + test_cases/q3/grade.solution | 1 + test_cases/q3/grade.test | 12 + test_cases/q4/CONFIG | 2 + test_cases/q4/grade.solution | 1 + test_cases/q4/grade.test | 14 + test_cases/q5/CONFIG | 2 + test_cases/q5/contest.solution | 1 + test_cases/q5/contest.test | 13 + test_cases/q5/suicide.solution | 1 + test_cases/q5/suicide.test | 13 + test_cases/q6/CONFIG | 2 + test_cases/q6/contest.solution | 1 + test_cases/q6/contest.test | 14 + test_cases/q6/suicide.solution | 1 + test_cases/q6/suicide.test | 14 + textDisplay.py | 81 + textDisplay.pyc | Bin 0 -> 3672 bytes util.py | 653 + util.pyc | Bin 0 -> 34432 bytes 124 files changed, 3777046 insertions(+) create mode 100644 README.txt create mode 100644 VERSION create mode 100644 answers.py create mode 100644 answers.pyc create mode 100644 autograder.py create mode 100644 classificationAgents.py create mode 100644 classificationMethod.py create mode 100644 classificationMethod.pyc create mode 100644 classificationTestClasses.py create mode 100644 classificationTestClasses.pyc create mode 100644 data.zip create mode 100644 dataClassifier.py create mode 100644 dataClassifier.pyc create mode 100644 game.py create mode 100644 game.pyc create mode 100644 ghostAgents.py create mode 100644 ghostAgents.pyc create mode 100644 grading.py create mode 100644 grading.pyc create mode 100644 graphicsDisplay.py create mode 100644 graphicsDisplay.pyc create mode 100644 graphicsUtils.py create mode 100644 graphicsUtils.pyc create mode 100644 images/2002_08_07_img_1578.0.jpg create mode 100644 images/2002_09_05_img_12098.0.jpg create mode 100644 images/2002_09_26_img_513.1.jpg create mode 100644 images/2002_10_17_img_647.0.jpg create mode 100644 images/2002_10_30_img_478.0.jpg create mode 100644 images/2002_10_31_img_507.0.jpg create mode 100644 images/2003_02_13_img_642.0.jpg create mode 100644 images/2003_05_15_img_1197.0.jpg create mode 100644 images/2003_07_04_img_502.0.jpg create mode 100644 images/2003_07_12_img_589.0.jpg create mode 100644 images/i1.jpg create mode 100644 images/i2.jpg create mode 100644 images/i3.jpg create mode 100644 images/i4.jpg create mode 100644 images/i5.jpg create mode 100644 images/i6.jpg create mode 100644 images/i7.jpg create mode 100644 images/i8.jpg create mode 100644 images/i9.jpg create mode 100644 images/image_0056.jpg create mode 100644 images/image_0067.jpg create mode 100644 images/image_0128.jpg create mode 100644 images/image_0193.jpg create mode 100644 images/image_0196.jpg create mode 100644 images/image_0332.jpg create mode 100644 images/img2 create mode 100644 images/img2.gif create mode 100644 keyboardAgents.py create mode 100644 layout.py create mode 100644 layout.pyc create mode 100644 layouts/capsuleClassic.lay create mode 100644 layouts/contestClassic.lay create mode 100644 layouts/mediumClassic.lay create mode 100644 layouts/minimaxClassic.lay create mode 100644 layouts/openClassic.lay create mode 100644 layouts/originalClassic.lay create mode 100644 layouts/smallClassic.lay create mode 100644 layouts/testClassic.lay create mode 100644 layouts/trappedClassic.lay create mode 100644 layouts/trickyClassic.lay create mode 100644 mira.py create mode 100644 mira.pyc create mode 100644 mostFrequent.py create mode 100644 mostFrequent.pyc create mode 100644 naiveBayes.py create mode 100644 naiveBayes.pyc create mode 100644 pacman.py create mode 100644 pacman.pyc create mode 100644 pacmanAgents.py create mode 100644 pacmanAgents.pyc create mode 100644 pacmandata/contest_test.pkl create mode 100644 pacmandata/contest_training.pkl create mode 100644 pacmandata/contest_validation.pkl create mode 100644 pacmandata/food_test.pkl create mode 100644 pacmandata/food_training.pkl create mode 100644 pacmandata/food_validation.pkl create mode 100644 pacmandata/stop_test.pkl create mode 100644 pacmandata/stop_training.pkl create mode 100644 pacmandata/stop_validation.pkl create mode 100644 pacmandata/suicide_test.pkl create mode 100644 pacmandata/suicide_training.pkl create mode 100644 pacmandata/suicide_validation.pkl create mode 100644 perceptron.py create mode 100644 perceptron.pyc create mode 100644 perceptron_pacman.py create mode 100644 perceptron_pacman.pyc create mode 100644 projectParams.py create mode 100644 projectParams.pyc create mode 100644 samples.py create mode 100644 samples.pyc create mode 100644 testClasses.py create mode 100644 testClasses.pyc create mode 100644 testParser.py create mode 100644 testParser.pyc create mode 100644 test_cases/CONFIG create mode 100644 test_cases/q1/CONFIG create mode 100644 test_cases/q1/grade.solution create mode 100644 test_cases/q1/grade.test create mode 100644 test_cases/q2/CONFIG create mode 100644 test_cases/q2/grade.solution create mode 100644 test_cases/q2/grade.test create mode 100644 test_cases/q3/CONFIG create mode 100644 test_cases/q3/grade.solution create mode 100644 test_cases/q3/grade.test create mode 100644 test_cases/q4/CONFIG create mode 100644 test_cases/q4/grade.solution create mode 100644 test_cases/q4/grade.test create mode 100644 test_cases/q5/CONFIG create mode 100644 test_cases/q5/contest.solution create mode 100644 test_cases/q5/contest.test create mode 100644 test_cases/q5/suicide.solution create mode 100644 test_cases/q5/suicide.test create mode 100644 test_cases/q6/CONFIG create mode 100644 test_cases/q6/contest.solution create mode 100644 test_cases/q6/contest.test create mode 100644 test_cases/q6/suicide.solution create mode 100644 test_cases/q6/suicide.test create mode 100644 textDisplay.py create mode 100644 textDisplay.pyc create mode 100644 util.py create mode 100644 util.pyc diff --git a/README.txt b/README.txt new file mode 100644 index 0000000..1eb890c --- /dev/null +++ b/README.txt @@ -0,0 +1,14 @@ +Name: Austin Balogh +CSU ID: 2597975 + +My implementation of the project involves finding ranges of +data and labels. Then returning a max or min depending on +what is needed or performing math where it is needed. +Thoughout the project the counter class functions are used to +update weights with the proper math needed for them. Much like +previous projects, the pacman enhanced features involve finding +distances to ghosts and food. My MIRA implementation seems to +somewhat long to complete due to the amount of loops used for +the grid. There may be some way to optimize it, but I am not +sure how. I was uncertain how to do question 4, so I did not +submit it. I spent about 25 hours on this project. \ No newline at end of file diff --git a/VERSION b/VERSION new file mode 100644 index 0000000..52f6cb3 --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +v1.001 diff --git a/answers.py b/answers.py new file mode 100644 index 0000000..b7e7d13 --- /dev/null +++ b/answers.py @@ -0,0 +1,17 @@ +# 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 ***" + return 'a' \ No newline at end of file diff --git a/answers.pyc b/answers.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ecbea396352ccf0542dd8fd2d16b032dada99f9b GIT binary patch literal 228 zcmZSn%*&NC!#pCH0SXv_v;z}hA1F|ks(-v2`GZ1&KabR1*8rpT`UIV zX=!OGMEZvYDLDJPxGH$K2DvH#g-SpsCu#r*1~j6W3nY_QT%KA~tXEK30": """ + 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': { + "": """ + 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)) diff --git a/classificationAgents.py b/classificationAgents.py new file mode 100644 index 0000000..51db76b --- /dev/null +++ b/classificationAgents.py @@ -0,0 +1,75 @@ +# 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() diff --git a/classificationMethod.py b/classificationMethod.py new file mode 100644 index 0000000..98e588f --- /dev/null +++ b/classificationMethod.py @@ -0,0 +1,62 @@ +# 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 diff --git a/classificationMethod.pyc 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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 + + diff --git a/classificationTestClasses.pyc b/classificationTestClasses.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a37bc501eec1778e3ec71b06507ac7579c098008 GIT binary patch literal 9444 zcmdT~U2_}fbv_G_00EIAB~g@U%Mv7+Hk>$g+&FeeBg>{qN@62X%nSMpwSp{nm*i3l zfWo_oNSB;m$kV#*eJ3;d1?}`NG&AWh=uCb@dX<~rcG^DAd3OO))>NV|+9`oOc)!2S 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    DzD zkDVja%^Wpe-OdXc0pG=Je2}xuYUZ#bGYP(eT@?17fJ?>AydN^W@B~eee;zsUYUzieGL8;n}2Y=<)aPa6XnsXy<-%D zCLSt5nsfM^roo{@eg^|*!}GTw4b#^Fl0wv$wh-5Yp{rmJgq{PT5Tdv+#KK(ik6g$? zK9_+~{g*;obAVzO1d=*>KAHC(kqL2NY?3Sl>CM4tTC1`E87aoXY(j0*R#)`Gpxn!g z=b3eE-2zDeIC?%MdvOUov|k zFx`zyZ(`obLKl^d=NWlb(Rd-Sf6QD6?*8@k4L@7MAWK7)d5aA-ZuT|YAu2WCUoHIZ z(WOJ;gEEkAz1F;B_-ttYEy{nGr}*Fn;gbdoI;f`%>Cf?%?GG!zLHcjo3O(KTz4C@Z zPgzRyYzw-u>T9gXC~Ek>+D4qLLd0joAonbldA1!NvHTY0KWy9YyCHo)AA)XcD9p2M zHQo9fr2n=JeF=5`?QRHKxl(psaz6F7nS&KgK1dD-{8#gudr~gk{{@0B+Qa81=Robk rK>uN(W5xwI9tMMOW=ivt^G&SnLagQ+DX$=nxi 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 { : , ... } + """ + features = util.Counter() + successor = state.generateSuccessor(0, action) #Generate successor for current state + pacmanPosition = successor.getPacmanPosition() + closestFood = 0 + foodList = successor.getFood().asList() #Make list of food + closestGhost = 0 + ghostPosition = successor.getGhostPositions() #Find ghost positions + + if not foodList: + pass + else: # Find minimum distance to food + closestFood = min([util.manhattanDistance(i, pacmanPosition) for i in foodList]) + if closestFood: + closestFood = 9.0 / closestFood + else: + closestFood + features['closestFood'] = closestFood #Designate feature name for closest food + + if not ghostPosition: + pass + else: #Find minimum distance to ghost + closestGhost = min(util.manhattanDistance(i, pacmanPosition) for i in ghostPosition) + if closestGhost: + closestGhost = 9.0 / closestGhost + else: + closestGhost + features['closestGhost'] = closestGhost #Designate feature name for closest ghost + + features['remainingFood'] = successor.getNumFood() #Designate feature name for remaining food + + 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() 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 + 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, 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zj8i3o`BI(m183Uwl`}QZ*y>C1x{DMQNg5bc{}CS(7L2wN#fP%qu`|l7!=kzyU7{c#Sd= 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() diff --git a/game.pyc b/game.pyc new file mode 100644 index 0000000000000000000000000000000000000000..efc5e9c600661bcb95eb93ded9c5ebc4e6b7c25a GIT binary patch literal 24441 zcmch9Yj9lGec!pei^ncOf+R?gkZ9>r5+zWANlBKah^8!)07%h8ftm|gkZHl2#qI^L z!y=7Y4d1P*L_Snolet!>D29qPCD&O z^P#Ex`~Cj+v4H5*GuSDBdvNZ#=bp#^{r{hHZt;H{tSo)`rMK!X`6=T6CvZgro^uWS zd9LN$0&@k|^4v{NrN|fD%|eAeb9NH7arzK+jDONx1!i@ge%Q<=-2$!Fuqieo1M159>!~(#+1Ljg*vqzzt)O6X}x$YR3j^$ 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z)$$V{_=Rt=m<9AmqV^J<{OERbqiUm)7uCZj+7>p6I_Agk!-W@(b+_6t(H{RlY_T-% zm9f^Sqrb%?*yS{a@G&=Ul(GK!1TLP_IIM$Nd|Jj`8Nv23)H=d*I(mFWZCAX3!m+|hwEu|rC@%Jg&(9Xd3i(}0 zEwb(5!l+j%Vg_jG6nf$DE0&%tjt`6%4!fg;Lpa*}Q1KLIRK&Bxdb6zXWd;4t8G^}* zJ)e9LeV16qkbrR=B~&24N#2sUQ#|=!@E}Kxa{Itf@GEG}BXY 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 diff --git a/ghostAgents.pyc b/ghostAgents.pyc new file mode 100644 index 0000000000000000000000000000000000000000..696d80c39e745654c24ba5f951b11b7d49966577 GIT binary patch literal 3531 zcmb_e-EJF26h5=-pEz-TA#GD6x|Eh$s3creDoBCSCMq{5RO^U>MQd$#$IiO;Zn86J z)5^XILgEqNg7@GNcnfa1L*fD8J7@gUa*NlQ$;_FVGiT2E&X521^5T=9?tUB7`1A4q z3pDd9x&(iU`a}aoEk%mwp3FUJd2a5@+^3fB<|XQvs8y1)W$Kq{P@z_3x>lxEnUV@= zMIA*+m3|<)Ao{LVrDTD0Rj9Qob%Bz_33X{otx-~w9aF?R-RXnO_Q^}T z01oYTnx(egzKpTq`$&nnEH_U@+)puptUVF>Lea=03+X+=7_WZjQPCjlQO~C)W$ zYtTb&&x*_hQLvYvWmXq~&f+{tvo1(1`9v{6{v3;u18mU2;{$uFgOehEo+dg81) zEX&_+CwUCG@R6AeGMa-RZ-MJgG{f(%sEV>{7)(<`)PEBvJ{BMF_7Bkg2PZHQ$9tqh zn9H99e&%dAaJ~VXlR|fNVFH^6#hEz{9z`*7&+Q;Df=*xS;Ki}d0ux7tPPV(h{LJ5< z_hRP7jiD$2x`x3te*g@eNB|XI2FOGlcG-LfEzuhVc1$F9n|mI>I$9Ln1quCp3;PoJvC=eM1PZpbE~!$@0r^l+K3 z@DYhqn|J-)(+l+&CwuG6Ut!b~9J!RAG7KRxe}CL-h<|euLIEH?*5rgO5h>gRS^RLa z_9!>0R0j-Qhtk7zpo@@sT8_t7=!3s0PQAeCJ>j& ztMo*oXZSYuh8!J3P9q}mEHX+qlQbZLTP{E)0gPj-usT86$Z;z*&6qt|>QZx}brN!- zf$frqLJ80%(bmVQIK-y5N9^x$3yqoH-s{7XwbL*<^)#3bi z{JH|B(5`bbkjOJl2H_R3a76>NoyjSqIYgr^aQz5vTrX7AYPrGbNs7oWN|+E663Dsg tl-24oyTc1(ORA2Lvr5SP<}RfeaM#sPpL6kKIA5(P{?)m8msT$={0mNC@elw2 literal 0 HcmV?d00001 diff --git a/grading.py b/grading.py new file mode 100644 index 0000000..0ef07a9 --- /dev/null +++ b/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

     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("
    ") + + # first sum + total_possible = sum(self.maxes.values()) + total_score = sum(self.points.values()) + checkOrX = '' + if (total_score >= total_possible): + checkOrX = '' + header = """ +

    + Total score ({total_score} / {total_possible}) +

    + """.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 = '' + if (self.points[q] == self.maxes[q]): + checkOrX = '' + #messages = '\n
    \n'.join(self.messages[q]) + messages = "
    %s
    " % '\n'.join(self.messages[q]) + output = """ +
    +
    +
    + Question {q} ({points}/{max}) {checkOrX} +
    +
    + {messages} +
    +
    +
    + """.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("
    ") + 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()) + diff --git a/grading.pyc b/grading.pyc new file mode 100644 index 0000000000000000000000000000000000000000..14ad9548a03c3c3a206e564d6f499ed798f704bf GIT binary patch literal 9828 zcmcIq&2Jn>cCVfp4ml%Iq$x^T%1%(Wto2xyNZV_~woD5vQyG*X&eQ|4NkG`ovr zlQYx9t{#)L$VfE|7EnkDRhtV1dmc$K>~W)iWGYAC3}c z$gZibdiCnnt5@%{eRcn}J zxZiq;voF>vB7R6zf zmGORuVD`vbKSQ(qI}`?M#k?pB$`ttzT7QCtg31Dk!D?cFp}i>WWiEj2!}%Jxq_VVB z1Pn_6&#GJ`!LW{sR7O=)mCBfkP6_0wAUrMgF&Qz&-2WPIRb9WXnYO%&Gw?9Ww)%e^rYjN9HE?iherq~c%$i+zKd=y~ad#eX6 zbFKHH+jz_Sx4Xt>aniOo+R^=B*VvnovTHif&Ddr-S<|&1gp9hPTfwgGByl^lx?@t^ z>wR1BL9&#JDY&sv{W@xkS&+)sLE0}xS1(06$WXa<>D~8TQLmi=SjLml!)CJ*-bCQJ zr8%fK|23>4pdv+&Dw`?Z} z&Fn)Qrw`_vfwl2k90uIuS(}ElJYJBQna-YYuF;6wan@*1CE4!~b6I=i-l+GESMko_ zaew2*kW1u4Qs(gpv^yg>1k;EklX0UL}5T19F)}V*B;m9;p`_Klm|2@^aqcl9PO;Y zSAd?My0fr=Di5}t>P;NoA+Q2XB?3Ti5V*`bj`dbarGLZMO|&R=AR2cc`Uvp&A69E6 z&a!<@wVn1ER_QOuC@2HKduaXoYqfV#nyR!7)IJ~uWC0^)f(7!Fx7>cHO4+E|s;YF< z^A1LotOvRWvy7Ftt(dQT8MXvxWAhx*N3bzt>UDDd zw7Rpf`<{&DzI>NTJp%bJMEB3$ax{T~(JT3+o9Br(`;NSxQJd3z1#!^pm;|aheXHQe zbCA)b+A6RyIs-J|e>8<}LR?VkX9D_aj}KtbVvVQT{uv5Uht5!I6NDRmMVaf}{21O( zqHYtKncBQ1b!YK{X7h-KU+cC*QPaBC!I9Q++rimAJLAULA2%A*>y5@^r;F_<$>?b> zeaVXU_%x>I$9RpOSi7+`@xpcmev^NHB4Harpb&R7_V0i46)JFF|<0+ z=L5Z0szkMFLTI96!@6-Zvbx*BfW4#(SEp@vU^?ja3?P`P##f-<#Cq7bLE7ybckf-( zOD1i_?I1Hz&KbH5oZjq)rbC|0)a`2kuU@=(aS-1%7@1jE8#Gt#Gy!fy)!7XoI%a_4 zB6^*{`Yz$1I=_l7FbB=t8>xQ!)W=Q&bb$PPI%&T8)JHaBDanFn4i}Rm`&;OuYAz8p z?gO72_wMNha6sSjZ`5x-yfHs>4DQ~8`W=1m?&r7k(!wo$XW_x((q}(fT)K)<4UPM8}JwyU!Mm2 z{&PRasC+J*bQ}SGF(`Xq68zWzvh>^LI5GoR56-^a5dAr3`L>yk&LVs9ZF4J&&m1x#Mj?D6H_&MgtfE>vS zzg}{{5%3qVN9H^R{37tDK)J_H6JD*_zomZtf=0lFc{+eWf&I~YCex`|hyM%bIhxUW zDe1pEtKkMP0LkrC6?SSZX*QD`2N1ViK$)a(S-swkBhz$|5;27){>tbOxMPGhxgpB^ z@tJCk2E?aE_vs{xJKr>IpJ9OWU+IrW@!A@wYllVvI!VWWh0t^;yII_n2!T%fLl~$` zVu&TgA4xa6ottlo57teFU^W%#TRkI|(MyKA+Hbh;LK=a}Qh4;MLAaHTE8Mz&8Khg)9m|Pg@hIvO_2msE_CU(#0CEEi5eTOOgQ9Mj z2O*NXaK1bHj(9NeHW8ro7IQ!7hQEvj_%^A46mBKl1(i<(E^~!~c8uN;(=v^#~qwj)O^v&KoLBcyD?`-doW4q@=ko& zz06F&CK3>6m1GQBZ$3W5_`?CZ5W?I3KrJJ7Sgc9h`j5aar|rJUHj;?0noEv|x7gnR zod?8M&>X{Cv~VtQ(1ghh2H^wco|+)rF}iKgM#pbGov_aEOs?wO$kXd?x^k zWeuzZg;ikJv-*dr_Tdlv0rVk;lz;p->tzaO=M!4n*w7R|y)~JMfAaayycM&<$}gf8N8; zi!&K9mX-Z$$ujP~DN*JTu|kjsNrP~?m~Q(YAXW_2xdU~_%a2fX8zWqHngNQ6AlY8W zX+3B!{}VL$%sm{c1pj>kQ7;(krn@-10Tm^+(srIfspndTxE`#D%IJ47B|;S?qp8`B zjD$k*=g@m%dwUtkeu_tARpIPcy>f9JiH~vbEWRg;Wv>LC5Q>-rjti5M{r70WZex3~ zuL!vhu+P{Sydr;ybIo{PPBd7JqfKEzZqYRkk^gxk_NF9f9f^a2Vz?YIM+nJblqo3! zMC1@5#NYs6^v$tF<}zizJgVOP7nyGuy5pCUZveQ3zz?V~Gkw+d+9p^Db#^Ku>%lSwwhl50*8G_d>4KtE; zjnr7goz~23WG$CJ+p$z8b5Z=&bqNdSkS{|Xo|&>w-=HBv)-TN7n7wY#6K=3~pzp`% z>$^R38{fO$f1&Rqksi{rRJye%Vr(1ob^y;YfA1tn&8;@gfeY6;Y;LxPNTh#Y9C9|0 zI~wgqZ=6_IB!0uK2kE1jZi@?@DXOB7FWI?SDrR@2tp~~N6UpWG)|?@4`onA;05BgMa#oUK}!ys8q|35X! zlU}Cx5Xo^Av&X1E((hc3B3J~Xd}e6HPqK8D9H7%ekR0(AYQlFaz@&p*FePUvjgj@8 z;ZU~wvn)7XJSb5Uc2 zc)Ni23!{KSz3fetXeO%|1ye~j_nvuTdF4|#9+5T$$Fb@R42=vam}lP<3OWzqj!Oc2 z&{Z_lJmK{ig>q?i@$VU+NUmL|KQT&w1hoH-r%>G>jx1CCaxL{+Ab(l9Kl&fh?LPwQlj5E88S49XCg|$^V?S zWfUj2=ED^chpty*vZJ+`d$llKvpT0(CJJ%gtnYdu*U0z

    Uuc;btBy{i?tLO5Z#^T4I8>vQ*3oV9Mr7pAkFe@kD&zsTYWi#Jh- zZa}Og>eiSR2XUSHThpxDHy@YjsBTZ^Q2~XZZfWF}Z}@ejlfjM4`Y=9QN^Z4~bYNr|88{g(ro3eH z@PX%FqJ+cyKDYikbexzpabKPyNn!3>oOpp*lMKxWAbZy%5@|LX?$)Hykd#cL5hY=x z;V+SH>NfumS$xQXYuqZOadNr*>29QGL=t5F#IZUfo@B_av@90U~v z7VhUDC52yRaJwyMfhWcv0Jy7`{C6k%U9^fgQKUG_Ji+O^>`l?0Bl^K%;wi!}T$bCD zxO_xNgUiPnuM#og;v>v`#DZ}`pM^NOjRvAhcvUTI2e;~nX73iGNN4D15#_wa(XMdx zB-k1P={QA&^C={Grr@`P 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 diff --git a/graphicsDisplay.pyc b/graphicsDisplay.pyc new file mode 100644 index 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+# 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 ) + _root_window.bind( "", _keyrelease ) + _root_window.bind( "", _clear_keys ) + _root_window.bind( "", _clear_keys ) + _root_window.bind( "", _leftclick ) + _root_window.bind( "", _rightclick ) + _root_window.bind( "", _rightclick ) + _root_window.bind( "", _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, 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(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 diff --git a/layout.py b/layout.py new file mode 100644 index 0000000..c6b377d --- /dev/null +++ b/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() diff --git a/layout.pyc b/layout.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d69f0fb3f122693400b2b639cb5bb6cbcc0aa47e GIT binary patch literal 6573 zcmb_gOLH7o6+U-f(#RvnkrPY8CLThiGE_h!v5BF|ByuF%aY`ABmJ-_{%2d0%rD=Jl zd(^jOkBqc(iYj*SBSNv@7qH2O6$^eqR)dm{^SZ@< zO;2t8=Gy&02LFoqUBYK~QH1z!q$AQbvT4MyU64*ex<%P6x^_`ICD|4-Ch|<=!RCa7pianVSZz|mNp@^vL{ZioQ`j#45B@a_YWa&Ox_;93^W0BX zVv90}TvrKQx3f5G<`vXd{eyHrAF1mV`N>S|Nm6!0SS@DSu$Nj(TS40jNd15n78#J+flsJ&ON<-_T2J2HgaS9 zPRD|Ck?k}M^GQ?!zi0P5knn2Y$U>fbcV zfD##gdh-(?@KPyTb~Ls0&Y8+j%ZHQ8bGz6R68X(!W_(;&~j2s;0=^1?JY`vMi;GV zIV~|Te3o!?v)&#zCgoUT>Ug7iyzzp(4Q)6oNxy9JbAz>k-Ic9>%TZbS|C9lBnuuQ6 z`cjT6I_gC^EX(|1MdtGvc~p?>OX>ef^7C?tBj}k|wtg?46y#_^j&PC*tykp;np2j; zs?1{U;grl4j?TzYP5O`J5NE5&Z2l5!*o_tFl9Aa*MX~#a(tJ^=+C4w&pRZ+qG>4@T zM~9O#zx(oFz3l>*FyFZ8-ZgV?Zvx+!g8%pS{w(*n4D|VepaR%`{VJ~~m%h1U-px~e zr*buq*>bj5;w%bOKVe(=?{8nIMHsf;RaG0dT;B=OyUu_Rc_$`*p`%Wa-fhfclp2*R z3ipFZEso8zoIq{0{4i{_RGD)Q{P@oLo%imn-r0E2`tZhvcjta<`Ns15H&yLvhQ0G# zjV7si*V}ksYwLGELX9Tr-p%z5t=zn^zMpPxXI%vaCR|_GpqA39?B=u*Yd#8Aowb1 z804-Xrx3%w`#Dga5*Wq)sCIB##^XEKh$b%fJMq)#$IQbFAxk4)Cp)X-)^@2O+>@4Or+1*m0F=1&+#in^hf zLh-3gda3nZA2x!tb6N-Hg~s+C9M%$QmYWKW67WungHPH2H5yPaIA&bKz*4GDgB*nH&6Fa~_0xjveL$&EGa*W#wfz4|P*t?+_dRed4IaP=ku`)@fP?oos%qnUYFtCu zJ@3aHjqfzV9_Akq@2&j|p}NhTfY(5@WJ1i6xoQHcH;oH_6?yNVvZGdLWow4VhbqIB zLK+l{ivr3TL}=j5uzV0l&}coXi~>5M%=;dT@1tmN6DNX6)OHxzQV2goBd0z@+ z+fC!dMc3@mlb#mS6kV$Y=ih2QK=pX&%DqO+YGyLz^c7{5{Y_=k)BDiy|I``ejfPRw zTTXiipB&?mTe5GYzkfhuV5yAE!Rz%urV2O`7+%@BNtNthA5t~;TiJSpKy>yVD4UxH zAm^!;FnUm3bfGLWEi`ltd*6j~Kvzk2K?Kr?XKk-+y`oBu*c~A{LiYkgU&OWptv}+p zmgd#YV%i%JmS(TaftS-X3**Gkk^cHwRNwBzyx+vK?ZTKz|L`ts2N^#xFv7>+3^D9bJz9TRVZsQKrZ zv6jAG#3+Hxl_rRi>LUlAFpZxU%cNypQ7;~#?*)+== z#F1*1i+X9VU|t`n+jjPX<6Uyf>1tWy(=@0a%~T7YrJ~97v<7-J$aJTj9VJgZncC5_ z01jXYilScbzs`50RRhqG0EpT zsHB5_tAok}KAwR_d>0NMrT+X%($VNth!)qFcf_yJcQuo=vtvwkz`3{PV3Am zP2+SXauJ*mY0J~%fL1&-rqN3gVfYPFqTv$-dJK%C0&}pB1LWl zn)f1tqQ&X?7)Ct!5Akq^Iz<|^kt*wCM1ZUMN~rMG8HAKp9{Rx})d$CxOPKcI;<7O? zo(~hBf){vThY7*M;5k#1nF4?>=aK%*z;vDmEnrAe#K!}v`TQj+E{Sx{aFDybpk@S| zL;AdVBTrxFs|MXWV+Vw*3KHA|_GGxa$Z_lUfEoSyS20b1*TsDBme*a literal 0 HcmV?d00001 diff --git a/layouts/capsuleClassic.lay b/layouts/capsuleClassic.lay new file mode 100644 index 0000000..06a5c51 --- /dev/null +++ b/layouts/capsuleClassic.lay @@ -0,0 +1,7 @@ +%%%%%%%%%%%%%%%%%%% +%G. 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GGGG%.%....% +%.%....%%%%%%.%..%.% +%.%....% oo%.%..%.% +%.%....% %%%%.%..%.% +%.%...........%..%.% +%.%%.%.%%%%%%.%.%%.% +%o...%...P....%...o% +%%%%%%%%%%%%%%%%%%%% diff --git a/mira.py b/mira.py new file mode 100644 index 0000000..90e7c08 --- /dev/null +++ b/mira.py @@ -0,0 +1,117 @@ +# 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. + """ + myCounter = util.Counter() + gridWeights = util.Counter() + label = util.Counter() + mySum = 0 + + for c in Cgrid: #Loop through C grid + for a in range(self.max_iterations): #Return max of iterations + for b in range(len(trainingData)): #Return training data length + for d in self.legalLabels: + label[d] = trainingData[b].__mul__(self.weights[d]) #Obtain label + if (trainingLabels[b] == label.argMax()): + pass + else: + maxLabel = label.argMax() #Get max label + countTwo = util.Counter() + tData = trainingData[b] + tLabel = trainingLabels[b] + weightCalc = (self.weights[maxLabel].__sub__(self.weights[tLabel])).__mul__(tData) + 1.0 + multData = (2.0 / (tData.__mul__(tData))) + calc = weightCalc / multData #Calculate weights + minArg = min(c, calc) #Return minimum value + + for e in trainingData[b].keys(): + countTwo[e] = minArg * trainingData[b][e] + self.weights[label.argMax()].__sub__(countTwo) + self.weights[trainingLabels[b]].__radd__(countTwo) + + gridWeights[c] = self.weights #Update counter + + for f in range(len(validationData)): #Return length of validation data + for g in validationLabels: + label[g] = validationData[f].__mul__(self.weights[g]) #Obtain label + if (validationLabels[f] != label.argMax()): + pass + else: + mySum += 1 #Add to sum if we are at max label + myCounter[c] = mySum #Update counter + + self.weights = gridWeights[myCounter.argMax()] #Update weights with highest value + + + 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 \ No newline at end of file diff --git a/mira.pyc b/mira.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6990049fadf64dca8567d6536c181df5eefe1cd0 GIT binary patch literal 3528 zcmaJ^-EQN?6&_NyY*Pj0K zlc%AmJ`Man!lNI6MEG+=BE*>x$`NO-i14>0qK5dZ5HE!I`m8}5$K7Rc$N$6U$ZTNv zB+lhQBDIeDvC3=V4wn9aNBOiZo6Kh9YvnAsfnZ5W{9HW~ny2Scucu z#4?G$Q>Q8(TpE3novA!yMfd=%lDrHXWiAYlPJ0QM-$p|On z{@iL$(Nr?L^b&Frh4DLBERjEIM@XrgQW8}ELR0&%iO8ic;BKL;MTxLfr!Q?!g9}T zyCEss!9z*Ff!Rl(4czNcP|D`Qcx7R{N|7sDV*HZ>&mW%u=#*<>967eeDOD+TkDc^Q?|wUZH^;te_fR8j0SC_LzAah#B^PBz&%~1(cbk)oDxic12b2v zq~0^6%0I;Qx5Q&-%8dmjZ_h-QcSNk;;hVVI^3yNtgW3ZWWJq;ou}7-f*0&gY+e(4-gft{6Y0vS8QV z=Nj2vbnX4xwfCXUzn~5zfTPyuSwrM)_xgKh+M+7_5V6L~Dc+{=5)Qt87n(qaX65+~ z)CyLpWmUcEdmtQl5b9Sa)N^ccc??p7qrDkQ6&hLZJ^9ps+yg6B41?Ibzj^&Ew?Kx1 zC=|ik0ktwt`-k-@K)%$|fHvxYa}eZiIEZiT?1j?ClVMooGMspsJ?fWrX8RiHU5oxw z+ufr)y8*03uwrh&Hw=g89x7Gh{JaRMe|kED8q(AQmdUnrJSgC3&0K{!iSjK3m$Iy^rTWn%$3 zm&EuD#zq%wug*Fy;BXlb(GXj<;qF~OU`(b~ydWGw)aiZtxUW@VQxuzUT?nBHuky}?EP?_=%bNJBmY(S zVc`Zq=>gG_3t&(vt$|AvAwG|1t7{r(O7ikdu{mo0513qHx4mkvG?4*qWJ<@`b-JsV zXT~5%6^Ley*-}SL9s3bMHpxJCX vtyZhqYPa6Ao5)l~3cfL&fgc5FI;7zX-G-gb?De2M$3(3ZfJnt!oK3pLUZ>u1OQiNx zF8nS25mW!if?vZh=VjBsQJ z!#{;FD20@;vE)KxNg4;IIV&}2QNJ$Ye!cBxU5(s)2NL&b%%{7f;5Tm`Gra8)`V;$n$?;1${XWU zj=QU3#Bi+(3AohOSvOWg&H8x=PGekSK6*lh&;}CDi4w381qXx>$dz*k+=i9hqZzNA z1{Sy}dxbOHf-|m+_hE^r6BFXX%$sb)w{RdcW1YPh(7uBy1>?8jb&rbNbamy~y6#y~ ze)kidn(l|PIDRqjV5->`uItORD?sKAem`C$@1P^F0yh=gj8`%16%{&P2A-`;OOrzh@3M>qy)MW5`bS@IbY$LN#7j@Zk)Q?7 zcd&b^GmCYQwJW<@*OqGfL&>x*x!hI+WTLpoDC-3*HQUiv6eK4XkkP~Nk6^~&IilUe zO}ao6&=#VvF`cY}bRp_G=`$frmCm4gc{y@>1?At^iCXGHddO9?C)I2AL^F~ka3sa- za*2@qv$s2a0^iF!qGssdOt4PpCP{b$>q(Lq!zz=hs3A#)MVcgi@%wkh+=saW7`!FC zoIbMBJq#~!S6VC4dUJg({GRq@g{AEL7pQ)K`-YE-Vc59-GV6_yarr<4V{dAXwl@W^ KJn?qaj{gB!FO3BN literal 0 HcmV?d00001 diff --git a/naiveBayes.py b/naiveBayes.py new file mode 100644 index 0000000..bfc2574 --- /dev/null +++ b/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] = + + 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 diff --git a/naiveBayes.pyc b/naiveBayes.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b068c738b2486370973c7201c9ee08d984a65989 GIT binary patch literal 5417 zcmcgwO>Z1o6@69hkF;aQPGUQ8Afy-+xs5U=AA*d6Gfv{pWHd?~s~j-#Oixp;s&1EE zUDd8vooNr(D>L? 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