76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# classificationAgents.py
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# -----------------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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# pacmanAgents.py
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# ---------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to
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# http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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from pacman import Directions
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from game import Agent
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import random
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import game
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import util
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class DummyOptions:
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def __init__(self):
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self.data = "pacman"
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self.training = 25000
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self.test = 100
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self.odds = False
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self.weights = False
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import perceptron_pacman
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class ClassifierAgent(Agent):
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def __init__(self, trainingData=None, validationData=None, classifierType="perceptron", agentToClone=None, numTraining=3):
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from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
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legalLabels = ['Stop', 'West', 'East', 'North', 'South']
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if(classifierType == "perceptron"):
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classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,numTraining)
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self.classifier = classifier
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self.featureFunction = enhancedFeatureExtractorPacman
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args = {'featureFunction': self.featureFunction,
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'classifier':self.classifier,
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'printImage':None,
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'trainingData':trainingData,
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'validationData':validationData,
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'agentToClone': agentToClone,
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}
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options = DummyOptions()
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options.classifier = classifierType
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runClassifier(args, options)
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def getAction(self, state):
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from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
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features = self.featureFunction(state)
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action = self.classifier.classify([features])[0]
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return action
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def scoreEvaluation(state):
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return state.getScore()
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