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