Add project 5 sources.

p5_classification/classificationMethod.py

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+# classificationMethod.py
+# -----------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# This file contains the abstract class ClassificationMethod
+
+class ClassificationMethod:
+    """
+    ClassificationMethod is the abstract superclass of
+     - MostFrequentClassifier
+     - NaiveBayesClassifier
+     - PerceptronClassifier
+     - MiraClassifier
+
+    As such, you need not add any code to this file.  You can write
+    all of your implementation code in the files for the individual
+    classification methods listed above.
+    """
+    def __init__(self, legalLabels):
+        """
+        For digits dataset, the set of legal labels will be 0,1,..,9
+        For faces dataset, the set of legal labels will be 0 (non-face) or 1 (face)
+        """
+        self.legalLabels = legalLabels
+
+
+    def train(self, trainingData, trainingLabels, validationData, validationLabels):
+        """
+        This is the supervised training function for the classifier.  Two sets of
+        labeled data are passed in: a large training set and a small validation set.
+
+        Many types of classifiers have a common training structure in practice: using
+        training data for the main supervised training loop but tuning certain parameters
+        with a small held-out validation set.
+
+        For some classifiers (naive Bayes, MIRA), you will need to return the parameters'
+        values after training and tuning step.
+
+        To make the classifier generic to multiple problems, the data should be represented
+        as lists of Counters containing feature descriptions and their counts.
+        """
+        abstract
+
+    def classify(self, data):
+        """
+        This function returns a list of labels, each drawn from the set of legal labels
+        provided to the classifier upon construction.
+
+        To make the classifier generic to multiple problems, the data should be represented
+        as lists of Counters containing feature descriptions and their counts.
+        """
+        abstract