Add project 5 sources.

p5_classification/perceptron.py

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+# perceptron.py
+# -------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# Perceptron implementation
+import util
+PRINT = True
+
+class PerceptronClassifier:
+    """
+    Perceptron classifier.
+
+    Note that the variable 'datum' in this code refers to a counter of features
+    (not to a raw samples.Datum).
+    """
+    def __init__( self, legalLabels, max_iterations):
+        self.legalLabels = legalLabels
+        self.type = "perceptron"
+        self.max_iterations = max_iterations
+        self.weights = {}
+        for label in legalLabels:
+            self.weights[label] = util.Counter() # this is the data-structure you should use
+
+    def setWeights(self, weights):
+        assert len(weights) == len(self.legalLabels);
+        self.weights = weights;
+
+    def train( self, trainingData, trainingLabels, validationData, validationLabels ):
+        """
+        The training loop for the perceptron passes through the training data several
+        times and updates the weight vector for each label based on classification errors.
+        See the project description for details.
+
+        Use the provided self.weights[label] data structure so that
+        the classify method works correctly. Also, recall that a
+        datum is a counter from features to values for those features
+        (and thus represents a vector a values).
+        """
+
+        self.features = trainingData[0].keys() # could be useful later
+        # DO NOT ZERO OUT YOUR WEIGHTS BEFORE STARTING TRAINING, OR
+        # THE AUTOGRADER WILL LIKELY DEDUCT POINTS.
+
+        for iteration in range(self.max_iterations):
+            print "Starting iteration ", iteration, "..."
+            for i in range(len(trainingData)):
+                "*** YOUR CODE HERE ***"
+                util.raiseNotDefined()
+
+    def classify(self, data ):
+        """
+        Classifies each datum as the label that most closely matches the prototype vector
+        for that label.  See the project description for details.
+
+        Recall that a datum is a util.counter...
+        """
+        guesses = []
+        for datum in data:
+            vectors = util.Counter()
+            for l in self.legalLabels:
+                vectors[l] = self.weights[l] * datum
+            guesses.append(vectors.argMax())
+        return guesses
+
+
+    def findHighWeightFeatures(self, label):
+        """
+        Returns a list of the 100 features with the greatest weight for some label
+        """
+        featuresWeights = []
+
+        "*** YOUR CODE HERE ***"
+        util.raiseNotDefined()
+
+        return featuresWeights