# 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 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 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 findHighOddsFeatures(self, label1, label2): """ Returns a list of the 100 features with the greatest difference in weights: w_label1 - w_label2 """ featuresOdds = [] "*** YOUR CODE HERE ***" util.raiseNotDefined() return featuresOdds