perceptron.py

class perceptron(object):
	def __init__(self, input_num, actiavtor):
		self.actiavtor = actiavtor
		self.weights = [0.0 for  i in range(input_num)]
		self.bias = 0.0

	def __str__(self):
		return "weights\t:%s\nbias\t:%f\n"% (self.weights, self.bias)

	def predict(self, input_vec):
		return self.actiavtor(
				reduce(lambda a, b: a+b, 
					map(lambda (x,w): x*w,
						zip(input_vec, self.weights))
					,0.0) + self.bias)

	def train(self, input_vecs, labels, iteration, rate):
		for i in range(iteration):
			self._one_iteration(input_vecs, labels, rate)

	def _one_iteration(self, input_vecs, labels, rate):
		samples = zip(input_vecs, labels)
		for (input_vecs, label) in samples:
			output = self.predict(input_vec)
			self._update_weights(input_vec, output, label, rate)

	def _update_weights(self, input_vec, output, label, rate):
		delta = label - output
		self.weights = map(
				lambda (x,w):w + rate*delta * x,
				zip(input_vec, self.weights))
		self.bias + rate * delta