import numpy as np

class Perceptron(object):
	def __init__(self, eta=0.01, n_iter=10):
		self.eta = eta
		self.n_iter = n_iter
		
	def fit(self, X, y):
		self.w_ = np.zeros(1 + X.shape[1])
		self.err_ = []
		for _ in range(self.n_iter):
			err = 0
			for xi, target in zip(X, y):
				update = self.eta * (target - self.predict(xi))
				self.w_[1:] += update * xi
				self.w_[0] += update
				err += int(update != 0.0)
			self.err_.append(err)
		return self

	def net_input(self, X):
		return np.dot(X, self.w_[1:]) + self.w_[0]

	def predict(self, X):
		return np.where(self.net_input(X) >= 0.0, 1, -1)