# -*- coding: utf-8 -*- 
import numpy as np
class Perceptron(object):

	def _init_(self, eta=0.01, n_iter=10):
		self.eta = eta
		self.iter= n_iter

	def fit(self, x, y):
		self.w_ = np.zeros(1 + X.shape[1])
		self.errors_ = []

		for _ in range(self.n_iter):
			errors = 0
			for xi, target in zip(X, y):
				update = self.eta * (target - self.predict(xi))
				self.w_[1:] += update * xi
				self.w_[0] += update
			self.errors_.append(errors)
		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)


