import numpy as np

class Perceptron(object):
	""" Perceptron Classifier

	Parameters
	------------
	rate : float
		Learning rate (ranging from 0.0 to 1.0)
	number_of_iteration : int
		Number of iterations over the input dataset.

	Attributes:
	------------

	weight_matrix : 1d-array
		Weights after fitting.

	error_matrix : list
		Number of misclassification in every epoch(one full training cycle on the training set)

	"""

	def __init__(self, rate = 0.01, number_of_iterations = 100):
		self.rate = rate
		self.number_of_iterations = number_of_iterations

	def fit(self, X, y):
		""" Fit training data

		Parameters:
		------------
		X : array-like, shape = [number_of_samples, number_of_features]
			Training vectors.
		y : array-like, shape = [number_of_samples]
			Target values.

		Returns
		------------
		self : object

		"""

		self.weight_matrix = np.zeros(1 + X.shape[1])
		self.errors_list = []

		for _ in range(self.number_of_iterations):
			errors = 0
			for xi, target in zip(X, y):
				update = self.rate * (target - self.predict(xi))
				self.weight_matrix[1:] += update * xi
				self.weight_matrix[0] += update
				errors += int(update != 0.0)
			self.errors_list.append(errors)
		return self

	def dot_product(self, X):
		""" Calculate the dot product """
		return (np.dot(X, self.weight_matrix[1:]) + self.weight_matrix[0])

	def predict(self, X):
		""" Predicting the label for the input data """
		return np.where(self.dot_product(X) >= 0.0, 1, 0)


if __name__ == '__main__':
	X = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0]])
	y = np.array([0, 1, 1, 1, 1, 1, 1])
	p = Perceptron()
	p.fit(X, y)
	print("Predicting the output of [1, 1, 1] = {}".format(p.predict([1, 1, 1])))