'''

	Perceptron
	w = w + N * (d(k) - y) * x(k)

	Using perceptron network for oil analysis,
	with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2
	p1 = -1
	p2 = 1

'''
from __future__ import print_function

import random


class Perceptron:
 def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1):
 self.sample = sample
 self.exit = exit
 self.learn_rate = learn_rate
 self.epoch_number = epoch_number
 self.bias = bias
 self.number_sample = len(sample)
 self.col_sample = len(sample[0])
 self.weight = []

 def trannig(self):
 for sample in self.sample:
 sample.insert(0, self.bias)

 for i in range(self.col_sample):
 self.weight.append(random.random())

 self.weight.insert(0, self.bias)

 epoch_count = 0

 while True:
 erro = False
 for i in range(self.number_sample):
 u = 0
 for j in range(self.col_sample + 1):
 u = u + self.weight[j] * self.sample[i][j]
 y = self.sign(u)
 if y != self.exit[i]:

 for j in range(self.col_sample + 1):

 self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
 erro = True
 #print('Epoch: \n',epoch_count)
 epoch_count = epoch_count + 1
 # if you want controle the epoch or just by erro
 if erro == False:
 print(('\nEpoch:\n',epoch_count))
 print('------------------------\n')
 #if epoch_count > self.epoch_number or not erro:
 break

 def sort(self, sample):
 sample.insert(0, self.bias)
 u = 0
 for i in range(self.col_sample + 1):
 u = u + self.weight[i] * sample[i]

 y = self.sign(u)

 if y == -1:
 print(('Sample: ', sample))
 print('classification: P1')
 else:
 print(('Sample: ', sample))
 print('classification: P2')

 def sign(self, u):
 return 1 if u >= 0 else -1


samples = [
 [-0.6508, 0.1097, 4.0009],
 [-1.4492, 0.8896, 4.4005],
 [2.0850, 0.6876, 12.0710],
 [0.2626, 1.1476, 7.7985],
 [0.6418, 1.0234, 7.0427],
 [0.2569, 0.6730, 8.3265],
 [1.1155, 0.6043, 7.4446],
 [0.0914, 0.3399, 7.0677],
 [0.0121, 0.5256, 4.6316],
 [-0.0429, 0.4660, 5.4323],
 [0.4340, 0.6870, 8.2287],
 [0.2735, 1.0287, 7.1934],
 [0.4839, 0.4851, 7.4850],
 [0.4089, -0.1267, 5.5019],
 [1.4391, 0.1614, 8.5843],
 [-0.9115, -0.1973, 2.1962],
 [0.3654, 1.0475, 7.4858],
 [0.2144, 0.7515, 7.1699],
 [0.2013, 1.0014, 6.5489],
 [0.6483, 0.2183, 5.8991],
 [-0.1147, 0.2242, 7.2435],
 [-0.7970, 0.8795, 3.8762],
 [-1.0625, 0.6366, 2.4707],
 [0.5307, 0.1285, 5.6883],
 [-1.2200, 0.7777, 1.7252],
 [0.3957, 0.1076, 5.6623],
 [-0.1013, 0.5989, 7.1812],
 [2.4482, 0.9455, 11.2095],
 [2.0149, 0.6192, 10.9263],
 [0.2012, 0.2611, 5.4631]

]

exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]

network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)

network.trannig()

while True:
 sample = []
 for i in range(3):
 sample.insert(i, float(input('value: ')))
 network.sort(sample)