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import numpy as np
import matplotlib as plt


    

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class Perceptron(object):
    def __init__(self, n):
        self.n = n
        lc = 0.05
        weights = np.random.rand(n) * 2 - 1
        
        
        print(weights)
    
    
    def activate(self, sum_):
        if sum_ > 0:
            return 1
        else:
            return 0
    
    def feedforward(self, inputs):
        sum_ = 0
        for i in range(len(weights)):
            sum_ += inputs[i]*weights[i]
        return activate(sum_)
    
    def train(self, inputs, desired):
        guess = feedforward(inputs)
        error = desired - guess
        for i in range(len(weights)):
            weights[i] = lc*error*inputs[i]
            
            
class Trainer(object):
    def __init__(self, x, y, answer):
        self.x = x
        self.y = y
        self.answer = answer
        
        inputs = [x, y, 1]
        answer = answer
        

        
        
def func(x):
    return 2*x + 1

x = np.random.rand() * 10 - 5
y = np.random.rand() * 10 - 5
yline = func(x)

if y < yline:
    answer = -1
else:
    answer = 1
    
    
    
t = Trainer(x, y, answer)
ptron = Perceptron(3)


    

    




[ 0.45543549 -0.47138545  0.98493958]

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