import matplotlib.pyplot as plt
import numpy as py
import numpy as np
from numpy import random

class Neuron:
    def __int__ (self, weight_count):
        random.seed(1)
        weight_count = 0
        self.weights = weight_count = 2 * random.random((1, 1))-1

    def tanh_derivative (self, x):
        return 1 - np.tanh(x) ** 2

    def step(self,x):
        dot_product = np.dot(x, self.weights)
        return np.tanh(dot_product)

    def train (self, iteration, train_inputs, train_outputs):
        for i in range(iterations):
            output = self.step(train_outputs)
            error = train_outputs - output
            adjustment = np.dot(train_inputs.T, (error * self.tanh_derivative(output)))
            self.weights += adjustment
    def function(x):
        return 2 * x

x = [i/100 for i in range(300)]
y = [Neuron.function(i/100) for i in range(300)]
data = []
for i in range(300):
    data.append(Neuron.function(i/100)+random.randint(1, 100)/50)

plt.plot(data, "b.")
plt.show()

x = np.asarray([x])/100
y = np.asarray([y])/100

neurObject = Neuron(300)
x = x.reshape(300, 1)
y = y.T
neuron.train(10000,x,y)
consant = neuron.weights[0][0]
print(constant)
test_data = []
for i in x:
    test_data.append(i*100*constant)

plt.plot(data, "bo")
plt.plot(test_data, "r-")
plt.show()