import numpy as np
import generatory_danych as gen
import matplotlib.pyplot as plt

class Perceptron:
    def __init__(self, dim, ):
        self.wagi = np.random.sample(dim)
        self.bias = np.random.random()

    def sumator(self, X):
        return np.dot(self.wagi, X) + self.bias

    def fa(self, X):
        if self.sumator(X) > 0:
            return 1
        else:
            return -1

    def predict(self, data):
        Y_pred = np.zeros(len(data))
        for idx, x in enumerate(data):
            Y_pred[idx] = self.fa(x)
        return Y_pred

    def back(self, X, Y):
        if (self.fa(X) != Y):
            self.wagi += Y*X
            self.bias += Y

    def fit(self, data, target, epochs):
        for e in range(epochs):
            for x, y in zip(data, target):
                self.back(x, y)
            print("Epoka {0}: {1}".format(e, self.accuracy(data, target)))

    def accuracy(self, data, target):
        Y_pred = self.predict(data)
        return np.sum(np.abs(Y_pred-target))/2

ile = 100
zakres = [[-5,5],[-10,10]]
wek=[2,1,0]

dane, klasy = gen.gen_lin_sep(ile, zakres, wek)


pr1 = Perceptron(2)
pr1.accuracy(dane, klasy)
pr1.fit(dane, klasy, epochs=10)

plt.scatter(dane[:, 0], dane[:, 1], c=klasy)