import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

class AdalineGB():
    def __init__(self, rychlost_ucenia=0.01, pocet_iteracii=10):
        self.rychlost_ucenia = rychlost_ucenia
        self.pocet_iteracii = pocet_iteracii

    def fit(self, X,y):
        self.w = np.zeros(1+ X.shape[1])
        self.costs= []

        for i in range(self.pocet_iteracii):
            output = self.net_input(X)
            errors = (y - output)

            self.w[1:] += self.rychlost_ucenia * X.T.dot(errors)
            self.w[0] += self.rychlost_ucenia * errors.sum()

            cost=(errors ** 2).sum()/2.0
            self.costs.append(cost)
        return self

    def net_input(self, X):
        return np.dot(X, self.w[1:])+ self.w[0]

    def linearna_aktivacia(self, X):
        return self.net_input(X)

    def predikcia(self, X):
        return np.where(self.linearna_aktivacia(X) >= 0.0, 1, -1)

kvetiny = pd.read_csv('kvetiny.csv', index_col = None)

x = kvetiny.iloc[0:100, [0,2]].values
y = kvetiny.iloc[0:100, 4].values

y = np.where(y == 'Iris-setosa', -1, 1)
ppn = AdalineGB()
ppn.fit(x,y)

plt.plot(ppn.costs)