from __future__ import print_function
from matplotlib import pyplot as plt
import numpy as np

def predict (input, weights):
    activation =0.0
    for i, w in zip(input, weights):
        activation +=i*w
    return 1.0 if activation>=0.0 else 0.0

def plot (matrix, weights=None, title='Prediction Matrix'):

        fig, ax = plt.subplots()# возвращает кортеж из фигуры и осей
        ax.set_title(title)
        ax.set_xlabel("i1")
        ax.set_ylabel("i2")

        if weights != None:
            map_min = 0.0
            map_max = 1.1
            y_res = 0.001
            x_res = 0.001
            ys = np.arange(map_min, map_max, y_res)
            xs = np.arange(map_min, map_max, x_res)
            zs = []
            for cur_y in np.arange(map_min, map_max, y_res):
                for cur_x in np.arange(map_min, map_max, x_res):
                    zs.append(predict([1.0, cur_x, cur_y], weights))
            xs, ys = np.meshgrid(xs, ys)
            zs = np.array(zs)
            zs = zs.reshape(xs.shape)
            cp = plt.contourf(xs, ys, zs, levels=[-1, -0.0001, 0, 1], colors=('b', 'r'), alpha=0.1) # линии уровня

        c1_data = [[], []]
        c0_data = [[], []]
        for i in range(len(matrix)):
            cur_i1 = matrix[i][1]
            cur_i2 = matrix[i][2]
            cur_y = matrix[i][-1]
            if cur_y == 1:
                c1_data[0].append(cur_i1)
                c1_data[1].append(cur_i2)
            else:
                c0_data[0].append(cur_i1)
                c0_data[1].append(cur_i2)

        plt.xticks(np.arange(0.0, 1.1, 0.1))
        plt.yticks(np.arange(0.0, 1.1, 0.1))
        plt.xlim(0, 1.05)
        plt.ylim(0, 1.05)

        c0s = plt.scatter(c0_data[0], c0_data[1], s=40.0, c='r', label='Class -1')
        c1s = plt.scatter(c1_data[0], c1_data[1], s=40.0, c='b', label='Class 1')

        plt.legend(fontsize=10, loc=1)
        plt.show()
        return



def accuracy (matrix, weights):
        num_correct =0.0
        preds =[]
        for i in range(len(matrix)):
            pred = predict(matrix[i][:-1], weights)
            preds.append(pred)
            if pred ==matrix[i][-1]: num_correct+=1.0
        print('Predictions:', preds)
        return num_correct/float(len(matrix))

def train_weights(matrix, weights, nb_epoch=10, l_rate=1.00, do_plot=False, stop_early=True):
        for epoch in range(nb_epoch):
            cur_acc = accuracy(matrix, weights)
            print("\nEpoch %d \nWeights: " %epoch, weights)
            print("Accuracy: ", cur_acc)

            if cur_acc==1.0 and stop_early: break
            if do_plot: plot(matrix, weights, title='Epoch %d'%epoch)

            for i in range(len(matrix)):
                prediction = predict(matrix[i][:-1], weights)
                error = matrix[i][-1] - prediction

                for j in range(len(weights)):

                    weights[j] = weights[j] + (l_rate * error * matrix[i][j])

        plot(matrix, weights, title='Final Epoch')
        return weights

def main():

    nb_epoch =10
    l_rate=1.0
    plot_each_epoch = True
    stop_early =True

    matrix = [[1.00, 0.08, 0.72, 1.0],
                  [1.00, 0.10, 1.00, 0.0],
                  [1.00, 0.26, 0.58, 1.0],
                  [1.00, 0.35, 0.95, 0.0],
                  [1.00, 0.45, 0.15, 1.0],
                  [1.00, 0.60, 0.30, 1.0],
                  [1.00, 0.70, 0.65, 0.0],
                  [1.00, 0.92, 0.45, 0.0]]
    weights = [	0.20,	1.00,  -1.00]



    train_weights(matrix, weights=weights, nb_epoch=nb_epoch, l_rate=l_rate, do_plot=plot_each_epoch, stop_early=stop_early)

main()