def sample():
X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
y = np.array([[0], [1], [1], [0]])
nn = NeuralNetwork(X, y)
for i in range(1500):
nn.feedforward()
nn.backprop()
print(nn.output)
error_count += error
if error_count == 1:
network.train(data, resp)
generation += 1
score = 0
error_count = 0
ball_pos = ball.position[0] - SCREEN_SIZE[0] / 2
bar_pos = bar.position[0] - SCREEN_SIZE[0] / 2
bar_pos += bar.size[0] / 2
ball_pos = ball_pos / 100
bar_pos = bar_pos / 100
direction = network.feedforward(np.array([ball_pos, bar_pos]))
direction = 1 if direction > 0.5 else 0
'''
df.loc[index] = [ball_pos, bar_pos, direction]
index += 1
'''
bar.move(direction)
text = font.render('Score: ' + str(score), 1, (255, 255, 255))
text2 = font.render('Geração: ' + str(generation), 1, (255, 255, 255))
screen.fill((0, 0, 0))
screen.blit(text, (10, 10))
screen.blit(text2, (10, 40))
