def train(self,
neuralNetwork: NeuralNetwork,
dataset,
acceptable_error=0,
examples_in_epoch=1000):
counter = 0
error = 0
epoch = 0
counter_history = []
error_history = []
for x, y in dataset:
x = np.array(x)
neuralNetwork.run(x)
error += (y - neuralNetwork.output)**2
counter += 1
neuralNetwork.correction(np.array([y]))
if counter % examples_in_epoch == 0 and counter != 0:
relative_error = error / counter
error_history.append(relative_error)
counter_history.append(epoch)
epoch += 1
print("Epoch: ", epoch, " | relative error: ", relative_error)
if (error <= acceptable_error):
print('Learinig finished at epoch: ', epoch)
break
counter = 0
error = 0
return counter_history, error_history
