def main(argv, input_break, target_break, apply_target, S, max_epoch, lr, B=1):
if (len(argv) != 3):
print('Error en la entrada')
[_, model, data] = argv
# lectura y pre procesamiento de datos
(input, target) = load_dataset(data, input_break, target_break,
apply_target)
if (not path.exists(model + '.p')):
# entrenamiento
errors, W = train(input, target, S, max_epoch, lr, B)
plot_error(errors)
pickle.dump(errors, open(model + '_errors.p', 'wb'))
pickle.dump(W, open(model + '.p', 'wb'))
else:
# testing
# carga de modelo entrenado
W = pickle.load(open(model + '.p', 'rb'))
# testeo
r, Y, Z = test(input, target, S, W)
if Z.shape[1] == 1:
print('precisión: {} (aciertos/total)'.format(r))
else:
print('error cuadratico medio: {}'.format(r))
def exp(self, S, max_epoch, lr, train_break, test_break, B):
errors = self.train(S, max_epoch, lr, train_break, B)
prec, Y, Z = self.test(test_break)
plot_target_1(Y, Z)
plot_error(errors)
V = []
J = []
Y = np.array(Y)
Z = np.array(Z)
for i in range(len(Y)):
if (Y[i] > -0.95 and Y[i] < 0.95) or not (np.sign(Y[i]) == Z[i]):
V.append(Y[i])
J.append(Z[i])
plot_scat_1(Y, Z)
plot_scat_1(V, J)
print('precision: {}'.format(prec))
return errors
from dataset import load_dataset
from hebbiano import Hebbiano
from plotter import plot, plot_error
data, labels = load_dataset('./dataset/tp2_training_dataset.csv')
data_train = data[:700]
labels_train = labels[:700]
data_test = data[700:]
labels_test = labels[700:]
modelo = Hebbiano(data_train)
errors = modelo.train('oja', 9, 0.00001, 0.001, 1500, 100)
plot_error(errors)
plot(modelo.test(data_train), labels_train)
plot(modelo.test(data_test), labels_test)
def exp_2(self, S, max_epoch, lr, train_break, test_break, B):
errors = self.train(S, max_epoch, lr, train_break, B)
ecm, Y, Z = self.test(test_break)
plot_error(errors)
print('error cuadratico medio: {}'.format(ecm))
return errors