# run data through autoencoder (so that it can be pulled into classifier)
ae_predictions = ae.predict_mse(x_train)
#print 'ae_predictions.shape: ', ae_predictions.shape
# format the output vectors
y_train_vector = np.array([
1 if y_train[np.where(item >= 1)].size else 0 for item in y_train
])
y_test_vector = np.array(
[1 if y_test[np.where(item >= 1)].size else 0 for item in y_test])
#print 'y_train_vector shape :', y_train_vector.shape
from classifier import Classifier
cf = Classifier('classical', conf)
#print 'building %s classifier...' % cf.get_model_type()
cf.add_dense()
cf.train_classifier(ae_predictions, y_train_vector)
cf_model = cf.get_model()
# predict nom nom
predictions = np.array([
1 if item >= 0.5 else 0
for item in cf_model.predict(ae.predict_mse(x_test))
])
predictions_train = np.array([
1 if item >= 0.5 else 0
for item in cf_model.predict(ae.predict_mse(x_train))
])
p.plot_wave(predictions, '[test] Classifier Predictions')
p.plot_wave(predictions_train, '[train] Classifier predicitions')
