def run_latent_data(base_path, trn_data, tst_data, outpath, name, fold): trndata = ANN_Executioner_Helper.get_latent_data(base_path, trn_data['name_index'], trn_data['stress'], use_input_sensitivity=True, normalize=True) tstdata = ANN_Executioner_Helper.get_latent_data(base_path, tst_data['name_index'], tst_data['stress'], use_input_sensitivity=True, normalize=True) ANN_Executioner_Helper.run_nn_train_until_convergence(trndata, tstdata, outpath, name, fold) pass
def run(fold_object_path, number_of_fold, outpath, feature, duration, delta_bool, delta2_bool, latent_data_path): print 'Start at Fold object path : {}'.format(fold_object_path) for i in range(number_of_fold): trn_obj, tst_obj = ANN_Executioner_Helper.get_train_and_test_fold( fold_object_path, number_of_fold, i) trn_data = ANN_Executioner_Helper.get_data_from_obj( trn_obj, feature, duration, get_only_stress=None) tst_data = ANN_Executioner_Helper.get_data_from_obj( tst_obj, feature, duration, get_only_stress=None) trn_data_set = ANN_Executioner_Helper.get_ClassificationDataSet( trn_data['Y'], trn_data['stress']) tst_data_set = ANN_Executioner_Helper.get_ClassificationDataSet( tst_data['Y'], tst_data['stress']) print "Number of training patterns: ", len(trn_data_set) print "Input and output dimensions: ", trn_data_set.indim, trn_data_set.outdim print 'Plain data : ' ANN_Executioner_Helper.run_nn(trn_data_set, tst_data_set, outpath, 'Plain_data', i) print 'Latent data : ' run_latent_data(latent_data_path, trn_data, tst_data, outpath, 'Latent_data', i) print
def run_latent_data(base_path, trn_data, tst_data, outpath, name, fold): trndata = ANN_Executioner_Helper.get_latent_data( base_path, trn_data['name_index'], trn_data['tone'], use_input_sensitivity=True, normalize=False) tstdata = ANN_Executioner_Helper.get_latent_data( base_path, tst_data['name_index'], tst_data['tone'], use_input_sensitivity=True, normalize=False) ANN_Executioner_Helper.run_nn(trndata, tstdata, outpath, name, fold) pass