diff --git a/classifiers/inception.py b/classifiers/inception.py
index 068f222..81a6d4c 100755
--- a/classifiers/inception.py
+++ b/classifiers/inception.py
@@ -10,7 +10,7 @@
 
 class Classifier_INCEPTION:
 
-    def __init__(self, output_directory, input_shape, nb_classes, verbose=False, build=True, batch_size=64,
+    def __init__(self, output_directory, input_shape, nb_classes, verbose=True, build=True, batch_size=64,
                  nb_filters=32, use_residual=True, use_bottleneck=True, depth=6, kernel_size=41, nb_epochs=1500):
 
         self.output_directory = output_directory
diff --git a/classifiers/nne.py b/classifiers/nne.py
index 6a0a492..e637153 100755
--- a/classifiers/nne.py
+++ b/classifiers/nne.py
@@ -10,7 +10,7 @@
 
 class Classifier_NNE:
 
-    def create_classifier(self, model_name, input_shape, nb_classes, output_directory, verbose=False,
+    def create_classifier(self, model_name, input_shape, nb_classes, output_directory, verbose=True,
                           build=True):
         if self.check_if_match('inception*', model_name):
             from classifiers import inception
@@ -22,7 +22,7 @@ def check_if_match(self, rex, name2):
         pattern = re.compile(rex)
         return pattern.match(name2)
 
-    def __init__(self, output_directory, input_shape, nb_classes, verbose=False, nb_iterations=5,
+    def __init__(self, output_directory, input_shape, nb_classes, verbose=True, nb_iterations=5,
                  clf_name='inception'):
         self.classifiers = [clf_name]
         out_add = ''
diff --git a/utils/constants.py b/utils/constants.py
index c515a06..18e828a 100755
--- a/utils/constants.py
+++ b/utils/constants.py
@@ -18,8 +18,8 @@
 
 UNIVARIATE_DATASET_NAMES = ['Meat', 'Coffee']
 
-UNIVARIATE_ARCHIVE_NAMES = ['TSC', 'InlineSkateXPs', 'SITS']
-UNIVARIATE_ARCHIVE_NAMES = ['TSC']
+UNIVARIATE_ARCHIVE_NAMES = ['Univariate_arff','mts_archive', 'coto_data', 'InlineSkateXPs', 'SITS']
+
 
 SITS_DATASETS = ['SatelliteFull_TRAIN_c301', 'SatelliteFull_TRAIN_c200', 'SatelliteFull_TRAIN_c451',
                  'SatelliteFull_TRAIN_c89', 'SatelliteFull_TRAIN_c677', 'SatelliteFull_TRAIN_c59',
@@ -29,6 +29,6 @@
                            'InlineSkate-256', 'InlineSkate-512', 'InlineSkate-1024',
                            'InlineSkate-2048']
 
-dataset_names_for_archive = {'TSC': UNIVARIATE_DATASET_NAMES,
+dataset_names_for_archive = {'Univariate_arff': UNIVARIATE_DATASET_NAMES,
                              'SITS': SITS_DATASETS,
                              'InlineSkateXPs': InlineSkateXPs_DATASETS}
diff --git a/utils/utils.py b/utils/utils.py
index 15bb6f1..12a554d 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -28,9 +28,11 @@ def check_if_file_exits(file_name):
 
 
 def readucr(filename, delimiter=','):
-    data = np.loadtxt(filename, delimiter=delimiter)
-    Y = data[:, 0]
-    X = data[:, 1:]
+    data = np.loadtxt(filename)
+    Y = data[:,0]
+    X = data[:,1:]
+    print(X.shape, Y.shape)
+    print(X[0], Y[0])
     return X, Y
 
 
@@ -56,11 +58,31 @@ def create_directory(directory_path):
 def read_dataset(root_dir, archive_name, dataset_name):
     datasets_dict = {}
 
-    file_name = root_dir + '/archives/' + archive_name + '/' + dataset_name + '/' + dataset_name
-    x_train, y_train = readucr(file_name + '_TRAIN')
-    x_test, y_test = readucr(file_name + '_TEST')
-    datasets_dict[dataset_name] = (x_train.copy(), y_train.copy(), x_test.copy(),
-                                   y_test.copy())
+    if archive_name == 'coto_data':
+        file_name = root_dir+'/archives/'+archive_name+'/'+dataset_name+'/'
+        x_train = np.load(file_name + 'x_train.npy')
+        y_train = np.load(file_name + 'y_train.npy')
+        x_test = np.load(file_name + 'x_test.npy')
+        y_test = np.load(file_name + 'y_test.npy')
+
+        datasets_dict[dataset_name] = (x_train.copy(), y_train.copy(), x_test.copy(),
+                                       y_test.copy())
+    elif archive_name == 'mts_archive':
+        file_name = root_dir+'/archives/'+archive_name+'/'+dataset_name+'/'
+        x_train = np.load(file_name + 'x_train.npy')
+        y_train = np.load(file_name + 'y_train.npy')
+        x_test = np.load(file_name + 'x_test.npy')
+        y_test = np.load(file_name + 'y_test.npy')
+
+        datasets_dict[dataset_name] = (x_train.copy(), y_train.copy(), x_test.copy(),
+                                       y_test.copy())
+
+    else:
+        file_name = root_dir + '/archives/' + archive_name + '/' + dataset_name + '/' + dataset_name
+        x_train, y_train = readucr(file_name + '_TRAIN.txt')
+        x_test, y_test = readucr(file_name + '_TEST.txt')
+        datasets_dict[dataset_name] = (x_train.copy(), y_train.copy(), x_test.copy(),
+                                    y_test.copy())
 
     return datasets_dict
 
@@ -70,12 +92,12 @@ def read_all_datasets(root_dir, archive_name):
 
     dataset_names_to_sort = []
 
-    if archive_name == 'TSC':
+    if archive_name == 'Univariate_arff':
         for dataset_name in DATASET_NAMES:
             root_dir_dataset = root_dir + '/archives/' + archive_name + '/' + dataset_name + '/'
             file_name = root_dir_dataset + dataset_name
-            x_train, y_train = readucr(file_name + '_TRAIN')
-            x_test, y_test = readucr(file_name + '_TEST')
+            x_train, y_train = readucr(file_name + '_TRAIN.txt')
+            x_test, y_test = readucr(file_name + '_TEST.txt')
 
             datasets_dict[dataset_name] = (x_train.copy(), y_train.copy(), x_test.copy(),
                                            y_test.copy())