Change ensemble to bagging

1 files changed, 9 insertions, 9 deletions

diff --git a/train.py b/train.py
index ef2df18..faa6f65 100755
--- a/train.py
+++ b/train.py
@@ -191,7 +191,7 @@ def main():
     parser.add_argument("-i", "--data", help="Input CSV file", required=True)
     parser.add_argument("-m", "--eigen", help="Number of eigenvalues in model", type=int, default = 10 )
     parser.add_argument("-M", "--reigen", help="Number of eigenvalues in model", type=int)
-    parser.add_argument("-e", "--ensemble", help="Number of bagging ensembles to use", type=int)
+    parser.add_argument("-b", "--bagging", help="Number of bagging baggings to use", type=int)
     parser.add_argument("-n", "--neighbors", help="How many neighbors to use", type=int, default = 1)
 ##USING STANDARD 1 FOR NN ACCURACY
     parser.add_argument("-f", "--faces", help="Show faces", type=int, default = 0)
@@ -218,14 +218,14 @@ def main():
 
     faces_train, faces_test, target_train, target_test = test_split(n_faces, raw_faces, args.split, args.seed)
 
-    if args.ensemble:
+    if args.bagging:
         n_training_faces = int(round(n_cases*(1 - args.split)))
-        faces_train_bagged = np.zeros((args.ensemble, n_faces, n_training_faces, n_pixels))
-        for x in range(args.ensemble):
+        faces_train_bagged = np.zeros((args.bagging, n_faces, n_training_faces, n_pixels))
+        for x in range(args.bagging):
             for k in range(n_faces):
                 samples = random.choices(range(n_training_faces), k=n_training_faces)
                 faces_train_bagged[x][k] = [faces_train[i+n_training_faces*k] for i in samples]
-        faces_train_bagged = faces_train_bagged.reshape(args.ensemble, n_faces*n_training_faces, n_pixels)
+        faces_train_bagged = faces_train_bagged.reshape(args.bagging, n_faces*n_training_faces, n_pixels)
 
     if args.classifyalt:
         faces_train = faces_train.reshape(n_faces, int(faces_train.shape[0]/n_faces), n_pixels)
@@ -254,10 +254,10 @@ def main():
         plt.ylabel('Recognition Accuracy (%)')
         plt.grid(True)
         plt.show()
-    elif args.ensemble:
-        rec_error = np.zeros((args.ensemble, n_faces, faces_test.shape[0]))
-        target_pred = np.zeros((args.ensemble, target_test.shape[0]))
-        for i in range(args.ensemble):
+    elif args.bagging:
+        rec_error = np.zeros((args.bagging, n_faces, faces_test.shape[0]))
+        target_pred = np.zeros((args.bagging, target_test.shape[0]))
+        for i in range(args.bagging):
             target_pred[i], rec_error[i] = test_model(args.eigen, faces_train_bagged[i], faces_test, target_train, target_test, args)
 
         target_pred_comb = np.zeros(target_pred.shape[1])