Split up long lines

1 files changed, 10 insertions, 5 deletions

diff --git a/train.py b/train.py
index 3c867a2..0d8f6b4 100755
--- a/train.py
+++ b/train.py
@@ -126,7 +126,8 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         faces_train = np.dot(faces_train, e_vecs.T)
         faces_test = np.dot(faces_test, e_vecs.T)
 
-        rec_vecs = np.add(np.tile(average_face, (faces_test.shape[0], 1)), np.dot(faces_test, e_vecs) * deviations_tr)
+        rec_vecs = np.add(np.tile(average_face,
+            (faces_test.shape[0], 1)), np.dot(faces_test, e_vecs) * deviations_tr)
         distances = LA.norm(raw_faces_test - rec_vecs, axis=1);
 
         if args.reconstruct:
@@ -211,7 +212,8 @@ def main():
     parser.add_argument("-t", "--split", help="Fractoin of data to use for testing", type=float, default=0.3)
     ### best split for lda = 22
     ### best plit for pca = 20
-    parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components", action='store_true')
+    parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components",
+            action='store_true')
     parser.add_argument("-p", "--pca", help="Use PCA", action='store_true')
     parser.add_argument("-l", "--lda", help="Use LDA", action='store_true')
     parser.add_argument("-r", "--reconstruct", help="Use PCA reconstruction, specify face NR", type=int, default=0)
@@ -248,7 +250,8 @@ def main():
 
         distances = np.zeros((n_faces, faces_test.shape[0]))
         for i in range(n_faces):
-            target_pred, distances[i] = test_model(args.eigen, faces_train[i], faces_test, target_train[i], target_test, args)
+            target_pred, distances[i] = test_model(args.eigen, faces_train[i],
+                    faces_test, target_train[i], target_test, args)
         target_pred = np.argmin(distances, axis=0)
     elif args.reigen:
         target_pred = np.zeros((args.reigen-args.eigen, target_test.shape[0]))
@@ -257,7 +260,8 @@ def main():
 
         for M in range(args.eigen, args.reigen):
             start = timer()
-            target_pred[M - args.eigen], rec_error[M - args.eigen] = test_model(M, faces_train, faces_test, target_train, target_test, args)
+            target_pred[M - args.eigen], rec_error[M - args.eigen] = test_model(M, faces_train,
+                    faces_test, target_train, target_test, args)
             end = timer()
             print("Run with", M, "eigenvalues completed in ", end-start, "seconds")
             print("Memory Used:", psutil.Process(os.getpid()).memory_info().rss)
@@ -273,7 +277,8 @@ def main():
         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):
-            target_pred[i], rec_error[i] = test_model(args.eigen, faces_train_ens[i], faces_test, target_train, target_test, args)
+            target_pred[i], rec_error[i] = test_model(args.eigen, faces_train_ens[i],
+                    faces_test, target_train, target_test, args)
 
         target_pred_comb = np.zeros(target_pred.shape[1])
         target_pred = target_pred.astype(int).T