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-rwxr-xr-xtrain.py15
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