Remove old code comments and redundant lines

1 files changed, 0 insertions, 18 deletions

diff --git a/train.py b/train.py
index b4b8194..3a15fa0 100755
--- a/train.py
+++ b/train.py
@@ -90,14 +90,10 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
     if args.pca or args.pca_r:
         # faces_pca containcts the principial components or the M most variant eigenvectors
         average_face = np.mean(faces_train, axis=0)
-        #PLOTTING MEAN FACE
-        #plt.imshow(average_face.reshape([46,56]).T, cmap = 'gist_gray')
-        plt.show()
         if args.classifyalt:
             deviations_tr = np.ones(n_pixels)
         else:
             deviations_tr =  np.std(faces_train, axis=0)
-        # deviations_tst = np.std(faces_test, axis=0)
         faces_train = normalise_faces(deviations_tr, average_face, faces_train)
         faces_test = normalise_faces(deviations_tr, average_face, faces_test)
         if (args.pca_r):
@@ -106,11 +102,6 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             e_vecs = e_vecs/LA.norm(e_vecs, axis = 0)
         else:
             e_vals, e_vecs = LA.eigh(np.cov(faces_train.T))
-            # e_vecs = normalise_faces(np.mean(e_vecs,axis=0), e_vecs)
-        #PLOTTING NON-ZERO EVALS
-        #if args.pca:
-        #    plt.semilogy(range(2576), np.absolute(416*np.flip(e_vals)))
-        #    plt.show()
 
         e_vals = np.flip(e_vals)
         e_vecs = np.fliplr(e_vecs).T
@@ -125,7 +116,6 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         e_vecs = e_vecs[:M]
 
         deviations_tr = np.flip(deviations_tr)
-        # deviations_tst = np.flip(deviations_tst)
         faces_train = np.dot(faces_train, e_vecs.T)
         faces_test = np.dot(faces_test, e_vecs.T)
 
@@ -151,7 +141,6 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         faces_test = lda.transform(faces_test)
         class_means = lda.means_
         e_vals = lda.explained_variance_ratio_
-        # scatter_matrix = lda.covariance_; print("Rank of scatter:", LA.matrix_rank(scatter_matrix))
 
     if args.faces:
         if args.lda:
@@ -171,7 +160,6 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         plt.show()
 
     if args.grapheigen:
-        #graph_eigen()
         # Colors for distinct individuals
         cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(n_faces)]
         pltCol = [cols[int(k)] for k in target_train]
@@ -192,11 +180,9 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         avg_targer_prob = np.zeros(n_faces)
         for i in range (n_faces):
             avg_targer_prob[i] = (targer_prob_vec[2*i] + targer_prob_vec[2*i + 1])/2
-        #WE CAN FIX THIS BY RESHAPING TARGER_PROB_VEC AND TAKING THE MEAN ON THE RIGHT AXIS
         plt.bar(range(n_faces), avg_targer_prob)
         plt.show()
 
-        #Better n_neighbors = 2
     return target_pred, distances
 
 def main():
@@ -208,13 +194,10 @@ def main():
     parser.add_argument("-b", "--bagging", help="Number of bags to use", action='store_true')
     parser.add_argument("-R", "--random", help="Number of eigen value to randomise", 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)
     parser.add_argument("-c", "--principal", help="Show principal components", action='store_true')
     parser.add_argument("-s", "--seed", help="Seed to use", type=int, default=0)
     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("-p", "--pca", help="Use PCA", action='store_true')
@@ -305,4 +288,3 @@ def main():
     draw_results(args, target_test, target_pred)
 
 if __name__ == "__main__":
-    main()