Plotting + Fix m to M iteration code

1 files changed, 21 insertions, 8 deletions

diff --git a/train.py b/train.py
index 25993f7..b4aaa89 100755
--- a/train.py
+++ b/train.py
@@ -59,23 +59,28 @@ def test_split(n_faces, raw_faces, split, seed):
 
 def draw_conf_mat(args, target_test, target_pred):
     cm = confusion_matrix(target_test, target_pred)
-    print(cm)
+    acc_sc = accuracy_score(target_test, target_pred)
+    print('Accuracy: ', acc_sc)
     if (args.conf_mat):
         plt.matshow(cm, cmap='Blues')
         plt.colorbar()
         plt.ylabel('Actual')
         plt.xlabel('Predicted')
         plt.show()
-    return accuracy_score(target_test, target_pred)
+    return acc_sc 
 
 def test_model(M, faces_train, faces_test, target_train, target_test, args):
     raw_faces_train = faces_train
+    raw_faces_test = faces_test
 
     explained_variances = ()
 
     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()
         deviations_tr =  np.std(faces_train, axis=0)
         deviations_tst = np.std(faces_train, axis=0)
         faces_train = normalise_faces(average_face, faces_train)
@@ -89,6 +94,10 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             print('Standard PCA')
             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)[:M]
         e_vecs = np.fliplr(e_vecs).T[:M]
@@ -200,17 +209,21 @@ def main():
     raw_faces = genfromtxt(args.data, delimiter=',')
     targets = np.repeat(np.arange(n_faces),n_cases)
 
-
     faces_train, faces_test, target_train, target_test = test_split(n_faces, raw_faces, args.split, args.seed)
-
-
+    
     if args.reigen:
-        for M in range(args.eigen, args,reigen):
+        accuracy = np.zeros(args.reigen - args.eigen)
+        for M in range(args.eigen, args.reigen):
             start = timer()
-            accuracy[M] = test_model(M, faces_train, faces_test, target_train, target_test, args)
+            accuracy[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")
-
+        #plot
+        plt.plot(range(args.eigen, args.reigen), 100*accuracy)
+        plt.xlabel('Number of Eigenvectors used (M)')
+        plt.ylabel('Recognition Accuracy (%)')
+        plt.grid(True)
+        plt.show()
     else:
         M = args.eigen
         start = timer()