Implement reconstruction for PCA

1 files changed, 15 insertions, 17 deletions

diff --git a/train.py b/train.py
index 0b0ce0e..0927943 100755
--- a/train.py
+++ b/train.py
@@ -89,32 +89,30 @@ 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) 
+    deviations =  np.std(faces_train, axis=0)
     faces_train = normalise_faces(average_face, faces_train)
     faces_test = normalise_faces(average_face, faces_test)
     if (args.pca_r):
         e_vals, e_vecs = LA.eigh(np.cov(faces_train))
-        e_vecs_original = e_vecs
         e_vecs = np.dot(faces_train.T, e_vecs) 
-        # e_vecs = normalise_faces(np.mean(e_vecs,axis=0), e_vecs)
-        e_vecs = sc.fit_transform(e_vecs)
-        ###TODO Maybe replace with our normalising function
-
-        if (args.reconstruct):
-            rec_vec = np.divide(average_face, np.std(average_face)).T 
-            e_vecs_t = e_vecs.T
-            for i in range (M): 
-                rec_vec = np.add(rec_vec, np.dot(e_vecs_original[i][args.reconstruct], e_vecs_t[i]))
-            plt.imshow(rec_vec.reshape([46,56]).T, cmap = 'gist_gray')
-            plt.show()
     else:
         e_vals, e_vecs = LA.eigh(np.cov(faces_train.T))
+        # e_vecs = normalise_faces(np.mean(e_vecs,axis=0), e_vecs)
+        # e_vecs = sc.fit_transform(e_vecs)
+
+    e_vals = np.flip(e_vals)[:M]
+    e_vecs = np.fliplr(e_vecs).T[:M]
+    deviations = np.flip(deviations)
 
-    e_vals = np.flip(e_vals)
-    e_vecs = np.fliplr(e_vecs).T
-    faces_train = np.dot(faces_train, e_vecs[:M].T)
-    faces_test = np.dot(faces_test, e_vecs[:M].T)
-#FOR THE ASSESSMENT PRINT EIGENVALUES AND EIGENVECTORS OF BOTH CASES AND COMPARE RESULTS WITH PHYSICAL EXPLAINATIONS
+    faces_train = np.dot(faces_train, e_vecs.T)
+    faces_test = np.dot(faces_test, e_vecs.T)
 
+    if (args.reconstruct):
+        for face in range(args.reconstruct):
+            rec_vec = np.add(average_face, np.dot(faces_train[face], e_vecs) * deviations)
+            ar = plt.subplot(2, args.reconstruct/2, face + 1)
+            ar.imshow(rec_vec.reshape([46,56]).T, cmap = 'gist_gray')
+        plt.show()
     
 if args.lda or (args.pca and args.lda):
     lda = LinearDiscriminantAnalysis(n_components=M)