Add reconstruction with different M

1 files changed, 22 insertions, 8 deletions

diff --git a/train.py b/train.py
index 69806fc..5367d8b 100755
--- a/train.py
+++ b/train.py
@@ -115,13 +115,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             rec_vec = np.add(average_face, np.dot(faces_train[args.reconstruct], e_vecs) * deviations_tr)
             rec_faces_test = np.add(average_face, np.dot(faces_test, e_vecs) * deviations_tst)
 #THERE MIGHT BE A RECONSTRUCTION PROBLEM DUE TO DEVIATIONS_TST
-            rec_error = LA.norm(np.subtract(raw_faces_train[args.reconstruct], rec_vec))
-            ar = plt.subplot(2, 1, 1)
-            ar.imshow(rec_vec.reshape([46,56]).T, cmap = 'gist_gray')
-            ar = plt.subplot(2, 1, 2)
-            ar.imshow(raw_faces_train[args.reconstruct].reshape([46,56]).T, cmap = 'gist_gray')
-            plt.show()
-
+            
     if args.lda:
         if args.pca_r or (args.pca and M > n_training_faces - n_faces):
             lda = LinearDiscriminantAnalysis(n_components=M, solver='eigen')
@@ -184,7 +178,10 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             plt.show()
 
         #Better n_neighbors = 2
-    return draw_conf_mat(args, target_test, target_pred)
+    if args.reconstruct:
+        return rec_vec
+    else:
+        return draw_conf_mat(args, target_test, target_pred)
 
 def main():
     parser = argparse.ArgumentParser()
@@ -228,6 +225,23 @@ def main():
         plt.ylabel('Recognition Accuracy (%)')
         plt.grid(True)
         plt.show()
+    elif args.reconstruct:
+        M = args.eigen
+        i = 0
+        q = 0
+        rec_vecs = np.zeros((5, 2576))
+        while M < 400: 
+            rec_vecs[i] = test_model(M, faces_train, faces_test, target_train, target_test, args)
+            M = M+100
+            i = i+1
+        while q < i:
+            ax = plt.subplot(1, i+1, q+1)
+            ax.imshow(rec_vecs[q].reshape([46, 56]).T, cmap = 'gist_gray')
+            q = q+1
+        ax = plt.subplot(1, i+1, i+1)
+        ax.imshow(faces_train[args.reconstruct].reshape([46, 56]).T, cmap = 'gist_gray')
+        plt.show()
+
     else:
         M = args.eigen
         start = timer()