From f8ee4b9bafe082b552773ccc604f41e104250760 Mon Sep 17 00:00:00 2001
From: Vasil Zlatanov <v@skozl.com>
Date: Tue, 6 Nov 2018 21:27:13 +0000
Subject: Revert "Add reconstruction with different M"

This reverts commit 9de78f26ac4d575ba8f6be16a67711690d263c16.
---
 train.py | 30 ++++++++----------------------
 1 file changed, 8 insertions(+), 22 deletions(-)

diff --git a/train.py b/train.py
index 5367d8b..69806fc 100755
--- a/train.py
+++ b/train.py
@@ -115,7 +115,13 @@ 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')
@@ -178,10 +184,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             plt.show()
 
         #Better n_neighbors = 2
-    if args.reconstruct:
-        return rec_vec
-    else:
-        return draw_conf_mat(args, target_test, target_pred)
+    return draw_conf_mat(args, target_test, target_pred)
 
 def main():
     parser = argparse.ArgumentParser()
@@ -225,23 +228,6 @@ 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()
-- 
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