Fix guess probability, remove one line for eigengraph, add flags

1 files changed, 18 insertions, 7 deletions

diff --git a/train.py b/train.py
index a16cb04..86e7337 100755
--- a/train.py
+++ b/train.py
@@ -101,6 +101,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         if (args.reconstruct):
             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')
@@ -139,7 +140,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         plt.show()
 
     if args.grapheigen:
-        graph_eigen()
+        #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]
@@ -150,16 +151,24 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
 
     classifier = KNeighborsClassifier(n_neighbors=args.neighbors)
     if (args.reconstruct):
-        cgassifier.fit(raw_faces_train, target_train)
+        classifier.fit(raw_faces_train, target_train)
         target_pred = classifier.predict(rec_faces_test)
         #Better Passing n_neighbors = 1
     else:
         classifier.fit(faces_train, target_train)
         target_pred = classifier.predict(faces_test)
-        targer_prob = np.max(classifier.predict_proba(faces_test), axis=1).reshape([52,2])
-        targer_prob = np.mean(targer_prob, axis=1)
-        plt.bar(range(52), targer_prob)
-        plt.show()
+        if args.prob:
+            targer_prob = classifier.predict_proba(faces_test)
+            targer_prob_vec = np.zeros(104)
+            for i in range (104):
+                j = int(np.floor(i/2))
+                targer_prob_vec [i] = targer_prob[i][j]
+            avg_targer_prob = np.zeros(52)
+            for i in range (52):
+                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(52), avg_targer_prob)
+            plt.show()
 
         #Better n_neighbors = 2
     return draw_conf_mat(args, target_test, target_pred)
@@ -169,7 +178,8 @@ def main():
     parser.add_argument("-i", "--data", help="Input CSV file", required=True)
     parser.add_argument("-m", "--eigen", help="Number of eigenvalues in model", type=int, default = 10 )
     parser.add_argument("-M", "--reigen", help="Number of eigenvalues in model", type=int)
-    parser.add_argument("-n", "--neighbors", help="How many neighbors to use", type=int, default = 3)
+    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)
@@ -183,6 +193,7 @@ def main():
     parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
 
     parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true')
+    parser.add_argument("-pr", "--prob", help="Certainty on each guess", action='store_true')
 
     args = parser.parse_args()