Add alternative method

1 files changed, 11 insertions, 6 deletions

diff --git a/train.py b/train.py
index b4aaa89..45e0d02 100755
--- a/train.py
+++ b/train.py
@@ -9,6 +9,8 @@ import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 import sys
 import random
+import os
+import psutil
 from random import randint
 
 from sklearn.neighbors import KNeighborsClassifier
@@ -103,7 +105,10 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         e_vecs = np.fliplr(e_vecs).T[:M]
         deviations_tr = np.flip(deviations_tr)
         deviations_tst = np.flip(deviations_tst)
-
+        if (args.classifyalt):
+            faces_train = np.mean(faces_train.reshape([n_faces,8,2576]), axis=1)
+            target_train = range(n_faces)
+            raw_faces_train = np.mean(raw_faces_train.reshape([n_faces,8,2576]), axis=1)
         faces_train = np.dot(faces_train, e_vecs.T)
         faces_test = np.dot(faces_test, e_vecs.T)
 
@@ -172,11 +177,11 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             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 = np.zeros(n_faces)
+            for i in range (n_faces):
                 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.bar(range(n_faces), avg_targer_prob)
             plt.show()
 
         #Better n_neighbors = 2
@@ -200,10 +205,9 @@ def main():
     parser.add_argument("-l", "--lda", help="Use LDA", action='store_true')
     parser.add_argument("-r", "--reconstruct", help="Use PCA reconstruction, specify face NR", type=int, default=0)
     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')
-
+    parser.add_argument("-alt", "--classifyalt", help="Alternative method ON", action='store_true')
     args = parser.parse_args()
 
     raw_faces = genfromtxt(args.data, delimiter=',')
@@ -218,6 +222,7 @@ def main():
             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")
+            print("Memory Used:", psutil.Process(os.getpid()).memory_info().rss)
         #plot
         plt.plot(range(args.eigen, args.reigen), 100*accuracy)
         plt.xlabel('Number of Eigenvectors used (M)')