diff options
| -rwxr-xr-x | train.py | 25 |
1 files changed, 18 insertions, 7 deletions
@@ -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() |