mode = args.mode[0]
return args, mode
if __name__ == '__main__':
args, mode = parse_args() # get argument from the command line
# load the data
train_set, train_labels, test_set, test_labels = load_data(train_set_path=args.train_set_path,
train_labels_path=args.train_labels_path,
test_set_path=args.test_set_path,
test_labels_path=args.test_labels_path)
if mode == 'feature_sel':
selected_features = feature_selection(train_set, train_labels)
print_features(selected_features)
elif mode == 'knn':
predictions = knn(train_set, train_labels, test_set, args.k)
print_predictions(predictions)
elif mode == 'alt':
predictions = alternative_classifier(train_set, train_labels, test_set)
print_predictions(predictions)
elif mode == 'knn_3d':
predictions = knn_three_features(train_set, train_labels, test_set, args.k)
print_predictions(predictions)
elif mode == 'knn_pca':
prediction = knn_pca(train_set, train_labels, test_set, args.k)
print_predictions(prediction)
else:
raise Exception('Unrecognised mode: {}. Possible modes are: {}'.format(mode, MODES))
args, mode = parse_args() # get argument from the command line
# load the data
train_set, train_labels, test_set, test_labels = load_data(
train_set_path=args.train_set_path,
train_labels_path=args.train_labels_path,
test_set_path=args.test_set_path,
test_labels_path=args.test_labels_path)
if mode == 'feature_sel':
selected_features = feature_selection(train_set, test_set,
train_labels)
print_features(selected_features)
elif mode == 'knn':
predictions = knn(train_set, train_labels, test_set, test_labels,
args.k)
print_predictions(predictions)
elif mode == 'alt':
predictions = alternative_classifier(train_set, train_labels, test_set)
print_predictions([float(i) for i in predictions])
elif mode == 'knn_3d':
predictions = knn_three_features(train_set, train_labels, test_set,
args.k)
print_predictions(predictions)
# print(labels)
elif mode == 'knn_pca':
prediction, scipy_train = knn_pca(train_set, train_labels, test_set,
args.k)
print_predictions(prediction)
#print(scipy_train)
#return confusion_matrix
