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))
return args, task if __name__ == '__main__': args, task = 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 task == 'feature_sel': selected_features = feature_selection(train_set, train_labels, args.f) print_features(np.array(selected_features) - 1) elif task == 'knn': predictions = knn(train_set, train_labels, test_set, args.k) print_predictions(predictions) elif task == 'alt': predictions = alternative_classifier(train_set, train_labels, test_set) print_predictions(predictions) elif task == 'knn_3d': predictions = knn_three_features(train_set, train_labels, test_set, args.k) print_predictions(predictions) elif task == 'knn_pca': predictions = knn_pca(train_set, train_labels, test_set, args.k) print_predictions(predictions) elif task == 'feature_plots': subplots(train_set1, n_features)