def search(b: board): stack = [] stack.append([b]) explored = set() while True: if len(stack) == 0: print(-1, len(explored)) return else: level_nodes = stack.pop() for current_node in level_nodes: moves = current_node.getPossibleMoves() current_node.fboxes = frozenset(current_node.boxes) explored.add(hash(current_node)) deep_level = [] for move in moves: child = deepcopy(current_node) child.move(move) if hash(child) not in explored: if child.is_win(): utils.print_output(child.path, len(explored), child.box_moves, "Iterative Deepening") return deep_level.append(child) stack.append(deep_level)
def run(self): """ :return: """ i = 0 while i < self.count: line = self.queue.get(True) if line: print '[%d]' % (i + 1, ), utils.print_output(line) i += 1 self.queue.task_done()
def main(args): # Read configuration file conf = configreader.parse(args.config) if not conf: sys.exit(1) # Get list of fields if args.fields: fields = args.fields.split(',') else: try: fields = conf['default']['fields'].split(',') except Exception as e: logger.error(f'Config missing `fields` in [default] section!') logger.exception(e) sys.exit(2) # Get github config for auth try: if conf['github']['token']: headers = { 'Authorization': f'token {conf["github"]["token"]}', 'Accept': 'application/json' } except Exception as e: logger.error(f'Config missing `token` in [github] section') logger.exception(e) sys.exit(2) # Assemble the data org = args.repository data = [] if args.repository: data = [handle_repository(args.repository, headers, fields, args.nolanguages)] elif args.organization: org = args.organization data = handle_organization(args.organization, headers, fields, args.nolanguages) elif args.user: org = args.user data = handle_organization(args.user, headers, fields, args.nolanguages, user_repos=True) else: print("Yay.... No work to do!") print("To exercise me specify either repository or organization to scan.") return if data: # Output the data print_output(data, fields, args.mode, org) # Analyze the data print(json.dumps(get_stats(get_csv_data_as_file(data, fields), org), indent=4))
def printer(r_name, r_count, r_queue, lock): """ :param r_name: :param r_count: :param r_queue: :return: """ i = 0 while i < r_count: line = r_queue.get(True) if line: lock.acquire() print '[%d]' % (i + 1,), utils.print_output(line) lock.release() i += 1 r_queue.task_done()
def search(b: board): q = Queue() q.put(b) explored = set() while True: if q.empty(): print(-1, len(explored)) return else: current_node = q.get() moves = current_node.getPossibleMoves() current_node.fboxes = frozenset(current_node.boxes) explored.add(hash(current_node)) for move in moves: child = deepcopy(current_node) child.move(move) if hash(child) not in explored: if child.is_win(): utils.print_output(child.path, len(explored), child.box_moves, "BFS") return q.put(child)
def search(b: board): board_list = [b] explored = set() while True: if len(board_list) == 0: print(-1, len(explored)) return else: board_list.sort(key=lambda b: len(b.path)) current_node = board_list[0] del board_list[0] moves = current_node.getPossibleMoves() current_node.fboxes = frozenset(current_node.boxes) explored.add(hash(current_node)) for move in moves: child = deepcopy(current_node) child.move(move) if hash(child) not in explored: if child.is_win(): utils.print_output(child.path, len(explored), child.box_moves, "UCS") return board_list.append(child)
def search(b: board): stack = [] stack.append(b) explored = set() while True: if len(stack) == 0: print(-1, len(explored)) return else: current_node = stack.pop() moves = current_node.getPossibleMoves() current_node.fboxes = frozenset(current_node.boxes) explored.add(hash(current_node)) for move in moves: child = deepcopy(current_node) child.move(move) if hash(child) not in explored: if child.is_win(): utils.print_output(child.path, len(explored), child.box_moves, "DFS") return stack.append(child)
columns=params["languages"]) return {"search_term": search_term, "results": results} else: return {"search_term": search_term} parser = argparse.ArgumentParser() if __name__ == '__main__': parser.add_argument("--term", type=str, help="Search Term", default="child") parser.add_argument("--lang", type=str, help="Language", default="english") args = parser.parse_args() supported_langs = [lang.lower() for lang in settings.LANG_LIST] if args.lang not in supported_langs: sys.exit("supported language(s): {}".format(supported_langs)) # search & translate output = translate_term( search_term=args.term, language=args.lang, ) print_output(output)
train_metric = metric_func(y_train, y_train_pred) t_lgbm_pred, y_test_pred = bench.measure_function_time(model_lgbm.predict, X_test, params=params) test_metric_lgbm = metric_func(y_test, y_test_pred) t_trans, model_daal = bench.measure_function_time( daal4py.get_gbt_model_from_lightgbm, model_lgbm, params=params) if hasattr(params, 'n_classes'): predict_algo = daal4py.gbt_classification_prediction( nClasses=params.n_classes, resultsToEvaluate='computeClassLabels', fptype='float') t_daal_pred, daal_pred = bench.measure_function_time( predict_algo.compute, X_test, model_daal, params=params) test_metric_daal = metric_func(y_test, daal_pred.prediction) else: predict_algo = daal4py.gbt_regression_prediction() t_daal_pred, daal_pred = bench.measure_function_time( predict_algo.compute, X_test, model_daal, params=params) test_metric_daal = metric_func(y_test, daal_pred.prediction) utils.print_output( library='modelbuilders', algorithm=f'lightgbm_{task}_and_modelbuilder', stages=['lgbm_train', 'lgbm_predict', 'daal4py_predict'], params=params, functions=['lgbm_dataset', 'lgbm_dataset', 'lgbm_train', 'lgbm_predict', 'lgbm_to_daal', 'daal_compute'], times=[t_creat_train, t_train, t_creat_test, t_lgbm_pred, t_trans, t_daal_pred], accuracy_type=metric_name, accuracies=[train_metric, test_metric_lgbm, test_metric_daal], data=[X_train, X_test, X_test])
solution = [ solve(case, sheeps[case]) for case in range(0, number_of_cases) ] return solution def solve(case, sheeps): N = int(sheeps[0]) digits = set() i = 1 while len(digits) < 10: M = N * i digitsArr = getDigits(M) for digit in digitsArr: digits.add(digit) i += 1 if N == 0: return "Case #" + str(case + 1) + ": INSOMNIA" return "Case #" + str(case + 1) + ": " + str(M) def getDigits(N): Ns = [int(d) for d in str(N)] return Ns filename = utils.getFilename() input = utils.read_input(filename) output = process(input) utils.print_output(filename, output)
y_train_pred = model_xgb.predict(dtrain) train_metric = metric_func(y_train, y_train_pred) t_xgb_pred, y_test_pred = measure_function_time(predict, params=params) test_metric_xgb = metric_func(y_test, y_test_pred) t_trans, model_daal = measure_function_time( daal4py.get_gbt_model_from_xgboost, model_xgb, params=params) if hasattr(params, 'n_classes'): predict_algo = daal4py.gbt_classification_prediction( nClasses=params.n_classes, resultsToEvaluate='computeClassLabels', fptype='float') t_daal_pred, daal_pred = measure_function_time( predict_algo.compute, X_test, model_daal, params=params) test_metric_daal = metric_func(y_test, daal_pred.prediction) else: predict_algo = daal4py.gbt_regression_prediction() t_daal_pred, daal_pred = measure_function_time( predict_algo.compute, X_test, model_daal, params=params) test_metric_daal = metric_func(y_test, daal_pred.prediction) print_output( library='modelbuilders', algorithm=f'xgboost_{task}_and_modelbuilder', stages=['xgboost_train', 'xgboost_predict', 'daal4py_predict'], columns=columns, params=params, functions=['xgb_dmatrix', 'xgb_dmatrix', 'xgb_train', 'xgb_predict', 'xgb_to_daal', 'daal_compute'], times=[t_creat_train, t_train, t_creat_test, t_xgb_pred, t_trans, t_daal_pred], accuracy_type=metric_name, accuracies=[train_metric, test_metric_xgb, test_metric_daal], data=[X_train, X_test, X_test])
# books_scores = np.asarray(res['scores']) # # num_of_books_in_library = np.asarray(res['books_in_library']) # signup_time_for_library = np.asarray(res['signup_time_for_library']) # books_per_day_from_lib = np.asarray(res['books_per_day_from_lib']) # book_ids_for_library = np.asarray(res['book_ids_for_library']) # # library_capacity = np.ceil(np.divide(num_of_books_in_library, books_per_day_from_lib)) # days_left_for_capacity = np.full(library_capacity.shape, days) - library_capacity # # library_time_score = days_left_for_capacity - signup_time_for_library # libraries_num = # books_output = np.argsort(, axis=0) (libraries_sort, books_sort, num_of_books_in_library, books_per_day_from_lib, days, signup_time_for_library) = solve_d.solve_d()#solve_c.solve_c() # libraries_sort = np.argsort(library_time_score)[::-1] libraries_end_date = utils.get_libraries_signup_end_date(libraries_sort, signup_time_for_library) # print (book_ids_for_library ) # print(get_books_scores()) # def score_per_library(): utils.print_output(libraries_sort, books_sort, num_of_books_in_library, books_per_day_from_lib, libraries_end_date, days, 'out_d.txt', signup_time_for_library)