def main(): test_false = [-3, 43, 10000, pow(3, 20)] test_true = [0, 4, 64, 1024, pow(2, 20)] for n in test_false: test(is_power_of_two(n), False) for n in test_true: test(is_power_of_two(n), True)
def main(): k = 4 n = 1000 arr = random.sample(range(n), n / 3) sort = sorted(arr, reverse = True) print 'test result...' for i in range(k): test(find_kth(arr, 0, len(arr) - 1, i), sort[i])
def main(): cases = [ [3, 4, 0, -3, 4, 2], [], [0,0,0], ['a','bc', 'ax','bb'] ] for arr in cases: print arr test(merge_sort(arr), sorted(arr))
def main(): parser = argparse.ArgumentParser() # 模型参数 parser.add_argument("--max_sequence_length", default=140, help="Bert input max sequence length", type=int) # 路径参数设置 parser.add_argument("--train_dataset_path", default='{}/dataset/src_data/train_dataset/nCoV_100k_train.labled.csv'.format(BASE_DIR), help="Train folder") parser.add_argument("--test_dataset_path", default='{}/dataset/src_data/test_dataset/nCov_10k_test.csv'.format(BASE_DIR), help="Test folder") parser.add_argument("--test_submit_example_path", default='{}/data/test_dataset/submit_example.csv'.format(BASE_DIR), help="submit_example folder") parser.add_argument("--bert_pretrain_path", default='{}/dataset/bert_base_chinese/'.format(BASE_DIR), help="Bert Pretrain folder") # others parser.add_argument("--input_categories", default="微博中文内容", help="输入文本的文本内容列") parser.add_argument("--output_categories", default="情感倾向", help="标签列") parser.add_argument("--epochs", default=2, help="train epochs", type=int) parser.add_argument("--batch_size", default=8, help="train batch_size", type=int) # 交叉验证参数 parser.add_argument("--n_splits", default=5, help="train n_splits", type=int) parser.add_argument("--use_cross_valid", default=True, help="是否使用交叉验证") parser.add_argument("--cross_dataset_path", default='{}/dataset/cross_data/'.format(BASE_DIR), help="Cross valid folder") # 数据集分割路径参数 parser.add_argument("--split_dataset_path", default='{}/dataset/split_data/'.format(BASE_DIR), help="Split dataset folder") # mode parser.add_argument("--mode", default='test', help="training or test options") parser.add_argument("--loss_type", default="focal_loss", help="loss type is focal_loss or cross_entropy") parser.add_argument("--learning_rate_1", default=1e-5, help="learning_rate_1") parser.add_argument("--learning_rate_2", default=1e-4, help="learning_rate_2 is None or 1e-4...") parser.add_argument("--use_different_learning_rate", default=True, help="是否使用不同的学习率") # checkpoint parser.add_argument("--model_checkpoint_dir", default='{}/ckpt'.format(BASE_DIR), help="Model folder") args = parser.parse_args() params = vars(args) gpus = tf.config.experimental.list_physical_devices(device_type='GPU') if gpus: tf.config.experimental.set_visible_devices(devices=gpus[0], device_type='GPU') if params["mode"] == "train": train(params) elif params["mode"] == "test": test(params)
def main(): print '**** test binary add by string ****' test(bi_add('11111','1'), '100000') test(bi_add('1010','1010'), '10100') print '**** test reverse polish notation (RPN) ****' test(reverse_polish(' 5 10 + 3 * 15 - '), 30.0) # exceptions reverse_polish('2 s + 4 0 /') reverse_polish('21 + s 0 /') reverse_polish('2 1 + 0 /') s = 'the same one' tests = ['', 'the same one', ' same', 'he', ' one', 'dif one', 'thesame', ' one '] print '**** test is_substring ****' for sub in tests: test(is_substring(s, sub), str.find(s, sub) > -1)
def main(args, config): dataset = create_dataset(args, config) train_loader, val_loader, test_loader = dataset.create_loaders() args.flow_args = [ args.n_blocks, args.flow_hidden_size, args.n_hidden, args.flow_model, args.flow_layer_type ] model = create_model(args, config).to(args.dev) print(vars(args)) print(model) print('number of parameters : {}'.format( sum([np.prod(x.shape) for x in model.parameters()]))) if not args.test: trained_model = train_graph_generation(args, config, train_loader, val_loader, test_loader, model) else: test_model = test(args, config, model, dataset)
def main(): # test list... cases = [ [], [1, 1, 1], [-4,-2, 0, 1, 4, 6, 7], ] for c in cases: tmp = c tmp.reverse() test(reverse(c), tmp) # string... cases = [ 'abccdwsef', 'h hh', 'I love rongchao, hope rongchao love me too @_@' ] for c in cases: print '*****test reverse string' test(reverse_str(c), c[::-1]) print '*****test is char unique' test(is_chars_unique(c, False), False) print '*****test replace space in string' test(replace_space(c), c.replace(' ', '%20')) print '*****test replace(old, new)' test(my_replace(c, 'rongchao', 'Danielle'), c.replace('rongchao', 'Danielle')) print '*****test if 2 strings anagrams' test(is_anagrams('abeed23', 'e2aeb3d'), True) print '*****test s1 is s2 rotation' test(is_rotation('waterbottle', 'erbottlewat'), True) test(is_rotation('ri', 'i'), False) print '*****test remove duplicate chars in a string' test(remove_dup('abeed223', True), 'abed23') test(remove_dup('abeed223', False), 'abed23')