def create_config(): return Config('config.json', foldername='Config', username='******', oauth='oauth: OAUTH', nick='NICK_NAME', channel='CHANNEL')
def __load_config(self, category: str) -> Config: """翻訳定義ファイルをロード :param str category: 翻訳定義ファイルの種類 :return Config: 翻訳データのコンフィグ :raise NotFoundError: 翻訳定義ファイルが存在しない """ return Config(self.__trans_path(self._lang, category))
def test_run(self): config = Config('config/app-dev.yml') App().run( config, { 'url': '/api/GetDevice', 'method': 'GET', 'headers': { 'Authorization': 'auth_key', }, 'queries': { 'hoge': 'fuga', }, 'body': { 'fizz': 'buzz', }, })
def test_run(self): config = Config('tests/unit/app/fixtures/lambdaapp/config.yml') response = App().run( config, { 'url': '/test/App', 'method': 'GET', 'headers': { 'Authorization': 'auth_key', }, 'queries': { 'hoge': 'fuga', }, 'body': { 'fizz': 'buzz', }, }) self.assertEqual({'success': True}, response)
def __init__(self, num_inputs, num_outputs, num_layers): super(LSTMController, self).__init__() self._config = Config() self.num_inputs = num_inputs self.num_outputs = num_outputs self.num_layers = num_layers self.lstm = nn.LSTM(input_size=num_inputs, hidden_size=num_outputs, num_layers=num_layers) # The hidden state is a learned parameter self.lstm_h_bias = Parameter( torch.randn(self.num_layers, 1, self.num_outputs) * self._config.h_lr) self.lstm_c_bias = Parameter( torch.randn(self.num_layers, 1, self.num_outputs) * self._config.c_lr) self.reset_parameters()
def test_get(self): config = Config('tests/unit/data/fixtures/config/config.yml') self.assertEqual(123, config.get('hoge.fuga.piyo')) self.assertEqual('value', config.get('arr.2.key'))
import pickle as pk import random from data.config import Config conf = Config() low_bound = conf.get_low_bound() up_bound = conf.get_up_bound() eos = conf.get_eos() # DP solution def mss(arr): dp = [] dp.append(arr[0]) result = arr[0] for i in range(1, len(arr)): curr = max(dp[i - 1] + arr[i], arr[i]) dp.append(curr) result = max(result, curr) return [result] def input_generator(length): arr = [] elements = random.randint(1, length) for i in range(elements): arr.append(random.randint(low_bound, up_bound)) return arr
def lambda_handler(event, context): config = Config(f'config/app-{os.environ["ENV"]}.yml') return App().run(config, event)