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)
