def test_defaults(self): cli = Cli() opt, vls = cli.parse() self.assertEquals(opt.message, '') self.assertEquals(opt.soundfile, None) self.assertEquals(opt.exitat, 5) self.assertEquals(opt.timeline, False) self.assertEquals(opt.fullscreen, False) self.assertEquals(opt.alarm, False)
def test_if_have_just_those_options(self): cli = Cli() opt, vls = cli.parse() #FIXME: need to find another way to test if 'Cli' just have that options self.assertEquals(opt, {'fullscreen': False, 'message': '', 'exitat': 5, 'soundfile': None, 'timeline': False, 'debug': False, 'alarm': False, 'color': ''})
def test_if_have_just_those_options(self): cli = Cli() opt, vls = cli.parse() #FIXME: need to find another way to test if 'Cli' just have that options self.assertEquals( opt, { 'fullscreen': False, 'message': '', 'exitat': 5, 'soundfile': None, 'timeline': False, 'debug': False, 'alarm': False, 'color': '' })
def test_e2e(self): cli = Cli(Feed()) cmds = [ "Josh -> As Josh used to say", "Josh", "Josh follows Bob", "Bob -> @Josh whats'up?", "John -> pancakes", "Bob -> @John you cooking pancakes?", "Josh -> mamma mia", "Josh wall", ] parsed = [cli.parse(cmd) for cmd in cmds] run = [cli.run(cmd) for cmd in parsed] expected = [ "mamma mia", "@John you cooking pancakes?", "@Josh whats'up?", "As Josh used to say", ] self.assertEqual(expected, run[-1])
from feed import Feed from cli import Cli if __name__ == "__main__": cli = Cli(Feed()) while True: cmd_str = input(">> ") cmd = cli.parse(cmd_str) res = cli.run(cmd) if res: print(res)
from dqn_agent_bananas import Agent from collections import deque from cli import Cli import numpy as np import torch import matplotlib.pyplot as plt from unityagents import UnityEnvironment import pandas as pd import os THIS_FOLDER = os.path.dirname(os.path.abspath(__file__)) cli = Cli() args = cli.parse() env = UnityEnvironment(file_name="./UnityEnv/Banana.exe") brain_name = env.brain_names[0] brain = env.brains[brain_name] def train(episodes=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995): agent = Agent(state_size=37, action_size=4, seed=0) scores = [] scores_window = deque(maxlen=100) eps = eps_start for i_episode in range(1, episodes + 1): env_info = env.reset(train_mode=True)[brain_name] state = env_info.vector_observations[0] score = 0
def test_parse_write(self): cli = Cli(None) res = cli.parse("Josh -> Hello!") parsed_cmd = self.josh_hello_cmd self.assertEqual(parsed_cmd, res)