simulation/decai/simulation/simulate_titanic_nb.py [18:37]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))

# FIXME Using MultinomialNB might not work well with the Titanic dataset because it requires discrete features.

class Runner(object):
    @inject
    def __init__(self,
                 data: DataLoader,
                 simulator: Simulator,
                 ):
        self._data = data
        self._s = simulator

    def run(self):
        init_train_data_portion = 0.10

        # Set up the agents that will act in the simulation.
        agents = [
            # Good
            Agent(address="Good",
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



simulation/decai/simulation/simulate_ttt_dt.py [22:40]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))


class Runner(object):
    @inject
    def __init__(self,
                 data: DataLoader,
                 simulator: Simulator,
                 ):
        self._data = data
        self._s = simulator

    def run(self):
        init_train_data_portion = 0.10

        # Set up the agents that will act in the simulation.
        agents = [
            # Good
            Agent(address="Good",
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -