def test_excluded_domain(capsys, logging_config):
log = Logger()
log._enable_all = False
log.spam('eggs')
out, err = capsys.readouterr()
assert out == ''
assert err == ''
def test_logging_config():
config = Config([__name__, '--log', 'v1', 'v2', 'v3'])
log = Logger()
assert (config.verbose is False)
assert ('v1' in log._enabled_keys)
assert ('v2' in log._enabled_keys)
assert ('v3' in log._enabled_keys)
assert ('v4' not in log._enabled_keys)
log._enabled_keys = {}
def test_enable_all(capsys, logging_config):
log = Logger()
log.set_enable_all(True)
log.spam('eggs')
out, err = capsys.readouterr()
assert out == ''
assert err.find("[spam] eggs\n") >= 0
log.set_enable_all(False)
def test_verbose_logging(capsys, temp_dot_bonsai):
config = Config([
__name__,
'--verbose'
])
log = Logger()
assert(config.verbose is True)
assert(log._enable_all is True)
def test_included_domain(capsys, logging_config):
log = Logger()
log.foo('bar')
log.baz('qux')
out, err = capsys.readouterr()
assert out == ''
assert err.find('[foo] bar\n') >= 0
assert err.find('[baz] qux\n') >= 0
def test_disable_domain(capsys, logging_config):
log = Logger()
log.set_enabled('spam', False)
assert log._enabled_keys['spam'] == False
log.spam('eggs')
out, err = capsys.readouterr()
assert out == ''
assert err == ''
"""
Provides the state transform, action transform, terminal function and reward
function. The functions in this file are called from cartpole_simulator.py.
"""
import math
from bonsai_ai.logger import Logger
log = Logger()
def state(model_state):
""" Convert the state as represented in CartPole to a format expected.
by the AI Engine.
"""
return {
'position': model_state[0],
'velocity': model_state[1],
'angle': model_state[2],
'rotation': model_state[3],
}
def terminal(model_state):
""" Return true if the state should end the episode. This includes both
failure terminals (such as when the model isout-of-bounds) and success
terminals (when the model is in a successful state)
"""
x, x_dot, theta, theta_dot = model_state
theta_threshold_radians = 12 * 2 * math.pi / 360
import numpy
import random
import sys
from bonsai_ai import Luminance
from bonsai_ai import Config, Brain, Simulator
from bonsai_ai.logger import Logger
log = Logger()
letters = {
"X": [[1, 0, 1], [0, 1, 0], [1, 0, 1]],
"O": [[0, 1, 0], [1, 0, 1], [0, 1, 0]],
" ": [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
}
POSMAP = {"X": 1, " ": 0, "O": -1}
class TicTacToeGame(object):
def __init__(self, seed=42):
random.seed(seed)
self.board = [" "] * 10
self.playerLetter, self.computerLetter = ('X', 'O')
self.terminal = False
self.reward = 0
self.framecount = 0
self.image = numpy.zeros((9, 9), dtype=numpy.uint8)
def render(self):
for i in range(3):
import sys
import gym
from bonsai_ai import Brain, Config, Predictor
from bonsai_ai.logger import Logger
log = Logger()
log.set_enabled("info")
def _state(observation):
""" Converts gym observation into Inkling state dictionary """
state = {
'position': observation[0],
'velocity': observation[1],
'angle': observation[2],
'rotation': observation[3]
}
return state
def _action(action):
""" Converts Inkling action into a gym action """
return action['command']
def _log_state_and_action(state, action):
log.info("The BRAIN received the following state: {}".format(state))
log.info("The BRAIN returned the following action: {}".format(action))
if __name__ == '__main__':
def test_verbose_logging():
config = Config([__name__, '--verbose'])
log = Logger()
assert (config.verbose is True)
assert (log._enable_all is True)
assert (len(log._enabled_keys.keys()) == 0)
