def test_instantiation():
"""
Testing common QLearner initial arguments and support functions.
"""
# Set-up:
STATES = 10
ACTIONS = 5
rmatrix_sq = np.random.rand(STATES, STATES)
rmatrix_rec = np.random.rand(STATES, ACTIONS)
tmatrix = np.random.randint(0, STATES, size=(STATES, ACTIONS))
# making sure tmatrix points to goal states:
tmatrix[:, ACTIONS - 1] = np.random.randint(0, 1, size=STATES)
goal_l = (0, 1)
goal_f = lambda x: x <= 1
np.savetxt('test.dat', rmatrix_sq)
global QLEARNER
# Test 1: list goal
temp = QLearner(rmatrix_sq, goal_l)
assert np.array_equal(temp.rmatrix,
rmatrix_sq), "R matrix not equal to arg."
assert temp.goal(0) and temp.goal(1) and not temp.goal(2) and not temp.goal(3), \
'List goal not working.'
QLEARNER = temp
# Test 2: function goal
temp = QLearner(rmatrix_sq, goal_f)
assert temp.goal(0) and temp.goal(
1) and not temp.goal(2), 'Function goal not working.'
QLEARNER = temp
# Test 3: File I/O
temp = QLearner('test.dat', goal_l)
assert temp.qmatrix.shape == rmatrix_sq.shape, "Q & R matrix dimension mismatch."
assert np.array_equal(temp.rmatrix,
rmatrix_sq), "R matrix not equal to arg."
QLEARNER = temp
# Test 4: rectangular r matrix, no tmatrix
try:
QLearner(rmatrix_rec, goal_l)
except ValueError:
pass
# Test 5: rectangular r matrix, t matrix of same dimension
temp = QLearner(rmatrix_rec, goal_f, tmatrix)
assert temp.next_state(1,
2) == tmatrix[1,
2], 'Next state prediction incorrect.'
QLEARNER = temp
# Test 6: episodes
l = set(temp.episodes(coverage=1.0, mode='bfs'))
assert l == set(range(temp.num_states)), 'Full episode coverage failed.'
# Finalize
os.remove('test.dat')
