def test_offset():
model = factored.create_model(num_factors=3)
eq_(model.offset, 0)
model.see_generated([1, 0, 0])
eq_(model.offset, 0)
model.see_added([1, 0])
eq_(model.offset, 0)
model.see_generated([1])
eq_(model.offset, 1)
model.see_generated([0])
eq_(model.offset, 2)
model.see_generated([0])
eq_(model.offset, 0)
def _get_trained_agent(num_percept_bits, num_action_bits,
num_remembered_steps):
train_seqs = saving.load_training_seqs()
#TEST: don't limit the number of used seqs
train_seqs = train_seqs[:15]
max_depth = (num_remembered_steps * (num_percept_bits + num_action_bits) +
num_action_bits)
agent_model = factored.create_model(max_depth=max_depth)
source_info = modeling.Interlaced(num_percept_bits, num_action_bits)
modeling.train_model(agent_model, train_seqs, bytes=False,
source_info=source_info)
return agent_model
def test_revert_generated():
model = factored.create_model(deterministic=True, max_depth=2,
num_factors=3)
model.see_generated([1, 0, 0])
model.see_generated([1, 0, 0])
eq_(model.predict_one(), 1.0)
model.see_generated([1])
eq_(model.predict_one(), 0.0)
model.revert_generated(1)
eq_(model.predict_one(), 1.0)
model.revert_generated(6)
eq_(model.offset, 0)
eq_(model.predict_one(), 0.5)
