ct.add(ctex)
ct.save(f'counterexamples_{problem}_nomutation.p')
eqc.onCounterexample(onct)
# Set up the teacher, with the system under learning and the equivalence checker
teacher = Teacher(sul, eqc)
# Set up the learner who only talks to the teacher
learner = TTTMealyLearner(teacher)
# learner.enable_checkpoints('checkpoints3')
# learner.load_checkpoint('/home/tom/projects/lstar/experiments/counterexampletracker/checkpoints3/cZsmSu/2020-05-06_20:00:33:790987')
# Get the learners hypothesis
hyp = learner.run(
show_intermediate=False,
render_options={'ignore_self_edges': ['error', 'invalid']},
on_hypothesis=lambda x: check_result(
x,
f'../../rers/TrainingSeqReachRers2019/{problem}/reachability-solution-{problem}.csv'
))
print(
"SUCCES",
check_result(
hyp,
f'../../rers/TrainingSeqReachRers2019/{problem}/reachability-solution-{problem}.csv'
))
hyp.render_graph(render_options={'ignore_self_edges': ['error', 'invalid']})
eqc = StackedChecker(
SmartWmethodEquivalenceCheckerV2(sul,
horizon=horizon,
stop_on={'invalid_input'},
stop_on_startswith={'error'}),
NuSMVEquivalenceChecker(sul, constrpath, mappingpath, n_unrolls=10),
# SmartWmethodEquivalenceCheckerV2(sul,
# horizon=horizon,
# stop_on={'invalid_input'},
# stop_on_startswith={'error'})
)
# Set up the teacher, with the system under learning and the equivalence checker
teacher = Teacher(sul, eqc)
# Set up the learner who only talks to the teacher
learner = TTTMealyLearner(teacher)
# Get the learners hypothesis
hyp = learner.run(show_intermediate=False, )
#hyp.render_graph()
nusmv = NuSMVUtils(constrpath, mappingpath)
ltl_answers = nusmv.run_ltl_check(hyp)
if 'Training' in problemset:
check_result(ltl_answers, solutionspath)
# Set up the teacher, with the system under learning and the equivalence checker
teacher = Teacher(sul, eqc)
# Set up the learner who only talks to the teacher
learner = TTTMealyLearner(teacher)
def stack(*args):
def chained(hypothesis):
for function in args:
function(hypothesis)
return chained
# Get the learners hypothesis
hyp = learner.run(
show_intermediate=False,
render_options={'ignore_self_edges': ['error', 'invalid']},
on_hypothesis=stack(
savehypothesis(f'{prefix}/hypotheses/{problemset}/{problem}',
f'{problem}'),
lambda h: check_result(
h,
f'../../rers/{problemset}/{problem}/constraints-solution-{problem}.txt'
)))
statstracker.write_log()
hyp.render_graph(render_options={'ignore_self_edges': ['error', 'invalid']})
# fuzzer_path=fuzzer_path,
# eqchecktype=EQCheckType.BOTH,
# enable_dtraces=False,
# minimize=False)
# eqc = SmartWmethodEquivalenceCheckerV4(sul,
# horizon=13,
# stop_on={'invalid_input'},
# stop_on_startswith={'error'},
# order_type='shortest first')
eqc = SmartWmethodEquivalenceCheckerV2(sul,
horizon=13,
stop_on={'invalid_input'},
stop_on_startswith={'error'})
#order_type='shortest first')
# Set up the teacher, with the system under learning and the equivalence checker
teacher = Teacher(sul, eqc)
# Set up the learner who only talks to the teacher
learner = TTTMealyLearner(teacher)
# Get the learners hypothesis
hyp = learner.run(show_intermediate=False,
render_options={'ignore_self_edges': ['error', 'invalid']},
on_hypothesis=savehypothesis(f'hypotheses/{problem}_wmethod',
f'{problem}'))
hyp.render_graph(render_options={'ignore_self_edges': ['error', 'invalid']})
check_result(
hyp,
f'../../rers/{problemset}/{problem}/reachability-solution-{problem}.csv')
sul = RersTrieCache(RERSConnectorV4(f'{path}/{problem}_Reach'),
storagepath=cache)
# We use a specialized W-method equivalence checker which features
# early stopping on invalid inputs, which speeds things up a lot
# eqc = RersWmethodEquivalenceChecker(sul, False, m=5)
eqc = SmartWmethodEquivalenceChecker(sul,
horizon=5,
stop_on={'invalid_input'},
stop_on_startswith={'error'})
# Set up the teacher, with the system under learning and the equivalence checker
teacher = Teacher(sul, eqc)
# Set up the learner who only talks to the teacher
# We let it save checkpoints of every intermediate hypothesis
learner = MealyLearner(teacher)\
.enable_checkpoints("checkpoints", problem)\
#.load_checkpoint('/home/tom/projects/lstar/examples/learn_rers/checkpoints/m34/2020-05-16_15:42:30:713416')
# Get the learners hypothesis
hyp = learner.run(show_intermediate=True,
render_options={'ignore_self_edges': ['error', 'invalid']},
on_hypothesis=lambda x: check_result(
x, f'{path}/solution_{problem}_reachability.csv'))
print("SUCCES", check_result(hyp,
f'{path}/solution_{problem}_reachability.csv'))
hyp.render_graph(tempfile.mktemp('.gv'))
