def get_models(finalDepth, traces, startValue, step, encoder, maxNumModels=1):
results = []
i = startValue
fg = encoder(i, traces)
fg.encodeFormula()
while len(results) < maxNumModels and i < finalDepth:
solverRes = fg.solver.check()
if not solverRes == sat:
logging.debug("not sat for i = {}".format(i))
i += step
fg = encoder(i, traces)
fg.encodeFormula()
else:
solverModel = fg.solver.model()
formula = fg.reconstructWholeFormula(solverModel)
logging.info("found formula {}".format(formula.prettyPrint()))
#print("found formula {}".format(formula))
formula = Formula.normalize(formula)
logging.info("normalized formula {}".format(formula))
if formula not in results:
results.append(formula)
#prevent current result from being found again
block = []
# pdb.set_trace()
# print(m)
infVariables = fg.getInformativeVariables()
logging.debug("informative variables of the model:")
for v in infVariables:
logging.debug((v, solverModel[v]))
logging.debug("===========================")
for d in solverModel:
# d is a declaration
if d.arity() > 0:
raise Z3Exception(
"uninterpreted functions are not supported")
# create a constant from declaration
c = d()
if is_array(c) or c.sort().kind() == Z3_UNINTERPRETED_SORT:
raise Z3Exception(
"arrays and uninterpreted sorts are not supported")
block.append(c != solverModel[d])
fg.solver.add(Or(block))
return results
def get_models(
traces,
startDepth=1,
maxDepth=float("inf"),
step=1,
optimizeDepth=float("inf"),
optimize='count',
minScore=0,
encoder=DagSATEncoding,
maxNumModels=1,
timeout=float("inf"),
):
if optimizeDepth < maxDepth and optimize is None:
logging.warning("Optimize objective not set. Ignoring optimization.")
tictoc_z3, time_z3 = TicToc(), 0
tictoc_total = TicToc()
tictoc_total.tic()
results = []
i = startDepth
fg = encoder(i, traces)
fg.encodeFormula(optimize=(optimize if i >= optimizeDepth else None))
while len(results) < maxNumModels and i < maxDepth:
if fg.set_timeout(timeout - tictoc_total.tocvalue()) <= 0: break
tictoc_z3.tic()
solverRes = fg.solver.check()
time_z3 += tictoc_z3.tocvalue()
acceptFormula = False
if solverRes == unsat:
logging.debug(f"not sat for i = {i}")
elif solverRes != sat:
logging.debug(f"unknown for i = {i}")
break
else:
acceptFormula = True
solverModel = fg.solver.model()
formula = fg.reconstructWholeFormula(solverModel)
if fg.optimize:
score = traces.get_score(formula, fg.optimize)
if score < minScore:
acceptFormula = False
logging.debug(
f"score too low for i = {i} ({fg.optimize}={score})")
if not acceptFormula:
i += step
fg = encoder(i, traces)
fg.encodeFormula(
optimize=(optimize if i >= optimizeDepth else None))
else:
if fg.optimize:
logging.info(
f"found formula {formula.prettyPrint()} ({fg.optimize}={score})"
)
else:
logging.info(f"found formula {formula.prettyPrint()}")
#print(f"found formula {formula}")
formula = Formula.normalize(formula)
logging.info(f"normalized formula {formula}")
if formula not in results:
results.append(formula)
#prevent current result from being found again
block = []
# pdb.set_trace()
# print(m)
infVariables = fg.getInformativeVariables()
logging.debug("informative variables of the model:")
for v in infVariables:
logging.debug((v, solverModel[v]))
logging.debug("===========================")
for d in solverModel:
# d is a declaration
if d.arity() > 0:
raise Z3Exception(
"uninterpreted functions are not supported")
# create a constant from declaration
c = d()
if is_array(c) or c.sort().kind() == Z3_UNINTERPRETED_SORT:
raise Z3Exception(
"arrays and uninterpreted sorts are not supported")
block.append(c != solverModel[d])
fg.solver.add(Or(block))
# time_total = tictoc_total.tocvalue()
# time_z3
# print(time_z3, time_total)
return results