def test_parameters(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [F s=5]", program)
model = stormpy.build_parametric_model(program, formulas)
model_parameters = model.collect_probability_parameters()
reward_parameters = model.collect_reward_parameters()
all_parameters = model.collect_all_parameters()
assert len(model_parameters) == 2
assert len(reward_parameters) == 0
assert len(all_parameters) == 2
program_reward = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp_rewards16_2.pm"))
formulas_reward = stormpy.parse_properties_for_prism_program(
"Rmin=? [ F \"target\" ]", program_reward)
model = stormpy.build_parametric_model(program_reward, formulas_reward)
model_parameters = model.collect_probability_parameters()
reward_parameters = model.collect_reward_parameters()
all_parameters = model.collect_all_parameters()
assert len(model_parameters) == 2
assert len(reward_parameters) == 2
assert len(all_parameters) == 4
model = stormpy.build_symbolic_parametric_model(program, formulas)
assert len(model.get_parameters()) == 4
model = stormpy.build_symbolic_parametric_model(
program_reward, formulas_reward)
assert len(model.get_parameters()) == 4
def test_parametric_bisimulation(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
assert program.nr_modules == 5
assert program.model_type == stormpy.PrismModelType.DTMC
assert program.has_undefined_constants
assert program.undefined_constants_are_graph_preserving
prop = "P=? [F s=5]"
properties = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, properties)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, properties[0])
ratFunc = result.at(initial_state)
model_bisim = stormpy.perform_bisimulation(
model, properties, stormpy.BisimulationType.STRONG)
assert model_bisim.nr_states == 324
assert model_bisim.nr_transitions == 452
assert model_bisim.model_type == stormpy.ModelType.DTMC
assert model_bisim.has_parameters
result_bisim = stormpy.model_checking(model_bisim, properties[0])
initial_state_bisim = model_bisim.initial_states[0]
assert initial_state_bisim == 316
ratFunc_bisim = result_bisim.at(initial_state_bisim)
assert ratFunc == ratFunc_bisim
def test_change_parametric_matrix_modelchecking(self):
import stormpy.logic
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
initial_state = model.initial_states[0]
assert initial_state == 0
matrix = model.transition_matrix
# Check matrix
one_pol = stormpy.RationalRF(1)
one_pol = stormpy.FactorizedPolynomial(one_pol)
one = stormpy.FactorizedRationalFunction(one_pol, one_pol)
for e in matrix:
assert e.value() == one or len(e.value().gather_variables()) > 0
# First model checking
result = stormpy.model_checking(model, formulas[0])
ratFunc = result.at(initial_state)
assert len(ratFunc.gather_variables()) > 0
# Change probabilities
two_pol = stormpy.RationalRF(2)
two_pol = stormpy.FactorizedPolynomial(two_pol)
new_val = stormpy.FactorizedRationalFunction(one_pol, two_pol)
for e in matrix:
if len(e.value().gather_variables()) > 0:
e.set_value(new_val)
for e in matrix:
assert e.value() == new_val or e.value() == one
# Second model checking
result = stormpy.model_checking(model, formulas[0])
ratFunc = result.at(initial_state)
assert len(ratFunc.gather_variables()) == 0
def test_pla(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.create_region_checker(env, model,
formulas[0].raw_formula)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion.create_from_string(
"0.7<=pL<=0.9,0.75<=pK<=0.95",
parameters,
splitting_threshold=None)
result = checker.check_region(env, region)
assert result == stormpy.pars.RegionResult.ALLSAT
region = stormpy.pars.ParameterRegion.create_from_string(
"0.4<=pL<=0.65,0.75<=pK<=0.95",
parameters,
splitting_threshold=None)
result = checker.check_region(
env, region, stormpy.pars.RegionResultHypothesis.UNKNOWN,
stormpy.pars.RegionResult.UNKNOWN, True)
assert result == stormpy.pars.RegionResult.EXISTSBOTH
region = stormpy.pars.ParameterRegion.create_from_string(
"0.1<=pL<=0.73,0.2<=pK<=0.715",
parameters,
splitting_threshold=None)
result = checker.check_region(env, region)
assert result == stormpy.pars.RegionResult.ALLVIOLATED
def example_parametric_models_01():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
path = stormpy.examples.files.prism_pdtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=3]"
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
print("Model supports parameters: {}".format(model.supports_parameters))
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
instantiator = stormpy.pars.PDtmcInstantiator(model)
point = dict()
for x in parameters:
print(x.name)
point[x] = stormpy.RationalRF(0.0)
instantiated_model = instantiator.instantiate(point)
result = stormpy.model_checking(instantiated_model, properties[0])
print(result)
def example_parametric_models_02():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
from pycarl.formula import FormulaType, Relation
if stormpy.info.storm_ratfunc_use_cln():
import pycarl.cln.formula
else:
import pycarl.gmp.formula
path = stormpy.examples.files.prism_pdtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=2]"
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
initial_state = model.initial_states[0]
result = stormpy.model_checking(model, properties[0])
print("Result: {}".format(result.at(initial_state)))
collector = stormpy.ConstraintCollector(model)
print("Well formed constraints:")
for formula in collector.wellformed_constraints:
print(formula.get_constraint())
print("Graph preserving constraints:")
for formula in collector.graph_preserving_constraints:
print(formula.get_constraint())
def test_mdp_simplification(self):
program = stormpy.parse_prism_program(get_example_path("pmdp", "two_dice.nm"))
formulas = stormpy.parse_properties_for_prism_program("Pmin=? [ F \"two\" ]", program)
formula = formulas[0].raw_formula
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 169
assert model.nr_transitions == 435
model, formula = stormpy.pars.simplify_model(model, formula)
assert model.nr_states == 17
assert model.nr_transitions == 50
def build_model(file, hybrid=False, sylvan_threads=1, sylvan_memory=4096):
"""
Build model from file and apply bisimulation.
:param file: File.
:param hybrid: If true, the model is built symbolically with BDDs and in the end converted to a sparse model.
If false, the model is built as a sparse model from the beginning.
:param sylvan_threads: Number of threads to use in Sylvan library.
:param sylvan_memory: Memory available to Sylvan.
:return: Tuple (sparse model, prism program, property, time (s) for building , time (s) for bisimulation).
"""
logging.debug("Build ({}) model for file {}".format("symbolic" if hybrid else "sparse", file))
# Build model
build_start = time.time()
program = stormpy.parse_prism_program(file)
prop = "R=? [F \"stable\"]"
properties = stormpy.parse_properties(prop, program)
assert (len(properties) == 1)
program, properties = stormpy.preprocess_prism_program(program, properties, "")
program = program.as_prism_program()
if hybrid:
# Set number of Sylvan threads to use
stormpy.set_settings(["--sylvan:threads", str(sylvan_threads)])
# Set memory for Sylvan
stormpy.set_settings(["--sylvan:maxmem", str(sylvan_memory)])
model = stormpy.build_symbolic_parametric_model(program, properties)
else:
model = stormpy.build_parametric_model(program, properties)
logging.info("Built ({}) model with {} states and {} transitions.".format("symbolic" if hybrid else "sparse", model.nr_states, model.nr_transitions))
build_end = time.time()
time_build = build_end - build_start
logging.info("Building model took {}s".format(time_build))
# Bisimulation
if hybrid:
# Extract bisimulation quotient as sparse model
stormpy.set_settings(["--bisimulation:quot", "sparse"])
model = stormpy.perform_symbolic_bisimulation(model, properties)
else:
model = stormpy.perform_bisimulation(model, properties, stormpy.BisimulationType.STRONG)
assert type(model) is stormpy.SparseParametricDtmc
logging.info("Model after bisimulation: {} states and {} transitions.".format(model.nr_states, model.nr_transitions))
time_bisim = time.time() - build_end
logging.info("Computing bisimulation quotient took {}s".format(time_bisim))
prop = properties[0]
# Simplify model (by eliminating constant transitions)
# Disabled for the moment as this leads to significantly more transitions in Herman
if False:
model, formula = stormpy.pars.simplify_model(model, prop.raw_formula)
logging.info("Model after simplification: {} states and {} transitions.".format(model.nr_states, model.nr_transitions))
return model, program, prop, time_build, time_bisim
def test_build_parametric_mdp(self):
program = stormpy.parse_prism_program(
get_example_path("pmdp", "two_dice.nm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F \"two\" ]", program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 169
assert model.nr_transitions == 435
assert model.model_type == stormpy.ModelType.MDP
assert model.supports_parameters
assert type(model) is stormpy.SparseParametricMdp
def test_dtmc_simplification(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
formula = formulas[0].raw_formula
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
model, formula = stormpy.pars.simplify_model(model, formula)
assert model.nr_states == 193
assert model.nr_transitions == 383
def test_build_dtmc_supporting_parameters(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F \"one\" ]", program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert model.model_type == stormpy.ModelType.DTMC
assert model.supports_parameters
assert not model.has_parameters
assert type(model) is stormpy.SparseParametricDtmc
def test_build_parametric_dtmc(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.supports_parameters
assert model.has_parameters
assert type(model) is stormpy.SparseParametricDtmc
def example_parametric_models_03():
path = stormpy.examples.files.prism_dtmc_brp
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F \"target\"]"
properties = stormpy.parse_properties_for_prism_program(
formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
print(model.model_type)
start = time.time()
t = 0
for state in model.states:
if state.id in model.initial_states:
print(state)
for action in state.actions:
for transition in action.transitions:
if transition.value().constant_part() == 1:
t += 1
#print("From state {}, with probability {}, go to state {}".format(state, transition.value(), transition.column))
print(time.time() - start)
print(t)
t2 = 0
start = time.time()
for row_group in range(model.nr_states):
for row in range(
model.transition_matrix.get_row_group_start(row_group),
model.transition_matrix.get_row_group_end(row_group)):
for entry in model.transition_matrix.get_row(row):
if entry.value().constant_part() == 1:
t2 += 1
print(time.time() - start)
print(t2)
states_and_transitions = []
for row_group in range(model.nr_states):
states_and_transitions.append([])
for row in range(
model.transition_matrix.get_row_group_start(row_group),
model.transition_matrix.get_row_group_end(row_group)):
for entry in model.transition_matrix.get_row(row):
states_and_transitions[-1].append(
(entry.value(), entry.column))
t3 = 0
start = time.time()
for s in states_and_transitions:
for (v, c) in s:
if v.constant_part() == 1:
t3 += 1
print(time.time() - start)
print(t3)
def test_derivatives(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
derivatives = stormpy.pars.gather_derivatives(model, list(parameters)[0])
assert len(derivatives) == 0
def test_parametric_model_checking_sparse(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
func = result.at(initial_state)
one = stormpy.FactorizedPolynomial(stormpy.RationalRF(1))
assert func.denominator == one
def test_build_parametric_dtmc_preprocess(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "herman5.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"R=? [ F \"stable\" ]", program)
trans_program, trans_formulas = stormpy.preprocess_symbolic_input(
program, formulas, "")
trans_prism = trans_program.as_prism_program()
model = stormpy.build_parametric_model(trans_prism, trans_formulas)
assert model.nr_states == 33
assert model.nr_transitions == 266
assert model.model_type == stormpy.ModelType.DTMC
assert model.supports_parameters
assert model.has_parameters
assert type(model) is stormpy.SparseParametricDtmc
def test_parametric_transitions(self):
program = stormpy.parse_prism_program(
get_example_path("pmdp", "two_dice.nm"))
model = stormpy.build_parametric_model(program)
assert model.states[1].id == 1
one = stormpy.FactorizedPolynomial(stormpy.RationalRF(1))
i = 0
for state in model.states:
assert state.id == i
i += 1
j = 0
for action in state.actions:
assert j == 0 or j == 1
j += 1
for transition in action.transitions:
assert transition.value().denominator == one
def test_label_parametric(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
labels = model.labeling.get_labels()
assert len(labels) == 3
assert "init" in labels
assert "(s = 5)" in labels
assert "init" in model.labels_state(0)
assert "(s = 5)" in model.labels_state(28)
assert "(s = 5)" in model.labels_state(611)
initial_states = model.initial_states
assert len(initial_states) == 1
assert 0 in initial_states
def test_dynasty(self):
program = stormpy.parse_prism_program(
"/home/simon/School/DP/synthesis/workspace/simon/die/sketch.templ")
prop = "P>=1/6 [F s=7 & d=1]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.MdpParameterLiftingModelChecker()
checker.specify(env, model, formulas[0].raw_formula, True)
parameters = model.collect_probability_parameters()
assert len(parameters) == 1
region = stormpy.pars.ParameterRegion.create_from_string(
"0.1<=p0<=0.62", parameters)
minr = checker.get_bound(env, region, False)
maxr = checker.get_bound(env, region, True)
result = checker.check_region(env, region)
assert True
def test_pla_manual(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.DtmcParameterLiftingModelChecker()
checker.specify(env, model, formulas[0].raw_formula)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion.create_from_string(
"0.7<=pL<=0.9,0.75<=pK<=0.95", parameters)
result = checker.get_bound(env, region, True)
assert math.isclose(float(result.constant_part()),
0.8369631383670559,
rel_tol=1e-6)
def test_pla_region_valuation(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.create_region_checker(env, model,
formulas[0].raw_formula)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
for par in parameters:
if par.name == "pL":
pL = par
elif par.name == "pK":
pK = par
else:
assert False
region_valuation = dict()
region_valuation[pL] = (stormpy.RationalRF(0.7),
stormpy.RationalRF(0.9))
region_valuation[pK] = (stormpy.RationalRF(0.75),
stormpy.RationalRF(0.95))
region = stormpy.pars.ParameterRegion(region_valuation)
result = checker.check_region(env, region)
assert result == stormpy.pars.RegionResult.ALLSAT
region_valuation[pL] = (stormpy.RationalRF(0.4),
stormpy.RationalRF(0.65))
region = stormpy.pars.ParameterRegion(region_valuation)
result = checker.check_region(
env, region, stormpy.pars.RegionResultHypothesis.UNKNOWN,
stormpy.pars.RegionResult.UNKNOWN, True)
assert result == stormpy.pars.RegionResult.EXISTSBOTH
region_valuation[pK] = (stormpy.RationalRF(0.2),
stormpy.RationalRF(0.715))
region_valuation[pL] = (stormpy.RationalRF(0.1),
stormpy.RationalRF(0.73))
region = stormpy.pars.ParameterRegion(region_valuation)
result = checker.check_region(env, region)
assert result == stormpy.pars.RegionResult.ALLVIOLATED
def test_instantiate_dtmc(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
instantiator = stormpy.pars.ModelInstantiator(model)
point = {p: stormpy.RationalRF("0.4") for p in parameters}
instantiated_model = instantiator.instantiate(point)
assert instantiated_model.nr_states == model.nr_states
assert not instantiated_model.has_parameters
assert "0.4" in str(instantiated_model.transition_matrix[1])
point = {p: stormpy.RationalRF("0.5") for p in parameters}
instantiated_model2 = instantiator.instantiate(point)
assert "0.5" in str(instantiated_model2.transition_matrix[1])
def test_region(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion.create_from_string("0.7<=pL<=0.9,0.75<=pK<=0.95", parameters)
assert region.area == stormpy.RationalRF(1) / stormpy.RationalRF(25)
for par in parameters:
if par.name == "pL":
pL = par
elif par.name == "pK":
pK = par
else:
assert False
dec = stormpy.RationalRF(100)
region_valuation = {pL: (stormpy.RationalRF(70) / dec, stormpy.RationalRF(90) / dec), pK: (stormpy.RationalRF(75) / dec, stormpy.RationalRF(95) / dec)}
region = stormpy.pars.ParameterRegion(region_valuation)
assert region.area == stormpy.RationalRF(1) / stormpy.RationalRF(25)
def test_pdtmc_exact_instantiation_checker(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "herman5.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"R=? [F \"stable\"]", program)
model = stormpy.build_parametric_model(program, formulas)
parameters = model.collect_probability_parameters()
inst_checker = stormpy.pars.PDtmcExactInstantiationChecker(model)
inst_checker.specify_formula(
stormpy.ParametricCheckTask(formulas[0].raw_formula, True))
inst_checker.set_graph_preserving(True)
env = stormpy.Environment()
point = {p: stormpy.RationalRF("1/2") for p in parameters}
result = inst_checker.check(env, point)
assert isinstance(result, stormpy.ExplicitExactQuantitativeCheckResult)
res = result.at(model.initial_states[0])
assert isinstance(res, stormpy.Rational)
assert res == stormpy.Rational("29/15")
def test_pla_bounds(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.create_region_checker(env, model,
formulas[0].raw_formula)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion("0.7<=pL<=0.9,0.75<=pK<=0.95",
parameters)
result = checker.get_bound(env, region, True)
assert math.isclose(float(result.constant_part()),
0.8369631383670559,
rel_tol=1e-6)
result_vec = checker.get_bound_all_states(env, region, True)
result = result_vec.at(model.initial_states[0])
assert math.isclose(result, 0.8369631383670559, rel_tol=1e-6)
def test_constraints_collector(self):
from pycarl.formula import FormulaType, Relation
if stormpy.info.storm_ratfunc_use_cln():
import pycarl.cln.formula
else:
import pycarl.gmp.formula
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
collector = stormpy.ConstraintCollector(model)
constraints_well_formed = collector.wellformed_constraints
for formula in constraints_well_formed:
assert formula.type == FormulaType.CONSTRAINT
constraint = formula.get_constraint()
assert constraint.relation == Relation.LEQ
constraints_graph_preserving = collector.graph_preserving_constraints
for formula in constraints_graph_preserving:
assert formula.type == FormulaType.CONSTRAINT
constraint = formula.get_constraint()
assert constraint.relation == Relation.NEQ
def test_pla_state_bounds(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.DtmcParameterLiftingModelChecker()
checker.specify(env,
model,
formulas[0].raw_formula,
allow_model_simplification=False)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion.create_from_string(
"0.7<=pL<=0.9,0.75<=pK<=0.95", parameters)
result_vec = checker.get_bound_all_states(env, region, True)
assert len(result_vec.get_values()) == model.nr_states
assert math.isclose(result_vec.at(model.initial_states[0]),
0.836963056082918,
rel_tol=1e-6)
def example_parametric_models_04():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
path = stormpy.examples.files.prism_pdtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=2]"
properties = stormpy.parse_properties(formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
# Modify
i = 0
for state in model.states:
if state.id in model.initial_states:
print(state)
for action in state.actions:
for transition in action.transitions:
if len(transition.value().gather_variables()) > 0:
new_var = pycarl.Variable("p{}".format(i))
i += 1
new_pol = stormpy.Polynomial(new_var)
pol_in_right_format = stormpy.FactorizedPolynomial(new_pol, transition.value().numerator.cache())
new_factorized_ratfunc = stormpy.FactorizedRationalFunction(pol_in_right_format)
transition.set_value(new_factorized_ratfunc)
# Display
for state in model.states:
if state.id in model.initial_states:
print(state)
for action in state.actions:
for transition in action.transitions:
print("From state {}, with probability {}, go to state {}".format(state, transition.value(), transition.column))
def example_parametric_models_01():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
from pycarl.formula import FormulaType, Relation
if stormpy.info.storm_ratfunc_use_cln():
import pycarl.cln.formula
else:
import pycarl.gmp.formula
# Prevent curious side effects from earlier runs (for tests only)
pycarl.clear_pools()
###
# How to apply an unknown FSC to obtain a pMC from a POMDP
path = stormpy.examples.files.prism_pomdp_maze
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [\"goal\"]"
properties = stormpy.parse_properties_for_prism_program(
formula_str, prism_program)
# construct the POMDP
pomdp = stormpy.build_model(prism_program, properties)
# make its representation canonic.
pomdp = stormpy.pomdp.make_canonic(pomdp)
# make the POMDP simple. This step is optional but often beneficial
pomdp = stormpy.pomdp.make_simple(pomdp)
# construct the memory for the FSC
# in this case, a selective counter with two states
memory_builder = stormpy.pomdp.PomdpMemoryBuilder()
memory = memory_builder.build(
stormpy.pomdp.PomdpMemoryPattern.selective_counter, 2)
# apply the memory onto the POMDP to get the cartesian product
pomdp = stormpy.pomdp.unfold_memory(pomdp, memory)
# apply the memory onto the POMDP to get the cartesian product
pmc = stormpy.pomdp.apply_unknown_fsc(
pomdp, stormpy.pomdp.PomdpFscApplicationMode.simple_linear)
####
# How to apply an unknown FSC to obtain a pMC from a pPOMDP
path = stormpy.examples.files.prism_par_pomdp_maze
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [\"goal\"]"
properties = stormpy.parse_properties_for_prism_program(
formula_str, prism_program)
# construct the pPOMDP
pomdp = stormpy.build_parametric_model(prism_program, properties)
# make its representation canonic.
pomdp = stormpy.pomdp.make_canonic(pomdp)
# make the POMDP simple. This step is optional but often beneficial
pomdp = stormpy.pomdp.make_simple(pomdp)
# construct the memory for the FSC
# in this case, a selective counter with two states
memory_builder = stormpy.pomdp.PomdpMemoryBuilder()
memory = memory_builder.build(
stormpy.pomdp.PomdpMemoryPattern.selective_counter, 2)
# apply the memory onto the POMDP to get the cartesian product
pomdp = stormpy.pomdp.unfold_memory(pomdp, memory)
# apply the unknown FSC to obtain a pmc from the POMDP
pmc = stormpy.pomdp.apply_unknown_fsc(
pomdp, stormpy.pomdp.PomdpFscApplicationMode.simple_linear)
export_pmc = False # Set to True to export the pMC as drn.
if export_pmc:
export_options = stormpy.core.DirectEncodingOptions()
export_options.allow_placeholders = False
stormpy.export_parametric_to_drn(pmc, "test.out", export_options)
def test2():
t0 = time.time()
#path = "collision_partial_obs_2d_upd_hobs_20_small.prism"
path, interval_path, formula_str, threshold = input_files()
prism_program = stormpy.parse_prism_program(path)
#formula_str = "P=? [!\"bad\" U \"goal\"]"
#formula_str = "P=? [F \"goal\"]"
#interval_path="collision_partial_obs_2d_upd_hobs_20_small.intervals"
opts = stormpy.DirectEncodingParserOptions()
opts.build_choice_labels = True
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
# construct the pPOMDP
import inspect
print(inspect.getfullargspec(stormpy.build_parametric_model))
pomdp = stormpy.build_parametric_model(prism_program, properties)
pomdp_parameters = pomdp.collect_probability_parameters()
stormpy.export_parametric_to_drn(pomdp, "pomdp_ex")
# make its representation canonic.
pomdp = stormpy.pomdp.make_canonic(pomdp)
#stormpy.export_parametric_to_drn(pomdp, "export_pomdp.drn")
# construct the memory for the FSC
# in this case, a selective counter with two states
memory_builder = stormpy.pomdp.PomdpMemoryBuilder()
memval=1
memory = memory_builder.build(stormpy.pomdp.PomdpMemoryPattern.selective_counter, memval)
# apply the memory onto the POMDP to get the cartesian product
pomdp = stormpy.pomdp.unfold_memory(pomdp, memory)
print("Number of pomdp states before simple:",pomdp.nr_states)
print("Number of transitions: {}".format(pomdp.nr_transitions))
# make the POMDP simple. This step is optional but often beneficial
pomdp = stormpy.pomdp.make_simple(pomdp)
print("Number of pomdp states after simple:",pomdp.nr_states)
print("Number of transitions: {}".format(pomdp.nr_transitions))
# apply the unknown FSC to obtain a pmc from the POMDP
pmc = stormpy.pomdp.apply_unknown_fsc(pomdp, stormpy.pomdp.PomdpFscApplicationMode.simple_linear)
print("Number of pomdp states after simple",pmc.nr_states)
print("Number of transitions: {}".format(pmc.nr_transitions))
print(pmc)
print("applied pmc")
path_pmc = "export_" + str(memval) + "_mem_" + path
print(path_pmc)
stormpy.export_parametric_to_drn(pmc, path_pmc)
print("built model")
#print(model.initial_states)
fsc_parameters = pmc.collect_probability_parameters() - pomdp.collect_probability_parameters()
print("number of pomdp parameters:",len(fsc_parameters))
#print(fsc_parameters)
#print(pomdp_parameters)
#intervals2, polyhedrons = interval_parser.parse_input(interval_path)
intervals, polyhedrons,items = interval_parser.parse_model_interval(pmc,pomdp_parameters,interval_path)
#for item in intervals:
#print(item,"printing in the main")
#for item in items:
# print(item,"printing in the main")
for p in polyhedrons:
#print(p)
p.compute_vertices()
properties = stormpy.parse_properties(formula_str)
print("Building model from {}".format(path))
#parameters = model.collect_probability_parameters()
print(pmc.nr_states)
prob0E, prob1A = stormpy.prob01max_states(pmc, properties[0].raw_formula.subformula)
#print(prob0E)
#threshold = 0.90
direction = "below" # can be "below" or "above"
options = QcqpOptions(mu=1e4, maxiter=10000, graph_epsilon=1e-2, silent=False)
# result = solver.run(reward_model_name ,model_rew, parameters_rew, rew0, rew_threshold, direction, options)
solver = QcqpSolver_affine_simple_fun()
result = solver.run(pmc, fsc_parameters, pomdp_parameters,properties, prob0E, prob1A, threshold, direction, options,intervals,items,True)
print("number of iterations={}".format(solver.iterations))
print("solver time={}".format(solver.solver_timer))
#compute the policy against the robust interval
#interval_path="collision_partial_obs_2d_upd_hobs_20_small.intervals"
intervals, polyhedrons,items = interval_parser.parse_model_interval(pmc,pomdp_parameters,interval_path)
regiondict = dict()
for x in pomdp_parameters:
for item in items:
if item.name == x.name:
regiondict[x] = (stormpy.RationalRF(item.lowerbound), stormpy.RationalRF(item.upperbound))
region = stormpy.pars.ParameterRegion(regiondict)
instantiator = stormpy.pars.PartialPDtmcInstantiator(pmc)
instantiated_model = instantiator.instantiate(solver.solver_params)
env = stormpy.Environment()
env.solver_environment.set_linear_equation_solver_type(stormpy.EquationSolverType.eigen)
env.solver_environment.native_solver_environment.method = stormpy.NativeLinearEquationSolverMethod.optimistic_value_iteration
env.solver_environment.native_solver_environment.precision = stormpy.Rational('0.01')
start_check = time.time()
region_checker = stormpy.pars.create_region_checker(env, instantiated_model, properties[0].raw_formula,
allow_model_simplification=False)
print("region check")
result = region_checker.get_bound_all_states(env, region, maximise=False)
end_check = time.time()
print("model checking ans:")
ansval=result.at(pmc.initial_states[0])
print(ansval)
tend = time.time()
print("Total time: ", str(tend - t0))
