def update_transition_system(self):
controller = KerasController(keras_model=self.controller_keras_obj,
cap_values=self.cap_values)
tr = TFControlledTransitionRelation(dynamics_obj=self.overt_obj,
controller_obj=controller)
self.ts = TransitionSystem(states=tr.states,
initial_set=self.init_set,
transition_relation=tr)
def run(self):
tr = TFControlledTransitionRelation(dynamics_obj=self.dynamics,
controller_obj=self.controller_obj)
init_set = dict(zip(tr.states.flatten(), self.init_range))
ts = TransitionSystem(states=tr.states,
initial_set=init_set,
transition_relation=tr)
solver = self.solver
prop = self.setup_property()
algo = self.algo(ts=ts, prop=prop, solver=solver)
return algo.check_invariant_until(self.n_steps)
def _simple_run(self, n_check_invariant):
self.setup_controller_obj()
self.setup_overt_dyn_obj()
tr = TFControlledTransitionRelation(dynamics_obj=self.overt_dyn_obj,
controller_obj=self.controller_obj,
turn_max_to_relu=True)
init_set = dict(zip(self.state_vars, self.init_range))
ts = TransitionSystem(states=tr.states,
initial_set=init_set,
transition_relation=tr)
for c in ts.transition_relation.constraints:
assert (not isinstance(c, MaxConstraint))
#solver = MarabouWrapper(n_worker=self.ncore)
solver = GurobiPyWrapper()
prop = self.setup_property()
algo = BMC(ts=ts, prop=prop, solver=solver)
return algo.check_invariant_until(n_check_invariant)
# print("single pendulum dynamics constraints = ", len(single_pendulum_dynamics.constraints))
# print("controler constraints = ", len(controller.constraints))
# create transition relation using controller and dynamics
tr = TFControlledTransitionRelation(dynamics_obj=single_pendulum_dynamics,
controller_obj=controller)
# initial set
x1_init_set = (0.5, 1)
x2_init_set = (-0.5, 0.5)
init_set = {states[0]: x1_init_set, states[1]: x2_init_set}
# build the transition system as an (S, I(S), TR) tuple
ts = TransitionSystem(states=tr.states,
initial_set=init_set,
transition_relation=tr)
# solver
solver = GurobiPyWrapper() #MarabouWrapper()
prop_list = []
p1 = Constraint(ConstraintType('GREATER'))
p1.monomials = [Monomial(1, states[0])]
p1.scalar = 0.3
prop_list.append(p1)
p2 = Constraint(ConstraintType('LESS'))
p2.monomials = [Monomial(1, states[0])]
p2.scalar = 1.15
prop_list.append(p2)
def test_marabou_interface(alpha,
prop_desc,
n_invar,
with_relu=False,
with_max=False):
# create controller object, this is just a place holder. I will modify the object later.
model = load_model(
"../OverApprox/models/single_pend_nn_controller_lqr_data.h5")
controller = KerasController(keras_model=model)
# rewrite to make a simple controller that is always equal to alpha*x
controller.control_outputs = [['c']]
controller.state_inputs = [['xc']]
fake_constraint = []
if with_relu:
alpha_times_x = 'var1'
monomial_list = [
Monomial(alpha, controller.state_inputs[0][0]),
Monomial(-1, alpha_times_x)
]
fake_constraint.append(
Constraint(ConstraintType('EQUALITY'), monomial_list, 0.0))
relu_constraint = [
ReluConstraint(varin=alpha_times_x,
varout=controller.control_outputs[0][0])
]
controller.constraints = relu_constraint + fake_constraint
controller.relus = relu_constraint
elif with_max:
alpha_times_x = 'var1'
monomial_list = [
Monomial(alpha, controller.state_inputs[0][0]),
Monomial(-1, alpha_times_x)
]
fake_constraint.append(
Constraint(ConstraintType('EQUALITY'), monomial_list, 0.0))
max_second_arg = 'var2'
fake_constraint.append(
Constraint(ConstraintType('EQUALITY'),
[Monomial(1, max_second_arg)], -1 / 2))
max_constraint = [
MaxConstraint(varsin=[alpha_times_x, max_second_arg],
varout=controller.control_outputs[0][0])
]
controller.constraints = max_constraint + fake_constraint
controller.relus = []
else:
monomial_list = [
Monomial(-1, controller.control_outputs[0][0]),
Monomial(alpha, controller.state_inputs[0][0])
]
fake_constraint = [
Constraint(ConstraintType('EQUALITY'), monomial_list, 0.0)
]
controller.constraints = fake_constraint
controller.relus = []
# create overt dynamics objects. this is just a place holder. I will modify the object later.
overt_obj = OvertConstraint(
"../OverApprox/models/single_pend_acceleration_overt.h5")
# rewrite to make a simple controller that is always equal to x
overt_obj.control_vars = [['cd']]
overt_obj.state_vars = [['x']]
overt_obj.output_vars = [['dx']]
monomial_list2 = [
Monomial(1, overt_obj.control_vars[0][0]),
Monomial(-1, overt_obj.output_vars[0][0])
]
fake_constraint2 = [
Constraint(ConstraintType('EQUALITY'), monomial_list2, 0.5)
]
overt_obj.constraints = fake_constraint2
simple_dynamics = Dynamics(np.array(['x']), np.array(['cd']))
next_states = simple_dynamics.next_states.reshape(1, )
# x_next = x + dt*dx
dt = 1
c1 = Constraint(ConstraintType('EQUALITY'))
c1.monomials = [
Monomial(1, overt_obj.state_vars[0][0]),
Monomial(dt, overt_obj.output_vars[0][0]),
Monomial(-1, next_states[0])
]
simple_dynamics.constraints = [c1] + overt_obj.constraints
print(len(simple_dynamics.constraints))
print(len(controller.constraints))
# create transition relation using controller and dynamics
tr = TFControlledTransitionRelation(dynamics_obj=simple_dynamics,
controller_obj=controller)
# initial set
init_set = {overt_obj.state_vars[0][0]: (0., 1.)}
# build the transition system as an (S, I(S), TR) tuple
ts = TransitionSystem(states=tr.states,
initial_set=init_set,
transition_relation=tr)
# property x< 0.105, x' < 0.2
p = Constraint(ConstraintType(prop_desc["type"]))
p.monomials = [Monomial(1, overt_obj.state_vars[0][0])]
p.scalar = prop_desc["scalar"] #
prop = ConstraintProperty([p], [overt_obj.state_vars[0][0]])
# solver
solver = MarabouWrapper()
algo = BMC(ts=ts, prop=prop, solver=solver)
result, vals, stats = algo.check_invariant_until(n_invar)
return result.name