def _assert(self, modelset, i, g, a):
g = new_graph('g')
a = new_action('a')
s = new_modelset('s')
t = new_modelset('t')
def is_plus(alpha, beta, a):
# Assert that alpha + beta = a. All of these are Actions.
# This is defined in Definition 2 of the L paper, on page 5.
# TODO: implement this once you have a clear API for Action.
pass
def is_join(modelset, s, t, g, a):
# Assert that modelset behaves, on inputs g and a, like s "joined" with t.
# "joined" is the |><| operator.
alpha = Action('alpha')
beta = Action('beta')
return z3_helpers.Iff(
has(modelset, g, a), z3.Exists(alpha, beta),
z3.And(has(s, g, alpha), has(t, g, beta),
is_plus(alpha, beta, a)))
return z3.ForAll([g, a],
z3.And(self.p1._assert(s, i, g, a),
self.p2._assert(t, i, g, a),
is_join(modelset, s, t, g, a)))
def _assert(self, modelset, i):
g = new_graph('g')
a = new_action('a')
s = new_modelset('s')
t = new_modelset('t')
return z3.Exists([s, t], z3.ForAll([g, a],
z3.And(self.p1._assert(s, i), self.p2._assert(t, i),
z3_helpers.Iff(has(modelset, g, a), z3.And(has(s, g, a),
has(t, g, a))))))
def _assert(self, modelset, i):
g = new_graph('g')
a = new_action('a')
s = new_modelset('s')
return z3.Exists([s], z3.ForAll([g, a],
z3.And(self.pred._assert(s, i),
z3_helpers.Iff(has(modelset, g, a), z3.Not(s(g, a))))))
def check_sat(self):
# returns a boolean
with solver.context():
modelset = new_modelset()
interpretation = z3.Function('interpretation', Variable, Node)
predicate = self._assert(modelset, interpretation)
#if DEBUG:
# import pdb; pdb.set_trace()
solver.add(predicate)
return solver.check()
def get_model(self):
# returns a set of sets of <graph, action> pairs, or at the very least
# something that behaves on the surface as such. It might not
# necessarily be a complete set. Actions should also behave as sets
# (sets of atomic actions).
with solver.context():
modelset = new_modelset()
interpretation = z3.Function('interpretation', Variable, Node)
predicate = self._assert(modelset, interpretation)
#if DEBUG:
# import pdb; pdb.set_trace()
solver.add(predicate)
if not solver.check():
raise ValueError("Tried to get model of unsat predicate")
return solver.model()
def _assert(self, modelset, i):
g = new_graph('g')
a = new_action('a')
s = new_modelset('s')
t = new_modelset('t')
def is_plus(alpha, beta, a):
# Assert that alpha + beta = a. All of these are Actions.
# This is defined in Definition 2 of the L paper, on page 5.
# TODO: implement this once you have a clear API for Action.
pass
def is_join(modelset, s, t, g, a):
# Assert that modelset behaves, on inputs g and a, like s "joined" with t.
# "joined" is the |><| operator.
alpha = Action('alpha')
beta = Action('beta')
return z3_helpers.Iff(has(modelset, g, a),
z3.Exists(alpha, beta),
z3.And(has(s, g, alpha), has(t, g, beta), is_plus(alpha, beta, a)))
return z3.Exists([s, t], z3.ForAll([g, a],
z3.And(self.p1._assert(s, i), self.p2._assert(t, i),
is_join(modelset, s, t, g, a))))
def get_predicate(self):
modelset = new_modelset()
interpretation = z3.Function('interpretation', Variable, Node)
predicate = self._assert(modelset, interpretation)
return predicate
def get_predicate(self):
# returns a z3 predicate
modelset = new_modelset()
interpretation = new_interpretation()
predicate = self._assert(modelset, interpretation, None, None)
return predicate
def get_predicate(self):
# returns a z3 predicate
modelset = new_modelset()
interpretation = new_interpretation()
predicate = self._assert(modelset, interpretation, None, None)
return predicate
