def test_one_player_rps():
from magnum.examples.rock_paper_scissors import rps as g
from magnum.examples.rock_paper_scissors import context
from stl.boolean_eval import pointwise_sat
res = encode_and_run(g)
phi = g.spec_as_stl(discretize=False)
dt = g.model.dt
assert pointwise_sat(phi, dt=dt)(res.solution)
not_rock = stl.parse('~(X(xRock))').inline_context(context)
not_paper = stl.parse('~(X(xPaper))').inline_context(context)
not_scissors = stl.parse('~(X(xScissors))').inline_context(context)
g = bind(g).specs.learned.set(not_rock)
res = encode_and_run(g)
phi = g.spec_as_stl(discretize=False)
dt = g.model.dt
assert pointwise_sat(phi, dt=dt)(res.solution)
g = bind(g).specs.learned.set(not_rock & not_paper)
res = encode_and_run(g)
phi = g.spec_as_stl(discretize=False)
dt = g.model.dt
assert pointwise_sat(phi, dt=dt)(res.solution)
g = bind(g).specs.learned.set(not_rock & not_paper & not_scissors)
res = encode_and_run(g)
phi = g.spec_as_stl(discretize=False)
assert res.solution is None
def test_encode_refuted_rec_sync():
from magnum.examples.feasible_example2 import feasible_example as g
from magnum.solvers import smt
from magnum.solvers import milp
from stl.boolean_eval import pointwise_sat
dt = g.model.dt
refuted = {'u': traces.TimeSeries([(0, 0.5)])}
phi = encode_refuted_rec(refuted, 0.1, g.times, dt=dt)
g = bind(g).specs.learned.set(phi)
res = smt.encode_and_run(g)
assert pointwise_sat(phi, dt=dt)(res.solution)
res = milp.encode_and_run(g)
assert pointwise_sat(phi, dt=dt)(res.solution)
refuted = {'u': traces.TimeSeries([(0, 1), (0.4, 1), (1, 0)])}
phi = encode_refuted_rec(refuted, 0.1, g.times, dt=dt)
g = bind(g).specs.learned.set(phi)
res = smt.encode_and_run(g)
assert pointwise_sat(phi, dt=dt)(res.solution)
res = milp.encode_and_run(g)
assert pointwise_sat(phi, dt=dt)(res.solution)
def dict2pdfa(mapping, start: PA.State):
"""Convert nested dictionary into a PDFA.
- mapping is a nested dictionary of the form:
mapping = {
<State>: (<Label>, {
<Action>: {
<State>: <Probability>
}
}
}
"""
label_map = fn.walk_values(ig(0), mapping)
transition_map = fn.walk_values(ig(1), mapping)
outputs = set(bind(mapping).Values()[0].collect())
inputs = set(bind(mapping).Values()[1].Keys().collect())
return PA.pdfa(
start=start,
label=label_map.get,
inputs=inputs,
outputs=outputs,
**_encode_two_player_game(transition_map)
)
def create_scenario(g, i):
def relabel(x):
return x if i == 0 or x in g.model.vars.input else f"{x}#{i}"
relabel_phi = stl.ast.lineq_lens.terms.Each().id.modify(relabel)
g = bind(g).specs.Each().modify(relabel_phi)
g = bind(g).model.vars.Each().Each().modify(relabel)
return g
def create_scenario(g, i):
def relabel(x):
return x if i == 0 or x in g.model.vars.input else f"{x}#{i}"
relabel_phi = stl.ast.lineq_lens.terms.Each().id.modify(relabel)
g = bind(g).specs.Each().modify(relabel_phi)
g = bind(g).model.vars.Each().Each().modify(relabel)
return g
def test_type_custom_class_immutable():
class C(object):
def __init__(self, a):
self._a = a
@property
def a(self):
return self._a
with pytest.raises(AttributeError):
bind(C(9)).a.set(7)
def __and__(self, g):
if self.model != g.model:
# TODO:
# 1. Check that the dynamics are compatible.
# 2. If so, merge states etc.
raise NotImplementedError
g2 = bind(self).specs.obj.modify(lambda x: x & g.specs.obj)
g2 = bind(self).specs.init.modify(lambda x: x & g.specs.init)
g2 = bind(self).specs.learned.modify(lambda x: x & g.specs.learned)
return g2
def invert(self):
# Swap Spec
g = bind(self).specs.obj.set(~self.specs.obj)
# Swap Inputs
g = bind(g).model.vars.env.set(self.model.vars.input)
g = bind(g).model.vars.input.set(self.model.vars.env)
# Swap Dynamics
g = bind(g).model.dyn.B.set(self.model.dyn.C)
g = bind(g).model.dyn.C.set(self.model.dyn.B)
return g
def _distribute_next(phi, i=0):
if isinstance(phi, (LinEq, AtomicPred)):
return stl.utils.next(phi, i=i)
elif isinstance(phi, Next):
return _distribute_next(phi.arg, i=i + 1)
children = tuple(_distribute_next(c, i) for c in phi.children)
if isinstance(phi, (And, Or)):
return bind(phi).args.set(children)
elif isinstance(phi, (Neg, Next)):
return bind(phi).arg.set(children[0])
def fromChild0(self,childId,newChildType): # only called from loop
nCT,curCh,nW = gotNewType(self.members[childId].curType,self.kidsType(childId))
if not curCh: return False
ctCh,self.curType = T.tupleFixUp(L.bind(self.curType).tMindx[childId].set(nCT))
assert not nW
self.up.receive(self.myChildId,self.curType) # check shadowing!! FIXME
return True
def pTrT(self, pt, rt):
assert pt.tMfamily == T.mfTuple and len(pt.tMindx) == 2
if pt.tMindx[0] == T.mvtEmpty: return T.mvtAny, T.mvtEmpty # fail
self.rt = H.intersection2(pt.tMindx[1], rt)
self.pt = L.bind(pt).tMindx[1].set(self.rt)
ch, self.pt = T.tupleFixUp(self.pt)
return self.pt, self.rt
def fromClosR0(self,iden,newType): # newType is closR's current val (or better)
newCur,curCh,nW = gotNewType(newType,self.extIds[iden].mtval)
if not curCh: return False # current value not changed
self.extIds[iden] = L.bind(self.extIds[iden]).mtval.set(newCur)
self.gotAllEIs = all ((eiv.mtval.tMsubset != None) for eiv in self.extIds.values())
assert not nW
return True
def updateAnId(self,name,newType):
if newType==T.mvtAny: return
newCur, curCh, newWrong = gotNewType(newType,self.myIds[name].mtval)
if curCh: # different from curType
self.myIds[name] = L.bind(self.myIds[name]).mtval.set(newCur)
for r in self.myIds[name].registry: # tell uses and closures about change
r.receive(name,newCur)
def visit_terms(self, _, children):
if isinstance(children[0], list):
term, _1, sgn, _2, terms = children[0]
terms = bind(terms)[0].coeff * sgn
return [term] + terms
else:
return children
def test_type_custom_class_frozen_dataclass():
@dataclasses.dataclass(frozen=True)
class C:
a: int
b: str
assert bind(C(1, "hello")).a.set(2) == C(2, "hello")
def _discretize(phi, dt):
if isinstance(phi, (LinEq, AtomicPred, _Top, _Bot)):
return phi
children = tuple(_discretize(arg, dt) for arg in phi.children)
if isinstance(phi, (And, Or)):
return bind(phi).args.set(children)
elif isinstance(phi, (Neg, Next)):
return bind(phi).arg.set(children[0])
# Only remaining cases are G and F
psi = children[0]
l, u = round(phi.interval.lower / dt), round(phi.interval.upper / dt)
psis = (next(psi, i) for i in range(l, u + 1))
opf = andf if isinstance(phi, G) else orf
return opf(*psis)
def test_lens_descriptor_zoom():
class MyClass(object):
def __init__(self, items):
self._private_items = items
def __eq__(self, other):
return self._private_items == other._private_items
def __repr__(self):
return "M({!r})".format(self._private_items)
first = lens._private_items[0]
data = (MyClass([1, 2, 3]), )
assert bind(data)[0].first.get() == 1
assert bind(data)[0].first.set(4) == (MyClass([4, 2, 3]), )
def ast_lens(phi,
bind=True,
*,
pred=lambda _: False,
focus_lens=lambda _: [lens],
getter=False):
child_lenses = _ast_lens(phi, pred=pred, focus_lens=focus_lens)
phi = lenses.bind(phi) if bind else lens
return (phi.Tuple if getter else phi.Fork)(*child_lenses)
def __get__(self, instance: Context | None, owner: Type[Context] | None) -> Node:
path: PathType
if instance is None:
path = tuple()
else:
path = instance._path
atoms = set(lenses.bind(self.condition.walk()).Each().Instance(_Atom).collect())
v = {a.id: _Atom(path + a.path, a.domain) for a in atoms}
return self.condition[v]
def test_type_custom_class_copy_and_mutate():
class C(object):
def __init__(self, a, b):
self.a = a
self.b = b
def __eq__(self, other):
return self.a == other.a and self.b == other.b
assert bind(C(C(0, 1), C(2, 3))).a.b.set(4) == C(C(0, 4), C(2, 3))
def pTrT(self, pt, rt):
assert pt.tMfamily == T.mfTuple and len(pt.tMindx) == 2
if pt.tMindx[1].tMsubset == rt.tMsubset == None: return pt, rt
assert pt.tMindx[1].tMfamily == T.mfType and len(
pt.tMindx[1].tMsubset) == 1
reqT = pt.tMindx[1].tMsubset[0]
irt = H.intersectionList([pt.tMindx[0], reqT,
rt]) # irt must have the value ?!?
return T.tupleFixUp(
L.bind(pt).tMindx[0].set(irt))[1], T.fixIfTuple(irt)
def path(self):
c = C(1, List(C(2), C(3, List(C(4, List(C(5))), C(6))), C(7)))
t = path_lens_pred(c, _.c, _.a, _ == 5).x
mod = lambda a: (a + 10
if isinstance(a, int) else bind(a).a.modify(_ + 20))
m = t.modify(lambda a: map(mod, a))
target = C(21, List(C(2), C(23, List(C(24, List(C(15))), C(6))), C(7)))
m.should.equal(target)
m2 = t.get()
t.set(map(mod, m2)).should.equal(target)
def pTrT(self, pt, rt):
assert pt.tMfamily == T.mfTuple and len(pt.tMindx) == 2
if pt.tMindx[1].tMsubset == None: return pt, rt
assert pt.tMindx[1].tMfamily == T.mfType and len(
pt.tMindx[1].tMsubset) == 1
reqT = pt.tMindx[1].tMsubset[0]
updown = H.isA(pt.tMindx[0], reqT)
if updown == None:
assert pt.tMindx[0].tMsubset == None
r = L.bind(pt).tMindx[0].set(reqT)
else:
if pt.tMindx[0].tMsubset == None:
r = L.bind(pt).tMindx[0].set(reqT)
else:
up, down = updown
val = up(pt.tMindx[0].tMsubset[0])
r = L.bind(pt).tMindx[0].set(T.typeWithVal(reqT, val))
r = T.tupleFixUp(r)[1]
return r, r.tMindx[0]
def tupleFixUp(t): # t:MtVal
if t==mvtEmpty: return False,t
assert t.tMfamily.famObj == P.mTuple
subs = tuple(fixIfTuple(t.tMindx[i]) for i in range(len(t.tMindx)))
if t.tMsubset!=None:
tupv = t.tMsubset[0] # only single elt subsets allowed
if all(subs[i].tMsubset!=None and subs[i].tMsubset[0]==tupv[i]
for i in range(len(subs))):
return False,t
assert all(subs[i].tMsubset==None or subs[i].tMsubset[0]==tupv[i]\
for i in range(len(subs)))
return True,L.bind(t).tMindx.set(tuple(
typeWithVal(subs[i],tupv[i]) for i in range(len(subs))))
# Only handle case where tMsubset has 1 element FIXME
# t.tMindx is an MVal for List(Type).
for tt in subs:
if tt.tMsubset==None: return False,t
#if all (tt.tMsubset!=None for tt in subs):
newtMsubset = (tuple(tt.tMsubset[0] for tt in subs),)
return True,L.bind(t).tMsubset.set(newtMsubset) # MtVal(t.tMfamily,t.tMindx,newtMsubset)
def update_board(s: State, b=EMPTY_BOARD):
with NamedTemporaryFile("w") as f:
f.write(to_sense_stim(s))
f.flush()
out = subprocess.check_output(f"aigsim -m sense.aig {f.name}",
shell=True)
colorize = unpack_sense_stim_result(out)
cursor = colorize(POS)
board = bind(b)[flat_index(s.x, s.y)].set(cursor)
return board
def directed_hausdorff(recs1, recs2, *, metric=dist_rec_bounds):
responses = _compute_responses(recs1, recs2)
values = bind(responses).Values()
d = Interval(max(values[0].collect()), max(values[1].collect()))
# TODO: can this be tightened?
potential_moves = {r for r in recs1 if responses[r] & d}
def is_required(r2):
return any(responses[r1] & metric(r1, r2) for r1 in potential_moves)
required_responses = {r2 for r2 in recs2 if is_required(r2)}
return d, (potential_moves, required_responses)
def _discretize(phi, dt, horizon):
if isinstance(phi, (LinEq, AtomicPred, _Top, _Bot)):
return phi
if not isinstance(phi, (F, G, Until)):
children = tuple(_discretize(arg, dt, horizon) for arg in phi.children)
if isinstance(phi, (And, Or)):
return bind(phi).args.set(children)
elif isinstance(phi, (Neg, Next)):
return bind(phi).arg.set(children[0])
raise NotImplementedError
elif isinstance(phi, Until):
raise NotImplementedError
# Only remaining cases are G and F
upper = min(phi.interval.upper, horizon)
l, u = round(phi.interval.lower / dt), round(upper / dt)
psis = (next(_discretize(phi.arg, dt, horizon - i), i)
for i in range(l, u + 1))
opf = andf if isinstance(phi, G) else orf
return opf(*psis)
def pTrT(self, pt, rt):
assert pt.tMfamily == T.mfTuple and len(pt.tMindx) == 2
cases = pt.tMindx[1].tMsubset[0]
p4c = pt.tMindx[0]
if p4c.tMsubset == None and rt.tMsubset == None: return pt, rt
pts = [None] * len(cases)
rts = [None] * len(cases)
for i in range(len(cases)):
cr = I.ClosureRun(
None, cases[i]) # I don't think callEt param is used????
didSomething, pts[i], rts[i] = cr.changePR(p4c, rt)
# Now we have possible params and results, we put together
# The parameters we are consistent with is the Union of pts, results is Union of rts
return L.bind(pt).tMindx[0].set(H.intersectionList(pts)), H.unionList(
rts) # tupleFixUp?FIXME
def test_type_custom_class_lens_setattr():
class C(object):
def __init__(self, a):
self._a = a
@property
def a(self):
return self._a
def __eq__(self, other):
return self.a == other.a
def _lens_setattr(self, key, value):
if key == "a":
return C(value)
assert bind(C(C(9))).a.a.set(4) == C(C(4))
def mpc(orig: Game, *, use_smt=False,):
prev_games = [(0, orig.scope, orig)]
while True:
max_age = max(map(lens[0].get(), prev_games))
age, _, g = prev_games[0]
g = g >> max_age - age
for age, _, g2 in prev_games[1:]:
g &= g2 >> max_age - age
res = solve(g, use_smt=use_smt)
msg = yield bind(res).solution.modify(time_shift(max_age))
new_init = measurement_to_stl(msg.measurement)
next_game = orig.reinit(new_init)
next_game = next_game.new_obj(msg.obj)
# Stale old games + Remove out of scope games + add new game.
prev_games = [(t+1, s, g) for t, s, g in prev_games if t < s]
prev_games.append((0, next_game.scope, next_game))
def _encode_two_player_game(mapping):
"""Convert transition probabilities from state to two player game
transition and distribution.
mapping: State => (Action => (State => Prob))
where (=>) denotes a Dictionary map.
"""
e_size = 0 # Environment Alphabet Size.
def reindex(state2prob):
"""Enumerates states and transforms the mapping:
State => Prob
to a mapping:
Index => (State, Prob)
"""
nonlocal e_size
e_size = max(e_size, len(state2prob))
return {i: (s, p) for i, (s, p) in enumerate(state2prob.items())}
# State => (Action => (EnvironmentAction => (State, Prob)))
mapping = bind(mapping).Values().Values().modify(reindex)
def transition(state, composite_action):
sys, env = composite_action
dist = mapping[state][sys]
if env not in dist: # Probability 0 event.
return state
return dist[env][0]
def env_dist(state, sys):
return {e: p for e, (_, p) in mapping[state][sys].items()}
return {
"env_inputs": range(e_size),
"transition": transition,
"env_dist": env_dist,
}
def new_horizon(self, H):
g = bind(self).specs.obj.modify(lambda x: stl.alw(x, lo=0, hi=H))
return g
def __rshift__(self, t):
return bind(self).specs.Each().call('__rshift__', t=t)
def learn(self, phi):
return bind(self).specs.learned.modify(lambda x: x & phi)
def test_ZoomTraversal_set():
l = b.GetitemLens(0) & b.ZoomTraversal()
data = [bind([1, 2, 3])[1]]
assert l.set(data, 7) == [[1, 7, 3]]
def reinit(self, phi):
return bind(self).specs.init.set(phi)
def new_obj(self, phi):
return bind(self).specs.obj.set(phi)
