def train_layer0(sg, generalizers, save_predictions = True):
layer0_partition_guess = numpy.array([generalizer.guess_partial(sg) for generalizer in generalizers])
for generalizer_index, generalizer in enumerate(generalizers):
if save_predictions:
Generalizer.save_partial(generalizer.name(),
layer0_partition_guess[generalizer_index])
print("log loss for {} : {}".format(
generalizer.name(),
log_loss(sg.train_target, layer0_partition_guess[generalizer_index, :, :])
))
layer0_whole_guess = numpy.array([generalizer.guess_whole(sg) for generalizer in generalizers])
for generalizer_index, generalizer in enumerate(generalizers):
if save_predictions:
Generalizer.save_whole(generalizer.name(),
layer0_whole_guess[generalizer_index])
return(layer0_partition_guess, layer0_whole_guess)
def train_layer0(sg, generalizers, save_predictions=True):
layer0_partition_guess = numpy.array(
[generalizer.guess_partial(sg) for generalizer in generalizers])
for generalizer_index, generalizer in enumerate(generalizers):
if save_predictions:
Generalizer.save_partial(generalizer.name(),
layer0_partition_guess[generalizer_index])
print("log loss for {} : {}".format(
generalizer.name(),
log_loss(sg.train_target,
layer0_partition_guess[generalizer_index, :, :])))
layer0_whole_guess = numpy.array(
[generalizer.guess_whole(sg) for generalizer in generalizers])
for generalizer_index, generalizer in enumerate(generalizers):
if save_predictions:
Generalizer.save_whole(generalizer.name(),
layer0_whole_guess[generalizer_index])
return (layer0_partition_guess, layer0_whole_guess)
def test_wp_generalize(tr):
x = Int('x')
y = Int('y')
z = Int('z')
c_1 = ConditionStmt(x + y >= 8)
c_1_1 = ConditionStmt(x - y >= 8)
a_1 = AssignmentStmt(z == x + y)
a_1_1 = AssignmentStmt(z == x - y)
c_2 = ConditionStmt(x <= y)
c_3 = ConditionStmt(z >= 9)
a_2 = AssignmentStmt(z == z - 1)
c_4 = ConditionStmt(z <= 8)
multitrace = BackwardTrace([[c_1, c_1_1], [a_1, a_1_1], [c_2], [c_3],
[a_2], [c_4]])
wp_gen_no_simpl = Generalizer(PreciseDomain(False))
wp_gen_with_simpl = Generalizer(PreciseDomain(True))
r = wp_gen_no_simpl.generalize_input(tr, print_annotation=True)
print("Final result generalize input from top no simplification: ", r,
"\n")
simplify(r)
print("Final result generalize input from top simplify once at end: ", r,
"\n")
r = wp_gen_with_simpl.generalize_input(tr, print_annotation=True)
print("Final result generalize input from top simplify after each step: ",
r, "\n")
r = wp_gen_no_simpl.generalize_trace(multitrace, print_annotation=True)
print("Final result generalize trace from top no simplification: ", r,
"\n")
r = wp_gen_with_simpl.generalize_trace(multitrace, print_annotation=True)
print("Final result generalize trace from top simplify after each step: ",
r, "\n")
r = wp_gen_no_simpl.generalize_trace(
multitrace,
initial_formula=PreciseDomain().get_bottom(),
print_annotation=True)
print("Final result generalize trace from bottom no simplification: ", r,
"\n")
def load_layer0(filenames):
layer0_partial_guess = numpy.array([Generalizer.load_partial(filename) for
filename in filenames])
layer0_whole_guess = numpy.array([Generalizer.load_whole(filename) for
filename in filenames])
return(layer0_partial_guess, layer0_whole_guess)
def load_layer0(filenames):
layer0_partial_guess = numpy.array(
[Generalizer.load_partial(filename) for filename in filenames])
layer0_whole_guess = numpy.array(
[Generalizer.load_whole(filename) for filename in filenames])
return (layer0_partial_guess, layer0_whole_guess)
I_4 = IntervalSet({"x": i_1, "y": i_2, "z": i_4})
I_5 = IntervalSet.get_top()
I_6 = IntervalSet.get_bottom()
I_7 = IntervalSet({"x": i_10, "y": i_7, "z": i_5})
I_8 = IntervalSet({"x": i_1, "y": i_2, "z": i_8, "w": i_9, "t": i_10})
f_1 = Or(x == 0, y == 1)
f_2 = Not(And(x > 0, x < 3))
f_3 = Or(Not(x < 0), And(Not(y > 7), y < 4))
f_4 = Not(Or(Not(x < 0), And(Not(y > 7), y < 4)))
f_5 = Implies(x > 0, y < 0)
f_6 = If(x > 0, y < 0, y > 0)
f_7 = Implies(And(Not(If(x > 0, y < 0, y > 0)), Or(z <= 7, x <= 8)), y == 2)
interval_domain = IntervalDomain()
interval_generalizer = Generalizer(interval_domain, debug=True)
wp_domain = PreciseDomain()
wp_generalizer = Generalizer(wp_domain, debug=True)
def test_interval():
assert not i_1.is_bottom()
assert not i_2.is_bottom()
assert not i_3.is_bottom()
assert not i_4.is_bottom()
assert not i_5.is_bottom()
assert i_6.is_bottom()
assert i_7.is_bottom()
assert not i_1.is_top()
assert not i_2.is_top()
assert i_3.is_top()
def test_generalization_with_interval():
interval_domain = IntervalDomain(True)
model = get_model_from_SSA_trace(tr_TFT)
g = Generalizer(interval_domain, True)
g.generalize_trace(mt_TFT, model=model, print_annotation=True)