def step1_evaluation(bn: SelectiveBooleanNetwork, na: int, ataus: dict,
nrho: float):
i = 4 * max(map(len, bn.atm.attractors)) * len(bn)**2
return test_contraints(bn, [
lambda o: constraints.test_attractors_number(o, na),
lambda o: constraints.test_attractors_transitions(o, ataus),
lambda o: constraints.test_bn_state_space_homogeneity(o, i, nrho)
])
def test_bnselector_atm_similarity(self):
N, K, P, Q, I, O = 5, 2, 0.5, 0.0, 1, 0
bnsg = template_selector_generator(N, K, P, Q, I, O)
atms = []
for _ in range(1000):
s = bnsg.new_selector()
if constraints.test_attractors_number(s, 2):
atms.append(s.atm.tableau)
# print(s.atm.tableau)
flatatm = [flat(list(a), to=tuple) for a in atms]
# print(len(set(flatatm)))
self.assertTrue(
all([
sum(a == b for b in atms) <= 1 + int(len(atms) / 2)
for a in atms
]))
############################################################
atms2 = []
for _ in range(1000):
s = bnsg.new_selector()
if constraints.test_attractors_number(s, 2):
atms2.append(s.atm.tableau)
# print(s.atm.tableau)
flatatm2 = [flat(list(a), to=tuple) for a in atms2]
# print(len(set(flatatm2)))
self.assertFalse(flatatm == flatatm2)
if isinstance(GLOBALS.slct_target_transition_tau, list):
tTau_map = {
'a0': {
'a1': max(GLOBALS.slct_target_transition_tau)
},
'a1': {
'a0': max(GLOBALS.slct_target_transition_tau)
}
}
else:
tTau_map = GLOBALS.slct_target_transition_tau
print(bn.atm.dtableau)
c1 = constraints.test_attractors_number(
bn, GLOBALS.slct_target_n_attractors)
c2 = constraints.test_attractors_transitions(bn, tTau_map)
c3 = constraints.test_bn_state_space_homogeneity(
bn, i, GLOBALS.slct_noise_rho)
print(time.perf_counter() - t)
t = time.perf_counter()
c4 = constraints.test_attraction_basins(bn,
GLOBALS.slct_input_steps_phi,
executor=mapper)
print(time.perf_counter() - t)
print(c1, c2, c3, bool(c4), end='\n\n')
pool.close()
def test_selector_constraint_1(self):
bn = SelectiveBooleanNetwork.from_json(
read_json('./test/bn_for_test.json'))
self.assertTrue(constraints.test_attractors_number(bn, 2))
self.assertFalse(constraints.test_attractors_number(bn, 4))
c2s = []
c2no1s = []
c3s = []
c4s = []
generator = template_selector_generator(N, K, P, Q, I, O)
for i in range(instances):
bn = generate_rbn(generator.new_selector, force_consistency=True)
tTau_map = build_tmap(tTau, len(bn.atm.attractors))
it = max(map(len, bn.atm.attractors)) * len(bn) * 20
c1 = constraints.test_attractors_number(bn, nA)
c2 = constraints.test_attractors_transitions(
bn, tTau_map) # if len(bn.atm.attractors) > 1 else False
c2no1 = constraints.test_attractors_transitions(
bn, tTau_map) if len(bn.atm.attractors) > 1 else False
c3 = constraints.test_bn_state_space_homogeneity(bn, it, nRho)
c4 = bool(constraints.test_attraction_basins(bn, isPhi))
files.append(
mpath /
f'{i}_bns_n{N}_k{K}_nrho{int(nRho*100)}_ttau{int(tTau*100)}_iphi{isPhi}_{FROZEN_DATE}.json'
)
write_json(bn.to_json(), files[-1])
c1s.append(c1)