def test_get_random_hmm(self):
configurations['MIN_NUM_OF_INNER_STATES'] = 1
configurations['MAX_NUM_OF_INNER_STATES'] = 5
configurations['RANDOM_HMM_MAX_EMISSIONS_PER_STATE'] = 5
self.initialise_segment_table("plural_english_segment_table.txt")
hmm = HMM.get_random_hmm(data=['dag', 'dagzook', 'kook', 'kooz'])
log_hmm(hmm)
self.write_to_dot_to_file(hmm, "random_hmm")
def test_remove_random(self):
self.initialise_segment_table("plural_english_segment_table.txt")
for _ in range(100000):
hmm = HMM.get_random_hmm()
try:
hmm.make_mutation()
# hmm.remove_random_state()
hmm.get_transducer()
except Exception:
from traceback import print_exc
log_hmm(hmm)
print_exc()
break
def test_random_hmms_crossover(self):
self.initialise_segment_table("plural_english_segment_table.txt")
for _ in range(1):
hmm_1 = HMM.get_random_hmm(data=['dag', 'zook', 'kook', 'kooz'])
hmm_2 = HMM.get_random_hmm(data=['dag', 'zook', 'kook', 'kooz'])
print('HMM 1')
log_hmm(hmm_1)
print('HMM 2')
log_hmm(hmm_2)
offspring_1, offspring_2 = HMM.crossover(hmm_1, hmm_2)
print('Offspring 1')
print('HMM', offspring_1.inner_states, 'TRANSITIONS', offspring_1.transitions, 'EMISSIONS',
offspring_1.emissions)
print('Offspring 2')
print('HMM', offspring_2.inner_states, 'TRANSITIONS', offspring_2.transitions, 'EMISSIONS',
offspring_2.emissions)
self.write_to_dot_to_file(hmm_1, 'random_parent_1')
self.write_to_dot_to_file(hmm_2, 'random_parent_2')
self.write_to_dot_to_file(offspring_1, 'random_offspring_1')
self.write_to_dot_to_file(offspring_2, 'random_offspring_2')
offspring_1.get_transducer()
offspring_2.get_transducer()
def get_random_hypothesis_randomized(cls, simulation, data, initial_hmm=None, initial_rules=None):
if initial_rules:
rule_set = RuleSet.load_from_flat_list(initial_rules)
elif not configurations['EVOLVE_RULES']:
rule_set = RuleSet.load_from_flat_list(deepcopy(simulation.target_tuple[1]))
else:
rule_set = RuleSet.get_random_rule_set()
if initial_hmm:
hmm = HMM(deepcopy(initial_hmm))
elif not configurations['EVOLVE_HMM']:
hmm = HMM(deepcopy(simulation.target_tuple[0]))
else:
hmm = HMM.get_random_hmm(data)
grammar = Grammar(hmm, rule_set)
return Hypothesis(grammar)
