def learn_inc(_data, _labels, _i, _k, _h):
# strategy = OneClassStrategy(RandomViolationsStrategy(10), thresholds)
strategy = RandomViolationsStrategy(10)
learner = KCnfSmtLearner(_k, _h, strategy, "mvn")
initial_indices = LearnOptions.initial_random(20)(list(range(len(_data))))
learner.add_observer(PlottingObserver(domain, directory, "run_{}_{}_{}".format(_i, _k, _h),
domain.real_vars[0], domain.real_vars[1], None, False))
return learner.learn(domain, _data, _labels, initial_indices)
def learn_inc(_data, _labels, _i, _k, _h):
strategy = OneClassStrategy(RandomViolationsStrategy(10), thresholds,
background_knowledge=bg_knowledge)
if negative_bootstrap > 0:
_data, _labels = OneClassStrategy.add_negatives(domain, _data, _labels, thresholds, negative_bootstrap)
learner = KCnfSmtLearner(_k, _h, strategy, symmetry_breaking)
random.seed(seed)
initial_indices = LearnOptions.initial_random(20)(list(range(len(_data))))
res = learner.learn(domain, _data, _labels, initial_indices)
return res
def learn_inc(_data, _labels, _i, _k, _h):
strategy = OneClassStrategy(
RandomViolationsStrategy(10),
thresholds) #, background_knowledge=(a | b) & (~a | ~b))
learner = KCnfSmtLearner(_k, _h, strategy, "mvn")
initial_indices = LearnOptions.initial_random(20)(list(
range(len(_data))))
# learner.add_observer(LoggingObserver(None, _k, _h, None, True))
learner.add_observer(
PlottingObserver(domain, "test_output/bg",
"run_{}_{}_{}".format(_i, _k,
_h), domain.real_vars[0],
domain.real_vars[1], None, False))
return learner.learn(domain, _data, _labels, initial_indices)