def learn_inc(_data, _labels, _i, _k, _h): strategy = OneClassStrategy(RandomViolationsStrategy(10), thresholds) 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/checker", "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=background_knowledge) 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)