def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.dir_path = r"C:\Users\Michał\PycharmProjects\mgr\sgcs\sgcs\data\example gramatics" self.filename = 'toy opt los 65534' self.sut = SymbolTranslator.create(os.path.join(self.dir_path, self.filename))
def generic_simulation(self, learning_path, testing_path, name): logging.info('starting %s', name) with open(os.path.join(r'C:\Users\Michał\PycharmProjects\mgr\runs\auto', name + '.txt'), 'w+') as file: learning_set = SymbolTranslator.create(learning_path) learning_set.negative_allowed = not self.algorithm_variant.is_stochastic testing_set = SymbolTranslator.create(testing_path) testing_set.negative_allowed = True result, ngen, grammar_estimator, population, *_ = self.sut.perform_simulation( learning_set, testing_set, self.configuration) print(result) print('NGen:', ngen) file.write(str(result)) file.write(str(ngen))
def prepare_simulation(self, runner, task_no, data_path, config_path, population_path=None): with open(config_path) as f: configuration = self.configuration_serializer.from_json(json.load(f)) is_stochastic = configuration.algorithm_variant == AlgorithmVariant.sgcs algorithm_variant = CykServiceVariationManager(is_stochastic) learning_set_path, testing_set_path = self.load_input_config(data_path) learning_set = SymbolTranslator.create(learning_set_path) learning_set.negative_allowed = configuration.statistics.negative_sentence_learning testing_set = SymbolTranslator.create(testing_set_path) testing_set.negative_allowed = True return (lambda conf: self._perform_simulation( algorithm_variant, learning_set, testing_set, conf, runner, task_no, population_path), configuration, self._mk_population_printer(learning_set) )
def _load_sentences(self): self.emit(QtCore.SIGNAL(AsyncProgressDialog.CHANGE_STEP_EVENT), 'Opening file...') self.employer.symbol_translator = SymbolTranslator.create( self.employer.selected_filename) return list(self.employer.symbol_translator.get_sentences())
def get_learned_translator(population_path): symbol_translator = SymbolTranslator.create(population_path) list(symbol_translator.get_sentences()) return symbol_translator