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
