def run(): """.""" get_options() prev_handler = np.seterrcall(lp.float_err_handler) prev_err = np.seterr(all='call') np.seterr(under='ignore') random.seed(options.seed) np.random.seed(options.seed) model_creator = eval(options.model + "(options)") model = model_creator.get_model() lp._print_sim_context(model._dataset) print "Number of training sequences: %d" % options.num_trials print "Start days of training sequences:", model._dataset.train_periods_desc gene_test_loop(model) ul.tempfeeder_exp().close()
def run(): """.""" get_options() prev_handler = np.seterrcall(lp.float_err_handler) prev_err = np.seterr(all='call') np.seterr(under='ignore') random.seed(options.seed) np.random.seed(options.seed) model_creator = eval(options.model + "(options)") model = model_creator.get_model() lp._print_sim_context(model._dataset) print "Number of training sequences: %d" % options.num_trials print "Start days of training sequences:", model._dataset.train_periods_desc gene_test_loop(model) ul.tempfeeder_exp().close()
def _prepare_dataset(self): """Return datasets for the experiment periods containing loads and temperatures for the given user, or for all users in the experiment if total_load is True.""" temps = read_temperatures() loads = ul.tempfeeder_exp()[self.user_id] return [self._join_temp_and_load(temps, loads, period) for period in ul.experiment_periods()]
def run(model_creator_class): """Main entry point for specific models. model_creator is an instance of a class used to set up the model and the data.""" get_options() if not is_mpi_slave(options): timer = SimpleTimer() prev_handler = np.seterrcall(float_err_handler) prev_err = np.seterr(all='call') np.seterr(under='ignore') random.seed(options.seed) np.random.seed(options.seed) model_creator = model_creator_class(options) model = model_creator.get_model() if not is_mpi_slave(options): _print_sim_context(model.dataset) _run_models([model], model.dataset) ul.tempfeeder_exp().close()
def _prepare_dataset(self): """Return datasets for the experiment periods containing loads and temperatures for the given dataset.""" loads = ul.tempfeeder_exp()[self.user_id] return [ ul.add_temperatures(loads, period) for period in ul.experiment_periods() ]
def run(model_creator_class): """Main entry point for specific models. model_creator is an instance of a class used to set up the model and the data.""" get_options() if not is_mpi_slave(options): timer = SimpleTimer() prev_handler = np.seterrcall(float_err_handler) prev_err = np.seterr(all='call') np.seterr(under='ignore') random.seed(options.seed) np.random.seed(options.seed) model_creator = model_creator_class(options) model = model_creator.get_model() if not is_mpi_slave(options): _print_sim_context(model.dataset) _run_models([model], model.dataset) ul.tempfeeder_exp().close()
def _prepare_dataset(self): """Return datasets for the experiment periods containing loads and temperatures for the given user, or for all users in the experiment if total_load is True.""" temps = read_temperatures() loads = ul.tempfeeder_exp()[self.user_id] return [ self._join_temp_and_load(temps, loads, period) for period in ul.experiment_periods() ]
def _get_userid(self, options): if options.userid is None: return self._rng.permutation(ul.tempfeeder_exp().user_ids)[0] return options.userid
def _get_userid(self, options): if options.userid is None: return self._rng.permutation(ul.tempfeeder_exp().user_ids)[0] return options.userid
def _get_userid(self): if options.userid is None: # rng.choice introduced in Numpy 1.7.0 return self._rng.permutation(ul.tempfeeder_exp().user_ids)[0] return options.userid
def _get_userid(self): if options.userid is None: # rng.choice introduced in Numpy 1.7.0 return self._rng.permutation(ul.tempfeeder_exp().user_ids)[0] return options.userid
def _prepare_dataset(self): """Return datasets for the experiment periods containing loads and temperatures for the given dataset.""" loads = ul.tempfeeder_exp()[self.user_id] return [ul.add_temperatures(loads, period) for period in ul.experiment_periods()]