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()]
