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logging.info('Initialized from DFA em_vector table is the following:')
logging.info(em_vector)
self._fill_table_entry(self.epsilon, self.epsilon)
# list(set([])) is used to remove duplicates, [1:0] to remove epsilon
for row in sorted(list(set(sm_vector + smi_vector)), key=len)[1:]:
for column in em_vector:
self._fill_table_entry(str(row), str(column))"
543,"def learn_sfa(self, mma=None):
""""""
Implements the high level loop of the algorithm for learning a
Mealy machine.
Args:
mma:
Returns:
MealyMachine: A model for the Mealy machine to be learned.
""""""
logging.info('Initializing learning procedure.')
if mma:
self._init_table_from_dfa(mma)
else:
self._init_table()
logging.info('Generating a closed and consistent observation table.')
while True:
closed = False
# Make sure that the table is closed
while not closed:
logging.debug('Checking if table is closed.')
closed, s = self.observation_table.is_closed()
if not closed:
logging.debug('Closing table.')
self._ot_make_closed(s)
else:
logging.debug('Table closed.')
# Create conjecture
sfa = self.get_sfa_conjecture()
logging.info('Generated conjecture machine with %d states.',
len(list(sfa.states)))
# _check correctness
logging.debug('Running equivalence query.')
found, counter_example = self._equivalence_query(sfa)
# Are we done?
if found:
logging.info('No counterexample found. Hypothesis is correct!')
break
# Add the new experiments into the table to reiterate the
# learning loop
logging.info(
'Processing counterexample %s with length %d.',
counter_example,
len(counter_example))
self._process_counter_example(sfa, counter_example)
logging.info('Learning complete.')
return '', sfa"
544,"def make_log_metric(level=logging.INFO, msg=""%d items in %.2f seconds""):
""""""Make a new metric function that logs at the given level
:arg int level: logging level, defaults to ``logging.INFO``
:arg string msg: logging message format string, taking ``count`` and ``elapsed``
:rtype: function
""""""
def log_metric(name, count, elapsed):
log_name = 'instrument.{}'.format(name) if name else 'instrument'
logging.getLogger(log_name).log(level, msg, count, elapsed)
return log_metric"
545,"def _auto_init(self, *args, **kwrds):
""""""Our decorator will add this as __init__ to target classes.""""""
for fld in getattr(self, '__fields__', []):
val = kwrds.get(fld.name, _NO_VAL)
if val is _NO_VAL:
val = fld.get_default_val()
setattr(self, fld.name, val)
if callable(getattr(self, 'setup', None)):
self.setup(*args, **kwrds)"
546,"def ctor_overridable(cls):
""""""Return true if cls has on overridable __init__.""""""
prev_init = getattr(cls, ""__init__"", None)
if not callable(prev_init):
return True
if prev_init in [object.__init__, _auto_init]:
return True
if getattr(prev_init, '_clobber_ok', False):
return True
print(cls, prev_init, getattr(prev_init, '_clobber_ok', 'missing'))
return False"
547,"def DBObject(table_name, versioning=VersioningTypes.NONE):
""""""Classes annotated with DBObject gain persistence methods.""""""
def wrapped(cls):