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