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return self._parser_func(desired_type, file_path, encoding, logger, **opts)
else:
return self._parser_func(desired_type, file_path, encoding, logger, **self.function_args, **opts)"
1228,"def queryByPortSensor(portiaConfig, edgeId, port, sensor, strategy=SummaryStrategies.PER_HOUR, interval=1, params={ 'from': None, 'to': None, 'order': None, 'precision': 'ms', 'fill':'none', 'min': True, 'max': True, 'sum': True, 'avg': True, 'median': False, 'mode': False, 'stddev': False, 'spread': False }):
""""""Returns a pandas data frame with the portia select resultset""""""
header = {'Accept': 'text/csv'}
endpoint = '/summary/device/{0}/port/{1}/sensor/{2}/{3}/{4}{5}'.format( edgeId, port, sensor, resolveStrategy(strategy), interval, utils.buildGetParams(params) )
response = utils.httpGetRequest(portiaConfig, endpoint, header)
if response.status_code == 200:
try:
dimensionSeries = pandas.read_csv( StringIO(response.text), sep=';' )
if portiaConfig['debug']:
print( '[portia-debug]: {0} rows'.format( len(dimensionSeries.index) ) )
return dimensionSeries
except:
raise Exception('couldn\'t create pandas data frame')
else:
raise Exception('couldn\'t retrieve data')"
1229,"def _process_counter_example(self, mma, w_string):
""""""""
Process a counterexample in the Rivest-Schapire way.
Args:
mma (DFA): The hypothesis automaton
w_string (str): The examined string to be consumed
Returns:
None
""""""
diff = len(w_string)
same = 0
membership_answer = self._membership_query(w_string)
while True:
i = (same + diff) / 2
access_string = self._run_in_hypothesis(mma, w_string, i)
if membership_answer != self._membership_query(access_string + w_string[i:]):
diff = i
else:
same = i
if diff - same == 1:
break
exp = w_string[diff:]
self.observation_table.em_vector.append(exp)
for row in self.observation_table.sm_vector + self.observation_table.smi_vector:
self._fill_table_entry(row, exp)
return 0"
1230,"def get_dfa_conjecture(self):
""""""
Utilize the observation table to construct a Mealy Machine.
The library used for representing the Mealy Machine is the python
bindings of the openFST library (pyFST).
Args:
None
Returns:
MealyMachine: A mealy machine build based on a closed and consistent
observation table.
""""""
dfa = DFA(self.alphabet)
for s in self.observation_table.sm_vector:
for i in self.alphabet:
dst = self.observation_table.equiv_classes[s + i]
# If dst == None then the table is not closed.
if dst == None:
logging.debug('Conjecture attempt on non closed table.')
return None
obsrv = self.observation_table[s, i]
src_id = self.observation_table.sm_vector.index(s)
dst_id = self.observation_table.sm_vector.index(dst)
dfa.add_arc(src_id, dst_id, i, obsrv)
# Mark the final states in the hypothesis automaton.
i = 0
for s in self.observation_table.sm_vector:
dfa[i].final = self.observation_table[s, self.epsilon]
i += 1
return dfa"
1231,"def _init_table(self):
""""""
Initialize the observation table.
""""""
self.observation_table.sm_vector.append(self.epsilon)
self.observation_table.smi_vector = list(self.alphabet)
self.observation_table.em_vector.append(self.epsilon)
self._fill_table_entry(self.epsilon, self.epsilon)
for s in self.observation_table.smi_vector:
self._fill_table_entry(s, self.epsilon)"
1232,"def learn_dfa(self, mma=None):
""""""
Implements the high level loop of the algorithm for learning a
Mealy machine.
Args:
mma (DFA): The input automaton
Returns:
MealyMachine: A string and a model for the Mealy machine to be learned.
""""""
logging.info('Initializing learning procedure.')