def cluster(self, messages):
if messages is None:
raise TypeError("Messages cannot be None")
if len(messages) == 0:
raise TypeError("There should be at least one message.")
for m in messages:
if not isinstance(m, AbstractMessage):
raise TypeError(
"At least one message ({0}) is not an AbstractMessage.".
format(str(m)))
self._logger.debug(
"Identify similar messages following their alignment (min_equivalence={0})"
.format(self.minEquivalence))
self._logger.debug(
"Initiating the clustering by alignment on {0} messages...".format(
len(messages)))
symbols = self._processUPGMA(messages, self.recomputeMatrixThreshold)
self._logger.debug("Clustering completed, computing final alignment.")
# Retrieve the alignment of each symbol and the build the associated regular expression
for symbol in symbols:
self._logger.debug("Align messages from symbol {0}".format(
symbol.name))
from netzob.Inference.Vocabulary.Format import Format
Format.splitAligned(symbol, useSemantic=False)
return symbols
def cluster(self, messages):
if messages is None:
raise TypeError("Messages cannot be None")
if len(messages) == 0:
raise TypeError("There should be at least one message.")
for m in messages:
if not isinstance(m, AbstractMessage):
raise TypeError(
"At least one message ({0}) is not an AbstractMessage.".
format(str(m)))
self._logger.debug(
"Identify similar messages following their alignment (min_equivalence={0})".
format(self.minEquivalence))
self._logger.debug(
"Initiating the clustering by alignment on {0} messages...".format(
len(messages)))
symbols = self._processUPGMA(messages, self.recomputeMatrixThreshold)
self._logger.debug("Clustering completed, computing final alignment.")
# Retrieve the alignment of each symbol and the build the associated regular expression
for symbol in symbols:
self._logger.debug(
"Align messages from symbol {0}".format(symbol.name))
from netzob.Inference.Vocabulary.Format import Format
Format.splitAligned(symbol, useSemantic=False)
return symbols
