def setParameter(self, name, index, value):
"""Set the value of a parameter."""
if name == "SVDSampleCount":
self._SVDSampleCount = value
elif name == "logPath":
self._logPath = value
else:
PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""Set the value of a parameter."""
if name == "SVDSampleCount":
self._SVDSampleCount = value
elif name == "logPath":
self._logPath = value
else:
PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "learningMode":
self.learningMode = bool(int(value))
elif name == "inferenceMode":
self.inferenceMode = bool(int(value))
else:
return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "learningMode":
self.learningMode = bool(int(value))
elif name == "inferenceMode":
self.inferenceMode = bool(int(value))
else:
return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "trainRecords":
# Ensure that the trainRecords can only be set to minimum of the ROWID in
# the saved states
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument(
"Invalid argument type \'%s\'. threshold "
"must be a number." % (type(value)))
if len(self._recordsCache
) > 0 and value < self._recordsCache[0].ROWID:
raise CLAModelInvalidArgument(
"Invalid value. autoDetectWaitRecord "
"value must be valid record within output stream. Current minimum "
" ROWID in output stream is %d." %
(self._recordsCache[0].ROWID))
self.trainRecords = value
# Remove any labels before the first cached record (wont be used anymore)
self._deleteRangeFromKNN(0, self._recordsCache[0].ROWID)
# Reclassify all states
self.classifyStates()
elif name == "anomalyThreshold":
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument(
"Invalid argument type \'%s\'. threshold "
"must be a number." % (type(value)))
self.anomalyThreshold = value
self.classifyStates()
elif name == "classificationMaxDist":
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument(
"Invalid argument type \'%s\'. "
"classificationMaxDist must be a number." % (type(value)))
self._classificationMaxDist = value
self.classifyStates()
elif name == "activeColumnCount":
self._activeColumnCount = value
else:
return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "trainRecords":
# Ensure that the trainRecords can only be set to minimum of the ROWID in
# the saved states
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument("Invalid argument type \'%s\'. threshold "
"must be a number." % (type(value)))
if len(self._recordsCache) > 0 and value < self._recordsCache[0].ROWID:
raise CLAModelInvalidArgument("Invalid value. autoDetectWaitRecord "
"value must be valid record within output stream. Current minimum "
" ROWID in output stream is %d." % (self._recordsCache[0].ROWID))
self.trainRecords = value
# Remove any labels before the first cached record (wont be used anymore)
self._deleteRangeFromKNN(0, self._recordsCache[0].ROWID)
# Reclassify all states
self.classifyStates()
elif name == "anomalyThreshold":
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument("Invalid argument type \'%s\'. threshold "
"must be a number." % (type(value)))
self.anomalyThreshold = value
self.classifyStates()
elif name == "classificationMaxDist":
if not (isinstance(value, float) or isinstance(value, int)):
raise CLAModelInvalidArgument("Invalid argument type \'%s\'. "
"classificationMaxDist must be a number." % (type(value)))
self._classificationMaxDist = value
self.classifyStates()
elif name == "activeColumnCount":
self._activeColumnCount = value
else:
return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "learningMode":
if int(value) and not self.learningMode:
self._restartLearning()
self.learningMode = bool(int(value))
self._epoch = 0
elif name == "inferenceMode":
self._epoch = 0
if int(value) and not self.inferenceMode:
self._finishLearning()
self.inferenceMode = bool(int(value))
elif name == "distanceNorm":
self._knn.distanceNorm = value
elif name == "distanceMethod":
self._knn.distanceMethod = value
elif name == "keepAllDistances":
self.keepAllDistances = bool(value)
if not self.keepAllDistances:
# Discard all distances except the latest
if self._protoScores is not None and self._protoScores.shape[
0] > 1:
self._protoScores = self._protoScores[-1, :]
if self._protoScores is not None:
self._protoScoreCount = 1
else:
self._protoScoreCount = 0
elif name == "clVerbosity":
self.verbosity = value
self._knn.verbosity = value
elif name == "doSelfValidation":
self.doSelfValidation = value
else:
return PyRegion.setParameter(self, name, index, value)
def setParameter(self, name, index, value):
"""
Set the value of the parameter.
@param name -- the name of the parameter to update, as defined
by the Node Spec.
@param value -- the value to which the parameter is to be set.
"""
if name == "learningMode":
if int(value) and not self.learningMode:
self._restartLearning()
self.learningMode = bool(int(value))
self._epoch = 0
elif name == "inferenceMode":
self._epoch = 0
if int(value) and not self.inferenceMode:
self._finishLearning()
self.inferenceMode = bool(int(value))
elif name == "distanceNorm":
self._knn.distanceNorm = value
elif name == "distanceMethod":
self._knn.distanceMethod = value
elif name == "keepAllDistances":
self.keepAllDistances = bool(value)
if not self.keepAllDistances:
# Discard all distances except the latest
if self._protoScores is not None and self._protoScores.shape[0] > 1:
self._protoScores = self._protoScores[-1,:]
if self._protoScores is not None:
self._protoScoreCount = 1
else:
self._protoScoreCount = 0
elif name == "clVerbosity":
self.verbosity = value
self._knn.verbosity = value
elif name == "doSelfValidation":
self.doSelfValidation = value
else:
return PyRegion.setParameter(self, name, index, value)
