def __init__(
self,
steps='1',
alpha=0.001,
clVerbosity=0,
implementation=None,
):
# Convert the steps designation to a list
self.steps = steps
self.stepsList = eval("[%s]" % (steps))
self.alpha = alpha
self.verbosity = clVerbosity
# Initialize internal structures
self._claClassifier = CLAClassifierFactory.create(
steps=self.stepsList,
alpha=self.alpha,
verbosity=self.verbosity,
implementation=implementation,
)
self.learningMode = True
self.inferenceMode = False
self._initEphemerals()
def __init__(self,
steps='1',
alpha=0.001,
clVerbosity=0,
implementation=None,
maxCategoryCount=None):
# Convert the steps designation to a list
self.steps = steps
self.stepsList = eval("[%s]" % (steps))
self.alpha = alpha
self.verbosity = clVerbosity
# Initialize internal structures
self._claClassifier = CLAClassifierFactory.create(
steps=self.stepsList,
alpha=self.alpha,
verbosity=self.verbosity,
implementation=implementation,
)
self.learningMode = True
self.inferenceMode = False
self.maxCategoryCount = maxCategoryCount
self.recordNum = 0
self._initEphemerals()
# Flag to know if the compute() function is ever called. This is to
# prevent backward compatibilities issues with the customCompute() method
# being called at the same time as the compute() method. Only compute()
# should be called via network.run(). This flag will be removed once we
# get to cleaning up the clamodel.py file.
self._computeFlag = False
def __init__(self,
steps='1',
alpha=0.001,
clVerbosity=0,
implementation=None,
maxCategoryCount=None
):
# Convert the steps designation to a list
self.steps = steps
self.stepsList = eval("[%s]" % (steps))
self.alpha = alpha
self.verbosity = clVerbosity
# Initialize internal structures
self._claClassifier = CLAClassifierFactory.create(
steps=self.stepsList,
alpha=self.alpha,
verbosity=self.verbosity,
implementation=implementation,
)
self.learningMode = True
self.inferenceMode = False
self.maxCategoryCount = maxCategoryCount
self.recordNum = 0
self._initEphemerals()
# Flag to know if the compute() function is ever called. This is to
# prevent backward compatibilities issues with the customCompute() method
# being called at the same time as the compute() method. Only compute()
# should be called via network.run(). This flag will be removed once we
# get to cleaning up the clamodel.py file.
self._computeFlag = False
def readFromProto(cls, proto):
"""Read state from proto object.
proto: CLAClassifierRegionProto capnproto object
"""
instance = cls()
instance.classifierImp = proto.classifierImp
instance.steps = proto.steps
instance.alpha = proto.alpha
instance.verbosity = proto.verbosity
instance.maxCategoryCount = proto.maxCategoryCount
instance._claClassifier = CLAClassifierFactory.read(proto)
return instance
def read(cls, proto):
"""Read state from proto object.
proto: PyRegionProto capnproto object
"""
regionImpl = proto.regionImpl.as_struct(CLAClassifierRegionProto)
instance = cls()
instance.classifierImp = regionImpl.classifierImp
instance.steps = regionImpl.steps
instance.alpha = regionImpl.alpha
instance.verbosity = regionImpl.verbosity
instance.maxCategoryCount = regionImpl.maxCategoryCount
instance._claClassifier = CLAClassifierFactory.read(regionImpl)
return instance
def read(cls, proto):
"""Read state from proto object.
proto: PyRegionProto capnproto object
"""
regionImpl = proto.regionImpl.as_struct(CLAClassifierRegionProto)
instance = cls()
instance.classifierImp = regionImpl.classifierImp
instance.steps = regionImpl.steps
instance.alpha = regionImpl.alpha
instance.verbosity = regionImpl.verbosity
instance.maxCategoryCount = regionImpl.maxCategoryCount
instance._claClassifier = CLAClassifierFactory.read(regionImpl)
return instance
def __init__(self,
steps='1',
alpha=0.001,
clVerbosity=0,
implementation=None,
):
# Convert the steps designation to a list
self.steps = steps
self.stepsList = eval("[%s]" % (steps))
self.alpha = alpha
self.verbosity = clVerbosity
# Initialize internal structures
self._claClassifier = CLAClassifierFactory.create(
steps=self.stepsList,
alpha=self.alpha,
verbosity=self.verbosity,
implementation=implementation,
)
self.learningMode = True
self.inferenceMode = False
self._initEphemerals()
