本文整理汇总了Python中KNNClassifierRegion.KNNClassifierRegion类的典型用法代码示例。如果您正苦于以下问题:Python KNNClassifierRegion类的具体用法?Python KNNClassifierRegion怎么用?Python KNNClassifierRegion使用的例子?那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了KNNClassifierRegion类的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
def __init__(self,
trainRecords,
anomalyThreshold,
cacheSize,
classificationVectorType=1,
activeColumnCount=40,
classificationMaxDist=0.30,
**classifierArgs):
# Internal Region Values
self._maxLabelOutputs = 16
self._activeColumnCount = activeColumnCount
self._prevPredictedColumns = numpy.array([])
self._anomalyVectorLength = None
self._classificationMaxDist = classificationMaxDist
self._iteration = 0
# Set to create deterministic classifier
classifierArgs['SVDDimCount'] = None
# Parameters
self.trainRecords = trainRecords
self.anomalyThreshold = anomalyThreshold
self.cacheSize = cacheSize
self.classificationVectorType = classificationVectorType
self._knnclassifierArgs = classifierArgs
self._knnclassifier = KNNClassifierRegion(**self._knnclassifierArgs)
self.labelResults = []
self.saved_categories = []
self._recordsCache = []
self._version = KNNAnomalyClassifierRegion.__VERSION__
示例2: __setstate__
def __setstate__(self, state):
"""
Set the state of ourself from a serialized state.
"""
if '_version' not in state or state['_version'] == 1:
knnclassifierProps = state.pop('_knnclassifierProps')
self.__dict__.update(state)
self._knnclassifier = KNNClassifierRegion(**self._knnclassifierArgs)
self._knnclassifier.__setstate__(knnclassifierProps)
self._version = KNNAnomalyClassifierRegion.__VERSION__
else:
raise Exception("Invalid KNNAnomalyClassifierRegion version. Current "
"version: %s" % (KNNAnomalyClassifierRegion.__VERSION__))
示例3: __setstate__
def __setstate__(self, state):
"""
Set the state of ourself from a serialized state.
"""
if '_version' not in state or state['_version'] == 1:
knnclassifierProps = state.pop('_knnclassifierProps')
self.__dict__.update(state)
self._knnclassifier = KNNClassifierRegion(**self._knnclassifierArgs)
self._knnclassifier.__setstate__(knnclassifierProps)
self._version = KNNAnomalyClassifierRegion.__VERSION__
else:
raise Exception("Invalid KNNAnomalyClassifierRegion version. Current "
"version: %s" % (KNNAnomalyClassifierRegion.__VERSION__))
# --------------------------------------------------------------------------
# Migrate from when Anomaly was separated to stand-alone class
if not hasattr(self, "_anomaly"):
self._anomaly = Anomaly()
示例4: getSpec
#.........这里部分代码省略.........
isDefaultInput=True,
requireSplitterMap=False),
tpLrnActiveStateT=dict(
description="""Active cells in the learn state at time T from TP.
This is used to classify on.""",
dataType='Real32',
count=0,
required=True,
regionLevel=False,
isDefaultInput=True,
requireSplitterMap=False)
),
outputs=dict(
),
parameters=dict(
trainRecords=dict(
description='Number of records to wait for training',
dataType='UInt32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
anomalyThreshold=dict(
description='Threshold used to classify anomalies.',
dataType='Real32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
cacheSize=dict(
description='Number of records to store in cache.',
dataType='UInt32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
classificationVectorType=dict(
description="""Vector type to use when classifying.
1 - Vector Column with Difference (TP and SP)
""",
dataType='UInt32',
count=1,
constraints='',
defaultValue=1,
accessMode='ReadWrite'),
activeColumnCount=dict(
description="""Number of active columns in a given step. Typically
equivalent to SP.numActivePerInhArea""",
dataType='UInt32',
count=1,
constraints='',
defaultValue=40,
accessMode='ReadWrite'),
classificationMaxDist=dict(
description="""Maximum distance a sample can be from an anomaly
in the classifier to be labeled as an anomaly.
Ex: With rawOverlap distance, a value of 0.65 means that the points
must be at most a distance 0.65 apart from each other. This
translates to they must be at least 35% similar.""",
dataType='Real32',
count=1,
constraints='',
defaultValue=0.65,
accessMode='Create'
)
),
commands=dict(
getLabels=dict(description=
"Returns a list of label dicts with properties ROWID and labels."
"ROWID corresponds to the records id and labels is a list of "
"strings representing the records labels. Takes additional "
"integer properties start and end representing the range that "
"will be returned."),
addLabel=dict(description=
"Takes parameters start, end and labelName. Adds the label "
"labelName to the records from start to end. This will recalculate "
"labels from end to the most recent record."),
removeLabels=dict(description=
"Takes additional parameters start, end, labelFilter. Start and "
"end correspond to range to remove the label. Remove labels from "
"each record with record ROWID in range from start to end, "
"noninclusive of end. Removes all records if labelFilter is None, "
"otherwise only removes the labels eqaul to labelFilter.")
)
)
ns['parameters'].update(KNNClassifierRegion.getSpec()['parameters'])
return ns
示例5: KNNAnomalyClassifierRegion
class KNNAnomalyClassifierRegion(PyRegion):
"""
KNNAnomalyClassifierRegion wraps the KNNClassifierRegion to classify clamodel
state. It allows for individual records to be classified as anomalies and
supports anomaly detection even after the model has learned the anomalous
sequence.
Methods:
compute() - called by clamodel during record processing
getLabels() - return points with classification records
addLabel() - add a set label to a given set of points
removeLabels() - remove labels from a given set of points
Parameters:
trainRecords - number of records to skip before classification
anomalyThreshold - threshold on anomaly score to automatically classify
record as an anomaly
cacheSize - number of records to keep in cache. Can only recalculate
records kept in cache when setting the trainRecords.
"""
@classmethod
def getSpec(cls):
ns = dict(
description=KNNAnomalyClassifierRegion.__doc__,
singleNodeOnly=True,
inputs=dict(
spBottomUpOut=dict(
description="""The output signal generated from the bottom-up inputs
from lower levels.""",
dataType='Real32',
count=0,
required=True,
regionLevel=False,
isDefaultInput=True,
requireSplitterMap=False),
tpTopDownOut=dict(
description="""The top-down inputsignal, generated from
feedback from upper levels""",
dataType='Real32',
count=0,
required=True,
regionLevel=False,
isDefaultInput=True,
requireSplitterMap=False),
tpLrnActiveStateT=dict(
description="""Active cells in the learn state at time T from TP.
This is used to classify on.""",
dataType='Real32',
count=0,
required=True,
regionLevel=False,
isDefaultInput=True,
requireSplitterMap=False)
),
outputs=dict(
),
parameters=dict(
trainRecords=dict(
description='Number of records to wait for training',
dataType='UInt32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
anomalyThreshold=dict(
description='Threshold used to classify anomalies.',
dataType='Real32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
cacheSize=dict(
description='Number of records to store in cache.',
dataType='UInt32',
count=1,
constraints='',
defaultValue=0,
accessMode='Create'),
classificationVectorType=dict(
description="""Vector type to use when classifying.
1 - Vector Column with Difference (TP and SP)
""",
dataType='UInt32',
count=1,
constraints='',
defaultValue=1,
accessMode='ReadWrite'),
activeColumnCount=dict(
description="""Number of active columns in a given step. Typically
equivalent to SP.numActivePerInhArea""",
#.........这里部分代码省略.........