本文整理匯總了Python中KNNClassifierRegion.KNNClassifierRegion.compute方法的典型用法代碼示例。如果您正苦於以下問題：Python KNNClassifierRegion.compute方法的具體用法？Python KNNClassifierRegion.compute怎麽用？Python KNNClassifierRegion.compute使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類KNNClassifierRegion.KNNClassifierRegion的用法示例。
在下文中一共展示了KNNClassifierRegion.compute方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: KNNAnomalyClassifierRegion
# 需要導入模塊: from KNNClassifierRegion import KNNClassifierRegion [as 別名]
# 或者: from KNNClassifierRegion.KNNClassifierRegion import compute [as 別名]
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""",
#.........這裏部分代碼省略.........