本文整理汇总了Python中KNNClassifierRegion.KNNClassifierRegion.compute方法的典型用法代码示例。如果您正苦于以下问题：Python KNNClassifierRegion.compute方法的具体用法？Python KNNClassifierRegion.compute怎么用？Python KNNClassifierRegion.compute使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类KNNClassifierRegion.KNNClassifierRegion的用法示例。
在下文中一共展示了KNNClassifierRegion.compute方法的1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: KNNAnomalyClassifierRegion
# 需要导入模块: from KNNClassifierRegion import KNNClassifierRegion [as 别名]
# 或者: from KNNClassifierRegion.KNNClassifierRegion import compute [as 别名]
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""",
#.........这里部分代码省略.........