本文整理汇总了Python中library.classes.GeneralMethods.approximateToNearest5Minutes方法的典型用法代码示例。如果您正苦于以下问题：Python GeneralMethods.approximateToNearest5Minutes方法的具体用法？Python GeneralMethods.approximateToNearest5Minutes怎么用？Python GeneralMethods.approximateToNearest5Minutes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library.classes.GeneralMethods的用法示例。
在下文中一共展示了GeneralMethods.approximateToNearest5Minutes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: dimensionsUpdateFrequencyEstimation
# 需要导入模块: from library.classes import GeneralMethods [as 别名]
# 或者: from library.classes.GeneralMethods import approximateToNearest5Minutes [as 别名]
def dimensionsUpdateFrequencyEstimation(estimationObject, currentMessageTime):
'''
Observe the new dimensions that get added to current dimension if the dimensions
are being updated at regular intervals.
For example, number of dimensions being added after 10m, 20m,... 5 horus.
As time increases the number of 'decayed' dimensions increase. The current dimensions
has a lot of unwanted decayed dimensions. Using this information identify the time
interval that is best suited to refresh dimensions.
Tentative: We decide to pick the time interval at which the rate of decay is maximum.
'''
def updatePhraseScore(phraseObject):
phraseObject.updateScore(currentMessageTime, 0, **estimationObject.stream_settings)
return phraseObject
dimensions = estimationObject.stream_settings['dimensions']
newList = [p.text for p in Phrase.sort((updatePhraseScore(p) for p in estimationObject.phraseTextToPhraseObjectMap.itervalues()), reverse=True)][:dimensions]
print currentMessageTime, len(newList)
if len(newList) >= dimensions:
idsOfDimensionsListToCompare = [(i, GeneralMethods.approximateToNearest5Minutes(currentMessageTime - i)) for i in estimationObject.dimensionUpdateTimeDeltas if GeneralMethods.approximateToNearest5Minutes(currentMessageTime - i) in estimationObject.dimensionListsMap]
dimensionsUpdateFrequency = {}
for td, id in idsOfDimensionsListToCompare:
oldList = estimationObject.dimensionListsMap[id]
dimensionsUpdateFrequency[str(td.seconds)] = len(set(newList).difference(oldList))
print len(estimationObject.dimensionListsMap), currentMessageTime, len(newList), [(k, dimensionsUpdateFrequency[k]) for k in sorted(dimensionsUpdateFrequency)]
iterationData = {
'time_stamp': getStringRepresentationForTweetTimestamp(currentMessageTime),
'total_number_of_phrases': len(estimationObject.phraseTextToPhraseObjectMap),
'settings': pprint.pformat(estimationObject.stream_settings),
ParameterEstimation.dimensionsUpdateFrequencyId:dimensionsUpdateFrequency
}
FileIO.writeToFileAsJson(iterationData, estimationObject.dimensionsUpdateFrequencyFile)
estimationObject.dimensionListsMap[GeneralMethods.approximateToNearest5Minutes(currentMessageTime)] = newList[:]
for key in estimationObject.dimensionListsMap.keys()[:]:
if currentMessageTime - key > estimationObject.dimensionUpdateTimeDeltas[-1]: del estimationObject.dimensionListsMap[key]