def __init__(self,
                 conceptCount=25,
                 conceptCountPerType = None,
                 conceptScoring = "importance",
                 articlesSampleSize = 10000,
                 returnInfo = ReturnInfo()):
        """
        get aggreate of concepts of resulting articles
        @param conceptCount: number of top concepts to return (at most 500)
        @param conceptCountPerType: if you wish to limit the number of top concepts per type (person, org, loc, wiki) then set this to some number.
            If you want to get equal number of concepts for each type then set conceptCountPerType to conceptCount/4 (since there are 4 concept types)
        @param conceptScoring: how should the top concepts be computed. Possible values are
            "importance" (takes into account how frequently a concept is mentioned and how relevant it is in an article),
            "frequency" (ranks the concepts simply by how frequently the concept is mentioned in the results) and
            "uniqueness" (computes what are the top concepts that are frequently mentioned in the results of your search query but less frequently mentioned in the news in general)
        @param articlesSampleSize: on what sample of results should the aggregate be computed (at most 20000)
        @param returnInfo: what details about the concepts should be included in the returned information
        """
        assert conceptCount <= 500
        assert articlesSampleSize <= 20000
        self.resultType = "conceptAggr"
        self.conceptAggrConceptCount = conceptCount
        self.conceptAggrSampleSize = articlesSampleSize
        self.conceptAggrScoring = conceptScoring
        if conceptCountPerType != None:
            self.conceptAggrConceptCountPerType = conceptCountPerType
        self.__dict__.update(returnInfo.getParams("conceptAggr"))
def __init__ ( self , conceptCount = 25 , conceptCountPerType = None , conceptScoring = "importance" , articlesSampleSize = 10000 , returnInfo = ReturnInfo ( ) ) : assert conceptCount <= 500 assert articlesSampleSize <= 20000 self . resultType = "conceptAggr" self . conceptAggrConceptCount = conceptCount self . conceptAggrSampleSize = articlesSampleSize self . conceptAggrScoring = conceptScoring if conceptCountPerType != None : self . conceptAggrConceptCountPerType = conceptCountPerType self . __dict__ . update ( returnInfo . getParams ( "conceptAggr" ) )
get aggreate of concepts of resulting articles
        @param conceptCount: number of top concepts to return (at most 500)
        @param conceptCountPerType: if you wish to limit the number of top concepts per type (person, org, loc, wiki) then set this to some number.
            If you want to get equal number of concepts for each type then set conceptCountPerType to conceptCount/4 (since there are 4 concept types)
        @param conceptScoring: how should the top concepts be computed. Possible values are
            "importance" (takes into account how frequently a concept is mentioned and how relevant it is in an article),
            "frequency" (ranks the concepts simply by how frequently the concept is mentioned in the results) and
            "uniqueness" (computes what are the top concepts that are frequently mentioned in the results of your search query but less frequently mentioned in the news in general)
        @param articlesSampleSize: on what sample of results should the aggregate be computed (at most 20000)
        @param returnInfo: what details about the concepts should be included in the returned information