(self, fieldname, text=None, top=10, numterms=5,
                       model=classify.Bo1Model, normalize=True, filter=None)
return self.searcher.more_like(self.docnum, fieldname, text=text,
                                       top=top, numterms=numterms, model=model,
                                       normalize=normalize, filter=filter)
Returns a new Results object containing documents similar to this
        hit, based on "key terms" in the given field::

            r = searcher.search(myquery)
            for hit in r:
                print(hit["title"])
                print("Top 3 similar documents:")
                for subhit in hit.more_like_this("content", top=3):
                  print("  ", subhit["title"])

        :param fieldname: the name of the field to use to test similarity.
        :param text: by default, the method will attempt to load the contents
            of the field from the stored fields for the document, or from a
            term vector. If the field isn't stored or vectored in the index,
            but you have access to the text another way (for example, loading
            from a file or a database), you can supply it using the ``text``
            parameter.
        :param top: the number of results to return.
        :param numterms: the number of "key terms" to extract from the hit and
            search for. Using more terms is slower but gives potentially more
            and more accurate results.
        :param model: (expert) a :class:`whoosh.classify.ExpansionModel` to use
            to compute "key terms".
        :param normalize: whether to normalize term weights.
Returns a new Results object containing documents similar to this
        hit, based on "key terms" in the given field::
Returns a new Results object containing documents similar to this hit based on key terms in the given field ::
def more_like_this(self, fieldname, text=None, top=10, numterms=5,
                       model=classify.Bo1Model, normalize=True, filter=None):
        """Returns a new Results object containing documents similar to this
        hit, based on "key terms" in the given field::

            r = searcher.search(myquery)
            for hit in r:
                print(hit["title"])
                print("Top 3 similar documents:")
                for subhit in hit.more_like_this("content", top=3):
                  print("  ", subhit["title"])

        :param fieldname: the name of the field to use to test similarity.
        :param text: by default, the method will attempt to load the contents
            of the field from the stored fields for the document, or from a
            term vector. If the field isn't stored or vectored in the index,
            but you have access to the text another way (for example, loading
            from a file or a database), you can supply it using the ``text``
            parameter.
        :param top: the number of results to return.
        :param numterms: the number of "key terms" to extract from the hit and
            search for. Using more terms is slower but gives potentially more
            and more accurate results.
        :param model: (expert) a :class:`whoosh.classify.ExpansionModel` to use
            to compute "key terms".
        :param normalize: whether to normalize term weights.
        """

        return self.searcher.more_like(self.docnum, fieldname, text=text,
                                       top=top, numterms=numterms, model=model,
                                       normalize=normalize, filter=filter)
def more_like_this ( self , fieldname , text = None , top = 10 , numterms = 5 , model = classify . Bo1Model , normalize = True , filter = None ) : return self . searcher . more_like ( self . docnum , fieldname , text = text , top = top , numterms = numterms , model = model , normalize = normalize , filter = filter )