def __getitem__(self, doc):
# get similarities of doc to all documents in the corpus
if self.normalize:
doc = matutils.unitVec(doc)
allSims = self.getSimilarities(doc)
# return either all similarities as a list, or only self.numBest most similar, depending on settings from the constructor
if self.numBest is None:
return allSims
else:
return matutils.full2sparse_clipped(allSims, self.numBest)
def __getitem__(self, doc):
# get similarities of doc to all documents in the corpus
if self.normalize:
doc = matutils.unitVec(doc)
allSims = self.getSimilarities(doc)
# return either all similarities as a list, or only self.numBest most similar, depending on settings from the constructor
if self.numBest is None:
return allSims
else:
tops = [(docNo, sim) for docNo, sim in enumerate(allSims) if sim > 0]
tops = sorted(tops, key = lambda item: -item[1]) # sort by -sim => highest cossim first
return tops[ : self.numBest] # return at most numBest top 2-tuples (docId, docSim)
def __getitem__(self, doc):
# get similarities of doc to all documents in the corpus
if self.normalize:
doc = matutils.unitVec(doc)
allSims = self.getSimilarities(doc)
# return either all similarities as a list, or only self.numBest most similar, depending on settings from the constructor
if self.numBest is None:
return allSims
else:
tops = [(docNo, sim) for docNo, sim in enumerate(allSims) if sim > 0]
tops = sorted(tops, key = lambda item: -item[1]) # sort by -sim => highest cossim first
return tops[ : self.numBest] # return at most numBest top 2-tuples (docId, docSim)
