def __getitem__(self, query):
"""Get similarities of document `query` to all documents in the corpus.
**or**
If `query` is a corpus (iterable of documents), return a matrix of similarities
of all query documents vs. all corpus document. Using this type of batch
query is more efficient than computing the similarities one document after
another.
"""
is_corpus, query = utils.is_corpus(query)
if self.normalize:
# self.normalize only works if the input is a plain gensim vector/corpus (as
# advertised in the doc). in fact, input can be a numpy or scipy.sparse matrix
# as well, but in that case assume tricks are happening and don't normalize
# anything (self.normalize has no effect).
if matutils.ismatrix(query):
logger.warning("non-gensim input must already come normalized")
else:
if is_corpus:
query = [matutils.unitvec(v) for v in query]
else:
query = matutils.unitvec(query)
result = self.get_similarities(query)
if self.num_best is None:
return result
# if the input query was a corpus (=more documents), compute the top-n
# most similar for each document in turn
if matutils.ismatrix(result):
return [matutils.full2sparse_clipped(v, self.num_best) for v in result]
else:
# otherwise, return top-n of the single input document
return matutils.full2sparse_clipped(result, self.num_best)
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)
