def sim_terms(term):
i = corpus.terms_int[term]
ra = similarity.similar_rows(i, matrix, norms=norms,
sort=False, filter_nan=False)
return ra['value']
def similar_terms(corpus, matrix, term,
norms=None, filter_nan=True,
rem_masked=True):
i = corpus.terms_int[term]
sim_vals = similarity.similar_rows(i, matrix,
norms=norms,
filter_nan=filter_nan)
out = []
for t,v in sim_vals:
term = corpus.terms[t]
if not (rem_masked and term is np.ma.masked):
out.append((term, v))
return out
