def most_similar(base_vector, words):
start = time.time()
# finds n words with smallest cosine similarity for a given word
words_with_distance = [
(v.cosine_similarity_normalized(base_vector, w.vector), w)
for w in words
]
# want as large as 1
sorted_by_dis = sorted(words_with_distance)
print(f"Elapsed {time.time() - start} s")
return sorted_by_dis
def most_similar(base_vector: Vector,
words: List[Word]) -> List[Tuple[float, Word]]:
"""Finds n words with smallest cosine similarity to a given word"""
words_with_distance = [
(v.cosine_similarity_normalized(base_vector, w.vector), w)
for w in words
]
# We want cosine similarity to be as large as possible (close to 1)
sorted_by_distance = sorted(words_with_distance,
key=lambda t: t[0],
reverse=True)
return sorted_by_distance
