Why the output of model.wv.similarity() in Word2Vec results different with model.wv.similar()? - word2vec

Question

I have trained a Word2Vec model and I am trying to use it.
When I input the most similar words of ‘动力', I got the output like this:
动力系统 0.6429724097251892
驱动力 0.5936785936355591
动能 0.5788494348526001
动力车 0.5579575300216675
引擎 0.5339343547821045
推动力 0.5152761936187744
扭力 0.501279354095459
新动力 0.5010953545570374
支撑力 0.48610919713974
精神力量 0.47970670461654663
But the problem is that if I input model.wv.similarity('动力','动力系统') I got the result 0.0, which is not equal with
0.6429724097251892
what confused me more was that when I got the next similarity of word '动力' and word '驱动力', it showed
3.689349e+19
So why ? Did I make misunderstanding with the similarity? I need someone to tell me!!
And the code is:
res = model.wv.most_similar('动力')
for r in res:
print(r[0],r[1])
print(model.wv.similarity('动力','动力系统'))
print(model.wv.similarity('动力','驱动力'))
print(model.wv.similarity('动力','动能'))
output:
动力系统 0.6429724097251892
驱动力 0.5936785936355591
动能 0.5788494348526001
动力车 0.5579575300216675
引擎 0.5339343547821045
推动力 0.5152761936187744
扭力 0.501279354095459
新动力 0.5010953545570374
支撑力 0.48610919713974
精神力量 0.47970670461654663
0.0
3.689349e+19
2.0

Answer 1

I have written a function to replace the model.wv.similarity method.
def Similarity(w1,w2,model):
A = model[w1]; B = model[w2]
return sum(A*B)/(pow(sum(pow(A,2)),0.5)*pow(sum(pow(B,2)),0.5)
Where w1 and w2 are the words you input, model is the Word2Vec model you have trained.

Answer 2

Using the similarity method directly from the model is deprecated. It has a bit extra logic in it that performs vector normalization before evaluating the result.
You should be using vw directly, because as stated in their documentation, for the word vectors it is of non importance how they were trained so they should be looked as independent structure, the model is just the means to obtain it.
Here is short discussion which should give you starting points if you want to investigate further.

Answer 3

It may be an encoding issue, where you are not actually comparing the same tokens.
Try the following, to see if it gives results closer to what you expect.
res = model.wv.most_similar('动力')
for r in res:
print(r[0],r[1])
print(model.wv.similarity('动力', res[0][0]))
print(model.wv.similarity('动力', res[1][0]))
print(model.wv.similarity('动力', res[2][0]))
If it does, you could look further into why the model might be reporting strings which print as 动力系统 (etc), but don't match your typed-in-code string literals like '动力系统' (etc). For example:
print(res[0][0]=='动力系统')
print(type(res[0][0]))
print(type('动力系统'))