text: Title: Strange embedding from FastText
  Body: I am struggled understanding word embeddings of FastText. According to the white paper [Enriching Word Vectors with Subword Information](https://arxiv.org/pdf/1607.04606.pdf), embeddings of a word is the mean (or sum) of embeddings of its subwords.
  
  I failed to verify this. On `common_text` imported from `gensim.test.utils`, embedding of `user` is `[-0.03062156 -0.02879291 -0.01737508 -0.02839565]`. The mean of embeddings of ['<us', 'use', 'ser', 'er>'] (setting `min_n=max_n=3`) is `[-0.047664   -0.01677518  0.02312234  0.03452689]`. The sum of embeddings also result in a different vector.
  
  Is it a mismatch between Gensim implementation and original FastText, or am I missing something?
  
  Below is my code:
  
  ```python
  import numpy as np
  from gensim.models import FastText
  from gensim.models._utils_any2vec import compute_ngrams
  from gensim.models.keyedvectors import FastTextKeyedVectors
  from gensim.test.utils import common_texts
  
  model = FastText(size=4, window=3, min_count=1)
  model.build_vocab(sentences=common_texts)
  model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10, min_n=3, max_n=3)
  
  print('survey' in model.wv.vocab)
  print('ser' in model.wv.vocab)
  print('ree' in model.wv.vocab)
  ngrams = compute_ngrams('user', 3, 3)
  print('num vector of "user": ', model.wv['user'])
  print('ngrams of "user": ', ngrams)
  print('mean of num vectors of {}: \n{}'.format(ngrams, np.mean([model.wv[c] for c in ngrams], axis=0)))
  ```
  