title: Using gensim's Doc2Vec to produce sentence vectors
body: <p>I'm trying to use Doc2Vec to read in a file that is a list of sentences like this:</p>
  
  <pre><code>The elephant flaps its large ears to cool the blood in them and its body.
  
  A house is a permanent building or structure for people or families to live in.
  
  ...
  </code></pre>
  
  <p>What I want to do is generate two files, one with unique words from these sentences and another that has one corresponding vector per line (if there's no vector output I want to output a vector of 0's)</p>
  
  <p>I'm getting the vocab fine with my code but I can't seem to figure out how to print out the individual sentence vectors. I have looked through the documentation and haven't found much help.  Here is what my code looks like so far. </p>
  
  <pre><code>sentences = []
  for uid, line in enumerate(open(filename)):
      sentences.append(LabeledSentence(words=line.split(), labels=['SENT_%s' %       uid]))
  
  model = Doc2Vec(alpha=0.025, min_alpha=0.025)
  model.build_vocab(sentences)
  for epoch in range(10):
      model.train(sentences)
      model.alpha -= 0.002
      model.min_alpha = model.alpha
  sent_reg = r'[SENT].*'
  for item in model.vocab.keys():
      sent = re.search(sent_reg, item)
      if sent:
          continue
      else:
          print item
  
  ###I'm not sure how to produce the vectors from here and this doesn't work##   
  sent_id = 0
  for item in model:
      print model["SENT_"+str(sent_id)]
      sent_id += 1
  </code></pre>