title: Scikit Learn Gaussian HMM: ValueError: startprob must sum to 1.0
body: <p>I am currently working with Scikit Learn and have been running into the following issue while trying to train a Gaussian HMM:</p>
  <blockquote>
  <p>File &quot;/Library/Python/2.7/site-packages/sklearn/hmm.py&quot;, line 443, in fit</p>
  <pre><code>self._do_mstep(stats, self.params)
  </code></pre>
  <p>File &quot;/Library/Python/2.7/site-packages/sklearn/hmm.py&quot;, line 798, in _do_mstep</p>
  <pre><code>super(GaussianHMM, self)._do_mstep(stats, params)
  </code></pre>
  <p>File &quot;/Library/Python/2.7/site-packages/sklearn/hmm.py&quot;, line 580, in _do_mstep</p>
  <pre><code>np.maximum(self.startprob_prior - 1.0 + stats['start'], 1e-20))
  </code></pre>
  <p>File &quot;/Library/Python/2.7/site-packages/sklearn/hmm.py&quot;, line 476, in _set_startprob</p>
  <pre><code>raise ValueError('startprob must sum to 1.0')
  </code></pre>
  <p>ValueError: startprob must sum to 1.0</p>
  </blockquote>
  <p>If I eliminate some of the features (fewer than 13 features per observation), it still works. I have checked that all of the input is valid and consists of only 2d-arrays of numpy.float64s for each training example. Any ideas on what is going wrong?
  Thanks!</p>