title: Supervised Learning HMM with Pomegranate
body: <p>thanks for your help, I am new about this topic.</p>
  <p>I have a dataset composed of a sequence of binary observable variables associated with a label, like this.</p>
  <pre><code>trainset = [ [0,0,1,0],[0,0,1,0],[0,0,0,1],...,]
  labels = [ 0,1,2,...,]
  states = 3 # number of unique labels
  </code></pre>
  <p>I am trying to train an HMM in this way</p>
  <pre><code>model = HiddenMarkovModel.from_samples(distribution = MultivariateGaussianDistribution, n_components = states,
                                         X= [trainset],
                                         labels = [labels],
                                         algorithm = 'labeled')
  </code></pre>
  <p>When I ran this model, I get this Exception</p>
  <blockquote>
  <p>Exception ignored in:
  'pomegranate.hmm.HiddenMarkovModel._labeled_summarize' KeyError: <code>3</code></p>
  </blockquote>
  <p>I figure out that the exception is raised when there are no observations for the state -<code>3</code>-.
  But , state <code>3</code> is in my labels.
  Can someone help me, please?
  thanks</p>