title: sklearn.mixture.DPGMM: Unexpected results
body: <p>The results I get from DPGMM are not what I expect. E.g.:</p>
  
  <pre><code>&gt;&gt;&gt; import sklearn.mixture
  &gt;&gt;&gt; sklearn.__version__
  '0.12-git'
  &gt;&gt;&gt; data = [[1.1],[0.9],[1.0],[1.2],[1.0], [6.0],[6.1],[6.1]]
  &gt;&gt;&gt; m = sklearn.mixture.DPGMM(n_components=5, n_iter=1000, alpha=1)
  &gt;&gt;&gt; m.fit(data)
  DPGMM(alpha=1, covariance_type='diag', init_params='wmc', min_covar=None,
     n_components=5, n_iter=1000, params='wmc',
     random_state=&lt;mtrand.RandomState object at 0x108a3f168&gt;, thresh=0.01,
     verbose=False)
  &gt;&gt;&gt; m.converged_
  True
  &gt;&gt;&gt; m.weights_
  array([ 0.2,  0.2,  0.2,  0.2,  0.2])
  &gt;&gt;&gt; m.means_
  array([[ 0.62019109],
         [ 1.16867356],
         [ 0.55713292],
         [ 0.36860511],
         [ 0.17886128]])
  </code></pre>
  
  <p>I expected the result to be more similar to the vanilla GMM; that is, two gaussians (around values 1 and 6), with non-uniform weights (like [ 0.625,  0.375]). I expected the "unused" gaussians to have weights near zero.</p>
  
  <p>Am I using the model incorrectly? </p>
  
  <p>I've also tried changing alpha without any luck.</p>