class_docstr: Dirichlet Process Gaussian Mixture Models
  
  .. deprecated:: 0.18
      This class will be removed in 0.20.
      Use :class:`sklearn.mixture.BayesianGaussianMixture` with
      parameter ``weight_concentration_prior_type='dirichlet_process'``
      instead.
code: def __init__(
      self,
      n_components=1,
      covariance_type="diag",
      alpha=1.0,
      random_state=None,
      tol=1e-3,
      verbose=0,
      min_covar=None,
      n_iter=10,
      params="wmc",
      init_params="wmc",
  ):
      super(DPGMM, self).__init__(
          n_components=n_components,
          covariance_type=covariance_type,
          alpha=alpha,
          random_state=random_state,
          tol=tol,
          verbose=verbose,
          min_covar=min_covar,
          n_iter=n_iter,
          params=params,
          init_params=init_params,
      )
  
imports: from __future__ import print_function
  import numpy
  from scipy.special import digamma, gammaln
  from scipy import linalg
  from scipy.spatial.distance import cdist
  from ..externals.six.moves import xrange
  from ..utils import check_random_state, check_array, deprecated
  from ..utils.extmath import logsumexp, pinvh, squared_norm
  from ..utils.validation import check_is_fitted
  from ..None import cluster
  from .gmm import _GMMBase
label_desc: Use image node_id 1 to create a new DPGMM object from inherited base classes: _DPGMMBase with example: obj = DPGMM(n_components, covariance_type, alpha, random_state, tol, verbose, min_covar, n_iter, params, init_params)