class_docstr: Base class for mixture models.
  
  This abstract class specifies an interface for all mixture classes and
  provides basic common methods for mixture models.
docstr: Predict posterior probability of data per each component.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      List of n_features-dimensional data points. Each row
      corresponds to a single data point.
  
  Returns
  -------
  resp : array, shape (n_samples, n_components)
      Returns the probability of the sample for each Gaussian
      (state) in the model.
code: def predict_proba(self, X):
      """Predict posterior probability of data per each component.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          List of n_features-dimensional data points. Each row
          corresponds to a single data point.
  
      Returns
      -------
      resp : array, shape (n_samples, n_components)
          Returns the probability of the sample for each Gaussian
          (state) in the model.
      """
      self._check_is_fitted()
      X = _check_X(X, None, self.means_.shape[1])
      _, log_resp = self._estimate_log_prob_resp(X)
      return np.exp(log_resp)
  
imports: from __future__ import print_function
  import warnings
  from abc import ABCMeta, abstractmethod
  from time import time
  import numpy
  from ..None import cluster
  from ..base import BaseEstimator
  from ..base import DensityMixin
  from ..externals import six
  from ..exceptions import ConvergenceWarning
  from ..utils import check_array, check_random_state
  from ..utils.extmath import logsumexp
label_desc: Use image node_id 15 for calling the BaseMixture obj's underlying member method code with example usage: obj.predict_proba(X) and returns: np