class_docstr: Particular IMM with an additional null class.
  The data is supplied together
  with a sample-related probability of being under the null.
class_docstr_tok: ["Particular","IMM","with","an","additional","null","class",".","The","data","is","supplied","together","with","a","sample-related","probability","of","being","under","the","null","."]
docstr: Parameters
  ----------
  alpha: float, optional,
         the parameter for cluster creation
  dim: int, optional,
       the dimension of the the data
  
  Note: use the function set_priors() to set adapted priors
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code: def __init__(self, alpha=0.5, dim=1):
      """
      Parameters
      ----------
      alpha: float, optional,
             the parameter for cluster creation
      dim: int, optional,
           the dimension of the the data
  
      Note: use the function set_priors() to set adapted priors
      """
      IMM.__init__(self, alpha, dim)
  
imports: import math
  import numpy
  from scipy.special import gammaln
  from .bgmm import BGMM, detsh
label_desc: Use image node_id 1 to create a new MixedIMM object from inherited base classes: IMM with example: obj = MixedIMM(alpha, dim)