class_docstr: GMM 1D distribution.
  
  Given a set of points, we create this object so that we
  can calculate likelihoods and generate samples from this
  1D Gaussian mixture model.
  
  Attributes
  ----------
  points : 1D array
      Set of points to create the GMM
  
  N : int
      Number of points to create the GMM
  
  min_limit : Optional[float], default : -inf
      Minimum limit for the distribution
  
  max_limit : Optional[float], default : inf
      Maximum limit for the distribution
  
  weights : Optional[1D array], default : array of ones
      Used to weight the points non-uniformly if required
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docstr: Return the GMM likelihood for given point(s).
  
  See :eq:`gmm-likelihood`.
  
  Arguments
  ---------
  x : scalar (or) 1D array of reals
      Point(s) at which likelihood needs to be computed
  
  Returns
  -------
  scalar (or) 1D array
      Likelihood values at the given point(s)
docstr_tok: ["Return","the","GMM","likelihood","for","given","point","(","s",")",".","See",":","eq",":","`","gmm-likelihood","`",".","Arguments","--","--","--","--","-","x",":","scalar","(","or",")","1D","array","of","reals","Point","(","s",")","at","which","likelihood","needs","to","be","computed","Returns","--","--","--","-","scalar","(","or",")","1D","array","Likelihood","values","at","the","given","point","(","s",")"]
code: def __call__(self, x):
      """Return the GMM likelihood for given point(s).
  
      See :eq:`gmm-likelihood`.
  
      Arguments
      ---------
      x : scalar (or) 1D array of reals
          Point(s) at which likelihood needs to be computed
  
      Returns
      -------
      scalar (or) 1D array
          Likelihood values at the given point(s)
      """
  
      if np.isscalar(x):
          return self.get_gmm_pdf(x)
      else:
          return np.array([self.get_gmm_pdf(t) for t in x])
  
file_path: brainiak/brainiak/hyperparamopt/hpo.py
imports: import logging
  import math
  import numpy
  from scipy.special import erf
  import scipy.stats
label_desc: Use image node_id 3 for calling the gmm_1d_distribution obj's underlying member method code with example usage: obj.__call__(x) and returns: self, np