class_docstr: Standard GMM.
  
  this class contains the following members
  k (int): the number of components in the mixture
  dim (int): is the dimension of the data
  prec_type = 'full' (string) is the parameterization
            of the precisions/covariance matrices:
            either 'full' or 'diagonal'.
  means: array of shape (k,dim):
        all the means (mean parameters) of the components
  precisions: array of shape (k,dim,dim):
             the precisions (inverse covariance matrix) of the components
  weights: array of shape(k): weights of the mixture
  
  fixme
  -----
  no copy method
class_docstr_tok: ["Standard","GMM",".","this","class","contains","the","following","members","k","(","int",")",":","the","number","of","components","in","the","mixture","dim","(","int",")",":","is","the","dimension","of","the","data","prec_type","=","'full","'","(","string",")","is","the","parameterization","of","the","precisions\/covariance","matrices",":","either","'full","'","or","'diagonal","'",".","means",":","array","of","shape","(","k",",","dim",")",":","all","the","means","(","mean","parameters",")","of","the","components","precisions",":","array","of","shape","(","k",",","dim",",","dim",")",":","the","precisions","(","inverse","covariance","matrix",")","of","the","components","weights",":","array","of","shape","(","k",")",":","weights","of","the","mixture","fixme","--","--","-","no","copy","method"]
docstr: Set the regularizing priors as weakly informative
  according to Fraley and raftery;
  Journal of Classification 24:155-181 (2007)
  
  Parameters
  ----------
  x array of shape (n_samples,dim)
    the data used in the estimation process
docstr_tok: ["Set","the","regularizing","priors","as","weakly","informative","according","to","Fraley","and","raftery",";","Journal","of","Classification","24:155-181","(","2007",")","Parameters","--","--","--","--","--","x","array","of","shape","(","n_samples",",","dim",")","the","data","used","in","the","estimation","process"]
code: def guess_regularizing(self, x, bcheck=1):
      """
      Set the regularizing priors as weakly informative
      according to Fraley and raftery;
      Journal of Classification 24:155-181 (2007)
  
      Parameters
      ----------
      x array of shape (n_samples,dim)
        the data used in the estimation process
      """
      small = 0.01
      # the mean of the data
      mx = np.reshape(x.mean(0), (1, self.dim))
  
      dx = x - mx
      vx = np.dot(dx.T, dx) / x.shape[0]
      if self.prec_type == "full":
          px = np.reshape(
              np.diag(1.0 / np.diag(vx)), (1, self.dim, self.dim)
          )
      else:
          px = np.reshape(1.0 / np.diag(vx), (1, self.dim))
      px *= np.exp(2.0 / self.dim * np.log(self.k))
      self.prior_means = np.repeat(mx, self.k, 0)
      self.prior_weights = np.ones(self.k) / self.k
      self.prior_scale = np.repeat(px, self.k, 0)
      self.prior_dof = self.dim + 2
      self.prior_shrinkage = small
      self.weights = np.ones(self.k) * 1.0 / self.k
      if bcheck:
          self.check()
  
imports: import numpy
  from scipy.linalg import eigvalsh
label_desc: Use image node_id 16 for calling the GMM obj's underlying member method code with example usage: obj.guess_regularizing(x, bcheck) without return types