class_docstr: Subclass of Bayesian GMMs (BGMM)
  that implements Variational Bayes estimation of the parameters
class_docstr_tok: ["Subclass","of","Bayesian","GMMs","(","BGMM",")","that","implements","Variational","Bayes","estimation","of","the","parameters"]
docstr: compute the population, i.e. the statistics of allocation
  
  Parameters
  ----------
  like array of shape (nb_samples, self.k):
    the likelihood of each item being in each class
docstr_tok: ["compute","the","population",",","i.e",".","the","statistics","of","allocation","Parameters","--","--","--","--","--","like","array","of","shape","(","nb_samples",",","self.k",")",":","the","likelihood","of","each","item","being","in","each","class"]
code: def pop(self, like, tiny=1.0e-15):
      """
      compute the population, i.e. the statistics of allocation
  
      Parameters
      ----------
      like array of shape (nb_samples, self.k):
        the likelihood of each item being in each class
      """
      slike = np.maximum(tiny, np.sum(like, 1))
      nlike = (like.T / slike).T
      return np.sum(nlike, 0)
  
imports: import math
  import numpy
  import numpy.random
  from scipy.linalg import cholesky, eigvalsh, inv
  from scipy.special import gammaln
  from .gmm import GMM
  from .utils import kmeans
label_desc: Use image node_id 9 for calling the VBGMM obj's underlying member method code with example usage: obj.pop(like, tiny) and returns: np