class_docstr: Simple class to work with 1D mixtures of Gaussians
  
  Parameters
  ----------
  means : array_like
      means of component distributions (default = 0)
  sigmas : array_like
      standard deviations of component distributions (default = 1)
  weights : array_like
      weight of component distributions (default = 1)
class_docstr_tok: ["Simple","class","to","work","with","1D","mixtures","of","Gaussians","Parameters","--","--","--","--","--","means",":","array_like","means","of","component","distributions","(","default","=","0",")","sigmas",":","array_like","standard","deviations","of","component","distributions","(","default","=","1",")","weights",":","array_like","weight","of","component","distributions","(","default","=","1",")"]
docstr: Random sample
docstr_tok: ["Random","sample"]
code: def sample(self, size):
      """Random sample"""
      return self._gmm.sample(size)
  
imports: import numpy
  from sklearn.mixture import GaussianMixture
label_desc: Use image node_id 2 for calling the GaussianMixture1D obj's underlying member method code with example usage: obj.sample(size) and returns: self