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: Compute probability distribution
docstr_tok: ["Compute","probability","distribution"]
code: def pdf(self, x):
      """Compute probability distribution"""
  
      if x.ndim == 1:
          x = x[:, np.newaxis]
      logprob = self._gmm.score_samples(x)
      return np.exp(logprob)
  
imports: import numpy
  from sklearn.mixture import GaussianMixture
label_desc: Use image node_id 3 for calling the GaussianMixture1D obj's underlying member method code with example usage: obj.pdf(x) and returns: np