class_docstr: A class to model one-parameter exponential
  families.
  
  INPUTS:
     link      -- a Link instance
     variance  -- a variance function (models means as a function
                  of mean)
class_docstr_tok: ["A","class","to","model","one-parameter","exponential","families",".","INPUTS",":","link","--","a","Link","instance","variance","--","a","variance","function","(","models","means","as","a","function","of","mean",")"]
docstr: Linear predictors based on given mu values.
  
  INPUTS:
     mu   -- mean parameter of one-parameter exponential family
  
  OUTPUTS: eta
     eta  -- link(mu), linear predictors, based on
             mean parameters mu
docstr_tok: ["Linear","predictors","based","on","given","mu","values",".","INPUTS",":","mu","--","mean","parameter","of","one-parameter","exponential","family","OUTPUTS",":","eta","eta","--","link","(","mu",")",",","linear","predictors",",","based","on","mean","parameters","mu"]
code: def predict(self, mu):
      """
      Linear predictors based on given mu values.
  
      INPUTS:
         mu   -- mean parameter of one-parameter exponential family
  
      OUTPUTS: eta
         eta  -- link(mu), linear predictors, based on
                 mean parameters mu
  
      """
      return self.link(mu)
  
imports: import numpy
  from .None import links
  from .None import varfuncs
label_desc: Use image node_id 8 for calling the Family obj's underlying member method code with example usage: obj.predict(mu) and returns: self