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: Fitted values based on linear predictors eta.
  
  INPUTS:
     eta  -- values of linear predictors, say,
             X beta in a generalized linear model.
  
  OUTPUTS: mu
     mu   -- link.inverse(eta), mean parameter based on eta
docstr_tok: ["Fitted","values","based","on","linear","predictors","eta",".","INPUTS",":","eta","--","values","of","linear","predictors",",","say",",","X","beta","in","a","generalized","linear","model",".","OUTPUTS",":","mu","mu","--","link.inverse","(","eta",")",",","mean","parameter","based","on","eta"]
code: def fitted(self, eta):
      """
      Fitted values based on linear predictors eta.
  
      INPUTS:
         eta  -- values of linear predictors, say,
                 X beta in a generalized linear model.
  
      OUTPUTS: mu
         mu   -- link.inverse(eta), mean parameter based on eta
  
      """
      return self.link.inverse(eta)
  
imports: import numpy
  from .None import links
  from .None import varfuncs
label_desc: Use image node_id 7 for calling the Family obj's underlying member method code with example usage: obj.fitted(eta) and returns: self