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: Weights for IRLS step.
  
  w = 1 / (link'(mu)**2 * variance(mu))
  
  INPUTS:
     mu  -- mean parameter in exponential family
  
  OUTPUTS:
     w   -- weights used in WLS step of GLM/GAM fit
docstr_tok: ["Weights","for","IRLS","step",".","w","=","1","\/","(","link","'","(","mu",")","*","*","2","*","variance","(","mu",")",")","INPUTS",":","mu","--","mean","parameter","in","exponential","family","OUTPUTS",":","w","--","weights","used","in","WLS","step","of","GLM\/GAM","fit"]
code: def weights(self, mu):
      """
      Weights for IRLS step.
  
      w = 1 / (link'(mu)**2 * variance(mu))
  
      INPUTS:
         mu  -- mean parameter in exponential family
  
      OUTPUTS:
         w   -- weights used in WLS step of GLM/GAM fit
  
      """
  
      return 1.0 / (self.link.deriv(mu) ** 2 * self.variance(mu))
  
imports: import numpy
  from .None import links
  from .None import varfuncs
label_desc: Use image node_id 4 for calling the Family obj's underlying member method code with example usage: obj.weights(mu) and returns: unknown