class_docstr: Aggregation Algorithm
  
  Parameters
  ----------
  learning_rate : float
      Learning rate for the Aggregation algorithm
  
  loss_type : string
      'absolute' or 'squared'
  
  match_window : int
      (default: 10) how many of the observations at the end of a model time 
      series to assess whether the data being used in each model is the same
docstr: Naive tuning of the the learning rate on the in-sample data
  
  Parameters
  ----------
  h : int
      How many steps to run Aggregate on
  
  parameter_list: list
      List of parameters to search for a good learning rate over
  
  Returns
  ----------
  - Void (changes self.learning_rate)
docstr_tok: ["Naive","tuning","of","the","the","learning","rate","on","the","in-sample","data","Parameters","--","--","--","--","--","h",":","int","How","many","steps","to","run","Aggregate","on","parameter_list",":","list","List","of","parameters","to","search","for","a","good","learning","rate","over","Returns","--","--","--","--","--","-","Void","(","changes","self.learning_rate",")"]
code: def tune_learning_rate(self, h, parameter_list=None):
      """Naive tuning of the the learning rate on the in-sample data
  
      Parameters
      ----------
      h : int
          How many steps to run Aggregate on
  
      parameter_list: list
          List of parameters to search for a good learning rate over
  
      Returns
      ----------
      - Void (changes self.learning_rate)
      """
  
      if parameter_list is None:
          parameter_list = [
              0.001,
              0.01,
              0.1,
              1.0,
              10.0,
              100.0,
              1000.0,
              10000.0,
              100000.0,
          ]
  
      for parameter in parameter_list:
          self.learning_rate = parameter
          _, losses, _ = self.run(h, recalculate=False)
          loss = losses[0]
          if parameter == parameter_list[0]:
              best_rate = parameter
              best_loss = loss
          else:
              if loss < best_loss:
                  best_loss = loss
                  best_rate = parameter
  
      self.learning_rate = best_rate
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 8 for calling the Aggregate obj's underlying member method code with example usage: obj.tune_learning_rate(h, parameter_list) without return types