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: Outputs predictions for the Aggregate algorithm on the in-sample data
  
  Parameters
  ----------
  h : int
      How many steps to run the aggregating algorithm on 
  
  Returns
  ----------
  - pd.DataFrame of ensemble predictions
docstr_tok: ["Outputs","predictions","for","the","Aggregate","algorithm","on","the","in-sample","data","Parameters","--","--","--","--","--","h",":","int","How","many","steps","to","run","the","aggregating","algorithm","on","Returns","--","--","--","--","--","-","pd.DataFrame","of","ensemble","predictions"]
code: def predict_is(self, h):
      """Outputs predictions for the Aggregate algorithm on the in-sample data
  
      Parameters
      ----------
      h : int
          How many steps to run the aggregating algorithm on
  
      Returns
      ----------
      - pd.DataFrame of ensemble predictions
      """
      result = pd.DataFrame([self.run(h=h)[2]]).T
      result.index = self.index[-h:]
      return result
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 12 for calling the Aggregate obj's underlying member method code with example usage: obj.predict_is(h) and returns: result