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:  Summarize the results for each model for h steps of the algorithm
  
  Parameters
   ----------
   h : int
       How many steps to run the aggregating algorithm on 
  
   Returns
   ----------
   - pd.DataFrame of losses for each model       
   
docstr_tok: ["Summarize","the","results","for","each","model","for","h","steps","of","the","algorithm","Parameters","--","--","--","--","--","h",":","int","How","many","steps","to","run","the","aggregating","algorithm","on","Returns","--","--","--","--","--","-","pd.DataFrame","of","losses","for","each","model"]
code: def summary(self, h):
      """
       Summarize the results for each model for h steps of the algorithm
  
      Parameters
       ----------
       h : int
           How many steps to run the aggregating algorithm on
  
       Returns
       ----------
       - pd.DataFrame of losses for each model
      """
      _, losses, _ = self.run(h=h)
      df = pd.DataFrame(losses)
      df.index = ["Ensemble"] + self.model_names
      df.columns = [self.loss_name]
      return df
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 13 for calling the Aggregate obj's underlying member method code with example usage: obj.summary(h) and returns: df