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: Run out-of-sample predicitons for Aggregate algorithm
  (This only works for non-exogenous variable models currently)
  
  Parameters
  ----------
  h : int
      How many out-of-sample steps to run the aggregating algorithm on 
  
  h_train : int
      How many in-sample steps to warm-up the ensemble weights on
  
  Returns
  ----------
  - pd.DataFrame of Aggregate out-of-sample predictions
docstr_tok: ["Run","out-of-sample","predicitons","for","Aggregate","algorithm","(","This","only","works","for","non-exogenous","variable","models","currently",")","Parameters","--","--","--","--","--","h",":","int","How","many","out-of-sample","steps","to","run","the","aggregating","algorithm","on","h_train",":","int","How","many","in-sample","steps","to","warm-up","the","ensemble","weights","on","Returns","--","--","--","--","--","-","pd.DataFrame","of","Aggregate","out-of-sample","predictions"]
code: def predict(self, h, h_train=40):
      """Run out-of-sample predicitons for Aggregate algorithm
      (This only works for non-exogenous variable models currently)
  
      Parameters
      ----------
      h : int
          How many out-of-sample steps to run the aggregating algorithm on
  
      h_train : int
          How many in-sample steps to warm-up the ensemble weights on
  
      Returns
      ----------
      - pd.DataFrame of Aggregate out-of-sample predictions
      """
  
      predictions, index = self._model_predict(h)
      normalized_weights = self.run(h=h_train)[0][-1, :]
      ensemble_prediction = np.zeros(h)
  
      for t in range(h):
          ensemble_prediction[t] = np.dot(
              normalized_weights, predictions.values[t, :]
          )
  
      result = pd.DataFrame(ensemble_prediction)
      result.index = index
  
      return result
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 11 for calling the Aggregate obj's underlying member method code with example usage: obj.predict(h, h_train) and returns: result