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: Plot the weights from the aggregating algorithm
  
  Parameters
  ----------
  h : int
      How many steps to run the aggregating algorithm on 
  
  Returns
  ----------
  - A plot of the weights for each model constituent over time
docstr_tok: ["Plot","the","weights","from","the","aggregating","algorithm","Parameters","--","--","--","--","--","h",":","int","How","many","steps","to","run","the","aggregating","algorithm","on","Returns","--","--","--","--","--","-","A","plot","of","the","weights","for","each","model","constituent","over","time"]
code: def plot_weights(self, h, **kwargs):
      """Plot the weights from the aggregating algorithm
  
      Parameters
      ----------
      h : int
          How many steps to run the aggregating algorithm on
  
      Returns
      ----------
      - A plot of the weights for each model constituent over time
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      weights, _, _ = self.run(h=h)
      plt.figure(figsize=figsize)
      plt.plot(self.index[-h:], weights)
      plt.legend(self.model_names)
      plt.show()
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 10 for calling the Aggregate obj's underlying member method code with example usage: obj.plot_weights(h) without return types