class_docstr: Exponentially Weighted Average regressor.
  
  Parameters
  ----------
  models
      The regressors to hedge.
  loss
      The loss function that has to be minimized. Defaults to `optim.losses.Squared`.
  learning_rate
      The learning rate by which the model weights are multiplied at each iteration.
  
  Examples
  --------
  
  >>> from river import datasets
  >>> from river import ensemble
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import optim
  >>> from river import preprocessing
  >>> from river import stream
  
  >>> optimizers = [
  ...     optim.SGD(0.01),
  ...     optim.RMSProp(),
  ...     optim.AdaGrad()
  ... ]
  
  >>> for optimizer in optimizers:
  ...
  ...     dataset = datasets.TrumpApproval()
  ...     metric = metrics.MAE()
  ...     model = (
  ...         preprocessing.StandardScaler() |
  ...         linear_model.LinearRegression(
  ...             optimizer=optimizer,
  ...             intercept_lr=.1
  ...         )
  ...     )
  ...
  ...     print(optimizer, evaluate.progressive_val_score(dataset, model, metric))
  SGD MAE: 0.558735
  RMSProp MAE: 0.522449
  AdaGrad MAE: 0.477289
  
  >>> dataset = datasets.TrumpApproval()
  >>> metric = metrics.MAE()
  >>> hedge = (
  ...     preprocessing.StandardScaler() |
  ...     ensemble.EWARegressor(
  ...         [
  ...             linear_model.LinearRegression(optimizer=o, intercept_lr=.1)
  ...             for o in optimizers
  ...         ],
  ...         learning_rate=0.005
  ...     )
  ... )
  
  >>> evaluate.progressive_val_score(dataset, hedge, metric)
  MAE: 0.496298
  
  References
  ----------
  [^1]: [Online Learning from Experts: Weighed Majority and Hedge](https://www.shivani-agarwal.net/Teaching/E0370/Aug-2011/Lectures/20-scribe1.pdf)
  [^2]: [Wikipedia page on the multiplicative weight update method](https://www.wikiwand.com/en/Multiplicative_weight_update_method)
  [^3]: [Kivinen, J. and Warmuth, M.K., 1997. Exponentiated gradient versus gradient descent for linear predictors. information and computation, 132(1), pp.1-63.](https://users.soe.ucsc.edu/~manfred/pubs/J36.pdf)
class_docstr_tok: ["Exponentially","Weighted","Average","regressor",".","Parameters","--","--","--","--","--","models","The","regressors","to","hedge",".","loss","The","loss","function","that","has","to","be","minimized",".","Defaults","to","`","optim.losses.Squared","`",".","learning_rate","The","learning","rate","by","which","the","model","weights","are","multiplied","at","each","iteration",".","Examples","--","--","--","--",">",">",">","from","river","import","datasets",">",">",">","from","river","import","ensemble",">",">",">","from","river","import","evaluate",">",">",">","from","river","import","linear_model",">",">",">","from","river","import","metrics",">",">",">","from","river","import","optim",">",">",">","from","river","import","preprocessing",">",">",">","from","river","import","stream",">",">",">","optimizers","=","[","...","optim.SGD","(","0.01",")",",","...","optim.RMSProp","(",")",",","...","optim.AdaGrad","(",")","...","]",">",">",">","for","optimizer","in","optimizers",":","...","...","dataset","=","datasets.TrumpApproval","(",")","...","metric","=","metrics.MAE","(",")","...","model","=","(","...","preprocessing.StandardScaler","(",")","|","...","linear_model.LinearRegression","(","...","optimizer=optimizer",",","...","intercept_lr=.1","...",")","...",")","...","...","print","(","optimizer",",","evaluate.progressive_val_score","(","dataset",",","model",",","metric",")",")","SGD","MAE",":","0.558735","RMSProp","MAE",":","0.522449","AdaGrad","MAE",":","0.477289",">",">",">","dataset","=","datasets.TrumpApproval","(",")",">",">",">","metric","=","metrics.MAE","(",")",">",">",">","hedge","=","(","...","preprocessing.StandardScaler","(",")","|","...","ensemble.EWARegressor","(","...","[","...","linear_model.LinearRegression","(","optimizer=o",",","intercept_lr=.1",")","...","for","o","in","optimizers","...","]",",","...","learning_rate=0.005","...",")","...",")",">",">",">","evaluate.progressive_val_score","(","dataset",",","hedge",",","metric",")","MAE",":","0.496298","References","--","--","--","--","--","[","^1","]",":","[","Online","Learning","from","Experts",":","Weighed","Majority","and","Hedge","]","(","https",":","\/\/www.shivani-agarwal.net\/Teaching\/E0370\/Aug-2011\/Lectures\/20-scribe1.pdf",")","[","^2","]",":","[","Wikipedia","page","on","the","multiplicative","weight","update","method","]","(","https",":","\/\/www.wikiwand.com\/en\/Multiplicative_weight_update_method",")","[","^3","]",":","[","Kivinen",",","J.","and","Warmuth",",","M.K.",",","1997",".","Exponentiated","gradient","versus","gradient","descent","for","linear","predictors",".","information","and","computation",",","132","(","1",")",",","pp.1-63",".","]","(","https",":","\/\/users.soe.ucsc.edu\/~manfred\/pubs\/J36.pdf",")"]
code: def learn_predict_one(self, x, y):
      y_pred_mean = 0.0
  
      # Make a prediction and update the weights accordingly for each model
      total = 0
      for i, regressor in enumerate(self):
          y_pred = regressor.predict_one(x=x)
          y_pred_mean += (
              self.weights[i] * (y_pred - y_pred_mean) / len(self)
          )
          loss = self.loss(y_true=y, y_pred=y_pred)
          self.weights[i] *= math.exp(-self.learning_rate * loss)
          total += self.weights[i]
          regressor.learn_one(x, y)
  
      # Normalize the weights so that they sum up to 1
      if total:
          for i, _ in enumerate(self.weights):
              self.weights[i] /= total
  
      return y_pred_mean
  
file_path: river/river/ensemble/ewa.py
imports: from __future__ import annotations
  import math
  from river import base, optim
  from river import linear_model
  from river import preprocessing
label_desc: Use image node_id 3 for calling the EWARegressor obj's underlying member method code with example usage: obj.learn_predict_one(x, y) and returns: y_pred_mean