class_docstr: Use hedge-style ensemble algorithms.
  
  Wrapper for hedge-style ensemble algorithms with a forecasting horizon and
  normalizing constant.
  
  Parameters
  ----------
  n_estimators : float
      number of estimators
  T : int
      forecasting horizon (in terms of timesteps)
  a : float
      normalizing constant
  loss_func : function
      loss function which follows sklearn.metrics API, for updating weights
class_docstr_tok: ["Use","hedge-style","ensemble","algorithms",".","Wrapper","for","hedge-style","ensemble","algorithms","with","a","forecasting","horizon","and","normalizing","constant",".","Parameters","--","--","--","--","--","n_estimators",":","float","number","of","estimators","T",":","int","forecasting","horizon","(","in","terms","of","timesteps",")","a",":","float","normalizing","constant","loss_func",":","function","loss","function","which","follows","sklearn.metrics","API",",","for","updating","weights"]
code: def __init__(self, n_estimators=10, T=10, a=1, loss_func=None):
      super().__init__(n_estimators=n_estimators, loss_func=loss_func)
      self.T = T
      self.a = a
      self._uniform_weights(n_estimators)
      self.loss_func = loss_func
  
imports: import numpy
  from scipy.optimize import bisect, nnls
label_desc: Use image node_id 1 to create a new HedgeExpertEnsemble object from inherited base classes: _PredictionWeightedEnsembler with example: obj = HedgeExpertEnsemble(n_estimators, T, a, loss_func)