class_docstr: Wrapper class to handle ensemble algorithms that use multiple forecasters.
  
  This implements default methods for setting uniform weights, updating
  and prediction.
  
  Parameters
  ----------
  n_estimators : float
      number of estimators
  loss_func : function
      loss function which follows sklearn.metrics API, for updating weights
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docstr: Reset weights for n estimator to uniform weights.
  
  Parameters
  ----------
  n_estimators : int
      number of estimators
docstr_tok: ["Reset","weights","for","n","estimator","to","uniform","weights",".","Parameters","--","--","--","--","--","n_estimators",":","int","number","of","estimators"]
code: def _uniform_weights(self, n_estimators):
      """Reset weights for n estimator to uniform weights.
  
      Parameters
      ----------
      n_estimators : int
          number of estimators
      """
      self.n = n_estimators
      self.weights = np.ones(n_estimators) / n_estimators
  
imports: import numpy
  from scipy.optimize import bisect, nnls
label_desc: Use image node_id 5 for calling the _PredictionWeightedEnsembler obj's underlying member method code with example usage: obj._uniform_weights(n_estimators) without return types