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: Make predictions by taking weighted average of forecaster predictions.
  
  Parameters
  ----------
  y_pred : np.array(), shape=(time_axis,estimator_axis)
      array with predictions from the estimators
  
  Returns
  -------
  predictions : np.array(), shape=(time_axis)
      array with our predictions
docstr_tok: ["Make","predictions","by","taking","weighted","average","of","forecaster","predictions",".","Parameters","--","--","--","--","--","y_pred",":","np.array","(",")",",","shape=","(","time_axis",",","estimator_axis",")","array","with","predictions","from","the","estimators","Returns","--","--","--","-","predictions",":","np.array","(",")",",","shape=","(","time_axis",")","array","with","our","predictions"]
code: def _predict(self, y_pred):
      """Make predictions by taking weighted average of forecaster predictions.
  
      Parameters
      ----------
      y_pred : np.array(), shape=(time_axis,estimator_axis)
          array with predictions from the estimators
  
      Returns
      -------
      predictions : np.array(), shape=(time_axis)
          array with our predictions
      """
      prediction = np.dot(self.weights, y_pred)
  
      return prediction
  
imports: import numpy
  from scipy.optimize import bisect, nnls
label_desc: Use image node_id 2 for calling the _PredictionWeightedEnsembler obj's underlying member method code with example usage: obj._predict(y_pred) and returns: prediction