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: Multiply pointwise the current weights with a new array of weights.
  
  Parameters
  ----------
  new_array : np.array()
      input array for pointwise multiplication
docstr_tok: ["Multiply","pointwise","the","current","weights","with","a","new","array","of","weights",".","Parameters","--","--","--","--","--","new_array",":","np.array","(",")","input","array","for","pointwise","multiplication"]
code: def _modify_weights(self, new_array):
      """Multiply pointwise the current weights with a new array of weights.
  
      Parameters
      ----------
      new_array : np.array()
          input array for pointwise multiplication
      """
      self.weights = self.weights * new_array
      self.weights /= np.sum(self.weights)
  
imports: import numpy
  from scipy.optimize import bisect, nnls
label_desc: Use image node_id 3 for calling the _PredictionWeightedEnsembler obj's underlying member method code with example usage: obj._modify_weights(new_array) without return types