code: def rmsle(expected, predicted):
      log_expected = np.log1p(expected + 1)
      log_predicted = np.log1p(predicted + 1)
      squared_log_error = np.square(log_expected - log_predicted)
      return np.sqrt(np.mean(squared_log_error))
  
file_path: neupy/examples/rbfn/grnn_params_selection.py
imports: import heapq
  import numpy
  from sklearn import datasets
  from sklearn.model_selection import RandomizedSearchCV
  from neupy import algorithms
label_desc: Use image node_id 1 for calling a global function with example usage: rmsle(expected, predicted) and returns: np