text: import numpy as np
  import torch
  
  if __name__ == "__main__":
  
      loadfile = "hyper-poc_2019-07-02-11-45-47.pth"
      # loadfile = "hyper-poc_2019-07-02-11-47-48.pth"
  
      with open(loadfile, "rb") as f:
          results = torch.load(f)
  
      # First, print the best by sum of losses
      def sum_of_losses(result):
          return sum(result[1])
  
      idxs = np.argsort([sum_of_losses(result) for result in results])
  
      print(idxs)
  
      # Print best 10
      for i in idxs[:10]:
          print(results[i])
  
      print(len(results))
      print(len(results) / len(np.unique([result[1][0] for result in results])))
  