code: def _get_maximal_adaptation(self, previous_weights):
          
          
          dimension = len(self._data[0])
          maximal_adaptation = 0.0
          
          for neuron_index in range(self._size):
              for dim in range(dimension):
                  current_adaptation = previous_weights[neuron_index][dim] - self._weights[neuron_index][dim]
                          
                  if current_adaptation < 0:
                      current_adaptation = -current_adaptation
                          
                  if maximal_adaptation < current_adaptation:
                      maximal_adaptation = current_adaptation
                      
          return maximal_adaptation
docs: !
          @brief Calculates maximum changes of weight in line with comparison between previous weights and current weights.
          
          @param[in] previous_weights (list): Weights from the previous step of learning process.
          
          @return (double) Value that represents maximum changes of weight after adaptation process.