  
          clustering_method = self._get_clustering_method(num_predictions, dims)
  
          kwargs = dict(method=method)
          if clustering_method == "linkage":
              kwargs["preserve_input"] = False
              func = linkage
          else:
              func = linkage_vector
  
 
</s> remove         else:
            func = linkage_vector
 </s> add  </s> remove         clustering_method = self._get_clustering_method(num_predictions, dims)

 </s> add  </s> remove         print(result_linkage.shape)
        exit(0)
        result_order = self.seriation(result_linkage,
                                      num_predictions,
                                      num_predictions + num_predictions - 2)

 </s> add         result_order = self._seriation(result_linkage,
                                       num_predictions,
                                       num_predictions + num_predictions - 2) </s> remove             retval = "linkage"
 </s> add             retval = False </s> remove         linkage_required = (((item_count ** 2) * np_float) / 1.7) / divider
 </s> add         linkage_required = (((item_count ** 2) * np_float) / 1.8) / divider
        if self._use_vector_linkage(num_predictions, dims):
            func = linkage_vector
        else:
  
          clustering_method = self._get_clustering_method(num_predictions, dims)
  
          kwargs = dict(method=method)
        if self._use_vector_linkage(num_predictions, dims):
            func = linkage_vector
        else:
              kwargs["preserve_input"] = False
              func = linkage
          else:
              func = linkage_vector
  
keep keep keep keep replace keep keep keep keep keep
 bugfix: tools.preview - Fix for new icons
bugfix: lib.vgg_face2_keras - Remove debug code
documentation: lib.vgg_face2_keras 
lib/vgg_face2_keras.py