Distance funtion of knn classifier

Dear,none

I used Bayesian Optimization to choose the best parameters for a knn classifier.

After optimizing I got a knn with JACCARD distance and INVERSE distance weights, and no standardization.

I suppose this means that the nearest points are those that are the most distant (when using the jaccard).

Is anyone able to tell me how may I reproduce the INVERSE jaccard (with no standardization) by using the KDTreeSearcher or the ExhaustiveSearcher??

In other words, I would like to find, for each point in a test set, all the points that are the nearest, according to the distance function used by the optimized knn classifier.

THANKS a lot for any help!