Use k-d tree to optimize performance in visual function

[TimoSci]

The visual function, which finds the winner neuron for an arbitrary "specimen" input, was using a brute force search to find the nearest neighbor among the neuron codes.

A k-d tree search is usually faster than a brute force search, especially when doing a large number of queries over a large number of neurons.

The disadvantage of a k-d tree is that the initial construction of the tree is costly, but once constructed, search queries are fast. That's why we can't use the k-d tree in the training algorithm - we would have to reconstruct after every step.

But for functions on an already-trained SOM, like mapToSOM, a k-d tree can be advantageous, in most practical use cases.

Example of a benchmark I have done

In this PR I suggest including an optimized version of the visual function.

PS. Caching the k-d tree in the SOM struct might also be a good idea.

[TimoSci]

This pull request was replaced with a new one (#19) after some refactoring and inclusion of a test.