[ML] Speed up the lat_long function

[tveasey]

Clustering can be the bottleneck for the lat_long function.

This reworks the calculation of the distances to the selected points in k-means++ initialisation. Before we were creating a k-d tree for each point we added and looking up nearest neighbours. This is unnecessary since we can simply update the distances directly, i.e. distance_i = min(distance_i, distance(selected, x_i)). The other main speedup comes from the fact that in #1037 I reworked online k-means to remove the points buffer. This saves us memory and we can spend this memory by accumulating more points before we re-cluster.

I also made some tweaks to cutdown the number of allocations (principally by moving various variables into place). Finally, I corrected CKMeansOnline::split to copy all the parent clustering parameters into each split and made a couple of other small tidy ups.

In total, I get around a 60% improvement in runtime from these changes for CXMeansOnline.

[droberts195]

LGTM