Implement discrete-continuous MI

[polsys]

Closes #32.

• Implement the (Ross 2014) algorithm for unconditional case with discrete y
• Apply the same tweaks to the unconditional case (don't know for sure if documented in literature)
• Add discrete_y parameter to estimate_mi()

Simply put, the idea is to modify the distance metric so that the different y values are "infinitely" far apart and therefore don't show up in neighbor searches of other y planes. With sufficiently spaced y, the existing algorithm would produce exactly the same results.

The implementation partitions the space according to y values, using one tree for each y. This reduces the vectorization benefit, but the individual searches are cheaper; by the simple benchmark, this case is faster than the continuous-continuous case.