KMedoids and MinibatchKMedoids

1. Two new mixtape estimators, `KMedoids` and `MinibatchKMedoids`.
2. Bugfix to libdistance, fixing the issue that was causing some sporadic test failures (e.g. in #306)
3. New function in libdistance to calculate sum of distances of pairs of elements
4. For RMSD, libdistance now requires the trajecrories to be precentered.
5. New example notebook, based code from sklearn for comparing clusterings
![image](https://cloud.githubusercontent.com/assets/641278/5100256/feb1047a-6f58-11e4-9964-ac2ef532c50f.png)

All of KCenters, RegularSpatial, KMedoids, and MinibatchKMedoids now use the same code for computing distances. They accept any of the regular vector distance metrics (euclidean, cityblock, cosine, etc) when the argument to `fit()` is a list of numpy arrays, and they accept `metric='rmsd'` when the argument to `fit()` is a list of trajectories. There is not `atomindices`argument -- if you want to tune the atomindices, you need to call `Trajectory.atom_slice` or `Trajectory.restrict_atoms` to cut the trajectorys down to only contain the atoms you want to compute with.