[MRG] faster convergence reporting in RBMs

Our RBMs used to take about 45% of their time just computing convergence information. This PR brings that down to a few percent. Also, sparse matrices are no longer densified in `score_samples`, given a memory reduction of several orders of magnitude (for my typical data, that can be up to eight orders). Finally, the reported figures should be more realistic because they reflect the actual change that occurred during an iteration.

The downside is that the `fit` procedure requires a bit more memory now because it computes pseudo-likelihood for the entire input matrix.

Ping @vene, and maybe @IssamLaradji will find this interesting too.