Refactor LMNN as a triplets learner

[zdk123]

Addresses my request in #210.

1. Introduces a _BaseLMNN/ LMNN class to operate on triplets, s.t. d(triplets[i, 0],triplets[i, 1]) < d(triplets[i, 0], triplets[i, 2]) - the same setup as SCML.

• from this definition of triplets, create a 'label mask', an nxn matrix with mask[i,j] = 1 and mask[i,k] = -1 for the set triplet[i,j,k] (else 0).
• This simply reformulates the loss_grad / _find_impostors to operate on this label mask (imposters that violate the large margin) are detected by evaluating the squared distances of entries implied by -1 values in the mask.
• the desired parameter k can be inferred from the triplets by counting unique occurrences of genuine and imposter pairs

2. Renames LMNN to LMNN_Supervised.