Implement hard and soft EM clustering for the Gens-Domingos algorithm.

[RenatoGeh]

Description: Our Gens implementation currently clusters instances with the DBSCAN and alternatively k-means clustering algorithms. We wish to add the EM clustering as cited in [1]. Additionally, we intend on implementing both hard and soft EM, as explained in the following quote extracted from [1]:

For soft EM, where instances can be fractionally assigned to clusters, T needs to be extended with a weight for each instance, and each instance is passed to each cluster it has nonzero weight in. However, this is considerably less efficient than hard EM, where each instance is wholly assigned to its most probable cluster, and we will use the latter method.

DBSCAN and k-means function like hard EM, assigning instances to the most problable cluster.

Files: learn/gens.go, utils/cluster/em.go

References:

• [1] http://spn.cs.washington.edu/papers/slspn.pdf