Change sampling strategy

[tribhuvanesh]

Right now, by setting solverOptions.sampleFrac, say 0.5, the master node samples 50% of the training data and hands each of the K workers 50/K% of the data.

This does not scale well. Since, adding more workers forces the users to change the sampling fraction in order to scale.
Rather, each worker should independently sample the data and then perform the computation.
Doing so implies adding more workers results in faster convergence.