Micro-optimisation of ALS

This change replaces some Scala `for` and `foreach` constructs with `while` constructs.  There may be a slight performance gain on the order of 1-2% when training an ALS model.

I trained an ALS model on the Movielens 10M-rating dataset repeatedly both with and without these changes.  All 7 runs in both columns were done in a Scala `for` loop like this:

    for (iter <- 0 to 10) {
      val before = System.currentTimeMillis()
      val model = ALS.train(rats, 20, 10)
      val after = System.currentTimeMillis()
      println("%d ms".format(after-before))
      println("rmse %g".format(computeRmse(model, rats, numRatings)))
    }

The timings were done on a multiuser machine, and I stopped one set of timings after 7 had been completed.  It would be nice if somebody with dedicated hardware could confirm my timings.

    After           Before
    121980 ms       122041 ms
    117069 ms       117127 ms
    115332 ms       117523 ms
    115381 ms       117402 ms
    114635 ms       116550 ms
    114140 ms       114076 ms
    112993 ms       117200 ms

Ratios are about 1.0005, 1.0005, 1.019, 1.0175, 1.01671, 0.99944, and 1.03723.  I therefore suspect these changes make for a slight performance gain on the order of 1-2%.