can be used by others now
Added a number of input parameters to neural nets.
Cosmetic changes for the NN model page. Show time left, etc.
Merge branch 'arno-deeplearning2'
Fix for PUB-410. Set max cols to display to max integer representation on user machine.
Merge branch 'master' of https://github.com/0xdata/h2o
When guessing headers, recognize dates as numbers

Also month is zero-based to match R's use of unix.
Merge branch 'master' of github.com:0xdata/h2o
Loosen the frame compatibility check

and move it into it's own function.
Complain only for misaligned Vecs of more than 1e4 rows.
just some stuff for exploring h2o quantiles (from summary, not exec)
Merge branch 'master' of github.com:0xdata/h2o

Conflicts:
	R/tests/testdir_golden/runit_NOPASS_glm2_9_iris_golden.R
back button in browser preserves user inputs
Merge branch 'master' of https://github.com/0xdata/h2o
Turn off forced_load_balancing by default.
Add option to limit the size of printed confusion matrices in JSON/GUI.
Add some Javadoc comments.
Revert "back button in browser preserves user inputs"

This broke auto-completion.

This reverts commit 5abf73d9c6bd6fcdd1a0382ade6a686fdf950a6d.
Merge branch 'arno-deeplearning2'
Max scoring duty cycle: 10%.
Set default mini-batch size to 0 (use entire dataset).
Merge branch 'master' of https://github.com/0xdata/h2o
taking a chance here. I don't think this is a final answer, but will be interesting to see what Irene thinks for R compare. I did some compares and I think it matches our R desires better. Basically return the estimated sampleQuantile always for Summary2. Don't use the new sampleQuantile that was introduced for Summary2.

And get rid of the 0.5*.. addition, which I think gets you the estimated midpoint for the bin implied by the threshold. For R, they want the 0.33/0.5/0.66 best estimates. Since our thresholds define those as bin edges (starts) ..the estimated midpoint, is not the best estimate for R.

We have the problem that for emulation of other quantile bin reports, we probably want the estimates for centers of a 0.5 "bin". it all depends on whether you think a 0.5 bin starts at 0.5, or is centered at 0.5. We make it start at 0.5 but return an estimate for 0.5 + half bin.

I put // kbn in the two places changed for easy undo. Only Summary2

here's where I think the half bin thing was
      // kbn. change so we get estimate at the threshold, not midpoint of bin.
      // I think this matches what R wants for a threshold. But probably doesn't match
      // what a binned quantile wants. (should we produce both?)
      qtiles[j] = _mins[0] + k*_binsz;
      // was, I think for midpoint estimation of the bin implied by the thresholds
      // qtiles[j] = _mins[0] + k*_binsz + ((_binsz > 1)?0.5*_binsz:0);