New test file for review from axm1820
2 Python tests and one R test
Reservoir Sampling v1.
Added LRResult object to encapsulate results (instead of just Json).
Merge remote-tracking branch 'upstream/master'
can be used by others now
Fix mystery NPE
Merge branch 'master' of github.com:0xdata/h2o
Fix a missing Nil in Shalala example.
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);
Merge branch 'master' of https://github.com/0xdata/h2o
fix for back button in browser + typeahead
Comments in code should be readable.
a little bit tricky. The quantile thresholds don't necessarily align to the bin boundaries.
So at the beginning and end, you have to add a percentage of the bin range, when you're creating a value for a quantile threshold..by making an assumption of linear distribution within the bin, which is true except for the last bin, which is potentially truncated from the expected bin size by _maxs[0]. So you want to change the linear distribution guess to the start of that bin, to _maxs[0]. Since you start with _mins[0], you don't have to worry about he other side. I eyeballed a bunch of cases, and this seems good. (The key thing: if the histogram is accurate, then just create the quantile estimate correctly from the histogram, accounting for these issues)
Merge branch 'master' of https://github.com/0xdata/h2o
get sync'ed with current legal exec expressions
Raise the overflow limit to the Long limit

Exponents below 1e18 still fit in a 64-bit long.
Also check scaling overflow in fewer situations.
Weaken assert to a warn; seeing 0 binsz
Add new time parse to VA
Merge branch 'master' of github.com:jgustave/h2o into jgustave-master

Conflicts:
	h2o-scala/build.sbt