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