+1 On Sat, Feb 18, 2017 at 10:48 PM, Mike Dusenberry <dusenberr...@gmail.com> wrote:
> +1 > > > -- > > Michael W. Dusenberry > GitHub: github.com/dusenberrymw > LinkedIn: linkedin.com/in/mikedusenberry > > On Sat, Feb 18, 2017 at 10:04 PM, Niketan Pansare <npan...@us.ibm.com> > wrote: > > > +1 > > > > Thanks, > > > > Niketan > > > > > On Feb 18, 2017, at 10:01 PM, Arvind Surve <ac...@yahoo.com.INVALID> > > wrote: > > > > > > +1 ------------------ Arvind Surve Spark Technology Center > > http://www.spark.tc/ > > > > > > From: Felix Schüler <fschue...@posteo.de> > > > To: dev@systemml.incubator.apache.org > > > Sent: Saturday, February 18, 2017 9:42 PM > > > Subject: Re: Weighted Statistical Estimates > > > > > > Sounds good! > > > > > > -Felix > > > > > >> On 18.02.2017 21:20, Matthias Boehm wrote: > > >> Going toward to our 1.0 release, I'd like to create consistency across > > our > > >> weighted statistics. Conceptually, theses weights represent frequency > > >> counts, i.e., multiplicities of input values. > > >> > > >> So far, our documentation does not state any restrictions on these > > weights > > >> but some runtime operations require integer data (I), while others > allow > > >> arbitrary floating point data as indicated below: > > >> > > >> * moment > > >> * cov > > >> * aggregate > > >> * table > > >> * median (I) > > >> * quantile (I) > > >> * interQuartileMean (I) > > >> > > >> This can lead to unexpected errors as shown by recent issues such as > > >> SYSTEMML-1265. Looking back to R and its packages like Hmisc or > > reldist, it > > >> turns out that they all allow arbitrary weights. > > >> > > >> So, relaxing any restrictions of integer weights seems like the right > > >> choice. As this changes the external behavior - albeit in a > generalizing > > >> manner - we should make this change now. If you have any concerns, let > > me > > >> know. > > >> > > >> Regards, > > >> Matthias > > >> > > > > > > > > > > > > > >
