There are about 10 files using classes from the math3.stat package in
the examples I mentioned. I have stayed away from math4 while it's
still snapshot.
Repo: https://github.com/paulk-asert/groovy-data-science

Slides: https://speakerdeck.com/paulk/groovy-data-science

Most of the examples are in the subprojects/HousePrices project with a
few others just using StatUtil.

It's not my full-time day job to be using those classes but I'd be
keen to have those examples working nicely.

Cheers, Paul.

On Fri, Jul 19, 2019 at 9:11 PM Gilles Sadowski <gillese...@gmail.com> wrote:
>
> Hi.
>
> Your experience as a user of "Commons Math" would be most useful
> to help us craft a better (or, at least, no worse) design for "Commons
> Statistics".
> Would you share pointers to actual use-cases?
>
> Thanks,
> Gilles
>
> 2019-07-19 7:03 UTC+02:00, Paul King <paul.king.as...@gmail.com>:
> > Cool. I'd be keen to try out the API, when you are ready, in my
> > "Apache Groovy for data science" examples which currently use the
> > commons math3 classes.
> >
> > Cheers, Paul.
> >
> > On Fri, Jul 19, 2019 at 9:51 AM Gilles Sadowski <gillese...@gmail.com>
> > wrote:
> >>
> >> Hi.
> >>
> >> Le ven. 19 juil. 2019 à 01:45, Paul King <paul.king.as...@gmail.com> a
> >> écrit :
> >> >
> >> > How does this relate to the OLS classes in commons math?
> >> > https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/stat/regression/OLSMultipleLinearRegression.html
> >>
> >> The new "Commons Statistics" component purports to replace the
> >> functionality
> >> currently defined in the package "org.apache.commons.math4.stat" of
> >> "Commons
> >> Math.
> >>
> >> Regards,
> >> Gilles
> >>
> >> > On Fri, Jul 19, 2019 at 8:50 AM Eric Barnhill <ericbarnh...@gmail.com>
> >> > wrote:
> >> > >
> >> > > I suggested the following grammar to aim for in our meeting today with
> >> > > the
> >> > > developing OLS module. If you see anything you'd prefer to change
> >> > > let's
> >> > > establish it now , if anyone doesn't like it later, it's on me.
> >> > >
> >> > > RegressionData data = RegressionDataLoader.of(double[][] y, double[]
> >> > > x);
> >> > > Regression ols = new OLSRegression();
> >> > > RegressionResults results = ols.regress(data);
> >> > > betas = results.getBetas() ;
> >> > >
> >> > > where:
> >> > > RegressionData is an interface
> >> > > RegressionDataLoader is a factory class and of() a (possibly
> >> > > overloaded)
> >> > > static method
> >> > > Regression is an interface, implemented by OLSRegression
> >> > > RegressionResults is an interface, the specific class returned is
> >> > > OLSResults which implements it.
> >> > > betas are the intercept and slopes of the regression model
> >> > >
> >> > > I think this preserves abstraction at the levels desired, since we
> >> > > will
> >> > > want in future flexibility as to regression type, posslble state
> >> > > parameters
> >> > > set on the regression object, and results contents and format. But
> >> > > also
> >> > > doesn't take on any unnecessary abstractions.
> >> > >
> >> > > Eric
>
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