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 > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org