Hi. 2019-10-28 22:12 UTC+01:00, Alex Herbert <alex.d.herb...@gmail.com>: > > >> On 28 Oct 2019, at 17:55, Eric Barnhill <ericbarnh...@gmail.com> wrote: >> >> Here is a schematic for how the interface might be made more abstract. >> >> https://imgur.com/a/izx5Xkh <https://imgur.com/a/izx5Xkh> > > Regression and RegressionResults both have a predict method with the same > signature. > >> >> In this case, we may want to just implement the simplest case, using >> Matrix >> and double[], for now. >> >> In principle the RegressionMetric class could extend a Metrics class >> later. >> >> Do you feel this would set up the library better for the future? > > I know that the use case for a diagonal matrix only was put forward > previously. So I can see the Matrix abstraction as useful. But should this > then be Matrix<E>. > > You have Vector<E> for most methods to pass a 1D set of numeric data. But > the RegressionData.of method accepts a double[]. This should also be > Vector<E>. > > I am assuming that Vector is an abstraction of a 1D data object. > > What are the possible values for <E>? > > Double > double[] > Possibly complex numbers. > … ? > > Such that Matrix<E> and Vector<E> just denote that the analysis is done on a > matrix and vector of the same type. > > This would then require abstraction of all operations required by the > regression objects such as: > > Vector<E> = Matrix<E>.multiply(Vector<E>) > Matrix<E> = Matrix<E>.transpose()
Is "Vector" necessary? Gilles > Etc. > > Then you start by using concrete classes for Matrix<double[]> and > Vector<double[]>. > > I see that the nomenclature Matrix<double[]> is a bit of a misnomer as it > may be confused for Matrix<double[][]>. So this would be documented as <E> > is the type of the entire data for a single matrix dimension. The matrix is > actually a E[]. > > >> >> Eric > > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
