Copyright	(c) Guilherme S I Aldeia 2018 Heitor R Savegnago 2018
License	GPL-3
Maintainer	guilherme.aldeia@aluno.ufabc.edu.br heitor.rodrigues@aluno.ufabc.edu.br
Stability	experimental
Portability	POSIX
Safe Haskell	None

AInet

Description

Symbolic regression algorithm. The regression search is done by means of the IT data structure, and the general structure of the algorithm is based on the AInet algorithm (artificial imunne network).

Synopsis

type NumGen = Int
type NumClones = Int
ainet' :: NumGen -> Pop -> LeSize -> NumClones -> SupressionT -> Dataset -> IO Pop
ainet :: NumGen -> PopSize -> LeSize -> NumClones -> SupressionT -> SimplifyT -> Dataset -> IO Le

Documentation

type NumGen = Int #

Number of generations to perform the regression

type NumClones = Int #

Highest number of clones to create on the AInet algorithm

ainet' :: NumGen -> Pop -> LeSize -> NumClones -> SupressionT -> Dataset -> IO Pop #

Recursive call of the AInet algorithm, for internal use only.

ainet :: NumGen -> PopSize -> LeSize -> NumClones -> SupressionT -> SimplifyT -> Dataset -> IO Le #

Performs an AInet based symbolic regression for the given number of generations.