gsll and gslp  ------ learning linear models in the least squares sense ----- regression only.

questions, bug report to olivier.teytaud@inria.fr    		(last update Jun 10, 2009)

0) what are gsll and gslp:
          0a) gsll takes as input 
                  -a matrix (predictor variables)
		  -a vector (variable to be predicted)
           and outputs a model (= predictor, = a linear function).
          0b) gslp takes as input
		  -a matrix (predictor variables)
                  -a model (as provided by gslp)
           and outputs predicted values.
   (see examples below)

1) download: you should find easily on www (google gsll gslp)
     (i) binary (statically linked) for linux 64 bits, linux 32 bits
     (ii) source code (requires linking with the gsl (gnu scientific library) which can be found by 
                sudo apt-get install gsl-bin gsl-ref-psdoc gsl-doc-pdf gsl-ref-html libgsl0ldbl    #### ubuntu / debian
                urpmi gsl-bin gsl-ref-psdoc gsl-doc-pdf gsl-ref-html libgsl0ldbl 

2) compiling 
  2a) compiling (if you downloaded the source) on a system with gsl
	g++ -o gsll gsll.c -lgsl -lgslcblas -lm 
	g++ -o gslp gslp.c -lgsl -lgslcblas -lm 

  2b) compiling (if you downloaded the source) on a system without gsl
   adapt the path to the gsl:

   g++ -I./gsl-1.9/ -o gsll gsll.c -L./gsl-1.9/.libs -lgsl -lm -Lgsl-1.9/cblas/.libs/ -lgslcblas
   g++ -I./gsl-1.9/ -o gslp gslp.c -L./gsl-1.9/.libs -lgsl -lm -Lgsl-1.9/cblas/.libs/ -lgslcblas

3) running

    You need a learning set (one is provided below for the example).
    Then, you can do as follows:

    ============================= example ==============
    #### learning
   ./gsll learningset model
    #### testing
   ./gslp learningset model outputvalues ### here the learning set is equal to the 
                                         ### test set - absolutely not required of course
    #### checking
    paste outputvalues learningset | awk ' { print $1 , " ", $2 }'      #### prints the predicted and the real values
    ======================================================

4) compatibility
    Should compile on any system
    Format compliant with libsvm / liblinear, but adapted for regression.

5) License
This is under Cecill license (GPL-compliant). Learn more on http://www.cecill.info/.
This comes with ABSOLUTELY NO WARRANTY, to the extent permitted by applicable law.