% PLSR Partial Least Squares Regression%%  W = PLSR%  W = PLSR([],MAXLV,METHOD)%%  [W, INFORM] = PLSR(A,MAXLV,METHOD)%% INPUT %   A             training dataset%   MAXLV         maximal number of latent variables (will be corrected%                 if > rank(A)); %                 MAXLV=inf means MAXLV=min(size(A)) -- theoretical%                 maximum number of LV; %                 by default = inf%   METHOD       'NIPALS' or 'SIMPLS'; by default = 'SIMPLS'%% OUTPUT %   W             PLS feature extraction mapping %   INFORM        extra algorithm output%% DESRIPTION% PRTools Adaptation of PLS_TRAIN/PLS_APPLY routines. No preprocessing% is done inside this mapping. It is the user responsibility to train% preprocessing on training data and apply it to the test data.%% Crisp labels will be converted into soft labels which will be used as% a target matrix.%% In order to do regression with the smaller number of latent variables% than the number of LV's selected during trainig do% d = w.data;% d.n = new_n;% w.data = d;%% SEE ALSO% PLS_TRAIN, PLS_TRANSFORM, PLS_APPLY% Copyright: S.Verzakov, s.verzakov@ewi.tudelft.nl % Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: plsr.m,v 1.1 2007/08/28 11:00:39 davidt Exp $%function [w,inform]=plsm(par1,par2,par3)	% No dataset given: return untrained mapping.if (nargin < 1) | (isempty(par1))  if nargin < 2    par2 = inf;  end    if nargin < 3    par3 = 'SIMPLS';  end    data = {par2,par3};  w = mapping(mfilename,'untrained',data);	w = setname(w,'Partial Least Squares Regression');	returnendisdataset(par1);           % Assert that A is a dataset.% trainingif nargin < 2 | ~isa(par2,'mapping')   % a*w when w is untrained or   if nargin < 2    par2 = inf;  end    if nargin < 3    par3 = 'SIMPLS';  end    maxLV  = par2;  method = par3; 	if strcmp(par1.labtype,'crisp')	  y=gettargets(setlabtype(par1,'soft'));	else  	y=gettargets(par1);  end	% options  Options.maxLV  = maxLV;  Options.method = method;	Options.X_centering=[];  Options.Y_centering=[];	Options.X_scaling=[];  Options.Y_scaling=[];	[B,XRes,YRes,Options]=pls_train(+par1,y,Options);	  clear B  data.n=Options.maxLV;  data.R=XRes.R;  data.C=YRes.C;  data.Options=Options;		% Save all useful data.	w = mapping(mfilename,'trained',data,[],size(XRes.R,1),size(YRes.C,1));	w = setname(w,'Partial Least Squares Mapping');	if nargout > 1	 inform.XRes=XRes;	 inform.YRes=YRes;	end	% executionelse 	data = getdata(par2); % Unpack the mapping.    if data.n > size(data.R,2)    ErrMsg = sprintf(['PLS: The nubmer of LV(s) asked (%d) is greater than\n'...                      'the number of LV(s) available (%d)'],data.n,size(data.R,2));    error(ErrMsg);  end  Y = pls_apply(+par1,data.R(:,1:data.n)*data.C(:,1:data.n)',data.Options);  w = setdat(par1,Y,par2);endreturn