www.gusucode.com > mbcmodels 工具箱 matlab 源码程序 > mbcmodels/@xregrbf/stepitrols.m
function [om,OK]=stepitrols(m) % XREGRBF/STETITROLS updates lambda while selecting centers % % Lambda selection is performed at the same time as center selection using rols. % Implemented as an xregoptmgr. This is normally a sub xregoptmgr (of e.g % trialwidths). This routine is faster than iteraterols. % Copyright 2000-2014 The MathWorks, Inc. and Ford Global Technologies, Inc. om= contextimplementation(xregoptmgr,m,@i_stepitrols,[],'StepItRols',@stepitrols); % fit parameters om= AddOption(om,'MaxNCenters','min(nObs/3,1000)','evalstr', 'Maximum number of centers',2);% percentage of data taken as centers - default 20 centers om= AddOption(om,'PercentCandidates','min(100,(2000/nObs)*100)','evalstr','Percentage of data to be candidate centers');% number of candidate centers,take min(nObs,200) om= AddOption(om,'CenterTol',0.01,{'numeric',[0 Inf]}, 'Center tolerance');% 1 for plotting om= AddOption(om,'StartLamUpdate',5,{'int',[1 Inf]},'Number of centers to add before updating');% when to start updating lambda (after how many centers have been added) om= AddOption(om,'UpdateInterval',10,{'int',[1 Inf]},'Number of terms between updating regularization parameter');% only update lambda every UpdateInterval iterations om= AddOption(om,'Tolerance',0.005,{'numeric',[eps 1]},'Minimum change in log10(GCV)');% stopping criterion om= AddOption(om,'MaxRep',10,{'int',[1 100]}, 'Maximum no. times log10(GCV) change is minimal');% number of times no large change in GCV is seen om= AddOption(om,'PlotFlag',0,'boolean','Display',false);%plot flag om= AddOption(om,'cheapmode',0,'boolean',[],false);% no cheaper version available, not gui-settable om= AddOption(om,'cost',Inf,'numeric',[],false); OK = 1; function [m,cost,OK,varargout]=i_stepitrols(m,om,~,x,y,varargin) % adaptation of the rols algorithm to iterate lambda to reduce GCV after each center selection % see 'Regularisation in the Selection of Rbf centers', Mark Orr % it seems that iteraterols performs better than this one % Inputs: % m - rbf object with centers % x - matrix of data points % y - target values % Outputs % m - new rbf object try CenterTol= get(om,'CenterTol'); UpdateInterval = get(om,'UpdateInterval'); catch CenterTol= 0; UpdateInterval= 1; end varargout = {}; set(m,'qr','rols'); nObs = size(x,1); maxCStr= get(om,'MaxNCenters'); maxncenters = calcMaxNCenters(m,maxCStr,nObs); if CenterTol>0 candcenters = curvefitlib.internal.uniqueWithinTol(x,CenterTol*ones(1,size(x,2))); else % take all data points as candidate centers candcenters = x; end startlamupdate = get(om,'StartLamUpdate'); maxrep = get(om,'MaxRep'); % maximum number of repeated GCV values before stopping % Get the parameters stored in m width = m.width;%width of the radial basis function nObs = size(x,1);%number of data points if any(width <0) m = defaultwidth(m,x);%set the default width end lam= get(m,'lambda'); if length(lam)>1 set(m,'lambda',lam(1)); end tol = get(om,'Tolerance');%stopping criteria for adding more centers 0<tol<1 % Set up the 'full' regression matrix, Phi centers = x;%take the initial centers to be the data points m.centers = centers; Phi = x2fx(m,x);% %%%%%%%%%%%adjust the number of candidate centers in pool PcCandStr = get(om,'PercentCandidates'); PercentCandidates = calcPercentCand(m,PcCandStr,nObs); ncand = max(1,round((PercentCandidates/100)*nObs)); % approximate number of candidate points to try if size(candcenters,1) > ncand [candcenters,sel] = xregrbfcentersel(ncand,candcenters); Phik= Phi(:,sel); else % take all data points as candidate centers candcenters = x; Phik = Phi; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 lambda = 1e-4;% start with lambda at default % choose centers with mx_rols and updateLamba algorithm updateLamba= 1; [rstats,centorder]= mx_rols(Phik,y,lambda,... [maxncenters,0,updateLamba,startlamupdate,maxrep,10^tol,0,0,UpdateInterval]); nchosen= rstats(1); bestlambda= rstats(4); set(m,'lambda',bestlambda); centers = candcenters((centorder(1:nchosen)),:);%note, centers are in the order that they were chosen m.centers = centers; % solve for weights using mx_rols and force center selection for all nchosen terms [rstats,~,WEIGHTS]= mx_rols(Phik(:,centorder(1:nchosen)),y,bestlambda,... [nchosen,0,0,startlamupdate,maxrep,10^tol,nchosen,0,UpdateInterval]); m = update(m,WEIGHTS); cost = log10(rstats(2)); if isnan(cost) cost = Inf; end m = setFitOpt(m,'cost',cost); OK =1;