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- function model = sop_adapt_train(X,Y,model)
- % SOP_ADAPT_TRAIN Second-order Perceptron algorithm, adaptive version
- %
- % MODEL = SOP_TRAIN(X,Y,MODEL) trains an classifier according to the
- % Second-order Perceptron algorithm, adaptive variant.
- %
- % Additional parameters:
- % - model.c is the aggressiveness parameter, used to trade-off the loss
- % and the regularization.
- % Default value is 1.
- %
- % References:
- % - Cesa-Bianchi, N., Conconi, A., & Gentile, C. (2005).
- % A Second Order Perceptron Algorithm.
- % SIAM J. COMPUT. 34(3), (pp. 640-668).
-
- % This file is part of the DOGMA library for MATLAB.
- % Copyright (C) 2009-2011, Francesco Orabona
- %
- % This program is free software: you can redistribute it and/or modify
- % it under the terms of the GNU General Public License as published by
- % the Free Software Foundation, either version 3 of the License, or
- % (at your option) any later version.
- %
- % This program is distributed in the hope that it will be useful,
- % but WITHOUT ANY WARRANTY; without even the implied warranty of
- % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- % GNU General Public License for more details.
- %
- % You should have received a copy of the GNU General Public License
- % along with this program. If not, see <http://www.gnu.org/licenses/>.
- %
- % Contact the author: francesco [at] orabona.com
-
- n = length(Y);
- d=size(X,1);
-
- if isfield(model,'c')==0
- model.c=1;
- end
-
- if isfield(model,'iter')==0
- model.iter=0;
- model.w=zeros(1,d);
- model.w2=zeros(1,d);
- model.errTot=0;
- model.numSV=zeros(numel(Y),1);
- model.aer=zeros(numel(Y),1);
- model.pred=zeros(numel(Y),1);
-
- model.SS=zeros(d);
- model.v=zeros(d,1);
- end
-
- for i=1:n
- model.iter=model.iter+1;
-
- SSnew=model.SS+X(:,i)*X(:,i)';
- model.w=(eye(d)*model.errTot*model.c+SSnew)^-1*model.v;
-
- %model.w2=model.w2+model.w;
-
- val_f=model.w'*X(:,i);
-
- Yi=Y(i);
-
- model.errTot=model.errTot+(sign(val_f)~=Yi);
- model.aer(model.iter)=model.errTot/model.iter;
- model.pred(model.iter)=val_f;
-
- if Yi*val_f<=0
- model.v=model.v+Yi*X(:,i);
-
- model.S(end+1)=model.iter;
- model.SV(:,end+1)=X(:,i);
- end
-
- model.numSV(model.iter)=numel(model.S);
-
- if mod(i,model.step)==0
- fprintf('#%.0f SV:%5.2f(%d)\tAER:%5.2f\n', ...
- ceil(i/1000),numel(model.S)/model.iter*100,numel(model.S),model.aer(model.iter)*100);
- end
- end
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