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- function model = pa_train(X,Y,model)
- % PA_TRAIN Passive-Aggressive algorithm
- %
- % MODEL = PA_TRAIN(X,Y,MODEL) trains a classifier according to the
- % Passive-Aggressive algorithm, PA-I and PA-II variants.
- %
- % Additional parameters:
- % - model.C is the aggressiveness parameter, used to trade-off the loss
- % on the current sample with the update on the current hyperplane.
- % Default value is 1.
- % - model.update is the used to select the update rule. A value of 1
- % selectes PA-I, 2 selects PA-II.
- % Default value is 1.
- %
- % References:
- % - Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., & Singer, Y. (2006).
- % Online Passive-Aggressive Algorithms.
- % Journal of Machine Learning Research 7(Mar), (pp. 551-585).
-
- % 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); % number of training samples
-
- if isfield(model,'iter')==0
- model.iter=0;
- model.w=zeros(1,size(X,1));
- model.w2=zeros(1,size(X,1));
- model.errTot=0;
- model.numSV=zeros(numel(Y),1);
- model.aer=zeros(numel(Y),1);
- model.pred=zeros(numel(Y),1);
- end
-
- if isfield(model,'update')==0
- model.update=1; %default update using PA-I
- end
-
- if isfield(model,'C')==0
- model.C=1;
- end
-
- for i=1:n
- model.iter=model.iter+1;
-
- 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<1
- norm_x_square=norm(X(:,i))^2;
- if model.update==1
- new_beta=min((1-Yi*val_f)/norm_x_square,model.C);
- else
- new_beta=(1-Yi*val_f)/(norm_x_square+1/(2*model.C));
- end
- model.w=model.w+new_beta*Yi*X(:,i)';
- model.S(end+1)=model.iter;
- end
-
- model.w2=model.w2+model.w;
-
- 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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