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- function model = adagrad_rda_sql2_diag_train(X,Y,model)
- % ADAGRAD_RDA_SQL2_DIAG_TRAIN Adagrad with RDA updates, squared L2
- % regularizer, hinge loss, and diagonal matrix.
- %
- % MODEL = ADAGRAD_RDA_SQL2_DIAG_TRAIN(X,Y,MODEL) trains a
- % classifier according to the Adaptive Gradient algorithm, using RDA
- % updates, squared L2 regularizer, hinge loss, and diagonal matrix.
- %
- % Additional parameters:
- % - model.eta is the learning rate parameter.
- % Default value is 1.
- % - model.delta is the parameter to initialize the matrix .
- % Default value is 1.
- %
- % References:
- % - Duchi, J., Hazan, E., & Singer, Y. (2011)
- % Adaptive Subgradient Methods forOnline Learning and Stochastic
- % Optimization
- % To appear in Journal of Machine Learning Research
-
- % 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);
-
- if isfield(model,'eta')==0
- model.eta = 1;
- end
-
- if isfield(model,'delta')==0
- model.delta = 1;
- end
-
- if isfield(model,'iter')==0
- model.iter = 0;
- model.w = spalloc(1,size(X,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);
-
- model.Kb = ones(size(X,1),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
- model.w = model.w+model.eta*Yi*X(:,i)'./(sqrt(model.Kb')+model.delta);
- model.Kb = model.Kb+X(:,i).^2;
-
- 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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