function [best_model,Errors] = evalsvm(arg1,arg2,arg3)% EVALSVM Trains and evaluates Support Vector Machines classifier.%% Synopsis:%  [model,Errors] = evalsvm(data,options)%  [model,Errors] = evalsvm(trn_data,val_data,options)%% Description:%  [model,Errors] = evalsvm(data,options) uses cross-validation%    to assess SVM classifiers with given kernel arguments and %    regularization constants.%   %    The kernel type is given in options.ker (see 'help kernel').%    The SVM solver to be used is specified by field options.solver %    (default 'smo'). Both binary and multi-class SVM solvers are %    allowed. The input data have the following format:%    be used with regards to the number of labels of training data %      data.X [dim x num_data] ... training vectors.%      data.y [1 x num_data] ... labels.%    The set of SVM parameters to be evaluated are specified in:%      options.arg [dimarg x nargs] ... enumeration of  kernel arguments; %        dimarg determins number of kernel argumens (e.g., dimarg = 1 %        for 'rbf' kernel and dimarg = 2 for 'sigmoid').%      options.C [1 x nc] ... enumeration of regularization constants.%%    Some extra parameters for the selected SVM solver can be%    specified in the field options.solver_options.%%    Each configuration of SVM paramaters is evaluated using the%    cross-validation. The number of folds is given in %    optios.num_folds (default 5). The trained SVM model with %    the smallest cross-validation error is returned. The computed%    cross-validation errors with respect to SVM parametes are %    returned in Errors [nc x nargs].%%    The progress info is displayed if options.verb is set to 1%    (default 0).%%  [model,Errors] = evalsvm(trn_data,val_data,options) each%    SVM is trained on the trn_data and evaluated on the %    validation val_data instead of using cross-validation.%   % Example:%  trn = load('riply_trn');%  tst = load('riply_tst');%  options.ker = 'rbf';%  options.arg = [0.1 0.5 1 5];%  options.C = [1 10 100];%  options.solver = 'smo';%  options.num_folds = 5;%  options.verb = 1;%  [model,Errors] = evalsvm(trn,options);%  figure; mesh(options.arg,options.C,Errors);%  hold on; xlabel('arg'); ylabel('C');%  ypred = svmclass(tst.X,model);%  cerror(ypred,tst.y)%% See also: %   SMO, SVMLIGHT, SVMCLASS.%% Modifications:% 17-sep-2004, VF, Help improved. Info about training stage added.% 18-aug-2004, VF, svm_options changed to solver_options% 4-june-2004, VF% 3-jun-2004, VFif nargin == 2,  % evaluation by cross-validation  %=================================================================  options = c2s(arg2);  if ~isfield(options,'verb'), options.verb = 0; end  if ~isfield(options,'solver'), options.solver = 'smo'; end  if ~isfield(options,'ker'), options.ker = 'linear'; end  if ~isfield(options,'num_folds'), options.num_folds = 5; end  if ~isfield(options,'solver_options'), options.solver_options = []; end  nargs = size(options.arg,2);  nc = length(options.C);  [dim,num_data] = size(arg1.X);  solver_options = options.solver_options;  solver_options.ker = options.ker;  min_error = inf;  Errors = [];  cnt_model = 0;  num_model = nc*nargs;    % data partitioning  [itrn,itst] = crossval(num_data,options.num_folds);    for i = 1:nargs,    arg = options.arg(:,i);    for j = 1:nc,      cnt_model = cnt_model + 1;      C = options.C(j);            % svm parameters      solver_options.C = C;      solver_options.arg = arg;          % display info      if options.verb == 1,        fprintf('Model %d/%d: ker=%s, C=%f, arg=', ...            cnt_model, num_model,solver_options.ker, C);        fprintf('%f ', arg);        fprintf('\n');      end            fold_error = zeros(options.num_folds,1);            for k=1:options.num_folds,        if options.verb == 1,          fprintf('fold %d/%d: training', k, options.num_folds);        end                trn.X = arg1.X(:,itrn{k});        trn.y = arg1.y(:,itrn{k});        tst.X = arg1.X(:,itst{k});        tst.y = arg1.y(:,itst{k});                % run solver        model = feval( options.solver, trn, solver_options );                if options.verb == 1,          ypred = feval( model.fun, trn.X, model );          fprintf(' err = %.4f, testing', cerror( ypred,trn.y));        end        % classify validation data        ypred = feval( model.fun, tst.X, model );                fold_error(k) = cerror( ypred, tst.y );        if options.verb == 1,          fprintf(' err = %.4f\n', fold_error(k));        end              end          % cross-validation error      err = mean( fold_error );      Errors(j,i) =  err;      if min_error > err,        min_error = err;        best_model = model;                if options.verb == 1,          fprintf('cross-validation error = %.4f (best so far)\n\n', err);        end      else        if options.verb == 1,          fprintf('cross-validation error = %.4f\n\n', err);        end      end          end  end    % disp info  if options.verb == 1,    fprintf('best model: ker=%s, C=%f, arg=', ...        best_model.options.ker, best_model.options.C);    fprintf('%f ', best_model.options.arg);    fprintf('\ncross-validation error = %.4f\n', min_error);  end  elseif nargin == 3,   % evaluation using val_data  %=================================================================    options = c2s(arg3);  if ~isfield(options,'verb'), options.verb = 0; end  if ~isfield(options,'ker'), options.ker = 'linear'; end  if ~isfield(options,'solver'), options.solver = 'smo'; end  if ~isfield(options,'solver_options'), options.solver_options = []; end  nargs = size(options.arg,2);  nc = length(options.C);  [dim,num_data] = size(arg1.X);  solver_options = options.solver_options;  solver_options.ker = options.ker;  min_error = inf;  Errors = [];  cnt_model = 0;  num_model = nc*nargs;    for i = 1:nargs,    arg = options.arg(:,i);    for j = 1:nc,      cnt_model = cnt_model + 1;      C = options.C(j);            % svm parameters      solver_options.C = C;      solver_options.arg = arg;          % display info      if options.verb == 1,        fprintf('Model %d/%d: ker=%s, C=%f, arg=', ...            cnt_model, num_model,solver_options.ker, C);        fprintf('%f ', arg);        fprintf('\n');      end      if options.verb == 1, fprintf('training'); end            % run SVM solver      model = feval( options.solver, arg1, solver_options );              if options.verb == 1,        ypred = feval( model.fun, arg1.X, model );        fprintf(' err = %.4f, testing', cerror( ypred,arg1.y));      end      if options.verb == 1, fprintf(', testing'); end              % classify validation data      ypred = feval( model.fun, arg2.X, model );      err = cerror(ypred,arg2.y);      Errors(j,i) =  err;      if min_error > err,        min_error = err;        best_model = model;                if options.verb == 1,          fprintf(', err = %.4f (best so far)\n\n', err);        end      else        if options.verb == 1,          fprintf(', err = %.4f\n\n', err);        end      end    end  end  % disp info  if options.verb == 1,    fprintf('best model: ker=%s, C=%f, arg=', ...        best_model.options.ker, best_model.options.C);    fprintf('%f ', best_model.options.arg);    fprintf('\ntesting error = %.4f\n', min_error);  end  end%EOF