function model=svm1d(data,options)% SVM1D Linear SVM for 1-dimensional input data.%% Synopsis:%  model = svm1d( data )%  model = svm1d( data, options )%% Description:%  model = svm1d( data ) trains the linear SVM binary%    classifier for the 1-dimensional training data.%    The optimizer is based on a modification of the %    Sequential Minimal Optimizer (SMO) [Platt98]. %    The trainined classfier is defined as%      q(x) = 1 if W*x + b >= 0%           = 2 if W*x + b < 0%%  model = svm1d( data, options ) use to set up control%    parameters for the SVM and the SMO algorithm.%% Input:%  data [struct] Input 1-dimensional binary labeled training data:%   .X [1 x num_data] Training numbers.%   .y [1 x num_data] Labels (1 or 2).%  %  options [struct] Control parameters:%   .C [1x1] SVM regularization constant (default C=inf). %   .eps [1x1] Tolerance of KKT-conditions (default eps=0.001).%   .tol [1x1] Minimal change of variables (default tol=0.001).%% Output:%  model [struct] Found SVM model:%   .Alpha [nsv x 1] Weights.%   .b [1x1] Bias of decision function.%   .sv.X [1 x nsv] Support vectors.%   .W [1x1] Explicit value of the normal vector (scalar).%%   .nsv [1x1] Number of Support Vectors.%   .kercnt [1x1] Number of kernel evaluations (multiplications %     in this 1-d linear case) used by the SMO.%   .trnerr [1x1] Training classification error.%   .margin [1x1] Margin of found classifier.%   .cputime [1x1] Used CPU time in seconds.%   .options [struct] Copy of used options.%% See also %  SMO, SVMCLASS, KFD, KFDQP.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 17-may-2004, VF% 14-may-2004, VF% 15-july-2003, VF% timertic;% Process input arguments % --------------------------[dim,num_data] = size(data.X);if dim ~= 1,  error('Inpu  data must be one-dimensional.');end  if nargin < 2,  options = []; else options=c2s(options); endif ~isfield(options,'C'), options.C = inf; endif ~isfield(options,'eps'), options.eps = 0.001; endif ~isfield(options,'tol'), options.tol = 0.001; end% call MEX function%---------------------------[model.Alpha, model.b, model.nsv, model.kercnt, model.trnerr, model.margin]...     = smo1d_mex(data.X, data.y, options.C, options.eps, options.tol);% fill up the output structure%---------------------------------inx = find( model.Alpha );model.sv.X = data.X(:,inx);model.sv.y = data.y(inx);model.sv.inx = inx;model.Alpha = model.Alpha(inx);model.Alpha( find(model.sv.y==2)) = -model.Alpha( find(model.sv.y==2 ));model.W = model.sv.X*model.Alpha;options.ker = 'linear';options.arg = 1;model.options = options;model.fun = 'svmclass';model.cputime = toc;return;% EOF