function [data,model] = genlsdata( dim, num_data, margin )% GENLSDATA Generates linearly separable binary data.%% Synopsis:%  data = genlsdata(dim,num_data,margin)%% Description:%  It generates randomly binary labeled vectors which %  are linearly separable with prescribed margin. % % Input:%  dim [1x1] Data dimension.%  num_data [1x1] Number of generated data.%  margin [1x1] Minimal ensured margin (distance of the closest%   vector to the separating hyperplane).%% Output:%  data [struct] Generated data:%   .X [dim x num_data] Sample data.%   .y [1 x num_data] Data labels (1 or 2).%%  model [struct] Ground truth linear classifier:%   .W [dim x 1] Normal vector of separating hyperplane.%   .b [1x1] Bias of the hyperplane.%% Example:%  data = genlsdata(2,50,1);%  model = ekozinec( data );%  model.margin%  figure; ppatterns(data); pline(model);%% See also %  PERCEPTRON, EKOZINEC, LINCLASS, SVM.%% 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:% 3-may-2004, VF% 16-Feb-2003, VF% 26-feb-2001 V.Francdata.X = 2*rand(dim, num_data )-1;if nargin == 3,  data.X = data.X*margin*10;endmodel.W = 2*rand(dim,1)-1;model.b = 0;data.y = linclass(data.X,model);model.b = rand(1);data.X = data.X - model.b/sum(model.W);if nargin ==3,  inx1 = find(data.y == 1);  proj1 = (model.W'*data.X(:,inx1) + model.b)/norm(model.W);  shift1 = max([ones(1,length(inx1))*margin ; proj1]) - proj1;  data.X(:,inx1) = data.X(:,inx1) + ...      (model.W*ones(1,length(inx1))/norm(model.W)).*(ones(dim,1)*shift1);  inx2 = find(data.y == 2);  proj2 = -(model.W'*data.X(:,inx2) + model.b)/norm(model.W);  shift2 = max([ones(1,length(inx2))*margin ; proj2]) - proj2;  data.X(:,inx2) = data.X(:,inx2) - ...      (model.W*ones(1,length(inx2))/norm(model.W)).*(ones(dim,1)*shift2);endreturn;