function [alpha,solution]=csvm(X)% CSVM separates point set from the origin by hyperplane.%  [alpha,solution]=csvm(X)%% CSVM finds a normal vector of a hyperplane passing the %  origin of coordinates and maximizing margin between given %  point set (its convex hull) and the origin.%%  In other words, the aim is to find such vector alpha lying %  inside the convex hull of given set and having the smallest norm.%%  Input:%   X [D x M] matrix which contains M training points in D-dimensional%      feature space. X=[x1,x2,..XM] where xi is i-th column%      vectors.%%  Output:%   alpha [Dx1] normal vector of finding decision hyperplane.%   solution [1x1] contains 1 if solution is found or 0 if solution%      is not found.%% See also LINSVM, PERCEPTR, KOZINEC, EKOZINEC, EKOZINEC2.%% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz% Written Vojtech Franc (diploma thesis) 29.10.1999% Modifications% 11-june-2001, V. Franc, new comments.% 24. 6.00 V. Hlavac, comments polished.N=size(X,1);K=size(X,2);H=eye(N);f=zeros(N,1);b=-ones(K,1);A=-X';% quadratic programming[alpha,lag,how]=qp(H,f,A,b,[],[],[],0,-1);if strcmp(lower(how),'ok'),   solution=1;else   solution=0;end