csvm.m

来自「用支持向量机进行分类」· M 代码 · 共 48 行

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