svmlspex02fun.m

数据挖掘的新方法－支持向量机书中算法例子

%SVMnLSPex02FUN.m
%Object function and Constrained function for Two Dimension SVM Problem, Two Class and Separable Situation;
%
%Method from Christopher J. C. Burges:
%"A Tutorial on Support Vector Machines for Pattern Recognition", page 9
%	
%  Objective:   min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16)
%  Subject to:  sum{ai*yi}=0 ,function (15);
%					 and ai>=0 for any i, the particular set of constraints C2 (page 9, line14). 
%
%The a.dat file contained both +/- classes samples and, "m1" and "m2" are thire number respectively.
%Vector A = [a1,a2,...] 

function [F,G]= SVMLSPex02FUN(A)

%Samples matrix X with (m*n), m samples with dimention n;
	fid=fopen('a.dat','r');					%Get the samples
   m1=fread(fid,1,'float');				%The number of Position Set
   m2=fread(fid,1,'float');				%The number of Negative Set
   n=fread(fid,1,'float');					%Dimension of the samples
   m=m1+m2;										%
	for k=1:n
		X(1:m,k)=fread(fid,m,'float');
	end
   fclose(fid);
   
	Y=[ones(m1,1);-ones(m2,1)];				%Classfication value for both Positive/Negative samples
   
%Object Function value F and Constrained function value G, G(i) is the value of i'th constrained function.
	ay=diag(A)*diag(Y);

	F=-sum(A)+sum(sum(0.5*((X*X')*ay)'*ay));		%function (16)
   G(1)=sum(sum(ay));									%Equality constrain function (15)
   G(2:m+1)=-A;											%condition ai>=0, for any i;
	G=G';