clustie.m

遗传算法的源程序

function [c,newx,newy]=clustie(x,y,n);
%
% Clustering programme
%
% HELLO!


%
% Determine the maximum and minimum of each
% input variable
%
xmax=max(x);
xmin=min(x);
xmean=mean(x);
range=xmax-xmin;
new_cent=c;

%
% Make some random centres
%
[D L]=size(x);
c=[];
for i = 1 : n
	r=rand(1,L);
	c(:,i)=(xmin+(r.*range))';
end


for k = 1:50
%
% Find the distances
%
distance=dist(x,c);

%
% Associate each reading with a centre
%
assoc=[];
for i = 1:D
	[m assoc(i,1)]=min(distance(i,:));
end

%
% Get the mean value of the x's for each of these clusters
%
ny=[];
for i = 1:n
	xdat=[];
	ydat=[];
	count=1;
	for j = 1:D
		if assoc(j,1)==i
			xdat(count,:)=x(j,:);
			ydat(count,:)=y(j,:);
			count=count+1;
		end
	end
	if count == 1
		new_cent(:,i)=c(:,i);
		ny(i,:)=mean(y);
	elseif count == 2
		new_cent(:,i)=xdat';
		ny(i,:)=ydat;
	else
		new_cent(:,i)=mean(xdat)';
		ny(i,:)=mean(ydat);
	end
end

%
% Calculate the error
%

er=sum(sum(c-new_cent)./sum(c));
c=new_cent;
new_cent=[];
if abs(er) < 0.0005
	break
end
end

newx=c';
newy=ny;