开始忘贴代码了，有朋友要求，就把修改后的代码贴在这里。改动不多，可以部分提升
效率。原来的也没删，注释掉了。供参考。
=========================================================================
clear;
close all
disp('The only input needed is a distance matrix file')
disp('The format of this file should be: ')
disp('Column 1: id of element i')
disp('Column 2: id of element j')
disp('Column 3: dist(i,j)')
if(0)
% mdist=input('name of the distance matrix file (with single quotes)?\n'
);
mdist = 'example_distances.dat';
disp('Reading input distance matrix')
xx=load(mdist);
N=size(xx,1);
ND=max(xx(:,2));
NL=max(xx(:,1));
if(NL>ND)
ND=NL;
end
dist = zeros(ND, ND);
% for i=1:ND
% for j=1:ND
% dist(i,j)=0;
% end
% end
for i=1:N
ii=xx(i,1);
jj=xx(i,2);
dist(ii,jj)=xx(i,3);
dist(jj,ii)=xx(i,3);
end
percent=2.0;
fprintf('average percentage of neighbours (hard coded): %5.6f\n',
percent);
position=round(N*percent/100);
sda=sort(xx(:,3));
dc=sda(position);
clear xx;
save('PeakCluData.mat');
return;
else
load('PeakCluData.mat');
end;
dc = dc * 3;
dc = 0.1;
% rhomin = 20;
% deltamin = 0.1;
rhomin = 10;
deltamin = 0.15;
percent=2.0;
fprintf('average percentage of neighbours (hard coded): %5.6f\n', percent);
fprintf('Computing Rho with gaussian kernel of radius: %12.6f\n', dc);
rho = zeros(1, ND);
% for i=1:ND
% rho(i)=0.;
% end
% %
% % Gaussian kernel
% %
% for i=1:ND-1
% for j=i+1:ND
% rho(i)=rho(i)+exp(-(dist(i,j)/dc)*(dist(i,j)/dc));
% rho(j)=rho(j)+exp(-(dist(i,j)/dc)*(dist(i,j)/dc));
% end
% end
% Kernel distance
k_dist = exp(-dist.^2/dc^2);
rho = sum(k_dist) - 1;
% %
% % "Cut off" kernel
% %
% for i=1:ND-1
% for j=i+1:ND
% if (dist(i,j)% rho(i)=rho(i)+1.;
% rho(j)=rho(j)+1.;
% end
% end
% end
maxd=max(max(dist));
% [rho_sorted,ordrho]=sort(rho,'descend');
[rho_sorted,ordrho]=sort(-rho);
rho_sorted = -rho_sorted;
delta(ordrho(1))=-1.;
nneigh(ordrho(1))=0;
% Find local peak among neighbors; mark the clustering trace
for ii=2:ND
delta(ordrho(ii))=maxd;
for jj=1:ii-1
if(dist(ordrho(ii),ordrho(jj))delta(ordrho(ii))=dist(ordrho(ii),ordrho(jj));
nneigh(ordrho(ii))=ordrho(jj);
end
end
end
delta(ordrho(1))=max(delta(:));
disp('Generated file:DECISION GRAPH')
disp('column 1:Density')
disp('column 2:Delta')
fid = fopen('DECISION_GRAPH', 'w');
for i=1:ND
fprintf(fid, '%6.2f %6.2f\n', rho(i),delta(i));
end
disp('Select a rectangle enclosing cluster centers')
scrsz = get(0,'ScreenSize');
%figure('Position',[6 72 scrsz(3)/4. scrsz(4)/1.3]);
figure('Position',[scrsz(3)/4 scrsz(4)/4 scrsz(3)/2. scrsz(4)/2]);
for i=1:ND
ind(i)=i;
gamma(i)=rho(i)*delta(i);
end
subplot(1,2,1)
tt=plot(rho(:),delta(:),'o','MarkerSize',5,'MarkerFaceColor','k','
MarkerEdgeColor','k');
title(sprintf('Decision Graph: dc = %.3f', dc));
xlabel ('\rho')
ylabel ('\delta')
subplot(1,2,1)
% Read the threshold set manually
% rect = getrect(1);
% rhomin=rect(1);
% deltamin=rect(4);
disp(sprintf('rhomin = %f, deltamin = %f', rhomin, deltamin));
NCLUST=0;
for i=1:ND
cl(i)=-1;
end
for i=1:ND
if ( (rho(i)>rhomin) && (delta(i)>deltamin))
NCLUST=NCLUST+1;
cl(i)=NCLUST;
icl(NCLUST)=i;
end
end
fprintf('NUMBER OF CLUSTERS: %i \n', NCLUST);
disp('Performing assignation')
%assignation
for i=1:ND
if (cl(ordrho(i))==-1)
cl(ordrho(i))=cl(nneigh(ordrho(i)));
end
end
%halo
% for i=1:ND
% halo(i)=cl(i);
% end
halo = cl;
if (NCLUST>1)
% for i=1:NCLUST
% bord_rho(i)=0.;
% end
bord_rho = zeros(1, NCLUST);
% For each cluster, calculate the average rho of border points
for i=1:ND-1
for j=i+1:ND
if ((cl(i)~=cl(j))&& (dist(i,j)<=dc))
rho_aver=(rho(i)+rho(j))/2.;
if (rho_aver>bord_rho(cl(i)))
bord_rho(cl(i))=rho_aver;
end
if (rho_aver>bord_rho(cl(j)))
bord_rho(cl(j))=rho_aver;
end
end
end
end
% Points whose rho is less than the average border rho will be considered
% as noise
for i=1:ND
if (rho(i)halo(i)=0;
end
end
end
for i=1:NCLUST
nc=0;
nh=0;
for j=1:ND
if (cl(j)==i)
nc=nc+1;
end
if (halo(j)==i)
nh=nh+1;
end
end
fprintf('CLUSTER: %i CENTER: %i ELEMENTS: %i CORE: %i HALO: %i \n', i,icl(
i),nc,nh,nc-nh);
end
cmap=colormap;
for i=1:NCLUST
ic=int8((i*64.)/(NCLUST*1.));
subplot(1,2,1)
hold on
plot(rho(icl(i)),delta(icl(i)),'o','MarkerSize',8,'MarkerFaceColor',cmap(
ic,:),'MarkerEdgeColor',cmap(ic,:));
end
subplot(1,2,2)
disp('Performing 2D nonclassical multidimensional scaling')
Y1 = mdscale(dist, 2, 'criterion','metricstress');
plot(Y1(:,1),Y1(:,2),'o','MarkerSize',2,'MarkerFaceColor','k','
MarkerEdgeColor','k');
%title ('2D Nonclassical multidimensional scaling','FontSize',15.0)
title (['Clu result: \rho_{min} = ', sprintf('%.2f', rhomin) ', \delta_{min}
= ' sprintf('%.2f', deltamin) ]);
xlabel ('X')
ylabel ('Y')
axis equal;
for i=1:ND
A(i,1)=0.;
A(i,2)=0.;
end
for i=1:NCLUST
nn=0;
ic=int8((i*64.)/(NCLUST*1.));
for j=1:ND
if (halo(j)==i)
nn=nn+1;
A(nn,1)=Y1(j,1);
A(nn,2)=Y1(j,2);
end
end
hold on
plot(A(1:nn,1),A(1:nn,2),'o','MarkerSize',2,'MarkerFaceColor',cmap(ic,:),'
MarkerEdgeColor',cmap(ic,:));
end
%for i=1:ND
% if (halo(i)>0)
% ic=int8((halo(i)*64.)/(NCLUST*1.));
% hold on
% plot(Y1(i,1),Y1(i,2),'o','MarkerSize',2,'MarkerFaceColor',cmap(ic,:),
'MarkerEdgeColor',cmap(ic,:));
% end
%end
faa = fopen('CLUSTER_ASSIGNATION', 'w');
disp('Generated file:CLUSTER_ASSIGNATION')
disp('column 1:element id')
disp('column 2:cluster assignation without halo control')
disp('column 3:cluster assignation with halo control')
for i=1:ND
fprintf(faa, '%i %i %i\n',i,cl(i),halo(i));
end
im = getframe(gcf);
imwrite(im.cdata, 'PeakCluRes.png');
