clear all

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)')

% mdist=input('name of the distance matrix file (with single quotes)?\n');

% disp('Reading input distance matrix')

% xx=load(mdist);

% xx = load('D:\example_distances.dat')

%x = load('C:\UseTraceOfAllUsers.txt');

x = load('D:\Courses\Cloud Computing\Clustering\dataWaitingForClustering4.2.txt');

minX = min(x);

maxX = max(x);

ran = maxX - minX;

nx(:,1) = (x(:,1) - minX(1,1)) / ran(1,1);

nx(:,2) = (x(:,2) - minX(1,2)) / ran(1,2);

dist = pdist2(nx, nx);

N = size(dist,1);

xx = zeros((N-1)*N/2, 3);

idx = 1;

for i=1:N

    for j=i+1:N

        xx(idx, 1) = i;

        xx(idx, 2) = j;

        xx(idx, 3) = dist(i, j);

        idx = idx + 1;

    end

end

N = size(xx, 1);

ND=max(xx(:,2));

NL=max(xx(:,1));

if (NL>ND)

  ND=NL;

end

% N=size(xx,1);

% 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;

% fprintf('average percentage of neighbours (hard coded): %5.6f\n', percent);

%

% position=round(N*percent/100);

% sda=sort(xx(:,3));

% dc=sda(position);

dc = 0.15;

fprintf('Computing Rho with gaussian kernel of radius: %12.6f\n', dc);

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

%

% "Cut off" kernel

%

% for i=1:ND-1

%  for j=i+1:ND

%    if (dist(i,j)<dc)S

%       rho(i)=rho(i)+1.;

%       rho(j)=rho(j)+1.;

%    end

%  end

% end

maxd=max(max(dist));

[rho_sorted,ordrho]=sort(rho,'descend');

delta(ordrho(1))=-1.;

nneigh(ordrho(1))=0;

for ii=2:ND

   delta(ordrho(ii))=maxd;

   for jj=1:ii-1

     if(dist(ordrho(ii),ordrho(jj))<delta(ordrho(ii)))

        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]);

for i=1:ND

  ind(i)=i;

  gamma(i)=rho(i)*delta(i);

end

subplot(2,1,1)

tt=plot(rho(:),delta(:),'o','MarkerSize',5,'MarkerFaceColor','k','MarkerEdgeColor','k');

title ('Decision Graph','FontSize',15.0)

xlabel ('\rho')

ylabel ('\delta')

subplot(2,1,1)

rect = getrect(1);

rhomin=rect(1);

deltamin=rect(4);

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

if (NCLUST>1)

  for i=1:NCLUST

    bord_rho(i)=0.;

  end

  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

  for i=1:ND

    if (rho(i)<bord_rho(cl(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(2,1,1)

   hold on

   plot(rho(icl(i)),delta(icl(i)),'o','MarkerSize',8,'MarkerFaceColor',cmap(ic,:),'MarkerEdgeColor',cmap(ic,:));

end

subplot(2,1,2)

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)

xlabel ('X')

ylabel ('Y')

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