clc
clear
close all
tic
%先形成初始点
center=[1,1,1,1,1;10,10,10,10,10;100,100,100,100,100;1000,1000,1000,1000,1000];
% center=[1;10;100;1000];
dataN=50000;% 生成多少个数据
Dim=5;
clusterN=100;
data=zeros(Dim,dataN);
% data=[11,101,1001,1,2,3];
for I=1:dataN
    i=round(1+(4-1)*rand());
    data(:,I)=center(i)*( -1+(1+1)*rand(Dim,1));
end

data=rand(Dim,dataN);


SetS=[1;];

for clusterI=1:clusterN-1
    maxG=-100*ones(dataN,1);
    for cluster=1:dataN
        if sum(ismember(cluster,SetS))>0
            continue
        end
        %选一个候选数据
        cddtI=cluster;
        cadSetS=[SetS;cddtI;];
        Cij=0;
        for J=1:dataN
            if sum(ismember(J,cadSetS))>0
                continue
            end
            d=data(:,J);
            %寻找最短距离
            minD=1e20;
            for D=1:length(SetS)%between j and the closest object in S.
%                 if J==cadSetS(D)
%                     continue
%                 end
                distance=sum((d-data(:,SetS(D))).^2).^.5;
                if distance<minD
                    minD=distance;
                end
            end
            distanceIJ=sum((d-data(:,cddtI)).^2).^.5;
            Cij=Cij+max([minD-distanceIJ,0]);
        end
        maxG(cluster)=Cij;
    end
    maxGInd=find(maxG==max(maxG));
    SetS=[SetS;maxGInd(1)];
end
SetS
%% 进入SWAP部分
% SetS=[1;2;3];
USetS=setxor(1:dataN,SetS);

while 1
    minKjih=1e20;
    for I=1:length(SetS)
        for H=1:length(USetS)
            %交换两个集合的元素
            cadSetS=SetS;
            cadUSetS=USetS;
            swap=cadSetS(I);
            cadSetS(I)=cadUSetS(H);
            cadUSetS(H)=swap;
            sumKjih=0;
            for D=1:length(cadUSetS)
                J=cadUSetS(D);
                d=data(:,J);
                if J==cadSetS(I)
                    continue
                end
                minD=1e20;
                min2D=minD;
                for G=1:length(SetS)%between j and the closest object in S.
                    distance=sum((d-data(:,SetS(G))).^2).^.5;
                    if distance<minD
                        minD=distance;
                    else
                        if distance<min2D
                            min2D=distance;
                        end
                    end
                    
                end
                distanceIJ=sum((d-data(:,cadUSetS(H))).^2).^.5;%S(I)已经等于U(H)
                distanceHJ=sum((d-data(:,cadSetS(I))).^2).^.5;
                if distanceIJ>minD
                    Kjih=min([distanceHJ-minD,0]);
                end
                if distanceIJ<minD
                    fprintf('Input must be a string\n')
                end
                if abs(distanceIJ-minD)<1e-5
                    Kjih=min([distanceHJ,min2D])-minD;
                end
                sumKjih=sumKjih+Kjih;
            end
            if sumKjih<minKjih
                minKjih=sumKjih;
                minSetS=cadSetS;
                minUSetS=cadUSetS;
            end
        end
    end
    if minKjih<0
        minKjih
        SetS=minSetS;
        USetS=minUSetS;
        SetS
    else
        minKjih
        SetS
        fprintf('clustering is done.\n')
        break;
    end
end
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