提交项目

Signed-off-by: facat@lab.com <facat@lab.com>

m.txt

@@ -0,0 +1,120 @@
+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=5000;% 生成多少个数据
+Dim=3;
+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);
+% data=load('data');
+% data=data.data;
+
+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
+            matDistance=repmat(d,1,length(SetS));
+            minD=matDistance-data(:,SetS);
+            minD=diag(minD'*minD);
+            minD=min(minD.^.5);
+            
+%             distanceIJ=sum((d-data(:,cddtI)).^2).^.5;
+            distanceIJ=metricFun( d,data(:,cddtI) );
+%             distanceIJ_t=UserSum(d,data(:,cddtI));
+            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
+                
+                matDistance=repmat(d,1,length(SetS));
+                minD=matDistance-data(:,SetS);
+                minD=diag(minD'*minD).^.5;
+                minD_t=min(minD);
+                minD(minD==minD_t)=1e20;
+                min2D=min(minD);
+                minD=minD_t; 
+                
+%                 distanceIJ=sum((d-data(:,cadUSetS(H))).^2).^.5;%S(I)已经等于U(H)
+                distanceIJ=metricFun( d,data(:,cadUSetS(H)) );
+%                 distanceHJ=sum((d-data(:,cadSetS(I))).^2).^.5;
+                distanceHJ=metricFun( d,data(:,cadSetS(I)) );
+                if distanceIJ>minD
+                    Kjih=min([distanceHJ-minD,0]);
+                end
+                if abs(distanceIJ-minD)<1e-5
+                    Kjih=min([distanceHJ,min2D])-minD;
+                elseif distanceIJ<minD
+                    fprintf('Input must be a string\n')
+                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
+toc

pam.go

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+// pam 用Go来写PAM
+package main
+
+import (
+	"fmt"
+)
+
+func main() {
+	fmt.Println("Hello World!")
+}