function weightcloudclass
global weight;
n=300;
x1(1,:)=[5.1,4.9,4.7,4.6,5.0,5.4,4.6,5.0,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5.0,5.0,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5.0,5.5,4.9,4.4,5.1,5.0,4.5,4.4,5.0,5.1,4.8,5.1,4.6,5.3,5.0];
x1(2,:)=[3.5,3.0,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3.0,3.0,4.0,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3.0,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.1,3.0,3.4,3.5,2.3,3.2,3.5,3.8,3.0,3.8,3.2,3.7,3.3];
x1(3,:)=[1.4,1.4,1.3,1.5,1.4,1.7,1.4,1.5,1.4,1.5,1.5,1.6,1.4,1.1,1.2,1.5,1.3,1.4,1.7,1.5,1.7,1.5,1.0,1.7,1.9,1.6,1.6,1.5,1.4,1.6,1.6,1.5,1.5,1.4,1.5,1.2,1.3,1.5,1.3,1.5,1.3,1.3,1.3,1.6,1.9,1.4,1.6,1.4,1.5,1.4];
x1(4,:)=[0.2,0.2,0.2,0.2,0.2,0.4,0.3,0.2,0.2,0.1,0.2,0.2,0.1,0.1,0.2,0.4,0.4,0.3,0.3,0.3,0.2,0.4,0.2,0.5,0.2,0.2,0.4,0.2,0.2,0.2,0.2,0.4,0.1,0.2,0.1,0.2,0.2,0.1,0.2,0.2,0.3,0.3,0.2,0.6,0.4,0.3,0.2,0.2,0.2,0.2];
x2(1,:)=[7.0,6.4,6.9,5.5,6.5,5.7,6.3,4.9,6.6,5.2,5.0,5.9,6.0,6.1,5.6,6.7,5.6,5.8,6.2,5.6,5.9,6.1,6.3,6.1,6.4,6.6,6.8,6.7,6.0,5.7,5.5,5.5,5.8,6.0,5.4,6.0,6.7,6.3,5.6,5.5,5.5,5.5,5.8,6.0,5.4,6.0,6.7,6.3,5.6,5.5];
x2(2,:)=[3.2,3.2,3.1,2.3,2.8,2.8,2.3,2.4,2.9,2.7,2.0,3.0,2.2,2.9,2.9,3.1,3.0,2.7,2.2,2.5,3.2,2.8,2.5,2.8,2.9,3.0,2.8,3.0,2.9,2.6,2.4,2.4,2.7,2.7,3.0,3.4,3.1,2.3,3.0,2.5,2.6,3.0,2.6,2.3,2.7,3.0,2.9,2.9,2.5,2.8];
x2(3,:)=[4.7,4.5,4.9,4.0,4.6,4.5,4.7,3.3,4.6,3.9,3.5,4.2,4.0,4.7,3.6,4.4,4.5,4.1,4.5,3.9,4.8,4.0,4.9,4.7,4.3,4.4,4.8,5.0,4.5,3.5,3.8,3.7,3.9,5.1,4.5,4.5,4.7,4.4,4.1,4.0,4.4,4.6,4.0,3.3,4.2,4.2,4.2,4.3,3.0,4.1];
x2(4,:)=[1.4,1.5,1.5,1.3,1.5,1.3,1.6,1.0,1.3,1.4,1.0,1.5,1.0,1.4,1.3,1.4,1.5,1.0,1.5,1.1,1.8,1.3,1.5,1.2,1.3,1.4,1.4,1.7,1.5,1.0,1.1,1.0,1.2,1.6,1.5,1.6,1.5,1.3,1.3,1.3,1.2,1.4,1.2,1.0,1.3,1.2,1.3,1.3,1.1,1.3];     
x3(1,:)=[6.3,5.8,7.1,6.3,6.5,7.6,4.9,7.3,6.7,7.2,6.5,6.4,6.8,5.7,5.8,6.4,6.5,7.7,7.7,6.0,6.9,5.6,7.7,6.3,6.7,7.2,6.2,6.1,6.4,7.2,7.4,7.9,6.4,6.3,6.1,7.7,6.3,6.4,6.0,6.9,6.7,6.9,5.8,6.8,6.7,6.7,6.3,6.5,6.2,5.9];
x3(2,:)=[3.3,2.7,3.0,2.9,3.0,3.0,2.5,2.9,2.5,3.6,3.2,2.7,3.0,2.5,2.8,3.2,3.0,3.8,2.6,2.2,3.2,2.8,2.8,2.7,3.3,3.2,2.8,3.0,2.8,3.0,2.8,3.8,2.8,2.8,2.6,3.0,3.4,3.1,3.0,3.1,3.1,3.1,2.7,3.2,3.3,3.0,2.5,3.0,3.4,3.0];
x3(3,:)=[6.0,5.1,5.9,5.6,5.8,6.6,4.5,6.3,5.8,6.1,5.1,5.3,5.5,5.0,5.1,5.3,5.5,6.7,6.9,5.0,5.7,4.9,6.7,4.9,5.7,6.0,4.8,4.9,5.6,5.8,6.1,6.6,6.4,5.6,5.1,6.1,5.6,5.5,4.8,5.4,5.6,5.1,5.1,5.9,5.7,5.2,5.0,5.2,5.4,5.1];
x3(4,:)=[2.5,1.9,2.1,1.8,2.2,2.1,1.7,1.8,1.8,2.5,2.0,1.9,2.1,2.0,2.4,2.3,1.8,2.2,2.3,1.5,2.3,2.0,2.0,1.8,2.1,1.8,1.8,1.8,2.1,1.6,1.9,2.0,2.2,1.5,1.4,2.3,2.4,1.8,1.8,2.1,2.4,2.3,1.9,2.3,2.5,2.3,1.9,2.0,2.3,1.8];
x(1,1,:)=x1(1,:);x(1,2,:)=x2(1,:);x(1,3,:)=x3(1,:);
x(2,1,:)=x1(2,:);x(2,2,:)=x2(2,:);x(2,3,:)=x3(2,:);
x(3,1,:)=x1(3,:);x(3,2,:)=x2(3,:);x(3,3,:)=x3(3,:);
x(4,1,:)=x1(4,:);x(4,2,:)=x2(4,:);x(4,3,:)=x3(4,:);

  %計算云模型的熵 
  for i=1:4
       for j=1:3   
       %en(i,j)=(max(x(i,j,:))-min(x(i,j,:)))/6;
       en(i,j)=std(x(i,j,:));
       ex(i,j)=mean(x(i,j,:));
       max1(i,j)=max(x(i,j,:));
       min1(i,j)=min(x(i,j,:));
       end
   end  
   min1
   max1
   ex
   for i=1:4
       [B(i,:),ix(i,:)]=sort(ex(i,:));              
   end
          
   %計算分解云的熵 
  % for i=1:4
   %   [B(i,:),ix(i,:)]=sort(ex(i,:));
    %  a=[x1(i,:)  x2(i,:) x3(i,:)];
     % exx(i)=mean(a);
      %enn(i)=(max(a)-min(a))/6;
       %en(i,1)=(B(i,1)-B(i,2))/(B(i,1)-exx(i)-3*enn(i))*enn(i);
       %en(i,2)=(B(i,2)-B(i,3))/(B(i,1)-exx(i)-3*enn(i))*enn(i);   
       %en(i,3)=(B(i,3)-exx(i)-3*enn(i))/(B(i,1)-exx(i)-3*enn(i))*enn(i);  
      %將云模型的熵調整為來次序 ,分解云的熵用en1表示  
       %for j=1:3          
       %   en1(i,ix(i,j))=en(i,j);
       %end
       %end
 
 
   %計算屬性的不相交個數，給屬性賦權值  
    for i=1:4       
       for j = 1:2
         %diff(i,j)=(max(x(i,ix(i,j+1),:))-min(x(i,ix(i,j),:)))/(max(x(i,ix(i,j+1),:))-B(i,j+1)+B(i,j)-min(x(i,ix(i,j),:)));
         diff(i,j)=(B(i,j+1)-B(i,j))/(B(i,j+1)-min(x(i,ix(i,j+1),:))+max(x(i,ix(i,j),:))-B(i,j));
     end      
       % diff(i)=diff(i)+(min(x(i,ix(i,j+1),:))-max(x(i,ix(i,j),:)))/(3*en(i,ix(i,j+1))+3*en(i,ix(i,j)));% 可用理論方法計算差異度,可能為負數
     end
 diff;
     %seem
  %屬性的差異由最小差異決定 
  for i=1:4
     dif(i)=min(diff(i,:));
   end
      for i=1:4   
         weight(i)=dif(i)/(dif(1)+dif(2)+dif(3)+dif(4));
      end
  weight
  %各模型分類結果的比較
     success1=0;
     success2=0;
     success3=0;
    y1=cloud(x2,ex(:,1),en(:,1));
    y2=cloud(x2,ex(:,2),en(:,2));  
    y3=cloud(x2,ex(:,3),en(:,3)); 
   
     
        for i=1:50
          [B,ixx]=sort([y1(i) y2(i) y3(i)]);
          if(ixx(3)==1)
              success1=success1+1;  
          end
          if(ixx(3)==2) 
              success2=success2+1;
          end
          if(ixx(3)==3) 
              success3=success3+1;               
          end    
        end
     success1=50-success1
     success2
     success3=50-success3 
   
    %求類別的擴展,擴展后的邊界
 % 由于類別2和類別有3% 的樣本被錯誤分類，兩類的中心移動各移動3%  
 %  for  i=1:4
 %    for j=1:2
 %            left(i,ix(i,j+1))=min(x(i,ix(i,j+1),:));
 %            right(i,ix(i,j))=max(x(i,ix(i,j),:));
 %           if (left(i,ix(i,j+1))> right(i,ix(i,j)))
 %             right(i,ix(i,j))=b+(a-b)*en(i,ix(i,j))/(en(i,ix(i,j+1))+en(i,ix(i,j)))
 %             left(i,ix(i,j+1)=a-(a-b)*en(i,ix(i,j+1))/(en(i,ix(i,j+1))+en(i,ix(i,j)))
 %            
 %           end
 %       end 
 % end   



 function y=cloud(x,ex,en,n)
 global weight;
 [a,b]=size(x);
     for j=1:b
          y(j)=1;
         for i=1:a
              y(j)=y(j)+weight(i)*exp(-0.5*(x(i,j)-ex(i))^2/en(i)^2);
          end
      end