www.pudn.com > Fuzzy_K.rar > Fuzzy_K.m, change:2007-06-04,size:4714b

%function [clusters] = Fuzzy_K(patterns_in,c,plot_on) 
clear all 
clc 
%read an image file 92av3c9.lan  from Multispec 
 
fid1=fopen('c:\image\92av3c9.lan','rb');  
status=fseek(fid1,128,'bof'); 
bandnum=9; 
row=145;							 
column=145;						 
X1=uint16(fread(fid1,[row*bandnum column],'uint16')); 
fclose(fid1); 
 
 
% change the multispect picture 92av3c9.lan to 3D 
for n=1:bandnum, 
     Y1(1:row,1:column,n)=X1(1+(n-1)*row:n*row,1:column)'; 
   
end 
 
%read ground truth from the file '92av3gt.gis' 
fid=fopen('C:\image\92AV3GT.GIS','rb'); 
status=fseek(fid,128,'bof'); 
row=145; 
column=145; 
X=uint8(fread(fid,[column row],'uint8')); 
fclose(fid); 
 
X=X'; 
X_ESSS=X; 
max=0; 
for i=1:row 
    for j=1:column 
        if max<X_ESSS(i,j) 
            max=X_ESSS(i,j); 
        end 
    end 
end 
count=zeros(16,1); 
for i=1:1:16 
    for j=1:row 
        for k=1:column 
            if X_ESSS(j,k)==i 
                count(i)=count(i)+1; 
            end 
        end 
    end 
end   
count         
 
% define the patterns that  needs to be classified 
 
for n=1:bandnum, 
 
pattern=Y1(:,:,n); 
pattern=pattern'; 
 
X=X'; 
X111=X; 
pattern1=pattern(find(X(:,:)==2)); 
 
pattern2=pattern(find(X(:,:)==5)); 
 
pattern3=pattern(find(X(:,:)==6)); 
 
pattern4=pattern(find(X(:,:)==8)); 
 
pattern5=pattern(find(X(:,:)==11)); 
 
pattern6=pattern(find(X(:,:)==14)); 
 
patterns_in(n,:)=[pattern1',pattern2',pattern3',pattern4',pattern5',pattern6']; 
 
end 
 
patterns_in=double(patterns_in); 
 
X=X'; 
% define the matrix for the coordinates of the patterns that are to be classified 
[R1,C1]=find(X(:,:)==2);  
co1=[R1,C1]'; 
[R2,C2]=find(X(:,:)==5); 
co2=[R2,C2]'; 
[R3,C3]=find(X(:,:)==6); 
co3=[R3,C3]'; 
[R4,C4]=find(X(:,:)==8); 
co4=[R4,C4]'; 
[R5,C5]=find(X(:,:)==11); 
co5=[R5,C5]'; 
[R6,C6]=find(X(:,:)==14); 
co6=[R6,C6]'; 
 
coordinates=[co1,co2,co3,co4,co5,co6]; 
 
 
 
% fuzzy K-means 
 
%Reduce the number of data points using the fuzzy k-means algorithm 
%Inputs: 
%	patterns_in	    - Input patterns 
%	c				- Number of output data points 
%   plot_on         - Plot stages of the algorithm 
% 
%Outputs 
%	patterns		- New patterns 
%	clusters	    - New clusters 
 
m		= 2; 
N		= size(patterns_in,2); 
c       = 6;%c--the number of clusters designated    
dist	= zeros(c,N);        
Dim     = size(patterns_in,1);%t = size(X,dim) returns the size of the dimension of X specified by scalar dim. 
 
%Initialize the V's 
%V--the clustering centroids 
V   = randn(Dim,c); 
V	= sqrtm(cov(patterns_in',1))*V + mean(patterns_in')'*ones(1,c); 
 
old_V	= zeros(Dim,c); 
 
%Initialize the U's 
%U--the membership matrix 
 
for i = 1:c, 
      dist(i,:) = sum((patterns_in(:,:) - V(:,i)*ones(1,N)).^2); 
      %the sum of the square of the distance from one pattern to eac clustering centroid  
   end 
    
  %Compute U's  
   U = (1./dist).^(1/(m-1)); 
   U = U ./ (ones(c,1) * sum(U));  
   %If A is a matrix, sum(A) treats the columns of A as vectors, returning a row vector of the sums of each column.  
    
   [R,C]=find(dist==0); 
   I=[R,C]; 
   if size(I,1)>0, 
     for  b=1:size(I,1), 
      U(R(b),:)=[0]; 
      U(R(i),C(i))=[1]; 
     end 
  end 
old_U	= zeros(c,N); 
 
while (sum(sum(abs(U - old_U) > 1e-5))> 0), 
     
   old_V = V; 
   old_U  = U; 
    
   %Classify all the patterns to one of the V's 
   for i = 1:c, 
      dist(i,:) = sum((patterns_in(:,:) - V(:,i)*ones(1,N)).^2); 
      %the sum of the square of the distance from one pattern to eac clustering centroid  
   end 
    
  %Recompute U's  
   U = (1./dist).^(1/(m-1)); 
   U = U ./ (ones(c,1) * sum(U));  
   %If A is a matrix, sum(A) treats the columns of A as vectors, returning a row vector of the sums of each column.  
    
   [R,C]=find(dist==0); 
   I=[R,C]; 
   if size(I,1)>0, 
     for  b=1:size(I,1), 
      U(R(b),:)=[0]; 
      U(R(i),C(i))=[1]; 
     end 
  end 
       
   %Recompute the V's 
   U  = U.^m; 
   V = (patterns_in * U') ./ (ones(Dim,1)*sum(U')); 
    
   
end 
 
%Classify the patterns 
[t,label] = max(U); 
 
cluster1=coordinates(:,find(label==1)); 
cluster2=coordinates(:,find(label==2)); 
cluster3=coordinates(:,find(label==3)); 
cluster4=coordinates(:,find(label==4)); 
cluster5=coordinates(:,find(label==5)); 
cluster6=coordinates(:,find(label==6)); 
 
clusters=[cluster1,cluster2,cluster3,cluster4,cluster5,cluster6]; 
 
%plot the clusters 
figure, 
plot(cluster1(1,:), cluster1(2,:),'.','color','b');hold on; 
plot(cluster2(1,:), cluster2(2,:),'.','color','g') 
plot(cluster3(1,:), cluster3(2,:),'.','color','r') 
plot(cluster4(1,:), cluster4(2,:),'.','color','c') 
plot(cluster5(1,:), cluster5(2,:),'.','color','m') 
plot(cluster6(1,:), cluster6(2,:),'.','color','y') 
 
hold off;