Question

急求FCM算法在C或MATLAB上实现

要有步骤,比如给出一组数据,怎么用FCM算法把它处理
要有程序.

如果回答的好,我还会加分,谢谢,急用
懂的人加下我QQ,123565913,需要详细点,谢谢

Answer 1

function [U,V,num_it]=fcm(U0,X)

% MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.

% Hathaway on June 21, 1994.) The fuzzification constant

% m = 2, and the stopping criterion for successive partitions is epsilon =??????.

%*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********

% Purpose:The function fcm attempts to find a useful clustering of the

% objects represented by the object data in X using the initial partition in U0.

%

% Usage: [U,V,num_it]=fcm(U0,X)

%

% where: U0 = on entry, the initial partition matrix of size c x n

% X = on entry, the object data matrix of size s x n

% U = on exit, the final partition matrix of size c x n

% V = on exit, the final prototype matrix of size s x c

% num_it = on exit, the number of iterations done

% Check for legal input values of U0 and X:

%

[c,n]=size(U0);

[s,nn]=size(X);

if min(min(U0)) < 0 | max(max(U0)) > 1 | any(abs(sum(U0) - 1) > .001),

error('U0 is not properly initialized.')

elseif nn ~= n,

error('Dimensions of U0 and X are inconsistent.')

end;

%

% Initialize variables:

%

temp=zeros(c,n); num_it=0; max_it=1000; U=U0; d=zeros(c,n);

epsilon=.00001;min_d=1.0e-100; step_size=epsilon; Vones=zeros(s,n);

%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%

% Begin the main loop:

%

while num_it < max_it & step_size >= epsilon,

num_it = num_it + 1;

U0 = U;

%

% Get new V prototypes:

%

temp = U0 .* U0;

work = sum(temp');

V = X*temp';

for i=1:c, V(:,i) = V(:,i) / work(i); end

%

% Get new squared-distance values d:

%

% First, get new initial values for d:

for i=1:c,

for j=1:s,

Vones(j,:)=V(j,i)*ones(1,n);

end

temp = X - Vones;

temp = temp.*temp;

if s > 1,

d(i,:) = sum(temp);

else

d(i,:) = temp;

end

end

% Second, adjust all d values to be at least as big as min_d:

j = find(d < min_d);

d(j) = d(j) - d(j) + min_d;

%

% Get new partition matrix U:

%

U = 1 ./ d;

work = sum(U);

for i=1:c, U(i,:) = U(i,:) ./ work; end

%

% Calculate step_size and return to top of loop:

%

step_size=max(max(abs(U-U0)));

%

% End the main loop:

%

end

%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

return

Answer 2

function [U,center,result,w,obj_fcn]= fenlei(data)
[data_n,in_n] = size(data);
m= 2; % Exponent for U
max_iter = 100; % Max. iteration
min_impro =1e-5; % Min. improvement
c=3;
[center, U, obj_fcn] = fcm(data, c);
for i=1:max_iter
if F(U)>0.98
break;
else
w_new=eye(in_n,in_n);
center1=sum(center)/c;
a=center1(1)./center1;
deta=center-center1(ones(c,1),:);
w=sqrt(sum(deta.^2)).*a;
for j=1:in_n
w_new(j,j)=w(j);
end
data1=data*w_new;
[center, U, obj_fcn] = fcm(data1, c);
center=center./w(ones(c,1),:);
obj_fcn=obj_fcn/sum(w.^2);
end
end
display(i);
result=zeros(1,data_n);U_=max(U);
for i=1:data_n
for j=1:c
if U(j,i)==U_(i)
result(i)=j;continue;
end
end
end