fcm演算法實現
A. 模糊c-均值聚類演算法的FCM 演算法簡介
假設樣本集合為X={x1 ，x2 ，…，xn }，將其分成c 個模糊組，並求每組的聚類中心cj （ j=1，2，…，C） ，使目標函數達到最小。
B. matlab中的功能函數FCM如何使用
模糊C均值聚類演算法，可將輸入的數據集data聚為指定的cluster_n類
【函數描述】
語法格式
[center, U, obj_fcn] = FCM(data, cluster_n, options)
用法：
1. [center,U,obj_fcn] = FCM(Data,N_cluster,options);
2. [center,U,obj_fcn] = FCM(Data,N_cluster);
輸入變數
data ---- n*m矩陣,表示n個樣本,每個樣本具有m維特徵值
cluster_n ---- 標量,表示聚合中心數目,即類別數
options ---- 4*1列向量，其中
options(1): 隸屬度矩陣U的指數，>1(預設值: 2.0)
options(2): 最大迭代次數(預設值: 100)
options(3): 隸屬度最小變化量,迭代終止條件(預設值: 1e-5)
options(4): 每次迭代是否輸出信息標志(預設值: 0)
輸出變數
center ---- 聚類中心
U ---- 隸屬度矩陣
obj_fcn ---- 目標函數值
C. matlab中的fcm演算法中的u矩陣怎麼初始化
matlab中的fcm演算法中的u矩陣怎麼初始化
模糊C均值聚類演算法，可將輸入的數據集data聚為指定的cluster_n類
【函數描述】
語法格式
[center, U, obj_fcn] = FCM(data, cluster_n, options)
用法：
1. [center,U,obj_fcn] = FCM(Data,N_cluster,options);
2. [center,U,obj_fcn] = FCM(Data,N_cluster);
D. 急求FCM演算法在C或MATLAB上實現
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
E. 在matlab中做模糊C均值聚類（fcm）演算法如何體現初始隸屬度
它的程序裡面是用rand函數隨機初始化了一個矩陣N*c，然後對這個隨機矩陣進行歸一化，即滿足一行（也可能是列記不清楚了），反正是讓它滿足隸屬度的每個樣本屬於所有類隸屬度為1的條件。用這個矩陣進行初始化，計算新的中心 新的隸屬度 新的中心。。。。 知道滿足閾值。matlab裡面自己有函數一招就能找到
F. python 中如何調用FCM演算法
以下代碼調試通過：
1234567classLuciaClass:#定義類defluciaprint(self,text):#類裡面的方法print(' ',text)#方法就是輸出textx=LuciaClass()#方法的實例xx.luciaprint('todayisabadday~~~')#實例調用類方法
運行效果：