matlab中kmeans算法程序如下 我要做图像分类 主程序改怎么写那？知道的写下 谢谢了

function [cid,nr,centers] = cskmeans(x,k,nc)
　　% CSKMEANS K-Means clustering - general method.
　　%
　　warning off
　　[n,d] = size(x);
　　if nargin < 3
　　% Then pick some observations to be the cluster centers.
　　ind = ceil(n*rand(1,k));
　　% We will add some noise to make it interesting.
　　nc = x(ind,:) + randn(k,d);
　　end
　　% set up storage
　　% integer 1,...,k indicating cluster membership
　　cid = zeros(1,n);
　　% Make this different to get the loop started.
　　oldcid = ones(1,n);
　　% The number in each cluster.
　　nr = zeros(1,k);
　　% Set up maximum number of iterations.
　　maxiter = 100;
　　iter = 1;
　　while ~isequal(cid,oldcid) & iter < maxiter
　　% Implement the hmeans algorithm
　　% For each point, find the distance to all cluster centers
　　for i = 1:n
　　dist = sum((repmat(x(i,:),k,1)-nc).^2,2);
　　[m,ind] = min(dist); % assign it to this cluster center
　　cid(i) = ind;
　　end
　　% Find the new cluster centers
　　for i = 1:k
　　% find all points in this cluster
　　ind = find(cid==i);
　　% find the centroid
　　nc(i,:) = mean(x(ind,:));
　　% Find the number in each cluster;
　　nr(i) = length(ind);
　　end
　　iter = iter + 1;
　　end
　　% Now check each observation to see if the error can be minimized some more.
　　% Loop through all points.
　　maxiter = 2;
　　iter = 1;
　　move = 1;
　　while iter < maxiter & move ~= 0
　　move = 0;
　　% Loop through all points.
　　for i = 1:n
　　% find the distance to all cluster centers
　　dist = sum((repmat(x(i,:),k,1)-nc).^2,2);
　　r = cid(i); % This is the cluster id for x
　　%%nr,nr+1;
　　dadj = nr./(nr+1).*dist'; % All adjusted distances
　　[m,ind] = min(dadj); % minimum should be the cluster it belongs to
　　if ind ~= r % if not, then move x
　　cid(i) = ind;
　　ic = find(cid == ind);
　　nc(ind,:) = mean(x(ic,:));
　　move = 1;
　　end
　　end
　　iter = iter+1;
　　end
　　centers = nc;
　　if move == 0
　　disp('No points were moved after the initial clustering procedure.')
　　else
　　disp('Some points were moved after the initial clustering procedure.')
　　end
　　warning on
咱能靠谱点不？这回答的是些什么啊

function [mu,mask]=kmeans(ima,k)%k为指定类别数
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% kmeans image segmentation
%
% Input:
% ima: grey color image灰度图像
% k: Number of classes指定的图像中类别数目
% Output:
% mu: vector of class means 每个类的均值
% mask: clasification image mask分类后的图像掩膜（mask)
%
% Author: Jose Vicente Manjon Herrera
% Email: jmanjon@fis.upv.es
% Date: 27-08-2005
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check image
ima=double(ima);
copy=ima; % make a copy
ima=ima(:); % vectorize ima将图像向量化，即一维化。
mi=min(ima); % deal with negative
ima=ima-mi+1; % and zero values

s=length(ima);%获得图像像素个数

% create image histogram%创建图像直方图

m=max(ima)+1;%最大像素值加1
h=zeros(1,m);%直方图，有m个bin
hc=zeros(1,m);%标号矩阵，每个像素点的值为该点所隶属的类别号

for i=1:s%s是图像象素个数，即考查每个像素
if(ima(i)>0) h(ima(i))=h(ima(i))+1;end;%直方图中对应bin加1
end
ind=find(h);%找到直方图中不为零的那些bin的序号。
hl=length(ind);%直方图中非零bin的个数

% initiate centroids

mu=(1:k)*m/(k+1);%k为指定的类别数，mu为不同类的分割点，相当于坐标轴上的整点

% start process

while(true)

oldmu=mu;
% current classification

for i=1:hl
c=abs(ind(i)-mu);%就是相当于考察ind(i)在坐标轴上离哪个整点最近！注意mu总共就k个
cc=find(c==min(c));%cc保留距离ind(i)最近整点的序号，序号为1、2、3...k
hc(ind(i))=cc(1);
end

%recalculation of means 下面的程序用于计算每一类的均值位置

for i=1:k,
a=find(hc==i);
mu(i)=sum(a.*h(a))/sum(h(a));%h为直方图
end

if(mu==oldmu) break;end;%循环结束条件

end

% calculate mask
s=size(copy);
mask=zeros(s);
mask1=mask;%增加一个显示矩阵
size(mask1)
for i=1:s(1),
for j=1:s(2),
c=abs(copy(i,j)-mu);
a=find(c==min(c));
mask(i,j)=a(1);
end
end

mu=mu+mi-1; % recover real range

for i = 1 : k
p=find(mask==i);
mask1(p)=1/k*i;
end
figure,imshow(mask1)追问

程序有错 34行，，，，，，

追答

for i=1:s%s是图像象素个数，即考查每个像素
if(ima(i)>0)
h(ima(i))=h(ima(i))+1;
end;%直方图中对应bin加1
end
%%%%%%%%%%%%%34行写成这样的形式即可！

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