int main()
{
int len=0
int len2=0
FILE* stream
FILE* stream1
FILE* stream2
char buf[50]
char buf1[50]
char buf2[50]
char text[1024]
printf("input anfile path to open:")
scanf("%s",buf)
stream=fopen(buf,"r+")
fseek(stream,0,SEEK_END)
len=ftell(stream)
printf("the file %s length is %d!\n",buf,len)
len2 = len/2
printf("intput 2 file name: \n")
scanf("%s%s",buf1,buf2)
fseek(stream,0,SEEK_SET)
stream1=fopen(buf1,"w+")
stream2=fopen(buf2,"w+")
fread(text,len2,1,stream)
fwrite(text,len2,1,stream1)
fread(text,len-len2,1,stream)
fwrite(text,len-len2,1,stream2)
fclose(stream)
fclose(stream1)
fclose(stream2)
return 0
}
文件合并
#include<stdio.h>
int main()
{
int len=0
int len2=0
FILE* stream
FILE* stream1
char buf[50]
char buf1[50]
char text[1024]
printf("input anfile path to open:")
scanf("%s",buf)
stream=fopen(buf,"r+")
fseek(stream,0,SEEK_END)
printf("intput another file name: \n")
scanf("%s",buf1)
stream1=fopen(buf1,"r+")
fseek(stream1,0,SEEK_END)
len=ftell(stream1)
fseek(stream1,0,SEEK_SET)
fread(text,len,1,stream1)
fwrite(text,len,1,stream)
fclose(stream)
fclose(stream1)
remove(buf1)//remove the another file
return 0
}
需要在程序目录下有文件存在
unction [U,P,Dist,Cluster_Res,Obj_Fcn,iter]=fuzzycm(Data,C,plotflag,M,epsm)% 模糊 C 均值聚类 FCM: 从随机初始化划分矩阵开始迭代
% [U,P,Dist,Cluster_Res,Obj_Fcn,iter] = fuzzycm(Data,C,plotflag,M,epsm)
% 输入:
% Data: N×S 型矩阵,聚类的原始数据,即一组有限的观测样本集,
% Data 的每一行为一个观测样本的特征矢量,S 为特征矢量
% 的维数,N 为样本点的个数
% C:聚类数,1<C<N
% plotflag: 聚类结果 2D/3D 绘图标记,0 表示不绘图,为缺省值
% M:加权指数,缺省值为 2
% epsm: FCM 算法的迭代停止阈值,缺省值为 1.0e-6
% 输出:
% U:C×N 型矩阵,FCM 的划分矩阵
% P:C×S 型矩阵,FCM 的聚类中心,每一行对应一个聚类原型
% Dist: C×N 型矩阵,FCM 各聚类中心到各样本点的距离,聚类中
% 心 i 到样本点 j 的距离为 Dist(i,j)
% Cluster_Res: 聚类结果,共 C 行,每一行对应一类
% Obj_Fcn: 目标函数值
% iter: FCM 算法迭代次数
% See also: fuzzydist maxrowf fcmplot
Data=imread('qq.jpg')
C=10%%
if nargin<5
epsm=1.0e-6
end
if nargin<4
M=2
end
if nargin<3
plotflag=0
end
[N,S]=size(Data)
m=2/(M-1)
iter=0
Dist(C,N)=0
U(C,N)=0
P(C,S)=0
% 随机初始化划分矩阵
U0 = rand(C,N)
U0=U0./(ones(C,1)*sum(U0))
% FCM 的迭代算法
while true
% 迭代计数器
iter=iter+1
% 计算或更新聚类中心 P
Data=uint8(Data)
P=uint8(P)
Um=U0.^M Um=uint8(Um)
P=Um.*Data./(ones(S,1).*sum(Um'))' %错误行
% 更新划分矩阵 U
for i=1:C
for j=1:N
Dist(i,j)=fuzzydist(P(i,:),Data(j,:))
end
end
U=1./(Dist.^m.*(ones(C,1)*sum(Dist.^(-m))))
% 目标函数值: 类内加权平方误差和
if nargout>4 || plotflag
Obj_Fcn(iter)=sum(sum(Um.*Dist.^2))
end
% FCM 算法迭代停止条件
if norm(U-U0,Inf)<epsm
break
end
U0=U
end
% 聚类结果
if nargout >3
res = maxrowf(U)
for c = 1:C
v = find(res==c)
Cluster_Res(c,1:length(v))=v
end
end
% 绘图
if plotflag
fcmplot(Data,U,P,Obj_Fcn)
end
function
[U,P,Dist,Cluster_Res,Obj_Fcn,iter]=fuzzycm2(Data,P0,plotflag,M,epsm)
% 模糊 C 均值聚类 FCM: 从指定初始聚类中心开始迭代
% [U,P,Dist,Cluster_Res,Obj_Fcn,iter] = fuzzycm2(Data,P0,plotflag,M,epsm)
% 输入: Data,plotflag,M,epsm: 见 fuzzycm.m
% P0: 初始聚类中心
% 输出: U,P,Dist,Cluster_Res,Obj_Fcn,iter: 见 fuzzycm.m
% See also: fuzzycm
if nargin<5
epsm=1.0e-6
end
if nargin<4
M=2
end
if nargin<3
plotflag=0
end
[N,S] = size(Data)
m = 2/(M-1)
iter = 0
C=size(P0,1)
Dist(C,N)=0
U(C,N)=0
P(C,S)=0% FCM 的迭代算法
while true% 迭代计数器
iter=iter+1 % 计算或更新划分矩阵 U
for i=1:C
for j=1:N
Dist(i,j)=fuzzydist(P0(i,:),Data(j,:))
end
end
U=1./(Dist.^m.*(ones(C,1)*sum(Dist.^(-m)))) % 更新聚类中心 P
Um=U.^M
P=Um*Data./(ones(S,1)*sum(Um'))' % 目标函数值: 类内加权平方误差和
if nargout>4 | plotflag
Obj_Fcn(iter)=sum(sum(Um.*Dist.^2))
end % FCM 算法迭代停止条件
if norm(P-P0,Inf)<epsm
break
end
P0=P
end % 聚类结果
if nargout >3
res = maxrowf(U)
for c = 1:C
v = find(res==c)
Cluster_Res(c,1:length(v))=v
end
end
% 绘图
if plotflag
fcmplot(Data,U,P,Obj_Fcn)
end
function f = addr(a,strsort)
% 返回向量升序或降序排列后各分量在原始向量中的索引
% 函数调用:f = addr(a,strsort)
% strsort: 'ascend' or 'descend'
% default is 'ascend'
% -------- example --------
% addr([ 4 5 1 2 ]) returns ans:
% [ 3 4 1 2 ]
if nargin==1
strsort='ascend'
end
sa=sort(a)
ca=a
la=length(a)
f(la)=0
for i=1:la
f(i)=find(ca==sa(i),1)
ca(f(i))=NaN
end
if strcmp(strsort,'descend')
f=fliplr(f)
end
function ellipse(a,b,center,style,c_3d)
% 绘制一个椭圆
% 调用: ellipse(a,b,center,style,c_3d)
% 输入:
% a: 椭圆的轴长(平行于 x 轴)
% b: 椭圆的轴长(平行于 y 轴)
% center: 椭圆的中心 [x0,y0],缺省值为 [0,0]
% style: 绘制的线型和颜色,缺省值为实线蓝色
% c_3d: 椭圆的中心在 3D 空间中的 z 轴坐标,可缺省
if nargin<4
style='b'
end
if nargin<3 | isempty(center)
center=[0,0]
end
t=1:360
x=a/2*cosd(t)+center(1)
y=b/2*sind(t)+center(2)
if nargin>4
plot3(x,y,ones(1,360)*c_3d,style)
else
plot(x,y,style)
end
function fcmplot(Data,U,P,Obj_Fcn)
% FCM 结果绘图函数
% See also: fuzzycm maxrowf ellipse
[C,S] = size(P)
res = maxrowf(U)
str = 'po*x+d^v><.h'
% 目标函数绘图
figure(1),plot(Obj_Fcn)
title('目标函数值变化曲线','fontsize',8)
% 2D 绘图
if S==2
figure(2),plot(P(:,1),P(:,2),'rs'),hold on
for i=1:C
v=Data(find(res==i),:)
plot(v(:,1),v(:,2),str(rem(i,12)+1))
ellipse(max(v(:,1))-min(v(:,1)), ...
max(v(:,2))-min(v(:,2)), ...
[max(v(:,1))+min(v(:,1)), ...
max(v(:,2))+min(v(:,2))]/2,'r:')
end
grid on,title('2D 聚类结果图','fontsize',8),hold off
end
% 3D 绘图
if S>2
figure(2),plot3(P(:,1),P(:,2),P(:,3),'rs'),hold on
for i=1:C
v=Data(find(res==i),:)
plot3(v(:,1),v(:,2),v(:,3),str(rem(i,12)+1))
ellipse(max(v(:,1))-min(v(:,1)), ...
max(v(:,2))-min(v(:,2)), ...
[max(v(:,1))+min(v(:,1)), ...
max(v(:,2))+min(v(:,2))]/2, ...
'r:',(max(v(:,3))+min(v(:,3)))/2)
end
grid on,title('3D 聚类结果图','fontsize',8),hold off
end
function D=fuzzydist(A,B)
% 模糊聚类分析: 样本间的距离
% D = fuzzydist(A,B)
D=norm(A-B)
function mr=maxrowf(U,c)
% 求矩阵 U 每列第 c 大元素所在行,c 的缺省值为1
% 调用格式: mr = maxrowf(U,c)
% See also: addr
if nargin<2
c=1
end
N=size(U,2)mr(1,N)=0
for j=1:N
aj=addr(U(:,j),'descend')
mr(j)=aj(c)
end
[MH MW]=textread('C:\Users\Administrator\Desktop\MALE.txt','%f %f')
Data(1:50,1)=FH
Data(51:100,1)=MH
Data(1:50,2)=FW
Data(51:100,2)=MW
C=input('????C??')
[U,P,Dist,Cluster_Res,Obj_Fcn,iter]=fuzzycm(Data,C)
plot(Data(:,1), Data(:,2),'o')
hold on
maxU = max(U)
index1 = find(U(1,:) == maxU)
index2 = find(U(2,:) == maxU)
line(Data(index1,1),Data(index1,2),'marker','*','color','g')
line(Data(index2,1),Data(index2,2),'marker','*','color','r')
plot([P([1 2],1)],[P([1 2],2)],'*','color','k')
hold off
