/*
本程序在linux g++下编译通过
bool svd(vector<vector<double>>A, int K, vector<vector<double>>&U, vector<double>&S, vector<vector<double>>&V)
A: 输入待分解矩阵
U[0],U[1],...,U[K-1]: 前K大奇异值对应的左奇异向量
S[0],S[1],...,S[K-1]: 前K大奇异值 S[0]>=S[1]>=...>=S[K-1]
V[0],V[1],...,V[K-1]: 前K大奇异值对应的右奇异向量
*/
#include <cmath>
#include <iostream>
#include <清梁迹iomanip>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <vector>
using namespace std
const int MAX_ITER=100000
const double eps=0.0000001
double get_norm(double *x, int n){
double r=0
for(int i=0i<ni++)
r+=x[i]*x[i]
return sqrt(r)
}
double normalize(double *x, int n){
double r=get_norm(x,n)
if(r<eps)
return 0
for(int i=0i<ni++)
x[i]/=r
return r
}
inline double product(double*a, double *b,int n){
double r=0
for(int i=0i<ni++)
r+=a[i]*b[i]
return r
}
void orth(double *a, double *b, int n){//|a|=1
double r=product(a,b,n)
for(int i=0i<ni++)
b[i]-=r*a[i]
}
bool svd(vector<vector<double>>A, int K, vector<vector<double>>&U, vector<double>&S, vector<vector<double>>&V){
int M=A.size()
int N=A[0].size()
U.clear()
V.clear()
S.clear()
S.resize(K,0)
U.resize(K)
for(int i=0i<Ki++)
U[i].resize(M,0)
V.resize(K)
for(int i=0i<Ki++)
V[i].resize(N,0)
srand(time(0))
double *left_vector=new double[M]
答并 渣拍double *next_left_vector=new double[M]
double *right_vector=new double[N]
double *next_right_vector=new double[N]
int col=0
for(int col=0col<Kcol++){
double diff=1
double r=-1
while(1){
for(int i=0i<Mi++)
left_vector[i]= (float)rand() / RAND_MAX
if(normalize(left_vector, M)>eps)
break
}
for(int iter=0diff>=eps &&iter<MAX_ITERiter++){
memset(next_left_vector,0,sizeof(double)*M)
memset(next_right_vector,0,sizeof(double)*N)
for(int i=0i<Mi++)
for(int j=0j<Nj++)
next_right_vector[j]+=left_vector[i]*A[i][j]
r=normalize(next_right_vector,N)
if(r<eps) break
for(int i=0i<coli++)
orth(&V[i][0],next_right_vector,N)
normalize(next_right_vector,N)
for(int i=0i<Mi++)
for(int j=0j<Nj++)
next_left_vector[i]+=next_right_vector[j]*A[i][j]
r=normalize(next_left_vector,M)
if(r<eps) break
for(int i=0i<coli++)
orth(&U[i][0],next_left_vector,M)
normalize(next_left_vector,M)
diff=0
for(int i=0i<Mi++){
double d=next_left_vector[i]-left_vector[i]
diff+=d*d
}
memcpy(left_vector,next_left_vector,sizeof(double)*M)
memcpy(right_vector,next_right_vector,sizeof(double)*N)
}
if(r>=eps){
S[col]=r
memcpy((char *)&U[col][0],left_vector,sizeof(double)*M)
memcpy((char *)&V[col][0],right_vector,sizeof(double)*N)
}else{
cout<<r<<endl
break
}
}
delete [] next_left_vector
delete [] next_right_vector
delete [] left_vector
delete [] right_vector
return true
}
void print(vector<vector<double>>&A){
}
int main(){
int m=10
int n=8
int k=5
//分解一个10*8的矩阵A,求其前5个奇异值和奇异向量
srand(time(0))
vector<vector<double>>A
A.resize(m)
for(int i=0i<mi++){
A[i].resize(n)
for(int j=0j<nj++)
A[i][j]=(float)rand()/RAND_MAX-0.5
}
cout<<"A="<<endl
for(int i=0i<A.size()i++){
for(int j=0j<A[i].size()j++){
cout<<setw(12)<<A[i][j]<<' '
}
cout<<endl
}
cout<<endl
vector<vector<double>>U
vector<double>S
vector<vector<double>>V
svd(A,k,U,S,V)
cout<<"U="<<endl
for(int i=0i<U[0].size()i++){
for(int j=0j<U.size()j++){
cout<<setw(12)<<U[j][i]<<' '
}
cout<<endl
}
cout<<endl
cout<<"S="<<endl
for(int i=0i<S.size()i++){
cout<<setw(7)<<S[i]<<' '
}
cout<<endl
cout<<"V="<<endl
for(int i=0i<V[0].size()i++){
for(int j=0j<V.size()j++){
cout<<setw(12)<<V[j][i]<<' '
}
cout<<endl
}
return 0
}
你自己看灶掘matlab对svd的说明,X= U*S*V'
这里的V是转置的
所以跟你的C程序结果是转置
其次正负号可以随便分配的,例如你把U的符号全取负号,那么V也是全取负号
然后就是精度问题,你在matlab 先输入format long
就能看到更多位数了,大小基本是一样的
所以其实结果是等价的,说白了就是S按特征值敬蠢大小排序的话是唯一的,U,V不唯隐稿核一
不明白可追问
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