头文件
#ifndef DEBUG_LRN_SVD_H
#define DEBUG_LRN_SVD_H
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int test_SVD();
void print_matrix(const vector<vector<float>>& vec);
#endif //DEBUG_LRN_SVD_H
源文件
#include "svd.h"
void print_matrix(const vector<vector<float>>& vec){
if(vec.empty()){
return;
}
for ( auto row: vec){
if(row.empty()){
return;
}
for ( auto elem: row){
cout << elem << ", ";
}
cout << endl;
}
cout << endl;
}
int test_SVD()
{
std::vector<std::vector<float>> vec{ { 0.68f, 0.597f },
{ -0.211f, 0.823f },
{ 0.566f, -0.605f } };
cout << "source matrix:" << endl;
print_matrix(vec);
// cout << "C++ implement SVD:" << endl;
// vector<vector<float>> matD, matU, matVt;
// if (svd(vec, matD, matU, matVt) != 0) {
// cout << "C++ implement singular value decomposition fail!" << endl;
// return -1;
// }
// cout << "singular values:" << endl;
// print_matrix(matD);
// cout << "left singular vectors:" << endl;
// print_matrix(matU);
// cout << "transposed matrix of right singular values:" << endl;
// print_matrix(matVt);
cout << "opencv singular value decomposition:" << endl;
const auto rows = (int)vec.size();
const auto cols = (int)vec[0].size();
cv::Mat mat(rows, cols, CV_32FC1);
for (int y = 0; y < rows; ++y) {
for (int x = 0; x < cols; ++x) {
mat.at<float>(y, x) = vec.at(y).at(x);
}
}
cv::Mat matD, matU, matVt;
cv::SVD::compute(mat, matD, matU, matVt, 4);
cout << "singular values:" << endl;
cout << matD << endl;
cout << "left singular vectors:" << endl;
cout << matU << endl;
cout << "transposed matrix of right singular values:" << endl;
cout << matVt << endl;
cv::Mat matUt, matV;
cv::transpose(matU, matUt);
cv::transpose(matVt, matV);
cout << "verify if the result is correct:" << endl;
cv::Mat reconMatD = matUt * mat * matV;
cout << reconMatD << endl;
return 0;
}
原文地址:https://www.cnblogs.com/theodoric008/p/9506015.html
时间: 2024-10-11 15:57:15