#include <iostream>
#include <string>
#include <iostream>
#include <vector>
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <opencv/ml.h>
#include <string>
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <opencv/ml.h>
using namespace std;
using namespace cv;
int main()
{
//待降维的训练数据（此转载自其他地方，出处忘了）
float Coordinates[7*31] = {
101.5, 100.4, 97.0, 98.7, 100.8, 114.2, 104.2
, 100.8, 93.5, 95.9, 100.7, 106.7, 104.3, 106.4
, 100.8, 97.4, 98.2, 98.2, 99.5, 103.6, 102.4
, 99.4, 96.0, 98.2, 97.8, 99.1, 98.3, 104.3
, 101.8, 97.7, 99.0, 98.1, 98.4, 102.0, 103.7
, 101.8, 96.8, 96.4, 92.7, 99.6, 101.3, 103.4
, 101.3, 98.2, 99.4, 103.7, 98.7, 101.4, 105.3
, 101.9, 100.0, 98.4, 96.9, 102.7, 100.3, 102.3
, 100.3, 98.9, 97.2, 97.4, 98.1, 102.1, 102.3
, 99.3, 97.7, 97.6, 101.1, 96.8, 110.1, 100.4
, 98.7, 98.4, 97.0, 99.6, 95.6, 107.2, 99.8
, 99.7, 97.7, 98.0, 99.3, 97.3, 104.1, 102.7
, 97.6, 96.5, 97.6, 102.5, 97.2, 100.6, 99.9
, 98.0, 98.4, 97.1, 100.5, 101.4, 103.0, 99.9
, 101.1, 98.6, 98.7, 102.4, 96.9, 108.2, 101.7
, 100.4, 98.6, 98.0, 100.7, 99.4, 102.4, 103.3
, 99.3, 96.9, 94.0, 98.1, 99.7, 109.7, 99.2
, 98.6, 97.4, 96.4, 99.8, 97.4, 102.1, 100.0
, 98.2, 98.2, 99.4, 99.3, 99.7, 101.5, 99.9
, 98.5, 96.3, 97.0, 97.7, 98.7, 112.6, 100.4
, 98.4, 99.2, 98.1, 100.2, 98.0, 98.2, 97.8
, 99.2, 97.4, 95.7, 98.9, 102.4, 114.8, 102.6
, 101.3, 97.9, 99.2, 98.8, 105.4, 111.9, 99.9
, 98.5, 97.8, 94.6, 102.4, 107.0, 115.0, 99.5
, 98.3, 96.3, 98.5, 106.2, 92.5, 98.6, 101.6
, 99.3, 101.1, 99.4, 100.1, 103.6, 98.7, 101.3
, 99.2, 97.3, 96.2, 99.7, 98.2, 112.6, 100.5
, 100.0, 99.9, 98.2, 98.3, 103.6, 123.2, 102.8
, 102.2, 99.4, 96.2, 98.6, 102.4, 115.3, 101.2
, 100.1, 98.7, 97.4, 99.8, 100.6, 112.4, 102.5
, 104.3, 98.7, 100.2, 116.1, 105.2, 101.6, 102.6
};
//测试降维用的特征向量
float Coordinates_test[7*2] = {104.3, 98.7, 100.2, 116.1, 105.2, 101.6, 102.6,};
//将待测试数组转为mat数组
Mat pcaSet(31, 7, CV_32FC1); //原始数据
Mat Coordinates1(1, 7, CV_32FC1);
for (int i = 0; i < 31; i++)
for (int j = 0; j < 7; j++)
pcaSet.at<float>(i, j) = Coordinates[i*j + j];
for (int i = 0; i < 1; i++)
for (int j = 0; j < 7; j++)
Coordinates1.at<float>(i, j) = Coordinates_test[i*j + j];
//降维处理，生成降维所需的特征向量：
int pca_num = 5;//降至5维
cout << "正在进行PCA降维..." << endl;
PCA pca(pcaSet, Mat(), CV_PCA_DATA_AS_ROW, pca_num);
Mat eigenvectors = pca.eigenvectors.clone();//eigenvectors用来存放降维后的特征向量
FileStorage f_pca("eigenvectors.xml", FileStorage::WRITE);
f_pca << "eigenvectors" << eigenvectors;
f_pca.release();
//读取上面的特征向量，对输入数据进行真正的降维处理：
FileStorage f_pca("eigenvectors.xml", FileStorage::READ);
f_pca["eigenvectors"] >> eigenvectors;
f_pca.release();
Mat data_to_pca = eigenvectors*Coordinates1.t();//对特征向量进行降维
//保存数据
FileStorage save_data("data_to_pca .xml", FileStorage::WRITE);
save_data<< "data_to_pca " << data_to_pca ;
save_data.release();
return 0;
}
