opencv/C++ 进行数据的PCA降维

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最新推荐文章于 2024-11-17 21:22:05 发布

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分类专栏： C++基础编程

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博客介绍了使用OpenCV和C++进行数据的PCA降维。PCA降维是数据处理中的重要技术，能有效减少数据维度，保留关键信息。借助OpenCV库和C++语言，可高效完成数据的PCA降维操作，在信息技术领域有广泛应用。

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#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;
}