PCA应用1

参考http://www.xxkb.com.cn/?action=show&id=69所提出得步骤，依葫芦画瓢，果然好用！

代码奉上：

#include <cv.h>
#include <highgui.h>
#include <fstream> 
#include <iostream>
#include <algorithm>
using namespace std;
void print_cvmat(CvMat* M)
{
	for (int i = 0; i < M->rows;++i)
		for(int j = 0;j < M->cols; ++j)
			cout << cvmGet(M,i,j) << " ";
}
int main()
{
	ifstream myfile( "E:\\feature_size.txt "); 
	if (!myfile.is_open()) 
		cout << "Unable to open file";
	int rows = 0,cols = 0;
	myfile >> rows >> cols;
	myfile.close();
	//初始化数据
	//每一行表示一个样本
	CvMat *pData = cvCreateMat(rows,cols,CV_32FC1);;
	ifstream fin("E:\\features.txt ",ifstream::in);
	for(int i = 0;i < rows; ++i)
		for(int j = 0;j < cols; ++j)
	   {
			fin >> pData->data.fl[i*cols+j];
		}         
	fin.close();
    CvMat* pMean = cvCreateMat(1, cols, CV_32FC1);
	//pEigVals中的每个数表示一个特征值
	CvMat* pEigVals = cvCreateMat(1, min(rows,cols), CV_32FC1);
	//每一行表示一个特征向量
	CvMat* pEigVecs = cvCreateMat( min(rows,cols), cols, CV_32FC1);
	//PCA处理,计算出平均向量pMean,特征值pEigVals和特征向量pEigVecs
	cvCalcPCA( pData, pMean, pEigVals, pEigVecs, CV_PCA_DATA_AS_ROW );
	print_cvmat(pEigVals);
	//选出前P个特征向量(主成份),然后投影,结果保存在pResult中，pResult中包含了P个系数
	CvMat* pResult = cvCreateMat( rows, 20, CV_32FC1 );
	cvProjectPCA( pData, pMean, pEigVecs, pResult );
	//重构,结果保存在pRecon中,这步貌似对我没必要
	//CvMat* pRecon = cvCreateMat( rows, cols, CV_32FC1 );
	//cvBackProjectPCA( pResult, pMean, pEigVecs, pRecon );
	//存储起来
	ofstream file( "E:\\feature_pca.txt "); 
	for (int i = 0; i < pResult->rows;++i)
	{
		for(int j = 0;j < pResult->cols; ++j)
		{
           file << cvmGet(pResult,i,j) << " ";
		}
		file << "\n";
	}
}