PCA降维

http://blog.csdn.net/a784763307/article/details/17289317

这篇比较棒

---

openCV版

void PrintMatrix(CvMat *Matrix,int Rows,int Cols)  
{  
    for(int i=0;i<Rows;i++)  
    {  
        for(int j=0;j<Cols;j++)  
        {  
            printf("%.2f ",cvGet2D(Matrix,i,j).val[0]);  
        }  
        printf("\n");  
    }  
}  
int main()
{
    float Coordinates[20]={1.5,2.3,  
                                 3.0,1.7,  
                                 1.2,2.9,  
                                 2.1,2.2,  
                                 3.1,3.1,  
                                 1.3,2.7,  
                                 2.0,1.7,  
                                 1.0,2.0,  
                                 0.5,0.6,  
                                 1.0,0.9};  
    CvMat *Vector1;  
    CvMat *AvgVector;  
    CvMat *EigenValue_Row;  
    CvMat *EigenVector;  
  
    Vector1=cvCreateMat(10,2,CV_32FC1);  
    cvSetData(Vector1,Coordinates,Vector1->step);  
    AvgVector=cvCreateMat(1,2,CV_32FC1);  
    EigenValue_Row=cvCreateMat(2,1,CV_32FC1);  
    EigenVector=cvCreateMat(2,2,CV_32FC1);  
  
    cvCalcPCA(Vector1,AvgVector,EigenValue_Row,EigenVector,CV_PCA_DATA_AS_ROW);  
  
    printf("Original Data:\n");  
    PrintMatrix(Vector1,10,2);  
  
    printf("==========\n");  
    PrintMatrix(AvgVector,1,2);  
  
    printf("\nEigne Value:\n");  
    PrintMatrix(EigenValue_Row,2,1);  
  
    printf("\nEigne Vector:\n");   //每一行是一个特征向量。
    PrintMatrix(EigenVector,2,2);  
  cout<<"******************************"<<endl;



system("pause");
return 0;

}