OpenCV机器学习（1）：贝叶斯分类器实现代码分析
OpenCV的机器学习类定义在ml.hpp文件中，基础类是CvStatModel，其他各种分类器从这里继承而来。
今天研究CvNormalBayesClassifier分类器。
1.类定义
在ml.hpp中有以下类定义：
class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
{
public:
CV_WRAP CvNormalBayesClassifier();
virtual ~CvNormalBayesClassifier();
CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
protected:
int var_count, var_all;
CvMat* var_idx;
CvMat* cls_labels;
CvMat** count;
CvMat** sum;
CvMat** productsum;
CvMat** avg;
CvMat** inv_eigen_values;
CvMat** cov_rotate_mats;
CvMat* c;
};
2.示例
此类使用方法如下：（引用别人的代码，忘记出处了，非常抱歉这个。。。）
#include "global_include.h"
using namespace std;
using namespace cv;
double inputArr[10][13] =
{
1,0.708333,1,1,-0.320755,-0.105023,-1,1,-0.419847,-1,-0.225806,0,1,
-1,0.583333,-1,0.333333,-0.603774,1,-1,1,0.358779,-1,-0.483871,0,-1,
1,0.166667,1,-0.333333,-0.433962,-0.383562,-1,-1,0.0687023,-1,-0.903226,-1,-1,
-1,0.458333,1,1,-0.358491,-0.374429,-1,-1,-0.480916,1,-0.935484,0,-0.333333,
-1,0.875,-1,-0.333333,-0.509434,-0.347032,-1,1,-0.236641,1,-0.935484,-1,-0.333333,
-1,0.5,1,1,-0.509434,-0.767123,-1,-1,0.0534351,-1,-0.870968,-1,-1,
1,0.125,1,0.333333,-0.320755,-0.406393,1,1,0.0839695,1,-0.806452,0,-0.333333,
1,0.25,1,1,-0.698113,-0.484018,-1,1,0.0839695,1,-0.612903,0,-0.333333,
1,0.291667,1,1,-0.132075,-0.237443,-1,1,0.51145,-1,-0.612903,0,0.333333,
1,0.416667,-1,1,0.0566038,0.283105,-1,1,0.267176,-1,0.290323,0,1
};
double testArr[]=
{
0.25,1,1,-0.226415,-0.506849,-1,-1,0.374046,-1,-0.83871,0,-1
};
int _tmain(int argc, _TCHAR* argv[])
{
Mat trainData(10, 12, CV_32FC1);
for (int i=0; i<10; i++)
{
for (int j=0; j<12; j++)
{
trainData.at<float>(i, j) = inputArr[i][j+1];
}
}
Mat trainResponse(10, 1, CV_32FC1);
for (int i=0; i<10; i++)
{
trainResponse.at<float>(i, 0) = inputArr[i][0];
}
CvNormalBayesClassifier nbc;
bool trainFlag = nbc.train(trainData, trainResponse);
if (trainFlag)
{
cout<<"train over..."<<endl;
nbc.save("normalBayes.txt");
}
else
{
cout<<"train error..."<<endl;
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