转载：http://blog.csdn.net/yang_xian521/article/details/6967515
PS：很多时候，我们并不需要特别精通某个理论，而且有的时候即便你非常精通，但是当你真正去用它时，也会错误百出。
也就是说，要做到会用（这是终极目标），出现问题时，能够快速解决就ok了。对理论的熟悉，可以通过简单的实例来加深。
最厉害的，往往是那些深入浅出的人。。。
openCV中与贝叶斯分类器相关的API函数有以下几个：
(1)CvNormalBayesClassifier::CvNormalBayesClassifier();
该函数为默认构造函数；
(2)CvNormalBayesClassifier::CvNormalBayesClassifier(const Mat& trainData, const Mat& response, const Mat& varIdx=Mat(),const Mat& sampleIdx=Mat());
该函数实际是在默认构造函数内部调用train()函数进行分类器训练；
(3)bool CvNormalBayesClassifier::train(const Mat& trainData, const Mat& response, const Mat& varIdx=Mat(),const Mat& sampleIdx=Mat());
该函数进行贝叶斯分类器的训练，输入向量必须为行向量；变量response必须为整数，但其在初始化时类型可被设置为CV_32FC1;
所有特征向量必须完整，不允许训练样本集的某一个向量存在数据缺失；
(4)float CvNormalBayesClassifier::predict(const Mat& samples, Mat* result=0);
该函数根据用户输入的测试样本的特征向量，返回其所属的类别；注意，如果输入是很多个测试样本的特征向量组成的矩阵，返回值为result矩阵；
- //openCV中贝叶斯分类器的API函数用法举例
- //运行环境：winXP + VS2008 + openCV2.3.0
- #include "stdafx.h"
- #include "opencv.hpp"
- #include "iostream"
- using namespace cv;
- using namespace std;
- //10个样本特征向量维数为12的训练样本集，第一列为该样本的类别标签
- 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
- };
- //一个测试样本的特征向量
- 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);//构建训练样本的特征向量
- 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);//构建训练样本的类别标签
- for (int i=0; i<10; i++)
- {
- trainResponse.at<float>(i, 0) = inputArr[i][0];
- }
- CvNormalBayesClassifier nbc;
- bool trainFlag = nbc.train(trainData, trainResponse);//进行贝叶斯分类器训练
- if (trainFlag)
- {
- cout<<"train over..."<<endl;
- nbc.save("c:/normalBayes.txt");
- }
- else
- {
- cout<<"train error..."<<endl;
- system("pause");
- exit(-1);
- }
- CvNormalBayesClassifier testNbc;
- testNbc.load("c:/normalBayes.txt");
- Mat testSample(1, 12, CV_32FC1);//构建测试样本
- for (int i=0; i<12; i++)
- {
- testSample.at<float>(0, i) = testArr[i];
- }
- float flag = testNbc.predict(testSample);//进行测试
- cout<<"flag = "<<flag<<endl;
- system("pause");
- return 0;
- }