参考文献:
http://blog.csdn.net/homechao/article/details/9056827
http://hi.baidu.com/lin65505578/item/20798e9748d936b7cd80e5dd
例程一:
//openCV中贝叶斯分类器的API函数用法举例 //运行环境:winXP + VS2008 + openCV2.1.0 #include "stdafx.h" #include <cv.h> #include <highgui.h> #include <ml.h> #include <iostream> using namespace std; int main() { float inputArr[120] = { 0.708333f,1.0f,1.0f,-0.320755f,-0.105023f,-1.0f,1.0f,-0.419847f,-1.0f,-0.225806f,0.f,1.f, 0.583333f,-1.f,0.333333f,-0.603774f,1.f,-1.f,1.f,0.358779f,-1.f,-0.483871f,0.f,-1.f, 0.166667f,1.f,-0.333333f,-0.433962f,-0.383562f,-1.f,-1.f,0.0687023f,-1.f,-0.903226f,-1.f,-1.f, 0.458333f,1.f,1.f,-0.358491f,-0.374429f,-1.f,-1.f,-0.480916f,1.f,-0.935484f,0.f,-0.333333f, 0.875f,-1.f,-0.333333f,-0.509434f,-0.347032f,-1.f,1.f,-0.236641f,1.f,-0.935484f,-1.f,-0.333333f, 0.5f,1.f,1.f,-0.509434f,-0.767123f,-1.f,-1.f,0.0534351f,-1.f,-0.870968f,-1.f,-1.f, 0.125f,1.f,0.333333f,-0.320755f,-0.406393f,1.f,1.f,0.0839695f,1.f,-0.806452f,0.f,-0.333333f, 0.25f,1.f,1.f,-0.698113f,-0.484018f,-1.f,1.f,0.0839695f,1.f,-0.612903f,0.f,-0.333333f, 0.291667f,1.f,1.f,-0.132075f,-0.237443f,-1.f,1.f,0.51145f,-1.f,-0.612903f,0.f,0.333333f, 0.416667f,-1.f,1.f,0.0566038f,0.283105f,-1.f,1.f,0.267176f,-1.f,0.290323f,0.f,1.f }; CvMat trainData=cvMat(10,12,CV_32FC1,inputArr); //标签 int label[10]={1,-1,1,-1,-1,-1,1,1,1,1}; CvMat trainResponse=cvMat(10,1,CV_32SC1,label); //一个测试样本的特征向量 float testArr[]= { 0.25f,1.f,1.f,-0.226415f,-0.506849f,-1.f,-1.f,0.374046f,-1.f,-0.83871f,0.f,-1.f }; CvMat testSample=cvMat(1,12,CV_32FC1,testArr); 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("normalBayes.xml");//将训练出的模型以xml或yaml格式保存到硬盘 } else { cout<<"train error..."<<endl; system("pause"); exit(-1); } CvNormalBayesClassifier testNbc; testNbc.load("normalBayes.xml"); float flag = testNbc.predict(&testSample);//进行测试 cout<<"flag = "<<flag<<endl; system("pause"); return 0; }
例程二:
//openCV中贝叶斯分类器的API函数用法举例 //运行环境:winXP + VS2008 + openCV2.1.0 #include "stdafx.h" #include "cxcore.h" #include "highgui.h" #include "cvaux.h" #include "string.h" #include "stdio.h" #include "time.h" #include <ml.h> #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; }