OpenCV Machine Learning 之正态贝叶斯分类器（Normal Bayes Classifier）的用法实例

本编博客通过以学生成绩为特征对学生进行分类，判断他是文科生还是理科生向大家分享OpenCV 贝叶斯分类器的用法。

该类的详细解释请看博文：

http://blog.csdn.net/zhjm07054115/article/details/27631913

分类器的用法大致分三步：

第一步： 构造已知标签的训练样本数据集

第二步： 训练分类器 （对应于 train函数）

第三步： 用训练好的分类器对未知标签的样本进行分类（对应于predict函数）

在实例中，我们假定：（所有课程的成绩范围是 0到100分）

文科生的数学成绩服从 均值为 60，标准差为10的正态分布，文科生的语文成绩服从 均值为80，标准差为10的正态分布

理科生的数学成绩服从 均值为 80，标准差为10的正态分布，理科生的语文成绩服从 均值为60，标准差为10的正态分布

分布范围如下所示：

下面看代码吧：（注释的很详细）

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#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
 
//生成具有一个特征的指定类别的训练数据集
void GenerateTrainData_two_feature(int sample_counts,double miu1,double sigma1,double miu2,double sigma2,Mat& train_data);
//把属于两个类的训练数据集混和起来，并记录对应的类别标签
void MixTrainDataFromTwoClasses(Mat& train_data1,Mat& train_data2,Mat& total_train_data,Mat& total_train_data_response);
int main(int argc, char* argv[])
{
 
int features_count = 2;  //每个样本包含两个特征: 数学成绩 与 语文成绩
//生成类别一的训练数据集： 若干文科生的 数学成绩 和 语文成绩，假定这两个特征都服从高斯分布
int SampleCount1 = 200;  //训练数据集1 的 样本个数
Mat TrainData1(SampleCount1,features_count,CV_32FC1); //训练数据集1 的 样本特征向量
//假定文科生的数学成绩的概率密度分布参数：均值 60，和 标准差 10
double sigma11 = 10;double miu11 = 60;  
//假定文科生的语文成绩的概率密度分布参数：均值 80，和 标准差 10
double sigma12 = 10;double miu12 = 80;  
//调用该函数生成文科生特征训练集
GenerateTrainData_two_feature(SampleCount1,miu11,sigma11,miu12,sigma12,TrainData1); 
cout<<endl<<"文科生的成绩分布规律"<<endl;
cout<<"数学成绩的分布参数：均值 60，和 标准差 10"<<endl;
cout<<"语文成绩的分布参数：均值 80，和 标准差 10"<<endl;
//生成类别二的训练数据集： 若干理科生的 数学成绩 和 语文成绩，假定这两个特征都服从高斯分布
int SampleCount2 = 200;
Mat TrainData2(SampleCount2,features_count,CV_32FC1); 
//假定理科生的数学成绩的概率密度分布参数：均值 80，和 标准差 10
double sigma21 = 10; double miu21 = 80;
//假定理科生的语文成绩的概率密度分布参数：均值 60，和 标准差 10
double sigma22 = 10; double miu22 = 60;
 //调用该函数生成理科生特征训练集
GenerateTrainData_two_feature(SampleCount2,miu21,sigma21,miu22,sigma22,TrainData2);
cout<<endl<<"理科生的成绩分布规律"<<endl;
cout<<"数学成绩的分布参数：均值 80，和 标准差 10"<<endl;
cout<<"语文成绩的分布参数：均值 60，和 标准差 10"<<endl;
//生成混合训练样本
int TotalSamplesCount = SampleCount1 + SampleCount2;
Mat TotalSamplesData(TotalSamplesCount,features_count,CV_32FC1); //包含两个类的训练样本
Mat SamplesResponse(TotalSamplesCount,1,CV_32FC1);  //样本集合的类别标签
//将两个类的样本均匀混合 ，产生训练数据集合
MixTrainDataFromTwoClasses(TrainData1,TrainData2,TotalSamplesData,SamplesResponse); 
//声明一个正态贝叶斯分类器对象
CvNormalBayesClassifier nb_classifier = CvNormalBayesClassifier(); //不带训练数据的默认构造函数
//使用训练数据训练分类器
Mat VarIdx = Mat();     //默认使用样本的所有特征训练分类器：总共两个特征
Mat SampleIdx = Mat();  //默认使用样本集合中的所有样本训练分类器
nb_classifier.train(TotalSamplesData,SamplesResponse,VarIdx,SampleIdx,false);
 
//使用训练好的分类器分类给定的新测试样本
float testsample[10][2] = {{30,50},{40,90},{50,60},{60,60},{65,70},{70,40},{75,30},{80,50},{90,60},{100,100}};
Mat test_sample1(10,2,CV_32FC1,testsample);
cout<<endl<<"10个学生成绩测试样本："<<endl<<"数学：语文 "<<endl<<test_sample1<<endl;
cout<<endl<<"文科生类别标签是 1 ，理科生类别标签是 2 "<<endl;
Mat test_result2D(10,1,CV_32FC1);
nb_classifier.predict(test_sample1,&test_result2D);
cout<<"数学语文联合特征分类结果："<<test_result2D<<endl;
//声明一个正态贝叶斯分类器对象:只使用数学成绩特征进行分类
CvNormalBayesClassifier nb_classifier1D0 = CvNormalBayesClassifier(); //不带训练数据的默认构造函数
//只使用单个特征训练分类器
int feature_index0[1] = {0}; // ==0 ：只用第一个特征即数学成绩构造分类器； 
Mat FeatureIdx0(1,1,CV_32SC1,feature_index0);
nb_classifier1D0.train(TotalSamplesData,SamplesResponse,FeatureIdx0,SampleIdx,false);
Mat test_result1D0(10,1,CV_32FC1);
nb_classifier1D0.predict(test_sample1,&test_result1D0);
cout<<"以数学成绩为特征的分类结果："<<test_result1D0<<endl;
//声明一个正态贝叶斯分类器对象:只使用语文特征进行分类
CvNormalBayesClassifier nb_classifier1D1 = CvNormalBayesClassifier(); //不带训练数据的默认构造函数
//只使用单个特征训练分类器
int feature_index1[1] = {1}; //  == 1：只用第二个特征即语文成绩构造分类器 
Mat FeatureIdx1(1,1,CV_32SC1,feature_index1);
nb_classifier1D1.train(TotalSamplesData,SamplesResponse,FeatureIdx1,SampleIdx,false);
Mat test_result1D1(10,1,CV_32FC1);
nb_classifier1D1.predict(test_sample1,&test_result1D1);
cout<<"以语文成绩为特征的分类结果："<<test_result1D1<<endl;
//新测试样本,绘制分类器界面
Mat TestSamples(101*101,2,CV_32FC1);
for(int i= 0; i<101;i++)
{
for(int j=0;j<101;j++)
{
TestSamples.at<float>(i*101+j,0) = i;  //第一个特征是数学成绩，0到100
TestSamples.at<float>(i*101+j,1) = j;  //第二个特征是语文成绩, 0到100
}
}
Mat TestSamplesResult(101*101,1,CV_32FC1);//总共有101*101个测试样本
nb_classifier.predict(TestSamples,&TestSamplesResult); //调用分类器对测试样本进行分类
//绘制分类结果
Mat result_map(101,101,CV_8UC3);
for(int i=0;i<101;i++)
{
for(int j=0;j<101;j++)
{
int label = (int)TestSamplesResult.at<float>(i*101+j,0);
Vec3b v = label==1? Vec3b(255,0,0) : Vec3b(0,0,255); //蓝色代表第1类，红色代表第二类
result_map.at<Vec3b>(i,j) = v;   //判决面中从左往右的顺序是语文成绩依次增加，从上往下的顺序是数学成绩依次增加
}
}
namedWindow("分类器判决面",0);
imshow("分类器判决面",result_map);
cout<<endl<<"判决面中 蓝色代表第1类：文科生，红色代表第二类：理科生"<<endl;
cout<<"判决面中从左往右的顺序是语文成绩依次增加，从上往下的顺序是数学成绩依次增加"<<endl;
waitKey(0);
return 0;
}
 
//把属于两个类的训练数据集混和起来，并记录对应的类别标签
void MixTrainDataFromTwoClasses(Mat& train_data1,Mat& train_data2,Mat& total_train_data,Mat& total_train_data_response)
{
//均匀混合两个类的训练数据
Size sz1 = train_data1.size();
Size sz2 = train_data2.size();
CV_Assert(sz1.width==sz2.width); //保证每个类的训练数据的特征维数是一样的
total_train_data = Mat::zeros(sz1.height+sz2.height,sz1.width,CV_32FC1);
total_train_data_response = Mat::zeros(sz1.height+sz2.height,1,CV_32FC1);
int loops = std::min(sz1.height,sz2.height);
bool more1 = (loops == sz2.height); 
int k = 0;
//两个类的训练样本交替出现
for(int i=0;i <loops;i++)
{ 
memcpy(total_train_data.ptr<float>(k),train_data1.ptr<float>(i),sz1.width*(sizeof(float)));
total_train_data_response.at<float>(k) = 1;
k++;
memcpy(total_train_data.ptr<float>(k),train_data2.ptr<float>(i),sz2.width*(sizeof(float))); 
total_train_data_response.at<float>(k) = 2;
k++;
}
//如果两类样本的数目不一样，则把多余的样本追加到末尾
if(sz1.height > sz2.height)
{
for(int i = sz2.height;i<sz1.height;i++)
{
memcpy(total_train_data.ptr<float>(k),train_data1.ptr<float>(i),sz1.width*(sizeof(float)));
total_train_data_response.at<float>(k) = 1;
k++;
}
}
if(sz2.height > sz1.height)
{
for(int i = sz1.height;i<sz2.height;i++)
{
memcpy(total_train_data.ptr<float>(k),train_data2.ptr<float>(i),sz2.width*(sizeof(float)));
total_train_data_response.at<float>(k) = 2;
k++;
}
}
}
//生成具有两个特征的指定类别的训练数据集
void GenerateTrainData_two_feature(int sample_counts,double miu1,double sigma1,double miu2,double sigma2,Mat& train_data)
{
int64 seed =/*100*/  getTickCount();//随机数种子，每次生成不一样的随机数
RNG rng1(seed);  //创建一个随机数发生器对象
RNG rng2(seed*2);  //创建一个随机数发生器对象
double MinFeatureValue = 0;  // 特征的最小值
double MaxFeatureValue = 100;// 特征的最大值
 //训练数据集矩阵初始化，样本按行存储，每一行是一个样本的2维特征向量,
train_data = Mat::zeros(sample_counts,2,CV_32FC1); 
 
//循环生成样本特征集合
for(int i=0;i<sample_counts;i++)
 {
 double a = miu1 + rng1.gaussian(sigma1); //返回一个均值为 miu1 ,标准差为 sigma1 的 随机数
 double b = miu2 + rng2.gaussian(sigma2); //返回一个均值为 miu2 ,标准差为 sigma2 的 随机数
 if(a>=MinFeatureValue && a<=MaxFeatureValue)
 { 
 //如果返回的随机数在指定的特征范围区间内部，则把它加入样本集
 train_data.at<float>(i,0) = a ;
 }
 else
 {
 //如果返回的随机数超出了指定的特征范围的边界，为简单起见，直接把均值加入样本集
 train_data.at<float>(i,0) = miu1 ;
 }
 if(b>=MinFeatureValue && b<=MaxFeatureValue)
 { 
 //如果返回的随机数在指定的特征范围区间内部，则把它加入样本集
 train_data.at<float>(i,1) = b ;
 }
 else
 {
 //如果返回的随机数超出了指定的特征范围的边界，为简单起见，直接把均值加入样本集
 train_data.at<float>(i,1) = miu2 ;
 }
 }
}

 来自CODE的代码片

normalbayesclassifier.cpp

下面是运行结果：

生成样本：数学成绩从0到100，语文成绩从0到100，总共101*101个测试数据，就可以大概的绘制出分类器的判决界面啦。

单一特征的分类判决界面

网址：http://blog.csdn.net/zhjm07054115/article/details/28326439