knn进行二维样本集分类

#include <ml.h>#include <highgui.h>int main( int argc, char** argv ){    const int K = 10;    int i, j, k, accuracy;    float response;    int train_sample_count = 100;    CvRNG rng_state = cvRNG(-1); //初始化随机数生成器    CvMat* trainData = cvCreateMat( train_sample_count, 2, CV_32FC1 );    CvMat* trainClasses = cvCreateMat( train_sample_count, 1, CV_32FC1 );    IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );    float _sample[2];    CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );    cvZero( img );    CvMat trainData1, trainData2, trainClasses1, trainClasses2;    // 行程训练样本    cvGetRows( trainData, &trainData1, 0, train_sample_count/2 );    //用随机数填充数组并更新RNG状态    cvRandArr( &rng_state, &trainData1, CV_RAND_NORMAL, cvScalar(200,200), cvScalar(50,50) );    cvGetRows( trainData, &trainData2, train_sample_count/2, train_sample_count );    cvRandArr( &rng_state, &trainData2, CV_RAND_NORMAL, cvScalar(300,300), cvScalar(50,50) );    cvGetRows( trainClasses, &trainClasses1, 0, train_sample_count/2 );    cvSet( &trainClasses1, cvScalar(1) );    cvGetRows( trainClasses, &trainClasses2, train_sample_count/2, train_sample_count );    cvSet( &trainClasses2, cvScalar(2) );    // learn classifier    CvKNearest knn( trainData, trainClasses, 0, false, K );    CvMat* nearests = cvCreateMat( 1, K, CV_32FC1);    for( i = 0; i < img->height; i++ )    {        for( j = 0; j < img->width; j++ )        {            sample.data.fl[0] = (float)j;            sample.data.fl[1] = (float)i;            // estimates the response and get the neighbors' labels            response = knn.find_nearest(&sample,K,0,0,nearests,0);            // compute the number of neighbors representing the majority            for( k = 0, accuracy = 0; k < K; k++ )            {                if( nearests->data.fl[k] == response)                    accuracy++;            }            // highlight the pixel depending on the accuracy (or confidence)            cvSet2D( img, i, j, response == 1 ?                (accuracy > 5 ? CV_RGB(180,0,0) : CV_RGB(180,120,0)) :                (accuracy > 5 ? CV_RGB(0,180,0) : CV_RGB(120,120,0)) );        }    }    // 显示原始训练样本    for( i = 0; i < train_sample_count/2; i++ )    {        CvPoint pt;        pt.x = cvRound(trainData1.data.fl[i*2]);        pt.y = cvRound(trainData1.data.fl[i*2+1]);        cvCircle( img, pt, 2, CV_RGB(255,0,0), CV_FILLED );        pt.x = cvRound(trainData2.data.fl[i*2]);        pt.y = cvRound(trainData2.data.fl[i*2+1]);        cvCircle( img, pt, 2, CV_RGB(0,255,0), CV_FILLED );    }    cvNamedWindow( "classifier result", 1 );    cvShowImage( "classifier result", img );    cvWaitKey(0);    cvReleaseMat( &trainClasses );    cvReleaseMat( &trainData );    return 0;}