KNN（一）--简单KNN原理及实现

#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);

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;



// form the training samples

cvGetRows(trainData, &trainData1, 0, train_sample_count / 2);

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 height; i++)

{

for (j = 0; j 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 
{

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)));

}

}



// display the original training samples

for (i = 0; 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;

}