//
// void main( --> int main( 
//
//


#include "cxcore.h"
#include "highgui.h"
#include "ml.h"

int main( int argc, char** argv )
{



 float priors[] = { 1.0, 10.0}; // Edible vs poisonos weights

 CvMat* var_type;
 CvMat* data; // jmh add
 data = cvCreateMat( 20, 30, CV_8U ); // jmh add

 var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
 cvSet( var_type, cvScalarAll(CV_VAR_CATEGORICAL) ); // all these vars 
 // are categorical
 CvDTree* dtree;
 dtree = new CvDTree;
 dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
 CvDTreeParams( 8, // max depth
 10, // min sample count
 0, // regression accuracy: N/A here
 true, // compute surrogate split, 
 // as we have missing data
 15, // max number of categories 
 // (use sub-optimal algorithm for
 // larger numbers)
 10, // cross-validations 
 true, // use 1SE rule => smaller tree
 true, // throw away the pruned tree branches
 priors // the array of priors, the bigger 
 // p_weight, the more attention
 // to the poisonous mushrooms
 )
 );



 dtree->save("tree.xml","MyTree");
 dtree->clear();
 dtree->load("tree.xml","MyTree");



 #define MAX_CLUSTERS 5
 CvScalar color_tab[MAX_CLUSTERS];
 IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
 CvRNG rng = cvRNG(0xffffffff);
 
 color_tab[0] = CV_RGB(255,0,0);
 color_tab[1] = CV_RGB(0,255,0);
 color_tab[2] = CV_RGB(100,100,255);
 color_tab[3] = CV_RGB(255,0,255);
 color_tab[4] = CV_RGB(255,255,0);

 cvNamedWindow( "clusters", 1 );

 for(;;)
 {
 int k, cluster_count = cvRandInt(&rng)%MAX_CLUSTERS + 1;
 int i, sample_count = cvRandInt(&rng)%1000 + 1;
 CvMat* points = cvCreateMat( sample_count, 1, CV_32FC2 );
 CvMat* clusters = cvCreateMat( sample_count, 1, CV_32SC1 );

 /* generate random sample from multivariate 
 Gaussian distribution */
 for( k = 0; k < cluster_count; k++ )
 {
 CvPoint center;
 CvMat point_chunk;
 center.x = cvRandInt(&rng)%img->width;
 center.y = cvRandInt(&rng)%img->height;
 cvGetRows( points, &point_chunk, 
 k*sample_count/cluster_count,
 k == cluster_count - 1 ? sample_count : 
 (k+1)*sample_count/cluster_count );
 cvRandArr( &rng, &point_chunk, CV_RAND_NORMAL,
 cvScalar(center.x,center.y,0,0),
 cvScalar(img->width/6, img->height/6,0,0) );
 }

 /* shuffle samples */
 for( i = 0; i < sample_count/2; i++ )
 {
 CvPoint2D32f* pt1 = (CvPoint2D32f*)points->data.fl +
 cvRandInt(&rng)%sample_count;
 CvPoint2D32f* pt2 = (CvPoint2D32f*)points->data.fl + 
 cvRandInt(&rng)%sample_count;
 CvPoint2D32f temp;
 CV_SWAP( *pt1, *pt2, temp );
 }

 cvKMeans2( points, cluster_count, clusters,
 cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 
 10, 1.0 ));
 cvZero( img );
 for( i = 0; i < sample_count; i++ )
 {
 CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];
 int cluster_idx = clusters->data.i[i];
 cvCircle( img, cvPointFrom32f(pt), 2, 
 color_tab[cluster_idx], CV_FILLED );
 }

 cvReleaseMat( &points );
 cvReleaseMat( &clusters );

 cvShowImage( "clusters", img );

 int key = cvWaitKey(0);
 if( key == 27 ) // 'ESC'
 break;
 }
}