This is my first post, I am new to this site, but I have been hiding for some time. I know C well and very limited knowledge of C ++. I suppose. I am on Windows (XPx64), VS2008.
I am trying to wrap a C ++ library, kdtree2 so that I can use it from C. The main issues are regarding access to kdtree2 and kdtree2_result_vector. Since the authors of the ftp server are not responding, I downloaded a copy of the original kdtree2 src distribution
Just some brief information about a kd tree (binary tree form), "data" are coordinates in n-dimensional Cartesian space and index. What it is used for is searching for the nearest neighbor, so after building a tree (which will not be changed), you can query the tree for various types of nn searches. The results in this case are returned in the vector object structs (c-like structs).
struct kdtree2_result {
public:
float dis;
int idx;
};
My imaginary solution is to have an array of kdtree2 objects (one per stream). For the kdtree2_result_vector class, I do not have a solution yet, since I do not get the first base. No need to directly access the kdtree2 class .
I only need to fill it with data and then use it (as an example of the second function below). For this, I defined:
kdtree2 *global_kdtree2;
extern "C" void new_kdtree2 ( float **data, const int n, const int dim, bool arrange ) {
multi_array_ref<float,2> kdtree2_data ( ( float * ) &data [ 0 ][ 0 ], extents [ n ][ dim ], c_storage_order ( ) );
global_kdtree2 = new kdtree2 ( kdtree2_data, arrange );
}
To do this, using this tree, I defined:
extern "C" void n_nearest_around_point_kdtree2 ( int idxin, int correltime, int nn ) {
kdtree2_result_vector result;
global_kdtree2->n_nearest_around_point ( idxin, correltime, nn, result );
}
kdtree2_result_vector . , , C- C.
, n_nearest_around_point_kdtree2 . - , - /. c-test- :
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <stdbool.h>
#include "kdtree2.h"
#define MALLOC_2D(type,x,y) ((type**)malloc_2D_kdtree2((x),(y),sizeof(type)))
void **malloc_2D_kdtree2 ( const int x, const int y, const int type_size ) {
const int y_type_size = y * type_size;
void** x_idx = ( void ** ) malloc ( x * ( sizeof ( void ** ) + y_type_size ) );
if ( x_idx == NULL )
return NULL;
char* y_idx = ( char * ) ( x_idx + x );
for ( int i = 0; i < x; i++ )
x_idx [ i ] = y_idx + i * y_type_size;
return x_idx;
}
int main ( void ) {
float **data = MALLOC_2D ( float, 100, 3 );
for ( int i = 0; i < 100; i++ )
for ( int j = 0; j < 3; j++ )
data [ i ][ j ] = ( float ) ( 3 * i + j );
tnrp ( data, 100, 3, false );
new_kdtree2 ( data, 100, 3, false );
n_nearest_around_point_kdtree2 ( 9, 3, 6 );
delete_kdtree2 ( );
free ( data );
return 0;
}
, , , , - ( ) ++.
EDIT:
, larsmans. ( , larsmans):
class kdtree {
private:
float **data;
multi_array_ref<float,2> data_ref;
kdtree2 tree;
public:
kdtree2_result_vector result;
kdtree ( float **data, int n, int dim, bool arrange ) :
data_ref ( ( float * ) &data [ 0 ][ 0 ], extents [ n ][ dim ], c_storage_order ( ) ),
tree ( data_ref, arrange )
{
}
void n_nearest_brute_force ( std::vector<float>& qv ) {
tree.n_nearest_brute_force ( qv, result ); }
void n_nearest ( std::vector<float>& qv, int nn ) {
tree.n_nearest ( qv, nn, result ); }
void n_nearest_around_point ( int idxin, int correltime, int nn ) {
tree.n_nearest_around_point ( idxin, correltime, nn, result ); }
void r_nearest ( std::vector<float>& qv, float r2 ) {
tree.r_nearest ( qv, r2, result ); }
void r_nearest_around_point ( int idxin, int correltime, float r2 ) {
tree.r_nearest_around_point ( idxin, correltime, r2, result ); }
int r_count ( std::vector<float>& qv, float r2 ) {
return tree.r_count ( qv, r2 ); }
int r_count_around_point ( int idxin, int correltime, float r2 ) {
return tree.r_count_around_point ( idxin, correltime, r2 ); }
};
C:
kdtree* global_kdtree2 [ 8 ];
extern "C" void new_kdtree2 ( const int thread_id, float **data, const int n, const int dim, bool arrange ) {
global_kdtree2 [ thread_id ] = new kdtree ( data, n, dim, arrange );
}
extern "C" void delete_kdtree2 ( const int thread_id ) {
delete global_kdtree2 [ thread_id ];
}
extern "C" void n_nearest_around_point_kdtree2 ( const int thread_id, int idxin, int correltime, int nn, struct kdtree2_result **result ) {
global_kdtree2 [ thread_id ]->n_nearest_around_point ( idxin, correltime, nn );
*result = &( global_kdtree2 [ thread_id ]->result.front ( ) );
}
, , C-, :
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <stdbool.h>
#include "kdtree2.h"
int main ( void ) {
float **data = MALLOC_2D ( float, 100, 3 );
for ( int i = 0; i < 100; i++ )
for ( int j = 0; j < 3; j++ )
data [ i ][ j ] = ( float ) ( 3 * i + j );
int thread_id = 0;
new_kdtree2 ( thread_id, data, 100, 3, false );
struct kdtree2_result *result;
n_nearest_around_point_kdtree2 ( thread_id, 28, 3, 9, &result );
for ( int i = 0; i < 9; i++ )
printf ( "result[%d]= (%d,%f)\n", i , result [ i ].idx, result [ i ].dis );
printf ( "\n" );
n_nearest_around_point_kdtree2 ( thread_id, 9, 3, 6, &result );
for ( int i = 0; i < 6; i++ )
printf ( "result[%d]= (%d,%f)\n", i , result [ i ].idx, result [ i ].dis );
delete_kdtree2 ( thread_id );
free ( data );
return 0;
}