kdtree corrected

extensions/kdtree_nanoflann.h

@@ -25,6 +25,10 @@
  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
  * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *************************************************************************/
+/** \file kdtree_nanoflann.h */
+
+#ifndef KDTREE_NANOFLANN_H
+#define KDTREE_NANOFLANN_H
 
 #include <nanoflann.hpp>
 
@@ -47,16 +51,20 @@ typedef std::vector<WorldVector<double> > my_vector_of_vectors_t;
   *  The i'th vector represents a point in the state space.
   *
   *  \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
-  *  \tparam num_t The type of the point coordinates (typically, double or float).
+  *  \tparam value_type The type of the point coordinates (typically, double or float).
   *  \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
-  *  \tparam IndexType The type for indices in the KD-tree index (typically, size_t of int)
+  *  \tparam index_type The type for indices in the KD-tree index (typically, size_t of int)
   */
-template <class VectorOfVectorsType, typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2_Simple, typename IndexType = size_t>
+template <class VectorOfVectorsType, 
+		  typename value_type = double, 
+		  int DIM = -1, 
+		  class Distance = nanoflann::metric_L2_Simple, 
+		  typename index_type = size_t>
 struct KDTreeVectorOfWorldVectorsAdaptor
 {
-	typedef KDTreeVectorOfWorldVectorsAdaptor<VectorOfVectorsType,num_t,DIM,Distance> self_t;
-	typedef typename Distance::template traits<num_t,self_t>::distance_t metric_t;
-	typedef KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType>  index_t;
+	typedef KDTreeVectorOfWorldVectorsAdaptor<VectorOfVectorsType, value_type, DIM, Distance, index_type> self_t;
+	typedef typename Distance::template traits<value_type, self_t>::distance_t metric_t;
+	typedef KDTreeSingleIndexAdaptor< metric_t, self_t, DIM, index_type>  index_t;
 
 	index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index.
 
@@ -82,9 +90,9 @@ struct KDTreeVectorOfWorldVectorsAdaptor
 	  *  The user can also call index->... methods as desired.
 	  * \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
 	  */
-	inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int nChecks_IGNORED = 10) const
+	inline void query(const value_type *query_point, const size_t num_closest, index_type *out_indices, value_type *out_distances_sq, const int nChecks_IGNORED = 10) const
 	{
-		nanoflann::KNNResultSet<typename VectorOfVectorsType::Scalar,IndexType> resultSet(num_closest);
+		nanoflann::KNNResultSet<typename VectorOfVectorsType::Scalar,index_type> resultSet(num_closest);
 		resultSet.init(out_indices, out_distances_sq);
 		index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
 	}
@@ -105,18 +113,18 @@ struct KDTreeVectorOfWorldVectorsAdaptor
 	}
 
 	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline num_t kdtree_distance(const num_t *p1, const size_t idx_p2,size_t size) const
+	inline value_type kdtree_distance(const value_type *p1, const size_t idx_p2,size_t size) const
 	{
-		num_t s=0;
+		value_type s=0;
 		for (size_t i=0; i<size; i++) {
-			const num_t d= p1[i]-m_data[idx_p2][i];
+			const value_type d= p1[i]-m_data[idx_p2][i];
 			s+=d*d;
 		}
 		return s;
 	}
 
 	// Returns the dim'th component of the idx'th point in the class:
-	inline num_t kdtree_get_pt(const size_t idx, int dim) const {
+	inline value_type kdtree_get_pt(const size_t idx, int dim) const {
 		return m_data[idx][dim];
 	}
 
@@ -136,4 +144,4 @@ typedef KDTreeVectorOfWorldVectorsAdaptor< my_vector_of_vectors_t, double >  KD_
 
 } // end namespace experimental
 
-
+#endif