Removed requirement of function kdtree_distance()

benchmarkTool/randomTests/nanoflann_testRandom.cpp

@@ -48,15 +48,6 @@ struct PointCloud
     // Must return the number of data points
     inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-    // Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-    inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
-    {
-        const T d0=p1[0]-pts[idx_p2].x;
-        const T d1=p1[1]-pts[idx_p2].y;
-        const T d2=p1[2]-pts[idx_p2].z;
-        return d0*d0+d1*d1+d2*d2;
-    }
-
     // Returns the dim'th component of the idx'th point in the class:
     // Since this is inlined and the "dim" argument is typically an immediate value, the
     //  "if/else's" are actually solved at compile time.

benchmarkTool/realTests/nanoflann_testReal.cpp

@@ -50,15 +50,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/KDTreeVectorOfVectorsAdaptor.h

@@ -99,17 +99,6 @@ struct KDTreeVectorOfVectorsAdaptor
 		return m_data.size();
 	}
 
-	// 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
-	{
-		num_t s=0;
-		for (size_t i=0; i<size; i++) {
-			const num_t 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 {
 		return m_data[idx][dim];

examples/dynamic_pointcloud_example.cpp

@@ -51,15 +51,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/example_with_pkgconfig/pointcloud_example.cpp

@@ -48,15 +48,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline float kdtree_distance(const float *p1, const size_t idx_p2,size_t size) const
-	{
-		float d0=p1[0]-pts[idx_p2].x;
-		float d1=p1[1]-pts[idx_p2].y;
-		float d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/pointcloud_adaptor_example.cpp

@@ -68,15 +68,6 @@ struct PointCloudAdaptor
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return derived().pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline coord_t kdtree_distance(const coord_t *p1, const size_t idx_p2,size_t /*size*/) const
-	{
-		const coord_t d0=p1[0]-derived().pts[idx_p2].x;
-		const coord_t d1=p1[1]-derived().pts[idx_p2].y;
-		const coord_t d2=p1[2]-derived().pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/pointcloud_example.cpp

@@ -51,15 +51,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/pointcloud_kdd_radius.cpp

@@ -49,15 +49,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

examples/saveload_example.cpp

@@ -51,15 +51,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size */) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

include/nanoflann.hpp

@@ -368,7 +368,12 @@ namespace nanoflann
 		L2_Simple_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
 		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
-			return data_source.kdtree_distance(a,b_idx,size);
+			DistanceType result = DistanceType();
+			for (int i=0; i<size; ++i) {
+				const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
+				result += diff * diff;
+			}
+			return result;
 		}
 
 		template <typename U, typename V>
@@ -389,6 +394,7 @@ namespace nanoflann
 		InnerProdQuat_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
 		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
+			// Update this
 			return data_source.kdtree_distance(a,b_idx,size);
 		}
 
@@ -410,6 +416,7 @@ namespace nanoflann
 		acosInnerProdQuat_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
 		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
+			// Update this
 			return data_source.kdtree_distance(a,b_idx,size);
 		}
 
@@ -1018,8 +1025,6 @@ namespace nanoflann
 	 *   // Must return the number of data poins
 	 *   inline size_t kdtree_get_point_count() const { ... }
 	 *
-	 *   // [Only if using the metric_L2_Simple type] Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	 *   inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
 	 *
 	 *   // Must return the dim'th component of the idx'th point in the class:
 	 *   inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
@@ -1396,9 +1401,6 @@ namespace nanoflann
 	 *   // Must return the number of data poins
 	 *   inline size_t kdtree_get_point_count() const { ... }
 	 *
-	 *   // [Only if using the metric_L2_Simple type] Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	 *   inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
-	 *
 	 *   // Must return the dim'th component of the idx'th point in the class:
 	 *   inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
 	 *

perf-tests/nanoflann/test_leaf_max_size.cpp

@@ -66,15 +66,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const
-	{
-		T d0=p1[0]-pts[idx_p2].x;
-		T d1=p1[1]-pts[idx_p2].y;
-		T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

perf-tests/nanoflann/test_nanoflann.cpp

@@ -63,15 +63,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline float kdtree_distance(const float *p1, const size_t idx_p2,size_t size) const
-	{
-		float d0=p1[0]-pts[idx_p2].x;
-		float d1=p1[1]-pts[idx_p2].y;
-		float d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.

tests/test_main.cpp

@@ -61,15 +61,6 @@ struct PointCloud
 	// Must return the number of data points
 	inline size_t kdtree_get_point_count() const { return pts.size(); }
 
-	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
-	inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /* size*/) const
-	{
-		const T d0=p1[0]-pts[idx_p2].x;
-		const T d1=p1[1]-pts[idx_p2].y;
-		const T d2=p1[2]-pts[idx_p2].z;
-		return d0*d0+d1*d1+d2*d2;
-	}
-
 	// Returns the dim'th component of the idx'th point in the class:
 	// Since this is inlined and the "dim" argument is typically an immediate value, the
 	//  "if/else's" are actually solved at compile time.