[SpatialPartitioning] Rename KdTreeImplBase to KdTreeBase

mazestic · mazestic · commit 5a1a7846ea21 · 2023-12-21T15:51:00.000+01:00
diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeKNearestQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeKNearestQueries.h
@@ -25,7 +25,7 @@ class KdTreeKNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
     using QueryAccelType = KdTreeQuery<Traits>;
     using Iterator       = IteratorType<typename Traits::IndexType, typename Traits::DataPoint>;
 
-    inline KdTreeKNearestQueryBase(const KdTreeImplBase<Traits>* kdtree, IndexType k, typename QueryType::InputType input) :
+    inline KdTreeKNearestQueryBase(const KdTreeBase<Traits>* kdtree, IndexType k, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(k, input) { }
 
 public:
diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeNearestQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeNearestQueries.h
@@ -25,7 +25,7 @@ class KdTreeNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
     using QueryAccelType = KdTreeQuery<Traits>;
     using Iterator       = IteratorType<typename Traits::IndexType>;
 
-    KdTreeNearestQueryBase(const KdTreeImplBase<Traits>* kdtree, typename QueryType::InputType input) :
+    KdTreeNearestQueryBase(const KdTreeBase<Traits>* kdtree, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(input){}
 
 public:
diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeQuery.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeQuery.h
@@ -10,7 +10,7 @@
 #include "../../../Common/Containers/stack.h"
 
 namespace Ponca {
-template <typename Traits> class KdTreeImplBase;
+template <typename Traits> class KdTreeBase;
 
 template <typename Traits>
 class KdTreeQuery
@@ -21,7 +21,7 @@ class KdTreeQuery
     using Scalar     = typename DataPoint::Scalar;
     using VectorType = typename DataPoint::VectorType;
 
-    explicit inline KdTreeQuery(const KdTreeImplBase<Traits>* kdtree) : m_kdtree( kdtree ), m_stack() {}
+    explicit inline KdTreeQuery(const KdTreeBase<Traits>* kdtree) : m_kdtree( kdtree ), m_stack() {}
 
 protected:
     /// \brief Init stack for a new search
@@ -30,7 +30,7 @@ class KdTreeQuery
         m_stack.push({0,0});
     }
 
-    const KdTreeImplBase<Traits>* m_kdtree { nullptr };
+    const KdTreeBase<Traits>* m_kdtree { nullptr };
     Stack<IndexSquaredDistance<IndexType, Scalar>, 2 * Traits::MAX_DEPTH> m_stack;
 
     template<typename LeafPreparationFunctor,
diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeRangeQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeRangeQueries.h
@@ -30,7 +30,7 @@ class KdTreeRangeQueryBase : public KdTreeQuery<Traits>, public QueryType
     friend Iterator;
 
 public:
-    KdTreeRangeQueryBase(const KdTreeImplBase<Traits>* kdtree, Scalar radius, typename QueryType::InputType input) :
+    KdTreeRangeQueryBase(const KdTreeBase<Traits>* kdtree, Scalar radius, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(radius, input){}
 
 public:
diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.h b/Ponca/src/SpatialPartitioning/KdTree/kdTree.h
@@ -25,18 +25,18 @@
 #include "Query/kdTreeRangeQueries.h"
 
 namespace Ponca {
-template <typename Traits> class KdTreeImplBase;
+template <typename Traits> class KdTreeBase;
 template <typename Traits> class KdTreeDenseBase;
 template <typename Traits> class KdTreeSparseBase;
 
 /*!
  * \brief Base type for default KdTree implementations
  *
  * \see KdTreeDefaultTraits for the default trait interface documentation.
- * \see KdTreeImplBase for complete API
+ * \see KdTreeBase for complete API
  */
 template <typename DataPoint>
-using KdTreeImpl = KdTreeImplBase<KdTreeDefaultTraits<DataPoint>>;
+using KdTree = KdTreeBase<KdTreeDefaultTraits<DataPoint>>;
 
 /*!
  * \brief Public interface for dense KdTree datastructure.
@@ -74,7 +74,7 @@ using KdTreeSparse = KdTreeSparseBase<KdTreeDefaultTraits<DataPoint>>;
  * \todo Better handle sampling: do not store non-selected points (requires to store original indices)
  */
 template <typename Traits>
-class KdTreeImplBase
+class KdTreeBase
 {
 public:
     using DataPoint      = typename Traits::DataPoint; ///< DataPoint given by user via Traits
@@ -277,7 +277,7 @@ public :
 
     // Internal ----------------------------------------------------------------
 protected:
-    inline KdTreeImplBase() = default;
+    inline KdTreeBase() = default;
 
     /// Generate a tree sampled from a custom contained type converted using a `Converter`
     /// \tparam PointUserContainer Input point, transformed to PointContainer
@@ -291,7 +291,7 @@ public :
                                   IndexUserContainer sampling,
                                   Converter c);
 
-    /// Generate a tree sampled from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Generate a tree sampled from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     /// \tparam PointUserContainer Input points, transformed to PointContainer
     /// \tparam IndexUserContainer Input sampling, transformed to IndexContainer
     /// \param points Input points
@@ -323,17 +323,17 @@ public :
  * \see KdTreeDefaultTraits for the trait interface documentation.
  */
 template <typename Traits>
-class KdTreeDenseBase : public KdTreeImplBase<Traits>
+class KdTreeDenseBase : public KdTreeBase<Traits>
 {
 private:
-    using Base = KdTreeImplBase<Traits>;
+    using Base = KdTreeBase<Traits>;
 
 public:
     /// Default constructor creating an empty tree
     /// \see build
     KdTreeDenseBase() = default;
 
-    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     template<typename PointUserContainer>
     inline explicit KdTreeDenseBase(PointUserContainer&& points)
         : Base()
@@ -357,10 +357,10 @@ class KdTreeDenseBase : public KdTreeImplBase<Traits>
  * \see KdTreeDefaultTraits for the trait interface documentation.
  */
 template <typename Traits>
-class KdTreeSparseBase : public KdTreeImplBase<Traits>
+class KdTreeSparseBase : public KdTreeBase<Traits>
 {
 private:
-    using Base = KdTreeImplBase<Traits>;
+    using Base = KdTreeBase<Traits>;
 
 public:
     static constexpr bool SUPPORTS_SUBSAMPLING = false;
@@ -369,15 +369,15 @@ class KdTreeSparseBase : public KdTreeImplBase<Traits>
     /// \see build
     KdTreeSparseBase() = default;
 
-    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     template<typename PointUserContainer>
     inline explicit KdTreeSparseBase(PointUserContainer&& points)
         : Base()
     {
         this->build(std::forward<PointUserContainer>(points));
     }
 
-    /// Constructor generating a tree sampled from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree sampled from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     /// \tparam PointUserContainer Input points, transformed to PointContainer
     /// \tparam IndexUserContainer Input sampling, transformed to IndexContainer
     /// \param point Input points
@@ -396,7 +396,7 @@ class KdTreeSparseBase : public KdTreeImplBase<Traits>
 } // namespace Ponca
 
 template <typename Traits>
-std::ostream& operator<<(std::ostream& os, const Ponca::KdTreeImplBase<Traits>& kdtree)
+std::ostream& operator<<(std::ostream& os, const Ponca::KdTreeBase<Traits>& kdtree)
 {
     kdtree.print(os);
     return os;
diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp b/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp
@@ -8,15 +8,15 @@
 
 template<typename Traits>
 template<typename PointUserContainer, typename Converter>
-inline void KdTreeImplBase<Traits>::build(PointUserContainer&& points, Converter c)
+inline void KdTreeBase<Traits>::build(PointUserContainer&& points, Converter c)
 {
     IndexContainer ids(points.size());
     std::iota(ids.begin(), ids.end(), 0);
     this->buildWithSampling(std::forward<PointUserContainer>(points), std::move(ids), std::move(c));
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::clear()
+void KdTreeBase<Traits>::clear()
 {
     m_points.clear();
     m_nodes.clear();
@@ -25,7 +25,7 @@ void KdTreeImplBase<Traits>::clear()
 }
 
 template<typename Traits>
-bool KdTreeImplBase<Traits>::valid() const
+bool KdTreeBase<Traits>::valid() const
 {
     if (m_points.empty())
         return m_nodes.empty() && m_indices.empty();
@@ -72,7 +72,7 @@ bool KdTreeImplBase<Traits>::valid() const
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const
+void KdTreeBase<Traits>::print(std::ostream& os, bool verbose) const
 {
     os << "KdTree:";
     os << "\n  MaxNodes: " << MAX_NODE_COUNT;
@@ -118,9 +118,9 @@ void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const
 
 template<typename Traits>
 template<typename PointUserContainer, typename IndexUserContainer, typename Converter>
-inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& points,
-                                                      IndexUserContainer sampling,
-                                                      Converter c)
+inline void KdTreeBase<Traits>::buildWithSampling(PointUserContainer&& points,
+                                                  IndexUserContainer sampling,
+                                                  Converter c)
 {
     PONCA_DEBUG_ASSERT(points.size() <= MAX_POINT_COUNT);
     this->clear();
@@ -140,7 +140,7 @@ inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& point
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)
+void KdTreeBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)
 {
     NodeType& node = m_nodes[node_id];
     AabbType aabb;
@@ -174,7 +174,7 @@ void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, I
 }
 
 template<typename Traits>
-auto KdTreeImplBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)
+auto KdTreeBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)
     -> IndexType
 {
     const auto& points = m_points;
diff --git a/Ponca/src/SpatialPartitioning/KnnGraph/knnGraph.h b/Ponca/src/SpatialPartitioning/KnnGraph/knnGraph.h
@@ -68,7 +68,7 @@ template <typename Traits> class KnnGraphBase
     /// \warning Stores a const reference to kdtree.point_data()
     /// \warning KdTreeTraits compatibility is checked with static assertion
     template<typename KdTreeTraits>
-    inline KnnGraphBase(const KdTreeImplBase<KdTreeTraits>& kdtree, int k = 6)
+    inline KnnGraphBase(const KdTreeBase<KdTreeTraits>& kdtree, int k = 6)
             : m_k(std::min(k,kdtree.sample_count()-1)),
               m_kdTreePoints(kdtree.points())
     {
diff --git a/doc/src/ponca_module_spatialpartitioning.mdoc b/doc/src/ponca_module_spatialpartitioning.mdoc
@@ -92,12 +92,12 @@ fit.computeWithIds( myDataStructure.range_neighbors(fitInitPos, scale), vectorPo
 
   We call indices into the underlying point storage *point indices* and indices into the sample storage *sample
   indices*. Note that the sample storage stores point indices: sample number 0 may, for example, refer to point number 25.
-  The point storage can be accessed with KdTreeImplBase::points, while the array of samples can be accessed with
-  KdTreeImplBase::samples.
+  The point storage can be accessed with KdTreeBase::points, while the array of samples can be accessed with
+  KdTreeBase::samples.
 
   While most of the KdTree API is built on using point indices, it can still be useful to iterate over samples instead
   (e.g. when using a Ponca::KdTreeSparse). If you ever need to convert sample indices to point indices, see
-  KdTreeImplBase::pointFromSample (see also KdTreeImplBase::pointDataFromSample).
+  KdTreeBase::pointFromSample (see also KdTreeBase::pointDataFromSample).
 
   \subsection spatialpartitioning_kdtree_examples Examples
 
@@ -120,8 +120,8 @@ fit.computeWithIds( myDataStructure.range_neighbors(fitInitPos, scale), vectorPo
   The kd-tree is a binary search tree that
   - is balanced (cuts at the median at each node)
   - cuts along the dimension that extends the most at each node
-  - has a maximal depth (KdTreeImplBase::MAX_DEPTH)
-  - has a minimal number of points per leaf (KdTreeImplBase::m_min_cell_size)
+  - has a maximal depth (KdTreeBase::MAX_DEPTH)
+  - has a minimal number of points per leaf (KdTreeBase::m_min_cell_size)
   - only stores points in the leafs
   - uses depth-first search with a static stack for queries
   - keeps the initial order of points
diff --git a/examples/cpp/nanoflann/ponca_nanoflann.cpp b/examples/cpp/nanoflann/ponca_nanoflann.cpp
@@ -98,7 +98,7 @@ int test_raw(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p)
     return f.getNumNeighbors();
 }
 
-int test_ponca_kdtree(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p, const KdTreeImpl<MyPoint>& tree, Scalar tmax){
+int test_ponca_kdtree(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p, const KdTree<MyPoint>& tree, Scalar tmax){
     f.init(_p);
     if(! (
             //! [Use Ponca KdTree]
diff --git a/examples/cpp/ponca_basic_cpu.cpp b/examples/cpp/ponca_basic_cpu.cpp
@@ -76,7 +76,7 @@ using Fit3 = BasketDiff< Fit1, FitSpaceDer, OrientedSphereDer, GLSDer, Curvature
 using Fit4 = Basket<MyPoint,WeightFunc,SphereFit, GLSParam>;
 
 template<typename Fit>
-void test_fit(Fit& _fit, const KdTreeImpl<MyPoint>& tree, const VectorType& _p)
+void test_fit(Fit& _fit, const KdTree<MyPoint>& tree, const VectorType& _p)
 {
   Scalar tmax = 100.0;
 
diff --git a/tests/src/basket.cpp b/tests/src/basket.cpp
@@ -26,7 +26,7 @@ using namespace std;
 using namespace Ponca;
 
 template<typename DataPoint>
-typename DataPoint::Scalar generateData(KdTreeImpl<DataPoint>& tree)
+typename DataPoint::Scalar generateData(KdTree<DataPoint>& tree)
 {
     typedef typename DataPoint::Scalar Scalar;
     typedef typename DataPoint::VectorType VectorType;
@@ -61,7 +61,7 @@ typename DataPoint::Scalar generateData(KdTreeImpl<DataPoint>& tree)
 }
 
 template<typename Fit>
-void testBasicFunctionalities(const KdTreeImpl<typename Fit::DataPoint>& tree, typename Fit::Scalar analysisScale)
+void testBasicFunctionalities(const KdTree<typename Fit::DataPoint>& tree, typename Fit::Scalar analysisScale)
 {
     using DataPoint = typename Fit::DataPoint;
 
@@ -124,7 +124,7 @@ void testBasicFunctionalities(const KdTreeImpl<typename Fit::DataPoint>& tree, t
 }
 
 template<typename Fit1, typename Fit2, typename Functor>
-void testIsSame(const KdTreeImpl<typename Fit1::DataPoint>& tree,
+void testIsSame(const KdTree<typename Fit1::DataPoint>& tree,
                 typename Fit1::Scalar analysisScale,
                 Functor f)
 {
diff --git a/tests/src/queries_knearest.cpp b/tests/src/queries_knearest.cpp
@@ -18,7 +18,7 @@ template<typename DataPoint>
 void testKdTreeKNearestIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
@@ -77,7 +77,7 @@ template<typename DataPoint>
 void testKdTreeKNearestPoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
diff --git a/tests/src/queries_nearest.cpp b/tests/src/queries_nearest.cpp
@@ -18,7 +18,7 @@ template<typename DataPoint>
 void testKdTreeNearestIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
@@ -61,7 +61,7 @@ template<typename DataPoint>
 void testKdTreeNearestPoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
diff --git a/tests/src/queries_range.cpp b/tests/src/queries_range.cpp
@@ -18,14 +18,14 @@ template<typename DataPoint, bool SampleKdTree = true>
 void testKdTreeRangeIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 5000;
 	auto points = VectorContainer(N);
     std::generate(points.begin(), points.end(), []() {return DataPoint(VectorType::Random()); });
 
-    KdTreeImpl<DataPoint> *kdtree {nullptr};
+    KdTree<DataPoint> *kdtree {nullptr};
 
     std::vector<int> sampling; // we need sampling for GT computation
     if(SampleKdTree){
@@ -89,7 +89,7 @@ template<typename DataPoint>
 void testKdTreeRangePoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;

Original file line number	Diff line number	Diff line change
`@@ -8,15 +8,15 @@`
`8`	`8`
`9`	`9`	`template<typename Traits>`
`10`	`10`	`template<typename PointUserContainer, typename Converter>`
`11`		`-inline void KdTreeImplBase<Traits>::build(PointUserContainer&& points, Converter c)`
	`11`	`+inline void KdTreeBase<Traits>::build(PointUserContainer&& points, Converter c)`
`12`	`12`	`{`
`13`	`13`	`IndexContainer ids(points.size());`
`14`	`14`	`std::iota(ids.begin(), ids.end(), 0);`
`15`	`15`	`this->buildWithSampling(std::forward<PointUserContainer>(points), std::move(ids), std::move(c));`
`16`	`16`	`}`
`17`	`17`
`18`	`18`	`template<typename Traits>`
`19`		`-void KdTreeImplBase<Traits>::clear()`
	`19`	`+void KdTreeBase<Traits>::clear()`
`20`	`20`	`{`
`21`	`21`	`m_points.clear();`
`22`	`22`	`m_nodes.clear();`
`@@ -25,7 +25,7 @@ void KdTreeImplBase<Traits>::clear()`
`25`	`25`	`}`
`26`	`26`
`27`	`27`	`template<typename Traits>`
`28`		`-bool KdTreeImplBase<Traits>::valid() const`
	`28`	`+bool KdTreeBase<Traits>::valid() const`
`29`	`29`	`{`
`30`	`30`	`if (m_points.empty())`
`31`	`31`	`return m_nodes.empty() && m_indices.empty();`
`@@ -72,7 +72,7 @@ bool KdTreeImplBase<Traits>::valid() const`
`72`	`72`	`}`
`73`	`73`
`74`	`74`	`template<typename Traits>`
`75`		`-void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const`
	`75`	`+void KdTreeBase<Traits>::print(std::ostream& os, bool verbose) const`
`76`	`76`	`{`
`77`	`77`	`os << "KdTree:";`
`78`	`78`	`os << "\n MaxNodes: " << MAX_NODE_COUNT;`
`@@ -118,9 +118,9 @@ void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const`
`118`	`118`
`119`	`119`	`template<typename Traits>`
`120`	`120`	`template<typename PointUserContainer, typename IndexUserContainer, typename Converter>`
`121`		`-inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& points,`
`122`		`- IndexUserContainer sampling,`
`123`		`- Converter c)`
	`121`	`+inline void KdTreeBase<Traits>::buildWithSampling(PointUserContainer&& points,`
	`122`	`+ IndexUserContainer sampling,`
	`123`	`+ Converter c)`
`124`	`124`	`{`
`125`	`125`	`PONCA_DEBUG_ASSERT(points.size() <= MAX_POINT_COUNT);`
`126`	`126`	`this->clear();`
`@@ -140,7 +140,7 @@ inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& point`
`140`	`140`	`}`
`141`	`141`
`142`	`142`	`template<typename Traits>`
`143`		`-void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)`
	`143`	`+void KdTreeBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)`
`144`	`144`	`{`
`145`	`145`	`NodeType& node = m_nodes[node_id];`
`146`	`146`	`AabbType aabb;`
`@@ -174,7 +174,7 @@ void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, I`
`174`	`174`	`}`
`175`	`175`
`176`	`176`	`template<typename Traits>`
`177`		`-auto KdTreeImplBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)`
	`177`	`+auto KdTreeBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)`
`178`	`178`	`-> IndexType`
`179`	`179`	`{`
`180`	`180`	`const auto& points = m_points;`
Original file line number	Diff line number	Diff line change
`@@ -68,7 +68,7 @@ template <typename Traits> class KnnGraphBase`
`68`	`68`	`/// \warning Stores a const reference to kdtree.point_data()`
`69`	`69`	`/// \warning KdTreeTraits compatibility is checked with static assertion`
`70`	`70`	`template<typename KdTreeTraits>`
`71`		`- inline KnnGraphBase(const KdTreeImplBase<KdTreeTraits>& kdtree, int k = 6)`
	`71`	`+ inline KnnGraphBase(const KdTreeBase<KdTreeTraits>& kdtree, int k = 6)`
`72`	`72`	`: m_k(std::min(k,kdtree.sample_count()-1)),`
`73`	`73`	`m_kdTreePoints(kdtree.points())`
`74`	`74`	`{`
Original file line number	Diff line number	Diff line change
`@@ -98,7 +98,7 @@ int test_raw(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p)`
`98`	`98`	`return f.getNumNeighbors();`
`99`	`99`	`}`
`100`	`100`
`101`		`-int test_ponca_kdtree(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p, const KdTreeImpl<MyPoint>& tree, Scalar tmax){`
	`101`	`+int test_ponca_kdtree(FitType& f, const std::vector<MyPoint>& _vecs, VectorType _p, const KdTree<MyPoint>& tree, Scalar tmax){`
`102`	`102`	`f.init(_p);`
`103`	`103`	`if(! (`
`104`	`104`	`//! [Use Ponca KdTree]`
Original file line number	Diff line number	Diff line change
`@@ -76,7 +76,7 @@ using Fit3 = BasketDiff< Fit1, FitSpaceDer, OrientedSphereDer, GLSDer, Curvature`
`76`	`76`	`using Fit4 = Basket<MyPoint,WeightFunc,SphereFit, GLSParam>;`
`77`	`77`
`78`	`78`	`template<typename Fit>`
`79`		`-void test_fit(Fit& _fit, const KdTreeImpl<MyPoint>& tree, const VectorType& _p)`
	`79`	`+void test_fit(Fit& _fit, const KdTree<MyPoint>& tree, const VectorType& _p)`
`80`	`80`	`{`
`81`	`81`	`Scalar tmax = 100.0;`
`82`	`82`