kdtree structures currected

extensions/kdtree_nanoflann.h

@@ -42,11 +42,6 @@ using namespace AMDiS;
 
 namespace experimental {
 
-// =====================================================================================
-typedef std::vector<WorldVector<double> > my_vector_of_vectors_t;
-// =====================================================================================
-
-
 /** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage.
   *  The i'th vector represents a point in the state space.
   *
@@ -140,7 +135,7 @@ struct KDTreeVectorOfWorldVectorsAdaptor
 
 }; // end of KDTreeVectorOfVectorsAdaptor
 
-typedef KDTreeVectorOfWorldVectorsAdaptor< my_vector_of_vectors_t, double >  KD_Tree;
+typedef KDTreeVectorOfWorldVectorsAdaptor<std::vector<WorldVector<double> >, double >  KD_Tree;
 
 } // end namespace experimental
 

extensions/kdtree_nanoflann_dof.h

@@ -42,12 +42,6 @@ using namespace AMDiS;
 
 namespace experimental {
 
-// =====================================================================================
-typedef WorldVector<double> PointType;
-typedef DOFVector<PointType> VectorOfVectors_Dof;
-// =====================================================================================
-
-
   /** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage.
     *  The i'th vector represents a point in the state space.
     *
@@ -168,7 +162,7 @@ typedef DOFVector<PointType> VectorOfVectors_Dof;
     *  == evalAtPoint_simple
     **/
     template<typename T>
-    T evalAtPoint(const DOFVector<T>& vec, const PointType& x, int strategy = 0, int nrOfPoints = -1)
+    T evalAtPoint(const DOFVector<T>& vec, const WorldVector<double>& x, int strategy = 0, int nrOfPoints = -1)
     {
       const size_t nnp = 1;
       std::vector<DegreeOfFreedom> ret_indexes(nnp);
@@ -187,7 +181,7 @@ typedef DOFVector<PointType> VectorOfVectors_Dof;
 
   }; // end of KDTreeDOFVectorOfWorldVectorsAdaptor
 
-  typedef KDTreeDOFVectorOfWorldVectorsAdaptor< VectorOfVectors_Dof, double >  KD_Tree_Dof;
+  typedef KDTreeDOFVectorOfWorldVectorsAdaptor<DOFVector<WorldVector<double> >, double >  KD_Tree_Dof;
   static std::map<const FiniteElemSpace*, std::pair<int, KD_Tree_Dof*> > kdtreeMap_Dof;
 
   inline KD_Tree_Dof* provideKDTree(const FiniteElemSpace* feSpace)

extensions/kdtree_nanoflann_double.h

+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2011 Jose Luis Blanco (joseluisblancoc@gmail.com).
+ *   All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (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_double.h */
+
+#ifndef KDTREE_NANOFLANN_DOUBLE_H
+#define KDTREE_NANOFLANN_DOUBLE_H
+
+#include <nanoflann.hpp>
+
+#include <cstdlib>
+#include <iostream>
+
+using namespace nanoflann;
+using namespace AMDiS;
+
+namespace experimental {
+
+/** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage.
+  *  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 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 index_type The type for indices in the KD-tree index (typically, size_t of int)
+  */
+template <class VectorOfVectorsType, 
+		  typename value_type = double, 
+		  int DIM = -1, 
+		  class Distance = nanoflann::metric_L2_Simple, 
+		  typename index_type = size_t>
+struct KDTreeVectorOfDoublesAdaptor
+{
+	typedef KDTreeVectorOfDoublesAdaptor<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.
+
+	/// Constructor: takes a const ref to the vector of vectors object with the data points
+	KDTreeVectorOfDoublesAdaptor(const int dimensionality, const VectorOfVectorsType &mat, const int leaf_max_size = 10) : m_data(mat)
+	{
+		assert(mat.size()!=0);
+		const size_t dims = 1;
+		if (DIM>0 && static_cast<int>(dims)!=DIM)
+			throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
+		index = new index_t( dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size, dims ) );
+		index->buildIndex();
+	}
+
+	~KDTreeVectorOfDoublesAdaptor() {
+		delete index;
+	}
+
+	const VectorOfVectorsType &m_data;
+
+	/** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]).
+	  *  Note that this is a short-cut method for index->findNeighbors().
+	  *  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 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,index_type> resultSet(num_closest);
+		resultSet.init(out_indices, out_distances_sq);
+		index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+	}
+
+	/** @name Interface expected by KDTreeSingleIndexAdaptor
+	  * @{ */
+
+	const self_t & derived() const {
+		return *this;
+	}
+	self_t & derived()       {
+		return *this;
+	}
+
+	// Must return the number of data points
+	inline size_t kdtree_get_point_count() const {
+		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 value_type kdtree_distance(const value_type *p1, const size_t idx_p2,size_t size) const
+	{
+		return sqr(p1[0]-m_data[idx_p2]);
+	}
+
+	// Returns the dim'th component of the idx'th point in the class:
+	inline value_type kdtree_get_pt(const size_t idx, int dim) const {
+		return m_data[idx];
+	}
+
+	// Optional bounding-box computation: return false to default to a standard bbox computation loop.
+	//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
+	//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
+	template <class BBOX>
+	bool kdtree_get_bbox(BBOX &bb) const {
+		return false;
+	}
+
+	/** @} */
+
+}; // end of KDTreeVectorOfVectorsAdaptor
+
+typedef KDTreeVectorOfDoublesAdaptor<std::vector<double>, double >  KD_Tree_Double;
+
+} // end namespace experimental
+
+#endif