Fixed most of the old benchmarks

Spatial_searching/benchmark/Spatial_searching/Compare_ANN_STANN_CGAL.cpp

@@ -11,11 +11,13 @@
 #include <CGAL/K_neighbor_search.h>
 #include <CGAL/Search_traits_3.h>
 
-#include<sfcnn.hpp>
-
 #include <ANN/ANN.h>
 #include <ANN/ANNperf.h>
 
+#include<sfcnn.hpp>
+
+
+
 typedef CGAL::Simple_cartesian<double> Kernel;
 typedef Kernel::Point_3 Point_3;
 typedef CGAL::Random_points_in_cube_3<Point_3> Random_points_iterator;

Spatial_searching/benchmark/Spatial_searching/Nearest_neighbor_searching.cpp

@@ -32,7 +32,7 @@ int main() {
   std::cout << "Enter input file name containing data points: \n" ;
   std::cin >> filename_data_points;
   
-  ifstream in_data_points, in_query_points;
+  std::ifstream in_data_points, in_query_points;
   int data_point_number;
   int query_point_number;
   int N, N_data_points, N_query_points; // dimension of input data
@@ -59,20 +59,20 @@ int main() {
   point_list query_points, data_points;
 
   for (int i = 0; i < query_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_query_points >> p[j];
         } 	
-        Point_d Pnt(N,p,p+N);
+        Point_d Pnt(N,p.begin(),p.end());
         query_points.push_back(Pnt);
   }; 
   
  for (int i = 0; i < data_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_data_points >> p[j];
         } 	
-        Point_d Pnt(N,p,p+N);
+        Point_d Pnt(N,p.begin(),p.end());
         data_points.push_back(Pnt);
   }; 
   

Spatial_searching/benchmark/Spatial_searching/Nearest_neighbor_searching_2D.cpp

@@ -35,7 +35,7 @@ int main() {
   std::cout << "Enter input file name containing data points: \n" ;
   std::cin >> filename_data_points;
   
-  ifstream in_data_points, in_query_points;
+  std::ifstream in_data_points, in_query_points;
   int data_point_number;
   int query_point_number;
   int N, N_data_points, N_query_points; // dimension of input data
@@ -62,7 +62,7 @@ int main() {
   point_list query_points, data_points;
 
   for (int i = 0; i < query_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_query_points >> p[j];
         } 	
@@ -71,7 +71,7 @@ int main() {
   }; 
   
  for (int i = 0; i < data_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_data_points >> p[j];
         } 	

Spatial_searching/benchmark/Spatial_searching/Nearest_neighbor_searching_2D_user_defined.cpp

@@ -40,7 +40,7 @@ int main() {
   std::cout << "Enter input file name containing data points: \n" ;
   std::cin >> filename_data_points;
   
-  ifstream in_data_points, in_query_points;
+  std::ifstream in_data_points, in_query_points;
   int data_point_number;
   int query_point_number;
   int N, N_data_points, N_query_points; // dimension of input data
@@ -67,7 +67,7 @@ int main() {
   point_list query_points, data_points;
 
   for (int i = 0; i < query_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_query_points >> p[j];
         } 	
@@ -76,7 +76,7 @@ int main() {
   }; 
   
  for (int i = 0; i < data_point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in_data_points >> p[j];
         } 	
@@ -90,7 +90,6 @@ int main() {
   Tree tree(data_points.begin(), data_points.end(), sliding_midpoint);
   t.stop();
 
-  std::cout << "bucket size is " << tree.bucket_size() << std::endl;
   data_points.clear();
    
   std::cout << "created binary search tree containing" << std::endl

Spatial_searching/benchmark/Spatial_searching/Split_data.cpp

@@ -29,8 +29,8 @@ int main() {
   std::cout << "Enter output file name containing data points: \n" ;
   std::cin >> filename_out_data_points;
 
-  ifstream in;
+  std::ifstream in;
-  ofstream out_query, out_data;
+  std::ofstream out_query, out_data;
 
   int data_point_number, query_point_number;
 
@@ -52,7 +52,7 @@ int main() {
   std::cout << "data point number = " << data_point_number << std::endl;
   std::cout << "query point number = " << query_point_number << std::endl;
 
-  int query_point[point_number];
+  std::vector<int> query_point(point_number);
 
 
   for (int ii = 0; ii < point_number; ii++) query_point[ii]=0;
@@ -68,11 +68,11 @@ int main() {
   
 
  for (int i = 0; i < point_number; i++) {
-	double p[N];
+	std::vector<double> p(N);
         for (int j = 0; j < N; j++) {
           in >> p[j]; 
         } 	
-        Point_d Pnt(N,p,p+N);
+        Point_d Pnt(N,p.begin(),p.end());
         all_points.push_back(Pnt);
   }; 
 

Spatial_searching/benchmark/Spatial_searching/nn3nanoflan.cpp

@@ -156,15 +156,10 @@ void kdtree_demo(int argc, char** argv)
 			if(dump)
 			std::cerr <<cloud.pts[ret_index[k]] << std::endl;
 		  }
-		  if(dump){
-			  std::cerr << "Tree has:\n"<<index.items<< " items\n"<<index.internals <<" internals\n"<< index.leafs<<" leafs\n"<<index.depth()<<" depth\n";
-			  index.print_mem_usage();
-		  }
 		  dump=false;
         }
         timer.stop();
         std::cout << sum << " done in " << timer.time() << " sec."<< std::endl;
-	std::cerr << index.count_items <<" items "<<index.count_internals<< " internals "<<index.count_leafs<<  " leafs visited\n";
 }
 
 int main(int argc, char** argv)

Spatial_searching/doc/Spatial_searching/Spatial_searching.txt

@@ -10,6 +10,9 @@ namespace CGAL {
 
 \section Spatial_searchingIntroduction Introduction
 
+\cgalModifBegin
+Pointed out worst cases for splitters and added example
+\cgalModifEnd
 The spatial searching package implements exact and approximate
 distance browsing by providing implementations of algorithms
 supporting
@@ -145,7 +148,9 @@ because in general the query time will be less then using the default value.
 Instead of using the default splitting rule `Sliding_midpoint` described below,
 a user may, depending upon the data, select 
 one from the following splitting rules,
-which determine how a separating hyperplane is computed:
+which determine how a separating hyperplane is computed. Every splitter has 
+degenerated worst cases, which may lead to a linear tree and a stack overflow. 
+Switching the splitting rule to one of a different kind will solve the problem.
 
 <DL>
 
@@ -219,14 +224,15 @@ generate empty cells.
 
 \section Spatial_searchingExample Example Programs
 
-We give six examples. The first example illustrates k nearest neighbor
+We give seven examples. The first example illustrates k nearest neighbor
 searching, and the second example incremental neighbor searching. 
 The third is an example of approximate furthest neighbor searching
 using a `d`-dimensional iso-rectangle as an query object. Approximate
 range searching is illustrated by the fourth example. The fifth
 example illustrates k neighbor searching for a user defined point
-class. The last example shows how to choose another splitting rule in the
+class. The sixth example shows how to choose another splitting rule in the
-`k-d` tree that is used as search tree.
+`k-d` tree that is used as search tree. The last example shows two worst-case
+scenarios for different splitter types.
 
 \subsection Spatial_searchingExampleforKNeighborSearching Example for K Neighbor Searching
 
@@ -333,6 +339,17 @@ splitting rule, needed to set the maximal allowed bucket size.
 
 \cgalExample{Spatial_searching/using_fair_splitting_rule.cpp}
 
+\subsection Spatial_searchingExampleforWorstCaseScenarios Example for worst-case scenarios for different splitters
+
+This example program has two 2-dimensional data sets: The first one containing 
+colinear points with exponential increasing distances and the second
+one with colinear points in dimension 1 and one point with a distance
+exceeding the spread of the other points in distance 2. These are 
+the worst cases for the midpoint/median rules and can also occur in 
+higher dimensions. 
+
+\cgalExample{Spatial_searching/splitter_worst_cases.cpp}
+
 \section Spatial_searchingSoftware Software Design
 
 \subsection Kd_tree_subsection The k-d tree 

Spatial_searching/doc/Spatial_searching/examples.txt

@@ -14,4 +14,5 @@
 \example Spatial_searching/user_defined_point_and_distance.cpp
 \example Spatial_searching/using_fair_splitting_rule.cpp
 \example Spatial_searching/weighted_Minkowski_distance.cpp
+\example Spatial_searching/splitter_worst_cases.cpp
 */