Merge branch 'main' of github.com:algo-hhu/k-min-sum-radii

Author: giuliabaldini

build_extension.py

@@ -71,7 +71,10 @@ class BuildExt(build_ext):
     """A custom build extension for adding -stdlib arguments for clang++."""
 
     def build_extensions(self) -> None:
-        support = check_openmp_support()
+        if os.name == "nt":
+            support = True
+        else:
+            support = check_openmp_support()
 
         # '-std=c++11' is added to `extra_compile_args` so the code can compile
         # with clang++. This works across compilers (ignored by MSVC).

kmsr/cpp/gonzales.cpp

@@ -25,7 +25,7 @@ double gonzalesrmax(const vector<Point> &points, int k, int seed)
   while (static_cast<int>(centers.size()) < k)
   {
     // Choose the next center based on the maximum distance
-    int nextCenterIndex = distance(
+    size_t nextCenterIndex = distance(
         distances.begin(), max_element(distances.begin(), distances.end()));
     Point nextCenter = points[nextCenterIndex];
     centers.push_back(nextCenter);

kmsr/cpp/heuristic.cpp

@@ -18,7 +18,7 @@ using namespace std;
 vector<Cluster> gonzales(vector<Point> &points, int k, int seed)
 {
   srand(seed);
-  int n = points.size();
+  int n = static_cast<int>(points.size());
   vector<Point> centers;
   centers.push_back(points[rand() % n]);
 
@@ -54,7 +54,7 @@ vector<Cluster> gonzales(vector<Point> &points, int k, int seed)
 
 vector<Cluster> kMeansPlusPlus(vector<Point> &points, int k, int seed)
 {
-  int n = points.size();
+  int n = static_cast<int>(points.size());
   vector<Point> centers;
   mt19937 gen(seed);
   uniform_int_distribution<> dis(0, n - 1);
@@ -130,7 +130,7 @@ vector<Cluster> kMeansPlusPlus(vector<Point> &points, int k, int seed)
 
 vector<Cluster> heuristik(vector<Point> &points, int k)
 {
-  int n = points.size();
+  int n = static_cast<int>(points.size());
   vector<Cluster> bestCluster;
   bestCluster.push_back(
       Cluster(points)); // Initialize with all points in one cluster

kmsr/cpp/k_MSR.cpp

@@ -20,7 +20,7 @@ vector<vector<double>> getRadii(double rmax, int k, double epsilon)
   vector<double> set;
 
   // Calculate the number of radii needed to ensure sufficient coverage
-  int limit = ceil(logBase((k / epsilon), (1 + epsilon)));
+  int limit = static_cast<int>(ceil(logBase((k / epsilon), (1 + epsilon))));
 
   // Create the set of radii whose permutations will be formed.
   for (int i = 0; i <= limit; i++)
@@ -63,7 +63,7 @@ vector<vector<double>> getRandomRadii(double rmax, int k, double epsilon,
   vector<double> set;
 
   // Calculate the number of radii needed to ensure sufficient coverage
-  int limit = ceil(logBase((k / epsilon), (1 + epsilon)));
+  int limit = static_cast<int>(ceil(logBase((k / epsilon), (1 + epsilon))));
 
   // Create the set of radii whose permutations will be formed.
   for (int i = 0; i <= limit; i++)
@@ -77,7 +77,7 @@ vector<vector<double>> getRandomRadii(double rmax, int k, double epsilon,
   mt19937 gen(seed);
 
   // Define a uniform distribution for integers between 0 and set.size()-1.
-  uniform_int_distribution<> distrib(0, set.size() - 1);
+  uniform_int_distribution<> distrib(0, static_cast<int>(set.size()) - 1);
 
   // Generate numVectors number of vectors.
   for (int i = 0; i < numRadiiVectors; i++)
@@ -131,7 +131,7 @@ vector<vector<int>> getU(int n, int k, double epsilon, int numUVectors, int seed
 vector<Ball> selection(const vector<Point> &points, int k, const vector<int> &u,
                        const vector<double> &radii, double epsilon)
 {
-  vector<Ball> balls(k, Ball(points.front().getCoordinates().size()));
+  vector<Ball> balls(k, Ball(static_cast<int>(points.front().getCoordinates().size())));
   vector<vector<Point>> Si(k);
   double lambda = 1 + epsilon + 2 * sqrt(epsilon);
 
@@ -181,7 +181,7 @@ vector<Cluster> clustering(const vector<Point> &points, int k, double epsilon,
   // Calculate the radii and u values based on 'rmax', 'k', and 'epsilon'.
   vector<vector<double>> radii =
       getRandomRadii(rmax, k, epsilon, numRadiiVectors, seed);
-  vector<vector<int>> u = getU(points.size(), k, epsilon, numUVectors, seed);
+  vector<vector<int>> u = getU(static_cast<int>(points.size()), k, epsilon, numUVectors, seed);
 
   // Initialize the 'bestCluster' by making all points part of a cluster.
   bestCluster[0].setPoints(points);

kmsr/cpp/util.cpp

@@ -31,7 +31,7 @@ bool clustersOverlap(const Cluster &c1, const Cluster &c2)
 std::vector<Cluster> assignPointsToCluster(const std::vector<Point> &points,
                                       const std::vector<Point> &centers, int k)
 {
-  int n = points.size();
+  int n = static_cast<int>(points.size());
   std::vector<Cluster> clusters(k);
 
   // Create clusters based on the centers
@@ -59,7 +59,7 @@ std::vector<Cluster> assignPointsToCluster(const std::vector<Point> &points,
 // Computes the centroid of the cluster
 Point computeCentroid(const std::vector<Point> &points)
 {
-  int dimension = points[0].getCoordinates().size();
+  int dimension = static_cast<int>(points[0].getCoordinates().size());
   std::vector<double> centroidCoords(dimension, 0.0);
 
   // Sum of the coordinates of all points in the cluster

kmsr/cpp/welzl.cpp

@@ -18,7 +18,7 @@ struct PointCoordAccessor
 
 Ball findMinEnclosingBall(const vector<Point> &points)
 {
-  int dimension = points.front().getCoordinates().size();
+  int dimension = static_cast<int>(points.front().getCoordinates().size());
 
   Miniball::Miniball<PointCoordAccessor> mb(dimension, points.begin(),
                                             points.end());

kmsr/cpp/yildirim.cpp

@@ -8,7 +8,7 @@ using namespace std;
 // Function to find the furthest point from a given point
 int findFurthestPoint(const std::vector<Point> &points, const Point &p)
 {
-  int furthestIndex = 0;
+  size_t furthestIndex = 0;
   double maxDistSquared = 0.0;
   for (size_t i = 0; i < points.size(); i++)
   {
@@ -21,7 +21,7 @@ int findFurthestPoint(const std::vector<Point> &points, const Point &p)
       furthestIndex = i;
     }
   }
-  return furthestIndex;
+  return static_cast<int>(furthestIndex);
 }
 
 // Function to calculate the weighted sum of the squares of the coordinates