Adapting headers for crossplatform compile

Author: quentinhaenn

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "radius-clustering"
-version = "1.0.0"
+version = "1.0.1"
 description = "A Clustering under radius constraints algorithm using minimum dominating sets"
 readme = "README.md"
 authors = [

radius_clustering/radius_clustering.py

@@ -10,7 +10,7 @@
 
 import os
 import numpy as np
-import scipy.spatial as sp_spatial
+from sklearn.metrics import pairwise_distances
 from sklearn.base import BaseEstimator, ClusterMixin
 from sklearn.utils.validation import check_array
 
@@ -38,7 +38,7 @@ class RadiusClustering(BaseEstimator, ClusterMixin):
     -----------
     X : array-like, shape (n_samples, n_features)
         The input data.
-    centers : list
+    centers\_ : list
         The indices of the cluster centers.
     labels\_ : array-like, shape (n_samples,)
         The cluster labels for each point in the input data.
@@ -50,6 +50,9 @@ def __init__(self, manner="approx", threshold=0.5):
         self.manner = manner
         self.threshold = threshold
 
+    def _check_symmetric(self, a, tol=1e-8):
+        return np.allclose(a, a.T, atol=tol)
+
     def fit(self, X, y=None):
         """
         Fit the MDS clustering model to the input data.
@@ -87,10 +90,19 @@ def fit(self, X, y=None):
         self.X = check_array(X)
 
         # Create dist and adj matrices
-        dist_mat = sp_spatial.distance_matrix(self.X, self.X)
+        if not self._check_symmetric(self.X):
+            dist_mat = pairwise_distances(self.X, metric="euclidean")
+        else:
+            dist_mat = self.X
         adj_mask = np.triu((dist_mat <= self.threshold), k=1)
         self.nb_edges = np.sum(adj_mask)
-        self.edges = np.argwhere(adj_mask).astype(np.int32)
+        if self.nb_edges == 0:
+            self.centers_ = list(range(self.X.shape[0]))
+            self.labels_ = self.centers_
+            self.effective_radius = 0
+            self._mds_exec_time = 0
+            return self
+        self.edges = np.argwhere(adj_mask).astype(np.int32) #TODO: changer en uint32
         self.dist_mat = dist_mat
 
         self._clustering()

radius_clustering/utils/mds3-util.h

@@ -15,8 +15,16 @@
 #include <stdlib.h>
 #include <string.h>
 #include <assert.h>
+#include <time.h>
+
+#ifdef _WIN32
+#include <windows.h>
+#elif defined(__APPLE__) || defined(__linux__)
 #include <sys/time.h>
 #include <sys/resource.h>
+#else
+#error "Unsupported platform"
+#endif
 
 #define WORD_LENGTH 100
 #define TRUE 1
@@ -200,10 +208,27 @@ struct Result {
 };
 
 static double get_utime() {
-  struct rusage utime;
-  getrusage(RUSAGE_SELF, &utime);
-  return (double) (utime.ru_utime.tv_sec
-		   + (double) utime.ru_utime.tv_usec / 1000000);
+  #ifdef _WIN32
+      FILETIME createTime;
+      FILETIME exitTime;
+      FILETIME kernelTime;
+      FILETIME userTime;
+      if (GetProcessTimes(GetCurrentProcess(),
+                          &createTime, &exitTime,
+                          &kernelTime, &userTime) != 0) {
+         ULARGE_INTEGER li = {{userTime.dwLowDateTime, userTime.dwHighDateTime}};
+         return li.QuadPart * 1e-7;
+      }
+        return 0.0;
+  #elif defined(__APPLE__) || defined(__linux__)
+    struct rusage utime;
+    if (getrusage(RUSAGE_SELF, &utime) == 0) {
+      return (double)utime.ru_utime.tv_sec + (double)utime.ru_utime.tv_usec * 1e-6;
+    }
+    return 0.0;
+  #else
+    return (double)clock() / CLOCKS_PER_SEC;
+  #endif
 }
 
 static int cmp_branching_vertex_score(const void * a, const void *b){