mimic sklearn weighted random init

sklearn_numba_dpex/kmeans/engine.py

@@ -188,16 +188,13 @@ def init_centroids(self, X, sample_weight):
         else:
             # NB: sampling without replacement must be executed sequentially so
             # it's better done on CPU
+            sample_weight_numpy = dpt.asnumpy(sample_weight)
+            p = sample_weight_numpy / sample_weight_numpy.sum()
             centers_idx = self.random_state.choice(
-                X.shape[0], size=n_clusters, replace=False
+                X.shape[0], size=n_clusters, replace=False, p=p
             )
             # Poor man's fancy indexing
-            # TODO: write a kernel ? or replace with better equivalent when available ?
-            # Relevant issue: https://github.com/IntelPython/dpctl/issues/1003
-            centers_t = dpt.concat(
-                [dpt.expand_dims(X[center_idx], axis=1) for center_idx in centers_idx],
-                axis=1,
-            )
+            centers_t = dpt.take(X.T, dpt.asarray(centers_idx), axis=1)
 
         return centers_t