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lesteve · lesteve · commit 3af1470e9f0b · 2025-06-16T14:48:24.000+02:00
diff --git a/sklearn/mixture/_base.py b/sklearn/mixture/_base.py
@@ -142,9 +142,9 @@ def _initialize_parameters(self, X, random_state, xp=None):
             # TODO: when array API supports __setitem__ with fancy indexing we
             # can use the previous code:
             # resp[indices, xp.arange(self.n_components)] = 1
-            # Until we use a for loop one on dimension.
-            for count, index in enumerate(indices):
-                resp[index, count] = 1
+            # Until then we use a for loop on one dimension.
+            for col, index in enumerate(indices):
+                resp[index, col] = 1
         elif self.init_params == "k-means++":
             resp = np.zeros((n_samples, self.n_components), dtype=X.dtype)
             _, indices = kmeans_plusplus(
diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py
@@ -662,7 +662,6 @@ def test_logsumexp_like_scipy_logsumexp(array_namespace, device_, dtype_name, ax
 
     # if torch on CPU or array api strict on default device
     # check that _logsumexp works when array API dispatch is disabled
-    # TODO is there a better way for this
     if (array_namespace == "torch" and device_ == "cpu") or (
         array_namespace == "array_api_strict" and "CPU" in str(device_)
     ):
