MNT Removed out of date note about KMeans parallelism on macOS
@@ -205,6 +205,9 @@ computing cluster centers and values of inertia. For example, assigning a
weight of 2 to a sample is equivalent to adding a duplicate of that sample
to the dataset :math:`X`.
+K-means can be used for vector quantization. This is achieved using the
+transform method of a trained model of :class:`KMeans`.
+
Low-level parallelism
---------------------
@@ -213,16 +216,6 @@ chunks of data (256 samples) are processed in parallel, which in addition
yields a low memory footprint. For more details on how to control the number of
threads, please refer to our :ref:`parallelism` notes.
-.. warning::
-
- The parallel version of K-Means is broken on OS X when `numpy` uses the
- `Accelerate` Framework. This is expected behavior: `Accelerate` can be called
- after a fork but you need to execv the subprocess with the Python binary
- (which multiprocessing does not do under posix).
-
-K-means can be used for vector quantization. This is achieved using the
-transform method of a trained model of :class:`KMeans`.
-
.. topic:: Examples:
* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`: Demonstrating when
