Pushing the docs to dev/ for branch: master, commit db2290ce38aea1d1d561e0966f952e9e7be05368

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nOnline learning of a dictionary of parts of faces\n==================================================\n\nThis example uses a large dataset of faces to learn a set of 20 x 20\nimages patches that constitute faces.\n\nFrom the programming standpoint, it is interesting because it shows how\nto use the online API of the scikit-learn to process a very large\ndataset by chunks. The way we proceed is that we load an image at a time\nand extract randomly 50 patches from this image. Once we have accumulated\n500 of these patches (using 10 images), we run the `partial_fit` method\nof the online KMeans object, MiniBatchKMeans.\n\nThe verbose setting on the MiniBatchKMeans enables us to see that some\nclusters are reassigned during the successive calls to\npartial-fit. This is because the number of patches that they represent\nhas become too low, and it is better to choose a random new\ncluster.\n\n"
+        "\nOnline learning of a dictionary of parts of faces\n==================================================\n\nThis example uses a large dataset of faces to learn a set of 20 x 20\nimages patches that constitute faces.\n\nFrom the programming standpoint, it is interesting because it shows how\nto use the online API of the scikit-learn to process a very large\ndataset by chunks. The way we proceed is that we load an image at a time\nand extract randomly 50 patches from this image. Once we have accumulated\n500 of these patches (using 10 images), we run the\n:func:`~sklearn.cluster.MiniBatchKMeans.partial_fit` method\nof the online KMeans object, MiniBatchKMeans.\n\nThe verbose setting on the MiniBatchKMeans enables us to see that some\nclusters are reassigned during the successive calls to\npartial-fit. This is because the number of patches that they represent\nhas become too low, and it is better to choose a random new\ncluster.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -9,7 +9,8 @@
 to use the online API of the scikit-learn to process a very large
 dataset by chunks. The way we proceed is that we load an image at a time
 and extract randomly 50 patches from this image. Once we have accumulated
-500 of these patches (using 10 images), we run the `partial_fit` method
+500 of these patches (using 10 images), we run the
+:func:`~sklearn.cluster.MiniBatchKMeans.partial_fit` method
 of the online KMeans object, MiniBatchKMeans.
 
 The verbose setting on the MiniBatchKMeans enables us to see that some