Pushing the docs to dev/ for branch: master, commit b2723582f206bb1f0d6847a478ddc5295a1ca8b1

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Wikipedia principal eigenvector\n\n\nA classical way to assert the relative importance of vertices in a\ngraph is to compute the principal eigenvector of the adjacency matrix\nso as to assign to each vertex the values of the components of the first\neigenvector as a centrality score:\n\n    https://en.wikipedia.org/wiki/Eigenvector_centrality\n\nOn the graph of webpages and links those values are called the PageRank\nscores by Google.\n\nThe goal of this example is to analyze the graph of links inside\nwikipedia articles to rank articles by relative importance according to\nthis eigenvector centrality.\n\nThe traditional way to compute the principal eigenvector is to use the\npower iteration method:\n\n    https://en.wikipedia.org/wiki/Power_iteration\n\nHere the computation is achieved thanks to Martinsson's Randomized SVD\nalgorithm implemented in the scikit.\n\nThe graph data is fetched from the DBpedia dumps. DBpedia is an extraction\nof the latent structured data of the Wikipedia content.\n\n"
+        "\n# Wikipedia principal eigenvector\n\n\nA classical way to assert the relative importance of vertices in a\ngraph is to compute the principal eigenvector of the adjacency matrix\nso as to assign to each vertex the values of the components of the first\neigenvector as a centrality score:\n\n    https://en.wikipedia.org/wiki/Eigenvector_centrality\n\nOn the graph of webpages and links those values are called the PageRank\nscores by Google.\n\nThe goal of this example is to analyze the graph of links inside\nwikipedia articles to rank articles by relative importance according to\nthis eigenvector centrality.\n\nThe traditional way to compute the principal eigenvector is to use the\npower iteration method:\n\n    https://en.wikipedia.org/wiki/Power_iteration\n\nHere the computation is achieved thanks to Martinsson's Randomized SVD\nalgorithm implemented in scikit-learn.\n\nThe graph data is fetched from the DBpedia dumps. DBpedia is an extraction\nof the latent structured data of the Wikipedia content.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -23,7 +23,7 @@
     https://en.wikipedia.org/wiki/Power_iteration
 
 Here the computation is achieved thanks to Martinsson's Randomized SVD
-algorithm implemented in the scikit.
+algorithm implemented in scikit-learn.
 
 The graph data is fetched from the DBpedia dumps. DBpedia is an extraction
 of the latent structured data of the Wikipedia content.