Pushing the docs to dev/ for branch: master, commit 9d535ad42ca9ff423e518ce9272246b0f42b94d7

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation\n\n\nThis is an example of applying Non-negative Matrix Factorization\nand Latent Dirichlet Allocation on a corpus of documents and\nextract additive models of the topic structure of the corpus.\nThe output is a list of topics, each represented as a list of terms\n(weights are not shown).\n\nThe default parameters (n_samples / n_features / n_topics) should make\nthe example runnable in a couple of tens of seconds. You can try to\nincrease the dimensions of the problem, but be aware that the time\ncomplexity is polynomial in NMF. In LDA, the time complexity is\nproportional to (n_samples * iterations).\n\n"
+        "\n# Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation\n\n\nThis is an example of applying :class:`sklearn.decomposition.NMF`\nand :class:`sklearn.decomposition.LatentDirichletAllocation` on a corpus of documents and\nextract additive models of the topic structure of the corpus.\nThe output is a list of topics, each represented as a list of terms\n(weights are not shown).\n\nThe default parameters (n_samples / n_features / n_topics) should make\nthe example runnable in a couple of tens of seconds. You can try to\nincrease the dimensions of the problem, but be aware that the time\ncomplexity is polynomial in NMF. In LDA, the time complexity is\nproportional to (n_samples * iterations).\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}

dev/_downloads/auto_examples_python.zip

@@ -3,8 +3,8 @@
 Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
 =======================================================================================
 
-This is an example of applying Non-negative Matrix Factorization
-and Latent Dirichlet Allocation on a corpus of documents and
+This is an example of applying :class:`sklearn.decomposition.NMF`
+and :class:`sklearn.decomposition.LatentDirichletAllocation` on a corpus of documents and
 extract additive models of the topic structure of the corpus.
 The output is a list of topics, each represented as a list of terms
 (weights are not shown).