Pushing the docs to dev/ for branch: master, commit 553b5fb8f84ba05c8397f26dd079deece2b05029

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n#  Comparison of Manifold Learning methods\n\n\nAn illustration of dimensionality reduction on the S-curve dataset\nwith various manifold learning methods.\n\nFor a discussion and comparison of these algorithms, see the\n`manifold module page <manifold>`\n\nFor a similar example, where the methods are applied to a\nsphere dataset, see `sphx_glr_auto_examples_manifold_plot_manifold_sphere.py`\n\nNote that the purpose of the MDS is to find a low-dimensional\nrepresentation of the data (here 2D) in which the distances respect well\nthe distances in the original high-dimensional space, unlike other\nmanifold-learning algorithms, it does not seeks an isotropic\nrepresentation of the data in the low-dimensional space.\n\n"
+        "\n# Comparison of Manifold Learning methods\n\n\nAn illustration of dimensionality reduction on the S-curve dataset\nwith various manifold learning methods.\n\nFor a discussion and comparison of these algorithms, see the\n`manifold module page <manifold>`\n\nFor a similar example, where the methods are applied to a\nsphere dataset, see `sphx_glr_auto_examples_manifold_plot_manifold_sphere.py`\n\nNote that the purpose of the MDS is to find a low-dimensional\nrepresentation of the data (here 2D) in which the distances respect well\nthe distances in the original high-dimensional space, unlike other\nmanifold-learning algorithms, it does not seeks an isotropic\nrepresentation of the data in the low-dimensional space.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -1,6 +1,6 @@
 """
 =========================================
- Comparison of Manifold Learning methods
+Comparison of Manifold Learning methods
 =========================================
 
 An illustration of dimensionality reduction on the S-curve dataset

dev/_downloads/plot_compare_methods.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n=============================================================================\n t-SNE: The effect of various perplexity values on the shape\n=============================================================================\n\nAn illustration of t-SNE on the two concentric circles and the S-curve\ndatasets for different perplexity values.\n\nWe observe a tendency towards clearer shapes as the preplexity value increases.\n\nThe size, the distance and the shape of clusters may vary upon initialization,\nperplexity values and does not always convey a meaning.\n\nAs shown below, t-SNE for higher perplexities finds meaningful topology of\ntwo concentric circles, however the size and the distance of the circles varies\nslightly from the original. Contrary to the two circles dataset, the shapes\nvisually diverge from S-curve topology on the S-curve dataset even for\nlarger perplexity values.\n\nFor further details, \"How to Use t-SNE Effectively\"\nhttp://distill.pub/2016/misread-tsne/ provides a good discussion of the\neffects of various parameters, as well as interactive plots to explore\nthose effects.\n\n"
+        "\n=============================================================================\nt-SNE: The effect of various perplexity values on the shape\n=============================================================================\n\nAn illustration of t-SNE on the two concentric circles and the S-curve\ndatasets for different perplexity values.\n\nWe observe a tendency towards clearer shapes as the preplexity value increases.\n\nThe size, the distance and the shape of clusters may vary upon initialization,\nperplexity values and does not always convey a meaning.\n\nAs shown below, t-SNE for higher perplexities finds meaningful topology of\ntwo concentric circles, however the size and the distance of the circles varies\nslightly from the original. Contrary to the two circles dataset, the shapes\nvisually diverge from S-curve topology on the S-curve dataset even for\nlarger perplexity values.\n\nFor further details, \"How to Use t-SNE Effectively\"\nhttp://distill.pub/2016/misread-tsne/ provides a good discussion of the\neffects of various parameters, as well as interactive plots to explore\nthose effects.\n\n"
       ]
     },
     {

dev/_downloads/plot_compare_methods.py

@@ -1,6 +1,6 @@
 """
 =============================================================================
- t-SNE: The effect of various perplexity values on the shape
+t-SNE: The effect of various perplexity values on the shape
 =============================================================================
 
 An illustration of t-SNE on the two concentric circles and the S-curve