update example with rst titles

1 files changed, 7 insertions, 7 deletions

diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb
index 0b5ca5c..5b3fd06 100644
--- a/docs/source/auto_examples/plot_otda_mapping.ipynb
+++ b/docs/source/auto_examples/plot_otda_mapping.ipynb
@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n===============================================\nOT mapping estimation for domain adaptation [8]\n===============================================\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8]\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n    \"Mapping estimation for discrete optimal transport\",\n    Neural Information Processing Systems (NIPS), 2016.\n\n"
+        "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n    \"Mapping estimation for discrete optimal transport\",\n    Neural Information Processing Systems (NIPS), 2016.\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -33,7 +33,7 @@
     }, 
     {
       "source": [
-        "generate data\n#############################################################################\n\n"
+        "Generate data\n#############################################################################\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -51,7 +51,7 @@
     }, 
     {
       "source": [
-        "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n"
+        "Plot data\n#############################################################################\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -60,7 +60,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n    kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n    max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n    kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n    max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)"
+        "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')"
       ], 
       "outputs": [], 
       "metadata": {
@@ -69,7 +69,7 @@
     }, 
     {
       "source": [
-        "plot data\n#############################################################################\n\n"
+        "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -78,7 +78,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')"
+        "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n    kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n    max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n    kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n    max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)"
       ], 
       "outputs": [], 
       "metadata": {
@@ -87,7 +87,7 @@
     }, 
     {
       "source": [
-        "plot transported samples\n#############################################################################\n\n"
+        "Plot transported samples\n#############################################################################\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}