From 3b0732b041d46df66cb182b17f6ece040c578722 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Apr 2020 09:07:30 +0200 Subject: correct url for examples --- README.md | 6 +++--- docs/source/readme.rst | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 893f200..b9b8f45 100644 --- a/README.md +++ b/README.md @@ -37,11 +37,11 @@ POT provides the following generic OT solvers (links to examples): POT provides the following Machine Learning related solvers: * [Optimal transport for domain - adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) + adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html) with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html). -* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. -* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/domain-adaptation/plot_WDA.html) [11] (requires autograd + pymanopt). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt). * [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27]. Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 2707a07..c96f191 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -70,7 +70,7 @@ POT provides the following generic OT solvers (links to examples): POT provides the following Machine Learning related solvers: - `Optimal transport for domain - adaptation `__ + adaptation `__ with `group lasso regularization `__, `Laplacian @@ -80,10 +80,10 @@ POT provides the following Machine Learning related solvers: - `Linear OT mapping `__ [14] and `Joint OT mapping - estimation `__ + estimation `__ [8]. - `Wasserstein Discriminant - Analysis `__ + Analysis `__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target shift `__ -- cgit v1.2.3