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| author | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-27 09:07:30 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-27 09:07:30 +0200 |
| commit | 3b0732b041d46df66cb182b17f6ece040c578722 (patch) | |
| tree | 60fafb54d66aa843965c000d48d249780afdcf75 | |
| parent | 71e79844a54ea88babd04b01688766a17b3de614 (diff) | |
correct url for examples
| -rw-r--r-- | README.md | 6 | ||||
| -rw-r--r-- | docs/source/readme.rst | 6 |
2 files changed, 6 insertions, 6 deletions
@@ -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 <auto_examples/plot_otda_classes.html>`__ + adaptation <auto_examples/domain-adaptation/plot_otda_classes.html>`__ with `group lasso regularization <auto_examples/domain-adaptation/plot_otda_classes.html>`__, `Laplacian @@ -80,10 +80,10 @@ POT provides the following Machine Learning related solvers: - `Linear OT mapping <auto_examples/domain-adaptation/plot_otda_linear_mapping.html>`__ [14] and `Joint OT mapping - estimation <auto_examples/plot_otda_mapping.html>`__ + estimation <auto_examples/domain-adaptation/plot_otda_mapping.html>`__ [8]. - `Wasserstein Discriminant - Analysis <auto_examples/domain-adaptation/plot_WDA.html>`__ + Analysis <auto_examples/others/plot_WDA.html>`__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target shift <auto_examples/domain-adaptation/plot_otda_jcpot.html>`__ |