[MRG] Actually run sphinx-gallery (#146)

* generate gallery

* remove mock

* add sklearn to requirermnt?txt for example

* remove latex from fgw example

* add networks for graph example

* remove all

* add requirement.txt rtd

* rtd debug

* update readme

* eradthedoc with redirection

* add conf rtd

1 files changed, 0 insertions, 291 deletions

diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst
deleted file mode 100644
index 6d8e429..0000000
--- a/docs/source/auto_examples/plot_otda_d2.rst
+++ /dev/null
@@ -1,291 +0,0 @@
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_d2.py>`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
-
-    .. _sphx_glr_auto_examples_plot_otda_d2.py:
-
-
-===================================================
-OT for domain adaptation on empirical distributions
-===================================================
-
-This example introduces a domain adaptation in a 2D setting. It explicits
-the problem of domain adaptation and introduces some optimal transport
-approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-
-
-.. code-block:: default
-
-
-    # Authors: Remi Flamary <remi.flamary@unice.fr>
-    #          Stanislas Chambon <stan.chambon@gmail.com>
-    #
-    # License: MIT License
-
-    import matplotlib.pylab as pl
-    import ot
-    import ot.plot
-
-
-
-
-
-
-
-
-generate data
--------------
-
-
-.. code-block:: default
-
-
-    n_samples_source = 150
-    n_samples_target = 150
-
-    Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
-    Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
-    # Cost matrix
-    M = ot.dist(Xs, Xt, metric='sqeuclidean')
-
-
-
-
-
-
-
-
-
-Instantiate the different transport algorithms and fit them
------------------------------------------------------------
-
-
-.. code-block:: default
-
-
-    # EMD Transport
-    ot_emd = ot.da.EMDTransport()
-    ot_emd.fit(Xs=Xs, Xt=Xt)
-
-    # Sinkhorn Transport
-    ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-    ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-    # Sinkhorn Transport with Group lasso regularization
-    ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
-    ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
-
-    # transport source samples onto target samples
-    transp_Xs_emd = ot_emd.transform(Xs=Xs)
-    transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
-    transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
-
-
-
-
-
-
-
-
-
-Fig 1 : plots source and target samples + matrix of pairwise distance
----------------------------------------------------------------------
-
-
-.. code-block:: default
-
-
-    pl.figure(1, figsize=(10, 10))
-    pl.subplot(2, 2, 1)
-    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.legend(loc=0)
-    pl.title('Source  samples')
-
-    pl.subplot(2, 2, 2)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.legend(loc=0)
-    pl.title('Target samples')
-
-    pl.subplot(2, 2, 3)
-    pl.imshow(M, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Matrix of pairwise distances')
-    pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png
-    :class: sphx-glr-single-img
-
-
-
-
-
-Fig 2 : plots optimal couplings for the different methods
----------------------------------------------------------
-
-
-.. code-block:: default
-
-    pl.figure(2, figsize=(10, 6))
-
-    pl.subplot(2, 3, 1)
-    pl.imshow(ot_emd.coupling_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Optimal coupling\nEMDTransport')
-
-    pl.subplot(2, 3, 2)
-    pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Optimal coupling\nSinkhornTransport')
-
-    pl.subplot(2, 3, 3)
-    pl.imshow(ot_lpl1.coupling_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
-    pl.subplot(2, 3, 4)
-    ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])
-    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Main coupling coefficients\nEMDTransport')
-
-    pl.subplot(2, 3, 5)
-    ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])
-    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Main coupling coefficients\nSinkhornTransport')
-
-    pl.subplot(2, 3, 6)
-    ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])
-    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Main coupling coefficients\nSinkhornLpl1Transport')
-    pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_002.png
-    :class: sphx-glr-single-img
-
-
-
-
-
-Fig 3 : plot transported samples
---------------------------------
-
-
-.. code-block:: default
-
-
-    # display transported samples
-    pl.figure(4, figsize=(10, 4))
-    pl.subplot(1, 3, 1)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-               label='Target samples', alpha=0.5)
-    pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
-               marker='+', label='Transp samples', s=30)
-    pl.title('Transported samples\nEmdTransport')
-    pl.legend(loc=0)
-    pl.xticks([])
-    pl.yticks([])
-
-    pl.subplot(1, 3, 2)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-               label='Target samples', alpha=0.5)
-    pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
-               marker='+', label='Transp samples', s=30)
-    pl.title('Transported samples\nSinkhornTransport')
-    pl.xticks([])
-    pl.yticks([])
-
-    pl.subplot(1, 3, 3)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-               label='Target samples', alpha=0.5)
-    pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
-               marker='+', label='Transp samples', s=30)
-    pl.title('Transported samples\nSinkhornLpl1Transport')
-    pl.xticks([])
-    pl.yticks([])
-
-    pl.tight_layout()
-    pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_otda_d2.py:172: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      pl.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  21.323 seconds)
-
-
-.. _sphx_glr_download_auto_examples_plot_otda_d2.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: plot_otda_d2.py <plot_otda_d2.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: plot_otda_d2.ipynb <plot_otda_d2.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_