summaryrefslogtreecommitdiff
path: root/docs/source/auto_examples/plot_otda_classes.rst
diff options
context:
space:
mode:
authorRémi Flamary <remi.flamary@gmail.com>2018-05-30 09:58:51 +0200
committerRémi Flamary <remi.flamary@gmail.com>2018-05-30 09:58:51 +0200
commitb5e45bbc83fd8cd8c1634a78f2f983d1cf28af73 (patch)
tree965b01f0313ff5ac2c2013239adda48f767ba992 /docs/source/auto_examples/plot_otda_classes.rst
parent90e42f32bdf0dd06667edaf172c51f4d4fce2c8b (diff)
update examples and notebooks
Diffstat (limited to 'docs/source/auto_examples/plot_otda_classes.rst')
-rw-r--r--docs/source/auto_examples/plot_otda_classes.rst61
1 files changed, 33 insertions, 28 deletions
diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst
index a5ab285..19756ff 100644
--- a/docs/source/auto_examples/plot_otda_classes.rst
+++ b/docs/source/auto_examples/plot_otda_classes.rst
@@ -42,8 +42,8 @@ Generate data
n_source_samples = 150
n_target_samples = 150
- Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)
- Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)
+ Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
+ Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)
@@ -94,29 +94,29 @@ Instantiate the different transport algorithms and fit them
It. |Loss |Delta loss
--------------------------------
- 0|1.003747e+01|0.000000e+00
- 1|1.953263e+00|-4.138821e+00
- 2|1.744456e+00|-1.196969e-01
- 3|1.689268e+00|-3.267022e-02
- 4|1.666355e+00|-1.374998e-02
- 5|1.656125e+00|-6.177356e-03
- 6|1.651753e+00|-2.646960e-03
- 7|1.647261e+00|-2.726957e-03
- 8|1.642274e+00|-3.036672e-03
- 9|1.639926e+00|-1.431818e-03
- 10|1.638750e+00|-7.173837e-04
- 11|1.637558e+00|-7.281753e-04
- 12|1.636248e+00|-8.002067e-04
- 13|1.634555e+00|-1.036074e-03
- 14|1.633547e+00|-6.166646e-04
- 15|1.633531e+00|-1.022614e-05
- 16|1.632957e+00|-3.510986e-04
- 17|1.632853e+00|-6.380944e-05
- 18|1.632704e+00|-9.122988e-05
- 19|1.632237e+00|-2.861276e-04
+ 0|9.566309e+00|0.000000e+00
+ 1|2.169680e+00|-3.409088e+00
+ 2|1.914989e+00|-1.329986e-01
+ 3|1.860251e+00|-2.942498e-02
+ 4|1.838073e+00|-1.206621e-02
+ 5|1.827064e+00|-6.025122e-03
+ 6|1.820899e+00|-3.386082e-03
+ 7|1.817290e+00|-1.985705e-03
+ 8|1.814644e+00|-1.458223e-03
+ 9|1.812661e+00|-1.093816e-03
+ 10|1.810239e+00|-1.338121e-03
+ 11|1.809100e+00|-6.296940e-04
+ 12|1.807939e+00|-6.420646e-04
+ 13|1.806965e+00|-5.389118e-04
+ 14|1.806822e+00|-7.889599e-05
+ 15|1.806193e+00|-3.482356e-04
+ 16|1.805735e+00|-2.536930e-04
+ 17|1.805321e+00|-2.292667e-04
+ 18|1.804389e+00|-5.170222e-04
+ 19|1.803908e+00|-2.661907e-04
It. |Loss |Delta loss
--------------------------------
- 20|1.632174e+00|-3.896483e-05
+ 20|1.803696e+00|-1.178279e-04
Fig 1 : plots source and target samples
@@ -161,7 +161,7 @@ Fig 2 : plot optimal couplings and transported samples
.. code-block:: python
- param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}
+ param_img = {'interpolation': 'nearest'}
pl.figure(2, figsize=(15, 8))
pl.subplot(2, 4, 1)
@@ -236,11 +236,13 @@ Fig 2 : plot optimal couplings and transported samples
-**Total running time of the script:** ( 0 minutes 2.308 seconds)
+**Total running time of the script:** ( 0 minutes 1.423 seconds)
-.. container:: sphx-glr-footer
+.. only :: html
+
+ .. container:: sphx-glr-footer
.. container:: sphx-glr-download
@@ -253,6 +255,9 @@ Fig 2 : plot optimal couplings and transported samples
:download:`Download Jupyter notebook: plot_otda_classes.ipynb <plot_otda_classes.ipynb>`
-.. rst-class:: sphx-glr-signature
- `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_