CircleCI update of dev docs (2648).

Author: Circle CI

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -106,7 +106,7 @@ <h1>Source code for ot.optim</h1><div class="highlight"><pre>
     <span class="k">try</span><span class="p">:</span>
         <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
     <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">scipy.optimize.linesearch</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
+        <span class="kn">from</span> <span class="nn">scipy.optimize._linesearch</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
 
 <span class="c1"># The corresponding scipy function does not work for matrices</span>
 

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -195,26 +195,26 @@ Estimating dual variables for entropic OT
 
  .. code-block:: none
 
-    Iter:   0, loss=0.20204949002247308
-    Iter:  10, loss=-19.551101352817334
-    Iter:  20, loss=-31.551458051531817
-    Iter:  30, loss=-35.7636881046527
-    Iter:  40, loss=-39.034476256142355
-    Iter:  50, loss=-40.6280858859757
-    Iter:  60, loss=-41.604004073406124
-    Iter:  70, loss=-41.818288329560566
-    Iter:  80, loss=-41.989647890237734
-    Iter:  90, loss=-42.03180987676141
-    Iter: 100, loss=-42.055013109267094
-    Iter: 110, loss=-42.062629072113054
-    Iter: 120, loss=-42.06626885119863
-    Iter: 130, loss=-42.06844988963407
-    Iter: 140, loss=-42.069939208182966
-    Iter: 150, loss=-42.070996679188674
-    Iter: 160, loss=-42.07177543369965
-    Iter: 170, loss=-42.0723760924461
-    Iter: 180, loss=-42.07284348639993
-    Iter: 190, loss=-42.07320986183685
+    Iter:   0, loss=0.2020494900225641
+    Iter:  10, loss=-19.541676622733295
+    Iter:  20, loss=-31.735836715687295
+    Iter:  30, loss=-36.618793395554086
+    Iter:  40, loss=-39.83845867036572
+    Iter:  50, loss=-41.14518853070982
+    Iter:  60, loss=-41.66991355578482
+    Iter:  70, loss=-41.84706534362833
+    Iter:  80, loss=-41.88857395701371
+    Iter:  90, loss=-41.91035463004136
+    Iter: 100, loss=-41.91743385705979
+    Iter: 110, loss=-41.91959207805565
+    Iter: 120, loss=-41.92049197287366
+    Iter: 130, loss=-41.92105290866285
+    Iter: 140, loss=-41.92130510788596
+    Iter: 150, loss=-41.92146925094368
+    Iter: 160, loss=-41.92159132695289
+    Iter: 170, loss=-41.92169055089339
+    Iter: 180, loss=-41.92177233929467
+    Iter: 190, loss=-41.92184312657864
 
 
 
@@ -319,25 +319,25 @@ Estimating dual variables for quadratic OT
  .. code-block:: none
 
     Iter:   0, loss=-0.0018442196020623663
-    Iter:  10, loss=-19.47816608787497
-    Iter:  20, loss=-31.50033642838461
-    Iter:  30, loss=-35.52005725731263
-    Iter:  40, loss=-38.945130956940254
-    Iter:  50, loss=-40.363092092514094
-    Iter:  60, loss=-41.379390235877494
-    Iter:  70, loss=-41.67855907657883
-    Iter:  80, loss=-41.83972370966888
-    Iter:  90, loss=-41.90216019622647
-    Iter: 100, loss=-41.92186511597178
-    Iter: 110, loss=-41.930491719790005
-    Iter: 120, loss=-41.93460180784624
-    Iter: 130, loss=-41.9372894164
-    Iter: 140, loss=-41.93949157907267
-    Iter: 150, loss=-41.94154733605073
-    Iter: 160, loss=-41.94315709103125
-    Iter: 170, loss=-41.944223378820006
-    Iter: 180, loss=-41.94487174493441
-    Iter: 190, loss=-41.94525245478749
+    Iter:  10, loss=-19.55017434263893
+    Iter:  20, loss=-31.347698856134237
+    Iter:  30, loss=-36.30414973124457
+    Iter:  40, loss=-39.44424288116018
+    Iter:  50, loss=-40.8600905041555
+    Iter:  60, loss=-41.43875932962067
+    Iter:  70, loss=-41.7017040151525
+    Iter:  80, loss=-41.753059572856095
+    Iter:  90, loss=-41.793122830389215
+    Iter: 100, loss=-41.802319075834625
+    Iter: 110, loss=-41.80715842088403
+    Iter: 120, loss=-41.80855728888973
+    Iter: 130, loss=-41.80914880131256
+    Iter: 140, loss=-41.80928209396796
+    Iter: 150, loss=-41.80935061812178
+    Iter: 160, loss=-41.809379249526906
+    Iter: 170, loss=-41.8093945438798
+    Iter: 180, loss=-41.8094025908204
+    Iter: 190, loss=-41.80940862825199
 
 
 
@@ -380,7 +380,7 @@ Plot the estimated quadratic OT plan
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.049 seconds)
+   **Total running time of the script:** (0 minutes 17.411 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_dual_ot_pytorch.py:

master/_images/sphx_glr_plot_OT_2D_samples_001.png

@@ -395,14 +395,14 @@ classes
     Estimated weights :  [0.29985821 0.18926744 0.51087435]
     True proportions :  [0.5 0.3 0.2]
 
-    <matplotlib.colorbar.Colorbar object at 0x7f561af3f4c0>
+    <matplotlib.colorbar.Colorbar object at 0x7fe974122e00>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.077 seconds)
+   **Total running time of the script:** (0 minutes 9.330 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_optim_gromov_pytorch.py: