CircleCI update of dev docs (2626).

Author: Circle CI

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -132,11 +132,11 @@ <h1>Source code for ot.gromov._gw</h1><div class="highlight"><pre>
 
 <span class="sd">    Where :</span>
 
-<span class="sd">    - :math:`\mathbf{C_1}`: Metric cost matrix in the source space</span>
-<span class="sd">    - :math:`\mathbf{C_2}`: Metric cost matrix in the target space</span>
-<span class="sd">    - :math:`\mathbf{p}`: distribution in the source space</span>
-<span class="sd">    - :math:`\mathbf{q}`: distribution in the target space</span>
-<span class="sd">    - `L`: loss function to account for the misfit between the similarity matrices</span>
+<span class="sd">    - :math:`\mathbf{C_1}`: Metric cost matrix in the source space.</span>
+<span class="sd">    - :math:`\mathbf{C_2}`: Metric cost matrix in the target space.</span>
+<span class="sd">    - :math:`\mathbf{p}`: Distribution in the source space.</span>
+<span class="sd">    - :math:`\mathbf{q}`: Distribution in the target space.</span>
+<span class="sd">    - `L`: Loss function to account for the misfit between the similarity matrices.</span>
 
 <span class="sd">    .. note:: This function is backend-compatible and will work on arrays</span>
 <span class="sd">        from all compatible backends. But the algorithm uses the C++ CPU backend</span>
@@ -151,39 +151,39 @@ <h1>Source code for ot.gromov._gw</h1><div class="highlight"><pre>
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    C1 : array-like, shape (ns, ns)</span>
-<span class="sd">        Metric cost matrix in the source space</span>
+<span class="sd">        Metric cost matrix in the source space.</span>
 <span class="sd">    C2 : array-like, shape (nt, nt)</span>
-<span class="sd">        Metric cost matrix in the target space</span>
+<span class="sd">        Metric cost matrix in the target space.</span>
 <span class="sd">    p : array-like, shape (ns,), optional</span>
 <span class="sd">        Distribution in the source space.</span>
 <span class="sd">        If let to its default value None, uniform distribution is taken.</span>
 <span class="sd">    q : array-like, shape (nt,), optional</span>
 <span class="sd">        Distribution in the target space.</span>
 <span class="sd">        If let to its default value None, uniform distribution is taken.</span>
 <span class="sd">    loss_fun : str, optional</span>
-<span class="sd">        loss function used for the solver either &#39;square_loss&#39; or &#39;kl_loss&#39;</span>
+<span class="sd">        Loss function used for the solver either &#39;square_loss&#39; or &#39;kl_loss&#39;.</span>
 <span class="sd">    symmetric : bool, optional</span>
 <span class="sd">        Either C1 and C2 are to be assumed symmetric or not.</span>
 <span class="sd">        If let to its default None value, a symmetry test will be conducted.</span>
 <span class="sd">        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).</span>
 <span class="sd">    verbose : bool, optional</span>
-<span class="sd">        Print information along iterations</span>
+<span class="sd">        Print information along iterations.</span>
 <span class="sd">    log : bool, optional</span>
-<span class="sd">        record log if True</span>
+<span class="sd">        Record log if True.</span>
 <span class="sd">    armijo : bool, optional</span>
-<span class="sd">        If True the step of the line-search is found via an armijo research. Else closed form is used.</span>
-<span class="sd">        If there are convergence issues use False.</span>
+<span class="sd">        If True, the step of the line-search is found via an armijo search. Else closed form is used.</span>
+<span class="sd">        If there are convergence issues, use False.</span>
 <span class="sd">    G0: array-like, shape (ns,nt), optional</span>
-<span class="sd">        If None the initial transport plan of the solver is pq^T.</span>
+<span class="sd">        If None, the initial transport plan of the solver is pq^T.</span>
 <span class="sd">        Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.</span>
 <span class="sd">    max_iter : int, optional</span>
-<span class="sd">        Max number of iterations</span>
+<span class="sd">        Max number of iterations.</span>
 <span class="sd">    tol_rel : float, optional</span>
-<span class="sd">        Stop threshold on relative error (&gt;0)</span>
+<span class="sd">        Stop threshold on relative error (&gt;0).</span>
 <span class="sd">    tol_abs : float, optional</span>
-<span class="sd">        Stop threshold on absolute error (&gt;0)</span>
+<span class="sd">        Stop threshold on absolute error (&gt;0).</span>
 <span class="sd">    **kwargs : dict</span>
-<span class="sd">        parameters can be directly passed to the ot.optim.cg solver</span>
+<span class="sd">        Parameters can be directly passed to the ot.optim.cg solver.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -264,7 +264,7 @@ <h1>Source code for ot.gromov._gw</h1><div class="highlight"><pre>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="n">nx</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">(</span><span class="n">C10</span><span class="p">):</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;Input structure matrix consists of integer. The transport plan will be &quot;</span>
+            <span class="s2">&quot;Input structure matrix consists of integers. The transport plan will be &quot;</span>
             <span class="s2">&quot;casted accordingly, possibly resulting in a loss of precision. &quot;</span>
             <span class="s2">&quot;If this behaviour is unwanted, please make sure your input &quot;</span>
             <span class="s2">&quot;structure matrix consists of floating point elements.&quot;</span><span class="p">,</span>

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -195,26 +195,26 @@ Estimating dual variables for entropic OT
 
  .. code-block:: none
 
-    Iter:   0, loss=0.20204949002247302
-    Iter:  10, loss=-19.280160148558775
-    Iter:  20, loss=-30.567874987030468
-    Iter:  30, loss=-35.151316321027515
-    Iter:  40, loss=-37.83916433937388
-    Iter:  50, loss=-39.101012589887894
-    Iter:  60, loss=-39.769128850689675
-    Iter:  70, loss=-40.14293192992
-    Iter:  80, loss=-40.209100602299934
-    Iter:  90, loss=-40.25757599015733
-    Iter: 100, loss=-40.27467643168675
-    Iter: 110, loss=-40.28036559784292
-    Iter: 120, loss=-40.28349709996101
-    Iter: 130, loss=-40.285093682279374
-    Iter: 140, loss=-40.28592700498707
-    Iter: 150, loss=-40.28645833308449
-    Iter: 160, loss=-40.2868274997363
-    Iter: 170, loss=-40.28709895511043
-    Iter: 180, loss=-40.28731251255663
-    Iter: 190, loss=-40.2874807478744
+    Iter:   0, loss=0.202049490022473
+    Iter:  10, loss=-19.638326466943862
+    Iter:  20, loss=-30.72704216650675
+    Iter:  30, loss=-35.27578567378329
+    Iter:  40, loss=-37.980242759466826
+    Iter:  50, loss=-39.59946841604584
+    Iter:  60, loss=-40.1603378250191
+    Iter:  70, loss=-40.509522418595274
+    Iter:  80, loss=-40.58265534387766
+    Iter:  90, loss=-40.63298504629522
+    Iter: 100, loss=-40.64778917467728
+    Iter: 110, loss=-40.65249154126749
+    Iter: 120, loss=-40.6553819728475
+    Iter: 130, loss=-40.65675787022022
+    Iter: 140, loss=-40.657476105996466
+    Iter: 150, loss=-40.65794484779828
+    Iter: 160, loss=-40.658279443754125
+    Iter: 170, loss=-40.65852676980854
+    Iter: 180, loss=-40.65871971171536
+    Iter: 190, loss=-40.658875505800786
 
 
 
@@ -319,25 +319,25 @@ Estimating dual variables for quadratic OT
  .. code-block:: none
 
     Iter:   0, loss=-0.0018442196020623663
-    Iter:  10, loss=-19.489961278333244
-    Iter:  20, loss=-30.23588633005069
-    Iter:  30, loss=-34.81483628908168
-    Iter:  40, loss=-37.56310559827427
-    Iter:  50, loss=-38.903224800000885
-    Iter:  60, loss=-39.58890908096657
-    Iter:  70, loss=-39.96864839940656
-    Iter:  80, loss=-40.067879312969175
-    Iter:  90, loss=-40.132293665491126
-    Iter: 100, loss=-40.15394647149833
-    Iter: 110, loss=-40.16411621223928
-    Iter: 120, loss=-40.16835381642544
-    Iter: 130, loss=-40.170330415583564
-    Iter: 140, loss=-40.17119413421853
-    Iter: 150, loss=-40.17164213441122
-    Iter: 160, loss=-40.17189100126094
-    Iter: 170, loss=-40.17203774844194
-    Iter: 180, loss=-40.17212962936345
-    Iter: 190, loss=-40.17219050531965
+    Iter:  10, loss=-19.754000811648837
+    Iter:  20, loss=-30.46925040523962
+    Iter:  30, loss=-34.78656396011775
+    Iter:  40, loss=-37.70509387644338
+    Iter:  50, loss=-39.34567647298549
+    Iter:  60, loss=-39.95406884964521
+    Iter:  70, loss=-40.33816852310141
+    Iter:  80, loss=-40.451478406578254
+    Iter:  90, loss=-40.50853175598522
+    Iter: 100, loss=-40.526794582157635
+    Iter: 110, loss=-40.535098863701585
+    Iter: 120, loss=-40.53819903244734
+    Iter: 130, loss=-40.53960000636185
+    Iter: 140, loss=-40.54020999546969
+    Iter: 150, loss=-40.54051667313033
+    Iter: 160, loss=-40.54070400382129
+    Iter: 170, loss=-40.54082205703948
+    Iter: 180, loss=-40.5409036840701
+    Iter: 190, loss=-40.54096844988613
 
 
 
@@ -380,7 +380,7 @@ Plot the estimated quadratic OT plan
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.668 seconds)
+   **Total running time of the script:** (0 minutes 13.360 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 0x7f7bee427430>
+    <matplotlib.colorbar.Colorbar object at 0x7faecf45b490>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.372 seconds)
+   **Total running time of the script:** (0 minutes 8.359 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_optim_gromov_pytorch.py: