CircleCI update of dev docs (2713).

Author: Circle CI

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -148,7 +148,7 @@ <h1>Source code for ot.gnn._layers</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">n_tplt</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_tplt_nodes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_node_weights</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">multi_alpha</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">feature_init_mean</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">feature_init_std</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Template Fused Gromov-Wasserstein (TFGW) layer. This layer is a pooling layer for graph neural networks.</span>
 <span class="sd">            Computes the fused Gromov-Wasserstein distances between the graph and a set of templates.</span>
 
@@ -281,7 +281,7 @@ <h1>Source code for ot.gnn._layers</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">n_tplt</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_tplt_nodes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">train_node_weights</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">feature_init_mean</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">feature_init_std</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Template Wasserstein (TW) layer, also kown as OT-GNN layer. This layer is a pooling layer for graph neural networks.</span>
 <span class="sd">            Computes the Wasserstein distances between the features of the graph features and a set of templates.</span>
 

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -322,7 +322,7 @@ <h1>Source code for ot.gromov._quantized</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../../gen_modules/ot.gromov.html#ot.gromov.get_graph_partition">[docs]</a>
 <span class="k">def</span> <span class="nf">get_graph_partition</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">part_method</span><span class="o">=</span><span class="s1">&#39;random&#39;</span><span class="p">,</span> <span class="n">F</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>
                         <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">nx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Partitioning a given graph with structure matrix :math:`\mathbf{C} \in R^{n \times n}`</span>
 <span class="sd">    into `npart` partitions either &#39;random&#39;, or using one of {&#39;louvain&#39;, &#39;fluid&#39;}</span>
 <span class="sd">    algorithms from networkx, or &#39;spectral&#39; clustering from scikit-learn,</span>
@@ -430,7 +430,7 @@ <h1>Source code for ot.gromov._quantized</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="get_graph_representants">
 <a class="viewcode-back" href="../../../gen_modules/ot.gromov.html#ot.gromov.get_graph_representants">[docs]</a>
 <span class="k">def</span> <span class="nf">get_graph_representants</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">part</span><span class="p">,</span> <span class="n">rep_method</span><span class="o">=</span><span class="s1">&#39;pagerank&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">nx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get representative node for each partition given by :math:`\mathbf{part} \in R^{n}`</span>
 <span class="sd">    of a graph with structure matrix :math:`\mathbf{C} \in R^{n \times n}`.</span>
 <span class="sd">    Selection is either done randomly or using &#39;pagerank&#39; algorithm from networkx.</span>
@@ -506,7 +506,7 @@ <h1>Source code for ot.gromov._quantized</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../../gen_modules/ot.gromov.html#ot.gromov.format_partitioned_graph">[docs]</a>
 <span class="k">def</span> <span class="nf">format_partitioned_graph</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">part</span><span class="p">,</span> <span class="n">rep_indices</span><span class="p">,</span> <span class="n">F</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                              <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">nx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Format an attributed graph :math:`(\mathbf{C}, \mathbf{F}, \mathbf{p})`</span>
 <span class="sd">    with structure matrix :math:`(\mathbf{C} \in R^{n \times n}`, feature matrix</span>
 <span class="sd">    :math:`(\mathbf{F} \in R^{n \times d}` and node relative importance</span>
@@ -865,7 +865,7 @@ <h1>Source code for ot.gromov._quantized</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../../gen_modules/ot.gromov.html#ot.gromov.get_partition_and_representants_samples">[docs]</a>
 <span class="k">def</span> <span class="nf">get_partition_and_representants_samples</span><span class="p">(</span>
         <span class="n">X</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;kmeans&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">nx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute `npart` partitions and representants over samples :math:`\mathbf{X} \in R^{n \times d}`</span>
 <span class="sd">    using either a random or a kmeans algorithm.</span>
 
@@ -961,7 +961,7 @@ <h1>Source code for ot.gromov._quantized</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../../gen_modules/ot.gromov.html#ot.gromov.format_partitioned_samples">[docs]</a>
 <span class="k">def</span> <span class="nf">format_partitioned_samples</span><span class="p">(</span>
         <span class="n">X</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">part</span><span class="p">,</span> <span class="n">rep_indices</span><span class="p">,</span> <span class="n">F</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">nx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Format an attributed graph :math:`(\mathbf{D}(\mathbf{X}), \mathbf{F}, \mathbf{p})`</span>
 <span class="sd">    with euclidean structure matrix :math:`(\mathbf{D}(\mathbf{X}) \in R^{n \times n}`,</span>
 <span class="sd">    feature matrix :math:`(\mathbf{F} \in R^{n \times d}` and node relative importance</span>

master/_images/sphx_glr_plot_OT_2D_samples_001.png

@@ -195,26 +195,26 @@ Estimating dual variables for entropic OT
 
  .. code-block:: none
 
-    Iter:   0, loss=0.20204949002249467
-    Iter:  10, loss=-19.60860574650993
-    Iter:  20, loss=-31.482478746465016
-    Iter:  30, loss=-36.175626847270706
-    Iter:  40, loss=-39.22212045703235
-    Iter:  50, loss=-40.61255600476051
-    Iter:  60, loss=-41.173521857417356
-    Iter:  70, loss=-41.380839787597345
-    Iter:  80, loss=-41.4460443975386
-    Iter:  90, loss=-41.48422729091434
-    Iter: 100, loss=-41.49625400646669
-    Iter: 110, loss=-41.50316442246139
-    Iter: 120, loss=-41.5058921752514
-    Iter: 130, loss=-41.50777558324456
-    Iter: 140, loss=-41.50905893744227
-    Iter: 150, loss=-41.50991611620446
-    Iter: 160, loss=-41.510557684181926
-    Iter: 170, loss=-41.5110341217496
-    Iter: 180, loss=-41.51140167587627
-    Iter: 190, loss=-41.51168859183621
+    Iter:   0, loss=0.20204949002247666
+    Iter:  10, loss=-19.52029446797639
+    Iter:  20, loss=-31.56589317416366
+    Iter:  30, loss=-36.44149718032852
+    Iter:  40, loss=-39.865784184986445
+    Iter:  50, loss=-41.75994464253616
+    Iter:  60, loss=-42.51664938835618
+    Iter:  70, loss=-42.85456456760184
+    Iter:  80, loss=-42.92631997334403
+    Iter:  90, loss=-42.97161089599306
+    Iter: 100, loss=-42.98404519979
+    Iter: 110, loss=-42.987862631339674
+    Iter: 120, loss=-42.99026698249955
+    Iter: 130, loss=-42.99142554440141
+    Iter: 140, loss=-42.99195268362308
+    Iter: 150, loss=-42.992328640487635
+    Iter: 160, loss=-42.99257458826821
+    Iter: 170, loss=-42.99276568827521
+    Iter: 180, loss=-42.992914403589744
+    Iter: 190, loss=-42.99303652537613
 
 
 
@@ -319,25 +319,25 @@ Estimating dual variables for quadratic OT
  .. code-block:: none
 
     Iter:   0, loss=-0.0018442196020623663
-    Iter:  10, loss=-19.543862196129865
-    Iter:  20, loss=-31.259747958753433
-    Iter:  30, loss=-35.6732125884935
-    Iter:  40, loss=-39.02711072361268
-    Iter:  50, loss=-40.36085807634747
-    Iter:  60, loss=-41.014144338144405
-    Iter:  70, loss=-41.19892103529712
-    Iter:  80, loss=-41.31973154479462
-    Iter:  90, loss=-41.356297973746095
-    Iter: 100, loss=-41.37305575144209
-    Iter: 110, loss=-41.37955006947374
-    Iter: 120, loss=-41.38380401883411
-    Iter: 130, loss=-41.38607383837321
-    Iter: 140, loss=-41.38752418540599
-    Iter: 150, loss=-41.38850405120806
-    Iter: 160, loss=-41.38922284097558
-    Iter: 170, loss=-41.38975293469908
-    Iter: 180, loss=-41.390142774135064
-    Iter: 190, loss=-41.39042904145647
+    Iter:  10, loss=-19.5218368360571
+    Iter:  20, loss=-31.36444262698248
+    Iter:  30, loss=-36.2653733193638
+    Iter:  40, loss=-39.718767823729024
+    Iter:  50, loss=-41.50474805129267
+    Iter:  60, loss=-42.351018653800374
+    Iter:  70, loss=-42.66655441621151
+    Iter:  80, loss=-42.784517384616485
+    Iter:  90, loss=-42.82632038232454
+    Iter: 100, loss=-42.84400873392061
+    Iter: 110, loss=-42.848647735590504
+    Iter: 120, loss=-42.85075563083304
+    Iter: 130, loss=-42.85153039869369
+    Iter: 140, loss=-42.85179248312254
+    Iter: 150, loss=-42.851919039576615
+    Iter: 160, loss=-42.85196395244533
+    Iter: 170, loss=-42.8519875130725
+    Iter: 180, loss=-42.85199843808929
+    Iter: 190, loss=-42.85200505273594
 
 
 
@@ -380,7 +380,7 @@ Plot the estimated quadratic OT plan
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 11.843 seconds)
+   **Total running time of the script:** (0 minutes 20.121 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_dual_ot_pytorch.py:

master/_images/sphx_glr_plot_OT_2D_samples_002.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 0x7f19e0346050>
+    <matplotlib.colorbar.Colorbar object at 0x7f09dd383b20>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.891 seconds)
+   **Total running time of the script:** (0 minutes 9.053 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_optim_gromov_pytorch.py: