GitHub Actions build openturns/otagrum 6591896081

otagrum/master/_downloads/5d5ea88ebb95e0f402b04a4daf048e90/plot_using_otagrum.ipynb

@@ -171,7 +171,7 @@
       },
       "outputs": [],
       "source": [
-        "learner = otagrum.ContinuousPC(sample, maxConditioningSetSize=5, alpha=0.1)"
+        "learner = otagrum.ContinuousPC(sample, 5, 0.1)"
       ]
     },
     {

otagrum/master/_downloads/c785e393d248af29337db07a92c50d35/plot_using_otagrum.py

@@ -103,7 +103,7 @@ def showDot(dotstring):
 # Now that we have data, we can use it to learn the structure with the continuous PC algorithm.
 
 # %%
-learner = otagrum.ContinuousPC(sample, maxConditioningSetSize=5, alpha=0.1)
+learner = otagrum.ContinuousPC(sample, 5, 0.1)
 
 # %%
 # We first learn the skeleton, that is the undirected structure.

otagrum/master/_sources/auto_examples/plot_plant_growth.rst.txt

@@ -159,7 +159,7 @@ light has 2 attributes : Dim and Bright
  .. code-block:: none
 
 
-    (pyAgrum.LabelizedVariable@0x55a895ed7ed0) Light:Labelized({Dim|Bright})
+    (pyAgrum.LabelizedVariable@0x55ffbf667760) Light:Labelized({Dim|Bright})
 
 
 
@@ -183,7 +183,7 @@ moisture has 2 attributes : Dry and Wet
  .. code-block:: none
 
 
-    (pyAgrum.LabelizedVariable@0x55a895e16b30) Moisture:Labelized({Dry|Wet})
+    (pyAgrum.LabelizedVariable@0x55ffbf58cb30) Moisture:Labelized({Dry|Wet})
 
 
 
@@ -274,7 +274,7 @@ Add arcs
  .. code-block:: none
 
 
-    (pyAgrum.BayesNet<double>@0x55a899021030) BN{nodes: 3, arcs: 3, domainSize: 56, dim: 55, mem: 496o}
+    (pyAgrum.BayesNet<double>@0x55ffc22f22d0) BN{nodes: 3, arcs: 3, domainSize: 56, dim: 55, mem: 496o}
 
 
 
@@ -468,7 +468,7 @@ Variability of the plant growth on my balcony
 
     Probability (height > 40cm) =  0.32857134257593257
 
-    <openturns.viewer.View object at 0x7f5392bc4890>
+    <openturns.viewer.View object at 0x7f1b49379850>
 
 
 
@@ -515,7 +515,7 @@ Variability of the plant growth in my cellar
 
     Probability (height > 40cm)|Light=Dim =  0.11428571428571432
 
-    <openturns.viewer.View object at 0x7f538b03cf10>
+    <openturns.viewer.View object at 0x7f1b48291150>
 
 
 
@@ -562,7 +562,7 @@ Variability of the plant growth when the atmosphere is very wet
 
     Probability (height > 40cm)|Moisture=Wet =  0.6571426851518651
 
-    <openturns.viewer.View object at 0x7f538b2bb990>
+    <openturns.viewer.View object at 0x7f1b49053350>
 
 
 
@@ -593,7 +593,7 @@ Get the distribution of the variable "Light"
 
     UserDefined({x = [0], p = 0.4}, {x = [1], p = 0.6})
 
-    <openturns.viewer.View object at 0x7f538a938210>
+    <openturns.viewer.View object at 0x7f1b43a66f50>
 
 
 
@@ -629,7 +629,7 @@ Get the joint distribution [H, M]
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.865 seconds)
+   **Total running time of the script:** (0 minutes 0.685 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_plant_growth.py:

otagrum/master/_sources/auto_examples/plot_using_otagrum.rst.txt

@@ -139,7 +139,7 @@ To do so, we need a NamedDAG structure...
  .. code-block:: none
 
 
-    (pyAgrum.DAG@0x55a898aa6300) {0,1,2,3} , {2->3,0->2,1->2}
+    (pyAgrum.DAG@0x55ffc29550c0) {0,1,2,3} , {2->3,0->2,1->2}
 
 
 
@@ -251,7 +251,7 @@ Now that we have data, we can use it to learn the structure with the continuous
 
 .. code-block:: default
 
-    learner = otagrum.ContinuousPC(sample, maxConditioningSetSize=5, alpha=0.1)
+    learner = otagrum.ContinuousPC(sample, 5, 0.1)
 
 
 
@@ -293,7 +293,7 @@ We first learn the skeleton, that is the undirected structure.
  .. code-block:: none
 
 
-    (pyAgrum.UndiGraph@0x55a898bb5e00) {0,1,2,3} , {2--3,0--2,1--2}
+    (pyAgrum.UndiGraph@0x55ffc2871450) {0,1,2,3} , {2--3,0--2,1--2}
 
 
 
@@ -330,7 +330,7 @@ Then we look for the v-structures, leading to a Partially Directed Acyclic Graph
  .. code-block:: none
 
 
-    (pyAgrum.MixedGraph@0x55a898aa8980) {0,1,2,3} , {1->2,0->2} , {2--3}
+    (pyAgrum.MixedGraph@0x55ffc2850790) {0,1,2,3} , {1->2,0->2} , {2--3}
 
 
 
@@ -460,7 +460,7 @@ As with PC algorithm we can learn the skeleton, PDAG and DAG using
  .. code-block:: none
 
 
-    (pyAgrum.UndiGraph@0x55a898a998c0) {0,1,2,3} , {2--3,0--2,1--2}
+    (pyAgrum.UndiGraph@0x55ffc2681c70) {0,1,2,3} , {2--3,0--2,1--2}
 
 
 
@@ -494,7 +494,7 @@ As with PC algorithm we can learn the skeleton, PDAG and DAG using
  .. code-block:: none
 
 
-    (pyAgrum.MixedGraph@0x55a8988e94c0) {0,1,2,3} , {2->3,1->2,0->2} , {}
+    (pyAgrum.MixedGraph@0x55ffc1ed9cd0) {0,1,2,3} , {2->3,1->2,0->2} , {}
 
 
 
@@ -647,7 +647,7 @@ And compare the mean loglikelihood between the true and the learned models
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.354 seconds)
+   **Total running time of the script:** (0 minutes 0.303 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_using_otagrum.py:

otagrum/master/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,10 +6,10 @@
 
 Computation times
 =================
-**00:01.218** total execution time for **auto_examples** files:
+**00:00.989** total execution time for **auto_examples** files:
 
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_plant_growth.py` (``plot_plant_growth.py``)   | 00:00.865 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_plant_growth.py` (``plot_plant_growth.py``)   | 00:00.685 | 0.0 MB |
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_using_otagrum.py` (``plot_using_otagrum.py``) | 00:00.354 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_using_otagrum.py` (``plot_using_otagrum.py``) | 00:00.303 | 0.0 MB |
 +---------------------------------------------------------------------------------+-----------+--------+

otagrum/master/auto_examples/plot_plant_growth.html

@@ -85,7 +85,7 @@ <h3>Navigation</h3>
 <li><p>Some remembrance of biology trainings:</p>
 <ul>
 <li><p>in plain light, if the air is moist, the plant is very happy: it grows
-90cm on average with a variation of <img class="math" src="data:image/svg+xml;base64,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" alt="\pm" style="vertical-align: -1px"/> 10 cm. If the air is too dry,
+90cm on average with a variation of <img class="math" src="data:image/svg+xml;base64,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" alt="\pm" style="vertical-align: -1px"/> 10 cm. If the air is too dry,
 it will not grow more than 30 cm but we reasonably can expect about a
 15 cm growth.</p></li>
 <li><p>in the darkness, if the air is too dry, the plant suffers: it will not
@@ -139,15 +139,15 @@ <h3>Navigation</h3>
 <span class="n">light</span><span class="o">.</span><span class="n">addLabel</span><span class="p">(</span><span class="s2">&quot;Bright&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.LabelizedVariable@0x55a895ed7ed0) Light:Labelized({Dim|Bright})
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.LabelizedVariable@0x55ffbf667760) Light:Labelized({Dim|Bright})
 </pre></div>
 </div>
 <p>moisture has 2 attributes : Dry and Wet</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moisture</span><span class="o">.</span><span class="n">addLabel</span><span class="p">(</span><span class="s2">&quot;Dry&quot;</span><span class="p">)</span>
 <span class="n">moisture</span><span class="o">.</span><span class="n">addLabel</span><span class="p">(</span><span class="s2">&quot;Wet&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.LabelizedVariable@0x55a895e16b30) Moisture:Labelized({Dry|Wet})
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.LabelizedVariable@0x55ffbf58cb30) Moisture:Labelized({Dry|Wet})
 </pre></div>
 </div>
 <p>height is a discretized variable</p>
@@ -177,7 +177,7 @@ <h3>Navigation</h3>
 <span class="n">bn</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.BayesNet&lt;double&gt;@0x55a899021030) BN{nodes: 3, arcs: 3, domainSize: 56, dim: 55, mem: 496o}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.BayesNet&lt;double&gt;@0x55ffc22f22d0) BN{nodes: 3, arcs: 3, domainSize: 56, dim: 55, mem: 496o}
 </pre></div>
 </div>
 <p>The next step is the quantification of the Bayes net.</p>
@@ -287,7 +287,7 @@ <h3>Navigation</h3>
 </div>
 <img src="../_images/sphx_glr_plot_plant_growth_001.png" srcset="../_images/sphx_glr_plot_plant_growth_001.png" alt="Height PDF" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Probability (height &gt; 40cm) =  0.32857134257593257
 
-&lt;openturns.viewer.View object at 0x7f5392bc4890&gt;
+&lt;openturns.viewer.View object at 0x7f1b49379850&gt;
 </pre></div>
 </div>
 <p>Variability of the plant growth in my cellar</p>
@@ -303,7 +303,7 @@ <h3>Navigation</h3>
 </div>
 <img src="../_images/sphx_glr_plot_plant_growth_002.png" srcset="../_images/sphx_glr_plot_plant_growth_002.png" alt="Height|Light=Dim PDF" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Probability (height &gt; 40cm)|Light=Dim =  0.11428571428571432
 
-&lt;openturns.viewer.View object at 0x7f538b03cf10&gt;
+&lt;openturns.viewer.View object at 0x7f1b48291150&gt;
 </pre></div>
 </div>
 <p>Variability of the plant growth when the atmosphere is very wet</p>
@@ -319,7 +319,7 @@ <h3>Navigation</h3>
 </div>
 <img src="../_images/sphx_glr_plot_plant_growth_003.png" srcset="../_images/sphx_glr_plot_plant_growth_003.png" alt="Height|Moisture=Wet PDF" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Probability (height &gt; 40cm)|Moisture=Wet =  0.6571426851518651
 
-&lt;openturns.viewer.View object at 0x7f538b2bb990&gt;
+&lt;openturns.viewer.View object at 0x7f1b49053350&gt;
 </pre></div>
 </div>
 <p>Get the distribution of the variable “Light”</p>
@@ -330,7 +330,7 @@ <h3>Navigation</h3>
 </div>
 <img src="../_images/sphx_glr_plot_plant_growth_004.png" srcset="../_images/sphx_glr_plot_plant_growth_004.png" alt="Light PDF" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>UserDefined({x = [0], p = 0.4}, {x = [1], p = 0.6})
 
-&lt;openturns.viewer.View object at 0x7f538a938210&gt;
+&lt;openturns.viewer.View object at 0x7f1b43a66f50&gt;
 </pre></div>
 </div>
 <p>Get the joint distribution [H, M]</p>
@@ -347,7 +347,7 @@ <h3>Navigation</h3>
 UserDefined({x = [0], p = 0.5}, {x = [1], p = 0.5})
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.865 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.685 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-plant-growth-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/82c3675708534fe0d37186acf7047e67/plot_plant_growth.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_plant_growth.py</span></code></a></p>

otagrum/master/auto_examples/plot_using_otagrum.html

@@ -97,7 +97,7 @@ <h3>Navigation</h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dag</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.DAG@0x55a898aa6300) {0,1,2,3} , {2-&gt;3,0-&gt;2,1-&gt;2}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.DAG@0x55ffc29550c0) {0,1,2,3} , {2-&gt;3,0-&gt;2,1-&gt;2}
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">structure</span> <span class="o">=</span> <span class="n">otagrum</span><span class="o">.</span><span class="n">NamedDAG</span><span class="p">(</span><span class="n">dag</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">mapping</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
@@ -135,7 +135,7 @@ <h3>Navigation</h3>
 </div>
 <p>Learning the structure with continuous PC:
 Now that we have data, we can use it to learn the structure with the continuous PC algorithm.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">learner</span> <span class="o">=</span> <span class="n">otagrum</span><span class="o">.</span><span class="n">ContinuousPC</span><span class="p">(</span><a href="http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Sample.html#openturns.Sample" title="openturns.Sample" class="sphx-glr-backref-module-openturns sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sample</span></a><span class="p">,</span> <span class="n">maxConditioningSetSize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">learner</span> <span class="o">=</span> <span class="n">otagrum</span><span class="o">.</span><span class="n">ContinuousPC</span><span class="p">(</span><a href="http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Sample.html#openturns.Sample" title="openturns.Sample" class="sphx-glr-backref-module-openturns sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sample</span></a><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>We first learn the skeleton, that is the undirected structure.</p>
@@ -145,7 +145,7 @@ <h3>Navigation</h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">skeleton</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.UndiGraph@0x55a898bb5e00) {0,1,2,3} , {2--3,0--2,1--2}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.UndiGraph@0x55ffc2871450) {0,1,2,3} , {2--3,0--2,1--2}
 </pre></div>
 </div>
 <p>Then we look for the v-structures, leading to a Partially Directed Acyclic Graph (PDAG)</p>
@@ -155,7 +155,7 @@ <h3>Navigation</h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pdag</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.MixedGraph@0x55a898aa8980) {0,1,2,3} , {1-&gt;2,0-&gt;2} , {2--3}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.MixedGraph@0x55ffc2850790) {0,1,2,3} , {1-&gt;2,0-&gt;2} , {2--3}
 </pre></div>
 </div>
 <p>Finally, the remaining edges are oriented by propagating constraints</p>
@@ -201,7 +201,7 @@ <h3>Navigation</h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">skeleton</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.UndiGraph@0x55a898a998c0) {0,1,2,3} , {2--3,0--2,1--2}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.UndiGraph@0x55ffc2681c70) {0,1,2,3} , {2--3,0--2,1--2}
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pdag</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">learnPDAG</span><span class="p">()</span>
@@ -210,7 +210,7 @@ <h3>Navigation</h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pdag</span>
 </pre></div>
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-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.MixedGraph@0x55a8988e94c0) {0,1,2,3} , {2-&gt;3,1-&gt;2,0-&gt;2} , {}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(pyAgrum.MixedGraph@0x55ffc1ed9cd0) {0,1,2,3} , {2-&gt;3,1-&gt;2,0-&gt;2} , {}
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dag</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">learnDAG</span><span class="p">()</span>
@@ -267,7 +267,7 @@ <h3>Navigation</h3>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.15241697100666607
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 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.354 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.303 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-using-otagrum-py">
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 <p><a class="reference download internal" download="" href="../_downloads/c785e393d248af29337db07a92c50d35/plot_using_otagrum.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_using_otagrum.py</span></code></a></p>