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6 changes: 3 additions & 3 deletions dev/_sources/auto_examples/plot_pc_alg.rst.txt
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.. code-block:: none
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1825.79it/s]
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1731.39it/s]
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w [1, 2, 0]
dtype: object

<graphviz.graphs.Digraph object at 0x7fdd21801af0>
<graphviz.graphs.Digraph object at 0x7f2f04e5e430>



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.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 0.159 seconds)
**Total running time of the script:** ( 0 minutes 0.158 seconds)


.. _sphx_glr_download_auto_examples_plot_pc_alg.py:
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4 changes: 2 additions & 2 deletions dev/_sources/auto_examples/plot_score_alg.rst.txt
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Expand Up @@ -187,7 +187,7 @@ This will then induce a causal graph, which we can visualize.

.. code-block:: none
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1692.96it/s]
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1493.43it/s]
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.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 3.286 seconds)
**Total running time of the script:** ( 0 minutes 3.082 seconds)


.. _sphx_glr_download_auto_examples_plot_score_alg.py:
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4 changes: 2 additions & 2 deletions dev/_sources/auto_examples/prior_know_score.rst.txt
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Expand Up @@ -137,7 +137,7 @@ This will then induce a causal graph, which we can visualize.

.. code-block:: none
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1884.23it/s]
Fitting causal models: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node x: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node y: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node z: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 0%| | 0/4 [00:00<?, ?it/s] Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1758.62it/s]
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.. rst-class:: sphx-glr-timing

**Total running time of the script:** ( 0 minutes 0.921 seconds)
**Total running time of the script:** ( 0 minutes 1.219 seconds)


.. _sphx_glr_download_auto_examples_prior_know_score.py:
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6 changes: 3 additions & 3 deletions dev/auto_examples/plot_pc_alg.html
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Expand Up @@ -368,7 +368,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
Fitting causal mechanism of node y: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node z: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1825.79it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1731.39it/s]
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Expand All @@ -383,7 +383,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
w [1, 2, 0]
dtype: object

&lt;graphviz.graphs.Digraph object at 0x7fdd21801af0&gt;
&lt;graphviz.graphs.Digraph object at 0x7f2f04e5e430&gt;
</pre></div>
</div>
</section>
Expand Down Expand Up @@ -477,7 +477,7 @@ <h2>Run structure learning algorithm<a class="headerlink" href="#run-structure-l
<img src="../_images/sphx_glr_plot_pc_alg_002.png" srcset="../_images/sphx_glr_plot_pc_alg_002.png" alt="plot pc alg" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;ci_cpdag.png&#39;
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.159 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.158 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pc-alg-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/f1c5e835df0a73798ede9531ab050d9e/plot_pc_alg.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_pc_alg.py</span></code></a></p>
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Expand Up @@ -406,7 +406,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
Fitting causal mechanism of node y: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node z: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1692.96it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1493.43it/s]
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Expand Down Expand Up @@ -506,7 +506,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
One is the fully connected graph associated to the inferred topological order
<code class="docutils literal notranslate"><span class="pre">[z,</span> <span class="pre">x,</span> <span class="pre">y,</span> <span class="pre">w]</span></code> of the graph nodes. The other is the sparser graph after the pruning
step, corresponding to the causal graph inferred by SCORE.</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.286 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.082 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-score-alg-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/7a0af3d327be5615defba776eff15f30/plot_score_alg.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_score_alg.py</span></code></a></p>
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Expand Up @@ -367,7 +367,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
Fitting causal mechanism of node y: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node z: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 0%| | 0/4 [00:00&lt;?, ?it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1884.23it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1758.62it/s]
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Expand Down Expand Up @@ -445,7 +445,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
This example can be generalized to the case of <code class="docutils literal notranslate"><span class="pre">NoGAM</span></code>, <code class="docutils literal notranslate"><span class="pre">DAS</span></code>, and <code class="docutils literal notranslate"><span class="pre">CAM</span></code> methods.
For a detailed example on order-based discovery approaches, see this
<a class="reference internal" href="plot_score_alg.html#ex-score-algorithm"><span class="std std-ref">tutorial</span></a>.</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.921 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.219 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-prior-know-score-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/54fa84cb158e29731e39a59414e303a3/prior_know_score.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">prior_know_score.py</span></code></a></p>
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