update doc pages

docs/pages/html/Continuous Data.html

@@ -212,7 +212,7 @@ <h1>Working with Continous data<a class="headerlink" href="#working-with-contino
 <section id="discretize-continuous-datasets-manually">
 <h1>Discretize continuous datasets manually<a class="headerlink" href="#discretize-continuous-datasets-manually" title="Link to this heading"></a></h1>
 <p>Discretizing continuous datasets manually using domain knowledge involves dividing a continuous variable into a set of discrete intervals based on an understanding of the data’s context and the relationships between variables. This method allows for meaningful groupings of data points, which can simplify analysis and improve interpretability in models.</p>
-<p>By leveraging expertise in the subject matter, the intervals or thresholds can be chosen to reflect real-world significance, such as categorizing age into meaningful ranges (e.g., “freezing,” “warm,” “hot”). This approach contrasts with automatic binning methods (as depicted in approach 2), such as equal-width or equal-frequency binning, where intervals may not correspond to meaningful domain-specific boundaries.</p>
+<p>By leveraging expertise in the subject matter, the intervals or thresholds can be chosen to reflect real-world significance, such as categorizing weather conditions into meaningful ranges (e.g., “freezing,” “warm,” “hot”). This approach contrasts with automatic binning methods (as depicted in approach 2), such as equal-width or equal-frequency binning, where intervals may not correspond to meaningful domain-specific boundaries.</p>
 <p>For instance, lets load the auto mpg data set and based on automotive standards, we can define horsepower categories:</p>
 <ul class="simple">
 <li><p>Low: Cars with horsepower less than 100 (typically small, fuel-efficient cars)</p></li>

docs/pages/html/_sources/Continuous Data.rst.txt

@@ -15,7 +15,7 @@ Discretize continuous datasets manually
 
 Discretizing continuous datasets manually using domain knowledge involves dividing a continuous variable into a set of discrete intervals based on an understanding of the data's context and the relationships between variables. This method allows for meaningful groupings of data points, which can simplify analysis and improve interpretability in models. 
 
-By leveraging expertise in the subject matter, the intervals or thresholds can be chosen to reflect real-world significance, such as categorizing age into meaningful ranges (e.g., "freezing," "warm," "hot"). This approach contrasts with automatic binning methods (as depicted in approach 2), such as equal-width or equal-frequency binning, where intervals may not correspond to meaningful domain-specific boundaries.
+By leveraging expertise in the subject matter, the intervals or thresholds can be chosen to reflect real-world significance, such as categorizing weather conditions into meaningful ranges (e.g., "freezing," "warm," "hot"). This approach contrasts with automatic binning methods (as depicted in approach 2), such as equal-width or equal-frequency binning, where intervals may not correspond to meaningful domain-specific boundaries.
 
 For instance, lets load the auto mpg data set and based on automotive standards, we can define horsepower categories:
 

docs/pages/html/_sources/index.rst.txt

@@ -5,6 +5,16 @@ BNLearn's Documentation
 
 |python| |pypi| |docs| |LOC| |downloads_month| |downloads_total| |license| |forks| |open issues| |project status| |medium| |colab| |DOI| |donate|
 
+.. |logo| image:: ../figs/logo.png
+
+.. table::
+   :align: center
+
+   +----------+
+   | |logo|   |
+   +----------+
+
+
 .. tip::
 	* `Guide in detecting causal relationships using Bayesian Structure Learning in Python. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_
 	* `Guide in designing knowledge-driven models using Bayesian theorem. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_

docs/pages/html/bnlearn.bnlearn.html

@@ -141,6 +141,7 @@
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.adjmat2vec"><code class="docutils literal notranslate"><span class="pre">adjmat2vec()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.check_model"><code class="docutils literal notranslate"><span class="pre">check_model()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.compare_networks"><code class="docutils literal notranslate"><span class="pre">compare_networks()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.compute_logp"><code class="docutils literal notranslate"><span class="pre">compute_logp()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.dag2adjmat"><code class="docutils literal notranslate"><span class="pre">dag2adjmat()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.df2onehot"><code class="docutils literal notranslate"><span class="pre">df2onehot()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="#bnlearn.bnlearn.get_edge_properties"><code class="docutils literal notranslate"><span class="pre">get_edge_properties()</span></code></a></li>
@@ -297,6 +298,11 @@
 </dl>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="bnlearn.bnlearn.compute_logp">
+<span class="sig-prename descclassname"><span class="pre">bnlearn.bnlearn.</span></span><span class="sig-name descname"><span class="pre">compute_logp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p_value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bnlearn.bnlearn.compute_logp" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="bnlearn.bnlearn.dag2adjmat">
 <span class="sig-prename descclassname"><span class="pre">bnlearn.bnlearn.</span></span><span class="sig-name descname"><span class="pre">dag2adjmat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bnlearn.bnlearn.dag2adjmat" title="Link to this definition"></a></dt>

docs/pages/html/bnlearn.structure_learning.html

@@ -191,7 +191,7 @@
 <dt>To learn model structure (a DAG) from a data set, there are three broad techniques:</dt><dd><ol class="arabic simple">
 <li><p>Score-based structure learning: using scoring functions as defined in scoretype and search strategy as defined in methodtype.</p></li>
 <li><p>Constraint-based structure learning (PC): Using statistics such as chi-square test for strength of edges prior the modeling.</p></li>
-<li><p>Hybrid structure learning (The combination of both techniques) (MMHC)</p></li>
+<li><p>Hybrid structure learning (The combination of both techniques)</p></li>
 </ol>
 </dd>
 </dl>
@@ -216,9 +216,9 @@ <h2>Score-based Structure Learning<a class="headerlink" href="#score-based-struc
 </dd>
 <dt class="field-odd">param methodtype<span class="colon">:</span></dt>
 <dd class="field-odd"><p>String Search strategy for structure_learning.
-‘hc’ or ‘hillclimbsearch’ (default)
-‘ex’ or ‘exhaustivesearch’
 ‘pc’ or ‘cs’ or ‘constraintsearch’
+‘ex’ or ‘exhaustivesearch’
+‘hc’ or ‘hillclimbsearch’ (default)
 ‘cl’ or ‘chow-liu’ (requires setting root_node parameter)
 ‘nb’ or ‘naivebayes’ (requires &lt;root_node&gt;)
 ‘tan’ (requires &lt;root_node&gt; and &lt;class_node&gt; parameter)

docs/pages/html/genindex.html

@@ -254,6 +254,8 @@ <h2 id="C">C</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="bnlearn.bnlearn.html#bnlearn.bnlearn.compare_networks">compare_networks() (in module bnlearn.bnlearn)</a>
+</li>
+      <li><a href="bnlearn.bnlearn.html#bnlearn.bnlearn.compute_logp">compute_logp() (in module bnlearn.bnlearn)</a>
 </li>
   </ul></td>
 </tr></table>

docs/pages/html/index.html

@@ -177,6 +177,12 @@
   <section id="bnlearn-s-documentation">
 <h1>BNLearn’s Documentation<a class="headerlink" href="#bnlearn-s-documentation" title="Link to this heading"></a></h1>
 <p><a class="reference external" href="https://erdogant.github.io/bnlearn/"><img alt="|Python" src="https://img.shields.io/pypi/pyversions/bnlearn.svg" /></a> <a class="reference external" href="https://pypi.org/project/bnlearn/"><img alt="|Python Version" src="https://img.shields.io/pypi/v/bnlearn.svg" /></a> <a class="reference external" href="https://erdogant.github.io/bnlearn/"><img alt="Sphinx documentation" src="https://img.shields.io/badge/Sphinx-Docs-blue.svg" /></a> <a class="reference external" href="https://github.com/erdogant/bnlearn"><img alt="lines of code" src="https://sloc.xyz/github/erdogant/bnlearn/?category=code" /></a> <a class="reference external" href="https://pepy.tech/project/bnlearn"><img alt="Downloads per month" src="https://static.pepy.tech/personalized-badge/bnlearn?period=month&amp;units=international_system&amp;left_color=grey&amp;right_color=brightgreen&amp;left_text=PyPI%20downloads/month" /></a> <a class="reference external" href="https://pepy.tech/project/bnlearn"><img alt="Downloads in total" src="https://static.pepy.tech/personalized-badge/bnlearn?period=total&amp;units=international_system&amp;left_color=grey&amp;right_color=brightgreen&amp;left_text=Downloads" /></a> <a class="reference external" href="https://github.com/erdogant/bnlearn/blob/master/LICENSE"><img alt="License" src="https://img.shields.io/badge/license-MIT-green.svg" /></a> <a class="reference external" href="https://github.com/erdogant/bnlearn/network"><img alt="Github Forks" src="https://img.shields.io/github/forks/erdogant/bnlearn.svg" /></a> <a class="reference external" href="https://github.com/erdogant/bnlearn/issues"><img alt="Open Issues" src="https://img.shields.io/github/issues/erdogant/bnlearn.svg" /></a> <a class="reference external" href="http://www.repostatus.org/#active"><img alt="Project Status" src="http://www.repostatus.org/badges/latest/active.svg" /></a> <a class="reference external" href="https://erdogant.github.io/bnlearn/pages/html/Documentation.html#medium-blog"><img alt="Medium Blog" src="https://img.shields.io/badge/Medium-Blog-green.svg" /></a> <a class="reference external" href="https://erdogant.github.io/bnlearn/pages/html/Documentation.html#colab-notebook"><img alt="Colab example" src="https://colab.research.google.com/assets/colab-badge.svg" /></a> <a class="reference external" href="https://zenodo.org/badge/latestdoi/231263493"><img alt="Cite" src="https://zenodo.org/badge/231263493.svg" /></a> <a class="reference external" href="https://erdogant.github.io/bnlearn/pages/html/Documentation.html#"><img alt="donate" src="https://img.shields.io/badge/Support%20this%20project-grey.svg?logo=github%20sponsors" /></a></p>
+<table class="docutils align-center">
+<tbody>
+<tr class="row-odd"><td><p><img alt="logo" src="_images/logo.png" /></p></td>
+</tr>
+</tbody>
+</table>
 <div class="admonition tip">
 <p class="admonition-title">Tip</p>
 <ul class="simple">
@@ -398,6 +404,7 @@ <h1>Contents<a class="headerlink" href="#contents" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.adjmat2vec"><code class="docutils literal notranslate"><span class="pre">adjmat2vec()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.check_model"><code class="docutils literal notranslate"><span class="pre">check_model()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.compare_networks"><code class="docutils literal notranslate"><span class="pre">compare_networks()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.compute_logp"><code class="docutils literal notranslate"><span class="pre">compute_logp()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.dag2adjmat"><code class="docutils literal notranslate"><span class="pre">dag2adjmat()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.df2onehot"><code class="docutils literal notranslate"><span class="pre">df2onehot()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="bnlearn.bnlearn.html#bnlearn.bnlearn.get_edge_properties"><code class="docutils literal notranslate"><span class="pre">get_edge_properties()</span></code></a></li>

docs/source/index.rst

@@ -5,6 +5,16 @@ BNLearn's Documentation
 
 |python| |pypi| |docs| |LOC| |downloads_month| |downloads_total| |license| |forks| |open issues| |project status| |medium| |colab| |DOI| |donate|
 
+.. |logo| image:: ../figs/logo.png
+
+.. table::
+   :align: center
+
+   +----------+
+   | |logo|   |
+   +----------+
+
+
 .. tip::
 	* `Guide in detecting causal relationships using Bayesian Structure Learning in Python. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_
 	* `Guide in designing knowledge-driven models using Bayesian theorem. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_