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<div class="section" id="module-pebl.cpd">
<span id="cpd-conditional-probability-distributions"></span><h1><tt class="xref py py-mod docutils literal"><span class="pre">cpd</span></tt> – Conditional probability distributions<a class="headerlink" href="#module-pebl.cpd" title="Permalink to this headline">¶</a></h1>
<p>A cpd (conditional probability distribution) determines the probability of a
variable given its parents. Currently, pebl inbcludes a pure-python and a C
implementation of a multinomial cpd.</p>
<p>A cpd has only three public methods:</p>
<dl class="class">
<dt id="pebl.cpd.CPD">
<em class="property">class </em><tt class="descclassname">pebl.cpd.</tt><tt class="descname">CPD</tt><big>(</big><em>data_</em><big>)</big><a class="headerlink" href="#pebl.cpd.CPD" title="Permalink to this definition">¶</a></dt>
<dd><p>Conditional probability distributions.</p>
<p>Currently, pebl only includes multinomial cpds and there are two versions:
a pure-python and a fast C implementation. The C implementation will be
used if available.</p>
<p>Create a CPD.</p>
<p><a href="#id1"><span class="problematic" id="id2">data_</span></a> should only contain data for the nodes involved in this CPD. The
first column should be for the child node and the rest for its parents.</p>
<p>The Dataset.subset method can be used to create the required dataset:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">d</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">fromfile</span><span class="p">(</span><span class="s">"somedata.txt"</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">random_network</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">variables</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">subset</span><span class="p">([</span><span class="n">child</span><span class="p">]</span> <span class="o">+</span> <span class="n">n</span><span class="o">.</span><span class="n">edges</span><span class="o">.</span><span class="n">parents</span><span class="p">(</span><span class="n">child</span><span class="p">))</span>
</pre></div>
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<dl class="method">
<dt id="pebl.cpd.CPD.loglikelihood">
<tt class="descname">loglikelihood</tt><big>(</big><big>)</big><a class="headerlink" href="#pebl.cpd.CPD.loglikelihood" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates the loglikelihood of the data.</p>
<p>This method implements the log of the g function (equation 12) from:</p>
<p>Cooper, Herskovitz. A Bayesian Method for the Induction of
Probabilistic Networks from Data.</p>
</dd></dl>
<dl class="method">
<dt id="pebl.cpd.CPD.replace_data">
<tt class="descname">replace_data</tt><big>(</big><em>oldrow</em>, <em>newrow</em><big>)</big><a class="headerlink" href="#pebl.cpd.CPD.replace_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Replaces a data row with a new one.</p>
<p>Missing values are handled using some form of sampling over the
possible values and this requires making small changes to the data.
Instead of recreating a CPD after every change, it’s far more efficient
to simply make a small change in the CPD.</p>
</dd></dl>
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