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  <div class="section" id="module-pebl.discretizer">
<span id="discretizer-discretization-algorithms"></span><h1><tt class="xref py py-mod docutils literal"><span class="pre">discretizer</span></tt> &#8211; Discretization algorithms<a class="headerlink" href="#module-pebl.discretizer" title="Permalink to this headline"></a></h1>
<p>Currently, Pebl only includes one discretization implementation but more may
come. Discretization and other data pre-processing steps can have a big impact
on the final results.</p>
<dl class="function">
<dt id="pebl.discretizer.maximum_entropy_discretize">
<tt class="descclassname">pebl.discretizer.</tt><tt class="descname">maximum_entropy_discretize</tt><big>(</big><em>indata</em>, <em>includevars=None</em>, <em>excludevars=</em><span class="optional">[</span><span class="optional">]</span>, <em>numbins=3</em><big>)</big><a class="headerlink" href="#pebl.discretizer.maximum_entropy_discretize" title="Permalink to this definition"></a></dt>
<dd><p>Performs a maximum-entropy discretization of data in-place.</p>
<p>Requirements for this implementation:</p>
<blockquote>
<div><blockquote>
<div><ol class="arabic simple">
<li>Try to make all bins equal sized (maximize the entropy)</li>
<li>If datum x==y in the original dataset, then disc(x)==disc(y) 
For example, all datapoints with value 3.245 discretize to 1
even if it violates requirement 1.</li>
<li>Number of bins reflects only the non-missing data.</li>
</ol>
</div></blockquote>
<p>Example:</p>
<blockquote>
<div><p>input:  [3,7,4,4,4,5]
output: [0,1,0,0,0,1]</p>
<p>Note that all 4s discretize to 0, which makes bin sizes unequal.</p>
</div></blockquote>
<p>Example:</p>
<blockquote>
<div><p>input:  [1,2,3,4,2,1,2,3,1,x,x,x]
output: [0,1,2,2,1,0,1,2,0,0,0,0]</p>
<p>Note that the missing data (&#8216;x&#8217;) gets put in the bin with 0.0.</p>
</div></blockquote>
</div></blockquote>
</dd></dl>

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