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<h1>pygsti.tools.logl_max<a class="headerlink" href="#pygsti-tools-logl-max" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.tools.logl_max">
<code class="descclassname">pygsti.tools.</code><code class="descname">logl_max</code><span class="sig-paren">(</span><em>dataset</em>, <em>gatestring_list=None</em>, <em>countVecMx=None</em>, <em>poissonPicture=True</em>, <em>check=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pygsti.tools.logl_max" title="Permalink to this definition">¶</a></dt>
<dd><p>The maximum log-likelihood possible for a DataSet. That is, the
log-likelihood obtained by a maximal model that can fit perfectly
the probability of each gate string.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>dataset</strong> (<a class="reference internal" href="pygsti.objects.DataSet.html#pygsti.objects.DataSet" title="pygsti.objects.DataSet"><em>DataSet</em></a>) – the data set to use.</li>
<li><strong>gatestring_list</strong> (<em>list of (tuples or GateStrings), optional</em>) – Each element specifies a gate string to include in the max-log-likelihood
sum. Default value of None implies all the gate strings in dataset should
be used.</li>
<li><strong>countVecMx</strong> (<em>numpy array, optional</em>) – Two-dimensional numpy array whose rows correspond to the data set’s spam
labels (i.e. dataset.get_spam_labels()). Each row is contains the
dataset counts for that spam label for each gate string in gatestring_list.
Use fill_count_vecs(...) to generate this quantity when it is useful elsewhere
(e.g. for logl(...) calls).</li>
<li><strong>poissonPicture</strong> (<em>boolean, optional</em>) – Whether the Poisson-picture maximum log-likelihood should be returned.</li>
<li><strong>check</strong> (<em>boolean, optional</em>) – Whether additional check is performed which computes the max logl another
way an compares to the faster method.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
</td>
</tr>
</tbody>
</table>
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