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pygsti.tools.logl.html
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<h1>pygsti.tools.logl<a class="headerlink" href="#pygsti-tools-logl" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.tools.logl">
<code class="descclassname">pygsti.tools.</code><code class="descname">logl</code><span class="sig-paren">(</span><em>gateset</em>, <em>dataset</em>, <em>gatestring_list=None</em>, <em>minProbClip=1e-06</em>, <em>probClipInterval=None</em>, <em>radius=0.0001</em>, <em>evalTree=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" title="Permalink to this definition">¶</a></dt>
<dd><p>The log-likelihood function.</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>gateset</strong> (<a class="reference internal" href="pygsti.objects.GateSet.html#pygsti.objects.GateSet" title="pygsti.objects.GateSet"><em>GateSet</em></a>) – Gateset of parameterized gates</li>
<li><strong>dataset</strong> (<a class="reference internal" href="pygsti.objects.DataSet.html#pygsti.objects.DataSet" title="pygsti.objects.DataSet"><em>DataSet</em></a>) – Probability data</li>
<li><strong>gatestring_list</strong> (<em>list of (tuples or GateStrings), optional</em>) – Each element specifies a gate string to include in the log-likelihood
sum. Default value of None implies all the gate strings in dataset
should be used.</li>
<li><strong>minProbClip</strong> (<em>float, optional</em>) – The minimum probability treated normally in the evaluation of the log-likelihood.
A penalty function replaces the true log-likelihood for probabilities that lie
below this threshold so that the log-likelihood never becomes undefined (which improves
optimizer performance).</li>
<li><strong>probClipInterval</strong> (<em>2-tuple or None, optional</em>) – (min,max) values used to clip the probabilities predicted by gatesets during MLEGST’s
search for an optimal gateset (if not None). Defaults to no clipping.</li>
<li><strong>radius</strong> (<em>float, optional</em>) – Specifies the severity of rounding used to “patch” the zero-frequency
terms of the log-likelihood.</li>
<li><strong>evalTree</strong> (<em>evaluation tree, optional</em>) – given by a prior call to bulk_evaltree for the same gatestring_list.
Significantly speeds up evaluation of log-likelihood, even more so
when accompanied by countVecMx (see below).</li>
<li><strong>countVecMx</strong> (<em>numpy array, optional</em>) – Two-dimensional numpy array whose rows correspond to the gate’s spam
labels (i.e. gateset.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 once for multiple
evaluations of the log-likelihood function which use the same dataset.</li>
<li><strong>poissonPicture</strong> (<em>boolean, optional</em>) – Whether the log-likelihood-in-the-Poisson-picture terms should be included
in the returned logl value.</li>
<li><strong>check</strong> (<em>boolean, optional</em>) – If True, perform extra checks within code to verify correctness. Used
for testing, and runs much slower when True.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The log likelihood</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>
</dd></dl>
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