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pygsti.chi2.html
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<h1>pygsti.chi2<a class="headerlink" href="#pygsti-chi2" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.chi2">
<code class="descclassname">pygsti.</code><code class="descname">chi2</code><span class="sig-paren">(</span><em>dataset</em>, <em>gateset</em>, <em>gateStrings=None</em>, <em>returnGradient=False</em>, <em>returnHessian=False</em>, <em>G0=True</em>, <em>SP0=True</em>, <em>SPAM=True</em>, <em>gates=True</em>, <em>minProbClipForWeighting=0.0001</em>, <em>clipTo=None</em>, <em>useFreqWeightedChiSq=False</em>, <em>check=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pygsti.chi2" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the total chi^2 for a set of gate strings.</p>
<p>The chi^2 test statistic obtained by summing up the
contributions of a given set of gate strings or all
the strings available in a dataset. Optionally,
the gradient and/or Hessian of chi^2 can be returned too.</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 used to specify frequencies and counts</li>
<li><strong>gateset</strong> (<a class="reference internal" href="pygsti.objects.GateSet.html#pygsti.objects.GateSet" title="pygsti.objects.GateSet"><em>GateSet</em></a>) – The gate set used to specify the probabilities and SPAM labels</li>
<li><strong>gateStrings</strong> (<em>list of GateStrings or tuples, optional</em>) – List of gate strings whose terms will be included in chi^2 sum.
Default value (None) means “all strings in dataset”.</li>
<li><strong>returnHessian</strong> (<em>returnGradient,</em>) – Whether to compute and return the gradient and/or Hessian of chi^2.</li>
<li><strong>SP0, SPAM, gates</strong> (<em>G0,</em>) – How to parameterize the gateset:
G0 == whether first row of gate matrices are parameterized
SP0 == whether first element of each rho vector is parameterized
SPAM == whether SPAM (rho and E) vectors are parameterized
gates == whether gate matrices are parameterized</li>
<li><strong>minProbClipForWeighting</strong> (<em>float, optional</em>) – defines the clipping interval for the statistical weight (see chi2fn).</li>
<li><strong>clipTo</strong> (<em>2-tuple, optional</em>) – (min,max) to clip probabilities to within GateSet probability
computation routines (see GateSet.bulk_fill_probs)</li>
<li><strong>useFreqWeightedChiSq</strong> (<em>bool, optional</em>) – Whether or not frequencies (instead of probabilities) should be used
in statistical weight factors.</li>
<li><strong>check</strong> (<em>bool, 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 last"><ul class="simple">
<li><strong>chi2</strong> (<em>float</em>) –
chi^2 value, equal to the sum of chi^2 terms from all specified gate strings</li>
<li><strong>dchi2</strong> (<em>numpy array</em>) –
Only returned if returnGradient == True. The gradient vector of
length nGatesetParams, the number of gateset parameters.
(nGatesetParams depends on values of G0, SP0, SPAM, and gates).</li>
<li><strong>d2chi2</strong> (<em>numpy array</em>) –
Only returned if returnHessian == True. The Hessian matrix of
shape (nGatesetParams, nGatesetParams).</li>
</ul>
</p>
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