Merge pull request #29 from fabiankueppers/develop

Updates for new release 1.3.0
EFS-OpenSource · Oct 18, 2022 · cc19d1b · cc19d1b
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diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.html
diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.html
diff --git a/docs/build/html/_autosummary/_autosummary_metric/netcal.metrics.ACE.html b/docs/build/html/_autosummary/_autosummary_metric/netcal.metrics.ACE.html
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diff --git a/docs/build/html/_autosummary/_autosummary_metric/netcal.metrics.NLL.html b/docs/build/html/_autosummary/_autosummary_metric/netcal.metrics.NLL.html
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+<li class="toctree-l4"><a class="reference internal" href="#netcal.metrics.NLL"><code class="docutils literal notranslate"><span class="pre">NLL</span></code></a></li>
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+  <section id="netcal-metrics-nll">
+<h1>netcal.metrics.NLL<a class="headerlink" href="#netcal-metrics-nll" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="netcal.metrics.NLL">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">netcal.metrics.</span></span><span class="sig-name descname"><span class="pre">NLL</span></span><a class="headerlink" href="#netcal.metrics.NLL" title="Permalink to this definition">¶</a></dt>
+<dd><p>Negative log likelihood (NLL) for probabilistic regression models.
+If a probabilistic forecaster outputs a probability density function (PDF) <span class="math notranslate nohighlight">\(f_Y(y)\)</span> targeting the ground-truth
+<span class="math notranslate nohighlight">\(y\)</span>, the negative log likelihood is defined by</p>
+<div class="math notranslate nohighlight">
+\[\text{NLL} = -\sum^N_{n=1} \log\big(f_Y(y)\big) ,\]</div>
+<p>with <span class="math notranslate nohighlight">\(N\)</span> as the number of samples within the examined data set.</p>
+<p><strong>Note:</strong> the input field for the standard deviation might also be given as quadratic NumPy arrays of shape
+(n, d, d) with d dimensions. In this case, this method asserts covariance matrices as input
+for each sample and the NLL is calculated for multivariate distributions.</p>
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">measure</span></code>(X, y, *[, kind, reduction])</p></td>
+<td><p>Measures the negative log likelihood (NLL) for probabilistic regression models.</p></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</section>
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