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Automated Docs Update
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github-actions committed Feb 4, 2022
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25 changes: 12 additions & 13 deletions docs/AIMSim.ops.html
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Expand Up @@ -105,29 +105,28 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
<blockquote>
<div><dl>
<dt>clustering_method (str): Label for the specific algorithm used.</dt><dd><p>Supported methods are:
‘kmedoids’ for the K-Medoids algorithm [1]. This method is useful</p>
‘kmedoids’:</p>
<blockquote>
<div><p>when the molecular descriptors are continuous / Euclidean
<div><p>for the K-Medoids algorithm [1]. This method is useful
when the molecular descriptors are continuous / Euclidean
since it relies on the existence of a sensible medoid.</p>
</div></blockquote>
<dl class="simple">
<dt>‘complete_linkage’, ‘complete’ for complete linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘complete_linkage’, ‘complete’: complete linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
</dd>
<dt>‘average_linkage’, ‘average’ for average linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘average_linkage’, ‘average’:</dt><dd><p>average linkage agglomerative hierarchical clustering [2].</p>
</dd>
<dt>‘single_linkage’, ‘single’ for single linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘single_linkage’, ‘single’:</dt><dd><p>single linkage agglomerative hierarchical clustering [2].</p>
</dd>
<dt>‘ward’ for Ward’s algorithm [2]. This method is useful for</dt><dd><p>Euclidean descriptors.</p>
<dt>‘ward’:</dt><dd><p>for Ward’s algorithm [2]. This method is useful for
Euclidean descriptors.</p>
</dd>
</dl>
</dd>
</dl>
<p>n_clusters (int): Number of clusters.
<a href="#id1"><span class="problematic" id="id2">model_</span></a> (sklearn.cluster.AgglomerativeClustering or</p>
<blockquote>
<div><p>sklearn_extra.cluster.KMedoids): The clustering estimator.</p>
</div></blockquote>
<dl class="simple">
<dt>n_clusters (int):</dt><dd><p>Number of clusters.</p>
</dd>
<dt><a href="#id1"><span class="problematic" id="id2">model_</span></a> (sklearn.cluster.AgglomerativeClustering or sklearn_extra.cluster.KMedoids):</dt><dd><p>The clustering estimator.</p>
</dd>
<dt><a href="#id3"><span class="problematic" id="id4">labels_</span></a> (np.ndarray of shape (n_samples,)):</dt><dd><p>cluster labels of the training set samples.</p>
</dd>
</dl>
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25 changes: 12 additions & 13 deletions docs/_build/html/AIMSim.ops.html
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Expand Up @@ -105,29 +105,28 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
<blockquote>
<div><dl>
<dt>clustering_method (str): Label for the specific algorithm used.</dt><dd><p>Supported methods are:
‘kmedoids’ for the K-Medoids algorithm [1]. This method is useful</p>
‘kmedoids’:</p>
<blockquote>
<div><p>when the molecular descriptors are continuous / Euclidean
<div><p>for the K-Medoids algorithm [1]. This method is useful
when the molecular descriptors are continuous / Euclidean
since it relies on the existence of a sensible medoid.</p>
</div></blockquote>
<dl class="simple">
<dt>‘complete_linkage’, ‘complete’ for complete linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘complete_linkage’, ‘complete’: complete linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
</dd>
<dt>‘average_linkage’, ‘average’ for average linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘average_linkage’, ‘average’:</dt><dd><p>average linkage agglomerative hierarchical clustering [2].</p>
</dd>
<dt>‘single_linkage’, ‘single’ for single linkage agglomerative</dt><dd><p>hierarchical clustering [2].</p>
<dt>‘single_linkage’, ‘single’:</dt><dd><p>single linkage agglomerative hierarchical clustering [2].</p>
</dd>
<dt>‘ward’ for Ward’s algorithm [2]. This method is useful for</dt><dd><p>Euclidean descriptors.</p>
<dt>‘ward’:</dt><dd><p>for Ward’s algorithm [2]. This method is useful for
Euclidean descriptors.</p>
</dd>
</dl>
</dd>
</dl>
<p>n_clusters (int): Number of clusters.
<a href="#id1"><span class="problematic" id="id2">model_</span></a> (sklearn.cluster.AgglomerativeClustering or</p>
<blockquote>
<div><p>sklearn_extra.cluster.KMedoids): The clustering estimator.</p>
</div></blockquote>
<dl class="simple">
<dt>n_clusters (int):</dt><dd><p>Number of clusters.</p>
</dd>
<dt><a href="#id1"><span class="problematic" id="id2">model_</span></a> (sklearn.cluster.AgglomerativeClustering or sklearn_extra.cluster.KMedoids):</dt><dd><p>The clustering estimator.</p>
</dd>
<dt><a href="#id3"><span class="problematic" id="id4">labels_</span></a> (np.ndarray of shape (n_samples,)):</dt><dd><p>cluster labels of the training set samples.</p>
</dd>
</dl>
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