updated

Author: hamidrgh

docs/_modules/idpet/comparison.html

@@ -937,8 +937,8 @@ <h1>Source code for idpet.comparison</h1><div class="highlight"><pre>
 <span class="sd">        above features.</span>
 <span class="sd">    bootstrap_iters: int, optional</span>
 <span class="sd">        Number of bootstrap iterations. By default its value is None. In</span>
-<span class="sd">        this case, IDPET will directly compare each pair of ensemble $i$ and</span>
-<span class="sd">        $j$ by using all of their conformers and perform the comparison only</span>
+<span class="sd">        this case, IDPET will directly compare each pair of ensemble *i* and</span>
+<span class="sd">        *j* by using all of their conformers and perform the comparison only</span>
 <span class="sd">        once. On the other hand, if providing an integer value to this</span>
 <span class="sd">        argument, each pair of ensembles *i* and *j* will be compared</span>
 <span class="sd">        `bootstrap_iters` times by randomly selecting (bootstrapping)</span>

docs/_modules/idpet/dimensionality_reduction.html

@@ -304,7 +304,7 @@ <h1>Source code for idpet.dimensionality_reduction</h1><div class="highlight"><p
 <span class="sd">    metric : str, optional</span>
 <span class="sd">        The metric to use for distance calculation. Default is &#39;euclidean&#39;.</span>
 <span class="sd">    range_n_clusters : range or List, optional</span>
-<span class="sd">        Range of cluster values to consider for silhouette scoring. Default is range(2, 10, 1).</span>
+<span class="sd">        Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</span>
 <span class="sd">    random_state : int, optional</span>
 <span class="sd">        Random state of the UMAP implementation.</span>
 <span class="sd">    &quot;&quot;&quot;</span>

docs/_modules/idpet/ensemble_analysis.html

@@ -494,7 +494,7 @@ <h1>Source code for idpet.ensemble_analysis</h1><div class="highlight"><pre>
 <span class="sd">            - learning_rate : float, optional</span>
 <span class="sd">                Learning rate. Default is 100.0.</span>
 <span class="sd">            - range_n_clusters : List[int], optional</span>
-<span class="sd">                Range of cluster values. Default is range(2, 10, 1).</span>
+<span class="sd">                Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</span>
 <span class="sd">            - random_state: int, optional</span>
 <span class="sd">                Random seed for sklearn.</span>
 <span class="sd">        - umap:</span>
@@ -511,7 +511,7 @@ <h1>Source code for idpet.ensemble_analysis</h1><div class="highlight"><pre>
 <span class="sd">            - random_state: int, optional</span>
 <span class="sd">                Random seed for sklearn.    </span>
 <span class="sd">            - range_n_clusters : List[int], optional</span>
-<span class="sd">                Range of cluster values. Default is range(2, 10, 1).</span>
+<span class="sd">                Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</span>
 
 <span class="sd">        - kpca:</span>
 <span class="sd">            - circular : bool, optional</span>

docs/comparison.html

@@ -136,8 +136,8 @@ <h2>Parameters<a class="headerlink" href="#parameters" title="Permalink to this
 above features.</p>
 </dd>
 <dt>bootstrap_iters: int, optional</dt><dd><p>Number of bootstrap iterations. By default its value is None. In
-this case, IDPET will directly compare each pair of ensemble $i$ and
-$j$ by using all of their conformers and perform the comparison only
+this case, IDPET will directly compare each pair of ensemble <em>i</em> and
+<em>j</em> by using all of their conformers and perform the comparison only
 once. On the other hand, if providing an integer value to this
 argument, each pair of ensembles <em>i</em> and <em>j</em> will be compared
 <cite>bootstrap_iters</cite> times by randomly selecting (bootstrapping)

docs/ensemble_analysis.html

@@ -436,7 +436,7 @@ <h4>Additional Parameters<a class="headerlink" href="#additional-parameters" tit
 </dl>
 </li>
 <li><dl class="simple">
-<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Range of cluster values. Default is range(2, 10, 1).</p>
+<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</p>
 </dd>
 </dl>
 </li>
@@ -482,7 +482,7 @@ <h4>Additional Parameters<a class="headerlink" href="#additional-parameters" tit
 </dl>
 </li>
 <li><dl class="simple">
-<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Range of cluster values. Default is range(2, 10, 1).</p>
+<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</p>
 </dd>
 </dl>
 </li>

docs/idpet.html

@@ -270,8 +270,8 @@ <h3>Parameters<a class="headerlink" href="#parameters" title="Permalink to this
 above features.</p>
 </dd>
 <dt>bootstrap_iters: int, optional</dt><dd><p>Number of bootstrap iterations. By default its value is None. In
-this case, IDPET will directly compare each pair of ensemble $i$ and
-$j$ by using all of their conformers and perform the comparison only
+this case, IDPET will directly compare each pair of ensemble <em>i</em> and
+<em>j</em> by using all of their conformers and perform the comparison only
 once. On the other hand, if providing an integer value to this
 argument, each pair of ensembles <em>i</em> and <em>j</em> will be compared
 <cite>bootstrap_iters</cite> times by randomly selecting (bootstrapping)
@@ -1279,7 +1279,7 @@ <h3>Parameters<a class="headerlink" href="#id66" title="Permalink to this headin
 </dd>
 <dt>metric<span class="classifier">str, optional</span></dt><dd><p>The metric to use for distance calculation. Default is ‘euclidean’.</p>
 </dd>
-<dt>range_n_clusters<span class="classifier">range or List, optional</span></dt><dd><p>Range of cluster values to consider for silhouette scoring. Default is range(2, 10, 1).</p>
+<dt>range_n_clusters<span class="classifier">range or List, optional</span></dt><dd><p>Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</p>
 </dd>
 <dt>random_state<span class="classifier">int, optional</span></dt><dd><p>Random state of the UMAP implementation.</p>
 </dd>
@@ -1926,7 +1926,7 @@ <h4>Additional Parameters<a class="headerlink" href="#additional-parameters" tit
 </dl>
 </li>
 <li><dl class="simple">
-<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Range of cluster values. Default is range(2, 10, 1).</p>
+<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</p>
 </dd>
 </dl>
 </li>
@@ -1972,7 +1972,7 @@ <h4>Additional Parameters<a class="headerlink" href="#additional-parameters" tit
 </dl>
 </li>
 <li><dl class="simple">
-<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Range of cluster values. Default is range(2, 10, 1).</p>
+<dt>range_n_clusters<span class="classifier">List[int], optional</span></dt><dd><p>Highly disordered ensembles typically do not form more than ~10 distinct, visually separable clusters. Therefore, exploring more than 10 clusters is usually unnecessary. But users can modify this parameter based on their specific datasets and research questions. Default is range(2, 10, 1).</p>
 </dd>
 </dl>
 </li>