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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.compute_optics_graph</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.compute_optics_graph"><code class="docutils literal notranslate"><span class="pre">compute_optics_graph</span></code></a></li>
</ul>
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<section id="sklearn-cluster-compute-optics-graph">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.compute_optics_graph<a class="headerlink" href="#sklearn-cluster-compute-optics-graph" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.compute_optics_graph">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">compute_optics_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/cluster/_optics.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.compute_optics_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the OPTICS reachability graph.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#optics"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples) if metric=’precomputed’</span></dt><dd><p>A feature array, or array of distances between samples if
metric=’precomputed’.</p>
</dd>
<dt><strong>min_samples</strong><span class="classifier">int > 1 or float between 0 and 1</span></dt><dd><p>The number of samples in a neighborhood for a point to be considered
as a core point. Expressed as an absolute number or a fraction of the
number of samples (rounded to be at least 2).</p>
</dd>
<dt><strong>max_eps</strong><span class="classifier">float, default=np.inf</span></dt><dd><p>The maximum distance between two samples for one to be considered as
in the neighborhood of the other. Default value of <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> will
identify clusters across all scales; reducing <code class="docutils literal notranslate"><span class="pre">max_eps</span></code> will result
in shorter run times.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">str or callable, default=’minkowski’</span></dt><dd><p>Metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.</p>
<p>If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy’s metrics, but is less
efficient than passing the metric name as a string. If metric is
“precomputed”, X is assumed to be a distance matrix and must be square.</p>
<p>Valid values for metric are:</p>
<ul class="simple">
<li><p>from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’,
‘manhattan’]</p></li>
<li><p>from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’,
‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’,
‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’,
‘yule’]</p></li>
</ul>
<p>See the documentation for scipy.spatial.distance for details on these
metrics.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">'kulsinski'</span></code> is deprecated from SciPy 1.9 and will be removed in SciPy 1.11.</p>
</div>
</dd>
<dt><strong>p</strong><span class="classifier">float, default=2</span></dt><dd><p>Parameter for the Minkowski metric from
<a class="reference internal" href="sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-class docutils literal notranslate"><span class="pre">pairwise_distances</span></code></a>. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a>.</p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a>.</p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> method. (default)</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, default=30</span></dt><dd><p>Leaf size passed to <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a> or
<a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a>. This can affect the speed of the
construction and query, as well as the memory required to store the
tree. The optimal value depends on the nature of the problem.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.4.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>ordering_</strong><span class="classifier">array of shape (n_samples,)</span></dt><dd><p>The cluster ordered list of sample indices.</p>
</dd>
<dt><strong>core_distances_</strong><span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Distance at which each sample becomes a core point, indexed by object
order. Points which will never be core have a distance of inf. Use
<code class="docutils literal notranslate"><span class="pre">clust.core_distances_[clust.ordering_]</span></code> to access in cluster order.</p>
</dd>
<dt><strong>reachability_</strong><span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Reachability distances per sample, indexed by object order. Use
<code class="docutils literal notranslate"><span class="pre">clust.reachability_[clust.ordering_]</span></code> to access in cluster order.</p>
</dd>
<dt><strong>predecessor_</strong><span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Point that a sample was reached from, indexed by object order.
Seed points have a predecessor of -1.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r61802d06a170-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Ankerst, Mihael, Markus M. Breunig, Hans-Peter Kriegel,
and Jörg Sander. “OPTICS: ordering points to identify the clustering
structure.” ACM SIGMOD Record 28, no. 2 (1999): 49-60.</p>
</div>
</div>
</dd></dl>
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