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<?xml version="1.0" encoding="ascii"?>
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<a href="Scientific-module.html">Package Scientific</a> ::
<a href="Scientific.Clustering-module.html">Package Clustering</a> ::
<a href="Scientific.Clustering.AffinityPropagation-module.html">Module AffinityPropagation</a> ::
Class DataSet
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class DataSet</h1><p class="nomargin-top"></p>
<pre class="base-tree">
object --+
|
<strong class="uidshort">DataSet</strong>
</pre>
<hr />
<p>A collection of data items with similarities</p>
<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
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<span class="table-header">Instance Methods</span></td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Scientific.Clustering.AffinityPropagation.DataSet-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">items</span>,
<span class="summary-sig-arg">similarities</span>,
<span class="summary-sig-arg">symmetric</span>=<span class="summary-sig-default">False</span>,
<span class="summary-sig-arg">minimal_similarity</span>=<span class="summary-sig-default">None</span>)</span><br />
x.__init__(...) initializes x; see x.__class__.__doc__ for signature</td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Scientific.Clustering.AffinityPropagation.DataSet-class.html#findClusters" class="summary-sig-name">findClusters</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">preferences</span>,
<span class="summary-sig-arg">max_iterations</span>=<span class="summary-sig-default">500</span>,
<span class="summary-sig-arg">convergence</span>=<span class="summary-sig-default">50</span>,
<span class="summary-sig-arg">damping</span>=<span class="summary-sig-default">0.5</span>)</span></td>
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<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__delattr__</code>,
<code>__getattribute__</code>,
<code>__hash__</code>,
<code>__new__</code>,
<code>__reduce__</code>,
<code>__reduce_ex__</code>,
<code>__repr__</code>,
<code>__setattr__</code>,
<code>__str__</code>
</p>
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<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__class__</code>
</p>
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<!-- ==================== METHOD DETAILS ==================== -->
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<span class="table-header">Method Details</span></td>
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">items</span>,
<span class="sig-arg">similarities</span>,
<span class="sig-arg">symmetric</span>=<span class="sig-default">False</span>,
<span class="sig-arg">minimal_similarity</span>=<span class="sig-default">None</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
>
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<p>x.__init__(...) initializes x; see x.__class__.__doc__ for
signature</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>items</code></strong> (sequence) - a sequence of data items</li>
<li><strong class="pname"><code>similarities</code></strong> - similarity values for item pairs. This parameter can have one of
three forms:
<ul>
<li>
a list if triples (index1, index2, similarity), where the
indices point into the item list and the similarity is a real
number.
</li>
<li>
a callable object (typically a function or a bound method)
that is called with two items and returns the similarity.
</li>
<li>
an array of shape (N, N), where N is the number of items,
containing the similarities. The diagonal elements are not
used.
</li>
</ul></li>
<li><strong class="pname"><code>symmetric</code></strong> (<code>bool</code>) - if <code>True</code>, the similarity measure is assumed to be
symmetric. If <code>False</code>, no such assumption is made and
the input data (if a list) must contain both directions for each
pair. If the similarity is defined by a function, it will be
called twice of symmtric=False and once if symmetric=True. If the
similarity is defined by an array, this parameter is not used.</li>
<li><strong class="pname"><code>minimal_similarity</code></strong> (<code>float</code>) - a cutoff value for the similarities; values smaller than this
cutoff are discarded. This is of use for large data sets because
both the runtime and the memory consumption increase with the
number of similarity values.</li>
</ul></dd>
<dt>Overrides:
object.__init__
</dt>
</dl>
</td></tr></table>
</div>
<a name="findClusters"></a>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">findClusters</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">preferences</span>,
<span class="sig-arg">max_iterations</span>=<span class="sig-default">500</span>,
<span class="sig-arg">convergence</span>=<span class="sig-default">50</span>,
<span class="sig-arg">damping</span>=<span class="sig-default">0.5</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>preferences</code></strong> (<code>float</code> or sequence of <code>float</code>) - the preference values for the cluster identification. This can be
either a single number, or a sequence with one value per item.</li>
<li><strong class="pname"><code>max_iterations</code></strong> (<code>int</code>) - the number of iterations at which the algorithm is stopped even
if there is no convergence.</li>
<li><strong class="pname"><code>convergence</code></strong> (<code>int</code>) - the number of iterations during which the cluster decomposition
must remain stable before it is returned as converged.</li>
<li><strong class="pname"><code>damping</code></strong> (<code>float</code>) - a number between 0 and 1 that influences by fast affinity and
responsibility values can change.</li>
</ul></dd>
</dl>
</td></tr></table>
</div>
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