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<div class="subTitle">org.apache.spark.mllib.clustering</div>
<h2 title="Class EMLDAOptimizer" class="title">Class EMLDAOptimizer</h2>
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<li>java.lang.Object</li>
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<li>org.apache.spark.mllib.clustering.EMLDAOptimizer</li>
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<dt>All Implemented Interfaces:</dt>
<dd><a href="../../../../../org/apache/spark/mllib/clustering/LDAOptimizer.html" title="interface in org.apache.spark.mllib.clustering">LDAOptimizer</a></dd>
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<pre>public final class <span class="strong">EMLDAOptimizer</span>
extends java.lang.Object
implements <a href="../../../../../org/apache/spark/mllib/clustering/LDAOptimizer.html" title="interface in org.apache.spark.mllib.clustering">LDAOptimizer</a></pre>
<div class="block">:: DeveloperApi ::
<p>
Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.
<p>
Currently, the underlying implementation uses Expectation-Maximization (EM), implemented
according to the Asuncion et al. (2009) paper referenced below.
<p>
References:
- Original LDA paper (journal version):
Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
- This class implements their "smoothed" LDA model.
- Paper which clearly explains several algorithms, including EM:
Asuncion, Welling, Smyth, and Teh.
"On Smoothing and Inference for Topic Models." UAI, 2009.</div>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#EMLDAOptimizer()">EMLDAOptimizer</a></strong>()</code> </td>
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<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#checkpointInterval()">checkpointInterval</a></strong>()</code> </td>
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<tr class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#docConcentration()">docConcentration</a></strong>()</code> </td>
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<td class="colFirst"><code>breeze.linalg.DenseVector<java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#globalTopicTotals()">globalTopicTotals</a></strong>()</code>
<div class="block">Aggregate distributions over topics from all term vertices.</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><breeze.linalg.DenseVector<java.lang.Object>,java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#graph()">graph</a></strong>()</code>
<div class="block">The following fields will only be initialized through the initialize() method</div>
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<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#k()">k</a></strong>()</code> </td>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#topicConcentration()">topicConcentration</a></strong>()</code> </td>
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<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/EMLDAOptimizer.html#vocabSize()">vocabSize</a></strong>()</code> </td>
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<pre>public EMLDAOptimizer()</pre>
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<h4>graph</h4>
<pre>public <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><breeze.linalg.DenseVector<java.lang.Object>,java.lang.Object> graph()</pre>
<div class="block">The following fields will only be initialized through the initialize() method</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>k</h4>
<pre>public int k()</pre>
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<pre>public int vocabSize()</pre>
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<pre>public double docConcentration()</pre>
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<h4>topicConcentration</h4>
<pre>public double topicConcentration()</pre>
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<pre>public int checkpointInterval()</pre>
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<pre>public breeze.linalg.DenseVector<java.lang.Object> globalTopicTotals()</pre>
<div class="block">Aggregate distributions over topics from all term vertices.
<p>
Note: This executes an action on the graph RDDs.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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