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<div class="subTitle">org.apache.spark.mllib.clustering</div>
<h2 title="Class OnlineLDAOptimizer" class="title">Class OnlineLDAOptimizer</h2>
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<li>java.lang.Object</li>
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<li>org.apache.spark.mllib.clustering.OnlineLDAOptimizer</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">OnlineLDAOptimizer</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>
An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA
algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic
distribution adaptively.
<p>
Original Online LDA paper:
Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.</div>
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<th class="colOne" scope="col">Constructor and Description</th>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#OnlineLDAOptimizer()">OnlineLDAOptimizer</a></strong>()</code> </td>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#getKappa()">getKappa</a></strong>()</code>
<div class="block">Learning rate: exponential decay rate</div>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#getMiniBatchFraction()">getMiniBatchFraction</a></strong>()</code>
<div class="block">Mini-batch fraction, which sets the fraction of document sampled and used in each iteration</div>
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<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#getOptimizeDocConcentration()">getOptimizeDocConcentration</a></strong>()</code>
<div class="block">Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for
document-topic distribution) will be optimized during training.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#getTau0()">getTau0</a></strong>()</code>
<div class="block">A (positive) learning parameter that downweights early iterations.</div>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#setKappa(double)">setKappa</a></strong>(double kappa)</code>
<div class="block">Learning rate: exponential decay rate---should be between
(0.5, 1.0] to guarantee asymptotic convergence.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#setMiniBatchFraction(double)">setMiniBatchFraction</a></strong>(double miniBatchFraction)</code>
<div class="block">Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in
each iteration.</div>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#setOptimizeDocConcentration(boolean)">setOptimizeDocConcentration</a></strong>(boolean optimizeDocConcentration)</code>
<div class="block">Sets whether to optimize docConcentration parameter during training.</div>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html#setTau0(double)">setTau0</a></strong>(double tau0)</code>
<div class="block">A (positive) learning parameter that downweights early iterations.</div>
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<h4>OnlineLDAOptimizer</h4>
<pre>public OnlineLDAOptimizer()</pre>
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<h4>getTau0</h4>
<pre>public double getTau0()</pre>
<div class="block">A (positive) learning parameter that downweights early iterations. Larger values make early
iterations count less.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setTau0</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a> setTau0(double tau0)</pre>
<div class="block">A (positive) learning parameter that downweights early iterations. Larger values make early
iterations count less.
Default: 1024, following the original Online LDA paper.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>tau0</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>getKappa</h4>
<pre>public double getKappa()</pre>
<div class="block">Learning rate: exponential decay rate</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setKappa</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a> setKappa(double kappa)</pre>
<div class="block">Learning rate: exponential decay rate---should be between
(0.5, 1.0] to guarantee asymptotic convergence.
Default: 0.51, based on the original Online LDA paper.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>kappa</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>getMiniBatchFraction</h4>
<pre>public double getMiniBatchFraction()</pre>
<div class="block">Mini-batch fraction, which sets the fraction of document sampled and used in each iteration</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setMiniBatchFraction</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a> setMiniBatchFraction(double miniBatchFraction)</pre>
<div class="block">Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in
each iteration.
<p>
Note that this should be adjusted in synch with <code>LDA.setMaxIterations()</code>
so the entire corpus is used. Specifically, set both so that
maxIterations * miniBatchFraction >= 1.
<p>
Default: 0.05, i.e., 5% of total documents.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>miniBatchFraction</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>getOptimizeDocConcentration</h4>
<pre>public boolean getOptimizeDocConcentration()</pre>
<div class="block">Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for
document-topic distribution) will be optimized during training.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setOptimizeDocConcentration</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html" title="class in org.apache.spark.mllib.clustering">OnlineLDAOptimizer</a> setOptimizeDocConcentration(boolean optimizeDocConcentration)</pre>
<div class="block">Sets whether to optimize docConcentration parameter during training.
<p>
Default: false</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>optimizeDocConcentration</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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