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<h2 title="Class DistributedLDAModel" class="title">Class DistributedLDAModel</h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">org.apache.spark.mllib.clustering.LDAModel</a></li>
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<pre>public class <span class="strong">DistributedLDAModel</span>
extends <a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></pre>
<div class="block">:: Experimental ::
<p>
Distributed LDA model.
This model stores the inferred topics, the full training dataset, and the topic distributions.
When computing topics for new documents, it may give more accurate answers
than the <a href="../../../../../org/apache/spark/mllib/clustering/LocalLDAModel.html" title="class in org.apache.spark.mllib.clustering"><code>LocalLDAModel</code></a>.</div>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
</tr>
<tr class="altColor">
<td class="colFirst"><code>scala.Tuple2<int[],double[]>[]</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#describeTopics(int)">describeTopics</a></strong>(int maxTermsPerTopic)</code>
<div class="block">Return the topics described by weighted terms.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#docConcentration()">docConcentration</a></strong>()</code>
<div class="block">Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>protected java.lang.String</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#formatVersion()">formatVersion</a></strong>()</code>
<div class="block">Current version of model save/load format.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>protected double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#gammaShape()">gammaShape</a></strong>()</code>
<div class="block">Shape parameter for random initialization of variational parameter gamma.</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code>breeze.linalg.DenseVector<java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#globalTopicTotals()">globalTopicTotals</a></strong>()</code> </td>
</tr>
<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/DistributedLDAModel.html#graph()">graph</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/DistributedLDAModel.html#iterationTimes()">iterationTimes</a></strong>()</code> </td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a><scala.Tuple3<java.lang.Long,int[],int[]>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#javaTopicAssignments()">javaTopicAssignments</a></strong>()</code>
<div class="block">Java-friendly version of <code>topicAssignments</code></div>
</td>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/api/java/JavaPairRDD.html" title="class in org.apache.spark.api.java">JavaPairRDD</a><java.lang.Long,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#javaTopicDistributions()">javaTopicDistributions</a></strong>()</code>
<div class="block">Java-friendly version of <code>topicDistributions</code></div>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a><scala.Tuple3<java.lang.Long,int[],double[]>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#javaTopTopicsPerDocument(int)">javaTopTopicsPerDocument</a></strong>(int k)</code>
<div class="block">Java-friendly version of <code>topTopicsPerDocument</code></div>
</td>
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<tr class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#k()">k</a></strong>()</code>
<div class="block">Number of topics</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html" title="class in org.apache.spark.mllib.clustering">DistributedLDAModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#load(org.apache.spark.SparkContext, java.lang.String)">load</a></strong>(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
java.lang.String path)</code> </td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#logLikelihood()">logLikelihood</a></strong>()</code>
<div class="block">Log likelihood of the observed tokens in the training set,
given the current parameter estimates:
log P(docs | topics, topic distributions for docs, alpha, eta)</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/DistributedLDAModel.html#logPrior()">logPrior</a></strong>()</code>
<div class="block">Log probability of the current parameter estimate:
log P(topics, topic distributions for docs | alpha, eta)</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#save(org.apache.spark.SparkContext, java.lang.String)">save</a></strong>(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
java.lang.String path)</code>
<div class="block">Java-friendly version of <code>topicDistributions</code></div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/clustering/LocalLDAModel.html" title="class in org.apache.spark.mllib.clustering">LocalLDAModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#toLocal()">toLocal</a></strong>()</code>
<div class="block">Convert model to a local model.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>scala.Tuple2<long[],double[]>[]</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topDocumentsPerTopic(int)">topDocumentsPerTopic</a></strong>(int maxDocumentsPerTopic)</code>
<div class="block">Return the top documents for each topic</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple3<java.lang.Object,int[],int[]>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topicAssignments()">topicAssignments</a></strong>()</code>
<div class="block">Return the top topic for each (doc, term) pair.</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topicConcentration()">topicConcentration</a></strong>()</code>
<div class="block">Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<java.lang.Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topicDistributions()">topicDistributions</a></strong>()</code>
<div class="block">For each document in the training set, return the distribution over topics for that document
("theta_doc").</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topicsMatrix()">topicsMatrix</a></strong>()</code>
<div class="block">Inferred topics, where each topic is represented by a distribution over terms.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple3<java.lang.Object,int[],double[]>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#topTopicsPerDocument(int)">topTopicsPerDocument</a></strong>(int k)</code>
<div class="block">For each document, return the top k weighted topics for that document and their weights.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html#vocabSize()">vocabSize</a></strong>()</code>
<div class="block">Vocabulary size (number of terms or terms in the vocabulary)</div>
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<h3>Methods inherited from class org.apache.spark.mllib.clustering.<a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></h3>
<code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#describeTopics()">describeTopics</a></code></li>
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<h3>Methods inherited from class java.lang.Object</h3>
<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
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<a name="load(org.apache.spark.SparkContext, java.lang.String)">
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<h4>load</h4>
<pre>public static <a href="../../../../../org/apache/spark/mllib/clustering/DistributedLDAModel.html" title="class in org.apache.spark.mllib.clustering">DistributedLDAModel</a> load(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
java.lang.String path)</pre>
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<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>
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<pre>public breeze.linalg.DenseVector<java.lang.Object> globalTopicTotals()</pre>
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<h4>k</h4>
<pre>public int k()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#k()">LDAModel</a></code></strong></div>
<div class="block">Number of topics</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#k()">k</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
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<h4>vocabSize</h4>
<pre>public int vocabSize()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#vocabSize()">LDAModel</a></code></strong></div>
<div class="block">Vocabulary size (number of terms or terms in the vocabulary)</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#vocabSize()">vocabSize</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
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<pre>public <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> docConcentration()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#docConcentration()">LDAModel</a></code></strong></div>
<div class="block">Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
<p>
This is the parameter to a Dirichlet distribution.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#docConcentration()">docConcentration</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>topicConcentration</h4>
<pre>public double topicConcentration()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#topicConcentration()">LDAModel</a></code></strong></div>
<div class="block">Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
<p>
This is the parameter to a symmetric Dirichlet distribution.
<p>
Note: The topics' distributions over terms are called "beta" in the original LDA paper
by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#topicConcentration()">topicConcentration</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>iterationTimes</h4>
<pre>public double[] iterationTimes()</pre>
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<h4>gammaShape</h4>
<pre>protected double gammaShape()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#gammaShape()">LDAModel</a></code></strong></div>
<div class="block">Shape parameter for random initialization of variational parameter gamma.
Used for variational inference for perplexity and other test-time computations.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#gammaShape()">gammaShape</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>toLocal</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/clustering/LocalLDAModel.html" title="class in org.apache.spark.mllib.clustering">LocalLDAModel</a> toLocal()</pre>
<div class="block">Convert model to a local model.
The local model stores the inferred topics but not the topic distributions for training
documents.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>topicsMatrix</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> topicsMatrix()</pre>
<div class="block">Inferred topics, where each topic is represented by a distribution over terms.
This is a matrix of size vocabSize x k, where each column is a topic.
No guarantees are given about the ordering of the topics.
<p>
WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#topicsMatrix()">topicsMatrix</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>describeTopics</h4>
<pre>public scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic)</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#describeTopics(int)">LDAModel</a></code></strong></div>
<div class="block">Return the topics described by weighted terms.
<p></div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html#describeTopics(int)">describeTopics</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/clustering/LDAModel.html" title="class in org.apache.spark.mllib.clustering">LDAModel</a></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>maxTermsPerTopic</code> - Maximum number of terms to collect for each topic.</dd>
<dt><span class="strong">Returns:</span></dt><dd>Array over topics. Each topic is represented as a pair of matching arrays:
(term indices, term weights in topic).
Each topic's terms are sorted in order of decreasing weight.</dd></dl>
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<h4>topDocumentsPerTopic</h4>
<pre>public scala.Tuple2<long[],double[]>[] topDocumentsPerTopic(int maxDocumentsPerTopic)</pre>
<div class="block">Return the top documents for each topic
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>maxDocumentsPerTopic</code> - Maximum number of documents to collect for each topic.</dd>
<dt><span class="strong">Returns:</span></dt><dd>Array over topics. Each element represent as a pair of matching arrays:
(IDs for the documents, weights of the topic in these documents).
For each topic, documents are sorted in order of decreasing topic weights.</dd></dl>
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<h4>topicAssignments</h4>
<pre>public <a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple3<java.lang.Object,int[],int[]>> topicAssignments()</pre>
<div class="block">Return the top topic for each (doc, term) pair. I.e., for each document, what is the most
likely topic generating each term?
<p></div>
<dl><dt><span class="strong">Returns:</span></dt><dd>RDD of (doc ID, assignment of top topic index for each term),
where the assignment is specified via a pair of zippable arrays
(term indices, topic indices). Note that terms will be omitted if not present in
the document.</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a><scala.Tuple3<java.lang.Long,int[],int[]>> javaTopicAssignments()</pre>
<div class="block">Java-friendly version of <code>topicAssignments</code></div>
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<h4>logLikelihood</h4>
<pre>public double logLikelihood()</pre>
<div class="block">Log likelihood of the observed tokens in the training set,
given the current parameter estimates:
log P(docs | topics, topic distributions for docs, alpha, eta)
<p>
Note:
- This excludes the prior; for that, use <code>logPrior</code>.
- Even with <code>logPrior</code>, this is NOT the same as the data log likelihood given the
hyperparameters.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public double logPrior()</pre>
<div class="block">Log probability of the current parameter estimate:
log P(topics, topic distributions for docs | alpha, eta)</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>topicDistributions</h4>
<pre>public <a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<java.lang.Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> topicDistributions()</pre>
<div class="block">For each document in the training set, return the distribution over topics for that document
("theta_doc").
<p></div>
<dl><dt><span class="strong">Returns:</span></dt><dd>RDD of (document ID, topic distribution) pairs</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/api/java/JavaPairRDD.html" title="class in org.apache.spark.api.java">JavaPairRDD</a><java.lang.Long,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> javaTopicDistributions()</pre>
<div class="block">Java-friendly version of <code>topicDistributions</code></div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>topTopicsPerDocument</h4>
<pre>public <a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple3<java.lang.Object,int[],double[]>> topTopicsPerDocument(int k)</pre>
<div class="block">For each document, return the top k weighted topics for that document and their weights.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>RDD of (doc ID, topic indices, topic weights)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a><scala.Tuple3<java.lang.Long,int[],double[]>> javaTopTopicsPerDocument(int k)</pre>
<div class="block">Java-friendly version of <code>topTopicsPerDocument</code></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>protected java.lang.String formatVersion()</pre>
<div class="block"><strong>Description copied from interface: <code><a href="../../../../../org/apache/spark/mllib/util/Saveable.html#formatVersion()">Saveable</a></code></strong></div>
<div class="block">Current version of model save/load format.</div>
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<h4>save</h4>
<pre>public void save(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
java.lang.String path)</pre>
<div class="block">Java-friendly version of <code>topicDistributions</code></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sc</code> - (undocumented)</dd><dd><code>path</code> - (undocumented)</dd></dl>
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