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                         <h1 class="title">Clustering - spark.ml</h1>


                     <p>In this section, we introduce the pipeline API for <a href="mllib-clustering.html">clustering in mllib</a>.</p>

 <p><strong>Table of Contents</strong></p>

 <ul id="markdown-toc">
   <li><a href="#k-means" id="markdown-toc-k-means">K-means</a>    <ul>
       <li><a href="#input-columns" id="markdown-toc-input-columns">Input Columns</a></li>
       <li><a href="#output-columns" id="markdown-toc-output-columns">Output Columns</a></li>
       <li><a href="#example" id="markdown-toc-example">Example</a></li>
     </ul>
   </li>
   <li><a href="#latent-dirichlet-allocation-lda" id="markdown-toc-latent-dirichlet-allocation-lda">Latent Dirichlet allocation (LDA)</a></li>
 </ul>

 <h2 id="k-means">K-means</h2>

 <p><a href="http://en.wikipedia.org/wiki/K-means_clustering">k-means</a> is one of the
 most commonly used clustering algorithms that clusters the data points into a
 predefined number of clusters. The MLlib implementation includes a parallelized
 variant of the <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">k-means++</a> method
 called <a href="http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf">kmeans||</a>.</p>

 <p><code>KMeans</code> is implemented as an <code>Estimator</code> and generates a <code>KMeansModel</code> as the base model.</p>

 <h3 id="input-columns">Input Columns</h3>

 <table class="table">
   <thead>
     <tr>
       <th align="left">Param name</th>
       <th align="left">Type(s)</th>
       <th align="left">Default</th>
       <th align="left">Description</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <td>featuresCol</td>
       <td>Vector</td>
       <td>"features"</td>
       <td>Feature vector</td>
     </tr>
   </tbody>
 </table>

 <h3 id="output-columns">Output Columns</h3>

 <table class="table">
   <thead>
     <tr>
       <th align="left">Param name</th>
       <th align="left">Type(s)</th>
       <th align="left">Default</th>
       <th align="left">Description</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <td>predictionCol</td>
       <td>Int</td>
       <td>"prediction"</td>
       <td>Predicted cluster center</td>
     </tr>
   </tbody>
 </table>

 <h3 id="example">Example</h3>

 <div class="codetabs">

 <div data-lang="scala">
     <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.clustering.KMeans">Scala API docs</a> for more details.</p>

     <div class="highlight"><pre><span class="k">import</span> <span class="nn">org.apache.spark.ml.clustering.KMeans</span>
 <span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span>

 <span class="c1">// Crates a DataFrame</span>
 <span class="k">val</span> <span class="n">dataset</span><span class="k">:</span> <span class="kt">DataFrame</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
   <span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)),</span>
   <span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">)),</span>
   <span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.2</span><span class="o">,</span> <span class="mf">0.2</span><span class="o">,</span> <span class="mf">0.2</span><span class="o">)),</span>
   <span class="o">(</span><span class="mi">4</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">9.0</span><span class="o">,</span> <span class="mf">9.0</span><span class="o">,</span> <span class="mf">9.0</span><span class="o">)),</span>
   <span class="o">(</span><span class="mi">5</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">9.1</span><span class="o">,</span> <span class="mf">9.1</span><span class="o">,</span> <span class="mf">9.1</span><span class="o">)),</span>
   <span class="o">(</span><span class="mi">6</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">9.2</span><span class="o">,</span> <span class="mf">9.2</span><span class="o">,</span> <span class="mf">9.2</span><span class="o">))</span>
 <span class="o">)).</span><span class="n">toDF</span><span class="o">(</span><span class="s">&quot;id&quot;</span><span class="o">,</span> <span class="s">&quot;features&quot;</span><span class="o">)</span>

 <span class="c1">// Trains a k-means model</span>
 <span class="k">val</span> <span class="n">kmeans</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">KMeans</span><span class="o">()</span>
   <span class="o">.</span><span class="n">setK</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
   <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">)</span>
   <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">dataset</span><span class="o">)</span>

 <span class="c1">// Shows the result</span>
 <span class="n">println</span><span class="o">(</span><span class="s">&quot;Final Centers: &quot;</span><span class="o">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">clusterCenters</span><span class="o">.</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span>
 </pre></div>
     <div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala" in the Spark repo.</small></div>
   </div>

 <div data-lang="java">
     <p>Refer to the <a href="api/java/org/apache/spark/ml/clustering/KMeans.html">Java API docs</a> for more details.</p>

     <div class="highlight"><pre><span class="kn">import</span> <span class="nn">org.apache.spark.ml.clustering.KMeansModel</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.ml.clustering.KMeans</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.VectorUDT</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.Metadata</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructField</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructType</span><span class="o">;</span>

 <span class="c1">// Loads data</span>
 <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span> <span class="n">points</span> <span class="o">=</span> <span class="n">jsc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="n">inputFile</span><span class="o">).</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="nf">ParsePoint</span><span class="o">());</span>
 <span class="n">StructField</span><span class="o">[]</span> <span class="n">fields</span> <span class="o">=</span> <span class="o">{</span><span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">,</span> <span class="k">new</span> <span class="nf">VectorUDT</span><span class="o">(),</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">())};</span>
 <span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StructType</span><span class="o">(</span><span class="n">fields</span><span class="o">);</span>
 <span class="n">DataFrame</span> <span class="n">dataset</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">points</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>

 <span class="c1">// Trains a k-means model</span>
 <span class="n">KMeans</span> <span class="n">kmeans</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">KMeans</span><span class="o">()</span>
   <span class="o">.</span><span class="na">setK</span><span class="o">(</span><span class="n">k</span><span class="o">);</span>
 <span class="n">KMeansModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">dataset</span><span class="o">);</span>

 <span class="c1">// Shows the result</span>
 <span class="n">Vector</span><span class="o">[]</span> <span class="n">centers</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">clusterCenters</span><span class="o">();</span>
 <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">&quot;Cluster Centers: &quot;</span><span class="o">);</span>
 <span class="k">for</span> <span class="o">(</span><span class="n">Vector</span> <span class="nl">center:</span> <span class="n">centers</span><span class="o">)</span> <span class="o">{</span>
   <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">center</span><span class="o">);</span>
 <span class="o">}</span>
 </pre></div>
     <div><small>Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java" in the Spark repo.</small></div>
   </div>

 </div>

 <h2 id="latent-dirichlet-allocation-lda">Latent Dirichlet allocation (LDA)</h2>

 <p><code>LDA</code> is implemented as an <code>Estimator</code> that supports both <code>EMLDAOptimizer</code> and <code>OnlineLDAOptimizer</code>,
 and generates a <code>LDAModel</code> as the base models. Expert users may cast a <code>LDAModel</code> generated by
 <code>EMLDAOptimizer</code> to a <code>DistributedLDAModel</code> if needed.</p>

 <div class="codetabs">

 <div data-lang="scala">

     <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.clustering.LDA">Scala API docs</a> for more details.</p>

     <div class="highlight"><pre><span class="k">import</span> <span class="nn">org.apache.spark.ml.clustering.LDA</span>
 <span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
 <span class="k">import</span> <span class="nn">org.apache.spark.sql.types.</span><span class="o">{</span><span class="nc">StructField</span><span class="o">,</span> <span class="nc">StructType</span><span class="o">}</span>

 <span class="c1">// Loads data</span>
 <span class="k">val</span> <span class="n">rowRDD</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="n">input</span><span class="o">).</span><span class="n">filter</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">nonEmpty</span><span class="o">)</span>
   <span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">&quot; &quot;</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toDouble</span><span class="o">)).</span><span class="n">map</span><span class="o">(</span><span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="nc">Row</span><span class="o">(</span><span class="k">_</span><span class="o">))</span>
 <span class="k">val</span> <span class="n">schema</span> <span class="k">=</span> <span class="nc">StructType</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="nc">StructField</span><span class="o">(</span><span class="nc">FEATURES_COL</span><span class="o">,</span> <span class="k">new</span> <span class="nc">VectorUDT</span><span class="o">,</span> <span class="kc">false</span><span class="o">)))</span>
 <span class="k">val</span> <span class="n">dataset</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="o">(</span><span class="n">rowRDD</span><span class="o">,</span> <span class="n">schema</span><span class="o">)</span>

 <span class="c1">// Trains a LDA model</span>
 <span class="k">val</span> <span class="n">lda</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LDA</span><span class="o">()</span>
   <span class="o">.</span><span class="n">setK</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
   <span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
   <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="nc">FEATURES_COL</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">lda</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">dataset</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">transformed</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">dataset</span><span class="o">)</span>

 <span class="k">val</span> <span class="n">ll</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">logLikelihood</span><span class="o">(</span><span class="n">dataset</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">lp</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">logPerplexity</span><span class="o">(</span><span class="n">dataset</span><span class="o">)</span>

 <span class="c1">// describeTopics</span>
 <span class="k">val</span> <span class="n">topics</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">describeTopics</span><span class="o">(</span><span class="mi">3</span><span class="o">)</span>

 <span class="c1">// Shows the result</span>
 <span class="n">topics</span><span class="o">.</span><span class="n">show</span><span class="o">(</span><span class="kc">false</span><span class="o">)</span>
 <span class="n">transformed</span><span class="o">.</span><span class="n">show</span><span class="o">(</span><span class="kc">false</span><span class="o">)</span>
 </pre></div>
     <div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala" in the Spark repo.</small></div>
   </div>

 <div data-lang="java">

     <p>Refer to the <a href="api/java/org/apache/spark/ml/clustering/LDA.html">Java API docs</a> for more details.</p>

     <div class="highlight"><pre><span class="kn">import</span> <span class="nn">java.util.regex.Pattern</span><span class="o">;</span>

 <span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.api.java.function.Function</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.ml.clustering.LDA</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.ml.clustering.LDAModel</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.VectorUDT</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.catalyst.expressions.GenericRow</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.Metadata</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructField</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructType</span><span class="o">;</span>

 <span class="kd">private</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">ParseVector</span> <span class="kd">implements</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Row</span><span class="o">&gt;</span> <span class="o">{</span>
   <span class="kd">private</span> <span class="kd">static</span> <span class="kd">final</span> <span class="n">Pattern</span> <span class="n">separator</span> <span class="o">=</span> <span class="n">Pattern</span><span class="o">.</span><span class="na">compile</span><span class="o">(</span><span class="s">&quot; &quot;</span><span class="o">);</span>

   <span class="nd">@Override</span>
   <span class="kd">public</span> <span class="n">Row</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="o">{</span>
     <span class="n">String</span><span class="o">[]</span> <span class="n">tok</span> <span class="o">=</span> <span class="n">separator</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="n">line</span><span class="o">);</span>
     <span class="kt">double</span><span class="o">[]</span> <span class="n">point</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[</span><span class="n">tok</span><span class="o">.</span><span class="na">length</span><span class="o">];</span>
     <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">tok</span><span class="o">.</span><span class="na">length</span><span class="o">;</span> <span class="o">++</span><span class="n">i</span><span class="o">)</span> <span class="o">{</span>
       <span class="n">point</span><span class="o">[</span><span class="n">i</span><span class="o">]</span> <span class="o">=</span> <span class="n">Double</span><span class="o">.</span><span class="na">parseDouble</span><span class="o">(</span><span class="n">tok</span><span class="o">[</span><span class="n">i</span><span class="o">]);</span>
     <span class="o">}</span>
     <span class="n">Vector</span><span class="o">[]</span> <span class="n">points</span> <span class="o">=</span> <span class="o">{</span><span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">point</span><span class="o">)};</span>
     <span class="k">return</span> <span class="k">new</span> <span class="nf">GenericRow</span><span class="o">(</span><span class="n">points</span><span class="o">);</span>
   <span class="o">}</span>
 <span class="o">}</span>

 <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>

   <span class="n">String</span> <span class="n">inputFile</span> <span class="o">=</span> <span class="s">&quot;data/mllib/sample_lda_data.txt&quot;</span><span class="o">;</span>

   <span class="c1">// Parses the arguments</span>
   <span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">&quot;JavaLDAExample&quot;</span><span class="o">);</span>
   <span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
   <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>

   <span class="c1">// Loads data</span>
   <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span> <span class="n">points</span> <span class="o">=</span> <span class="n">jsc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="n">inputFile</span><span class="o">).</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="nf">ParseVector</span><span class="o">());</span>
   <span class="n">StructField</span><span class="o">[]</span> <span class="n">fields</span> <span class="o">=</span> <span class="o">{</span><span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">,</span> <span class="k">new</span> <span class="nf">VectorUDT</span><span class="o">(),</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">())};</span>
   <span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StructType</span><span class="o">(</span><span class="n">fields</span><span class="o">);</span>
   <span class="n">DataFrame</span> <span class="n">dataset</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">points</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>

   <span class="c1">// Trains a LDA model</span>
   <span class="n">LDA</span> <span class="n">lda</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LDA</span><span class="o">()</span>
     <span class="o">.</span><span class="na">setK</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
     <span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span>
   <span class="n">LDAModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">lda</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">dataset</span><span class="o">);</span>

   <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">model</span><span class="o">.</span><span class="na">logLikelihood</span><span class="o">(</span><span class="n">dataset</span><span class="o">));</span>
   <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">model</span><span class="o">.</span><span class="na">logPerplexity</span><span class="o">(</span><span class="n">dataset</span><span class="o">));</span>

   <span class="c1">// Shows the result</span>
   <span class="n">DataFrame</span> <span class="n">topics</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">describeTopics</span><span class="o">(</span><span class="mi">3</span><span class="o">);</span>
   <span class="n">topics</span><span class="o">.</span><span class="na">show</span><span class="o">(</span><span class="kc">false</span><span class="o">);</span>
   <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">dataset</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="kc">false</span><span class="o">);</span>

   <span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span>
 <span class="o">}</span>
 </pre></div>
     <div><small>Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java" in the Spark repo.</small></div>
   </div>

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