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author | Michael Armbrust <marmbrus@apache.org> | 2016-01-04 17:53:21 +0000 |
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committer | Michael Armbrust <marmbrus@apache.org> | 2016-01-04 17:53:21 +0000 |
commit | 0d824b0442707b6842ef19a589cb8a2af997273b (patch) | |
tree | 4ac1758b478ee6cb74382733fd3eb3157b6c3161 /site/docs/1.6.0/ml-clustering.html | |
parent | ebcd167cbcb4ba33d8926b66850eff0cce12d365 (diff) | |
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Add Spark 1.6.0 docs
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diff --git a/site/docs/1.6.0/ml-clustering.html b/site/docs/1.6.0/ml-clustering.html new file mode 100644 index 000000000..6b4c1423d --- /dev/null +++ b/site/docs/1.6.0/ml-clustering.html @@ -0,0 +1,588 @@ + +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>Clustering - spark.ml - Spark 1.6.0 Documentation</title> + + + + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + + <!-- Google analytics script --> + <script type="text/javascript"> + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-2']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 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href="ml-features.html"> + + Extracting, transforming and selecting features + + </a> + </li> + + + <li> + <a href="ml-classification-regression.html"> + + Classification and Regression + + </a> + </li> + + + <li> + <a href="ml-clustering.html"> + + <b>Clustering</b> + + </a> + </li> + + + <li> + <a href="ml-advanced.html"> + + Advanced topics + + </a> + </li> + + +</ul> + + <h3><a href="mllib-guide.html">spark.mllib package</a></h3> + +<ul> + + <li> + <a href="mllib-data-types.html"> + + Data types + + </a> + </li> + + + <li> + <a href="mllib-statistics.html"> + + Basic statistics + + </a> + </li> + + + <li> + <a href="mllib-classification-regression.html"> + + Classification and regression + + </a> + </li> + + + <li> + <a href="mllib-collaborative-filtering.html"> + + Collaborative filtering + + </a> + </li> + + + <li> + <a href="mllib-clustering.html"> + + Clustering + + </a> + </li> + + + <li> + <a href="mllib-dimensionality-reduction.html"> + + Dimensionality reduction + + </a> + </li> + + + <li> + <a href="mllib-feature-extraction.html"> + + Feature extraction and transformation + + </a> + </li> + + + <li> + <a href="mllib-frequent-pattern-mining.html"> + + Frequent pattern mining + + </a> + </li> + + + <li> + <a href="mllib-evaluation-metrics.html"> + + Evaluation metrics + + </a> + </li> + + + <li> + <a href="mllib-pmml-model-export.html"> + + PMML model export + + </a> + </li> + + + <li> + <a href="mllib-optimization.html"> + + Optimization (developer) + + </a> + </li> + + +</ul> + + </div> +</div> + <input id="nav-trigger" class="nav-trigger" checked type="checkbox"> + <label for="nav-trigger"></label> + <div class="content-with-sidebar" id="content"> + + <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">"id"</span><span class="o">,</span> <span class="s">"features"</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">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</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">"Final Centers: "</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"><</span><span class="n">Row</span><span class="o">></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">"features"</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">"Cluster Centers: "</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">" "</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"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Row</span><span class="o">></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">" "</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"><</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">"data/mllib/sample_lda_data.txt"</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">"JavaLDAExample"</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"><</span><span class="n">Row</span><span class="o">></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">"features"</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> + +</div> + + + </div> + + <!-- /container --> + </div> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script 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