1 files changed, 0 insertions, 77 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java
deleted file mode 100644
index de8e739ac9..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.mllib;
-
-import scala.Tuple2;
-
-import org.apache.spark.api.java.*;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.mllib.clustering.DistributedLDAModel;
-import org.apache.spark.mllib.clustering.LDA;
-import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.SparkConf;
-
-public class JavaLDAExample {
-  public static void main(String[] args) {
-    SparkConf conf = new SparkConf().setAppName("LDA Example");
-    JavaSparkContext sc = new JavaSparkContext(conf);
-
-    // Load and parse the data
-    String path = "data/mllib/sample_lda_data.txt";
-    JavaRDD<String> data = sc.textFile(path);
-    JavaRDD<Vector> parsedData = data.map(
-        new Function<String, Vector>() {
-          public Vector call(String s) {
-            String[] sarray = s.trim().split(" ");
-            double[] values = new double[sarray.length];
-            for (int i = 0; i < sarray.length; i++) {
-              values[i] = Double.parseDouble(sarray[i]);
-            }
-            return Vectors.dense(values);
-          }
-        }
-    );
-    // Index documents with unique IDs
-    JavaPairRDD<Long, Vector> corpus = JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map(
-        new Function<Tuple2<Vector, Long>, Tuple2<Long, Vector>>() {
-          public Tuple2<Long, Vector> call(Tuple2<Vector, Long> doc_id) {
-            return doc_id.swap();
-          }
-        }
-    ));
-    corpus.cache();
-
-    // Cluster the documents into three topics using LDA
-    DistributedLDAModel ldaModel = (DistributedLDAModel)new LDA().setK(3).run(corpus);
-
-    // Output topics. Each is a distribution over words (matching word count vectors)
-    System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize()
-        + " words):");
-    Matrix topics = ldaModel.topicsMatrix();
-    for (int topic = 0; topic < 3; topic++) {
-      System.out.print("Topic " + topic + ":");
-      for (int word = 0; word < ldaModel.vocabSize(); word++) {
-        System.out.print(" " + topics.apply(word, topic));
-      }
-      System.out.println();
-    }
-    sc.stop();
-  }
-}