diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index d0a299cb894b2..b352847ab6cc2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -295,7 +295,7 @@ class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   /**
-   * The function is for test only now. In the future, it can help support training strop/resume
+   * The function is for test only now. In the future, it can help support training stop/resume
    */
   private[clustering] def setLambda(lambda: BDM[Double]): this.type = {
     this.lambda = lambda
@@ -310,8 +310,9 @@ class OnlineLDAOptimizer extends LDAOptimizer {
     this
   }
 
-  override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA):
-  OnlineLDAOptimizer = {
+  override private[clustering] def initialize(
+      docs: RDD[(Long, Vector)],
+      lda: LDA):  OnlineLDAOptimizer = {
     this.k = lda.getK
     this.corpusSize = docs.count()
     this.vocabSize = docs.first()._2.size
@@ -333,7 +334,6 @@ class OnlineLDAOptimizer extends LDAOptimizer {
     submitMiniBatch(batch)
   }
 
-
   /**
    * Submit a subset (like 1%, decide by the miniBatchFraction) of the corpus to the Online LDA
    * model, and it will update the topic distribution adaptively for the terms appearing in the
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 2e8a9e99f6d01..dd61f054b18c7 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -109,35 +109,38 @@ public void distributedLDAModel() {
     assert(model.logPrior() < 0.0);
   }
 
-
   @Test
   public void OnlineOptimizerCompatibility() {
-      int k = 3;
-      double topicSmoothing = 1.2;
-      double termSmoothing = 1.2;
-
-      // Train a model
-      OnlineLDAOptimizer op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51)
-              .setGammaShape(1e40).setMiniBatchFraction(0.5);
-      LDA lda = new LDA();
-      lda.setK(k)
-         .setDocConcentration(topicSmoothing)
-         .setTopicConcentration(termSmoothing)
-         .setMaxIterations(5)
-         .setSeed(12345)
-         .setOptimizer(op);
-
-      LDAModel model = lda.run(corpus);
-
-      // Check: basic parameters
-      assertEquals(model.k(), k);
-      assertEquals(model.vocabSize(), tinyVocabSize);
-
-      // Check: topic summaries
-      Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics();
-      assertEquals(roundedTopicSummary.length, k);
-      Tuple2<int[], double[]>[] roundedLocalTopicSummary = model.describeTopics();
-      assertEquals(roundedLocalTopicSummary.length, k);
+    int k = 3;
+    double topicSmoothing = 1.2;
+    double termSmoothing = 1.2;
+
+    // Train a model
+    OnlineLDAOptimizer op = new OnlineLDAOptimizer()
+      .setTau_0(1024)
+      .setKappa(0.51)
+      .setGammaShape(1e40)
+      .setMiniBatchFraction(0.5);
+
+    LDA lda = new LDA();
+    lda.setK(k)
+      .setDocConcentration(topicSmoothing)
+      .setTopicConcentration(termSmoothing)
+      .setMaxIterations(5)
+      .setSeed(12345)
+      .setOptimizer(op);
+
+    LDAModel model = lda.run(corpus);
+
+    // Check: basic parameters
+    assertEquals(model.k(), k);
+    assertEquals(model.vocabSize(), tinyVocabSize);
+
+    // Check: topic summaries
+    Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics();
+    assertEquals(roundedTopicSummary.length, k);
+    Tuple2<int[], double[]>[] roundedLocalTopicSummary = model.describeTopics();
+    assertEquals(roundedLocalTopicSummary.length, k);
   }
 
   private static int tinyK = LDASuite$.MODULE$.tinyK();