[MINOR][DOCS] Use multi-line JavaDoc comments in Scala code.

## What changes were proposed in this pull request?

This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes.
(All comment-only changes over 77 files: +786 lines, −747 lines)

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12130 from dongjoon-hyun/use_multiine_javadoc_comments.

5 files changed, 21 insertions, 17 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
index 2c7ffdb7ba..1b0a9a12e8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
@@ -38,9 +38,9 @@ class ElementwiseProduct(override val uid: String)
   def this() = this(Identifiable.randomUID("elemProd"))
 
   /**
-    * the vector to multiply with input vectors
-    * @group param
-    */
+   * the vector to multiply with input vectors
+   * @group param
+   */
   val scalingVec: Param[Vector] = new Param(this, "scalingVec", "vector for hadamard product")
 
   /** @group setParam */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
index a689b09341..364d5eea08 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
@@ -24,15 +24,15 @@ import org.apache.spark.mllib.clustering.GaussianMixtureModel
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
-  * Wrapper around GaussianMixtureModel to provide helper methods in Python
-  */
+ * Wrapper around GaussianMixtureModel to provide helper methods in Python
+ */
 private[python] class GaussianMixtureModelWrapper(model: GaussianMixtureModel) {
   val weights: Vector = Vectors.dense(model.weights)
   val k: Int = weights.size
 
   /**
-    * Returns gaussians as a List of Vectors and Matrices corresponding each MultivariateGaussian
-    */
+   * Returns gaussians as a List of Vectors and Matrices corresponding each MultivariateGaussian
+   */
   val gaussians: Array[Byte] = {
     val modelGaussians = model.gaussians.map { gaussian =>
       Array[Any](gaussian.mu, gaussian.sigma)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/Word2VecModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/Word2VecModelWrapper.scala
index 073f03e16f..05273c3434 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/Word2VecModelWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/Word2VecModelWrapper.scala
@@ -27,8 +27,8 @@ import org.apache.spark.mllib.feature.Word2VecModel
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
-  * Wrapper around Word2VecModel to provide helper methods in Python
-  */
+ * Wrapper around Word2VecModel to provide helper methods in Python
+ */
 private[python] class Word2VecModelWrapper(model: Word2VecModel) {
   def transform(word: String): Vector = {
     model.transform(word)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 6e571fe35a..8c09b69b3c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -123,14 +123,18 @@ sealed trait Matrix extends Serializable {
   @Since("1.4.0")
   def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth)
 
-  /** Map the values of this matrix using a function. Generates a new matrix. Performs the
-    * function on only the backing array. For example, an operation such as addition or
-    * subtraction will only be performed on the non-zero values in a `SparseMatrix`. */
+  /**
+   * Map the values of this matrix using a function. Generates a new matrix. Performs the
+   * function on only the backing array. For example, an operation such as addition or
+   * subtraction will only be performed on the non-zero values in a `SparseMatrix`.
+   */
   private[spark] def map(f: Double => Double): Matrix
 
-  /** Update all the values of this matrix using the function f. Performed in-place on the
-    * backing array. For example, an operation such as addition or subtraction will only be
-    * performed on the non-zero values in a `SparseMatrix`. */
+  /**
+   * Update all the values of this matrix using the function f. Performed in-place on the
+   * backing array. For example, an operation such as addition or subtraction will only be
+   * performed on the non-zero values in a `SparseMatrix`.
+   */
   private[mllib] def update(f: Double => Double): Matrix
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index e8f4422fd4..84764963b5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -81,8 +81,8 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   }
 
   /**
-    * Set the number of iterations of gradient descent to run per update. Default: 50.
-    */
+   * Set the number of iterations of gradient descent to run per update. Default: 50.
+   */
   @Since("1.1.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)