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author | Dongjoon Hyun <dongjoon@apache.org> | 2016-04-02 17:50:40 -0700 |
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committer | Reynold Xin <rxin@databricks.com> | 2016-04-02 17:50:40 -0700 |
commit | 4a6e78abd9d5edc4a5092738dff0006bbe202a89 (patch) | |
tree | 5ecbee86bb057139128b65b0f99405c51e637e38 /mllib/src/main | |
parent | f705037617d55bb479ec60bcb1e55c736224be94 (diff) | |
download | spark-4a6e78abd9d5edc4a5092738dff0006bbe202a89.tar.gz spark-4a6e78abd9d5edc4a5092738dff0006bbe202a89.tar.bz2 spark-4a6e78abd9d5edc4a5092738dff0006bbe202a89.zip |
[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.
Diffstat (limited to 'mllib/src/main')
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) |