[SPARK-1357 (fix)] remove empty line after :: DeveloperApi/Experiment…

…al :: Remove empty line after :: DeveloperApi/Experimental :: in comments to make the original doc show up in the preview of the generated html docs. Thanks @andrewor14 ! Author: Xiangrui Meng <meng@databricks.com> Closes #373 from mengxr/api and squashes the following commits: 9c35bdc [Xiangrui Meng] remove the empty line after :: DeveloperApi/Experimental ::
amplab · Apr 10, 2014 · 0adc932 · 0adc932
1 parent eb5f2b6
commit 0adc932
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Showing 33 changed files with 21 additions and 71 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -30,7 +30,6 @@ import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
- *
  * The Java stubs necessary for the Python mllib bindings.
  */
 @DeveloperApi

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -29,7 +29,6 @@ import org.apache.spark.rdd.RDD
 
 /**
  * :: Experimental ::
- *
  * Model for Naive Bayes Classifiers.
  *
  * @param labels list of labels

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -78,7 +78,6 @@ class KMeans private (
 
   /**
    * :: Experimental ::
-   *
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
@@ -398,9 +397,6 @@ object KMeans {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
 
-  /**
-   * :: Experimental ::
-   */
   @Experimental
   def main(args: Array[String]) {
     if (args.length < 4) {

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -34,7 +34,6 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
 
 /**
  * :: Experimental ::
- *
  * Represents a matrix in coordinate format.
  *
  * @param entries matrix entries

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -26,15 +26,13 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 
 /**
  * :: Experimental ::
- *
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
  */
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
 
 /**
  * :: Experimental ::
- *
  * Represents a row-oriented [[org.apache.spark.mllib.linalg.distributed.DistributedMatrix]] with
  * indexed rows.
  *

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -30,7 +30,6 @@ import org.apache.spark.Logging
 
 /**
  * :: Experimental ::
- *
  * Represents a row-oriented distributed Matrix with no meaningful row indices.
  *
  * @param rows rows stored as an RDD[Vector]

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
  * :: DeveloperApi ::
- *
  * Class used to compute the gradient for a loss function, given a single data point.
  */
 @DeveloperApi
@@ -56,7 +55,6 @@ abstract class Gradient extends Serializable {
 
 /**
  * :: DeveloperApi ::
- *
  * Compute gradient and loss for a logistic loss function, as used in binary classification.
  * See also the documentation for the precise formulation.
  */
@@ -100,7 +98,6 @@ class LogisticGradient extends Gradient {
 
 /**
  * :: DeveloperApi ::
- *
  * Compute gradient and loss for a Least-squared loss function, as used in linear regression.
  * This is correct for the averaged least squares loss function (mean squared error)
  *              L = 1/n ||A weights-y||^2
@@ -135,7 +132,6 @@ class LeastSquaresGradient extends Gradient {
 
 /**
  * :: DeveloperApi ::
- *
  * Compute gradient and loss for a Hinge loss function, as used in SVM binary classification.
  * See also the documentation for the precise formulation.
  * NOTE: This assumes that the labels are {0,1}

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -28,7 +28,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
  * :: DeveloperApi ::
- *
  * Class used to solve an optimization problem using Gradient Descent.
  * @param gradient Gradient function to be used.
  * @param updater Updater to be used to update weights after every iteration.
@@ -113,7 +112,6 @@ class GradientDescent(private var gradient: Gradient, private var updater: Updat
 
 /**
  * :: DeveloperApi ::
- *
  * Top-level method to run gradient descent.
  */
 @DeveloperApi

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector
 
 /**
  * :: DeveloperApi ::
- *
  * Trait for optimization problem solvers.
  */
 @DeveloperApi

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
@@ -26,7 +26,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
  * :: DeveloperApi ::
- *
  * Class used to perform steps (weight update) using Gradient Descent methods.
  *
  * For general minimization problems, or for regularized problems of the form
@@ -64,7 +63,6 @@ abstract class Updater extends Serializable {
 
 /**
  * :: DeveloperApi ::
- *
  * A simple updater for gradient descent *without* any regularization.
  * Uses a step-size decreasing with the square root of the number of iterations.
  */
@@ -86,7 +84,6 @@ class SimpleUpdater extends Updater {
 
 /**
  * :: DeveloperApi ::
- *
  * Updater for L1 regularized problems.
  *          R(w) = ||w||_1
  * Uses a step-size decreasing with the square root of the number of iterations.
@@ -131,7 +128,6 @@ class L1Updater extends Updater {
 
 /**
  * :: DeveloperApi ::
- *
  * Updater for L2 regularized problems.
  *          R(w) = 1/2 ||w||^2
  * Uses a step-size decreasing with the square root of the number of iterations.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -140,7 +140,6 @@ class ALS private (
 
   /**
    * :: Experimental ::
-   *
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */
   @Experimental

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -70,7 +70,6 @@ class MatrixFactorizationModel(
 
   /**
    * :: DeveloperApi ::
-   *
    * Predict the rating of many users for many products.
    * This is a Java stub for python predictAll()
    *

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -103,7 +103,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * :: Experimental ::
-   *
    * Set if the algorithm should validate data before training. Default true.
    */
   @Experimental

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -35,7 +35,6 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * :: Experimental ::
- *
  * A class that implements a decision tree algorithm for classification and regression. It
  * supports both continuous and categorical features.
  * @param strategy The configuration parameters for the tree algorithm which specify the type

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- *
  * Enum to select the algorithm for the decision tree
  */
 @Experimental

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- *
  * Enum to describe whether a feature is "continuous" or "categorical"
  */
 @Experimental

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- *
  * Enum for selecting the quantile calculation strategy
  */
 @Experimental

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 
 /**
  * :: Experimental ::
- *
  * Stores all the configuration options for tree construction
  * @param algo classification or regression
  * @param impurity criterion used for information gain calculation

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 
 /**
  * :: Experimental ::
- *
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
  */
@@ -32,7 +31,6 @@ object Entropy extends Impurity {
 
   /**
    * :: DeveloperApi ::
-   *
    * entropy calculation
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 
 /**
  * :: Experimental ::
- *
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
@@ -31,7 +30,6 @@ object Gini extends Impurity {
 
   /**
    * :: DeveloperApi ::
-   *
    * Gini coefficient calculation
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -21,15 +21,13 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 
 /**
  * :: Experimental ::
- *
  * Trait for calculating information gain.
  */
 @Experimental
 trait Impurity extends Serializable {
 
   /**
    * :: DeveloperApi ::
-   *
    * information calculation for binary classification
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1
@@ -40,7 +38,6 @@ trait Impurity extends Serializable {
 
   /**
    * :: DeveloperApi ::
-   *
    * information calculation for regression
    * @param count number of instances
    * @param sum sum of labels

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 
 /**
  * :: Experimental ::
- *
  * Class for calculating variance during regression
  */
 @Experimental
@@ -31,7 +30,6 @@ object Variance extends Impurity {
 
   /**
    * :: DeveloperApi ::
-   *
    * variance calculation
    * @param count number of instances
    * @param sum sum of labels

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector
 
 /**
  * :: Experimental ::
- *
  * Model to store the decision tree parameters
  * @param topNode root node
  * @param algo algorithm type -- classification or regression

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -21,7 +21,6 @@ import org.apache.spark.annotation.DeveloperApi
 
 /**
  * :: DeveloperApi ::
- *
  * Information gain statistics for each split
  * @param gain information gain value
  * @param impurity current node impurity

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector
 
 /**
  * :: DeveloperApi ::
- *
  * Node in a decision tree
  * @param id integer node id
  * @param predict predicted value at the node

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -22,7 +22,6 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 
 /**
  * :: DeveloperApi ::
- *
  * Split applied to a feature
  * @param feature feature index
  * @param threshold threshold for continuous feature

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
@@ -24,7 +24,6 @@ import org.apache.spark.mllib.regression.LabeledPoint
 
 /**
  * :: DeveloperApi ::
- *
  * A collection of methods used to validate data before applying ML algorithms.
  */
 @DeveloperApi

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
@@ -25,7 +25,6 @@ import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
- *
  * Generate test data for KMeans. This class first chooses k cluster centers
  * from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian
  * cluster with scale 1 around each center.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -30,7 +30,6 @@ import org.apache.spark.mllib.regression.LabeledPoint
 
 /**
  * :: DeveloperApi ::
- *
  * Generate sample data used for Linear Data. This class generates
  * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the
  * response variable `Y`.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -27,7 +27,6 @@ import org.apache.spark.mllib.linalg.Vectors
 
 /**
  * :: DeveloperApi ::
- *
  * Generate test data for LogisticRegression. This class chooses positive labels
  * with probability `probOne` and scales features for positive examples by `eps`.
  */