diff options
20 files changed, 93 insertions, 86 deletions
diff --git a/core/src/main/scala/org/apache/spark/Accumulator.scala b/core/src/main/scala/org/apache/spark/Accumulator.scala index 7bea636c94..9d5fbefc82 100644 --- a/core/src/main/scala/org/apache/spark/Accumulator.scala +++ b/core/src/main/scala/org/apache/spark/Accumulator.scala @@ -24,8 +24,7 @@ package org.apache.spark * They can be used to implement counters (as in MapReduce) or sums. Spark natively supports * accumulators of numeric value types, and programmers can add support for new types. * - * An accumulator is created from an initial value `v` by calling - * [[SparkContext#accumulator SparkContext.accumulator]]. + * An accumulator is created from an initial value `v` by calling `SparkContext.accumulator`. * Tasks running on the cluster can then add to it using the `+=` operator. * However, they cannot read its value. Only the driver program can read the accumulator's value, * using its [[#value]] method. diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 766aea213a..9544475ff0 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -166,7 +166,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key). * - * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to + * This method differs from `sampleByKey` in that we make additional passes over the RDD to * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) * over all key values with a 99.99% confidence. When sampling without replacement, we need one * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need @@ -184,7 +184,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key). * - * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to + * This method differs from `sampleByKey` in that we make additional passes over the RDD to * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) * over all key values with a 99.99% confidence. When sampling without replacement, we need one * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index eda16d957c..91ae1002ab 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -393,7 +393,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def treeReduce(f: JFunction2[T, T, T], depth: Int): T = rdd.treeReduce(f, depth) /** - * [[org.apache.spark.api.java.JavaRDDLike#treeReduce]] with suggested depth 2. + * `org.apache.spark.api.java.JavaRDDLike.treeReduce` with suggested depth 2. */ def treeReduce(f: JFunction2[T, T, T]): T = treeReduce(f, 2) @@ -440,7 +440,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { } /** - * [[org.apache.spark.api.java.JavaRDDLike#treeAggregate]] with suggested depth 2. + * `org.apache.spark.api.java.JavaRDDLike.treeAggregate` with suggested depth 2. */ def treeAggregate[U]( zeroValue: U, diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index ec12b9963e..d7bfdbad84 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -109,7 +109,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * functions. This method is here for backward compatibility. It does not provide combiner * classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, @@ -127,7 +127,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * This method is here for backward compatibility. It does not provide combiner * classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, @@ -608,7 +608,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * existing partitioner/parallelism level. This method is here for backward compatibility. It * does not provide combiner classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 374abccf6a..a7e01f397e 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -1841,7 +1841,7 @@ abstract class RDD[T: ClassTag]( * Defines implicit functions that provide extra functionalities on RDDs of specific types. * * For example, [[RDD.rddToPairRDDFunctions]] converts an RDD into a [[PairRDDFunctions]] for - * key-value-pair RDDs, and enabling extra functionalities such as [[PairRDDFunctions.reduceByKey]]. + * key-value-pair RDDs, and enabling extra functionalities such as `PairRDDFunctions.reduceByKey`. */ object RDD { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala index c55a5885ba..b3a3420b84 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala @@ -331,7 +331,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab /** * Merges multiple edges between two vertices into a single edge. For correct results, the graph - * must have been partitioned using [[partitionBy]]. + * must have been partitioned using `partitionBy`. * * @param merge the user-supplied commutative associative function to merge edge attributes * for duplicate edges. diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index 90907300be..475bccf9bf 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -428,7 +428,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali * Compute the connected component membership of each vertex and return a graph with the vertex * value containing the lowest vertex id in the connected component containing that vertex. * - * @see [[org.apache.spark.graphx.lib.ConnectedComponents$#run]] + * @see `org.apache.spark.graphx.lib.ConnectedComponents.run` */ def connectedComponents(): Graph[VertexId, ED] = { ConnectedComponents.run(graph) @@ -438,7 +438,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali * Compute the connected component membership of each vertex and return a graph with the vertex * value containing the lowest vertex id in the connected component containing that vertex. * - * @see [[org.apache.spark.graphx.lib.ConnectedComponents$#run]] + * @see `org.apache.spark.graphx.lib.ConnectedComponents.run` */ def connectedComponents(maxIterations: Int): Graph[VertexId, ED] = { ConnectedComponents.run(graph, maxIterations) diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 08e9cb9ba8..b76dc5f931 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -83,11 +83,11 @@ abstract class PipelineStage extends Params with Logging { /** * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each - * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the - * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will + * of which is either an [[Estimator]] or a [[Transformer]]. When `Pipeline.fit` is called, the + * stages are executed in order. If a stage is an [[Estimator]], its `Estimator.fit` method will * be called on the input dataset to fit a model. Then the model, which is a transformer, will be * used to transform the dataset as the input to the next stage. If a stage is a [[Transformer]], - * its [[Transformer#transform]] method will be called to produce the dataset for the next stage. + * its `Transformer.transform` method will be called to produce the dataset for the next stage. * The fitted model from a [[Pipeline]] is a [[PipelineModel]], which consists of fitted models and * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as * an identity transformer. @@ -121,9 +121,9 @@ class Pipeline @Since("1.4.0") ( /** * Fits the pipeline to the input dataset with additional parameters. If a stage is an - * [[Estimator]], its [[Estimator#fit]] method will be called on the input dataset to fit a model. + * [[Estimator]], its `Estimator.fit` method will be called on the input dataset to fit a model. * Then the model, which is a transformer, will be used to transform the dataset as the input to - * the next stage. If a stage is a [[Transformer]], its [[Transformer#transform]] method will be + * the next stage. If a stage is a [[Transformer]], its `Transformer.transform` method will be * called to produce the dataset for the next stage. The fitted model from a [[Pipeline]] is an * [[PipelineModel]], which consists of fitted models and transformers, corresponding to the * pipeline stages. If there are no stages, the output model acts as an identity transformer. diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 9adb0fa618..ab0620ca75 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -728,7 +728,7 @@ trait Params extends Identifiable with Serializable { } /** - * [[extractParamMap]] with no extra values. + * `extractParamMap` with no extra values. */ final def extractParamMap(): ParamMap = { extractParamMap(ParamMap.empty) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index ae33698209..7fd722a332 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -237,7 +237,7 @@ class LocalLDAModel private[spark] ( vocabSize) /** - * Java-friendly version of [[logLikelihood]] + * Java-friendly version of `logLikelihood` */ @Since("1.5.0") def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = { @@ -259,7 +259,9 @@ class LocalLDAModel private[spark] ( -logLikelihood(documents) / corpusTokenCount } - /** Java-friendly version of [[logPerplexity]] */ + /** + * Java-friendly version of `logPerplexity` + */ @Since("1.5.0") def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = { logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]]) @@ -365,7 +367,9 @@ class LocalLDAModel private[spark] ( } } - /** Get a method usable as a UDF for [[topicDistributions()]] */ + /** + * Get a method usable as a UDF for `topicDistributions()` + */ private[spark] def getTopicDistributionMethod(sc: SparkContext): Vector => Vector = { val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.asBreeze.toDenseMatrix.t).t) val expElogbetaBc = sc.broadcast(expElogbeta) @@ -414,7 +418,7 @@ class LocalLDAModel private[spark] ( } /** - * Java-friendly version of [[topicDistributions]] + * Java-friendly version of `topicDistributions` */ @Since("1.4.1") def topicDistributions( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala index dca031477d..85a90fa959 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala @@ -80,7 +80,9 @@ class AssociationRules private[fpm] ( }.filter(_.confidence >= minConfidence) } - /** Java-friendly version of [[run]]. */ + /** + * Java-friendly version of `run`. + */ @Since("1.5.0") def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]): JavaRDD[Rule[Item]] = { val tag = fakeClassTag[Item] diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala index e3cf0d4979..635da00b69 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala @@ -218,7 +218,9 @@ class FPGrowth private ( new FPGrowthModel(freqItemsets) } - /** Java-friendly version of [[run]]. */ + /** + * Java-friendly version of `run`. + */ @Since("1.3.0") def run[Item, Basket <: JavaIterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item] = { implicit val tag = fakeClassTag[Item] 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 index 8979707666..07a67a9e71 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala @@ -279,7 +279,7 @@ object GradientDescent extends Logging { } /** - * Alias of [[runMiniBatchSGD]] with convergenceTol set to default value of 0.001. + * Alias of `runMiniBatchSGD` with convergenceTol set to default value of 0.001. */ def runMiniBatchSGD( data: RDD[(Double, Vector)], diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala index 85d4d7f37f..258b1763bb 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala @@ -57,7 +57,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#uniformRDD]]. + * Java-friendly version of `RandomRDDs.uniformRDD`. */ @Since("1.1.0") def uniformJavaRDD( @@ -69,7 +69,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#uniformJavaRDD]] with the default seed. + * `RandomRDDs.uniformJavaRDD` with the default seed. */ @Since("1.1.0") def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = { @@ -77,7 +77,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#uniformJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.uniformJavaRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = { @@ -107,7 +107,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#normalRDD]]. + * Java-friendly version of `RandomRDDs.normalRDD`. */ @Since("1.1.0") def normalJavaRDD( @@ -119,7 +119,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#normalJavaRDD]] with the default seed. + * `RandomRDDs.normalJavaRDD` with the default seed. */ @Since("1.1.0") def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = { @@ -127,7 +127,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#normalJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.normalJavaRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = { @@ -157,7 +157,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#poissonRDD]]. + * Java-friendly version of `RandomRDDs.poissonRDD`. */ @Since("1.1.0") def poissonJavaRDD( @@ -170,7 +170,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#poissonJavaRDD]] with the default seed. + * `RandomRDDs.poissonJavaRDD` with the default seed. */ @Since("1.1.0") def poissonJavaRDD( @@ -182,7 +182,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#poissonJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.poissonJavaRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = { @@ -212,7 +212,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#exponentialRDD]]. + * Java-friendly version of `RandomRDDs.exponentialRDD`. */ @Since("1.3.0") def exponentialJavaRDD( @@ -225,7 +225,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#exponentialJavaRDD]] with the default seed. + * `RandomRDDs.exponentialJavaRDD` with the default seed. */ @Since("1.3.0") def exponentialJavaRDD( @@ -237,7 +237,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#exponentialJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.exponentialJavaRDD` with the default number of partitions and the default seed. */ @Since("1.3.0") def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = { @@ -269,7 +269,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#gammaRDD]]. + * Java-friendly version of `RandomRDDs.gammaRDD`. */ @Since("1.3.0") def gammaJavaRDD( @@ -283,7 +283,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#gammaJavaRDD]] with the default seed. + * `RandomRDDs.gammaJavaRDD` with the default seed. */ @Since("1.3.0") def gammaJavaRDD( @@ -296,7 +296,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#gammaJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.gammaJavaRDD` with the default number of partitions and the default seed. */ @Since("1.3.0") def gammaJavaRDD( @@ -332,7 +332,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#logNormalRDD]]. + * Java-friendly version of `RandomRDDs.logNormalRDD`. */ @Since("1.3.0") def logNormalJavaRDD( @@ -346,7 +346,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#logNormalJavaRDD]] with the default seed. + * `RandomRDDs.logNormalJavaRDD` with the default seed. */ @Since("1.3.0") def logNormalJavaRDD( @@ -359,7 +359,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#logNormalJavaRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.logNormalJavaRDD` with the default number of partitions and the default seed. */ @Since("1.3.0") def logNormalJavaRDD( @@ -419,7 +419,7 @@ object RandomRDDs { /** * :: DeveloperApi :: - * [[RandomRDDs#randomJavaRDD]] with the default seed. + * `RandomRDDs.randomJavaRDD` with the default seed. */ @DeveloperApi @Since("1.6.0") @@ -433,7 +433,7 @@ object RandomRDDs { /** * :: DeveloperApi :: - * [[RandomRDDs#randomJavaRDD]] with the default seed & numPartitions + * `RandomRDDs.randomJavaRDD` with the default seed & numPartitions */ @DeveloperApi @Since("1.6.0") @@ -469,7 +469,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#uniformVectorRDD]]. + * Java-friendly version of `RandomRDDs.uniformVectorRDD`. */ @Since("1.1.0") def uniformJavaVectorRDD( @@ -482,7 +482,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#uniformJavaVectorRDD]] with the default seed. + * `RandomRDDs.uniformJavaVectorRDD` with the default seed. */ @Since("1.1.0") def uniformJavaVectorRDD( @@ -494,7 +494,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#uniformJavaVectorRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.uniformJavaVectorRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def uniformJavaVectorRDD( @@ -527,7 +527,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#normalVectorRDD]]. + * Java-friendly version of `RandomRDDs.normalVectorRDD`. */ @Since("1.1.0") def normalJavaVectorRDD( @@ -540,7 +540,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#normalJavaVectorRDD]] with the default seed. + * `RandomRDDs.normalJavaVectorRDD` with the default seed. */ @Since("1.1.0") def normalJavaVectorRDD( @@ -552,7 +552,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#normalJavaVectorRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.normalJavaVectorRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def normalJavaVectorRDD( @@ -590,7 +590,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#logNormalVectorRDD]]. + * Java-friendly version of `RandomRDDs.logNormalVectorRDD`. */ @Since("1.3.0") def logNormalJavaVectorRDD( @@ -605,7 +605,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#logNormalJavaVectorRDD]] with the default seed. + * `RandomRDDs.logNormalJavaVectorRDD` with the default seed. */ @Since("1.3.0") def logNormalJavaVectorRDD( @@ -619,7 +619,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#logNormalJavaVectorRDD]] with the default number of partitions and + * `RandomRDDs.logNormalJavaVectorRDD` with the default number of partitions and * the default seed. */ @Since("1.3.0") @@ -657,7 +657,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#poissonVectorRDD]]. + * Java-friendly version of `RandomRDDs.poissonVectorRDD`. */ @Since("1.1.0") def poissonJavaVectorRDD( @@ -671,7 +671,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#poissonJavaVectorRDD]] with the default seed. + * `RandomRDDs.poissonJavaVectorRDD` with the default seed. */ @Since("1.1.0") def poissonJavaVectorRDD( @@ -684,7 +684,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#poissonJavaVectorRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.poissonJavaVectorRDD` with the default number of partitions and the default seed. */ @Since("1.1.0") def poissonJavaVectorRDD( @@ -721,7 +721,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#exponentialVectorRDD]]. + * Java-friendly version of `RandomRDDs.exponentialVectorRDD`. */ @Since("1.3.0") def exponentialJavaVectorRDD( @@ -735,7 +735,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#exponentialJavaVectorRDD]] with the default seed. + * `RandomRDDs.exponentialJavaVectorRDD` with the default seed. */ @Since("1.3.0") def exponentialJavaVectorRDD( @@ -748,7 +748,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#exponentialJavaVectorRDD]] with the default number of partitions + * `RandomRDDs.exponentialJavaVectorRDD` with the default number of partitions * and the default seed. */ @Since("1.3.0") @@ -788,7 +788,7 @@ object RandomRDDs { } /** - * Java-friendly version of [[RandomRDDs#gammaVectorRDD]]. + * Java-friendly version of `RandomRDDs.gammaVectorRDD`. */ @Since("1.3.0") def gammaJavaVectorRDD( @@ -803,7 +803,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#gammaJavaVectorRDD]] with the default seed. + * `RandomRDDs.gammaJavaVectorRDD` with the default seed. */ @Since("1.3.0") def gammaJavaVectorRDD( @@ -817,7 +817,7 @@ object RandomRDDs { } /** - * [[RandomRDDs#gammaJavaVectorRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.gammaJavaVectorRDD` with the default number of partitions and the default seed. */ @Since("1.3.0") def gammaJavaVectorRDD( @@ -857,7 +857,7 @@ object RandomRDDs { /** * :: DeveloperApi :: - * Java-friendly version of [[RandomRDDs#randomVectorRDD]]. + * Java-friendly version of `RandomRDDs.randomVectorRDD`. */ @DeveloperApi @Since("1.6.0") @@ -873,7 +873,7 @@ object RandomRDDs { /** * :: DeveloperApi :: - * [[RandomRDDs#randomJavaVectorRDD]] with the default seed. + * `RandomRDDs.randomJavaVectorRDD` with the default seed. */ @DeveloperApi @Since("1.6.0") @@ -888,7 +888,7 @@ object RandomRDDs { /** * :: DeveloperApi :: - * [[RandomRDDs#randomJavaVectorRDD]] with the default number of partitions and the default seed. + * `RandomRDDs.randomJavaVectorRDD` with the default number of partitions and the default seed. */ @DeveloperApi @Since("1.6.0") 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 index 499c80767a..e5aece7798 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -83,8 +83,8 @@ object DecisionTree extends Serializable with Logging { * categorical), depth of the tree, quantile calculation strategy, etc. * @return DecisionTreeModel that can be used for prediction. * - * @note Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] - * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] + * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier` + * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor` * is recommended to clearly separate classification and regression. */ @Since("1.0.0") @@ -105,8 +105,8 @@ object DecisionTree extends Serializable with Logging { * 1 internal node + 2 leaf nodes). * @return DecisionTreeModel that can be used for prediction. * - * @note Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] - * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] + * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier` + * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor` * is recommended to clearly separate classification and regression. */ @Since("1.0.0") @@ -133,8 +133,8 @@ object DecisionTree extends Serializable with Logging { * @param numClasses Number of classes for classification. Default value of 2. * @return DecisionTreeModel that can be used for prediction. * - * @note Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] - * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] + * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier` + * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor` * is recommended to clearly separate classification and regression. */ @Since("1.2.0") @@ -167,8 +167,8 @@ object DecisionTree extends Serializable with Logging { * indexed from 0: {0, 1, ..., k-1}. * @return DecisionTreeModel that can be used for prediction. * - * @note Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] - * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] + * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier` + * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor` * is recommended to clearly separate classification and regression. */ @Since("1.0.0") @@ -218,7 +218,7 @@ object DecisionTree extends Serializable with Logging { } /** - * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] + * Java-friendly API for `org.apache.spark.mllib.tree.DecisionTree.trainClassifier` */ @Since("1.1.0") def trainClassifier( @@ -262,7 +262,7 @@ object DecisionTree extends Serializable with Logging { } /** - * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] + * Java-friendly API for `org.apache.spark.mllib.tree.DecisionTree.trainRegressor` */ @Since("1.1.0") def trainRegressor( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index 3e85678906..df2c1b02f4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -136,7 +136,7 @@ object GradientBoostedTrees extends Logging { } /** - * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]] + * Java-friendly API for `org.apache.spark.mllib.tree.GradientBoostedTrees.train` */ @Since("1.2.0") def train( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala index 1f6cb086ce..d1331a57de 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala @@ -172,7 +172,7 @@ object RandomForest extends Serializable with Logging { } /** - * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainClassifier]] + * Java-friendly API for `org.apache.spark.mllib.tree.RandomForest.trainClassifier` */ @Since("1.2.0") def trainClassifier( @@ -259,7 +259,7 @@ object RandomForest extends Serializable with Logging { } /** - * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainRegressor]] + * Java-friendly API for `org.apache.spark.mllib.tree.RandomForest.trainRegressor` */ @Since("1.2.0") def trainRegressor( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala index 6bb3271aac..de66c7ca1d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala @@ -149,7 +149,7 @@ object MLUtils extends Logging { * Save labeled data in LIBSVM format. * @param data an RDD of LabeledPoint to be saved * @param dir directory to save the data - * @see [[org.apache.spark.mllib.util.MLUtils#loadLibSVMFile]] + * @see `org.apache.spark.mllib.util.MLUtils.loadLibSVMFile` */ @Since("1.0.0") def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: String) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala index 2caf723669..375df64d39 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala @@ -259,7 +259,7 @@ abstract class SQLImplicits extends LowPrioritySQLImplicits { * Conflicting implicits are placed here to disambiguate resolution. * * Reasons for including specific implicits: - * newProductEncoder - to disambiguate for [[List]]s which are both [[Seq]] and [[Product]] + * newProductEncoder - to disambiguate for `List`s which are both `Seq` and `Product` */ trait LowPrioritySQLImplicits { /** @since 1.6.0 */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 9a080fd3c9..cabe1f4563 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -633,7 +633,7 @@ object functions { def skewness(columnName: String): Column = skewness(Column(columnName)) /** - * Aggregate function: alias for [[stddev_samp]]. + * Aggregate function: alias for `stddev_samp`. * * @group agg_funcs * @since 1.6.0 @@ -641,7 +641,7 @@ object functions { def stddev(e: Column): Column = withAggregateFunction { StddevSamp(e.expr) } /** - * Aggregate function: alias for [[stddev_samp]]. + * Aggregate function: alias for `stddev_samp`. * * @group agg_funcs * @since 1.6.0 @@ -717,7 +717,7 @@ object functions { def sumDistinct(columnName: String): Column = sumDistinct(Column(columnName)) /** - * Aggregate function: alias for [[var_samp]]. + * Aggregate function: alias for `var_samp`. * * @group agg_funcs * @since 1.6.0 @@ -725,7 +725,7 @@ object functions { def variance(e: Column): Column = withAggregateFunction { VarianceSamp(e.expr) } /** - * Aggregate function: alias for [[var_samp]]. + * Aggregate function: alias for `var_samp`. * * @group agg_funcs * @since 1.6.0 |