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author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-06-06 09:35:47 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-06-06 09:35:47 +0100 |
commit | fd8af397132fa1415a4c19d7f5cb5a41aa6ddb27 (patch) | |
tree | a653b3542d0671c8cb8b3ff7fa3755525c0606a4 /mllib/src | |
parent | 32f2f95dbdfb21491e46d4b608fd4e8ac7ab8973 (diff) | |
download | spark-fd8af397132fa1415a4c19d7f5cb5a41aa6ddb27.tar.gz spark-fd8af397132fa1415a4c19d7f5cb5a41aa6ddb27.tar.bz2 spark-fd8af397132fa1415a4c19d7f5cb5a41aa6ddb27.zip |
[MINOR] Fix Typos 'an -> a'
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #13515 from zhengruifeng/an_a.
Diffstat (limited to 'mllib/src')
9 files changed, 10 insertions, 10 deletions
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 b02aea92b7..25e56d70c2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -82,7 +82,7 @@ abstract class PipelineStage extends Params with Logging { * 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. - * The fitted model from a [[Pipeline]] is an [[PipelineModel]], which consists of fitted models and + * 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. */ diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index cecf3c43df..1ea4d90e16 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -854,7 +854,7 @@ class BinaryLogisticRegressionSummary private[classification] ( /** * Returns the receiver operating characteristic (ROC) curve, - * which is an Dataframe having two fields (FPR, TPR) + * which is a Dataframe having two fields (FPR, TPR) * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. * * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]]. @@ -874,7 +874,7 @@ class BinaryLogisticRegressionSummary private[classification] ( lazy val areaUnderROC: Double = binaryMetrics.areaUnderROC() /** - * Returns the precision-recall curve, which is an Dataframe containing + * Returns the precision-recall curve, which is a Dataframe containing * two fields recall, precision with (0.0, 1.0) prepended to it. * * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]]. diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala index be3792eb77..71c8c42ce5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala @@ -491,7 +491,7 @@ private[spark] object RandomForest extends Logging { timer.start("chooseSplits") // In each partition, iterate all instances and compute aggregate stats for each node, - // yield an (nodeIndex, nodeAggregateStats) pair for each node. + // yield a (nodeIndex, nodeAggregateStats) pair for each node. // After a `reduceByKey` operation, // stats of a node will be shuffled to a particular partition and be combined together, // then best splits for nodes are found there. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index e4cc784cfe..e4cbf5acbc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -86,7 +86,7 @@ class LogisticRegressionModel @Since("1.3.0") ( /** * Sets the threshold that separates positive predictions from negative predictions * in Binary Logistic Regression. An example with prediction score greater than or equal to - * this threshold is identified as an positive, and negative otherwise. The default value is 0.5. + * this threshold is identified as a positive, and negative otherwise. The default value is 0.5. * It is only used for binary classification. */ @Since("1.0.0") diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index 783c1c835b..7c3ccbb40b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -44,7 +44,7 @@ class SVMModel @Since("1.1.0") ( /** * Sets the threshold that separates positive predictions from negative predictions. An example - * with prediction score greater than or equal to this threshold is identified as an positive, + * with prediction score greater than or equal to this threshold is identified as a positive, * and negative otherwise. The default value is 0.0. */ @Since("1.0.0") diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala index ca7385128d..9db725097a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala @@ -53,7 +53,7 @@ trait VectorTransformer extends Serializable { } /** - * Applies transformation on an JavaRDD[Vector]. + * Applies transformation on a JavaRDD[Vector]. * * @param data JavaRDD[Vector] to be transformed. * @return transformed JavaRDD[Vector]. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 02fd60da7d..91f065831c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -731,7 +731,7 @@ object DenseVector { } /** - * A sparse vector represented by an index array and an value array. + * A sparse vector represented by an index array and a value array. * * @param size size of the vector. * @param indices index array, assume to be strictly increasing. 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 index 97b03b340f..008b03d1cc 100644 --- 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 @@ -24,7 +24,7 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors} import org.apache.spark.rdd.RDD /** - * Represents an entry in an distributed matrix. + * Represents an entry in a distributed matrix. * @param i row index * @param j column index * @param value value of the entry diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala index af4bc9f290..e28e1af5b0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala @@ -47,7 +47,7 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Se combOp = (queue1, queue2) => { queue1 ++= queue2 } - ).mapValues(_.toArray.sorted(ord.reverse)) // This is an min-heap, so we reverse the order. + ).mapValues(_.toArray.sorted(ord.reverse)) // This is a min-heap, so we reverse the order. } } |