[MINOR] Fix Typos 'a -> an'

## What changes were proposed in this pull request?

`a` -> `an`

I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.

## How was this patch tested?

local build
`lint-java` checking

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13317 from zhengruifeng/a_an.

7 files changed, 8 insertions, 8 deletions

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 0ab4459bdb..61e355ab9f 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
@@ -686,7 +686,7 @@ object LogisticRegressionModel extends MLReadable[LogisticRegressionModel] {
 /**
  * MultiClassSummarizer computes the number of distinct labels and corresponding counts,
  * and validates the data to see if the labels used for k class multi-label classification
- * are in the range of {0, 1, ..., k - 1} in a online fashion.
+ * are in the range of {0, 1, ..., k - 1} in an online fashion.
  *
  * Two MultilabelSummarizer can be merged together to have a statistical summary of the
  * corresponding joint dataset.
@@ -923,7 +923,7 @@ class BinaryLogisticRegressionSummary private[classification] (
 
 /**
  * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used
- * in binary classification for instances in sparse or dense vector in a online fashion.
+ * in binary classification for instances in sparse or dense vector in an online fashion.
  *
  * Note that multinomial logistic loss is not supported yet!
  *
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 82f2de7ccd..ecec61a72f 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
@@ -790,7 +790,7 @@ trait Params extends Identifiable with Serializable {
  * :: DeveloperApi ::
  * Java-friendly wrapper for [[Params]].
  * Java developers who need to extend [[Params]] should use this class instead.
- * If you need to extend a abstract class which already extends [[Params]], then that abstract
+ * If you need to extend an abstract class which already extends [[Params]], then that abstract
  * class should be Java-friendly as well.
  */
 @DeveloperApi
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
index e63eb71080..00ef6ccc74 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
@@ -396,7 +396,7 @@ object AFTSurvivalRegressionModel extends MLReadable[AFTSurvivalRegressionModel]
 
 /**
  * AFTAggregator computes the gradient and loss for a AFT loss function,
- * as used in AFT survival regression for samples in sparse or dense vector in a online fashion.
+ * as used in AFT survival regression for samples in sparse or dense vector in an online fashion.
  *
  * The loss function and likelihood function under the AFT model based on:
  * Lawless, J. F., Statistical Models and Methods for Lifetime Data,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 37552194c5..4d66b0eb37 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -731,7 +731,7 @@ class LinearRegressionSummary private[regression] (
 
 /**
  * LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function,
- * as used in linear regression for samples in sparse or dense vector in a online fashion.
+ * as used in linear regression for samples in sparse or dense vector in an online fashion.
  *
  * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of
  * the corresponding joint dataset.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
index b836d2a234..7d12f447f7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
@@ -74,7 +74,7 @@ class ParamGridBuilder @Since("1.2.0") {
   }
 
   /**
-   * Adds a int param with multiple values.
+   * Adds an int param with multiple values.
    */
   @Since("1.2.0")
   def addGrid(param: IntParam, values: Array[Int]): this.type = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala
index 1d2d777c00..b0fa287473 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala
@@ -126,7 +126,7 @@ private[fpm] object FPTree {
     def isRoot: Boolean = parent == null
   }
 
-  /** Summary of a item in an FP-Tree. */
+  /** Summary of an item in an FP-Tree. */
   private class Summary[T] extends Serializable {
     var count: Long = 0L
     val nodes: ListBuffer[Node[T]] = ListBuffer.empty
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 98404be260..d4de0fd7d5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -24,7 +24,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  * :: DeveloperApi ::
  * MultivariateOnlineSummarizer implements [[MultivariateStatisticalSummary]] to compute the mean,
  * variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector
- * format in a online fashion.
+ * format in an online fashion.
  *
  * Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of
  * the corresponding joint dataset.