From aa41dcea4a41899507dfe4ec1eceaabb5edf728f Mon Sep 17 00:00:00 2001
From: zhengruifeng <ruifengz@foxmail.com>
Date: Fri, 12 Jul 2019 11:00:16 -0700
Subject: [PATCH] [SPARK-28159][ML][FOLLOWUP] fix typo & (0 until
 v.size).toList => List.range(0, v.size)

## What changes were proposed in this pull request?
fix typo in spark-28159
`transfromWithMean` -> `transformWithMean`

## How was this patch tested?
existing test

Closes #25129 from zhengruifeng/to_ml_vec_cleanup.

Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
---
 mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala | 2 +-
 .../scala/org/apache/spark/ml/feature/StandardScaler.scala    | 2 +-
 .../scala/org/apache/spark/mllib/feature/StandardScaler.scala | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
index aa81037014..681bb95156 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
@@ -490,7 +490,7 @@ abstract class LDAModel private[ml] (
         Vectors.zeros(k)
       } else {
         val (ids: List[Int], cts: Array[Double]) = vector match {
-          case v: DenseVector => ((0 until v.size).toList, v.values)
+          case v: DenseVector => (List.range(0, v.size), v.values)
           case v: SparseVector => (v.indices.toList, v.values)
           case other =>
             throw new UnsupportedOperationException(
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index 17f2c17c95..81cf2e1a4f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -169,7 +169,7 @@ class StandardScalerModel private[ml] (
           case d: DenseVector => d.values.clone()
           case v: Vector => v.toArray
         }
-        val newValues = scaler.transfromWithMean(values)
+        val newValues = scaler.transformWithMean(values)
         Vectors.dense(newValues)
     } else if ($(withStd)) {
       vector: Vector =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index 578b779cd5..19e53e7eac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -141,7 +141,7 @@ class StandardScalerModel @Since("1.3.0") (
         case d: DenseVector => d.values.clone()
         case v: Vector => v.toArray
       }
-      val newValues = transfromWithMean(values)
+      val newValues = transformWithMean(values)
       Vectors.dense(newValues)
     } else if (withStd) {
       vector match {
@@ -161,7 +161,7 @@ class StandardScalerModel @Since("1.3.0") (
     }
   }
 
-  private[spark] def transfromWithMean(values: Array[Double]): Array[Double] = {
+  private[spark] def transformWithMean(values: Array[Double]): Array[Double] = {
     // By default, Scala generates Java methods for member variables. So every time when
     // the member variables are accessed, `invokespecial` will be called which is expensive.
     // This can be avoid by having a local reference of `shift`.