在Apache Spark Scala中，如何从CSV填充DataFrame中的Vectors.dense？

import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

val spark = SparkSession.builder()
  .appName("CSV to DataFrame")
  .master("local")
  .getOrCreate()

val csvPath = "path/to/csv/file.csv"
val df = spark.read.format("csv")
  .option("header", "true")
  .option("inferSchema", "true")
  .load(csvPath)

col1,col2,col3
1.0,2.0,3.0
4.0,5.0,6.0

val vectorCol = "features"
val inputCols = df.columns.filter(_ != vectorCol)

val dfWithVectors = df.withColumn(vectorCol, udf((cols: Seq[Double]) => Vectors.dense(cols.toArray)).apply(array(inputCols.map(col): _*)))