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我正在尝试实施KMeans using Apache Spark.

val data = sc.textFile(irisDatasetString)
val parsedData = data.map(_.split(',').map(_.toDouble)).cache()

val clusters = KMeans.train(parsedData,3,numIterations = 20)

我收到以下错误：

error: overloaded method value train with alternatives:
  (data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector],k: Int,maxIterations: Int,runs: Int)org.apache.spark.mllib.clustering.KMeansModel <and>
  (data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector],k: Int,maxIterations: Int)org.apache.spark.mllib.clustering.KMeansModel <and>
  (data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector],k: Int,maxIterations: Int,runs: Int,initializationMode: String)org.apache.spark.mllib.clustering.KMeansModel
 cannot be applied to (org.apache.spark.rdd.RDD[Array[Double]], Int, numIterations: Int)
       val clusters = KMeans.train(parsedData,3,numIterations = 20)

所以我尝试将 Array[Double] 转换为 Vector，如下所示

scala> val vectorData: Vector = Vectors.dense(parsedData)

我收到以下错误：

error: type Vector takes type parameters
   val vectorData: Vector = Vectors.dense(parsedData)
                   ^
error: overloaded method value dense with alternatives:
  (values: Array[Double])org.apache.spark.mllib.linalg.Vector <and>
  (firstValue: Double,otherValues: Double*)org.apache.spark.mllib.linalg.Vector
 cannot be applied to (org.apache.spark.rdd.RDD[Array[Double]])
       val vectorData: Vector = Vectors.dense(parsedData)

所以我推断这org.apache.spark.rdd.RDD[Array[Double]]与 Array[Double] 不一样

我该如何处理我的数据org.apache.spark.rdd.RDD[Array[Double]]？或者我该如何转换org.apache.spark.rdd.RDD[Array[Double]] to Array[Double]？

4

1 回答 1

6

KMeans.train期待RDD[Vector]而不是RDD[Array[Double]]. 在我看来，你需要做的就是改变

val parsedData = data.map(_.split(',').map(_.toDouble)).cache()

至

val parsedData = data.map(x => Vectors.dense(x.split(',').map(_.toDouble))).cache()
于 2015-01-08T20:17:41.420 回答