如何在spark中将矩阵转换为RDD [Vector]

Question

How to convert from org.apache.spark.mllib.linalg.Matrix to RDD[org.apache.spark.mllib.linalg.Vector] in Spark?

The matrix is generated from SVD, and I am using the results from SVD to do clustering analysis.

Answer 1

MLlib's Matrix is a small local matrix. It would probably be more efficient to analyze it locally instead of turning it into an RDD.

Anyway, if your clustering only supports RDD as its input, here's how you can do the transformation:

import org.apache.spark.mllib.linalg._
def toRDD(m: Matrix): RDD[Vector] = {
  val columns = m.toArray.grouped(m.numRows)
  val rows = columns.toSeq.transpose // Skip this if you want a column-major RDD.
  val vectors = rows.map(row => new DenseVector(row.toArray))
  sc.parallelize(vectors)
}