Operations on a Distributed Block Matrix in scala spark





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I am forming a distributed block matrix from an indexed row matrix by controlling the size of the blocks formed.



var populationSize   = 1000000
var chromosomeLength = 4
var mutationRate = 0.05
var mutationNumber = 1000000*(mutationRate)

var populationMatrix = new IndexedRowMatrix(
RandomRDDs.uniformVectorRDD(sc, populationSize, chromosomeLength).zipWithIndex.map {case (v, i) => IndexedRow(i, v)}
)

var mutationBlockMatrix = populationMatrix.toBlockMatrix(mutationNumber.toInt,chromosomeLength)


Now only one random row from these distributed blocks needs to be selected in a distributed manner and then collected into moved into another distributed matrix



var s = mutationBlockMatrix.colsPerBlock
var r = mutationBlockMatrix.rowsPerBlock
var t = mutationBlockMatrix.numRowBlocks
var w = mutationBlockMatrix.blocks.toDF


s: Int = 4
r: Int = 50000
t: Int = 20
w: org.apache.spark.sql.DataFrame = [_1: struct<_1: int, _2: int>, _2: matrix]


In this for example - 'w' is and RDD of block matrices of dimensions (4,20) in this example and there are 50000 such matrices. My goal is to randomly select a row of 4 out 20 for each 'w' and create a new distributed block matrix having 50000 block matrices of dimensions(4,1) . This has to be a distributed function hopefully .










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    I am forming a distributed block matrix from an indexed row matrix by controlling the size of the blocks formed.



    var populationSize   = 1000000
    var chromosomeLength = 4
    var mutationRate = 0.05
    var mutationNumber = 1000000*(mutationRate)

    var populationMatrix = new IndexedRowMatrix(
    RandomRDDs.uniformVectorRDD(sc, populationSize, chromosomeLength).zipWithIndex.map {case (v, i) => IndexedRow(i, v)}
    )

    var mutationBlockMatrix = populationMatrix.toBlockMatrix(mutationNumber.toInt,chromosomeLength)


    Now only one random row from these distributed blocks needs to be selected in a distributed manner and then collected into moved into another distributed matrix



    var s = mutationBlockMatrix.colsPerBlock
    var r = mutationBlockMatrix.rowsPerBlock
    var t = mutationBlockMatrix.numRowBlocks
    var w = mutationBlockMatrix.blocks.toDF


    s: Int = 4
    r: Int = 50000
    t: Int = 20
    w: org.apache.spark.sql.DataFrame = [_1: struct<_1: int, _2: int>, _2: matrix]


    In this for example - 'w' is and RDD of block matrices of dimensions (4,20) in this example and there are 50000 such matrices. My goal is to randomly select a row of 4 out 20 for each 'w' and create a new distributed block matrix having 50000 block matrices of dimensions(4,1) . This has to be a distributed function hopefully .










    share|improve this question

























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      I am forming a distributed block matrix from an indexed row matrix by controlling the size of the blocks formed.



      var populationSize   = 1000000
      var chromosomeLength = 4
      var mutationRate = 0.05
      var mutationNumber = 1000000*(mutationRate)

      var populationMatrix = new IndexedRowMatrix(
      RandomRDDs.uniformVectorRDD(sc, populationSize, chromosomeLength).zipWithIndex.map {case (v, i) => IndexedRow(i, v)}
      )

      var mutationBlockMatrix = populationMatrix.toBlockMatrix(mutationNumber.toInt,chromosomeLength)


      Now only one random row from these distributed blocks needs to be selected in a distributed manner and then collected into moved into another distributed matrix



      var s = mutationBlockMatrix.colsPerBlock
      var r = mutationBlockMatrix.rowsPerBlock
      var t = mutationBlockMatrix.numRowBlocks
      var w = mutationBlockMatrix.blocks.toDF


      s: Int = 4
      r: Int = 50000
      t: Int = 20
      w: org.apache.spark.sql.DataFrame = [_1: struct<_1: int, _2: int>, _2: matrix]


      In this for example - 'w' is and RDD of block matrices of dimensions (4,20) in this example and there are 50000 such matrices. My goal is to randomly select a row of 4 out 20 for each 'w' and create a new distributed block matrix having 50000 block matrices of dimensions(4,1) . This has to be a distributed function hopefully .










      share|improve this question














      I am forming a distributed block matrix from an indexed row matrix by controlling the size of the blocks formed.



      var populationSize   = 1000000
      var chromosomeLength = 4
      var mutationRate = 0.05
      var mutationNumber = 1000000*(mutationRate)

      var populationMatrix = new IndexedRowMatrix(
      RandomRDDs.uniformVectorRDD(sc, populationSize, chromosomeLength).zipWithIndex.map {case (v, i) => IndexedRow(i, v)}
      )

      var mutationBlockMatrix = populationMatrix.toBlockMatrix(mutationNumber.toInt,chromosomeLength)


      Now only one random row from these distributed blocks needs to be selected in a distributed manner and then collected into moved into another distributed matrix



      var s = mutationBlockMatrix.colsPerBlock
      var r = mutationBlockMatrix.rowsPerBlock
      var t = mutationBlockMatrix.numRowBlocks
      var w = mutationBlockMatrix.blocks.toDF


      s: Int = 4
      r: Int = 50000
      t: Int = 20
      w: org.apache.spark.sql.DataFrame = [_1: struct<_1: int, _2: int>, _2: matrix]


      In this for example - 'w' is and RDD of block matrices of dimensions (4,20) in this example and there are 50000 such matrices. My goal is to randomly select a row of 4 out 20 for each 'w' and create a new distributed block matrix having 50000 block matrices of dimensions(4,1) . This has to be a distributed function hopefully .







      scala apache-spark






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      asked Nov 16 '18 at 13:03









      LeothornLeothorn

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