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From brkyvz <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4409][MLlib] Additional Linear Algebra ...
Date Tue, 16 Dec 2014 07:34:41 GMT
Github user brkyvz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3319#discussion_r21881678
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala ---
    @@ -197,6 +295,171 @@ class SparseMatrix(
       }
     
       override def copy = new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
    +
    +  private[mllib] def map(f: Double => Double) =
    +    new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.map(f))
    +
    +  private[mllib] def update(f: Double => Double): SparseMatrix = {
    +    val len = values.length
    +    var i = 0
    +    while (i < len) {
    +      values(i) = f(values(i))
    +      i += 1
    +    }
    +    this
    +  }
    +}
    +
    +/**
    + * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
    + */
    +object SparseMatrix {
    +
    +  /**
    +   * Generate an Identity Matrix in `SparseMatrix` format.
    +   * @param n number of rows and columns of the matrix
    +   * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
    +   */
    +  def speye(n: Int): SparseMatrix = {
    +    new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
    +  }
    +
    +  /** Generates a SparseMatrix given an Array[Double] of size numRows * numCols. The
number of
    +    * non-zeros in `raw` is provided for efficiency. */
    +  private def genRand(
    +      numRows: Int,
    +      numCols: Int,
    +      raw: Array[Double],
    +      nonZero: Int): SparseMatrix = {
    +    val sparseA: ArrayBuffer[Double] = new ArrayBuffer(nonZero)
    +    val sCols: ArrayBuffer[Int] = new ArrayBuffer(numCols + 1)
    +    val sRows: ArrayBuffer[Int] = new ArrayBuffer(nonZero)
    +
    +    var i = 0
    +    var nnz = 0
    +    var lastCol = -1
    +    raw.foreach { v =>
    +      val r = i % numRows
    +      val c = (i - r) / numRows
    +      if ( v != 0.0) {
    --- End diff --
    
    Right now, it's not. Currently users can supply zero values during the construction of
SparseMatrix. Two things:
    1) Should I add a check in the constructor of SparseMatrix?
    2) Should I transform genRand into something like .toSparse() inside DenseMatrix, and
add a .toDense() inside SparseMatrix? (I actually had these two methods in my multi model
training repo)


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