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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