Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3319#discussion_r22120288
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -197,6 +335,167 @@ 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
+ }
+
+ /** Generate a `DenseMatrix` from the given `SparseMatrix`. */
+ def toDense(): DenseMatrix = {
+ new DenseMatrix(numRows, numCols, toArray)
+ }
+}
+
+/**
+ * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
+ */
+object SparseMatrix {
+
+ /**
+ * Generate a `SparseMatrix` from Coordinate List (COO) format. Input
must be an array of
+ * (row, column, value) tuples.
+ * @param numRows number of rows of the matrix
+ * @param numCols number of columns of the matrix
+ * @param entries Array of (row, column, value) tuples
+ * @return The corresponding `SparseMatrix`
+ */
+ def fromCOO(numRows: Int, numCols: Int, entries: Array[(Int, Int,
Double)]): SparseMatrix = {
+ val sortedEntries = entries.sortBy(v => (v._2, v._1))
+ val colPtrs = new Array[Int](numCols + 1)
+ var nnz = 0
+ var lastCol = -1
+ val values = sortedEntries.map { case (i, j, v) =>
+ while (j != lastCol) {
--- End diff --
We can add a check on `j >=0`. If there is `j = -2` in the input, we get a
dead loop.
It is a little hard to read if we put the `colPtrs` construction code
inside the map. We are separate the code. There is no performance penalty.
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