Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15628#discussion_r107549283
--- Diff:
mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala ---
@@ -291,31 +395,60 @@ class DenseMatrix @Since("2.0.0") (
override def numActives: Int = values.length
/**
- * Generate a `SparseMatrix` from the given `DenseMatrix`. The new
matrix will have isTransposed
- * set to false.
+ * Generate a `SparseMatrix` from the given `DenseMatrix`.
+ *
+ * @param colMajor Whether the resulting `SparseMatrix` values will be
in column major order.
*/
- @Since("2.0.0")
- def toSparse: SparseMatrix = {
- val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
- val colPtrs: Array[Int] = new Array[Int](numCols + 1)
- val rowIndices: MArrayBuilder[Int] = new MArrayBuilder.ofInt
- var nnz = 0
- var j = 0
- while (j < numCols) {
- var i = 0
- while (i < numRows) {
- val v = values(index(i, j))
- if (v != 0.0) {
- rowIndices += i
- spVals += v
- nnz += 1
+ private[ml] override def toSparseMatrix(colMajor: Boolean): SparseMatrix
= {
+ if (!colMajor) this.transpose.toSparseMatrix(colMajor = true).transpose
+ else {
+ val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
+ val colPtrs: Array[Int] = new Array[Int](numCols + 1)
+ val rowIndices: MArrayBuilder[Int] = new MArrayBuilder.ofInt
+ var nnz = 0
+ var j = 0
+ while (j < numCols) {
+ var i = 0
+ while (i < numRows) {
+ val v = values(index(i, j))
+ if (v != 0.0) {
+ rowIndices += i
+ spVals += v
+ nnz += 1
+ }
+ i += 1
}
- i += 1
+ j += 1
+ colPtrs(j) = nnz
}
- j += 1
- colPtrs(j) = nnz
+ new SparseMatrix(numRows, numCols, colPtrs, rowIndices.result(),
spVals.result())
+ }
+ }
+
+ /**
+ * Generate a `DenseMatrix` from this `DenseMatrix`.
+ *
+ * @param colMajor Whether the resulting `DenseMatrix` values will be in
column major order.
+ */
+ private[ml] override def toDenseMatrix(colMajor: Boolean): DenseMatrix =
{
+ if (!(isTransposed ^ colMajor)) {
+ val newValues = new Array[Double](numCols * numRows)
--- End diff --
Hm, I don't think this solution is better. The entire point of abstract
methods is to allow subclasses to implement a method differently. Since we need
different implementations depending on the subclass, we should just implement
them in the subclasses. We can do this with the following:
````scala
trait Matrix {
def toDenseMatrix(colMajor: Boolean): Matrix
}
class DenseMatrix extends Matrix {
private[ml] override def toDenseMatrix(colMajor: Boolean): DenseMatrix
= {
if (isTransposed && colMajor) {
new DenseMatrix(numRows, numCols, toArray, isTransposed = false)
} else if (!isTransposed && !colMajor) {
new DenseMatrix(numRows, numCols, transpose.toArray, isTransposed =
true)
} else {
this
}
}
}
class SparseMatrix extends Matrix {
private[ml] override def toDenseMatrix(colMajor: Boolean): DenseMatrix = {
if (colMajor) new DenseMatrix(numRows, numCols, toArray)
else new DenseMatrix(numRows, numCols, this.transpose.toArray,
isTransposed = true)
}
}
````
Which is less verbose than the previous code. I'm going to put that in the
next commit. Let me know what you think.
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