Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5081#discussion_r26664038
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -100,6 +100,50 @@ sealed trait Matrix extends Serializable {
* corresponding value in the matrix with type `Double`.
*/
private[spark] def foreachActive(f: (Int, Int, Double) => Unit)
+
+ override def hashCode(): Int = {
+ var result: Int = numRows * numCols + 31
+ this.foreachActive { case (rowInd, colInd, value) =>
+ // ignore explict 0 for comparison between sparse and dense
+ if (value != 0) {
+ result = 31 * result + rowInd + (numRows * colInd)
+ // refer to {@link java.util.Arrays.equals} for hash algorithm
+ val bits = java.lang.Double.doubleToLongBits(value)
+ result = 31 * result + (bits ^ (bits >>> 32)).toInt
+ }
+ }
+ result
+ }
+
+ override def equals(other: Any): Boolean = {
+ other match {
+ case mat: Matrix =>
+ if (mat.numRows != this.numRows || mat.numCols != this.numCols)
return false
+ (this, mat) match {
+ case (dm1: DenseMatrix, dm2: DenseMatrix) =>
+ Arrays.equals(dm1.toArray, dm2.toArray)
+ case (sm1: SparseMatrix, sm2: SparseMatrix) =>
+ // For the case in which one matrix is CSC and the other is CSR
+ // the values, colPtrs and rowIndices need not be the same.
+ // When both matrices are of the same type, it is sufficient
to check that
+ // the values, colPtrs and rowIndices are the same.
+ if (sm1.isTransposed != sm2.isTransposed) {
+ if (sm1.values.length != sm2.values.length) return false
+ sm1.foreachActive {
+ case (i, j, value) => if (value != sm2(i, j)) return false
+ }
+ } else {
+ if (sm1.values != sm2.values) return false
--- End diff --
This is not correct because one matrix may contain explicit zeros. Please
include this case in the unit test.
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