srowen commented on a change in pull request #32734:
URL: https://github.com/apache/spark/pull/32734#discussion_r643407060
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File path:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
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@@ -439,7 +439,8 @@ class RowMatrix @Since("1.0.0") (
" Cannot compute the covariance of a RowMatrix with <= 1 row.")
val mean = Vectors.fromML(summary.mean)
- if (rows.first().isInstanceOf[DenseVector]) {
+ // If all the rows are sparse vectors, then compute based on
`computeSparseVectorCovariance`.
+ if (!rows.filter(_.isInstanceOf[DenseVector]).isEmpty()) {
Review comment:
How about `rows.exists(_.isInstanceOf[DenseVector])`? might
short-circuit faster. This could be expensive I guess if it really is all
sparse, but correctness is more important.
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