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https://issues.apache.org/jira/browse/SPARK-19382?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15850369#comment-15850369
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Miao Wang commented on SPARK-19382:
-----------------------------------
In addition,
def merge(other: MultivariateOnlineSummarizer): this.type = {
if (this.totalWeightSum != 0.0 && other.totalWeightSum != 0.0) {
require(n == other.n, s"Dimensions mismatch when merging with another
summarizer. " +
s"Expecting $n but got ${other.n}.")
So mixed DenseVector and SparseVector will throw the above exception,
`Dimensions mismatch when merging with another summarizer.` due to the finding
in the previous reply.
I will create separate test cases for SparseVector. Thanks!
> Test sparse vectors in LinearSVCSuite
> -------------------------------------
>
> Key: SPARK-19382
> URL: https://issues.apache.org/jira/browse/SPARK-19382
> Project: Spark
> Issue Type: Test
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> Currently, LinearSVCSuite does not test sparse vectors. We should. I
> recommend that generateSVMInput be modified to create a mix of dense and
> sparse vectors, rather than adding an additional test.
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