Github user Lewuathe commented on a diff in the pull request:
https://github.com/apache/spark/pull/9180#discussion_r42927682
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala ---
@@ -125,6 +124,58 @@ object LinearDataGenerator {
}
/**
+ * @param intercept Data intercept
+ * @param weights Weights to be applied.
+ * @param xMean the mean of the generated features. Lots of time, if the
features are not properly
+ * standardized, the algorithm with poor implementation
will have difficulty
+ * to converge.
+ * @param xVariance the variance of the generated features.
+ * @param nPoints Number of points in sample.
+ * @param seed Random seed
+ * @param eps Epsilon scaling factor.
+ * @return Seq of LabeledPoint includes sparse vectors..
+ */
--- End diff --
Yes, I also thought it is good idea. But `LinearDataGenerator` is used as
static object, then we have to pass `sparsity` as parameter to
`generateLinearInput`. This method seems to be used a lot of suites. It is
necessary to change a lot of method reference.
Therefore it might be better to do in separate JIRA. What do you thing
about?
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