Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/40#discussion_r10181447
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
---
@@ -149,7 +149,13 @@ object GradientDescent extends Logging {
// Initialize weights as a column vector
var weights = new DoubleMatrix(initialWeights.length, 1,
initialWeights:_*)
- var regVal = 0.0
+
+ /**
+ * For the first iteration, the regVal will be initialized as sum of
sqrt of
+ * weights if it's L2 update; for L1 update; the same logic is
followed.
+ */
+ var regVal = updater.compute(weights,
+ new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
--- End diff --
I saw code in spark codebase written in this way.
var regVal = updater.compute(
weights,
new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
What do you think?
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