Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5707#discussion_r30188085
--- Diff: python/pyspark/mllib/util.py ---
@@ -169,6 +169,25 @@ def loadLabeledPoints(sc, path, minPartitions=None):
minPartitions = minPartitions or min(sc.defaultParallelism, 2)
return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
+ @staticmethod
+ def appendBias(data):
+ """
+ Returns a new vector with `1.0` (bias) appended to
+ the end of the input vector.
+ """
+ vec = _convert_to_vector(data)
+ if isinstance(vec, Vector):
+ vec = vec.toArray()
--- End diff --
This is going to make sparse vectors become dense. It should treat
SparseVector and scipy.sparse column matrices specially so data size does not
blow up. There are probably numpy or scipy methods which could help with that.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
