Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5707#discussion_r32770638
--- Diff: python/pyspark/mllib/util.py ---
@@ -169,6 +175,32 @@ 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, SparseVector):
+ if _have_scipy:
--- End diff --
Oh, I just realized that convert_to_vector already handles scipy for you.
You may assume that you have either a SparseVector or a DenseVector. No need
to handle scipy here.
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