Github user Lewuathe commented on a diff in the pull request:
https://github.com/apache/spark/pull/5707#discussion_r30941923
--- Diff: python/pyspark/mllib/util.py ---
@@ -169,6 +170,27 @@ 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):
+ return sp.csc_matrix(np.append(vec.toArray(), 1.0))
--- End diff --
@jkbradley Thank you for response. I think if the returned value of
`appendBias` should be `SparseVector`, there seems no space to use scipy.sparse
because SparseVector can be constructed with given data without scipy.sparse.
Is it correct?
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