Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/10298#discussion_r47959777
--- Diff: python/pyspark/mllib/feature.py ---
@@ -174,6 +174,38 @@ def setWithStd(self, withStd):
self.call("setWithStd", withStd)
return self
+ @property
+ @since('2.0.0')
+ def withStd(self):
+ """
+ Returns if the model scales the data to unit standard deviation.
+ """
+ return self.call("withStd")
+
+ @property
+ @since('2.0.0')
+ def withMean(self):
+ """
+ Returns if the model centers the data before scaling.
+ """
+ return self.call("withMean")
+
+ @property
+ @since('2.0.0')
+ def std(self):
+ """
+ Return the column standard deviation values. Only set if model was
trained withStd.
+ """
+ return self.call("std")
+
+ @property
+ @since('2.0.0')
+ def mean(self):
--- End diff --
Thats a good point, so it turns out the comment was a little off (currently
we compute the mean and std regardless although there is a todo to skip
computation when both are set to false - although I'm not sure that makes any
sense from a producing a usable model point of view). I've updated the comment
text.
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