GitHub user hhbyyh opened a pull request:
https://github.com/apache/spark/pull/6039
[Spark-7514][MLlib] Add MinMaxNormalizer to feature transformation
Add a new scaling method to feature component, which is commonly known as
min-max normalization or Rescaling.
Core function is,
Normalized(x) = (x - min) / (max - min) * scale + newBase
where newBase the new minimum number for the feature, and scale controls
the range after transformation. This is a little complicated than the basic
MinMax normalization, yet it provides flexibility so that users can control the
range more specifically. like [0.1, 0.9] in some NN application.
for case that max == min, 0.5 is used as the raw value.
reference:
http://en.wikipedia.org/wiki/Feature_scaling
http://stn.spotfire.com/spotfire_client_help/index.htm#norm/norm_scale_between_0_and_1.htm
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/hhbyyh/spark minMaxNorm
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/6039.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #6039
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commit d285a191e282cb9e028b30eb94aca272b062f79b
Author: Yuhao Yang <[email protected]>
Date: 2015-05-10T08:38:41Z
initial checkin for minMaxNorm
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