I would call it Scaler. You might want to add it to the spark.ml pipieline api. Please check the spark.ml.HashingTF implementation. Note that this should handle sparse vectors efficiently.
Hadamard and FFTs are quite useful. If you are intetested, make sure that we call an FFT libary that is license-compatible with Apache. -Xiangrui On Jan 24, 2015 8:27 AM, "Octavian Geagla" <ogea...@gmail.com> wrote: > Hello, > > I found it useful to implement the Hadamard Product > <https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29http://> > as > a VectorTransformer. It can be applied to scale (by a constant) a certain > dimension (column) of the data set. > > Since I've already implemented it and am using it, I thought I'd see if > there's interest in this feature going in as Experimental. I'm not sold on > the name 'Weighter', either. > > Here's the current branch with the work (docs, impl, tests). > <https://github.com/ogeagla/spark/compare/spark-mllib-weighting> > > The implementation was heavily inspired by those of StandardScalerModel and > Normalizer. > > Thanks > Octavian > > > > -- > View this message in context: > http://apache-spark-developers-list.1001551.n3.nabble.com/Any-interest-in-weighting-VectorTransformer-which-does-component-wise-scaling-tp10265.html > Sent from the Apache Spark Developers List mailing list archive at > Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > >
