View source: R/gensvm.maxabs.scale.R
gensvm.maxabs.scale | R Documentation |
Scaling a dataset can greatly decrease the computation time of GenSVM. This function scales the data by dividing each column of a matrix by the maximum absolute value of that column. This preserves sparsity in the data while mapping each column to the interval [-1, 1].
Optionally a test dataset can be provided as well. In this case, the scaling
will be computed on the first argument (x
) and applied to the test
dataset. Note that the return value is a list when this argument is
supplied.
gensvm.maxabs.scale(x, x.test = NULL)
x |
a matrix to scale |
x.test |
(optional) a test matrix to scale as well. |
if x.test=NULL a scaled matrix where the maximum value of the
columns is 1 and the minimum value of the columns isn't below -1. If x.test
is supplied, a list with elements x
and x.test
representing
the scaled datasets.
Gerrit J.J. van den Burg, Patrick J.F. Groenen
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
Van den Burg, G.J.J. and Groenen, P.J.F. (2016). GenSVM: A Generalized Multiclass Support Vector Machine, Journal of Machine Learning Research, 17(225):1–42. URL https://jmlr.org/papers/v17/14-526.html.
gensvm
, gensvm.grid
,
gensvm.train.test.split
, gensvm-package
x <- iris[, -5] # check the min and max of the columns apply(x, 2, min) apply(x, 2, max) # scale the data x.scale <- gensvm.maxabs.scale(x) # check again (max should be 1.0, min shouldn't be below -1) apply(x.scale, 2, min) apply(x.scale, 2, max) # with a train and test dataset split <- gensvm.train.test.split(x) x.train <- split$x.train x.test <- split$x.test scaled <- gensvm.maxabs.scale(x.train, x.test) x.train.scl <- scaled$x x.test.scl <- scaled$x.test
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