GenSVM R package

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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gensvm.maxabs.scale.R
+\name{gensvm.maxabs.scale}
+\alias{gensvm.maxabs.scale}
+\title{Scale each column of a matrix by its maximum absolute value}
+\usage{
+gensvm.maxabs.scale(x, x.test = NULL)
+}
+\arguments{
+\item{x}{a matrix to scale}
+
+\item{x.test}{(optional) a test matrix to scale as well.}
+}
+\value{
+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 \code{x} and \code{x.test} representing 
+the scaled datasets.
+}
+\description{
+Scaling a dataset can creatly 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 (\code{x}) and applied to the test 
+dataset. Note that the return value is a list when this argument is 
+supplied.
+}
+\examples{
+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
+x <- iris[, -5]
+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
+
+}
+\author{
+Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
+Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
+}
+\references{
+Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized 
+Multiclass Support Vector Machine}, Journal of Machine Learning Research, 
+17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
+}
+