1 files changed, 62 insertions, 0 deletions
diff --git a/man/gensvm.train.test.split.Rd b/man/gensvm.train.test.split.Rd
new file mode 100644
index 0000000..a99940f
--- /dev/null
+++ b/man/gensvm.train.test.split.Rd
@@ -0,0 +1,62 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gensvm.train.test.split.R
+\name{gensvm.train.test.split}
+\alias{gensvm.train.test.split}
+\title{Create a train/test split of a dataset}
+\usage{
+gensvm.train.test.split(x, y = NULL, train.size = NULL, test.size = NULL,
+  shuffle = TRUE, random.state = NULL, return.idx = FALSE)
+}
+\arguments{
+\item{x}{array to split}
+
+\item{y}{another array to split (typically this is a vector)}
+
+\item{train.size}{size of the training dataset. This can be provided as 
+float or as int. If it's a float, it should be between 0.0 and 1.0 and 
+represents the fraction of the dataset that should be placed in the training 
+dataset.  If it's an int, it represents the exact number of samples in the 
+training dataset. If it is NULL, the complement of \code{test.size} will be 
+used.}
+
+\item{test.size}{size of the test dataset. Similarly to train.size both a 
+float or an int can be supplied. If it's NULL, the complement of train.size 
+will be used. If both train.size and test.size are NULL, a default test.size 
+of 0.25 will be used.}
+
+\item{shuffle}{shuffle the rows or not}
+
+\item{random.state}{seed for the random number generator (int)}
+}
+\description{
+Often it is desirable to split a dataset into a training and 
+testing sample. This function is included in GenSVM to make it easy to do 
+so. The function is inspired by a similar function in Scikit-Learn.
+}
+\examples{
+x <- iris[, -5]
+y <- iris[, 5]
+
+# using the default values
+split <- gensvm.train.test.split(x, y)
+
+# using the split in a GenSVM model
+fit <- gensvm(split$x.train, split$y.train)
+gensvm.accuracy(split$y.test, predict(fit, split$x.test))
+
+# using attach makes the results directly available
+attach(gensvm.train.test.split(x, y))
+fit <- gensvm(x.train, y.train)
+gensvm.accuracy(y.test, predict(fit, 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}.
+}
+