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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-03-27 12:31:28 +0100 |
|---|---|---|
| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-03-27 12:31:28 +0100 |
| commit | 004941896bac692d354c41a3334d20ee1d4627f7 (patch) | |
| tree | 2b11e42d8524843409e2bf8deb4ceb74c8b69347 /man/predict.gensvm.Rd | |
| parent | updates to GenSVM C library (diff) | |
| download | rgensvm-004941896bac692d354c41a3334d20ee1d4627f7.tar.gz rgensvm-004941896bac692d354c41a3334d20ee1d4627f7.zip | |
GenSVM R package
Diffstat (limited to 'man/predict.gensvm.Rd')
| -rw-r--r-- | man/predict.gensvm.Rd | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/man/predict.gensvm.Rd b/man/predict.gensvm.Rd new file mode 100644 index 0000000..0c55a43 --- /dev/null +++ b/man/predict.gensvm.Rd @@ -0,0 +1,50 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/predict.gensvm.R +\name{predict.gensvm} +\alias{predict} +\alias{predict.gensvm} +\title{Predict class labels with the GenSVM model} +\usage{ +\method{predict}{gensvm}(fit, x.test, ...) +} +\arguments{ +\item{fit}{Fitted \code{gensvm} object} + +\item{x.test}{Matrix of new values for \code{x} for which predictions need +to be made.} + +\item{\dots}{further arguments are ignored} +} +\value{ +a vector of class labels, with the same type as the original class +labels. +} +\description{ +This function predicts the class labels of new data using a +fitted GenSVM model. +} +\examples{ +x <- iris[, -5] +y <- iris[, 5] + +# create a training and test sample +attach(gensvm.train.test.split(x, y)) +fit <- gensvm(x.train, y.train) + +# predict the class labels of the test sample +y.test.pred <- predict(fit, x.test) + +# compute the accuracy with gensvm.accuracy +gensvm.accuracy(y.test, y.test.pred) + +} +\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}. +} + |
