GenSVM R package

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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gensvm.R
+\name{gensvm}
+\alias{gensvm}
+\title{Fit the GenSVM model}
+\usage{
+gensvm(X, y, p = 1, lambda = 1e-08, kappa = 0, epsilon = 1e-06,
+  weights = "unit", kernel = "linear", gamma = "auto", coef = 1,
+  degree = 2, kernel.eigen.cutoff = 1e-08, verbose = FALSE,
+  random.seed = NULL, max.iter = 1e+08, seed.V = NULL)
+}
+\arguments{
+\item{X}{data matrix with the predictors}
+
+\item{y}{class labels}
+
+\item{p}{parameter for the L_p norm of the loss function (1.0 <= p <= 2.0)}
+
+\item{lambda}{regularization parameter for the loss function (lambda > 0)}
+
+\item{kappa}{parameter for the hinge function in the loss function (kappa > 
+-1.0)}
+
+\item{weights}{type of instance weights to use. Options are 'unit' for unit 
+weights and 'group' for group size correction weight (eq. 4 in the paper).}
+
+\item{kernel}{the kernel type to use in the classifier. It must be one of 
+'linear', 'poly', 'rbf', or 'sigmoid'. See the section "Kernels in GenSVM" 
+in \code{\link{gensvm-package}} for more info.}
+
+\item{gamma}{kernel parameter for the rbf, polynomial, and sigmoid kernel.
+If gamma is 'auto', then 1/n_features will be used.}
+
+\item{coef}{parameter for the polynomial and sigmoid kernel.}
+
+\item{degree}{parameter for the polynomial kernel}
+
+\item{kernel.eigen.cutoff}{Cutoff point for the reduced eigendecomposition 
+used with kernel-GenSVM. Eigenvectors for which the ratio between their 
+corresponding eigenvalue and the largest eigenvalue is smaller than this 
+cutoff value will be dropped.}
+
+\item{verbose}{Turn on verbose output and fit progress}
+
+\item{random.seed}{Seed for the random number generator (useful for 
+reproducible output)}
+
+\item{max.iter}{Maximum number of iterations of the optimization algorithm.}
+
+\item{seed.V}{Matrix to warm-start the optimization algorithm. This is 
+typically the output of \code{coef(fit)}. Note that this function will 
+silently drop seed.V if the dimensions don't match the provided data.}
+}
+\value{
+A "gensvm" S3 object is returned for which the print, predict, coef, 
+and plot methods are available. It has the following items:
+\item{call}{The call that was used to construct the model.}
+\item{p}{The value of the lp norm in the loss function}
+\item{lambda}{The regularization parameter used in the model.}
+\item{kappa}{The hinge function parameter used.}
+\item{epsilon}{The stopping criterion used.}
+\item{weights}{The instance weights type used.}
+\item{kernel}{The kernel function used.}
+\item{gamma}{The value of the gamma parameter of the kernel, if applicable}
+\item{coef}{The value of the coef parameter of the kernel, if applicable}
+\item{degree}{The degree of the kernel, if applicable}
+\item{kernel.eigen.cutoff}{The cutoff value of the reduced 
+eigendecomposition of the kernel matrix.}
+\item{verbose}{Whether or not the model was fitted with progress output}
+\item{random.seed}{The random seed used to seed the model.}
+\item{max.iter}{Maximum number of iterations of the algorithm.}
+\item{n.objects}{Number of objects in the dataset}
+\item{n.features}{Number of features in the dataset}
+\item{n.classes}{Number of classes in the dataset}
+\item{classes}{Array with the actual class labels}
+\item{V}{Coefficient matrix}
+\item{n.iter}{Number of iterations performed in training}
+\item{n.support}{Number of support vectors in the final model}
+\item{training.time}{Total training time}
+\item{X.train}{When training with nonlinear kernels, the training data is 
+needed to perform prediction. For these kernels it is therefore stored in 
+the fitted model.}
+}
+\description{
+Fits the Generalized Multiclass Support Vector Machine model 
+with the given parameters. See the package documentation 
+(\code{\link{gensvm-package}}) for more general information about GenSVM.
+}
+\note{
+This function returns partial results when the computation is interrupted by 
+the user.
+}
+\examples{
+x <- iris[, -5]
+y <- iris[, 5]
+
+# fit using the default parameters
+fit <- gensvm(x, y)
+
+# fit and show progress
+fit <- gensvm(x, y, verbose=T)
+
+# fit with some changed parameters
+fit <- gensvm(x, y, lambda=1e-8)
+
+# Early stopping defined through epsilon
+fit <- gensvm(x, y, epsilon=1e-3)
+
+# Early stopping defined through max.iter
+fit <- gensvm(x, y, max.iter=1000)
+
+# Nonlinear training
+fit <- gensvm(x, y, kernel='rbf')
+fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0)
+
+# Setting the random seed and comparing results
+fit <- gensvm(x, y, random.seed=123)
+fit2 <- gensvm(x, y, random.seed=123)
+all.equal(coef(fit), coef(fit2))
+
+
+}
+\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}.
+}
+\seealso{
+\code{\link{coef}}, \code{\link{print}}, \code{\link{predict}}, 
+\code{\link{plot}}, and \code{\link{gensvm.grid}}.
+}
+