% 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. \cr\cr
Note that for SVMs categorical features should be converted to binary dummy
features. This can be done with using the \code{\link{model.matrix}}
function (i.e. \code{model.matrix( ~ var - 1)}).}

\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{epsilon}{Stopping parameter for the optimization algorithm. The 
optimization will stop if the relative change in the loss function is below 
this value.}

\item{weights}{type or vector of instance weights to use. Options are 'unit'
for unit weights and 'group' for group size correction weights (eq. 4 in the
paper). Alternatively, a vector of weights can be provided.}

\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}
}
\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 and show progress
fit <- gensvm(x, y, verbose=TRUE)

# fit with some changed parameters
fit <- gensvm(x, y, lambda=1e-6)

# 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', max.iter=1000)
fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=1000)

# Setting the random seed and comparing results
fit <- gensvm(x, y, random.seed=123, max.iter=1000)
fit2 <- gensvm(x, y, random.seed=123, max.iter=1000)
all.equal(coef(fit), coef(fit2))


}
\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{https://jmlr.org/papers/v17/14-526.html}.
}
\seealso{
\code{\link{coef}}, \code{\link{print}}, \code{\link{predict}},
\code{\link{plot}}, \code{\link{gensvm.grid}}, \code{\link{gensvm-package}}
}
\author{
Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
}