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#' @title Fit the GenSVM model
#'
#' @description Fits the Generalized Multiclass Support Vector Machine model
#' with the given parameters.
#'
#' @param X data matrix with the predictors
#' @param y class labels
#' @param p parameter for the L_p norm of the loss function (1.0 <= p <= 2.0)
#' @param lambda regularization parameter for the loss function (lambda > 0)
#' @param kappa parameter for the hinge function in the loss function (kappa >
#' -1.0)
#' @param 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).
#' @param kernel the kernel type to use in the classifier. It must be one of
#' 'linear', 'poly', 'rbf', or 'sigmoid'.
#' @param gamma kernel parameter for the rbf, polynomial, and sigmoid kernel.
#' If gamma is 'auto', then 1/n_features will be used.
#' @param coef parameter for the polynomial and sigmoid kernel.
#' @param degree parameter for the polynomial kernel
#' @param 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.
#' @param verbose Turn on verbose output and fit progress
#' @param random.seed Seed for the random number generator (useful for
#' reproducible output)
#' @param max.iter Maximum number of iterations of the optimization algorithm.
#'
#' @return 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{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{random.seed}{The random seed used to seed the model.}
#' \item{max.iter}{Maximum number of iterations of the algorithm.}
#'
#' @author
#' Gerrit J.J. van den Burg, Patrick J.F. Groenen
#' 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}}.
#'
#' @export
#'
#' @examples
#' X <-
#'
gensvm <- function(X, y, p=1.0, lambda=1e-5, kappa=0.0, epsilon=1e-6,
weights='unit', kernel='linear', gamma='auto', coef=0.0,
degree=2.0, kernel.eigen.cutoff=1e-8, verbose=0,
random.seed=NULL, max.iter=1e8, seed.V=NULL)
{
call <- match.call()
# TODO: generate the random.seed value in R if it is NULL. Then you can
# return it and people can still reproduce even if they forgot to set it
# explicitly.
# TODO: Store a labelencoder in the object, preferably as a partially
# hidden item. This can then be used with prediction.
n.objects <- nrow(X)
n.features <- ncol(X)
n.classes <- length(unique(y))
# Convert labels to integers
y.clean <- label.encode(y)
# Convert weights to index
weight.idx <- which(c("unit", "group") == weights)
if (length(weight.idx) == 0) {
stop("Incorrect weight specification. ",
"Valid options are 'unit' and 'group'")
}
# Convert kernel to index
kernel.idx <- which(c("linear", "poly", "rbf", "sigmoid") == kernel)
if (length(kernel.idx) == 0) {
stop("Incorrect kernel specification. ",
"Valid options are 'linear', 'poly', 'rbf', and 'sigmoid'")
}
out <- .Call("R_gensvm_train",
as.matrix(t(X)),
as.integer(y.clean),
p,
lambda,
kappa,
epsilon,
weight.idx,
kernel.idx,
gamma,
coef,
degree,
kernel.eigen.cutoff,
verbose,
max.iter,
random.seed,
seed.V)
object <- list(call = call, lambda = lambda, kappa = kappa,
epsilon = epsilon, weights = weights, kernel = kernel,
gamma = gamma, coef = coef, degree = degree,
kernel.eigen.cutoff = kernel.eigen.cutoff,
random.seed = random.seed, max.iter = max.iter,
V = out$V, n.iter = out$n.iter, n.support = out$n.support)
class(object) <- "gensvm"
return(object)
}
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