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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)
+}