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+#' @title Cross-validated grid search for GenSVM
+#'
+#' @description This function performs a cross-validated grid search of the 
+#' model parameters to find the best hyperparameter configuration for a given 
+#' dataset. This function takes advantage of GenSVM's ability to use warm 
+#' starts to speed up computation. The function also uses the GenSVM C library 
+#' for speed. 
+#'
+#' There are two ways to use this function: either by providing a data frame 
+#' with the parameter configurations to try or by giving each of the function 
+#' inputs a vector of values to evaluate. In the latter case all combinations 
+#' of the provided values will be used (i.e. the product set).
+#'
+#' @param X training data matrix. We denote the size of this matrix by 
+#' n_samples x n_features.
+#' @param y training vector of class labes of length n_samples. The number of 
+#' unique labels in this vector is denoted by n_classes.
+#' @param df Data frame with parameter configurations to evaluate.  
+#' If this is provided it overrides the other parameter ranges provided. The 
+#' data frame must provide *all* required columns, as described below.
+#' @param p vector of values to try for the \eqn{p} hyperparameter 
+#' for the \eqn{\ell_p} norm in the loss function. All values should be on the 
+#' interval [1.0, 2.0].
+#' @param lambda vector of values for the regularization parameter 
+#' \eqn{\lambda} in the loss function. All values should be larger than 0.
+#' @param kappa vector of values for the hinge function parameter in 
+#' the loss function. All values should be larger than -1.
+#' @param weights vector of values for the instance weights. Values 
+#' should be either 'unit', 'group', or both.
+#' @param kernel vector of values for the kernel type. Possible 
+#' values are: 'linear', 'rbf', 'poly', or 'sigmoid', or any combination of 
+#' these values. See the article \link[=gensvm-kernels]{Kernels in GenSVM} for 
+#' more information.
+#' @param gamma kernel parameter for the 'rbf', 'poly', and 'sigmoid' kernels.  
+#' If it is 'auto', 1/n_features will be used. See the article 
+#' \link[=gensvm-kernels]{Kernels in GenSVM} for more information.
+#' @param coef kernel parameter for the 'poly' and 'sigmoid' 
+#' kernels. See the article \link[=gensvm-kernels]{Kernels in GenSVM} for more 
+#' information.
+#' @param degree kernel parameter for the 'poly' kernel. See the 
+#' article \link[=gensvm-kernels]{Kernels in GenSVM} for more information.
+#' @param max.iter maximum number of iterations to run in the 
+#' optimization algorithm.
+#' @param refit boolean variable. If true, the best model from cross validation 
+#' is fitted again on the entire dataset.
+#' @param scoring metric to use to evaluate the classifier performance during 
+#' cross validation. The metric should be an R function that takes two 
+#' arguments: y_true and y_pred and that returns a float such that higher 
+#' values are better. If it is NULL, the accuracy score will be used.
+#' @param cv the number of cross-validation folds to use or a vector with the 
+#' same length as \code{y} where each unique value denotes a test split.
+#' @param verbose boolean variable to indicate whether training details should 
+#' be printed.
+#'
+#' @return A "gensvm.grid" S3 object with the following items:
+#' \item{cv.results}{A data frame with the cross validation results}
+#' \item{best.estimator}{If refit=TRUE, this is the GenSVM model fitted with 
+#' the best hyperparameter configuration, otherwise it is NULL}
+#' \item{best.score}{Mean cross-validated score for the model with the best 
+#' hyperparameter configuration}
+#' \item{best.params}{Parameter configuration that provided the highest mean 
+#' cross-validated score}
+#' \item{best.index}{Row index of the cv.results data frame that corresponds to 
+#' the best hyperparameter configuration}
+#' \item{n.splits}{The number of cross-validation splits}
+#'
+#'
+#'
+#' @section Using a DataFrame:
+#' ...
+#'
+#'
+#' @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.grid <- function(X, y,
+                        df=NULL,
+                        p=c(1.0, 1.5, 2.0), 
+                        lambda=c(1e-8, 1e-6, 1e-4, 1e-2, 1),
+                        kappa=c(-0.9, 0.5, 5.0),
+                        weights=c('unit', 'group'),
+                        kernel=c('linear'),
+                        gamma=c('auto'),
+                        coef=c(0.0),
+                        degree=c(2.0),
+                        max.iter=c(1e8),
+                        refit=TRUE,
+                        scoring=NULL,
+                        cv=3,
+                        verbose=TRUE)
+{
+    call <- match.call()
+
+
+
+    object <- list(...)
+    class(object) <- "gensvm.grid"
+    return(object)
+}