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#' @title Predict class labels with the GenSVM model
#'
#' @description This function predicts the class labels of new data using a
#' fitted GenSVM model.
#'
#' @param fit Fitted \code{gensvm} object
#' @param x.test Matrix of new values for \code{x} for which predictions need
#' to be made.
#' @param \dots further arguments are ignored
#'
#' @return a vector of class labels, with the same type as the original class
#' labels.
#'
#' @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{plot.gensvm}}, \code{\link{predict.gensvm.grid}},
#' \code{\link{gensvm}}, \code{\link{gensvm-package}}
#'
#' @export
#' @aliases predict
#'
#' @examples
#' x <- iris[, -5]
#' y <- iris[, 5]
#'
#' # create a training and test sample
#' attach(gensvm.train.test.split(x, y))
#' fit <- gensvm(x.train, y.train)
#'
#' # predict the class labels of the test sample
#' y.test.pred <- predict(fit, x.test)
#'
#' # compute the accuracy with gensvm.accuracy
#' gensvm.accuracy(y.test, y.test.pred)
#'
predict.gensvm <- function(fit, x.test, ...)
{
## Implementation note:
## - It might seem that it would be faster to do the prediction directly in
## R here, since we then don't need to switch to C, construct model and
## data structures, copy the data, etc. before doing the prediction.
## However, if you actually implement it and compare, we find that calling
## the C implementation is *much* faster than doing it in R.
# Sanity check
if (ncol(x.test) != fit$n.features) {
cat("Error: Number of features of fitted model and testing",
"data disagree.\n")
return
}
x.train <- fit$X.train
if (fit$kernel != 'linear' && is.null(x.train)) {
cat("Error: The training data is needed to compute predictions for ",
"nonlinear GenSVM. This data is not present in the fitted ",
"model!\n", sep="")
}
if (!is.null(x.train) && ncol(x.train) != fit$n.features) {
cat("Error: Number of features of fitted model and training",
"data disagree.\n")
return
}
if (fit$kernel == 'linear') {
y.pred.c <- .Call("R_gensvm_predict",
as.matrix(x.test),
as.matrix(fit$V),
as.integer(nrow(x.test)),
as.integer(ncol(x.test)),
as.integer(fit$n.classes)
)
} else {
kernels <- c("linear", "poly", "rbf", "sigmoid")
kernel.idx <- which(kernels == fit$kernel) - 1
y.pred.c <- .Call("R_gensvm_predict_kernels",
as.matrix(x.test),
as.matrix(x.train),
as.matrix(fit$V),
as.integer(nrow(fit$V)),
as.integer(ncol(fit$V)),
as.integer(nrow(x.train)),
as.integer(nrow(x.test)),
as.integer(fit$n.features),
as.integer(fit$n.classes),
as.integer(kernel.idx),
fit$gamma,
fit$coef,
fit$degree,
fit$kernel.eigen.cutoff
)
}
yhat <- fit$classes[y.pred.c]
return(yhat)
}
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