From 004941896bac692d354c41a3334d20ee1d4627f7 Mon Sep 17 00:00:00 2001
From: Gertjan van den Burg <gertjanvandenburg@gmail.com>
Date: Tue, 27 Mar 2018 12:31:28 +0100
Subject: GenSVM R package

---
 R/predict.gensvm.grid.R | 47 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 R/predict.gensvm.grid.R

(limited to 'R/predict.gensvm.grid.R')

diff --git a/R/predict.gensvm.grid.R b/R/predict.gensvm.grid.R
new file mode 100644
index 0000000..81a0207
--- /dev/null
+++ b/R/predict.gensvm.grid.R
@@ -0,0 +1,47 @@
+#' @title Predict class labels from the GenSVMGrid class
+#'
+#' @description Predict class labels using the best model from a grid search.  
+#' After doing a grid search with the \code{\link{gensvm.grid}} function, this 
+#' function can be used to make predictions of class labels. It uses the best 
+#' GenSVM model found during the grid search to do the predictions. Note that 
+#' this model is only available if \code{refit=TRUE} was specified in the 
+#' \code{\link{gensvm.grid}} call (the default).
+#'
+#' @param grid A \code{gensvm.grid} object trained with \code{refit=TRUE}
+#' @param newx Matrix of new values for \code{x} for which predictions need to 
+#' be computed.
+#' @param \dots further arguments are passed to predict.gensvm()
+#'
+#' @return a vector of class labels, with the same type as the original class 
+#' labels provided to gensvm.grid()
+#'
+#' @export
+#'
+#' @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}.
+#'
+#' @examples
+#' x <- iris[, -5]
+#' y <- iris[, 5]
+#'
+#' # run a grid search
+#' grid <- gensvm.grid(x, y)
+#'
+#' # predict training sample
+#' y.hat <- predict(grid, x)
+#'
+predict.gensvm.grid <- function(grid, newx, ...)
+{
+    if (is.null(grid$best.estimator)) {
+        cat("Error: Can't predict, the best.estimator element is NULL\n")
+        return
+    }
+
+    return(predict(grid$best.estimator, newx, ...))
+}
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