GenSVM R package

1 files changed, 57 insertions, 10 deletions

diff --git a/R/predict.gensvm.R b/R/predict.gensvm.R
index 5c8f2e7..7e04fe4 100644
--- a/R/predict.gensvm.R
+++ b/R/predict.gensvm.R
@@ -4,8 +4,8 @@
 #' fitted GenSVM model.
 #'
 #' @param fit Fitted \code{gensvm} object
-#' @param newx Matrix of new values for \code{x} for which predictions need to 
-#' be made.
+#' @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 
@@ -15,7 +15,7 @@
 #' @aliases predict
 #'
 #' @author
-#' Gerrit J.J. van den Burg, Patrick J.F. Groenen
+#' Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
 #' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
 #'
 #' @references
@@ -24,10 +24,20 @@
 #' 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
 #'
 #' @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)
 #'
-predict.gensvm <- function(fit, newx, ...)
+#' # 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 
@@ -37,16 +47,53 @@ predict.gensvm <- function(fit, newx, ...)
     ## the C implementation is *much* faster than doing it in R.
 
     # Sanity check
-    if (ncol(newx) != fit$n.features)
-        stop("Number of features of fitted model and supplied data disagree.")
+    if (ncol(x.test) != fit$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
+    }
 
-    y.pred.c <- .Call("R_gensvm_predict",
-                      as.matrix(newx),
+    if (fit$kernel == 'linear') {
+        y.pred.c <- .Call("R_gensvm_predict",
+                      as.matrix(x.test),
                       as.matrix(fit$V),
-                      as.integer(nrow(newx)),
-                      as.integer(ncol(newx)),
+                      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)