Implement fitting and prediction

1 files changed, 21 insertions, 4 deletions

diff --git a/R/predict.gensvm.R b/R/predict.gensvm.R
index 6cc8851..5c8f2e7 100644
--- a/R/predict.gensvm.R
+++ b/R/predict.gensvm.R
@@ -3,7 +3,7 @@
 #' @description This function predicts the class labels of new data using a 
 #' fitted GenSVM model.
 #'
-#' @param object Fitted \code{gensvm} object
+#' @param fit Fitted \code{gensvm} object
 #' @param newx Matrix of new values for \code{x} for which predictions need to 
 #' be made.
 #' @param \dots further arguments are ignored
@@ -27,10 +27,27 @@
 #'
 #'
 #'
-predict.gensvm <- function(object, newx, ...)
+predict.gensvm <- function(fit, newx, ...)
 {
-    # TODO: C library fitting prediction here (or not? with the column-major 
-    # order it may be faster to do it directly in R)
+    ## 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(newx) != fit$n.features)
+        stop("Number of features of fitted model and supplied data disagree.")
+
+    y.pred.c <- .Call("R_gensvm_predict",
+                      as.matrix(newx),
+                      as.matrix(fit$V),
+                      as.integer(nrow(newx)),
+                      as.integer(ncol(newx)),
+                      as.integer(fit$n.classes)
+                      )
+    yhat <- fit$classes[y.pred.c]
 
     return(yhat)
 }