Fixes to get the input data from the call

2 files changed, 41 insertions, 58 deletions

diff --git a/R/plot.gensvm.R b/R/plot.gensvm.R
index ac40ccf..d0c1eb2 100644
--- a/R/plot.gensvm.R
+++ b/R/plot.gensvm.R
@@ -6,10 +6,9 @@
 #' dimensional to easily visualize.
 #'
 #' @param fit A fitted \code{gensvm} object
-#' @param x the dataset to plot
-#' @param y.true the true data labels. If provided the objects will be colored 
-#' using the true labels instead of the predicted labels. This makes it easy to 
-#' identify misclassified objects.
+#' @param x the dataset to plot (if NULL the training data is used)
+#' @param y the labels to color points with (if NULL the predicted labels are 
+#' used)
 #' @param with.margins plot the margins
 #' @param with.shading show shaded areas for the class regions
 #' @param with.legend show the legend for the class labels
@@ -48,19 +47,19 @@
 #' fit <- gensvm(x, y)
 #'
 #' # plot the simplex space
-#' plot(fit, x)
+#' plot(fit)
 #'
 #' # plot and use the true colors (easier to spot misclassified samples)
-#' plot(fit, x, y.true=y)
+#' plot(fit, y)
 #'
 #' # plot only misclassified samples
-#' x.mis <- x[predict(fit, x) != y, ]
-#' y.mis.true <- y[predict(fit, x) != y]
-#' plot(fit, x.mis)
-#' plot(fit, x.mis, y.true=y.mis.true)
+#' x.mis <- x[predict(fit) != y, ]
+#' y.mis.true <- y[predict(fit) != y]
+#' plot(fit, x.test=x.mis)
+#' plot(fit, y.mis.true, x.test=x.mis)
 #'
-plot.gensvm <- function(fit, x, y.true=NULL, with.margins=TRUE, 
-                        with.shading=TRUE, with.legend=TRUE, center.plot=TRUE,
+plot.gensvm <- function(fit, y, x.test=NULL, with.margins=TRUE, 
+                        with.shading=TRUE, with.legend=TRUE, center.plot=TRUE, 
                         xlim=NULL, ylim=NULL, ...)
 {
     if (!(fit$n.classes %in% c(2,3))) {
@@ -68,44 +67,32 @@ plot.gensvm <- function(fit, x, y.true=NULL, with.margins=TRUE,
         return(invisible(NULL))
     }
 
+    x.test <- if(is.null(x.test)) eval.parent(fit$call$x) else x.test
+
     # Sanity check
-    if (ncol(x) != fit$n.features) {
+    if (ncol(x.test) != fit$n.features) {
         cat("Error: Number of features of fitted model and testing data 
             disagree.\n")
-        invisible(NULL)
-    }
-
-    x.train <- fit$X.train
-    if (fit$kernel != 'linear' && is.null(x.train)) {
-        cat("Error: The training data is needed to plot data for ",
-             "nonlinear GenSVM. This data is not present in the fitted ",
-             "model!\n", sep="")
-        invisible(NULL)
-    }
-    if (!is.null(x.train) && ncol(x.train) != fit$n.features) {
-        cat("Error: Number of features of fitted model and training data ",
-            "disagree.\n", sep="")
-        invisible(NULL)
+        return(invisible(NULL))
     }
 
-    x <- as.matrix(x)
-
+    x.train <- eval.parent(fit$call$x)
     if (fit$kernel == 'linear') {
         V <- coef(fit)
-        Z <- cbind(matrix(1, dim(x)[1], 1), x)
+        Z <- cbind(matrix(1, dim(x.test)[1], 1), as.matrix(x.test))
         S <- Z %*% V
-        y.pred.orig <- predict(fit, x)
+        y.pred.orig <- predict(fit, x.test)
     } else {
         kernels <- c("linear", "poly", "rbf", "sigmoid")
         kernel.idx <- which(kernels == fit$kernel) - 1
         plotdata <- .Call("R_gensvm_plotdata_kernels",
-                          as.matrix(x),
+                          as.matrix(x.test),
                           as.matrix(x.train),
                           as.matrix(fit$V),
                           as.integer(nrow(fit$V)),
                           as.integer(ncol(fit$V)),
                           as.integer(nrow(x.train)),
-                          as.integer(nrow(x)),
+                          as.integer(nrow(x.test)),
                           as.integer(fit$n.features),
                           as.integer(fit$n.classes),
                           as.integer(kernel.idx),
@@ -125,16 +112,13 @@ plot.gensvm <- function(fit, x, y.true=NULL, with.margins=TRUE,
         y.pred <- y.pred.orig
     }
 
-    labels <- if(is.null(y.true)) y.pred else y.true
-    classes <- unique(labels)
-
-    colors <- gensvm.plot.colors(length(classes))
-    markers <- gensvm.plot.markers(length(classes))
+    labels <- if(missing(y)) y.pred else match(y, classes)
 
-    indices <- match(labels, classes)
+    colors <- gensvm.plot.colors(fit$n.classes)
+    markers <- gensvm.plot.markers(fit$n.classes)
 
-    col.vector <- colors[indices]
-    mark.vector <- markers[indices]
+    col.vector <- colors[labels]
+    mark.vector <- markers[labels]
 
     if (fit$n.classes == 2)
         S <- cbind(S, matrix(0, nrow(S), 1))
diff --git a/R/predict.gensvm.R b/R/predict.gensvm.R
index 558f80b..2cf7401 100644
--- a/R/predict.gensvm.R
+++ b/R/predict.gensvm.R
@@ -4,8 +4,9 @@
 #' 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 newdata Matrix of new data for which predictions need to be made.
+#' @param add.rownames add the rownames from the training data to the 
+#' predictions
 #' @param \dots further arguments are ignored
 #'
 #' @return a vector of class labels, with the same type as the original class 
@@ -41,7 +42,7 @@
 #' # compute the accuracy with gensvm.accuracy
 #' gensvm.accuracy(y.test, y.test.pred)
 #'
-predict.gensvm <- function(fit, x.test, ...)
+predict.gensvm <- function(fit, newdata, add.rownames=FALSE, ...)
 {
     ## Implementation note:
     ## - It might seem that it would be faster to do the prediction directly in 
@@ -50,26 +51,19 @@ predict.gensvm <- function(fit, x.test, ...)
     ## However, if you actually implement it and compare, we find that calling 
     ## the C implementation is *much* faster than doing it in R.
 
+    if (missing(newdata)) {
+        newdata <- eval.parent(fit$call$x)
+    }
+    x.test <- as.matrix(newdata)
+
     # Sanity check
     if (ncol(x.test) != fit$n.features) {
         cat("Error: Number of features of fitted model and testing",
             "data disagree.\n")
-        invisible(NULL)
-    }
-
-    x.train <- fit$X.train
-    if (fit$kernel != 'linear' && is.null(x.train)) {
-        cat("Error: The training data is needed to compute predictions for ",
-             "nonlinear GenSVM. This data is not present in the fitted ",
-             "model!\n", sep="")
-        invisible(NULL)
-    }
-    if (!is.null(x.train) && ncol(x.train) != fit$n.features) {
-        cat("Error: Number of features of fitted model and training",
-            "data disagree.\n")
-        invisible(NULL)
+        return(invisible(NULL))
     }
 
+    x.train <- eval.parent(fit$call$x)
     if (fit$kernel == 'linear') {
         y.pred.c <- .Call("R_gensvm_predict",
                       as.matrix(x.test),
@@ -101,5 +95,10 @@ predict.gensvm <- function(fit, x.test, ...)
 
     yhat <- fit$classes[y.pred.c]
 
+    if (add.rownames) {
+        yhat <- matrix(yhat, length(yhat), 1)
+        rownames(yhat) <- rownames(x.train)
+    }
+
     return(yhat)
 }