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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-04 17:39:31 -0400 |
|---|---|---|
| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-04 17:39:31 -0400 |
| commit | a76eaee63a9e34c491473c03d47a150b275f5003 (patch) | |
| tree | 00563b543240e51c034536fd0434aa308e06b188 | |
| parent | Update docs (diff) | |
| download | rgensvm-a76eaee63a9e34c491473c03d47a150b275f5003.tar.gz rgensvm-a76eaee63a9e34c491473c03d47a150b275f5003.zip | |
Simplify plot code
| -rw-r--r-- | R/plot.gensvm.R | 21 |
1 files changed, 7 insertions, 14 deletions
diff --git a/R/plot.gensvm.R b/R/plot.gensvm.R index e05a690..054c374 100644 --- a/R/plot.gensvm.R +++ b/R/plot.gensvm.R @@ -93,7 +93,8 @@ plot.gensvm <- function(x, labels, newdata=NULL, with.margins=TRUE, V <- coef(fit) Z <- cbind(matrix(1, dim(newdata)[1], 1), as.matrix(newdata)) S <- Z %*% V - y.pred.orig <- predict(fit, newdata) + y.pred <- predict(fit, newdata) + y.pred.int <- match(y.pred, fit$classes) } else { kernels <- c("linear", "poly", "rbf", "sigmoid") kernel.idx <- which(kernels == fit$kernel) - 1 @@ -114,22 +115,14 @@ plot.gensvm <- function(x, labels, newdata=NULL, with.margins=TRUE, fit$kernel.eigen.cutoff ) S <- plotdata$ZV - y.pred.orig <- plotdata$y.pred + y.pred.int <- plotdata$y.pred } - classes <- fit$classes - if (is.factor(y.pred.orig)) { - y.pred <- match(y.pred.orig, classes) - } else { - y.pred <- y.pred.orig - } - - labels <- if(missing(labels)) y.pred else match(labels, classes) - colors <- gensvm.plot.colors(fit$n.classes) markers <- gensvm.plot.markers(fit$n.classes) - indices <- match(labels, classes) + indices <- if (missing(labels)) y.pred.int else match(labels, fit$classes) + col.vector <- colors[indices] mark.vector <- markers[indices] @@ -150,10 +143,10 @@ plot.gensvm <- function(x, labels, newdata=NULL, with.margins=TRUE, } if (fit$n.classes == 3) - gensvm.plot.2d(classes, with.margins, with.shading, with.legend, + gensvm.plot.2d(fit$classes, with.margins, with.shading, with.legend, center.plot) else - gensvm.plot.1d(classes, with.margins, with.shading, with.legend, + gensvm.plot.1d(fit$classes, with.margins, with.shading, with.legend, center.plot) invisible(fit) |