From 8cfaa3e50d6b96605b9efbc37d1b628bac29f324 Mon Sep 17 00:00:00 2001 From: Gertjan van den Burg Date: Thu, 5 Apr 2018 11:52:48 -0400 Subject: make examples faster --- R/gensvm.R | 4 ++-- R/plot.gensvm.R | 2 +- man/gensvm.Rd | 4 ++-- man/plot.gensvm.Rd | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/R/gensvm.R b/R/gensvm.R index db023e2..c541b5a 100644 --- a/R/gensvm.R +++ b/R/gensvm.R @@ -107,8 +107,8 @@ #' fit <- gensvm(x, y, max.iter=1000) #' #' # Nonlinear training -#' fit <- gensvm(x, y, kernel='rbf') -#' fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0) +#' fit <- gensvm(x, y, kernel='rbf', max.iter=5000) +#' fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=5000) #' #' # Setting the random seed and comparing results #' fit <- gensvm(x, y, random.seed=123) diff --git a/R/plot.gensvm.R b/R/plot.gensvm.R index 054c374..500adf5 100644 --- a/R/plot.gensvm.R +++ b/R/plot.gensvm.R @@ -66,7 +66,7 @@ #' # plot a 2-d model #' xx <- x[y %in% c('versicolor', 'virginica'), ] #' yy <- y[y %in% c('versicolor', 'virginica')] -#' fit <- gensvm(xx, yy, kernel='rbf', max.iter=5000) +#' fit <- gensvm(xx, yy, kernel='rbf', max.iter=1000) #' plot(fit) #' plot.gensvm <- function(x, labels, newdata=NULL, with.margins=TRUE, diff --git a/man/gensvm.Rd b/man/gensvm.Rd index 4f5d614..cee2b35 100644 --- a/man/gensvm.Rd +++ b/man/gensvm.Rd @@ -115,8 +115,8 @@ fit <- gensvm(x, y, epsilon=1e-3) fit <- gensvm(x, y, max.iter=1000) # Nonlinear training -fit <- gensvm(x, y, kernel='rbf') -fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0) +fit <- gensvm(x, y, kernel='rbf', max.iter=5000) +fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=5000) # Setting the random seed and comparing results fit <- gensvm(x, y, random.seed=123) diff --git a/man/plot.gensvm.Rd b/man/plot.gensvm.Rd index 6e19228..dc4ca38 100644 --- a/man/plot.gensvm.Rd +++ b/man/plot.gensvm.Rd @@ -66,7 +66,7 @@ plot(fit, y.mis.true, newdata=x.mis) # plot a 2-d model xx <- x[y \%in\% c('versicolor', 'virginica'), ] yy <- y[y \%in\% c('versicolor', 'virginica')] -fit <- gensvm(xx, yy, kernel='rbf', max.iter=5000) +fit <- gensvm(xx, yy, kernel='rbf', max.iter=1000) plot(fit) } -- cgit v1.2.3