Nothing
#' @export
print.parallelSVM <- function(x, ...){
# Calculate average number of support Vectors
nSV <- c()
for (i in 1:length(x)){
nSV <- c(nSV,x[[i]]$tot.nSV)
}
tot.nSV <- round(mean(nSV))
# Convert the type
tempie <- x[[1]]$type
tempie <- ifelse(tempie == 0, "C-classficiation",
ifelse(tempie == 1,"nu-classification",
ifelse(tempie == 2, "one-classification",
ifelse(tempie == 3, "eps-regression", "nu-regression"))))
# Convert the kernel
kernie <- x[[1]]$kernel
kernie <- ifelse(kernie == 0, "linear",
ifelse(kernie == 1,"polynomial",
ifelse(kernie == 2, "radial", "sigmoid")))
# Print the call, Parameters and average number of Support Vectors
cat("\nCall:\n")
cat(attributes(x)$call)
cat("\n\n\nParameters: \n")
cat(" SVM-Type: ")
cat(tempie)
cat("\n SVM-Kernel: ")
cat(kernie)
cat("\n cost: ")
cat(x[[1]]$cost)
cat("\n gamma: ")
cat(x[[1]]$gamma)
cat("\n\nAverage Number of Support Vectors: ")
cat(tot.nSV)
cat("\n ")
}
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