# VariSel_mod <- list(type = "both",
# library = "VariSel",
# loop = NULL)
#
# prm <- data.frame(parameter = c("type", "lambda", "lambda1", "lambda2"),
# class = c("character",rep("numeric", 2)),
# label = c("Type", "Lambda", "Lambda1", "Lambda2"))
#
# VariSel_mod$parameters <- prm
#
# VariSelGrid <- function(X, Y, Sigma_12inv, search = "grid") {
# y <- as.numeric(Y %*% Sigma_12inv)
# x <- kronecker(Matrix::t(Sigma_12inv), X)
#
#
# library(kernlab)
# ## This produces low, middle and high values for sigma
# ## (i.e. a vector with 3 elements).
# sigmas <- kernlab::sigest(as.matrix(x), na.action = na.omit, scaled = TRUE)
# ## To use grid search:
# if(search == "grid") {
# out <- expand.grid(sigma = mean(as.vector(sigmas[-2])),
# C = 2 ^((1:len) - 3))
# } else {
# ## For random search, define ranges for the parameters then
# ## generate random values for them
# rng <- extendrange(log(sigmas), f = .75)
# out <- data.frame(sigma = exp(runif(len, min = rng[1], max = rng[2])),
# C = 2^runif(len, min = -5, max = 8))
# }
# out
# }
# filter <- ksvm(type~.,data=spamtrain,kernel="rbfdot",
# kpar=list(sigma=c(0.05,0.1)),C=c(5,2),cross=3)
# filter
#
#
# get_lambda <- function(x, y, type){
# if(grepl("lasso", type)){
# mysd <- function(y) sqrt(sum((y-mean(y))^2)/length(y))
# sx <- scale(x,scale=apply(x, 2, mysd))
# lambda.max <- max(abs(t(sx) %*% y))/length(y)
# lambda.min.ratio <- ifelse(nrow(sx)< ncol(sx), 0.01,0.0001)
# nlambda <- 100
# lambdapath <- round(exp(seq(log(lambda_max),
# log(lambda_max*lambda.min.ratio),
# length.out = nlambda)), digits = 10)
# }
# }
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