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#' @import utils
utils::globalVariables(c("dat"))
#------------------------------------------------------------------------------
# SVM regression ga function
#------------------------------------------------------------------------------
svm.reg.ga <- function(x = x, y = y, cross = cross, fast = fast, loss = loss) {
dat <- as.data.frame(cbind(y, x))
# initialize list
results <- list()
#------------------------------------------------------------------------------
# SVM regression resub function
#------------------------------------------------------------------------------
# Function for regular speed
svm.reg.opt.resub <- function(params, dat, loss) {
pr <- NULL
try(pr <- e1071::svm(y ~ ., data = dat, cost = params[1],
gamma = 2^params[2], epsilon = params[3]))
if(!is.null(pr)){
l <- loss.reg(pred = pr$fitted, true_y = dat$y, loss = loss)
} else {
l <- 1e+150
}
l
}
#------------------------------------------------------------------------------
# SVM regression CV function
#------------------------------------------------------------------------------
svm.reg.opt.cv <- function(params, dat, cross, loss) {
pr <- NULL
if(loss == "mse") {
try(pr <- e1071::svm(y ~ ., data = dat, cost = params[1],
gamma = 2^params[2], epsilon = params[3],
cross = cross))
l <- pr$tot.MSE[1, 1]
} else if(loss == "mae") {
try(pr <- cv.pred.svm.reg(dat = dat, params = params, cross = cross))
l <- loss.reg(pred = pr, true_y = dat$y, loss = "mae")
} else {
l <- 1e+150
}
l
}
#------------------------------------------------------------------------------
# SVM regression fast functions
#------------------------------------------------------------------------------
svm.reg.pred.fast <- function(x, y, n, cost, gamma, epsilon, loss) {
dat <- cbind(y, x)
dat2 <- dat[sample(nrow(dat)), ]
train <- dat2[c(1:n), ]
test <- dat2[-c(1:n), ]
svm.t <- e1071::svm(y ~ ., data = train, cost = cost, gamma = gamma,
epsilon = epsilon)
pred <- stats::predict(svm.t, newdata = test[, -1])
loss.reg(pred = pred, true_y = test$y, loss = loss)
}
svm.reg.opt.fast <- function(params, dat, n, loss){
pr <- NULL
try(pr <- svm.reg.pred.fast(dat[, -1], dat[, 1], n = n, cost = params[1],
gamma = 2^params[2], epsilon = params[3],
loss = loss))
if(!is.null(pr)){
l <- pr
} else {
l <- 1e+150
}
l
}
# setup fitness function based on user inputs
if(is.null(cross) & !fast) {
fit <- function(p) {-1 * svm.reg.opt.resub(p, dat, loss)}
} else if (fast > 0) {
if(fast > 1) {
n <- fast
} else if(fast < 1) {
n <- round(fast * nrow(dat))
} else {
n <- find.n(dat, fast)
}
fit <- function(p) {-1 * svm.reg.opt.fast(p, dat, n, loss)}
results$n <- n
} else if(!is.null(cross)) {
if(cross >= 2) {
fit <- function(p) {-1 * svm.reg.opt.cv(p, dat, cross, loss)}
} else {
stop("Invalid number of folds for cross-validation. Use integer > 1.")
}
results$nfold <- cross
} else {
warning("Invalid option for fast. Default for fast used in computations.")
n <- find.n(dat, fast)
fit <- function(p) {-1 * svm.reg.opt.fast(p, dat, n, loss)}
results$n <- n
}
ga.obj <- GA::ga(type = "real-valued", fitness = fit, parallel = 2,
maxiter = 10, run = 5, lower = c(1, -10, 0),
upper = c(1042, 5, 0.5))
results$cost <- as.numeric(ga.obj@solution[1, 1])
results$gamma <- as.numeric(2^ga.obj@solution[1, 2])
results$epsilon <- as.numeric(ga.obj@solution[1, 3])
results$loss <- as.numeric(-1 * ga.obj@fitnessValue)
results$model <- e1071::svm(y ~ ., data = dat, cost = results$cost,
gamma = results$gamma, epsilon = results$epsilon)
results
}
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