Question

library(e1071)
library(caret)
library(kernlab)
data <- read.table(file.choose(), header = T, sep=";", quote="")
s <-sample(213,149)
tra <- data[s,]
tes<- data[-s,]
x <- subset(tes, select = -Cardio)
y <- tes$Cardio

# objective function
f <- function(y,data,C_exp, gamma_exp)
{
  set.seed(123)
  C = 10 ** C_exp
  gamma = 10 ** gamma_exp
  clf = svm.SVC(C=C, gamma=gamma)

  wts <- 100 / table(data$Cardio)
  svm.model <- svm(Cardio ~ ., data = tra, scale = FALSE,kernel="radial",gamma=gamma,cost=0.01,class.weights = wts)
  predictedY <- predict(svm.model, x)
  error <- data$Cardio - predictedY
  # Check accuracy:
  table( predictedY, y)
  # compute decision values and probabilities:
  pred <- predict(svm.model, x, decision.values = TRUE)
 # attr(pred, "decision.values")[1:4,]
    mean(pred==tes[,72])
  return (RMSE(error))
}
n <- 50
m.l <- 50
w <- 0.95
c1 <- 0.2
c2 <- 0.2
xmin <- c(-5.12, -5.12)
xmax <- c(5.12, 5.12)
vmax <- c(4, 4)
optimum <-psoptim(f, n=n, max.loop=m.l, w=w, c1=c1, c2=c2,xmin=xmin, xmax=xmax, vmax=vmax, seed=5, anim=FALSE)

我正在尝试用PSO技术优化svm参数，但我仍然感到困惑，因为我收到了这个错误

FUN（x）出错：缺少参数“C_exp”，没有默认值

请提前帮助并谢谢

支持向量机组合粒子群算法优化其参数

0 个答案: