SHARE
TWEET

Untitled

a guest Jun 26th, 2019 68 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. data<- pca.train
  2. train<-data.matrix(pca.train, rownames.force = NA)
  3.  
  4. rmse <- function(error)
  5. {
  6.   sqrt(mean(error^2))
  7. }
  8.  
  9. f <- function(V1, V2,V3,x,y)
  10. {
  11.   V1 <-  log10(B1)
  12.   V2 <-  log10(B2)
  13.   V3 <-  log10(B3)
  14.  
  15.  
  16.   svm.model <- svm(x=train, y=data$y1,scale=F, type= "eps-regression",kernel="radial",cost = V1,epsilon= V2,gamma= V3)
  17.   error<- pca.train$y1- svm.model$fitted
  18.   return (rmse(error))
  19. }
  20.  
  21. B3<-  seq(1, 2,0.1)
  22. B1<-  2^(2:9)
  23. B2 <- seq(0.1,1, 0.1)
  24.  
  25. n <- 50
  26. m.l <- 50
  27. w <- 0.95
  28. c1 <- 0.2
  29. c2 <- 0.2
  30. xmin <- c(-5.12, -5.12)
  31. xmax <- c(5.12, 5.12)
  32. vmax <- c(4, 4)
  33.  
  34. 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)
  35. OPTIMIM.VALUE<- f(optimum)
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!
 
Top