R Under development (unstable) (2026-02-18 r89435 ucrt) -- "Unsuffered Consequences"
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> set.seed(19970224)
> nt = 100
> n = 100
> d = 200
> p = 3
> m = d * p
> t = 3
> 
> library(splines)
> 
> 
> X = 0.5*matrix(runif(n*d),n,d) + matrix(rep(0.5*runif(n),d),n,d)
> y = -2*sin(X[,1]) + X[,2]^2-1/3 + X[,3]-1/2 + exp(-X[,4])+exp(-1)-1
> 
> 
> 
> genZ_t = proc.time()
> Z = matrix(0,n,m)
> for(j in 1:d){
+   tmp = (j-1)*p + c(1:p)
+   tmp0 = ns(X[,j],df=p)
+   Z[,tmp] = tmp0
+ }
> 
> Z <-cbind(rep(1, n), Z)
> colnames(Z) <- c("Intercept", paste("X", 1:m, sep = ""))
> 
> genZ_t = proc.time() - genZ_t
> 
> Xt = 0.5*matrix(runif(nt*d),nt,d) + matrix(rep(0.5*runif(nt),d),nt,d)
> yt = -2*sin(Xt[,1]) + Xt[,2]^2-1/3 + Xt[,3]-1/2 + exp(-Xt[,4])+exp(-1)-1
> 
> Zt = matrix(0,nt,m)
> for(j in 1:d){
+   tmp = (j-1)*p + c(1:p)
+   tmp0 = ns(Xt[,j],df=p)
+   Zt[,tmp] = tmp0
+ }
> 
> Zt <-cbind(rep(1, nt), Zt)
> colnames(Zt) <- c("Intercept", paste("X", 1:m, sep = ""))
> 
> index <- NA
> for (i in 1:d) {
+   index <- c(index, rep(i, p))
+ }
> 
> # SAM
> 
> library(SAM)
> 
> total_t = 0
> total_l = 0
> nlamb = 30
> for (i in 1:t) {
+   t0 = proc.time()
+   out.trn = samQL(X, y, nlambda=nlamb)
+   total_t = total_t + proc.time() - t0
+   out.tst = predict(out.trn, Xt)
+   total_l = total_l + mean((out.tst$values[,nlamb]-yt)^2/2)
+ }
> print("sam lin-reg:")
[1] "sam lin-reg:"
> print(total_t / t - genZ_t)
      user     system    elapsed 
0.01000000 0.01000000 0.01333333 
> print(total_l / t)
[1] 0.000264342
> 
> 
> total_t = 0
> total_l = 0
> nlamb = 30
> for (i in 1:t) {
+   t0 = proc.time()
+   out.trn = samQL(X, y, nlambda=nlamb, regfunc="MCP")
+   total_t = total_t + proc.time() - t0
+   out.tst = predict(out.trn, Xt)
+   total_l = total_l + mean((out.tst$values[,nlamb]-yt)^2/2)
+ }
> print("sam lin-reg with MCP:")
[1] "sam lin-reg with MCP:"
> print(total_t / t - genZ_t)
       user      system     elapsed 
0.006666667 0.006666667 0.006666667 
> print(total_l / t)
[1] 0.0002486487
> 
> proc.time()
   user  system elapsed 
   1.68    0.31    1.93