- d <- nrow(β)
- K <- ncol(β)
- io <- generateSampleIO(n, p, β, b, link)
- op <- optimParams(K, link, list(X=io$X, Y=io$Y))
- # N random starting points gaussian (TODO: around true β?)
- res <- matrix(nrow=d*K+1, ncol=N)
- for (i in seq_len(N))
- {
- β_init <- rnorm(d*K)
- par <- op$run( c(rep(1/K,K-1), β_init, rep(0,K)) )
- par <- op$linArgs(par)
- Qn <- op$f(par)
- res[,i] = c(Qn, par[K:(K+d*K-1)])
- }
- res #TODO: plot this, not just return it...
+ d <- nrow(β)
+ K <- ncol(β)
+ io <- generateSampleIO(n, p, β, b, link)
+ op <- optimParams(K, link, list(X=io$X, Y=io$Y))
+ # N random starting points gaussian (TODO: around true β?)
+ res <- matrix(nrow=d*K+1, ncol=N)
+ for (i in seq_len(N))
+ {
+ β_init <- rnorm(d*K)
+ par <- op$run( c(rep(1/K,K-1), β_init, rep(0,K)) )
+ par <- op$linArgs(par)
+ Qn <- op$f(par)
+ res[,i] = c(Qn, par[K:(K+d*K-1)])
+ }
+ res #TODO: plot this, not just return it...