-
Notifications
You must be signed in to change notification settings - Fork 2
/
tests.R
36 lines (35 loc) · 1008 Bytes
/
tests.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
data(neuroblastomaProcessed, package="penaltyLearning")
nb.err <- with(neuroblastomaProcessed$errors, data.frame(
example=paste0(profile.id, ".", chromosome),
min.lambda,
max.lambda,
fp, fn))
X.sc <- scale(neuroblastomaProcessed$feature.mat)
keep <- apply(is.finite(X.sc), 2, all)
X.keep <- X.sc[,keep]
weight.vec <- rep(0, ncol(X.keep))
nb.diffs <- aum::aum_diffs_penalty(nb.err, rownames(X.keep))
biggest.max.it <- 4278103
logseq <- function(x,by=0.25){
as.integer(10^seq(1, log10(x), by=by))
}
test.list <- list(
"N=data,iterations=N"=list(
expr=quote(aum::aum_line_search(
some.diffs,
feature.mat=X.keep,
weight.vec=weight.vec,
maxIterations = nrow(some.diffs))),
N=logseq(nrow(nb.diffs)),
setup=quote(some.diffs <- nb.diffs[1:N])
),
"N=iterations,data=full"=list(
expr=quote(aum::aum_line_search(
nb.diffs,
feature.mat=X.keep,
weight.vec=weight.vec,
maxIterations = N)),
N=logseq(biggest.max.it),
setup={}
)
)