Nothing
weight.aggregate <- function(dat,
d=300,
B=100,
k,
classlabel,
sample.n=100,
method.phi=c("correspondence","Rand","cRand","NMI"),
method.dist=c("pearson","kendall","spearman","standardizedEuclid",
"euclidean","pearson.u","kendall.u","spearman.u"),
leave.k.out=c("sample","none","combn"),
leave.by=c("count.class","flat","percent.class"),
leave.k=1)
{
wm <- weight.matrix(dat=dat,
d=d,
B=B,
k=k,
classlabel=classlabel,
sample.n=sample.n,
method.phi=method.phi,
method.dist=method.dist,
leave.k.out=leave.k.out,
leave.by=leave.by,
leave.k=leave.k)
ret <- data.frame(sum=weight.sum(wm), n=weight.n(wm))
row.names(ret) <- row.names(dat)
ret
}
weight.matrix <-
function(dat,
d=300,
B=100,
k,
classlabel,
sample.n=100,
method.phi=c("correspondence","Rand","cRand","NMI","gdbr"),
method.dist=c("pearson","kendall","spearman","standardizedEuclid",
"euclidean","pearson.u","kendall.u","spearman.u"),
leave.k.out=c("sample","none","combn"),
leave.by=c("count.class","flat","percent.class"),
leave.k=1)
## use fs.agreement.part to weight the features based on its partition
## agreement with the classlabel
{
method <- match.arg(method.phi)
if(method =='correspondence') method <- 'euclidean'
method.dist <- match.arg(method.dist)
leave.k.out <- match.arg(leave.k.out)
leave.by <- match.arg(leave.by)
dat.nrow <- dim(dat)[1]
weights.matrix <- matrix(ncol=B,nrow=dat.nrow)
idx <- matrix(sample(seq(dat.nrow),size=B*d,replace=T),
nrow=B)
if(identical(leave.k.out,"none")) {
agreement.measure <- apply(idx,
1,
fs.agreement.part,
c.idx=seq(ncol(dat)),
dt=dat,
classlabel=classlabel,
k=k,
method.agreement=method,
method.dist=method.dist)
}
else if (identical(leave.k.out,"combn")) {
agreement.measure <- apply(idx,
1,
fs.leave.k.out.combn,
dt=dat,
classlabel=classlabel,
k=k,
method.agreement=method,
method.dist=method.dist,
leave.by=leave.by,
leave.k=leave.k)
}
else if(identical(leave.k.out,"sample")) {
agreement.measure <- apply(idx,
1,
fs.leave.k.out.sample,
dt=dat,
classlabel=classlabel,
k=k,
n=sample.n,
method.agreement=method,
method.dist=method.dist,
leave.by=leave.by,
leave.k=leave.k)
}
else stop("Unsupported leave.k.out,only none,combn or sample are supported")
for (i in seq(B)) {
weights.matrix[idx[i,],i] <- agreement.measure[i]
}
weights.matrix
}
weight.sum <- function(wm) {
rowSums(wm, na.rm=T)
}
weight.n <- function(wm) {
rowSums(!is.na(wm))
}
weight.mean <- function(wm) {
rowMeans(wm, na.rm=T)
}
weight.sd <- function(wm) {
apply(wm, 1, sd, na.rm=T)
}
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