"getClusterInfo" <-
function(useGUI=FALSE) {
upgma <- list()
diana <- list()
som <- list()
kmeans <- list()
#else, ask for input from command line
cat("Choose your clustering methods:\n")
answer <- readline("Use upgma clustering? (y/n) ")
if(substr(answer, 1, 1) == "n")
{
upgma$use <- FALSE
}
else {upgma$use <- TRUE
answer <- readline("Use Euclidean Distance? (y/n) ")
if(substr(answer, 1, 1) == "n")
{upgma$euclidean <- FALSE}
else{upgma$euclidean <- TRUE}
answer <- readline("Use Correlation Distance? (y/n) ")
if(substr(answer, 1, 1) == "n")
{upgma$correlation <- FALSE}
else{upgma$correlation <- TRUE}
}
answer <- readline("Use Diana clustering? (y/n)")
if(substr(answer, 1, 1) == "n")
{diana$use <- FALSE}
else{diana$use <- TRUE
answer <- readline("Use Euclidean Distance? (y/n) ")
if(substr(answer, 1, 1) == "n")
{diana$euclidean <- FALSE}
else{diana$euclidean <- TRUE}
answer <- readline("Use Correlation Distance? (y/n) ")
if(substr(answer, 1, 1) == "n")
{diana$correlation <- FALSE}
else{diana$correlation <- TRUE}
}
kans <- "c"
answer <- readline("Use Self-Organized Maps? (y/n) ")
if(substr(answer, 1, 1) == "n")
{ som$use <- FALSE
}
else {som$use <- TRUE
som$xdim <- as.numeric(readline("Enter X dimension of map: "))
som$ydim <- as.numeric(readline("Enter Y dimension of map: "))
}
answer <- readline("Use K-means clustering? (y/n)")
if(substr(answer, 1, 1) == "n")
{kmeans$use <- FALSE
}
else {kmeans$use <- TRUE
kmeans$k <-
as.numeric(readline("Enter number of clusters k: "))
kmeans$iterations <-
as.numeric(readline("Enter Maximum Iterations (Default 10000):"))
if(is.na(kmeans$iterations))
{kmeans$iterations <- 10000}
}
i <- 1
methodlist <- list()
if((upgma$use == TRUE) && (upgma$euclidean == TRUE)) {
id <- "UPGMAEUC"
func <- agglomOutput
method <- "upgma"
clustMethod <- "average"
distfunc <- "euclidean"
k <- kans
params <- list(id = id, method=method, func=func,
distfunc = distfunc, k=k,
clustMethod = clustMethod)
methodlist[[i]] <- params
i <- i + 1
}
if((upgma$use == TRUE) && (upgma$correlation == TRUE)) {
id <- "UPGMACOR"
func <- agglomOutput
method <- "upgma"
k <- kans
clustMethod <- "average"
distfunc <- "correlation"
params <- list(id = id, method=method, func=func,
distfunc = distfunc,
clustMethod = clustMethod)
methodlist[[i]] <- params
i <- i + 1
}
if((diana$use == TRUE) && (diana$euclidean == TRUE)) {
id <- "DIANAEUC"
func <- dianaOutput
method <- "diana"
distfunc <- "euclidean"
params <- list(id = id, method=method, func=func, distfunc = distfunc)
methodlist[[i]] <- params
i <- i + 1
}
if((diana$use == TRUE) && (diana$correlation == TRUE)) {
id <- "DIANACOR"
func <- dianaOutput
method <- "diana"
distfunc <- "correlation"
params <-
list(id = id, method=method, func=func, distfunc = distfunc)
methodlist[[i]] <- params
i <- i + 1
}
if(som$use == TRUE) {
id <- "SOM1"
func <- somOutput
method <- "som"
distfunc <- "euclidean"
xdim <- som$xdim
ydim <- som$ydim
params <-
list(id=id, method=method, func=func, xdim=xdim, ydim=ydim,
distfunc=distfunc)
methodlist[[i]] <- params
i <- i + 1
}
if(kmeans$use == TRUE) {
id <- "KMEANS1"
func <- kmeansOutput
method <- "kmeans"
distfunc <- "euclidean"
k <- kmeans$k
iterations <- kmeans$iterations
params <- list(id = id, method=method, func=func, k=k,
iterations = iterations, distfunc=distfunc)
methodlist[[i]] <- params
i <- i + 1
}
methodlist
}
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