Nothing
confusion.matrix <- function(clust1,clust2)
{
clust1 = cls.id.vect.validity(clust1, "clust1")
clust2 = cls.id.vect.validity(clust2, "clust2")
if( length(clust1) != length(clust2) )
stop("Bad input data: both input vectors should have the same length.")
clust_num1 = max(clust1)
clust_num2 = max(clust2)
result = .Call("confusionMatrix",
clust1,
clust2,
as.integer( c(clust_num1, clust_num2) ),
PACKAGE="clv"
)
return(result)
}
similarity.index <- function(cnf.mx)
{
cnf.mx = data.validity.int(cnf.mx, "cnf.mx")
if( TRUE %in% (cnf.mx < 0) )
stop("Bad input data: each 'cnf.mx' matrix element should be equal or greater than 0.")
return(similarity.index.int(cnf.mx))
}
similarity.index.int <- function(cnf.mx, opt.assign=NULL)
{
if( dim(cnf.mx)[1] > dim(cnf.mx)[2] ) cnf.mx = t(cnf.mx)
if(is.null(opt.assign)) opt.assign = ( .Call("clv_optimalAssignment", cnf.mx, PACKAGE="clv") + 1 )
app = 0
opt.asgn.len = length(opt.assign)
for( i in 1:opt.asgn.len ) app = app + cnf.mx[i,opt.assign[i]]
result = (app - 1)/(sum(cnf.mx) - 1)
return(result)
}
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