R Under development (unstable) (2024-04-27 r86487 ucrt) -- "Unsuffered Consequences"
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> library(Thresher)
Loading required package: ClassDiscovery
Loading required package: cluster
Loading required package: oompaBase
Loading required package: PCDimension
> # get saved data
> data(savedSims)
> reap <- savedReap[[5]]
> dset <- reap@data
> labels <- factor(paste("C", predict(reap@fit), sep=""))
> 
> # use a different clustering algorithm
> hc <- hclust(distanceMatrix(dset, "uncentered"), "average")
> newLabels <- paste("New", cutree(hc, k=3), sep="")
> tab <- table(OLD=labels, NEW=newLabels)
> tab
    NEW
OLD  New1 New2 New3
  C1    0    0   10
  C2    4    0    0
  C3    0    6    0
> 
> # now test the methods
> indices <- labelMatcher(tab)
> tab[indices$ii, indices$jj]
    NEW
OLD  New3 New1 New2
  C1   10    0    0
  C2    0    4    0
  C3    0    0    6
> 
> matchLabels(tab)
    NEW
OLD  New3 New1 New2
  C1   10    0    0
  C2    0    4    0
  C3    0    0    6
> 
> countAgreement(tab)
[1] 20
> 
> labelAccuracy(dset, labels)
               pearson               spearman              euclidean 
                  1.00                   1.00                   1.00 
uncentered correlation       absolute pearson           sqrt pearson 
                  1.00                   0.75                   1.00 
             minkowski                maximum                  weird 
                  1.00                   1.00                   1.00 
> 
> bestMetric(dset, labels)
[1] "pearson"
> 
> proc.time()
   user  system elapsed 
   0.87    0.09    0.96