value2 = as.integer()

HierarchicalClustering <- function(couplingdata, tcluster, met="complete")
{
  d <- dist(as.matrix(couplingdata))
  dendrogram <- hclust(d, method = met)
  plot(dendrogram, check = TRUE, labels = colnames(couplingdata))
  rect.hclust(dendrogram,k = tcluster, border = 2)
  # making clusters variable as global
  clusters <<- cutree(dendrogram, k= tcluster)
  clusters
}

Precision <- function(observed, gold1)
{
  library(clusterCrit)
  print(observed)
  print(gold1)
  ext <- extCriteria(observed, gold1,"all")
  return(ext)
}
Recall <- function(observed, gold1)
{
  library(clusterCrit)
  value2 <- as.numeric(extCriteria(observed, gold1,"Recall"))
  return(value2)
}

# initialization of variables
totalClusters = 4
method = "ward.D2"
goldStandard = as.integer(JUnitGoldStandard)

# performing hierarchical clustering
HierarchicalClustering(ClassCouplingData, totalClusters, method)

# generating all possible permutations
val <- vector()
library(combinat)
for(i in 1: totalClusters)
  val <- c(val, i)
permutation <- permn(val) 

# evaluating clustering obtained
for(i in 1: length(permutation))
{
  str2 = ""
  library(car)
  print(permutation[i])
  gold1 <- as.integer(recode(goldStandard,"1=permutation[[i]][1];2=permutation[[i]][2];3=permutation[[i]][3]; 4=permutation[[i]][4]"),as.numeric.result=FALSE)
  precision = 0
  ext <- Precision(clusters, gold1)
  recall = Recall(clusters, gold1)
  cat("Iteration = ", i, " Precision =  ", ext$precision," Recall= ",recall, "\n")
}