#we compare with the sliouette  function
##############
set.seed(451)
data=rbind(matrix(runif(20,1,5), nrow = 2, ncol = 10),matrix(runif(20,20,30), nrow = 2, ncol = 10),matrix(runif(20,50,70), nrow = 2, ncol = 10))
k=3
seed=5

distance<- function(vect1, vect2){
  sqrt(sum((vect1-vect2)^2))
}

centers_function<-function(data, grouping){
  center=matrix(0,length(grouping), dim(data)[2])
  for (i in 1:(length(grouping))){

    if(length(grouping[[i]])==1){
      center[i,]=data[grouping[[i]],]
    }else{
      center[i,]=apply(data[grouping[[i]],],2,mean)
    }
  }
  return(center)
}

init_centers=init_centers_random

#with kmeans
if(!is.null(seed)){
  set.seed(seed)
}
centers <- data[sample(1:nrow(data), k), ]
kmeas_R=kmeans(data,centers, 10, algorithm ="Hartigan-Wong")
dimnames(kmeas_R$centers) <- NULL

#With our function
FHW_output=Hartigan_and_Wong(data, distance,k,centers_function,init_centers   , seed=seed, 10)

Ous=Silhouette(data, FHW_output, distance)


Rs=cluster::silhouette(kmeas_R$cluster, dist(data))




#We make sure they are the same
test_that("Silhouette", {
  expect_equal(Ous,  Rs[,3],tolerance=1e-6)
})