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#' @title Compute the performances of the l1-spectral clustering algorithm
#' @description This function computes the performances of the l1-spectral clustering algorithm in terms of Normalized Mutualized Information (NMI).
#' @param Results Output of the function \code{l1_spectralclustering()}.
#' @param A The adjacency matrix of the graph to cluster.
#' @importFrom aricode NMI
#' @importFrom aricode AMI
#' @importFrom aricode ARI
#' @seealso \code{\link{l1_spectralclustering}}, \code{\link{l1spectral}}.
#' @return The Normalized Mutualized Information (NMI), Adjusted Mutualized Information (AMI) and Adjusted Rand Index (ARI) scores.
#' @author Camille Champion, Magali Champion
#' @export
#' @examples
#' #############################################################
#' # Computing the performances
#' #############################################################
#'
#' data(ToyData)
#'
#' results <- l1_spectralclustering(A = ToyData$A_hat, pen = "lasso",
#' k=2, elements = c(1,4))
#'
#' ComputePerformances(Results=results,A=ToyData$A)
#'
ComputePerformances <- function(Results, A){
# Results: output of the function l1_spectralclustering()
# A: true adjacency matrix
# Output: NMI, AMI and ARI scores
# first, find the clusters in the adjacency matrix
graph <- graph_from_adjacency_matrix(A,mode="undirected")
clusters <- components(graph)$membership
if (!is.null(ncol(Results$comm))){
clus_est <- Results$comm%*%c(1:ncol(Results$comm))
} else {
clus_est <- Results$comm
}
clus_est <- as.vector(clus_est)
NMI <- NMI(clus_est,clusters)
AMI <- AMI(clus_est,clusters)
ARI <- ARI(clus_est,clusters)
return(score=list(NMI=NMI,AMI=AMI,ARI=ARI))
}
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