Extract Clusters + Silhouette Value in STA Library

[AC3112]

I've been using a topological data analysis library for clustering in R known as Semi-Supervised Topological Analysis "STA."

However, unlike many libraries I've used before for clustering, there is no call to extract the clusters, e.g. x$cluster. So, I'd like to how to extract things such as cluster assignment and Silhouette values?

Code and links below.

Semi-supervised topological analysis

https://rdrr.io/github/TianshuFeng/SemiMapper/man/mapper.sta.html

#INSTALLATION AND LOADING

devtools::install_github("TianshuFeng/STA")
library(STA)

#DUMMY DATA

x1 = rep(1:3, times = 100) 
x2 = rep(1:3, times = 100)
x3 = rep(1:3, times = 100)
x4 = rep(1:3, times = 100)
x5 = rep(1:3, times = 100)

DAT <- data.frame(x1, x2,x3,x4,x5)
DAT <- data.frame(lapply(DAT, function(x) as.numeric(as.character(x))))

#STA CODE

MAP <- mapper.sta(DAT,
                  filter_values = DAT$x1,
                  num_intervals = 5,
                  percent_overlap = 40,
                  dist_method = "manhattan",
                  cluster_method = "hierarchical",
                  NbClust_cluster_method = "single",
                  num_bins_when_clustering = 10,
                  cluster_index = "silhouette"
)

simple_visNet(MAP, filter = DAT$x1, color_filter = TRUE)

####SIDE NOTE
#PLEASE ONLY RUN THIS LAST PIECE OF CODE IF THERE IS AN ERROR MESSAGE WITH DEPENDENCIES

cluster_cutoff_at_first_empty_bin <- function(heights, diam, num_bins_when_clustering) {
  
  # if there are only two points (one height value), then we have a single cluster
  if (length(heights) == 1) {
    if (heights == diam) {
      cutoff <- Inf
      return(cutoff)
    }
  }
  
  bin_breaks <- seq(from=min(heights), to=diam, 
                    by=(diam - min(heights))/num_bins_when_clustering)
  if (length(bin_breaks) == 1) { bin_breaks <- 1 }
  
  myhist <- hist(c(heights,diam), breaks=bin_breaks, plot=FALSE)
  z <- (myhist$counts == 0)
  if (sum(z) == 0) {
    cutoff <- Inf
    return(cutoff)
  } else {
    #  which returns the indices of the logical vector (z == TRUE), min gives the smallest index
    cutoff <- myhist$mids[ min(which(z == TRUE)) ]
    return(cutoff)
  }
  
}