#' Perform a cluster analysis.
#'
#' This function will cluster the data using [stats::hclust()] and
#' [stats::cutree()]. Every cluster with at least two members qualifies for
#' further analysis. Clusters are then ranked based on their size in relation
#' to the total number of values. The return value is a final score between
#' 0.0 and 1.0. Lower ranking clusters contribute less to this score.
#'
#' @param data The values that should be scored.
#' @param span The maximum span of values considered to be in one cluster.
#' @param weight The weight that will be given to the next largest cluster in
#'   relation to the previous one. For example, if `weight` is 0.7 (the
#'   default), the first cluster will weigh 1.0, the second 0.7, the third 0.49
#'   etc.
clusteriness <- function(data, span = 1000000, weight = 0.7) {
    n <- length(data)

    # Return a score of 0.0 if there is just one or no value at all.
    if (n < 2) {
        return(0.0)
    }

    # Cluster the data and compute the cluster sizes.

    tree <- stats::hclust(stats::dist(data))
    clusters <- stats::cutree(tree, h = span)
    cluster_sizes <- sort(tabulate(clusters), decreasing = TRUE)

    # Compute the "clusteriness" score.

    score <- 0.0

    for (i in seq_along(cluster_sizes)) {
        cluster_size <- cluster_sizes[i]

        if (cluster_size >= 2) {
            cluster_score <- cluster_size / n
            score <- score + weight^(i - 1) * cluster_score
        }
    }

    score
}

#' Process genes clustering their distance to telomeres.
#'
#' The result will be cached and can be reused for different presets, because
#' it is independent of the reference genes in use.
#'
#' @return An object of class `geposan_method`.
#'
#' @seealso [clusteriness()]
#'
#' @export
clustering <- function() {
    method(
        id = "clustering",
        name = "Clustering",
        description = "Clustering of genes",
        function(preset, progress) {
            species_ids <- preset$species_ids
            gene_ids <- preset$gene_ids

            cached("clustering", c(species_ids, gene_ids), {
                scores <- data.table(gene = gene_ids)

                # Prefilter the input data by species.
                distances <- geposan::distances[species %chin% species_ids]

                genes_done <- 0
                genes_total <- length(gene_ids)

                # Perform the cluster analysis for one gene.
                compute <- function(gene_id) {
                    data <- distances[gene == gene_id, distance]
                    score <- clusteriness(data)

                    genes_done <<- genes_done + 1
                    progress(genes_done / genes_total)

                    score
                }

                scores[, score := compute(gene), by = gene]

                result(
                    method = "clustering",
                    scores = scores
                )
            })
        }
    )
}