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fast_anticlustering.R
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fast_anticlustering.R
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#' Fast anticlustering
#'
#' Increasing the speed of (k-means / k-plus) anticlustering by (1)
#' conducting fewer exchanges during the optimization and (2) using an alternative
#' formulation of the k-means objective. Makes anticlustering applicable to
#' quite large data sets.
#'
#' @param x A numeric vector, matrix or data.frame of data points.
#' Rows correspond to elements and columns correspond to
#' features. A vector represents a single numeric feature.
#' @param K How many anticlusters should be created. Alternatively:
#' (a) A vector describing the size of each group, or (b) a vector
#' of length \code{nrow(x)} describing how elements are assigned
#' to anticlusters before the optimization starts.
#' @param k_neighbours The number of nearest neighbours that serve as
#' exchange partner for each element. See details.
#' @param categories A vector, data.frame or matrix representing one
#' or several categorical constraints.
#' @param exchange_partners Optional argument. A list of length
#' \code{NROW(x)} specifying for each element the indices of the
#' elements that serve as exchange partners. If used, this
#' argument overrides the \code{k_neighbours} argument. See
#' examples.
#'
#' @importFrom RANN nn2
#'
#' @seealso
#'
#' \code{\link{anticlustering}}
#'
#' \code{\link{kplus_moment_variables}}
#'
#' \code{\link{categories_to_binary}}
#'
#' \code{\link{variance_objective}}
#'
#' \code{\link{generate_exchange_partners}}
#'
#' @export
#'
#' @author
#' Martin Papenberg \email{martin.papenberg@@hhu.de}
#'
#'
#' @references
#'
#'
#' Papenberg, M., & Klau, G. W. (2021). Using anticlustering to partition
#' data sets into equivalent parts. Psychological Methods, 26(2),
#' 161–174. https://doi.org/10.1037/met0000301.
#'
#' Papenberg, M. (2024). K-plus Anticlustering: An Improved k-means Criterion for
#' Maximizing Between-Group Similarity. British Journal of Mathematical and
#' Statistical Psychology, 77(1), 80--102. https://doi.org/10.1111/bmsp.12315
#'
#' Späth, H. (1986). Anticlustering: Maximizing the variance criterion.
#' Control and Cybernetics, 15, 213-218.
#'
#'
#' @details
#'
#' This function was created to make anticlustering applicable to
#' large data sets (e.g., several 100,000 elements). It optimizes the
#' k-means objective because computing all pairwise distances as is
#' done when optimizing the "diversity" (i.e., the default in
#' \code{\link{anticlustering}}) is not feasible for very large data
#' sets (for about N > 20000 on my personal computer). Using
#' \code{fast_anticlustering} for k-plus anticlustering is also
#' possible by applying \code{\link{kplus_moment_variables}} on the
#' input (and possibly by using the argument \code{exchange_partners},
#' see Examples).
#'
#' The function \code{fast_anticlustering} employs a speed-optimized
#' exchange method, which is basically equivalent to \code{method =
#' "exchange"} in \code{\link{anticlustering}}, but may reduce the number
#' of exchanges that are investigated for each input element. The number of
#' exchange partners per element has to be set using the argument \code{k_neighbours}. By
#' default, it is set to \code{Inf}, meaning that all possible swaps are
#' tested. If \code{k_neighbours} is set differently (which is usually recommended when running
#' this function), the default behaviour is to generate exchange partners using a
#' nearest neighbour search (using the function \code{\link[RANN]{nn2}}
#' from the \code{RANN} package). Using more exchange partners can improve the quality of
#' the results, but also increase run time. Note that for very large
#' data sets, anticlustering generally becomes "easier" (even a random
#' split may yield satisfactory results), so using few exchange
#' partners is usually not a problem.
#'
#' It is possible to specify custom exchange partners using the
#' argument \code{exchange_partners} instead of relying on the default
#' nearest neighbour search. When using \code{exchange_partners}, it
#' is not necessary that each element has the same number of exchange
#' partners; this is why the argument \code{exchange_partners} has to
#' be a \code{list} instead of a \code{matrix} or
#' \code{data.frame}. Exchange partners can for example be generated
#' by \code{\link{generate_exchange_partners}} (see Examples), but a
#' custom list may also be used. Note that categorical constraints
#' induced via \code{categories} may not be respected during the
#' optimization if the \code{exchange_partners} argument allows
#' exchanges between members of different categories, so care must be
#' taken when combining the arguments \code{exchange_partners} and
#' \code{categories}.
#'
#' In \code{anticlustering(..., objective = "variance")}, the run time
#' of computing the k-means objective is in O(M N), where N is the
#' total number of elements and M is the number of variables. This is
#' because the variance is computed as the sum of squared distances
#' between all data points and their cluster centers. The function
#' \code{fast_anticlustering} uses a different - but equivalent -
#' formulation of the k-means objective, where the re-computation of
#' the objective only depends and M but no longer on N. In
#' particular, this variant of k-means anticlustering minimizes
#' the weighted sum of squared distances between
#' cluster centroids and the overall data centroid; the distances
#' between all individual data points and their cluster center are not
#' computed (Späth, 1986). Using the different objective formulation
#' reduces the run time by an
#' order of magnitude and makes k-means anticlustering applicable to
#' very large data sets (even in the millions). For a fixed number of
#' exchange partners (specified using the argument
#' \code{k_neighbours}), the approximate run time of
#' \code{fast_anticlustering} is in O(M N). The algorithm
#' \code{method = "exchange"} in \code{\link{anticlustering}} with
#' \code{objective = "variance"} has a run time of O(M N^3).
#' Thus, \code{fast_anticlustering} can improve the run time
#' by two orders of magnitude as compared to the standard exchange
#' algorithm. The nearest neighbour search, which is done in the
#' beginning usually does not strongly contribute to the overall
#' run time. It is nevertheless possible to suppress the nearest
#' neighbour search by using the \code{exchange_partners} argument.
#'
#' When setting the \code{categories} argument, exchange partners
#' (i.e., nearest neighbours) will be generated from the same
#' category. Note that when \code{categories} has multiple columns,
#' each combination of these categories is treated as a distinct
#' category by the exchange method. You can also use
#' \code{\link{categories_to_binary}} to potentially improve results
#' for several categorical variables, instead of using the argument
#' \code{categories}.
#'
#' @examples
#'
#' ## Use fewer or more exchange partners to adjust speed (vs. quality tradeoff)
#' features <- iris[, - 5]
#' N <- nrow(features)
#' init <- sample(rep_len(1:3, N)) # same starting point for all calls:
#' groups1 <- fast_anticlustering(features, K = init) # default: all exchanges
#' groups2 <- fast_anticlustering(features, K = init, k_neighbours = 20)
#' groups3 <- fast_anticlustering(features, K = init, k_neighbours = 2)
#'
#' variance_objective(features, groups1)
#' variance_objective(features, groups2)
#' variance_objective(features, groups3)
#'
#' # K-plus anticlustering is straight forward when sticking with the default
#' # for k_neighbours
#' kplus_anticlusters <- fast_anticlustering(
#' kplus_moment_variables(features, T = 2),
#' K = 3
#' )
#' mean_sd_tab(features, kplus_anticlusters)
#'
#' # Some care is needed when applying k-plus using with this function
#' # while using a reduced number of exchange partners generated in the
#' # nearest neighbour search. Then we:
#' # 1) Use kplus_moment_variables() on the numeric input
#' # 2) Generate custom exchange_partners because otherwise nearest
#' # neighbours are internally selected based on the extended k-plus
#' # variables returned by kplus_moment_variables()
#' # (which does not really make sense)
#' kplus_anticlusters <- fast_anticlustering(
#' kplus_moment_variables(features, T = 2),
#' K = 3,
#' exchange_partners = generate_exchange_partners(120, features = features, method = "RANN")
#' )
#' mean_sd_tab(features, kplus_anticlusters)
#' # Or we use random exchange partners:
#' kplus_anticlusters <- fast_anticlustering(
#' kplus_moment_variables(features, T = 2),
#' K = 3,
#' exchange_partners = generate_exchange_partners(120, N = nrow(features), method = "random")
#' )
#' mean_sd_tab(features, kplus_anticlusters)
#'
#'
#' # Working on several 1000 elements is very fast (Here n = 10000, m = 2)
#' data <- matrix(rnorm(10000 * 2), ncol = 2)
#' start <- Sys.time()
#' groups <- fast_anticlustering(data, K = 5, k_neighbours = 5)
#' Sys.time() - start
#'
fast_anticlustering <- function(x, K, k_neighbours = Inf, categories = NULL,
exchange_partners = NULL) {
input_validation_anticlustering(
x, K, "variance", "exchange", FALSE, categories, NULL
)
categories <- merge_into_one_variable(categories)
x <- as.matrix(x)
N <- nrow(x)
if (argument_exists(exchange_partners)) {
validate_exchange_partners(exchange_partners, N)
} else {
if (!isTRUE(k_neighbours == Inf)) {
validate_input(k_neighbours, "k_neighbours", objmode = "numeric", len = 1,
must_be_integer = TRUE, greater_than = 0, not_na = TRUE)
}
exchange_partners <- all_exchange_partners(x, k_neighbours, categories)
}
exchange_partners <- cleanup_exchange_partners(exchange_partners, N)
c_anticlustering(
x, initialize_clusters(N, K, categories),
categories = NULL, objective = "fast-kmeans",
exchange_partners - 1
)
}
#' Get exchange partners for k-means anticlustering
#'
#' @details
#'
#' Computes the k nearest neighbours for each input element using
#' RANN::nn2. If no nearest neighbours are required, argument
#' `k_neighbours` will be `Inf`. May compute nearest neighbors within
#' categories.
#'
#' @return A list of length `N`. Each element is a vector
#' of exchange partners (that may be nearest neighbors).
#'
#' @noRd
all_exchange_partners <- function(features, k_neighbours, categories) {
# Case 1: no nearest neighbor search needed
if (is.infinite(k_neighbours)) {
return(all_exchange_partners_(nrow(features), categories))
}
# Case 2: NN search needed
return(nearest_neighbours(features, k_neighbours, categories))
}
# Generate all possible exchange partners
all_exchange_partners_ <- function(N, categories) {
# Case 1: Exchange partners are from the same category
if (argument_exists(categories)) {
return(list_idx_by_category(categories))
}
# Case 2: Everyone is potential exchange partner
rep(list(1:N), N)
}
# convert list of exchange partners to matrix for C;
# when doing that, possibly "fill" empty entries with "N+1" (in C -> N), if some
# list elements are shorter than others (if categorical variables have been used and are
# unevenly distributed)
cleanup_exchange_partners <- function(exchange_partners, N) {
max_exchanges_partners <- max(lengths(exchange_partners))
exchange_partners <- lapply(exchange_partners, function(x) c(x[1:length(x)], rep(N+1, max(0, max_exchanges_partners - length(x)))))
# remove potential NAs
exchange_partners <- lapply(exchange_partners, function(x) x[!is.na(x)])
exchange_partners <- unname(t(t(as.data.frame(exchange_partners))))
# `exchange_partners` is passed as matrix to C; there it is converted to a 1-dimensional "vector".
# Here we pass it as a matrix where elements = columns; cols = exchange partners.
# Usually it would be the other way around, e.g. RANN returns the standard "N x k_neighbours"
# format, but I pass a "k_neighbours x N" matrix to C, which is more easily handled there.
exchange_partners
}
validate_exchange_partners <- function(exchange_partners, N) {
if (!inherits(exchange_partners, "list")) {
stop("Argument `exchange_partners` must be a list.")
}
if (length(exchange_partners) != N) {
stop("If used, argument `exchange_partners` must have the same length as you have data points.")
}
if (any(lengths(exchange_partners)) > N) {
stop("Argument `exchange_partners` has problems: Some elements have more exchange partners than there are data points.")
}
unlisted <- unlist(exchange_partners)
if (any(unlisted) > N) {
stop("Argument `exchange_partners` has problems: The maximum index is larger than the number of elements.")
}
if (any(unlisted) < 1) {
stop("Argument `exchange_partners` has problems: Some indices are lower than 1.")
}
if (any(as.integer(unlisted) != unlisted)) {
stop("Argument `exchange_partners` has problems: Only integer indices are allowed.")
}
}
#' Get exchange partners for fast_anticlustering()
#'
#' @param n_exchange_partners The number of exchange partners per
#' element
#' @param N The number of elements for which exchange partners; can be
#' \code{NULL} if \code{features} is passed (it is ignored if
#' \code{features} is passed).
#' @param features The features for which nearest neighbours are
#' sought if \code{method = "RANN"}. May be NULL if random
#' exchange partners are generated.
#' @param method Currently supports "random" (default), "RANN" and
#' "restricted_random". See details.
#' @param categories A vector, data.frame or matrix representing one
#' or several categorical constraints.
#'
#' @return A list of length \code{N}. Is usually used as input to the
#' argument \code{exchange_partners} in
#' \code{\link{fast_anticlustering}}. Then, the i'th element of
#' the list contains the indices of the exchange partners that are
#' used for the i'th element.
#'
#' @export
#'
#' @details
#'
#' The \code{method = "RANN"} generates exchange partners using a
#' nearest neighbour search via \code{\link[RANN]{nn2}} from the
#' \code{RANN} package; \code{methode = "restricted_random"} generates
#' random exchange partners but ensures that for each element, no
#' duplicates are generated and that the element itself does not occur
#' as exchange partner (this is the slowest method, and I would not
#' recommend it for large N); \code{method = "random"} (default) does
#' not impose these restrictions and generates unrescricted random
#' partners (it may therefore generate duplicates and the element
#' itself as exchange partner).
#'
#' When setting the \code{categories} argument and using \code{method
#' = "RANN"}, exchange partners (i.e., nearest neighbours) will be
#' generated from the same category; \code{methode =
#' "restricted_random"} will also adhere to categorical constraints
#' induced via \code{categories} (i.e. each element only receives
#' exchange partners from the same category as itself); \code{methode
#' = "random"} cannot incoorporate categorical restrictions.
#'
#'
#' @examples
#'
#' # Restricted random method generates no duplicates per element and cannot return
#' # the element itself as exchange partner
#' generate_exchange_partners(5, N = 10, method = "restricted_random")
#' # "random" simply randomizes with replacement and without restrictions
#' # (categorical restrictions are also not possible; is much faster for large data sets)
#' generate_exchange_partners(5, N = 10, method = "random")
#' # May return less than 5 exchange partners if there are not enough members
#' # of the same category:
#' generate_exchange_partners(
#' 5, N = 10,
#' method = "restricted_random",
#' categories = cbind(schaper2019$room, schaper2019$frequency)
#' )
#' # using nearest neighbour search (unlike RANN::nn2, this does not
#' # return the ID of the element itself as neighbour)
#' generate_exchange_partners(5, features = schaper2019[, 3:5], method = "RANN")[1:3]
#' # compare with RANN directly:
#' RANN::nn2(schaper2019[, 3:5], k = 6)$nn.idx[1:3, ] # note k = 6
#'
generate_exchange_partners <- function(n_exchange_partners, N = NULL, features = NULL, method = "random", categories = NULL) {
if (argument_exists(features)) {
validate_data_matrix(features)
N <- NROW(features)
}
categories <- merge_into_one_variable(categories)
validate_input(n_exchange_partners, "n_exchange_partners", objmode = "numeric", len = 1,
must_be_integer = TRUE, greater_than = 0, not_na = TRUE)
validate_input(method, "method", objmode = "character", len = 1,
not_na = TRUE, not_function = TRUE, input_set = c("RANN", "random", "restricted_random"))
if (argument_exists(features) && method == "RANN") {
return(nearest_neighbours(features, n_exchange_partners, categories))
} else if (method == "restricted_random") {
init_list <- NULL
if (argument_exists(categories)) {
init_list <- list_idx_by_category(categories)
}
return(random_exchange_partners(N, n_exchange_partners, init_list))
} else if (method == "random" && !argument_exists(categories)) {
return(completely_random_exchange_partners(N, n_exchange_partners))
}
else {
stop("Argument method must be 'RANN' or 'random' or 'restricted_random'.\n'",
"When method is 'RANN', the argument 'features' must be used.\n'",
"When method is 'random', you cannot use the argument 'categories'.")
}
}
list_idx_by_category <- function(categories) {
category_ids <- lapply(1:max(categories), function(i) which(categories == i))
category_ids[categories]
}
# Generate exchange partners via nearest neighbor search using RANN::nn2
nearest_neighbours <- function(features, k_neighbours, categories) {
if (!argument_exists(categories)) {
idx <- matrix_to_list(RANN::nn2(features, k = min(k_neighbours + 1, nrow(features)))$nn.idx)
} else {
# compute nearest neighbors within each category
nns <- list()
# track the indices when dividing by category. problem is:
# in nearest neighbour search, there is no guarantee that
# the nearest neighbour is the element itself; if it were,
# restoring original order would be easy
new_order <- list()
for (i in 1:max(categories)) {
tmp_indices <- which(categories == i)
new_order[[i]] <- tmp_indices
tmp_features <- features[tmp_indices, , drop = FALSE]
tmp_nn <- RANN::nn2(tmp_features, k = min(k_neighbours + 1, nrow(tmp_features)))$nn.idx
# get original index per category
nns[[i]] <- which(categories == i)[tmp_nn]
# restore matrix structure, gets lost
dim(nns[[i]]) <- dim(tmp_nn)
}
# per category, convert matrix to list
idx_list <- lapply(nns, matrix_to_list)
# in the end, merge all lists into 1 list
idx <- merge_lists(idx_list)
# restore original order after dividing by category
original_order <- order(unlist(new_order))
idx <- idx[original_order]
}
# return as list
remove_self(idx)
}
# Convert a matrix to list - each row becomes list element
matrix_to_list <- function(x) {
as.list(as.data.frame(t(x)))
}
# Merge a list of lists into one list
merge_lists <- function(list_of_lists) {
do.call(c, list_of_lists)
}
completely_random_exchange_partners <- function(N, n_exchange_partners) {
ret <- matrix_to_list(matrix(sample(N, size = N * n_exchange_partners, replace = TRUE), ncol = n_exchange_partners))
unname(ret)
}
# generate list of random exchange partners; will not generate itself as exchange partner
# and has no duplicates for each element
random_exchange_partners <- function(N, n_exchange_partners, init_partners = NULL) {
if (is.null(init_partners)) {
init_partners <- rep(list(1:N), N)
}
init_partners <- remove_self(init_partners)
partners <- lapply(init_partners, function(x) sample_(x)[1:n_exchange_partners])
# possibly remove NAs
lapply(partners, function(x) x[!is.na(x)])
}
# remove in a list for each element the ID of the list elements position
remove_self <- function(idx_list) {
N <- length(idx_list)
lapply(1:N, function(i) idx_list[[i]][idx_list[[i]] != i])
}
#### THIS IS AN OLD AN OBSOLETE R IMPLEMENTATION; I AM KEEPING IT FOR THE TEST FILES
#' Solve anticlustering using the fast exchange method
#'
#' @param data the data -- an N x M table of item features
#' @param clusters An initial cluster assignment
#' @param all_exchange_partners A list of exchange partners
#'
#' @return The anticluster assignment
#'
#' @noRd
#'
fast_exchange_ <- function(data, clusters, all_exchange_partners) {
N <- nrow(data)
best_total <- variance_objective_(clusters, data)
centers <- cluster_centers(data, clusters)
distances <- dist_from_centers(data, centers, squared = TRUE)
## frequencies of each cluster are required for updating cluster centers:
tab <- c(table(clusters))
for (i in 1:N) {
# cluster of current item
cluster_i <- clusters[i]
# get exchange partners for item i
exchange_partners <- all_exchange_partners[[i]]
# exchange partners are not in the same cluster:
exchange_partners <- exchange_partners[clusters[exchange_partners] != clusters[i]]
# Sometimes an exchange cannot take place
if (length(exchange_partners) == 0) {
next
}
# container to store objectives associated with each exchange of item i:
comparison_objectives <- rep(NA, length(exchange_partners))
for (j in seq_along(exchange_partners)) {
## Swap item i with all legal exchange partners and check out objective
# (a) Determine clusters of to-be-swapped elements
tmp_clusters <- clusters
tmp_swap <- exchange_partners[j]
cluster_j <- tmp_clusters[tmp_swap]
# (b) Swap the elements
tmp_clusters[i] <- cluster_j
tmp_clusters[tmp_swap] <- cluster_i
# (c) Update cluster centers after swap
tmp_centers <- update_centers(centers, data, i, tmp_swap, cluster_i, cluster_j, tab)
# (d) Update distances from centers after swap
tmp_distances <- update_distances(data, tmp_centers, distances, cluster_i, cluster_j)
# (e) Compute objective after swap
comparison_objectives[j] <- sum(tmp_distances[cbind(1:nrow(tmp_distances), tmp_clusters)])
}
## If an improvement of the objective occured, do the swap
best_this_round <- max(comparison_objectives)
if (best_this_round > best_total) {
# which element has to be swapped
swap <- exchange_partners[comparison_objectives == best_this_round][1]
# Update cluster centers
centers <- update_centers(centers, data, i, swap, clusters[i], clusters[swap], tab)
# Update distances
distances <- update_distances(data, centers, distances, cluster_i, clusters[swap])
# Actually swap the elements - i.e., update clusters
clusters[i] <- clusters[swap]
clusters[swap] <- cluster_i
# Update best solution
best_total <- best_this_round
}
}
clusters
}
#' Recompute distances from cluster centers after swapping two elements
#' @param distances distances from cluster centers per element (old)
#' @param cluster_i the cluster of element i
#' @param cluster_j the cluster of element j
#' @return The new distances
#' @noRd
update_distances <- function(features, centers, distances, cluster_i, cluster_j) {
for (k in c(cluster_i, cluster_j)) {
distances[, k] <- colSums((t(features) - centers[k,])^2)
}
distances
}
#' Update a cluster center after swapping two elements
#'
#' @param centers The current cluster centers
#' @param features The features
#' @param i the index of the first element to be swapped
#' @param j the index of the second element to be swapped
#' @param cluster_i the cluster of element i
#' @param cluster_j the cluster of element j
#' @param tab A table of the cluster frequencies
#'
#' @details
#'
#' This should make the fast exchange method much faster, because
#' most time is spent on finding the cluster centers. After swapping
#' only two elements, it should be possible to update the two centers
#' very fast
#' @noRd
update_centers <- function(centers, features, i, j, cluster_i, cluster_j, tab) {
## First cluster: item i is removed, item j is added
centers[cluster_i, ] <- centers[cluster_i, ] - (features[i, ] / tab[cluster_i]) + (features[j, ] / tab[cluster_i])
## Other cluster: item j is removed, item i is added
centers[cluster_j, ] <- centers[cluster_j, ] + (features[i, ] / tab[cluster_j]) - (features[j, ] / tab[cluster_j])
centers
}
############ END OLD OBSOLETE IMPLEMENTATION