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#' K-Means Alignment Algorithm
#'
#' This is an implementation of the k-means alignment algorithm originally
#' described in Sangalli et al. (2010), with improvements as proposed in Vantini
#' (2012).
#'
#' @inherit fdacluster::fdakmeans
#' @export
#'
#' @examples
#' #----------------------------------
#' # Extracts 15 out of the 30 simulated curves in `simulated30_sub` data set
#' idx <- c(1:5, 11:15, 21:25)
#' x <- fdacluster::simulated30$x[idx, ]
#' y <- fdacluster::simulated30$y[idx, , ]
#'
#' #----------------------------------
#' # Runs a k-means clustering with affine alignment, searching for 2 clusters
#' res <- kma(
#' x,
#' y,
#' n_clusters = 2,
#' seeds = c(1, 11),
#' warping_class = "affine",
#' centroid_type = "medoid",
#' metric = "pearson"
#' )
kma <- function(x, y = NULL,
n_clusters = 1L,
seeds = NULL,
seeding_strategy = c("kmeans++", "exhaustive-kmeans++", "exhaustive", "hclust"),
warping_class = c("affine", "dilation", "none", "shift", "srsf"),
centroid_type = "mean",
metric = c("l2", "pearson"),
cluster_on_phase = FALSE,
use_verbose = TRUE,
warping_options = c(0.15, 0.15),
maximum_number_of_iterations = 100L,
number_of_threads = 1L,
parallel_method = 0L,
distance_relative_tolerance = 0.001,
use_fence = FALSE,
check_total_dissimilarity = TRUE,
compute_overall_center = FALSE,
add_silhouettes = TRUE,
expand_domain = TRUE) {
fdacluster::fdakmeans(
x = x, y = y,
n_clusters = n_clusters,
seeds = seeds,
seeding_strategy = seeding_strategy,
warping_class = warping_class,
centroid_type = centroid_type,
metric = metric,
cluster_on_phase = cluster_on_phase,
use_verbose = use_verbose,
warping_options = warping_options,
maximum_number_of_iterations = maximum_number_of_iterations,
number_of_threads = number_of_threads,
parallel_method = parallel_method,
distance_relative_tolerance = distance_relative_tolerance,
use_fence = use_fence,
check_total_dissimilarity = check_total_dissimilarity,
compute_overall_center = compute_overall_center,
add_silhouettes = add_silhouettes,
expand_domain = expand_domain
)
}
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