R/RcppExports.R

In deadwood: Outlier Detection via Trimming of Mutual Reachability Minimum Spanning Trees

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' @title Knee/Elbow Point Detection
#'
#' @description
#' Finds the most significant knee/elbow using the Kneedle algorithm
#' with exponential smoothing.
#'
#' @param x data vector (increasing)
#'
#' @param convex whether the data in \code{x} are convex-ish (elbow detection)
#'         or not (knee lookup)
#'
#' @param dt controls the smoothing parameter \eqn{\alpha = 1-\exp(-dt)}
#'         of the exponential moving average,
#'         \eqn{y_i = \alpha x_i + (1-\alpha) x_{i-1}}, \eqn{y_1 = x_1}
#'
#'
#' @return
#' Returns the index of the knee/elbow point; 1 if not found.
#'
#' @references
#' V. Satopaa, J. Albrecht, D. Irwin, B. Raghavan,
#' Finding a "Kneedle" in a haystack: Detecting knee points in system behavior,
#' In: 31st Intl. Conf. Distributed Computing Systems Workshops,
#' 2011, 166-171, \doi{10.1109/ICDCSW.2011.20}
#'
#' @name kneedle
#' @rdname kneedle
#' @export
kneedle_increasing <- function(x, convex = TRUE, dt = 0.01) {
    .Call(`_deadwood_kneedle_increasing`, x, convex, dt)
}

.deadwood <- function(mst, cut_edges, max_contamination, ema_dt, max_debris_size, verbose) {
    .Call(`_deadwood_dot_deadwood`, mst, cut_edges, max_contamination, ema_dt, max_debris_size, verbose)
}

.oldmst.matrix <- function(X, distance = "euclidean", M = 0L, cast_float32 = FALSE, verbose = FALSE) {
    .Call(`_deadwood_dot_oldmst_matrix`, X, distance, M, cast_float32, verbose)
}

.oldmst.dist <- function(d, M = 0L, verbose = FALSE) {
    .Call(`_deadwood_dot_oldmst_dist`, d, M, verbose)
}