# knn: Simple implementation of k-nearest-neighbours
# A package for the R statistical environment
# Copyright (C) 2015 Matthew Upson <ivyleavedtoadflax@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#' @title Calculate centroid mean
#'
#' @description \code{centroid_mean} calculate centroid mean
#'
#' @param X \code{X} Matrix of \code{ncol >=2}.
#' @param group \code{group} Vector of groups for which centroid means will be calculated.
#'
#' @examples
#'
#' # knn(X)
#'
#' @export
#'
#'
knn <- function(X, centroids = NULL, k = 3, imax = NULL, ...) {
## Run one iteration as standard
idx <- find_group(X, k = k)
centroids <- centroid_mean(X, idx)
idx2 <- find_group(X, centroids, k = k)
centroids2 <- centroid_mean(X, idx2)
i <- 1
## Setup a df for storing the outputs
out_mat <- cbind(
rbind(centroids, centroids2),
c(
rep(0, nrow(centroids)),
rep(i, nrow(centroids))
)
)
## Set a threshold for 'convergence'. Some thought needs to go into how best to
## do this.
while (sum(centroids - centroids2) > 0.000005 ) {
## If a number of iterations was specified, break the function when max
## iterations has been reached
if (!is.null(imax)) {
if(i == imax) break
}
idx <- find_group(X, centroids2, k = k)
centroids <- centroid_mean(X, idx)
idx2 <- find_group(X, centroids, k = k)
centroids2 <- centroid_mean(X, idx2)
## Add to accumulator
i <- i + 1
## Output results for plot function
out_mat <- rbind(
out_mat,
cbind(
centroids2,
i
)
)
}
out <- list(
groups = idx2,
centroids = centroids2,
steps = i,
centroid_path = out_mat
)
return(out)
}
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