Nothing
# SOMnn topology-based classifier
# Copyright (C) 2017 Andreas Dominik
# THM University of Applied Sciences
# Gießen, Germany
#
# 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/>.
#
#' Tricubic distance functions for topological k-NN classifier
#'
#' The tricubic function is used as distance-dependent weight \eqn{w} for
#' k-NN voting.
#'
#' The function returns 1.0 for \eqn{x = 0}, 0.0 for \eqn{x \ge \sigma} and
#'
#' \deqn{w(x) = (1 - x^3 / \sigma^3)^3}
#'
#' for \eqn{0 < x < \sigma}.
#'
#'
#'
#' @param x Distance or \code{numeric} vector or matrix of distances.
#' @param sigma Maximum distance to be considered.
#'
#' @return Distance-dependent weight.
#'
#' @export
dist.fun.tricubic <- function(x, sigma=1){
result <- (1 - x^3/sigma^3)^3
result[x>sigma] <- 0
return( result)
}
#' Inverse exponential distance functions for topological k-NN classifier
#'
#' The function is used as distance-dependent weight \eqn{w} for k-NN voting.
#'
#' The function returns 1.0 for \eqn{x = 0}, 0.0 for \eqn{x \ge \sigma} and
#'
#' \deqn{1 / (x+1)^(1/sigma)}
#'
#' for \eqn{0 < x < \sigma}.
#'
#'
#' @param x Distance or \code{numeric} vector or matrix of distances.
#' @param sigma Maximum distance to be considered. Default is 1.1.
#'
#' @return Distance-dependent weight.
#'
#' @export
dist.fun.inverse <- function(x, sigma=1.1){
result <- 1 / (x+1)^(1/sigma)
result[x>sigma] <- 0
return( result)
}
#' Linear distance functions for topological k-NN classifier
#'
#' The function is used as distance-dependent weight \eqn{w} for k-NN voting.
#'
#' The function returns 1.0 for \eqn{x = 0}, 0.0 for \eqn{x \ge \sigma} and
#'
#' \deqn{1 - x / \sigma}
#'
#' for \eqn{0 < x < \sigma}.
#'
#'
#' @param x Distance or \code{numeric} vector of distances.
#' @param sigma Maximum distance to be considered. Default is 1.1.
#'
#' @return Distance-dependent weight.
#'
#' @export
dist.fun.linear <- function(x, sigma=1.1){
result <- 1 - x/sigma
result[x>sigma] <- 0
return( result)
}
#' Bubble distance functions for topological k-NN classifier
#'
#' The function is used as distance-dependent weight \eqn{w} for k-NN voting.
#'
#' The function returns 1.0 for \eqn{0 < x \le \sigma} and 0.0 for \eqn{x > \sigma}.
#'
#'
#' @param x Distance or \code{numeric} vector or matrix of distances.
#' @param sigma Maximum distance to be considered. Default is 1.1.
#'
#' @return Distance-dependent weight.
#'
#' @export
dist.fun.bubble <- function(x, sigma=1.1){
result <- ifelse(x > sigma, 0, 1)
return( result)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.