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# 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/>.
#
#' Hexagonal som training
#'
#' A self-organising map with hexagonal tolology is trained and
#' a model of Type SOMnn created for prediction of unknown samples.
#' In contrast to a "normal" som, class-labels for all samples of
#' the training set are required to build the topological model after SOM training.
#'
#' Besides of the predefined kernels
#' \code{"internal", "gaussian", "SOM", "kohonen" or "som"},
#' any specified custom kernel function can be used for som training. The function must match the
#' signature \code{kernel(data, grid, rlen, alpha, radius, init, toroidal)}, with
#' arguments:
#' \itemize{
#' \item \code{data:} \code{numeric} matrix of training data; one sample per row
#' \item \code{classes:} optional \code{charater} vector of classes for training data
#' \item \code{grid:} somgrid, generated with \code{\link[class]{somgrid}}
#' \item \code{rlen:} number of training steps
#' \item \code{alpha:} training rate
#' \item \code{radius:} training radius
#' \item \code{init:} \code{numeric} matrix of initial codebook vectors; one code per row
#' \item \code{toroidal:} \code{logical}; TRUE, if the topology of grid is toroidal
#' }
#' The returned value must be a list with at minimum one element
#' \itemize{
#' \item \code{codes:} \code{numeric} matrix of result codebook vectors; one code per row
#' }
#'
#'
#' @param x data.fame with training data. Samples are requested as rows and taken randomly for the
#' training steps. All
#' columns except of the class lables are considered to be attributes and parts of
#' the training vector.
#' One column is needed as class labels. The column with class
#' lables is selected by the argument \code{class.col}.
#'
#' @param class.col single string or number. If class is a string, it is considered to be the
#' name of the column with class labels.
#' If class is a number, the respective column will be used as class labels
#' (after beeing coerced to character).
#' Default is 1.
#' @param kernel kernel for som training. One of the predefined kernels
#' \code{"bubble"}: train with the R-implementation or
#' \code{"gaussian"}: train with the R-implementation of the Gaussian kernel or
#' \code{"SOM"}: train with \code{\link[class]{SOM}} (\code{class::SOM}) or
#' \code{"kohonen"}: train with \code{\link[kohonen]{som}} (\code{kohonen::som}) or
#' \code{"som"}: train with \code{\link[som]{som}} (\code{som::som}).
#' If a function is specified (as closure, not as character)
#' the specified custom function is used for training.
#' @param xdim dimension in x-direction.
#' @param ydim dimension in y-direction.
#' @param toroidal \code{logical}; if TRUE an endless som is trained as on the
#' surface of a torus. default: FALSE.
#' @param len number of steps to be trained (steps - not epochs!).
#' @param alpha initial training rate; the learning rate is decreased linearly to 0.0 for the laset training step.
#' Default: 0.02.
#' @param radius inital radius for SOM training.
#' If Gaussian distance function is used, radius corresponds to sigma.
#' The distance is decreased linearly to 1.0 for the last training step.
#' If \code{radius = 0} (default), the diameter of the SOM is used as initial
#' radius.
#'
#' @param norm logical; if TRUE, input data is normalised by \code{scale(x, TRUE, TRUE)}.
#'
#' @param dist.fun parameter for k-NN prediction: Function used to calculate
#' distance-dependent weights. Any distance function must accept the two parameters
#' \code{x} (distance) and \code{sigma} (maximum distance to give a weight > 0.0).
#' Default is \code{dist.fun.inverse}.
#' @param max.dist parameter for k-NN prediction: Parameter \code{sigma} for dist.fun.
#' Default is 2.1. In order to avoid rounding issues, it is recommended not to
#' use exact integers as limit, but values like 1.1 to make sure, that all
#' neurons within distance 1 are included.
#' @param strict Minimum vote for the winner (if the winner's vote is smaller than strict,
#' "unknown" is reported as class label (\code{default = 0.8}).
#'
#' @param name optional name for the model. Name will be stored as slot \code{model@name} in the
#' trained model.
#'
#' @return S4 object of type \code{\link{SOMnn}} with the trained model
#'
#' @example man/examples/example.train.R
#'
#' @export
som.nn.train <- function(
x, class.col = 1, kernel = "internal",
xdim = 7, ydim = 5, toroidal = FALSE,
len = 0, alpha = 0.2, radius = 0,
norm = TRUE, # som parameters
dist.fun = dist.fun.inverse, max.dist = 1.1, # predictor parameter
strict = 0.8,
name = "som.nn job"
){
# find class column:
if (typeof(class.col) == "character") {
class.idx <- which(names(x) == class.col)
} else {
class.idx <- class.col
}
len.total <- 0
# init and run som:
return(som.nn.do.train( x = x, class.idx = class.idx,
kernel = kernel,
xdim = xdim, ydim = ydim, toroidal = toroidal,
len = len, alpha = alpha, radius = radius,
norm = norm, norm.center = 0, norm.scale = 1,
dist.fun = dist.fun, max.dist = max.dist, strict = strict,
name = name,
continue = FALSE, len.total = 0, codes = NULL))
}
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