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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/>.
#
#' predict method for S4 class \code{SOMnn}
#'
#' Predicts categories for a table of data, based on the hexagonal som in the model.
#' This S4 method is a wrapper for the predict method stored in the slot \code{predict}
#' of a model of type SOMnn.
#'
#' The function returns the winner neuron in \code{codes} for
#' each test vector in \code{x}.
#' \code{x} is organised as one vector per row and must have
#' the same number of columns (i.e. dimensions) and the identical column names
#' as stored in the SOMnn object.
#'
#' If data have been normalised during training, the same normalisation is applied
#' to the unknown data to be predicted.
#'
#' Probablilities are softmax normalised by default.
#'
#' @rdname predict-methods
#' @aliases predict,SOMnn-method
#'
#' @param object object of type \code{SOMnn}.
#' @param x \code{data.frame} with rows of data to be predicted.
#'
#' @return \code{data.frame} with columns:
#' \code{winner}, \code{x}, \code{y}, the predicted probabilities
#' for all categories and the prediction
#' as category index (column name \code{prediction}) and
#' class label (column name \code{pred.class}).
#'
#' @example examples/example.predict.R
#'
#' @export
setMethod( f = "predict", signature = "SOMnn",
definition = function(object, x){
som <- object
return( som@predict(x))
})
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