R/rbfDDA.R

In RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

#############################################################################
#
#   This file is part of the R package "RSNNS".
#
#   Author: Christoph Bergmeir
#   Supervisor: José M. Benítez
#   Copyright (c) DiCITS Lab, Sci2s group, DECSAI, University of Granada.
#
#   This library is free software; you can redistribute it and/or
#   modify it under the terms of the GNU Library General Public
#   License as published by the Free Software Foundation; either
#   version 2 of the License, or (at your option) any later version.
# 
#   This library 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
#   Library General Public License for more details.
# 
#   You should have received a copy of the GNU Library General Public License
#   along with this library; see the file COPYING.LIB.  If not, write to
#   the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor,
#   Boston, MA 02110-1301, USA.
#
#############################################################################


#' Create and train an RBF network with the dynamic decay adjustment (DDA) algorithm. 
#' This type of network can only be used for classification. The training typically begins
#' with an empty network, i.e., a network only consisting of input and output units, and
#' adds new units successively. It is a lot easier to use than normal RBF, because it only 
#' requires two quite uncritical parameters.
#' 
#' The default functions do not have to be altered. The learning function \code{RBF-DDA} has
#' three parameters: a positive threshold, and a negative threshold, that controls adding units to 
#' the network, and a parameter for display purposes in the original SNNS. This parameter has 
#' no effect in RSNNS. See p 74 of the original SNNS User Manual for details.
#' 
#' @title Create and train an RBF network with the DDA algorithm
#' @references 
#' Berthold, M. R. & Diamond, J. (1995), Boosting the Performance of RBF Networks with Dynamic Decay Adjustment, in 'Advances in Neural Information Processing Systems', MIT Press, , pp. 521--528.
#' 
#' Hudak, M. (1993), 'RCE classifiers: theory and practice', Cybernetics and Systems 23(5), 483--515.
#' 
#' Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. 
#' \url{https://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html}
#' @export
rbfDDA <- function(x, ...) UseMethod("rbfDDA")


#' @param x a matrix with training inputs for the network
#' @param y the corresponding targets values
#' @param maxit maximum of iterations to learn
#' @param initFunc the initialization function to use
#' @param initFuncParams the parameters for the initialization function
#' @param learnFunc the learning function to use
#' @param learnFuncParams the parameters for the learning function
#' @param updateFunc the update function to use
#' @param updateFuncParams the parameters for the update function
#' @param shufflePatterns should the patterns be shuffled?
#' @param linOut sets the activation function of the output units to linear or logistic
#' @param ... additional function parameters (currently not used)
#' @return an \code{\link{rsnns}} object.
#' @export
# @S3method rbfDDA default
#' @method rbfDDA default
#' @rdname rbfDDA
#' @examples 
#' \dontrun{demo(iris)}
#' \dontrun{demo(rbfDDA_spiralsSnnsR)}
#' 
#' 
#' data(iris)
#' iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
#' irisValues <- iris[,1:4]
#' irisTargets <- decodeClassLabels(iris[,5])
#' iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15)
#' iris <- normTrainingAndTestSet(iris)
#' 
#' model <- rbfDDA(iris$inputsTrain, iris$targetsTrain)
#' 
#' summary(model)
#' plotIterativeError(model)
rbfDDA.default <- function(x, y, maxit=1, 
    initFunc="Randomize_Weights", initFuncParams=c(-0.3, 0.3), 
    learnFunc="RBF-DDA", learnFuncParams=c(0.4, 0.2, 5), 
    updateFunc="Topological_Order", updateFuncParams=c(0.0),
    shufflePatterns=TRUE, linOut=FALSE, ...) {
  
  #TODO: does linOut make sense here?  
  #as in the beginning no hidden units are present, the init function is not needed
  #is one iteration always sufficient?
  
  x <- as.matrix(x)
  y <- as.matrix(y)
  
  checkInput(x,y)
  
  nInputs <- dim(x)[2L]
  nOutputs <- dim(y)[2L]
  
  snns <- rsnnsObjectFactory(subclass=c("rbfDDA"), nInputs=nInputs, maxit=maxit, 
      initFunc=initFunc, initFuncParams=initFuncParams, 
      learnFunc=learnFunc, learnFuncParams=learnFuncParams, 
      updateFunc=updateFunc, 
      updateFuncParams=updateFuncParams,
      shufflePatterns=shufflePatterns, computeIterativeError=FALSE)
  
  snns$archParams <- NULL#list(size=size)
  
  snns$snnsObject$setUnitDefaults(c(0,0,1,0,1,"Act_Logistic","Out_Identity"))
  snns$snnsObject$createNet(c(nInputs,nOutputs), fullyConnectedFeedForward = FALSE)
  
  if(linOut) {
    outputActFunc <- "Act_Identity"
  } else {
    outputActFunc <- "Act_Logistic"
  }
  
  snns$snnsObject$setTTypeUnitsActFunc("UNIT_INPUT", "Act_Identity")
  snns$snnsObject$setTTypeUnitsActFunc("UNIT_OUTPUT", outputActFunc)
  
  
  snns <- train(snns, inputsTrain=x, targetsTrain=y)
  
  snns

}