R/ART_Update_Weights.r

In gbaquer3/fuzzyARTMAP: Implementation of a fuzzy ARTMAP neural network

#' Updates the weight matrix of an ART network.
#' 
#' This function returns a new weight matrix that has "learned" the input
#' in the given category, as well as a value correspoding to whether or 
#' not the weight matrix was changed (0 = no change; 1 = change).
#' @title ART_Update_Weights
#' @param input Vector of size NumFeatures that contains the input signal into the network.
#' @param weight Matrix of size NumFeatures-by-NumCategories which holds the weights of the network.
#' @param categoryNumer Number of the category that codes the current input.
#' @param learningRate Rate at which the network should learn new inputs.
#' @return List containing the UPDATEDWEIGHT and WEIGHTCHANGE. The UPDATEDWEIGHT is a matrix of size NumFeatures-by-NumCategories
#' that holds the new weights of the network after the input has been successfully learned.
#' The WEIGHTCHANGE is a value (0 or 1) which relays whether or not
#' the weight matrix was changed during this updating. Here, 0 represents
#' no change and 1 represents a change.
#' @details The length of the INPUT vector must equal the number of rows in the WEIGHT matrix, the CATEGORYNUMBER must
#' be in the range [1, NumCategories], and the LEARNINGRATE must be in the range [0, 1].
ART_Update_Weights = function(input, weight, categoryNumber, learningRate)
{
  # Get the number of features from the weight matrix.
  numFeatures = ncol(weight);
  numCategories = nrow(weight);
  # Check the input parameters for correct ranges.
  if(length(input) != numFeatures)
  {
    stop('The length of the input and rows of the weights do not match.');
  }
  if((categoryNumber < 1) | (categoryNumber > numCategories))
  {
    stop('The category number must be in the range [1, NumCategories].');
  }
  if((learningRate < 0) | (learningRate > 1))
  {
    stop('The learning rate must be within the range [0, 1].');
  }
  
  weightChange = 0;
  for (i in 1:numFeatures)
  {
    if(input[i] < weight[categoryNumber,i])
    {
      weight[categoryNumber,i] = (learningRate * input[i]) + ((1 - learningRate) * weight[categoryNumber,i]);
      weightChange = 1;
    }
  }
  
  # Return the updated weight matrix.
  updatedWeight = weight;
  return (list(updatedWeight, weightChange));
}