R/calculate_info_symbols.R

In allgebrist/algodyn: Algorithmic Information Dynamics

#' Calculate information contribution of symbols
#'
#' @param x character string
#' @param block_size block size required to estimate the Kolmogorov-Chaitin complexity of \code{x} using the 1-dimensional Block Decomposition Method (BDM).
#' @param offset offset required to estimate the Kolmogorov-Chaitin complexity of \code{x} using the 1-dimensional Block Decomposition Method (BDM).
#' @param base number of symbols in the alphabet over which \code{x} is defined.
#'
#' @details The information contribution of a symbol \eqn{i} to a string \eqn{s} is given by \eqn{I(s,i)=C(s)-C(s-i)}, where \eqn{C(s)} denotes the information content of \eqn{s} and \eqn{C(s-i)} the information content of \eqn{s} after removing \eqn{i}.
#'
#' @import acss
#' @import stringr
#' @return A data frame containing the perturbations on the original string's characters and their corresponding information contributions.
#'
#' @examples
#' \dontrun{
#' ex_str <- "1010010111010"
#' calculate_info_symbols(ex_str, 12, 11, 2)
#' }
#'
#' @export
calculate_info_symbols <- function(x, block_size, offset, base) {

    x_bdm_value <- bdm1D(x, block_size, offset, base)
    x_char_vector <- unlist(str_split(x, pattern = ""))

    perturbations <- c()

    for (i in 1:length(x_char_vector)) {
        index <- !logical(length(x_char_vector))
        index[i] <- FALSE

        back <- paste(x_char_vector[index], sep = "", collapse = "")
        perturbations <- c(perturbations, back)
    }

    perturbations_bdm_values <- unlist(lapply(perturbations, bdm1D, block_size, offset, base))

    bdm_difference <- x_bdm_value - perturbations_bdm_values

    bdm_df <- data.frame(perturbations = perturbations, bdm_difference = bdm_difference, stringsAsFactors = FALSE)

    return(bdm_df)
}