R/2_2AFBDTW.R

In DTWBI: Imputation of Time Series Based on Dynamic Time Warping

#' @title Adaptive Feature Based Dynamic Time Warping algorithm
#' @description 
#' This function estimates a distance matrix which is used as an input in dtw() function (package dtw) to align two univariate signals following Adaptative Feature Based Dynamic Time Warping algorithm (AFBDTW).
#' @param q query vector
#' @param r reference vector
#' @param w1 weight of local feature VS global feature.
#'  By default, w1 = 0.5, and by definition, w2 = 1 - w1.
#' @return A list containing the following elements:
#' \item{query}{The query vector.}
#' \item{response}{The response vector.}
#' \item{query_local}{Local features of the query.}
#' \item{response_local}{Local features of the response vector.}
#' \item{query_global}{Global features of the query.}
#' \item{response_global}{Global features of the response vector.}
#' \item{dist_local}{Distance matrix of the local features.}
#' \item{dist_global}{Distance matrix of the global features.}
#' \item{distAFBDTW}{AFBDTW distance matrix.}
#' @import rlist stats
#' @examples
#' data(dataDTWBI)
#' X <- dataDTWBI[, 1] ; Y <- dataDTWBI[, 2]
#' AFBDTW_Dist <- dist_afbdtw(X, Y)
#' @export

dist_afbdtw <- function(q, r, w1=0.5){
  
  w2 <- 1-w1
  
  if(w1<=0){stop("Weights should be positive")}
  
  ql <- .local_feature(q)
  rl <- .local_feature(r)
  qg <- .global_feature(q)
  rg <- .global_feature(r)
  dist_local <- .dist_matrix(ql, rl)
  dist_global <- .dist_matrix(qg, rg)
  dist <- w1*dist_local + w2*dist_global
  
  outputAFBDTW <- list("query" = q,
                       "response" = r,
                       "query_local" = ql,
                       "response_local" = rl,
                       "query_global" = qg,
                       "response_global" = rg,
                       "dist_local" = dist_local,
                       "dist_global" = dist_global,
                       "distAFBDTW" = dist)
}