R/mass.R

In franzbischoff/tsmp: Time Series with Matrix Profile

#' Calculates the distance profile using MASS_V2 algorithm
#'
#' Mueen's Algorithm for Similarity Search is The Fastest Similarity Search Algorithm for Time
#' Series Subsequences under Euclidean Distance and Correlation Coefficient.
#'
#' @param data_fft precomputed data product.
#' @param query_window a `vector` of `numeric`. Query window.
#' @param data_size an `int`. The length of the reference data.
#' @param window_size an `int`. Sliding window size.
#' @param data_mean precomputed data moving average.
#' @param data_sd precomputed data moving standard deviation.
#' @param query_mean precomputed query average.
#' @param query_sd precomputed query standard deviation.
#'
#' @return Returns the `distance_profile` for the given query and the `last_product` for STOMP
#'   algorithm.
#' @seealso [mass_pre()] to precomputation of input values.
#'
#' @references * Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy Viswanathan,
#'   Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity Search Algorithm for Time
#'   Series Subsequences under Euclidean Distance
#' @references Website: <https://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html>

#' @name mass-deprecated
#' @usage mass(data_fft, query_window, data_size, window_size, data_mean, data_sd,
#'  query_mean, query_sd)
#' @seealso \code{\link{tsmp-deprecated}}
#' @keywords internal
NULL

#' @rdname tsmp-deprecated
#' @section \code{mass}:
#' For \code{mass}, use \code{\link{dist_profile}}. Original documentation at \code{\link{mass-deprecated}}.
#'
#' @export

mass <- function(data_fft, query_window, data_size, window_size, data_mean, data_sd,
                 query_mean, query_sd) {
  .Deprecated("dist_profile")
  # pre-process query for fft
  query_window <- rev(query_window)
  pad_size <- length(data_fft)
  query_window[(window_size + 1):pad_size] <- 0
  # compute the product
  prod <- data_fft * stats::fft(query_window)
  z <- Re(stats::fft(prod, inverse = TRUE) / length(prod))
  # compute the distance profile
  last_product <- z[window_size:data_size]
  distance_profile <- 2 * (window_size - (last_product - window_size * data_mean * query_mean) / (data_sd * query_sd))
  distance_profile[distance_profile < 0] <- 0

  return(list(distance_profile = distance_profile, last_product = last_product))
}