dec_dm | R Documentation |
Update the warping path to omit observations of the alignment of two time series.
dec_dm(dm, Ndec, diffM = NULL)
dm |
direction matrix, output from dtw(Q=Q, C=C, ws=ws) |
Ndec |
integer, number of observations (columns) to be reduced |
diffM |
matrix of differences |
wp |
warping path |
ii |
indices of Q of the optimal path |
jj |
indices of C of the optimal path |
diffp |
path of differences (only returned if diffM is not NULL) |
Leodolter, M.; Pland, C.; Brändle, N; IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping. Journal of Statistical Software, 99(9), 1-23. doi: 10.18637/jss.v099.i09
Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.26, no.1, pp. 43-49, Feb 1978. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055
Q <- cos(1:100) C <- cumsum(rnorm(80)) # the ordinary calculation result_base <- dtw(Q=Q, C=C, return_wp = TRUE) # the ordinary calculation without the last 4 observations result_decr <- dtw(Q=Q, C=C[1:(length(C) - 4)], return_wp = TRUE) # the decremental step: reduce C for 4 observation result_decr2 <- dec_dm(result_base$dm, Ndec = 4) # compare ii, jj and wp of result_decr and those of result_decr$ii result_decr2$ii identical(result_decr$ii, result_decr2$ii) result_decr$jj result_decr2$jj identical(result_decr$jj, result_decr2$jj) result_decr$wp result_decr2$wp identical(result_decr$wp, result_decr2$wp)
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