idtw | R Documentation |
Update the DTW distance, cost matrices and direction matrices including the warping path for new observations of two time series.
idtw(Q, C, newObs, gcm, dm, dist_method = c("norm1", "norm2", "norm2_square"), step_pattern = c("symmetric2", "symmetric1"), diffM = NULL, ws = NULL, return_cm = FALSE, return_diffM = FALSE, return_wp = FALSE, return_diffp = FALSE, return_QC = FALSE)
Q |
numeric vector, or matrix (see also |
C |
numeric vector, or matrix |
newObs |
vector or matrix of new observations to be appended to C |
gcm |
global cost matrix, output from |
dm |
direction matrix, output from |
dist_method |
character, describes the method of distance measure. See also |
step_pattern |
character, describes the step pattern. See also |
diffM |
differences matrix, output from |
ws |
integer, describes the window size for the sakoe chiba window. If NULL, then no window is applied. (default = NULL) |
return_cm |
logical, if TRUE then the Matrix of costs (the absolute value) is returned. (default = FALSE) |
return_diffM |
logical, if TRUE then the Matrix of differences (not the absolute value) is returned. (default = FALSE) |
return_wp |
logical, if TRUE then the warping path is returned. (default = FALSE) If return_diffp == TRUE, then return_wp is set to TRUE as well. |
return_diffp |
logical, if TRUE then the path of differences (not the absolute value) is returned. (default = FALSE) |
return_QC |
logical, if TRUE then the input vectors Q and C are appended to the returned list. This is useful for the |
The dynamic time warping distance is the element in the last row and last column of the global cost matrix.
distance |
the DTW distance, that is the element of the last row and last column of gcm |
gcm |
global cost matrix |
dm |
direction matrix (3=up, 1=diagonal, 2=left) |
wp |
warping path |
ii |
indices of Q of the optimal path |
jj |
indices of C of the optimal path |
cm |
Matrix of costs |
diffM |
Matrix of differences |
diffp |
path of differences |
Q |
input Q |
C |
input C |
normalized_distance |
the normalized DTW distance, see also |
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
#--- Compare the incremental calculation with the basic # calculation from scratch. Q <- cumsum(rnorm(100)) C <- Q[11:100] + rnorm(90, 0, 0.5) newObs <- c(2, 3)# new observation base <- dtw(Q = Q, C = C, ws = 15, return_diffM = TRUE) base # recalculation from scratch with new observations result0 <- dtw(Q = Q, C = c(C, newObs), ws = 15, return_diffM = TRUE) # the incremental step with new observations result1 <- idtw(Q, C, ws = 15, newO = newObs, gcm = base$gcm, dm = base$dm, diffM = base$diffM, return_diffp = TRUE, return_diffM = TRUE, return_QC = TRUE) print(result1, digits = 2) plot(result1) #--- Compare the incremental calculation with external calculated # costMatrix cm_add with the basic calculation from scratch. cm_add <- cm(Q, newObs) result2 <- idtw(Q = cm_add, C = "cm_add", ws = 15, newO = newObs, gcm = base$gcm, dm = base$dm) c(result0$distance, result1$distance, result2$distance)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.