IncDTW (version 1.0.2)

dtw: Dynamic Time Warping

Description

Wrapper function for the C++ calculations of the global cost matrix and the BACKTRACK_cpp that finds the cheapest warping path.

Usage

dtw(Q, C, ws = NULL, return_cm = FALSE,
                     return_diffM = FALSE,
                     return_wp = FALSE,
                     return_diffp = FALSE,
                     return_QC = FALSE)

Arguments

Q

one dimensional vector, query time series. Alternatively Q can also be a matrix of differences/ costs (diffM, cm).

C

one dimensional vector, time series to be fitted to C, and for which values are constantly observed. If Q is not a vector but a matrix, then C needs to be one of the following strings ('diffM', 'cm').

ws

integer, describes the window size for the sakoe chiba window. If NULL, then no window is applied. (default = NULL)

return_cm

boolean, if TRUE then the Matrix of costs (the absolute value) is returned. (default = FALSE)

return_diffM

boolean, if TRUE then the Matrix of differences (not the absolute value) is returned. (default = FALSE)

return_wp

boolean, 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

boolean, if TRUE then the path of differences (not the absolute value) is returned. (default = FALSE)

return_QC

boolean, if TRUE then the input vectors Q and C are appended to the returned list. This is useful for the plot.idtw function. (default = FALSE)

Value

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 C of the optimal path

jj

indices of Q of the optimal path

cm

Matrix of costs

diffM

Matrix of differences

diffp

path of differences

Q

input Q

C

input C

Details

The dynamic time warping distance is the element in the last row and last column of the global cost matrix.

References

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

Examples

# NOT RUN {
Q <- cumsum(rnorm(100))
C <- Q[11:100] + rnorm(90, 0, 0.5)
dtw(Q = Q, C = C, ws = 15, return_diffM = FALSE)

# compare different input variations
dtw_base <- dtw(Q = Q, C = C, ws = 15, return_diffM = TRUE)
dtw_diffM <- dtw(Q = dtw_base$diffM, C = "diffM", ws = 15, return_diffM = TRUE)
dtw_cm <- dtw(Q = abs(dtw_base$diffM), C = "cm", ws = 15, return_diffM = TRUE)

identical(dtw_base$gcm, dtw_cm$gcm)
identical(dtw_base$gcm, dtw_diffM$gcm)

# of course no diffM is returned in the 'cm'-case
dtw_cm$diffM
# }