warp {dtw} | R Documentation |
Returns the indexing required to apply the optimal warping curve to a given timeseries (warps either into a query or into a template).
warp(d,index.template=FALSE)
d |
dtw object specifying the warping curve to apply |
index.template |
TRUE to warp a template, FALSE
to warp a query |
The warping is returned as a set of indices, which can be used to
subscript the timeseries to be warped (or rows in a matrix, if one
wants to warp a multivariate time series). In other words,
warp
converts the warping curve, or its inverse, into a
function in the explicit form.
Multiple indices that would be mapped to a single point are averaged, with a warning. Gaps in the index sequence are filled by linear interpolation.
A list of indices to subscript the timeseries.
Toni Giorgino
Examples in dtw
show how to graphically
apply the warping via parametric plots.
idx<-seq(0,6.28,len=100); query<-sin(idx)+runif(100)/10; template<-cos(idx) alignment<-dtw(query,template); wq<-warp(alignment,index.template=FALSE); wt<-warp(alignment,index.template=TRUE); old.par <- par(no.readonly = TRUE); par(mfrow=c(2,1)); plot(template,main="Warping query"); lines(query[wq],col="blue"); plot(query,type="l",col="blue", main="Warping template"); points(template[wt]); par(old.par); ############## ## ## Asymmetric step makes it "natural" to warp ## the template, because every query index has ## exactly one image (q->t is a function) ## alignment<-dtw(query,template,step="a") wt<-warp(alignment,index.template=TRUE); plot(query,type="l",col="blue", main="Warping template, asymmetric step"); points(template[wt]);