Revision **0baa1688488a27e95ac3d245dfab5fc6c18c26d5** authored by Toni Giorgino on **17 June 2008, 00:00:00 UTC**, committed by Gabor Csardi on **17 June 2008, 00:00:00 UTC**

globalCostMatrix.R

```
###############################################################
# #
# (c) Toni Giorgino <toni.giorgino@gmail.com> #
# Laboratory for Biomedical Informatics #
# University of Pavia - Italy #
# www.labmedinfo.org #
# #
# $Id: globalCostMatrix.R 142 2008-06-09 13:30:53Z tonig $
# #
###############################################################
########################################
## Compute the cost matrix from a local distance matrix
## Wrapper to the native function
`globalCostMatrix` <-
function(lm,
step.matrix=symmetric1,
window.function=noWindow,
native=TRUE,
...) {
## sanity check - be extra cautions w/ binary
if (!is.stepPattern(step.matrix))
stop("step.matrix is no stepMatrix object");
# i = 1 .. n in query sequence, on first index, ie rows
# j = 1 .. m on template sequence, on second index, ie columns
# Note: template is usually drawn vertically, up-wise
n <- nrow(lm);
m <- ncol(lm);
# number of individual steps (counting all patterns)
nsteps<-dim(step.matrix)[1];
# clear the cost and step matrix
# these will be the outputs of the binary
cm <- matrix(NA,nrow=n,ncol=m);
sm <- matrix(NA,nrow=n,ncol=m);
# initializer
cm[1,1] <- lm[1,1];
if(is.loaded("computeCM") && native){
## precompute windowing
wm <- matrix(FALSE,nrow=n,ncol=m);
wm[window.function(row(wm),col(wm),
query.size=n, template.size=m,
...)]<-TRUE;
## this call could be optimized
tmp<-.C("computeCM",NAOK=TRUE,PACKAGE="dtw",
as.integer(dim(cm)), # s
as.logical(wm), #
as.double(lm),
as.integer(nsteps),
as.double(step.matrix),
cmo=as.double(cm), # OUT
smo=as.integer(sm)); # OUT
cm<-matrix(tmp$cmo,nrow=n,ncol=m);
sm<-matrix(tmp$smo,nrow=n,ncol=m);
} else {
####################
## INTERPRETED PURE-R IMPLEMENTATION
warning("Native dtw implementation not available: using (slow) interpreted fallback");
# now walk through the matrix, column-wise and row-wise,
# and recursively compute the accumulated distance. Unreachable
# elements are handled via NAs (removed)
dir <- step.matrix;
npats <- attr(dir,"npat");
for (j in 1:m) {
for (i in 1:n) {
## It is ok to window on the arrival point (?)
if(!window.function(i,j, query.size=n, template.size=m, ...)) { next; }
clist<-numeric(npats)+NA;
for (s in 1:nsteps) {
## current pattern
p<-dir[s,1];
## ii,jj is the cell from where potentially we could
## have come from.
ii<-i-dir[s,2]; # previous step in inp
jj<-j-dir[s,3]; # previous step in tpl
if(ii>=1 && jj>=1) { # element exists?
cc<- dir[s,4]; # step penalty
if(cc == -1) { # -1? cumulative cost:
clist[p]<-cm[ii,jj]; # there must be exactly 1 per pattern
} else { # a cost for
clist[p]<-clist[p]+cc*lm[ii,jj];
}
}
}
## no NAs in clist at this point BUT clist can be empty
## store in cost matrix
minc<-which.min(clist); # pick the least cost
if(length(minc)>0) { # false if clist has all NAs
cm[i,j]<-clist[minc];
sm[i,j]<-minc; # remember the pattern picked
}
}
}
}
## END PURE-R IMPLEMENTATION
####################
out<-list();
out$costMatrix<-cm; # to get distance
out$directionMatrix<-sm; # to backtrack
out$stepPattern<-step.matrix; # to backtrack
return(out);
}
```

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