# R/globalCostMatrix.R In dtw: Dynamic Time Warping Algorithms

```###############################################################
#                                                             #
#   (c) Toni Giorgino <toni.giorgino,gmail.com>               #
#       Istituto di Neuroscienze (IN-CNR)                 #
#       Consiglio Nazionale delle Ricerche                           #
#       www.isib.cnr.it                                    #
#                                                             #
#   \$Id: globalCostMatrix.R 436 2018-05-17 14:23:15Z 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,
seed=NULL,
...) {

## 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 reference sequence, on second index, ie columns
#   Note:  reference 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
# for  cm use  seed if given
if(!is.null(seed)) {
cm <- seed;
} else {
cm <- matrix(NA,nrow=n,ncol=m);
cm[1,1] <- lm[1,1];                 # Questionable.
}

sm <- matrix(NA,nrow=n,ncol=m);

#if(is.loaded("computeCM_Call") && native){ # -- not working any more?
if(native){
## precompute windowing
wm <- matrix(FALSE,nrow=n,ncol=m);
wm[window.function(row(wm),col(wm),
query.size=n, reference.size=m,
...)]<-TRUE;

storage.mode(wm) <- "logical";
storage.mode(lm) <- "double";
storage.mode(cm) <- "double";
storage.mode(step.matrix) <- "double";
out <- .Call(C_computeCM_Call,
wm,lm,cm,step.matrix);

} 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, reference.size=m, ...)) { next; }

if(!is.na(cm[i,j])) { 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
}
}
}
out <- list(costMatrix=cm,directionMatrix=sm);
}

## END PURE-R IMPLEMENTATION
####################

## At this point out\$cmo and out\$smo should be set
out\$stepPattern <- step.matrix;
return(out);
}
```

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dtw documentation built on May 18, 2018, 9:03 a.m.