Revision

**47b430f6e116f153b5f406d88ce26f868027a447**authored by Toni Giorgino on**30 November 2007, 00:00:00 UTC**, committed by Gabor Csardi on**30 November 2007, 00:00:00 UTC****1 parent**f3fa0a2

globalCostMatrix.R

```
###############################################################
# #
# (c) Toni Giorgino <toni.giorgino@gmail.com> #
# Laboratory for Biomedical Informatics #
# University of Pavia - Italy #
# www.labmedinfo.org #
# #
# $Id: globalCostMatrix.R 51 2007-12-11 10:59:08Z tonig $
# #
###############################################################
########################################
## Compute the cost matrix from a local distance matrix
## We assume that all arguments are expanded from
## char shortands. This includes:
## step.matrix - should be a stepMatrix object
## window.function - should be a function
`globalCostMatrix` <-
function(lm,
step.matrix=symmetric1,
window.function=noWindow,
...) {
# a shorthand
dir <- step.matrix;
# 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
query.size <- n <- dim(lm)[1];
template.size <- m <- dim(lm)[2];
# number of individual steps (counting all patterns)
nsteps<-dim(dir)[1];
# number of step patterns defined
npats<-max(dir[,1]);
# clear the cost and step matrix
cm <- matrix(NA,nrow=n,ncol=m);
sm <- matrix(NA,nrow=n,ncol=m);
# initializer
cm[1,1] <- lm[1,1];
# now walk through the matrix, column-wise and row-wise,
# and recursively compute the accumulated distance. Unreachable
# elements are handled via NAs (removed)
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, template.size, ...)) { 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();
out$costMatrix<-cm; # to get distance
out$directionMatrix<-sm; # to backtrack
out$stepPatterns<-dir; # to backtrack
return(out);
}
```

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