https://github.com/cran/dtw
Tip revision: 0baa1688488a27e95ac3d245dfab5fc6c18c26d5 authored by Toni Giorgino on 17 June 2008, 00:00:00 UTC
version 1.9-1
version 1.9-1
Tip revision: 0baa168
dtw.R
###############################################################
# #
# (c) Toni Giorgino <toni.giorgino@gmail.com> #
# Laboratory for Biomedical Informatics #
# University of Pavia - Italy #
# www.labmedinfo.org #
# #
# $Id: dtw.R 155 2008-06-19 15:12:04Z tonig $
# #
###############################################################
##
## Frontend stuff, including coercing shorthand types
##
`dtw` <-
function(x, y=NULL,
dist.method="Euclidean",
step.pattern="s",
window.type="none",
keep.internals=FALSE,
distance.only=FALSE,
partial=FALSE,
... ) {
lm <- NULL;
## if matrix given
if(is.null(y)) {
if(!is.matrix(x))
stop("Single argument requires a global cost matrix");
lm <- x;
} else {
## two timeseries or vectors given
## as.matrix coerces ts or mts to matrices
x <- as.matrix(x);
y <- as.matrix(y);
lm <- proxy::dist(x,y,method=dist.method);
}
## Now we have a function
wfun<-.canonicalizeWindowFunction(window.type);
## Now we have a step pattern
dir<-.canonicalizeStepPattern(step.pattern);
## shorthand names
n <- nrow(lm);
m <- ncol(lm);
## perform the computation
gcm <- globalCostMatrix(lm, step.matrix=dir,
window.function=wfun, ...);
## remember size
gcm$N <- n;
gcm$M <- m;
## remember call
gcm$call <- match.call();
## last column, normalized
norm <- attr(dir,"norm");
lastcol <- gcm$costMatrix[n,];
if(is.na(norm)) {
# NO-OP
} else if(norm == "N+M") {
lastcol <- lastcol/(n+(1:m));
} else if(norm == "N") {
lastcol <- lastcol/n;
} else if(norm == "M") {
lastcol <- lastcol/m;
}
## for complete alignment
gcm$jmin <- m;
## for partial alignment: normalize
if (partial) {
if(is.na(norm)) {
warning("Unknown normalization with partial=TRUE: using N+M");
lastcol <- lastcol/(n+(1:m));
}
gcm$jmin <- which.min(lastcol);
}
## result: distance
gcm$distance <- gcm$costMatrix[n,gcm$jmin];
## alignment valid?
if(is.na(gcm$distance)) {
stop("No warping paths exists that is allowed by costraints");
}
## normalized distance
if(! is.na(norm)) {
gcm$normalizedDistance <- lastcol[gcm$jmin];
} else {
gcm$normalizedDistance <- NA;
}
if(!distance.only) {
## perform the backtrack
mapping <- backtrack(gcm);
gcm$index1 <- mapping$index1;
gcm$index2 <- mapping$index2;
}
## don't keep internals: delete sizey intermediate steps
if(!keep.internals) {
gcm$costMatrix<-NULL;
gcm$directionMatrix<-NULL;
} else {
## keep internals: add data
gcm$localCostMatrix <- lm;
if(! is.null(y)) {
gcm$query <- x;
gcm$template <- y;
}
}
## if a dtw object is to be sponsored:
class(gcm) <- "dtw";
return(gcm);
}
##############################
## OO class check
is.dtw <- function(d) {
return(inherits(d,"dtw"));
}
## Replace char window.type with appropriate
## windowing FUNCTION
.canonicalizeWindowFunction <- function(w) {
if(is.function(w)) {
return(w);
}
# else
wt<-pmatch(w,c("none","sakoechiba","itakura","slantedband"));
if(is.na(wt)) {
stop("Ambiguous or unsupported char argument for window.type");
}
wfun<-switch(wt,
noWindow,
sakoeChibaWindow,
itakuraWindow,
slantedBandWindow);
return(wfun);
}
## Replace char step.pattern with matrix equivalent
## Step patterns: \delta i, \delta j, cost
## ...
## all deltas MUST be positive (otherwise we violate monotonicity)
.canonicalizeStepPattern <- function(step.pattern) {
if(is.stepPattern(step.pattern))
return(step.pattern);
# else
sp<-pmatch(step.pattern,c("symmetric","asymmetric"));
if(is.na(sp))
stop("Ambiguous or unsupported char argument for step.pattern");
dir<-switch(sp,
symmetric1,
asymmetric);
return(dir);
}