##### https://github.com/cran/dtw
Tip revision: d32f6f7
dtwDist.Rd
\name{dtwDist}
\alias{dtwDist}
\title{Compute a dissimilarity matrix}

\description{Compute the dissimilarity matrix  between
a set of single-variate timeseries.  }

\usage{
dtwDist(mx,my=mx,...)
# dist(mx,my=mx,method="DTW",...)
}
\arguments{
\item{mx}{numeric matrix, containing timeseries as rows}
\item{my}{numeric matrix, containing timeseries as rows (for cross-distance)}
\item{...}{arguments passed to the \code{\link{dtw}} call}
}

\value{
A square matrix whose element \code{[i,j]} holds the Dynamic Time Warp
distance between row \code{i} (query) and \code{j} (reference) of
\code{mx} and \code{my}, i.e.  \code{dtw(mx[i,],my[j,])distance}. } \details{ \code{dtwDist} computes a dissimilarity matrix, akin to \code{\link{dist}}, based on the Dynamic Time Warping definition of a distance between single-variate timeseries. The \code{dtwDist} command is a synonym for the \code{\link[proxy]{dist}} function of package \pkg{proxy}; the DTW distance is registered as \code{method="DTW"} (see examples below). The timeseries are stored as rows in the matrix argument \code{m}. In other words, if \code{m} is an N * T matrix, \code{dtwDist} will build N*N ordered pairs of timeseries, perform the corresponding N*N \code{dtw} alignments, and return all of the results in a matrix. Each of the timeseries is T elements long. \code{dtwDist} returns a square matrix, whereas the \code{dist} object is lower-triangular. This makes sense because in general the DTW "distance" is not symmetric (see e.g. asymmetric step patterns). To make a square matrix with the \code{\link[proxy]{dist}} function sematics, use the two-arguments call as \code{dist(m,m)}. This will return a square \code{crossdist} object. } \note{ To convert a square cross-distance matrix (\code{crossdist} object) to a symmetric \code{\link{dist}} object, use a suitable conversion strategy (see examples). } \seealso{Other "distance" functions are: \code{\link{dist}}, \code{\link[vegan]{vegdist}} in package \code{vegan}, \code{\link[analogue]{distance}} in package \code{analogue}, etc. } \examples{ ## Symmetric step pattern => symmetric dissimilarity matrix; ## no problem coercing it to a dist object: m <- matrix(0,ncol=3,nrow=4) m <- row(m) dist(m,method="DTW"); # Old-fashioned call style would be: # dtwDist(m) # as.dist(dtwDist(m)) ## Find the optimal warping _and_ scale factor at the same time. ## (There may be a better, analytic way) # Prepare a query and a reference query<-sin(seq(0,4*pi,len=100)) reference<-cos(seq(0,4*pi,len=100)) # Make a set of several references, scaled from 0 to 3 in .1 increments. # Put them in a matrix, in rows scaleSet <- seq(0.1,3,by=.1) referenceSet<-outer(1/scaleSet,reference) # The query has to be made into a 1-row matrix. # Perform all of the alignments at once, and normalize the result. dist(t(query),referenceSet,meth="DTW")->distanceSet # The optimal scale for the reference is 1.0 plot(scaleSet,scaleSet*distanceSet, xlab="Reference scale factor (denominator)", ylab="DTW distance",type="o", main="Sine vs scaled cosine alignment, 0 to 4 pi") ## Asymmetric step pattern: we can either disregard part of the pairs ## (as.dist), or average with the transpose mm <- matrix(runif(12),ncol=3) dm <- dist(mm,mm,method="DTW",step=asymmetric); # a crossdist object # Old-fashioned call style would be: # dm <- dtwDist(mm,step=asymmetric) # as.dist(dm) ## Symmetrize by averaging: (dm+t(dm))/2 ## check definition stopifnot(dm[2,1]==dtw(mm[2,],mm[1,],step=asymmetric)distance)

}

\author{Toni Giorgino}
\keyword{ts}