https://github.com/cran/dtw
Tip revision: da02fed50f656619ef5518cab97c0d00fb23d318 authored by Toni Giorgino on 08 January 2008, 00:00:00 UTC
version 1.4-3
version 1.4-3
Tip revision: da02fed
stepPattern.R
###############################################################
# #
# Author: Toni Giorgino <toni.giorgino@gmail.com> #
# Laboratory for Biomedical Informatics #
# University of Pavia - Italy #
# www.labmedinfo.org #
# #
# $Id: stepPattern.R 87 2008-01-07 13:59:07Z tonig $
# #
###############################################################
## For pre-defined step patterns see below.
#############################
## Methods for accessing and creating step.patterns
stepPattern <- function(v) {
if(!is.vector(v)) {
stop("stepPattern creation only supported from vectors");
}
obj<-matrix(v,ncol=4,byrow=TRUE);
class(obj)<-"stepPattern";
attr(obj,"npat") <- max(obj[,1]);
return(obj);
}
is.stepPattern <- function(x) {
return(inherits(x,"stepPattern"));
}
## pretty-print the matrix meaning,
## so it will not be as write-only as now
print.stepPattern <-function(x,...) {
step.pattern<-x; # for clarity
np<-max(step.pattern[,1]); #no. of patterns
head<-"g[i,j] = min(\n";
body<-"";
## cycle over available step patterns
for(p in 1:np) {
steps<-.extractpattern(step.pattern,p);
ns<-dim(steps)[1];
## restore row order
steps<-matrix(steps[ns:1,],ncol=3); # enforce a matrix
## cycle over steps s in the current pattern p
for(s in 1:ns) {
di<-steps[s,1]; # delta in query
dj<-steps[s,2]; # delta in templ
cc<-steps[s,3]; # step cost multiplier
## make pretty-printable negative increments
dis<-ifelse(di==0,"",-di); # 4 -> -4; 0 -> .
djs<-ifelse(dj==0,"",-dj); # 0 maps to empty string
## cell origin, as coordinate pair
dijs<-sprintf("i%2s,j%2s",dis,djs);
if(cc==-1) { # g
gs<-sprintf(" g[%s]",dijs);
body<-paste(body,gs);
} else {
## prettyprint step cost multiplier in ccs: 1 -> .; 2 -> 2 *
ccs<-ifelse(cc==1," ",sprintf("%2.2g *",cc));
ds<-sprintf("+%s d[%s]",ccs,dijs);
body<-paste(body,ds);
}
}
body<-paste(body,",\n",s="");
}
tail<-")\n\n";
rv<-paste(head,body,tail);
cat("Step pattern recursion:\n");
cat(rv);
}
## TODO: sanity check on the step pattern definition
.checkpattern <- function(sp) {
## must have 4 x n elements
## all integers
## first column in ascending order from 1, no missing steps
## 2nd, 3rd row non-negative
## 4th: first for each step is -1
}
## Extract rows belonging to pattern no. sn
## with first element stripped
## in reverse order
.extractpattern <- function(sp,sn) {
sbs<-sp[,1]==sn; # pick only rows beginning by sn
spl<-sp[sbs,-1,drop=FALSE];
# of those: take only column Di, Dj, cost
# (drop first - pattern no. column)
nr<-nrow(spl); # how many are left
spl<-spl[nr:1,,drop=FALSE]; # invert row order
return(spl);
}
##################################################
##################################################
##
## Various step patterns, defined as internal variables
##
## First column: enumerates step patterns.
## Second step in query index
## Third step in template index
## Fourth weight if positive, or -1 if starting point
##
## For \cite{} see dtw.bib in the package
##
## Widely-known variants
## White-Neely symmetric (default)
## aka Quasi-symmetric \cite{White1976}
## normalization: no (N+M?)
symmetric1 <- stepPattern(c(
1,0,1,-1,
1,0,0,1,
2,1,1,-1,
2,0,0,1,
3,1,0,-1,
3,0,0,1
));
## Normal symmetric
## normalization: N+M
symmetric2 <- stepPattern(c(
1,0,1,-1,
1,0,0,1,
2,1,1,-1,
2,0,0,2,
3,1,0,-1,
3,0,0,1
));
## classic asymmetric pattern: max slope 2, min slope 0
## normalization: N
asymmetric <- stepPattern(c(
1,1,0,-1,
1,0,0,1,
2,1,1,-1,
2,0,0,1,
3,1,2,-1,
3,0,0,1
));
## normalization: max[N,M]
## note: local distance matrix is 1-d
## \cite{Velichko}
.symmetricVelichkoZagoruyko <- stepPattern(c(
1, 0, 1, -1,
2, 1, 1, -1,
2, 0, 0, -1.001,
3, 1, 0, -1 ));
## Itakura slope-limited asymmetric \cite{Itakura1975}
## Max slope: 2; min slope: 1/2
## normalization: N
asymmetricItakura <- stepPattern(c(
1, 1, 2, -1,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 1,
3, 2, 1, -1,
3, 1, 0, 1,
3, 0, 0, 1,
4, 2, 2, -1,
4, 1, 0, 1,
4, 0, 0, 1
));
#############################
## Slope-limited versions
##
## Taken from Table I, page 47 of "Dynamic programming algorithm
## optimization for spoken word recognition," Acoustics, Speech, and
## Signal Processing, vol.26, no.1, pp. 43-49, Feb 1978 URL:
## http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055
##
## Mostly unchecked
## Row P=0
symmetricP0 <- symmetric2;
## uhmmmm......
## normalization: N ?
asymmetricP0 <- stepPattern(c(
1,0,1,-1,
2,1,1,-1,
2,0,0,1,
3,1,0,-1,
3,0,0,1
));
## Row P=1/2
symmetricP05 <- stepPattern(c(
1 , 1, 3 , -1,
1 , 0, 2 , 2,
1 , 0, 1 , 1,
1 , 0, 0 , 1,
2 , 1, 2 , -1,
2 , 0, 1 , 2,
2 , 0, 0 , 1,
3 , 1, 1 , -1,
3 , 0, 0 , 2,
4 , 2, 1 , -1,
4 , 1, 0 , 2,
4 , 0, 0 , 1,
5 , 3, 1 , -1,
5 , 2, 0 , 2,
5 , 1, 0 , 1,
5 , 0, 0 , 1
));
asymmetricP05 <- stepPattern(c(
1 , 1 , 3 , -1,
1 , 0 , 2 ,1/3,
1 , 0 , 1 ,1/3,
1 , 0 , 0 ,1/3,
2 , 1 , 2 , -1,
2 , 0 , 1 , .5,
2 , 0 , 0 , .5,
3 , 1 , 1 , -1,
3 , 0 , 0 , 1 ,
4 , 2 , 1 , -1,
4 , 1 , 0 , 1 ,
4 , 0 , 0 , 1 ,
5 , 3 , 1 , -1,
5 , 2 , 0 , 1 ,
5 , 1 , 0 , 1 ,
5 , 0 , 0 , 1
));
## Row P=1
## Implementation of Sakoe's P=1, Symmetric algorithm
symmetricP1 <- stepPattern(c(
1,1,2,-1, # First branch: g(i-1,j-2)+
1,0,1,2, # + 2d(i ,j-1)
1,0,0,1, # + d(i ,j)
2,1,1,-1, # Second branch: g(i-1,j-1)+
2,0,0,2, # +2d(i, j)
3,2,1,-1, # Third branch: g(i-2,j-1)+
3,1,0,2, # + 2d(i-1,j)
3,0,0,1 # + d( i,j)
));
asymmetricP1 <- stepPattern(c(
1, 1 , 2 , -1 ,
1, 0 , 1 , .5 ,
1, 0 , 0 , .5 ,
2, 1 , 1 , -1 ,
2, 0 , 0 , 1 ,
3, 2 , 1 , -1 ,
3, 1 , 0 , 1 ,
3, 0 , 0 , 1
));
## Row P=2
symmetricP2 <- stepPattern(c(
1, 2, 3, -1,
1, 1, 2, 2,
1, 0, 1, 2,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 2,
3, 3, 2, -1,
3, 2, 1, 2,
3, 1, 0, 2,
3, 0, 0, 1
));
asymmetricP2 <- stepPattern(c(
1, 2 , 3 , -1,
1, 1 , 2 ,2/3,
1, 0 , 1 ,2/3,
1, 0 , 0 ,2/3,
2, 1 , 1 ,-1 ,
2, 0 , 0 ,1 ,
3, 3 , 2 ,-1 ,
3, 2 , 1 ,1 ,
3, 1 , 0 ,1 ,
3, 0 , 0 ,1
));
################################
## Taken from Table III, page 49.
## Four varieties of DP-algorithm compared
## 1st row: asymmetric
## 2nd row: symmetricVelichkoZagoruyko
## 3rd row: symmetric1
## 4th row: asymmetricItakura
#############################
## Classified according to Rabiner
##
## Taken from chapter 2, Myers' thesis [4]. Letter is
## the weighting function:
##
## rule norm unbiased
## a min step ~N NO
## b max step ~N NO
## c x step N YES
## d x+y step N+M YES
##
## Mostly unchecked
typeIa <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1,
1, 0, 0, 0,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, 1,
3, 0, 0, 0
));
typeIb <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, 1,
3, 0, 0, 1
));
typeIc <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, 1,
3, 0, 0, 0
));
typeId <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 2,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 2,
3, 1, 2, -1,
3, 0, 1, 2,
3, 0, 0, 1
));
## ----------
## smoothed variants of above
typeIas <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, .5,
1, 0, 0, .5,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, .5,
3, 0, 0, .5
));
typeIbs <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, 1,
3, 0, 0, 1
));
typeIcs <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1,
1, 0, 0, 1,
2, 1, 1, -1,
2, 0, 0, 1,
3, 1, 2, -1,
3, 0, 1, .5,
3, 0, 0, .5
));
typeIds <- stepPattern(c(
1, 2, 1, -1,
1, 1, 0, 1.5,
1, 0, 0, 1.5,
2, 1, 1, -1,
2, 0, 0, 2,
3, 1, 2, -1,
3, 0, 1, 1.5,
3, 0, 0, 1.5
));
## ----------
typeIIa <- stepPattern(c(
1, 1, 1, -1,
1, 0, 0, 1,
2, 1, 2, -1,
2, 0, 0, 1,
3, 2, 1, -1,
3, 0, 0, 1
));
typeIIb <- stepPattern(c(
1, 1, 1, -1,
1, 0, 0, 1,
2, 1, 2, -1,
2, 0, 0, 2,
3, 2, 1, -1,
3, 0, 0, 2
));
typeIIc <- stepPattern(c(
1, 1, 1, -1,
1, 0, 0, 1,
2, 1, 2, -1,
2, 0, 0, 1,
3, 2, 1, -1,
3, 0, 0, 2
));
typeIId <- stepPattern(c(
1, 1, 1, -1,
1, 0, 0, 2,
2, 1, 2, -1,
2, 0, 0, 3,
3, 2, 1, -1,
3, 0, 0, 3
));
## ----------
## Myers (p. 56) claims this rule is not exaclty equal to Itakura's,
## but I am not convinced.
typeIIIc <- asymmetricItakura;
## ----------
## numbers follow as production rules in fig 2.16
typeIVc <- stepPattern(c(
1, 1, 1, -1,
1, 0, 0, 1,
2, 1, 2, -1,
2, 0, 0, 1,
3, 1, 3, -1,
3, 0, 0, 1,
4, 2, 1, -1,
4, 1, 0, 1,
4, 0, 0, 1,
5, 2, 2, -1,
5, 1, 0, 1,
5, 0, 0, 1,
6, 2, 3, -1,
6, 1, 0, 1,
6, 0, 0, 1,
7, 3, 1, -1,
7, 2, 0, 1,
7, 1, 0, 1,
7, 0, 0, 1,
8, 3, 2, -1,
8, 2, 0, 1,
8, 1, 0, 1,
8, 0, 0, 1,
9, 3, 3, -1,
9, 2, 0, 1,
9, 1, 0, 1,
9, 0, 0, 1
));
