A stepPattern object lists the transitions
allowed while searching for the minimum-distance path. DTW variants
are implemented by passing one of the objects described in this page
to the stepPattern argument of the dtw call.
## Well-known step patterns
symmetric1
symmetric2
asymmetric## Step patterns classified according to Rabiner-Juang [Rabiner1993]
rabinerJuangStepPattern(type,slope.weighting="d",smoothed=FALSE)
## Slope-constrained step patterns from Sakoe-Chiba [Sakoe1978]
symmetricP0; asymmetricP0
symmetricP05; asymmetricP05
symmetricP1; asymmetricP1
symmetricP2; asymmetricP2
## Step patterns classified according to Rabiner-Myers [Myers1980]
typeIa; typeIb; typeIc; typeId;
typeIas; typeIbs; typeIcs; typeIds; # smoothed
typeIIa; typeIIb; typeIIc; typeIId;
typeIIIc; typeIVc;
## Miscellaneous
mori2006;
rigid;
# S3 method for stepPattern
print(x,...)
# S3 method for stepPattern
plot(x,...)
# S3 method for stepPattern
t(x)
stepPattern(v,norm=NA)
is.stepPattern(x)
a step pattern object
path specification, integer 1..7 (see [Rabiner1993], table 4.5)
slope weighting rule: character "a"
to "d" (see [Rabiner1993], sec. 4.7.2.5)
logical, whether to use smoothing (see [Rabiner1993], fig. 4.44)
a vector defining the stepPattern structure
normalization hint (character)
additional arguments to print.
A step pattern characterizes the matching model and slope constraint specific of a DTW variant. They also known as local- or slope-constraints, transition types, production or recursion rules [GiorginoJSS].
print.stepPattern prints an user-readable
description of the recurrence equation defined by the given pattern.
plot.stepPattern graphically displays the step patterns
productions which can lead to element (0,0). Weights are
shown along the step leading to the corresponding element.
t.stepPattern transposes the productions and normalization hint
so that roles of query and reference become reversed.
A variety of classifications have been proposed for step patterns,
including Sakoe-Chiba [Sakoe1978]; Rabiner-Juang [Rabiner1993]; and Rabiner-Myers [Myers1980].
The dtw package implements all of the transition types found in
those papers, with the exception of Itakura's and Velichko-Zagoruyko's
steps, which require subtly different algorithms (this may be rectified
in the future). Itakura recursion is almost, but not quite, equivalent
to typeIIIc.
For convenience, we shall review pre-defined step patterns grouped by classification. Note that the same pattern may be listed under different names. Refer to paper [GiorginoJSS] for full details.
1. Well-known step patterns
Common DTW implementations are based on one of the following transition types.
symmetric2 is the normalizable, symmetric, with no local slope
constraints. Since one diagonal step costs as much as the two
equivalent steps along the sides, it can be normalized dividing by
N+M (query+reference lengths). It is widely used and
the default.
asymmetric is asymmetric, slope constrained between 0 and
2. Matches each element of the query time series exactly once, so
the warping path index2~index1 is guaranteed to
be single-valued. Normalized by N (length of query).
symmetric1 (or White-Neely) is quasi-symmetric,
no local constraint, non-normalizable. It is biased
in favor of oblique steps.
2. The Rabiner-Juang set
A comprehensive table of step patterns is proposed in Rabiner-Juang's
book [Rabiner1993], tab. 4.5. All of them can be constructed through
the rabinerJuangStepPattern(type,slope.weighting,smoothed)
function.
The classification foresees seven families, labelled with Roman
numerals I-VII; here, they are selected through the integer argument
type. Each family has four slope weighting sub-types, named in
sec. 4.7.2.5 as "Type (a)" to "Type (d)"; they are selected passing a
character argument slope.weighting, as in the table
below. Furthermore, each subtype can be either plain or smoothed (figure
4.44); smoothing is enabled setting the logical argument
smoothed. (Not all combinations of arguments make sense.)
| Subtype | Rule | Norm | Unbiased |
| a | min step | -- | NO |
| b | max step | -- | NO |
| c | Di step | N | YES |
| d | Di+Dj step | N+M | YES |
3. The Sakoe-Chiba set
Sakoe-Chiba [Sakoe1978] discuss a family of slope-constrained
patterns; they are implemented as shown in page 47, table I. Here,
they are called symmetricP<x> and asymmetricP<x>, where
<x> corresponds to Sakoe's integer slope parameter P.
Values available are accordingly: 0 (no constraint), 1,
05 (one half) and 2. See [Sakoe1978] for details.
4. The Rabiner-Myers set
The type<XX><y> step patterns follow the older Rabiner-Myers'
classification proposed in [Myers1980] and [MRR1980]. Note that this
is a subset of the Rabiner-Juang set [Rabiner1993], and the latter should be
preferred in order to avoid confusion. <XX> is a Roman numeral
specifying the shape of the transitions; <y> is a letter in the
range a-d specifying the weighting used per step, as above;
typeIIx patterns also have a version ending in s,
meaning the smoothing is used (which does not permit skipping
points). The typeId, typeIId and typeIIds are unbiased
and symmetric.
5. Other
The rigid pattern enforces a fixed unitary slope. It only makes
sense in combination with open.begin=T, open.end=T to
find gapless subsequences. It may be seen as the \(P \to
\infty\) limiting case in Sakoe's classification.
mori2006 is Mori's asymmetric step-constrained pattern
[Mori2006]. It is normalized by the matched reference length.
mvmStepPattern() implements Latecki's Minimum
Variance Matching algorithm, and it is described in its own page.
[GiorginoJSS] Toni Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24. http://www.jstatsoft.org/v31/i07/ [Itakura1975] Itakura, F., Minimum prediction residual principle applied to speech recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.23, no.1, pp. 67-72, Feb 1975. URL: http://dx.doi.org/10.1109/TASSP.1975.1162641 [MRR1980] Myers, C.; Rabiner, L. & Rosenberg, A. Performance tradeoffs in dynamic time warping algorithms for isolated word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1980, 28, 623-635. URL: http://dx.doi.org/10.1109/TASSP.1980.1163491 [Mori2006] Mori, A.; Uchida, S.; Kurazume, R.; Taniguchi, R.; Hasegawa, T. & Sakoe, H. Early Recognition and Prediction of Gestures Proc. 18th International Conference on Pattern Recognition ICPR 2006, 2006, 3, 560-563. URL: http://dx.doi.org/10.1109/ICPR.2006.467 [Myers1980] Myers, Cory S. A Comparative Study Of Several Dynamic Time Warping Algorithms For Speech Recognition, MS and BS thesis, Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, archived Jun 20 1980, http://hdl.handle.net/1721.1/27909 [Rabiner1993] Rabiner, L. R., & Juang, B.-H. (1993). Fundamentals of speech recognition. Englewood Cliffs, NJ: Prentice Hall. [Sakoe1978] Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.26, no.1, pp. 43-49, Feb 1978 URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055
mvmStepPattern, implementing
Latecki's Minimal Variance Matching algorithm.
# NOT RUN {
#########
##
## The usual (normalizable) symmetric step pattern
## Step pattern recursion, defined as:
## g[i,j] = min(
## g[i,j-1] + d[i,j] ,
## g[i-1,j-1] + 2 * d[i,j] ,
## g[i-1,j] + d[i,j] ,
## )
print(symmetric2) # or just "symmetric2"
#########
##
## The well-known plotting style for step patterns
plot(symmetricP2,main="Sakoe's Symmetric P=2 recursion")
#########
##
## Same example seen in ?dtw , now with asymmetric step pattern
idx<-seq(0,6.28,len=100);
query<-sin(idx)+runif(100)/10;
reference<-cos(idx);
## Do the computation
asy<-dtw(query,reference,keep=TRUE,step=asymmetric);
dtwPlot(asy,type="density",main="Sine and cosine, asymmetric step")
#########
##
## Hand-checkable example given in [Myers1980] p 61
##
`tm` <-
structure(c(1, 3, 4, 4, 5, 2, 2, 3, 3, 4, 3, 1, 1, 1, 3, 4, 2,
3, 3, 2, 5, 3, 4, 4, 1), .Dim = c(5L, 5L))
# }
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