1.1.2(1) of NFPA 30 that a combustible material, having a melting point at or above 100°F, be outside. Opening(s) only to the distance specified by Table 7.3.3 for the specific source(s) that requires area classifica-tion? Issue Edition: 2003 Reference: Table 8.2.2.
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Latest version
Released:
Distance measures for time series
Project description
Library for time series distances (e.g. Dynamic Time Warping) used in the DTAI Research Group.The library offers a pure Python implementation and a faster implementation in C.
Documentation: http://dtaidistance.readthedocs.io
Citing this work:
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Installation
This packages is available on PyPI (requires Python 3):
In case the C based version is not available, see the documentation for alternative installation options.In case OpenMP is not available on your system add the
--noopenmp
global option.
The source code is available at github.com/wannesm/dtaidistance.
Usage
Dynamic Time Warping (DTW) Distance Measure
DTW Distance Measure Between Two Series
Only the distance measure based on two sequences of numbers:
The fastest version (30-300 times) uses c directly but requires an array as input (with the double type):
Or you can use a numpy array (with dtype double or float):
Check the
__doc__
for information about the available arguments:
A number of options are foreseen to early stop some paths the dynamic programming algorithm is exploring or tunethe distance measure computation:
window
: Only allow for shifts up to this amount away from the two diagonals.max_dist
: Stop if the returned distance measure will be larger than this value.max_step
: Do not allow steps larger than this value.max_length_diff
: Return infinity if difference in length of two series is larger.penalty
: Penalty to add if compression or expansion is applied (on top of the distance).psi
: Psi relaxation to ignore begin and/or end of sequences (for cylical sequencies) [2].
DTW Distance Measure all warping paths
If, next to the distance, you also want the full matrix to see all possible warping paths:
The matrix with all warping paths can be visualised as follows:
Notice the
psi
parameter that relaxes the matching at the beginning and end.In this example this results in a perfect match even though the sine waves are slightly shifted.
DTW Distance Measures Between Set of Series
To compute the DTW distance measures between all sequences in a list of sequences, use the method
dtw.distance_matrix
.You can set variables to use more or less c code (use_c
and use_nogil
) and parallel or serial execution(parallel
).
The
distance_matrix
method expects a list of lists/arrays:
or a matrix (in case all series have the same length):
DTW Distance Measures Between Set of Series, limited to block
You can instruct the computation to only fill part of the distance measures matrix.For example to distribute the computations over multiple nodes, or to onlycompare source series to target series.
The output in this case will be:
Clustering
A distance matrix can be used for time series clustering. You can use existing methods such as
scipy.cluster.hierarchy.linkage
or one of two included clustering methods (the latter is awrapper for the SciPy linkage method).
For models that keep track of the full clustering tree (
HierarchicalTree
or LinkageTree
), thetree can be visualised:
Dependencies
Optional:
Development:
Contact
- Wannes Meert
<[email protected]>
References
- T. K. Vintsyuk,Speech discrimination by dynamic programming.Kibernetika, 4:81–88, 1968.
- H. Sakoe and S. Chiba,Dynamic programming algorithm optimization for spoken word recognition.IEEE Transactions on Acoustics, Speech and Signal Processing, 26(1):43–49, 1978.
- C. S. Myers and L. R. Rabiner,A comparative study of several dynamic time-warping algorithms for connected-word recognition.The Bell System Technical Journal, 60(7):1389–1409, Sept 1981.
- Mueen, A and Keogh, E,Extracting Optimal Performance from Dynamic Time Warping,Tutorial, KDD 2016
- D. F. Silva, G. E. A. P. A. Batista, and E. Keogh.On the effect of endpoints on dynamic time warping,In SIGKDD Workshop on Mining and Learning from Time Series, II. Association for Computing Machinery-ACM, 2016.
- C. Yanping, K. Eamonn, H. Bing, B. Nurjahan, B. Anthony, M. Abdullah and B. Gustavo.The UCR Time Series Classification Archive, 2015.
License
Release historyRelease notifications
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