Python implementation of soft-DTW
Python implementation of soft-DTW.
What is it?
The celebrated dynamic time warping (DTW)  defines the discrepancy between two time series, of possibly variable length, as their minimal alignment cost. Although the number of possible alignments is exponential in the length of the two time series,  showed that DTW can be computed in only quadractic time using dynamic programming.
Soft-DTW  proposes to replace this minimum by a soft minimum. Like the original DTW, soft-DTW can be computed in quadratic time using dynamic programming. However, the main advantage of soft-DTW stems from the fact that it is differentiable everywhere and that its gradient can also be computed in quadratic time. This enables to use soft-DTW for time series averaging or as a loss function, between a ground-truth time series and a time series predicted by a neural network, trained end-to-end using backpropagation.
- PyTorch function
from sdtw import SoftDTW from sdtw.distance import SquaredEuclidean # Time series 1: numpy array, shape = [m, d] where m = length and d = dim X = ... # Time series 2: numpy array, shape = [n, d] where n = length and d = dim Y = ... # D can also be an arbitrary distance matrix: numpy array, shape [m, n] D = SquaredEuclidean(X, Y) sdtw = SoftDTW(D, gamma=1.0) # soft-DTW discrepancy, approaches DTW as gamma -> 0 value = sdtw.compute() # gradient w.r.t. D, shape = [m, n], which is also the expected alignment matrix E = sdtw.grad() # gradient w.r.t. X, shape = [m, d] G = D.jacobian_product(E)
Binary packages are not available.
This project can be installed from its git repository. It is assumed that you have a working C compiler.
Obtain the sources by:
git clone https://github.com/mblondel/soft-dtw.git
or, if git is unavailable, download as a ZIP from GitHub.
Install the dependencies:
# via pip pip install numpy scipy scikit-learn cython nose # via conda conda install numpy scipy scikit-learn cython nose
Build and install soft-dtw:
cd soft-dtw python setup.py install
|||Hiroaki Sakoe, Seibi Chiba. Dynamic programming algorithm optimization for spoken word recognition. In: IEEE Trans. on Acoustics, Speech, and Sig. Proc, 1978.|
|||Marco Cuturi, Mathieu Blondel. Soft-DTW: a Differentiable Loss Function for Time-Series. In: Proc. of ICML 2017. [PDF]|
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