tslearn.barycenters.softdtw_barycenter¶
- tslearn.barycenters.softdtw_barycenter(X, gamma=1.0, weights=None, method='L-BFGS-B', tol=0.001, max_iter=50, init=None)[source]¶
Compute barycenter (time series averaging) under the soft-DTW [1] geometry.
Soft-DTW was originally presented in [1].
- Parameters:
- Xarray-like, shape=(n_ts, sz, d)
Time series dataset.
- gamma: float
Regularization parameter. Lower is less smoothed (closer to true DTW).
- weights: None or array
Weights of each X[i]. Must be the same size as len(X). If None, uniform weights are used.
- method: string
Optimization method, passed to scipy.optimize.minimize. Default: L-BFGS.
- tol: float
Tolerance of the method used.
- max_iter: int
Maximum number of iterations.
- init: array or None (default: None)
Initial barycenter to start from for the optimization process. If None, euclidean barycenter is used as a starting point.
- Returns:
- numpy.array of shape (bsz, d) where bsz is the size of the init array if provided or sz otherwise
Soft-DTW barycenter of the provided time series dataset.
References
[1]M. Cuturi, M. Blondel “Soft-DTW: a Differentiable Loss Function for Time-Series,” ICML 2017.
Examples
>>> time_series = [[1, 2, 3, 4], [1, 2, 4, 5]] >>> softdtw_barycenter(time_series, max_iter=5) array([[1.25161574], [2.03821705], [3.5101956 ], [4.36140605]]) >>> time_series = [[1, 2, 3, 4], [1, 2, 3, 4, 5]] >>> softdtw_barycenter(time_series, max_iter=5) array([[1.21349933], [1.8932251 ], [2.67573269], [3.51057026], [4.33645802]])
