tslearn.barycenters.dtw_barycenter_averaging

tslearn.barycenters.dtw_barycenter_averaging(X, barycenter_size=None, init_barycenter=None, max_iter=30, tol=1e-05, weights=None, metric_params=None, verbose=False, n_init=1)[source]

DTW Barycenter Averaging (DBA) method estimated through Expectation-Maximization algorithm.

DBA was originally presented in [1]. This implementation is based on a idea from [2] (Majorize-Minimize Mean Algorithm).

Parameters:
X : array-like, shape=(n_ts, sz, d)

Time series dataset.

barycenter_size : int or None (default: None)

Size of the barycenter to generate. If None, the size of the barycenter is that of the data provided at fit time or that of the initial barycenter if specified.

init_barycenter : array or None (default: None)

Initial barycenter to start from for the optimization process.

max_iter : int (default: 30)

Number of iterations of the Expectation-Maximization optimization procedure.

tol : float (default: 1e-5)

Tolerance to use for early stopping: if the decrease in cost is lower than this value, the Expectation-Maximization procedure stops.

weights: None or array

Weights of each X[i]. Must be the same size as len(X). If None, uniform weights are used.

metric_params: dict or None (default: None)

DTW constraint parameters to be used. See tslearn.metrics.dtw_path for a list of accepted parameters If None, no constraint is used for DTW computations.

verbose : boolean (default: False)

Whether to print information about the cost at each iteration or not.

n_init : int (default: 1)

Number of different initializations to be tried (useful only is init_barycenter is set to None, otherwise, all trials will reach the same performance)

Returns:
numpy.array of shape (barycenter_size, d) or (sz, d) if barycenter_size is None

DBA barycenter of the provided time series dataset.

References

[1]F. Petitjean, A. Ketterlin & P. Gancarski. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, Elsevier, 2011, Vol. 44, Num. 3, pp. 678-693
[2]D. Schultz and B. Jain. Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces. Pattern Recognition, 74, 340-358.

Examples

>>> time_series = [[1, 2, 3, 4], [1, 2, 4, 5]]
>>> dtw_barycenter_averaging(time_series, max_iter=5)
array([[1. ],
       [2. ],
       [3.5],
       [4.5]])
>>> time_series = [[1, 2, 3, 4], [1, 2, 3, 4, 5]]
>>> dtw_barycenter_averaging(time_series, max_iter=5)
array([[1. ],
       [2. ],
       [3. ],
       [4. ],
       [4.5]])
>>> dtw_barycenter_averaging(time_series, max_iter=5,
...                          metric_params={"itakura_max_slope": 2})
array([[1. ],
       [2. ],
       [3. ],
       [3.5],
       [4.5]])
>>> dtw_barycenter_averaging(time_series, max_iter=5, barycenter_size=3)
array([[1.5       ],
       [3.        ],
       [4.33333333]])
>>> dtw_barycenter_averaging([[0, 0, 0], [10, 10, 10]], max_iter=1,
...                          weights=numpy.array([0.75, 0.25]))
array([[2.5],
       [2.5],
       [2.5]])

Examples using tslearn.barycenters.dtw_barycenter_averaging