# tslearn.metrics.subsequence_path¶

tslearn.metrics.subsequence_path(acc_cost_mat, idx_path_end)[source]

Compute the optimal path through a accumulated cost matrix given the endpoint of the sequence.

Parameters: acc_cost_mat: array, shape = (sz1, sz2) The accumulated cost matrix comparing subsequence from a longer sequence. idx_path_end: int The end position of the matched subsequence in the longer sequence. path: list of tuples of integer pairs Matching path represented as a list of index pairs. In each pair, the first index corresponds to subseq and the second one corresponds to longseq. The startpoint of the Path is $$P_0 = (0, ?)$$ and it ends at $$P_L = (len(subseq)-1, idx\_path\_end)$$

dtw_subsequence_path
Get the similarity score for DTW
subsequence_cost_matrix
Calculate the required cost matrix

Examples

>>> acc_cost_mat = numpy.array([[1., 0., 0., 1., 4.],
...                             [5., 1., 1., 0., 1.]])
>>> # calculate the globally optimal path
>>> optimal_end_point = numpy.argmin(acc_cost_mat[-1, :])
>>> path = subsequence_path(acc_cost_mat, optimal_end_point)
>>> path
[(0, 2), (1, 3)]


## Examples using tslearn.metrics.subsequence_path¶ sDTW multi path matching