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You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.9.1.

Temporal segmentation

Recurrence and self-similarity

cross_similarity(data, data_ref[, k, ...])

Compute cross-similarity from one data sequence to a reference sequence.

recurrence_matrix(data[, k, width, metric, ...])

Compute a recurrence matrix from a data matrix.

recurrence_to_lag(rec[, pad, axis])

Convert a recurrence matrix into a lag matrix.

lag_to_recurrence(lag[, axis])

Convert a lag matrix into a recurrence matrix.

timelag_filter(function[, pad, index])

Filtering in the time-lag domain.

path_enhance(R, n[, window, max_ratio, ...])

Multi-angle path enhancement for self- and cross-similarity matrices.

Temporal clustering

agglomerative(data, k[, clusterer, axis])

Bottom-up temporal segmentation.

subsegment(data, frames[, n_segments, axis])

Sub-divide a segmentation by feature clustering.