Caution

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.