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.

Sequential modeling

Sequence alignment

dtw([X, Y, C, metric, step_sizes_sigma, ...])

Dynamic time warping (DTW).

rqa(sim[, gap_onset, gap_extend, ...])

Recurrence quantification analysis (RQA)

Viterbi decoding

viterbi(prob, transition[, p_init, return_logp])

Viterbi decoding from observation likelihoods.

viterbi_discriminative(prob, transition[, ...])

Viterbi decoding from discriminative state predictions.

viterbi_binary(prob, transition[, p_state, ...])

Viterbi decoding from binary (multi-label), discriminative state predictions.

Transition matrices

transition_uniform(n_states)

Construct a uniform transition matrix over n_states.

transition_loop(n_states, prob)

Construct a self-loop transition matrix over n_states.

transition_cycle(n_states, prob)

Construct a cyclic transition matrix over n_states.

transition_local(n_states, width[, window, wrap])

Construct a localized transition matrix.