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.