viterbi

Builtin index · Statistics & ML

result = hmm.viterbi(obs)

Input: obs · Output: result

What it is

Runs the Viterbi algorithm on an hmm model hmm for a length-T integer observation sequence obs (same encoding as forward).

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

model = hmm(transition, emission, initial)
path = model.viterbi(obs)          # one call, entire 8000-step decode

from tour

P = [0.9, 0.5, 0.1, 0.5] as matrix(2, 2)   # column-major: rows [0.9 0.1], [0.5 0.5]

chain = markov_chain(P, 0)
print("stationary: {round(chain.stationary(), 4)}")
print("3-step:     {round(chain.transition ^ 3, 4)}")

trans = [0.7, 0.4, 0.3, 0.6] as matrix(2, 2)
emit  = [0.5, 0.1, 0.4, 0.3, 0.1, 0.6] as matrix(2, 3)
h = hmm(trans, emit, [0.6, 0.4])
print("P(normal, cold, dizzy) = {round(h.forward([0, 1, 2]), 5)}")
print("most likely path       = {h.viterbi([0, 1, 2])}")

stationary: [0.8333, 0.1667]
3-step:     [0.844, 0.156; 0.78, 0.22]
P(normal, cold, dizzy) = 0.03628
most likely path       = [0, 0, 1]

from statistics-ml

How it works

viterbi(model, obs) — callable as model.viterbi(obs). The HMM's most likely hidden-state path given the 0-based observation-symbol sequence obs (Viterbi decoding). See hmm_viterbi's own doc comment for the dynamic program.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.