Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms.
See Keras example for an example of how to use the Keras HMMLayer.
See test_hmm.py for usage examples. Here is an excerpt of the documentation from hmm.py for reference for now.
See also viterbi_wikipedia_example.py which replicates the viterbi example on wikipedia.
class HMM(object):
"""
A class for Hidden Markov Models.
The model attributes are:
- K :: the number of states
- P :: the K by K transition matrix (from state i to state j,
(i, j) in [1..K])
- p0 :: the initial distribution (defaults to starting in state 0)
"""
def __init__(self, P, p0=None):
class HMMTensorflow(HMM):
def forward_backward(self, y):
"""
runs forward backward algorithm on state probabilities y
Arguments
---------
y : np.array : shape (T, K) where T is number of timesteps and
K is the number of states
Returns
-------
(posterior, forward, backward)
posterior : list of length T of tensorflow graph nodes representing
the posterior probability of each state at each time step
forward : list of length T of tensorflow graph nodes representing
the forward probability of each state at each time step
backward : list of length T of tensorflow graph nodes representing
the backward probability of each state at each time step
"""
def viterbi_decode(self, y, nT):
"""
Runs viterbi decode on state probabilies y.
Arguments
---------
y : np.array : shape (T, K) where T is number of timesteps and
K is the number of states
nT : int : number of timesteps in y
Returns
-------
(s, pathScores)
s : list of length T of tensorflow ints : represents the most likely
state at each time step.
pathScores : list of length T of tensorflow tensor of length K
each value at (t, k) is the log likliehood score in state k at
time t. sum(pathScores[t, :]) will not necessary == 1
"""
