tensorflow_hmm-0.4.0.tar.gz

[](https://travis-ci.org/dwiel/tensorflow_hmm) # tensorflow_hmm Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms. See [Keras example](https://github.com/dwiel/tensorflow_hmm/blob/master/tensorflow_hmm/hmm_layer.py) 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 """ ```