问题
I tried to use hmmlearn from GitHub to run a binary hidden markov model. This does not work:
import hmmlearn.hmm as hmm
transmat = np.array([[0.7, 0.3],
[0.3, 0.7]])
emitmat = np.array([[0.9, 0.1],
[0.2, 0.8]])
obs = np.array([0, 0, 1, 0, 0])
startprob = np.array([0.5, 0.5])
h = hmm.MultinomialHMM(n_components=2, startprob=startprob,
transmat=transmat)
h.emissionprob_ = emitmat
# fails
h.fit([0, 0, 1, 0, 0])
# fails
h.decode([0, 0, 1, 0, 0])
print h
I get this error:
ValueError: zero-dimensional arrays cannot be concatenated
What is the right way to use this module? Note I am using the version of hmmlearn that was separated from sklearn, because apparently sklearn doesn't maintain hmmlearn anymore.
回答1:
Fit accepts list of sequences and not a single sequence (as in general you can have multiple, independent sequences observed from different runs of your experiments/observations). Thus simply put your list inside another list
import hmmlearn.hmm as hmm
import numpy as np
transmat = np.array([[0.7, 0.3],
[0.3, 0.7]])
emitmat = np.array([[0.9, 0.1],
[0.2, 0.8]])
startprob = np.array([0.5, 0.5])
h = hmm.MultinomialHMM(n_components=2, startprob=startprob,
transmat=transmat)
h.emissionprob_ = emitmat
# works fine
h.fit([[0, 0, 1, 0, 0]])
# h.fit([[0, 0, 1, 0, 0], [0, 0], [1,1,1]]) # this is the reason for such
# syntax, you can fit to multiple
# sequences
print h.decode([0, 0, 1, 0, 0])
print h
gives
(-4.125363362578882, array([1, 1, 1, 1, 1]))
MultinomialHMM(algorithm='viterbi',
init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ',
n_components=2, n_iter=10,
params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ',
random_state=<mtrand.RandomState object at 0x7fe245ac7510>,
startprob=None, startprob_prior=1.0, thresh=0.01, transmat=None,
transmat_prior=1.0)
来源:https://stackoverflow.com/questions/34379911/how-to-run-hidden-markov-models-in-python-with-hmmlearn