I'm trying to fit a multinomial model with my observed data. I have a dataset containing trajectories with different lengths. Since my observations are discrete I try to fit with a multinomial model. The number of observation symbols is 3147 and the number of trajectories(sequences) is 4760. Whilst I give the observation sequences (X with an array shape defined in hmmlearn class) and the length of observation with () to fit method with the this code:
X
array([[31],
[ 1],
[17],
...,
[ 4],
[ 1],
[16]])lengths
[28,
6,
11,
7,
2,
2,
...]model = hmm.MultinomialHMM(n_components=10).fit(X, lengths)
I got an error. Can someone help me and explain what I am doing wrong. Thanks.
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-72-565aadfd0ae4> in <module>()
----> 1 model = hmm.MultinomialHMM(n_components=10).fit(X, lengths)
C:\Python\Anaconda2\lib\site-packages\hmmlearn\base.pyc in fit(self, X, lengths)
427 curr_logprob = 0
428 for i, j in iter_from_X_lengths(X, lengths):
--> 429 framelogprob = self._compute_log_likelihood(X[i:j])
430 logprob, fwdlattice = self._do_forward_pass(framelogprob)
431 curr_logprob += logprob
C:\Python\Anaconda2\lib\site-packages\hmmlearn\hmm.pyc in _compute_log_likelihood(self, X)
403
404 def _compute_log_likelihood(self, X):
--> 405 return np.log(self.emissionprob_)[:, np.concatenate(X)].T
406
407 def _generate_sample_from_state(self, state, random_state=None):
IndexError: index 3147 is out of bounds for axis 1 with size 3147The value of your X should start from zero rather than from one.