MLE errors

[Tomáš Šabata]

I have found two errors in function maximum likelihood estimation:

hmm = hmms.DtHMM.random(2,5)
states, seq = hmm.generate(1000);
hmm.maximum_likelihood_estimation(states, seq)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "hmms/dthmm.pyx", line 332, in hmms.dthmm.DtHMM.maximum_likelihood_estimation (hmms/dthmm.c:11523)
  File "hmms/dthmm.pyx", line 352, in hmms.dthmm.DtHMM.maximum_likelihood_estimation (hmms/dthmm.c:10930)
TypeError: Cannot convert numpy.int64 to numpy.ndarray

This can be fixed using it in following way (which is not too intuitive).

hmm = hmms.DtHMM.random(2,5)
states, seq = hmm.generate(1000);
hmm.maximum_likelihood_estimation(numpy.ndarray(states), numpy.ndarray(seq))

The second problem appears when the sequence is too long.

ValueError: sequence too large; cannot be greater than 32

[lopatovsky]

Hello,
for the input of the Baum-Welch algorithm functions and MLE function can be for now only two dimensional numpy array or list of one dimensional numpy arrays.

So you can use it like this:

 hmm = hmms.DtHMM.random(2,5)
 states, seq = hmm.generate( 1000 );
 hmm.maximum_likelihood_estimation( [states], [seq] )

or use the function generate_data directly:

 hmm = hmms.DtHMM.random(2,5)
 states, seq = hmm.generate_data( ( 1,1000 ) );
 hmm.maximum_likelihood_estimation(states, seq)

We will consider supporting one dimensional arrays later in development.
Thank you for your feedback!

best,

Lukas