What steps will reproduce the problem?
It seems there is no way to control the iteration of the Baum-Welch learner to
compute the likelihood of the current parameters as the algorithm iterates.
This is aggravated by the fact that there is there is no straightforward way to
write the likelihood function in a subclass because the relevant parameters are
buried deep inside the iterate function.
What is the expected output? What do you see instead?
Most learning algorithms allow the likelihood to be computed during each
iteration. This allows local convergence to be perfectly checked during EM, as
well as verify correctness because it should increase monotonically. The KL
measurement thingy is only a crude approximation to this.
What version of the product are you using? On what operating system?
jahmm 0.6.2 on Ubuntu 12.04
Please provide any additional information below.
I wish you guys would host this project on GitHub instead of Google code. Then
it would be easier for people like me to fork the project, implement things
like this, and then send a pull request with updates.
Original issue reported on code.google.com by miz...@gmail.com on 16 Sep 2013 at 2:16
Original issue reported on code.google.com by
miz...@gmail.comon 16 Sep 2013 at 2:16