Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Initialization of normals in HMM model #76

Closed
lonerzzz opened this issue Feb 4, 2017 · 1 comment
Closed

Initialization of normals in HMM model #76

lonerzzz opened this issue Feb 4, 2017 · 1 comment

Comments

@lonerzzz
Copy link

lonerzzz commented Feb 4, 2017

Hello,

Thanks for the work on this software. I am eager to get to know it in depth.

I am not sure if I am missing something but I cannot seem to find a way to set the initial normal values for an HMM. I am trying the following:

`

			IOHMM.initializeLearning();

			list = IOHMM.getModel().getConditionalDistributionsTimeT();

			np = (Normal_MultinomialParents)list.get( 0 );

			normalList = np.getNormalDistributions();

			Normal normal = normalList.get( 0 );

			normal.setMean( 0 );

			normal.setVariance( 32 );

`

However, in tracing the code, the extracted values are all newly created objects so their values cannot be set and then used. Is there a way to initialize these Normal values? I am trying to do so because HMMs are notoriously sensitive to local minima and I have an idea of some of the values of certain variables in certain situations.

If this is not currently possible, then please consider this an enhancement request.

@andresmasegosa
Copy link
Contributor

andresmasegosa commented Feb 7, 2017

Hi,

Thanks for your interest in the toolbox.

The feature you are looking for is not current available. Initial parameters are randomly initialized. The only thing you can do is to change the seed of the random initializer. I'll open a new issue with this enhancement.

#77

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants