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

MVPA toolbox did not account for the probability of underflow problem #5

Open
GoogleCodeExporter opened this issue Sep 28, 2015 · 0 comments

Comments

@GoogleCodeExporter
Copy link

What steps will reproduce the problem?
1. subj = load_afni_mask(subj,'wholebrain','wholebrain+orig');

2. class_args.train_funct_name = 'train_gnb';
   class_args.test_funct_name = 'test_gnb';
   class_args.nHidden = 0;
3.

What is the expected output? What do you see instead?
The output I see is: Perf = [ 0.4793 0.4793 0.4793 0.4793     0.4793     0.4793 
0.4793 0.4793 0.4793 0.4793]
Perf_total = 0.4793 and I noticed the value of "acts" is all
NaN, please see the attachment!


What version of the product are you using? On what operating system?
Matlab R2014a.  Windows 7 (64bit)

Please provide any additional information below.
I just simply modify the "tutorial_easy.m" as follows
1) I change the mask to the intra-cranial"wholebrain" mask.
2) I change the Backprop Classifier to Gaussian Naive Bayes Classifier.
Then I run the code and find that MVPA toolbox did not account for the 
possibility of underflow.
I paste part of the result here. Please take a look at it!

Original issue reported on code.google.com by [email protected] on 29 Jan 2015 at 10:13

Attachments:

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

No branches or pull requests

1 participant