thank you for supporting our work.
I'm so sorry for that remaining mistake.
Du to changes, each time you call
you must pass data' (transposed) instead of simply data.
Make the change on lines 77, 292, 309, 413, 420, 495, 605 and 626.
I'm trying to work with the function learn_struct_bnpc and it always returns a fully independent bayes network. I used your 3 test_bnpc files provided with your code and some other data in your repository and none of them can manage to learn a structure.
I appreciate your help to find what went wrong.
there is a typo here.
The input 'app' is the same as 'data' and is the learning dataset where
data(n,l) is supposed to be the l-th observed value of the n-th node/variable.
Note that the dataset is supposed the be passed transposed (than usual for most user) in all SLP function (and BNT also).
The Tree Augmented Bayesian Network classifier is described in the literature, for instance in "Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets (O.C.H. François, P. Leray)in prooceddings of the third European Workshop on Probabilistic Graphical Models, PGM'06, Prague, Czech Republic, 2006"
Hi, could you please tell me more about the learn_struct_tan function, right now it just says: learn_struct_tan(app, class, root, node_sizes)
data(i,m) is the value of node i in case m
class_node is the class node
root is the root node of the tree part of the dag (must be different from the class node)
node_sizes = 1 if gaussian node,
scoring_fn = 'bic' (default value) or 'mutual_info'
So what is the data and the app, and why it says the node(i,m)?