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SDD memory error & d-DNNF example ignoring #118
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Hi Zarach Could you perhaps give is an example of this behaviour? Kind regards, |
Hi Robin, not that easy, because I run it in python, but I'll try. For the python run I also uploaded a txt-file with the list of examples, in python it is done with the Term() objects which you can't see in the txt file. examples_small.txt And another example file which should show the memory alloc error for sdd: Kind regards, |
Hi Benjamin It seems that you only give the training data, but there's no program attached. We'll need this as well to look into it in more depth. |
Hi Robin, program_small.txt is the program which should be executable in problog standalone mode. |
I have solved the issue with the inconsistent evidence error by setting the initial probabilities to t(0.1) for all words, and by using log-space calculations, i.e. by running it with I'll now look into the memory issue |
I have noticed a calloc when using SDDs. Have you tried using -k sddx ? |
Dear problog team,
I try to learn a Naive Bayes classifier for documents based on occuring words.
There are about 1000 examples and they should be classified (to be precise, the probability of the words, used at a class should be learned) like in your online example but to one of 4 classes.
If I run LFI with SDD a memory error occures.
If I run it with d-DNNF, it ignores nearly all of the given examples.
It runs into the following error because the calculated weight is very low, even at the beginning of the learning process:
I guess this is the wanted behavior, but could you explain in an abstract way, why examples get ignored from the beginning?
Does it mean, that there is not enough information (not enough words used) in these examples to learn parameters?
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