Calculation of <prob_topics> in <Latent Dirichlet Allocation.ipynb> about data-science-and-machine-learning-from-scratch HOT 4 OPEN

@DaveRockt Thanks for raising the issue, I just noticed this. Could you be a bit more specific and compare the formula I have with the one in wikipedia?

I will review my LDA notebook in the meantime and see if I can spot a mistake.

from data-science-and-machine-learning-from-scratch.

Thank you for your answer and sorry for not being specific:

In the formula on Wikipedia, I cannot find what you call 'denom1'. In the meantime, however, I found out that after normalising, I get the same result as you.

However, I have another question:

In your code in the 'Main part of LDA algorithm' under 'Add in current word back into count matrixes', you use 'init_topic_assign'. Does this make sense? Shouldn't you use the new assigned topic?

Thank you for this project by the way, it really helped me to understand LDA better.

Best,
David

from data-science-and-machine-learning-from-scratch.

Hi David,

Sorry for the late reply, I skimmed over the code and you may be right. I think one way to check would be to use a python library and run LDA on the same data set and see whether the results match. Could you work on doing that?

If there is indeed a mistake in the code, feel free to fix it and do a pull request.

from data-science-and-machine-learning-from-scratch.

from data-science-and-machine-learning-from-scratch.