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The Hierarchical Dirichlet Process model in Gensim uses a clever trick to efficiently normalize some variational parameters. It does this using a function called log_normalize, see line 49 and line 342 in the HDP code.
This could also be used in ldamodel.py, to make the normaization faster.
The text was updated successfully, but these errors were encountered:
On a related note, it would be nice if the log_normalize function was able to take a 2D array, and normalize along a single axis. At the moment it does support normalizing along both axes in a 2D array.
I just discovered something you might find useful. From the usage of log_normalize in the HDP code, it seemed to me that if you want to normalize a vector v, you pass it to the function and obtain a normalized log v in return. That is not the case, you need to pass log v to the function, and then return a normalized log v.
This has caused me a lot of pain and confusion lately, so I though I'd warn you :)
The time taken for the ldamodel tests to run worsens for the small file testcorpus.mm.Maybe log normalization will perform better for a larger corpus.I will test it out and post results soon.
The Hierarchical Dirichlet Process model in Gensim uses a clever trick to efficiently normalize some variational parameters. It does this using a function called
log_normalize
, see line 49 and line 342 in the HDP code.This could also be used in
ldamodel.py
, to make the normaization faster.The text was updated successfully, but these errors were encountered: