Closed roger-mahler closed 3 years ago
Hi! I need to check this in the paper myself to be sure. Currently, I'm quite busy with paper submission, but later this week I'll check this!
Thanks for the heads up Roger! Will check it out when time permits!
On Tue, Jun 2, 2020 at 8:43 AM Måns Magnusson notifications@github.com wrote:
Hi! I need to check this in the paper myself to be sure. Currently, I'm quite busy with paper submission, but later this week I'll check this!
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Yes. It looks like a bug in the code. We should supply p_t() instead of p_w() to the function. I guess this is a real bug if the documentation is correct. But it depends what is supplied to the function if we supply p_t to p_w it would still maybe be correct.
Anyway the notation should be fixed/correct. Also we should check that the function uses the correct p_t().
Again, many thanks @roger-mahler for the heads up. I have now corrected this in commit https://github.com/lejon/PartiallyCollapsedLDA/commit/fd2b1aff9720a4b2218cc2c748132f0ae6f5811f
Cheers!
I'm in the process of implementing a Python-wrapper for PC-LDA to be used in the Welfare state analytics research project. I have a question concerning the distinctiveness calculation here.
The formula according to [Chung et. a.l 2012] is
Σ p(t|w) ∙ log(p(t|w) / p(t))
, whilst the code implements the formulaΣ p(t|w) ∙ log(p(t|w) / p(w))
i.e. word probability is used instead of topic probability. Is this a bug, or am I missing something?@MansMeg