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View Code? Open in Web Editor NEWGibbs sampler for for a Naive Bayes document classifier
Gibbs sampler for for a Naive Bayes document classifier
Hi,wpm, Thanks for your great code, it helps a lot.
I have some confusion about the line 143 and 146,
num = word_counts[category_index].sum() + \ self.hyp_pi[category_index] - 1.0
According to the paper,(formula 46) ,it should be:
num = category_counts[category_index].sum() + \ self.hyp_pi[category_index] - 1.0
Maybe I have misunderstood it.
I have just modified number of categories, vocabulary size and number of documents in main as
categories = 3 # number of categories
vocabulary = 5 # vocabulary size
documents = 7 # dataset size
and this code break down, i think estimated_labels = zeros(self._categories(), int)
in estimate_labels
may be estimated_labels = zeros(self._documents(), int)
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