kzhai / pynpb Goto Github PK

Hi,

I'am experiencing your NP prior, in order to try it in some relational model.

When running ugs HDP over a NIPS 2012 text corpus, the log-likelihood decrease with iterations. Did you observe some convergence or a likelihood improvement in your experiments ?

Output that I get:

sampling in progress 10%
total number of topics 3, log-likelihood is -608665.500427
sampling in progress 20%
total number of topics 4, log-likelihood is -611818.654787
sampling in progress 30%
total number of topics 4, log-likelihood is -618855.827824
sampling in progress 40%
total number of topics 4, log-likelihood is -621922.608209
sampling in progress 50%
total number of topics 5, log-likelihood is -627783.435882
sampling in progress 60%
total number of topics 5, log-likelihood is -631405.448569

Is there an implementation of collapsed gibbs sampling in hierarchical dirichlet process?

The parameters of the HDP variationnal inference, self._alpha, self._gamma and other are not defined or initialized or I am missing something here ?

File "vb.py", line 218, in velb
log_likelihood[0] = self._K * numpy.log(self._alpha / self._K) + (self._alpha / self._K - 1.) * numpy.sum(psi_tau[0, :] - psi_sum_tau)
AttributeError: 'VariationalBayes' object has no attribute '_alpha'