def log_likelihood(self):
nzw, ndz, nz = self._num_zw, self._num_dz, self._num_z
alpha = self.alpha
beta = self.beta
nd = np.sum(ndz, axis=1).astype(np.intc)
# call c function via _lda.pyx
return _lda._loglikelihood(nzw, ndz, nz, nd, alpha, beta)
def loglikelihood(self):
"""Calculate complete log likelihood, log p(w,z)
Formula used is log p(w,z) = log p(w|z) + log p(z)
"""
nd = np.sum(self.ndz, axis=1).astype(np.intc)
return _lda._loglikelihood(self.nzw, self.ndz, self.nz, nd, self.alpha,
self.beta)
def loglikelihood(self):
"""Calculate complete log likelihood, log p(w,z)
Formula used is log p(w,z) = log p(w|z) + log p(z)
"""
nzw, ndz, nz = self.nzw_, self.ndz_, self.nz_
alpha = self.alpha
eta = self.eta
nd = np.sum(ndz, axis=1).astype(np.intc)
return _lda._loglikelihood(nzw, ndz, nz, nd, alpha, eta)
def loglikelihood(self):
"""Calculate complete log likelihood, log p(w,z)
Formula used is log p(w,z) = log p(w|z) + log p(z)
"""
nd = np.sum(self.ndz, axis=1).astype(np.intc)
return _lda._loglikelihood(self.nzw, self.ndz, self.nz, nd, self.alpha, self.beta)
def loglikelihood_inference(self):
""" Computes the log-likelihood using just the LDA part:
log p(w,z) = log p(w|z) + log p(z)
"""
return _lda._loglikelihood(self.nzw_, self.ndz_, self.nz_, self.nd_,
self.alpha, self.eta)