def _priors(self):
""" Measure likelihood of seeing topic proportions"""
loss = None
for categorical_feature_name in self.categorical_feature_names:
name = categorical_feature_name + "_mixture"
dl = dirichlet_likelihood(self[name].weights)
loss = dl if loss is None else dl + loss
return loss
def _priors(self, contexts):
""" Measure likelihood of seeing topic proportions"""
loss = None
for categorical_feature_name in self.categorical_feature_names:
name = categorical_feature_name + "_mixture"
dl = dirichlet_likelihood(self[name].weights)
loss = dl if loss is None else dl + loss
return loss
def _priors(self):
""" Measure likelihood of seeing topic proportions"""
loss = None
for cat_feat_name, vals in self.categorical_features.items():
embedding, transform, loss_func, penalty = vals
name = cat_feat_name + "_mixture"
dl = dirichlet_likelihood(self[name].weights)
if penalty:
factors = self[name].factors.W
cc = F.cross_covariance(factors, factors)
dl += cc
loss = dl if loss is None else dl + loss
return loss
def _priors(self):
""" Measure likelihood of seeing topic proportions"""
loss = None
for cat_feat_name, vals in self.categorical_features.items():
embedding, transform, loss_func, penalty = vals
name = cat_feat_name + "_mixture"
dl = dirichlet_likelihood(self[name].weights)
if penalty:
factors = self[name].factors.W
cc = F.cross_covariance(factors, factors)
dl += cc
loss = dl if loss is None else dl + loss
return loss
def prior(self):
# defaults to inialization with uniform prior (1/n_topics)
return DL.dirichlet_likelihood(self.mixture.Doc_Embedding,
alpha=self.alpha)
