def resample(self):
# 1. Resample table assignments
for i, (w, p, s) in enumerate(self.analyses):
self.decrement(w, p, s)
w, p, s = self.increment(w)
self.analyses[i] = (w, p, s)
# 2. Resample table dishes
new_analyses = []
new_tables = {}
new_ncustomers = {}
for (w, old_p, old_s), tables in self.tables.iteritems():
for c in tables:
self.p_counts[old_p] -= 1
self.s_counts[old_s] -= 1
p, s = mult_sample(((p, s), self.base.prob(p, s))
for p, s in self.seg_mappings[w])
self.p_counts[p] += 1
self.s_counts[s] += 1
if (w, p, s) not in new_tables:
new_tables[w, p, s] = []
new_ncustomers[w, p, s] = 0
new_tables[w, p, s].append(c)
new_ncustomers[w, p, s] += c
new_analyses.extend([(w, p, s)] * c)
self.analyses = new_analyses
self.tables = new_tables
self.ncustomers = new_ncustomers
def increment(self, word, initialize=False):
if initialize:
p, s = random.choice([a for a, _ in self._analysis_probs(word)])
else:
p, s = mult_sample(self._analysis_probs(word))
self.prefix_model.increment(p)
self.suffix_model.increment(s)
self.analyses[word].append((p, s))
def increment(self, w, initialize=False):
"""Sample a segmentation and a table assignment for word #w"""
# sample a table
(p, s, seat) = mult_sample(self.seating_probs(w, initialize))
# seat to the table
if self._seat_to((w, p, s), seat):
self.base.increment(p, s)
# increment dish count
self.analyses[w][p, s] += 1
def sample_topics(doc, model, n_iter):
assignments = [None] * len(doc)
doc_topic = DirichletMultinomial(model.n_topics, model.alpha)
for it in xrange(n_iter):
for i, word in enumerate(doc):
if it > 0: doc_topic.decrement(assignments[i])
assignments[i] = mult_sample((k, (doc_topic.prob(k)
* model.topic_word[k].prob(word))) for k in xrange(model.n_topics))
doc_topic.increment(assignments[i])
return topic_vector(doc_topic, model)
def increment(self, k):
# Sample analysis & store assignment
i = (0 if len(self.analyses[k]) == 1 else
mult_sample((i, self.analysis_prob(analysis))
for i, analysis in enumerate(self.analyses[k])))
self.assignments[k].append(i)
analysis = self.analyses[k][i]
# Increment models
self.stem_model.increment(analysis.stem)
self.pattern_model.increment(analysis.pattern)
def resample_labels(self):
new_analyses = [Counter() for _ in xrange(len(self.word_vocabulary))]
new_tables = {}
new_ncustomers = {}
for (w, old_p, old_s), tables in self.tables.iteritems():
for c in tables:
# remove (old_p, old_s)
self.base.decrement(old_p, old_s)
# resample
(p, s) = mult_sample(((p, s), self.base.prob(p, s))
for p, s in self.segmentations(w))
# add (p, s)
if (w, p, s) not in new_tables:
new_tables[w, p, s] = []
new_ncustomers[w, p, s] = 0
new_tables[w, p, s].append(c)
new_ncustomers[w, p, s] += c
new_analyses[w][p, s] += c
self.base.increment(p, s)
self.analyses = new_analyses
self.tables = new_tables
self.ncustomers = new_ncustomers
def increment(self, w, initialize=False):
(w, p, s, seat) = mult_sample(self.seating_probs(w, initialize))
if self._seat_to((w, p, s), seat):
self.p_counts[p] += 1
self.s_counts[s] += 1
return (w, p, s)
