def energy(self,data):
# TODO TODO this function is horrible
assert data.ndim == 1
if np.isnan(data).any():
return 0.
from util.stats import sample_discrete
likes = np.array([c.log_likelihood(data) for c in self.components]).reshape((-1,))
likes += np.log(self.weights.weights)
label = sample_discrete(np.exp(likes - likes.max()))
return self.components[label].energy(data)
def energy(self,data):
# TODO TODO this function is horrible
assert data.ndim == 1
if np.isnan(data).any():
return 0.
from util.stats import sample_discrete
likes = np.array([c.log_likelihood(data) for c in self.components]).reshape((-1,))
likes += np.log(self.weights.weights)
label = sample_discrete(np.exp(likes - likes.max()))
return self.components[label].energy(data)
def rvs(self,customer_counts):
'''
Number of distinct tables. Not complete CRPs. customer_counts is a list
of customer counts, and len(customer_counts) is the number of
restaurants.
'''
assert isinstance(customer_counts,list) or isinstance(customer_counts,int)
if isinstance(customer_counts,int):
customer_counts = [customer_counts]
restaurants = []
for num in customer_counts:
tables = []
for c in range(num):
newidx = sample_discrete(np.array(tables + [self.concentration]))
if newidx == len(tables):
tables += [1]
else:
tables[newidx] += 1
restaurants.append(tables)
return restaurants if len(restaurants) > 1 else restaurants[0]
def _generate(self,N):
# run a CRP forwards
alpha_0 = self.alpha_0
self.z = np.zeros(N,dtype=np.int32)
for n in range(N):
self.z[n] = sample_discrete(np.concatenate((np.bincount(self.z[:n]),(alpha_0,))))
def rvs(self,size=None):
return sample_discrete(self.weights,size)
def sample_next(self):
next_table = sample_discrete(self._get_distr())
self.assignments.append(next_table)
return next_table
def sample_next(self,**kwargs):
label = sample_discrete(self.counts)
self.counts[label] += 1
return self.components[label].sample_next(**self.arggetters[label](kwargs))
def sample_next(self, taboo):
next_table = sample_discrete(self._get_distr(taboo))
self.assignments.append(next_table)
return next_table
def sample_next(self, **kwargs):
label = sample_discrete(self.counts)
self.counts[label] += 1
return self.components[label].sample_next(
**self.arggetters[label](kwargs))
