def sample(self, i):
s = self.data[i]
ss = self.dd[i]
probs = []
for t in range(self.K):
prior = math.log(self.counts[t])
ll = self.mvNormalLL(s, ss, 1, self.mu[t], self.precision[t], self.logdet[t])
probs.append(prior + ll)
if self.K < self.con.pruningfactor:
prior = self.con.logalpha
ll = self.integrated[i]
probs.append(prior + ll)
normed = logNormalize(probs)
return sampleIndex(normed)
def sample(self, i):
probs = []
for t in range(self.K):
prior = math.log(self.counts[t])
ll = self.posteriorPredictive(set([i]), self.data[i], self.dd[i], t)
indices = self.getIndices(t)
suffprior = np.zeros(len(indices))
for j, ind in enumerate(indices):
suffprior[j] = self.prior[i, ind]
avgprior = np.log(suffprior.sum() / len(indices))
probs.append(prior + avgprior + ll)
if self.K < self.con.pruningfactor:
prior = self.con.logalpha
ll = self.integrated[i]
probs.append(prior + ll)
normed = logNormalize(probs)
return sampleIndex(normed)