def E(self, x):
return batch_mvn.E(x, self.mu, self.inv_L, self.diag)
def E(self, x):
y = -x.clone()
y[:, 1] = x[:, 1] + 0.5 * x[:, 0]**2 + 1
return batch_mvn.E(y, self.mu, self.inv_L, self.diag)
def E(self, x):
return batch_mvn.E(x, 0, self._sigma, True)
def E(self, x):
vec = torch.zeros((x.size(0), self.K), device=self.L.device)
for k in range(self.K):
vec[:, k] = -batch_mvn.E(x, self.mu[k].unsqueeze(0), self.inv_L[k])
return -torch.logsumexp(vec, dim=1)