other = Gaussian(p*0.2, mtp*0.3) other2 = Gaussian(p*0.5, mtp*0.5) self.update(other, other2) self[1].update(other[1], other2[1]) self *= other p = torch.ones((3, )) * 4. mtp = torch.ones((3, )) * 5. self[0, :] = Gaussian(p, mtp) self.entropy() self.negative_entropy() Gaussian.point_mass(p) self.cuda() self.split((1, 2), 1) Gaussian.cat([self, other, other2], 0) import torch from NNVI.vmp.bernoulli import Bernoulli self = Bernoulli(torch.rand((3, 2))).cuda() self.entropy() self.is_point_mass self.is_uniform
def prior(cls, child: Gaussian, mean: float, variance: torch.Tensor):
shape = child.shape
parent = Gaussian.point_mass(torch.full(shape, mean))
obj = cls(parent, child, variance)
obj._prior = True
return obj
