def clip_mvnormal_proposal(old_point):
# batches of old points, possibly with weird shapes
samp = MultivariateNormal(old_point,
0.3 * torch.eye(old_point.shape[-1])).sample()
samp.clamp_min_(0.0)
samp.clamp_max_(1.0)
return samp
def forward(self, x):
samp = MultivariateNormal(x, self.sigma *
torch.eye(x.shape[-1])).sample()
samp.clamp_min_(self.min_val)
samp.clamp_max_(self.max_val)
return samp
