def sample_partitions(B, N, K, alpha=1.0, rand_K=True, device='cpu'):
pi = Dirichlet(alpha * torch.ones(K)).sample([B]).to(device)
if rand_K:
to_use = (torch.rand(B, K) < 0.5).float().to(device)
to_use[..., 0] = 1
pi = pi * to_use
pi = pi / pi.sum(1, keepdim=True)
labels = Categorical(probs=pi).sample([N]).to(device)
labels = labels.transpose(0, 1).contiguous()
return labels
def sample_labels(B, N, K_low, K_high, alpha=1.0):
pi = Dirichlet(alpha * torch.ones(K_high)).sample([B])
K = torch.randint(K_low, K_high + 1, size=(B, ))
to_use = torch.zeros(B, K_high).int()
for i, k in enumerate(K):
to_use[i, :k] = 1
pi = pi * to_use
pi = pi / pi.sum(1, keepdim=True)
labels = Categorical(probs=pi).sample([N])
labels = labels.transpose(0, 1).contiguous()
return labels
