def sample_fn(): for _ in xrange(skip): for bs in bounded_states: kernel(bs, r) key = tuple(tuple(permutation_canonical(bs.assignments())) for bs in bounded_states) return idmap[key]
def sample_fn(): r.run(r=prng, niters=10) new_latent = r.get_latent() key = tuple( tuple(permutation_canonical(new_latent.assignments(i))) for i in xrange(len(domains))) return idmap[key]
def sample_iter(): r.run(r=prng, niters=10) for latent in r.get_latents(): key = tuple( tuple(permutation_canonical(latent.assignments(i))) for i in xrange(len(domains))) yield idmap[key]
def sample_fn(): for _ in xrange(skip): for bs in bounded_states: kernel(bs, r) key = tuple( tuple(permutation_canonical(bs.assignments())) for bs in bounded_states) return idmap[key]
def sample_fn(): r.run(r=prng, niters=10) new_latent = r.get_latent() key = tuple(tuple(permutation_canonical(new_latent.assignments(i))) for i in xrange(len(domains))) return idmap[key]
def sample_iter(): r.run(r=prng, niters=10) for latent in r.get_latents(): key = tuple(tuple(permutation_canonical(latent.assignments(i))) for i in xrange(len(domains))) yield idmap[key]
def sample_fn(): for _ in xrange(skip): kernel(bs) return idmap[tuple(permutation_canonical(bs.assignments()))]
def sample_fn(): for _ in xrange(skip): kernel(bs) return idmap[tuple(permutation_canonical(bs.assignments()))]
def sample_fn(): r.run(r=prng, niters=10) new_latent = r.get_latent() return idmap[tuple(permutation_canonical(new_latent.assignments()))]
def sample_iter(): r.run(r=prng, niters=10) for latent in r.get_latents(): yield idmap[tuple(permutation_canonical(latent.assignments()))]
def sample_fn(): sample = permutation_canonical(_sample_crp(N, alpha)) return idmap[tuple(sample)]
def sample_fn(): sample = permutation_canonical(_sample_crp(N, alpha)) return idmap[tuple(sample)]