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)]
