def hmm_sample(h: HyperParams, params, rng: random.Generator):
a0, a, b = map(partial(quantized_cdf_to_mass, h), params)
xs = []
for t in range(h.T):
z = rng.choice(h.latent_K, p=a[z] if t else a0)
xs.append(rng.choice(h.obs_K, p=b[z]))
return np.array(xs)
def iteration(self):
i = randint(self.Lx)
j = randint(self.Ly)
en_old = self.energy_local(i,j)
old_s = self.Spin[i,j]
self.Spin[i,j] = (old_s + Generator.choice([1,2]) + 1)%3 -1
en_new = self.energy_local(i,j)
deltaE = en_new - en_old
if deltaE > 0:
p = np.exp(-deltaE)
if random.rand() > p :
self.Spin[i,j] = old_s
