def test_mf_hamiltonian(self):
h = np.random.normal()
j = np.random.normal()
j_matrix = [[0, j], [j, 0]]
h_vector = [h, h]
full_model = IsingModel(2)
mf_model = IsingModel(2)
full_model.import_ising01(h_vector, j_matrix)
mf_model.import_uniform01(h, j)
state = np.random.choice([True, False], size=2)
self.assertAlmostEqual(full_model.hamiltonian(state),
mf_model.hamiltonian(state))
def test_diff_mf(self):
h = np.random.normal()
j = np.random.normal()
model = IsingModel(2)
model.import_uniform01(h, j)
spins = np.random.choice([True, False], size=2)
spin = np.random.choice([0, 1])
spins[spin] = True
pos_energy = model.hamiltonian(spins)
spins[spin] = False
neg_energy = model.hamiltonian(spins)
p = neg_energy - pos_energy
self.assertAlmostEqual(p, model.energydiff(spins, spin))
def sample_e_with_beta(beta):
model = IsingModel(numspin)
model.import_uniform01(beta * h, beta * j)
sampled_states = model.sample(n)
states = np.empty([n, numspin], dtype=bool)
energies = np.empty(n)
for i, state in enumerate(sampled_states):
states[i] = state
energies[i] = model.hamiltonian(state)
np.save(resdir + "states_" + "beta" + str(beta) + "_n" +
str(numspin) + ".npy", states)
np.save(resdir + "energies_" + "beta" + str(beta) + "_n" +
str(numspin) + ".npy", energies)
def test_physical(self):
n = 35
h = -0.5
j = 1 / n
model = IsingModel(n)
# disordered phase, should be .5 on average
beta = 1
model.import_uniform01(beta * h, beta * j)
sample = from_shaped_iter(model.sample(5000), bool, [5000, n])[200:]
self.assertAlmostEqual(np.mean(sample), .5, places=1)
# ordered phase, sol can be 0 or 1
beta = 16
model.import_uniform01(beta * h, beta * j)
sample = from_shaped_iter(model.sample(2000), bool, [2000, n])[200:]
self.assertAlmostEqual(abs(2 * np.mean(sample) - 1), 1, places=2)
from time import time
numspin = 300
n = 100
seed = np.random.choice(1000)
j0 = np.random.random()
h0 = np.random.normal()
j = np.ones((numspin, numspin)) * j0
h = np.ones(numspin) * h0
j[np.diag_indices_from(j)] = np.zeros(numspin)
model_full = IsingModel(numspin)
model_full.import_ising01(h, j)
np.random.seed(seed)
t = time()
for x in model_full.sample(n):
pass
print(time() - t)
model_mf = IsingModel(numspin)
model_mf.import_uniform01(h0, j0)
np.random.seed(seed)
t = time()
for x in model_mf.sample(n):
pass
print(time() - t)
