def test_diff_2d(self):
h = 0 # np.random.normal()
j = np.random.normal()
def get_energy(spins, h, j):
return -np.sum(j*spins*(
np.roll(spins, 1, axis=0) +
np.roll(spins, -1, axis=0) +
np.roll(spins, 1, axis=1) +
np.roll(spins, -1, axis=1))
) - h * np.sum(spins)
shape = (3, 3)
model = IsingModel(np.product(shape))
model.import_2d01(h, j, shape)
model.random_state()
# model.spins = np.repeat([True], np.product(shape))
for i in range(model.numspin):
print(model.spins[i])
state = np.copy(model.spins)
state[i] = True
e1 = get_energy(state.reshape(shape), h, j)
state[i] = False
e0 = get_energy(state.reshape(shape), h, j)
self.assertAlmostEqual(e0 - e1, model.energydiff(model.spins, i))
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 test_diff_full(self):
h = np.random.normal(size=2)
j = np.random.normal()
j_matrix = [[0, j], [j, 0]]
model = IsingModel(2)
model.import_ising01(h, j_matrix)
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))
