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_simple_hamiltonian(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)
self.assertAlmostEqual(model.hamiltonian(np.array([0, 0])), 0)
h = np.array([-1, 1, -1])
j_matrix = np.array([[0, 1, 2], [1, 0, -1], [2, -1, 0]])
s = np.array([1, 1, 0])
a = np.dot(h, s) + .5 * np.dot(s, np.dot(j_matrix, s))
model = IsingModel(3)
model.import_ising01(h, j_matrix)
self.assertAlmostEqual(model.hamiltonian(s), -a)
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))
def test_rbm_hamiltonian(self):
nvis, nhid = 10, 5
vishid = np.random.normal(size=(nvis, nhid))
model = IsingModel(nvis + nhid)
model.import_rbm01(nvis, nhid, np.zeros(nvis), np.zeros(nhid), vishid)
state = np.random.choice([True, False], size=nvis + nhid)
vis, hid = state[:nvis], state[nvis:]
energy = -np.dot(vis, np.dot(hid, vishid.T))
self.assertAlmostEqual(model.hamiltonian(state), energy)
def test_submodel(self):
j = np.random.normal(0, 1, size=(10, 10))
j += j.T
j /= 2.
j[np.diag_indices_from(j)] = np.zeros(10)
h = np.random.random(size=10)
model = IsingModel(10)
model.import_ising01(h, j)
subm = model.submodel(5)
inp = [True, True, True, True, True, False, False, False, False, False]
h10 = model.hamiltonian(inp)
h5 = subm.hamiltonian(inp[:5])
self.assertAlmostEqual(h5, h10)
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)
