def test_infer_isolated():
gr_size = 1000
model = PairWiseFiniteModel(gr_size, 2)
model.set_field(np.array([[0, 1]] * gr_size))
res = model.infer(algorithm='bruteforce')
assert np.allclose(res.log_pf, gr_size * np.log(1 + np.exp(1)))
assert np.allclose(res.marg_prob - softmax([0, 1]), 0)
def tree_potts_model(gr_size=5,
al_size=2,
seed=111,
same_j=None,
zero_field=False) -> PairWiseFiniteModel:
"""Generates random PairWiseFinteModel on a random tree.
:param gr_size: Size of the graph (number of variables).
:param al_size: Alphabet size.
:param seed: Random set.
:param same_j: If set, interaction matrix for all edges.
:param zero_field: Whether model should be zero-field.
:return: Generated Potts Model.
"""
np.random.seed(seed)
tree = networkx.generators.trees.random_tree(gr_size, seed=seed)
model = PairWiseFiniteModel(gr_size, al_size)
if not zero_field:
model.set_field(-3.0 + 6.0 * np.random.random((gr_size, al_size)))
for v1, v2 in tree.edges:
J = np.random.random((al_size, al_size)) * 5.0
if same_j is not None:
J = same_j
model.add_interaction(v1, v2, J)
return model
def test_max_likelihood():
model = PairWiseFiniteModel(3, 2)
model.set_field(np.array([[0.4, 0.6], [0.4, 0.6], [0.4, 0.6]]))
model.add_interaction(0, 1, np.array([[0, 10], [10, 0]]))
model.add_interaction(1, 2, np.array([[0, 10], [10, 0]]))
state = model.max_likelihood(algorithm='bruteforce')
assert np.allclose(state, np.array([1, 0, 1]))
def test_infer_1_variable():
al_size = 10
probs = _stochastic_vector(al_size)
model = PairWiseFiniteModel(1, al_size)
model.set_field(np.log(probs).reshape(1, al_size))
result = infer_bruteforce(model)
assert np.allclose(result.log_pf, 0)
assert np.allclose(result.marg_prob, probs)
def clique_potts_model(gr_size=5, al_size=2, seed=0) -> PairWiseFiniteModel:
"""Generates random PairWiseFinteModel on a clique."""
np.random.seed(seed)
model = PairWiseFiniteModel(gr_size, al_size)
model.set_field(-3.0 + 6.0 * np.random.random((gr_size, al_size)))
for i in range(gr_size):
for j in range(i + 1, gr_size):
inter = np.random.random((al_size, al_size)) * 5.0
model.add_interaction(i, j, inter)
return model
def test_isolated_exact():
np.random.seed(0)
gr_size = 1000
al_size = 5
model = PairWiseFiniteModel(gr_size, al_size)
model.set_field(np.random.random((gr_size, al_size)))
gt = model.infer(algorithm='bruteforce')
result = model.infer(algorithm='mean_field')
assert_results_close(result, gt)
def test_sample_bruteforce():
gr_size, num_samples = 3, 50
model = PairWiseFiniteModel(gr_size, 2)
model.set_field(np.array([[0, 20]] * gr_size))
samples = sample_bruteforce(model, num_samples=num_samples)
assert np.allclose(samples, np.ones((num_samples, gr_size)))
def test_max_likelihood_isolated():
gr_size = 1000
model = PairWiseFiniteModel(gr_size, 2)
model.set_field(np.array([[0, 1]] * gr_size))
result = model.max_likelihood(algorithm='bruteforce')
assert np.allclose(result, np.ones(gr_size))