def infer(self, algorithm='auto', **kwargs):
"""Calculates partition function.
Available algorithms
* ``auto`` - Automatic.
* ``edge_elimination`` - Edge elimination.
:param algorithm: Which algorithm to use. String.
:return: Partition function.
"""
if algorithm == 'auto':
return infer_edge_elimination(self)
elif algorithm == 'edge_elimination':
return infer_edge_elimination(self)
else:
raise ValueError('Unknown algorithm %s' % algorithm)
def test_infer_compare_with_pairwise_tree():
pw_model = tree_potts_model(gr_size=50, al_size=5, seed=0)
true_pf = np.exp(pw_model.infer(algorithm='tree_dp').log_pf)
nfg_model = inferlo.NormalFactorGraphModel.from_model(pw_model)
pf = infer_edge_elimination(nfg_model)
assert np.allclose(true_pf, pf)
def test_infer_compare_with_pairwise_grid_5x5():
pw_model = grid_potts_model(5, 5, al_size=2, seed=0)
true_pf = np.exp(pw_model.infer(algorithm='path_dp').log_pf)
nfg_model = inferlo.NormalFactorGraphModel.from_model(pw_model)
pf = infer_edge_elimination(nfg_model)
assert np.allclose(true_pf, pf)
def test_infer_disconnected():
pw_model = PairWiseFiniteModel(5, al_size=2)
pw_model.set_field(np.random.random(size=(5, 2)))
pw_model.add_interaction(2, 3, np.random.random(size=(2, 2)))
true_pf = np.exp(pw_model.infer(algorithm='bruteforce').log_pf)
nfg_model = inferlo.NormalFactorGraphModel.from_model(pw_model)
pf = infer_edge_elimination(nfg_model)
assert np.allclose(true_pf, pf)
def test_ifer_clique_3vars():
al_size = 5
np.random.seed(10)
domain = inferlo.DiscreteDomain.range(al_size)
model = inferlo.NormalFactorGraphModel(3, domain)
f01 = np.random.random(size=(al_size, al_size))
f12 = np.random.random(size=(al_size, al_size))
f20 = np.random.random(size=(al_size, al_size))
model *= inferlo.DiscreteFactor(model, [0, 1], f01)
model *= inferlo.DiscreteFactor(model, [1, 2], f12)
model *= inferlo.DiscreteFactor(model, [2, 0], f20)
model.build()
z = infer_edge_elimination(model)
assert np.allclose(z, model.part_func_bruteforce())
def test_infer_small_line():
np.random.seed(10)
domain = inferlo.DiscreteDomain.range(5)
model = inferlo.NormalFactorGraphModel(3, domain)
f0 = np.random.random(size=(5,))
f01 = np.random.random(size=(5, 5))
f12 = np.random.random(size=(5, 5))
f2 = np.random.random(size=(5,))
model *= inferlo.DiscreteFactor(model, [0], f0)
model *= inferlo.DiscreteFactor(model, [0, 1], f01)
model *= inferlo.DiscreteFactor(model, [1, 2], f12)
model *= inferlo.DiscreteFactor(model, [2], f2)
model.build()
z = infer_edge_elimination(model)
assert np.allclose(z, f0 @ f01 @ f12 @ f2)