def test_cycle(self):
xy_factor = Factor(
[self.random_variables[0], self.random_variables[1]])
xz_factor = Factor(
[self.random_variables[0], self.random_variables[2]])
yz_factor = Factor(
[self.random_variables[1], self.random_variables[2]])
graphical_model = GraphicalModel()
graphical_model.add_factor(xy_factor).add_factor(xz_factor).add_factor(
yz_factor)
marginal_inference = MpMarginalInference(graphical_model)
marginal_inference.get_marginal_factor([self.random_variables[0]], {})
def test_add_factors(self):
graphical_model = GraphicalModel()
factor1 = Factor([RandomVariable('x', (1, 2))])
factor2 = Factor(
[RandomVariable('y', (2, 3, 4)),
RandomVariable('z', (2, ))])
factor3 = Factor(
[RandomVariable('x', (1, 2)),
RandomVariable('z', (2, ))])
graphical_model.add_factor(factor1).add_factor(factor2).add_factor(
factor3)
print(graphical_model.random_variables)
def _generate_two_variables_model(self):
x_factor = Factor([self.random_variables[0]])
x_factor.add_value([1], 1).add_value([2], 2)
y_factor = Factor([self.random_variables[1]])
y_factor.add_value([3], 5).add_value([4], 10).add_value([5], 15)
xy_factor = Factor(
[self.random_variables[0], self.random_variables[1]])
xy_factor.add_value([1, 3], 10).add_value([1, 4], 10).add_value([2, 5],
20)
graphical_model = GraphicalModel()
graphical_model.add_factor(x_factor).add_factor(y_factor).add_factor(
xy_factor)
return graphical_model
def generate_complete(nb_vars, delta):
model = GraphicalModel()
interaction = delta * np.random.uniform(
-1.0, 1.0, nb_vars*(nb_vars-1))
bias = np.random.uniform(-0.1, 0.1, [nb_vars])
for i in range(nb_vars):
model.add_variable(ith_object_name('V', i))
for i in range(nb_vars):
for j in range(i+1, nb_vars):
beta = interaction[i * nb_vars + j ]
log_values = np.array([beta, -beta, -beta, beta]).reshape([2,2])
factor = Factor(
name = ijth_object_name('F', i, j),
variables = [ith_object_name('V', i), ith_object_name('V', j)],
log_values = log_values)
model.add_factor(factor)
for i in range(nb_vars):
factor = Factor(
name = ith_object_name('B', i),
variables = [ith_object_name('V', i)],
log_values = np.array([bias[i], -bias[i]]))
model.add_factor(factor)
return model
def _generate_three_variables_model(self):
xy_factor = Factor(
[self.random_variables[0], self.random_variables[1]])
xy_factor.add_value([1, 3], 5).add_value([1, 4], 20).add_value([2, 5],
30)
xz_factor = Factor(
[self.random_variables[0], self.random_variables[2]])
xz_factor.add_value([1, 2], 10).add_value([2, 2], 20)
yz_factor = Factor(
[self.random_variables[1], self.random_variables[2]])
yz_factor.add_value([3, 2], 10).add_value([4, 2], 20).add_value([5, 2],
30)
graphical_model = GraphicalModel()
graphical_model.add_factor(xy_factor).add_factor(xz_factor).add_factor(
yz_factor)
return graphical_model
def test_two_variables(self):
x_factor = Factor([self.random_variables[0]])
x_factor.add_value([1], 1).add_value([2], 2)
y_factor = Factor([self.random_variables[1]])
y_factor.add_value([3], 5).add_value([4], 10).add_value([5], 15)
xy_factor = Factor(
[self.random_variables[0], self.random_variables[1]])
xy_factor.add_value([1, 3], 10).add_value([1, 4], 10).add_value([2, 5],
20)
graphical_model = GraphicalModel()
graphical_model.add_factor(x_factor).add_factor(y_factor).add_factor(
xy_factor)
marginal_inference = MpMarginalInference(graphical_model)
marginal_factor = marginal_inference.get_marginal_factor(
self.random_variables[0])
self.assertAlmostEqual(0.2, marginal_factor.get_value([1]))
self.assertAlmostEqual(0.8, marginal_factor.get_value([2]))
def test_marginal_y_three_variables(self):
x_factor = Factor([self.random_variables[0]])
x_factor.add_value([1], 1).add_value([2], 2)
xy_factor = Factor(
[self.random_variables[0], self.random_variables[1]])
xy_factor.add_value([1, 3], 5).add_value([1, 4], 20).add_value([2, 5],
30)
xz_factor = Factor(
[self.random_variables[0], self.random_variables[2]])
xz_factor.add_value([1, 2], 12).add_value([2, 2], 20)
graphical_model = GraphicalModel()
graphical_model.add_factor(x_factor).add_factor(xy_factor).add_factor(
xz_factor)
marginal_inference = MpMarginalInference(graphical_model)
marginal_factor = marginal_inference.get_marginal_factor(
self.random_variables[1])
self.assertAlmostEqual(0.04, marginal_factor.get_value([3]))
self.assertAlmostEqual(0.16, marginal_factor.get_value([4]))
self.assertAlmostEqual(0.8, marginal_factor.get_value([5]))
def generate_uai(model_name):
model = GraphicalModel()
model.name=model_name
with open(UAI_PATH+model_name+'.uai') as f:
a = f.readlines()
content = []
for c in a:
content.extend(c.split())
cnt = 1
nb_vars = int(content[cnt])
cardinalities = dict()
for t in range(nb_vars):
cnt += 1
newvar = 'V' + str(t)
model.add_variable(newvar)
cardinalities[newvar] = int(content[cnt])
cnt += 1
nfactors = int(content[cnt])
factor_variables = dict()
for t in range(nfactors):
cnt += 1
newfac_name = 'F' + str(t)
factor_size = int(content[cnt])
factor_variables[newfac_name] = []
for t2 in range(factor_size):
cnt += 1
factor_variables[newfac_name].append('V' + str(content[cnt]))
for t in range(nfactors):
cnt += 1
value_num = int(content[cnt])
newfac_name = 'F' + str(t)
values = []
for vt2 in range(value_num):
cnt += 1
values.append(float(content[cnt]))
values = np.reshape(
values,
[cardinalities[var] for var in factor_variables[newfac_name]])
factor = Factor(
name = newfac_name,
variables = factor_variables[newfac_name],
values = values)
model.add_factor(factor)
return model
def generate_grid(nb_vars, delta):
model = GraphicalModel()
grid_size = int(nb_vars**0.5)
interaction = delta * np.random.uniform(-1.0, 1.0, 2*grid_size*(grid_size-1))
bias = np.random.uniform(-0.1, 0.1, [grid_size,grid_size])
for i in range(grid_size):
for j in range(grid_size):
model.add_variable(ijth_object_name('V', i,j))
edge_set = []
for x in range(grid_size*grid_size):
q, m = divmod(x, grid_size)
if m != grid_size-1:
edge_set.append([x,x+1])
if q != grid_size-1:
edge_set.append([x,x+grid_size])
for i, e in enumerate(edge_set):
beta = interaction[i]
q1, m1 = divmod(e[0], grid_size)
V1 = ijth_object_name('V', q1,m1)
q2, m2 = divmod(e[1],grid_size)
V2 = ijth_object_name('V', q2,m2)
log_values = np.array([beta, -beta, -beta, beta]).reshape([2,2])
factor = Factor(name = ith_object_name('F', i),
variables = [V1, V2],
log_values = log_values)
model.add_factor(factor)
for i in range(grid_size):
for j in range(grid_size):
log_values = np.array([bias[i,j], -bias[i,j]])
model.add_factor(Factor(name = ith_object_name('B', i*grid_size + j),
variables = [ijth_object_name('V', i,j)],
log_values = log_values))
return model