class TestUndirectedGraphFactorOperations(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_add_factor_raises_error(self):
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles'),
('Charles', 'Debbie'), ('Debbie', 'Alice')])
factor = Factor(['Alice', 'Bob', 'John'], [2, 2, 2], np.random.rand(8))
self.assertRaises(ValueError, self.graph.add_factors, factor)
def test_add_single_factor(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi = Factor(['a', 'b'], [2, 2], range(4))
self.graph.add_factors(phi)
self.assertListEqual(self.graph.get_factors(), [phi])
def test_add_multiple_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = Factor(['a', 'b'], [2, 2], range(4))
phi2 = Factor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.assertListEqual(self.graph.get_factors(), [phi1, phi2])
def test_remove_single_factor(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = Factor(['a', 'b'], [2, 2], range(4))
phi2 = Factor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1)
self.assertListEqual(self.graph.get_factors(), [phi2])
def test_remove_multiple_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = Factor(['a', 'b'], [2, 2], range(4))
phi2 = Factor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1, phi2)
self.assertListEqual(self.graph.get_factors(), [])
def test_partition_function(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = Factor(['a', 'b'], [2, 2], range(4))
phi2 = Factor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertEqual(self.graph.get_partition_function(), 22.0)
def test_partition_function_raises_error(self):
self.graph.add_nodes_from(['a', 'b', 'c', 'd'])
phi1 = Factor(['a', 'b'], [2, 2], range(4))
phi2 = Factor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError,
self.graph.get_partition_function)
def tearDown(self):
del self.graph
# values=np.array([[1,1],
# [1,1]])) for edge in G.edges()]
phi = [
DiscreteFactor(['x2', 'x1'],
cardinality=[2, 2],
values=np.array([[1, 2], [3, 4]])),
DiscreteFactor(['x3', 'x1'],
cardinality=[2, 2],
values=np.array([[1, 2], [3, 4]])),
DiscreteFactor(['x1'], cardinality=[2], values=np.array([2, 2]))
]
G.add_factors(*phi)
print "factors:", G.get_factors
print "partition function =", G.get_partition_function()
def eval_partition_func_random_glass_spin(N):
'''
Inputs:
-N: int, generate a random NxN glass spin model
Outputs:
'''
G = MarkovModel()
#create an NxN grid of nodes
node_names = ['x%d%d' % (r, c) for r in range(N) for c in range(N)]
from pgmpy.models import MarkovModel
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.inference import VariableElimination
phi_1 = DiscreteFactor(['A', 'B'], [2, 2], [30, 5, 1, 10])
phi_2 = DiscreteFactor(['B', 'C'], [2, 2], [100, 1, 1, 100])
phi_3 = DiscreteFactor(['C', 'D'], [2, 2], [1, 100, 100, 1])
phi_4 = DiscreteFactor(['D', 'A'], [2, 2], [100, 1, 1, 100])
model = MarkovModel([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')])
model.add_factors(phi_1, phi_2, phi_3, phi_4)
phi = phi_1 * phi_2 * phi_3 * phi_4
Z = model.get_partition_function()
normalized = phi.values / Z
print(normalized)
class TestUndirectedGraphFactorOperations(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_add_factor_raises_error(self):
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles'),
('Charles', 'Debbie'), ('Debbie', 'Alice')])
factor = DiscreteFactor(['Alice', 'Bob', 'John'], [2, 2, 2],
np.random.rand(8))
self.assertRaises(ValueError, self.graph.add_factors, factor)
def test_add_single_factor(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi = DiscreteFactor(['a', 'b'], [2, 2], range(4))
self.graph.add_factors(phi)
six.assertCountEqual(self, self.graph.factors, [phi])
def test_add_multiple_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.factors, [phi1, phi2])
def test_get_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
six.assertCountEqual(self, self.graph.get_factors(), [])
self.graph.add_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.get_factors(), [phi1, phi2])
def test_remove_single_factor(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1)
six.assertCountEqual(self, self.graph.factors, [phi2])
def test_remove_multiple_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.factors, [])
def test_partition_function(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertEqual(self.graph.get_partition_function(), 22.0)
def test_partition_function_raises_error(self):
self.graph.add_nodes_from(['a', 'b', 'c', 'd'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.get_partition_function)
def tearDown(self):
del self.graph
from pgmpy.models import MarkovModel
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.inference import BeliefPropagation
import numpy as np
# Construct a graph
PGM = MarkovModel()
PGM.add_nodes_from(['w1', 'w2', 'w3'])
PGM.add_edges_from([('w1', 'w2'), ('w2', 'w3')])
tr_matrix = np.array([1, 2, 3, 10, 1, 3, 3, 5, 2]).reshape(3, 3).T.reshape(-1)
phi = [DiscreteFactor(edge, [3, 3], tr_matrix) for edge in PGM.edges()]
PGM.add_factors(*phi)
# Calculate partition funtion
Z = PGM.get_partition_function()
print('The partition function is:', Z)
# Calibrate the click
belief_propagation = BeliefPropagation(PGM)
belief_propagation.calibrate()
# Output calibration result, which you should get
query = belief_propagation.query(variables=["w2"], joint=False)
print('After calibration you should get the following mu(S):', query["w2"] * Z)
# Get marginal distribution over third word
query = belief_propagation.query(variables=['w3'],
joint=False) #, evidence = {'w2':0})
print('Marginal distribution over the third word is:\n', query["w3"])
