class TestFactorGraphCreation(unittest.TestCase):
def setUp(self):
self.graph = FactorGraph()
def test_class_init_without_data(self):
self.assertIsInstance(self.graph, FactorGraph)
def test_class_init_data_string(self):
self.graph = FactorGraph([('a', 'phi1'), ('b', 'phi1')])
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'phi1'])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['a', 'phi1'], ['b', 'phi1']])
def test_add_single_node(self):
self.graph.add_node('phi1')
self.assertEqual(self.graph.nodes(), ['phi1'])
def test_add_multiple_nodes(self):
self.graph.add_nodes_from(['a', 'b', 'phi1'])
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'phi1'])
def test_add_single_edge(self):
self.graph.add_edge('a', 'phi1')
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'phi1'])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['a', 'phi1']])
def test_add_multiple_edges(self):
self.graph.add_edges_from([('a', 'phi1'), ('b', 'phi1')])
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'phi1'])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['a', 'phi1'], ['b', 'phi1']])
def test_add_self_loop_raises_error(self):
self.assertRaises(ValueError, self.graph.add_edge, 'a', 'a')
def test_add_edge_between_variable_nodes_raises_error(self):
self.graph.add_edges_from([('a', 'phi1'), ('b', 'phi1')])
self.assertRaises(ValueError, self.graph.add_edge, 'a', 'b')
def tearDown(self):
del self.graph
class TestFactorGraphCreation(unittest.TestCase):
def setUp(self):
self.graph = FactorGraph()
def test_class_init_without_data(self):
self.assertIsInstance(self.graph, FactorGraph)
def test_class_init_data_string(self):
self.graph = FactorGraph([("a", "phi1"), ("b", "phi1")])
self.assertListEqual(sorted(self.graph.nodes()), ["a", "b", "phi1"])
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()), [["a", "phi1"], ["b", "phi1"]]
)
def test_add_single_node(self):
self.graph.add_node("phi1")
self.assertEqual(list(self.graph.nodes()), ["phi1"])
def test_add_multiple_nodes(self):
self.graph.add_nodes_from(["a", "b", "phi1"])
self.assertListEqual(sorted(self.graph.nodes()), ["a", "b", "phi1"])
def test_add_single_edge(self):
self.graph.add_edge("a", "phi1")
self.assertListEqual(sorted(self.graph.nodes()), ["a", "phi1"])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()), [["a", "phi1"]])
def test_add_multiple_edges(self):
self.graph.add_edges_from([("a", "phi1"), ("b", "phi1")])
self.assertListEqual(sorted(self.graph.nodes()), ["a", "b", "phi1"])
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()), [["a", "phi1"], ["b", "phi1"]]
)
def test_add_self_loop_raises_error(self):
self.assertRaises(ValueError, self.graph.add_edge, "a", "a")
def tearDown(self):
del self.graph
def compute_pk(self, type_list, fid):
assert len(type_list) == 5, print("ComputePk Error: number of type_list should be 5")
constraint_name = ['m', 'r', 's', 'd', 'v']
'''
m, r, s, d, v = type_list
p_m, p_r, p_s, p_d, p_v = self.p_observation
p_ktox, p_xtok = self.p_implication
p_ktom, p_ktor, p_ktos, p_ktod, p_ktov = p_ktox
p_mtok, p_rtok, p_stok, p_dtok, p_vtok = p_xtok
'''
fg = FactorGraph()
fg.add_node('k')
for i in range(len(type_list)):
if type_list[i] == 0:
fg = self.add_constraints_k2x_x2k(fg, self.p_observation[fid][i], self.p_implication[fid][0][i], self.p_implication[fid][1][i], constraint_name[i])
elif type_list[i] == 1:
fg = self.add_constraints_k2x(fg, self.p_observation[fid][i], self.p_implication[fid][0][i], constraint_name[i])
elif type_list[i] == 2:
fg = self.add_constraints_x2k(fg, self.p_observation[fid][i], self.p_implication[fid][1][i], constraint_name[i])
'''
if m == 0:
fg = add_constraints_kv_vk(fg, p_m, p_ktom, p_mtok, 'm')
elif m == 1:
fg = add_constraints_kv(fg, p_m, p_mtok, 'm')
elif m == 2:
fg = add_constraints_vk(fg, p_m, p_mtok, 'm')
if r == 0:
fg = add_constraints_kv_vk(fg, p_r, p_ktor, p_rtok, 'r')
elif r == 1:
fg = add_constraints_kv(fg, p_r, p_ktor, 'r')
elif r == 2:
fg = add_constraints_vk(fg, p_r, p_rtok, 'r')
if s == 0:
fg = add_constraints_kv_vk(fg, p_s, p_ktos, p_stok, 's')
elif s == 1:
fg = add_constraints_kv(fg, p_s, p_ktos, 's')
elif s == 2:
fg = add_constraints_vk(fg, p_s, p_stok, 's')
if d == 0:
fg = add_constraints_kv_vk(fg, p_d, p_ktod, p_dtok, 'd')
elif d == 1:
fg = add_constraints_kv(fg, p_d, p_ktod, 'd')
elif d == 2:
fg = add_constraints_vk(fg, p_d, p_dtok, 'd')
if v == 0:
fg = add_constraints_kv_vk(fg, p_v, p_ktov, p_vtok, 'v')
elif v == 1:
fg = add_constraints_kv(fg, p_v, p_ktov, 'v')
elif v == 2:
fg = add_constraints_vk(fg, p_v, p_vtok, 'v')
'''
bp = BeliefPropagation(fg)
#result = bp.query(variables=['k'])['k']
#result = bp.query(variables=['k'], joint=False)['k']
result = bp.query(variables=['k'])
result.normalize()
#print(result)
return result.values[1]
import numpy as np from pgmpy.models import FactorGraph from pgmpy.factors.discrete import DiscreteFactor from pgmpy.inference import BeliefPropagation G = FactorGraph() G.add_node(0) G.add_node(1) G.add_node(2) f01 = DiscreteFactor([0, 1], [2, 2], np.random.rand(4)) f02 = DiscreteFactor([0, 2], [2, 2], np.random.rand(4)) f12 = DiscreteFactor([1, 2], [2, 2], np.random.rand(4)) G.add_factors(f01) G.add_factors(f02) G.add_factors(f12) G.add_edges_from([(0, f01), (1, f01), (0, f02), (2, f02), (1, f12), (2, f12)]) bp = BeliefPropagation(G) bp.calibrate()
