('accident', 'traffic_jam'),
('traffic_jam', 'long_queues'),
('traffic_jam', 'late_for_school'),
('getting_up_late', 'late_for_school')])
cpd_rain = TabularCPD('rain', 2, [[0.4], [0.6]])
cpd_accident = TabularCPD('accident', 2, [[0.2], [0.8]])
cpd_traffic_jam = TabularCPD('traffic_jam', 2,
[[0.9, 0.6, 0.7, 0.1],
[0.1, 0.4, 0.3, 0.9]],
evidence=['rain', 'accident'],
evidence_card=[2, 2])
cpd_getting_up_late = TabularCPD('getting_up_late', 2, [[0.6], [0.4]])
cpd_late_for_school = TabularCPD('late_for_school', 2,
[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
evidence=['getting_up_late',
'traffic_jam'],
evidence_card=[2, 2])
cpd_long_queues = TabularCPD('long_queues', 2,
[[0.9, 0.2],
[0.1, 0.8]],
evidence=['traffic_jam'],
evidence_card=[2])
model.add_cpds(cpd_rain, cpd_accident,
cpd_traffic_jam, cpd_getting_up_late,
cpd_late_for_school, cpd_long_queues)
junction_tree_bm = model.to_junction_tree()
type(junction_tree_bm)
junction_tree_bm.nodes()
junction_tree_bm.edges()
from pgmpy.models import BayesianModel, MarkovModel
from pgmpy.factors import TabularCPD, Factor
# Create a bayesian model as we did in the previous chapters
model = BayesianModel([('rain', 'traffic_jam'), ('accident', 'traffic_jam'),
('traffic_jam', 'long_queues'),
('traffic_jam', 'late_for_school'),
('getting_up_late', 'late_for_school')])
cpd_rain = TabularCPD('rain', 2, [[0.4], [0.6]])
cpd_accident = TabularCPD('accident', 2, [[0.2], [0.8]])
cpd_traffic_jam = TabularCPD('traffic_jam',
2, [[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
evidence=['rain', 'accident'],
evidence_card=[2, 2])
cpd_getting_up_late = TabularCPD('getting_up_late', 2, [[0.6], [0.4]])
cpd_late_for_school = TabularCPD(
'late_for_school',
2, [[0.9, 0.45, 0.8, 0.1], [0.1, 0.55, 0.2, 0.9]],
evidence=['getting_up_late', 'traffic_jam'],
evidence_card=[2, 2])
cpd_long_queues = TabularCPD('long_queues',
2, [[0.9, 0.2], [0.1, 0.8]],
evidence=['traffic_jam'],
evidence_card=[2])
model.add_cpds(cpd_rain, cpd_accident, cpd_traffic_jam, cpd_getting_up_late,
cpd_late_for_school, cpd_long_queues)
junction_tree_bm = model.to_junction_tree()
type(junction_tree_bm)
junction_tree_bm.nodes()
junction_tree_bm.edges()
values=[[0.01], [0.99]])
cpd_do = TabularCPD(variable='dog_out',
variable_card=2,
values=[[0.99, 0.9, 0.97, 0.3], [0.01, 0.1, 0.03, 0.7]],
evidence=['family_out', 'bowel_problem'],
evidence_card=[2, 2])
cpd_lo = TabularCPD(variable='light_on',
variable_card=2,
values=[[0.6, 0.05], [0.4, 0.95]],
evidence=['family_out'],
evidence_card=[2])
cpd_hb = TabularCPD(variable='hear_bark',
variable_card=2,
values=[[0.7, 0.01], [0.3, 0.99]],
evidence=['dog_out'],
evidence_card=[2])
#integrity checking
model.add_cpds(cpd_fo, cpd_bp, cpd_do, cpd_lo, cpd_hb)
model.check_model()
junction_tree = model.to_junction_tree()
print(junction_tree.nodes())
infer_bp = BeliefPropagation(junction_tree)
print(
infer_bp.query(['family_out'], evidence={
'light_on': 0,
'hear_bark': 1
})['family_out'])
