机器学习（八）：贝叶斯网络——福尔摩斯推理、草地喷水器推断

from pgmpy.models import BayesianNetwork
from pgmpy.factors.discrete import TabularCPD
from pgmpy.inference import VariableElimination
model = BayesianNetwork([('B', 'A'), ('E', 'A'), ('A', 'W'), ('E', 'R')])
cpd_1 = TabularCPD(variable='B', variable_card=2, values=[[0.9], [0.1]])
cpd_2 = TabularCPD(variable='E', variable_card=2, values=[[0.99], [0.01]])
cpd_A = TabularCPD(variable='A', variable_card=2,
                   values=[[0.99, 0.1, 0.1, 0.01],
                           [0.01, 0.9, 0.9, 0.99]],
                   evidence=['E', 'B'],#父节点
                   evidence_card=[2, 2])#警铃
cpd_W = TabularCPD(variable='W', variable_card=2,
                   values=[[0.99, 0.35],
                           [0.01, 0.65]],
                   evidence=['A'],#父节点
                   evidence_card=[2])#华生致电福尔摩斯
cpd_R = TabularCPD(variable='R', variable_card=2,
                     values=[[0.999, 0.01],
                            [0.001, 0.99]],
                     evidence=['E'],#父节点
                     evidence_card=[2])#广播播报地震
model.add_cpds(cpd_1, cpd_2, cpd_A, cpd_W, cpd_R)
infer = VariableElimination(model)
print(infer.query(variables=['B'],evidence={'A':1}))
print(infer.query(variables=['E'],evidence={'A':1,'R':1}))
print(infer.query(variables=['B'],evidence={'A':1,'R':1}))

model = BayesianNetwork([('R', 'S'), ('R', 'G'), ('S', 'G')])
cpd_1 = TabularCPD(variable='R', variable_card=2, values=[[0.8], [0.2]])
cpd_S = TabularCPD(variable='S', variable_card=2,
                   values=[[0.6, 0.99],
                           [0.4, 0.01]],
                   evidence=['R'],#父节点
                   evidence_card=[2])
cpd_G = TabularCPD(variable='G', variable_card=2,
                   values=[[1.0, 0.2, 0.1, 0.01],
                           [0.0, 0.8, 0.9, 0.99]],
                   evidence=['S', 'R'],#父节点
                   evidence_card=[2, 2])#警铃
                   
model.add_cpds(cpd_1, cpd_S, cpd_G)
infer = VariableElimination(model)
print(infer.query(variables=['R'],evidence={'G':1}))

print(infer.query(variables=['S'],evidence={'G':1}))