图形式的分类概率

from pgmpy.models import BayesianModel
from pgmpy.factors.discrete import TabularCPD

# 定义模型结构
model = BayesianModel([('Symptom1', 'Disease'), ('Symptom2', 'Disease')])

# 定义条件概率分布
cpd_symptom1 = TabularCPD(variable='Symptom1', variable_card=2, values=[[0.6], [0.4]])
cpd_symptom2 = TabularCPD(variable='Symptom2', variable_card=2, values=[[0.7], [0.3]])
cpd_disease = TabularCPD(variable='Disease', variable_card=2,
                        values=[[0.9, 0.3, 0.8, 0.1], [0.1, 0.7, 0.2, 0.9]],
                        evidence=['Symptom1', 'Symptom2'],
                        evidence_card=[2, 2])

# 添加CPD到模型
model.add_cpds(cpd_symptom1, cpd_symptom2, cpd_disease)

# 检查模型
print(model.check_model())