本文整理汇总了Python中pgmpy.models.BayesianModel.node[var]方法的典型用法代码示例。如果您正苦于以下问题：Python BayesianModel.node[var]方法的具体用法？Python BayesianModel.node[var]怎么用？Python BayesianModel.node[var]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pgmpy.models.BayesianModel的用法示例。
在下文中一共展示了BayesianModel.node[var]方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setUp
# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import node[var] [as 别名]
def setUp(self):
nodes = {'c': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(c) Brain Tumor',
'YPOS': '11935',
'XPOS': '15250',
'TYPE': 'discrete'},
'a': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(a) Metastatic Cancer',
'YPOS': '10465',
'XPOS': '13495',
'TYPE': 'discrete'},
'b': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(b) Serum Calcium Increase',
'YPOS': '11965',
'XPOS': '11290',
'TYPE': 'discrete'},
'e': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(e) Papilledema',
'YPOS': '13240',
'XPOS': '17305',
'TYPE': 'discrete'},
'd': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(d) Coma',
'YPOS': '12985',
'XPOS': '13960',
'TYPE': 'discrete'}}
model = BayesianModel([('b', 'd'), ('a', 'b'), ('a', 'c'), ('c', 'd'), ('c', 'e')])
cpd_distribution = {'a': {'TYPE': 'discrete', 'DPIS': np.array([[0.2, 0.8]])},
'e': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.6, 0.4]]), 'CONDSET': ['c'], 'CARDINALITY': [2]},
'b': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.2, 0.8]]), 'CONDSET': ['a'], 'CARDINALITY': [2]},
'c': {'TYPE': 'discrete', 'DPIS': np.array([[0.2, 0.8],
[0.05, 0.95]]), 'CONDSET': ['a'], 'CARDINALITY': [2]},
'd': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.9, 0.1],
[0.7, 0.3],
[0.05, 0.95]]), 'CONDSET': ['b', 'c'], 'CARDINALITY': [2, 2]}}
tabular_cpds = []
for var, values in cpd_distribution.items():
evidence = values['CONDSET'] if 'CONDSET' in values else []
cpd = values['DPIS']
evidence_card = values['CARDINALITY'] if 'CARDINALITY' in values else []
states = nodes[var]['STATES']
cpd = TabularCPD(var, len(states), cpd,
evidence=evidence,
evidence_card=evidence_card)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for var, properties in nodes.items():
model.node[var] = properties
self.maxDiff = None
self.writer = XMLBeliefNetwork.XBNWriter(model=model)示例2: get_model
# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import node[var] [as 别名]
def get_model(self):
"""
Returns an instance of Bayesian Model.
"""
model = BayesianModel(self.edges)
model.name = self.model_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
evidence = values['CONDSET'] if 'CONDSET' in values else []
cpd = values['DPIS']
evidence_card = values['CARDINALITY'] if 'CARDINALITY' in values else []
states = self.variables[var]['STATES']
cpd = TabularCPD(var, len(states), cpd,
evidence=evidence,
evidence_card=evidence_card)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for var, properties in self.variables.items():
model.node[var] = properties
return model