本文整理汇总了Python中pgmpy.models.BayesianModel.node[var][prop_name]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][prop_name]方法的具体用法？Python BayesianModel.node[var][prop_name]怎么用？Python BayesianModel.node[var][prop_name]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][prop_name]方法的1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: 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][prop_name] [as 别名]
def get_model(self):
"""
Returns the model instance of the ProbModel.
Return
---------------
model: an instance of BayesianModel.
Examples
-------
>>> reader = ProbModelXMLReader()
>>> reader.get_model()
"""
if self.probnet.get('type') == "BayesianNetwork":
model = BayesianModel(self.probnet['edges'].keys())
tabular_cpds = []
cpds = self.probnet['Potentials']
for cpd in cpds:
var = list(cpd['Variables'].keys())[0]
states = self.probnet['Variables'][var]['States']
evidence = cpd['Variables'][var]
evidence_card = [len(self.probnet['Variables'][evidence_var]['States'])
for evidence_var in evidence]
arr = list(map(float, cpd['Values'].split()))
values = np.array(arr)
values = values.reshape((len(states), values.size//len(states)))
tabular_cpds.append(TabularCPD(var, len(states), values, evidence, evidence_card))
model.add_cpds(*tabular_cpds)
variables = model.nodes()
for var in variables:
for prop_name, prop_value in self.probnet['Variables'][var].items():
model.node[var][prop_name] = prop_value
edges = model.edges()
for edge in edges:
for prop_name, prop_value in self.probnet['edges'][edge].items():
model.edge[edge[0]][edge[1]][prop_name] = prop_value
return model
else:
raise ValueError("Please specify only Bayesian Network.")