本文整理汇总了Python中pgmpy.models.BayesianModel.node[node][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[node][prop_name]方法的具体用法？Python BayesianModel.node[node][prop_name]怎么用？Python BayesianModel.node[node][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[node][prop_name]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: get_model
# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import node[node][prop_name] [as 别名]
def get_model(self):
"""
Returns the fitted bayesian model
Example
----------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_model()
<pgmpy.models.BayesianModel.BayesianModel object at 0x7f20af154320>
"""
try:
model = BayesianModel(self.variable_edges)
model.name = self.network_name
model.add_nodes_from(self.variable_names)
tabular_cpds = []
for var in sorted(self.variable_cpds.keys()):
values = self.variable_cpds[var]
cpd = TabularCPD(var, len(self.variable_states[var]), values,
evidence=self.variable_parents[var],
evidence_card=[len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_properties.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(), prop.split('='))
model.node[node][prop_name] = prop_value
return model
except AttributeError:
raise AttributeError('First get states of variables, edges, parents and network name')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[node][prop_name] [as 别名]
def get_model(self):
model = BayesianModel(self.get_edges())
model.name = self.network_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
cpd = TabularCPD(var, len(self.variable_states[var]), values,
evidence=self.variable_parents[var],
evidence_card=[len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_property.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(), prop.split('='))
model.node[node][prop_name] = prop_value
return model