生成非二元贝叶斯网络时出错

问题描述

我有两个类来生成贝叶斯网络，BayesNode 为网络生成节点，BayesNet 在名为 probabilityPlus.py 的文件中生成网络.

class BayesNode:
   """A conditional probability distribution,P(X | parents). Part of a BayesNet."""

    def __init__(self,X,parents,cpt):
        """X is a variable name,and parents a sequence of variable
        names or a space-separated string. cpt,the conditional
        probability table,takes one of these forms:

        * A number,the unconditional probability P(X=true). You can
          use this form when there are no parents.

        * A dict {v: p,...},the conditional probability distribution
          P(X=true | parent=v) = p. When there's just one parent.

        * A dict {(v1,v2,...): p,the distribution P(X=true |
          parent1=v1,parent2=v2,...) = p. Each key must have as many
          values as there are parents. You can use this form always;
          the first two are just conveniences.

        In all cases the probability of X being false is left implicit,since it follows from P(X=true).
        """
        if isinstance(parents,str):
            parents = parents.split()

        # We store the table always in the third form above.
        if isinstance(cpt,(float,int)):  # no parents,0-tuple
            cpt = {(): cpt}
        elif isinstance(cpt,dict):
            # one parent,1-tuple
            if cpt and isinstance(list(cpt.keys())[0],bool):
                cpt = {(v,): p for v,p in cpt.items()}

        assert isinstance(cpt,dict)
        for vs,p in cpt.items():
            assert isinstance(vs,tuple) and len(vs) == len(parents)      #line 163 »for ERROR MESSAGE
            assert all(isinstance(v,bool) for v in vs)
            assert 0 <= p <= 1

        self.variable = X
        self.parents = parents
        self.cpt = cpt
        self.children = []

    def p(self,value,event):
        """Return the conditional probability
        P(X=value | parents=parent_values),where parent_values
        are the values of parents in event. (event must assign each
        parent a value.)"""
        assert isinstance(value,bool)
        ptrue = self.cpt[event_values(event,self.parents)]
        return ptrue if value else 1 - ptrue

    def sample(self,event):
        """Sample from the distribution for this variable conditioned
        on event's values for parent_variables. That is,return True/False
        at random according with the conditional probability given the
        parents."""
        return probability(self.p(True,event))

    def __repr__(self):
        return repr((self.variable,' '.join(self.parents)))

和

class BayesNet:
    """Bayesian network"""

    def __init__(self,node_specs=None):
        """Nodes must be ordered with parents before children."""
        self.nodes = []
        self.variables = []
        node_specs = node_specs or []
        for node_spec in node_specs:
            self.add(node_spec)           #line 207 »for ERROR MESSAGE

    def add(self,node_spec):
        """Add a node to the net. Its parents must already be in the
        net,and its variable must not."""
        node = BayesNode(*node_spec)           #line 212 »for ERROR MESSAGE
        assert node.variable not in self.variables
        assert all((parent in self.variables) for parent in node.parents)
        self.nodes.append(node)
        self.variables.append(node.variable)
        for parent in node.parents:
            self.variable_node(parent).children.append(node.variable)

    def variable_node(self,var):
        """Return the node for the variable named var.
        """
        for n in self.nodes:
            if n.variable == var:
                return n
        raise Exception("No such variable: {}".format(var))

    def variable_values(self,var):
        """Return the domain of var."""   
        return [True,False]

    def __repr__(self):
        return 'BayesNet({0!r})'.format(self.nodes)

当我这样做时它运行没有问题：

T,F = True,False

burglary = BayesNet([
    ('Burglary','',0.001),('Earthquake',0.002),('Alarm','Burglary Earthquake',{(T,T): 0.95,(T,F): 0.94,(F,T): 0.29,F): 0.001}),('JohnCalls','Alarm',{T: 0.90,F: 0.05}),('MaryCalls',{T: 0.70,F: 0.01})
])

burglary

返回：

BayesNet([('Burglary',''),'Burglary Earthquake'),'Alarm'),'Alarm')])

没关系，然后我执行 burglary.variables 并返回

['Burglary','Earthquake','JohnCalls','MaryCalls']

就像我尝试时应该但是：

T,False

burglary2 = BayesNet([
    ('Burglary',{"strong":1,"light":10,"none":89 }),'strong'): 0.95,'light'): 0.80,'none'): 0.76,'strong'): 0.8,'light'): 0.3,'none'): 0.001}),F: 0.01})
])

burglary2

导致此错误

并且（逻辑上）不允许我对最后一个网络做任何事情；但我不明白为什么以及如何解决它。有没有人知道出了什么问题以及如何解决？

解决方法

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