Method specificquery

libpgm/discretebayesiannetwork.py:108–138

.. note: Shortcut method to the *specificquery* method in :doc:`tablecpdfactorization` Eliminate all variables except for the ones specified by *query*. Adjust all distributions to reflect *evidence*. Return the entry that matches the exact probability of a specific event, as speci

(self, query, evidence)

            self.Vdata = j["Vdata"] 

    def specificquery(self, query, evidence):
        '''
        .. note: Shortcut method to the *specificquery* method in :doc:`tablecpdfactorization`

        Eliminate all variables except for the ones specified by *query*. Adjust all distributions to reflect *evidence*. Return the entry that matches the exact probability of a specific event, as specified by *query*.
        
        Arguments:
            1. *query* -- A dict containing (key: value) pairs reflecting (variable: value) that represents what outcome to calculate the probability of. The value of the query is a list of one or more values that can be taken by the variable.
            2. *evidence* -- A dict containing (key: value) pairs reflecting (variable: value) evidence that is known about the system. 
                    
        Returns:
            - the probability that the event (or events) specified will occur, represented as a float between 0 and 1.

        Note that in this function, queries of the type P((x=A or x=B) and (y=C or y=D)) are permitted. They are executed by formatting the *query* dictionary like so::

            {
                "x": ["A", "B"],
                "y": ["C", "D"]
            }

        '''
        # validate
        if not (hasattr(self, "V") and hasattr(self, "E") and hasattr(self, "Vdata")):
            raise notloadedError("Bayesian network is missing essential attributes")
        assert isinstance(query, dict) and isinstance(evidence, dict), "query and evidence must be dicts"
        for k in query.keys():
            assert isinstance(query[k], list), "the values of your query must be lists, even if singletons" 

        # calculate
        fn = TableCPDFactorization(self)
        return fn.specificquery(query, evidence)
            
    def randomsample(self, n, evidence=None):
        '''