Method randomsample

libpgm/hybayesiannetwork.py:75–152

Produce *n* random samples from the Bayesian networki, subject to *evidence*, and return them in a list. This function requires the *nodes* attribute to be instantiated. This function takes the following arguments: 1. *n* -- The number of random samples to prod

(self, n, evidence=None)

            assert sorted(self.V) == sorted(self.Vdata.keys()), "Node data did not match graph skeleton nodes."
    
    def randomsample(self, n, evidence=None):
        '''
        Produce *n* random samples from the Bayesian networki, subject to *evidence*, and return them in a list. This function requires the *nodes* attribute to be instantiated.
        
        This function takes the following arguments:

            1. *n* -- The number of random samples to produce.
            2. *evidence* -- (Optional) A dict containing (vertex: value) pairs that describe the evidence. To be used carefully because it does manually overrides the nodes with evidence instead of affecting the joint probability distribution of the entire graph.
        
        And returns:
            A list of *n* independent random samples, each element of which is a dict containing (vertex: value) pairs.
        
        Usage example: this would generate a sequence of 10 random samples::
            
            import json

            from libpgm.nodedata import NodeData
            from libpgm.graphskeleton import GraphSkeleton
            from libpgm.hybayesiannetwork import HyBayesianNetwork

            # load nodedata and graphskeleton
            nd = NodeData()
            skel = GraphSkeleton()
            nd.load("../tests/unittesthdict.txt")   # an input file
            skel.load("../tests/unittestdict.txt")

            # topologically order graphskeleton
            skel.toporder()

            # convert nodes to class instances
            nd.entriestoinstances()

            # load bayesian network
            hybn = HyBayesianNetwork(skel, nd)

            # sample 
            result = hybn.randomsample(10)

            # output
            print json.dumps(result, indent=2)
            


        '''
        assert (isinstance(n, int) and n > 0), "Argument must be a positive integer."
        
        random.seed()
        seq = []
        for _ in range(n):
            outcome = dict()
            for vertex in self.V:
                outcome[vertex] = "default"
            
            def assignnode(name, node):
                
                # check if node is already observed
                if (evidence != None):
                    if name in evidence.keys():