Method randomsample

libpgm/discretebayesiannetwork.py:140–228

Produce *n* random samples from the Bayesian network, subject to *evidence*, and return them in a list. This function takes the following arguments: 1. *n* -- The number of random samples to produce. 2. *evidence* -- (Optional) A dict containin

(self, n, evidence=None)

        return fn.specificquery(query, evidence)
            
    def randomsample(self, n, evidence=None):
        '''
        Produce *n* random samples from the Bayesian network, subject to *evidence*, and return them in a list.             

        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.discretebayesiannetwork import DiscreteBayesianNetwork
            
            # load nodedata and graphskeleton
            nd = NodeData()
            skel = GraphSkeleton()
            nd.load("../tests/unittestdict.txt")    # any input file
            skel.load("../tests/unittestdict.txt")

            # topologically order graphskeleton
            skel.toporder()

            # load bayesian network
            bn = DiscreteBayesianNetwork(skel, nd)

            # sample 
            result = bn.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(s):
                
                if (evidence != None):
                    if s in evidence.keys():
                        return evidence[s]
                
                # find entry in dictionary and store
                Vdataentry = self.Vdata[s]