Method gibbssample

libpgm/tablecpdfactorization.py:310–435

Return a sequence of *n* samples using the Gibbs sampling method, given evidence specified by *evidence*. Gibbs sampling is a technique wherein for each sample, each variable in turn is erased and calculated conditioned on the outcomes of its neighbors. This method starts by sampling from t

(self, evidence, n)

        return fanswer

    def gibbssample(self, evidence, n):
        '''
        Return a sequence of *n* samples using the Gibbs sampling method, given evidence specified by *evidence*. Gibbs sampling is a technique wherein for each sample, each variable in turn is erased and calculated conditioned on the outcomes of its neighbors. This method starts by sampling from the 'prior distribution,' which is the distribution not conditioned on evidence, but the samples provably get closer and closer to the posterior distribution, which is the distribution conditioned on the evidence. It is thus a good way to deal with evidence when generating random samples.
        
        Arguments: 
            1. *evidence* -- A dict containing (key: value) pairs reflecting (variable: value) that represents what is known about the system.
            2. *n* -- The number of samples to return.
        
        Returns:
        
        A list of *n* random samples, each element of which is a dict containing (vertex: value) pairs.
        
        For more information, cf. Koller et al. Ch. 12.3.1

        Usage example: This code would generate a sequence of 10 samples::

            import json
            
            from libpgm.graphskeleton import GraphSkeleton
            from libpgm.nodedata import NodeData
            from libpgm.discretebayesiannetwork import DiscreteBayesianNetwork
            from libpgm.tablecpdfactorization import TableCPDFactorization
            
            # load nodedata and graphskeleton
            nd = NodeData()
            skel = GraphSkeleton()
            nd.load("../tests/unittestdict.txt")
            skel.load("../tests/unittestdict.txt")

            # toporder graph skeleton
            skel.toporder()

            # load evidence
            evidence = dict(Letter='weak')

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

            # load factorization
            fn = TableCPDFactorization(bn)

            # sample 
            result = fn.gibbssample(evidence, 10)

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

        '''
        self.refresh()
        random.seed() 
        
        # declare result array
        seq = []
        
        # create initial instantiation 
        initial = self.bn.randomsample(1)
        for key in evidence.keys():
            initial[0][key] = evidence[key]