ca25da7c55ba436ab557410d5f2e2e9b557d2840,pgmpy/models/MarkovModel.py,MarkovModel,to_junction_tree,#MarkovModel#,412

Before Change

        // Create clique trees by minimum (or maximum) spanning tree method
        clique_trees = JunctionTree(nx.minimum_spanning_tree(complete_graph).edges())

        factor = self.factors[0]
        factor = factor.product(*[self.factors[i] for i in
                                  range(1, len(self.factors))])
        if set(factor.scope()) != set(self.nodes()):
            ValueError("Factor for all the random variables not specified")

        all_vars = set(self.nodes())

        for node in clique_trees.nodes():
            marginalised_nodes = all_vars - set(node)
            factor_copy = factor.marginalize(list(marginalised_nodes),
                                             inplace=False)
            clique_trees.add_factors(factor_copy)

        return clique_trees

After Change

        is_used = {factor: False for factor in self.factors}

        for node in clique_trees.nodes():
            clique_factors = []
            for factor in self.factors:
                if not is_used[factor] and set(factor.scope()).issubset(node):
                    clique_factors.append(factor)
                    is_used[factor] = True

            clique_potential = factor_product(*clique_factors)
            clique_trees.add_factors(clique_potential)

Instances