1c8608f922bd80188a6c708ea42789627e34bdb5,pgmpy/inference/Sampling.py,BayesianModelSampling,forward_sample,#BayesianModelSampling#,33

Before Change

        [{"intel": "intel_0", "grade": "grade_1", "diff": "diff_1"},
        {"intel":  "intel_1", "grade": "grade_0", "diff": "diff_0"}]
        
        sampled = []
        for _ in range(size):
            particle = {}
            for node in self.topological_order:
                cpd = self.cpds[node]
                if cpd.evidence:
                    evid = []
                    for var in cpd.evidence:
                        evid.append(particle[var])
                    weights = cpd.reduce(evid, inplace=False).values
                else:
                    weights = cpd.values
                particle[node] = sample_discrete(cpd.variables[cpd.variable], weights)
            sampled.append(particle)
        return sampled

After Change

        0  diff_1  intel_0  grade_1
        1  diff_1  intel_0  grade_2
        
        sampled = DataFrame(index=range(0, size),
                            columns=self.topological_order)
        for i in range(size):
            particle = {}
            for node in self.topological_order:
                cpd = self.cpds[node]

Instances