dd25b0e36c5808a4ef6428b14cf9f39d3b0dd5ea,pgmpy/inference/Sampling.py,BayesianModelSampling,forward_sample,#BayesianModelSampling#Any#,32

Before Change

                weights = list(map(lambda t: cpd.reduce(t, inplace=False).values, evidence))
                sampled[node] = sample_discrete(cpd.variables[cpd.variable], weights)
            else:
                sampled[node] = sample_discrete(cpd.variables[cpd.variable], cpd.values, size)
        return sampled

    def rejection_sample(self, evidence=None, size=1):
        

After Change

                weights = list(map(lambda t: cpd.reduce(t, inplace=False).values, evidence))
                sampled[node] = list(map(lambda t: State(node, t), sample_discrete(states, weights)))
            else:
                sampled[node] = list(map(lambda t: State(node, t),
                                    sample_discrete(states, cpd.values, size)))
        return sampled

    def rejection_sample(self, evidence=None, size=1):

Instances