f58745ab284f48b7ef4ce813f5f8cd26bdb3c0a8,pgmpy/inference/Sampling.py,BayesianModelSampling,forward_sample,#BayesianModelSampling#,31

Before Change

            cpd = self.cpds[node]
            if cpd.evidence:
                weights = []
                for i in range(size):
                    evid = [sampled[var][i] for var in cpd.evidence]
                    weights.append(cpd.reduce(evid, inplace=False).values)
                sampled[node] = sample_discrete(cpd.variables[cpd.variable], weights)
            else:
                // can generate the column at once
                weights = cpd.values

After Change

        for index, node in enumerate(self.topological_order):
            cpd = self.cpds[node]
            if cpd.evidence:
                evidence = sampled.values[:, :index].tolist()
                weights = np.apply_along_axis(lambda t: cpd.reduce(t, inplace=False).values, 1, evidence)
                sampled[node] = sample_discrete(cpd.variables[cpd.variable], weights)
            else:
                sampled[node] = sample_discrete(cpd.variables[cpd.variable], cpd.values, size)

Instances