Generates sample(s) from joint distribution of the bayesian network.

        Parameters
        ----------
        size: int
            size of sample to be generated

        return_type: string (dataframe | recarray)
            Return type for samples, either of 'dataframe' or 'recarray'.
            Defaults to 'dataframe'

        Returns
        -------
        sampled: A pandas.DataFrame or a numpy.recarray object depending upon return_type argument
            the generated samples


        Examples
        --------
        >>> from pgmpy.models.BayesianModel import BayesianModel
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> from pgmpy.sampling import BayesianModelSampling
        >>> student = BayesianModel([('diff', 'grade'), ('intel', 'grade')])
        >>> cpd_d = TabularCPD('diff', 2, [[0.6], [0.4]])
        >>> cpd_i = TabularCPD('intel', 2, [[0.7], [0.3]])
        >>> cpd_g = TabularCPD('grade', 3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25,
        ...                0.08, 0.3], [0.3, 0.7, 0.02, 0.2]],
        ...                ['intel', 'diff'], [2, 2])
        >>> student.add_cpds(cpd_d, cpd_i, cpd_g)
        >>> inference = BayesianModelSampling(student)
        >>> inference.forward_sample(size=2, return_type='recarray')
        rec.array([(0, 0, 1), (1, 0, 2)], dtype=
                  [('diff', '<i8'), ('intel', '<i8'), ('grade', '<i8')])

def forward_sample(self, size=1, return_type='dataframe'):
        """
        Generates sample(s) from joint distribution of the bayesian network.

        Parameters
        ----------
        size: int
            size of sample to be generated

        return_type: string (dataframe | recarray)
            Return type for samples, either of 'dataframe' or 'recarray'.
            Defaults to 'dataframe'

        Returns
        -------
        sampled: A pandas.DataFrame or a numpy.recarray object depending upon return_type argument
            the generated samples


        Examples
        --------
        >>> from pgmpy.models.BayesianModel import BayesianModel
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> from pgmpy.sampling import BayesianModelSampling
        >>> student = BayesianModel([('diff', 'grade'), ('intel', 'grade')])
        >>> cpd_d = TabularCPD('diff', 2, [[0.6], [0.4]])
        >>> cpd_i = TabularCPD('intel', 2, [[0.7], [0.3]])
        >>> cpd_g = TabularCPD('grade', 3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25,
        ...                0.08, 0.3], [0.3, 0.7, 0.02, 0.2]],
        ...                ['intel', 'diff'], [2, 2])
        >>> student.add_cpds(cpd_d, cpd_i, cpd_g)
        >>> inference = BayesianModelSampling(student)
        >>> inference.forward_sample(size=2, return_type='recarray')
        rec.array([(0, 0, 1), (1, 0, 2)], dtype=
                  [('diff', '<i8'), ('intel', '<i8'), ('grade', '<i8')])
        """
        types = [(var_name, 'int') for var_name in self.topological_order]
        sampled = np.zeros(size, dtype=types).view(np.recarray)

        for node in self.topological_order:
            cpd = self.model.get_cpds(node)
            states = range(self.cardinality[node])
            evidence = cpd.variables[:0:-1]
            if evidence:
                cached_values = self.pre_compute_reduce(variable=node)
                evidence = np.vstack([sampled[i] for i in evidence])
                weights = list(map(lambda t: cached_values[tuple(t)], evidence.T))
            else:
                weights = cpd.values
            sampled[node] = sample_discrete(states, weights, size)

        return _return_samples(return_type, sampled)