Returns the independent variables in the joint probability distribution.
        Returns marginally independent variables if condition=None.
        Returns conditionally independent variables if condition!=None

        Parameter
        ---------
        condition: array_like
                Random Variable on which to condition the Joint Probability Distribution.

        Examples
        --------
        >>> import numpy as np
        >>> from pgmpy.factors.discrete import JointProbabilityDistribution
        >>> prob = JointProbabilityDistribution(['x1', 'x2', 'x3'], [2, 3, 2], np.ones(12)/12)
        >>> prob.get_independencies()
        (x1 _|_ x2)
        (x1 _|_ x3)
        (x2 _|_ x3)

def get_independencies(self, condition=None):
        """
        Returns the independent variables in the joint probability distribution.
        Returns marginally independent variables if condition=None.
        Returns conditionally independent variables if condition!=None

        Parameter
        ---------
        condition: array_like
                Random Variable on which to condition the Joint Probability Distribution.

        Examples
        --------
        >>> import numpy as np
        >>> from pgmpy.factors.discrete import JointProbabilityDistribution
        >>> prob = JointProbabilityDistribution(['x1', 'x2', 'x3'], [2, 3, 2], np.ones(12)/12)
        >>> prob.get_independencies()
        (x1 _|_ x2)
        (x1 _|_ x3)
        (x2 _|_ x3)
        """
        JPD = self.copy()
        if condition:
            JPD.conditional_distribution(condition)
        independencies = Independencies()
        for variable_pair in itertools.combinations(list(JPD.variables), 2):
            if (JPD.marginal_distribution(variable_pair, inplace=False) ==
                    JPD.marginal_distribution(variable_pair[0], inplace=False) *
                    JPD.marginal_distribution(variable_pair[1], inplace=False)):
                independencies.add_assertions(variable_pair)
        return independencies