Returns a list of strings representing the values about
        which the discretization method calculates the probabilty
        masses.

        Default value is the points -
        [low, low+step, low+2*step, ......... , high-step]
        unless the method is overridden by a subclass.

        Examples
        --------
        >>> from pgmpy.factors import ContinuousNode
        >>> from pgmpy.discretize import BaseDiscretizer
        >>> class ChildDiscretizer(BaseDiscretizer):
        ...     def get_discrete_values(self):
        ...         pass
        >>> from scipy.stats import norm
        >>> node = ContinuousNode(norm(0).pdf)
        >>> child = ChildDiscretizer(node, -5, 5, 20)
        >>> chld.get_labels()
        ['x=-5.0', 'x=-4.5', 'x=-4.0', 'x=-3.5', 'x=-3.0', 'x=-2.5',
         'x=-2.0', 'x=-1.5', 'x=-1.0', 'x=-0.5', 'x=0.0', 'x=0.5', 'x=1.0',
         'x=1.5', 'x=2.0', 'x=2.5', 'x=3.0', 'x=3.5', 'x=4.0', 'x=4.5']

def get_labels(self):
        """
        Returns a list of strings representing the values about
        which the discretization method calculates the probabilty
        masses.

        Default value is the points -
        [low, low+step, low+2*step, ......... , high-step]
        unless the method is overridden by a subclass.

        Examples
        --------
        >>> from pgmpy.factors import ContinuousNode
        >>> from pgmpy.discretize import BaseDiscretizer
        >>> class ChildDiscretizer(BaseDiscretizer):
        ...     def get_discrete_values(self):
        ...         pass
        >>> from scipy.stats import norm
        >>> node = ContinuousNode(norm(0).pdf)
        >>> child = ChildDiscretizer(node, -5, 5, 20)
        >>> chld.get_labels()
        ['x=-5.0', 'x=-4.5', 'x=-4.0', 'x=-3.5', 'x=-3.0', 'x=-2.5',
         'x=-2.0', 'x=-1.5', 'x=-1.0', 'x=-0.5', 'x=0.0', 'x=0.5', 'x=1.0',
         'x=1.5', 'x=2.0', 'x=2.5', 'x=3.0', 'x=3.5', 'x=4.0', 'x=4.5']

        """
        step = (self.high - self.low) / self.cardinality
        labels = ['x={i}'.format(i=str(i)) for i in np.round(
            np.arange(self.low, self.high, step), 3)]
        return labels