Creates random points within the bounds of the space.

        Returns
        ----------
        data: ndarray
            [num x dim] array points with dimensions corresponding to `self._keys`

        Example
        -------
        >>> target_func = lambda p1, p2: p1 + p2
        >>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
        >>> space = TargetSpace(target_func, pbounds, random_state=0)
        >>> space.random_points(1)
        array([[ 55.33253689,   0.54488318]])

def random_sample(self):
        """
        Creates random points within the bounds of the space.

        Returns
        ----------
        data: ndarray
            [num x dim] array points with dimensions corresponding to `self._keys`

        Example
        -------
        >>> target_func = lambda p1, p2: p1 + p2
        >>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
        >>> space = TargetSpace(target_func, pbounds, random_state=0)
        >>> space.random_points(1)
        array([[ 55.33253689,   0.54488318]])
        """
        # TODO: support integer, category, and basic scipy.optimize constraints
        data = np.empty((1, self.dim))
        for col, (lower, upper) in enumerate(self._bounds):
            data.T[col] = self.random_state.uniform(lower, upper, size=1)
        return data.ravel()