def nn_x(self, x, k=1, radius=np.inf, eps=0.0, p=2):
        """Find the k nearest neighbors of x in the observed input data
        @see Databag.nn() for argument description
        @return  distance and indexes of found nearest neighbors.
        """
        assert len(x) == self.dim_x
        k_x = min(k, self.size)
        # Because linear models requires x vector to be extended to [1.0]+x
        # to accomodate a constant, we store them that way.
        return self._nn(DATA_X, x, k=k_x, radius=radius, eps=eps, p=p)
Find the k nearest neighbors of x in the observed input data
        @see Databag.nn() for argument description
        @return  distance and indexes of found nearest neighbors.