def K_to_F(self, K):
        """
        Compute agent 1's best value-maximizing response F, given K.

        Parameters
        ----------
        K : array_like(float, ndim=2)
            A j x n array

        Returns
        -------
        F : array_like(float, ndim=2)
            The policy function for a given K
        P : array_like(float, ndim=2)
            The value function for a given K

        """
        A1 = self.A + dot(self.C, K)
        B1 = self.B
        Q1 = self.Q
        R1 = self.beta * self.theta * dot(K.T, K) - self.R
        lq = LQ(Q1, R1, A1, B1, beta=self.beta)
        P, F, d = lq.stationary_values()

        return F, P
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/robustlq.py/RBLQ.K_to_F/swapped operands of `-`