'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'

def K_to_F(self, K):

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