def test_noninteractive(self):
"Test case for when agents don't interact with each other"
# Copied these values from test_lqcontrol
a = np.array([[.95, 0.], [0, .95]])
b1 = np.array([.95, 0.])
b2 = np.array([0., .95])
r1 = np.array([[-.25, 0.], [0., 0.]])
r2 = np.array([[0., 0.], [0., -.25]])
q1 = np.array([[-.15]])
q2 = np.array([[-.15]])
f1, f2, p1, p2 = nnash(a, b1, b2, r1, r2, q1, q2, 0, 0, 0, 0, 0, 0,
tol=1e-8, max_iter=10000)
alq = a[:1, :1]
blq = b1[:1].reshape((1, 1))
rlq = r1[:1, :1]
qlq = q1
lq_obj = LQ(qlq, rlq, alq, blq, beta=1.)
p, f, d = lq_obj.stationary_values()
assert_allclose(f1, f2[:, ::-1])
assert_allclose(f1[0, 0], f[0])
assert_allclose(p1[0, 0], p2[1, 1])
assert_allclose(p1[0, 0], p[0, 0])
def test_noninteractive(self):
"Test case for when agents don't interact with each other"
# Copied these values from test_lqcontrol
a = np.array([[.95, 0.], [0, .95]])
b1 = np.array([.95, 0.])
b2 = np.array([0., .95])
r1 = np.array([[-.25, 0.], [0., 0.]])
r2 = np.array([[0., 0.], [0., -.25]])
q1 = np.array([[-.15]])
q2 = np.array([[-.15]])
f1, f2, p1, p2 = nnash(a, b1, b2, r1, r2, q1, q2, 0, 0, 0, 0, 0, 0,
tol=1e-8, max_iter=10000)
alq = a[:1, :1]
blq = b1[:1].reshape((1, 1))
rlq = r1[:1, :1]
qlq = q1
lq_obj = LQ(qlq, rlq, alq, blq, beta=1.)
p, f, d = lq_obj.stationary_values()
assert_allclose(f1, f2[:, ::-1])
assert_allclose(f1[0, 0], f[0])
assert_allclose(p1[0, 0], p2[1, 1])
assert_allclose(p1[0, 0], p[0, 0])
plt.plot(xs,density(xs))
plt.show()
############
# AD-HOC #
###########
theta =2
R=-R
F, P, K = quantecon.robustlq.RBLQ(Q,R,A,B,C,beta,theta).robust_rule()
P = quantecon.solve_discrete_riccati(A, B, R, Q)
from quantecon.lqcontrol import LQ
I=np.eye(4)
lq = LQ(-beta*I*theta, R, A, C, beta=beta)
Ptest, ftest, dtest = lq.stationary_values()
theta =901
A=np.matrix([[rho1,0,0,0],
[0,rho2,0,0],
[0,0,rho3,0],
[0,0,0,rho4]])
B=np.zeros((4,4))
C=np.identity(4)
B = np.matrix([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]) C = np.matrix([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) Q = np.matrix([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]) R = np.matrix([[r1, 0, 0, 0], [0, r2, 0, 0], [0, 0, r3, 0], [0, 0, 0, r4]]) P = np.ones(4) I = np.ones(4) Z = np.zeros(4) quantecon.robustlq.RBLQ(Q, R, A, B, C, beta, theta).robust_rule() F, K, P = quantecon.robustlq.RBLQ.robust_rule() lq = LQ(-beta * I * theta, R, A, C, beta=beta) P, f, d = lq.stationary_values() #Define M for Schur-Decomposition M11 = A + C * np.linalg.inv(beta * theta * A) * C.T * R M12 = C * np.linalg.inv(beta * theta * A) * C.T M21 = np.linalg.inv(A) * R M22 = np.linalg.inv(A) M1 = np.hstack((M11, M12)) M2 = np.hstack((M21, M22)) M = np.vstack((M1, M2)) [T, Z, sdim] = scipy.linalg.schur(M, output='real', sort='iuc')
