def setUpClass(cls):
jv = JvWorker(A=A, alpha=alpha, beta=beta, grid_size=grid_size)
cls.jv = jv
# compute solution
v_init = _get_vf_guess(jv)
cls.V = compute_fixed_point(jv.bellman_operator, v_init)
cls.s_pol, cls.phi_pol = jv.bellman_operator(cls.V * 0.999,
return_policies=True)
def setUpClass(cls):
jv = JvWorker(A=A, alpha=alpha, beta=beta, grid_size=grid_size)
cls.jv = jv
# compute solution
v_init = _get_vf_guess(jv)
cls.V = compute_fixed_point(jv.bellman_operator, v_init)
cls.s_pol, cls.phi_pol = jv.bellman_operator(cls.V * 0.999,
return_policies=True)
"""
Origin: QE by John Stachurski and Thomas J. Sargent
Filename: jv_test.py
Authors: John Stachurski and Thomas Sargent
LastModified: 11/08/2013
Tests jv.py with a particular parameterization.
"""
import matplotlib.pyplot as plt
from quantecon import compute_fixed_point
from jv import JvWorker
# === solve for optimal policy === #
wp = JvWorker(grid_size=25)
v_init = wp.x_grid * 0.5
V = compute_fixed_point(wp.bellman_operator, v_init, max_iter=40)
s_policy, phi_policy = wp.bellman_operator(V, return_policies=True)
# === plot policies === #
fig, ax = plt.subplots()
ax.set_xlim(0, max(wp.x_grid))
ax.set_ylim(-0.1, 1.1)
ax.plot(wp.x_grid, phi_policy, 'b-', label='phi')
ax.plot(wp.x_grid, s_policy, 'g-', label='s')
ax.set_xlabel("x")
ax.legend()
plt.show()