return v, phi_vfi, phi_pfi, new_v
# model parameters
beta = 0.95
c = 0.6
F_a = 1
F_b = 1
G_a = 3
G_b = 1.2
w_max = 2
w_grid_size = 40
pi_grid_size = 40
sp = SearchProblem(beta, c, F_a, F_b, G_a, G_b, w_max, w_grid_size,
pi_grid_size)
v, phi_vfi, phi_pfi, new_v = _get_data(sp)
def test_h5_access():
"odu: test access to data file"
assert_true(v is not None)
assert_true(phi_vfi is not None)
assert_true(phi_pfi is not None)
def test_vfi_v_phi_same_shape():
"odu: vfi value and policy same shape"
assert_equal(v.shape, phi_vfi.shape)
""" Filename: odu_vfi_plots.py Authors: John Stachurski and Thomas Sargent """ import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.axes3d import Axes3D from matplotlib import cm from scipy.interpolate import LinearNDInterpolator import numpy as np from quantecon import compute_fixed_point from quantecon.models import SearchProblem sp = SearchProblem(w_grid_size=100, pi_grid_size=100) v_init = np.zeros(len(sp.grid_points)) + sp.c / (1 - sp.beta) v = compute_fixed_point(sp.bellman_operator, v_init) policy = sp.get_greedy(v) # Make functions from these arrays by interpolation vf = LinearNDInterpolator(sp.grid_points, v) pf = LinearNDInterpolator(sp.grid_points, policy) pi_plot_grid_size, w_plot_grid_size = 100, 100 pi_plot_grid = np.linspace(0.001, 0.99, pi_plot_grid_size) w_plot_grid = np.linspace(0, sp.w_max, w_plot_grid_size) #plot_choice = 'value_function' plot_choice = 'policy_function' if plot_choice == 'value_function':
""" Filename: odu_plot_densities.py Authors: John Stachurski, Thomas J. Sargent """ import numpy as np import matplotlib.pyplot as plt from quantecon.models import SearchProblem sp = SearchProblem(F_a=1, F_b=1, G_a=3, G_b=1.2) grid = np.linspace(0, 2, 150) fig, ax = plt.subplots() ax.plot(grid, sp.f(grid), label=r'$f$', lw=2) ax.plot(grid, sp.g(grid), label=r'$g$', lw=2) ax.legend(loc=0) plt.show()
