Skip to content

[ar1_processes.md] Update np.random → Generator API #794

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
Chihiro2000GitHub wants to merge 1 commit into
base: main
Choose a base branch
from
Open

[ar1_processes.md] Update np.random → Generator API #794

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions lectures/ar1_processes.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -409,13 +409,12 @@ from numba import njit
from scipy.special import factorial2

@njit
def sample_moments_ar1(k, m=100_000, mu_0=0.0, sigma_0=1.0, seed=1234):
np.random.seed(seed)
def sample_moments_ar1(k, rng, m=100_000, mu_0=0.0, sigma_0=1.0):
sample_sum = 0.0
x = mu_0 + sigma_0 * np.random.randn()
x = mu_0 + sigma_0 * rng.standard_normal()
for t in range(m):
sample_sum += (x - mu_star)**k
x = a * x + b + c * np.random.randn()
x = a * x + b + c * rng.standard_normal()
return sample_sum / m

def true_moments_ar1(k):
Expand All @@ -429,7 +428,8 @@ sample_moments = np.empty(len(k_vals), dtype=float)
true_moments = np.empty(len(k_vals), dtype=float)

for k_idx, k in enumerate(k_vals):
sample_moments[k_idx] = sample_moments_ar1(k)
rng = np.random.default_rng(1234)
sample_moments[k_idx] = sample_moments_ar1(k, rng)
true_moments[k_idx] = true_moments_ar1(k)

fig, ax = plt.subplots()
Expand Down Expand Up @@ -622,8 +622,9 @@ s_next = np.sqrt(a**2 * s**2 + c**2)

```{code-cell} ipython3
n = 2000
rng = np.random.default_rng()
x_draws = ψ.rvs(n)
x_draws_next = a * x_draws + b + c * np.random.randn(n)
x_draws_next = a * x_draws + b + c * rng.standard_normal(n)
kde = KDE(x_draws_next)

x_grid = np.linspace(μ - 1, μ + 1, 100)
Expand Down
Loading