From 72d4c8568b72e3579e177dc806f0e2308fb97d55 Mon Sep 17 00:00:00 2001 From: Chihiro Watanabe Date: Wed, 3 Jun 2026 07:42:08 +0900 Subject: [PATCH] Update rng usage in rand_resp.md Co-Authored-By: Claude Sonnet 4.6 --- lectures/rand_resp.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/lectures/rand_resp.md b/lectures/rand_resp.md index 1986812f2..2c3bdd00e 100644 --- a/lectures/rand_resp.md +++ b/lectures/rand_resp.md @@ -226,9 +226,9 @@ class Comparison: A = self.A n = self.n df = self.template.copy() - np.random.seed(seed) - sample = np.random.rand(size, self.n) <= A - random_device = np.random.rand(size, n) + rng = np.random.default_rng(seed) + sample = rng.random((size, self.n)) <= A + random_device = rng.random((size, n)) mse_rd = {} for p in self.p_arr: spinner = random_device <= p @@ -237,8 +237,8 @@ class Comparison: pi_hat = (p - 1) / (2 * p - 1) + n1 / n / (2 * p - 1) mse_rd[p] = np.sum((pi_hat - A)**2) for inum, irow in df.iterrows(): - truth_a = np.random.rand(size, self.n) <= irow.T_a - truth_b = np.random.rand(size, self.n) <= irow.T_b + truth_a = rng.random((size, self.n)) <= irow.T_a + truth_b = rng.random((size, self.n)) <= irow.T_b trad_answer = sample * truth_a + (1 - sample) * (1 - truth_b) pi_trad = trad_answer.sum(axis=1) / n df.loc[inum, 'Bias'] = pi_trad.mean() - A