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

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