def online_random_sample_runner(executor, stream, k):
results = executor.run(lambda : [online_random_sample(iter(stream), k) for _ in range(100000)])
total_possible_outcomes = binomial_coefficient(len(stream), k)
stream = sorted(stream)
comb_to_idx = {
tuple(compute_combination_idx(stream, len(stream), k, i)): i
for i in range(binomial_coefficient(len(stream), k))
}
return check_sequence_is_uniformly_random(
[comb_to_idx.get(tuple(sorted(result)), 0)
for result in results], total_possible_outcomes, 0.01)
def random_subset_runner(executor, n, k):
results = executor.run(
lambda: [random_subset(n, k) for _ in range(100000)])
total_possible_outcomes = binomial_coefficient(n, k)
comb_to_idx = {
tuple(compute_combination_idx(list(range(n)), n, k, i)): i
for i in range(binomial_coefficient(n, k))
}
return check_sequence_is_uniformly_random(
[comb_to_idx.get(tuple(sorted(result)), 0)
for result in results], total_possible_outcomes, 0.01)
def compute_random_permutation_runner(executor, n):
def permutation_index(perm):
p = copy.deepcopy(perm)
idx = 0
n = len(p)
while p:
a = p.pop(0)
idx += a * math.factorial(n - 1)
for i, b in enumerate(p):
if b > a:
p[i] -= 1
n -= 1
return idx
result = executor.run(
lambda: [compute_random_permutation(n) for _ in range(1000000)])
return check_sequence_is_uniformly_random(
[permutation_index(perm)
for perm in result], math.factorial(n), 0.01)
def random_sampling_runner(executor, k, A):
result = []
def populate_random_sampling_result():
for _ in range(100000):
random_sampling(k, A)
result.append(A[:k])
executor.run(populate_random_sampling_result)
total_possible_outcomes = binomial_coefficient(len(A), k)
A = sorted(A)
comb_to_idx = {
tuple(compute_combination_idx(A, len(A), k, i)): i
for i in range(binomial_coefficient(len(A), k))
}
return check_sequence_is_uniformly_random(
[comb_to_idx[tuple(sorted(a))]
for a in result], total_possible_outcomes, 0.01)
def random_sampling_runner(executor, k, A):
result = []
def populate_random_sampling_result():
for _ in range(100000):
random_sampling(k, A)
result.append(A[:k])
executor.run(populate_random_sampling_result)
total_possible_outcomes = binomial_coefficient(len(A), k)
A = sorted(A)
comb_to_idx = {
tuple(compute_combination_idx(A, len(A), k, i)): i
for i in range(binomial_coefficient(len(A), k))
}
return check_sequence_is_uniformly_random(
[comb_to_idx[tuple(sorted(a))]
for a in result], total_possible_outcomes, 0.01)
def compute_random_permutation_runner(executor, n):
def permutation_index(perm):
p = copy.deepcopy(perm)
idx = 0
n = len(p)
while p:
a = p.pop(0)
idx += a * math.factorial(n - 1)
for i, b in enumerate(p):
if b > a:
p[i] -= 1
n -= 1
return idx
result = executor.run(
lambda: [compute_random_permutation(n) for _ in range(1000000)])
return check_sequence_is_uniformly_random(
[permutation_index(perm) for perm in result], math.factorial(n),
0.01)
def uniform_random_runner(executor, lower_bound, upper_bound):
result = executor.run(lambda : [uniform_random(lower_bound, upper_bound) for _ in range(100000)])
return check_sequence_is_uniformly_random(
[a - lower_bound
for a in result], upper_bound - lower_bound + 1, 0.01)
def uniform_random_runner(executor, lower_bound, upper_bound):
result = executor.run(lambda : [uniform_random(lower_bound, upper_bound) for _ in range(100000)])
return check_sequence_is_uniformly_random(
[a - lower_bound
for a in result], upper_bound - lower_bound + 1, 0.01)
