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 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 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(timer, n, k):
timer.start()
result = [random_subset(n, k) for _ in range(100000)]
timer.stop()
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[tuple(sorted(a))] for a in result],
total_possible_outcomes, 0.01)
def online_random_sample_runner(timer, stream, k):
timer.start()
result = [online_random_sample(iter(stream), k) for _ in range(100000)]
timer.stop()
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[tuple(sorted(a))] for a in result],
total_possible_outcomes, 0.01)
def random_sampling_runner(timer, k, A):
result = []
timer.start()
for _ in range(100000):
random_sampling(k, A)
result.append(A[:k])
timer.stop()
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)
