def it_stops_sampling_when_the_new_equilibria_are_all_similar(self):
Nash.replicator_dynamics = lambda g, mix, iters, converge_threshold: \
array([0.110001, 0.899999]) if list(mix) == [0.11, 0.89] else array([0.9999, 0.0001])
matrix = create_observation_matrix()
evaluator = EquilibriumCompareEvaluator(0.05)
evaluator.old_equilibria = [array([0.11, 0.89]), array([1.0, 0.0])]
assert evaluator.continue_sampling(matrix) == False
def it_requests_sampling_when_delta_is_in_interval(self):
class FakeCI:
def two_sided_interval(self, matrix, profile, alpha):
return [0.0, 1.0]
ci_calculator = FakeCI()
matrix = create_observation_matrix()
evaluator = ConfidenceIntervalEvaluator(matrix.toGame(), matrix.knownProfiles(), 0.5, 0.95, ci_calculator)
assert evaluator.continue_sampling(matrix) == True
def it_requests_further_sampling_when_new_equilibria_are_found(self):
Nash.replicator_dynamics = lambda g, mix, iters, converge_threshold: \
array([0.1, 0.9]) if mix is array([0.11, 0.89]) else array([0.999, 0.001])
matrix = create_observation_matrix()
evaluator = EquilibriumCompareEvaluator(0.05)
evaluator.old_equilibria = [array([0.11, 0.89])]
assert evaluator.continue_sampling(matrix) == True
for eq in evaluator.old_equilibria:
assert [[0.1, 0.9], [0.999, 0.001]].count(list(eq)) == 1
def it_does_not_request_sampling_when_delta_is_outside_all_intervals(self):
class FakeCI:
def __init__(self):
self.count = 0
def two_sided_interval(self, matrix, profile, alpha):
self.count += 1
if self.count % 2 == 0:
return [0.6, 1.0]
else:
return [0.0, 0.4]
ci_calculator = FakeCI()
matrix = create_observation_matrix()
evaluator = ConfidenceIntervalEvaluator(matrix.toGame(), matrix.knownProfiles(), 0.5, 0.95, ci_calculator)
assert evaluator.continue_sampling(matrix) == False
def it_tracks_whether_or_not_a_profile_is_a_delta_nash(self):
class FakeCI:
def __init__(self):
self.count = 0
def two_sided_interval(self, matrix, profile, alpha):
self.count += 1
if self.count % 2 == 0:
return [0.6, 1.0]
else:
return [0.0, 0.4]
ci_calculator = FakeCI()
matrix = create_observation_matrix()
evaluator = ConfidenceIntervalEvaluator(matrix.toGame(), matrix.knownProfiles(), 0.5, 0.95, ci_calculator)
evaluator.continue_sampling(matrix)
count = 0
print evaluator.target_hash.items()
for result in evaluator.target_hash.values():
count += 1
if count % 2 == 0:
assert result == "No"
else:
assert result == "Yes"
def it_requests_further_sampling_when_the_new_equilibrium_is_distant(self):
Nash.replicator_dynamics = lambda g, mix, iters, converge_threshold: array([0.1, 0.9])
matrix = create_observation_matrix()
evaluator = EquilibriumCompareEvaluator(0.01)
evaluator.old_equilibria = [array([0.5, 0.5])]
assert evaluator.continue_sampling(matrix) == True
def it_requests_further_sampling_when_there_is_data_but_no_old_equilibria(self):
Nash.replicator_dynamics = lambda g, mix, iters, converge_threshold: array([0.1, 0.9])
matrix = create_observation_matrix()
evaluator = EquilibriumCompareEvaluator(0.01)
assert evaluator.continue_sampling(matrix) == True
assert_array_almost_equal(evaluator.old_equilibria[0], array([0.1, 0.9]))
