def test_weighted_order_doesnt_lose_or_add_items(self):
original_list = [('Something', 10), ('Another', 5), ('Again', 1),
('And', 700), ('More', 5)]
shuffled = rand.weighted_order(original_list)
self.assertEqual(len(original_list), len(shuffled))
for item in original_list:
self.assertIn(item[0], shuffled)
def test_weighted_order_output_type(self):
original_list = [('Something', 10),
('Another', 5),
('Again', 1),
('And', 700),
('More', 5)]
shuffled = rand.weighted_order(original_list)
for item in shuffled:
self.assertIsInstance(item, str)
def test_weighted_order_doesnt_modify_original(self):
original_list = [('Something', 10),
('Another', 5),
('Again', 1),
('And', 700),
('More', 5)]
input_list = original_list[:]
shuffled = rand.weighted_order(input_list)
self.assertEqual(input_list, original_list)
def test_weighted_order_doesnt_lose_or_add_items(self):
original_list = [('Something', 10),
('Another', 5),
('Again', 1),
('And', 700),
('More', 5)]
shuffled = rand.weighted_order(original_list)
self.assertEqual(len(original_list), len(shuffled))
for item in original_list:
self.assertIn(item[0], shuffled)
def test_weighted_order_with_extremely_strong_weight(self):
original_list = [('Something', 10), ('Another', 5), ('Again', 1),
('And', 7000), ('More', 5)]
success_count = 0
TRIALS = 500
for i in range(TRIALS):
shuffled = rand.weighted_order(original_list)
if shuffled.index('And') == 0:
success_count += 1
self.assertGreater(success_count, TRIALS * 0.75)
def test_weighted_order_with_extremely_strong_weight(self):
original_list = [('Something', 10),
('Another', 5),
('Again', 1),
('And', 7000),
('More', 5)]
success_count = 0
TRIALS = 500
for i in range(TRIALS):
shuffled = rand.weighted_order(original_list)
if shuffled.index('And') == 0:
success_count += 1
self.assertGreater(success_count, TRIALS * 0.75)
def test_weighted_order_with_all_1_weights(self):
# When every weight is 0, the probability spread should be uniform
original_list = [('Something', 1), ('Another', 1), ('Again', 1),
('And', 1), ('More', 1)]
landing_positions = {
'Something': [],
'Another': [],
'Again': [],
'And': [],
'More': []
}
for i in range(1000):
shuffled_list = rand.weighted_order(original_list)
for index, value in enumerate(shuffled_list):
landing_positions[value].append(index)
# With an even distribution, landing indices should average near 2.5
for positions in landing_positions.values():
average = sum(positions) / len(positions)
self.assertLess(abs(average - 2.5), 1)
def test_weighted_order_with_all_1_weights(self):
# When every weight is 0, the probability spread should be uniform
original_list = [('Something', 1),
('Another', 1),
('Again', 1),
('And', 1),
('More', 1)]
landing_positions = {'Something': [],
'Another': [],
'Again': [],
'And': [],
'More': []}
for i in range(1000):
shuffled_list = rand.weighted_order(original_list)
for index, value in enumerate(shuffled_list):
landing_positions[value].append(index)
# With an even distribution, landing indices should average near 2.5
for positions in landing_positions.values():
average = sum(positions) / len(positions)
self.assertLess(abs(average - 2.5), 1)
def test_weighted_order_with_empty_list_returns_empty_list(self):
self.assertEqual(rand.weighted_order([]), [])
def test_weighted_order_with_invalid_weight(self):
with self.assertRaises(rand.ProbabilityUndefinedError):
foo = rand.weighted_order([('bar', 0), ('baz', 5), ('buzz', -3)])
def test_weighted_order_with_empty_list_returns_empty_list(self):
self.assertEqual(rand.weighted_order([]), [])
def test_weighted_order_with_invalid_weight(self):
with self.assertRaises(rand.ProbabilityUndefinedError):
foo = rand.weighted_order([('bar', 0), ('baz', 5), ('buzz', -3)])
def test_weighted_order_output_type(self):
original_list = [('Something', 10), ('Another', 5), ('Again', 1),
('And', 700), ('More', 5)]
shuffled = rand.weighted_order(original_list)
for item in shuffled:
self.assertIsInstance(item, str)
def test_weighted_order_doesnt_modify_original(self):
original_list = [('Something', 10), ('Another', 5), ('Again', 1),
('And', 700), ('More', 5)]
input_list = original_list[:]
shuffled = rand.weighted_order(input_list)
self.assertEqual(input_list, original_list)